From d7037b1c07c0eeb60e99c88bcd652d5f0223f6ce Mon Sep 17 00:00:00 2001 From: julian-evers <133691040+julian-evers@users.noreply.github.com> Date: Sat, 16 Dec 2023 00:48:28 +0100 Subject: [PATCH 01/10] merge_conflicts --- CHANGELOG.md | 1 + .../examples/notebooks/change-settings.ipynb | 8 ++--- .../tutorial-4-setting-parameter-values.ipynb | 4 +-- .../notebooks/models/composite_particle.ipynb | 4 +-- .../examples/notebooks/models/latexify.ipynb | 20 +++++------ .../notebooks/models/pouch-cell-model.ipynb | 4 +-- .../notebooks/models/using-submodels.ipynb | 2 +- .../parameterization/parameterization.ipynb | 20 +++++------ examples/scripts/emperical_hysteresis.py | 6 ++-- .../minimal_example_of_lookup_tables.py | 6 ++-- pybamm/input/parameters/lithium_ion/Ai2020.py | 4 +-- .../input/parameters/lithium_ion/Chen2020.py | 4 +-- .../lithium_ion/Chen2020_composite.py | 6 ++-- .../input/parameters/lithium_ion/Ecker2015.py | 4 +-- .../Ecker2015_graphite_halfcell.py | 2 +- .../lithium_ion/MSMR_example_set.py | 4 +-- .../parameters/lithium_ion/Marquis2019.py | 4 +-- .../parameters/lithium_ion/Mohtat2020.py | 4 +-- .../parameters/lithium_ion/NCA_Kim2011.py | 4 +-- .../input/parameters/lithium_ion/OKane2022.py | 4 +-- .../OKane2022_graphite_SiOx_halfcell.py | 2 +- .../parameters/lithium_ion/ORegan2022.py | 4 +-- .../input/parameters/lithium_ion/Prada2013.py | 4 +-- .../parameters/lithium_ion/Ramadass2004.py | 4 +-- pybamm/input/parameters/lithium_ion/Xu2019.py | 2 +- pybamm/parameters/bpx.py | 33 ++++++++++++------- pybamm/parameters/lithium_ion_parameters.py | 2 +- pybamm/util.py | 5 +++ .../test_compare_outputs_two_phase.py | 2 +- .../test_simulation_with_experiment.py | 2 +- tests/unit/test_models/test_model_info.py | 6 ++-- tests/unit/test_parameters/test_bpx.py | 12 +++---- .../test_parameter_sets/test_Ai2020.py | 4 +-- .../test_parameter_sets/test_Ecker2015.py | 4 +-- .../test_Ecker2015_graphite_halfcell.py | 2 +- .../test_LCO_Ramadass2004.py | 4 +-- .../test_LGM50_ORegan2022.py | 4 +-- .../test_parameter_sets/test_OKane2022.py | 4 +-- .../test_OKane2022_negative_halfcell.py | 2 +- tests/unit/test_util.py | 4 +++ 40 files changed, 121 insertions(+), 100 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 82f07dea29..7e5205425f 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,7 @@ ## Features +- Renamed "electrode diffusivity" to "particle diffusivity" as a non-breaking change with a deprecation warning ([#3604](https://github.com/pybamm-team/PyBaMM/pull/3604)) - Added method to get QuickPlot axes by variable ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596)) - Added custom experiment terminations ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596)) - Mechanical parameters are now a function of stoichiometry and temperature ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576)) diff --git a/docs/source/examples/notebooks/change-settings.ipynb b/docs/source/examples/notebooks/change-settings.ipynb index c54da8754c..6b4f71c519 100644 --- a/docs/source/examples/notebooks/change-settings.ipynb +++ b/docs/source/examples/notebooks/change-settings.ipynb @@ -174,7 +174,7 @@ " 'Negative electrode charge transfer coefficient': 0.5,\n", " 'Negative electrode conductivity [S.m-1]': 100.0,\n", " 'Negative electrode density [kg.m-3]': 1657.0,\n", - " 'Negative electrode diffusivity [m2.s-1]': ,\n", + " 'Negative particle diffusivity [m2.s-1]': ,\n", " 'Negative electrode double-layer capacity [F.m-2]': 0.2,\n", " 'Negative electrode electrons in reaction': 1.0,\n", " 'Negative electrode exchange-current density [A.m-2]': ,\n", @@ -209,7 +209,7 @@ " 'Positive electrode charge transfer coefficient': 0.5,\n", " 'Positive electrode conductivity [S.m-1]': 10.0,\n", " 'Positive electrode density [kg.m-3]': 3262.0,\n", - " 'Positive electrode diffusivity [m2.s-1]': ,\n", + " 'Positive particle diffusivity [m2.s-1]': ,\n", " 'Positive electrode double-layer capacity [F.m-2]': 0.2,\n", " 'Positive electrode electrons in reaction': 1.0,\n", " 'Positive electrode exchange-current density [A.m-2]': ,\n", @@ -313,7 +313,7 @@ "Current function [A] 0.680616\n", "Negative electrode conductivity [S.m-1] 100.0\n", "Maximum concentration in negative electrode [mol.m-3] 24983.2619938437\n", - "Negative electrode diffusivity [m2.s-1] \n", + "Negative particle diffusivity [m2.s-1] \n", "Negative electrode OCP [V] \n", "Negative electrode porosity 0.3\n", "Negative electrode active material volume fraction 0.6\n", @@ -331,7 +331,7 @@ "Negative electrode OCP entropic change [V.K-1] \n", "Positive electrode conductivity [S.m-1] 10.0\n", "Maximum concentration in positive electrode [mol.m-3] 51217.9257309275\n", - "Positive electrode diffusivity [m2.s-1] \n", + "Positive particle diffusivity [m2.s-1] \n", "Positive electrode OCP [V] \n", "Positive electrode porosity 0.3\n", "Positive electrode active material volume fraction 0.5\n", diff --git a/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb b/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb index 64a345c312..1265c58b1c 100644 --- a/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb +++ b/docs/source/examples/notebooks/getting_started/tutorial-4-setting-parameter-values.ipynb @@ -122,7 +122,7 @@ " 'Negative electrode charge transfer coefficient': 0.5,\n", " 'Negative electrode conductivity [S.m-1]': 215.0,\n", " 'Negative electrode density [kg.m-3]': 1657.0,\n", - " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", + " 'Negative particle diffusivity [m2.s-1]': 3.3e-14,\n", " 'Negative electrode double-layer capacity [F.m-2]': 0.2,\n", " 'Negative electrode electrons in reaction': 1.0,\n", " 'Negative electrode exchange-current density [A.m-2]': ,\n", @@ -154,7 +154,7 @@ " 'Positive electrode charge transfer coefficient': 0.5,\n", " 'Positive electrode conductivity [S.m-1]': 0.18,\n", " 'Positive electrode density [kg.m-3]': 3262.0,\n", - " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", + " 'Positive particle diffusivity [m2.s-1]': 4e-15,\n", " 'Positive electrode double-layer capacity [F.m-2]': 0.2,\n", " 'Positive electrode electrons in reaction': 1.0,\n", " 'Positive electrode exchange-current density [A.m-2]': ,\n", diff --git a/docs/source/examples/notebooks/models/composite_particle.ipynb b/docs/source/examples/notebooks/models/composite_particle.ipynb index 59fa9c957e..123d099b3f 100644 --- a/docs/source/examples/notebooks/models/composite_particle.ipynb +++ b/docs/source/examples/notebooks/models/composite_particle.ipynb @@ -86,8 +86,8 @@ "param.update({\n", " \"Primary: Maximum concentration in negative electrode [mol.m-3]\":28700,\n", " \"Primary: Initial concentration in negative electrode [mol.m-3]\":23000,\n", - " \"Primary: Negative electrode diffusivity [m2.s-1]\":5.5E-14,\n", - " \"Secondary: Negative electrode diffusivity [m2.s-1]\":1.67E-14,\n", + " \"Primary: Negative particle diffusivity [m2.s-1]\":5.5E-14,\n", + " \"Secondary: Negative particle diffusivity [m2.s-1]\":1.67E-14,\n", " \"Secondary: Initial concentration in negative electrode [mol.m-3]\":277000,\n", " \"Secondary: Maximum concentration in negative electrode [mol.m-3]\":278000\n", "})" diff --git a/docs/source/examples/notebooks/models/latexify.ipynb b/docs/source/examples/notebooks/models/latexify.ipynb index c2a45ff2c8..5e50c1c12e 100644 --- a/docs/source/examples/notebooks/models/latexify.ipynb +++ b/docs/source/examples/notebooks/models/latexify.ipynb @@ -69,7 +69,7 @@ "data": { "image/png": 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", "text/latex": [ - "$\\displaystyle \\large{\\underline{\\textbf{Single Particle Model Equations}}}\\\\\\\\\\\\ \\textbf{Discharge capacity [A.h]}\\\\\\\\\\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}\\\\\\\\Q_{Ah} = 0.0\\quad \\text{at}\\; t=0\\\\\\\\\\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}\\\\\\\\\\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}\\\\\\\\\\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = - \\frac{j_{\\mathrm{p}}}{D_{\\mathrm{p}}^{surf} F}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{Voltage [V]}\\\\\\\\V = - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) + \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{p}}} - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{n}}}\\\\\\\\\\\\ \\textbf{Parameters and Variables}\\\\\\\\I = \\text{Current function [A]}\\\\\\\\\\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}\\\\\\\\T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}\\\\\\\\c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}\\\\\\\\\\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}\\\\\\\\c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}$" + "$\\displaystyle \\large{\\underline{\\textbf{Single Particle Model Equations}}}\\\\\\\\\\\\ \\textbf{Discharge capacity [A.h]}\\\\\\\\\\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}\\\\\\\\Q_{Ah} = 0.0\\quad \\text{at}\\; t=0\\\\\\\\\\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}\\\\\\\\\\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}\\\\\\\\\\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0\\\\\\\\\\nabla \\overline{c}_{\\mathrm{s,p}} = - \\frac{j_{\\mathrm{p}}}{D_{\\mathrm{p}}^{surf} F}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{Voltage [V]}\\\\\\\\V = - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) + \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{p}}} - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{n}}}\\\\\\\\\\\\ \\textbf{Parameters and Variables}\\\\\\\\I = \\text{Current function [A]}\\\\\\\\\\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{n}} = \\text{Negative particle diffusivity [m2.s-1]}\\\\\\\\T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}\\\\\\\\c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}\\\\\\\\\\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{p}} = \\text{Positive particle diffusivity [m2.s-1]}\\\\\\\\c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}$" ], "text/plain": [ "\\large{\\underline{\\textbf{Single Particle Model Equations}}}\\\\\\\\\\\\ \\textbf{Dis\n", @@ -91,10 +91,10 @@ "\\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}_{\\mathrm{n}}}\\\\\\\\\\\\ \\textbf{Parameter\n", "s and Variables}\\\\\\\\I = \\text{Current function [A]}\\\\\\\\\\overline{c}_{\\mathrm{s\n", ",n}} = \\text{X-averaged negative particle concentration [mol.m-3]}\\\\\\\\D_{\\math\n", - "rm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}\\\\\\\\T_{\\mathrm{amb}} = \n", + "rm{n}} = \\text{Negative particle diffusivity [m2.s-1]}\\\\\\\\T_{\\mathrm{amb}} = \n", "\\text{Ambient temperature [K]}\\\\\\\\c_{\\mathrm{n}}_{\\mathrm{s,p}} = \\text{X-aver\n", "aged positive particle concentration [mol.m-3]}\\\\\\\\D_{\\mathrm{p}} = \\text{Posi\n", - "tive electrode diffusivity [m2.s-1]}\\\\\\\\c_{\\mathrm{p}}__{\\mathrm{typ}}\\\\\\\\\\ove\n", + "tive particle diffusivity [m2.s-1]}\\\\\\\\c_{\\mathrm{p}}__{\\mathrm{typ}}\\\\\\\\\\ove\n", "rline{c}__{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0\\\\\\\\\\nabla \\overline{c}__{\n", "surf} F L__{\\mathrm{typ}}\\\\\\\\\\\\ \\textbf{X-averaged positive particle concentra\n", "tion [mol.m-3]}\\\\\\\\\\frac{\\partial}{\\partial t} \\overline{c}__{\\mathrm{typ}}\\\\\\\n", @@ -132,7 +132,7 @@ "data": { "image/png": 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", "text/latex": [ - "$\\displaystyle \\left[ \\large{\\underline{\\textbf{Single Particle Model Equations}}}, \\ \\\\ \\textbf{Discharge capacity [A.h]}, \\ \\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}, \\ Q_{Ah} = 0.0\\quad \\text{at}\\; t=0, \\ \\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}, \\ \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}, \\ \\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\ \\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0, \\ \\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}, \\ \\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}, \\ \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}, \\ \\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\ \\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0, \\ \\nabla \\overline{c}_{\\mathrm{s,p}} = - \\frac{j_{\\mathrm{p}}}{D_{\\mathrm{p}}^{surf} F}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}, \\ \\\\ \\textbf{Voltage [V]}, \\ V = - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) + \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{p}}} - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{n}}}, \\ \\\\ \\textbf{Parameters and Variables}, \\ I = \\text{Current function [A]}, \\ \\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}, \\ D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}, \\ T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}, \\ c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}, \\ \\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}, \\ D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}, \\ c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}\\right]$" + "$\\displaystyle \\left[ \\large{\\underline{\\textbf{Single Particle Model Equations}}}, \\ \\\\ \\textbf{Discharge capacity [A.h]}, \\ \\frac{d}{d t} Q_{Ah} = \\frac{I}{3600}, \\ Q_{Ah} = 0.0\\quad \\text{at}\\; t=0, \\ \\\\ \\textbf{X-averaged negative particle concentration [mol.m-3]}, \\ \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,n}} = \\nabla\\cdot \\left(D_{\\mathrm{n}} \\left(\\nabla \\overline{c}_{\\mathrm{s,n}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{n}}^{\\mathrm{typ}}, \\ \\overline{c}_{\\mathrm{s,n}} = \\int c_{\\mathrm{n}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\ \\nabla \\overline{c}_{\\mathrm{s,n}} = 0.0\\quad \\text{at } r = 0, \\ \\nabla \\overline{c}_{\\mathrm{s,n}} = - \\frac{i_{\\mathrm{cell}}}{D_{\\mathrm{n}}^{surf} F L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}}}\\quad \\text{at } r = R_{\\mathrm{n}}^{\\mathrm{typ}}, \\ \\\\ \\textbf{X-averaged positive particle concentration [mol.m-3]}, \\ \\frac{\\partial}{\\partial t} \\overline{c}_{\\mathrm{s,p}} = \\nabla\\cdot \\left(D_{\\mathrm{p}} \\left(\\nabla \\overline{c}_{\\mathrm{s,p}}\\right)\\right)\\quad 0 < r < R_{\\mathrm{p}}^{\\mathrm{typ}}, \\ \\overline{c}_{\\mathrm{s,p}} = \\int c_{\\mathrm{p}}^{\\mathrm{init}}\\, dxn\\quad \\text{at}\\; t=0, \\ \\nabla \\overline{c}_{\\mathrm{s,p}} = 0.0\\quad \\text{at } r = 0, \\ \\nabla \\overline{c}_{\\mathrm{s,p}} = - \\frac{j_{\\mathrm{p}}}{D_{\\mathrm{p}}^{surf} F}\\quad \\text{at } r = R_{\\mathrm{p}}^{\\mathrm{typ}}, \\ \\\\ \\textbf{Voltage [V]}, \\ V = - U_\\mathrm{n}(c^\\mathrm{surf}_\\mathrm{s,n}, T) + U_\\mathrm{p}(c^\\mathrm{surf}_\\mathrm{s,p}, T) + \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 j_{\\mathrm{p}}}{j_{\\mathrm{p}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{p}}} - \\frac{2.0 R T_{\\mathrm{amb}} \\operatorname{asinh}{\\left(\\frac{0.5 i_{\\mathrm{cell}}}{L_{\\mathrm{n}} \\overline{a}_{\\mathrm{n}} j_{\\mathrm{n}}^{\\mathrm{0}}} \\right)}}{F ne_{\\mathrm{n}}}, \\ \\\\ \\textbf{Parameters and Variables}, \\ I = \\text{Current function [A]}, \\ \\overline{c}_{\\mathrm{s,n}} = \\text{X-averaged negative particle concentration [mol.m-3]}, \\ D_{\\mathrm{n}} = \\text{Negative particle diffusivity [m2.s-1]}, \\ T_{\\mathrm{amb}} = \\text{Ambient temperature [K]}, \\ c_{\\mathrm{n}}^{\\mathrm{max}} = \\text{Maximum concentration in negative electrode [mol.m-3]}, \\ \\overline{c}_{\\mathrm{s,p}} = \\text{X-averaged positive particle concentration [mol.m-3]}, \\ D_{\\mathrm{p}} = \\text{Positive particle diffusivity [m2.s-1]}, \\ c_{\\mathrm{p}}^{\\mathrm{max}} = \\text{Maximum concentration in positive electrode [mol.m-3]}\\right]$" ], "text/plain": [ "⎡ \n", @@ -270,7 +270,7 @@ " \n", " \n", " \n", - "_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}, T_{\\mathrm{amb\n", + "_{\\mathrm{n}} = \\text{Negative particle diffusivity [m2.s-1]}, T_{\\mathrm{amb\n", " \n", "\n", " \n", @@ -294,7 +294,7 @@ " \n", " \n", " \n", - "p}} = \\text{Positive electrode diffusivity [m2.s-1]}, c_{\\mathrm{p}}__{\\mathrm\n", + "p}} = \\text{Positive particle diffusivity [m2.s-1]}, c_{\\mathrm{p}}__{\\mathrm\n", " \n", "\n", " ⎤\n", @@ -725,10 +725,10 @@ "data": { "image/png": "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", "text/latex": [ - "$\\displaystyle D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}$" + "$\\displaystyle D_{\\mathrm{n}} = \\text{Negative particle diffusivity [m2.s-1]}$" ], "text/plain": [ - "D_{\\mathrm{n}} = \\text{Negative electrode diffusivity [m2.s-1]}" + "D_{\\mathrm{n}} = \\text{Negative particle diffusivity [m2.s-1]}" ] }, "metadata": {}, @@ -779,10 +779,10 @@ "data": { "image/png": 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"text/latex": [ - "$\\displaystyle D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}$" + "$\\displaystyle D_{\\mathrm{p}} = \\text{Positive particle diffusivity [m2.s-1]}$" ], "text/plain": [ - "D_{\\mathrm{p}} = \\text{Positive electrode diffusivity [m2.s-1]}" + "D_{\\mathrm{p}} = \\text{Positive particle diffusivity [m2.s-1]}" ] }, "metadata": {}, diff --git a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb index 2c58b1861f..f0c3869985 100644 --- a/docs/source/examples/notebooks/models/pouch-cell-model.ipynb +++ b/docs/source/examples/notebooks/models/pouch-cell-model.ipynb @@ -136,8 +136,8 @@ "param.update(\n", " {\n", " \"Current function [A]\": I_1C * 3, \n", - " \"Negative electrode diffusivity [m2.s-1]\": 3.9 * 10 ** (-14),\n", - " \"Positive electrode diffusivity [m2.s-1]\": 10 ** (-13),\n", + " \"Negative particle diffusivity [m2.s-1]\": 3.9 * 10 ** (-14),\n", + " \"Positive particle diffusivity [m2.s-1]\": 10 ** (-13),\n", " \"Negative current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n", " \"Positive current collector surface heat transfer coefficient [W.m-2.K-1]\": 10,\n", " \"Negative tab heat transfer coefficient [W.m-2.K-1]\": 10,\n", diff --git a/docs/source/examples/notebooks/models/using-submodels.ipynb b/docs/source/examples/notebooks/models/using-submodels.ipynb index 211e3346d8..3f11755498 100644 --- a/docs/source/examples/notebooks/models/using-submodels.ipynb +++ b/docs/source/examples/notebooks/models/using-submodels.ipynb @@ -276,7 +276,7 @@ "{Variable(0x3825da4a5fc4eb0b, Discharge capacity [A.h], children=[], domains={}): Multiplication(0x7678edd47e530eec, *, children=['0.0002777777777777778', 'Current function [A]'], domains={}),\n", " Variable(-0x7fb8d0e6e9632372, Throughput capacity [A.h], children=[], domains={}): Multiplication(-0x7c65e8600b424661, *, children=['0.0002777777777777778', 'abs(Current function [A])'], domains={}),\n", " Variable(0x69f725db1a464db8, Average negative particle concentration [mol.m-3], children=[], domains={'primary': ['current collector']}): MatrixMultiplication(0xf98a766c86b2483, @, children=['mass(Average negative particle concentration [mol.m-3])', '-3.0 * Current function [A] / (Number of electrodes connected in parallel to make a cell * Electrode width [m] * Electrode height [m]) / Negative electrode thickness [m] / x-average(3.0 * Negative electrode active material volume fraction / Negative particle radius [m]) / Faraday constant [C.mol-1] / x-average(Negative particle radius [m])'], domains={'primary': ['current collector']}),\n", - " Variable(0x48143b39c7603013, X-averaged positive particle concentration [mol.m-3], children=[], domains={'primary': ['positive particle'], 'secondary': ['current collector']}): Divergence(0x17c75a81711ad510, div, children=['Positive electrode diffusivity [m2.s-1] * grad(X-averaged positive particle concentration [mol.m-3])'], domains={'primary': ['positive particle'], 'secondary': ['current collector']})}" + " Variable(0x48143b39c7603013, X-averaged positive particle concentration [mol.m-3], children=[], domains={'primary': ['positive particle'], 'secondary': ['current collector']}): Divergence(0x17c75a81711ad510, div, children=['Positive particle diffusivity [m2.s-1] * grad(X-averaged positive particle concentration [mol.m-3])'], domains={'primary': ['positive particle'], 'secondary': ['current collector']})}" ] }, "execution_count": 9, diff --git a/docs/source/examples/notebooks/parameterization/parameterization.ipynb b/docs/source/examples/notebooks/parameterization/parameterization.ipynb index 3ec04e9654..63515b2dc9 100644 --- a/docs/source/examples/notebooks/parameterization/parameterization.ipynb +++ b/docs/source/examples/notebooks/parameterization/parameterization.ipynb @@ -481,10 +481,10 @@ "Negative electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", "Negative electrode exchange-current density [A.m-2] (FunctionParameter with input(s) 'Electrolyte concentration [mol.m-3]', 'Negative particle surface concentration [mol.m-3]', 'Maximum negative particle surface concentration [mol.m-3]', 'Temperature [K]')\n", "Positive electrode OCP [V] (FunctionParameter with input(s) 'Positive particle stoichiometry')\n", - "Positive electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Temperature [K]')\n", + "Positive particle diffusivity [m2.s-1] (FunctionParameter with input(s) 'Positive particle stoichiometry', 'Temperature [K]')\n", "Positive electrode porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", "Initial concentration in negative electrode [mol.m-3] (FunctionParameter with input(s) 'Radial distance (r) [m]', 'Through-cell distance (x) [m]')\n", - "Negative electrode diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\n", + "Negative particle diffusivity [m2.s-1] (FunctionParameter with input(s) 'Negative particle stoichiometry', 'Temperature [K]')\n", "Negative electrode active material volume fraction (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", "Separator porosity (FunctionParameter with input(s) 'Through-cell distance (x) [m]')\n", "Current function [A] (FunctionParameter with input(s) 'Time[s]')\n", @@ -533,7 +533,7 @@ " 'Nominal cell capacity [A.h]': 0.680616,\n", " 'Current function [A]': 0.680616,\n", " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n", - " 'Negative electrode diffusivity [m2.s-1]': ,\n", + " 'Negative particle diffusivity [m2.s-1]': ,\n", " 'Negative electrode OCP [V]': ,\n", " 'Negative electrode porosity': 0.3,\n", " 'Negative electrode active material volume fraction': 0.6,\n", @@ -543,7 +543,7 @@ " 'Negative electrode exchange-current density [A.m-2]': ,\n", " 'Negative electrode OCP entropic change [V.K-1]': ,\n", " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n", - " 'Positive electrode diffusivity [m2.s-1]': ,\n", + " 'Positive particle diffusivity [m2.s-1]': ,\n", " 'Positive electrode OCP [V]': ,\n", " 'Positive electrode porosity': 0.3,\n", " 'Positive electrode active material volume fraction': 0.5,\n", @@ -706,7 +706,7 @@ " 0.08709427, 0.08503284, 0.07601531]))),\n", " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", " 'Negative electrode active material volume fraction': 0.75,\n", - " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", + " 'Negative particle diffusivity [m2.s-1]': 3.3e-14,\n", " 'Negative electrode electrons in reaction': 1.0,\n", " 'Negative electrode exchange-current density [A.m-2]': ,\n", " 'Negative electrode porosity': 0.25,\n", @@ -816,7 +816,7 @@ " 3.5684922 , 3.5672133 , 3.52302167]))),\n", " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n", " 'Positive electrode active material volume fraction': 0.665,\n", - " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", + " 'Positive particle diffusivity [m2.s-1]': 4e-15,\n", " 'Positive electrode electrons in reaction': 1.0,\n", " 'Positive electrode exchange-current density [A.m-2]': ,\n", " 'Positive electrode porosity': 0.335,\n", @@ -1355,7 +1355,7 @@ " 'Typical current [A]': 5.0,\n", " 'Current function [A]': 5.0,\n", " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", - " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", + " 'Negative particle diffusivity [m2.s-1]': 3.3e-14,\n", " 'Negative electrode OCP [V]': ('graphite_LGM50_ocp_Chen2020', neg_ocp),\n", " 'Negative electrode porosity': 0.25,\n", " 'Negative electrode active material volume fraction': 0.75,\n", @@ -1366,7 +1366,7 @@ " 'Negative electrode exchange-current density [A.m-2]': graphite_LGM50_electrolyte_exchange_current_density_Chen2020,\n", " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", - " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", + " 'Positive particle diffusivity [m2.s-1]': 4e-15,\n", " 'Positive electrode OCP [V]': ('nmc_LGM50_ocp_Chen2020', pos_ocp),\n", " 'Positive electrode porosity': 0.335,\n", " 'Positive electrode active material volume fraction': 0.665,\n", @@ -1420,7 +1420,7 @@ " 'Nominal cell capacity [A.h]': 5.0,\n", " 'Current function [A]': 5.0,\n", " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n", - " 'Negative electrode diffusivity [m2.s-1]': 3.3e-14,\n", + " 'Negative particle diffusivity [m2.s-1]': 3.3e-14,\n", " 'Negative electrode OCP [V]': ,\n", " 'Negative electrode porosity': 0.25,\n", " 'Negative electrode active material volume fraction': 0.75,\n", @@ -1430,7 +1430,7 @@ " 'Negative electrode exchange-current density [A.m-2]': ,\n", " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n", " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n", - " 'Positive electrode diffusivity [m2.s-1]': 4e-15,\n", + " 'Positive particle diffusivity [m2.s-1]': 4e-15,\n", " 'Positive electrode OCP [V]': ,\n", " 'Positive electrode porosity': 0.335,\n", " 'Positive electrode active material volume fraction': 0.665,\n", diff --git a/examples/scripts/emperical_hysteresis.py b/examples/scripts/emperical_hysteresis.py index 3f3fc7c640..e21d201b4c 100644 --- a/examples/scripts/emperical_hysteresis.py +++ b/examples/scripts/emperical_hysteresis.py @@ -122,9 +122,9 @@ def exchange_current_density_average(sto): "": exchange_current_density_lithiation, "Negative electrode delithiation exchange-current density [A.m-2]" "": exchange_current_density_delithiation, - "Negative electrode diffusivity [m2.s-1]": 3.3e-14, - "Negative electrode lithiation diffusivity [m2.s-1]": 4e-14, - "Negative electrode delithiation diffusivity [m2.s-1]": 2.6e-14, + "Negative particle diffusivity [m2.s-1]": 3.3e-14, + "Negative particle lithiation diffusivity [m2.s-1]": 4e-14, + "Negative particle