Simplified_SEI_Model2_Full.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2bf3605d",
   "metadata": {},
   "source": [
    "# Simplify SPMe+SEI Model "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3df340b3",
   "metadata": {},
   "source": [
    "The main purpose of this section is to reformulate the SPMe+RF model from. \n",
    "\n",
    "Assumptions:\n",
    "\n",
    "<ol>\n",
    "    <li> The diffusivity parameter of lithium ion in the negative particle is constant.</li>\n",
    "    <li> The concentration of lithium ions in the electrolyte is constant.</li>\n",
    "    <li> The conductivity and transport efficiency of the electrolyte are also constant </li>\n",
    "    </ol>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d60860a",
   "metadata": {},
   "source": [
    "$$  \n",
    "\\begin{equation}\n",
    "\\frac{d \\bar{\\hat{c}}_{\\mathrm{n0}}}{d\\hat{t}}=\\frac{-3}{\\alpha_n }\\hat{J}_\\mathrm{n}^\\mathrm{total}.\n",
    "\\end{equation}\n",
    "$$\n",
    "\n",
    "$$\n",
    " \\frac{\\partial \\varepsilon_\\mathrm{n}}{\\partial t}= \\frac{M_\\mathrm{SR}}{n_\\mathrm{SR} \\rho_\\mathrm{SR} F} \\bar{J}_\\mathrm{SR},\n",
    " $$\n",
    "$$\n",
    "  L_\\mathrm{f, n}=L_\\mathrm{f,n,init}-\\frac{1}{a_\\mathrm{n}}(\\varepsilon_\\mathrm{n}-\\varepsilon_\\mathrm{n, init}) \n",
    " $$\n",
    " \n",
    "$$\n",
    "    J_\\mathrm{n}^{\\mathrm{total}}=\\frac{i_\\mathrm{app}}{L_\\mathrm{n}}-\\bar{J}_\\mathrm{SR}, \n",
    "$$\n",
    "\n",
    "$$\n",
    "    \\bar{J}_\\mathrm{SR}=\\frac{1}{L_\\mathrm{n}} \\int_0^{L_\\mathrm{n}} J_\\mathrm{SR} d\\scriptstyle{\\mathcal{X}},\n",
    "$$\n",
    "\n",
    "$$\n",
    "    J_{\\mathrm{SR}}=-a_\\mathrm{n} j_\\mathrm{SR} \\mathrm{exp}\\left(-\\alpha_\\mathrm{SR}\\frac{F}{RT} \\left(\\phi_\\mathrm{n}-\\phi_\\mathrm{e}-U_\\mathrm{SR}-\\frac{i_\\mathrm{app}L_\\mathrm{f, n}}{a_\\mathrm{n} \\sigma_\\mathrm{f, n} L_\\mathrm{n}} \\right ) \\right ).\n",
    "$$\n",
    "\n",
    "\\begin{equation}\n",
    "\\begin{aligned}\n",
    "&\\phi_\\mathrm{n}=U_\\mathrm{n}(\\bar{c}_\\mathrm{n}|_{r=R_{n}}) \\\\\n",
    "  &\\phi_e=- \\frac{i_\\mathrm{app} }{6 \\sigma_\\mathrm{e} {\\scriptstyle\\mathcal{B}} L_n}\\scriptstyle\\mathcal{X}^3\n",
    "  \\end{aligned}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8881317e",
   "metadata": {},
   "source": [
    "So, Basically we are trying two solve DAEs with (1,2 3, 6, 7) for the variables $\\bar{c}$, $\\varepsilon$, $L_\\mathrm{f, n}$,  $\\phi_e$ and $\\phi_n$ with approperiate initial conditions. For Simiplicity (now) we only conider first terms for $\\phi_e$ and $\\phi_n$."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b4e9becb",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pybamm\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "# import scipy.sparse as sparse\n",
    "# pybamm.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "4579537b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Ambient temperature [K]': 298.15,\n",
       " 'Boltzmann constant [J.K-1]': 1.380649e-23,\n",
       " 'Bulk solvent concentration [mol.m-3]': 2636.0,\n",
       " 'Cation transference number': 0.2594,\n",
       " 'Cell cooling surface area [m2]': 0.00531,\n",
       " 'Cell thermal expansion coefficient [m.K-1]': 1.1e-06,\n",
       " 'Cell volume [m3]': 2.42e-05,\n",
       " 'Contact resistance [Ohm]': 0,\n",
       " 'Current function [A]': 5.0,\n",
       " 'EC diffusivity [m2.s-1]': 2e-18,\n",
       " 'EC initial concentration in electrolyte [mol.m-3]': 4541.0,\n",
       " 'Electrode height [m]': 0.065,\n",
       " 'Electrode width [m]': 1.58,\n",
       " 'Electrolyte conductivity [S.m-1]': <function electrolyte_conductivity_Nyman2008 at 0x7f1ddcb479a0>,\n",
       " 'Electrolyte diffusivity [m2.s-1]': <function electrolyte_diffusivity_Nyman2008 at 0x7f1ddcb47910>,\n",
