From b27ae28634479268648be5898889f118af799316 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 27 Apr 2022 11:24:45 +0100 Subject: [PATCH 01/36] Partially completed first draft of SEI on cracks --- .../full_battery_models/base_battery_model.py | 4 + .../submodels/interface/sei/base_sei.py | 90 +++++++++++- .../submodels/interface/sei/sei_growth.py | 138 ++++++++++++++++-- pybamm/parameters/lithium_ion_parameters.py | 2 + 4 files changed, 220 insertions(+), 14 deletions(-) diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index b5bafeb339..ee6b888763 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -143,6 +143,9 @@ class BatteryModelOptions(pybamm.FuzzyDict): * "SEI porosity change" : str Whether to include porosity change due to SEI formation, can be "false" (default) or "true". + * "SEI on cracks" : str + Whether to include SEI growth on particle cracks, can be "false" + (default) or "true". * "stress-induced diffusion" : str Whether to include stress-induced diffusion, can be "false" or "true". The default is "false" if "particle mechanics" is "none" and "true" @@ -238,6 +241,7 @@ def __init__(self, extra_options): ], "SEI film resistance": ["none", "distributed", "average"], "SEI porosity change": ["false", "true"], + "SEI on cracks": ["false", "true"], "stress-induced diffusion": ["false", "true"], "surface form": ["false", "differential", "algebraic"], "thermal": ["isothermal", "lumped", "x-lumped", "x-full"], diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py index 3014d9a5d2..df230e6934 100644 --- a/pybamm/models/submodels/interface/sei/base_sei.py +++ b/pybamm/models/submodels/interface/sei/base_sei.py @@ -100,6 +100,49 @@ def _get_standard_thickness_variables(self, L_inner, L_outer): return variables + def _get_standard_thickness_variables_cracks(self, L_inner_cr, L_outer_cr): + """ + A private function to obtain the standard variables which + can be derived from the local SEI thickness on cracks. + + Parameters + ---------- + L_inner_cr : :class:`pybamm.Symbol` + The inner SEI thickness on cracks. + L_outer_cr : :class:`pybamm.Symbol` + The outer SEI thickness on cracks. + + Returns + ------- + variables : dict + The variables which can be derived from the SEI thicknesses. + """ + param = self.param + L_scale = param.L_sei_0_dim + + variables = { + "Inner SEI thickness on cracks": L_inner_cr, + "Inner SEI thickness on cracks [m]": L_inner_cr * L_scale, + "Outer SEI thickness on cracks": L_outer_cr, + "Outer SEI thickness on cracks [m]": L_outer_cr * L_scale, + } + + L_inner_cr_av = pybamm.x_average(L_inner_cr) + L_outer_cr_av = pybamm.x_average(L_outer_cr) + variables.update( + { + "X-averaged inner SEI thickness on cracks": L_inner_cr_av, + "X-averaged inner SEI thickness on cracks [m]": L_inner_cr_av * L_scale, + "X-averaged outer SEI thickness on cracks": L_outer_cr_av, + "X-averaged outer SEI thickness on cracks [m]": L_outer_cr_av * L_scale, + } + ) + # Get variables related to the total thickness + L_sei_cr = L_inner_cr + L_outer_cr + variables.update(self._get_standard_total_thickness_variables_cracks(L_sei_cr)) + + return variables + def _get_standard_total_thickness_variables(self, L_sei): """Update variables related to total SEI thickness.""" if isinstance(self, pybamm.sei.NoSEI): @@ -130,6 +173,28 @@ def _get_standard_total_thickness_variables(self, L_sei): ) return variables + def _get_standard_total_thickness_variables_cracks(self, L_sei_cr): + """Update variables related to total SEI thickness on cracks.""" + L_scale = self.param.L_sei_0_dim + R_sei_dim = self.param.R_sei_dimensional + + variables = { + "SEI thickness on cracks": L_sei_cr, + "SEI thickness on cracks [m]": L_sei_cr * L_scale, + "Total SEI thickness on cracks": L_sei_cr, + "Total SEI thickness on cracks [m]": L_sei_cr * L_scale, + } + L_sei_cr_av = pybamm.x_average(L_sei_cr) + variables.update( + { + "X-averaged SEI thickness on cracks": L_sei_cr_av, + "X-averaged SEI thickness on cracks [m]": L_sei_cr_av * L_scale, + "X-averaged total SEI thickness on cracks": L_sei_cr_av, + "X-averaged total SEI thickness on cracks [m]": L_sei_cr_av * L_scale, + } + ) + return variables + def _get_standard_concentration_variables(self, variables): """Update variables related to the SEI concentration.""" param = self.param @@ -170,15 +235,30 @@ def _get_standard_concentration_variables(self, variables): L_inner = variables["Inner SEI thickness"] L_outer = variables["Outer SEI thickness"] - n_inner = L_inner # inner SEI concentration - n_outer = L_outer # outer SEI concentration + if self.options["SEI on cracks"] == True: + L_inner_cr = variables["Inner SEI thickness on cracks"] + L_outer_cr = variables["Outer SEI thickness on cracks"] + else: + L_inner_cr = 0 * L_inner + L_outer_cr = 0 * L_outer + + n_inner = (L_inner + L_inner_cr) # inner SEI concentration + n_outer = (L_outer + L_outer_cr) # outer SEI concentration - n_inner_av = pybamm.x_average(L_inner) - n_outer_av = pybamm.x_average(L_outer) + n_inner_av = pybamm.x_average(n_inner) + n_outer_av = pybamm.x_average(n_outer) n_SEI = n_inner + n_outer / v_bar # SEI concentration n_SEI_av = pybamm.yz_average(pybamm.x_average(n_SEI)) - delta_n_SEI = n_SEI_av - (L_inner_0 + L_outer_0 / v_bar) + + # Calculate change in SEI concentration with respect to initial state + if self.options["SEI on cracks"] == True: + rho_cr = param.rho_cr_n + n_SEI_init = L_inner_0 + L_outer_0 / v_bar + n_SEI_cr_init = 2 * rho_cr * (L_inner_0 + L_outer_0 / v_bar) / 10000 + delta_n_SEI = n_SEI_av - n_SEI_init - n_SEI_cr_init + else: + delta_n_SEI = n_SEI_av - (L_inner_0 + L_outer_0 / v_bar) # Q_sei in mol if self.reaction_loc == "interface": diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py index 859180d0d6..ec61a81cef 100644 --- a/pybamm/models/submodels/interface/sei/sei_growth.py +++ b/pybamm/models/submodels/interface/sei/sei_growth.py @@ -39,10 +39,41 @@ def get_fundamental_variables(self): L_inner = pybamm.standard_variables.L_inner_interface L_outer = pybamm.standard_variables.L_outer_interface + if self.options["SEI on cracks"] == True: + if self.reaction_loc == "x-average": + L_inner_cr_av = pybamm.Variable( + "X-averaged inner SEI thickness on cracks", + domain="current collector", + ) + L_inner_cr = pybamm.PrimaryBroadcast( + L_inner_cr_av, "negative electrode" + ) + L_outer_cr_av = pybamm.Variable( + "X-averaged outer SEI thickness on cracks", + domain="current collector", + ) + L_outer_cr = pybamm.PrimaryBroadcast( + L_outer_cr_av, "negative electrode" + ) + elif self.reaction_loc == "full electrode": + L_inner_cr = pybamm.Variable( + "Inner SEI thickness on cracks", + domain="negative electrode", + auxiliary_domains={"secondary": "current collector"}, + ) + if self.options["SEI"] == "ec reaction limited": L_inner = 0 * L_inner # Set L_inner to zero, copying domains + if self.options["SEI on cracks"] == True: + L_inner_cr = 0 * L_inner_cr variables = self._get_standard_thickness_variables(L_inner, L_outer) + + if self.options["SEI on cracks"] == True: + variables.update( + self._get_standard_thickness_variables_cracks(L_inner_cr,L_outer_cr) + ) + variables.update(self._get_standard_concentration_variables(variables)) return variables @@ -88,18 +119,27 @@ def get_coupled_variables(self, variables): # need to revise for thermal case j_sei = -(1 / C_sei) * pybamm.exp(-0.5 * (delta_phi - j * L_sei * R_sei)) + if self.options["SEI on cracks"] == True: + j_sei_cr = j_sei + elif self.options["SEI"] == "electron-migration limited": U_inner = self.param.U_inner_electron C_sei = self.param.C_sei_electron j_sei = (phi_s_n - U_inner) / (C_sei * L_sei_inner) + if self.options["SEI on cracks"] == True: + j_sei_cr = (phi_s_n - U_inner) / (C_sei * L_sei_cr_inner) elif self.options["SEI"] == "interstitial-diffusion limited": C_sei = self.param.C_sei_inter j_sei = -pybamm.exp(-delta_phi) / (C_sei * L_sei_inner) + if self.options["SEI on cracks"] == True: + j_sei_cr = -pybamm.exp(-delta_phi) / (C_sei * L_sei_cr_inner) elif self.options["SEI"] == "solvent-diffusion limited": C_sei = self.param.C_sei_solvent j_sei = -1 / (C_sei * L_sei_outer) + if self.options["SEI on cracks"] == True: + j_sei_cr = -1 / (C_sei * L_sei_cr_outer) elif self.options["SEI"] == "ec reaction limited": C_sei_ec = self.param.C_sei_ec @@ -117,6 +157,9 @@ def get_coupled_variables(self, variables): C_sei_exp = C_sei_ec * pybamm.exp(-0.5 * (delta_phi - j * L_sei * R_sei)) j_sei = -C_sei_exp / (1 + L_sei * C_ec * C_sei_exp) c_ec = 1 / (1 + L_sei * C_ec * C_sei_exp) + if self.options["SEI on cracks"] == True: + j_sei_cr = -C_sei_exp / (1 + L_sei_cr * C_ec * C_sei_exp) + c_ec_cr = 1 / (1 + L_sei_cr * C_ec * C_sei_exp) # Get variables related to the concentration c_ec_av = pybamm.x_average(c_ec) @@ -129,18 +172,40 @@ def get_coupled_variables(self, variables): "X-averaged EC surface concentration": c_ec_av, "X-averaged EC surface concentration [mol.m-3]": c_ec_av * c_ec_scale, + "EC concentration on cracks": c_ec_cr, + "EC concentration on cracks [mol.m-3]": c_ec_cr * c_ec_scale, + "X-averaged EC concentration on cracks": c_ec_cr_av, + "X-averaged EC concentration on cracks [mol.m-3]": c_ec_cr_av + * c_ec_scale, } ) + if self.options["SEI on cracks"] == True: + c_ec_cr_av = pybamm.x_average(c_ec_cr) + variables.update( + { + "EC concentration on cracks": c_ec_cr, + "EC concentration on cracks [mol.m-3]": c_ec_cr * c_ec_scale, + "X-averaged EC concentration on cracks": c_ec_cr_av, + "X-averaged EC concentration on cracks [mol.m-3]": c_ec_cr_av + * c_ec_scale, + } + ) + if self.options["SEI"] == "ec reaction limited": alpha = 0 else: - alpha = 0.5 + alpha = self.param.alpha_SEI j_inner = alpha * j_sei j_outer = (1 - alpha) * j_sei - variables.update(self._get_standard_reaction_variables(j_inner, j_outer)) + if self.options["SEI on cracks"] == True: + j_inner_cr = alpha * j_sei_cr + j_outer_cr = (1 - alpha) * j_sei_cr + variables.update( + self._get_standard_reaction_variables_cracks(j_inner_cr,j_outer_cr) + ) # Update whole cell variables, which also updates the "sum of" variables variables.update(super().get_coupled_variables(variables)) @@ -165,17 +230,50 @@ def set_rhs(self, variables): a = 1 else: a = variables["Negative electrode surface area to volume ratio"] + if self.options["SEI on cracks"] == True: + if self.reaction_loc == "x-average": + L_inner = variables["X-averaged inner SEI thickness"] + L_outer = variables["X-averaged outer SEI thickness"] + j_inner = variables["X-averaged inner SEI interfacial current density"] + j_outer = variables["X-averaged outer SEI interfacial current density"] + a = variables["X-averaged negative electrode surface area to volume ratio"] + l_cr = variables["X-averaged negative particle crack length"] + dl_cr = variables["X-averaged negative particle cracking rate"] + else: + L_inner_cr = variables["Inner SEI thickness on cracks"] + L_outer_cr = variables["Outer SEI thickness on cracks"] + j_inner_cr = variables["Inner SEI interfacial current density on cracks"] + j_outer_cr = variables["Outer SEI interfacial current density on cracks"] + a = variables["Negative electrode surface area to volume ratio"] + l_cr = variables["Negative particle crack length"] + dl_cr = variables["Negative particle cracking rate"] + spreading_outer = dl_cr / l_cr * (self.param.L_outer_0 / 10000 - L_outer) + spreading_inner = dl_cr / l_cr * (self.param.L_inner_0 / 10000 - L_inner) Gamma_SEI = self.param.Gamma_SEI if self.options["SEI"] == "ec reaction limited": - self.rhs = {L_outer: -Gamma_SEI * a * j_outer / 2} + if self.options["SEI on cracks"] == True: + self.rhs = { + L_outer: -Gamma_SEI * a * j_outer / 2, + L_outer_cr: -Gamma_SEI * a * j_outer_cr / 2 + spreading_outer, + } + else: + self.rhs = {L_outer: -Gamma_SEI * a * j_outer / 2} else: v_bar = self.param.v_bar - self.rhs = { - L_inner: -Gamma_SEI * a * j_inner, - L_outer: -v_bar * Gamma_SEI * a * j_outer, - } + if self.options["SEI on cracks"] == True: + self.rhs = { + L_inner: -Gamma_SEI * a * j_inner, + L_outer: -v_bar * Gamma_SEI * a * j_outer, + L_inner_cr: -Gamma_SEI * a * j_inner_cr + spreading_inner, + L_outer_cr: -v_bar * Gamma_SEI * a * j_outer_cr + spreading_outer, + } + else: + self.rhs = { + L_inner: -Gamma_SEI * a * j_inner, + L_outer: -v_bar * Gamma_SEI * a * j_outer, + } def set_initial_conditions(self, variables): if self.reaction_loc == "x-average": @@ -184,10 +282,32 @@ def set_initial_conditions(self, variables): else: L_inner = variables["Inner SEI thickness"] L_outer = variables["Outer SEI thickness"] + + if self.options["SEI on cracks"] == True: + if self.reaction_loc == "x-average": + L_inner_cr = variables["X-averaged inner SEI thickness on cracks"] + L_outer_cr = variables["X-averaged outer SEI thickness on cracks"] + else: + L_inner_cr = variables["Inner SEI thickness on cracks"] + L_outer_cr = variables["Outer SEI thickness on cracks"] L_inner_0 = self.param.L_inner_0 L_outer_0 = self.param.L_outer_0 if self.options["SEI"] == "ec reaction limited": - self.initial_conditions = {L_outer: L_inner_0 + L_outer_0} + if self.options["SEI on cracks"] == True: + self.initial_conditions = { + L_outer: L_inner_0 + L_outer_0, + L_outer_cr: (L_inner_0 + L_outer_0) / 10000, + } + else: + self.initial_conditions = {L_outer: L_inner_0 + L_outer_0} else: - self.initial_conditions = {L_inner: L_inner_0, L_outer: L_outer_0} + if self.options["SEI on cracks"] == True: + self.initial_conditions = { + L_inner: L_inner_0, + L_outer: L_outer_0, + L_inner_cr: L_inner_0 / 10000, + L_outer_cr: L_outer_0 / 10000, + } + else: + self.initial_conditions = {L_inner: L_inner_0, L_outer: L_outer_0} diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 0f86f99ae8..a943f6d32c 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -768,6 +768,8 @@ def _set_dimensionless_parameters(self): self.T_amb = self.therm.T_amb # SEI parameters + self.alpha_SEI = pybamm.Parameter("Inner SEI reaction proportion") # was 0.5 + self.C_sei_reaction = (self.j_scale_n / self.m_sei_dimensional) * pybamm.exp( -(self.F * self.U_n_ref / (2 * self.R * self.T_ref)) ) From 23a0a6fb478ed6bcfaeb7d4ea83a7e598bfe3bb9 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Thu, 28 Apr 2022 23:19:05 +0100 Subject: [PATCH 02/36] Second attempt at adding SEI on cracks --- .../lithium_ion/base_lithium_ion_model.py | 11 +- .../submodels/interface/base_interface.py | 3 + .../submodels/interface/sei/base_sei.py | 271 ++++++++++++++---- .../submodels/interface/sei/sei_growth.py | 175 ++++++----- 4 files changed, 304 insertions(+), 156 deletions(-) diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index f4689ff87d..36ddd49f86 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -137,11 +137,13 @@ def set_degradation_variables(self): # Different way of measuring LLI but should give same value LLI_sei = self.variables["Loss of lithium to SEI [mol]"] if self.half_cell: + LLI_sei_cracks = pybamm.Scalar(0) LLI_pl = pybamm.Scalar(0) else: + LLI_sei_cracks = self.variable["Loss of lithium to SEI on cracks [mol]"] LLI_pl = self.variables["Loss of lithium to lithium plating [mol]"] - LLI_reactions = LLI_sei + LLI_pl + LLI_reactions = LLI_sei + LLI_sei_cracks + LLI_pl self.variables.update( { "Total lithium lost to side reactions [mol]": LLI_reactions, @@ -203,8 +205,13 @@ def set_sei_submodel(self): self.submodels["sei"] = pybamm.sei.ConstantSEI(self.param, self.options) else: self.submodels["sei"] = pybamm.sei.SEIGrowth( - self.param, reaction_loc, self.options + self.param, reaction_loc, cracks=False, self.options ) + # Run SEI growth model again, this time on cracks + if self.options["SEI on cracks"] == "true": + self.submodels["sei on cracks"] = pybamm.sei.SEIGrowth( + self.param, reaction_loc, cracks=True, self.options + ) def set_lithium_plating_submodel(self): if self.options["lithium plating"] == "none": diff --git a/pybamm/models/submodels/interface/base_interface.py b/pybamm/models/submodels/interface/base_interface.py index 279ba09059..b17793e5e4 100644 --- a/pybamm/models/submodels/interface/base_interface.py +++ b/pybamm/models/submodels/interface/base_interface.py @@ -41,6 +41,9 @@ def __init__(self, param, domain, reaction, options=None): elif reaction == "SEI": self.reaction_name = " SEI" self.Reaction_icd = "SEI interfacial current density" + elif reaction == "SEI on cracks": + self.reaction_name = " SEI on cracks" + self.Reaction_icd = "SEI on cracks interfacial current density" elif reaction == "lithium plating": self.reaction_name = " lithium plating" self.Reaction_icd = "Lithium plating interfacial current density" diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py index df230e6934..55a30090c0 100644 --- a/pybamm/models/submodels/interface/sei/base_sei.py +++ b/pybamm/models/submodels/interface/sei/base_sei.py @@ -18,32 +18,59 @@ class BaseModel(BaseInterface): **Extends:** :class:`pybamm.interface.BaseInterface` """ - def __init__(self, param, options=None): - reaction = "SEI" + def __init__(self, param, cracks, options=None): + if self.cracks == True: + reaction = "SEI on cracks" + else: + reaction = "SEI" domain = "Negative" super().__init__(param, domain, reaction, options=options) - def get_coupled_variables(self, variables): + def get_coupled_variables(self, variables, cracks): # Update some common variables zero_av = pybamm.PrimaryBroadcast(0, "current collector") zero = pybamm.FullBroadcast(0, "positive electrode", "current collector") if self.reaction_loc != "interface": + if cracks = True: + variables.update( + { + "X-averaged negative electrode SEI on cracks " + "interfacial current density": variables[ + "X-averaged SEI on cracks interfacial current density" + ], + "Negative electrode SEI on cracks interfacial current ": + "density": variables["SEI interfacial current density"], + } + ) + else: + variables.update( + { + "X-averaged negative electrode SEI interfacial current " + "density": variables[ + "X-averaged SEI interfacial current density" + ], + "Negative electrode SEI interfacial current " + "density": variables["SEI interfacial current density"], + } + ) + if cracks == True: variables.update( { - "X-averaged negative electrode SEI interfacial current " - "density": variables["X-averaged SEI interfacial current density"], - "Negative electrode SEI interfacial current " - "density": variables["SEI interfacial current density"], + "X-averaged positive electrode SEI on cracks " + "interfacial current density": zero_av, + "Positive electrode SEI on cracks " + "interfacial current density": zero, + } + ) + else: + variables.update( + { + "X-averaged positive electrode SEI interfacial current " + "density": zero_av, + "Positive electrode SEI interfacial current density": zero, } ) - variables.update( - { - "X-averaged positive electrode SEI interfacial current " - "density": zero_av, - "Positive electrode SEI interfacial current density": zero, - } - ) variables.update( self._get_standard_whole_cell_interfacial_current_variables(variables) ) @@ -100,46 +127,46 @@ def _get_standard_thickness_variables(self, L_inner, L_outer): return variables - def _get_standard_thickness_variables_cracks(self, L_inner_cr, L_outer_cr): + def _get_standard_thickness_variables_cracks(self, L_inner, L_outer): """ A private function to obtain the standard variables which - can be derived from the local SEI thickness on cracks. + can be derived from the local SEI on cracks thickness. Parameters ---------- - L_inner_cr : :class:`pybamm.Symbol` - The inner SEI thickness on cracks. - L_outer_cr : :class:`pybamm.Symbol` - The outer SEI thickness on cracks. + L_inner : :class:`pybamm.Symbol` + The inner SEI on cracks thickness. + L_outer : :class:`pybamm.Symbol` + The outer SEI on cracks thickness. Returns ------- variables : dict - The variables which can be derived from the SEI thicknesses. + The variables which can be derived from the SEI on cracks thicknesses. """ param = self.param L_scale = param.L_sei_0_dim variables = { - "Inner SEI thickness on cracks": L_inner_cr, - "Inner SEI thickness on cracks [m]": L_inner_cr * L_scale, - "Outer SEI thickness on cracks": L_outer_cr, - "Outer SEI thickness on cracks [m]": L_outer_cr * L_scale, + "Inner SEI on cracks thickness": L_inner, + "Inner SEI on cracks thickness [m]": L_inner * L_scale, + "Outer SEI on cracks thickness": L_outer, + "Outer SEI on cracks thickness [m]": L_outer * L_scale, } - L_inner_cr_av = pybamm.x_average(L_inner_cr) - L_outer_cr_av = pybamm.x_average(L_outer_cr) + L_inner_av = pybamm.x_average(L_inner) + L_outer_av = pybamm.x_average(L_outer) variables.update( { - "X-averaged inner SEI thickness on cracks": L_inner_cr_av, - "X-averaged inner SEI thickness on cracks [m]": L_inner_cr_av * L_scale, - "X-averaged outer SEI thickness on cracks": L_outer_cr_av, - "X-averaged outer SEI thickness on cracks [m]": L_outer_cr_av * L_scale, + "X-averaged inner SEI on cracks thickness": L_inner_av, + "X-averaged inner SEI on cracks thickness [m]": L_inner_av * L_scale, + "X-averaged outer SEI on cracks thickness": L_outer_av, + "X-averaged outer SEI on cracks thickness [m]": L_outer_av * L_scale, } ) # Get variables related to the total thickness - L_sei_cr = L_inner_cr + L_outer_cr - variables.update(self._get_standard_total_thickness_variables_cracks(L_sei_cr)) + L_sei = L_inner + L_outer + variables.update(self._get_standard_total_thickness_variables_cracks(L_sei)) return variables @@ -173,24 +200,24 @@ def _get_standard_total_thickness_variables(self, L_sei): ) return variables - def _get_standard_total_thickness_variables_cracks(self, L_sei_cr): - """Update variables related to total SEI thickness on cracks.""" + def _get_standard_total_thickness_variables_cracks(self, L_sei): + """Update variables related to total SEI on cracks thickness.""" L_scale = self.param.L_sei_0_dim R_sei_dim = self.param.R_sei_dimensional variables = { - "SEI thickness on cracks": L_sei_cr, - "SEI thickness on cracks [m]": L_sei_cr * L_scale, - "Total SEI thickness on cracks": L_sei_cr, - "Total SEI thickness on cracks [m]": L_sei_cr * L_scale, + "SEI on cracks thickness": L_sei, + "SEI on cracks thickness [m]": L_sei * L_scale, + "Total SEI on cracks thickness": L_sei, + "Total SEI on cracks thickness [m]": L_sei * L_scale, } - L_sei_cr_av = pybamm.x_average(L_sei_cr) + L_sei_av = pybamm.x_average(L_sei) variables.update( { - "X-averaged SEI thickness on cracks": L_sei_cr_av, - "X-averaged SEI thickness on cracks [m]": L_sei_cr_av * L_scale, - "X-averaged total SEI thickness on cracks": L_sei_cr_av, - "X-averaged total SEI thickness on cracks [m]": L_sei_cr_av * L_scale, + "X-averaged SEI on cracks thickness": L_sei_av, + "X-averaged SEI on cracksthickness [m]": L_sei_av * L_scale, + "X-averaged total SEI on cracks thickness": L_sei_av, + "X-averaged total SEI on cracks thickness [m]": L_sei_av * L_scale, } ) return variables @@ -235,15 +262,8 @@ def _get_standard_concentration_variables(self, variables): L_inner = variables["Inner SEI thickness"] L_outer = variables["Outer SEI thickness"] - if self.options["SEI on cracks"] == True: - L_inner_cr = variables["Inner SEI thickness on cracks"] - L_outer_cr = variables["Outer SEI thickness on cracks"] - else: - L_inner_cr = 0 * L_inner - L_outer_cr = 0 * L_outer - - n_inner = (L_inner + L_inner_cr) # inner SEI concentration - n_outer = (L_outer + L_outer_cr) # outer SEI concentration + n_inner = L_inner # inner SEI concentration + n_outer = L_outer # outer SEI concentration n_inner_av = pybamm.x_average(n_inner) n_outer_av = pybamm.x_average(n_outer) @@ -252,13 +272,7 @@ def _get_standard_concentration_variables(self, variables): n_SEI_av = pybamm.yz_average(pybamm.x_average(n_SEI)) # Calculate change in SEI concentration with respect to initial state - if self.options["SEI on cracks"] == True: - rho_cr = param.rho_cr_n - n_SEI_init = L_inner_0 + L_outer_0 / v_bar - n_SEI_cr_init = 2 * rho_cr * (L_inner_0 + L_outer_0 / v_bar) / 10000 - delta_n_SEI = n_SEI_av - n_SEI_init - n_SEI_cr_init - else: - delta_n_SEI = n_SEI_av - (L_inner_0 + L_outer_0 / v_bar) + delta_n_SEI = n_SEI_av - (L_inner_0 + L_outer_0 / v_bar) # Q_sei in mol if self.reaction_loc == "interface": @@ -291,6 +305,80 @@ def _get_standard_concentration_variables(self, variables): return variables + def _get_standard_concentration_variables_cracks(self, variables): + """Update variables related to the SEI on cracks concentration.""" + param = self.param + + if self.reaction_loc == "interface": + # scales in mol/m2 (n is an interfacial quantity) + n_scale = param.L_sei_0_dim / param.V_bar_inner_dimensional + n_outer_scale = param.L_sei_0_dim / param.V_bar_outer_dimensional + else: + # scales in mol/m3 (n is a bulk quantity) + n_scale = ( + param.L_sei_0_dim * param.a_n_typ / param.V_bar_inner_dimensional + ) + n_outer_scale = ( + param.L_sei_0_dim * param.a_n_typ / param.V_bar_outer_dimensional + ) + + v_bar = param.v_bar + # Set scales for the "EC Reaction Limited" model + if self.options["SEI"] == "ec reaction limited": + L_inner_0 = 0 + L_outer_0 = 1 + li_mols_per_sei_mols = 2 + else: + L_inner_0 = param.L_inner_0 + L_outer_0 = param.L_outer_0 + li_mols_per_sei_mols = 1 + + L_inner = variables["Inner SEI on cracks thickness"] + L_outer = variables["Outer SEI on cracks thickness"] + roughness = variables["Negative electrode roughness ratio"] + + n_inner = L_inner * (roughness - 1) # inner SEI concentration + n_outer = L_outer * (roughness - 1) # outer SEI concentration + + n_inner_av = pybamm.x_average(n_inner) + n_outer_av = pybamm.x_average(n_outer) + + n_SEI = n_inner + n_outer / v_bar # SEI concentration + n_SEI_av = pybamm.yz_average(pybamm.x_average(n_SEI)) + + # Calculate change in SEI concentration with respect to initial state + rho_cr = param.rho_cr_n + n_SEI_init = L_inner_0 + L_outer_0 / v_bar + n_SEI_cr_init = 2 * rho_cr * (L_inner_0 + L_outer_0 / v_bar) / 10000 + delta_n_SEI = n_SEI_av - n_SEI_init - n_SEI_cr_init + + # Q_sei in mol + Q_sei = ( + li_mols_per_sei_mols + * delta_n_SEI + * n_scale + * L_n + * self.param.L_y + * self.param.L_z + ) + + variables.update( + { + "Inner SEI on cracks concentration [mol.m-3]": n_inner * n_scale, + "X-averaged inner SEI on cracks concentration [mol.m-3]": n_inner_av + * n_scale, + "Outer SEI on cracks concentration [mol.m-3]": n_outer * n_outer_scale, + "X-averaged outer SEI on cracks concentration [mol.m-3]": n_outer_av + * n_outer_scale, + "SEI on cracks concentration [mol.m-3]": n_SEI * n_scale, + "X-averaged SEI on cracks concentration [mol.m-3]": n_SEI_av * n_scale, + "Loss of lithium to SEI on cracks [mol]": Q_sei, + "Loss of capacity to SEI on cracks [A.h]": Q_sei * self.param.F / 3600, + } + ) + + return variables + def _get_standard_reaction_variables(self, j_inner, j_outer): """ A private function to obtain the standard variables which @@ -306,7 +394,7 @@ def _get_standard_reaction_variables(self, j_inner, j_outer): Returns ------- variables : dict - The variables which can be derived from the SEI thicknesses. + The variables which can be derived from the SEI currents. """ j_scale = self.param.j_scale_n j_i_av = pybamm.x_average(j_inner) @@ -330,6 +418,47 @@ def _get_standard_reaction_variables(self, j_inner, j_outer): return variables + def _get_standard_reaction_variables_cracks(self, j_inner, j_outer): + """ + A private function to obtain the standard variables which + can be derived from the SEI on cracks interfacial reaction current + + Parameters + ---------- + j_inner : :class:`pybamm.Symbol` + The inner SEI on cracks interfacial reaction current. + j_outer : :class:`pybamm.Symbol` + The outer SEI on cracks interfacial reaction current. + + Returns + ------- + variables : dict + The variables which can be derived from the SEI on cracks currents. + """ + j_scale = self.param.j_scale_n + j_i_av = pybamm.x_average(j_inner) + j_o_av = pybamm.x_average(j_outer) + + variables = { + "Inner SEI on cracks interfacial current density": j_inner, + "Inner SEI on cracks interfacial current density [A.m-2]": j_inner + * j_scale, + "X-averaged inner SEI on cracks interfacial current density": j_i_av, + "X-averaged inner SEI on cracks interfacial current density [A.m-2]": + j_i_av * j_scale, + "Outer SEI on cracks interfacial current density": j_outer, + "Outer SEI on cracks interfacial current density [A.m-2]": j_outer + * j_scale, + "X-averaged outer SEI on cracks interfacial current density": j_o_av, + "X-averaged outer SEI on cracks interfacial current density [A.m-2]": + j_o_av * j_scale, + } + + j_sei = j_inner + j_outer + variables.update(self._get_standard_total_reaction_variables_cracks(j_sei)) + + return variables + def _get_standard_total_reaction_variables(self, j_sei): """Update variables related to total SEI interfacial current density.""" j_scale = self.param.j_scale_n @@ -350,3 +479,23 @@ def _get_standard_total_reaction_variables(self, j_sei): ) return variables + + def _get_standard_total_reaction_variables(self, j_sei): + """Update variables related to total SEI on cracks interfacial current density.""" + j_scale = self.param.j_scale_n + + variables = { + "SEI on cracks interfacial current density": j_sei, + "SEI on cracks interfacial current density [A.m-2]": j_sei * j_scale, + } + + j_sei_av = pybamm.x_average(j_sei) + variables.update( + { + "X-averaged SEI on cracks interfacial current density": j_sei_av, + "X-averaged SEI on cracks interfacial current density [A.m-2]": + j_sei_av * j_scale, + } + ) + + return variables diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py index ec61a81cef..6af0e60bc2 100644 --- a/pybamm/models/submodels/interface/sei/sei_growth.py +++ b/pybamm/models/submodels/interface/sei/sei_growth.py @@ -22,59 +22,60 @@ class SEIGrowth(BaseModel): **Extends:** :class:`pybamm.sei.BaseModel` """ - def __init__(self, param, reaction_loc, options=None): - super().__init__(param, options=options) + def __init__(self, param, reaction_loc, cracks=False, options=None): + super().__init__(param, cracks, options=options) self.reaction_loc = reaction_loc def get_fundamental_variables(self): - if self.reaction_loc == "x-average": - L_inner_av = pybamm.standard_variables.L_inner_av - L_outer_av = pybamm.standard_variables.L_outer_av - L_inner = pybamm.PrimaryBroadcast(L_inner_av, "negative electrode") - L_outer = pybamm.PrimaryBroadcast(L_outer_av, "negative electrode") - elif self.reaction_loc == "full electrode": - L_inner = pybamm.standard_variables.L_inner - L_outer = pybamm.standard_variables.L_outer - elif self.reaction_loc == "interface": - L_inner = pybamm.standard_variables.L_inner_interface - L_outer = pybamm.standard_variables.L_outer_interface - - if self.options["SEI on cracks"] == True: + if self.cracks == True: if self.reaction_loc == "x-average": - L_inner_cr_av = pybamm.Variable( - "X-averaged inner SEI thickness on cracks", - domain="current collector", - ) - L_inner_cr = pybamm.PrimaryBroadcast( - L_inner_cr_av, "negative electrode" - ) - L_outer_cr_av = pybamm.Variable( - "X-averaged outer SEI thickness on cracks", - domain="current collector", - ) - L_outer_cr = pybamm.PrimaryBroadcast( - L_outer_cr_av, "negative electrode" - ) + L_inner_av = pybamm.Variable( + "X-averaged inner SEI on cracks thickness", + domain="current collector", + ) + L_inner = pybamm.PrimaryBroadcast( + L_inner_av, "negative electrode" + ) + L_outer_av = pybamm.Variable( + "X-averaged outer SEI on cracks thickness", + domain="current collector", + ) + L_outer = pybamm.PrimaryBroadcast( + L_outer_av, "negative electrode" + ) elif self.reaction_loc == "full electrode": - L_inner_cr = pybamm.Variable( - "Inner SEI thickness on cracks", - domain="negative electrode", - auxiliary_domains={"secondary": "current collector"}, - ) + L_inner = pybamm.Variable( + "Inner SEI on cracks thickness", + domain="negative electrode", + auxiliary_domains={"secondary": "current collector"}, + ) + L_outer = pybamm.Variable( + "Outer SEI on cracks thickness", + domain="negative electrode", + auxiliary_domains={"secondary": "current collector"}, + ) + else: + if self.reaction_loc == "x-average": + L_inner_av = pybamm.standard_variables.L_inner_av + L_outer_av = pybamm.standard_variables.L_outer_av + L_inner = pybamm.PrimaryBroadcast(L_inner_av, "negative electrode") + L_outer = pybamm.PrimaryBroadcast(L_outer_av, "negative electrode") + elif self.reaction_loc == "full electrode": + L_inner = pybamm.standard_variables.L_inner + L_outer = pybamm.standard_variables.L_outer + elif self.reaction_loc == "interface": + L_inner = pybamm.standard_variables.L_inner_interface + L_outer = pybamm.standard_variables.L_outer_interface if self.options["SEI"] == "ec reaction limited": L_inner = 0 * L_inner # Set L_inner to zero, copying domains - if self.options["SEI on cracks"] == True: - L_inner_cr = 0 * L_inner_cr - variables = self._get_standard_thickness_variables(L_inner, L_outer) - - if self.options["SEI on cracks"] == True: - variables.update( - self._get_standard_thickness_variables_cracks(L_inner_cr,L_outer_cr) - ) - - variables.update(self._get_standard_concentration_variables(variables)) + if self.cracks == True: + variables = self._get_standard_thickness_variables_cracks(L_inner, L_outer) + variables.update(self._get_standard_concentration_variables_cracks(variables)) + else: + variables = self._get_standard_thickness_variables(L_inner, L_outer) + variables.update(self._get_standard_concentration_variables(variables)) return variables @@ -106,40 +107,38 @@ def get_coupled_variables(self, variables): + " electrode total interfacial current density" ] - L_sei_inner = variables["Inner SEI thickness"] - L_sei_outer = variables["Outer SEI thickness"] - L_sei = variables["Total SEI thickness"] - + if self.cracks == True: + L_sei_inner = variables["Inner SEI on cracks thickness"] + L_sei_outer = variables["Outer SEI on cracks thickness"] + L_sei = variables["Total SEI on cracks thickness"] + else: + L_sei_inner = variables["Inner SEI thickness"] + L_sei_outer = variables["Outer SEI thickness"] + L_sei = variables["Total SEI thickness"] + + T = variables["Negative electrode temperature"] R_sei = self.param.R_sei + # thermal prefactor for reaction, interstitial and EC models + prefactor = -1 / (2 * (1 + self.param.Theta * T)) if self.options["SEI"] == "reaction limited": # alpha = param.alpha C_sei = param.C_sei_reaction - - # need to revise for thermal case - j_sei = -(1 / C_sei) * pybamm.exp(-0.5 * (delta_phi - j * L_sei * R_sei)) - - if self.options["SEI on cracks"] == True: - j_sei_cr = j_sei + eta_SEI = delta_phi - j * L_sei * R_sei + j_sei = -(1 / C_sei) * pybamm.exp(prefactor * eta_SEI) elif self.options["SEI"] == "electron-migration limited": U_inner = self.param.U_inner_electron C_sei = self.param.C_sei_electron j_sei = (phi_s_n - U_inner) / (C_sei * L_sei_inner) - if self.options["SEI on cracks"] == True: - j_sei_cr = (phi_s_n - U_inner) / (C_sei * L_sei_cr_inner) elif self.options["SEI"] == "interstitial-diffusion limited": C_sei = self.param.C_sei_inter - j_sei = -pybamm.exp(-delta_phi) / (C_sei * L_sei_inner) - if self.options["SEI on cracks"] == True: - j_sei_cr = -pybamm.exp(-delta_phi) / (C_sei * L_sei_cr_inner) + j_sei = -pybamm.exp(2 * prefactor * delta_phi) / (C_sei * L_sei_inner) elif self.options["SEI"] == "solvent-diffusion limited": C_sei = self.param.C_sei_solvent j_sei = -1 / (C_sei * L_sei_outer) - if self.options["SEI on cracks"] == True: - j_sei_cr = -1 / (C_sei * L_sei_cr_outer) elif self.options["SEI"] == "ec reaction limited": C_sei_ec = self.param.C_sei_ec @@ -153,41 +152,31 @@ def get_coupled_variables(self, variables): # so # j_sei = -C_sei_ec * exp() / (1 + L_sei * C_ec * C_sei_ec * exp()) # c_ec = 1 / (1 + L_sei * C_ec * C_sei_ec * exp()) - # need to revise for thermal case - C_sei_exp = C_sei_ec * pybamm.exp(-0.5 * (delta_phi - j * L_sei * R_sei)) + C_sei_exp = C_sei_ec * pybamm.exp(prefactor * eta_SEI) j_sei = -C_sei_exp / (1 + L_sei * C_ec * C_sei_exp) c_ec = 1 / (1 + L_sei * C_ec * C_sei_exp) - if self.options["SEI on cracks"] == True: - j_sei_cr = -C_sei_exp / (1 + L_sei_cr * C_ec * C_sei_exp) - c_ec_cr = 1 / (1 + L_sei_cr * C_ec * C_sei_exp) # Get variables related to the concentration c_ec_av = pybamm.x_average(c_ec) c_ec_scale = self.param.c_ec_0_dim - variables.update( - { - "EC surface concentration": c_ec, - "EC surface concentration [mol.m-3]": c_ec * c_ec_scale, - "X-averaged EC surface concentration": c_ec_av, - "X-averaged EC surface concentration [mol.m-3]": c_ec_av - * c_ec_scale, - "EC concentration on cracks": c_ec_cr, - "EC concentration on cracks [mol.m-3]": c_ec_cr * c_ec_scale, - "X-averaged EC concentration on cracks": c_ec_cr_av, - "X-averaged EC concentration on cracks [mol.m-3]": c_ec_cr_av - * c_ec_scale, - } - ) - - if self.options["SEI on cracks"] == True: - c_ec_cr_av = pybamm.x_average(c_ec_cr) + if self.cracks == True: + variables.update( + { + "EC concentration on cracks": c_ec, + "EC concentration on cracks [mol.m-3]": c_ec * c_ec_scale, + "X-averaged EC concentration on cracks": c_ec_av, + "X-averaged EC concentration on cracks [mol.m-3]": c_ec_av + * c_ec_scale, + } + ) + else: variables.update( { - "EC concentration on cracks": c_ec_cr, - "EC concentration on cracks [mol.m-3]": c_ec_cr * c_ec_scale, - "X-averaged EC concentration on cracks": c_ec_cr_av, - "X-averaged EC concentration on cracks [mol.m-3]": c_ec_cr_av + "EC surface concentration": c_ec, + "EC surface concentration [mol.m-3]": c_ec * c_ec_scale, + "X-averaged EC surface concentration": c_ec_av, + "X-averaged EC surface concentration [mol.m-3]": c_ec_av * c_ec_scale, } ) @@ -199,16 +188,16 @@ def get_coupled_variables(self, variables): j_inner = alpha * j_sei j_outer = (1 - alpha) * j_sei - variables.update(self._get_standard_reaction_variables(j_inner, j_outer)) - if self.options["SEI on cracks"] == True: - j_inner_cr = alpha * j_sei_cr - j_outer_cr = (1 - alpha) * j_sei_cr + + if cracks == True: variables.update( - self._get_standard_reaction_variables_cracks(j_inner_cr,j_outer_cr) + self._get_standard_reaction_variables_cracks(j_inner, j_outer) ) + else: + variables.update(self._get_standard_reaction_variables(j_inner, j_outer)) # Update whole cell variables, which also updates the "sum of" variables - variables.update(super().get_coupled_variables(variables)) + variables.update(super().get_coupled_variables(variables, cracks)) return variables From d17473a7143c7d8c43a7818a2d73e0fa67611bda Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Thu, 12 May 2022 14:20:07 +0100 Subject: [PATCH 03/36] Fixed some bugs in crack SEI model --- .../full_battery_models/base_battery_model.py | 8 +- .../lithium_ion/base_lithium_ion_model.py | 6 +- .../submodels/interface/sei/base_sei.py | 66 ++++----- .../submodels/interface/sei/sei_growth.py | 137 +++++++++--------- .../test_base_battery_model.py | 1 + 5 files changed, 112 insertions(+), 106 deletions(-) diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index ee6b888763..cc74ccb0f0 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -140,12 +140,12 @@ class BatteryModelOptions(pybamm.FuzzyDict): .. math:: \\eta_r = \\frac{F}{RT} * (\\phi_s - \\phi_e - U - R_{sei} * L_{sei} * \\frac{I}{aL}) - * "SEI porosity change" : str - Whether to include porosity change due to SEI formation, can be "false" - (default) or "true". * "SEI on cracks" : str Whether to include SEI growth on particle cracks, can be "false" (default) or "true". + * "SEI porosity change" : str + Whether to include porosity change due to SEI formation, can be "false" + (default) or "true". * "stress-induced diffusion" : str Whether to include stress-induced diffusion, can be "false" or "true". The default is "false" if "particle mechanics" is "none" and "true" @@ -240,8 +240,8 @@ def __init__(self, extra_options): "ec reaction limited", ], "SEI film resistance": ["none", "distributed", "average"], - "SEI porosity change": ["false", "true"], "SEI on cracks": ["false", "true"], + "SEI porosity change": ["false", "true"], "stress-induced diffusion": ["false", "true"], "surface form": ["false", "differential", "algebraic"], "thermal": ["isothermal", "lumped", "x-lumped", "x-full"], diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index 36ddd49f86..ae43cf6080 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -140,7 +140,7 @@ def set_degradation_variables(self): LLI_sei_cracks = pybamm.Scalar(0) LLI_pl = pybamm.Scalar(0) else: - LLI_sei_cracks = self.variable["Loss of lithium to SEI on cracks [mol]"] + LLI_sei_cracks = self.variables["Loss of lithium to SEI on cracks [mol]"] LLI_pl = self.variables["Loss of lithium to lithium plating [mol]"] LLI_reactions = LLI_sei + LLI_sei_cracks + LLI_pl @@ -205,12 +205,12 @@ def set_sei_submodel(self): self.submodels["sei"] = pybamm.sei.ConstantSEI(self.param, self.options) else: self.submodels["sei"] = pybamm.sei.SEIGrowth( - self.param, reaction_loc, cracks=False, self.options + self.param, reaction_loc, self.options, cracks=False ) # Run SEI growth model again, this time on cracks if self.options["SEI on cracks"] == "true": self.submodels["sei on cracks"] = pybamm.sei.SEIGrowth( - self.param, reaction_loc, cracks=True, self.options + self.param, reaction_loc, self.options, cracks=True ) def set_lithium_plating_submodel(self): diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py index 55a30090c0..b77101c988 100644 --- a/pybamm/models/submodels/interface/sei/base_sei.py +++ b/pybamm/models/submodels/interface/sei/base_sei.py @@ -18,56 +18,59 @@ class BaseModel(BaseInterface): **Extends:** :class:`pybamm.interface.BaseInterface` """ - def __init__(self, param, cracks, options=None): - if self.cracks == True: + def __init__(self, param, options=None, cracks=False): + if cracks is True: reaction = "SEI on cracks" else: reaction = "SEI" domain = "Negative" super().__init__(param, domain, reaction, options=options) - def get_coupled_variables(self, variables, cracks): + def get_coupled_variables(self, variables, cracks=False): # Update some common variables zero_av = pybamm.PrimaryBroadcast(0, "current collector") zero = pybamm.FullBroadcast(0, "positive electrode", "current collector") if self.reaction_loc != "interface": - if cracks = True: + if cracks is True: variables.update( { - "X-averaged negative electrode SEI on cracks " - "interfacial current density": variables[ + "X-averaged negative electrode SEI on cracks interfacial " + "current density": variables[ "X-averaged SEI on cracks interfacial current density" ], - "Negative electrode SEI on cracks interfacial current ": - "density": variables["SEI interfacial current density"], + "Negative electrode SEI on cracks interfacial " + "current density": variables[ + "SEI on cracks interfacial current density" + ], } ) else: variables.update( { - "X-averaged negative electrode SEI interfacial current " - "density": variables[ + "X-averaged negative electrode SEI interfacial " + "current density": variables[ "X-averaged SEI interfacial current density" ], - "Negative electrode SEI interfacial current " - "density": variables["SEI interfacial current density"], + "Negative electrode SEI interfacial current density": variables[ + "SEI interfacial current density" + ], } ) - if cracks == True: + if cracks is True: variables.update( { - "X-averaged positive electrode SEI on cracks " - "interfacial current density": zero_av, - "Positive electrode SEI on cracks " - "interfacial current density": zero, + "X-averaged positive electrode SEI on cracks interfacial " + "current density": zero_av, + "Positive electrode SEI on cracks interfacial " + "current density": zero, } ) else: variables.update( { - "X-averaged positive electrode SEI interfacial current " - "density": zero_av, + "X-averaged positive electrode SEI interfacial " + "current density": zero_av, "Positive electrode SEI interfacial current density": zero, } ) @@ -203,7 +206,6 @@ def _get_standard_total_thickness_variables(self, L_sei): def _get_standard_total_thickness_variables_cracks(self, L_sei): """Update variables related to total SEI on cracks thickness.""" L_scale = self.param.L_sei_0_dim - R_sei_dim = self.param.R_sei_dimensional variables = { "SEI on cracks thickness": L_sei, @@ -315,13 +317,11 @@ def _get_standard_concentration_variables_cracks(self, variables): n_outer_scale = param.L_sei_0_dim / param.V_bar_outer_dimensional else: # scales in mol/m3 (n is a bulk quantity) - n_scale = ( - param.L_sei_0_dim * param.a_n_typ / param.V_bar_inner_dimensional - ) + n_scale = param.L_sei_0_dim * param.a_n_typ / param.V_bar_inner_dimensional n_outer_scale = ( param.L_sei_0_dim * param.a_n_typ / param.V_bar_outer_dimensional ) - + v_bar = param.v_bar # Set scales for the "EC Reaction Limited" model if self.options["SEI"] == "ec reaction limited": @@ -357,7 +357,7 @@ def _get_standard_concentration_variables_cracks(self, variables): li_mols_per_sei_mols * delta_n_SEI * n_scale - * L_n + * self.param.L_n * self.param.L_y * self.param.L_z ) @@ -444,14 +444,14 @@ def _get_standard_reaction_variables_cracks(self, j_inner, j_outer): "Inner SEI on cracks interfacial current density [A.m-2]": j_inner * j_scale, "X-averaged inner SEI on cracks interfacial current density": j_i_av, - "X-averaged inner SEI on cracks interfacial current density [A.m-2]": - j_i_av * j_scale, + "X-averaged inner SEI on cracks interfacial current density [A.m-2]": j_i_av + * j_scale, "Outer SEI on cracks interfacial current density": j_outer, "Outer SEI on cracks interfacial current density [A.m-2]": j_outer * j_scale, "X-averaged outer SEI on cracks interfacial current density": j_o_av, - "X-averaged outer SEI on cracks interfacial current density [A.m-2]": - j_o_av * j_scale, + "X-averaged outer SEI on cracks interfacial current density [A.m-2]": j_o_av + * j_scale, } j_sei = j_inner + j_outer @@ -480,8 +480,8 @@ def _get_standard_total_reaction_variables(self, j_sei): return variables - def _get_standard_total_reaction_variables(self, j_sei): - """Update variables related to total SEI on cracks interfacial current density.""" + def _get_standard_total_reaction_variables_cracks(self, j_sei): + """Update variables related to total SEI on cracks current density.""" j_scale = self.param.j_scale_n variables = { @@ -493,8 +493,8 @@ def _get_standard_total_reaction_variables(self, j_sei): variables.update( { "X-averaged SEI on cracks interfacial current density": j_sei_av, - "X-averaged SEI on cracks interfacial current density [A.m-2]": - j_sei_av * j_scale, + "X-averaged SEI on cracks interfacial current density [A.m-2]": j_sei_av + * j_scale, } ) diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py index 6af0e60bc2..b41ebcf453 100644 --- a/pybamm/models/submodels/interface/sei/sei_growth.py +++ b/pybamm/models/submodels/interface/sei/sei_growth.py @@ -22,12 +22,13 @@ class SEIGrowth(BaseModel): **Extends:** :class:`pybamm.sei.BaseModel` """ - def __init__(self, param, reaction_loc, cracks=False, options=None): - super().