delithiation diffusivity [m2.s-1]": 2.6e-14, }, check_already_exists=False, ) diff --git a/examples/scripts/minimal_example_of_lookup_tables.py b/examples/scripts/minimal_example_of_lookup_tables.py index 1c93e311c0..24aba26a44 100644 --- a/examples/scripts/minimal_example_of_lookup_tables.py +++ b/examples/scripts/minimal_example_of_lookup_tables.py @@ -22,7 +22,7 @@ def process_2D(name, data): parameter_values = pybamm.ParameterValues(pybamm.parameter_sets.Chen2020) # overwrite the diffusion coefficient with a 2D lookup table -D_s_n = parameter_values["Negative electrode diffusivity [m2.s-1]"] +D_s_n = parameter_values["Negative particle diffusivity [m2.s-1]"] df = pd.DataFrame( { "T": [0, 0, 25, 25, 45, 45], @@ -31,7 +31,7 @@ def process_2D(name, data): } ) df["T"] = df["T"] + 273.15 -D_s_n_data = process_2D("Negative electrode diffusivity [m2.s-1]", df) +D_s_n_data = process_2D("Negative particle diffusivity [m2.s-1]", df) def D_s_n(sto, T): @@ -39,7 +39,7 @@ def D_s_n(sto, T): return pybamm.Interpolant(x, y, [T, sto], name) -parameter_values["Negative electrode diffusivity [m2.s-1]"] = D_s_n +parameter_values["Negative particle diffusivity [m2.s-1]"] = D_s_n k_n = parameter_values["Negative electrode exchange-current density [A.m-2]"] diff --git a/pybamm/input/parameters/lithium_ion/Ai2020.py b/pybamm/input/parameters/lithium_ion/Ai2020.py index abae3087ea..2287a54a84 100644 --- a/pybamm/input/parameters/lithium_ion/Ai2020.py +++ b/pybamm/input/parameters/lithium_ion/Ai2020.py @@ -586,7 +586,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 100.0, "Maximum concentration in negative electrode [mol.m-3]": 28700.0, - "Negative electrode diffusivity [m2.s-1]": graphite_diffusivity_Dualfoil1998, + "Negative particle diffusivity [m2.s-1]": graphite_diffusivity_Dualfoil1998, "Negative electrode OCP [V]": graphite_ocp_Enertech_Ai2020, "Negative electrode porosity": 0.33, "Negative electrode active material volume fraction": 0.61, @@ -621,7 +621,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 10.0, "Maximum concentration in positive electrode [mol.m-3]": 49943.0, - "Positive electrode diffusivity [m2.s-1]": lico2_diffusivity_Dualfoil1998, + "Positive particle diffusivity [m2.s-1]": lico2_diffusivity_Dualfoil1998, "Positive electrode OCP [V]": lico2_ocp_Ai2020, "Positive electrode porosity": 0.32, "Positive electrode active material volume fraction": 0.62, diff --git a/pybamm/input/parameters/lithium_ion/Chen2020.py b/pybamm/input/parameters/lithium_ion/Chen2020.py index e526b480c4..7d5463b5cf 100644 --- a/pybamm/input/parameters/lithium_ion/Chen2020.py +++ b/pybamm/input/parameters/lithium_ion/Chen2020.py @@ -277,7 +277,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 215.0, "Maximum concentration in negative electrode [mol.m-3]": 33133.0, - "Negative electrode diffusivity [m2.s-1]": 3.3e-14, + "Negative particle diffusivity [m2.s-1]": 3.3e-14, "Negative electrode OCP [V]": graphite_LGM50_ocp_Chen2020, "Negative electrode porosity": 0.25, "Negative electrode active material volume fraction": 0.75, @@ -295,7 +295,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 0.18, "Maximum concentration in positive electrode [mol.m-3]": 63104.0, - "Positive electrode diffusivity [m2.s-1]": 4e-15, + "Positive particle diffusivity [m2.s-1]": 4e-15, "Positive electrode OCP [V]": nmc_LGM50_ocp_Chen2020, "Positive electrode porosity": 0.335, "Positive electrode active material volume fraction": 0.665, diff --git a/pybamm/input/parameters/lithium_ion/Chen2020_composite.py b/pybamm/input/parameters/lithium_ion/Chen2020_composite.py index f7e27c8d52..742bbfa1d1 100644 --- a/pybamm/input/parameters/lithium_ion/Chen2020_composite.py +++ b/pybamm/input/parameters/lithium_ion/Chen2020_composite.py @@ -400,7 +400,7 @@ def get_parameter_values(): "Negative electrode conductivity [S.m-1]": 215.0, "Primary: Maximum concentration in negative electrode [mol.m-3]": 28700.0, "Primary: Initial concentration in negative electrode [mol.m-3]": 27700.0, - "Primary: Negative electrode diffusivity [m2.s-1]": 5.5e-14, + "Primary: Negative particle diffusivity [m2.s-1]": 5.5e-14, "Primary: Negative electrode OCP [V]": graphite_ocp_Enertech_Ai2020, "Negative electrode porosity": 0.25, "Primary: Negative electrode active material volume fraction": 0.735, @@ -417,7 +417,7 @@ def get_parameter_values(): "Primary: Negative electrode OCP entropic change [V.K-1]": 0.0, "Secondary: Maximum concentration in negative electrode [mol.m-3]": 278000.0, "Secondary: Initial concentration in negative electrode [mol.m-3]": 276610.0, - "Secondary: Negative electrode diffusivity [m2.s-1]": 1.67e-14, + "Secondary: Negative particle diffusivity [m2.s-1]": 1.67e-14, "Secondary: Negative electrode lithiation OCP [V]" "": silicon_ocp_lithiation_Mark2016, "Secondary: Negative electrode delithiation OCP [V]" @@ -431,7 +431,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 0.18, "Maximum concentration in positive electrode [mol.m-3]": 63104.0, - "Positive electrode diffusivity [m2.s-1]": 4e-15, + "Positive particle diffusivity [m2.s-1]": 4e-15, "Positive electrode OCP [V]": nmc_LGM50_ocp_Chen2020, "Positive electrode porosity": 0.335, "Positive electrode active material volume fraction": 0.665, diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015.py b/pybamm/input/parameters/lithium_ion/Ecker2015.py index 3f37db6e61..9af47ca456 100644 --- a/pybamm/input/parameters/lithium_ion/Ecker2015.py +++ b/pybamm/input/parameters/lithium_ion/Ecker2015.py @@ -466,7 +466,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 14.0, "Maximum concentration in negative electrode [mol.m-3]": 31920.0, - "Negative electrode diffusivity [m2.s-1]": graphite_diffusivity_Ecker2015, + "Negative particle diffusivity [m2.s-1]": graphite_diffusivity_Ecker2015, "Negative electrode OCP [V]": graphite_ocp_Ecker2015, "Negative electrode porosity": 0.329, "Negative electrode active material volume fraction": 0.372403, @@ -482,7 +482,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 68.1, "Maximum concentration in positive electrode [mol.m-3]": 48580.0, - "Positive electrode diffusivity [m2.s-1]": nco_diffusivity_Ecker2015, + "Positive particle diffusivity [m2.s-1]": nco_diffusivity_Ecker2015, "Positive electrode OCP [V]": nco_ocp_Ecker2015, "Positive electrode porosity": 0.296, "Positive electrode active material volume fraction": 0.40832, diff --git a/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py index f6bc8e4d93..0ee289a5d0 100644 --- a/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py +++ b/pybamm/input/parameters/lithium_ion/Ecker2015_graphite_halfcell.py @@ -388,7 +388,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 14.0, "Maximum concentration in positive electrode [mol.m-3]": 31920.0, - "Positive electrode diffusivity [m2.s-1]": graphite_diffusivity_Ecker2015, + "Positive particle diffusivity [m2.s-1]": graphite_diffusivity_Ecker2015, "Positive electrode OCP [V]": graphite_ocp_Ecker2015, "Positive electrode porosity": 0.329, "Positive electrode active material volume fraction": 0.372403, diff --git a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py index fce5c7f068..55033431bd 100644 --- a/pybamm/input/parameters/lithium_ion/MSMR_example_set.py +++ b/pybamm/input/parameters/lithium_ion/MSMR_example_set.py @@ -136,7 +136,7 @@ def get_parameter_values(): "j0_ref_n_5": 2.7, "Negative electrode conductivity [S.m-1]": 215.0, "Maximum concentration in negative electrode [mol.m-3]": 33133.0, - "Negative electrode diffusivity [m2.s-1]": 3.3e-14, + "Negative particle diffusivity [m2.s-1]": 3.3e-14, "Negative electrode porosity": 0.25, "Negative electrode active material volume fraction": 0.75, "Negative particle radius [m]": 5.86e-06, @@ -167,7 +167,7 @@ def get_parameter_values(): "j0_ref_p_3": 1e6, "Positive electrode conductivity [S.m-1]": 0.18, "Maximum concentration in positive electrode [mol.m-3]": 63104.0, - "Positive electrode diffusivity [m2.s-1]": 4e-15, + "Positive particle diffusivity [m2.s-1]": 4e-15, "Positive electrode porosity": 0.335, "Positive electrode active material volume fraction": 0.665, "Positive particle radius [m]": 5.22e-06, diff --git a/pybamm/input/parameters/lithium_ion/Marquis2019.py b/pybamm/input/parameters/lithium_ion/Marquis2019.py index d3bddc6e30..f1ed1329d4 100644 --- a/pybamm/input/parameters/lithium_ion/Marquis2019.py +++ b/pybamm/input/parameters/lithium_ion/Marquis2019.py @@ -403,7 +403,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 100.0, "Maximum concentration in negative electrode [mol.m-3]": 24983.2619938437, - "Negative electrode diffusivity [m2.s-1]" + "Negative particle diffusivity [m2.s-1]" "": graphite_mcmb2528_diffusivity_Dualfoil1998, "Negative electrode OCP [V]": graphite_mcmb2528_ocp_Dualfoil1998, "Negative