removing dissolution for exchange current density

degradation_model/try_spme_deg.ipynb

@@ -0,0 +1,79 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pybamm\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import math\n",
+    "import dfols\n",
+    "import signal\n",
+    "from scipy.integrate import solve_ivp\n",
+    "from scipy.fft import fft, fftfreq, fftshift\n",
+    "from scipy.signal import savgol_filter\n",
+    "from scipy.signal import find_peaks\n",
+    "from scipy.io import savemat\n",
+    "from scipy import interpolate\n",
+    "import os, sys\n",
+    "import pickle\n",
+    "sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(\"__file__\"))))\n",
+    "from batfuns import *\n",
+    "plt.rcParams = set_rc_params(plt.rcParams)\n",
+    "\n",
+    "eSOH_DIR = \"./data/esoh/\"\n",
+    "oCV_DIR = \"./data/ocv/\"\n",
+    "cyc_DIR = \"./data/cycling/\"\n",
+    "fig_DIR = \"./figures/\"\n",
+    "res_DIR = \"./results/\"\n",
+    "resistance_DIR = \"./data/resistance/\"\n",
+    "%matplotlib widget"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# loading parameter values without aging model parameters, just battery parameters\n",
+    "parameter_values = get_parameter_values()\n",
+    "# we are defing the model\n",
+    "spme = pybamm.lithium_ion.SPMe(\n",
+    "    {\n",
+    "        \"SEI\": \"ec reaction limited\",\n",
+    "        \"loss of active material\": \"stress-driven\",\n",
+    "        \"lithium plating\": \"irreversible\",\n",
+    "        \"stress-induced diffusion\": \"false\",\n",
+    "    }\n",
+    ")\n",
+    "param=spme.param"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "windows-dev",
+   "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": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

degradation_model/try_spme_deg.py

@@ -0,0 +1,10 @@
+import pybamm
+
+spme = pybamm.lithium_ion.SPMe(
+    {
+        "SEI": "ec reaction limited",
+        "loss of active material": "stress-driven",
+        "lithium plating": "irreversible",
+        "stress-induced diffusion": "false",
+    }
+)

pybamm/input/parameters/lithium_ion/negative_electrodes/graphite_UMBL_Siegel2022/graphite_electrolyte_exchange_current_density_Siegel.py

@@ -1,7 +1,7 @@
 from pybamm import exp, constants
 
 
-def graphite_electrolyte_exchange_current_density_Siegel(c_e, c_s_surf, c_s_max, T, N_nick):
+def graphite_electrolyte_exchange_current_density_Siegel(c_e, c_s_surf, c_s_max, T):
     """
     Exchange-current density for Butler-Volmer reactions between graphite and LiPF6 in
     EC:DMC.

pybamm/input/parameters/lithium_ion/positive_electrodes/NMC_UMBL_Siegel2022/NMC_electrolyte_exchange_current_density_Siegel.py

@@ -1,7 +1,7 @@
 from pybamm import exp, constants
 
 
-def NMC_electrolyte_exchange_current_density_Siegel(c_e, c_s_surf, c_s_max, T, N_nick):
+def NMC_electrolyte_exchange_current_density_Siegel(c_e, c_s_surf, c_s_max, T):
     """
     Exchange-current density for Butler-Volmer reactions between NMC and LiPF6 in
     EC:DMC.

pybamm/models/submodels/interface/base_interface.py

@@ -71,7 +71,9 @@ def _get_exchange_current_density(self, variables):
         c_e = variables[f"{Domain} electrolyte concentration"]
         T = variables[f"{Domain} electrode temperature"]
 
-        N_nick= variables["X-averaged positive electrode loss nickel dissolution"]
+        #N_nick= variables["X-averaged positive electrode loss nickel dissolution"]
+
+        # N_nick= variables[f"{Domain} loss nickel dissolution"]
 
         if self.reaction == "lithium-ion main":
             # For "particle-size distribution" submodels, take distribution version
@@ -114,7 +116,7 @@ def _get_exchange_current_density(self, variables):
                     c_e = c_e.orphans[0]
                     T = T.orphans[0]
 
-            j0 = phase_param.j0(c_e, c_s_surf, T, N_nick)
+            j0 = phase_param.j0(c_e, c_s_surf, T)
 
         elif self.reaction == "lithium metal plating":
             j0 = param.j0_plating(c_e, 1, T)

pybamm/parameters/lithium_ion_parameters.py

@@ -824,7 +824,7 @@ def D_dimensional(self, sto, T):
             inputs,
         )
 
-    def j0_dimensional(self, c_e, c_s_surf, T, N_nick):
+    def j0_dimensional(self, c_e, c_s_surf, T):
         """Dimensional exchange-current density [A.m-2]"""
         inputs = {
             "Electrolyte concentration [mol.m-3]": c_e,
@@ -833,8 +833,7 @@ def j0_dimensional(self, c_e, c_s_surf, T, N_nick):
             "surface concentration [mol.m-3]": self.c_max,
             "Temperature [K]": T,
             f"{self.phase_prefactor}Maximum {self.domain.lower()} particle "
-            "surface concentration [mol.m-3]": self.c_max,
-            f"{self.domain} dissolution nickel intercalation coefficient": N_nick,            
+            "surface concentration [mol.m-3]": self.c_max,           
 
         }
         return pybamm.FunctionParameter(
@@ -906,7 +905,7 @@ def _set_scales(self):
 
         # Reference exchange-current density
         self.j0_ref_dimensional = (
-            self.j0_dimensional(main.c_e_typ, self.c_max / 2, main.T_ref, 0) * 2
+            self.j0_dimensional(main.c_e_typ, self.c_max / 2, main.T_ref) * 2
         )
 
         # Reaction timescales
@@ -1052,7 +1051,7 @@ def D(self, c_s, T):
         T_dim = self.main_param.Delta_T * T + self.main_param.T_ref
         return self.D_dimensional(sto, T_dim) / self.D_typ_dim
 
-    def j0(self, c_e, c_s_surf, T, N_nick):
+    def j0(self, c_e, c_s_surf, T):
         """Dimensionless exchange-current density"""
         tol = pybamm.settings.tolerances["j0__c_e"]
         c_e = pybamm.maximum(c_e, tol)
@@ -1062,7 +1061,7 @@ def j0(self, c_e, c_s_surf, T, N_nick):
         c_s_surf_dim = c_s_surf * self.c_max
         T_dim = self.main_param.Delta_T * T + self.main_param.T_ref
 
-        return self.j0_dimensional(c_e_dim, c_s_surf_dim, T_dim,N_nick) / self.j_scale
+        return self.j0_dimensional(c_e_dim, c_s_surf_dim, T_dim) / self.j_scale
 
     def U(self, c_s, T, lithiation=None):
         """Dimensionless open-circuit potential in the electrode"""