__init__(param, cracks, options=options) + def __init__(self, param, reaction_loc, options=None, cracks=False): + super().__init__(param, options=options, cracks=cracks) self.reaction_loc = reaction_loc + self.cracks = cracks def get_fundamental_variables(self): - if self.cracks == True: + if self.cracks is True: if self.reaction_loc == "x-average": L_inner_av = pybamm.Variable( "X-averaged inner SEI on cracks thickness", @@ -70,9 +71,11 @@ def get_fundamental_variables(self): if self.options["SEI"] == "ec reaction limited": L_inner = 0 * L_inner # Set L_inner to zero, copying domains - if self.cracks == True: + if self.cracks is True: variables = self._get_standard_thickness_variables_cracks(L_inner, L_outer) - variables.update(self._get_standard_concentration_variables_cracks(variables)) + variables.update( + self._get_standard_concentration_variables_cracks(variables) + ) else: variables = self._get_standard_thickness_variables(L_inner, L_outer) variables.update(self._get_standard_concentration_variables(variables)) @@ -107,7 +110,7 @@ def get_coupled_variables(self, variables): + " electrode total interfacial current density" ] - if self.cracks == True: + if self.cracks is True: L_sei_inner = variables["Inner SEI on cracks thickness"] L_sei_outer = variables["Outer SEI on cracks thickness"] L_sei = variables["Total SEI on cracks thickness"] @@ -115,7 +118,7 @@ def get_coupled_variables(self, variables): L_sei_inner = variables["Inner SEI thickness"] L_sei_outer = variables["Outer SEI thickness"] L_sei = variables["Total SEI thickness"] - + T = variables["Negative electrode temperature"] R_sei = self.param.R_sei # thermal prefactor for reaction, interstitial and EC models @@ -160,7 +163,7 @@ def get_coupled_variables(self, variables): c_ec_av = pybamm.x_average(c_ec) c_ec_scale = self.param.c_ec_0_dim - if self.cracks == True: + if self.cracks is True: variables.update( { "EC concentration on cracks": c_ec, @@ -189,7 +192,7 @@ def get_coupled_variables(self, variables): j_inner = alpha * j_sei j_outer = (1 - alpha) * j_sei - if cracks == True: + if self.cracks is True: variables.update( self._get_standard_reaction_variables_cracks(j_inner, j_outer) ) @@ -197,66 +200,72 @@ def get_coupled_variables(self, variables): variables.update(self._get_standard_reaction_variables(j_inner, j_outer)) # Update whole cell variables, which also updates the "sum of" variables - variables.update(super().get_coupled_variables(variables, cracks)) + variables.update(super().get_coupled_variables(variables, self.cracks)) return variables def set_rhs(self, variables): - if self.reaction_loc == "x-average": - L_inner = variables["X-averaged inner SEI thickness"] - L_outer = variables["X-averaged outer SEI thickness"] - j_inner = variables["X-averaged inner SEI interfacial current density"] - j_outer = variables["X-averaged outer SEI interfacial current density"] - # Note a is dimensionless (has a constant value of 1 if the surface - # area does not change) - a = variables["X-averaged negative electrode surface area to volume ratio"] - else: - L_inner = variables["Inner SEI thickness"] - L_outer = variables["Outer SEI thickness"] - j_inner = variables["Inner SEI interfacial current density"] - j_outer = variables["Outer SEI interfacial current density"] - if self.reaction_loc == "interface": - a = 1 - else: - a = variables["Negative electrode surface area to volume ratio"] - if self.options["SEI on cracks"] == True: + if self.cracks is True: if self.reaction_loc == "x-average": - L_inner = variables["X-averaged inner SEI thickness"] - L_outer = variables["X-averaged outer SEI thickness"] - j_inner = variables["X-averaged inner SEI interfacial current density"] - j_outer = variables["X-averaged outer SEI interfacial current density"] - a = variables["X-averaged negative electrode surface area to volume ratio"] + L_inner = variables["X-averaged inner SEI on cracks thickness"] + L_outer = variables["X-averaged outer SEI on cracks thickness"] + j_inner = variables[ + "X-averaged inner SEI on cracks interfacial current density" + ] + j_outer = variables[ + "X-averaged outer SEI on cracks interfacial current density" + ] + a = variables[ + "X-averaged negative electrode surface area to volume ratio" + ] l_cr = variables["X-averaged negative particle crack length"] dl_cr = variables["X-averaged negative particle cracking rate"] else: - L_inner_cr = variables["Inner SEI thickness on cracks"] - L_outer_cr = variables["Outer SEI thickness on cracks"] - j_inner_cr = variables["Inner SEI interfacial current density on cracks"] - j_outer_cr = variables["Outer SEI interfacial current density on cracks"] + L_inner = variables["Inner SEI on cracks thickness"] + L_outer = variables["Outer SEI on cracks thickness"] + j_inner = variables["Inner SEI on cracks interfacial current density"] + j_outer = variables["Outer SEI on cracks interfacial current density"] a = variables["Negative electrode surface area to volume ratio"] l_cr = variables["Negative particle crack length"] dl_cr = variables["Negative particle cracking rate"] spreading_outer = dl_cr / l_cr * (self.param.L_outer_0 / 10000 - L_outer) spreading_inner = dl_cr / l_cr * (self.param.L_inner_0 / 10000 - L_inner) + else: + if self.reaction_loc == "x-average": + L_inner = variables["X-averaged inner SEI thickness"] + L_outer = variables["X-averaged outer SEI thickness"] + j_inner = variables["X-averaged inner SEI interfacial current density"] + j_outer = variables["X-averaged outer SEI interfacial current density"] + # Note a is dimensionless (has a constant value of 1 if the surface + # area does not change) + a = variables[ + "X-averaged negative electrode surface area to volume ratio" + ] + else: + L_inner = variables["Inner SEI thickness"] + L_outer = variables["Outer SEI thickness"] + j_inner = variables["Inner SEI interfacial current density"] + j_outer = variables["Outer SEI interfacial current density"] + if self.reaction_loc == "interface": + a = 1 + else: + a = variables["Negative electrode surface area to volume ratio"] Gamma_SEI = self.param.Gamma_SEI if self.options["SEI"] == "ec reaction limited": - if self.options["SEI on cracks"] == True: + if self.cracks is True: self.rhs = { - L_outer: -Gamma_SEI * a * j_outer / 2, - L_outer_cr: -Gamma_SEI * a * j_outer_cr / 2 + spreading_outer, + L_outer: -Gamma_SEI * a * j_outer / 2 + spreading_outer, } else: self.rhs = {L_outer: -Gamma_SEI * a * j_outer / 2} else: v_bar = self.param.v_bar - if self.options["SEI on cracks"] == True: + if self.cracks is True: self.rhs = { - L_inner: -Gamma_SEI * a * j_inner, - L_outer: -v_bar * Gamma_SEI * a * j_outer, - L_inner_cr: -Gamma_SEI * a * j_inner_cr + spreading_inner, - L_outer_cr: -v_bar * Gamma_SEI * a * j_outer_cr + spreading_outer, + L_inner: -Gamma_SEI * a * j_inner + spreading_inner, + L_outer: -v_bar * Gamma_SEI * a * j_outer + spreading_outer, } else: self.rhs = { @@ -265,38 +274,34 @@ def set_rhs(self, variables): } def set_initial_conditions(self, variables): - if self.reaction_loc == "x-average": - L_inner = variables["X-averaged inner SEI thickness"] - L_outer = variables["X-averaged outer SEI thickness"] + if self.cracks is True: + if self.reaction_loc == "x-average": + L_inner = variables["X-averaged inner SEI on cracks thickness"] + L_outer = variables["X-averaged outer SEI on cracks thickness"] + else: + L_inner = variables["Inner SEI on cracks thickness"] + L_outer = variables["Outer SEI on cracks thickness"] else: - L_inner = variables["Inner SEI thickness"] - L_outer = variables["Outer SEI thickness"] - - if self.options["SEI on cracks"] == True: if self.reaction_loc == "x-average": - L_inner_cr = variables["X-averaged inner SEI thickness on cracks"] - L_outer_cr = variables["X-averaged outer SEI thickness on cracks"] + L_inner = variables["X-averaged inner SEI thickness"] + L_outer = variables["X-averaged outer SEI thickness"] else: - L_inner_cr = variables["Inner SEI thickness on cracks"] - L_outer_cr = variables["Outer SEI thickness on cracks"] + L_inner = variables["Inner SEI thickness"] + L_outer = variables["Outer SEI thickness"] L_inner_0 = self.param.L_inner_0 L_outer_0 = self.param.L_outer_0 + if self.options["SEI"] == "ec reaction limited": - if self.options["SEI on cracks"] == True: - self.initial_conditions = { - L_outer: L_inner_0 + L_outer_0, - L_outer_cr: (L_inner_0 + L_outer_0) / 10000, - } + if self.cracks is True: + self.initial_conditions = {L_outer: (L_inner_0 + L_outer_0) / 10000} else: self.initial_conditions = {L_outer: L_inner_0 + L_outer_0} else: - if self.options["SEI on cracks"] == True: + if self.cracks is True: self.initial_conditions = { - L_inner: L_inner_0, - L_outer: L_outer_0, - L_inner_cr: L_inner_0 / 10000, - L_outer_cr: L_outer_0 / 10000, + L_inner: L_inner_0 / 10000, + L_outer: L_outer_0 / 10000, } else: self.initial_conditions = {L_inner: L_inner_0, L_outer: L_outer_0} diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index 2a16da3981..4419beae6d 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -34,6 +34,7 @@ 'particle size': 'single' (possible: ['single', 'distribution']) 'SEI': 'none' (possible: ['none', 'constant', 'reaction limited', 'solvent-diffusion limited', 'electron-migration limited', 'interstitial-diffusion limited', 'ec reaction limited']) 'SEI film resistance': 'none' (possible: ['none', 'distributed', 'average']) +'SEI on cracks': 'false' (possible: ['false', 'true']) 'SEI porosity change': 'false' (possible: ['false', 'true']) 'stress-induced diffusion': 'true' (possible: ['false', 'true']) 'surface form': 'differential' (possible: ['false', 'differential', 'algebraic']) From e105d4009962f45114827d56fef91e947d8f5ad6 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 18 May 2022 13:13:32 +0100 Subject: [PATCH 04/36] Ensure loss of lithium to SEI on cracks is always defined --- .../submodels/interface/sei/base_sei.py | 120 ++++++++---------- .../submodels/interface/sei/sei_growth.py | 8 +- 2 files changed, 55 insertions(+), 73 deletions(-) diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py index b77101c988..19fb3b1aac 100644 --- a/pybamm/models/submodels/interface/sei/base_sei.py +++ b/pybamm/models/submodels/interface/sei/base_sei.py @@ -305,77 +305,61 @@ def _get_standard_concentration_variables(self, variables): } ) - return variables - - def _get_standard_concentration_variables_cracks(self, variables): - """Update variables related to the SEI on cracks concentration.""" - param = self.param - - if self.reaction_loc == "interface": - # scales in mol/m2 (n is an interfacial quantity) - n_scale = param.L_sei_0_dim / param.V_bar_inner_dimensional - n_outer_scale = param.L_sei_0_dim / param.V_bar_outer_dimensional - else: - # scales in mol/m3 (n is a bulk quantity) - n_scale = param.L_sei_0_dim * param.a_n_typ / param.V_bar_inner_dimensional - n_outer_scale = ( - param.L_sei_0_dim * param.a_n_typ / param.V_bar_outer_dimensional + if self.options["SEI on cracks"] == "true": + L_inner_cr = variables["Inner SEI on cracks thickness"] + L_outer_cr = variables["Outer SEI on cracks thickness"] + roughness = variables["Negative electrode roughness ratio"] + + n_inner_cr = L_inner_cr * (roughness - 1) # inner SEI cracks concentration + n_outer_cr = L_outer_cr * (roughness - 1) # outer SEI cracks concentration + + n_inner_cr_av = pybamm.x_average(n_inner) + n_outer_cr_av = pybamm.x_average(n_outer) + + n_SEI_cr = n_inner_cr + n_outer_cr / v_bar # SEI on cracks concentration + n_SEI_cr_av = pybamm.yz_average(pybamm.x_average(n_SEI)) + + # Calculate change in SEI cracks concentration with respect to initial state + rho_cr = param.rho_cr_n + n_SEI_cr_init = 2 * rho_cr * (L_inner_0 + L_outer_0 / v_bar) / 10000 + delta_n_SEI_cr = n_SEI_cr_av - n_SEI_cr_init + + # Q_sei_cr in mol + Q_sei_cr = ( + li_mols_per_sei_mols + * delta_n_SEI_cr + * n_scale + * self.param.L_n + * self.param.L_y + * self.param.L_z ) - v_bar = param.v_bar - # Set scales for the "EC Reaction Limited" model - if self.options["SEI"] == "ec reaction limited": - L_inner_0 = 0 - L_outer_0 = 1 - li_mols_per_sei_mols = 2 + variables.update( + { + "Inner SEI on cracks concentration [mol.m-3]": n_inner_cr * n_scale, + "X-averaged inner SEI on cracks concentration [mol.m-3]": + n_inner_cr_av * n_scale, + "Outer SEI on cracks concentration [mol.m-3]": n_outer_cr + * n_outer_scale, + "X-averaged outer SEI on cracks concentration [mol.m-3]": + n_outer_cr_av * n_outer_scale, + "SEI on cracks concentration [mol.m-3]": n_SEI_cr * n_scale, + "X-averaged SEI on cracks concentration [mol.m-3]": n_SEI_cr_av + * n_scale, + "Loss of lithium to SEI on cracks [mol]": Q_sei_cr, + "Loss of capacity to SEI on cracks [A.h]": Q_sei_cr + * self.param.F / 3600, + } + ) else: - L_inner_0 = param.L_inner_0 - L_outer_0 = param.L_outer_0 - li_mols_per_sei_mols = 1 - - L_inner = variables["Inner SEI on cracks thickness"] - L_outer = variables["Outer SEI on cracks thickness"] - roughness = variables["Negative electrode roughness ratio"] - - n_inner = L_inner * (roughness - 1) # inner SEI concentration - n_outer = L_outer * (roughness - 1) # outer SEI concentration - - n_inner_av = pybamm.x_average(n_inner) - n_outer_av = pybamm.x_average(n_outer) - - n_SEI = n_inner + n_outer / v_bar # SEI concentration - n_SEI_av = pybamm.yz_average(pybamm.x_average(n_SEI)) - - # Calculate change in SEI concentration with respect to initial state - rho_cr = param.rho_cr_n - n_SEI_init = L_inner_0 + L_outer_0 / v_bar - n_SEI_cr_init = 2 * rho_cr * (L_inner_0 + L_outer_0 / v_bar) / 10000 - delta_n_SEI = n_SEI_av - n_SEI_init - n_SEI_cr_init - - # Q_sei in mol - Q_sei = ( - li_mols_per_sei_mols - * delta_n_SEI - * n_scale - * self.param.L_n - * self.param.L_y - * self.param.L_z - ) - - variables.update( - { - "Inner SEI on cracks concentration [mol.m-3]": n_inner * n_scale, - "X-averaged inner SEI on cracks concentration [mol.m-3]": n_inner_av - * n_scale, - "Outer SEI on cracks concentration [mol.m-3]": n_outer * n_outer_scale, - "X-averaged outer SEI on cracks concentration [mol.m-3]": n_outer_av - * n_outer_scale, - "SEI on cracks concentration [mol.m-3]": n_SEI * n_scale, - "X-averaged SEI on cracks concentration [mol.m-3]": n_SEI_av * n_scale, - "Loss of lithium to SEI on cracks [mol]": Q_sei, - "Loss of capacity to SEI on cracks [A.h]": Q_sei * self.param.F / 3600, - } - ) + zero = pybamm.Scalar(0) + # Degradation variables are required even if SEI on cracks is turned off + variables.update( + { + "Loss of lithium to SEI on cracks [mol]": zero, + "loss of capacity to SEI on cracks [A.h]": zero, + } + ) return variables diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py index b41ebcf453..a6eaa419b9 100644 --- a/pybamm/models/submodels/interface/sei/sei_growth.py +++ b/pybamm/models/submodels/interface/sei/sei_growth.py @@ -73,12 +73,10 @@ def get_fundamental_variables(self): if self.cracks is True: variables = self._get_standard_thickness_variables_cracks(L_inner, L_outer) - variables.update( - self._get_standard_concentration_variables_cracks(variables) - ) else: variables = self._get_standard_thickness_variables(L_inner, L_outer) - variables.update(self._get_standard_concentration_variables(variables)) + + variables.update(self._get_standard_concentration_variables(variables)) return variables @@ -121,13 +119,13 @@ def get_coupled_variables(self, variables): T = variables["Negative electrode temperature"] R_sei = self.param.R_sei + eta_SEI = delta_phi - j * L_sei * R_sei # thermal prefactor for reaction, interstitial and EC models prefactor = -1 / (2 * (1 + self.param.Theta * T)) if self.options["SEI"] == "reaction limited": # alpha = param.alpha C_sei = param.C_sei_reaction - eta_SEI = delta_phi - j * L_sei * R_sei j_sei = -(1 / C_sei) * pybamm.exp(prefactor * eta_SEI) elif self.options["SEI"] == "electron-migration limited": From e4d93d84f92b10d7d1d9ef9be90f9518ec46641e Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Tue, 14 Jun 2022 14:29:57 +0100 Subject: [PATCH 05/36] Replaced most references to 'SEI' and 'SEI on cracks' with self.reaction --- examples/notebooks/models/SEI-on-cracks.ipynb | 88 +++++ .../submodels/interface/sei/base_sei.py | 331 +++++------------- .../submodels/interface/sei/sei_growth.py | 138 +++----- 3 files changed, 232 insertions(+), 325 deletions(-) create mode 100644 examples/notebooks/models/SEI-on-cracks.ipynb diff --git a/examples/notebooks/models/SEI-on-cracks.ipynb b/examples/notebooks/models/SEI-on-cracks.ipynb new file mode 100644 index 0000000000..20f1b99483 --- /dev/null +++ b/examples/notebooks/models/SEI-on-cracks.ipynb @@ -0,0 +1,88 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "eb5375ae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33mWARNING: You are using pip version 22.0.4; however, version 22.1.2 is available.\n", + "You should consider upgrading via the '/home/sokane/PyBaMM/env/bin/python3 -m pip install --upgrade pip' command.\u001b[0m\u001b[33m\n", + "\u001b[0mNote: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "%pip install pybamm -q # install PyBaMM if it is not installed\n", + "import pybamm\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "05c30b49", + "metadata": {}, + "outputs": [ + { + "ename": "KeyError", + "evalue": "'Negative electrode roughness ratio'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m parameter_values \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mParameterValues(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAi2020\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m spm \u001b[38;5;241m=\u001b[39m \u001b[43mpybamm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlithium_ion\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSPM\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparticle mechanics\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mswelling and cracking\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 4\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSEI\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msolvent-diffusion limited\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 5\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSEI on cracks\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtrue\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 6\u001b[0m \u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/lithium_ion/spm.py:67\u001b[0m, in \u001b[0;36mSPM.__init__\u001b[0;34m(self, options, name, build)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_li_metal_counter_electrode_submodels()\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m build:\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMPM\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 70\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mcitations\u001b[38;5;241m.\u001b[39mregister(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMarquis2019\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:852\u001b[0m, in \u001b[0;36mBaseBatteryModel.build_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 849\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStart building \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname))\n\u001b[1;32m 851\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_built_fundamental_and_external \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m--> 852\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_fundamental_and_external\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 854\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_coupled_variables()\n\u001b[1;32m 856\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_model_equations()\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:740\u001b[0m, in \u001b[0;36mBaseBatteryModel.build_fundamental_and_external\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 734\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m submodel_name, submodel \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubmodels\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 735\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\n\u001b[1;32m 736\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGetting fundamental variables for \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m submodel (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 737\u001b[0m submodel_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname\n\u001b[1;32m 738\u001b[0m )\n\u001b[1;32m 739\u001b[0m )\n\u001b[0;32m--> 740\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables\u001b[38;5;241m.\u001b[39mupdate(\u001b[43msubmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_fundamental_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 742\u001b[0m \u001b[38;5;66;03m# Set the submodels that are external\u001b[39;00m\n\u001b[1;32m 743\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sub \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexternal submodels\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/submodels/interface/sei/sei_growth.py:74\u001b[0m, in \u001b[0;36mSEIGrowth.get_fundamental_variables\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 71\u001b[0m L_inner \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;241m*\u001b[39m L_inner \u001b[38;5;66;03m# Set L_inner to zero, copying domains\u001b[39;00m\n\u001b[1;32m 73\u001b[0m variables \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_standard_thickness_variables(L_inner, L_outer)\n\u001b[0;32m---> 74\u001b[0m variables\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_standard_concentration_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 76\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m variables\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/submodels/interface/sei/base_sei.py:231\u001b[0m, in \u001b[0;36mBaseModel._get_standard_concentration_variables\u001b[0;34m(self, variables)\u001b[0m\n\u001b[1;32m 229\u001b[0m L_inner_cr \u001b[38;5;241m=\u001b[39m variables[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInner SEI on cracks thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 230\u001b[0m L_outer_cr \u001b[38;5;241m=\u001b[39m variables[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOuter SEI on cracks thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 231\u001b[0m roughness \u001b[38;5;241m=\u001b[39m \u001b[43mvariables\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdomain\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m electrode roughness ratio\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 233\u001b[0m n_inner_cr \u001b[38;5;241m=\u001b[39m L_inner_cr \u001b[38;5;241m*\u001b[39m (roughness \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# inner SEI cracks concentration\u001b[39;00m\n\u001b[1;32m 234\u001b[0m n_outer_cr \u001b[38;5;241m=\u001b[39m L_outer_cr \u001b[38;5;241m*\u001b[39m (roughness \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# outer SEI cracks concentration\u001b[39;00m\n", + "\u001b[0;31mKeyError\u001b[0m: 'Negative electrode roughness ratio'" + ] + } + ], + "source": [ + "parameter_values = pybamm.ParameterValues(\"Ai2020\")\n", + "spm = pybamm.lithium_ion.SPM({\n", + " \"particle mechanics\": \"swelling and cracking\",\n", + " \"SEI\": \"solvent-diffusion limited\",\n", + " \"SEI on cracks\": \"true\",\n", + "})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c3884817", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py index 19fb3b1aac..bb16f2f213 100644 --- a/pybamm/models/submodels/interface/sei/base_sei.py +++ b/pybamm/models/submodels/interface/sei/base_sei.py @@ -26,54 +26,29 @@ def __init__(self, param, options=None, cracks=False): domain = "Negative" super().__init__(param, domain, reaction, options=options) - def get_coupled_variables(self, variables, cracks=False): + def get_coupled_variables(self, variables): # Update some common variables zero_av = pybamm.PrimaryBroadcast(0, "current collector") zero = pybamm.FullBroadcast(0, "positive electrode", "current collector") if self.reaction_loc != "interface": - if cracks is True: - variables.update( - { - "X-averaged negative electrode SEI on cracks interfacial " - "current density": variables[ - "X-averaged SEI on cracks interfacial current density" - ], - "Negative electrode SEI on cracks interfacial " - "current density": variables[ - "SEI on cracks interfacial current density" - ], - } - ) - else: - variables.update( - { - "X-averaged negative electrode SEI interfacial " - "current density": variables[ - "X-averaged SEI interfacial current density" - ], - "Negative electrode SEI interfacial current density": variables[ - "SEI interfacial current density" - ], - } - ) - if cracks is True: - variables.update( - { - "X-averaged positive electrode SEI on cracks interfacial " - "current density": zero_av, - "Positive electrode SEI on cracks interfacial " - "current density": zero, - } - ) - else: variables.update( { - "X-averaged positive electrode SEI interfacial " - "current density": zero_av, - "Positive electrode SEI interfacial current density": zero, + f"X-averaged negative electrode {self.reaction} interfacial " + "current density": variables[ + f"X-averaged {self.reaction} interfacial current density" + ], + f"Negative electrode {self.reaction} interfacial current " + "density": variables[f"{self.reaction} interfacial current density"], } ) + variables.update( + { + f"X-averaged positive electrode {self.reaction} interfacial " + "current density": zero_av, + f"Positive electrode {self.reaction} interfacial current density": zero, + } + ) variables.update( self._get_standard_whole_cell_interfacial_current_variables(variables) ) @@ -107,10 +82,10 @@ def _get_standard_thickness_variables(self, L_inner, L_outer): L_scale = param.L_sei_0_dim variables = { - "Inner SEI thickness": L_inner, - "Inner SEI thickness [m]": L_inner * L_scale, - "Outer SEI thickness": L_outer, - "Outer SEI thickness [m]": L_outer * L_scale, + f"Inner {self.reaction} thickness": L_inner, + f"Inner {self.reaction} thickness [m]": L_inner * L_scale, + f"Outer {self.reaction} thickness": L_outer, + f"Outer {self.reaction} thickness [m]": L_outer * L_scale, } if self.reaction_loc != "interface": @@ -118,10 +93,12 @@ def _get_standard_thickness_variables(self, L_inner, L_outer): L_outer_av = pybamm.x_average(L_outer) variables.update( { - "X-averaged inner SEI thickness": L_inner_av, - "X-averaged inner SEI thickness [m]": L_inner_av * L_scale, - "X-averaged outer SEI thickness": L_outer_av, - "X-averaged outer SEI thickness [m]": L_outer_av * L_scale, + f"X-averaged inner {self.reaction} thickness": L_inner_av, + f"X-averaged inner {self.reaction} thickness [m]": L_inner_av + * L_scale, + f"X-averaged outer {self.reaction} thickness": L_outer_av, + f"X-averaged outer {self.reaction} thickness [m]": L_outer_av + * L_scale, } ) # Get variables related to the total thickness @@ -130,49 +107,6 @@ def _get_standard_thickness_variables(self, L_inner, L_outer): return variables - def _get_standard_thickness_variables_cracks(self, L_inner, L_outer): - """ - A private function to obtain the standard variables which - can be derived from the local SEI on cracks thickness. - - Parameters - ---------- - L_inner : :class:`pybamm.Symbol` - The inner SEI on cracks thickness. - L_outer : :class:`pybamm.Symbol` - The outer SEI on cracks thickness. - - Returns - ------- - variables : dict - The variables which can be derived from the SEI on cracks thicknesses. - """ - param = self.param - L_scale = param.L_sei_0_dim - - variables = { - "Inner SEI on cracks thickness": L_inner, - "Inner SEI on cracks thickness [m]": L_inner * L_scale, - "Outer SEI on cracks thickness": L_outer, - "Outer SEI on cracks thickness [m]": L_outer * L_scale, - } - - L_inner_av = pybamm.x_average(L_inner) - L_outer_av = pybamm.x_average(L_outer) - variables.update( - { - "X-averaged inner SEI on cracks thickness": L_inner_av, - "X-averaged inner SEI on cracks thickness [m]": L_inner_av * L_scale, - "X-averaged outer SEI on cracks thickness": L_outer_av, - "X-averaged outer SEI on cracks thickness [m]": L_outer_av * L_scale, - } - ) - # Get variables related to the total thickness - L_sei = L_inner + L_outer - variables.update(self._get_standard_total_thickness_variables_cracks(L_sei)) - - return variables - def _get_standard_total_thickness_variables(self, L_sei): """Update variables related to total SEI thickness.""" if isinstance(self, pybamm.sei.NoSEI): @@ -183,45 +117,28 @@ def _get_standard_total_thickness_variables(self, L_sei): R_sei_dim = self.param.R_sei_dimensional variables = { - "SEI thickness": L_sei, - "SEI thickness [m]": L_sei * L_scale, - "Total SEI thickness": L_sei, - "Total SEI thickness [m]": L_sei * L_scale, + f"{self.reaction} thickness": L_sei, + f"{self.reaction} [m]": L_sei * L_scale, + f"Total {self.reaction} thickness": L_sei, + f"Total {self.reaction} thickness [m]": L_sei * L_scale, } if self.reaction_loc != "interface": L_sei_av = pybamm.x_average(L_sei) variables.update( { - "X-averaged SEI thickness": L_sei_av, - "X-averaged SEI thickness [m]": L_sei_av * L_scale, - "X-averaged total SEI thickness": L_sei_av, - "X-averaged total SEI thickness [m]": L_sei_av * L_scale, - "X-averaged " - + self.domain.lower() - + " electrode resistance [Ohm.m2]": L_sei_av * L_scale * R_sei_dim, + f"X-averaged {self.reaction} thickness": L_sei_av, + f"X-averaged {self.reaction} thickness [m]": L_sei_av * L_scale, + f"X-averaged total {self.reaction} thickness": L_sei_av, + f"X-averaged total {self.reaction} thickness [m]": L_sei_av * L_scale, } ) - return variables - - def _get_standard_total_thickness_variables_cracks(self, L_sei): - """Update variables related to total SEI on cracks thickness.""" - L_scale = self.param.L_sei_0_dim - - variables = { - "SEI on cracks thickness": L_sei, - "SEI on cracks thickness [m]": L_sei * L_scale, - "Total SEI on cracks thickness": L_sei, - "Total SEI on cracks thickness [m]": L_sei * L_scale, - } - L_sei_av = pybamm.x_average(L_sei) - variables.update( - { - "X-averaged SEI on cracks thickness": L_sei_av, - "X-averaged SEI on cracksthickness [m]": L_sei_av * L_scale, - "X-averaged total SEI on cracks thickness": L_sei_av, - "X-averaged total SEI on cracks thickness [m]": L_sei_av * L_scale, - } - ) + if self.reaction == "SEI": + variables.update( + { + f"X-averaged {self.domain.lower()} electrode resistance " + "[Ohm.m2]": L_sei_av * L_scale * R_sei_dim, + } + ) return variables def _get_standard_concentration_variables(self, variables): @@ -261,54 +178,57 @@ def _get_standard_concentration_variables(self, variables): L_outer_0 = param.L_outer_0 li_mols_per_sei_mols = 1 - L_inner = variables["Inner SEI thickness"] - L_outer = variables["Outer SEI thickness"] + if self.reaction == "SEI": + L_inner = variables["Inner SEI thickness"] + L_outer = variables["Outer SEI thickness"] - n_inner = L_inner # inner SEI concentration - n_outer = L_outer # outer SEI concentration + n_inner = L_inner # inner SEI concentration + n_outer = L_outer # outer SEI concentration - n_inner_av = pybamm.x_average(n_inner) - n_outer_av = pybamm.x_average(n_outer) + n_inner_av = pybamm.x_average(n_inner) + n_outer_av = pybamm.x_average(n_outer) - n_SEI = n_inner + n_outer / v_bar # SEI concentration - n_SEI_av = pybamm.yz_average(pybamm.x_average(n_SEI)) + n_SEI = n_inner + n_outer / v_bar # SEI concentration + n_SEI_av = pybamm.yz_average(pybamm.x_average(n_SEI)) - # Calculate change in SEI concentration with respect to initial state - delta_n_SEI = n_SEI_av - (L_inner_0 + L_outer_0 / v_bar) + # Calculate change in SEI concentration with respect to initial state + delta_n_SEI = n_SEI_av - (L_inner_0 + L_outer_0 / v_bar) - # Q_sei in mol - if self.reaction_loc == "interface": - L_n = 1 - else: - L_n = self.param.L_n - - Q_sei = ( - li_mols_per_sei_mols - * delta_n_SEI - * n_scale - * L_n - * self.param.L_y - * self.param.L_z - ) + # Q_sei in mol + if self.reaction_loc == "interface": + L_n = 1 + else: + L_n = self.param.L_n - variables.update( - { - "Inner SEI concentration [mol.m-3]": n_inner * n_scale, - "X-averaged inner SEI concentration [mol.m-3]": n_inner_av * n_scale, - "Outer SEI concentration [mol.m-3]": n_outer * n_outer_scale, - "X-averaged outer SEI concentration [mol.m-3]": n_outer_av - * n_outer_scale, - "SEI concentration [mol.m-3]": n_SEI * n_scale, - "X-averaged SEI concentration [mol.m-3]": n_SEI_av * n_scale, - "Loss of lithium to SEI [mol]": Q_sei, - "Loss of capacity to SEI [A.h]": Q_sei * self.param.F / 3600, - } - ) + Q_sei = ( + li_mols_per_sei_mols + * delta_n_SEI + * n_scale + * L_n + * self.param.L_y + * self.param.L_z + ) - if self.options["SEI on cracks"] == "true": + variables.update( + { + "Inner SEI concentration [mol.m-3]": n_inner * n_scale, + "X-averaged inner SEI concentration [mol.m-3]": n_inner_av + * n_scale, + "Outer SEI concentration [mol.m-3]": n_outer * n_outer_scale, + "X-averaged outer SEI concentration [mol.m-3]": n_outer_av + * n_outer_scale, + "SEI concentration [mol.m-3]": n_SEI * n_scale, + "X-averaged SEI concentration [mol.m-3]": n_SEI_av * n_scale, + "Loss of lithium to SEI [mol]": Q_sei, + "Loss of capacity to SEI [A.h]": Q_sei * self.param.F / 3600, + } + ) + + # Concentration variables are handled slightly differently for SEI on cracks + if self.reaction == "SEI on cracks": L_inner_cr = variables["Inner SEI on cracks thickness"] L_outer_cr = variables["Outer SEI on cracks thickness"] - roughness = variables["Negative electrode roughness ratio"] + roughness = variables[self.domain + " electrode roughness ratio"] n_inner_cr = L_inner_cr * (roughness - 1) # inner SEI cracks concentration n_outer_cr = L_outer_cr * (roughness - 1) # outer SEI cracks concentration @@ -385,16 +305,18 @@ def _get_standard_reaction_variables(self, j_inner, j_outer): j_o_av = pybamm.x_average(j_outer) variables = { - "Inner SEI interfacial current density": j_inner, - "Inner SEI interfacial current density [A.m-2]": j_inner * j_scale, - "X-averaged inner SEI interfacial current density": j_i_av, - "X-averaged inner SEI interfacial current density [A.m-2]": j_i_av + f"Inner {self.reaction} interfacial current density": j_inner, + f"Inner {self.reaction} interfacial current density [A.m-2]": j_inner * j_scale, - "Outer SEI interfacial current density": j_outer, - "Outer SEI interfacial current density [A.m-2]": j_outer * j_scale, - "X-averaged outer SEI interfacial current density": j_o_av, - "X-averaged outer SEI interfacial current density [A.m-2]": j_o_av + f"X-averaged inner {self.reaction} interfacial current density": j_i_av, + f"X-averaged inner {self.reaction} interfacial current density [A.m-2]": + j_i_av * j_scale, + f"Outer {self.reaction} interfacial current density": j_outer, + f"Outer {self.reaction} interfacial current density [A.m-2]": j_outer * j_scale, + f"X-averaged outer {self.reaction} interfacial current density": j_o_av, + f"X-averaged outer {self.reaction} interfacial current density [A.m-2]": + j_o_av * j_scale, } j_sei = j_inner + j_outer @@ -402,84 +324,23 @@ def _get_standard_reaction_variables(self, j_inner, j_outer): return variables - def _get_standard_reaction_variables_cracks(self, j_inner, j_outer): - """ - A private function to obtain the standard variables which - can be derived from the SEI on cracks interfacial reaction current - - Parameters - ---------- - j_inner : :class:`pybamm.Symbol` - The inner SEI on cracks interfacial reaction current. - j_outer : :class:`pybamm.Symbol` - The outer SEI on cracks interfacial reaction current. - - Returns - ------- - variables : dict - The variables which can be derived from the SEI on cracks currents. - """ - j_scale = self.param.j_scale_n - j_i_av = pybamm.x_average(j_inner) - j_o_av = pybamm.x_average(j_outer) - - variables = { - "Inner SEI on cracks interfacial current density": j_inner, - "Inner SEI on cracks interfacial current density [A.m-2]": j_inner - * j_scale, - "X-averaged inner SEI on cracks interfacial current density": j_i_av, - "X-averaged inner SEI on cracks interfacial current density [A.m-2]": j_i_av - * j_scale, - "Outer SEI on cracks interfacial current density": j_outer, - "Outer SEI on cracks interfacial current density [A.m-2]": j_outer - * j_scale, - "X-averaged outer SEI on cracks interfacial current density": j_o_av, - "X-averaged outer SEI on cracks interfacial current density [A.m-2]": j_o_av - * j_scale, - } - - j_sei = j_inner + j_outer - variables.update(self._get_standard_total_reaction_variables_cracks(j_sei)) - - return variables - def _get_standard_total_reaction_variables(self, j_sei): """Update variables related to total SEI interfacial current density.""" j_scale = self.param.j_scale_n variables = { - "SEI interfacial current density": j_sei, - "SEI interfacial current density [A.m-2]": j_sei * j_scale, + f"{self.reaction} interfacial current density": j_sei, + f"{self.reaction} interfacial current density [A.m-2]": j_sei * j_scale, } if self.reaction_loc != "interface": j_sei_av = pybamm.x_average(j_sei) variables.update( { - "X-averaged SEI interfacial current density": j_sei_av, - "X-averaged SEI interfacial current density [A.m-2]": j_sei_av - * j_scale, + f"X-averaged {self.reaction} interfacial current density": j_sei_av, + f"X-averaged {self.reaction} interfacial current density [A.m-2]": + j_sei_av * j_scale, } ) return variables - - def _get_standard_total_reaction_variables_cracks(self, j_sei): - """Update variables related to total SEI on cracks current density.""" - j_scale = self.param.j_scale_n - - variables = { - "SEI on cracks interfacial current density": j_sei, - "SEI on cracks interfacial current density [A.m-2]": j_sei * j_scale, - } - - j_sei_av = pybamm.x_average(j_sei) - variables.update( - { - "X-averaged SEI on cracks interfacial current density": j_sei_av, - "X-averaged SEI on cracks interfacial current density [A.m-2]": j_sei_av - * j_scale, - } - ) - - return variables diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py index a6eaa419b9..2d7fb9a952 100644 --- a/pybamm/models/submodels/interface/sei/sei_growth.py +++ b/pybamm/models/submodels/interface/sei/sei_growth.py @@ -25,10 +25,9 @@ class SEIGrowth(BaseModel): def __init__(self, param, reaction_loc, options=None, cracks=False): super().__init__(param, options=options, cracks=cracks) self.reaction_loc = reaction_loc - self.cracks = cracks def get_fundamental_variables(self): - if self.cracks is True: + if self.reaction == "SEI on cracks": if self.reaction_loc == "x-average": L_inner_av = pybamm.Variable( "X-averaged inner SEI on cracks thickness", @@ -71,11 +70,7 @@ def get_fundamental_variables(self): if self.options["SEI"] == "ec reaction limited": L_inner = 0 * L_inner # Set L_inner to zero, copying domains - if self.cracks is True: - variables = self._get_standard_thickness_variables_cracks(L_inner, L_outer) - else: - variables = self._get_standard_thickness_variables(L_inner, L_outer) - + variables = self._get_standard_thickness_variables(L_inner, L_outer) variables.update(self._get_standard_concentration_variables(variables)) return variables @@ -108,14 +103,9 @@ def get_coupled_variables(self, variables): + " electrode total interfacial current density" ] - if self.cracks is True: - L_sei_inner = variables["Inner SEI on cracks thickness"] - L_sei_outer = variables["Outer SEI on cracks thickness"] - L_sei = variables["Total SEI on cracks thickness"] - else: - L_sei_inner = variables["Inner SEI thickness"] - L_sei_outer = variables["Outer SEI thickness"] - L_sei = variables["Total SEI thickness"] + L_sei_inner = variables[f"Inner {self.reaction} thickness"] + L_sei_outer = variables[f"Outer {self.reaction} thickness"] + L_sei = variables[f"Total {self.reaction} thickness"] T = variables["Negative electrode temperature"] R_sei = self.param.R_sei @@ -161,7 +151,7 @@ def get_coupled_variables(self, variables): c_ec_av = pybamm.x_average(c_ec) c_ec_scale = self.param.c_ec_0_dim - if self.cracks is True: + if self.reaction == "SEI on cracks": variables.update( { "EC concentration on cracks": c_ec, @@ -190,113 +180,81 @@ def get_coupled_variables(self, variables): j_inner = alpha * j_sei j_outer = (1 - alpha) * j_sei - if self.cracks is True: - variables.update( - self._get_standard_reaction_variables_cracks(j_inner, j_outer) - ) - else: - variables.update(self._get_standard_reaction_variables(j_inner, j_outer)) + variables.update(self._get_standard_reaction_variables(j_inner, j_outer)) # Update whole cell variables, which also updates the "sum of" variables - variables.update(super().get_coupled_variables(variables, self.cracks)) + variables.update(super().get_coupled_variables(variables)) return variables def set_rhs(self, variables): - if self.cracks is True: - if self.reaction_loc == "x-average": - L_inner = variables["X-averaged inner SEI on cracks thickness"] - L_outer = variables["X-averaged outer SEI on cracks thickness"] - j_inner = variables[ - "X-averaged inner SEI on cracks interfacial current density" - ] - j_outer = variables[ - "X-averaged outer SEI on cracks interfacial current density" - ] - a = variables[ - "X-averaged negative electrode surface area to volume ratio" + if self.reaction_loc == "x-average": + L_inner = variables[f"X-averaged inner {self.reaction} thickness"] + L_outer = variables[f"X-averaged outer {self.reaction} thickness"] + j_inner = variables[ + f"X-averaged inner {self.reaction} interfacial current density" + ] + j_outer = variables[ + f"X-averaged outer {self.reaction} interfacial current density" ] + # Note a is dimensionless (has a constant value of 1 if the surface + # area does not change) + a = variables[ + "X-averaged negative electrode surface area to volume ratio" + ] + else: + L_inner = variables[f"Inner {self.reaction} thickness"] + L_outer = variables[f"Outer {self.reaction} thickness"] + j_inner = variables[f"Inner {self.reaction} interfacial current density"] + j_outer = variables[f"Outer {self.reaction} interfacial current density"] + if self.reaction_loc == "interface": + a = 1 + else: + a = variables["Negative electrode surface area to volume ratio"] + + # Get variables specific to cracks + if self.reaction == "SEI on cracks": + if self.reaction_loc == "x-average": l_cr = variables["X-averaged negative particle crack length"] dl_cr = variables["X-averaged negative particle cracking rate"] else: - L_inner = variables["Inner SEI on cracks thickness"] - L_outer = variables["Outer SEI on cracks thickness"] - j_inner = variables["Inner SEI on cracks interfacial current density"] - j_outer = variables["Outer SEI on cracks interfacial current density"] - a = variables["Negative electrode surface area to volume ratio"] l_cr = variables["Negative particle crack length"] dl_cr = variables["Negative particle cracking rate"] spreading_outer = dl_cr / l_cr * (self.param.L_outer_0 / 10000 - L_outer) spreading_inner = dl_cr / l_cr * (self.param.L_inner_0 / 10000 - L_inner) else: - if self.reaction_loc == "x-average": - L_inner = variables["X-averaged inner SEI thickness"] - L_outer = variables["X-averaged outer SEI thickness"] - j_inner = variables["X-averaged inner SEI interfacial current density"] - j_outer = variables["X-averaged outer SEI interfacial current density"] - # Note a is dimensionless (has a constant value of 1 if the surface - # area does not change) - a = variables[ - "X-averaged negative electrode surface area to volume ratio" - ] - else: - L_inner = variables["Inner SEI thickness"] - L_outer = variables["Outer SEI thickness"] - j_inner = variables["Inner SEI interfacial current density"] - j_outer = variables["Outer SEI interfacial current density"] - if self.reaction_loc == "interface": - a = 1 - else: - a = variables["Negative electrode surface area to volume ratio"] + spreading_outer = 0 + spreading_inner = 0 Gamma_SEI = self.param.Gamma_SEI if self.options["SEI"] == "ec reaction limited": - if self.cracks is True: - self.rhs = { - L_outer: -Gamma_SEI * a * j_outer / 2 + spreading_outer, - } - else: - self.rhs = {L_outer: -Gamma_SEI * a * j_outer / 2} + self.rhs = {L_outer: -Gamma_SEI * a * j_outer / 2 + spreading_outer} else: v_bar = self.param.v_bar - if self.cracks is True: - self.rhs = { - L_inner: -Gamma_SEI * a * j_inner + spreading_inner, - L_outer: -v_bar * Gamma_SEI * a * j_outer + spreading_outer, - } - else: - self.rhs = { - L_inner: -Gamma_SEI * a * j_inner, - L_outer: -v_bar * Gamma_SEI * a * j_outer, - } + self.rhs = { + L_inner: -Gamma_SEI * a * j_inner + spreading_inner, + L_outer: -v_bar * Gamma_SEI * a * j_outer + spreading_outer, + } def set_initial_conditions(self, variables): - if self.cracks is True: - if self.reaction_loc == "x-average": - L_inner = variables["X-averaged inner SEI on cracks thickness"] - L_outer = variables["X-averaged outer SEI on cracks thickness"] - else: - L_inner = variables["Inner SEI on cracks thickness"] - L_outer = variables["Outer SEI on cracks thickness"] + if self.reaction_loc == "x-average": + L_inner = variables[f"X-averaged inner {self.reaction} thickness"] + L_outer = variables[f"X-averaged outer {self.reaction} thickness"] else: - if self.reaction_loc == "x-average": - L_inner = variables["X-averaged inner SEI thickness"] - L_outer = variables["X-averaged outer SEI thickness"] - else: - L_inner = variables["Inner SEI thickness"] - L_outer = variables["Outer SEI thickness"] + L_inner = variables[f"Inner {self.reaction} thickness"] + L_outer = variables[f"Outer {self.reaction} thickness"] L_inner_0 = self.param.L_inner_0 L_outer_0 = self.param.L_outer_0 if self.options["SEI"] == "ec reaction limited": - if self.cracks is True: + if self.reaction == "SEI on cracks": self.initial_conditions = {L_outer: (L_inner_0 + L_outer_0) / 10000} else: self.initial_conditions = {L_outer: L_inner_0 + L_outer_0} else: - if self.cracks is True: + if self.reaction == "SEI on cracks": self.initial_conditions = { L_inner: L_inner_0 / 10000, L_outer: L_outer_0 / 10000, From ecfcdc4113e2522dbe943a7c5c6a2d6fbc70db26 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Mon, 20 Jun 2022 16:30:14 +0100 Subject: [PATCH 06/36] Added NoMechanics subclass --- examples/notebooks/models/SEI-on-cracks.ipynb | 22 ++++++---- .../full_battery_models/base_battery_model.py | 11 +++++ .../lithium_ion/base_lithium_ion_model.py | 15 ++++++- .../submodels/interface/sei/base_sei.py | 20 +++------ .../models/submodels/interface/sei/no_sei.py | 4 +- .../submodels/interface/sei/sei_growth.py | 2 +- .../submodels/particle_mechanics/__init__.py | 1 + .../particle_mechanics/no_mechanics.py | 41 +++++++++++++++++++ 8 files changed, 88 insertions(+), 28 deletions(-) create mode 100644 pybamm/models/submodels/particle_mechanics/no_mechanics.py diff --git a/examples/notebooks/models/SEI-on-cracks.ipynb b/examples/notebooks/models/SEI-on-cracks.ipynb index 20f1b99483..e25a7313d1 100644 --- a/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/examples/notebooks/models/SEI-on-cracks.ipynb @@ -36,23 +36,29 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m parameter_values \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mParameterValues(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAi2020\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m spm \u001b[38;5;241m=\u001b[39m \u001b[43mpybamm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlithium_ion\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mSPM\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\n\u001b[1;32m 3\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mparticle mechanics\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m 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\u001b[0;36mSPM.