electrode porosity": 0.3, @@ -423,7 +423,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 10.0, "Maximum concentration in positive electrode [mol.m-3]": 51217.9257309275, - "Positive electrode diffusivity [m2.s-1]": lico2_diffusivity_Dualfoil1998, + "Positive particle diffusivity [m2.s-1]": lico2_diffusivity_Dualfoil1998, "Positive electrode OCP [V]": lico2_ocp_Dualfoil1998, "Positive electrode porosity": 0.3, "Positive electrode active material volume fraction": 0.5, diff --git a/pybamm/input/parameters/lithium_ion/Mohtat2020.py b/pybamm/input/parameters/lithium_ion/Mohtat2020.py index 29535b9f3d..d57637c6c7 100644 --- a/pybamm/input/parameters/lithium_ion/Mohtat2020.py +++ b/pybamm/input/parameters/lithium_ion/Mohtat2020.py @@ -393,7 +393,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 100.0, "Maximum concentration in negative electrode [mol.m-3]": 28746.0, - "Negative electrode diffusivity [m2.s-1]": graphite_diffusivity_PeymanMPM, + "Negative particle diffusivity [m2.s-1]": graphite_diffusivity_PeymanMPM, "Negative electrode OCP [V]": graphite_ocp_PeymanMPM, "Negative electrode porosity": 0.3, "Negative electrode active material volume fraction": 0.61, @@ -415,7 +415,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 100.0, "Maximum concentration in positive electrode [mol.m-3]": 35380.0, - "Positive electrode diffusivity [m2.s-1]": NMC_diffusivity_PeymanMPM, + "Positive particle diffusivity [m2.s-1]": NMC_diffusivity_PeymanMPM, "Positive electrode OCP [V]": NMC_ocp_PeymanMPM, "Positive electrode porosity": 0.3, "Positive electrode active material volume fraction": 0.445, diff --git a/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py b/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py index b5543ea6c2..2878720164 100644 --- a/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py +++ b/pybamm/input/parameters/lithium_ion/NCA_Kim2011.py @@ -372,7 +372,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 100.0, "Maximum concentration in negative electrode [mol.m-3]": 28700.0, - "Negative electrode diffusivity [m2.s-1]": graphite_diffusivity_Kim2011, + "Negative particle diffusivity [m2.s-1]": graphite_diffusivity_Kim2011, "Negative electrode OCP [V]": graphite_ocp_Kim2011, "Negative electrode porosity": 0.4, "Negative electrode active material volume fraction": 0.51, @@ -390,7 +390,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 10.0, "Maximum concentration in positive electrode [mol.m-3]": 49000.0, - "Positive electrode diffusivity [m2.s-1]": nca_diffusivity_Kim2011, + "Positive particle diffusivity [m2.s-1]": nca_diffusivity_Kim2011, "Positive electrode OCP [V]": nca_ocp_Kim2011, "Positive electrode porosity": 0.4, "Positive electrode active material volume fraction": 0.41, diff --git a/pybamm/input/parameters/lithium_ion/OKane2022.py b/pybamm/input/parameters/lithium_ion/OKane2022.py index e3718fb9ee..d1b0528586 100644 --- a/pybamm/input/parameters/lithium_ion/OKane2022.py +++ b/pybamm/input/parameters/lithium_ion/OKane2022.py @@ -572,7 +572,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 215.0, "Maximum concentration in negative electrode [mol.m-3]": 33133.0, - "Negative electrode diffusivity [m2.s-1]": graphite_LGM50_diffusivity_Chen2020, + "Negative particle diffusivity [m2.s-1]": graphite_LGM50_diffusivity_Chen2020, "Negative electrode OCP [V]": graphite_LGM50_ocp_Chen2020, "Negative electrode porosity": 0.25, "Negative electrode active material volume fraction": 0.75, @@ -605,7 +605,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 0.18, "Maximum concentration in positive electrode [mol.m-3]": 63104.0, - "Positive electrode diffusivity [m2.s-1]": nmc_LGM50_diffusivity_Chen2020, + "Positive particle diffusivity [m2.s-1]": nmc_LGM50_diffusivity_Chen2020, "Positive electrode OCP [V]": nmc_LGM50_ocp_Chen2020, "Positive electrode porosity": 0.335, "Positive electrode active material volume fraction": 0.665, diff --git a/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py b/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py index e13d27fad0..1e95252148 100644 --- a/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py +++ b/pybamm/input/parameters/lithium_ion/OKane2022_graphite_SiOx_halfcell.py @@ -460,7 +460,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 215.0, "Maximum concentration in positive electrode [mol.m-3]": 33133.0, - "Positive electrode diffusivity [m2.s-1]": graphite_LGM50_diffusivity_Chen2020, + "Positive particle diffusivity [m2.s-1]": graphite_LGM50_diffusivity_Chen2020, "Positive electrode OCP [V]": graphite_LGM50_ocp_Chen2020, "Positive electrode porosity": 0.25, "Positive electrode active material volume fraction": 0.75, diff --git a/pybamm/input/parameters/lithium_ion/ORegan2022.py b/pybamm/input/parameters/lithium_ion/ORegan2022.py index 3ea7ab06ce..3ca5f6824c 100644 --- a/pybamm/input/parameters/lithium_ion/ORegan2022.py +++ b/pybamm/input/parameters/lithium_ion/ORegan2022.py @@ -953,7 +953,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 215.0, "Maximum concentration in negative electrode [mol.m-3]": 29583.0, - "Negative electrode diffusivity [m2.s-1]" + "Negative particle diffusivity [m2.s-1]" "": graphite_LGM50_diffusivity_ORegan2022, "Negative electrode OCP [V]": graphite_LGM50_ocp_Chen2020, "Negative electrode porosity": 0.25, @@ -976,7 +976,7 @@ def get_parameter_values(): "Positive electrode conductivity [S.m-1]" "": nmc_LGM50_electronic_conductivity_ORegan2022, "Maximum concentration in positive electrode [mol.m-3]": 51765.0, - "Positive electrode diffusivity [m2.s-1]": nmc_LGM50_diffusivity_ORegan2022, + "Positive particle diffusivity [m2.s-1]": nmc_LGM50_diffusivity_ORegan2022, "Positive electrode OCP [V]": nmc_LGM50_ocp_Chen2020, "Positive electrode porosity": 0.335, "Positive electrode active material volume fraction": 0.665, diff --git a/pybamm/input/parameters/lithium_ion/Prada2013.py b/pybamm/input/parameters/lithium_ion/Prada2013.py index 2d3d0e7ceb..31b95473e7 100644 --- a/pybamm/input/parameters/lithium_ion/Prada2013.py +++ b/pybamm/input/parameters/lithium_ion/Prada2013.py @@ -187,7 +187,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 215.0, "Maximum concentration in negative electrode [mol.m-3]": 30555, - "Negative electrode diffusivity [m2.s-1]": 3e-15, + "Negative particle diffusivity [m2.s-1]": 3e-15, "Negative electrode OCP [V]": graphite_LGM50_ocp_Chen2020, "Negative electrode porosity": 0.36, "Negative electrode active material volume fraction": 0.58, @@ -202,7 +202,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 0.33795074, "Maximum concentration in positive electrode [mol.m-3]": 22806.0, - "Positive electrode diffusivity [m2.s-1]": 5.9e-18, + "Positive particle diffusivity [m2.s-1]": 5.9e-18, "Positive electrode OCP [V]": LFP_ocp_Afshar2017, "Positive electrode porosity": 0.426, "Positive electrode active material volume fraction": 0.374, diff --git a/pybamm/input/parameters/lithium_ion/Ramadass2004.py b/pybamm/input/parameters/lithium_ion/Ramadass2004.py index 13aa86fe8e..16a59fb4b5 100644 --- a/pybamm/input/parameters/lithium_ion/Ramadass2004.py +++ b/pybamm/input/parameters/lithium_ion/Ramadass2004.py @@ -412,7 +412,7 @@ def get_parameter_values(): # negative electrode "Negative electrode conductivity [S.m-1]": 100.0, "Maximum concentration in negative electrode [mol.m-3]": 30555.0, - "Negative electrode diffusivity [m2.s-1]" + "Negative particle diffusivity [m2.s-1]" "": graphite_mcmb2528_diffusivity_Dualfoil1998, "Negative electrode OCP [V]": graphite_ocp_Ramadass2004, "Negative electrode porosity": 0.485, @@ -432,7 +432,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 100.0, "Maximum concentration in positive electrode [mol.m-3]": 51555.0, - "Positive electrode diffusivity [m2.s-1]": lico2_diffusivity_Ramadass2004, + "Positive particle diffusivity [m2.s-1]": lico2_diffusivity_Ramadass2004, "Positive electrode OCP [V]": lico2_ocp_Ramadass2004, "Positive electrode porosity": 0.385, "Positive electrode active material volume fraction": 0.59, diff --git a/pybamm/input/parameters/lithium_ion/Xu2019.py b/pybamm/input/parameters/lithium_ion/Xu2019.py index d96afc3f04..edf3bd40b0 100644 --- a/pybamm/input/parameters/lithium_ion/Xu2019.py +++ b/pybamm/input/parameters/lithium_ion/Xu2019.py @@ -257,7 +257,7 @@ def get_parameter_values(): # positive electrode "Positive electrode conductivity [S.m-1]": 100.0, "Maximum concentration in positive electrode [mol.m-3]": 48230.0, - "Positive electrode diffusivity [m2.s-1]": 1e-14, + "Positive particle diffusivity [m2.s-1]": 1e-14, "Positive electrode OCP [V]": nmc_ocp_Xu2019, "Positive electrode porosity": 0.331, "Positive electrode active material volume fraction": 0.518, diff --git a/pybamm/parameters/bpx.py b/pybamm/parameters/bpx.py index 8efd26cd57..ea4e247be2 100644 --- a/pybamm/parameters/bpx.py +++ b/pybamm/parameters/bpx.py @@ -43,12 +43,21 @@ class Domain: pre_name="Positive electrode ", short_pre_name="Positive ", ) +negative_particle = Domain( + name="negative particle", + pre_name="Negative particle ", + short_pre_name="Negative ", +) +positive_particle = Domain( + name="positive particle", + pre_name="Positive particle ", + short_pre_name="Positive ", +) positive_current_collector = Domain( name="positive current collector", pre_name="Positive current collector ", short_pre_name="", ) - negative_current_collector = Domain( name="negative current collector", pre_name="Negative current collector ", @@ -324,16 +333,17 @@ def _positive_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T): Ea_D_n = pybamm_dict.get( negative_electrode.pre_name + "diffusivity activation energy [J.mol-1]", 0.0 ) + pybamm_dict[negative_particle.pre_name + "diffusivity activation energy [J.mol-1]"] = Ea_D_n D_n_ref = pybamm_dict[negative_electrode.pre_name + "diffusivity [m2.s-1]"] if callable(D_n_ref): - def _negative_electrode_diffusivity(sto, T): + def _negative_particle_diffusivity(sto, T): return arrhenius(Ea_D_n, T) * D_n_ref(sto) elif isinstance(D_n_ref, tuple): - def _negative_electrode_diffusivity(sto, T): + def _negative_particle_diffusivity(sto, T): name, (x, y) = D_n_ref return arrhenius(Ea_D_n, T) * pybamm.Interpolant( x, y, sto, name=name, interpolator="linear" @@ -341,27 +351,28 @@ def _negative_electrode_diffusivity(sto, T): else: - def _negative_electrode_diffusivity(sto, T): + def _negative_particle_diffusivity(sto, T): return arrhenius(Ea_D_n, T) * D_n_ref - pybamm_dict[negative_electrode.pre_name + "diffusivity [m2.s-1]"] = _copy_func( - _negative_electrode_diffusivity + pybamm_dict[negative_particle.pre_name + "diffusivity [m2.s-1]"] = _copy_func( + _negative_particle_diffusivity ) # positive electrode Ea_D_p = pybamm_dict.get( positive_electrode.pre_name + "diffusivity activation energy [J.mol-1]", 0.0 ) + pybamm_dict[positive_particle.pre_name + "diffusivity activation energy [J.mol-1]"] = Ea_D_p D_p_ref = pybamm_dict[positive_electrode.pre_name + "diffusivity [m2.s-1]"] if callable(D_p_ref): - def _positive_electrode_diffusivity(sto, T): + def _positive_particle_diffusivity(sto, T): return arrhenius(Ea_D_p, T) * D_p_ref(sto) elif isinstance(D_p_ref, tuple): - def _positive_electrode_diffusivity(sto, T): + def _positive_particle_diffusivity(sto, T): name, (x, y) = D_p_ref return arrhenius(Ea_D_p, T) * pybamm.Interpolant( x, y, sto, name=name, interpolator="linear" @@ -369,11 +380,11 @@ def _positive_electrode_diffusivity(sto, T): else: - def _positive_electrode_diffusivity(sto, T): + def _positive_particle_diffusivity(sto, T): return arrhenius(Ea_D_p, T) * D_p_ref - pybamm_dict[positive_electrode.pre_name + "diffusivity [m2.s-1]"] = _copy_func( - _positive_electrode_diffusivity + pybamm_dict[positive_particle.pre_name + "diffusivity [m2.s-1]"] = _copy_func( + _positive_particle_diffusivity ) # electrolyte diff --git a/pybamm/parameters/lithium_ion_parameters.py b/pybamm/parameters/lithium_ion_parameters.py index 12196c4044..3fbef57803 100644 --- a/pybamm/parameters/lithium_ion_parameters.py +++ b/pybamm/parameters/lithium_ion_parameters.py @@ -570,7 +570,7 @@ def D(self, c_s, T, lithiation=None): "Temperature [K]": T, } return pybamm.FunctionParameter( - f"{self.phase_prefactor}{Domain} electrode {lithiation}" + f"{self.phase_prefactor}{Domain} particle {lithiation}" "diffusivity [m2.s-1]", inputs, ) diff --git a/pybamm/util.py b/pybamm/util.py index 71883e3d27..8b9e291724 100644 --- a/pybamm/util.py +++ b/pybamm/util.py @@ -58,6 +58,11 @@ def __getitem__(self, key): try: return super().__getitem__(key) except KeyError: + if "electrode diffusivity" in key: + warn( + f"The parameter '{key}' has been renamed to '{key.replace('electrode', 'particle')}'", DeprecationWarning + ) + return super().__getitem__(key.replace("electrode", "particle")) if key in ["Negative electrode SOC", "Positive electrode SOC"]: domain = key.split(" ")[0] raise KeyError( diff --git a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs_two_phase.py b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs_two_phase.py index 48cda791b1..b82c726e75 100644 --- a/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs_two_phase.py +++ b/tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_compare_outputs_two_phase.py @@ -31,7 +31,7 @@ def compare_outputs_two_phase_graphite_graphite(self, model_class): "Maximum concentration in negative electrode [mol.m-3]", "Initial concentration in negative electrode [mol.m-3]", "Negative particle radius [m]", - "Negative electrode diffusivity [m2.s-1]", + "Negative particle diffusivity [m2.s-1]", "Negative electrode exchange-current density [A.m-2]", ]: parameter_values_two_phase.update( diff --git a/tests/unit/test_experiments/test_simulation_with_experiment.py b/tests/unit/test_experiments/test_simulation_with_experiment.py index cc04177ba2..ea0e582ddd 100644 --- a/tests/unit/test_experiments/test_simulation_with_experiment.py +++ b/tests/unit/test_experiments/test_simulation_with_experiment.py @@ -458,7 +458,7 @@ def test_inputs(self): # Change a parameter to an input param = pybamm.ParameterValues("Marquis2019") - param["Negative electrode diffusivity [m2.s-1]"] = ( + param["Negative particle diffusivity [m2.s-1]"] = ( pybamm.InputParameter("Dsn") * 3.9e-14 ) diff --git a/tests/unit/test_models/test_model_info.py b/tests/unit/test_models/test_model_info.py index 936c9d8449..144d763bf1 100644 --- a/tests/unit/test_models/test_model_info.py +++ b/tests/unit/test_models/test_model_info.py @@ -9,11 +9,11 @@ class TestModelInfo(TestCase): def test_find_parameter_info(self): model = pybamm.lithium_ion.SPM() - model.info("Negative electrode diffusivity [m2.s-1]") + model.info("Negative particle diffusivity [m2.s-1]") model = pybamm.lithium_ion.SPMe() - model.info("Negative electrode diffusivity [m2.s-1]") + model.info("Negative particle diffusivity [m2.s-1]") model = pybamm.lithium_ion.DFN() - model.info("Negative electrode diffusivity [m2.s-1]") + model.info("Negative particle diffusivity [m2.s-1]") model.info("Not a parameter") diff --git a/tests/unit/test_parameters/test_bpx.py b/tests/unit/test_parameters/test_bpx.py index 2559641d7e..5de4031c72 100644 --- a/tests/unit/test_parameters/test_bpx.py +++ b/tests/unit/test_parameters/test_bpx.py @@ -170,7 +170,7 @@ def check_constant_output(func): self.assertEqual(p_vals[0], p_vals[1]) for electrode in ["Negative", "Positive"]: - D = param[f"{electrode} electrode diffusivity [m2.s-1]"] + D = param[f"{electrode} particle diffusivity [m2.s-1]"] dUdT = param[f"{electrode} electrode OCP entropic change [V.K-1]"] check_constant_output(D) check_constant_output(dUdT) @@ -227,7 +227,7 @@ def test_table_data(self): D = param["Electrolyte diffusivity [m2.s-1]"](c, 298.15) self.assertIsInstance(D, pybamm.Interpolant) for electrode in ["Negative", "Positive"]: - D = param[f"{electrode} electrode diffusivity [m2.s-1]"](c, 298.15) + D = param[f"{electrode} particle diffusivity [m2.s-1]"](c, 298.15) self.assertIsInstance(D, pybamm.Interpolant) OCP = param[f"{electrode} electrode OCP [V]"](c) self.assertIsInstance(OCP, pybamm.Interpolant) @@ -283,8 +283,8 @@ def arrhenius_assertion(pv, param_key, Ea_key): param_keys = [ "Electrolyte conductivity [S.m-1]", "Electrolyte diffusivity [m2.s-1]", - "Negative electrode diffusivity [m2.s-1]", - "Positive electrode diffusivity [m2.s-1]", + "Negative particle diffusivity [m2.s-1]", + "Positive particle diffusivity [m2.s-1]", "Positive electrode exchange-current density [A.m-2]", "Negative electrode exchange-current density [A.m-2]", ] @@ -292,8 +292,8 @@ def arrhenius_assertion(pv, param_key, Ea_key): Ea_keys = [ "Electrolyte conductivity activation energy [J.mol-1]", "Electrolyte diffusivity activation energy [J.mol-1]", - "Negative electrode diffusivity activation energy [J.mol-1]", - "Positive electrode diffusivity activation energy [J.mol-1]", + "Negative particle diffusivity activation energy [J.mol-1]", + "Positive particle diffusivity activation energy [J.mol-1]", "Positive electrode reaction rate constant activation energy [J.mol-1]", "Negative electrode reaction rate constant activation energy [J.mol-1]", ] diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ai2020.py b/tests/unit/test_parameters/test_parameter_sets/test_Ai2020.py index 43eee82175..8816551ab6 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_Ai2020.