__init__\u001b[0;34m(self, options, name, build)\u001b[0m\n\u001b[1;32m 64\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_li_metal_counter_electrode_submodels()\n\u001b[1;32m 66\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m build:\n\u001b[0;32m---> 67\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMPM\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m 70\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mcitations\u001b[38;5;241m.\u001b[39mregister(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mMarquis2019\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:852\u001b[0m, in \u001b[0;36mBaseBatteryModel.build_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 849\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStart building \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname))\n\u001b[1;32m 851\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_built_fundamental_and_external \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m--> 852\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_fundamental_and_external\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 854\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_coupled_variables()\n\u001b[1;32m 856\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_model_equations()\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:740\u001b[0m, in \u001b[0;36mBaseBatteryModel.build_fundamental_and_external\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 734\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m submodel_name, submodel \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubmodels\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 735\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\n\u001b[1;32m 736\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGetting fundamental variables for \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m submodel (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 737\u001b[0m submodel_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname\n\u001b[1;32m 738\u001b[0m )\n\u001b[1;32m 739\u001b[0m )\n\u001b[0;32m--> 740\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables\u001b[38;5;241m.\u001b[39mupdate(\u001b[43msubmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_fundamental_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 742\u001b[0m \u001b[38;5;66;03m# Set the submodels that are external\u001b[39;00m\n\u001b[1;32m 743\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sub \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexternal submodels\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/submodels/interface/sei/sei_growth.py:74\u001b[0m, in \u001b[0;36mSEIGrowth.get_fundamental_variables\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 71\u001b[0m L_inner \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m \u001b[38;5;241m*\u001b[39m L_inner \u001b[38;5;66;03m# Set L_inner to zero, copying domains\u001b[39;00m\n\u001b[1;32m 73\u001b[0m variables \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_standard_thickness_variables(L_inner, L_outer)\n\u001b[0;32m---> 74\u001b[0m variables\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_standard_concentration_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 76\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m variables\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/submodels/interface/sei/base_sei.py:231\u001b[0m, in \u001b[0;36mBaseModel._get_standard_concentration_variables\u001b[0;34m(self, variables)\u001b[0m\n\u001b[1;32m 229\u001b[0m L_inner_cr \u001b[38;5;241m=\u001b[39m variables[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInner SEI on cracks thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 230\u001b[0m L_outer_cr \u001b[38;5;241m=\u001b[39m variables[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOuter SEI on cracks thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 231\u001b[0m roughness \u001b[38;5;241m=\u001b[39m \u001b[43mvariables\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdomain\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m electrode roughness ratio\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 233\u001b[0m n_inner_cr \u001b[38;5;241m=\u001b[39m L_inner_cr \u001b[38;5;241m*\u001b[39m (roughness \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# inner SEI cracks concentration\u001b[39;00m\n\u001b[1;32m 234\u001b[0m n_outer_cr \u001b[38;5;241m=\u001b[39m L_outer_cr \u001b[38;5;241m*\u001b[39m (roughness \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# outer SEI cracks concentration\u001b[39;00m\n", + "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m parameter_values \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mParameterValues(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAi2020\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m model1 \u001b[38;5;241m=\u001b[39m \u001b[43mpybamm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlithium_ion\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDFN\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSEI\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msolvent-diffusion limited\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m model2 \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mlithium_ion\u001b[38;5;241m.\u001b[39mDFN({\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparticle mechanics\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mswelling and cracking\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSEI\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msolvent-diffusion limited\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSEI on cracks\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrue\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 7\u001b[0m })\n\u001b[1;32m 8\u001b[0m experiment \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mExperiment([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDischarge at 1C until 3 V\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/lithium_ion/dfn.py:66\u001b[0m, in \u001b[0;36mDFN.__init__\u001b[0;34m(self, options, name, build)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_li_metal_counter_electrode_submodels()\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m build:\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 68\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mcitations\u001b[38;5;241m.\u001b[39mregister(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDoyle1993\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:863\u001b[0m, in \u001b[0;36mBaseBatteryModel.build_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 860\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStart building \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname))\n\u001b[1;32m 862\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_built_fundamental_and_external \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m--> 863\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_fundamental_and_external\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_coupled_variables()\n\u001b[1;32m 867\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_model_equations()\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:751\u001b[0m, in \u001b[0;36mBaseBatteryModel.build_fundamental_and_external\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 745\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m submodel_name, submodel \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubmodels\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 746\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\n\u001b[1;32m 747\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGetting fundamental variables for \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m submodel (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 748\u001b[0m submodel_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname\n\u001b[1;32m 749\u001b[0m )\n\u001b[1;32m 750\u001b[0m )\n\u001b[0;32m--> 751\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables\u001b[38;5;241m.\u001b[39mupdate(\u001b[43msubmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_fundamental_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 753\u001b[0m \u001b[38;5;66;03m# Set the submodels that are external\u001b[39;00m\n\u001b[1;32m 754\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sub \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexternal submodels\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/submodels/interface/sei/no_sei.py:37\u001b[0m, in \u001b[0;36mNoSEI.get_fundamental_variables\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 33\u001b[0m zero \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mFullBroadcast(\n\u001b[1;32m 34\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mScalar(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnegative electrode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcurrent collector\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 35\u001b[0m )\n\u001b[1;32m 36\u001b[0m variables \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_standard_thickness_variables(zero, zero)\n\u001b[0;32m---> 37\u001b[0m variables\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_standard_concentration_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 38\u001b[0m variables\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_standard_reaction_variables(zero, zero))\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m variables\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/submodels/interface/sei/base_sei.py:230\u001b[0m, in \u001b[0;36mBaseModel._get_standard_concentration_variables\u001b[0;34m(self, variables)\u001b[0m\n\u001b[1;32m 228\u001b[0m L_inner_cr \u001b[38;5;241m=\u001b[39m variables[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInner SEI on cracks thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 229\u001b[0m L_outer_cr \u001b[38;5;241m=\u001b[39m variables[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOuter SEI on cracks thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 230\u001b[0m roughness \u001b[38;5;241m=\u001b[39m \u001b[43mvariables\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdomain\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m electrode roughness ratio\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 232\u001b[0m n_inner_cr \u001b[38;5;241m=\u001b[39m L_inner_cr \u001b[38;5;241m*\u001b[39m (roughness \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# inner SEI cracks concentration\u001b[39;00m\n\u001b[1;32m 233\u001b[0m n_outer_cr \u001b[38;5;241m=\u001b[39m L_outer_cr \u001b[38;5;241m*\u001b[39m (roughness \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# outer SEI cracks concentration\u001b[39;00m\n", "\u001b[0;31mKeyError\u001b[0m: 'Negative electrode roughness ratio'" ] } ], "source": [ "parameter_values = pybamm.ParameterValues(\"Ai2020\")\n", - "spm = pybamm.lithium_ion.SPM({\n", + "model1 = pybamm.lithium_ion.DFN({\"SEI\": \"solvent-diffusion limited\"})\n", + "model2 = pybamm.lithium_ion.DFN({\n", " \"particle mechanics\": \"swelling and cracking\",\n", " \"SEI\": \"solvent-diffusion limited\",\n", " \"SEI on cracks\": \"true\",\n", - "})" + "})\n", + "experiment = pybamm.Experiment([\"Discharge at 1C until 3 V\"])\n", + "sim1 = pybamm.Simulation(model1, parameter_values=parameter_values, experiment=experiment)\n", + "sol1 = sim1.solve()\n", + "sim2 = pybamm.Simulation(model2, parameter_values=parameter_values, experiment=experiment)\n", + "sol2 = sim2.solve()" ] }, { diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 751ab9128b..1545c58fc1 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -336,6 +336,17 @@ def __init__(self, extra_options): "current density as a state' must be 'true'" ) + if options["SEI on cracks"] == "true": + if options["particle mechanics"] != "swelling and cracking": + raise pybamm.OptionError( + "To model SEI on cracks, 'particle mechanics' must be set to " + "'swelling and cracking'." + ) + elif options["working electrode"] == "positive": + raise NotImplementedError( + "SEI on cracks not yet implemented for lithium metal eleectrode." + ) + # Options not yet compatible with particle-size distributions if options["particle size"] == "distribution": if options["lithium plating"] != "none": diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index 683c3d6618..20359fbd8f 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -201,17 +201,26 @@ def set_sei_submodel(self): if self.options["SEI"] == "none": self.submodels["sei"] = pybamm.sei.NoSEI(self.param, self.options) + self.submodels["sei on cracks"] = pybamm.sei.NoSEI( + self.param, self.options, cracks=True + ) elif self.options["SEI"] == "constant": self.submodels["sei"] = pybamm.sei.ConstantSEI(self.param, self.options) + self.submodels["sei on cracks"] = pybamm.sei.NoSEI( + self.param, self.options, cracks=True + ) else: self.submodels["sei"] = pybamm.sei.SEIGrowth( self.param, reaction_loc, self.options, cracks=False ) - # Run SEI growth model again, this time on cracks if self.options["SEI on cracks"] == "true": self.submodels["sei on cracks"] = pybamm.sei.SEIGrowth( self.param, reaction_loc, self.options, cracks=True ) + else: + self.submodels["sei on cracks"] = pybamm.sei.NoSEI( + self.param, self.options, cracks=True + ) def set_lithium_plating_submodel(self): if self.options["lithium plating"] == "none": @@ -235,7 +244,9 @@ def set_crack_submodel(self): for domain in ["Negative", "Positive"]: crack = getattr(self.options, domain.lower())["particle mechanics"] if crack == "none": - pass + self.submodels[ + domain.lower() + " particle mechanics" + ] = pybamm.particle_mechanics.NoMechanics(self.param, domain) elif crack == "swelling only": self.submodels[ domain.lower() + " particle mechanics" diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py index 490378b364..fcdd7c210f 100644 --- a/pybamm/models/submodels/interface/sei/base_sei.py +++ b/pybamm/models/submodels/interface/sei/base_sei.py @@ -223,9 +223,8 @@ def _get_standard_concentration_variables(self, variables): "Loss of capacity to SEI [A.h]": Q_sei * self.param.F / 3600, } ) - # Concentration variables are handled slightly differently for SEI on cracks - if self.reaction == "SEI on cracks": + elif self.reaction == "SEI on cracks": L_inner_cr = variables["Inner SEI on cracks thickness"] L_outer_cr = variables["Outer SEI on cracks thickness"] roughness = variables[self.domain + " electrode roughness ratio"] @@ -233,14 +232,14 @@ def _get_standard_concentration_variables(self, variables): n_inner_cr = L_inner_cr * (roughness - 1) # inner SEI cracks concentration n_outer_cr = L_outer_cr * (roughness - 1) # outer SEI cracks concentration - n_inner_cr_av = pybamm.x_average(n_inner) - n_outer_cr_av = pybamm.x_average(n_outer) + n_inner_cr_av = pybamm.x_average(n_inner_cr) + n_outer_cr_av = pybamm.x_average(n_outer_cr) n_SEI_cr = n_inner_cr + n_outer_cr / v_bar # SEI on cracks concentration - n_SEI_cr_av = pybamm.yz_average(pybamm.x_average(n_SEI)) + n_SEI_cr_av = pybamm.yz_average(pybamm.x_average(n_SEI_cr)) # Calculate change in SEI cracks concentration with respect to initial state - rho_cr = param.rho_cr_n + rho_cr = param.n.rho_cr n_SEI_cr_init = 2 * rho_cr * (L_inner_0 + L_outer_0 / v_bar) / 10000 delta_n_SEI_cr = n_SEI_cr_av - n_SEI_cr_init @@ -271,15 +270,6 @@ def _get_standard_concentration_variables(self, variables): * self.param.F / 3600, } ) - else: - zero = pybamm.Scalar(0) - # Degradation variables are required even if SEI on cracks is turned off - variables.update( - { - "Loss of lithium to SEI on cracks [mol]": zero, - "loss of capacity to SEI on cracks [A.h]": zero, - } - ) return variables diff --git a/pybamm/models/submodels/interface/sei/no_sei.py b/pybamm/models/submodels/interface/sei/no_sei.py index 2c6c8005ce..e86a0ce403 100644 --- a/pybamm/models/submodels/interface/sei/no_sei.py +++ b/pybamm/models/submodels/interface/sei/no_sei.py @@ -19,8 +19,8 @@ class NoSEI(BaseModel): **Extends:** :class:`pybamm.sei.BaseModel` """ - def __init__(self, param, options=None): - super().__init__(param, options=options) + def __init__(self, param, options=None, cracks=False): + super().__init__(param, options=options, cracks=cracks) if self.half_cell: self.reaction_loc = "interface" else: diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py index 2d7fb9a952..543949cfb1 100644 --- a/pybamm/models/submodels/interface/sei/sei_growth.py +++ b/pybamm/models/submodels/interface/sei/sei_growth.py @@ -71,7 +71,6 @@ def get_fundamental_variables(self): L_inner = 0 * L_inner # Set L_inner to zero, copying domains variables = self._get_standard_thickness_variables(L_inner, L_outer) - variables.update(self._get_standard_concentration_variables(variables)) return variables @@ -180,6 +179,7 @@ def get_coupled_variables(self, variables): j_inner = alpha * j_sei j_outer = (1 - alpha) * j_sei + variables.update(self._get_standard_concentration_variables(variables)) variables.update(self._get_standard_reaction_variables(j_inner, j_outer)) # Update whole cell variables, which also updates the "sum of" variables diff --git a/pybamm/models/submodels/particle_mechanics/__init__.py b/pybamm/models/submodels/particle_mechanics/__init__.py index 9a6fc1f830..9b89a95d55 100644 --- a/pybamm/models/submodels/particle_mechanics/__init__.py +++ b/pybamm/models/submodels/particle_mechanics/__init__.py @@ -1,3 +1,4 @@ from .base_mechanics import BaseMechanics from .crack_propagation import CrackPropagation from .swelling_only import SwellingOnly +from .no_mechanics import NoMechanics diff --git a/pybamm/models/submodels/particle_mechanics/no_mechanics.py b/pybamm/models/submodels/particle_mechanics/no_mechanics.py new file mode 100644 index 0000000000..c0c81532a4 --- /dev/null +++ b/pybamm/models/submodels/particle_mechanics/no_mechanics.py @@ -0,0 +1,41 @@ +# +# Class for no mechanics +# +import pybamm +from .base_mechanics import BaseMechanics + + +class NoMechanics(BaseMechanics): + """ + Class for swelling only (no cracking) + + Parameters + ---------- + param : parameter class + The parameters to use for this submodel + domain : str + The domain of the model either 'Negative' or 'Positive' + + **Extends:** :class:`pybamm.particle_mechanics.BaseMechanics` + """ + + def __init__(self, param, domain): + super().__init__(param, domain) + + def get_fundamental_variables(self): + zero = pybamm.FullBroadcast( + pybamm.Scalar(0), self.domain.lower() + " electrode", "current collector" + ) + zero_av = pybamm.x_average(zero) + variables = self._get_standard_variables(zero) + variables.update( + { + self.domain + " particle cracking rate": zero, + "X-averaged " + self.domain + " particle cracking rate": zero_av, + } + ) + return variables + + def get_coupled_variables(self, variables): + variables.update(self._get_standard_surface_variables(variables)) + return variables \ No newline at end of file From fea7d4c2c26e998341600f2ca05c8c8d2a35017c Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Mon, 20 Jun 2022 16:33:37 +0100 Subject: [PATCH 07/36] flake8 --- pybamm/models/submodels/interface/sei/base_sei.py | 9 ++++++--- pybamm/models/submodels/interface/sei/sei_growth.py | 4 ++-- .../models/submodels/particle_mechanics/no_mechanics.py | 2 +- 3 files changed, 9 insertions(+), 6 deletions(-) diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py index fcdd7c210f..5a877529ab 100644 --- a/pybamm/models/submodels/interface/sei/base_sei.py +++ b/pybamm/models/submodels/interface/sei/base_sei.py @@ -36,10 +36,12 @@ def get_coupled_variables(self, variables): { f"X-averaged negative electrode {self.reaction} interfacial " "current density": variables[ - f"X-averaged {self.reaction} interfacial current density" + f"X-averaged {self.reaction} interfacial current density" ], f"Negative electrode {self.reaction} interfacial current " - "density": variables[f"{self.reaction} interfacial current density"], + "density": variables[ + f"{self.reaction} interfacial current density" + ], } ) variables.update( @@ -129,7 +131,8 @@ def _get_standard_total_thickness_variables(self, L_sei): f"X-averaged {self.reaction} thickness": L_sei_av, f"X-averaged {self.reaction} thickness [m]": L_sei_av * L_scale, f"X-averaged total {self.reaction} thickness": L_sei_av, - f"X-averaged total {self.reaction} thickness [m]": L_sei_av * L_scale, + f"X-averaged total {self.reaction} thickness [m]": L_sei_av + * L_scale, } ) if self.reaction == "SEI": diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py index 543949cfb1..b667d4e51a 100644 --- a/pybamm/models/submodels/interface/sei/sei_growth.py +++ b/pybamm/models/submodels/interface/sei/sei_growth.py @@ -196,7 +196,7 @@ def set_rhs(self, variables): ] j_outer = variables[ f"X-averaged outer {self.reaction} interfacial current density" - ] + ] # Note a is dimensionless (has a constant value of 1 if the surface # area does not change) a = variables[ @@ -211,7 +211,7 @@ def set_rhs(self, variables): a = 1 else: a = variables["Negative electrode surface area to volume ratio"] - + # Get variables specific to cracks if self.reaction == "SEI on cracks": if self.reaction_loc == "x-average": diff --git a/pybamm/models/submodels/particle_mechanics/no_mechanics.py b/pybamm/models/submodels/particle_mechanics/no_mechanics.py index c0c81532a4..6ecadaa1aa 100644 --- a/pybamm/models/submodels/particle_mechanics/no_mechanics.py +++ b/pybamm/models/submodels/particle_mechanics/no_mechanics.py @@ -38,4 +38,4 @@ def get_fundamental_variables(self): def get_coupled_variables(self, variables): variables.update(self._get_standard_surface_variables(variables)) - return variables \ No newline at end of file + return variables From 5b321a72dd65613fd58318d969b1a11d452e9202 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Tue, 21 Jun 2022 13:39:40 +0100 Subject: [PATCH 08/36] Added if statement to set roughness to 1 if not in variables --- examples/notebooks/models/SEI-on-cracks.ipynb | 32 +++++++++++++------ .../submodels/interface/sei/base_sei.py | 5 ++- 2 files changed, 26 insertions(+), 11 deletions(-) diff --git a/examples/notebooks/models/SEI-on-cracks.ipynb b/examples/notebooks/models/SEI-on-cracks.ipynb index e25a7313d1..f1ae2f361a 100644 --- a/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/examples/notebooks/models/SEI-on-cracks.ipynb @@ -30,19 +30,31 @@ "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'Negative electrode roughness ratio'", + "name": "stderr", + "output_type": "stream", + "text": [ + "At t = 0.00698581, , mxstep steps taken before reaching tout.\n" + ] + }, + { + "ename": "SolverError", + "evalue": "Could not solve for summary variables, run `sim.solve(calc_esoh=False)` to skip this step", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m parameter_values \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mParameterValues(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAi2020\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m model1 \u001b[38;5;241m=\u001b[39m \u001b[43mpybamm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlithium_ion\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDFN\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSEI\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msolvent-diffusion limited\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m model2 \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mlithium_ion\u001b[38;5;241m.\u001b[39mDFN({\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparticle mechanics\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mswelling and cracking\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSEI\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msolvent-diffusion limited\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSEI on cracks\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrue\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 7\u001b[0m })\n\u001b[1;32m 8\u001b[0m experiment \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mExperiment([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDischarge at 1C until 3 V\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/lithium_ion/dfn.py:66\u001b[0m, in \u001b[0;36mDFN.__init__\u001b[0;34m(self, options, name, build)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_li_metal_counter_electrode_submodels()\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m build:\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 68\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mcitations\u001b[38;5;241m.\u001b[39mregister(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDoyle1993\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:863\u001b[0m, in \u001b[0;36mBaseBatteryModel.build_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 860\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStart building \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname))\n\u001b[1;32m 862\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_built_fundamental_and_external \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m--> 863\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_fundamental_and_external\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_coupled_variables()\n\u001b[1;32m 867\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_model_equations()\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:751\u001b[0m, in \u001b[0;36mBaseBatteryModel.build_fundamental_and_external\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 745\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m submodel_name, submodel \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubmodels\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 746\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\n\u001b[1;32m 747\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGetting fundamental variables for \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m submodel (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 748\u001b[0m submodel_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname\n\u001b[1;32m 749\u001b[0m )\n\u001b[1;32m 750\u001b[0m )\n\u001b[0;32m--> 751\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables\u001b[38;5;241m.\u001b[39mupdate(\u001b[43msubmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_fundamental_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 753\u001b[0m \u001b[38;5;66;03m# Set the submodels that are external\u001b[39;00m\n\u001b[1;32m 754\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sub \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexternal submodels\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/submodels/interface/sei/no_sei.py:37\u001b[0m, in \u001b[0;36mNoSEI.get_fundamental_variables\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 33\u001b[0m zero \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mFullBroadcast(\n\u001b[1;32m 34\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mScalar(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnegative electrode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcurrent collector\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 35\u001b[0m )\n\u001b[1;32m 36\u001b[0m variables \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_standard_thickness_variables(zero, zero)\n\u001b[0;32m---> 37\u001b[0m variables\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_standard_concentration_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 38\u001b[0m variables\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_standard_reaction_variables(zero, zero))\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m variables\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/submodels/interface/sei/base_sei.py:230\u001b[0m, in \u001b[0;36mBaseModel._get_standard_concentration_variables\u001b[0;34m(self, variables)\u001b[0m\n\u001b[1;32m 228\u001b[0m L_inner_cr \u001b[38;5;241m=\u001b[39m variables[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInner SEI on cracks thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 229\u001b[0m L_outer_cr \u001b[38;5;241m=\u001b[39m variables[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOuter SEI on cracks thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 230\u001b[0m roughness \u001b[38;5;241m=\u001b[39m \u001b[43mvariables\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdomain\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m electrode roughness ratio\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 232\u001b[0m n_inner_cr \u001b[38;5;241m=\u001b[39m L_inner_cr \u001b[38;5;241m*\u001b[39m (roughness \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# inner SEI cracks concentration\u001b[39;00m\n\u001b[1;32m 233\u001b[0m n_outer_cr \u001b[38;5;241m=\u001b[39m L_outer_cr \u001b[38;5;241m*\u001b[39m (roughness \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# outer SEI cracks concentration\u001b[39;00m\n", - "\u001b[0;31mKeyError\u001b[0m: 'Negative electrode roughness ratio'" + "\u001b[0;31mSolverError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m~/PyBaMM/pybamm/solvers/solution.py:918\u001b[0m, in \u001b[0;36mget_cycle_summary_variables\u001b[0;34m(cycle_solution, esoh_sim)\u001b[0m\n\u001b[1;32m 917\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 918\u001b[0m esoh_sol \u001b[38;5;241m=\u001b[39m \u001b[43mesoh_sim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msolver\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msolver\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 919\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m pybamm\u001b[38;5;241m.\u001b[39mSolverError: \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n", + "File \u001b[0;32m~/PyBaMM/pybamm/simulation.py:710\u001b[0m, in \u001b[0;36mSimulation.solve\u001b[0;34m(self, t_eval, solver, check_model, save_at_cycles, calc_esoh, starting_solution, initial_soc, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 696\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 697\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 698\u001b[0m \u001b[38;5;124;03m The largest timestep in t_eval ({}) is larger than\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 707\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mSolverWarning,\n\u001b[1;32m 708\u001b[0m )\n\u001b[0;32m--> 710\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solution \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuilt_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_eval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 712\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moperating_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwith experiment\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", + "File \u001b[0;32m~/PyBaMM/pybamm/solvers/base_solver.py:1083\u001b[0m, in \u001b[0;36mBaseSolver.solve\u001b[0;34m(self, model, t_eval, external_variables, inputs, initial_conditions, nproc, calculate_sensitivities)\u001b[0m\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ninputs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m-> 1083\u001b[0m new_solution \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_integrate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1084\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1085\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_eval_dimensionless\u001b[49m\u001b[43m[\u001b[49m\u001b[43mstart_index\u001b[49m\u001b[43m:\u001b[49m\u001b[43mend_index\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1086\u001b[0m \u001b[43m \u001b[49m\u001b[43mext_and_inputs_list\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1087\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1088\u001b[0m new_solutions \u001b[38;5;241m=\u001b[39m [new_solution]\n", + "File \u001b[0;32m~/PyBaMM/pybamm/solvers/casadi_algebraic_solver.py:184\u001b[0m, in \u001b[0;36mCasadiAlgebraicSolver._integrate\u001b[0;34m(self, model, t_eval, inputs_dict)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m success:\n\u001b[0;32m--> 184\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m pybamm\u001b[38;5;241m.\u001b[39mSolverError(\n\u001b[1;32m 185\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not find acceptable solution: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(message)\n\u001b[1;32m 186\u001b[0m )\n\u001b[1;32m 187\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28many\u001b[39m(np\u001b[38;5;241m.\u001b[39misnan(fun)):\n", + "\u001b[0;31mSolverError\u001b[0m: Could not find acceptable solution: .../casadi/core/rootfinder.cpp:280: rootfinder process failed. Set 'error_on_fail' option to false to ignore this error.", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[0;31mSolverError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m experiment \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mExperiment([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDischarge at 1C until 3 V\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m 9\u001b[0m sim1 \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mSimulation(model1, parameter_values\u001b[38;5;241m=\u001b[39mparameter_values, experiment\u001b[38;5;241m=\u001b[39mexperiment)\n\u001b[0;32m---> 10\u001b[0m sol1 \u001b[38;5;241m=\u001b[39m \u001b[43msim1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m sim2 \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mSimulation(model2, parameter_values\u001b[38;5;241m=\u001b[39mparameter_values, experiment\u001b[38;5;241m=\u001b[39mexperiment)\n\u001b[1;32m 12\u001b[0m sol2 \u001b[38;5;241m=\u001b[39m sim2\u001b[38;5;241m.\u001b[39msolve()\n", + "File \u001b[0;32m~/PyBaMM/pybamm/simulation.py:858\u001b[0m, in \u001b[0;36mSimulation.solve\u001b[0;34m(self, t_eval, solver, check_model, save_at_cycles, calc_esoh, starting_solution, initial_soc, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 856\u001b[0m \u001b[38;5;66;03m# At the final step of the inner loop we save the cycle\u001b[39;00m\n\u001b[1;32m 857\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(steps) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 858\u001b[0m cycle_sol \u001b[38;5;241m=\u001b[39m \u001b[43mpybamm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmake_cycle_solution\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 859\u001b[0m \u001b[43m \u001b[49m\u001b[43msteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43mesoh_sim\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_this_cycle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_this_cycle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 863\u001b[0m cycle_solution, cycle_sum_vars, cycle_first_state \u001b[38;5;241m=\u001b[39m cycle_sol\n\u001b[1;32m 864\u001b[0m all_cycle_solutions\u001b[38;5;241m.\u001b[39mappend(cycle_solution)\n", + "File \u001b[0;32m~/PyBaMM/pybamm/solvers/solution.py:812\u001b[0m, in \u001b[0;36mmake_cycle_solution\u001b[0;34m(step_solutions, esoh_sim, save_this_cycle)\u001b[0m\n\u001b[1;32m 808\u001b[0m cycle_solution\u001b[38;5;241m.\u001b[39mset_up_time \u001b[38;5;241m=\u001b[39m sum_sols\u001b[38;5;241m.\u001b[39mset_up_time\n\u001b[1;32m 810\u001b[0m cycle_solution\u001b[38;5;241m.\u001b[39msteps \u001b[38;5;241m=\u001b[39m step_solutions\n\u001b[0;32m--> 812\u001b[0m cycle_summary_variables \u001b[38;5;241m=\u001b[39m \u001b[43mget_cycle_summary_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcycle_solution\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mesoh_sim\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 814\u001b[0m cycle_first_state \u001b[38;5;241m=\u001b[39m cycle_solution\u001b[38;5;241m.\u001b[39mfirst_state\n\u001b[1;32m 816\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save_this_cycle:\n", + "File \u001b[0;32m~/PyBaMM/pybamm/solvers/solution.py:920\u001b[0m, in \u001b[0;36mget_cycle_summary_variables\u001b[0;34m(cycle_solution, esoh_sim)\u001b[0m\n\u001b[1;32m 918\u001b[0m esoh_sol \u001b[38;5;241m=\u001b[39m esoh_sim\u001b[38;5;241m.\u001b[39msolve([\u001b[38;5;241m0\u001b[39m], inputs\u001b[38;5;241m=\u001b[39minputs, solver\u001b[38;5;241m=\u001b[39msolver)\n\u001b[1;32m 919\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m pybamm\u001b[38;5;241m.\u001b[39mSolverError: \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[0;32m--> 920\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m pybamm\u001b[38;5;241m.\u001b[39mSolverError(\n\u001b[1;32m 921\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not solve for summary variables, run \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 922\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`sim.solve(calc_esoh=False)` to skip this step\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 923\u001b[0m )\n\u001b[1;32m 924\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m esoh_sim\u001b[38;5;241m.\u001b[39mbuilt_model\u001b[38;5;241m.\u001b[39mvariables:\n\u001b[1;32m 925\u001b[0m cycle_summary_variables[var] \u001b[38;5;241m=\u001b[39m esoh_sol[var]\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;241m0\u001b[39m]\n", + "\u001b[0;31mSolverError\u001b[0m: Could not solve for summary variables, run `sim.solve(calc_esoh=False)` to skip this step" ] } ], diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py index 5a877529ab..124272cdf5 100644 --- a/pybamm/models/submodels/interface/sei/base_sei.py +++ b/pybamm/models/submodels/interface/sei/base_sei.py @@ -230,7 +230,10 @@ def _get_standard_concentration_variables(self, variables): elif self.reaction == "SEI on cracks": L_inner_cr = variables["Inner SEI on cracks thickness"] L_outer_cr = variables["Outer SEI on cracks thickness"] - roughness = variables[self.domain + " electrode roughness ratio"] + if self.domain + " electrode roughness ratio" in variables: + roughness = variables[self.domain + " electrode roughness ratio"] + else: + roughness = 1 n_inner_cr = L_inner_cr * (roughness - 1) # inner SEI cracks concentration n_outer_cr = L_outer_cr * (roughness - 1) # outer SEI cracks concentration From 6bfddd1c8d794937fadf544cd2e820b6df2215b9 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 22 Jun 2022 14:52:55 +0100 Subject: [PATCH 09/36] Revert "Added if statement to set roughness to 1 if not in variables" This reverts commit 5b321a72dd65613fd58318d969b1a11d452e9202. --- examples/notebooks/models/SEI-on-cracks.ipynb | 32 ++++++------------- .../submodels/interface/sei/base_sei.py | 5 +-- 2 files changed, 11 insertions(+), 26 deletions(-) diff --git a/examples/notebooks/models/SEI-on-cracks.ipynb b/examples/notebooks/models/SEI-on-cracks.ipynb index f1ae2f361a..e25a7313d1 100644 --- a/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/examples/notebooks/models/SEI-on-cracks.ipynb @@ -30,31 +30,19 @@ "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "At t = 0.00698581, , mxstep steps taken before reaching tout.\n" - ] - }, - { - "ename": "SolverError", - "evalue": "Could not solve for summary variables, run `sim.solve(calc_esoh=False)` to skip this step", + "ename": "KeyError", + "evalue": "'Negative electrode roughness ratio'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mSolverError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32m~/PyBaMM/pybamm/solvers/solution.py:918\u001b[0m, in \u001b[0;36mget_cycle_summary_variables\u001b[0;34m(cycle_solution, esoh_sim)\u001b[0m\n\u001b[1;32m 917\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 918\u001b[0m esoh_sol \u001b[38;5;241m=\u001b[39m \u001b[43mesoh_sim\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43msolver\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msolver\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 919\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m pybamm\u001b[38;5;241m.\u001b[39mSolverError: \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n", - "File \u001b[0;32m~/PyBaMM/pybamm/simulation.py:710\u001b[0m, in \u001b[0;36mSimulation.solve\u001b[0;34m(self, t_eval, solver, check_model, save_at_cycles, calc_esoh, starting_solution, initial_soc, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 696\u001b[0m warnings\u001b[38;5;241m.\u001b[39mwarn(\n\u001b[1;32m 697\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 698\u001b[0m \u001b[38;5;124;03m The largest timestep in t_eval ({}) is larger than\u001b[39;00m\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 707\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mSolverWarning,\n\u001b[1;32m 708\u001b[0m )\n\u001b[0;32m--> 710\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_solution \u001b[38;5;241m=\u001b[39m \u001b[43msolver\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuilt_model\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mt_eval\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 712\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moperating_mode \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwith experiment\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n", - "File \u001b[0;32m~/PyBaMM/pybamm/solvers/base_solver.py:1083\u001b[0m, in \u001b[0;36mBaseSolver.solve\u001b[0;34m(self, model, t_eval, external_variables, inputs, initial_conditions, nproc, calculate_sensitivities)\u001b[0m\n\u001b[1;32m 1082\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ninputs \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m-> 1083\u001b[0m new_solution \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_integrate\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1084\u001b[0m \u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1085\u001b[0m \u001b[43m \u001b[49m\u001b[43mt_eval_dimensionless\u001b[49m\u001b[43m[\u001b[49m\u001b[43mstart_index\u001b[49m\u001b[43m:\u001b[49m\u001b[43mend_index\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1086\u001b[0m \u001b[43m \u001b[49m\u001b[43mext_and_inputs_list\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1087\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1088\u001b[0m new_solutions \u001b[38;5;241m=\u001b[39m [new_solution]\n", - "File \u001b[0;32m~/PyBaMM/pybamm/solvers/casadi_algebraic_solver.py:184\u001b[0m, in \u001b[0;36mCasadiAlgebraicSolver._integrate\u001b[0;34m(self, model, t_eval, inputs_dict)\u001b[0m\n\u001b[1;32m 183\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m success:\n\u001b[0;32m--> 184\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m pybamm\u001b[38;5;241m.\u001b[39mSolverError(\n\u001b[1;32m 185\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not find acceptable solution: \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(message)\n\u001b[1;32m 186\u001b[0m )\n\u001b[1;32m 187\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28many\u001b[39m(np\u001b[38;5;241m.\u001b[39misnan(fun)):\n", - "\u001b[0;31mSolverError\u001b[0m: Could not find acceptable solution: .../casadi/core/rootfinder.cpp:280: rootfinder process failed. Set 'error_on_fail' option to false to ignore this error.", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mSolverError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 8\u001b[0m experiment \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mExperiment([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDischarge at 1C until 3 V\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[1;32m 9\u001b[0m sim1 \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mSimulation(model1, parameter_values\u001b[38;5;241m=\u001b[39mparameter_values, experiment\u001b[38;5;241m=\u001b[39mexperiment)\n\u001b[0;32m---> 10\u001b[0m sol1 \u001b[38;5;241m=\u001b[39m \u001b[43msim1\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msolve\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m sim2 \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mSimulation(model2, parameter_values\u001b[38;5;241m=\u001b[39mparameter_values, experiment\u001b[38;5;241m=\u001b[39mexperiment)\n\u001b[1;32m 12\u001b[0m sol2 \u001b[38;5;241m=\u001b[39m sim2\u001b[38;5;241m.\u001b[39msolve()\n", - "File \u001b[0;32m~/PyBaMM/pybamm/simulation.py:858\u001b[0m, in \u001b[0;36mSimulation.solve\u001b[0;34m(self, t_eval, solver, check_model, save_at_cycles, calc_esoh, starting_solution, initial_soc, callbacks, **kwargs)\u001b[0m\n\u001b[1;32m 856\u001b[0m \u001b[38;5;66;03m# At the final step of the inner loop we save the cycle\u001b[39;00m\n\u001b[1;32m 857\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(steps) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m--> 858\u001b[0m cycle_sol \u001b[38;5;241m=\u001b[39m \u001b[43mpybamm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmake_cycle_solution\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 859\u001b[0m \u001b[43m \u001b[49m\u001b[43msteps\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 860\u001b[0m \u001b[43m \u001b[49m\u001b[43mesoh_sim\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 861\u001b[0m \u001b[43m \u001b[49m\u001b[43msave_this_cycle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msave_this_cycle\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 862\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 863\u001b[0m cycle_solution, cycle_sum_vars, cycle_first_state \u001b[38;5;241m=\u001b[39m cycle_sol\n\u001b[1;32m 864\u001b[0m all_cycle_solutions\u001b[38;5;241m.\u001b[39mappend(cycle_solution)\n", - "File \u001b[0;32m~/PyBaMM/pybamm/solvers/solution.py:812\u001b[0m, in \u001b[0;36mmake_cycle_solution\u001b[0;34m(step_solutions, esoh_sim, save_this_cycle)\u001b[0m\n\u001b[1;32m 808\u001b[0m cycle_solution\u001b[38;5;241m.\u001b[39mset_up_time \u001b[38;5;241m=\u001b[39m sum_sols\u001b[38;5;241m.\u001b[39mset_up_time\n\u001b[1;32m 810\u001b[0m cycle_solution\u001b[38;5;241m.\u001b[39msteps \u001b[38;5;241m=\u001b[39m step_solutions\n\u001b[0;32m--> 812\u001b[0m cycle_summary_variables \u001b[38;5;241m=\u001b[39m \u001b[43mget_cycle_summary_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mcycle_solution\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mesoh_sim\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 814\u001b[0m cycle_first_state \u001b[38;5;241m=\u001b[39m cycle_solution\u001b[38;5;241m.\u001b[39mfirst_state\n\u001b[1;32m 816\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save_this_cycle:\n", - "File \u001b[0;32m~/PyBaMM/pybamm/solvers/solution.py:920\u001b[0m, in \u001b[0;36mget_cycle_summary_variables\u001b[0;34m(cycle_solution, esoh_sim)\u001b[0m\n\u001b[1;32m 918\u001b[0m esoh_sol \u001b[38;5;241m=\u001b[39m esoh_sim\u001b[38;5;241m.\u001b[39msolve([\u001b[38;5;241m0\u001b[39m], inputs\u001b[38;5;241m=\u001b[39minputs, solver\u001b[38;5;241m=\u001b[39msolver)\n\u001b[1;32m 919\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m pybamm\u001b[38;5;241m.\u001b[39mSolverError: \u001b[38;5;66;03m# pragma: no cover\u001b[39;00m\n\u001b[0;32m--> 920\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m pybamm\u001b[38;5;241m.\u001b[39mSolverError(\n\u001b[1;32m 921\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not solve for summary variables, run \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 922\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`sim.solve(calc_esoh=False)` to skip this step\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 923\u001b[0m )\n\u001b[1;32m 924\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m var \u001b[38;5;129;01min\u001b[39;00m esoh_sim\u001b[38;5;241m.\u001b[39mbuilt_model\u001b[38;5;241m.\u001b[39mvariables:\n\u001b[1;32m 925\u001b[0m cycle_summary_variables[var] \u001b[38;5;241m=\u001b[39m esoh_sol[var]\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;241m0\u001b[39m]\n", - "\u001b[0;31mSolverError\u001b[0m: Could not solve for summary variables, run `sim.solve(calc_esoh=False)` to skip this step" + "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m parameter_values \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mParameterValues(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAi2020\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m model1 \u001b[38;5;241m=\u001b[39m \u001b[43mpybamm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlithium_ion\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDFN\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSEI\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msolvent-diffusion limited\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m model2 \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mlithium_ion\u001b[38;5;241m.\u001b[39mDFN({\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparticle mechanics\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mswelling and cracking\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSEI\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msolvent-diffusion limited\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSEI on cracks\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrue\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 7\u001b[0m })\n\u001b[1;32m 8\u001b[0m experiment \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mExperiment([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDischarge at 1C until 3 V\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/lithium_ion/dfn.py:66\u001b[0m, in \u001b[0;36mDFN.__init__\u001b[0;34m(self, options, name, build)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_li_metal_counter_electrode_submodels()\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m build:\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 68\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mcitations\u001b[38;5;241m.\u001b[39mregister(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDoyle1993\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:863\u001b[0m, in \u001b[0;36mBaseBatteryModel.build_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 860\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStart building \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname))\n\u001b[1;32m 862\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_built_fundamental_and_external \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m--> 863\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_fundamental_and_external\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_coupled_variables()\n\u001b[1;32m 867\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_model_equations()\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:751\u001b[0m, in \u001b[0;36mBaseBatteryModel.build_fundamental_and_external\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 745\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m submodel_name, submodel \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubmodels\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 746\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\n\u001b[1;32m 747\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGetting fundamental variables for \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m submodel (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 748\u001b[0m submodel_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname\n\u001b[1;32m 749\u001b[0m )\n\u001b[1;32m 750\u001b[0m )\n\u001b[0;32m--> 751\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables\u001b[38;5;241m.\u001b[39mupdate(\u001b[43msubmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_fundamental_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 753\u001b[0m \u001b[38;5;66;03m# Set the submodels that are external\u001b[39;00m\n\u001b[1;32m 754\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sub \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexternal submodels\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/submodels/interface/sei/no_sei.py:37\u001b[0m, in \u001b[0;36mNoSEI.get_fundamental_variables\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 33\u001b[0m zero \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mFullBroadcast(\n\u001b[1;32m 34\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mScalar(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnegative electrode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcurrent collector\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 35\u001b[0m )\n\u001b[1;32m 36\u001b[0m variables \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_standard_thickness_variables(zero, zero)\n\u001b[0;32m---> 37\u001b[0m variables\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_standard_concentration_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 38\u001b[0m variables\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_standard_reaction_variables(zero, zero))\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m variables\n", + "File \u001b[0;32m~/PyBaMM/pybamm/models/submodels/interface/sei/base_sei.py:230\u001b[0m, in \u001b[0;36mBaseModel._get_standard_concentration_variables\u001b[0;34m(self, variables)\u001b[0m\n\u001b[1;32m 228\u001b[0m L_inner_cr \u001b[38;5;241m=\u001b[39m variables[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInner SEI on cracks thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 229\u001b[0m L_outer_cr \u001b[38;5;241m=\u001b[39m variables[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOuter SEI on cracks thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 230\u001b[0m roughness \u001b[38;5;241m=\u001b[39m \u001b[43mvariables\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdomain\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m electrode roughness ratio\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 232\u001b[0m n_inner_cr \u001b[38;5;241m=\u001b[39m L_inner_cr \u001b[38;5;241m*\u001b[39m (roughness \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# inner SEI cracks concentration\u001b[39;00m\n\u001b[1;32m 233\u001b[0m n_outer_cr \u001b[38;5;241m=\u001b[39m L_outer_cr \u001b[38;5;241m*\u001b[39m (roughness \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# outer SEI cracks concentration\u001b[39;00m\n", + "\u001b[0;31mKeyError\u001b[0m: 'Negative electrode roughness ratio'" ] } ], diff --git a/pybamm/models/submodels/interface/sei/base_sei.py b/pybamm/models/submodels/interface/sei/base_sei.py index 124272cdf5..5a877529ab 100644 --- a/pybamm/models/submodels/interface/sei/base_sei.py +++ b/pybamm/models/submodels/interface/sei/base_sei.py @@ -230,10 +230,7 @@ def _get_standard_concentration_variables(self, variables): elif self.reaction == "SEI on cracks": L_inner_cr = variables["Inner SEI on cracks thickness"] L_outer_cr = variables["Outer SEI on cracks thickness"] - if self.domain + " electrode roughness ratio" in variables: - roughness = variables[self.domain + " electrode roughness ratio"] - else: - roughness = 1 + roughness = variables[self.domain + " electrode roughness ratio"] n_inner_cr = L_inner_cr * (roughness - 1) # inner SEI cracks concentration n_outer_cr = L_outer_cr * (roughness - 1) # outer SEI cracks concentration From 775626ffc671e52c72f2ebcf884debd975196472 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 22 Jun 2022 16:34:15 +0100 Subject: [PATCH 10/36] Added cracking parameters to graphite_Chen2020_plating --- .../graphite_cracking_rate_Ai2020.py | 35 ++++++++++++++ .../graphite_volume_change_Ai2020.py | 47 +++++++++++++++++++ .../graphite_Chen2020_plating/parameters.csv | 22 ++++++++- 3 files changed, 102 insertions(+), 2 deletions(-) create mode 100644 pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_cracking_rate_Ai2020.py create mode 100644 pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_volume_change_Ai2020.py diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_cracking_rate_Ai2020.py b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_cracking_rate_Ai2020.py new file mode 100644 index 0000000000..1bf7ce3e67 --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_cracking_rate_Ai2020.py @@ -0,0 +1,35 @@ +from pybamm import Parameter, constants, exp + + +def graphite_cracking_rate_Ai2020(T_dim): + """ + graphite particle cracking rate as a function of temperature [1, 2]. + + References + ---------- + .. [1] Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020). + Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity in + Lithium-Ion Pouch Cells. Journal of The Electrochemical Society, 167(1), 013512 + DOI: 10.1149/2.0122001JES. + .. [2] Deshpande, R., Verbrugge, M., Cheng, Y. T., Wang, J., & Liu, P. (2012). + Battery cycle life prediction with coupled chemical degradation and fatigue + mechanics. Journal of the Electrochemical Society, 159(10), A1730. + + Parameters + ---------- + T_dim: :class:`pybamm.Symbol` + temperature, [K] + + Returns + ------- + k_cr: :class:`pybamm.Symbol` + cracking rate, [m/(Pa.m0.5)^m_cr] + where m_cr is another Paris' law constant + """ + k_cr = 3.9e-20 + T_ref = Parameter("Reference temperature [K]") + Eac_cr = Parameter( + "Negative electrode activation energy for cracking rate [J.mol-1]" + ) + arrhenius = exp(Eac_cr / constants.R * (1 / T_dim - 1 / T_ref)) + return k_cr * arrhenius diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_volume_change_Ai2020.py b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_volume_change_Ai2020.py new file mode 100644 index 0000000000..c47f921c34 --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_volume_change_Ai2020.py @@ -0,0 +1,47 @@ +def graphite_volume_change_Ai2020(sto): + """ + Graphite particle volume change as a function of stochiometry [1, 2]. + + References + ---------- + .. [1] Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020). + Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity in + Lithium-Ion Pouch Cells. Journal of The Electrochemical Society, 167(1), 013512 + DOI: 10.1149/2.0122001JES. + .. [2] Rieger, B., Erhard, S. V., Rumpf, K., & Jossen, A. (2016). + A new method to model the thickness change of a commercial pouch cell + during discharge. Journal of The Electrochemical Society, 163(8), A1566-A1575. + + Parameters + ---------- + sto: :class:`pybamm.Symbol` + Electrode stochiometry, dimensionless + should be R-averaged particle concentration + Returns + ------- + t_change:class:`pybamm.Symbol` + volume change, dimensionless, normalised by particle volume + """ + p1 = 145.907 + p2 = -681.229 + p3 = 1334.442 + p4 = -1415.710 + p5 = 873.906 + p6 = -312.528 + p7 = 60.641 + p8 = -5.706 + p9 = 0.386 + p10 = -4.966e-05 + t_change = ( + p1 * sto ** 9 + + p2 * sto ** 8 + + p3 * sto ** 7 + + p4 * sto ** 6 + + p5 * sto ** 5 + + p6 * sto ** 4 + + p7 * sto ** 3 + + p8 * sto ** 2 + + p9 * sto + + p10 + ) + return t_change diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv index 195c5cdbef..1b4296d36d 100644 --- a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv @@ -29,6 +29,24 @@ Negative electrode specific heat capacity [J.kg-1.K-1],700,default, Negative electrode thermal conductivity [W.m-1.K-1],1.7,default, Negative electrode OCP entropic change [V.K-1],0,, ,,, -,,, -,,, +# Mechanical properties,,, +Negative electrode Poisson's ratio,0.3,, +Negative electrode Young's modulus [Pa],15e9,, +Negative electrode reference concentration for free of deformation [mol.m-3],0,, +Negative electrode partial molar volume [m3.mol-1],3.1e-6,, +Negative electrode volume change,[function]graphite_volume_change_Ai2020,Ai2020, +,,, +# Crack model,,, +Negative electrode initial crack length [m],20e-9,, +Negative electrode initial crack width [m],15e-9,, +Negative electrode number of cracks per unit area [m-2],3.18e15,, +Negative electrode Paris' law constant b,1.12,, +Negative electrode Paris' law constant m,2.2,, +Negative electrode cracking rate,[function]graphite_cracking_rate_Ai2020,Ai2020, +Negative electrode activation energy for cracking rate [J.mol-1],0,, +,,, +# Loss of active materials (LAM) model,,, +Negative electrode LAM constant proportional term,1E-3,OKane2022, +Negative electrode LAM constant exponential term,2,, +Negative electrode critical stress [Pa],60e6,, ,,, From 0ad8afe5e8fc79628ec933316e9e367f7d852fff Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Thu, 23 Jun 2022 18:24:04 +0100 Subject: [PATCH 11/36] Added get_coupled_variables to NoSEI class --- examples/notebooks/models/SEI-on-cracks.ipynb | 26 +- examples/scripts/SEI_on_cracks.py | 10 + .../cells/LGM50_Chen2020/parameters.csv | 1 + .../graphite_Chen2020_plating/parameters.csv | 2 +- .../nmc_OKane2022/README.md | 7 + .../nmc_OKane2022/__init__.py | 0 .../nmc_OKane2022/cracking_rate_Ai2020.py | 32 +++ .../nmc_LGM50_diffusivity_Chen2020.py | 33 +++ ...olyte_exchange_current_density_Chen2020.py | 38 +++ .../nmc_OKane2022/nmc_LGM50_ocp_Chen2020.csv | 243 ++++++++++++++++++ .../nmc_OKane2022/nmc_LGM50_ocp_Chen2020.py | 35 +++ .../nmc_OKane2022/parameters.csv | 50 ++++ .../nmc_OKane2022/volume_change_Ai2020.py | 29 +++ .../models/submodels/interface/sei/no_sei.py | 5 +- pybamm/parameters/parameter_sets.py | 2 +- 15 files changed, 493 insertions(+), 20 deletions(-) create mode 100644 examples/scripts/SEI_on_cracks.py create mode 100644 pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/README.md create mode 100644 pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/__init__.py create mode 100644 pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py create mode 100644 pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_diffusivity_Chen2020.py create mode 100644 pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py create mode 100644 pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_ocp_Chen2020.csv create mode 100644 pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_ocp_Chen2020.py create mode 100644 pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/parameters.csv create mode 100644 pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/volume_change_Ai2020.py diff --git a/examples/notebooks/models/SEI-on-cracks.ipynb b/examples/notebooks/models/SEI-on-cracks.ipynb index e25a7313d1..e311d7fd93 100644 --- a/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/examples/notebooks/models/SEI-on-cracks.ipynb @@ -25,24 +25,16 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 6, "id": "05c30b49", "metadata": {}, "outputs": [ { - "ename": "KeyError", - "evalue": "'Negative electrode roughness ratio'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mKeyError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [2]\u001b[0m, in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m parameter_values \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mParameterValues(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mAi2020\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m model1 \u001b[38;5;241m=\u001b[39m \u001b[43mpybamm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlithium_ion\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mDFN\u001b[49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mSEI\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43msolvent-diffusion limited\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3\u001b[0m model2 \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mlithium_ion\u001b[38;5;241m.\u001b[39mDFN({\n\u001b[1;32m 4\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mparticle mechanics\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mswelling and cracking\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 5\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSEI\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msolvent-diffusion limited\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mSEI on cracks\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrue\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 7\u001b[0m })\n\u001b[1;32m 8\u001b[0m experiment \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mExperiment([\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDischarge at 1C until 3 V\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/lithium_ion/dfn.py:66\u001b[0m, in \u001b[0;36mDFN.__init__\u001b[0;34m(self, options, name, build)\u001b[0m\n\u001b[1;32m 63\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mset_li_metal_counter_electrode_submodels()\n\u001b[1;32m 65\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m build:\n\u001b[0;32m---> 66\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 68\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mcitations\u001b[38;5;241m.\u001b[39mregister(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mDoyle1993\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:863\u001b[0m, in \u001b[0;36mBaseBatteryModel.build_model\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 860\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mStart building \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname))\n\u001b[1;32m 862\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_built_fundamental_and_external \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m:\n\u001b[0;32m--> 863\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbuild_fundamental_and_external\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 865\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_coupled_variables()\n\u001b[1;32m 867\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbuild_model_equations()\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/full_battery_models/base_battery_model.py:751\u001b[0m, in \u001b[0;36mBaseBatteryModel.build_fundamental_and_external\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 745\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m submodel_name, submodel \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msubmodels\u001b[38;5;241m.\u001b[39mitems():\n\u001b[1;32m 746\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mlogger\u001b[38;5;241m.\u001b[39mdebug(\n\u001b[1;32m 747\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mGetting fundamental variables for \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m submodel (\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m)\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m 748\u001b[0m submodel_name, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mname\n\u001b[1;32m 749\u001b[0m )\n\u001b[1;32m 750\u001b[0m )\n\u001b[0;32m--> 751\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvariables\u001b[38;5;241m.\u001b[39mupdate(\u001b[43msubmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_fundamental_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 753\u001b[0m \u001b[38;5;66;03m# Set the submodels that are external\u001b[39;00m\n\u001b[1;32m 754\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m sub \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39moptions[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mexternal submodels\u001b[39m\u001b[38;5;124m\"\u001b[39m]:\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/submodels/interface/sei/no_sei.py:37\u001b[0m, in \u001b[0;36mNoSEI.get_fundamental_variables\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 33\u001b[0m zero \u001b[38;5;241m=\u001b[39m pybamm\u001b[38;5;241m.\u001b[39mFullBroadcast(\n\u001b[1;32m 34\u001b[0m pybamm\u001b[38;5;241m.\u001b[39mScalar(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnegative electrode\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcurrent collector\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 35\u001b[0m )\n\u001b[1;32m 36\u001b[0m variables \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_standard_thickness_variables(zero, zero)\n\u001b[0;32m---> 37\u001b[0m variables\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_standard_concentration_variables\u001b[49m\u001b[43m(\u001b[49m\u001b[43mvariables\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[1;32m 38\u001b[0m variables\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_standard_reaction_variables(zero, zero))\n\u001b[1;32m 39\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m variables\n", - "File \u001b[0;32m~/PyBaMM/pybamm/models/submodels/interface/sei/base_sei.py:230\u001b[0m, in \u001b[0;36mBaseModel._get_standard_concentration_variables\u001b[0;34m(self, variables)\u001b[0m\n\u001b[1;32m 228\u001b[0m L_inner_cr \u001b[38;5;241m=\u001b[39m variables[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mInner SEI on cracks thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m 229\u001b[0m L_outer_cr \u001b[38;5;241m=\u001b[39m variables[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mOuter SEI on cracks thickness\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[0;32m--> 230\u001b[0m roughness \u001b[38;5;241m=\u001b[39m \u001b[43mvariables\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdomain\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[43m \u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m electrode roughness ratio\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\n\u001b[1;32m 232\u001b[0m n_inner_cr \u001b[38;5;241m=\u001b[39m L_inner_cr \u001b[38;5;241m*\u001b[39m (roughness \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# inner SEI cracks concentration\u001b[39;00m\n\u001b[1;32m 233\u001b[0m n_outer_cr \u001b[38;5;241m=\u001b[39m L_outer_cr \u001b[38;5;241m*\u001b[39m (roughness \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# outer SEI cracks concentration\u001b[39;00m\n", - "\u001b[0;31mKeyError\u001b[0m: 'Negative electrode roughness ratio'" + "name": "stderr", + "output_type": "stream", + "text": [ + "At t = 0.00698581, , mxstep steps taken before reaching tout.\n", + "At t = 0.00700083, , mxstep steps taken before reaching tout.\n" ] } ], @@ -52,13 +44,13 @@ "model2 = pybamm.lithium_ion.DFN({\n", " \"particle mechanics\": \"swelling and cracking\",\n", " \"SEI\": \"solvent-diffusion limited\",\n", - " \"SEI on cracks\": \"true\",\n", + " \"SEI on cracks\": \"false\",\n", "})\n", "experiment = pybamm.Experiment([\"Discharge at 1C until 3 V\"])\n", "sim1 = pybamm.Simulation(model1, parameter_values=parameter_values, experiment=experiment)\n", - "sol1 = sim1.solve()\n", + "sol1 = sim1.solve(calc_esoh=False)\n", "sim2 = pybamm.Simulation(model2, parameter_values=parameter_values, experiment=experiment)\n", - "sol2 = sim2.solve()" + "sol2 = sim2.solve(calc_esoh=False)" ] }, { diff --git a/examples/scripts/SEI_on_cracks.py b/examples/scripts/SEI_on_cracks.py new file mode 100644 index 0000000000..5461ed64c5 --- /dev/null +++ b/examples/scripts/SEI_on_cracks.py @@ -0,0 +1,10 @@ +import pybamm +import matplotlib.pyplot as plt +import numpy as np + +pybamm.set_logging_level("DEBUG") +parameter_values = pybamm.ParameterValues("OKane2022") +model1 = pybamm.lithium_ion.DFN({"SEI": "solvent-diffusion limited"}) +experiment = pybamm.Experiment(["Discharge at 1C until 2.5 V"]) +sim1 = pybamm.Simulation(model1, parameter_values=parameter_values, experiment=experiment) +sol1 = sim1.solve() \ No newline at end of file diff --git a/pybamm/input/parameters/lithium_ion/cells/LGM50_Chen2020/parameters.csv b/pybamm/input/parameters/lithium_ion/cells/LGM50_Chen2020/parameters.csv index 8853a4d451..038d2bc76d 100644 --- a/pybamm/input/parameters/lithium_ion/cells/LGM50_Chen2020/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/cells/LGM50_Chen2020/parameters.csv @@ -11,6 +11,7 @@ Electrode height [m],6.5E-2,Chen 2020, Electrode width [m],1.58,Chen 2020,accounts for both sides of unwound electrode (double-sided coating) Cell cooling surface area [m2],5.31E-3,Chen 2020,cylindrical Cell volume [m3],2.42E-5,Chen 2020,cylindrical +Cell thermal expansion coefficient [m.K-1],1.1E-6,Ai2020, ,,, # Current collector properties ,,, Negative current collector conductivity [S.m-1],58411000,CRC Handbook,copper diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv index 1b4296d36d..9473ccb90f 100644 --- a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv @@ -46,7 +46,7 @@ Negative electrode cracking rate,[function]graphite_cracking_rate_Ai2020,Ai2020, Negative electrode activation energy for cracking rate [J.mol-1],0,, ,,, # Loss of active materials (LAM) model,,, -Negative electrode LAM constant proportional term,1E-3,OKane2022, +Negative electrode LAM constant proportional term,1E-3,, Negative electrode LAM constant exponential term,2,, Negative electrode critical stress [Pa],60e6,, ,,, diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/README.md b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/README.md new file mode 100644 index 0000000000..9bd8671867 --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/README.md @@ -0,0 +1,7 @@ +# NMC 811 positive electrode parameters + +Parameters for an LG M50 NMC 811 positive electrode, from the paper + +> Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. ["Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models."](https://iopscience.iop.org/article/10.1149/1945-7111/ab9050) Journal of the Electrochemical Society 167 (2020): 080534 + +and references therein. diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/__init__.py b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py new file mode 100644 index 0000000000..138be48936 --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py @@ -0,0 +1,32 @@ +from pybamm import Parameter, constants, exp + + +def cracking_rate_Ai2020(T_dim): + """ + Particle cracking rate as a function of temperature [1, 2]. + References + ---------- + .. [1] > Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020). + Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity in + Lithium-Ion Pouch Cells. Journal of The Electrochemical Society, 167(1), 013512 + DOI: 10.1149/2.0122001JES. + .. [2] > Deshpande, R., Verbrugge, M., Cheng, Y. T., Wang, J., & Liu, P. (2012). + Battery cycle life prediction with coupled chemical degradation and fatigue + mechanics. Journal of the Electrochemical Society, 159(10), A1730. + Parameters + ---------- + T: :class:`pybamm.Symbol` + temperature, [K] + Returns + ------- + k_cr: :class:`pybamm.Symbol` + cracking rate, [m/(Pa.m0.5)^m_cr] + where m_cr is another Paris' law constant + """ + k_cr = 3.9e-20 + T_ref = Parameter("Reference temperature [K]") + Eac_cr = Parameter( + "Positive electrode activation energy for cracking rate [J.mol-1]" + ) + arrhenius = exp(Eac_cr / constants.R * (1 / T_dim - 1 / T_ref)) + return k_cr * arrhenius diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_diffusivity_Chen2020.py b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_diffusivity_Chen2020.py new file mode 100644 index 0000000000..bce5f98105 --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_diffusivity_Chen2020.py @@ -0,0 +1,33 @@ +from pybamm import exp, constants + + +def nmc_LGM50_diffusivity_Chen2020(sto, T): + """ + NMC diffusivity as a function of stoichiometry, in this case the + diffusivity is taken to be a constant. The value is taken from [1]. + + References + ---------- + .. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W. + Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for + Parameterization of Multi-scale Lithium-ion Battery Models." Journal of the + Electrochemical Society 167 (2020): 080534. + + Parameters + ---------- + sto: :class:`pybamm.Symbol` + Electrode stochiometry + T: :class:`pybamm.Symbol` + Dimensional temperature + + Returns + ------- + :class:`pybamm.Symbol` + Solid diffusivity + """ + + D_ref = 4e-15 + E_D_s = 25000 # O'Kane et al. (2022), after Cabanero et al. (2018) + arrhenius = exp(E_D_s / constants.R * (1 / 298.15 - 1 / T)) + + return D_ref * arrhenius diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py new file mode 100644 index 0000000000..0925f69260 --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py @@ -0,0 +1,38 @@ +from pybamm import exp, constants, Parameter + + +def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, T): + """ + Exchange-current density for Butler-Volmer reactions between NMC and LiPF6 in + EC:DMC. + + References + ---------- + .. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W. + Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for + Parameterization of Multi-scale Lithium-ion Battery Models." Journal of the + Electrochemical Society 167 (2020): 080534. + + Parameters + ---------- + c_e : :class:`pybamm.Symbol` + Electrolyte concentration [mol.m-3] + c_s_surf : :class:`pybamm.Symbol` + Particle concentration [mol.m-3] + T : :class:`pybamm.Symbol` + Temperature [K] + + Returns + ------- + :class:`pybamm.Symbol` + Exchange-current density [A.m-2] + """ + m_ref = 3.42e-6 # (A/m2)(mol/m3)**1.5 - includes ref concentrations + E_r = 17800 + arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T)) + + c_p_max = Parameter("Maximum concentration in positive electrode [mol.m-3]") + + return ( + m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5 + ) diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_ocp_Chen2020.csv b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_ocp_Chen2020.csv new file mode 100644 index 0000000000..7dc9adc71c --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_ocp_Chen2020.csv @@ -0,0 +1,243 @@ +# OCP data for LG M50 NMC positive electrode +# sto,ocp +# extra point to avoid extrapolation +0.248797280909757,4.40 +# experimentally measured data +0.266145163492257,4.29356530000000 +0.268867632314474,4.27686210000000 +0.271590113475307,4.26470180000000 +0.274312581168247,4.25403120000000 +0.277035051828500,4.24494460000000 +0.279757525339878,4.23648790000000 +0.282479995064952,4.23026470000000 +0.285202473437965,4.22255280000000 +0.287924947671149,4.21825740000000 +0.290647431425727,4.21329400000000 +0.293369917257952,4.20903730000000 +0.296092386692851,4.20512390000000 +0.298814868849636,4.20126770000000 +0.301537350055390,4.19815640000000 +0.304259830904681,4.19552180000000 +0.306982312259254,4.19311670000000 +0.309704783671781,4.18897440000000 +0.312427252746966,4.18815330000000 +0.315149732455743,4.18658830000000 +0.317872201316148,4.18502280000000 +0.320594658973297,4.18322850000000 +0.323317140454362,4.18088050000000 +0.326039617362371,4.18057490000000 +0.328762086363981,4.17895220000000 +0.331484555867675,4.17681460000000 +0.334207025471543,4.17681460000000 +0.336929501255821,4.17528720000000 +0.339651973273663,4.17311100000000 +0.342374462458239,4.17267180000000 +0.345096938615155,4.17108770000000 +0.347819410074246,4.17022850000000 +0.350541895855318,4.16879700000000 +0.353264376316384,4.16698310000000 +0.355986846136589,4.16551350000000 +0.358709322969457,4.16345170000000 +0.361431798807600,4.15982480000000 +0.364154276288401,4.15717120000000 +0.366876742342325,4.15407900000000 +0.369599211442129,4.15041350000000 +0.372321688828519,4.14665320000000 +0.375044179100704,4.14233880000000 +0.377766654413886,4.13823460000000 +0.380489134491185,4.13382480000000 +0.383211609132993,4.13057990000000 +0.385934081264202,4.12723920000000 +0.388656550366338,4.12281040000000 +0.391379033718590,4.11861090000000 +0.394101506024207,4.11418200000000 +0.396823981011550,4.10960050000000 +0.399546445031954,4.10469480000000 +0.402268917913251,4.10047580000000 +0.404991398895142,4.09564640000000 +0.407713868869987,4.09096960000000 +0.410436337723266,4.08646440000000 +0.413158819617673,4.08184480000000 +0.415881292009550,4.07768300000000 +0.418603765047837,4.07333090000000 +0.421326243264316,4.06907370000000 +0.424048721985195,4.06472160000000 +0.426771203785911,4.06086540000000 +0.429493683560302,4.05647470000000 +0.432216157859838,4.05275250000000 +0.434938637930869,4.04924010000000 +0.437661112840513,4.04502110000000 +0.440383593375701,4.04198600000000 +0.443106070903017,4.03847360000000 +0.445828558295681,4.03517100000000 +0.448551031796266,4.03204060000000 +0.451273507249511,4.02892880000000 +0.453996003855954,4.02597000000000 +0.456718484235626,4.02274370000000 +0.459440954945593,4.01997570000000 +0.462163428115050,4.01751330000000 +0.464885915164505,4.01497460000000 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+0.835141923021117,3.61776210000000 +0.837864385539901,3.61585310000000 +0.840586843407759,3.61283710000000 +0.843309308653791,3.61180620000000 +0.846031768598797,3.60945820000000 +0.848754235411593,3.60724380000000 +0.851476699467683,3.60499120000000 +0.854199161102801,3.60308220000000 +0.856921623426447,3.60126880000000 +0.859644087674256,3.59958890000000 +0.862366559650803,3.59764170000000 +0.865089024293406,3.59519840000000 +0.867811486910360,3.59384300000000 +0.870533951869352,3.59162860000000 +0.873256418218254,3.58949070000000 +0.875978884501499,3.58742900000000 +0.878701351769785,3.58529090000000 +0.881423826658424,3.58347750000000 +0.884146298809852,3.58177850000000 +0.886868771867091,3.58011770000000 +0.889591240142391,3.57788420000000 +0.892313713281813,3.57633810000000 +0.895036201752086,3.57378010000000 +0.897758683022040,3.57210020000000 +0.900481161991173,3.57021020000000 +0.903203643476430,3.56849220000000 +0.905926128940627,3.56721330000000 +# extra points to avoid extrapolation +1,3.52302166875714 \ No newline at end of file diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_ocp_Chen2020.py b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_ocp_Chen2020.py new file mode 100644 index 0000000000..2038c2eb76 --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_ocp_Chen2020.py @@ -0,0 +1,35 @@ +from pybamm import tanh + + +def nmc_LGM50_ocp_Chen2020(sto): + """ + LG M50 NMC open circuit potential as a function of stochiometry, fit taken + from [1]. + + References + ---------- + .. [1] Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W. + Dhammika Widanage, and Emma Kendrick. "Development of Experimental Techniques for + Parameterization of Multi-scale Lithium-ion Battery Models." Journal of the + Electrochemical Society 167 (2020): 080534. + + Parameters + ---------- + sto: :class:`pybamm.Symbol` + Electrode stochiometry + + Returns + ------- + :class:`pybamm.Symbol` + Open circuit potential + """ + + u_eq = ( + -0.8090 * sto + + 4.4875 + - 0.0428 * tanh(18.5138 * (sto - 0.5542)) + - 17.7326 * tanh(15.7890 * (sto - 0.3117)) + + 17.5842 * tanh(15.9308 * (sto - 0.3120)) + ) + + return u_eq diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/parameters.csv b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/parameters.csv new file mode 100644 index 0000000000..2ce9f3c60d --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/parameters.csv @@ -0,0 +1,50 @@ +Name [units],Value,Reference,Notes +# Empty rows and rows starting with ‘#’ will be ignored,,, +,,, +# Electrode properties,,, +Positive electrode conductivity [S.m-1],0.18,Chen 2020, +Maximum concentration in positive electrode [mol.m-3],63104,Chen 2020,tuned for 1C +Positive electrode diffusivity [m2.s-1],4E-15,Chen 2020,tuned for 1C +Positive electrode OCP [V],[function]nmc_LGM50_ocp_Chen2020,Chen 2020, +,,, +# Microstructure,,, +Positive electrode porosity,0.335,Chen 2020, +Positive electrode active material volume fraction,0.665,Chen 2020, +Positive particle radius [m],5.22E-6,Chen 2020, +Positive electrode Bruggeman coefficient (electrolyte),1.5,Chen 2020,theoretical +Positive electrode Bruggeman coefficient (electrode),1.5,default, +,,, +# Interfacial reactions,,, +Positive electrode cation signed stoichiometry,-1,, +Positive electrode electrons in reaction,1,, +Positive electrode charge transfer coefficient,0.5,Chen 2020, +Positive electrode double-layer capacity [F.m-2],0.2,, +Positive electrode exchange-current density [A.m-2],[function]nmc_LGM50_electrolyte_exchange_current_density_Chen2020,, +,,, +# Density,,, +Positive electrode density [kg.m-3],3262,default, +,,, +# Thermal parameters,,, +Positive electrode specific heat capacity [J.kg-1.K-1],700,default, +Positive electrode thermal conductivity [W.m-1.K-1],2.1,default, +Positive electrode OCP entropic change [V.K-1],0,, +,,, +# Mechanical properties,,, +Positive electrode Poisson's ratio,0.3,, +Positive electrode Young's modulus [Pa],375e9,, +Positive electrode reference concentration for free of deformation [mol.m-3],0,, +Positive electrode partial molar volume [m3.mol-1],12.5e-6,, +Positive electrode volume change,[function]volume_change_Ai2020,Ai2020, +,,, +# Crack model,,, +Positive electrode initial crack length [m],20e-9,, +Positive electrode initial crack width [m],15e-9,, +Positive electrode number of cracks per unit area [m-2],3.18e15,, +Positive electrode Paris' law constant b,1.12,, +Positive electrode Paris' law constant m,2.2,, +Positive electrode cracking rate,[function]cracking_rate_Ai2020,Ai2020, +Positive electrode activation energy for cracking rate [J.mol-1],0,, +# Loss of activate materials (LAM) model,,, +Positive electrode LAM constant proportional term,1E-3,, +Positive electrode LAM constant exponential term,2,, +Positive electrode critical stress [Pa],375e6,, \ No newline at end of file diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/volume_change_Ai2020.py b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/volume_change_Ai2020.py new file mode 100644 index 0000000000..63d5276cd1 --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/volume_change_Ai2020.py @@ -0,0 +1,29 @@ +from pybamm import Parameter + + +def volume_change_Ai2020(sto): + """ + Particle volume change as a function of stochiometry [1, 2]. + References + ---------- + .. [1] > Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020). + Electrochemical Thermal-Mechanical Modelling of Stress Inhomogeneity in + Lithium-Ion Pouch Cells. Journal of The Electrochemical Society, 167(1), 013512 + DOI: 10.1149/2.0122001JES. + .. [2] > Rieger, B., Erhard, S. V., Rumpf, K., & Jossen, A. (2016). + A new method to model the thickness change of a commercial pouch cell + during discharge. Journal of The Electrochemical Society, 163(8), A1566-A1575. + Parameters + ---------- + sto: :class:`pybamm.Symbol` + Electrode stochiometry, dimensionless + should be R-averaged particle concentration + Returns + ------- + t_change:class:`pybamm.Symbol` + volume change, dimensionless, normalised by particle volume + """ + omega = Parameter("Positive electrode partial molar volume [m3.mol-1]") + c_p_max = Parameter("Maximum concentration in positive electrode [mol.m-3]") + t_change = omega * c_p_max * sto + return t_change diff --git a/pybamm/models/submodels/interface/sei/no_sei.py b/pybamm/models/submodels/interface/sei/no_sei.py index e86a0ce403..9ebf5fd530 100644 --- a/pybamm/models/submodels/interface/sei/no_sei.py +++ b/pybamm/models/submodels/interface/sei/no_sei.py @@ -34,6 +34,9 @@ def get_fundamental_variables(self): pybamm.Scalar(0), "negative electrode", "current collector" ) variables = self._get_standard_thickness_variables(zero, zero) - variables.update(self._get_standard_concentration_variables(variables)) variables.update(self._get_standard_reaction_variables(zero, zero)) return variables + + def get_coupled_variables(self, variables): + variables.update(self._get_standard_concentration_variables(variables)) + return variables \ No newline at end of file diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py index 93bd171f9d..0ffa2dc668 100644 --- a/pybamm/parameters/parameter_sets.py +++ b/pybamm/parameters/parameter_sets.py @@ -256,7 +256,7 @@ "cell": "LGM50_Chen2020", "negative electrode": "graphite_Chen2020_plating", "separator": "separator_Chen2020", - "positive electrode": "nmc_Chen2020", + "positive electrode": "nmc_OKane2022", "electrolyte": "lipf6_Nyman2008", "experiment": "1C_discharge_from_full_Chen2020", "sei": "example", From a21484db98382619c5e89e9fa29f6166b56d07ca Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Fri, 24 Jun 2022 17:51:57 +0100 Subject: [PATCH 12/36] Cracking model works but OKane2022 parameters do not --- examples/notebooks/models/SEI-on-cracks.ipynb | 48 +++++++++++++++++-- examples/scripts/SEI_on_cracks.py | 10 ---- .../graphite_Chen2020_plating/parameters.csv | 2 +- .../nmc_OKane2022/cracking_rate_Ai2020.py | 3 ++ .../nmc_OKane2022/parameters.csv | 7 +-- .../nmc_OKane2022/volume_change_Ai2020.py | 2 + 6 files changed, 54 insertions(+), 18 deletions(-) delete mode 100644 examples/scripts/SEI_on_cracks.py diff --git a/examples/notebooks/models/SEI-on-cracks.ipynb b/examples/notebooks/models/SEI-on-cracks.ipynb index e311d7fd93..24497a3107 100644 --- a/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/examples/notebooks/models/SEI-on-cracks.ipynb @@ -25,7 +25,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "id": "05c30b49", "metadata": {}, "outputs": [ @@ -34,7 +34,7 @@ "output_type": "stream", "text": [ "At t = 0.00698581, , mxstep steps taken before reaching tout.