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_Ai2020.py @@ -17,7 +17,7 @@ def test_functions(self): fun_test = { # Positive electrode "Positive electrode cracking rate": ([T], 3.9e-20), - "Positive electrode diffusivity [m2.s-1]": ([sto, T], 5.387e-15), + "Positive particle diffusivity [m2.s-1]": ([sto, T], 5.387e-15), "Positive electrode exchange-current density [A.m-2]": ( [1e3, 1e4, c_p_max, T], 0.6098, @@ -29,7 +29,7 @@ def test_functions(self): "Positive electrode volume change": ([sto, c_p_max], -1.8179e-2), # Negative electrode "Negative electrode cracking rate": ([T], 3.9e-20), - "Negative electrode diffusivity [m2.s-1]": ([sto, T], 3.9e-14), + "Negative particle diffusivity [m2.s-1]": ([sto, T], 3.9e-14), "Negative electrode exchange-current density [A.m-2]": ( [1e3, 1e4, c_n_max, T], 0.4172, diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py index a537afc93d..49135c97ff 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015.py @@ -14,14 +14,14 @@ def test_functions(self): fun_test = { # Negative electrode - "Negative electrode diffusivity [m2.s-1]": ([sto, T], 1.219e-14), + "Negative particle diffusivity [m2.s-1]": ([sto, T], 1.219e-14), "Negative electrode exchange-current density [A.m-2]": ( [1000, 15960, 31920, T], 6.2517, ), "Negative electrode OCP [V]": ([sto], 0.124), # Positive electrode - "Positive electrode diffusivity [m2.s-1]": ([sto, T], 1.0457e-13), + "Positive particle diffusivity [m2.s-1]": ([sto, T], 1.0457e-13), "Positive electrode exchange-current density [A.m-2]": ( [1000, 24290, 48580, T], 2.5121, diff --git a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py index 6dde10cd9c..118f9ad9d1 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_Ecker2015_graphite_halfcell.py @@ -14,7 +14,7 @@ def test_functions(self): fun_test = { # Positive electrode - "Positive electrode diffusivity [m2.s-1]": ([sto, T], 1.219e-14), + "Positive particle diffusivity [m2.s-1]": ([sto, T], 1.219e-14), "Positive electrode exchange-current density [A.m-2]": ( [1000, 15960, 31920, T], 6.2517, diff --git a/tests/unit/test_parameters/test_parameter_sets/test_LCO_Ramadass2004.py b/tests/unit/test_parameters/test_parameter_sets/test_LCO_Ramadass2004.py index 1cb1477822..2de67b9e62 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_LCO_Ramadass2004.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_LCO_Ramadass2004.py @@ -16,7 +16,7 @@ def test_functions(self): c_n_max = param["Maximum concentration in negative electrode [mol.m-3]"] fun_test = { # Positive electrode - "Positive electrode diffusivity [m2.s-1]": ([sto, T], 1e-14), + "Positive particle diffusivity [m2.s-1]": ([sto, T], 1e-14), "Positive electrode exchange-current density [A.m-2]": ( [1e3, 1e4, c_p_max, T], 1.4517, @@ -27,7 +27,7 @@ def test_functions(self): ), "Positive electrode OCP [V]": ([sto], 4.1249), # Negative electrode - "Negative electrode diffusivity [m2.s-1]": ([sto, T], 3.9e-14), + "Negative particle diffusivity [m2.s-1]": ([sto, T], 3.9e-14), "Negative electrode exchange-current density [A.m-2]": ( [1e3, 1e4, c_n_max, T], 2.2007, diff --git a/tests/unit/test_parameters/test_parameter_sets/test_LGM50_ORegan2022.py b/tests/unit/test_parameters/test_parameter_sets/test_LGM50_ORegan2022.py index 037c2c87dd..f878b7d790 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_LGM50_ORegan2022.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_LGM50_ORegan2022.py @@ -23,7 +23,7 @@ def test_functions(self): [298.15], 902.6502, ), - "Positive electrode diffusivity [m2.s-1]": ([0.5, 298.15], 7.2627e-15), + "Positive particle diffusivity [m2.s-1]": ([0.5, 298.15], 7.2627e-15), "Positive electrode exchange-current density [A.m-2]": ( [1e3, 1e4, c_p_max, 298.15], 2.1939, @@ -40,7 +40,7 @@ def test_functions(self): [298.15], 847.7155, ), - "Negative electrode diffusivity [m2.s-1]": ([0.5, 298.15], 2.8655e-16), + "Negative particle diffusivity [m2.s-1]": ([0.5, 298.15], 2.8655e-16), "Negative electrode exchange-current density [A.m-2]": ( [1e3, 1e4, c_n_max, 298.15], 1.0372, 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 1abc5c3baf..1ab7e7930e 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022.py @@ -21,7 +21,7 @@ def test_functions(self): ), "Dead lithium decay rate [s-1]": ([1e-8], 5e-7), # Negative electrode - "Negative electrode diffusivity [m2.s-1]": ([sto, T], 3.3e-14), + "Negative particle diffusivity [m2.s-1]": ([sto, T], 3.3e-14), "Negative electrode exchange-current density [A.m-2]": ( [1000, 16566.5, 33133, T], 0.33947, @@ -29,7 +29,7 @@ def test_functions(self): "Negative electrode cracking rate": ([T], 3.9e-20), "Negative electrode volume change": ([sto, 33133], 0.0897), # Positive electrode - "Positive electrode diffusivity [m2.s-1]": ([sto, T], 4e-15), + "Positive particle diffusivity [m2.s-1]": ([sto, T], 4e-15), "Positive electrode exchange-current density [A.m-2]": ( [1000, 31552, 63104, T], 3.4123, diff --git a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py index 5c6971a7d5..04a19e1002 100644 --- a/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py +++ b/tests/unit/test_parameters/test_parameter_sets/test_OKane2022_negative_halfcell.py @@ -21,7 +21,7 @@ def test_functions(self): ), "Dead lithium decay rate [s-1]": ([1e-8], 5e-7), # Positive electrode - "Positive electrode diffusivity [m2.s-1]": ([sto, T], 3.3e-14), + "Positive particle diffusivity [m2.s-1]": ([sto, T], 3.3e-14), "Positive electrode exchange-current density [A.m-2]": ( [1000, 16566.5, 33133, T], 0.33947, diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py index 730e4cc08d..cc323ae8ac 100644 --- a/tests/unit/test_util.py +++ b/tests/unit/test_util.py @@ -47,6 +47,7 @@ def test_fuzzy_dict(self): "SEI current": 3, "Lithium plating current": 4, "A dimensional variable [m]": 5, + "Positive particle diffusivity [m2.s-1]": 6, } ) self.assertEqual(d["test"], 1) @@ -68,6 +69,9 @@ def test_fuzzy_dict(self): with self.assertRaisesRegex(KeyError, "Upper voltage"): d.__getitem__("Open-circuit voltage at 100% SOC [V]") + with self.assertWarns(DeprecationWarning): + self.assertEqual(d["Positive electrode diffusivity [m2.s-1]"], d["Positive particle diffusivity [m2.s-1]"]) + def test_get_parameters_filepath(self): tempfile_obj = tempfile.NamedTemporaryFile("w", dir=".") self.assertTrue( From e9c23569ed4e6493eca8f42e22de5a370e2bcd5b Mon Sep 17 00:00:00 2001 From: julian-evers <133691040+julian-evers@users.noreply.github.com> Date: Sat, 16 Dec 2023 00:55:51 +0100 Subject: [PATCH 02/10] merge_conflicts_ --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7e5205425f..b77f52ae36 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,7 +2,7 @@ ## Features -- Renamed "electrode diffusivity" to "particle diffusivity" as a non-breaking change with a deprecation warning ([#3604](https://github.com/pybamm-team/PyBaMM/pull/3604)) +- Renamed "electrode diffusivity" to "particle diffusivity" as a non-breaking change with a deprecation warning ([#3623](https://github.com/pybamm-team/PyBaMM/pull/3623)) - Added method to get QuickPlot axes by variable ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596)) - Added custom experiment terminations ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596)) - Mechanical parameters are now a function of stoichiometry and temperature ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576)) From 167b2bd3257fa51fdc073bc83aae6ccd1cc5e430 Mon Sep 17 00:00:00 2001 From: julian-evers <133691040+julian-evers@users.noreply.github.com> Date: Sat, 16 Dec 2023 01:07:03 +0100 Subject: [PATCH 03/10] update_pr_number --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index b77f52ae36..422a92384a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,7 +2,7 @@ ## Features -- Renamed "electrode diffusivity" to "particle diffusivity" as a non-breaking change with a deprecation warning ([#3623](https://github.com/pybamm-team/PyBaMM/pull/3623)) +- Renamed "electrode diffusivity" to "particle diffusivity" as a non-breaking change with a deprecation warning ([#3624](https://github.com/pybamm-team/PyBaMM/pull/3624)) - Added method to get QuickPlot axes by variable ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596)) - Added custom experiment terminations ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596)) - Mechanical parameters are now a function of stoichiometry and temperature ([#3576](https://github.com/pybamm-team/PyBaMM/pull/3576)) From 5194b25864355d55482981ff355b3ddf4c5794c0 Mon Sep 17 00:00:00 2001 From: julian-evers <133691040+julian-evers@users.noreply.github.com> Date: Sat, 16 Dec 2023 02:33:37 +0100 Subject: [PATCH 04/10] rename_empirical_hysteresis --- .../scripts/{emperical_hysteresis.py => empirical_hysteresis.py} | 0 1 file changed, 0 insertions(+), 0 deletions(-) rename examples/scripts/{emperical_hysteresis.py => empirical_hysteresis.py} (100%) diff --git a/examples/scripts/emperical_hysteresis.py b/examples/scripts/empirical_hysteresis.py similarity index 100% rename from