\n", - "At t = 0.00700083, , mxstep steps taken before reaching tout.\n" + "At t = 0.00694142, , mxstep steps taken before reaching tout.\n" ] } ], @@ -44,7 +44,7 @@ "model2 = pybamm.lithium_ion.DFN({\n", " \"particle mechanics\": \"swelling and cracking\",\n", " \"SEI\": \"solvent-diffusion limited\",\n", - " \"SEI on cracks\": \"false\",\n", + " \"SEI on cracks\": \"true\",\n", "})\n", "experiment = pybamm.Experiment([\"Discharge at 1C until 3 V\"])\n", "sim1 = pybamm.Simulation(model1, parameter_values=parameter_values, experiment=experiment)\n", @@ -55,10 +55,50 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "c3884817", "metadata": {}, "outputs": [], + "source": [ + "t1 = sol1[\"Time [s]\"].entries\n", + "SEI1 = sol1[\"Loss of capacity to SEI [A.h]\"].entries\n", + "t2 = sol2[\"Time [s]\"].entries\n", + "SEI2 = sol2[\"Loss of capacity to SEI [A.h]\"].entries + sol2[\"Loss of capacity to SEI on cracks [A.h]\"].entries" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d33e1d89", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "(fig, ax) = plt.subplots()\n", + "ax.plot(t1,SEI1,label=\"without cracking\")\n", + "ax.plot(t2,SEI2,label=\"with cracking\")\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9ca7991b", + "metadata": {}, + "outputs": [], "source": [] } ], diff --git a/examples/scripts/SEI_on_cracks.py b/examples/scripts/SEI_on_cracks.py deleted file mode 100644 index 5461ed64c5..0000000000 --- a/examples/scripts/SEI_on_cracks.py +++ /dev/null @@ -1,10 +0,0 @@ -import pybamm -import matplotlib.pyplot as plt -import numpy as np - -pybamm.set_logging_level("DEBUG") -parameter_values = pybamm.ParameterValues("OKane2022") -model1 = pybamm.lithium_ion.DFN({"SEI": "solvent-diffusion limited"}) -experiment = pybamm.Experiment(["Discharge at 1C until 2.5 V"]) -sim1 = pybamm.Simulation(model1, parameter_values=parameter_values, experiment=experiment) -sol1 = sim1.solve() \ No newline at end of file diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv index 9473ccb90f..a36306255b 100644 --- a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv @@ -46,7 +46,7 @@ Negative electrode cracking rate,[function]graphite_cracking_rate_Ai2020,Ai2020, Negative electrode activation energy for cracking rate [J.mol-1],0,, ,,, # Loss of active materials (LAM) model,,, -Negative electrode LAM constant proportional term,1E-3,, +Negative electrode LAM constant proportional term [s-1],2.7778E-7,0.001/3600, Negative electrode LAM constant exponential term,2,, Negative electrode critical stress [Pa],60e6,, ,,, diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py index 138be48936..c02e41d83a 100644 --- a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py @@ -4,6 +4,7 @@ def cracking_rate_Ai2020(T_dim): """ Particle cracking rate as a function of temperature [1, 2]. + References ---------- .. [1] > Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020). @@ -13,10 +14,12 @@ def cracking_rate_Ai2020(T_dim): .. [2] > Deshpande, R., Verbrugge, M., Cheng, Y. T., Wang, J., & Liu, P. (2012). Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10), A1730. + Parameters ---------- T: :class:`pybamm.Symbol` temperature, [K] + Returns ------- k_cr: :class:`pybamm.Symbol` diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/parameters.csv b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/parameters.csv index 2ce9f3c60d..61ebde7649 100644 --- a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/parameters.csv @@ -30,7 +30,7 @@ Positive electrode thermal conductivity [W.m-1.K-1],2.1,default, Positive electrode OCP entropic change [V.K-1],0,, ,,, # Mechanical properties,,, -Positive electrode Poisson's ratio,0.3,, +Positive electrode Poisson's ratio,0.2,, Positive electrode Young's modulus [Pa],375e9,, Positive electrode reference concentration for free of deformation [mol.m-3],0,, Positive electrode partial molar volume [m3.mol-1],12.5e-6,, @@ -44,7 +44,8 @@ Positive electrode Paris' law constant b,1.12,, Positive electrode Paris' law constant m,2.2,, Positive electrode cracking rate,[function]cracking_rate_Ai2020,Ai2020, Positive electrode activation energy for cracking rate [J.mol-1],0,, -# Loss of activate materials (LAM) model,,, -Positive electrode LAM constant proportional term,1E-3,, +,,, +# Loss of active materials (LAM) model,,, +Positive electrode LAM constant proportional term [s-1],2.7778E-7,0.001/3600, Positive electrode LAM constant exponential term,2,, Positive electrode critical stress [Pa],375e6,, \ No newline at end of file diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/volume_change_Ai2020.py b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/volume_change_Ai2020.py index 63d5276cd1..1273da5722 100644 --- a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/volume_change_Ai2020.py +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/volume_change_Ai2020.py @@ -4,6 +4,7 @@ def volume_change_Ai2020(sto): """ Particle volume change as a function of stochiometry [1, 2]. + References ---------- .. [1] > Ai, W., Kraft, L., Sturm, J., Jossen, A., & Wu, B. (2020). @@ -13,6 +14,7 @@ def volume_change_Ai2020(sto): .. [2] > Rieger, B., Erhard, S. V., Rumpf, K., & Jossen, A. (2016). A new method to model the thickness change of a commercial pouch cell during discharge. Journal of The Electrochemical Society, 163(8), A1566-A1575. + Parameters ---------- sto: :class:`pybamm.Symbol` From 7ec5fc8dcfd864c0606088d06c279b52084664a7 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 29 Jun 2022 13:51:34 +0100 Subject: [PATCH 13/36] Added Arrhenius temperature dependence to all SEI growth models --- examples/notebooks/models/SEI-on-cracks.ipynb | 39 +++++-------------- .../lithium_ion/seis/OKane2022/README.md | 12 ++++++ .../lithium_ion/seis/OKane2022/parameters.csv | 28 +++++++++++++ .../lithium_ion/seis/example/parameters.csv | 1 + .../submodels/interface/sei/sei_growth.py | 36 +++++++++-------- pybamm/parameters/lithium_ion_parameters.py | 7 +++- pybamm/parameters/parameter_sets.py | 2 +- 7 files changed, 76 insertions(+), 49 deletions(-) create mode 100644 pybamm/input/parameters/lithium_ion/seis/OKane2022/README.md create mode 100644 pybamm/input/parameters/lithium_ion/seis/OKane2022/parameters.csv diff --git a/examples/notebooks/models/SEI-on-cracks.ipynb b/examples/notebooks/models/SEI-on-cracks.ipynb index 24497a3107..59d98c6670 100644 --- a/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/examples/notebooks/models/SEI-on-cracks.ipynb @@ -25,26 +25,18 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "id": "05c30b49", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "At t = 0.00698581, , mxstep steps taken before reaching tout.\n", - "At t = 0.00694142, , mxstep steps taken before reaching tout.\n" - ] - } - ], + "outputs": [], "source": [ - "parameter_values = pybamm.ParameterValues(\"Ai2020\")\n", + "parameter_values = pybamm.ParameterValues(\"OKane2022\")\n", "model1 = pybamm.lithium_ion.DFN({\"SEI\": \"solvent-diffusion limited\"})\n", "model2 = pybamm.lithium_ion.DFN({\n", - " \"particle mechanics\": \"swelling and cracking\",\n", + " \"particle mechanics\": \"swelling only\",\n", + " \"loss of active material\": \"stress-driven\",\n", " \"SEI\": \"solvent-diffusion limited\",\n", - " \"SEI on cracks\": \"true\",\n", + " \"SEI on cracks\": \"false\",\n", "})\n", "experiment = pybamm.Experiment([\"Discharge at 1C until 3 V\"])\n", "sim1 = pybamm.Simulation(model1, parameter_values=parameter_values, experiment=experiment)\n", @@ -55,7 +47,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "c3884817", "metadata": {}, "outputs": [], @@ -68,23 +60,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "d33e1d89", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "(fig, ax) = plt.subplots()\n", "ax.plot(t1,SEI1,label=\"without cracking\")\n", diff --git a/pybamm/input/parameters/lithium_ion/seis/OKane2022/README.md b/pybamm/input/parameters/lithium_ion/seis/OKane2022/README.md new file mode 100644 index 0000000000..7e4df9e041 --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/seis/OKane2022/README.md @@ -0,0 +1,12 @@ +# SEI parameters + +Some example parameters for SEI growth from the papers: + +> Ramadass, P., Haran, B., Gomadam, P. M., White, R., & Popov, B. N. (2004). Development of first principles capacity fade model for Li-ion cells. Journal of the Electrochemical Society, 151(2), A196-A203. +> Ploehn, H. J., Ramadass, P., & White, R. E. (2004). Solvent diffusion model for aging of lithium-ion battery cells. Journal of The Electrochemical Society, 151(3), A456-A462. +> Single, F., Latz, A., & Horstmann, B. (2018). Identifying the mechanism of continued growth of the solid–electrolyte interphase. ChemSusChem, 11(12), 1950-1955. +> Safari, M., Morcrette, M., Teyssot, A., & Delacour, C. (2009). Multimodal Physics-Based Aging Model for Life Prediction of Li-Ion Batteries. Journal of The Electrochemical Society, 156(3), +> Yang, X., Leng, Y., Zhang, G., Ge, S., Wang, C. (2017). Modeling of lithium plating induced aging of lithium-ion batteries: Transition from linear to nonlinear aging. Journal of Power Sources, 360, 28-40. +> Waldmann, T., Wilka, M., Kasper, M., Fleischammer M., Wohlfahrt-Mehrens M. (2014). Temperature dependent ageing mechanisms in Lithium-ion batteris: A Post-Mortem study. Journal of Power Sources, 262, 129-135. + +Note: this parameter set does not claim to be representative of the true parameter values. Instead these are parameter values that were used to fit SEI models to observed experimental data in the referenced papers. diff --git a/pybamm/input/parameters/lithium_ion/seis/OKane2022/parameters.csv b/pybamm/input/parameters/lithium_ion/seis/OKane2022/parameters.csv new file mode 100644 index 0000000000..92af3830d1 --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/seis/OKane2022/parameters.csv @@ -0,0 +1,28 @@ +Name [units],Value,Reference,Notes +# Empty rows and rows starting with ‘#’ will be ignored,,, +,,, +# SEI properties,,, +Inner SEI reaction proportion,0,, +Inner SEI partial molar volume [m3.mol-1],9.585e-5, Safari paper, +Outer SEI partial molar volume [m3.mol-1],9.585e-5, Safari paper, +SEI reaction exchange current density [A.m-2],1.5E-7, Guess, +SEI resistivity [Ohm.m],2e5, Safari paper, +Outer SEI solvent diffusivity [m2.s-1],2.5E-22, Single paper, +Bulk solvent concentration [mol.m-3],2.636E3, Ploehn paper, +Ratio of inner and outer SEI exchange current densities,1, Assume same, +Inner SEI open-circuit potential [V],0.1,, +Outer SEI open-circuit potential [V],0.8,, +Inner SEI electron conductivity [S.m-1],8.95E-14, Single paper, +Inner SEI lithium interstitial diffusivity [m2.s-1],1E-20, Guess, +Lithium interstitial reference concentration [mol.m-3],15, Single paper, +Initial inner SEI thickness [m], 0, 1/2 of initial thickness in Safari paper, +Initial outer SEI thickness [m], 2.5E-9, 1/2 of initial thickness in Safari paper, +EC initial concentration in electrolyte [mol.m-3], 4.541E3, Safari paper, +EC diffusivity [m2.s-1], 2E-18, adjusted parameter in Yang paper, +SEI kinetic rate constant [m.s-1], 1e-12, adjusted parameter in Yang paper, +SEI open-circuit potential [V], 0.4, Safari paper, +SEI growth activation energy [J.mol-1], 38000, Waldmann paper, +,,, +# Reaction-driven LAM example,,, +Negative electrode reaction-driven LAM factor [m3.mol-1],0,, +Positive electrode reaction-driven LAM factor [m3.mol-1],0,, diff --git a/pybamm/input/parameters/lithium_ion/seis/example/parameters.csv b/pybamm/input/parameters/lithium_ion/seis/example/parameters.csv index 3cee296e21..e328d353f1 100644 --- a/pybamm/input/parameters/lithium_ion/seis/example/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/seis/example/parameters.csv @@ -21,6 +21,7 @@ EC initial concentration in electrolyte [mol.m-3], 4.541E3, Safari paper, EC diffusivity [m2.s-1], 2E-18, adjusted parameter in Yang paper, SEI kinetic rate constant [m.s-1], 1e-12, adjusted parameter in Yang paper, SEI open-circuit potential [V], 0.4, Safari paper, +SEI growth activation energy [J.mol-1], 0,, ,,, # Reaction-driven LAM example,,, Negative electrode reaction-driven LAM factor [m3.mol-1],0,, diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py index b667d4e51a..420cc686e1 100644 --- a/pybamm/models/submodels/interface/sei/sei_growth.py +++ b/pybamm/models/submodels/interface/sei/sei_growth.py @@ -107,32 +107,31 @@ def get_coupled_variables(self, variables): L_sei = variables[f"Total {self.reaction} thickness"] T = variables["Negative electrode temperature"] - R_sei = self.param.R_sei + R_sei = param.R_sei eta_SEI = delta_phi - j * L_sei * R_sei - # thermal prefactor for reaction, interstitial and EC models - prefactor = -1 / (2 * (1 + self.param.Theta * T)) + # Thermal prefactor for reaction, interstitial and EC models + prefactor = 1 / (1 + param.Theta * T) if self.options["SEI"] == "reaction limited": - # alpha = param.alpha C_sei = param.C_sei_reaction - j_sei = -(1 / C_sei) * pybamm.exp(prefactor * eta_SEI) + j_sei = -(1 / C_sei) * pybamm.exp(-0.5 * prefactor * eta_SEI) elif self.options["SEI"] == "electron-migration limited": - U_inner = self.param.U_inner_electron - C_sei = self.param.C_sei_electron + U_inner = param.U_inner_electron + C_sei = param.C_sei_electron j_sei = (phi_s_n - U_inner) / (C_sei * L_sei_inner) elif self.options["SEI"] == "interstitial-diffusion limited": - C_sei = self.param.C_sei_inter - j_sei = -pybamm.exp(2 * prefactor * delta_phi) / (C_sei * L_sei_inner) + C_sei = param.C_sei_inter + j_sei = -pybamm.exp(-(prefactor * delta_phi) / (C_sei * L_sei_inner)) elif self.options["SEI"] == "solvent-diffusion limited": - C_sei = self.param.C_sei_solvent + C_sei = param.C_sei_solvent j_sei = -1 / (C_sei * L_sei_outer) elif self.options["SEI"] == "ec reaction limited": - C_sei_ec = self.param.C_sei_ec - C_ec = self.param.C_ec + C_sei_ec = param.C_sei_ec + C_ec = param.C_ec # we have a linear system for j_sei and c_ec # c_ec = 1 + j_sei * L_sei * C_ec @@ -142,13 +141,13 @@ def get_coupled_variables(self, variables): # so # j_sei = -C_sei_ec * exp() / (1 + L_sei * C_ec * C_sei_ec * exp()) # c_ec = 1 / (1 + L_sei * C_ec * C_sei_ec * exp()) - C_sei_exp = C_sei_ec * pybamm.exp(prefactor * eta_SEI) + C_sei_exp = C_sei_ec * pybamm.exp(-0.5 * prefactor * eta_SEI) j_sei = -C_sei_exp / (1 + L_sei * C_ec * C_sei_exp) c_ec = 1 / (1 + L_sei * C_ec * C_sei_exp) # Get variables related to the concentration c_ec_av = pybamm.x_average(c_ec) - c_ec_scale = self.param.c_ec_0_dim + c_ec_scale = param.c_ec_0_dim if self.reaction == "SEI on cracks": variables.update( @@ -174,10 +173,13 @@ def get_coupled_variables(self, variables): if self.options["SEI"] == "ec reaction limited": alpha = 0 else: - alpha = self.param.alpha_SEI + alpha = param.alpha_sei - j_inner = alpha * j_sei - j_outer = (1 - alpha) * j_sei + # All SEI growth mechanisms assumed to have Arrhenius dependence + Arrhenius = pybamm.exp(param.E_over_RT_sei * (1 - prefactor)) + + j_inner = alpha * Arrhenius * j_sei + j_outer = (1 - alpha) * Arrhenius * j_sei variables.update(self._get_standard_concentration_variables(variables)) variables.update(self._get_standard_reaction_variables(j_inner, j_outer)) diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 50e9a5c107..fa2c3fdadf 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -155,6 +155,9 @@ def _set_dimensional_parameters(self): self.L_inner_0_dim = pybamm.Parameter("Initial inner SEI thickness [m]") self.L_outer_0_dim = pybamm.Parameter("Initial outer SEI thickness [m]") self.L_sei_0_dim = self.L_inner_0_dim + self.L_outer_0_dim + self.E_sei_dimensional = pybamm.Parameter( + "SEI growth activation energy [J.mol-1]" + ) # EC reaction self.c_ec_0_dim = pybamm.Parameter( @@ -328,7 +331,9 @@ def _set_dimensionless_parameters(self): ) # SEI parameters - self.alpha_SEI = pybamm.Parameter("Inner SEI reaction proportion") # was 0.5 + self.alpha_sei = pybamm.Parameter("Inner SEI reaction proportion") # was 0.5 + + self.E_over_RT_sei = self.E_sei_dimensional / self.R / self.T_ref self.C_sei_reaction = (self.n.j_scale / self.m_sei_dimensional) * pybamm.exp( -(self.F * self.n.U_ref / (2 * self.R * self.T_ref)) diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py index 0ffa2dc668..a324250af7 100644 --- a/pybamm/parameters/parameter_sets.py +++ b/pybamm/parameters/parameter_sets.py @@ -259,7 +259,7 @@ "positive electrode": "nmc_OKane2022", "electrolyte": "lipf6_Nyman2008", "experiment": "1C_discharge_from_full_Chen2020", - "sei": "example", + "sei": "OKane2022", "lithium plating": "okane2022_Li_plating", "citation": ["OKane2022", "Chen2020"], } From e106c1da608b4b9353ad90d2ce0a5554d19cb437 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 13 Jul 2022 12:14:07 +0100 Subject: [PATCH 14/36] Added zero temperature dependence for all other SEI parameter sets --- examples/notebooks/models/SEI-on-cracks.ipynb | 63 ++++++++++++++----- .../notebooks/models/compare-ecker-data.ipynb | 2 +- .../seis/ramadass2004/parameters.csv | 2 +- .../seis/yang2017_sei/parameters.csv | 1 + 4 files changed, 52 insertions(+), 16 deletions(-) diff --git a/examples/notebooks/models/SEI-on-cracks.ipynb b/examples/notebooks/models/SEI-on-cracks.ipynb index 59d98c6670..a406129a79 100644 --- a/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/examples/notebooks/models/SEI-on-cracks.ipynb @@ -28,47 +28,82 @@ "execution_count": 3, "id": "05c30b49", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "At t = 0.00698071 and h = 8.83032e-18, the corrector convergence failed repeatedly or with |h| = hmin.\n", + "At t = 0.00693594 and h = 1.78003e-18, the corrector convergence failed repeatedly or with |h| = hmin.\n" + ] + } + ], "source": [ - "parameter_values = pybamm.ParameterValues(\"OKane2022\")\n", + "parameter_values = pybamm.ParameterValues(\"Ai2020\")\n", + "parameter_values.update({\"Negative electrode cracking rate\": 1e-30, \"Positive electrode cracking rate\": 1e-30})\n", "model1 = pybamm.lithium_ion.DFN({\"SEI\": \"solvent-diffusion limited\"})\n", "model2 = pybamm.lithium_ion.DFN({\n", - " \"particle mechanics\": \"swelling only\",\n", - " \"loss of active material\": \"stress-driven\",\n", + " \"particle mechanics\": \"swelling and cracking\",\n", " \"SEI\": \"solvent-diffusion limited\",\n", - " \"SEI on cracks\": \"false\",\n", + " \"SEI on cracks\": \"true\",\n", "})\n", "experiment = pybamm.Experiment([\"Discharge at 1C until 3 V\"])\n", - "sim1 = pybamm.Simulation(model1, parameter_values=parameter_values, experiment=experiment)\n", + "var = pybamm.standard_spatial_vars\n", + "var_pts = {\n", + " var.x_n: 20,\n", + " var.x_s: 20,\n", + " var.x_p: 20,\n", + " var.r_n: 100,\n", + " var.r_p: 100,\n", + "}\n", + "sim1 = pybamm.Simulation(model1, parameter_values=parameter_values, experiment=experiment, var_pts=var_pts)\n", "sol1 = sim1.solve(calc_esoh=False)\n", - "sim2 = pybamm.Simulation(model2, parameter_values=parameter_values, experiment=experiment)\n", + "sim2 = pybamm.Simulation(model2, parameter_values=parameter_values, experiment=experiment, var_pts=var_pts)\n", "sol2 = sim2.solve(calc_esoh=False)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "c3884817", "metadata": {}, "outputs": [], "source": [ "t1 = sol1[\"Time [s]\"].entries\n", + "V1 = sol1[\"Terminal voltage [V]\"].entries\n", "SEI1 = sol1[\"Loss of capacity to SEI [A.h]\"].entries\n", "t2 = sol2[\"Time [s]\"].entries\n", + "V2 = sol2[\"Terminal voltage [V]\"].entries\n", "SEI2 = sol2[\"Loss of capacity to SEI [A.h]\"].entries + sol2[\"Loss of capacity to SEI on cracks [A.h]\"].entries" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "d33e1d89", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "(fig, ax) = plt.subplots()\n", - "ax.plot(t1,SEI1,label=\"without cracking\")\n", - "ax.plot(t2,SEI2,label=\"with cracking\")\n", - "plt.legend()\n", + "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18,4))\n", + "ax1.plot(t1,V1,label=\"without cracking\")\n", + "ax1.plot(t2,V2,label=\"with cracking\")\n", + "ax1.legend()\n", + "ax2.plot(t1,SEI1,label=\"without cracking\")\n", + "ax2.plot(t2,SEI2,label=\"with cracking\")\n", + "ax2.legend()\n", "plt.show()" ] }, diff --git a/examples/notebooks/models/compare-ecker-data.ipynb b/examples/notebooks/models/compare-ecker-data.ipynb index 24c4ca8f02..84bef377a8 100644 --- a/examples/notebooks/models/compare-ecker-data.ipynb +++ b/examples/notebooks/models/compare-ecker-data.ipynb @@ -262,7 +262,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.8.10" }, "toc": { "base_numbering": 1, diff --git a/pybamm/input/parameters/lithium_ion/seis/ramadass2004/parameters.csv b/pybamm/input/parameters/lithium_ion/seis/ramadass2004/parameters.csv index 0adc9cb90e..f07d512d19 100644 --- a/pybamm/input/parameters/lithium_ion/seis/ramadass2004/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/seis/ramadass2004/parameters.csv @@ -21,4 +21,4 @@ EC initial concentration in electrolyte [mol.m-3], 4.541e3, Safari paper, EC diffusivity [m2.s-1], 2e-18, adjusted parameter in Yang paper, SEI kinetic rate constant [m.s-1], 1e-12, adjusted parameter in Yang paper, SEI open-circuit potential [V], 0, Estimated, - +SEI growth activation energy [J.mol-1], 0,, diff --git a/pybamm/input/parameters/lithium_ion/seis/yang2017_sei/parameters.csv b/pybamm/input/parameters/lithium_ion/seis/yang2017_sei/parameters.csv index a5936b42eb..ccd97e4f0c 100644 --- a/pybamm/input/parameters/lithium_ion/seis/yang2017_sei/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/seis/yang2017_sei/parameters.csv @@ -11,3 +11,4 @@ EC initial concentration in electrolyte [mol.m-3],4.541E+03, Safari paper, EC diffusivity [m2.s-1],2.00E-18, adjusted parameter in Yang paper, SEI kinetic rate constant [m.s-1],1.00E-12, adjusted parameter in Yang paper, SEI open-circuit potential [V],0.4, Safari paper, +SEI growth activation energy [J.mol-1], 0,, From 82d5800e917d1e47ddbbad461dbb9aca64ba414d Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Fri, 15 Jul 2022 14:12:12 +0100 Subject: [PATCH 15/36] Completed example notebook --- .../Tutorial 9 - Changing the mesh.ipynb | 2 +- examples/notebooks/models/SEI-on-cracks.ipynb | 135 ++++++++++++++---- pybamm/parameters/lithium_ion_parameters.py | 2 +- 3 files changed, 106 insertions(+), 33 deletions(-) diff --git a/examples/notebooks/Getting Started/Tutorial 9 - Changing the mesh.ipynb b/examples/notebooks/Getting Started/Tutorial 9 - Changing the mesh.ipynb index 3cc5f88ae0..8834e2e6e8 100644 --- a/examples/notebooks/Getting Started/Tutorial 9 - Changing the mesh.ipynb +++ b/examples/notebooks/Getting Started/Tutorial 9 - Changing the mesh.ipynb @@ -325,7 +325,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.8.10" }, "toc": { "base_numbering": 1, diff --git a/examples/notebooks/models/SEI-on-cracks.ipynb b/examples/notebooks/models/SEI-on-cracks.ipynb index a406129a79..9e69ec9ce7 100644 --- a/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/examples/notebooks/models/SEI-on-cracks.ipynb @@ -1,5 +1,15 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "569864b3", + "metadata": {}, + "source": [ + "# Modelling SEI growth on particle cracks\n", + "\n", + "This notebook provides a short demonsration of how the SEI and particle mechanics submodels can be combined to simulate SEi growth on particle cracks." + ] + }, { "cell_type": "code", "execution_count": 1, @@ -23,48 +33,79 @@ "import numpy as np" ] }, + { + "cell_type": "markdown", + "id": "c46a0904", + "metadata": {}, + "source": [ + "Define two models. In model1, the only degradation mechanism is solvent-diffusion limited SEI growth. model2 includes the same SEI growth mechanism but also includes particle cracking and SEI growth on the cracks. The SEI model is run twice: once on the initial surface and once on the cracks. The equations for SEI on cracks are reported by O'Kane et al. [8]" + ] + }, { "cell_type": "code", - "execution_count": 3, - "id": "05c30b49", + "execution_count": 2, + "id": "b4df06fd", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "At t = 0.00698071 and h = 8.83032e-18, the corrector convergence failed repeatedly or with |h| = hmin.\n", - "At t = 0.00693594 and h = 1.78003e-18, the corrector convergence failed repeatedly or with |h| = hmin.\n" - ] - } - ], + "outputs": [], "source": [ - "parameter_values = pybamm.ParameterValues(\"Ai2020\")\n", - "parameter_values.update({\"Negative electrode cracking rate\": 1e-30, \"Positive electrode cracking rate\": 1e-30})\n", "model1 = pybamm.lithium_ion.DFN({\"SEI\": \"solvent-diffusion limited\"})\n", "model2 = pybamm.lithium_ion.DFN({\n", " \"particle mechanics\": \"swelling and cracking\",\n", " \"SEI\": \"solvent-diffusion limited\",\n", " \"SEI on cracks\": \"true\",\n", - "})\n", - "experiment = pybamm.Experiment([\"Discharge at 1C until 3 V\"])\n", - "var = pybamm.standard_spatial_vars\n", + "})" + ] + }, + { + "cell_type": "markdown", + "id": "01d45212", + "metadata": {}, + "source": [ + "Depending on the parameter set being used, the particle cracking model can require a large number of mesh points inside the particles to be numerically stable." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "19235041", + "metadata": {}, + "outputs": [], + "source": [ + "parameter_values = pybamm.ParameterValues(\"OKane2022\")\n", "var_pts = {\n", - " var.x_n: 20,\n", - " var.x_s: 20,\n", - " var.x_p: 20,\n", - " var.r_n: 100,\n", - " var.r_p: 100,\n", - "}\n", + " \"x_n\": 20, # negative electrode\n", + " \"x_s\": 20, # separator \n", + " \"x_p\": 20, # positive electrode\n", + " \"r_n\": 120, # negative particle\n", + " \"r_p\": 100, # positive particle\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "b70d357a", + "metadata": {}, + "source": [ + "Solve the models with and without cracking for a 1C discharge." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "05c30b49", + "metadata": {}, + "outputs": [], + "source": [ + "experiment = pybamm.Experiment([\"Discharge at 1C until 2.5 V\"])\n", "sim1 = pybamm.Simulation(model1, parameter_values=parameter_values, experiment=experiment, var_pts=var_pts)\n", - "sol1 = sim1.solve(calc_esoh=False)\n", + "sol1 = sim1.solve()\n", "sim2 = pybamm.Simulation(model2, parameter_values=parameter_values, experiment=experiment, var_pts=var_pts)\n", - "sol2 = sim2.solve(calc_esoh=False)" + "sol2 = sim2.solve()" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "id": "c3884817", "metadata": {}, "outputs": [], @@ -79,13 +120,13 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "d33e1d89", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -100,20 +141,52 @@ "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18,4))\n", "ax1.plot(t1,V1,label=\"without cracking\")\n", "ax1.plot(t2,V2,label=\"with cracking\")\n", + "ax1.set_xlabel(\"Time [s]\")\n", + "ax1.set_ylabel(\"Terminal voltage [V]\")\n", "ax1.legend()\n", "ax2.plot(t1,SEI1,label=\"without cracking\")\n", "ax2.plot(t2,SEI2,label=\"with cracking\")\n", + "ax2.set_xlabel(\"Time [s]\")\n", + "ax2.set_ylabel(\"Loss of capacity to SEI [A.h]\")\n", "ax2.legend()\n", "plt.show()" ] }, + { + "cell_type": "markdown", + "id": "4afa1ba8", + "metadata": {}, + "source": [ + "The SEI on cracks consumes far more capacity than the SEI on the initial surface, in agreement with the literature." + ] + }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "9ca7991b", "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", + "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", + "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", + "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[7] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", + "[8] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", + "[9] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[10] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "\n" + ] + } + ], + "source": [ + "pybamm.print_citations()" + ] } ], "metadata": { diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index fa2c3fdadf..921fa4d34e 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -429,7 +429,7 @@ def _set_dimensionless_parameters(self): self.ocv_init = (self.ocv_init_dim - self.ocv_ref) / self.potential_scale # Dimensionless mechanical parameters - self.t0_cr = 3600 / self.C_rate / self.timescale + self.t0_cr = 3600 / (self.C_rate * self.timescale) def chi(self, c_e, T): """ From 3dc6375a54e552676ec764335309a6c6f24730c9 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Fri, 15 Jul 2022 14:31:31 +0100 Subject: [PATCH 16/36] Updated CHANGELOG --- CHANGELOG.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index b8cf3965c1..3abfcfcca0 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,9 @@ - Moved general code about submodels to `BaseModel` instead of `BaseBatteryModel`, making it easier to build custom models from submodels. ([#2169](https://github.com/pybamm-team/PyBaMM/pull/2169)) - Events can now be plotted as a regular variable (under the name "Event: event_name", e.g. "Event: Minimum voltage [V]") ([#2158](https://github.com/pybamm-team/PyBaMM/pull/2158)) +- Added SEI growth on cracks ([#2104](https://github.com/pybamm-team/PyBaMM/pull/2104)) +- Added Arrhenius temperature dependence of SEI growth ([#2104](https://github.com/pybamm-team/PyBaMM/pull/2104)) +- The "Inner SEI reaction proportion" parameter actually gets used now ([#2104](https://github.com/pybamm-team/PyBaMM/pull/2104)) ## Optimizations From c78229ac59cb9fff7d8661ac715d99c269750ca4 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Mon, 18 Jul 2022 16:52:31 +0100 Subject: [PATCH 17/36] flake8 --- examples/notebooks/models/SEI-on-cracks.ipynb | 14 +++++++++++--- .../graphite_volume_change_Ai2020.py | 2 +- ...lectrolyte_exchange_current_density_Chen2020.py | 8 +++----- .../nmc_OKane2022/volume_change_Ai2020.py | 5 ++--- pybamm/models/submodels/interface/sei/no_sei.py | 2 +- .../models/submodels/interface/sei/sei_growth.py | 2 +- 6 files changed, 19 insertions(+), 14 deletions(-) diff --git a/examples/notebooks/models/SEI-on-cracks.ipynb b/examples/notebooks/models/SEI-on-cracks.ipynb index 9e69ec9ce7..3bf8eb082d 100644 --- a/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/examples/notebooks/models/SEI-on-cracks.ipynb @@ -120,13 +120,13 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "d33e1d89", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -162,7 +162,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "9ca7991b", "metadata": {}, "outputs": [ @@ -187,6 +187,14 @@ "source": [ "pybamm.print_citations()" ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86209382", + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_volume_change_Ai2020.py b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_volume_change_Ai2020.py index c47f921c34..40b5b69556 100644 --- a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_volume_change_Ai2020.py +++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_volume_change_Ai2020.py @@ -1,4 +1,4 @@ -def graphite_volume_change_Ai2020(sto): +def graphite_volume_change_Ai2020(sto, c_s_max): """ Graphite particle volume change as a function of stochiometry [1, 2]. diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py index 0925f69260..afac388e18 100644 --- a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py @@ -1,7 +1,7 @@ -from pybamm import exp, constants, Parameter +from pybamm import exp, constants -def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, T): +def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, c_s_max, T): """ Exchange-current density for Butler-Volmer reactions between NMC and LiPF6 in EC:DMC. @@ -31,8 +31,6 @@ def nmc_LGM50_electrolyte_exchange_current_density_Chen2020(c_e, c_s_surf, T): E_r = 17800 arrhenius = exp(E_r / constants.R * (1 / 298.15 - 1 / T)) - c_p_max = Parameter("Maximum concentration in positive electrode [mol.m-3]") - return ( - m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_p_max - c_s_surf) ** 0.5 + m_ref * arrhenius * c_e ** 0.5 * c_s_surf ** 0.5 * (c_s_max - c_s_surf) ** 0.5 ) diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/volume_change_Ai2020.py b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/volume_change_Ai2020.py index 1273da5722..39589ef50b 100644 --- a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/volume_change_Ai2020.py +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/volume_change_Ai2020.py @@ -1,7 +1,7 @@ from pybamm import Parameter -def volume_change_Ai2020(sto): +def volume_change_Ai2020(sto, c_s_max): """ Particle volume change as a function of stochiometry [1, 2]. @@ -26,6 +26,5 @@ def volume_change_Ai2020(sto): volume change, dimensionless, normalised by particle volume """ omega = Parameter("Positive electrode partial molar volume [m3.mol-1]") - c_p_max = Parameter("Maximum concentration in positive electrode [mol.m-3]") - t_change = omega * c_p_max * sto + t_change = omega * c_s_max * sto return t_change diff --git a/pybamm/models/submodels/interface/sei/no_sei.py b/pybamm/models/submodels/interface/sei/no_sei.py index 9ebf5fd530..dc021f0ed4 100644 --- a/pybamm/models/submodels/interface/sei/no_sei.py +++ b/pybamm/models/submodels/interface/sei/no_sei.py @@ -39,4 +39,4 @@ def get_fundamental_variables(self): def get_coupled_variables(self, variables): variables.update(self._get_standard_concentration_variables(variables)) - return variables \ No newline at end of file + return variables diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py index 420cc686e1..da528ac6e5 100644 --- a/pybamm/models/submodels/interface/sei/sei_growth.py +++ b/pybamm/models/submodels/interface/sei/sei_growth.py @@ -175,7 +175,7 @@ def get_coupled_variables(self, variables): else: alpha = param.alpha_sei - # All SEI growth mechanisms assumed to have Arrhenius dependence + # All SEI growth mechanisms assumed to have Arrhenius dependence Arrhenius = pybamm.exp(param.E_over_RT_sei * (1 - prefactor)) j_inner = alpha * Arrhenius * j_sei From 6e7fbcf883635bb8f428f73d7641c36b3f0957da Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Tue, 19 Jul 2022 10:52:53 +0100 Subject: [PATCH 18/36] Fixed get_coupled_variables in no_sei.py --- examples/notebooks/models/SEI-on-cracks.ipynb | 43 +++---------------- .../models/submodels/interface/sei/no_sei.py | 2 + 2 files changed, 8 insertions(+), 37 deletions(-) diff --git a/examples/notebooks/models/SEI-on-cracks.ipynb b/examples/notebooks/models/SEI-on-cracks.ipynb index 3bf8eb082d..67d0318433 100644 --- a/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/examples/notebooks/models/SEI-on-cracks.ipynb @@ -48,7 +48,7 @@ "metadata": {}, "outputs": [], "source": [ - "model1 = pybamm.lithium_ion.DFN({\"SEI\": \"solvent-diffusion limited\"})\n", + "model1 = pybamm.lithium_ion.DFN({\"SEI\": \"none\"})\n", "model2 = pybamm.lithium_ion.DFN({\n", " \"particle mechanics\": \"swelling and cracking\",\n", " \"SEI\": \"solvent-diffusion limited\",\n", @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "c3884817", "metadata": {}, "outputs": [], @@ -120,23 +120,10 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "d33e1d89", "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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j7rrdzF23iznrdvP0J2s4lJd/pG1G3VohEdG4Hh0y69KjRQP6t2lIvZSkMEwjs0tYul9U/BMeOgCH9kXL/mjZd3R99l7I2gPZu6Ofe47+3LkmJDWKSmAk1Q5DQBq1j5YOR5cGrTRjh4iICGBm/wXkAv8qZvstwC0Abdq0qcTIRESk0uQchC+mHk02bJoX1tduGGak6PizkHBo2C6WUVZJSj6UQWKC0aVpfbo0rX9k+s1Dufl8seMAq7btZ+XWfazcup9V2/bzweLNvDjjEAAJBl2bNWBQ24YMateQAW0a0qphbay4Xgi16oSFJicebPa+kITYtyX6uTkkJnasDF2Elr0HeYcKPGd9aNY7LM37hJ+Z3TW0Q0Qqxfnnn89zzz0HwHPPPcdtt90GhAJw999/P+PGjav0mO69917q1avHT37yk2PW33333YwYMYKzzjqr0mOqgdYDrQvcbxWtK6rNOjNLAtIIhSfL8tgvMbMbCcUoz/Ri5iF398eAxyBMtVmWA4k3es+JiBTiDpvnw4oJIeGw5hPIy4aEZGhzGpx5N3Q4IwyrUN2Gk6LkwwmqlZRApyb16NSkHtD0mG27D+Qwd/0uZqzeycw1O3n183U8Oy0M22jaIIVhHRszunsTRnTJpEFqOc+IkVIvLBkdi96enxeGeuxcBduXw+aFsGkuzPonfLo/tElIDnPNthoEbYaEN116Gw3dEJFy99ZbbwGwevVqHn744SNfhMpLbm4uSUnl81F33333lct+pEw+AzqbWXtC4uBa4CuF2owFvg5MJdRomODubmZjgefM7I+EgpOdgU9LerJoZo2fASPdvYxjIKsmvedERIC9m0KiYcWEUL9h/5awPrM7DL4ZOo6GtkND3T0pN+pvXwHS6iQzvHMmPzy7C//85qnMueccxn3vdO67pCentM9gwpItfPe5WQy4bzxf+XuY0nPN9v2VE1xCIqS3DuOSBt0EF9wPN78Hd62F786EK58M04nWy4T5r8Brt8Bf+sAfu8O/b4Tpf4ONc0ISQ0SkBH/4wx948MEHAfjhD3/I6NGjAZgwYQLXX389AO3atWPbtm3ceeedrFixgn79+vHTn/4UgH379nHllVfSrVs3rr/+eoq6GL18+XLOOuss+vbty4ABA1ixYgWTJk1i+PDhXHzxxfTo0QOASy+9lIEDB9KzZ08ee+yxI49/5513GDBgAH379uXMM8/80v7//ve/c95553Hw4EFuvPFGXn755SNx33PPPQwYMIDevXuzePFiALZu3crZZ59Nz549+eY3v0nbtm3Ztm1bef1Kawx3zwW+C7wLLAJecvcFZnafmV0cNXsCyIgKSv4IuDN67ALgJUJxyneA26OZLjCz5wnJiq5mts7Mbo729VegPjDezGab2aOVcqDlTO85vedEpBg5B0Oi4d3/goeHwgNd4fVbw7oOI+HSR+BHi+D2aTDmN9D5bCUeKoB6PlSCpMQEerVMo1fLNL42pB25efnMWruLDxZt4YNFm/nVuIX8atxCOmbW5Zyezbigd3N6tmhQ/PCMipCQCI07haXXFWFdfh5sWRTGPH0xDdZOhwWvhW21G4YERodRoRtSo/aVF6uIHL+37zw6ZrG8NOsN5/222M3Dhw/ngQce4I477mDGjBlkZ2eTk5PD5MmTGTFixDFtf/vb3zJ//nxmz54NhC7gs2bNYsGCBbRo0YJhw4bx8ccfc/rppx/zuOuvv54777yTyy67jKysLPLz81m7di2ff/458+fPp3378L/pySefpFGjRhw8eJDBgwdzxRVXkJ+fz7e+9S0++ugj2rdvz44dO47Z91//+lfGjx/P66+/TkpKoSmXgcaNG/P555/z8MMPc//99/P444/zy1/+ktGjR3PXXXfxzjvv8MQTT5zIb1YAd38LeKvQursL3M4Crirmsb8Gfl3E+uuKad/ppIItit5zes+JSOy4h+8xKybAig/CUIrcLEisFQ2luAc6nQlNe6v+XSVS8iEGkhITGNyuEYPbNeLO87rxxfYDfLB4Mx8s2sJjH63kkUkraN2oNuf3as55vZvTt1Va5SYiDktIhGa9wnLKt8K6XWtDIuLw9DIL3wjrG7Y7mojoMApqp1d+vCISVwYOHMjMmTPZs2cPKSkpDBgwgBkzZjB58uQjV2dLcsopp9CqVaiz069fP1avXn3MF6G9e/eyfv16LrvsMgBSU1OPeezhL0EADz74IK+9FpKna9euZdmyZWzdupURI0Ycadeo0dEq1c888wytW7fm9ddfJzm56OFxl19++ZHjfPXVVwGYMmXKkecZM2YMDRs2LPU4RcqL3nN6z4nUaAd2hO8nyyeEpMPeDWF9464w8Bsh2aChFDGl5EMcaJNRh28Ma883hrVn5/5DjF+4mbfmb+SJKav420craZlemzG9mnF+7+b0b51OQkIMay+ktw5Ln6tCRnH78qOVYOe9AjOfAksMb+zO50CXMdC4s+pFiMRaCVdLK0pycjLt27fnqaeeYujQofTp04eJEyeyfPlyunfvXurjC175TExMJDc3t8zPXbfu0ROLSZMm8f777zN16lTq1KnDqFGjyMrKKvHxvXv3Zvbs2axbt+6YL1RFxXe8sUkNofec3nMiUrHycmH9DFj+Piz/ADbMAhxS08PF0E5nhtoNaa1iHKgcpj4mcaZh3VpcPbg1T33jFGb+99ncf1VfujWrz7NT13DFI58w9LcTuHfsAj5bvYP8/BgX2jYLiYVTb4Hrnoefr4ab3oVh34eDO2H8/8BDg+HB/vD2z0MGMvdQqbsVkepj+PDh3H///YwYMYLhw4fz6KOP0r9//y/15qpfvz579+49rn3Xr1+fVq1a8frrrwOQnZ3NgQNfrhW4e/duGjZsSJ06dVi8eDHTpk0D4LTTTuOjjz5i1apVAMd0Ae/fvz9/+9vfuPjii9mwYUOZYxo2bBgvvfQSAO+99x47d+48rmMSOVl6z+k9J1Kt7VobLna+eAP8vgM8eS5MfgASkmDUnXDz+/CzlXD10zDga0o8xBn1fIhjaXWSuXJgK64c2Io9WTl8sGgzb83bxHOffsFTn6ymSf2UIz0iBrdrRGIse0QAJCaFMVRtToOz7gn/HJa9C0vfDf8kpj8KKQ1CAZduF0CnsyG1QWxjFpEKNXz4cH79618zZMgQ6tatS2pqKsOHD/9Su4yMDIYNG0avXr0477zzuOCCC8q0/2effZZvf/vb3H333SQnJ/Pvf//7S23GjBnDo48+Svfu3enatSunnXYaAJmZmTz22GNcfvnl5Ofn06RJE8aPH3/kcaeffjr3338/F1xwwTHrS3LPPfdw3XXX8eyzzzJkyBCaNWtG/fr1y/RYkfKg95zecyLVSk4WrPk49GxY8QFsDcVmadASelwcejd0GBXq0Uncs2Kmso5bgwYN8hkzZsQ6jJjal53LB4s28/a8TUxcsoXs3Hwy6tbizO5NOLdnM4Z1akxqcpzNQXvoAKz6EBb/B5a8DQe2hSk9O4wMiYiu50P9ZrGOUqRaWbRoUZm6Wkv5yc7OJjExkaSkJKZOncp3vvOdIwX9yltRr6+ZzXT3QRXyhHKMos5H9J6rfJX1ntNrK1KJtq+IhlK8D6smQ+7BUCiy7TDodFZIOGR207DuOFbc+Yh6PlRB9VKSuKRfSy7p15L92blMXLKF9xaEZMRLM9ZRp1YiI7tkck7Ppozu2pS0OkUXbqpUtepA1/PCkp8Haz+FxeNCMmLcD8PSajB0vwi6XQgZHWMdsYjIcfviiy+4+uqryc/Pp1atWvz973+PdUgi1ZrecyLVwKEDsHoyLBsfEg47w9AsGnUIQyc6nQntTlehyGpAyYcqrm5KEhf2acGFfVpwKDefaSu3897CTSEZMX8TSQnG4HaNOKNbJqO7NaFjZr3YzJxRUEIitB0SlnP+X5gGZ/F/YPGbMP7usDTpCd0vDMmIpr2U2RSRKqFz587MmjUr1mGI1Bh6z4lUQYeL1i8bD8vHw+qPIS8bkmpD+xEw5PZQKFIXI6sdJR+qkVpJCYzoksmILpncd3Ev5q7fzbsLNjFx8Rb+963F/O9bi2ndqDZndG3CGd2aMKRDRuyHZ5hB0x5hGflT2LkmSkSMgw9/Dx/+Lkzj2e3CsLQ+JSQvRKRM3D32CUcpd1VtyGRNovdc9aP3m0g5OLQ/DKFYPj4kHXatCesbd4HB34ymwRwGyakl70eqNNV8qCE27DrIxCVbmLh4Kx8v38bBnDxSkxM4pX0Gwzs1ZniXxnRtWj++Tpj2bYUlb8GiN0O9iLxDULdJGLrR/aKQGU1KKX0/IjXUqlWrqF+/PhkZGfH13paT4u5s376dvXv3fmlKQtV8qDxFnY/oPVf9lPR+E5FSbF8REg3L3oPVU0LvhuQ60H4kdD4r1G9o2C7WUUoFKO58pMKSD2aWCnwEpBB6WLzs7vcUatMGeBpIBxKBO939rZL2q+TDycvKyePTVTuYsHgLU5ZvY/mWfQBk1k/h9E6Nw9K5MU0bxFHmMWtP+Me1eFz4J3Zon2bOEClFTk4O69atIysrK9ahSDlLTU2lVatWJCcfW9NHyYfKU9T5iN5z1VNx7zcRKSQnC9ZMiRIO42HHirA+ozN0PickHNoO08XDGiAWyQcD6rr7PjNLBqYA33f3aQXaPAbMcvdHzKwH8Ja7tytpv0o+lL+Nuw8yedk2pizbxsfLt7F9/yEAOmbWZUjHDIZ0aMxpHRqRUS9O/lHkZIWeEIvePHbmjPYjoNv5YeaMBi1iHaWISKVT8qHy6HxERATYtTZcIFw2Ppyf5xyApNRwXt75nNC7oZF6DdU0lT7bhYesxr7obnK0FM50OHD4cnUasKGi4pHiNU+rzdWDWnP1oNbk5zuLNu1hyrJtTF25ndc+X88/p30BQNem9RnSMYPTOmRwWodGpNepFZuAk1Ohy7lhOTxzxpL/wOK34D8/DkuL/tD1gjBEo2lPFawUERERETlZebmw7lNY+m5IOGxZENant4F+14eEQ/vhkFw7tnFKXKrQmg9mlgjMBDoBD7n7zwttbw68BzQE6gJnufvMIvZzC3ALQJs2bQauWbOmwmKWY+Xk5TNv/W6mrtjOtJXb+Wz1DrJy8jELyYjDiYhT2mfQqG6MkhGHucO2pdEUnm/B+uiKVFpr6DImLO2Hq6uXiFRb6vlQedTzQURqjAM7whSYS9+B5R9A1i5ISIK2Q6PhFOeEwpG62CeRSh92UejJ04HXgO+5+/wC638UxfCAmQ0BngB6uXt+cfvSh31sHcrNZ866XUxfuZ1pK3cwc81ODubkAdClab0oGZHBqe3jYJjG3s2w7F1Y8g6smAC5ByG5LnQ8I/SI6HwO1GsS2xhFRMpRvCQfzGxPaU2Aje7epTLiqQg6HxGRassdtiwMyYal78K6z8DzoW4mdD4XupwDHc5QvTUpVkyTD1EAdwMH3P3+AusWAGPcfW10fyVwmrtvKW4/+rCPL4dy85m3fhfTVu5g2srtzFyzkwOH4jAZkXMQVn0UakQsfRf2RiN8WvQ/+k+0eX9ISIhdjCIiJymOkg+z3L3/ybaJZzofEZFqJScLVk8+mnDYvTasb94v6kGsc2Upu1gUnMwEctx9l5nVJgyv+J27jyvQ5m3gRXd/ysy6Ax8ALb2EoPRhH98OD9OYFvWMmLF6xzHJiCEdMhjSsTFDOmSQVidGVaPdYdNcWPpe6BmxbgbgYRrPzmeHHhEdz4DUtNjEJyJyguIo+dDB3VeebJt4pvMREany9mwM58JL34WVk0KxyMO9hLucG86J6zeLdZRSBcUi+dCHMI1mIpAAvOTu95nZfcAMdx8bzXDxd6Aeofjkz9z9vZL2qw/7qqVgMmLqiu3MWB2GaZhBrxZpDO2YwZCOGQxu14i6KRVW/7Rk+7eFcWzL3gs/s3aHcWytTw3JiE5nq2iliFQJ8ZJ8qAl0PiIiVY47bJwdkg1L3g63AdLaQNcxIeHQ9vRQ3F3kJMR82EV50Yd91Xa4ZsQny7fzyYptzPpiF4fy8klKMPq3Sef0Tpmc3jmDPq3SSU6MQbeuwxV8l42H5eNh07ywvn6LMDdxp7OhwyiNcRORuBRvyQczuxz4HdCEUOfBCBNiVfl/ojofEZEqIecgrPwQlh4eerwRMGg1OEo4nAdNuusim5QrJR8kLh08lMfMNTv5eMU2Pl6+jXnrd+MO9VKSOK1DBqd3yuD0zpl0zKyLxeKf4p6NoTfE8vGwYiJk7znaK6LTWWFp1lv/sEUkLsRh8mE5cJG7L4p1LOVN5yMiErf2bTnau+Fw0fVa9aLhFIeLrmfGOkqpxpR8kCph14FDTF2xncnLQzJizfYDALRIS2VEl0xGdslkaKfGpNWOQb2IvBxY++nRZMThXhH1mkWJiDNDr4g6jSo/NhER4jL58LG7D4t1HBVB5yMiEjfcYetiWPJWSDgcrmfWoFWY4a3rGGin6eal8ij5IFXS2h0HmLxsGx8t3crHy7exNzuXxASjX+t0RnbJZESXTPq0TCMhIQY9D/ZuCnMdLx8fsspZu8ESoMWAkIjoOBpaDoLEGNWyEJEaJ16SD9FwC4CRQDPgdSD78HZ3fzUGYZUrnY+ISEzl5cIXU6OEw1uwc3VY36I/dD0/JB2a9lLvXIkJJR+kysvJy2f22l18tHQrHy3dytxoiEZG3VqM7JLJqG5NGNk5MzazaOTlwvqZIQmx4oNw2/MhJQ06jAiJiA5nQKP2lR+biNQYcZR8+EcJm93db6q0YCqIzkdEpNJl7w0Xvpa8HWapOLgTEmtB+5FRD4fzoEGLWEcpouSDVD879h9i8rKtTFy8hQ+XbmXngRwSDAa2bcgZ3ZpwRtcmdGtWPza1Ig7sgFUfhg+IFRNgz/qwPr1N+IDoMAraj4B6TSo/NhGptuIl+VAezGwM8BfCrFmPu/tvC21PAZ4BBgLbgWvcfXW07S7gZiAPuMPd343WPwlcCGxx914F9tUIeBFoB6wGrnb3nSXFp/MREakUezeHng2L/xPOLfMOQe2G0Plc6HZ+uMCVUj/WUYocQ8kHqdby8p3Za3cxackWJizewoINewBomV6bs3s05azuTTm1Q6PYzKDhDtuWhQ+MlZNg1WTI3h22NekJHUaGcXhth4QPExGRE1QVkg9mdqG7jyulTSKwFDgbWAd8Blzn7gsLtLkN6OPut5rZtcBl7n5NNI3388ApQAvgfaCLu+eZ2QhgH/BMoeTD74Ed7v5bM7sTaOjuPy8pRp2PiEiF2bYMFo8LCYfD9RsatoOuF4SEQ+vTNKxX4pqSD1KjbN6TxcTFW3h/0WYmL9tGdm4+9VOTGNW1CWf3aMrILpmxKVoJkJ8X5lVeGSUj1k6H3CzAoFmvkIhodzq0GaLilSJyXKpI8uGX7n5PKW2GAPe6+7nR/bsA3P03Bdq8G7WZamZJwCYgE7izYNuC7aL77YBxhZIPS4BR7r7RzJoDk9y9a0kx6nxERMpNfj5s+BwWvRkSDtuXhfXN+0G3C8LSpIfqN0iVUdz5iFJmUi01bZDKtae04dpT2nDwUB5Tlm9j/MJNfLBoC2/O2UBSgjG0U2PG9GzGOT2b0rheJVb/TUiElgPDMvxHkJsdakSsngKrJ8OMJ2Haw4CFQkHthkHbaKmbUXlxiohUjIfL0KYlsLbA/XXAqcW1cfdcM9sNZETrpxV6bMtSnq+pu2+Mbm8CmhbVyMxuAW4BaNOmTelHISJSnNxDsGYKLBoXhlXs3Rimc287DE65JdRvSG8d6yhFypWSD1Lt1a6VyNk9mnJ2j6ZHhme8t3AT78zfxC9em8d/vz6Pwe0acV6vZpzbqxnN02pXboBJKdB2aFhG/ixKRnx+NBkx82mY/mhom9n92GRE/SLPj0VE4oqZpQNXAF8BuhOGQ8Qld3czK7JbqLs/BjwGoedDpQYmIlXfof1hyvZF42Dpu2EYbnKdMEtat4ugyzkagivVmpIPUqMkJhgD2zZkYNuG3DmmG4s37eXt+Zt4Z/5G7n1zIfe+uZB+rdO5sE9zLujTvPITERAlI4aEZeRPQ2Z8w6yQHV/9Mcx5AT57PLRt3OXoMI12w6FeZuXHKyJSBDOrDVxCSDj0B+oDlwIfleHh64GCl/xaReuKarMuGnaRRig8WZbHFrbZzJoXGHaxpQwxioiU7uDOkGhY9GZIPORmQe1G0P1C6HYhdDwDkmNwvikSA6r5IBJZsXUf78zfxNvzNzJ/fShYObhdQy7s04LzejejSf3UGEcYycuFTXOinhFTYM1UOLQ3bMvsdjQZ0X6EakaI1DDxUvPBzJ4DhgPvAS8AE4Dl7l6m+YajZMJS4ExC4uAz4CvuvqBAm9uB3gUKTl7u7lebWU/gOY4WnPwA6OzuedHj2vHlmg9/ALYXKDjZyN1/VlKMOh8RkWLt3RwKRi56M/Rizc+F+i2g+0Uh6dBmqApGSrWmgpMix2HVtv2Mm7OBcXM3smTzXhIMTm2fwUV9W3B+72ak16kV6xCPyssNBSxXTw4zaXwxDXL2AwbN+4ZpPTuMgjanKbMuUs3FUfJhNpBAmArzBXdfZ2Yr3b3DcezjfODPhKk2n3T3X5vZfcAMdx9rZqnAs4ReFTuAa919ZfTY/wJuAnKBH7j729H654FRQGNgM3CPuz9hZhnAS0AbYA1hqs0dJcWn8xEROcauL0KyYdGb4VwMh0YdocfFYUhFi/6QEINZ10RiQMkHkRO0dPPeI4mIldv2k5xojOySySX9WnJW96bUrpUY6xCPlZcTClgenk1j3ach456YEhIQHUZBp7OgWW9VTRapZuIl+QBgZt2A64BrgG1AV6CXu2+OaWDlROcjIsK25bDoDVg4NlwIAmjaO+rhcBE06a5zLamRlHwQOUnuzoINe3hj9nrGztnA5j3Z1K2VyLk9m3FJ/5YM65hBUmIcZrSz98GaT0IiYuUk2BL1Wq7XFDqeGYocdRytIRoi1UA8JR8KMrOBhETE1cA6dx8a45BOms5HRGogd9i6GBa+EZYtC8P6lgOh+8Uh4ZDRMbYxisSB404+mNnYMux3h7vfeJKxHRd92Es8yMt3pq/azhuzNvDW/I3szcqlcb1aXNinBRf3a0H/1ulYvGa6926CFRNC0aMVE0IhJCx8cHY6K1Rabq6ugSJVUbwmHw6z8I9xuLuXpehkXNP5iEgN4Q6b5x9NOGxbCliYpaz7xaGGQ1qrWEcpEldOJPmwDPhmSfsEHnL3nuUTYtnow17iTVZOHpOWbOWN2ev5YPEWDuXm06ZRHS7p14JL+rWgU5P6sQ6xePl5YSaN5e+HZd0MwKFuE+h8DnQ5N1RhTonjYxCRI+Il+WBmt0TTUp5Um3im8xGRaswdNs6Bha+HhMOOlWAJoaB3j0tCDQdNdy5SrBNJPlzt7i+VstNi20SFoD4CUghTer7s7vcUtQ/gXsCBOe7+lZKeUx/2Es/2ZOXw7vxNjJ2zgY+XbyPfoUfzBlzSrwXn925O60Z1Yh1iyfZvD0mIpe/A8g/C/NMJydBuGHQZA13Pg4btYh2liBQjjpIPK4GflNQEuK+yL2CUJ52PiFQz7qFuw4LXQ9Jh52qwxDB7WM9Lw7SYdRvHNESRquJEkg9XAW+6e9YJPqEBdd19n5klA1OA77v7tAJtOhOqS492951m1sTdS5xbWx/2UlVs2ZvFf+Zu5I3ZG5i9dhcAfVulcUGf5pzXqwokIvJyYO30kIhY+h5sWxLWN+kRkhBdL1DlZpE4E0fJh3+Uodlud/9BRcdSUXQ+IlINuIceoAtfD0mHXWsgIQnajzyacFBNLJHjdiLJh9eAYcC7wPPAu4fnyD6