examples/scripts/emperical_hysteresis.py rename to examples/scripts/empirical_hysteresis.py From 5dae2daf85d8690cd4180fb442cd9236e5070d3d Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Sat, 13 Jan 2024 12:09:02 +0000 Subject: [PATCH 05/10] style: pre-commit fixes --- pybamm/parameters/bpx.py | 8 ++++++-- pybamm/util.py | 3 ++- tests/unit/test_util.py | 5 ++++- 3 files changed, 12 insertions(+), 4 deletions(-) diff --git a/pybamm/parameters/bpx.py b/pybamm/parameters/bpx.py index cb3868c315..096416bec3 100644 --- a/pybamm/parameters/bpx.py +++ b/pybamm/parameters/bpx.py @@ -333,7 +333,9 @@ def _positive_electrode_exchange_current_density(c_e, c_s_surf, c_s_max, T): Ea_D_n = pybamm_dict.get( negative_electrode.pre_name + "diffusivity activation energy [J.mol-1]", 0.0 ) - pybamm_dict[negative_particle.pre_name + "diffusivity activation energy [J.mol-1]"] = Ea_D_n + pybamm_dict[ + negative_particle.pre_name + "diffusivity activation energy [J.mol-1]" + ] = Ea_D_n D_n_ref = pybamm_dict[negative_electrode.pre_name + "diffusivity [m2.s-1]"] if callable(D_n_ref): @@ -362,7 +364,9 @@ def _negative_particle_diffusivity(sto, T): Ea_D_p = pybamm_dict.get( positive_electrode.pre_name + "diffusivity activation energy [J.mol-1]", 0.0 ) - pybamm_dict[positive_particle.pre_name + "diffusivity activation energy [J.mol-1]"] = Ea_D_p + pybamm_dict[ + positive_particle.pre_name + "diffusivity activation energy [J.mol-1]" + ] = Ea_D_p D_p_ref = pybamm_dict[positive_electrode.pre_name + "diffusivity [m2.s-1]"] if callable(D_p_ref): diff --git a/pybamm/util.py b/pybamm/util.py index 228bcc2653..1149327cf7 100644 --- a/pybamm/util.py +++ b/pybamm/util.py @@ -60,7 +60,8 @@ def __getitem__(self, key): except KeyError: if "electrode diffusivity" in key: warn( - f"The parameter '{key}' has been renamed to '{key.replace('electrode', 'particle')}'", DeprecationWarning + f"The parameter '{key}' has been renamed to '{key.replace('electrode', 'particle')}'", + DeprecationWarning, ) return super().__getitem__(key.replace("electrode", "particle")) if key in ["Negative electrode SOC", "Positive electrode SOC"]: diff --git a/tests/unit/test_util.py b/tests/unit/test_util.py index 4f8f498e9b..24f204b6df 100644 --- a/tests/unit/test_util.py +++ b/tests/unit/test_util.py @@ -71,7 +71,10 @@ def test_fuzzy_dict(self): d.__getitem__("Open-circuit voltage at 100% SOC [V]") with self.assertWarns(DeprecationWarning): - self.assertEqual(d["Positive electrode diffusivity [m2.s-1]"], d["Positive particle diffusivity [m2.s-1]"]) + self.assertEqual( + d["Positive electrode diffusivity [m2.s-1]"], + d["Positive particle diffusivity [m2.s-1]"], + ) def test_get_parameters_filepath(self): tempfile_obj = tempfile.NamedTemporaryFile("w", dir=".") From 0d38e2300f294dcf29cd5fb46d6c0d2c5a7904f5 Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 26 Jan 2024 15:03:18 +0000 Subject: [PATCH 06/10] style: pre-commit fixes --- pybamm/parameters/bpx.py | 19 ++++++++++++------- 1 file changed, 12 insertions(+), 7 deletions(-) diff --git a/pybamm/parameters/bpx.py b/pybamm/parameters/bpx.py index acbaf12dc0..241f53eac5 100644 --- a/pybamm/parameters/bpx.py +++ b/pybamm/parameters/bpx.py @@ -338,11 +338,15 @@ def _conductivity(c_e, T, Ea, sigma_ref, constant=False): D_ref = pybamm_dict[phase_domain_pre_name + "diffusivity [m2.s-1]"] if callable(D_ref): - pybamm_dict[phase_domain_pre_name.replace('electrode', 'particle') + "diffusivity [m2.s-1]"] = partial( - _diffusivity, D_ref=D_ref, Ea=Ea_D - ) + pybamm_dict[ + phase_domain_pre_name.replace("electrode", "particle") + + "diffusivity [m2.s-1]" + ] = partial(_diffusivity, D_ref=D_ref, Ea=Ea_D) elif isinstance(D_ref, tuple): - pybamm_dict[phase_domain_pre_name.replace('electrode', 'particle') + "diffusivity [m2.s-1]"] = partial( + pybamm_dict[ + phase_domain_pre_name.replace("electrode", "particle") + + "diffusivity [m2.s-1]" + ] = partial( _diffusivity, D_ref=partial( _interpolant_func, name=D_ref[0], x=D_ref[1][0], y=D_ref[1][1] @@ -350,9 +354,10 @@ def _conductivity(c_e, T, Ea, sigma_ref, constant=False): Ea=Ea_D, ) else: - pybamm_dict[phase_domain_pre_name.replace('electrode', 'particle') + "diffusivity [m2.s-1]"] = partial( - _diffusivity, D_ref=D_ref, Ea=Ea_D, constant=True - ) + pybamm_dict[ + phase_domain_pre_name.replace("electrode", "particle") + + "diffusivity [m2.s-1]" + ] = partial(_diffusivity, D_ref=D_ref, Ea=Ea_D, constant=True) # electrolyte Ea_D_e = pybamm_dict.get( From cb6a517c9302a69f492a7df7b15100ebfd03b6e9 Mon Sep 17 00:00:00 2001 From: julian-evers Date: Fri, 26 Jan 2024 16:09:36 +0100 Subject: [PATCH 07/10] update changelog --- CHANGELOG.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 4504080374..55bafde9fb 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,7 @@ ## Features +- Renamed "electrode diffusivity" to "particle diffusivity" as a non-breaking change with a deprecation warning ([#3624](https://github.com/pybamm-team/PyBaMM/pull/3624)) - Add support for BPX version 0.4.0 which allows for blended electrodes and user-defined parameters in BPX([#3414](https://github.com/pybamm-team/PyBaMM/pull/3414)) - Added the ability to specify a custom solver tolerance in `get_initial_stoichiometries` and related functions ([#3714](https://github.com/pybamm-team/PyBaMM/pull/3714)) @@ -13,13 +14,13 @@ ## Breaking changes +- Renamed "electrode diffusivity" to "particle diffusivity" as a non-breaking change with a deprecation warning ([#3624](https://github.com/pybamm-team/PyBaMM/pull/3624)) - Dropped support for BPX version 0.3.0 and below ([#3414](https://github.com/pybamm-team/PyBaMM/pull/3414)) # [v24.1rc2](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc2) - 2024-01-24 ## Features -- Renamed "electrode diffusivity" to "particle diffusivity" as a non-breaking change with a deprecation warning ([#3624](https://github.com/pybamm-team/PyBaMM/pull/3624)) - The `pybamm_install_odes` command now includes support for macOS systems and can be used to set up SUNDIALS and install the `scikits.odes` solver on macOS ([#3417](https://github.com/pybamm-team/PyBaMM/pull/3417), [#3706](https://github.com/pybamm-team/PyBaMM/3706])) - Added support for Python 3.12 ([#3531](https://github.com/pybamm-team/PyBaMM/pull/3531)) - Added method to get QuickPlot axes by variable ([#3596](https://github.com/pybamm-team/PyBaMM/pull/3596)) From 0a19895c71c7a681b95e1e9acca6d839bdae9942 Mon Sep 17 00:00:00 2001 From: julian-evers Date: Fri, 26 Jan 2024 16:31:09 +0100 Subject: [PATCH 08/10] fix bpx --- pybamm/parameters/bpx.py | 1 + 1 file changed, 1 insertion(+) diff --git a/pybamm/parameters/bpx.py b/pybamm/parameters/bpx.py index 241f53eac5..1b9cc81514 100644 --- a/pybamm/parameters/bpx.py +++ b/pybamm/parameters/bpx.py @@ -335,6 +335,7 @@ def _conductivity(c_e, T, Ea, sigma_ref, constant=False): phase_domain_pre_name + "diffusivity activation energy [J.mol-1]", 0.0, ) + pybamm_dict[phase_domain_pre_name.replace("electrode", "particle") + "diffusivity activation energy [J.mol-1]"] = Ea_D D_ref = pybamm_dict[phase_domain_pre_name + "diffusivity [m2.s-1]"] if callable(D_ref): From 6b2ac994a5fc578bc0af75654bba9aa05873210b Mon Sep 17 00:00:00 2001 From: "pre-commit-ci[bot]" <66853113+pre-commit-ci[bot]@users.noreply.github.com> Date: Fri, 26 Jan 2024 15:31:51 +0000 Subject: [PATCH 09/10] style: pre-commit fixes --- pybamm/parameters/bpx.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/pybamm/parameters/bpx.py b/pybamm/parameters/bpx.py index 1b9cc81514..65322a5b99 100644 --- a/pybamm/parameters/bpx.py +++ b/pybamm/parameters/bpx.py @@ -335,7 +335,10 @@ def _conductivity(c_e, T, Ea, sigma_ref, constant=False): phase_domain_pre_name + "diffusivity activation energy [J.mol-1]", 0.0, ) - pybamm_dict[phase_domain_pre_name.replace("electrode", "particle") + "diffusivity activation energy [J.mol-1]"] = Ea_D + pybamm_dict[ + phase_domain_pre_name.replace("electrode", "particle") + + "diffusivity activation energy [J.mol-1]" + ] = Ea_D D_ref = pybamm_dict[phase_domain_pre_name + "diffusivity [m2.s-1]"] if callable(D_ref): From c8261abc1ce15c5a5693b5da217b69a5cc2dc153 Mon Sep 17 00:00:00 2001 From: julian-evers Date: Sat, 27 Jan 2024 12:11:35 +0100 Subject: [PATCH 10/10] update changelog --- CHANGELOG.md | 1 - 1 file changed, 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 55bafde9fb..6dcf36b2e9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -14,7 +14,6 @@ ## Breaking changes -- Renamed "electrode diffusivity" to "particle diffusivity" as a non-breaking change with a deprecation warning ([#3624](https://github.com/pybamm-team/PyBaMM/pull/3624)) - Dropped support for BPX version 0.3.0 and below ([#3414](https://github.com/pybamm-team/PyBaMM/pull/3414)) # [v24.1rc2](https://github.com/pybamm-team/PyBaMM/tree/v24.1rc2) - 2024-01-24