BJ69DSD58x92nF1j/e2Cpuz9e1n3pw16qoi+2H+Ct+Rt5a95G5q7bDYRExPm9m1eNHhEA21eERMSSt0MBS88LRSu7jIGu50OHkZrKUyTG4iX5UBPofESkinKHTXNhwWth2bm6QMLhMuh2gRIOIifphGa7MLMGwGXAtUA/4A3geXf/sIxPmgjMBDoR6kP8vND214GlhCRHInCvu79T0j71YS9V3eFExH/mbmTe+pCI6N68AWf3aMo5PZrSs0WD+C1WediBHbBsPCx5KwzPOLQXkuuEWTO6XQCdz4W6GbGOUqTGUfKh8uh8RKQKcQ8zU8x/NSQcdqwIQyo6jISelyvhIFLOTnqqTTPLAK4EbgMauXvr43jydOA14HvuPr/A+nFADmHqrVaEGhG93X1XocffAtwC0KZNm4Fr1qwp61OLxLUvth/gnQUbGb9wMzPX7CTfoXlaKmd1b8rZPZpyWocMaiXF+bCG3GxYPRkWvxV6RezdEIoytRkSekR0Ox8adYh1lCI1gpIPlUfJB5EqYOtSWPBqSDpsWxLOT9qPiHo4XKQLJSIV5KSSD2bWkJB4uA7oTCge+cPjDOBu4IC7319g3aPAdHf/R3T/A+BOd/+suP3ow16qq+37spmweAvjF25m8rJtHMzJo15KEsM6ZTCySxNGds2kZXqcD2s4PHZyyVshGbFlQVif2S0kIrqeH6b0VJ0IkQqh5EPl0fmISJzauTokG+a/CpvnARZmqeh5KXS/BOplxjhAkervRGo+1CMMubgO6A+MBV4AJnkZMhZmlgnkuPsuM6sNvAf8zt3HFWgzBrjO3b9uZo2BWUA/d99e3H71YS81QVZOHh8v38b7i7bw0dKtrN91EIDOTeoxsksmI7tmMrhdI1KTE2McaSl2rg5JiKVvw+qPQ52Iuk3CFJ5dz4cOo6BWFah3IVJFxEvywcx+VNJ2d/9jZcVSUXQ+IhJH9mwIwynmvwLrZ4Z1rU6BXleEqTEbNI9tfCI1THHnI0klPGY18A7wMKHYZM5xPmdz4Omo7kMC8JK7jzOz+4AZ7j6WUMzyHDNbCOQBPy0p8SBSU6QmJ3Jm96ac2b0p7s6KrfuYtGQrHy7dyjPT1vD4lFWkJicwuF0jhnTMYFjHxvRqmUZiQpzVimjYDobcFpaDO0N9iCVvhTmzZz0LSamhwFOXc8OS1irWEYtI+agf6wBEpJo7sCPMUjHvFVjzMeDQrA+cfV8YVpHeJtYRikghJfV8qO3uBys5nlLpSoPUdAcO5TJ95Q4+XLqVaSu3s3jTXgDqpyZxavsMhnbMYGinDLo0qU9CvCUjDss9FE4Ulr4bekXsXB3WN+0dJSLGQMsBkBDnPTtE4ky89HyoCXQ+IhID2XtDj8r5L8OKCZCfCxmdofeV0OtKaNwp1hGKCCc27OIxd7+llJ2W2qa86cNe5Fhb92YzbeV2PlmxnakrtrF6+wEA0monM6htQwa3b8Tgdo3o3TItPotXusO2ZWEaz6XvwhdTw/CMOhnQ8UzofE6YRUNFoURKFS/JBzN7yd2vjm7/ruBsV2b2nrufE7voyofOR0QqSe4hWP4+zHsJlrwDuQehQSvodXlIOjTrA/E+S5hIDXMiwy4uNbOskvYJnHHSkYnIScmsn8JFfVtwUd8WAKzfdZBpK7bz2eodfLpqBx8s3gJAanIC/VqnM7hdIwa0aUj/Numk16kVy9ADM8jsEpZhdxwdnrHsvaMnG1goVNn57LA076+ilSLxrXOB22cDBafaVrU3ESlZfn7oITnv32GoZtaucFGi//Whh0PrU3UeIFIFlZR8+GkZHj+5vAIRkfLRMr02VwxsxRUDQ/2ErXuzmbF6B5+u3sFnq3fw0MTl5Ecdnjpk1mVAm4ZhaZtO5yb1Y183onbDcCWj95Xh5GPDLFg+HpaNh0m/hUm/CScgHc6ATmeGnyokJRJvSipMXbY5vkWk5tk0H+a+CPNeDlN3J9eFbhdA76ug4xmQmBzrCEXkJBSbfHD3pyszEBGpGJn1Uzivd3PO6x2+oO/LzmXuul3M+mIXn6/ZyQeLNvPyzHUA1EtJolfLBvRtnU6/Vun0bZ1O87RULFbdGRMSoNXAsIy6E/ZvD2M8l78ffs5/ObRr0jOclHQcDW2HQnKcT0kqUv3VMbP+hILTtaPbFi16g4rIUbvXhR4Oc1+CLQshIQk6nQXn/Aq6nge16sY6QhEpJ8XWfIhXGmMpUr7cndXbDzDri53M+mIXc9ftYuHGPeTkhf8NmfVT6Nsqjb6t0undKo3eLdPIqJcS46gJvSK2LAhDNFZMCLUi8g6FGTTanBam8exwRhgLqq6ZUkPEUc2HSZTQw8Hdq/ywTZ2PiJyEg7tg0diQcFg9BfAwNWafq6Hn5arzJFLFHXfByXilD3uRipedm8eijXuZs3YXc9btYs7aXazYuv/I9pbptenTKo3erdLo0zKd3i3TSKsT466Qh/bD6o9h5URYOSlcPQGo3Qjaj4iSEaOgUfsYBilSseIl+VAT6HxE5Djl5YQLBnOehyVvQ142ZHSCPteEoZaNOsQ6QhEpJydScLLwDuq4+4HyDUtE4lFKUiL9WqfTr3X6kXV7snJYsH4P89bvYu663cxbv5u35286sr11o9r0bplGr5ahd0TvlmmVW9CyVl3ock5YAPZugpUfhkTEyolhLnAI8363HxkSEe1HQL0mlRejSA1hZoOBte6+Kbr/NeAKYA1wr7vviGV8IlJJ3EPtpjkvwPxX4MC2ULdp4I3Q9xpoMUAzVYjUIKX2fDCzocDjQD13b2NmfYFvu/ttlRFgYbrSIBI/dh04xPz1e5i7fhfz14eExNodB49sb9XwywmJhnVjMMOGO2xbGpIRqz6EVZMhe3fY1qQndBgZEhJth0Jqg8qPT6ScxEvPBzP7HDjL3XeY2QjgBeB7QD+gu7tfGcv4yoPOR0RKsHs9zH0hJB22LYXElFC/oe91oVi0CkeKVGsnPOzCzKYDVwJj3b1/tG6+u/eqkEhLoQ97kfh2OCExb/3uIwmJL3Yc7TTVMj0kJA7Xj4hJQiI/DzbOPpqM+GIa5GaBJULLAaFHRPuRYSqv5NTKjU3kJMRR8mGOu/eNbj8EbHX3e6P7s929XwzDKxc6HxEp5NB+WDQO5jwXPl9xaDME+l4LPS6F2ukxDlBEKstJDbtw97WFqt3nlVdgIlK9pNepxemdG3N658ZH1u0+kMP8DSERcTgp8c6Co0M2WjUMNSR6taykGhIJidByYFiG/whysmDdZ1GviI9gyp9h8gPhSk2bU6NkxCho0R8SyzxaTaQmSzSzJHfPBc4EbimwTW8ikeoiPx+++ARmPx+GNx7aB+ltYeTPQ9JBdZZEpICynACsjYZeuJklA98HFlVsWCJSnaTVSWZYp8YM61RMQiKqIfHWvKMJiTaN6tC7VRp9W6XRp1U6vVqmUS+lgr6zJKdC++FhAcjeC2umhmTEyg9hwv8D/h+kNIC2w44O02jSXWNVRYr2PPChmW0DDgKTAcysE7C7LDswszHAX4BE4HF3/22h7SnAM8BAYDtwjbuvjrbdBdxMuFhyh7u/W9I+zexM4A+EqUH3ATe6+/ITPXiRam/nmjCkYva/YNcaqFUPel4Kfb8SejtolikRKUJZhl00JnxQn0WYn/s94Pvuvr3iw/sydXMUqb4K1pCYuzYkJNbvCjUkzKBjZj36tEyjT6s0+rZOp0eLBqQkJVZ8YPu3hR4Rh5MRO1eF9XWbhF4RHc8IBSzTWlV8LCIliJdhFwBmdhrQHHjP3fdH67oQakh9XspjE4GlwNnAOuAz4Dp3X1igzW1AH3e/1cyuBS5z92vMrAch+XEK0AJ4H+gSPazIfZrZUuASd18U7fcUd7+xpBh1PiI1zqEDsOhNmP3P8JkIIRHf73rofmEo/CwiwkkMu3D3bcD1FRKViEgBRQ3Z2LYvm3nrdkczbOxi8vJtvDprPQDJiUaP5g3o2zqdvq3S6ds6nQ6N65KQUM69Eeo2hl6XhwVg1xdH60Ws/BDmvxzWZ3Q+mohodzqkppVvHCJViLtPK2Ld0jI+/BRgubuvBDCzF4BLgIUF2lwC3Bvdfhn4q4UxopcAL7h7NrDKzJZH+6OEfTpwuNpsGrChjHGKVG/usG4GzHoWFrwG2XvCsIpRv4B+14UZpEREyqjU5IOZPVjE6t3ADHd/o/xDEhE5qnG9FM7o1oQzuoUpMd2djbuzmLtuF7PX7mbO2l28+vl6npm6BoD6KUn0jaYJ7d8m/Myol1K+QaW3gQE3hMUdtiyEFRPDtJ6z/gmfPhaKV7YaBB3PDJW9W/QPtSZEpCxaAmsL3F8HnFpcG3fPNbPdQEa0flqhx7aMbhe3z28Cb5nZQWAPcFpRQZnZLUT1K9q00Zcuqcb2bQ2zVXz+LGxbAsl1oMcloZdD22EaViEiJ6QsA6hTgW7Av6P7VwCrgL5mdoa7/6CCYhMR+RIzo0V6bVqk12ZMr+YA5OU7K7fuY/baXcxZt4tZX+zikQ9XkJcfhpW1aVTnSDJiYNuGdG/egOTEcjpxMoOmPcMy9LuQmx2KV66YCCsmwKTfwKT/hdoNQ4+Iw8mIBi3K5/lFpDz8EDjf3aeb2U+BPxISEsdw98eAxyAMu6jcEEUqWF4urPgAPn8Glr4D+bnQajBc9GDo+ZdSP9YRikgVV5bkQx9gmLvnAZjZI4TCUacD8yowNhGRMklMMDo3rU/npvW5alBrAA4eymPe+t3MXruTWV/s4tNVOxg7J/SkTk1OoE/LdAa0bciANuFn4/LqHZGUEoZctDsdzvwf2L8dVkaJiOUfhG6rAE16QOezofO50PoUzXku1ZaZNQUGR3c/dfctZXjYeqB1gfutonVFtVlnZkmE4RLbS3nsl9abWSbQ192nR+tfBN4pQ4wi1cOOVaHX3ux/wd6NUKcxnHor9L8BmnSLdXQiUo2UJfnQEKjH0erUdYFG7p5nZtkVFpmIyEmoXSuRU9o34pT2jY6s27j7IJ+v2cXMNTv5/IudPDFlJY/mhYuX7TLqMLBtIwa3a8igdg3pmFkPK4+ZLOpmQO8rw3J4iMbyD2D5eJj6EHz8F0hJC7UiOp8Dnc6C+k1P/nlF4oCZXU2YRWISoWj1/5nZT9395VIe+hnQ2czaExIH1wJfKdRmLPB1YCpwJTDB3d3MxgLPmdkfCQUnOwOfRs9f1D53Amlm1iWqSXE2mtVLqrvcbFg8LvRyWDkJLCF8/pz3e+gyBpJqxTpCEamGypJ8+D0w28wmET64RwD/a2Z1CRWki2RmqcBHQEr0PC+7+z3FtL2CUCxqsLurdLSIVIjmabW5oE9tLugThmtk5eQxf/1uZq7Zycw1O5m4ZAuvfL4OgIZ1khnYthGD2jVkcLuG9G6ZTq2kkxyqUXCIxrA7IGtPKFq57D1YNj7MkQ6hPkTX88MJYLPems5TqrL/Iny2bwGIehm8T/jML1ZUw+G7wLuEaTGfdPcFZnYfoebUWOAJ4NmooOQOQjKBqN1LhEKSucDtBXpvfmmf0fpvAa+YWT4hGXFTef4SROLG1iUh4TDneTiwHdJah+KR/a/XjE0iUuFKnWoTwMyac7RS9GfuXmoV6KjidF1332dmycAUwhSd0wq1qw/8B6gFfLe05IOmthKRiuLurNq2nxmrd/LZ6h3MXLOTldv2A2GoxoA2DY/0phjQpiGpyeVYQNIdNs2DZe/C0ndDdXEcGrSCrmOgy3nQfngY1iFSjHiaahPAzOa5e+8C9xOAOQXXVVU6H5EqIycLFr4BM5+CLz6BhKSQ4B74dehwhoohi0i5O+GpNiNZwEZC8clOZtbJ3T8q6QEeshr7orvJ0VJUpuNXwO+An5YxFhGRCmFmdMisR4fMelw9OAwN37YvmxmrdzB91Q6mr9zBXz5YhnuY5rNvq3RO7dCIIR0aM7BtQ2rXOokTODNo3icsI34K+7aEHhFL3obZz8Fnj0NyXeg0GrpeAF3OhTqNSt+vSGy9Y2bvAs9H968B3o5hPCI1x9alIeEw5zk4uBMadYCzfgn9vgL1msQ6OhGpgUrt+WBm3wS+TyjMNJsw/dRUdx9d6s7NEoGZQCfgIXf/eaHtA4D/cvcromEdPymq50Ohqa0GrlmzpvQjExGpALsP5jBzTUhETFu1g/nrd5OX79RKTKBfm3SGdsxgSIcM+rVJJyWpnK4m5WTBqo9g6dshGbF3Y5jKs80Q6HYBdDsfGrYrn+eSKi3eej4AmNnlhCLVAJPd/bVYxlNe1PNB4lJuNix6E2b8A9ZMCb0cul0Ig74B7UZoikwRqRTFnY+UJfkwj1Clepq79zOzbsD/uvvlx/Hk6cBrwPfcfX60LgGYANzo7qtLSj4UpA97EYkn+7Jz+WzVDqau3M7UFduZv2E37mGYxuB2jTi9U2NO79yY7s0akJBQDrUb8vNh4yxY/BYseSsUsARo0hO6nhcSEc376wSzhoq35IOZ/a6ICw9fWlcV6XxE4srO1SHhMOufcGBbSEgPvBH6Xa9eDiJS6U4m+fCZuw82s9nAqe6ebWYL3L3ncQZwN3DA3e+P7qcBKzg6NKMZoWDUxSUlIPRhLyLxbPeBHKav2s4nK7bzyYptLN0c/sVl1K3FsCgRMbxzY5qn1S6fJ9yxMvSGWPxWGMvr+VC/eShW2e0CaDccklPL57kk7sVh8uFzdx9QaN1cd+8Tq5jKi85HJOby88LwvM+egOXvhxkrup4Hg26KajkoCS0isXEyNR/WRT0XXgfGm9lOoNRxD1FF6xx332VmtQlTV/3u8HZ33w00LtB+EmXo+SAiEs/S6iRzTs9mnNOzGQCbdmcxZfk2pizbypTl2xk7J9Tr7dykHqO6ZnJG1yYMatfoxGfSaNQBhtwelgM7wono4v/A3Jdg5j8K1Ik4P0zlWbdx6fsUOUlm9h3gNqCDmc0tsKk+8HFsohKpJvZtgc+fhplPw+61IeE88ucw4GuQ1jLW0YmIFKtMs10caWw2EkgD3nb3nFLa9gGeJkxnlQC85O73FZomq2D7SWjYhYhUY+7O4k17mbJsGx8u3cqnq3ZwKC+furUSGdqpMaO6ZjKqaxNappdDr4icLFg9OQzNOFwnAoNWg4/OntGku6bxrGbipedD1LuxIfAb4M4Cm/a6+47YRFW+dD4ilcod1n0Gnz4GC16H/BzoMAoG3Rx6OyQmxzpCEZEjTmbYxbPufkNp6yqLPuxFpLrYn53LJyu2M2nJFiYt2cr6XQcB6Nq0Pmd2b8KZ3ZvSr3U6iSdbKyI/HzbNgSXvwNJ3YOPssD69TRie0WUMtDtd03hWA/GSfKgJdD4ilSLnIMx7OSQdNs2FlAahjsPgm6Fx51hHJyJSpJNJPhwzXjOawWKeu/co/zBLpw97EamO3J0VW/cxcfFWJizewqerd5CX72TUrcWork04q3sThnfJpF5KWWdILsGeDbD03bCsnAS5B8PwjI5nhERE53OgftOTfx6pdEo+VB6dj0iF2rk6TLH8+bOQtQua9IBTvgW9r4aUerGOTkSkRMdd88HM7gJ+AdQ2sz2HVwOHgMcqJEoRkRrKzOjUpD6dmtTnWyM6sPtgDh8u3cqERZt5f9FmXvl8HcmJxmkdMji7R1PO6t6UFic6PKNBizDt2qBvhKtqqz4KPSKWvguLx4U2LQaERETXMdCsj4ZniIhUNPcwXG7ao2HInCVA94vglFug7VD9HxaRKq8sPR9+4+53VVI8pdKVBhGpaXLz8pm5ZicfLN7C+ws3s3LbfgB6tmjAWd2bcnaPpvRs0QA72RNTd9g8PyQilrwD62cCDmmtw5jirudB29MhqdbJH5RUiHjr+WBm3wP+6e47Yx1LedP5iJSbQwdg3ksw/W9h+uQ6GTDwG2HWChWQFJEq6LiHXZjZgCI3RNz983KK7bjow15EaroVW/fx/sLNjF+4mZlf7MQdmqelclb3ppzVoymndWhESlLiyT/Rvi2hN8SSt2DFxDA8I6UBdDorTOPZ+WxITTv555FyE4fJh/8HXAt8DjwJvOvHU+k6jul8RE7a7nXw6d/DzBUHd0LT3nDardDrCkgup+mYRURi4ESSDxNL2J+7++jyCu546MNeROSobfuymbB4C+MXbmbKsm0czMmjbq1ERnTJ5KzuTTmjWxMa1S2HngqHDoT6EEveCj0j9m+FxFrQcTT0uDT0iqidfvLPIycl3pIPABa65JwDfAMYBLwEPOHuK2Ia2EnS+YicsHUzYdpDYdYKHLpdCKfeqqEVIlJtHHfNB3c/o2JDEhGRk9W4XgpXD2rN1YNak5WTxycrtvH+oi18sGgzb8/fRILBwLYNObN7U87s1oROTeqd2PCMWnWg2/lhyc+DdTNg4RthWfoOJCSHgpU9Lg1tajcs92OVqsnd3cw2AZuAXMIUnC+b2Xh3/1lsoxOpJHm5oabOtIdh7fTQi+y078Cp3w4zD4mI1ABlqfmQDHwHGBGtmgT8zd1zKja0oulKg4hI6fLznfkbdvP+olAnYuHGUDe4TaM6jO7WhDO7N+GU9uUwPMM91IZY8BosHAu7vwiJiM5nQ7+vQOdzVSOiEsVbzwcz+z7wNWAb8DjwurvnmFkCsMzdO8Y0wJOg8xEpk6w9MOvZUERy9xfQsB2c+h3ofz2k1I91dCIiFeJkptp8HEgGno5W3QDkufs3yz3KMtCHvYjI8duw6yATl2xhwqItTFm+jezcfOrWSmR450xGd2/CGV2bkFk/5eSexB3Wfw4LXoV5/4Z9m0PhtN5Xh0RE8z7lczBSrDhMPvwSeNLd1xSxrbu7L4pBWOVC5yNSot3rYfqjMPMpyN4DbYfBabeFIWoJ5VCTR0Qkjp1M8mGOu/ctbV1l0Ye9iMjJOXgoj6krt/HBoi1MWLyFjbuzAOjbOp0zo14RPZqf5OwZebmwYgLM/leoE5F3CJr1hn7XQ59roE6jcjoaKSgOkw/PuvsNpa2rinQ+IkXaNA8++SvMfzkkZHteCkO+Cy1LrOMuIlKtHHfNhwLyzKzj4cJQZtYByCvvAEVEpHLUrpXI6G5NGd2tKe7Owo17mLBoCx8s3sKf3l/KH8cvpVmDVEZ3b8JZ3ZswtGNjUpOP80pdYhJ0OScsB3bA/Fdg9nPwzp3wwX3Q/wYYclvogizVWc+Cd8wsERgYo1hEKoY7rJwIn/xfSLom14VTbglFJBu2jXV0IiJxoyw9H84E/gGsBAxoC3zD3UuaDaPC6EqDiEjF2bo3m0lLtvDBoi1MXraV/YfyqFMrkRGdMzm7R1NGd2tCw5OZPWPTfJj6UBiW4XmhQOWwO6BF/3I7hposXno+mNldwC+A2sCBw6uBQ8Bj7n5XrGIrLzofEfLzYOHrMOXPsGku1GsWCkgO+oaK7opIjXbCwy6iB6cAXaO7S9w9u5zjKzN92IuIVI7s3DymrtjO+IWbeX/RZjbvySYxwRjcriFn92jGuT2b0qphnRPb+Z4NMO2Ro+Oh2w2HoXeEQpWaau6ExUvy4TAz+011SDQURecjNVhOFsx5Dj5+EHaugozOMOz70OdqSDrJ2jkiItXAydR8mAs8D7wUD3Ny68NeRKTy5ec789bvZvzCzYxfuJklm/cC0L9NOhf0bs75vZvTIr328e84aw98/nRIROxZD61Pgwv/CE17lv5Y+ZJ4ST6YWTd3X2xmRQ50d/fPKzum8qbzkRooazd89kT4f7V/C7QcCKf/ELpeAAkJsY5ORCRunEzyoS1wTbTkAy8SEhFfVESgpdGHvYhI7K3Zvp+35m1i3NwNLNgQpvEc2LbhkUREs7TU49th7iGY8zy8f2/oCXHabTDqTqhVt/yDr8biKPnwmLvfYmZFDdF0dx9d6UGVM52P1CD7t4XhYp89Hv4/dTwTTv9B6LGlnloiIl9yUsMuCuykM/A/wPXuHpN5gvRhLyISX1Zt289b8zYybu5GFm3cgxkM6ZDB1YNaM6ZXs+MrVnlgB4y/G2Y9C2mt4bzfQbcLKi74aiZekg81gc5HaoA9G0IRyRn/gNws6HEJDP8RNI/JhG8iIlVGcecjZeojZmZtzexnwAtAN+Bn5RyfiIhUUe0b1+X2Mzrx9veH88GPR/KDM7uwducBfvDibAb/+n3++/V5zF23izIlu+s0gkv+Cje9Cyn14YWvwPPXwa6YdLaTk2Rmt5tZeoH7Dc3stjI+doyZLTGz5WZ2ZxHbU8zsxWj7dDNrV2DbXdH6JWZ2bmn7tODXZrbUzBaZ2R0nesxSDexcDW/+AP7SF6b/DXpeBrd/Clc/rcSDiMhJKMuwi+lAMvASYbjFysoIrDi60iAiEv/y853pq3bw0oy1vDVvI9m5+XRrVp+rB7XmigGtSKuTXPpO8nJg2sMw6bfh/vn3Q//rKzbwKi7eej6Y2Wx371do3Sx3L3F6k2hKzqXA2cA64DPgOndfWKDNbUAfd7/VzK4FLnP3a8ysB6FW1SlAC+B9oEv0sCL3aWbfAM4AbnT3fDNr4u5bSopR5yPV0LblMPl+mPsSJCRC/6+GQpKaElhE5LgUdz6SVIbHfs3dl5zAE6YCHwEp0fO87O73FGrzI+CbQC6wFbjJ3dcc73OJiEh8SUgwhnTMYEjHDH55SU/enLOBlz5by33jFvLAe0u4/rS23Hx6e5o2KKE2RGJyOPHveRm8cTu8cRvsWgOj7tI466oj0czMoysdUVKhLHO1ngIsP3zBw8xeAC4BFhZocwlwb3T7ZeCvZmbR+heimblWmdnyaH+UsM/vAF9x93yA0hIPUs1sWwYf/SFMAZyUCqfeCkO/Bw2axzoyEZFqpdTkw4kkHiLZwGh332dmycAUM3vb3acVaDMLGOTuB8zsO8DvCYUtRUSkmmiQmsz1p7bl+lPbsmDDbh77aCWPT17JUx+v5vIBLfn2yI60b1xCYcn0NvDVV0M36A9/B7vWwkV/gaSyfIeVGHsHeNHM/hbd/3a0rjQtgbUF7q8DTi2ujbvnmtluICNaP63QY1tGt4vbZ0fgGjO7jHAx5A53X1Y4KDO7BbgFoE2bNmU4DIlr25bBh7+H+S+HpMOQ74Ypf+tlxjoyEZFqqSw9H05IdJVjX3Q3OVq8UJuCVbCnAV+tqHhERCT2erZI4y/X9ufHZ3fl75NX8uKMtbw4Yy3n92rOd0Z1pFfLtKIfmJgcakE0bAsTfw17N8DVz0BqMe0lXvyckHD4TnR/PPB47MIpVgqQ5e6DzOxy4ElgeOFG7v4Y8BiEYReVG6KUm61LQ0+Hw0mHod+DId9T0kFEpIJVWPIBjnSvnAl0Ah5y9+klNL8ZeLuY/ehKg4hINdImow6/urQXd5zZmX98vIpnp67hP/M2cmGf5txzUU8y66d8+UFmMPJnkNYKxn4PnjwPrv83pLX8cluJC9Ewhkei5XisB1oXuN8qWldUm3VmlgSkAdtLeWxx69cBr0a3XwP+cZzxSlWwYyVM+h3Me+lo0mHoHVC3cawjExGpEYpNPkSZ/2K5+6slbY/a5AH9okrXr5lZL3efX8RzfRUYBIwsZj+60iAiUg1l1k/hZ2O6ceuojjw5ZRUPT1zB5GXb+O8LunPlwFZYUbUd+n0FGrSAF2+Ax8+C61+CZr0rP3gpVTRF92+AHsCRAh/u3qGUh34GdDaz9oQEwbXAVwq1GQt8HZgKXAlMcHc3s7HAc2b2R0LByc7Ap4CVsM/XCQUnVxHORZaeyPFKnNq9LvR0mPVPSEiGIbfDsB8o6SAiUslK6vlwUQnbnKNXCErl7rvMbCIwBjgm+WBmZwH/BYyMikOJiEgN0yA1mR+c1YUL+7Tgrlfn8tOX5/L67PX85rI+tMmo8+UHdBgFN70D/7oq9IC47jloP6LS45ZS/QO4B/gT4cv9NyjDNN9RDYfvAu8CicCT7r7AzO4DZrj7WOAJ4NmooOQOQjKBqN1LhEKSucDt0cUQitpn9JS/Bf5lZj8kDBn9ZrkcvcTWvi0w+Y8w4wlwh0E3wfAfQ/1msY5MRKRGKnWqzRPesVkmkBMlHmoD7wG/c/dxBdr0J1SoHlNUYaeiaGorEZHqLT/fee7TL/jt24vJzc/nR2d34aZh7UlKLOI7654N8OzlsOsL+Nob0Hpw5QccR+Jwqs2Z7j7QzOa5e++C62Id28nS+UgcO7gTPv4LTP8b5GaH3lIjfxaK14qISIU7mak2MbMLgJ4c22XyvlIe1hx4Oqr7kAC85O7jCl21+ANQD/h31LX2C3e/uCwxiYhI9ZSQYHz1tLac1b0p//36fP73rcWMnbOBP1/Tj05N6h/buEEL+Nrr8OQY+NcVcONb0KxXTOKWImWbWQKwLOp1sJ7wuS9S/nIOwqePweQHIGsP9L4SRt4JjTvFOjIREaEMPR/M7FGgDqG75OOEcZWfuvvNFR/el+lKg4hIzeHuvD1/E//z+nxy850nvj6IQe0afbnhzjXwj/Mg7xB8450a+2UjDns+DAYWAenArwhFIX9faNrtKknnI3EkLxfmPA+TfgN71kPnc+DMe5SIFBGJkeLOR0oddwkMdfevATvd/ZfAEKBLeQcoIiJSmJlxfu/mvH77MBrVrcX1j0/nvQWbvtywYdsw7MIdnrkEdq2t/GDlS9z9M3ffB+wB7nD3y6tD4kHihDssfgseHQZjvxtqOXx9XJgFR4kHEZG4U5bkw8Ho5wEzawHkEIZUiIiIVIrWjerw8q1D6Na8Abf+cybPTf/iy40ad4YbXoPsvfDMxbB3c+UHKscws0FmNg+YC8wzszlmVuXrPUgcWPtZGG71wnWQnwtXPwvf/ADaD491ZCIiUoyyJB/GRVNl/gH4HFgNPF+BMYmIiHxJRr0Unv/WqYzskskvXpvHn99fypeGDjbvE6567t0Ez14GB3bEJlg57EngNndv5+7tgNsJM2CInJida+Dlm+CJs2DnKrjwz3DbdOhxMRQ1Na+IiMSNskx39St33+XurwBtgW7u/j8VH5qIiMix6tRK4rGvDeLKga348/vL+MVr88nNyz+2UZtT4drnYPuyMBVn9t7YBCsAee4++fAdd59CmP5S5Phk7Ybx98BfB4ehFiN/Dt/7HAZ9AxLLVD9dRERirKyzXQwF2h1ub2a4+zMVGJeIiEiRkhMT+MOVfWjaIIWHJq5g275s/u+6/qQmJx5t1PEMuOopePEGeOEr8JV/Q3JqsfuUCvOhmf2N0GPSgWuASWY2AMDdP49lcFIF5OXC50/BxN/AgW3Q9zoY/T+Q1jLWkYmIyHEqNflgZs8CHYHZQF602gElH0REJCbMjJ+e240m9VO5980F3PLsTP7+tYGkJBVIQHS7AC59BF67BV65Ga56WldIK1/f6Oc9hdb3J5xLjK7ccKRKWTER3rkTti6GtqfDuf8PWvSPdVQiInKCynIWNgjo4aXNySkiIlLJvj60HbWTE/nZK3P5wQuz+b/r+pOUWGBEYd9rIGsXvP0zGPs9uOQhSChLuSMpD+5+RqxjkCpo5xp49xeweBw0bAfX/CskE1XTQUSkSitL8mE+0AzYWMGxiIiIHLerB7dmb3Yuvxq3kDtfncfvr+hDQkKBLymnfhsO7oJJ/wupaTDmN/oSU4nM7AKgJ3Bk3Iu73xe7iCRu5RyEKX+Gj/8MlhCGVwz5roZMiYhUE2VJPjQGFprZp0D24ZXufnGFRSUiInIcbj69PXuzcvjz+8uol5LEPRf1wAomGEb+LPSAmPYw1E6HUXfGKtQaxcweBeoAZwCPA1cCn8Y0KIk/7rBoLLz737D7C+h5OZzzK0hrFevIRESkHJUl+XBvRQchIiJysr5/Zmf2HMzlyY9X0aB2Mj86u8vRjWZwzq9DxfxJv4HUdDjt1pjFWoMMdfc+ZjbX3X9pZg8Ab8c6KIkj25bBWz+BlZOgSU+48T/Q7vRYRyUiIhWg1OSDu39YGYGIiIicDDPjfy7szr7sHB78YBkNUpP45vAORxskJMBFD4YExDs/D0Mw+l0Xu4BrhoPRzwNm1gLYDjSPYTwSL3KyYMqfYMofIak2nPcHGHSTisKKiFRjxf6HN7Mp7n66me0lVKQ+sglwd29Q4dGJiIgcBzPjN5f3YX92Hv/vP4uol5LEtae0OdogMQmueAKeuxreuD0kILqdH7uAq79xZpYO/AH4nHA+8XhMI5LYW/khjPsh7FgBva8KvZLqN411VCIiUsGKTT64++nRz/qVF46IiMjJSUww/nRNP/Zl53LXa/Oom5LERX1bHG2QnArXPgdPXwQvfwO+9ga0OS12AVdj7v6r6OYrZjYOSHX33bGMSWJo31Z4779g7ovQqAPc8Bp01GyrIiI1RZnmGzOzhmbWx8wGHF4qOjAREZETVSspgUe/OpDBbRvxwxdnM3HJlmMbpNSD6/8NDVqGXhBbFsUm0GrOzG6Pej7g7tlAgpndFtuopNLl58PMp+CvA2H+qzDiZ/CdT5R4EBGpYUpNPpjZr4C5wP8BD0TL/RUcl4iIyEmpXSuRx28cRNdm9fnOP2fy6aodxzao2xhueBWSUuGfV8DudbEJtHr7lrvvOnzH3XcC34pdOFLpdqyCZy6GN78PTXuHpMPo/4Lk2rGOTEREKllZej5cDXR095Hufka0KFUtIiJxr0FqMs/cdAot0mtz81OfMX99oR7/DdvBV1+B7L3w7OVwYEeR+5ETlmgF5jw1s0SgVgzjkcqSnw/THoVHhsLGOaHY643jILNL6Y8VEZFqqSzJh/lA+vHu2MxSzexTM5tjZgvM7JdFtEkxsxfNbLmZTTezdsf7PCIiIiXJqJfCP28+lQa1k/n6k5+yYuu+Yxs06x1qQOxcBc9fC4cOxCbQ6ukd4EUzO9PMzgSej9ZJdbZtOfzjvDCrTLvT4bZpMPDrYcpbERGpscqSfPgNMMvM3jWzsYeXMjwuGxjt7n2BfsAYMytc0etmYKe7dwL+BPzuOGIXEREpkxbptXn25lMwgxsen876XQePbdB+OFz+d1j7Kbx8E+TlxibQ6ufnwATgO9HyAfCzmEYkFSc/Dz5+EB4dBlsXwaWPwldegrSWsY5MRETiQFkmU36akBSYB+SXdcfu7sDhy0vJ0eKFml0C3Bvdfhn4q5lZ9FgREZFy0yGzHs/cdCrXPDaVrz4+nZe+PYTM+ilHG/S8FA7cD//5MYz7AVz8f7pSe5LcPR94NFqkOtu+Al77Nqz7DLpeABf+Eeo3i3VUIiISR8rS8+GAuz/o7hPd/cPDS1l2bmaJZjYb2AKMd/fphZq0BNYCuHsusBvIKHv4IiIiZdejRQP+ceNgNu4+yNee/JTdB3OObTD4m6ES/6xn4ZMHYxOkAGBmY8xsSTQ0884ithc7dNPM7orWLzGzc49jnw+a2b7C66UE7mEmi0dPh23L4Ion4Np/KfEgIiJfUpbkw2Qz+42ZDTneqTbdPc/d+wGtgFPMrNeJBGlmt5jZDDObsXXr1hPZhYiICACD2jXibzcMYvmWvXzrmRlk5eQd2+CMX0CPS+CD+2D9zNgEWcNFhSkfAs4DegDXmVmPQs2KHLoZtbsW6AmMAR6OLoaUuE8zGwQ0rNADq272b4cXrg8zWbQaDLdNhd5XqseQiIgUqSzJh/7AacD/coJTbUbTbE0knAQUtB5oDWBmSUAasL2Ixz/m7oPcfVBmZubxPLWIiMiXjOySyQNX9+PTVTv44YuzycsvMNrPDC76C9RvDi/fDFl7YhdoFWVmz0Y/v3+CuzgFWO7uK939EPACYahmQZcQhoZCGLp5ZjSzxiXAC+6e7e6rgOXR/ordZ5SY+AOqR1F2y96HR4bA8vFw7v/CDa9DgxaxjkpEROJYicmH6MN4bIEpNss81aaZZZpZenS7NnA2sLhQs7HA16PbVwITVO9BREQqw8V9W/DfF3Tn7fmb+OWbCzjm46d2Q7jicdi1Bt76SeyCrLoGmlkL4CYza2hmjQouZXj8kWGZkXXRuiLbFBq6WdxjS9rndwnnOxtLCko9MYGcg/DWz+BfV0CdDPjWRBhyOySU5XqWiIjUZCUWnHT3PDO7jtCd8Xg1B56OEhgJwEvuPs7M7gNmuPtY4AngWTNbDuwgdJMUERGpFN8c3oEte7N57KOVNG2Qyu1ndDq6sc1pMOoumPhr6Dga+uoj6jg8SpjZogMwEyjYD9+j9XEhSpJcBYwqra27PwY8BjBo0KCad7Fky2L4941hJotTvwNn3QvJqbGOSkREqoiyzHbxsZn9FXgR2H94pbt/XtKD3H0uYchG4fV3F7idRfjAFxERiYk7x3Rjy54s/vDuEprUT+GqQa2Pbhz+Y1g5KcyA0WowZHSMWZxVibs/CDxoZo+4+3dOYBdHhmVGWkXrimqzrtDQzZIeW9T6/kAnYHkYtUEdM1se1ZKQw+b+G968A2rVha++Ap3OinVEIiJSxZQl+dAv+nlfgXUOlDr0QkREJN4lJBi/v7Iv2/cf4s5X59G4XgpndGsSbUyEyx+DR4bByzfBzeMhqVZsA65C3P07ZtYXGB6t+ii6OFGaz4DOZtaekCC4FvhKoTaHh25OpcDQTTMbCzxnZn8EWgCdgU8JvS++tE93XwAcmZrBzPYp8VBAbja8cxfMeALaDIEr/wENmsc6KhERqYJKHaBXRL2HMtV8EBERqSpqJSXwyFcH0r15fW771+fMXrvr6Ma0VnDJX2HjbJhwX3G7kCKY2R3Av4Am0fIvM/teaY+Lajh8F3gXWEQYurnAzO4zs4ujZk8AGdHQzR8Bd0aPXQC8BCwE3gFuj2bfKnKf5Xe01dDONfDEOSHxMPQO+PqbSjyIiMgJs9LqO5pZU8JMFy3c/bxoWqoh7v5EZQRY2KBBg3zGjBmxeGoREanmtu7N5opHPmFfdi6vfmco7RrXPbpx3I/Cl7A47nJuZjPdfVCs4zjMzOYSzhn2R/frAlPdvU9sIzt51f58ZMk78Nq3wR0uewS6XRDriEREpIoo7nykLKWJnyJcJTg8f9JS4AflFpmIiEicyKyfwtM3nUK+O3e8MItDuflHN577a8jsDq/dCvu2xC7IqsWAvAL38zi2+KTEm/w8eP9eeP4aSG8D3/5QiQcRESkXxSYfouJNAI3d/SUgH450hcwr7nEiIiJVWfvGdfnt5X2Yu243f3p/6dENybXhyifh4E748PexC7Bq+Qcw3czuNbN7gWmE4RISj7L3wgtfgSl/ggFfDzVOGrWPdVQiIlJNlNTz4dPo534zyyAUmcTMTiPMpS0iIlItjenVjGsHt+bRD1cwdcX2oxua9oC+18Hnz8DeTbELsIpw9z8C3yBMp70D+Ia7/zmmQUnRdq2FJ8fAsvFw/v1w8YOaRlNERMpVScmHw90if0SoKN3RzD4GngFKLRYlIiJSld19UQ/aZ9TlRy/NZteBQ0c3DP8R5OfCJ/8Xu+CqEHf/3N0fjJZZsY5HirBuBvx9NOz6Aq7/N5zyrVhHJCIi1VBJyYdMM/sRMAp4Dfg98DbwdyA+K22JiIiUkzq1kvjztf3YujebX7w2jyMFmht1gN5XwYwnYf+22AYpcrLmvwL/OB9q1QnDLDqdGeuIRESkmiop+ZAI1APqA3WBpGhdnWidiIhItdanVTo/Pqcrb83bxL9nrju6YfiPIecgTP1r7IITORnuMOl38PJN0HIgfHMCNOkW66hERKQaSyph20Z314TmIiJSo90yogMfLt3CvWMXMLhdI9o3rguZXaDnpfDp32HoHVCnUazDjEvR1JoH3T3fzLoA3YC33T0nxqHVbLmH4I3bYN6/oe9X4KI/Q1JKrKMSEZFqriw1H0RERGqsxATjj1f3IzkxgR+8MIucvGj6zRE/hUP7YPrfYhtgfPsISDWzlsB7wA2EKbwlVnIOhhkt5v0bzrwbLn1YiQcREakUJSUfNOhPREQEaJFem99c3ps563bz58PTbzbtCd0uhOmPQNae2AYYv8zdDwCXAw+7+1VAzxjHVHNl74V/XQXL34eL/hKGD5muNYmISOUoNvng7jsqMxAREZF4dn7v5lw9qBUPT1rBzDU7w8oRP4Gs3fDpY7ENLn6ZmQ0Brgf+E61LjGE8NdfBnfDsZbDmE7j87zDwxlhHJCIiNUxJPR9ERESkgHsu6klG3ZSjvR9a9IfO58DUhyB7X2yDi08/AO4CXnP3BWbWAZgY25BqoP3b4OmLYOMcuPoZ6HNVrCMSEZEaSMkHERGRMqqbksQ3h7dn8rJtzF23K6wc8VM4uANm/iOmscUjd//Q3S9299+ZWQKwzd3viHVcNcqeDfCP82DbcrjuBeh+YawjEhGRGkrJBxERkeNw/altaJCaxEMTl4cVrU+B9iPh4wdDMT85wsyeM7MG0awX84GFZvbTWMdVY+xcDU+OgT0b4YZXoZPKeYmISOwo+SAiInIc6qcmc+PQdry7YDPLNu8NK0f+DPZvgc+fiW1w8aeHu+8BLgXeBtoTZryQirZ7Hfzj/FCT5OtvQNuhsY5IRERquApLPphZazObaGYLzWyBmX2/iDZpZvammc2J2nyjouIREREpLzcOa0/t5EQembQirGh3OrQZClP+DLnZMY0tziSbWTIh+TDW3XMAj21INcDBnfDPK8LsFjeOg5YDYx2RiIhIhfZ8yAV+7O49gNOA282sR6E2twML3b0vMAp4wMxqVWBMIiIiJ61R3Vpcf2ob3pizgbU7DoSVI38KezfAwjdiG1x8+RuwGqgLfGRmbQHNS1qRcrLg+a/AjpVw7b+gWe9YRyQiIgJUYPLB3Te6++fR7b3AIqBl4WZAfTMzoB6wg5C0EBERiWvfHN6BRDMe/TDq/dB+FNRrBkveimVYccXdH3T3lu5+vgdrgDNiHVe1lZ8Hr90CX3wClz0K7UfEOiIREZEjKqXmg5m1A/oD0wtt+ivQHdgAzAO+7+75RTz+FjObYWYztm7dWtHhioiIlKpZWipXDGzFv2esY8ueLEhIgC7nwPIPIC8n1uHFhWh45R8Pf4ab2QOEXhBS3tzhnbtCz5tz/xd6XRHriERERI5R4ckHM6sHvAL8ICo6VdC5wGygBdAP+KuZNSi8D3d/zN0HufugzMzMCo5YRESkbG4d2YHc/Hwen7IqrOgyBrL3wBdTYxtY/HgS2AtcHS17AM1JWhE+eRA+/RsM+S4MuT3W0YiIiHxJhSYfoiJTrwD/cvdXi2jyDeDVqCvmcmAV0K0iYxIRESkvbTPqcnHfFvxz2hp2HTgUptxMTIGl78Y6tHjR0d3vcfeV0fJLoEOsg6p25v4bxt8NPS+Hs38V62hERESKVJGzXRjwBLDI3f9YTLMvgDOj9k2BrsDKiopJRESkvH1nVCcOHMrjHx+vhpR60H44LH0n1mHFi4NmdvrhO2Y2DDgYw3iqn5WT4PXvQLvhoc5DgmZRFxGR+FSRn1DDCHN5jzaz2dFyvpndama3Rm1+BQw1s3nAB8DP3X1bBcYkIiJSrro2q8/ZPZry1Cer2ZedG4ZebF8O25bHOrR4cCvwkJmtNrPVhFpP345tSNXIri/gxRugcWe45p+QlBLriERERIpVkbNdTHF3c/c+7t4vWt5y90fd/dGozQZ3P8fde7t7L3f/Z0XFIyIiUlFuP6MTuw/m8Nz0NdD5nLBymYZeuPucaDrtPkAfd+8PjC7LY81sjJktMbPlZnZnEdtTzOzFaPv0qLj14W13ReuXmNm5pe3TzP4VrZ9vZk9Gw0bjW34+vH4beD5c9zzUTo91RCIiIiVS3zwREZGT1K91Oqd3aszfJ68iq14raNIDlrwd67DihrvvKVB0+keltTezROAh4DygB3CdmfUo1OxmYKe7dwL+BPwuemwP4FqgJzAGeNjMEkvZ578INad6A7WBb57osVaaT/8GqyfDmN9Aw3axjkZERKRUSj6IiIiUg9tGdWTr3mz+M3cjdDk3zHhxcFesw4pHVoY2pwDLoyKVh4AXgEsKtbkEeDq6/TJwZlRv6hLgBXfPdvdVwPJof8XuM+qZ6e7uwKdAq5M7xAq2dQm8f28Y4tP/hlhHIyIiUiZKPoiIiJSDIR0zaFI/hQmLt0CX8yA/F1ZMiHVY8cjL0KYlsLbA/XXRuiLbuHsusBvIKOGxpe4zGm5xA1BkxVAzu8XMZpjZjK1bt5bhMCpAXg689m1IrgMXPQhWllyOiIhI7Cn5ICIiUg7MjFFdM/lo6VZymg+A2o1q7JSbZrbXzPYUsewFWsQ6vhI8DHzk7pOL2ujuj7n7IHcflJmZWcmhRSY/ABtmwUV/hvpNYxODiIjICVDyQUREpJyM7taEvdm5zPhiTyg8uew9yM+LdViVzt3ru3uDIpb67p5Uhl2sB1oXuN8qWldkGzNLAtKA7SU8tsR9mtk9QCZlqEkRM+s/hw9/D32ugR6FR6GIiIjENyUfREREysnpnTNJTjQmLtkS6j4c3AHrZsQ6rKroM6CzmbU3s1qEApJjC7UZC3w9un0lMCGq2TAWuDaaDaM90JlQx6HYfZrZN4FzgevcPb+Cj+3E5BwMwy3qNYXzfh/raERERI6bkg8iIiLlpF5KEqe0bxTqPnQcDQlJsLTI8gFSgqiGw3eBd4FFwEvuvsDM7jOzi6NmTwAZZrac0FvhzuixC4CXgIWE2g23u3tecfuM9vUo0BSYamazzezuSjnQ4/HBfbBtKVz6kKbVFBGRKqksXR9FRESkjM7o2oT/959FrD1Yi9ZthoS6D2fdE+uwqhx3fwt4q9C6uwvczgKuKuaxvwZ+XZZ9Ruvj+3xo1Ucw7WE45ZaQ1BIREamC1PNBRESkHI3u1gQgGnoxBrYsgF1fxDgqqbLy8+GN70JGJzjrl7GORkRE5IQp+SAiIlKO2jeuS9uMOtGUm2PCyho664WUg42zYNcaGPEzqFUn1tGIiIicMCUfREREypGZcUbXJkxdsZ2DDdqHK9aq+yAnatl4wKDTWbGORERE5KQo+SAiIlLORndrQnZuPp+s2BZ6P6z6CLL3xTosqYqWvQetBkHdjFhHIiIiclKUfBARESlnp3ZoRJ1aiUen3Mw7BKs+jHVYUtXs2wrrP4fO58Q6EhERkZOm5IOIiEg5S0lKZFinxkxcvBVvfRqkNNDQCzl+Kz4AHDqfHetIRERETpqSDyIiIhXgjK5NWL/rIEu3ZUOnM2Hpe2HmApGyWvYe1G0CzfrGOhIREZGTpuSDiIhIBTijWybA0Vkv9m2CjbNjG5RUHXm5sPyD0OshQadrIiJS9enTTEREpAI0T6tN9+YNQt2HDmeElV9Mi21QUnWsnwFZuzTkQkREqo0KSz6YWWszm2hmC81sgZl9v5h2o8xsdtRG1bhERKTaGN0tk5lrdrI7sRHUbwEbZsU6JKkqlr0Hlng0cSUiIlLFVWTPh1zgx+7eAzgNuN3MehRsYGbpwMPAxe7eE7iqAuMRERGpVKO7NSEv3/lo2VZo0V/JBym7Ze9Bm9OgdnqsIxERESkXSRW1Y3ffCGyMbu81s0VAS2BhgWZfAV519y+idlsqKh4REZHK1q91Q9LrJDNx8RYuatEflrwFWXsgtUGsQ5N4tmcDbJoHZ90b60hERCSO5OU7h3Lzyc7NIzs3n+ycfA7l5ZGVkx/u5+ZF26MlJ49DeaFd4e1NG6Rwy4iOlRp/hSUfCjKzdkB/YHqhTV2AZDObBNQH/uLuzxTx+FuAWwDatGlTobGKiIiUl8QEY2SXTCYt3Upev74k4rBpLrQ7PdahSTxb/n742fmc2MYhIiJHFP7if/h2Vk5+gS/4BbdF94vYfnh9wf1k5xaRQDjy2LA9N99P+jiSE42UpET6tk6rfskHM6sHvAL8wN33FPH8A4EzgdrAVDOb5u5LCzZy98eAxwAGDRp08r9xERGRSjK6WxPemL2BhXShN4ShF0o+SEmWvQcNWkKTHqW3FRGpAfLzPXwJL+2Le87R20X1ECiqB0DhfX4pgVABX/xTkhKolZRw5GdqciK1EhNITU4grXbykfVhXcH2iaQkJxx7PynhS9sP7+vo9qPrExKsHF6RE1OhyQczSyYkHv7l7q8W0WQdsN3d9wP7zewjoC+wtIi2IiIiVc7ILpkkGIxfk0/vtNaq+yAlyz0EKyZB7yvAYneCKCJymPvRL/6Fv5Af8yX/cEKg0Jf8Yr/UF7rCXzCJ8OV95p/0cSQl2NEv9UlHv9Qf+YKenED91KRjth37BT/xSMIgJbnQ/SKSCEU9LpZf/ONBhSUfzMyAJ4BF7v7HYpq9AfzVzJKAWsCpwJ8qKiYREZHKll6nFgPaNGTCki38qEU/2DA71iFJPFs7DQ7t1ZALETkiNy//S+P2C39hL3yV/0u9AYoZ918wkVBSL4KTlWCU8KU+3E+vUyv6Yn90fWlf+ItqX/BxqclHexAkJVbkXAtSFhXZ82EYcAMwz8xmR+t+AbQBcPdH3X2Rmb0DzAXygcfdfX4FxiQiIlLpzujWhD+8u4R93XpRb9GbcHCXZjGQoi17DxKSof3IWEciIoSr/sfTNb/kYQEld/svrhdAXjl09//SF/6oC/7hL+71U5OO6dKfUig5UPALfsEr/CkFrvAf6VFwZD9H1yclGKbeXDVeRc52MQUo9S/M3f8A/KGi4hAREYm1UV0z+cO7S5id157TATbOgQ76cilFWDYe2g2DlHqxjkQkLuTm5R/3l/qyJAWOLfAXzQpQuF1e2M/JKmqcf8Ev+rWTE0mvnVzktloFr+IXSAIUTCQcO4Tg2Da1EsM6ffGXeFAps12IiIjUZN2aNaBurUQm728Vkg8bZin5IF+2cw1sXQwDvhbrSESAY8f6F666X6Yv9sdR3b+4HgEne9XfLLrqn1hE9/zoC35a7WRS66d8qaBfwa77RSYPko698p+anECtxIIF/8LjE2v4OH+Rw5R8EBERqWCJCUbvVmlM3ZgH6W1UdFKKtnx8+Kl6DxLJz/cir/JnFfEl/0uJgVKTAV9uX1RvgZN1uMhfUeP4U5PDVf/D1f2L+8JfcIhAwer/RXb5L/Q8yYnq7i8SL5R8EBERqQT9WjfkiSkryevdj8SNs2MdjsSjZeOhYTvI6BTrSCSSl+8lfok/ppt+Gb7cH28yICfv5Mf6l3SlvlY01r9xEdtSkhNIOeYL/5d7BXzpMUX0GtBVfxE5TMkHERGRStCvdTo5ec6met1puXgsHNgBdRrFOiyJFzlZsPJDGHCDptgsoLjx/qV16y/LF/1SZwfIzSe3HLr8F75SH8bgH+2aX1KF/8JX/4sd41+oRsDhYoC1EjW1n4jEDyUfREREKkH/NukAzM3vQEuAjbOh4+gYRhTfzGwM8BcgkTAb1m8LbU8BngEGAtuBa9x9dbTtLuBmIA+4w93fLWmfZtYeeAHIAGYCN7j7oYo+xmOsmQK5B+NuyEVZv/xnFdUToLSr/GVoUx7j/VNLGcNfLyXpmGRASvKXq/WHL/WJZUoGFOxpoC7/IiJHKfkgIiJSCZo2SKV5WiqT9tTnPAh1H5R8KJKZJQIPAWcD64DPzGysuy8s0OxmYKe7dzKza4HfAdeYWQ/gWqAn0AJ438y6RI8pbp+/A/7k7i+Y2aPRvh+p+CMtYNl4SEqFdqcfszo37/BY/qNf0rNyivuyfnxX/ivjy3+CcaToXuEkwOEv8PVTkwpcxS85UVD81f/EAl/6j27T9H4iIvFDyQcREZFK0rdVOlM37IGG7VV0smSnAMvdfSWAmb0AXAIUTD5cAtwb3X4Z+KuFb5mXAC+4ezawysyWR/ujqH2a2SJgNPCVqM3T0X4rNfmw4bOxrLGe/Oj+T8q123+CceQLfWrhrvnR/QZlKPZX3Bf+EhMFSQkkJSaU029IRESqOiUfREREKslZPZrSoHYS+Q2vJCFRH8ElaAmsLXB/HXBqcW3cPdfMdhOGTbQEphV6bMvodlH7zAB2uXtuEe2PYWa3ALcAtGnT5viOqCQ5WXzRoD/La/dleEbjAtP2FfGFP/pyf3h4wJF6AoW+9B8uEqhu/yIiEi905iMiIlJJrhzYiisHtgL6xjoUOQHu/hjwGMCgQYNOfhqCw5JTOe0Hz3Faue1QREQk/qgvnIiIiMSb9UDrAvdbReuKbGNmSUAaofBkcY8tbv12ID3aR3HPJSIiIidJyQcRERGJN58Bnc2svZnVIhSQHFuozVjg69HtK4EJ7u7R+mvNLCWaxaIz8Glx+4weMzHaB9E+36jAYxMREamRNOxCRERE4kpUw+G7wLuEaTGfdPcFZnYfMMPdxwJPAM9GBSV3EJIJRO1eIhSnzAVud/c8gKL2GT3lz4EXzOz/AbOifYuIiEg5spDwrzoGDRrkM2bMiHUYIiIiccfMZrr7oFjHURPofERERKRoxZ2PaNiFiIiIiIiIiFQoJR9EREREREREpEIp+SAiIiIiIiIiFUrJBxERERERERGpUFWu4KSZbQXWlPNuGwPbynmf8UjHWb3UlOOEmnOsOs7qJRbH2dbdMyv5OWsknY+cFB1n9aLjrH5qyrHqOCtOkecjVS75UBHMbEZNqA6u46xeaspxQs05Vh1n9VJTjlPKT035m9FxVi86zuqnphyrjrPyadiFiIiIiIiIiFQoJR9EREREREREpEIp+RA8FusAKomOs3qpKccJNedYdZzVS005Tik/NeVvRsdZveg4q5+acqw6zkqmmg8iIiIiIiIiUqHU80FEREREREREKpSSDyIiIiIiIiJSoWp08sHMxpjZEjNbbmZ3xjqek2Vmq81snpnNNrMZ0bpGZjbezJZFPxtG683MHoyOfa6ZDYht9CUzsyfNbIuZzS+w7riPzcy+HrVfZmZfj8WxlKSY47zXzNZHr+tsMzu/wLa7ouNcYmbnFlgf13/bZtbazCaa2UIzW2Bm34/WV6vXtITjrFavqZmlmtmnZjYnOs5fRuvbm9n0KOYXzaxWtD4lur882t6uwL6KPP54UMJxPmVmqwq8nv2i9VXy71YqXzy/v0+E6XykSn92gc5HqttrWsJxVqvXtITP6fam85H4+Lt19xq5AInACqADUAuYA/SIdVwneUyrgcaF1v0euDO6fSfwu+j2+cDbgAGnAdNjHX8pxzYCGADMP9FjAxoBK6OfDaPbDWN9bGU4znuBnxTRtkf0d5sCtI/+nhOrwt820BwYEN2uDyyNjqdavaYlHGe1ek2j16VedDsZmB69Ti8B10brHwW+E92+DXg0un0t8GJJxx/r4yvDcT4FXFlE+yr5d6ul0v+u4vr9fYLHtBqdj1Tp/wHFHGe1+uyKYtf5SDV6TdH5yFPE+flITe75cAqw3N1Xuvsh4AXgkhjHVBEuAZ6Obj8NXFpg/TMeTAPSzax5DOIrE3f/CNhRaPXxHtu5wHh33+HuO4HxwJgKD/44FHOcxbkEeMHds919FbCc8Hcd93/b7r7R3T+Pbu8FFgEtqWavaQnHWZwq+ZpGr8u+6G5ytDgwGng5Wl/49Tz8Or8MnGlmRvHHHxdKOM7iVMm/W6l0cf3+Lkc6H6lC/wN0PlK9XlOdj+h8hDj5u63JyYeWwNoC99dR8puwKnDgPTObaWa3ROuauvvG6PYmoGl0uzoc//EeW1U+5u9G3aSePNz1j2pynFEXt/6ErG21fU0LHSdUs9fUzBLNbDawhfDhtQLY5e65UZOCMR85nmj7biCDKnic7n749fx19Hr+ycxSonVV9vWUSlUd/x50PhJUx/8B1eqzqyCdj1SP11TnI/F9PlKTkw/V0enuPgA4D7jdzEYU3OjuTslZsSqrOh8b8AjQEegHbAQeiGk05cjM6gGvAD9w9z0Ft1Wn17SI46x2r6m757l7P6AV4epAt9hGVDEKH6eZ9QLuIhzvYELXxZ/HLkKRuKDzkeqp2n12Habzkerzmup8JL7PR2py8mE90LrA/VbRuirL3ddHP7cArxHecJsPd1+Mfm6JmleH4z/eY6uSx+zum6N/MPnA3zna7atKH6eZJRM+AP/l7q9Gq6vda1rUcVbX1xTA3XcBE4EhhG59SdGmgjEfOZ5oexqwnap5nGOi7qzu7tnAP6hGr6dUimr396Dzkar/2VWU6vrZpfOR6veags5HiNPzkZqcfPgM6BxVP61FKDIyNsYxnTAzq2tm9Q/fBs4B5hOO6etRs68Db0S3xwJfi6qfngbsLtC9rKo43mN7FzjHzBpG3crOidbFtUJjXy8jvK4QjvPaqFJve6Az8ClV4G87Gk/3BLDI3f9YYFO1ek2LO87q9pqaWaaZpUe3awNnE8aTTgSujJoVfj0Pv85XAhOiK0vFHX9cKOY4Fxc4QTXCONKCr2eV+7uVShfX7+/jpfORqv/ZVZzq9tkFOh+pbq+pzkeqwPmIx0HFzlgthMqfSwljgf4r1vGc5LF0IFRlnQMsOHw8hHFLHwDLgPeBRtF6Ax6Kjn0eMCjWx1DK8T1P6A6WQxiPdPOJHBtwE6FozHLgG7E+rjIe57PRccwl/PNoXqD9f0XHuQQ4r8D6uP7bBk4ndGGcC8yOlvOr22tawnFWq9cU6APMio5nPnB3tL4D4cN6OfBvICVanxrdXx5t71Da8cfDUsJxTohez/nAPzlagbpK/t1qicnfVty+v0/gWHQ+UsU/u0o4zmr12RXFp/ORavSaovORuD8fsehJRUREREREREQqRE0ediEiIiIiIiIilUDJBxERERERERGpUEo+iIiIiIiIiEiFUvJBRERERERERCqUkg8iIiIiIiIiUqGUfBARERERERGRCqXkg0gNYmYZZjY7WjaZ2fro9j4ze7gCnu8pM1tlZreW0Ga4mS00s/nl/fwiIiISf3Q+IlIzmbvHOgYRiQEzuxfY5+73V+BzPAWMc/eXS2nXLmrXq6JiERERkfij8xGRmkM9H0QEMxtlZuOi2/ea2dNmNtnM1pjZ5Wb2ezObZ2bvmFly1G6gmX1oZjPN7F0za16G57nKzOab2Rwz+6iij0tERESqDp2PiFRvSj6ISFE6AqOBi4F/AhPdvTdwELgg+sD/P+BKdx8IPAn8ugz7vRs41937RvsWERERKY7OR0SqkaRYByAiceltd88xs3lAIvBOtH4e0A7oCvQCxpsZUZuNZdjvx8BTZvYS8Gp5By0iIiLVis5HRKoRJR9EpCjZAO6eb2Y5frQ4TD7h/4YBC9x9yPHs1N1vNbNTgQuAmWY20N23l2fgIiIiUm3ofESkGtGwCxE5EUuATDMbAmBmyWbWs7QHmVlHd5/u7ncDW4HWFRyniIiIVF86HxGpQtTzQUSOm7sfMrMrgQfNLI3wv+TPwIJSHvoHM+tMuFLxATCnQgMVERGRakvnIyJVi6baFJEKo6mtREREJNZ0PiISHzTsQkQq0m7gV2Z2a3ENzGw48CawrdKiEhERkZpE5yMicUA9H0RERERERESkQqnng4iIiIiIiIhUKCUfRERERERERKRCKfkgIiIiIiIiIhVKyQcRERERERERqVD/H+3fRJm7LEuhAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18,4))\n", "ax1.plot(t1,V1,label=\"without cracking\")\n", @@ -162,28 +149,10 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "9ca7991b", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", - "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", - "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", - "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", - "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", - "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[7] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", - "[8] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", - "[9] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[10] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "pybamm.print_citations()" ] diff --git a/pybamm/models/submodels/interface/sei/no_sei.py b/pybamm/models/submodels/interface/sei/no_sei.py index dc021f0ed4..4092704f97 100644 --- a/pybamm/models/submodels/interface/sei/no_sei.py +++ b/pybamm/models/submodels/interface/sei/no_sei.py @@ -39,4 +39,6 @@ def get_fundamental_variables(self): def get_coupled_variables(self, variables): variables.update(self._get_standard_concentration_variables(variables)) + # Update whole cell variables, which also updates the "sum of" variables + variables.update(super().get_coupled_variables(variables)) return variables From 0a93993b327864a4d8b5f65b289d344b9dc3c2b3 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Tue, 19 Jul 2022 13:46:52 +0100 Subject: [PATCH 19/36] Changed base_lithium_ion_model to avoid running SEI on cracks for half-cell case --- examples/notebooks/models/SEI-on-cracks.ipynb | 43 ++++++++++++++--- .../full_battery_models/base_battery_model.py | 2 +- .../lithium_ion/base_lithium_ion_model.py | 48 ++++++++++++------- 3 files changed, 69 insertions(+), 24 deletions(-) diff --git a/examples/notebooks/models/SEI-on-cracks.ipynb b/examples/notebooks/models/SEI-on-cracks.ipynb index 67d0318433..3bf8eb082d 100644 --- a/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/examples/notebooks/models/SEI-on-cracks.ipynb @@ -48,7 +48,7 @@ "metadata": {}, "outputs": [], "source": [ - "model1 = pybamm.lithium_ion.DFN({\"SEI\": \"none\"})\n", + "model1 = pybamm.lithium_ion.DFN({\"SEI\": \"solvent-diffusion limited\"})\n", "model2 = pybamm.lithium_ion.DFN({\n", " \"particle mechanics\": \"swelling and cracking\",\n", " \"SEI\": \"solvent-diffusion limited\",\n", @@ -105,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "c3884817", "metadata": {}, "outputs": [], @@ -120,10 +120,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "d33e1d89", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(18,4))\n", "ax1.plot(t1,V1,label=\"without cracking\")\n", @@ -149,10 +162,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "9ca7991b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", + "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", + "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", + "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[7] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", + "[8] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", + "[9] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", + "[10] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "\n" + ] + } + ], "source": [ "pybamm.print_citations()" ] diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 93e83f9631..2b1fcf97f4 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -358,7 +358,7 @@ def __init__(self, extra_options): ) elif options["working electrode"] == "positive": raise NotImplementedError( - "SEI on cracks not yet implemented for lithium metal eleectrode." + "SEI on cracks not yet implemented for lithium metal electrode." ) # Options not yet compatible with particle-size distributions diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index 4c21c046a0..4f77c240b9 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -214,6 +214,8 @@ def set_summary_variables(self): "Total lithium in negative electrode [mol]", "Loss of lithium to lithium plating [mol]", "Loss of capacity to lithium plating [A.h]", + "Loss of lithium to SEI on cracks [mol]", + "Loss of capacity to SEI on cracks [A.h]", ] self.summary_variables = summary_variables @@ -234,28 +236,40 @@ def set_sei_submodel(self): else: reaction_loc = "full electrode" - if self.options["SEI"] == "none": - self.submodels["sei"] = pybamm.sei.NoSEI(self.param, self.options) - self.submodels["sei on cracks"] = pybamm.sei.NoSEI( - self.param, self.options, cracks=True - ) - elif self.options["SEI"] == "constant": - self.submodels["sei"] = pybamm.sei.ConstantSEI(self.param, self.options) - self.submodels["sei on cracks"] = pybamm.sei.NoSEI( - self.param, self.options, cracks=True - ) + # Do not set "sei on cracks" submodel for half-cells + if reaction_loc == "interface": + if self.options["SEI"] == "none": + self.submodels["sei"] = pybamm.sei.NoSEI(self.param, self.options) + elif self.options["SEI"] == "constant": + self.submodels["sei"] = pybamm.sei.ConstantSEI(self.param, self.options) + else: + self.submodels["sei"] = pybamm.sei.SEIGrowth( + self.param, reaction_loc, self.options, cracks=False + ) + # For full cells, "sei on cracks" submodel must be set, even if it is zero else: - self.submodels["sei"] = pybamm.sei.SEIGrowth( - self.param, reaction_loc, self.options, cracks=False - ) - if self.options["SEI on cracks"] == "true": - self.submodels["sei on cracks"] = pybamm.sei.SEIGrowth( - self.param, reaction_loc, self.options, cracks=True + if self.options["SEI"] == "none": + self.submodels["sei"] = pybamm.sei.NoSEI(self.param, self.options) + self.submodels["sei on cracks"] = pybamm.sei.NoSEI( + self.param, self.options, cracks=True ) - else: + elif self.options["SEI"] == "constant": + self.submodels["sei"] = pybamm.sei.ConstantSEI(self.param, self.options) self.submodels["sei on cracks"] = pybamm.sei.NoSEI( self.param, self.options, cracks=True ) + else: + self.submodels["sei"] = pybamm.sei.SEIGrowth( + self.param, reaction_loc, self.options, cracks=False + ) + if self.options["SEI on cracks"] == "true": + self.submodels["sei on cracks"] = pybamm.sei.SEIGrowth( + self.param, reaction_loc, self.options, cracks=True + ) + else: + self.submodels["sei on cracks"] = pybamm.sei.NoSEI( + self.param, self.options, cracks=True + ) def set_lithium_plating_submodel(self): if self.options["lithium plating"] == "none": From 2ddb0c282910f29ee1369892dce55aaf605f0000 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Tue, 19 Jul 2022 14:04:52 +0100 Subject: [PATCH 20/36] flake8 --- .../full_battery_models/lithium_ion/base_lithium_ion_model.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index 4f77c240b9..4f586f58e8 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -236,7 +236,7 @@ def set_sei_submodel(self): else: reaction_loc = "full electrode" - # Do not set "sei on cracks" submodel for half-cells + # Do not set "sei on cracks" submodel for half-cells if reaction_loc == "interface": if self.options["SEI"] == "none": self.submodels["sei"] = pybamm.sei.NoSEI(self.param, self.options) From 0080825580bc6a66568dc895b357fa5f468d4cdc Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 20 Jul 2022 13:15:05 +0100 Subject: [PATCH 21/36] Fixed bug in equation for interstitial-diffusion limited SEI --- pybamm/models/submodels/interface/sei/sei_growth.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py index da528ac6e5..966038784e 100644 --- a/pybamm/models/submodels/interface/sei/sei_growth.py +++ b/pybamm/models/submodels/interface/sei/sei_growth.py @@ -123,7 +123,7 @@ def get_coupled_variables(self, variables): elif self.options["SEI"] == "interstitial-diffusion limited": C_sei = param.C_sei_inter - j_sei = -pybamm.exp(-(prefactor * delta_phi) / (C_sei * L_sei_inner)) + j_sei = -pybamm.exp(-prefactor * delta_phi) / (C_sei * L_sei_inner) elif self.options["SEI"] == "solvent-diffusion limited": C_sei = param.C_sei_solvent From c628f9526a3f864be85e0545b9490f99546f9b70 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 20 Jul 2022 16:39:00 +0100 Subject: [PATCH 22/36] Fixed examples, added SEI on cracks porosity change, added tests --- .../notebooks/models/using-submodels.ipynb | 5 ++- examples/scripts/custom_model.py | 3 +- .../porosity/reaction_driven_porosity.py | 4 +- .../test_base_battery_model.py | 4 ++ .../base_lithium_ion_tests.py | 41 +++++++++++++++++++ 5 files changed, 53 insertions(+), 4 deletions(-) diff --git a/examples/notebooks/models/using-submodels.ipynb b/examples/notebooks/models/using-submodels.ipynb index 16ee79e933..e2c4759086 100644 --- a/examples/notebooks/models/using-submodels.ipynb +++ b/examples/notebooks/models/using-submodels.ipynb @@ -478,7 +478,8 @@ "outputs": [], "source": [ "model.submodels[\"sei\"] = pybamm.sei.NoSEI(model.param)\n", - "model.submodels[\"lithium plating\"] = pybamm.lithium_plating.NoPlating(model.param)" + "model.submodels[\"lithium plating\"] = pybamm.lithium_plating.NoPlating(model.param)\n", + "model.submodels[\"sei on cracks\"] = pybamm.sei.NoSEI(model.param, cracks=True)" ] }, { @@ -616,7 +617,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.0" + "version": "3.8.10" }, "toc": { "base_numbering": 1, diff --git a/examples/scripts/custom_model.py b/examples/scripts/custom_model.py index c7c1fe68f9..9c0c2a3b07 100644 --- a/examples/scripts/custom_model.py +++ b/examples/scripts/custom_model.py @@ -87,7 +87,8 @@ model.param, "Positive" ) model.submodels["sei"] = pybamm.sei.NoSEI(model.param) -model.submodels["lithium plating"] = pybamm.lithium_plating.NoPlating(model.param) +model.submodels["sei on cracks"] = pybamm.sei.NoSEI(model.param) +model.submodels["lithium plating"] = pybamm.lithium_plating.NoPlating(model.param, cracks=True) # build model model.build_model() diff --git a/pybamm/models/submodels/porosity/reaction_driven_porosity.py b/pybamm/models/submodels/porosity/reaction_driven_porosity.py index 5988df0e1b..b4d1413703 100644 --- a/pybamm/models/submodels/porosity/reaction_driven_porosity.py +++ b/pybamm/models/submodels/porosity/reaction_driven_porosity.py @@ -28,8 +28,10 @@ def get_coupled_variables(self, variables): L_sei_0 = self.param.L_inner_0_dim + self.param.L_outer_0_dim L_pl_n = variables["Lithium plating thickness [m]"] L_dead_n = variables["Dead lithium thickness [m]"] + L_sei_cr_n = variables["Total SEI on cracks thickness [m]"] + roughness_n = variables["Negative electrode roughness ratio"] - L_tot = (L_sei_n - L_sei_0) + L_pl_n + L_dead_n + L_tot = (L_sei_n - L_sei_0) + L_pl_n + L_dead_n + L_sei_cr_n * (roughness_n - 1) a_n = variables["Negative electrode surface area to volume ratio [m-1]"] diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index 74d32114b1..21a89c6406 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -272,6 +272,10 @@ def test_options(self): with self.assertRaisesRegex(pybamm.OptionError, "particle cracking"): pybamm.BaseBatteryModel({"particle cracking": "bad particle cracking"}) + # SEI on cracks + with self.assertRaisesRegex(pybamm.OptionError,"SEI on cracks"): + pybamm.BaseBatteryModel({"SEI on cracks": "bad SEI on cracks"}) + # plating model with self.assertRaisesRegex(pybamm.OptionError, "lithium plating"): pybamm.BaseBatteryModel({"lithium plating": "bad plating"}) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index 07b3d88972..df12f6bfd6 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -197,6 +197,47 @@ def test_well_posed_mechanics_stress_induced_diffusion_mixed(self): } self.check_well_posedness(options) + def test_well_posed_sei_reaction_limited_on_cracks(self): + options = { + "SEI": "reaction limited", + "SEI on cracks": "true", + "particle mechanics": "swelling and cracking", + } + self.check_well_posedness(options) + + def test_well_posed_sei_solvent_diffusion_limited_on_cracks(self): + options = { + "SEI": "solvent-diffusion limited", + "SEI on cracks": "true", + "particle mechanics": "swelling and cracking", + } + self.check_well_posedness(options) + + def test_well_posed_sei_electron_migration_limited(self): + options = { + "SEI": "electron-migration limited", + "SEI on cracks": "true", + "particle mechanics": "swelling and cracking", + } + self.check_well_posedness(options) + + def test_well_posed_sei_interstitial_diffusion_limited(self): + options = { + "SEI": "interstitial-diffusion limited", + "SEI on cracks": "true", + "particle mechanics": "swelling and cracking", + } + self.check_well_posedness(options) + + def test_well_posed_sei_ec_reaction_limited(self): + options = { + "SEI": "ec reaction limited", + "SEI porosity change": "true", + "SEI on cracks": "true", + "particle mechanics": "swelling and cracking", + } + self.check_well_posedness(options) + def test_well_posed_reversible_plating(self): options = {"lithium plating": "reversible"} self.check_well_posedness(options) From f23b3ba33bc353023668c7d2999b766fff32ba83 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 20 Jul 2022 16:45:54 +0100 Subject: [PATCH 23/36] flake8 --- examples/scripts/custom_model.py | 4 ++-- .../test_full_battery_models/test_base_battery_model.py | 2 +- .../test_lithium_ion/base_lithium_ion_tests.py | 6 +++--- 3 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/scripts/custom_model.py b/examples/scripts/custom_model.py index 9c0c2a3b07..ba2f2e85da 100644 --- a/examples/scripts/custom_model.py +++ b/examples/scripts/custom_model.py @@ -87,8 +87,8 @@ model.param, "Positive" ) model.submodels["sei"] = pybamm.sei.NoSEI(model.param) -model.submodels["sei on cracks"] = pybamm.sei.NoSEI(model.param) -model.submodels["lithium plating"] = pybamm.lithium_plating.NoPlating(model.param, cracks=True) +model.submodels["sei on cracks"] = pybamm.sei.NoSEI(model.param, cracks=True) +model.submodels["lithium plating"] = pybamm.lithium_plating.NoPlating(model.param) # build model model.build_model() diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index 21a89c6406..b47e5f347d 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -273,7 +273,7 @@ def test_options(self): pybamm.BaseBatteryModel({"particle cracking": "bad particle cracking"}) # SEI on cracks - with self.assertRaisesRegex(pybamm.OptionError,"SEI on cracks"): + with self.assertRaisesRegex(pybamm.OptionError, "SEI on cracks"): pybamm.BaseBatteryModel({"SEI on cracks": "bad SEI on cracks"}) # plating model diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index df12f6bfd6..98ab1c2950 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -213,7 +213,7 @@ def test_well_posed_sei_solvent_diffusion_limited_on_cracks(self): } self.check_well_posedness(options) - def test_well_posed_sei_electron_migration_limited(self): + def test_well_posed_sei_electron_migration_limited_on_cracks(self): options = { "SEI": "electron-migration limited", "SEI on cracks": "true", @@ -221,7 +221,7 @@ def test_well_posed_sei_electron_migration_limited(self): } self.check_well_posedness(options) - def test_well_posed_sei_interstitial_diffusion_limited(self): + def test_well_posed_sei_interstitial_diffusion_limited_on_cracks(self): options = { "SEI": "interstitial-diffusion limited", "SEI on cracks": "true", @@ -229,7 +229,7 @@ def test_well_posed_sei_interstitial_diffusion_limited(self): } self.check_well_posedness(options) - def test_well_posed_sei_ec_reaction_limited(self): + def test_well_posed_sei_ec_reaction_limited_on_cracks(self): options = { "SEI": "ec reaction limited", "SEI porosity change": "true", From 5f0258e6173f6ab66ec86bf4059fbe820cf9f2c4 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Thu, 21 Jul 2022 16:08:04 +0100 Subject: [PATCH 24/36] Breaking change: particle mechanics now a compulsory submodel (none is acceptable) --- CHANGELOG.md | 1 + .../notebooks/models/using-submodels.ipynb | 8 +++- examples/scripts/custom_model.py | 6 +++ .../test_parameter_sets/test_OKane2022.py | 46 ++++++++++++++++++- 4 files changed, 58 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 3abfcfcca0..3c1b2b6536 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -24,6 +24,7 @@ ## Breaking changes - Exchange-current density functions (and some other functions) now take an additional argument, the maximum particle concentration for that phase ([#2134](https://github.com/pybamm-team/PyBaMM/pull/2134)) +- Loss of lithium to SEI on cracks is now a degradation variable, so setting a particle mechanics submodel is now compulsory (NoMechanics will suffice) # [v22.6](https://github.com/pybamm-team/PyBaMM/tree/v22.6) - 2022-06-30 diff --git a/examples/notebooks/models/using-submodels.ipynb b/examples/notebooks/models/using-submodels.ipynb index e2c4759086..7a9967ec22 100644 --- a/examples/notebooks/models/using-submodels.ipynb +++ b/examples/notebooks/models/using-submodels.ipynb @@ -468,7 +468,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We don't want any SEI formation or lithium plating in this model" + "We don't want any particle mechanics, SEI formation or lithium plating in this model" ] }, { @@ -477,6 +477,12 @@ "metadata": {}, "outputs": [], "source": [ + "model.submodels[\n", + " \"Negative particle mechanics\"\n", + "] = pybamm.particle_mechanics.NoMechanics(model.param, \"Negative\")\n", + "model.submodels[\n", + " \"Positive particle mechanics\"\n", + "] = pybamm.particle_mechanics.NoMechanics(model.param, \"Positive\")\n", "model.submodels[\"sei\"] = pybamm.sei.NoSEI(model.param)\n", "model.submodels[\"lithium plating\"] = pybamm.lithium_plating.NoPlating(model.param)\n", "model.submodels[\"sei on cracks\"] = pybamm.sei.NoSEI(model.param, cracks=True)" diff --git a/examples/scripts/custom_model.py b/examples/scripts/custom_model.py index ba2f2e85da..ba0d1be52a 100644 --- a/examples/scripts/custom_model.py +++ b/examples/scripts/custom_model.py @@ -86,6 +86,12 @@ ] = pybamm.electrolyte_conductivity.surface_potential_form.Explicit( model.param, "Positive" ) +model.submodels[ + "Negative particle mechanics" +] = pybamm.particle_mechanics.NoMechanics(model.param, "Negative") +model.submodels[ + "Positive particle mechanics" +] = pybamm.particle_mechanics.NoMechanics(model.param, "Positive") model.submodels["sei"] = pybamm.sei.NoSEI(model.param) model.submodels["sei on cracks"] = pybamm.sei.NoSEI(model.param, cracks=True) model.submodels["lithium plating"] = pybamm.lithium_plating.NoPlating(model.param) diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py index e827bcb674..05d5dd9f15 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py @@ -1,5 +1,5 @@ # -# Tests for O'Kane (2020) parameter set +# Tests for O'Kane (2022) parameter set # import pybamm import unittest @@ -23,6 +23,7 @@ def test_functions(self): param = pybamm.ParameterValues("OKane2022") T = pybamm.Scalar(298.15) + # Lithium plating p = "pybamm/input/parameters/lithium_ion/lithium_platings/okane2022_Li_plating/" k_path = os.path.join(root, p) @@ -32,7 +33,48 @@ def test_functions(self): [1e3, 1e4, T], 9.6485e-1, ), - "SEI_limited_dead_lithium_OKane2022.py": ([1e-8], 5e-7) + "SEI_limited_dead_lithium_OKane2022.py": ([1e-8], 5e-7), + } + + for name, value in fun_test.items(): + fun = pybamm.load_function(os.path.join(k_path, name)) + self.assertAlmostEqual(param.evaluate(fun(*value[0])), value[1], places=4) + + # Negative electrode + p = ( + "pybamm/input/parameters/lithium_ion/negative_electrodes/" + "graphite_Chen2020_plating/" + ) + k_path = os.path.join(root, p) + + fun_test = { + #"graphite_LGM50_diffusivity_Chen2020.py": ([0.9, T], 3.3e-14), + "graphite_LGM50_electrolyte_exchange_current_density_Chen2020.py": ( + [1000, 16566.5, 33133, T], + 0.33947, + ), + #"graphite_LGM50_ocp_Chen2020.py": ([0.9], 0.0861), + #"graphite_cracking_rate_Ai2020.py": ([T], 3.9e-20), + #"graphite_volume_change_Ai2020.py": ([0, 33133], -4.955e-5), + } + + for name, value in fun_test.items(): + fun = pybamm.load_function(os.path.join(k_path, name)) + self.assertAlmostEqual(param.evaluate(fun(*value[0])), value[1], places=4) + + # Positive electrode + p = "pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/" + k_path = os.path.join(root, p) + + fun_test = { + "nmc_LGM50_diffusivity_Chen2020.py": ([0.9, T], 4e-15), + "nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py": ( + [1000, 31552, 63104, T], + 3.4123, + ), + "nmc_LGM50_ocp_Chen2020.py": ([0.9], 3.5682), + "cracking_rate_Ai2020.py": ([T], 3.9e-20), + "volume_change_Ai2020.py": ([0.9, 63104], 0.70992), } for name, value in fun_test.items(): From 7ee18d81ba210304007a02cd330010024e5b750b Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Thu, 21 Jul 2022 18:14:27 +0100 Subject: [PATCH 25/36] coverage --- .../test_base_battery_model.py | 10 ++++++++++ .../test_parameter_sets/test_OKane2022.py | 8 ++++---- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index b47e5f347d..5271eba4aa 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -275,6 +275,16 @@ def test_options(self): # SEI on cracks with self.assertRaisesRegex(pybamm.OptionError, "SEI on cracks"): pybamm.BaseBatteryModel({"SEI on cracks": "bad SEI on cracks"}) + with self.assertRaisesRegex(pybamm.OptionError, "To model SEI on cracks"): + pybamm.BaseBatteryModel({ + "SEI on cracks": "true", + "particle mechanics": "swelling only", + }) + with self.assertRaisesRegex(NotImplementedError, "SEI on cracks not yet"): + pybamm.BaseBatteryModel({ + "SEI on cracks": "true", + "working electrode": "positive", + }) # plating model with self.assertRaisesRegex(pybamm.OptionError, "lithium plating"): diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py index 05d5dd9f15..4e3546ada9 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py @@ -48,14 +48,14 @@ def test_functions(self): k_path = os.path.join(root, p) fun_test = { - #"graphite_LGM50_diffusivity_Chen2020.py": ([0.9, T], 3.3e-14), + "graphite_LGM50_diffusivity_Chen2020.py": ([0.9, T], 3.3e-14), "graphite_LGM50_electrolyte_exchange_current_density_Chen2020.py": ( [1000, 16566.5, 33133, T], 0.33947, ), - #"graphite_LGM50_ocp_Chen2020.py": ([0.9], 0.0861), - #"graphite_cracking_rate_Ai2020.py": ([T], 3.9e-20), - #"graphite_volume_change_Ai2020.py": ([0, 33133], -4.955e-5), + "graphite_LGM50_ocp_Chen2020.py": ([0.9], 0.0861), + "graphite_cracking_rate_Ai2020.py": ([T], 3.9e-20), + #"graphite_volume_change_Ai2020.py": ([0.9, 33133], 0.0897), } for name, value in fun_test.items(): From 1d6319e8e62e862d194901c9e546bf02e2976b1e Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Thu, 21 Jul 2022 18:59:47 +0100 Subject: [PATCH 26/36] Fixed tests --- .../full_battery_models/base_battery_model.py | 36 +++++++++---------- .../test_base_battery_model.py | 8 ++--- 2 files changed, 21 insertions(+), 23 deletions(-) diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 2b1fcf97f4..61c3cc8388 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -282,14 +282,21 @@ def __init__(self, extra_options): # The "SEI film resistance" option will still be overridden by extra_options if # provided - # Change the default for particle mechanics based on which LAM option is - # provided - # return "none" if option not given - lam_option = extra_options.get("loss of active material", "none") - if "stress-driven" in lam_option or "stress and reaction-driven" in lam_option: - default_options["particle mechanics"] = "swelling only" + # Change the default for particle mechanics based on which SEI on cracks option + # is provided + # return "false" if option not given + SEI_cracks_option = extra_options.get("SEI on cracks", "false") + if SEI_cracks_option == "true": + default_options["particle mechanics"] = "swelling and cracking" else: - default_options["particle mechanics"] = "none" + # Change the default for particle mechanics based on which LAM option is + # provided + # return "none" if option not given + LAM_opt = extra_options.get("loss of active material", "none") + if "stress-driven" in LAM_opt or "stress and reaction-driven" in LAM_opt: + default_options["particle mechanics"] = "swelling only" + else: + default_options["particle mechanics"] = "none" # The "particle mechanics" option will still be overridden by extra_options if # provided @@ -350,17 +357,6 @@ def __init__(self, extra_options): "current density as a state' must be 'true'" ) - if options["SEI on cracks"] == "true": - if options["particle mechanics"] != "swelling and cracking": - raise pybamm.OptionError( - "To model SEI on cracks, 'particle mechanics' must be set to " - "'swelling and cracking'." - ) - elif options["working electrode"] == "positive": - raise NotImplementedError( - "SEI on cracks not yet implemented for lithium metal electrode." - ) - # Options not yet compatible with particle-size distributions if options["particle size"] == "distribution": if options["lithium plating"] != "none": @@ -455,6 +451,10 @@ def __init__(self, extra_options): f"X-lumped thermal submodels do not yet support {n}D " "current collectors in a half-cell configuration" ) + elif options ["SEI on cracks"] == "true": + raise NotImplementedError( + "SEI on cracks not yet implemented for half-cell models" + ) # Check options are valid for option, value in options.items(): diff --git a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py index 5271eba4aa..85149bbb30 100644 --- a/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py +++ b/tests/unit/test_models/test_full_battery_models/test_base_battery_model.py @@ -265,6 +265,9 @@ def test_options(self): model = pybamm.BaseBatteryModel({"loss of active material": "stress-driven"}) self.assertEqual(model.options["particle mechanics"], "swelling only") self.assertEqual(model.options["stress-induced diffusion"], "true") + model = pybamm.BaseBatteryModel({"SEI on cracks": "true"}) + self.assertEqual(model.options["particle mechanics"], "swelling and cracking") + self.assertEqual(model.options["stress-induced diffusion"], "true") # crack model with self.assertRaisesRegex(pybamm.OptionError, "particle mechanics"): @@ -275,11 +278,6 @@ def test_options(self): # SEI on cracks with self.assertRaisesRegex(pybamm.OptionError, "SEI on cracks"): pybamm.BaseBatteryModel({"SEI on cracks": "bad SEI on cracks"}) - with self.assertRaisesRegex(pybamm.OptionError, "To model SEI on cracks"): - pybamm.BaseBatteryModel({ - "SEI on cracks": "true", - "particle mechanics": "swelling only", - }) with self.assertRaisesRegex(NotImplementedError, "SEI on cracks not yet"): pybamm.BaseBatteryModel({ "SEI on cracks": "true", From f6b979f941cf8a0b8102ab76119c1d202e119c8c Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Fri, 22 Jul 2022 15:13:39 +0100 Subject: [PATCH 27/36] Fixed test_OKane2022 --- examples/notebooks/models/using-submodels.ipynb | 13 ++++++++++--- .../test_parameter_sets/test_OKane2022.py | 13 +++++++------ 2 files changed, 17 insertions(+), 9 deletions(-) diff --git a/examples/notebooks/models/using-submodels.ipynb b/examples/notebooks/models/using-submodels.ipynb index a5466155d1..2ee317e4dd 100644 --- a/examples/notebooks/models/using-submodels.ipynb +++ b/examples/notebooks/models/using-submodels.ipynb @@ -469,7 +469,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We don't want any SEI formation or lithium plating in this model" + "We don't want any particle mechanics, SEI formation or lithium plating in this model" ] }, { @@ -478,7 +478,14 @@ "metadata": {}, "outputs": [], "source": [ + "model.submodels[\n", + " \"Negative particle mechanics\"\n", + "] = pybamm.particle_mechanics.NoMechanics(model.param, \"Negative\")\n", + "model.submodels[\n", + " \"Positive particle mechanics\"\n", + "] = pybamm.particle_mechanics.NoMechanics(model.param, \"Positive\")\n", "model.submodels[\"sei\"] = pybamm.sei.NoSEI(model.param)\n", + "model.submodels[\"sei on cracks\"] = pybamm.sei.NoSEI(model.param, cracks=True)\n", "model.submodels[\"lithium plating\"] = pybamm.lithium_plating.NoPlating(model.param)" ] }, @@ -603,7 +610,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.12 ('conda_jl')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -617,7 +624,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.8.10" }, "toc": { "base_numbering": 1, diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py index 4e3546ada9..74a8cb19df 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py @@ -21,6 +21,7 @@ def test_load_params(self): def test_functions(self): root = pybamm.root_dir() param = pybamm.ParameterValues("OKane2022") + sto = pybamm.Scalar(0.9) T = pybamm.Scalar(298.15) # Lithium plating @@ -48,14 +49,14 @@ def test_functions(self): k_path = os.path.join(root, p) fun_test = { - "graphite_LGM50_diffusivity_Chen2020.py": ([0.9, T], 3.3e-14), + "graphite_LGM50_diffusivity_Chen2020.py": ([sto, T], 3.3e-14), "graphite_LGM50_electrolyte_exchange_current_density_Chen2020.py": ( [1000, 16566.5, 33133, T], 0.33947, ), - "graphite_LGM50_ocp_Chen2020.py": ([0.9], 0.0861), + "graphite_LGM50_ocp_Chen2020.py": ([sto], 0.0861), "graphite_cracking_rate_Ai2020.py": ([T], 3.9e-20), - #"graphite_volume_change_Ai2020.py": ([0.9, 33133], 0.0897), + "graphite_volume_change_Ai2020.py": ([sto, 33133], 0.0897), } for name, value in fun_test.items(): @@ -67,14 +68,14 @@ def test_functions(self): k_path = os.path.join(root, p) fun_test = { - "nmc_LGM50_diffusivity_Chen2020.py": ([0.9, T], 4e-15), + "nmc_LGM50_diffusivity_Chen2020.py": ([sto, T], 4e-15), "nmc_LGM50_electrolyte_exchange_current_density_Chen2020.py": ( [1000, 31552, 63104, T], 3.4123, ), - "nmc_LGM50_ocp_Chen2020.py": ([0.9], 3.5682), + "nmc_LGM50_ocp_Chen2020.py": ([sto], 3.5682), "cracking_rate_Ai2020.py": ([T], 3.9e-20), - "volume_change_Ai2020.py": ([0.9, 63104], 0.70992), + "volume_change_Ai2020.py": ([sto, 63104], 0.70992), } for name, value in fun_test.items(): From 5ae0a6caf57c7c8914cdb5fdcfb6269e32b597a9 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Fri, 22 Jul 2022 15:17:28 +0100 Subject: [PATCH 28/36] flake8 --- pybamm/models/full_battery_models/base_battery_model.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/pybamm/models/full_battery_models/base_battery_model.py b/pybamm/models/full_battery_models/base_battery_model.py index 61c3cc8388..86e2be7ee9 100644 --- a/pybamm/models/full_battery_models/base_battery_model.py +++ b/pybamm/models/full_battery_models/base_battery_model.py @@ -289,7 +289,7 @@ def __init__(self, extra_options): if SEI_cracks_option == "true": default_options["particle mechanics"] = "swelling and cracking" else: - # Change the default for particle mechanics based on which LAM option is + # Change the default for particle mechanics based on which LAM option is # provided # return "none" if option not given LAM_opt = extra_options.get("loss of active material", "none") @@ -451,7 +451,7 @@ def __init__(self, extra_options): f"X-lumped thermal submodels do not yet support {n}D " "current collectors in a half-cell configuration" ) - elif options ["SEI on cracks"] == "true": + elif options["SEI on cracks"] == "true": raise NotImplementedError( "SEI on cracks not yet implemented for half-cell models" ) From 63a9c79e66765f88c32bd10e6503cc893d064121 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Tue, 26 Jul 2022 13:37:15 +0100 Subject: [PATCH 29/36] Completed changes requested in review --- examples/notebooks/models/SEI-on-cracks.ipynb | 12 +-- .../notebooks/models/lithium-plating.ipynb | 92 +++++++++---------- .../graphite_Chen2020_plating/README.md | 9 -- .../graphite_OKane2022/README.md | 7 ++ .../__init__.py | 0 .../graphite_LGM50_diffusivity_Chen2020.py | 0 ...olyte_exchange_current_density_Chen2020.py | 0 .../graphite_LGM50_ocp_Chen2020.csv | 0 .../graphite_LGM50_ocp_Chen2020.py | 0 .../graphite_cracking_rate_Ai2020.py | 5 +- .../graphite_volume_change_Ai2020.py | 0 .../parameters.csv | 2 +- .../nmc_OKane2022/README.md | 2 +- .../nmc_OKane2022/cracking_rate_Ai2020.py | 3 +- .../lithium_ion/seis/OKane2022/parameters.csv | 1 - .../lithium_ion/seis/example/parameters.csv | 1 - .../seis/ramadass2004/parameters.csv | 1 - .../lithium_ion/Yang2017.py | 2 +- .../lithium_ion/base_lithium_ion_model.py | 42 +++------ .../submodels/interface/sei/sei_growth.py | 2 +- .../particle_mechanics/no_mechanics.py | 2 +- pybamm/parameters/lithium_ion_parameters.py | 3 - pybamm/parameters/parameter_sets.py | 15 +-- .../test_simulation_with_experiment.py | 4 +- .../test_lithium_ion/test_Yang2017.py | 2 +- .../test_lithium_ion/test_basic_models.py | 2 +- .../test_parameter_sets/test_OKane2020.py | 11 ++- .../test_parameter_sets/test_OKane2022.py | 2 +- 28 files changed, 97 insertions(+), 125 deletions(-) delete mode 100644 pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/README.md create mode 100644 pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/README.md rename pybamm/input/parameters/lithium_ion/negative_electrodes/{graphite_Chen2020_plating => graphite_OKane2022}/__init__.py (100%) rename pybamm/input/parameters/lithium_ion/negative_electrodes/{graphite_Chen2020_plating => graphite_OKane2022}/graphite_LGM50_diffusivity_Chen2020.py (100%) rename pybamm/input/parameters/lithium_ion/negative_electrodes/{graphite_Chen2020_plating => graphite_OKane2022}/graphite_LGM50_electrolyte_exchange_current_density_Chen2020.py (100%) rename pybamm/input/parameters/lithium_ion/negative_electrodes/{graphite_Chen2020_plating => graphite_OKane2022}/graphite_LGM50_ocp_Chen2020.csv (100%) rename pybamm/input/parameters/lithium_ion/negative_electrodes/{graphite_Chen2020_plating => graphite_OKane2022}/graphite_LGM50_ocp_Chen2020.py (100%) rename pybamm/input/parameters/lithium_ion/negative_electrodes/{graphite_Chen2020_plating => graphite_OKane2022}/graphite_cracking_rate_Ai2020.py (85%) rename pybamm/input/parameters/lithium_ion/negative_electrodes/{graphite_Chen2020_plating => graphite_OKane2022}/graphite_volume_change_Ai2020.py (100%) rename pybamm/input/parameters/lithium_ion/negative_electrodes/{graphite_Chen2020_plating => graphite_OKane2022}/parameters.csv (96%) diff --git a/examples/notebooks/models/SEI-on-cracks.ipynb b/examples/notebooks/models/SEI-on-cracks.ipynb index 3bf8eb082d..e93529d79d 100644 --- a/examples/notebooks/models/SEI-on-cracks.ipynb +++ b/examples/notebooks/models/SEI-on-cracks.ipynb @@ -98,9 +98,9 @@ "source": [ "experiment = pybamm.Experiment([\"Discharge at 1C until 2.5 V\"])\n", "sim1 = pybamm.Simulation(model1, parameter_values=parameter_values, experiment=experiment, var_pts=var_pts)\n", - "sol1 = sim1.solve()\n", + "sol1 = sim1.solve(calc_esoh=False)\n", "sim2 = pybamm.Simulation(model2, parameter_values=parameter_values, experiment=experiment, var_pts=var_pts)\n", - "sol2 = sim2.solve()" + "sol2 = sim2.solve(calc_esoh=False)" ] }, { @@ -126,7 +126,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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7tLq1aJNRl7YZ9WiTUZc26fXo0rw+zRvWDqZCSW8fLJxX8hPl5cD+7UEiImcXHNgLB/YU+bkHcsOfOTuDNrm7YMeq4HHOTsjdWfKxkxpAgxbBm6PRwaVNsNRvCfH6ExAREQEws18C+cALJW139yeBJyGY7aISQxMRkcqStx9WfhH2bhgX9GyHoJd69inQ9mdBwiE1K4pBVk365nkUcXFG24x6tM2ox4W9ghsn7s7qbftZtHE3y7bsYdnmvSzdvIeP5m1k694Dh/Zt3iCZvlmN6Nc6lb6tU+ncrD7xcSUMnUhMhsRmUL/Z8QdakAd7N8OejbBnU/hzY1Bhdeca2LY0GA5SkFvk5BKC2hRNexzusdG0e5Wf2kVEqo5zzjmHF198EYAXX3yRW2+9FQgKwD344IO8/fbblR7TvffeS7169fjJT35yxPq7776bYcOGcfrpp1d6TDXQWqBVkd9bhutKarPGzBKABgSFJ8uz79eY2Q0EdwlGeGXMQx4les+JiBRTWAgb54TJhjGwcmLwnSkuMajbMPz/oO1pwUyHqttwQpR8OA5mRmZaHTLT6gBNjti2c18eSzbvYfaaHUxduZ0py7fxv6/WAVAvKYHemQ0Z2CaNUztm0KVZfayi6jjEJ0L95sFSmsJC2L0eti+HbcuCZdOCYKzS7FcOt0vNChISLfpC5qBg/FJCUsXEKSJSxLvvvgvAihUrePTRRw99Eaoo+fn5JCRUzEfdfffdVyHHkXKZArQ3s2yCxMFVwNXF2owGrgcmEtRoGOPubmajgRfN7E8EBSfbA1+W9WThzBo/A05x93KMhay69J4TEQF2bwgSDUvHBEMp9m4O1md0hpO+Fcxi2HpwUINPKkzc0ZvIsWhQJ5G+rVO5YUg2j1zdh4l3Deezn5/GX6/qxUW9m7N5dy4PfLCQcx/+jIG//4SfvzqL9+esZ3dO3tEPfqLi4oIhGFknQ5/r4PR74eqX4Mfz4SdL4JuvwYh7gqzehtnw8T3w9Jlwfyb86xz45D5Y/HEwzENE5CgeeOABHn74YQB++MMfMnz4cADGjBnDNddcA0BWVhZbtmzhzjvvZOnSpfTq1Yuf/vSnAOzZs4fLLruMTp06cc0111DSzeglS5Zw+umn07NnT/r06cPSpUsZN24cQ4cO5YILLqBLly4AXHTRRfTt25euXbvy5JNPHtr//fffp0+fPvTs2ZMRI0Z87fj/+Mc/OPvss9m/fz833HADr7766qG477nnHvr06UP37t1ZsGABAJs3b+aMM86ga9eufPvb36Z169Zs2bKlov5Jawx3zwe+B3wAzAdecfe5ZnafmV0QNnsKSAsLSv4IuDPcdy7wCkFxyveB28KZLjCz/xAkKzqa2Roz+1Z4rEeAFOAjM5tpZo9XyolWML3n9J4TkVLk7Q9mJ/zgl/DoYHioI7x5S1A0ss2pcNFj8KP5cNskGPl7aH+GEg8RoJ4PEWZmtEytQ8vUOoeGbWzancOnCzczbuFm3p2znpenriYhzuiXlcrpnZtwdvdmtGhYu3IDrZcB7U4PloP2boFVE2HVpGDc02d/AX8ILC4YntHm1GCe2syBkFjJ8YrIsXnvziCpWJGadoez7y9189ChQ3nooYe4/fbbmTp1Krm5ueTl5TFhwgSGDRt2RNv777+fOXPmMHPmTCDoAj5jxgzmzp1L8+bNGTJkCJ9//jknn3zyEftdc8013HnnnVx88cXk5ORQWFjI6tWrmT59OnPmzCE7OxuAp59+mkaNGrF//35OOukkLr30UgoLC/nOd77D+PHjyc7OZtu2bUcc+5FHHuGjjz7izTffJCnp672/0tPTmT59Oo8++igPPvgg//znP/nVr37F8OHDueuuu3j//fd56qmnjudfVgB3fxd4t9i6u4s8zgEuL2Xf3wK/LWH9N0pp3+6Egi2J3nN6z4lI9LjDpvmw9JMg6bDyi2AoRXytoGf36b8Kejc06RbcoJVKoeRDFDROSebyfq24vF8r8goKmb5yO2MXbmbsgk385p35/Oad+fRs1ZBzujXl7G7NwuEdUVA3HTqfHywQFL1cOzUYB7V8PEz8O3z+18Pz2B5MRjTtoTexiNC3b1+mTZvGrl27SEpKok+fPkydOpUJEyYcujtblv79+9OyZUsAevXqxYoVK474IrR7927Wrl3LxRdfDEBycvIR+x78EgTw8MMP88YbbwCwevVqFi9ezObNmxk2bNihdo0aHa5S/eyzz9KqVSvefPNNEhMTS4zvkksuOXSer7/+OgCfffbZoecZOXIkqampRz1PkYqi95zecyI12r5th4dSLB0TDDcHSO8YDqUYEQ6liNJ3K1HyIdoS4+MY0CaNAW3SuPPsTqzYspf35mzg3dnr+f17C/j9ewvo1qI+53RvxjndmpGVHsXuP0n1wgTDqXDaXUEyYuXn4dQz4+Dje4F7oU46dDgLOowMMopJ9aIXs4gEyrhbGimJiYlkZ2fzzDPPMHjwYHr06MHYsWNZsmQJnTt3Pur+Re98xsfHk5+fX+7nrlv38P+V48aN4+OPP2bixInUqVOHU089lZycnDL37969OzNnzmTNmjVHfKEqKb5jjU1qCL3n9J4TkcgqyA9ujC75JCisv24G4JDcMPi+0m5E8F2kQcsoByoH6fZ0jMlKr8stp7blf98/mQk/O41fnNOJhLg4/vj+Qk59cBxn/3UCf/tkMUs374l2qEFSocNZwbioWyfCjxfCxU8Eb/YFb8Mr18Ifs+G5S+DLfwRTg4pIjTJ06FAefPBBhg0bxtChQ3n88cfp3bv314rtpqSksHv37mM6dkpKCi1btuTNN98EIDc3l337vl4rcOfOnaSmplKnTh0WLFjApEmTABg4cCDjx49n+fLlAEd0Ae/duzdPPPEEF1xwAevWrSt3TEOGDOGVV4ICvh9++CHbt28/pnMSOVF6z+k9J1Kt7VwD0/4NL18Lf2wDT58FEx4MZvE79U741sfws2Vwxb+DGndKPMQU9XyIYa0a1WHUsLaMGtaWtTv2897s9bw3ZwMPfbSIhz5aRMcmKZzdvSnndm9G+yYp0Q4XUppCz6uCpSAvqBWx6P1gefcnwdK4a1DApf2Z0Kp/MEuHiFRbQ4cO5be//S2DBg2ibt26JCcnM3To0K+1S0tLY8iQIXTr1o2zzz6bc889t1zHf+655/jud7/L3XffTWJiIv/973+/1mbkyJE8/vjjdO7cmY4dOzJw4EAAMjIyePLJJ7nkkksoLCykcePGfPTRR4f2O/nkk3nwwQc599xzj1hflnvuuYdvfOMbPPfccwwaNIimTZuSkhID/z9LjaH3nN5zItVKXg6s+uJw74bNQbFZ6reArhcGQynanAK1NeSqKrCqNpV1v379fOrUqdEOI6o27MzhvTnreW/2Bqas3IY7tMmoy1ldm3Jmlyb0bNmQuLgKmsKzomxZAoveg8UfBgVfCvMhqQG0PTVIRLQ7PUheiEiFmT9/frm6WkvFyc3NJT4+noSEBCZOnMgtt9xyqKBfRSvp9TWzae7eLyJPKEco6XpE77nKV1nvOb22IpVo69Ig0bDkY1g+AfL3Q3xSUK/hYIH8jI5gMfZ9Rw4p7XpEPR+qoKYNkrlxSDY3Dslm064c3p+7gQ/mbuDJ8ct4bNxSGqckcUaXJpzZtSmD2qRRKyEGRtekt4P078Pg70POLlj+aZCIWPwRzHsraNO8N3S5EDpfAGltoxuviMhxWLVqFVdccQWFhYXUqlWLf/zjH9EOSaRa03tOpBo4sBdWfBZ8L1jyMWwPhmbRqC30vT5INrQeokKR1YCSD1Vc4/rJXDcoi+sGZbFzXx5jFm7kw7kbeWPGWl6YvIqUpASGdcxgeMfGnNoxg7R6X5+6qtIl1z88i4Y7bJwTJCIWvBMUrfz43mA6sc4XBsmIjA7RjlhEpFzat2/PjBkzoh2GSI2h95xIFeQOWxbDko+ChMPBaTAT60D2MBh0W1AsslGbaEcqFUzJh2qkQZ1ELu7dkot7tyQnr4Avlm7hgzkbGbNwE+/MWo8Z9GjZkOEdG3Napwy6NW8Q/eEZZkGioWl3GPrjoCjl/P8FvSHG/iZYMjpDlwuCHhFNuqqLlcgxcPevFZqTqq+qDZmsSfSeq370fhOpAAf2wvLxYe+Gjw4Xok/vCP2/E/ZuGAwJMXCjVCJGNR9qgMJCZ976XYxZsIkxCzbx1ZoduEN6vSRO6ZDB0PbpDGmXTkZKjL3Zd607nIhY+QXgQQa08/lBIqJ5H4iLgSElIjFq+fLlpKSkkJaWpi9D1Yi7s3XrVnbv3v21KQlV86HylHQ9ovdc9VPW+01EyuAe1G5Y/GGQbFjxGRQcCHs3nBIUoG93OqS2jnakEgGlXY9ELPlgZsnAeCCJoIfFq+5+T7E2mcC/gYZAPHCnu79b1nGVfDhxW/fk8umizYxZsInPlmxhx748ALo0q8/Q9ukMbZ9Bv6xUkhPjoxxpEXs2BcMy5v8vqBdRmB9Uue10HnQ+DzIHaeYMkWLy8vJYs2YNOTk50Q5FKlhycjItW7YkMfHI//eUfKg8JV2P6D1XPZX2fhORYvL2w4rPw7puHx6u3ZDeAdqdESQc1LuhRohG8sGAuu6+x8wSgc+AO9x9UpE2TwIz3P0xM+sCvOvuWWUdV8mHilVQ6Mxdt5MJi7cwYfFmpq3cTl6Bk5QQR//sRgxum87gtml0a9GA+GgP0Tho/3ZY9EGQiFjyMeTnQHKDIHva8ZxgjJim2xGRGkjJh8qj6xEREWDHalj8QTCcYtmnwcwUCbWD2g3tw4RDala0o5RKVumzXXiQ1dgT/poYLsUzHQ7UDx83ANZFKh4pWXyc0aNlQ3q0bMhtp7Vjb24+Xy7fxvjFm/liyVb+8H4wl25KcgID26QxuG0aQ9ql075xveh1Ka2dCj2vCpbcPbBsHCx8L/iPb85rYPFBVrXDSOh4tmbOEBERERGpCAX5sHpycN296EPYPD9Yn5oFfa6D9mdC1hBIrB3VMCU2RbTmg5nFA9OAdsDf3f3nxbY3Az4EUoG6wOnuPq2E44wCRgFkZmb2XblyZcRiliNt3p3LxGVb+WLJFr5YupVV2/YBkF6vFgPapDGwTRqD2jSibUYUkxEHFRbC2mmw8F1Y9D5smhesT2sPHUdCh7Oh1QCIV51VEame1POh8qjng4jUGHu3Br2NF70PSz+BnJ0QlwitB0H7s6DDWZDWTkXh5ZBKH3ZR7MkbAm8A33f3OUXW/yiM4SEzGwQ8BXRz98LSjqUP++havW0fE5duZeKyrUxcupUNu4JxrUWTEQOyG9Euo170Z9LYviIYnrHwvaDITWFe0Gui3RlBMqLd6cFwDRGRaiJWkg9mtutoTYD17l5l51LW9YiIVFvusHFukGxY9AGsmQI41G0c9GzocBa0ORWS6x/tSFJDVfqwi6LcfYeZjQVGAnOKbPpWuA53nxgWqUwHNlVGXHLsWjWqQ6tGdbjipFa4O6u27WPSsq1MWraNiUu38s6s9QDUT06gT+tU+mam0rd1Kj1bNaRuUiX3OEjNggHfDZacXbB0TPCf6OIPYfYrEJcArQYGY9E6nAUZnZSxFRGpGEvdvXdZDcxsRmUFIyIiR5G3H5ZPOJxw2LUmWN+8N5zy8+BauVkvzTQnJyRi3wbNLAPICxMPtYEzgD8Ua7YKGAE8Y2adgWRgc6RikoplZrROq0vrtLpceVIm7s7KrfuYsmIb01dtZ9rK7YxbGLyccQadm9Wnf3YjTm6XzoA2adSrzGREcn3oelGwFBYEGdxFYXGcj+8JlgatwsI4Z0H2UKhVt/LiExGpXi6toDYiIhIpu9YdTjYcLBaZWBfangan3hlcF6c0jXaUUo1EcraLHgTTaMYDccAr7n6fmd0HTHX30eEMF/8A6hEUn/yZu39Y1nHVzbFq2bkvjxmrtzN95XamrgwSErn5hSTEGb0zG3JyuwxObp9Oz5YNSIiPUiZ159pg/uHFH8HSsZC3F+JrQeZAaDs8WJp0V6ZXRGJerAy7qAl0PSIiVU5hIayfGSYc3of1XwXrG2YGtdE6nAVZJ2sqTDlhUa35UJH0YV+15eQVMH3ldiYs2cJni7cwZ91O3CElKYGBbdMY2j6dk9ulk51eNzoFLPNzYeXnwRCNpWNhYzhKqE56MLat7fAgG1y/eeXHJiJyFLGWfDCzSwh6PTYmqPNgBBNiVfmBwroeEZEq4cA+WP5pUANt0QewZwNg0Kr/4ZnhNPRYKpiSDxKTtu89wBdLt/LZks1MWLyFNdv3A9CiYW1ObpfOye3TGdIunUZ1a0UnwN0bgqk8DyYj9oblSDI6B4mIdsMhczDUqhOd+EREiojB5MMS4Hx3nx/tWCqarkdEJGbt3ni4d8PSscFwilopwXVrh7ODopF106IdpVRjSj5IzDtYMyLoFbGZL5ZuZXdOPgDdWtRnWPsMhrbPoG/rVGolRGEIhHvQE2Lp2CAZsfILKMiF+KRgqqFDQzS6KXssIlERg8mHz919SLTjiARdj4hIzHCHTfOD6eYXvgdrw/+bGmQGM7x1PBtanwwJUbqZJzWOkg9S5eQXFDJr7U4+W7yFCYs3M33VDgoKnbq14hnUNo1hHYJkRFZanegM0cjbHw7RCJMRm+YF6+s2DoZmtB0ObU6DlCaVH5uI1EixknwIh1sAnAI0Bd4Ecg9ud/fXoxBWhdL1iIhEVUE+rJoYJBwWvAM7VgbrW/QNejd0PBuadNUNMYkKJR+kytudk8cXS7cyYfFmxi/awqpt+wDIbFSH0zpmcGrHxgxsk0btWvHRCXDXuiOHaOzbEqxv0u1wvYjMQRqiISIRE0PJh3+Vsdnd/aZKCyZCdD0iIpUudzcs+SRIOCz6AHJ2BD1w25wCHc8JEg6anUJigJIPUu2s3LqX8Ys28+mizXy+ZCv78wpISohjUNs0TuvYmFM7ZtA6LUrTZRYWwsbZh3tFrJoIBQcOz6LR5tRgadYL4qKULBGRaidWkg8VwcxGAn8lmDXrn+5+f7HtScCzQF9gK3Clu68It90FfAsoAG539w/C9U8D5wGb3L1bkWM1Al4GsoAVwBXuvr2s+HQ9IiKVYveGsHfDu0HhyIIDUDs1LBZ5TnBzK6letKMUOYKSD1Kt5eQV8OXybYxduIlPF25m2Za9ALTJqMsZnZswonMT+mQ2jN50ngf2waovwp4R44LEBEByQ8geFmSss0+BtHbqHicix60qJB/M7Dx3f/sobeKBRcAZwBpgCvANd59XpM2tQA93v9nMrgIudvcrw2m8/wP0B5oDHwMd3L3AzIYBe4BniyUf/ghsc/f7zexOINXdf15WjLoeEZGIcIcti4KhFAveOVy/ITULOp0XJBxaDYD4hKiGKVKW0q5H9Fcr1UJyYjzDOmQwrEMGnA8rtuxl3MJNfLJgE09/vpwnxi8jtU4ip3VqzOmdmzCsQwb1kirxz79WHWh3erAA7NkcZK+XjQuW+aOD9fWaBsmI7GGQPTT4oBERqV5OAspMPhAkDpa4+zIAM3sJuBCYV6TNhcC94eNXgUcsKAB0IfCSu+cCy8MZN/oDE919vJlllfB8FwKnho//DYwDykw+iIhUmMJCWDsNFrwdLFuXBOub94bT/h90Ohcad9YNKqnylHyQaikrvS43pGdzw5BsdufkMX7RFj6Zv5ExCzbx+vS11IqPY2DbNM7s0oQzujShSf3kyg2wXgZ0vyxY3GHbMlgxAZaPh2VjYfYrQbuGmZA1NBiqkTkY0trqg0dEqrpHy9GmBbC6yO9rgAGltXH3fDPbCaSF6ycV27fFUZ6vibuvDx9vAEqsFGxmo4BRAJmZmUc/CxGR0uQfCK79FrwdDKnYswHiEiDrZBhwc9DDocHR/usSqVqUfJBqLyU5kXN7NOPcHs3ILyhk2srtfDx/Ix/O28j/e3MO/+/NOfRq1ZAzuzbhzC5Nade4ksfNmQVJhbS20PeGIBmxeWGQiFj+aTBl0swXgrZ10sNExKBgadYD4hMrN14RkWNkZg2BS4Grgc4EwyFikru7mZU4JtXdnwSehGDYRaUGJiJV34G9sORjmP92UDAydyckhr1jO50HHc4M6jmIVFNKPkiNkhAfx4A2aQxok8YvzunM4k17+HDuBj6ct5E/vr+QP76/kDYZdRnZtSnn9mhGl2b1K38aTzNo3ClYBowKx/4tDopWrpoU/FwQ9lhOrAuZA6D1kKCHRPPemsNZRGKCmdUmGM5wNdAbSAEuAsaXY/e1QKsiv7cM15XUZo2ZJQANCApPlmff4jaaWTN3X29mzYBN5YhRROTo9m0LEg3z/wdLP4H8HKjdCDqfD53PCwqQJ9aOdpQilUIFJ0VC63bsD3pEzN3IxGVbKSh02qTX5bwezTivZ3M6NEmJdoiH7d4QJCFWToQVn8GmucH6xDrQqn/QZS9rGLToo54RIjVIrBScNLMXgaHAh8BLwBiCGg7Z5dw/gaDg5AiCxMEU4Gp3n1ukzW1A9yIFJy9x9yvMrCvwIocLTn4CtHf3gnC/LODtYgUnHwC2Fik42cjdf1ZWjLoeEZFS7d4Q3Cia/z9YPgG8AOq3CHo3dD4vGEqrgpFSjWm2C5FjsG3vAd6fs4G3Z61j0rKtFDp0aFKP83o057wezWiTEWNTGu3dCis/DxIRRZMRtVKCRMTBqT0zOqpmhEg1FkPJh5lAHMFUmC+5+xozW+bubY7hGOcAfyGYavNpd/+tmd0HTHX30WaWDDxH0KtiG3BVkQKVvwRuAvKBH7j7e+H6/xAUlkwHNgL3uPtTZpYGvAJkAisJptrcVlZ8uh4RkSNsXxkkG+b/D1ZPBjyYxazz+cHSvI+uwaTGUPJB5Dht2p0TJCK+Ws+Uldtwhy7N6nNez2ac1705mWl1oh3i1+3dCis/OzybxrZlwfp6TQ8nItoOh5QSa6qJSBUVK8kHADPrBHwDuBLYAnQEurn7xqgGVkF0PSIibFkM894KZi1b/1Wwrkl36HJBkHDI6KSEg9RISj6IVIANO3N4d/Z6/jdrHTNW7QCgZ8sGnNejOef2aEbzhjE6Zm/7yiJTe34K+7YE65v2gHYjgkJHrQZoiIZIFRdLyYeizKwvQSLiCmCNuw+OckgnTNcjIjWQO2yafzjhsCmc/bflSYd7ODQqdwcvkWrrmJMPZja6HMfd5u43nGBsx0Qf9hIr1mzfxzuz1vP2rPXMXrsTgH6tU7mwdwvO7d6MRnVjtPBjYSFsnB1UW17ySdA1sDA/GKLR5pQgGdH+LE3vJFIFxWry4SALKvgOdffyFJ2MaboeEakh3GHDLJg3Okg6bF0MGLQeDF0uDOo46JpJ5AjHk3xYDHy7rGMCf3f3rhUTYvnow15i0Yote3l71jpGf7WORRv3kBBnnNIhgwt7t+CMzk2oXSs+2iGWLmdXMK3nwWTEzlXB+qbdocPIYGneB+LiohuniBxVrCQfzGxUOC3lCbWJZboeEanG3GH9TJj7ZpBw2L4cLC6YWazLBdDpfA1dFSnD8SQfrnD3V45y0FLbhIWgxgNJBFN6vuru95R0DOBewIGv3P3qsp5TH/YSy9yd+et389bMtbw1cx0bduVQt1Y8Z3VtygW9mjOkXTqJ8TH8Jd4dNi+ERe8H00KtngReCHUzoP2Z0OEsaDsCkmKs4KaIADGVfFgG/KSsJsB9lX0DoyLpekSkmnGHdTNg3pthwmEFWHzQK/RgD4e66dGOUqRKOJ7kw+XA/9w95zif0IC67r7HzBKBz4A73H1SkTbtCapLD3f37WbW2N3LnFtbH/ZSVRQUOpOXb+WtGet4d856dufkk1onkZHdmnJej+YMbJNGfFyMFyHatw2WjgmSEYs/gpwdEJ8UfBB3PBs6ngMpTaMdpYiEYij58K9yNNvp7j+IdCyRousRkWrgYMJh7htB0mHHKohLCApzH0w41GkU7ShFqpzjST68AQwBPgD+A3xwcI7s43jyOgTJh1vcfXKR9X8EFrn7P8t7LH3YS1WUk1fA+EWbeXvWej6ev5F9BwpIr1eLs7s147wezTgpqxFxsZ6IKMgPekIseBcWvhPcEQBo0TdMRJwLjTurqrNIFMVK8qEm0PWISBXlHsxMMfeNYNmxMkw4nAZdLwpurCjhIHJCjmu2CzOrD1wMXAX0At4C/uPun5bzSeOBaUA7gvoQPy+2/U1gEUGSIx64193fL+uY+rCXqm7/gQLGLtzEO7PW88mCjeTkFZJeL4kRnRpzepcmnNwuPbZrRMDhas8L3w2WtdOC9anZ0Onc4IM7cyDExfh5iFQzSj5UHl2PiFQh7rBxzuGEw7Zl4ZCKU6HbJUo4iFSwE55q08zSgMuAW4FG7t7qGJ68IfAG8H13n1Nk/dtAHsHUWy0JakR0d/cdxfYfBYwCyMzM7Lty5cryPrVITNubm88nCzbxwdwNjF+4md25+SQlxDGkXTojOjdmRKcmNG2QHO0wj27Xelj0XtArYvmnUHAAajcKilV2Ohfanga16kY7SpFqT8mHyqPkg0gVsHkhzHkN5rwezFJh8ZA9DLpeHEyLqYSDSEScUPLBzFIJEg/fANoTFI/84TEGcDewz90fLLLucWCyu/8r/P0T4E53n1LacfRhL9XVgfxCpqzYxkfzNvLJgo2s3rYfgK7N6zOsQwZD26fTt3UqSQkx3psgd3cwc8aCd2HxB5CzExKSg7sLHc8JEhKqEC0SEUo+VB5dj4jEqG3LgmTDnNdh01zAIOvkoIdD5wtUNFKkEhxPzYd6BEMuvgH0BkYDLwHjvBwZCzPLAPLcfYeZ1QY+BP7g7m8XaTMS+Ia7X29m6cAMoJe7by3tuPqwl5rA3Vm8aQ8fzdvIp4s2M33ldvILndqJ8Qxs04ih7TMY1iGdthn1sFiusVCQBys/h4Vhr4idqwCDlv0OF6zM6KQ6ESIVJFaSD2b2o7K2u/ufKiuWSNH1iEgM2bUuTDi8GhSQBGg1ALpdGhSOVHFskUpV2vVIQhn7rADeBx4lKDaZd4zP2Qz4d1j3IQ54xd3fNrP7gKnuPpqgmOWZZjYPKAB+WlbiQaSmMDM6NEmhQ5MUbjutHXty85m0dCsTFm9mwuItjF04D4Cm9ZMZ1DYtWNqk0apRnShHXkx8YtDjoc2pMPJ+2Dg3SEQsfBc+uS9YUrOC3hAdzoLWQyAhKcpBi0gFSIl2ACJSze3dGsxQMec1WPkF4NCsF5zx62BYRcNyjxAXkUpSVs+H2u6+v5LjOSrdaRCB1dv2MWHxFj5fuoVJS7eyde8BAFo1qs2gNkEyYmCbNJo1qB3lSMtwsE7Eog9g2TjIz4Fa9YL6EO3PgvZnaniGyDGKlZ4PNYGuR0SiIHc3LHgHZv8Xlo4FL4D0jtD9Muh6CaS3i3aEIsLxDbt40t1HHeWgR21T0fRhL3Kkg0M0vliyhYnLtjJp2TZ27g86KrVMrc1JWY3CJZW2GfVic0rPA/tgxQRY9H6QjNi1NljfvDe0OwPanxFM6anZM0TKFCvJBzN7xd2vCB//oehsV2b2obufGb3oKoauR0QqSX5uUEtq9n+D3pP5OdAgE7pfGgyraNJNwzdFYszxJB82EdR4KPWYwEh3b18xIZaPPuxFylZQ6Mxfv4svl29jyoptTFmxnS17cgFoWCeRfq1T6ZfViD6ZqfRo2YDkxBj7Qn9wOqxF78Pij2DNFPBCqJ0KbUcEiYi2I6BeRrQjFYk5MZR8mOHuvcPH0929T0nbqjJdj4hEUGFBUDNq9n9h3ltB8eo66cFwiu6XQ6v+SjiIxLDjqfnw03Icd8LxhyQikRAfZ3Rr0YBuLRpw08nZuDsrt+4LExFBMuLj+ZsASIgzujavT+/MVPq2TqVP61SaN0iObhFLM2jaPViG/RT2bYOlY4K7Hks+DopJYdCsZzBEo81pQVGpxCowJalIzVFWYeryzfEtIjWLO2yYDbNfgdmvwu71wXDMTucFCYc2pwS1pESkyirXVJuxRHcaRE7c1j25zFi1g2mrtjN95XZmrdnJ/rwCAJrUT6JXq4b0apVKr1YN6dGyAXWTyspTVqLCQtjwFSz+GJZ+EvSKKMyHhNrQelCQiGhzatAFMy4u2tGKVLoY6vmwgGC2rDjgeeBqgh6TBjzv7p2jGF6F0PWISAXZsSro4TDrv7B5PsQlBEMue1wOHc6GWjFWTFtEjuqYh13EKn3Yi1S8vIJCFqzfzfRV25mxajszV+9gxdZ9AMQZdGiSEiYkGtKjZUM6NKlHQnwMfLnP3Q0rPg8KVi4bC5sXBOvrpEP2UMg+JbhTkpqt7plSI8RQ8mEcZfRwcPfTKi+ayND1iMgJ2L89GE4x65VgeAUEvRh7XAFdLoa6adGNT0ROiJIPInJMtu09wFerdzBj9Q5mrt7BV6t3HCpkmZQQR9fm9enRsiE9WzWge4uGtEmvG/1ilrvWh4mIcbD806DLJgSFqbKHBYmI7FM0i4ZUW7GSfKgJdD0icozyDwTDJ2e9BAvfh4JcSGsPPa4MZqtolB3tCEWkgpxw8sHM6rj7vgqP7Bjpw14kOgoLnZXb9jFrzQ5mrdnJrDU7mLN216HhGvWSEsKERAO6t2xI9xYNaN2oTvQSEu6wdcnhRMTyCZCzI9iW0elwr4jWQ6B2w+jEKFLBYiX5YGYnAavdfUP4+3XApcBK4F533xbN+CqCrkdEysEd1k4PEg5zXoN9W6FOGnS7DHpeCc37qGeiSDV03MkHMxsM/BOo5+6ZZtYT+K673xqZUMumD3uR2JFfUMjSzXv5as0OZq/Zyey1O5m3fhcH8gsBSElOoFvzBnRvGRTAjGpCorAANsyC5eNh2aewaiLk7QOLg2a9DveKaDVA40ulyoqh5MN04HR332Zmwwhmz/o+0Avo7O6XRTO+iqDrEZEy7FwDs16Gr16CLYsgPgk6nQM9roJ2I1Q4UqSaO5Hkw2TgMmB0kWmz5rh7t4hEehT6sBeJbXkFhSzauJs5a3cyK0xILFi/mwMFYUIiKYEuzevTPZyRo3vLBmSnRWHIRn4urJka9IpY9imsnRoUr4yvBS37B8M0sodBi76QUKtyYxM5TjGUfPjK3XuGj/8ObHb3e8PfZ7p7ryiGVyF0PSJSzIG9MP9/MPPFINGPQ+Zg6HkVdL0IkhtEO0IRqSTHM9XmIe6+utjUewUVFZiIVC+J8XF0bd6Ars0bcOVJwboD+UFCYu66IBkxZ+0unpu0ktz8wwmJbi0a0KNlA3q0DGbYaJlaO7JTfiYkQdaQYDntF0HxylWTwiEa42Hc72Hc7yCxDmQOCpMRQ4NeEnHxkYtLpHqIN7MEd88HRgCjimyLkelzROSEFRbCqi+ChMO8t+DAHkjNglPvDGo5qI6DiBRRnguA1eHQCzezROAOYH5kwxKR6qRWQhzdwp4OBxMS+QWFLN60h9lrdzI7rCHxr89XHOohkVonke4tG9Ij7B3RvUUDmjVIjlxCIikF2p8RLAD7tgUVuA8O0/j4nrBdgyBhkT0MsoZC4y6a1lPk6/4DfGpmW4D9wAQAM2sH7CzPAcxsJPBXIB74p7vfX2x7EvAs0BfYClzp7ivCbXcB3yK4WXK7u39Q1jHNbATwAMHUoHuAG9x9yfGevEi1t30lfPWfIOmwYyXUSoGuF0Ovq4OEveo4iEgJyjPsIp3gg/p0gvm5PwTucPetkQ/v69TNUaT6OpBfyMINu5m1dgezVu9k1tqdLNq4m4LC4P+p9Hq16B7Wjjg4ZKNp/QgmJIravRFWTAiSEcvHw/blwfo6aUEiou2IYBxr/eaRj0WkFLEy7ALAzAYCzYAP3X1vuK4DQQ2p6UfZNx5YBJwBrAGmAN9w93lF2twK9HD3m83sKuBid7/SzLoQJD/6A82Bj4EO4W4lHtPMFgEXuvv88Lj93f2GsmLU9YjUOAf2wfzRMOP54PMQC+ol9boGOp2nekkicshxD7tw9y3ANRGJSkSkiFoJcUEvh5YNuGZAsC4nr4B563cdriGxZiefLtpMmI8gvV6tQ8Usu4bFLZtHoodESpNgKrDuYZ28HauDi69lnwYzasx9I1jfuAu0HQ7tTg/u/iQmV2wcIlWEu08qYd2icu7eH1ji7ssAzOwl4EJgXpE2FwL3ho9fBR6x4I1/IfCSu+cCy81sSXg8yjimA/XDNg2AdeWMU6R6c4c1U2DGczDnDTiwOxhWcdovg1oODTOjHaGIVCFHTT6Y2cMlrN4JTHX3tyo+JBGRw5IT4+mTmUqfzNRD6/YdyGf++l3hDBu7mLtuJxMWbzmih0SvVqn0zmxIn8xUerRsQN2kCh5m3rBV0L2019XBxdmmecH85Us+gS+fhImPQEJtyDo56BHRdgSkt1dXVJHyaQGsLvL7GmBAaW3cPd/MdgJp4fpJxfZtET4u7ZjfBt41s/3ALmBgSUGZ2SjC+hWZmfrSJdXYnk3BTBUznoctCyGxblA0stc10HqwPstE5LiU52o8GegE/Df8/VJgOdDTzE5z9x9EKDYRkRLVqZVA39aN6Nu60aF1+w8UMH9D0EPiq9U7mbF6Ox/P3whAnEHHpvXpk9mQ3pmp9GrVkDbpFTjDhhk06RosQ+4IKn6v+CxIRiwdA+/fGbRr0AranhYkItqcArVTyz6uiFSWHwLnuPtkM/sp8CeChMQR3P1J4EkIhl1UbogiEVaQD0s+ChIOi94PZoBqNQAu+FtQzyEpJdoRikgVV57kQw9giLsXAJjZYwSFo04GZkcwNhGRcqtdq0gPiUHBuu17DzBzzQ5mrNzOjNU7GD1zHS9MXgVA/eQEerYKkhG9WzWkV6uGpNatoCk1a9WFDmcFC8D2FUESYsknMPdNmP4sWBy06Be2GxkkLnQnSaohM2sChKVm+dLdN5Vjt7VAqyK/twzXldRmjZklEAyX2HqUfb+23swygJ7uPjlc/zLwfjliFKketi0LEg4zXoA9G6BuBgy8FXp/EzI6Rjs6EalGypN8SAXqcbg6dV2gkbsXmFluxCITETlBqXVrcVrHxpzWsTEABYXO0s17mLlqBzNW72DGqu08MmbxofoR2el16dc6lZOyGnFSdiOy0upUTO2I1Czod1OwFOTD2qlBImLJRzDm18HSoNXhRETWUNWKkGrBzK4gmEViHEHR6r+Z2U/d/dWj7DoFaG9m2QSJg6uAq4u1GQ1cD0wELgPGuLub2WjgRTP7E0HByfbAl+Hzl3TM7UADM+sQ1qQ4A83qJdVdfi7M/1+QDF/+aZAQb38m9L42+CyKT4x2hCJSDZUn+fBHYKaZjSP44B4G/M7M6hJUkC6RmSUD44Gk8Hledfd7Sml7KUGxqJPcXaWjRSQi4uOMDk1S6NAkhStOCm6A7s3NZ9aancxcvYNpK7fz0fyN/HfaGiCoHdGvdSP6ZaXSP7sRXZrVJyH+BKfVjE+AzIHBMvyXsHsDLP4QFr4fTFk25Z+QWAfanAodz4b2ZwXFLkWqpl8SfLZvAgh7GXxM8JlfqrCGw/eADwimxXza3eea2X0ENadGA08Bz4UFJbcRJBMI271CUEgyH7itSO/Nrx0zXP8d4DUzKyRIRtxUkf8IIjFj0wKY/u9gmsz924OCkaf9v6B+UYMWR99fROQEHHWqTQAza8bhStFT3P2oVaDDitN13X2PmSUCnxFM0TmpWLsU4B2gFvC9oyUfNLWViERSYdg7YsqK7UxdsY0pK7exett+AOrWiqfPwZ4RWY3ondmQ5MT4invyvJygVsSi92DRB7AzrI3Xoh90HAkdzwlm09DwDClFLE21CWBms929e5Hf44Cviq6rqnQ9IlVG3n6Y9xZMewZWTYS4ROh0LvS9HrJPhbgTTKqLiBRT2vVIeZMPqQTdFg/1A3b38cfw5HUIkg+3FBlTeXDbX4CPgJ8CP1HyQURizfqd+5m6YjtTVmzjy+XbWLhxN+6QGG90b9GA/tlpDGmXxklZjSouGeEOG+cEPSIWvgvrpgfrG2QGiYgOI4OZNBKSKub5pFqIweTDAwS1o/4TrroSmO3uP4teVBVD1yMS8zYvDBIOM1+EnB3QqA30vQF6Xg31MqIcnIhUZ8edfDCzbwN3EBRmmkkw/dREdx9ejieNB6YB7YC/u/vPi23vA/zS3S8Nh3WUmHwoNrVV35UrVx7tqUVEImbnvjymrdrGl8uDhMSsNTvIK3BqJcTRNzOVk9unM7htGt1bNDjxYRoH7d4QVB9f+B4sGwf5OVArJZg9o+PZwVjduukV81xSZcVa8gHAzC4hKFINMMHd34hmPBVFyQeJSXk5MH80TP0XrPoi6OXQ+Xzod2NQT0g950SkEpxI8mE2QZXqSe7ey8w6Ab9z90uO4ckbAm8A33f3OeG6OGAMcIO7rygr+VCUPuxFJNbszc3nyxXb+GLJFj5bspX563cBkJKcwKA2aQzrkMEpHTJo1ahOxTzhgX1BgbCF4fCMPRsAg1b9gx4RHc+GjE66yKyBYi35YGZ/KOHGw9fWVUW6HpGYsm1ZkHCY+QLs26peDiISVSeSfJji7ieZ2UxggLvnmtlcd+96jAHcDexz9wfD3xsAS4E9YZOmBAWjLigrAaEPexGJdVv35DJx2VY+X7KFCYu3sGZ7UDMiO70uw9qnM6xDBgPbpFE3qTw1f4+isBA2fBUMz1j0Hqz/KljfsHWQhOhwFrQ+GRIqaBpRiWkxmHyY7u59iq2b5e49ohVTRdH1iERdQT4s/gCmPAVLPwGLh07nQL9vQfYpquUgIlFT2vVIea5814Q9F94EPjKz7cBRxz2EFa3z3H2HmdUmmLrqDwe3u/tOIL1I+3GUo+eDiEisS6uXxHk9mnNej+a4O8u27GX8os2MX7SZV6au4d8TV5IYb5yU1YhTO2ZwWsfGtGtc7/im9YyLg+a9g+W0u2DXunB4xvvBWN/JjwfDM9oNhw4Hh2ekVfg5ixRlZrcAtwJtzGxWkU0pwOfRiUqkmtizCab9G6b9C3athZTmcOpd0Oc6qN882tGJiJSqXAUnDzU2OwVoALzn7nlHadsD+DfBdFZxwCvufl+xabKKth+Hhl2ISDWXm1/A1BXb+XTRZj5duJmFG3cD0KJhbU4JExGD21ZQr4iShmdYHLTsHxatPBsyOmp4RjUSKz0fwt6NqcDvgTuLbNrt7tuiE1XF0vWIVCp3WD0ZvvxHMHNFYR60OQ1O+lbwf3l8BXxmiIhUkBMZdvGcu197tHWVRR/2IlKdrNuxn3ELNzNu4SY+X7KFvQcKqBUfR//sRgzv1JjTOzchM60CakUUFsL6mYeLVm4Ib0anZgUXrh1HQushEJ944s8lURMryYeaQNcjUikO7IPZ/4Up/4ANsyGpAfS+Jhhakd4u2tGJiJToRJIPR4zXDGewmO3uXSo+zKPTh72IVFcH8guZumIbYxduYsyCTSzdvBeA9o3rMaJzE07v3JjemanEx1VAT4Wda4NExKL3YdmnUJALSfWh3YhweMYZUKfRiT+PVColHyqPrkckorYthyn/hBnPQc5OaNwV+n8HelwBtepGOzoRkTIdc/LBzO4CfgHUBvYdXA0cAJ5097siFGuZ9GEvIjXFii17+WTBJj6Zv5Evl28jv9BpVLcWp3bMYESnJgztkE795AroqXBgbzB958HhGXs3BcMzWg0sMjyjw4k/j0Sckg+VR9cjUuHcg6Fyk58I/j+2OOhyAfQfBZmDNERORKqME+n58PtoJRpKog97EamJdu7PY/yizXwyfyPjFm1mx748EuKCopUjOjfmtE6NaZNe9/iKVhZVWAjrZgQzZyx8HzbODtand4BO50Hn86B5H10Ex6hYSz6Y2feB5919e7RjqWi6HpEKc2AvzHoZJj8Jm+dDnXTodyP0u0kFJEWkSjqeng99StwQcvfpFRTbMdGHvYjUdAWFzoxV2/lkwSbGLtjEgg1B0cqstDqc1qkxZ3RuwknZjUiMr4Bp1naugQXvwoL/wYrPwQugfgvodC50Ph8yB6vQWQyJweTDb4CrgOnA08AHfiyVrmOYrkfkhO1YDV8+CdOfhZwd0LQHDLgZul0KicnRjk5E5LgdT/JhbBnHc3cfXlHBHQt92IuIHGnN9n2MXbCJTxZs4oulWzmQX0hKcgKndmzM6Z0bc2qHxjSoUwHDM/ZtC2pEzH87mFM+PwdqpwbDMjqdC22HQ60KKI4pxy3Wkg8AFnTHORO4EegHvAI85e5LoxrYCdL1iBy31VNg0t9hXjjxW+fzYMAtkDlQvcpEpFo47mEXsUYf9iIipdt3IJ8Ji7fwyfyNjFmwiS17DhAfZ5yUlcrpnZswvFNj2mTUO/EnOrAXlnwcJCIWfxAUREtIDhIQnc6FDiOhbvqJP48ck1hMPgCYWU+C5MNIYCwwEPjI3X8W1cBOgK5H5JgU5MP80TDpUVgzJZi1ou910P+70LBVtKMTEalQJ1LzIRG4BRgWrhoHPOHueRUdZHnow15EpHwKC52Za3bwyfyNfDL/yOEZwzsFiYj+2Y2olXCCwzMK8mDl57DgnWCIxq41QaG0zMHQ/TLocqFmzqgksZZ8MLM7gOuALcA/gTfdPc/M4oDF7t42qgGeAF2PSLnk7AyGVUx+AnauhtRsGHgL9LoGkiogESwiEoNOJPnwTyAR+He46lqgwN2/XeFRloM+7EVEjk9JwzPq1opnaPsMhnduzPBOjUmvl3RiT+IO678KEhFz34CtiyEuEdqfCT0uD3pEJNaumBOSr4nB5MOvgKfdfWUJ2zq7+/wohFUhdD0iZdq5BiY9BtP+DQd2Q+uTYdBt0OEsiIuPdnQiIhF1IsmHr9y959HWVRZ92IuInLh9B/L5YslWxizcxJj5m9iwKwcz6JMZDM84o0tj2mbUO7HZMw4mImb/F2a/Cns2QK2UoFBlz6sge5jGN1ewGEw+POfu1x5tXVWk6xEp0fqv4ItHYO7rwf+BXS+Gwd+D5r2jHZmISKU5keTDdODyg4WhzKwN8Kq7lzkbRqTow15EpGK5O3PX7eLj+Rv5eP5G5qzdBUB2el1O79yY0zs3oV9WI+LjTiBRUFgAKyYEiYh5oyF3FzTpFtwJ7HYpJJxgjwsBYjL5ML3o9YKZxQOz3b1LFMOqELoekUPcgyK8nz8Myz+FWvWgz/Uw8GZomBnt6EREKt2JJB9GAP8ClgEGtAZudPeyZsOIGH3Yi4hE1rod+/lkwSY+nreRiUu3cqCgkEZ1a3F658ac1bUpQ9qlk5x4At2G83Jgzmsw8RHYNA/qNYX+3wnmtFdtiBMSK8kHM7sL+AVQG9h3cDVwAHjS3e+KVmwVRdcjQkE+zHsTPvsLbJwNKc2CqTL73gC1G0Y3NhGRKDqh2S7MLAnoGP660N1zKzi+ctOHvYhI5dmTm8+nCzfzwdwNjF2wid25+dSpFc+pHTM4q2tTTuvUmPrJxzmNpzssHRMkIZaOgcQ60OtqGHgrpFXZOoRRFSvJh4PM7PfVIdFQEl2P1GB5+2HG8/DF32DHSkjvAEPugO5XQEKtaEcnIhJ1J9LzYRbwH+CVWJiTWx/2IiLRcSC/kInLtvLB3A18NG8jm3fnkhhvDGyTxlldm3JmlyY0rp98fAffOA8m/h1mvxIkJU79OQz5IcQnVOxJVHOxknwws07uvsDMShyi6e7TKzumiqbrkRpo/3aY8k+Y9Djs2wItT4IhP4CO50DcCc4aJCJSjZxI8qE1cGW4FAIvEyQiVkUi0KPRh72ISPQVFjozVu/gw7kb+GDuBlZs3YcZ9G7VMEhEdG1KdnrdYz/w7g3wwS+CYRnNesFFj0GTKl8eoNLEUPLhSXcfZWYlDdF0dx9e6UFVMF2P1CB7NgXJ0SlPBTNXtDsDTv4htB6sorkiIiU4oWEXRQ7SHvg/4Bp3j8o8QfqwFxGJLe7Ooo17+GDuBj6ct+FQwcpOTVO4qHcLLuzVnGYNjnF6zblvwjs/gtzdcOpdMPh29YIoh1hJPlQEMxsJ/BWIB/7p7vcX254EPAv0BbYCV7r7inDbXcC3gALgdnf/oKxjWjCty2+Ay8N9HnP3h8uKT9cjNcDOtfDFwzDtGcjPDWauGPojaNo92pGJiMS0E635ULT3QwHwsrs/VOFRloM+7EVEYtua7fv4cO5G3p61jumrdmAGg9umcXHvlozs1pR6SeVMIuzZDO/+GOa9BS36Br0gMjoefb8aLNaSD2Z2G/CCu+8If08FvuHujx5lv3hgEXAGsAaYEu43r0ibW4Ee7n6zmV0FXOzuV5pZF4Lhov2B5sDHQIdwtxKPaWY3AqcBN7h7oZk1dvdNZcWo65FqbNsy+OzPMPM/gEOPq4KeDuntoh2ZiEiVcCLDLiYDicArBMMtlkUmxPLRh72ISNWxYste3pixljdmrGXVtn3UToznzK5NuLxvK4a0S8PK02V5zuvwzo/hwF4Y8X8w6Hvq6lyKGEw+zHT3XsXWzXD33kfZbxBwr7ufFf5+F4C7/75Imw/CNhPNLAHYAGQAdxZte7BduFuJxzSzL4Gr3X1Jec9N1yPV0JbFMP6BYErguEToc21QSFLTZYqIHJPSrkfKc/vpOndfeBxPmAyMB5LC53nV3e8p1uZHwLeBfGAzcJO7rzzW5xIRkdiUlV6XH57RgR+c3p5pK7fz+oy1vP3VOt6auY7Ozepz8yltOLd7MxLiyyjW1u0SyDoZ3v4hfPj/YP0suOBvkHicxS2lMsWbmXl4pyPs0VCe6QBaAKuL/L4GGFBaG3fPN7OdQFq4flKxfVuEj0s7ZlvgSjO7mOB65HZ3X1yOOKU62LwIxv8xqDWTkBzMuDP4+5DSNNqRiYhUK0dNPhxP4iGUCwx39z1mlgh8ZmbvuXvRC4IZQD9332dmtwB/JBjaISIi1YiZ0S+rEf2yGnHP+V0YPXMdj3+6lDtemsmDHy5k1NA2XN6vFcmJpZQTqtcYrnweJjwEY34dTG931YtQN71yT0SO1fvAy2b2RPj7d8N1sSYJyHH3fmZ2CfA0MLR4IzMbBYwCyMzU3fAqb/NC+DRMOiTWDnpVDb4d6mVEOzIRkWopYvMCeWBP+GtiuHixNmPdfV/46ySgZaTiERGR2JCUEM/l/Vrx0Q9P4Ylr+5JeL4n/e2suQ+4fwyNjFrNzX17JO5rBsJ/A5c/A+q/gH8Nh04JKjV2O2c+BscAt4fIJ8LNy7LcWaFXk95bhuhLbhMMuGhAUnixt37KOuQZ4PXz8BtCjpKDc/Ul37+fu/TIy9AW1ytq8EF69Cf4+ABa+B0Nuhx/MhjN/rcSDiEgERXRSYjOLN7OZwCbgI3efXEbzbwHvlXKcUWY21cymbt68OQKRiohIZYuLM87q2pTXbxnMy6MG0r1lAx78cBFD/ziGt2YW/55ZRNeL4YZ3IW8/PHUGLB1TeUHLMXH3Qnd/zN0vC5cn3L2gHLtOAdqbWbaZ1QKuAkYXazMauD58fBkwJhzeMRq4ysySzCwbaA98eZRjvklQcBLgFILClFLdbF0Kr48Kkw7vB/UcfjALzrhPvahERCpBqQUnw26HpXL318vaXuxYDQnuJHzf3eeUsP2bwPeAU9w9t6xjqcCTiEj1NW/dLn755mxmrNrBeT2a8ZuLutGwTiklAnashhevhM0L4JwH4KRvVW6wMSgGC062B34PdAEOFelw9zbl2Pcc4C8E02I+7e6/NbP7gKnuPjqsLfUc0BvYBlx1sCi2mf0SuImgptQP3P290o4Zrm8IvABkAnuAm939q7Li0/VIFbJjdVBIcsbzEF8L+n8nSDwo4SAiEhHHPNuFmf2rjOO5u990jAHcDexz9weLrT8d+BtB4qHMaa1AH/YiItVdfkEhj3+6lL98vJi0erV44LKeDOtQSlfo3N3w6rdg8Qcw8DY48zcQF9FOfTEtBpMPnwH3AH8GzgduBOLc/e6oBlYBdD1SBezeGNSJmRZe0va9EYb+SIUkRUQi7Jhnu3D3G0/wCTOAPHffYWa1CebV/kOxNr2BJ4CR5Uk8iIhI9ZcQH8f3hrfnlA6N+eErM7nu6S+5flBr7jy7M7VrFStImZQC3/gPfPALmPR3KMiFcx7UVJyxo7a7fxLOeLESuNfMpgFVPvkgMWz/dvjsLzD5CSg4AL2/CcN+Cg1bHXVXERGJnPJMtYmZnQt05cguk/cdZbdmwL/DabXigFfc/e2iXSaBB4B6wH/Dud5XufsFx34aIiJS3XRv2YC3v38yf3x/IU9/vpwJS7bw5yt60bNVwyMbxsXDyPshPhG++BvEJcLI3ysBERtyzSwOWGxm3yMo8FgvyjFJdZW3HyY/Dp/9GXJ2QY8r4JSfQ1rbaEcmIiKUI/lgZo8DdQgKMf2ToKjTl0fbz91nEYzDLL7+7iKPTz+WYEVEpGZJTozn7vO7MKJzY37y36+47PEvuP+SHlzat9jkSGZwxq+hIB8mPwbxCcHvSkBE2x0E1xC3A78GhnO4SKRIxSjIh5kvwLj7Yfc6aH8WnH4PNOka7chERKSI8vR8GOzuPcxslrv/ysweopRZKURERCJhSLt03r9jGLe8MI0f//crVm7bxw9Pb48VTS6YBT0eCvMO94AYcbcSEFHk7lMAwt4Pt7v77iiHJNWJOyx4Gz65D7YsgpYnwaX/hKwh0Y5MRERKUJ7kw/7w5z4za04wh3azyIUkIiLydQ3qJPLMjf355RuzefiTxazcupc/XtaDpIQidSDM4OwHoCAPPvtTUNn+tLuiF3QNZ2b9gH8BKeHvO4Gb3H1aVAOTqm/1l/DBL2HNl5DeAa58ATqdq2SjiEgMK0/y4e1wCqoHgOmAEwy/EBERqVS1EuL442U9yEqvywMfLGTdjv08cW0/GtUtMh1nXByc9xcoLIBP7w+GYAz7adRiruGeBm519wkAZnYyQTKiR1Sjkqpr+wr4+F6Y+wbUawrnPwy9rgne5yIiEtOO+j+1u/86fPiamb0NJLv7zsiGJSIiUjIz47bT2pHZqA4//u9XXPLo5/zrxv5kp9c93CguDi54OBiCMeY3QQ+IIXdEL+iaq+Bg4gHA3T8zs/xoBiRVVM5OGP9gUFDS4oNCkoNvhyTVLxURqSrKO9vFYCDrYHszw92fjWBcIiIiZTq/Z3OaN0zmO89O4+JHP+fJa/vRP7vR4QZx8XDho1CYDx/dDbXqwknfjl7ANdOnZvYE8B+CnpNXAuPMrA+Au0+PZnBSBRTkwbRnYNzvYd826HU1DP9/UL95tCMTEZFjVJ7ZLp4D2gIzgYJwtQNKPoiISFT1bd2IN24dzI3PTOGbT03m6etP4uT26YcbxCfAxU/Agb3wzk8guSF0vyxq8dZAPcOf9xRb35vgWmJ45YYjVcrSsfDez2HLQsgaCmf+Bpr3inZUIiJynMrT86Ef0MXdPdLBiIiIHKvWaXV57ebBfOMfk/j2s1P49439GdAm7XCD+ES4/Bl4/jJ447uQlAIdzopavDWJu58W7RikCtq+IigmueBtSM2Gq16EjueomKSISBUXV442c4CmkQ5ERETkeKXWrcXz3x5Ai4a1uemZKUxftf3IBom14Rv/gSbd4JXrYMXn0Qm0BjKzc83sZ2Z298El2jFJjDqwD8b+Dv4+AJaOCabKvW2yZrEQEakmypN8SAfmmdkHZjb64BLpwERERI5Fer0kXvzOQNJTkrj+6S+Zs7ZYbeTk+vDN16BhJvznKlg3Mypx1iRm9jhBnYfvAwZcDrSOalASe9xh7pvw9/7w6R+g03nwvakw9MeQkBTt6EREpIKUJ/lwL3AR8DvgoSKLiIhITGlSP5kXvzOQ+smJfPOpySzYsOvIBnXT4do3ILkBPH8pbFkcnUBrjsHufh2w3d1/BQwCOkQ5JoklW5bAsxfCf68ParLc+B5c9hQ0aBHtyEREpIIdNfng7p+WtFRGcCIiIseqRcPavPidASQnxPPNf05myaY9RzZo0BKueyvoxv3sRbBjdVTirCH2hz/3mVlzIA9oFsV4JFbk5cDY38Njg2D9TDjnQRg1DloPjnZkIiISIaUmH8zss/DnbjPbVWTZbWa7SttPREQk2lqn1eWF7wwAjGv+OYmVW/ce2SCtLXzzdcjdDc9dFEzhJ5Hwtpk1BB4ApgMrCKbdlJps6Vh4bDB8ej90uTAYYtH/O8HsNCIiUm2Vmnxw95PDnynuXr/IkuLu9SsvRBERkWPXNqMeL3x7AAfyC7n6H5NZt2P/kQ2a9YCrXw56Prz8TcjPjU6g1Zi7/9rdd7j7awS1Hjq5+/9FOy6Jkj2b4LXvBAk/HK59Ey79J9RrHOXARESkMpSn5gNmlmpmPcysz8El0oGJiIicqI5NU3juWwPYtT+Pbz41mS17iiUYWg+Cix6FlZ/D/+4ICt9JhTGz28KeD7h7LhBnZrdGNyqpdIWFMPVf8Eg/mPcmnPJzuGUitNVMrCIiNclRkw9m9mtgFvA3DhebfDDCcYmIiFSIbi0a8NQNJ7F2+36uf/pLduXkHdmg+2Vw6i/gq//AeH28VbDvuPuOg7+4+3bgO9ELRyrdtuXw7AXw9g+gaQ+45Qs47ReQmBztyEREpJKVp+fDFUBbdz/F3U8Ll+GRDkxERKSi9M9uxOPf7MvCDbv59jNT2X+g4MgGp/wMelwFY38Ds1+NTpDVU7yZ2cFfzCweqBXFeKSyFBbCpMeC2g7rv4LzH4br/wfp7aMdmYiIREl5kg9zgIYRjkNERCSiTuvUmD9f2YspK7dx6wvTOJBfeHijGVzwMLQeAm/eCqsmRy/Q6uV94GUzG2FmIwiKTb4f5Zgk0rYshn+dDe/fCVknw62ToO/1wftMRERqrPIkH34PzDCzD8xs9MHlaDuZWbKZfWlmX5nZXDP7VQltkszsZTNbYmaTzSzrOM5BRESkXM7v2ZzfXtSdsQs38+P/fkVBYZEaDwlJcOXz0KAFvPSNoLu4nKifA2OAW8LlE+BnUY1IIqcgHz77Czw2BDYvgIufgKtfCd5TIiJS45VnTqN/A38AZgOFR2lbVC4w3N33mFki8JmZvefuk4q0+Raw3d3bmdlV4fNceQzPISIickyuHpDJzv15/OH9BaQkJ/Dbi7pxaGRAnUZw9X/hqdPhxSvgWx9C7dToBlyFuXsh8Hi4SHW2ZQm8MQrWToNO58G5D0FK02hHJSIiMaQ8PR/2ufvD7j7W3T89uBxtJw/sCX9NDJfiZcQvJEhuALwKjCg6NlRERCQSbjm1LTef0pYXJ6/ijx8sPHJjeju48oWg58Mr1wV3c6XSmdlIM1sY9o68s4TtpfaeNLO7wvULzeysYzjmw2a2p/h6KYN7MJPFE0Nh61K49KmgB5ESDyIiUkx5kg8TzOz3ZjboWKfaNLN4M5sJbAI+cvfig2hbAKsB3D0f2AmklXCcUWY21cymbt68uTxPLSIiUqafj+zI1QMyeWzcUl6YvPLIjVlDghoQy8fDmPuiE2ANFham/DtwNtAF+IaZdSnW7FDvSeDPBL0nCdtdBXQFRgKPhtcjZR7TzPoB6uZyLPZugZeuDmayaNUfbp0YzB6j+0giIlKC8gy76B3+HFhknQNHnfHC3QuAXuEc32+YWTd3n3OsQbr7k8CTAP369dMk7CIicsLMjF9f2I31O/Zz91tzad2oLie3Tz/coNfVsGYKfP5XaDUAOp0bvWCrGDN7zt2vNbM73P2vx3GI/sASd18WHu8lgt6S84q0uRC4N3z8KvBI2HvyQuAld88FlpvZkvB4lHbMMDHxAHA1cPFxxFvzLP4oKM6aswPO+h0MuAXiynNPS0REaqoyPyXCD+PRRabYPK6pNsM5vscS3IEoai3QKnyuBKABsPVYji0iInK84uOMh7/Rm3YZ9bjlhWks2bT7yAYj74fmveGNW2DbsugEWTX1NbPmwE1mlmpmjYou5dj/UM/I0JpwXYltivWeLG3fso75PYLrnfVlBaWemMCBffDOT+CFy6BuBowaB4NuU+JBRESOqsxPirDnwjeO58BmlhH2eMDMagNnAAuKNRsNXB8+vgwY4+7q2SAiIpUmJTmRp27oR1JCHDc9M5Vtew8c3piQBJf/O+hG/sp1kLc/eoFWLY8TzGzRCZhWbJkaxbi+JkySXA787Wht3f1Jd+/n7v0yMjIiH1ys2bQA/nEaTPkHDLwNvjMGmnSNdlQiIlJFlCdN/bmZPWJmQ4+x5kMzYKyZzQKmENR8eNvM7jOzC8I2TwFpYZfIHwFfK/4kIiISaS1T6/Dkdf3YsCuH7z43ldz8gsMbU1vDJf+ADbPh3Z9GL8gqJCxU3Rl42t3buHt2kaVNOQ5xqGdkqGW4rsQ2xXpPlrZvaet7A+2AJWa2AqgTXpdIUbP+GyQe9m2Fa9+Akb+DxORoRyUiIlVIeWo+9Ap/Fq24ddSaD+4+i8P1Ioquv7vI4xyCuw0iIiJR1SczlYcu78n3/zODu16bzUNX9Dw8BWeHM2HYT2H8A5A5EHp/M7rBVhHufouZ9QSGhqvGh9cHRzMFaG9m2QQJgqsI6jEUdbD35ESK9J40s9HAi2b2J6A50B74ErCSjunuc4FDUzOY2Z6wiKUA5OfC+3fB1KcgczBc9jTUbxbtqEREpAo6avLB3U+rjEBERESi7fyezVm+ZS9/+mgRbTLq8r3h7Q9vPPUuWP0lvPNjaNoDmvWIXqBVhJndDowCXg9XvWBmT7p7mUMc3D3fzL4HfADEE/SgmGtm9wFT3X00Qe/J58JeCtsIkgmE7V4hKE6ZD9wWDiOlpGNW8ClXL9tXwCvXw/qZMOQHMPz/IL48961ERES+zo5WYsHMmgC/A5q7+9nhtFSD3P2pygiwuH79+vnUqTE1XFRERKoRd+eHL8/kzZnreOTq3pzXo/nhjXs2wxPDgloQo8ZB7YbRCrNEZjbN3ftFO46DwqGXg9x9b/h7XWCiu1f5zE21vx5Z8C68eXPw+KLHodM50Y1HRESqjNKuR8pT8+EZgrsEB6++FgE/qLDIREREYoiZcf+lPejbOpUfv/IV89fvOryxXgZc/gzsXA1v3QaqkXw0BhQpoEFBuE5iVWEBfHwvvPQNSM2G745X4kFERCpEqcmHsHgTQLq7vwIUwqHprApK209ERKSqS06M5/Fv9qVB7URufWE6u3PyDm/MHAAj7oYFb8OSj6MXZNXwL2Cymd1rZvcCkwiGS0gsyt0NL10Dn/0Z+t4IN30AqVnRjkpERKqJsno+fBn+3GtmaQRFJjGzgQRzaYuIiFRbGSlJ/O0bvVm5dS93vjabI4YpDrgFGmbCmF+r90MZ3P1PwI0ENRm2ATe6+1+iGpSUbMdqeHokLP4QznkQzv+LZrMQEZEKVVby4WC3yB8RVJRua2afA88C3490YCIiItE2oE0aPzmrI+/MXs+zE1ce3pBQKyhAuf4rmD86egFWAe4+PZx682F3nxHteKQEq6fAP4YHCYhvvgr9vxPtiEREpBoqK/mQYWY/Ak4F3gD+CLwH/AM4PfKhiYiIRN/Nw9oyvFNjfvPOPL5avePwhh5XQnoHGPPbYJy8SFU0+1V45lyoVQe+/RG0LXMmdRERkeNWVvIhHqgHpAB1CabljAfqhOtERESqvbg446HLe9I4JZlbX5jOjn0Hwg3xcNovYMtCmP3f6AYpcqzcYezv4LVvQct+8O0xkNEx2lGJiEg1VtZkzevd/b5Ki0RERCRGpdatxSNX9+aKJyby41e+4h/X9SMuzqDzhdC0O4z7PXS7FOITox1qTAmn1tzv7oVm1gHoBLzn7nlH2VUiKf8AvHkLzHkVen0TzvtzMJRIREQkgspT80FERKTG652Zyi/O6cwnCzbx5IRlwcq4OBj+f7B9Bcx4PqrxxajxQLKZtQA+BK4lmMJboiVvP7x8TZB4GHEPXPiIEg8iIlIpyko+jKi0KERERKqAGwZncU73pjzwwUK+XL4tWNn+TGjZH8Y/AHk50Q0w9pi77wMuAR5198uBrlGOqebK3Q0vXA6LP4Lz/wpDfwSme00iIlI5Sk0+uPu2ygxEREQk1pkZ91/ag1aptfn+f6azNzc/+PI24v9g11qY+nS0Q4w1ZmaDgGuAd8J18VGMp+bavx2evQhWfgGX/AP63hDtiEREpIYpq+eDiIiIFFM/OZEHL+/Jxl25PD8pnH4ze1iwTHgIcvdEN8DY8gPgLuANd59rZm2AsdENqQbasxmeOR82zIIrnoUel0c7IhERqYGUfBARETlG/bIaMbR9Ok+OX8a+A/nByuF3w74tMPnx6AYXQ9z9U3e/wN3/YGZxwBZ3vz3acdUoO9fCM+fA1iVw9cvQ+bxoRyQiIjWUkg8iIiLH4Y4R7dm69wAvTFoVrGh1EnQYCV88DPt3RDW2WGFmL5pZ/XDWiznAPDP7abTjqjG2LYd/jYRd6+Ha16Ht8GhHJCIiNZiSDyIiIsehX1YjTm6XzhPjl7L/QEGw8rRfQs5O+OJv0Q0udnRx913ARcB7QDbBjBcSaTvXwDPnBkUmrx8NrQdHOyIREanhIpZ8MLNWZjbWzOaZ2Vwzu6OENg3M7H9m9lXY5sZIxSMiIlLR7ji9PVv2HOCFyWHth2Y9oOvFMOkxOLAvusHFhkQzSyRIPox29zzAoxtSDbB/Ozx/WZh4+B+06BPtiERERCLa8yEf+LG7dwEGAreZWZdibW4D5rl7T+BU4CEz02TTIiJSJZyU1Ygh7dJ4/NMivR96XQN5e2H15OgGFxueAFYAdYHxZtYa2BXViKq7vBx46ZqgxsNVL0DT7tGOSEREBIhg8sHd17v79PDxbmA+0KJ4MyDFzAyoB2wjSFqIiIhUCXeM6HBk74fMgWDxsOKz6AYWA9z9YXdv4e7neGAlcFq046q2CgvgjVGw8nO4+PFgBhYREZEYUSk1H8wsC+gNFL8N9AjQGVgHzAbucPfCEvYfZWZTzWzq5s2bIx2uiIhIufXPbsSgNmk8MX4ZOXkFkJQCzXvDignRDi3qwuGVfzr4GW5mDxH0gpCK5g7v3wXz3oIzfwvdL4t2RCIiIkeIePLBzOoBrwE/CItOFXUWMBNoDvQCHjGz+sWP4e5Puns/d++XkZER4YhFRESOzR2nt2fz7lxemBzOfJF1MqydBgf2Rjew6Hsa2A1cES67gH9FNaLq6vO/wpdPwKDvweDvRTsaERGRr4lo8iEsMvUa8IK7v15CkxuB18OumEuA5UCnSMYkIiJS0Qa2SWNgm0Y8/unSoPdD9lAozFfdB2jr7ve4+7Jw+RXQJtpBVTtfvQwf3wNdL4Ezfh3taEREREoUydkuDHgKmO/ufyql2SpgRNi+CdARWBapmERERCLljhEd2Lw7lxcnr4JWAyEuAZbX+KEX+83s5IO/mNkQYH8U46l+lo6Bt26FrKFBnYc4zaIuIiKxKSGCxx5CMJf3bDObGa77BZAJ4O6PA78GnjGz2YABP3f3LRGMSUREJCIGtU1jQHbQ++HqAZkkN++jopNwM/CsmTUIf98OXB/FeKqXHavhlRsgvSNc+TwkJEU7IhERkVJFcraLz9zd3L2Hu/cKl3fd/fEw8YC7r3P3M929u7t3c/fnIxWPiIhIpN1xens27c7lpS9XBXUf1k2H3D3RDitq3P2rcDrtHkAPd+8NDC/PvmY20swWmtkSM7uzhO1JZvZyuH1yWNz64La7wvULzeysox3TzF4I188xs6fDYaOxrbAw6PFQmA9XPQ+1G0Y7IhERkTKpb56IiEgFGdQmjf7ZjXjs06UUZA4J6z5MinZYUefuu4oUnf7R0dqbWTzwd+BsoAvwDTPrUqzZt4Dt7t4O+DPwh3DfLsBVQFdgJPComcUf5ZgvENSc6g7UBr59vOdaab58EpaPh5G/g0YqoyEiIrFPyQcREZEKYmZcMyCTjbtymZfYJaj7oKEXxVk52vQHloRFKg8ALwEXFmtzIfDv8PGrwIiw3tSFwEvunuvuy4El4fFKPWbYM9Pd3YEvgZYndooRtnlRUGCy/VnQR6NYRESkalDyQUREpAINbJMGwOQ1OdCir4pOfp2Xo00LYHWR39eE60ps4+75wE4grYx9j3rMcLjFtcD7JQVlZqPMbKqZTd28eXM5TiMCCvLgjVGQWAcu+BtYeXI5IiIi0afkg4iISAVqUj+ZrLQ6TF6+LZiBYN0MyN0d7bAqlZntNrNdJSy7gebRjq8MjwLj3b3EjJG7P+nu/dy9X0ZGRiWHFprwUPA3dd6fIaVJdGIQERE5Dko+iIiIVLD+2Y2YsmIbha1PBi+AVZOjHVKlcvcUd69fwpLi7uWZaWst0KrI7y3DdSW2MbMEoAGwtYx9yzymmd0DZFCOmhRRs3Y6fPpH6H4FdL0o2tGIiIgcEyUfREREKlj/7DR27MtjSVJXiEuEFeOjHVJVMwVob2bZZlaLoIDk6GJtRnN42s7LgDFhzYbRwFXhbBjZQHuCOg6lHtPMvg2cBXzD3QsjfG7HJ28/vPFdqNcEznkg2tGIiIgcs/LcfRAREZFjMCC7EQCT1uyjQ8t+Kjp5jNw938y+B3wAxANPu/tcM7sPmOruo4GngOfMbAmwjSCZQNjuFWAekA/c5u4FACUdM3zKx4GVwMSgZiWvu/t9lXS65fPxr2DLIrj2DU2rKSIiVZKSDyIiIhWsZWptmjVIZvLybVyXdTJM+BPk7ILk+tEOrcpw93eBd4utu7vI4xzg8lL2/S3w2/IcM1wf29dDyz6FyY9B/1HQdni0oxERETkuGnYhIiJSwcyMAdmN+HL5NvxQ3YdJ0Q5LqiJ3+N/tkNYOTv9VtKMRERE5bko+iIiIRED/7DQ2785lZZ2uEF8LVmjKTTkOG2bB9hVw8o+gVp1oRyMiInLclHwQERGJgP5h3YfJa/ZDi35KPsjxWfxh8LP9GdGNQ0RE5AQp+SAiIhIBbTPqkla3FpOXbYPsobD+K8jZGe2wpKpZ/BE07wP1Gkc7EhERkROi5IOIiEgEmBn9sxsxefk2yDoZvFB1H+TY7NsGa6ZA+zOjHYmIiMgJU/JBREQkQgZkN2Ltjv2sqRfWfVg+PtohSVWydEyQtFLyQUREqgElH0RERCKkf3YaAFPW7IeW/WHFZ1GOSKqURR9AnXRo3jvakYiIiJwwJR9EREQipGPTFOonJ/DlwaEXG2bB/h3RDkuqgsICWPIxtDsd4nS5JiIiVZ8+zURERCIkPs44KavR4aKTXgirJkY7LKkK1k6H/dugg4ZciIhI9RCx5IOZtTKzsWY2z8zmmtkdpbQ71cxmhm0+jVQ8IiIi0dA/uxHLtuxlU4NuEJ+koRdSPos/BIuDtsOjHYmIiEiFSIjgsfOBH7v7dDNLAaaZ2UfuPu9gAzNrCDwKjHT3VWameaRERKRaGdAmrPuwej/ntuqvopNSPos/hFYDoHZqtCMRERGpEBFLPrj7emB9+Hi3mc0HWgDzijS7Gnjd3VeF7TZFKh4REZFo6Nq8PnVqxfPl8q2c23oIfPoHyN0DSfWiHZrEqt0bYf1MGHF3tCMREZEY4u7kFzq5+YUcyC8kN7+A3LxCDhQUkpsX/B6sD7cdelxIbl7R34P9vtE/k45NUyot/kj2fDjEzLKA3sDkYps6AIlmNg5IAf7q7s+WsP8oYBRAZmZmRGMVERGpSInxcfRtncrk5dtgRHvAYccqaNIl2qFJrFryUfBTU2yKiMSU/ILCr32BL/rFv+z1hUcmDfILi7Q7vO+B/K8/R9H9Cv3Ez6NWQhxJ8XGc0iGjeiUfzKwe8BrwA3ffVcLz9wVGALWBiWY2yd0XFW3k7k8CTwL069evAv65RUREKk//rEb86eNF7K7dnBSAHSuVfJDSLf4QUppDk27RjkREJGYc/OJf/M7+gWJf0ku741/yfsW3f713QNEkQoV98U+IIykhPvwZd3hdYjx1kxJoVPfw9lrhkpwYT634g+2C7bUS4qgVH2wrvj6p+H6J4br4OMzsxE/kOEQ0+WBmiQSJhxfc/fUSmqwBtrr7XmCvmY0HegKLSmgrIiJSJfXPboQ7TN/VgFMg6PkgUpKCPFg6FrpeDFG6OBQRKa6g0I/4cn7wcU5e2V/oi3+pLy1RcKDYF/6SthVUwDf/kr68JxVJBhT94l90W62iyYLEuPDLfPyRSYRw/cEv/Efsd3BbfBxxcTX3//aIJR8sSKc8Bcx39z+V0uwt4BEzSwBqAQOAP0cqJhERkWjo2aohtRLi+GwdnJJQG7avjHZIEqtWT4bcXRpyISKHuHupXfaL3qkv/sX+QEnbiu33tfoApWzLKzjxL/6J8VbiF/6Dd+RrJ8bToHZiidu+tt8Rd/NLP+YRvQBq+Bf/WBDJng9DgGuB2WY2M1z3CyATwN0fd/f5ZvY+MAsoBP7p7nMiGJOIiEilS06Mp1erhny5Yjs0zAyGXYiUZPGHEJcIbU6JdiQiQukF/op/YT9QUNr6kgv95Rb/0h/2FvjaeP/weU9UfJx97cv5wS/vB7vt10tKKOEOfzzJiV+/83/wS31ysbv7xYcTFH0uffGXSM528Rlw1L8wd38AeCBScYiIiMSCAdmNeHTcUvI7Z5Kg5IOUZvFH0HowJFVeATCRWFZY6MEX+DK++Bf/Al++u/1H6T1QZNuJ9vY349Cd96Qi3fGTi3xhb1A7kaSUpCPG6Zd4xz+hhO7+xRIFR+wTfvFPiI+rmBdE5ARUymwXIiIiNV3/7Eb8bcwSNsY1psWOL6MdjsSiHath0zw463fRjkTkkJKq+3+9Kn/5kwLl7eZ/aOhAwYnf9T84Dv/gnfrDX96DL+p1aiXQsE7coS/uxcf5B8mAr/cI+FqioKRifwnxJMZb1Ar8icQSJR9EREQqQZ/MVOLjjMUHGtEiZyfs3wG1G0Y7LIkliz8Mfqreg4Tci9z1L+lLeqnd+Y/tbn9Z+1bUXf+vddkvUn2/YZ1aR9zRL94roPRCf8W7+x9591/d/UVii5IPIiIilaBuUgLdWjRg2s4UToVgxgslH6SoxR9BahaktYt2JBIqLPSvfSE/UPD1Cv9HH9tfem+AotP4lTR04EQdLPJX/C79wbvztRPjaVg7scQp+UraL+mI3gGHewUcUROgSEFA3fUXkYOUfBAREakkA7Ib8dnn9fhxIkHRyWY9oh2SxIq8HFj+KfT+pqbYLKKg0MvxJb48d/yPvV5ARVX4L1qRv/iX91oJcaQkJ5Jer4Tp/IoPDzjibn8Jd/9LGCJQKyGOeN31F5EYoeSDiIhIJenQJIVXCtIgkaDng5TKzEYCfwXiCWbDur/Y9iTgWaAvsBW40t1XhNvuAr4FFAC3u/sHZR3TzLKBl4A0YBpwrbsfiPQ5HmHl55C3L+aGXJQ13v9o1f/L+lJf3vb5J9jnv6Qu/wcr+x9cn1q31qGu/MG2kiv3H1kE8Mip/o44ZtHkgrr8i4gcouSDiIhIJWmVWpsd1CM/oS4J2zXjRWnMLB74O3AGsAaYYmaj3X1ekWbfAra7ezszuwr4A3ClmXUBrgK6As2Bj82sQ7hPacf8A/Bnd3/JzB4Pj/1Y5M+0iMUfQUJtyDr5iNXF7/zn5BWU8aW92Jf6o33BL0ebghP88h9nwVSztYrfoS/yBb1uUkKp24pX/S9pmr+Segeo0J+ISOxR8kFERKSStGpUBzB2125Oqno+lKU/sMTdlwGY2UvAhUDR5MOFwL3h41eBRyz4lnkh8JK75wLLzWxJeDxKOqaZzQeGA1eHbf4dHrdSkw/rprzFSuvKjx78okLv/B/88l+8MF/Rgn71ayce0X2/pMr+pSYDEsvYL0HT+4mIyGFKPoiIiFSSJvWTOaNLE7Yln0lq4/rRDieWtQBWF/l9DTCgtDbunm9mOwmGTbQAJhXbt0X4uKRjpgE73D2/hPZHMLNRwCiAzMzMYzujsuTlsKp+bxbX7sXQtPQjvvQnH/pSX3axv9ISBYn68i8iIjFCyQcREZFKEh9n/OO6fkC/aIcix8HdnwSeBOjXr9+JVyI8KDGZgT94kYEVdkAREZHYo3S4iIiIxJq1QKsiv7cM15XYxswSgAYEhSdL27e09VuBhuExSnsuEREROUFKPoiIiEismQK0N7NsM6tFUEBydLE2o4Hrw8eXAWPc3cP1V5lZUjiLRXvgy9KOGe4zNjwG4THfiuC5iYiI1EgadiEiIiIxJazh8D3gA4JpMZ9297lmdh8w1d1HA08Bz4UFJbcRJBMI271CUJwyH7jN3QsASjpm+JQ/B14ys98AM8Jji4iISAWyIOFfdfTr18+nTp0a7TBERERijplNc3cVlKgEuh4REREpWWnXIxp2ISIiIiIiIiIRpeSDiIiIiIiIiESUkg8iIiIiIiIiElFKPoiIiIiIiIhIRFW5gpNmthlYWcGHTQe2VPAxY1FNOU+oOeeq86x+asq51pTzhMo919bunlFJz1Wj6XrkhNSU84Sac646z+qnppyrzjMySrweqXLJh0gws6k1oTp4TTlPqDnnqvOsfmrKudaU84Sada5yYmrK30pNOU+oOeeq86x+asq56jwrl4ZdiIiIiIiIiEhEKfkgIiIiIiIiIhGl5EPgyWgHUElqynlCzTlXnWf1U1POtaacJ9Ssc5UTU1P+VmrKeULNOVedZ/VTU85V51mJVPNBRERERERERCJKPR9EREREREREJKKUfBARERERERGRiKrRyQczG2lmC81siZndGe14KoKZrTCz2WY208ymhusamdlHZrY4/Jkarjczezg8/1lm1ie60ZfOzJ42s01mNqfIumM+LzO7Pmy/2Myuj8a5HE0p53qvma0NX9eZZnZOkW13hee60MzOKrI+pv++zayVmY01s3lmNtfM7gjXV6vXtYzzrFavqZklm9mXZvZVeJ6/Ctdnm9nkMOaXzaxWuD4p/H1JuD2ryLFKPP9YUca5PmNmy4u8pr3C9VXyb1cqTyy/t4+X6XqkSr/3SznPavW5dVAZn9PV6nUt4zyr1etaxmd0tul6JPp/u+5eIxcgHlgKtAFqAV8BXaIdVwWc1wogvdi6PwJ3ho/vBP4QPj4HeA8wYCAwOdrxl3Few4A+wJzjPS+gEbAs/JkaPk6N9rmV81zvBX5SQtsu4d9uEpAd/k3HV4W/b6AZ0Cd8nAIsCs+nWr2uZZxntXpNw9elXvg4EZgcvk6vAFeF6x8Hbgkf3wo8Hj6+Cni5rPOP9vmV81yfAS4roX2V/NvVUml/TzH93j6B81qBrkeq7Hu/lPOsVp9bReLX9Ug1el3R9UhMX4/U5J4P/YEl7r7M3Q8ALwEXRjmmSLkQ+Hf4+N/ARUXWP+uBSUBDM2sWhfiOyt3HA9uKrT7W8zoL+Mjdt7n7duAjYGTEgz9GpZxraS4EXnL3XHdfDiwh+NuO+b9vd1/v7tPDx7uB+UALqtnrWsZ5lqZKvqbh67In/DUxXBwYDrwari/+eh58nV8FRpiZUfr5x4wyzrU0VfJvVypNTL+3K5iuR6rIe7+mXIuArkfK2KVKvq66Hont65GanHxoAawu8vsayn4DVhUOfGhm08xsVLiuibuvDx9vAJqEj6v6v8GxnldVP9/vhV2knj7Y9Y9qcq5hF7feBBnbavu6FjtPqGavqZnFm9lMYBPBB9dSYIe754dNisZ86HzC7TuBNKrAecLXz9XdD76mvw1f0z+bWVK4rsq+plIpquvfga5Hqud7v1p9bhWn65Hq8brqegSI0euRmpx8qK5Odvc+wNnAbWY2rOhGd3fKzohVSdX1vIp4DGgL9ALWAw9FNZoKZGb1gNeAH7j7rqLbqtPrWsJ5VrvX1N0L3L0X0JLg7kCn6EYUOcXP1cy6AXcRnPNJBF0Xfx69CEWiTtcj1U+1+9wqStcj1ed11fVI7F6P1OTkw1qgVZHfW4brqjR3Xxv+3AS8QfCG23iw+2L4c1PYvKr/GxzreVXZ83X3jeF/LoXAPzjc7atKn6uZJRJ8AL7g7q+Hq6vd61rSeVbX1xTA3XcAY4FBBF36EsJNRWM+dD7h9gbAVqrQecIR5zoy7NLq7p4L/Itq9JpKRFXLvwNdjwDV7L1fnT+3dD1SPV9XXY/E3vVITU4+TAHah5VPaxEUGBkd5ZhOiJnVNbOUg4+BM4E5BOd1fdjseuCt8PFo4Lqw8ulAYGeR7mVVwbGe1wfAmWaWGnYpOzNcF/OKjX29mOB1heBcrwor9WYD7YEvqQJ/3+F4uqeA+e7+pyKbqtXrWtp5VrfX1MwyzKxh+Lg2cAbBeNKxwGVhs+Kv58HX+TJgTHhnqbTzjxmlnOuCIhepRjCWtOhrWuX+dqXSxPR7+3joeqRqf26Vprp9bh2k65Hq9brqeiTGr0c8Bip1RmshqPi5iGAc0C+jHU8FnE8bgqqsXwFzD54TwbilT4DFwMdAo3C9AX8Pz3820C/a51DGuf2HoCtYHsE4pG8dz3kBNxEUjFkC3Bjt8zqGc30uPJdZBP9xNCvS/pfhuS4Ezi6yPqb/voGTCbowzgJmhss51e11LeM8q9VrCvQAZoTnMwe4O1zfhuDDegnwXyApXJ8c/r4k3N7maOcfK0sZ5zomfE3nAM9zuAJ1lfzb1VKpf1Mx+94+zvPR9chRzivW3/ulnGe1+twqEqOuR6rR64quRyCGr0csfDIRERERERERkYioycMuRERERERERKQSKPkgIiIiIiIiIhGl5IOIiIiIiIiIRJSSDyIiIiIiIiISUUo+iIiIiIiIiEhEKfkgIiIiIiIiIhGl5INIDWJmaWY2M1w2mNna8PEeM3s0As/3jJktN7Oby2gz1Mzmmdmcin5+ERERiT26HhGpmczdox2DiESBmd0L7HH3ByP4HM8Ab7v7q0dplxW26xapWERERCT26HpEpOZQzwcRwcxONbO3w8f3mtm/zWyCma00s0vM7I9mNtvM3jezxLBdXzP71MymmdkHZtasHM9zuZnNMbOvzGx8pM9LREREqg5dj4hUb0o+iEhJ2gLDgQuA54Gx7t4d2A+cG37g/w24zN37Ak8Dvy3Hce8GznL3nuGxRUREREqj6xGRaiQh2gGISEx6z93zzGw2EA+8H66fDWQBHYFuwEdmRthmfTmO+znwjJm9Arxe0UGLiIhItaLrEZFqRMkHESlJLoC7F5pZnh8uDlNI8P+GAXPdfdCxHNTdbzazAcC5wDQz6+vuWysycBEREak2dD0iUo1o2IWIHI+FQIaZDQIws0Qz63q0ncysrbtPdve7gc1AqwjHKSIiItWXrkdEqhD1fBCRY+buB8zsMuBhM2tA8H/JX4C5R9n1ATNrT3Cn4hPgq4gGKiIiItWWrkdEqhZNtSkiEaOprURERCTadD0iEhs07EJEImkn8Gszu7m0BmY2FPgfsKXSohIREZGaRNcjIjFAPR9EREREREREJKLU80FEREREREREIkrJBxERERERERGJKCUfRERERERERCSilHwQERERERERkYj6/w2k0Lm/kL2hAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] @@ -176,10 +176,8 @@ "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[7] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", - "[8] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", - "[9] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[10] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "[7] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", + "[8] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } diff --git a/examples/notebooks/models/lithium-plating.ipynb b/examples/notebooks/models/lithium-plating.ipynb index 69cb48dfcc..49c9c95e42 100644 --- a/examples/notebooks/models/lithium-plating.ipynb +++ b/examples/notebooks/models/lithium-plating.ipynb @@ -75,7 +75,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 3, @@ -95,15 +95,15 @@ ")\n", "\n", "sim_discharge1 = pybamm.Simulation(model1, parameter_values=parameter_values, experiment=experiment_discharge)\n", - "sol_discharge1 = sim_discharge1.solve()\n", + "sol_discharge1 = sim_discharge1.solve(calc_esoh=False)\n", "model1.set_initial_conditions_from(sol_discharge1, inplace=True)\n", "\n", "sim_discharge2 = pybamm.Simulation(model2, parameter_values=parameter_values, experiment=experiment_discharge)\n", - "sol_discharge2 = sim_discharge2.solve()\n", + "sol_discharge2 = sim_discharge2.solve(calc_esoh=False)\n", "model2.set_initial_conditions_from(sol_discharge2, inplace=True)\n", "\n", "sim_discharge3 = pybamm.Simulation(model3, parameter_values=parameter_values, experiment=experiment_discharge)\n", - "sol_discharge3 = sim_discharge3.solve()\n", + "sol_discharge3 = sim_discharge3.solve(calc_esoh=False)\n", "model3.set_initial_conditions_from(sol_discharge3, inplace=True)" ] }, @@ -172,7 +172,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -182,20 +182,20 @@ ], "source": [ "sim1_2C = pybamm.Simulation(model1, experiment=experiment_2C, parameter_values=parameter_values)\n", - "sim1_2C.solve()\n", + "sim1_2C.solve(calc_esoh=False)\n", "sim1_1C = pybamm.Simulation(model1, experiment=experiment_1C, parameter_values=parameter_values)\n", - "sim1_1C.solve()\n", + "sim1_1C.solve(calc_esoh=False)\n", "sim1_Cover2 = pybamm.Simulation(model1, experiment=experiment_Cover2, parameter_values=parameter_values)\n", - "sim1_Cover2.solve()\n", + "sim1_Cover2.solve(calc_esoh=False)\n", "sim1_Cover4 = pybamm.Simulation(model1, experiment=experiment_Cover4, parameter_values=parameter_values)\n", - "sim1_Cover4.solve()\n", + "sim1_Cover4.solve(calc_esoh=False)\n", "sim1_Cover8 = pybamm.Simulation(model1, experiment=experiment_Cover8, parameter_values=parameter_values)\n", - "sim1_Cover8.solve()" + "sim1_Cover8.solve(calc_esoh=False)" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -252,12 +252,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -330,36 +330,36 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sim2_2C = pybamm.Simulation(model2, experiment=experiment_2C, parameter_values=parameter_values)\n", - "sim2_2C.solve()\n", + "sim2_2C.solve(calc_esoh=False)\n", "sim2_1C = pybamm.Simulation(model2, experiment=experiment_1C, parameter_values=parameter_values)\n", - "sim2_1C.solve()\n", + "sim2_1C.solve(calc_esoh=False)\n", "sim2_Cover2 = pybamm.Simulation(model2, experiment=experiment_Cover2, parameter_values=parameter_values)\n", - "sim2_Cover2.solve()\n", + "sim2_Cover2.solve(calc_esoh=False)\n", "sim2_Cover4 = pybamm.Simulation(model2, experiment=experiment_Cover4, parameter_values=parameter_values)\n", - "sim2_Cover4.solve()\n", + "sim2_Cover4.solve(calc_esoh=False)\n", "sim2_Cover8 = pybamm.Simulation(model2, experiment=experiment_Cover8, parameter_values=parameter_values)\n", - "sim2_Cover8.solve()" + "sim2_Cover8.solve(calc_esoh=False)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -417,12 +417,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -488,36 +488,36 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sim3_2C = pybamm.Simulation(model3, experiment=experiment_2C, parameter_values=parameter_values)\n", - "sim3_2C.solve()\n", + "sim3_2C.solve(calc_esoh=False)\n", "sim3_1C = pybamm.Simulation(model3, experiment=experiment_1C, parameter_values=parameter_values)\n", - "sim3_1C.solve()\n", + "sim3_1C.solve(calc_esoh=False)\n", "sim3_Cover2 = pybamm.Simulation(model3, experiment=experiment_Cover2, parameter_values=parameter_values)\n", - "sim3_Cover2.solve()\n", + "sim3_Cover2.solve(calc_esoh=False)\n", "sim3_Cover4 = pybamm.Simulation(model3, experiment=experiment_Cover4, parameter_values=parameter_values)\n", - "sim3_Cover4.solve()\n", + "sim3_Cover4.solve(calc_esoh=False)\n", "sim3_Cover8 = pybamm.Simulation(model3, experiment=experiment_Cover8, parameter_values=parameter_values)\n", - "sim3_Cover8.solve()" + "sim3_Cover8.solve(calc_esoh=False)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -575,12 +575,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -653,23 +653,23 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[1] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", - "[2] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", - "[3] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", - "[4] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", - "[5] Peyman Mohtat, Suhak Lee, Jason B Siegel, and Anna G Stefanopoulou. Towards better estimability of electrode-specific state of health: decoding the cell expansion. Journal of Power Sources, 427:101–111, 2019.\n", - "[6] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", - "[7] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", - "[8] Dongsheng Ren, Kandler Smith, Dongxu Guo, Xuebing Han, Xuning Feng, Languang Lu, and Minggao Ouyang. Investigation of lithium plating-stripping process in li-ion batteries at low temperature using an electrochemical model. Journal of the Electrochemistry Society, 165:A2167-A2178, 2018. doi:10.1149/2.0661810jes.\n", - "[9] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", - "[10] Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, and others. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature Methods, 17(3):261–272, 2020. doi:10.1038/s41592-019-0686-2.\n", + "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n", + "[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n", + "[3] Chang-Hui Chen, Ferran Brosa Planella, Kieran O'Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models. Journal of The Electrochemical Society, 167(8):080534, 2020. doi:10.1149/1945-7111/ab9050.\n", + "[4] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n", + "[5] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n", + "[6] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n", + "[7] Simon E. J. O'Kane, Ian D. Campbell, Mohamed W. J. Marzook, Gregory J. Offer, and Monica Marinescu. Physical origin of the differential voltage minimum associated with lithium plating in li-ion batteries. Journal of The Electrochemical Society, 167(9):090540, may 2020. URL: https://doi.org/10.1149/1945-7111/ab90ac, doi:10.1149/1945-7111/ab90ac.\n", + "[8] Simon E. J. O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Sophie Edge, Billy Wu, Gregory J. Offer, and Monica Marinescu. Lithium-ion battery degradation: how to model it. Phys. Chem. Chem. Phys., 24:7909-7922, 2022. URL: http://dx.doi.org/10.1039/D2CP00417H, doi:10.1039/D2CP00417H.\n", + "[9] Dongsheng Ren, Kandler Smith, Dongxu Guo, Xuebing Han, Xuning Feng, Languang Lu, and Minggao Ouyang. Investigation of lithium plating-stripping process in li-ion batteries at low temperature using an electrochemical model. Journal of the Electrochemistry Society, 165:A2167-A2178, 2018. doi:10.1149/2.0661810jes.\n", + "[10] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n", "\n" ] } diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/README.md b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/README.md deleted file mode 100644 index dbd07b7ca2..0000000000 --- a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/README.md +++ /dev/null @@ -1,9 +0,0 @@ -# LG M50 Graphite anode parameters - -Parameters for a LG M50 graphite anode, from the paper - -> Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. ["Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models."](https://iopscience.iop.org/article/10.1149/1945-7111/ab9050) Journal of the Electrochemical Society 167 (2020): 080534 - -and references therein. - -Speculative parameters for lithium plating have also been added. diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/README.md b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/README.md new file mode 100644 index 0000000000..d8f6b21220 --- /dev/null +++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/README.md @@ -0,0 +1,7 @@ +# LG M50 Graphite anode parameters + +Parameters for a LG M50 graphite anode, from the paper + +> Simon O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Edge, Billy Wu, Gregory Offer, and Monica Marinescu. ["Lithium-ion battery degradation: how to model it."](https://pubs.rsc.org/en/content/articlelanding/2022/cp/d2cp00417h) Physical Chemistry: Chemical Physics 24 (2022): 7909-7922 + +and references therein. diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/__init__.py b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/__init__.py similarity index 100% rename from pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/__init__.py rename to pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/__init__.py diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_diffusivity_Chen2020.py b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_LGM50_diffusivity_Chen2020.py similarity index 100% rename from pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_diffusivity_Chen2020.py rename to pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_LGM50_diffusivity_Chen2020.py diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_electrolyte_exchange_current_density_Chen2020.py b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_LGM50_electrolyte_exchange_current_density_Chen2020.py similarity index 100% rename from pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_electrolyte_exchange_current_density_Chen2020.py rename to pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_LGM50_electrolyte_exchange_current_density_Chen2020.py diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_ocp_Chen2020.csv b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_LGM50_ocp_Chen2020.csv similarity index 100% rename from pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_ocp_Chen2020.csv rename to pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_LGM50_ocp_Chen2020.csv diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_ocp_Chen2020.py b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_LGM50_ocp_Chen2020.py similarity index 100% rename from pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_LGM50_ocp_Chen2020.py rename to pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_LGM50_ocp_Chen2020.py diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_cracking_rate_Ai2020.py b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_cracking_rate_Ai2020.py similarity index 85% rename from pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_cracking_rate_Ai2020.py rename to pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_cracking_rate_Ai2020.py index 1bf7ce3e67..bde32ad16c 100644 --- a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_cracking_rate_Ai2020.py +++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_cracking_rate_Ai2020.py @@ -3,7 +3,7 @@ def graphite_cracking_rate_Ai2020(T_dim): """ - graphite particle cracking rate as a function of temperature [1, 2]. + Graphite particle cracking rate as a function of temperature [1, 2]. References ---------- @@ -27,9 +27,8 @@ def graphite_cracking_rate_Ai2020(T_dim): where m_cr is another Paris' law constant """ k_cr = 3.9e-20 - T_ref = Parameter("Reference temperature [K]") Eac_cr = Parameter( "Negative electrode activation energy for cracking rate [J.mol-1]" ) - arrhenius = exp(Eac_cr / constants.R * (1 / T_dim - 1 / T_ref)) + arrhenius = exp(Eac_cr / constants.R * (1 / T_dim - 1 / 298.15)) return k_cr * arrhenius diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_volume_change_Ai2020.py b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_volume_change_Ai2020.py similarity index 100% rename from pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/graphite_volume_change_Ai2020.py rename to pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_volume_change_Ai2020.py diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv similarity index 96% rename from pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv rename to pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv index a36306255b..eb9e3e15e9 100644 --- a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_Chen2020_plating/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv @@ -5,7 +5,7 @@ Name [units],Value,Reference,Notes Negative electrode conductivity [S.m-1],215,Chen 2020,graphite Maximum concentration in negative electrode [mol.m-3],33133,Chen 2020,tuned for 1C Negative electrode diffusivity [m2.s-1],[function]graphite_LGM50_diffusivity_Chen2020,Chen 2020,tuned for 1C -Negative electrode OCP [V],[function]graphite_LGM50_ocp_Chen2020,Chen 2020, +Negative electrode OCP [V],[data]graphite_LGM50_ocp_Chen2020,Chen 2020, ,,, # Microstructure,,, Negative electrode porosity,0.25,Chen 2020, diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/README.md b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/README.md index 9bd8671867..a3775e61b6 100644 --- a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/README.md +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/README.md @@ -2,6 +2,6 @@ Parameters for an LG M50 NMC 811 positive electrode, from the paper -> Chang-Hui Chen, Ferran Brosa Planella, Kieran O’Regan, Dominika Gastol, W. Dhammika Widanage, and Emma Kendrick. ["Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models."](https://iopscience.iop.org/article/10.1149/1945-7111/ab9050) Journal of the Electrochemical Society 167 (2020): 080534 +> Simon O'Kane, Weilong Ai, Ganesh Madabattula, Diego Alonso-Alvarez, Robert Timms, Valentin Sulzer, Jacqueline Edge, Billy Wu, Gregory Offer, and Monica Marinescu. ["Lithium-ion battery degradation: how to model it."](https://pubs.rsc.org/en/content/articlelanding/2022/cp/d2cp00417h) Physical Chemistry: Chemical Physics 24 (2022): 7909-7922 and references therein. diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py index c02e41d83a..a60b2daa2a 100644 --- a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py @@ -27,9 +27,8 @@ def cracking_rate_Ai2020(T_dim): where m_cr is another Paris' law constant """ k_cr = 3.9e-20 - T_ref = Parameter("Reference temperature [K]") Eac_cr = Parameter( "Positive electrode activation energy for cracking rate [J.mol-1]" ) - arrhenius = exp(Eac_cr / constants.R * (1 / T_dim - 1 / T_ref)) + arrhenius = exp(Eac_cr / constants.R * (1 / T_dim - 1 / 298.15)) return k_cr * arrhenius diff --git a/pybamm/input/parameters/lithium_ion/seis/OKane2022/parameters.csv b/pybamm/input/parameters/lithium_ion/seis/OKane2022/parameters.csv index 92af3830d1..fb2be7c3e6 100644 --- a/pybamm/input/parameters/lithium_ion/seis/OKane2022/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/seis/OKane2022/parameters.csv @@ -9,7 +9,6 @@ SEI reaction exchange current density [A.m-2],1.5E-7, Guess, SEI resistivity [Ohm.m],2e5, Safari paper, Outer SEI solvent diffusivity [m2.s-1],2.5E-22, Single paper, Bulk solvent concentration [mol.m-3],2.636E3, Ploehn paper, -Ratio of inner and outer SEI exchange current densities,1, Assume same, Inner SEI open-circuit potential [V],0.1,, Outer SEI open-circuit potential [V],0.8,, Inner SEI electron conductivity [S.m-1],8.95E-14, Single paper, diff --git a/pybamm/input/parameters/lithium_ion/seis/example/parameters.csv b/pybamm/input/parameters/lithium_ion/seis/example/parameters.csv index e328d353f1..c2751e5d78 100644 --- a/pybamm/input/parameters/lithium_ion/seis/example/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/seis/example/parameters.csv @@ -9,7 +9,6 @@ SEI reaction exchange current density [A.m-2],1.5E-7, Guess, SEI resistivity [Ohm.m],2e5, Safari paper, Outer SEI solvent diffusivity [m2.s-1],2.5E-22, Single paper, Bulk solvent concentration [mol.m-3],2.636E3, Ploehn paper, -Ratio of inner and outer SEI exchange current densities,1, Assume same, Inner SEI open-circuit potential [V],0.1,, Outer SEI open-circuit potential [V],0.8,, Inner SEI electron conductivity [S.m-1],8.95E-14, Single paper, diff --git a/pybamm/input/parameters/lithium_ion/seis/ramadass2004/parameters.csv b/pybamm/input/parameters/lithium_ion/seis/ramadass2004/parameters.csv index f07d512d19..16008063bb 100644 --- a/pybamm/input/parameters/lithium_ion/seis/ramadass2004/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/seis/ramadass2004/parameters.csv @@ -9,7 +9,6 @@ SEI reaction exchange current density [A.m-2],1.5e-6, Ramadass 2004, SEI resistivity [Ohm.m],2e5, Safari 2009, Outer SEI solvent diffusivity [m2.s-1],2.5E-22, Single paper, Bulk solvent concentration [mol.m-3],2.636E3, Ploehn paper, -Ratio of inner and outer SEI exchange current densities,1, Assume same, Inner SEI open-circuit potential [V],0.1,, Outer SEI open-circuit potential [V],0.8,, Inner SEI electron conductivity [S.m-1],8.95E-14, Single paper, diff --git a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py index 3c7186e22f..50ecf9ba6d 100644 --- a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py +++ b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py @@ -16,4 +16,4 @@ def __init__(self, options=None, name="Yang2017", build=True): @property def default_parameter_values(self): - return pybamm.ParameterValues("Chen2020_plating") + return pybamm.ParameterValues("OKane2022") diff --git a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py index 4f586f58e8..7ea1e4c510 100644 --- a/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py +++ b/pybamm/models/full_battery_models/lithium_ion/base_lithium_ion_model.py @@ -236,40 +236,28 @@ def set_sei_submodel(self): else: reaction_loc = "full electrode" + if self.options["SEI"] == "none": + self.submodels["sei"] = pybamm.sei.NoSEI(self.param, self.options) + elif self.options["SEI"] == "constant": + self.submodels["sei"] = pybamm.sei.ConstantSEI(self.param, self.options) + else: + self.submodels["sei"] = pybamm.sei.SEIGrowth( + self.param, reaction_loc, self.options, cracks=False + ) # Do not set "sei on cracks" submodel for half-cells - if reaction_loc == "interface": - if self.options["SEI"] == "none": - self.submodels["sei"] = pybamm.sei.NoSEI(self.param, self.options) - elif self.options["SEI"] == "constant": - self.submodels["sei"] = pybamm.sei.ConstantSEI(self.param, self.options) - else: - self.submodels["sei"] = pybamm.sei.SEIGrowth( - self.param, reaction_loc, self.options, cracks=False - ) # For full cells, "sei on cracks" submodel must be set, even if it is zero - else: - if self.options["SEI"] == "none": - self.submodels["sei"] = pybamm.sei.NoSEI(self.param, self.options) - self.submodels["sei on cracks"] = pybamm.sei.NoSEI( - self.param, self.options, cracks=True - ) - elif self.options["SEI"] == "constant": - self.submodels["sei"] = pybamm.sei.ConstantSEI(self.param, self.options) + if reaction_loc != "interface": + if ( + self.options["SEI"] in ["none", "constant"] + or self.options["SEI on cracks"] == "false" + ): self.submodels["sei on cracks"] = pybamm.sei.NoSEI( self.param, self.options, cracks=True ) else: - self.submodels["sei"] = pybamm.sei.SEIGrowth( - self.param, reaction_loc, self.options, cracks=False + self.submodels["sei on cracks"] = pybamm.sei.SEIGrowth( + self.param, reaction_loc, self.options, cracks=True ) - if self.options["SEI on cracks"] == "true": - self.submodels["sei on cracks"] = pybamm.sei.SEIGrowth( - self.param, reaction_loc, self.options, cracks=True - ) - else: - self.submodels["sei on cracks"] = pybamm.sei.NoSEI( - self.param, self.options, cracks=True - ) def set_lithium_plating_submodel(self): if self.options["lithium plating"] == "none": diff --git a/pybamm/models/submodels/interface/sei/sei_growth.py b/pybamm/models/submodels/interface/sei/sei_growth.py index 966038784e..9002c503ee 100644 --- a/pybamm/models/submodels/interface/sei/sei_growth.py +++ b/pybamm/models/submodels/interface/sei/sei_growth.py @@ -54,7 +54,7 @@ def get_fundamental_variables(self): domain="negative electrode", auxiliary_domains={"secondary": "current collector"}, ) - else: + elif self.reaction == "SEI": if self.reaction_loc == "x-average": L_inner_av = pybamm.standard_variables.L_inner_av L_outer_av = pybamm.standard_variables.L_outer_av diff --git a/pybamm/models/submodels/particle_mechanics/no_mechanics.py b/pybamm/models/submodels/particle_mechanics/no_mechanics.py index 6ecadaa1aa..5715eafbfe 100644 --- a/pybamm/models/submodels/particle_mechanics/no_mechanics.py +++ b/pybamm/models/submodels/particle_mechanics/no_mechanics.py @@ -7,7 +7,7 @@ class NoMechanics(BaseMechanics): """ - Class for swelling only (no cracking) + Class for no particle mechanics. Parameters ---------- diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index dff8b744b4..40d6b6d0cc 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -134,9 +134,6 @@ def _set_dimensional_parameters(self): self.c_sol_dimensional = pybamm.Parameter( "Bulk solvent concentration [mol.m-3]" ) - self.m_ratio = pybamm.Parameter( - "Ratio of inner and outer SEI exchange current densities" - ) self.U_inner_dimensional = pybamm.Parameter( "Inner SEI open-circuit potential [V]" ) diff --git a/pybamm/parameters/parameter_sets.py b/pybamm/parameters/parameter_sets.py index a324250af7..573368a4b9 100644 --- a/pybamm/parameters/parameter_sets.py +++ b/pybamm/parameters/parameter_sets.py @@ -167,19 +167,6 @@ "citation": "Chen2020", } -Chen2020_plating = { - "chemistry": "lithium_ion", - "cell": "LGM50_Chen2020", - "negative electrode": "graphite_Chen2020_plating", - "separator": "separator_Chen2020", - "positive electrode": "nmc_Chen2020", - "electrolyte": "lipf6_Nyman2008", - "experiment": "1C_discharge_from_full_Chen2020", - "sei": "example", - "lithium plating": "okane2020_Li_plating", - "citation": "Chen2020", -} - Mohtat2020 = { "chemistry": "lithium_ion", "cell": "UMBL_Mohtat2020", @@ -254,7 +241,7 @@ OKane2022 = { "chemistry": "lithium_ion", "cell": "LGM50_Chen2020", - "negative electrode": "graphite_Chen2020_plating", + "negative electrode": "graphite_OKane2022", "separator": "separator_Chen2020", "positive electrode": "nmc_OKane2022", "electrolyte": "lipf6_Nyman2008", diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py index f0c7bdf2d2..a8a01d4c69 100644 --- a/tests/unit/test_experiments/test_simulation_with_experiment.py +++ b/tests/unit/test_experiments/test_simulation_with_experiment.py @@ -344,8 +344,8 @@ def test_cycle_summary_variables(self): ) model = pybamm.lithium_ion.SPM() - # Chen 2020 plating: pos = function, neg = data - param = pybamm.ParameterValues("Chen2020_plating") + # O'Kane 2022: pos = function, neg = data + param = pybamm.ParameterValues("OKane2022") sim = pybamm.Simulation(model, experiment=experiment, parameter_values=param) sim.solve(solver=pybamm.CasadiSolver("fast with events"), save_at_cycles=2) diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py index b12294f205..7ef6acafce 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py @@ -13,7 +13,7 @@ def test_well_posed(self): def test_default_parameter_values(self): model = pybamm.lithium_ion.Yang2017() - parameter_values = pybamm.ParameterValues("Chen2020_plating") + parameter_values = pybamm.ParameterValues("OKane2022") for key, value in parameter_values.items(): if not isinstance(value, tuple): np.testing.assert_array_equal( diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py index 6141770d66..50a0789fa4 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_basic_models.py @@ -39,7 +39,7 @@ def test_basic_dfn_half_cell_simulation(self): model = pybamm.lithium_ion.BasicDFNHalfCell( options={"working electrode": "positive"} ) - param = pybamm.ParameterValues("Chen2020_plating") + param = pybamm.ParameterValues("OKane2022") param["Current function [A]"] = 2.5 sim = pybamm.Simulation(model=model, parameter_values=param) sim.solve([0, 100]) diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2020.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2020.py index 1e1180e6f2..5ab2b10aa6 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2020.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2020.py @@ -20,7 +20,16 @@ def test_load_params(self): def test_functions(self): root = pybamm.root_dir() - param = pybamm.ParameterValues("Chen2020_plating") + param = pybamm.ParameterValues(chemistry={ + "chemistry": "lithium_ion", + "cell": "LGM50_Chen2020", + "negative electrode": "graphite_Chen2020", + "separator": "separator_Chen2020", + "positive electrode": "nmc_Chen2020", + "electrolyte": "lipf6_Nyman2008", + "experiment": "1C_discharge_from_full_Chen2020", + "lithium plating": "okane2020_Li_plating", + }) T = pybamm.Scalar(298.15) p = "pybamm/input/parameters/lithium_ion/lithium_platings/okane2020_Li_plating/" diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py index 74a8cb19df..724eb2857f 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py @@ -44,7 +44,7 @@ def test_functions(self): # Negative electrode p = ( "pybamm/input/parameters/lithium_ion/negative_electrodes/" - "graphite_Chen2020_plating/" + "graphite_OKane2022/" ) k_path = os.path.join(root, p) From 21f0374dacb4573e2bdd72c12019599d72a1b4ac Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Tue, 26 Jul 2022 13:40:00 +0100 Subject: [PATCH 30/36] changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 3044c45f66..5c1666e702 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,7 @@ - Added SEI growth on cracks ([#2104](https://github.com/pybamm-team/PyBaMM/pull/2104)) - Added Arrhenius temperature dependence of SEI growth ([#2104](https://github.com/pybamm-team/PyBaMM/pull/2104)) - The "Inner SEI reaction proportion" parameter actually gets used now ([#2104](https://github.com/pybamm-team/PyBaMM/pull/2104)) +- New OKane2022 parameter set replaces Chen2020_plating ([#2104](https://github.com/pybamm-team/PyBaMM/pull/2104)) ## Optimizations From 4153103b30b5efaa694fd5586e530b74243691c3 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Tue, 26 Jul 2022 15:02:25 +0100 Subject: [PATCH 31/36] Modified tests to account for Chen2020_plating being deleted --- .../full_battery_models/lithium_ion/Yang2017.py | 12 +++++++++++- .../test_lithium_ion/base_lithium_ion_tests.py | 2 +- .../test_lithium_ion/test_basic_half_cell_models.py | 2 +- .../test_lithium_ion/test_Yang2017.py | 12 +++++++++++- .../test_parameter_sets/test_OKane2020.py | 2 +- 5 files changed, 25 insertions(+), 5 deletions(-) diff --git a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py index 50ecf9ba6d..a4c4e7ed93 100644 --- a/pybamm/models/full_battery_models/lithium_ion/Yang2017.py +++ b/pybamm/models/full_battery_models/lithium_ion/Yang2017.py @@ -16,4 +16,14 @@ def __init__(self, options=None, name="Yang2017", build=True): @property def default_parameter_values(self): - return pybamm.ParameterValues("OKane2022") + return pybamm.ParameterValues({ + "chemistry": "lithium_ion", + "cell": "LGM50_Chen2020", + "negative electrode": "graphite_Chen2020", + "separator": "separator_Chen2020", + "positive electrode": "nmc_Chen2020", + "electrolyte": "lipf6_Nyman2008", + "experiment": "1C_discharge_from_full_Chen2020", + "sei": "example", + "lithium plating": "okane2020_Li_plating", + }) diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index 286d028ef3..fe31fdf96c 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -162,7 +162,7 @@ def test_irreversible_plating_with_porosity(self): "lithium plating": "irreversible", "lithium plating porosity change": "true", } - param = pybamm.ParameterValues("Chen2020_plating") + param = pybamm.ParameterValues("OKane2022") self.run_basic_processing_test(options, parameter_values=param) def test_sei_reaction_limited(self): diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py index 6b953e78d9..59d80bc8bc 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py @@ -46,7 +46,7 @@ def test_runs_Chen2020(self): geometry = model.default_geometry # load parameter values - param = pybamm.ParameterValues("Chen2020_plating") + param = pybamm.ParameterValues("Chen2020") param["Current function [A]"] = 2.5 diff --git a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py index 7ef6acafce..5e1f65c171 100644 --- a/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py +++ b/tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_Yang2017.py @@ -13,7 +13,17 @@ def test_well_posed(self): def test_default_parameter_values(self): model = pybamm.lithium_ion.Yang2017() - parameter_values = pybamm.ParameterValues("OKane2022") + parameter_values = pybamm.ParameterValues({ + "chemistry": "lithium_ion", + "cell": "LGM50_Chen2020", + "negative electrode": "graphite_Chen2020", + "separator": "separator_Chen2020", + "positive electrode": "nmc_Chen2020", + "electrolyte": "lipf6_Nyman2008", + "experiment": "1C_discharge_from_full_Chen2020", + "sei": "example", + "lithium plating": "okane2020_Li_plating", + }) for key, value in parameter_values.items(): if not isinstance(value, tuple): np.testing.assert_array_equal( diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2020.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2020.py index 5ab2b10aa6..54fb2a8d21 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2020.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2020.py @@ -20,7 +20,7 @@ def test_load_params(self): def test_functions(self): root = pybamm.root_dir() - param = pybamm.ParameterValues(chemistry={ + param = pybamm.ParameterValues({ "chemistry": "lithium_ion", "cell": "LGM50_Chen2020", "negative electrode": "graphite_Chen2020", From 9ad8b756aef5e474005f2804eef74f285e067a5c Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Tue, 26 Jul 2022 15:50:17 +0100 Subject: [PATCH 32/36] Modified more tests to account for Chen2020_plating being deleted --- .../test_lithium_ion/base_lithium_ion_tests.py | 12 +++++++++++- .../test_lithium_ion/test_basic_half_cell_models.py | 12 +++++++++++- 2 files changed, 22 insertions(+), 2 deletions(-) diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py index fe31fdf96c..9ddbe4e6d8 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/base_lithium_ion_tests.py @@ -162,7 +162,17 @@ def test_irreversible_plating_with_porosity(self): "lithium plating": "irreversible", "lithium plating porosity change": "true", } - param = pybamm.ParameterValues("OKane2022") + param = pybamm.ParameterValues({ + "chemistry": "lithium_ion", + "cell": "LGM50_Chen2020", + "negative electrode": "graphite_Chen2020", + "separator": "separator_Chen2020", + "positive electrode": "nmc_Chen2020", + "electrolyte": "lipf6_Nyman2008", + "experiment": "1C_discharge_from_full_Chen2020", + "sei": "example", + "lithium plating": "okane2020_Li_plating", + }) self.run_basic_processing_test(options, parameter_values=param) def test_sei_reaction_limited(self): diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py index 59d80bc8bc..db254a32cd 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_basic_half_cell_models.py @@ -46,7 +46,17 @@ def test_runs_Chen2020(self): geometry = model.default_geometry # load parameter values - param = pybamm.ParameterValues("Chen2020") + param = pybamm.ParameterValues({ + "chemistry": "lithium_ion", + "cell": "LGM50_Chen2020", + "negative electrode": "graphite_Chen2020", + "separator": "separator_Chen2020", + "positive electrode": "nmc_Chen2020", + "electrolyte": "lipf6_Nyman2008", + "experiment": "1C_discharge_from_full_Chen2020", + "sei": "example", + "lithium plating": "okane2020_Li_plating", + }) param["Current function [A]"] = 2.5 From ebeffb19ff587c25ac520b339755c6790e3f9b0d Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Tue, 26 Jul 2022 18:58:54 +0100 Subject: [PATCH 33/36] Hard coded cracking activation energy (currently 0) --- .../graphite_OKane2022/graphite_cracking_rate_Ai2020.py | 6 ++---- .../nmc_OKane2022/cracking_rate_Ai2020.py | 6 ++---- 2 files changed, 4 insertions(+), 8 deletions(-) diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_cracking_rate_Ai2020.py b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_cracking_rate_Ai2020.py index bde32ad16c..7871321bdd 100644 --- a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_cracking_rate_Ai2020.py +++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/graphite_cracking_rate_Ai2020.py @@ -1,4 +1,4 @@ -from pybamm import Parameter, constants, exp +from pybamm import constants, exp def graphite_cracking_rate_Ai2020(T_dim): @@ -27,8 +27,6 @@ def graphite_cracking_rate_Ai2020(T_dim): where m_cr is another Paris' law constant """ k_cr = 3.9e-20 - Eac_cr = Parameter( - "Negative electrode activation energy for cracking rate [J.mol-1]" - ) + Eac_cr = 0 # to be implemented arrhenius = exp(Eac_cr / constants.R * (1 / T_dim - 1 / 298.15)) return k_cr * arrhenius diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py index a60b2daa2a..51299a21f0 100644 --- a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/cracking_rate_Ai2020.py @@ -1,4 +1,4 @@ -from pybamm import Parameter, constants, exp +from pybamm import constants, exp def cracking_rate_Ai2020(T_dim): @@ -27,8 +27,6 @@ def cracking_rate_Ai2020(T_dim): where m_cr is another Paris' law constant """ k_cr = 3.9e-20 - Eac_cr = Parameter( - "Positive electrode activation energy for cracking rate [J.mol-1]" - ) + Eac_cr = 0 # to be implemented arrhenius = exp(Eac_cr / constants.R * (1 / T_dim - 1 / 298.15)) return k_cr * arrhenius From 51d1c4fa6e0b9fc382a42e4d99aa242664f6d21d Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 27 Jul 2022 12:23:35 +0100 Subject: [PATCH 34/36] Removed cracking activation energy from parameter csv files --- .../negative_electrodes/graphite_OKane2022/parameters.csv | 1 - .../lithium_ion/positive_electrodes/nmc_OKane2022/parameters.csv | 1 - 2 files changed, 2 deletions(-) diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv index eb9e3e15e9..f835cba862 100644 --- a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv @@ -27,7 +27,6 @@ Negative electrode density [kg.m-3],1657,default, # Thermal parameters,,, Negative electrode specific heat capacity [J.kg-1.K-1],700,default, Negative electrode thermal conductivity [W.m-1.K-1],1.7,default, -Negative electrode OCP entropic change [V.K-1],0,, ,,, # Mechanical properties,,, Negative electrode Poisson's ratio,0.3,, diff --git a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/parameters.csv b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/parameters.csv index 61ebde7649..d5178d0115 100644 --- a/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/positive_electrodes/nmc_OKane2022/parameters.csv @@ -43,7 +43,6 @@ Positive electrode number of cracks per unit area [m-2],3.18e15,, Positive electrode Paris' law constant b,1.12,, Positive electrode Paris' law constant m,2.2,, Positive electrode cracking rate,[function]cracking_rate_Ai2020,Ai2020, -Positive electrode activation energy for cracking rate [J.mol-1],0,, ,,, # Loss of active materials (LAM) model,,, Positive electrode LAM constant proportional term [s-1],2.7778E-7,0.001/3600, From 851b44c77262a1416900497af82b024c6b6a5b70 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 27 Jul 2022 14:13:15 +0100 Subject: [PATCH 35/36] Deleted the correct parameter this time --- .../negative_electrodes/graphite_OKane2022/parameters.csv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv index f835cba862..bc2534eab5 100644 --- a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv @@ -27,6 +27,7 @@ Negative electrode density [kg.m-3],1657,default, # Thermal parameters,,, Negative electrode specific heat capacity [J.kg-1.K-1],700,default, Negative electrode thermal conductivity [W.m-1.K-1],1.7,default, +Positive electrode OCP entropic change [V.K-1],0,, ,,, # Mechanical properties,,, Negative electrode Poisson's ratio,0.3,, @@ -42,7 +43,6 @@ Negative electrode number of cracks per unit area [m-2],3.18e15,, Negative electrode Paris' law constant b,1.12,, Negative electrode Paris' law constant m,2.2,, Negative electrode cracking rate,[function]graphite_cracking_rate_Ai2020,Ai2020, -Negative electrode activation energy for cracking rate [J.mol-1],0,, ,,, # Loss of active materials (LAM) model,,, Negative electrode LAM constant proportional term [s-1],2.7778E-7,0.001/3600, From 2b77c81bee3986702119843c2ed0fac0a4cb6d19 Mon Sep 17 00:00:00 2001 From: DrSOKane Date: Wed, 27 Jul 2022 14:52:31 +0100 Subject: [PATCH 36/36] Third time lucky? --- .../negative_electrodes/graphite_OKane2022/parameters.csv | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv index bc2534eab5..062be61eaa 100644 --- a/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv +++ b/pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_OKane2022/parameters.csv @@ -27,7 +27,7 @@ Negative electrode density [kg.m-3],1657,default, # Thermal parameters,,, Negative electrode specific heat capacity [J.kg-1.K-1],700,default, Negative electrode thermal conductivity [W.m-1.K-1],1.7,default, -Positive electrode OCP entropic change [V.K-1],0,, +Negative electrode OCP entropic change [V.K-1],0,, ,,, # Mechanical properties,,, Negative electrode Poisson's ratio,0.3,,