       " 'Electron charge [C]': 1.602176634e-19,\n",
       " 'Faraday constant [C.mol-1]': 96485.33212,\n",
       " 'Ideal gas constant [J.K-1.mol-1]': 8.314462618,\n",
       " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n",
       " 'Initial concentration in negative electrode [mol.m-3]': 29866.0,\n",
       " 'Initial concentration in positive electrode [mol.m-3]': 17038.0,\n",
       " 'Initial inner SEI thickness [m]': 2.5e-09,\n",
       " 'Initial outer SEI thickness [m]': 2.5e-09,\n",
       " 'Initial temperature [K]': 298.15,\n",
       " 'Inner SEI electron conductivity [S.m-1]': 8.95e-14,\n",
       " 'Inner SEI lithium interstitial diffusivity [m2.s-1]': 1e-20,\n",
       " 'Inner SEI open-circuit potential [V]': 0.1,\n",
       " 'Inner SEI partial molar volume [m3.mol-1]': 9.585e-05,\n",
       " 'Inner SEI reaction proportion': 0.5,\n",
       " 'Lithium interstitial reference concentration [mol.m-3]': 15.0,\n",
       " 'Lower voltage cut-off [V]': 2.5,\n",
       " 'Maximum concentration in negative electrode [mol.m-3]': 33133.0,\n",
       " 'Maximum concentration in positive electrode [mol.m-3]': 63104.0,\n",
       " 'Negative current collector conductivity [S.m-1]': 58411000.0,\n",
       " 'Negative current collector density [kg.m-3]': 8960.0,\n",
       " 'Negative current collector specific heat capacity [J.kg-1.K-1]': 385.0,\n",
       " 'Negative current collector thermal conductivity [W.m-1.K-1]': 401.0,\n",
       " 'Negative current collector thickness [m]': 1.2e-05,\n",
       " 'Negative electrode Bruggeman coefficient (electrode)': 0,\n",
       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
       " 'Negative electrode OCP [V]': <function graphite_LGM50_ocp_Chen2020 at 0x7f1ddcb47490>,\n",
       " 'Negative electrode OCP entropic change [V.K-1]': 0.0,\n",
       " 'Negative electrode active material volume fraction': 0.75,\n",
       " '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 electrode double-layer capacity [F.m-2]': 0.2,\n",
       " 'Negative electrode exchange-current density [A.m-2]': <function graphite_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x7f1ddcb47520>,\n",
       " 'Negative electrode porosity': 0.25,\n",
       " 'Negative electrode reaction-driven LAM factor [m3.mol-1]': 0.0,\n",
       " 'Negative electrode specific heat capacity [J.kg-1.K-1]': 700.0,\n",
       " 'Negative electrode thermal conductivity [W.m-1.K-1]': 1.7,\n",
       " 'Negative electrode thickness [m]': 8.52e-05,\n",
       " 'Negative particle radius [m]': 5.86e-06,\n",
       " 'Nominal cell capacity [A.h]': 5.0,\n",
       " 'Number of cells connected in series to make a battery': 1.0,\n",
       " 'Number of electrodes connected in parallel to make a cell': 1.0,\n",
       " 'Outer SEI open-circuit potential [V]': 0.8,\n",
       " 'Outer SEI partial molar volume [m3.mol-1]': 9.585e-05,\n",
       " 'Outer SEI solvent diffusivity [m2.s-1]': 2.5000000000000002e-22,\n",
       " 'Positive current collector conductivity [S.m-1]': 36914000.0,\n",
       " 'Positive current collector density [kg.m-3]': 2700.0,\n",
       " 'Positive current collector specific heat capacity [J.kg-1.K-1]': 897.0,\n",
       " 'Positive current collector thermal conductivity [W.m-1.K-1]': 237.0,\n",
       " 'Positive current collector thickness [m]': 1.6e-05,\n",
       " 'Positive electrode Bruggeman coefficient (electrode)': 0,\n",
       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
       " 'Positive electrode OCP [V]': <function nmc_LGM50_ocp_Chen2020 at 0x7f1ddcb475b0>,\n",
       " 'Positive electrode OCP entropic change [V.K-1]': 0.0,\n",
       " 'Positive electrode active material volume fraction': 0.665,\n",
       " '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 electrode double-layer capacity [F.m-2]': 0.2,\n",
       " 'Positive electrode exchange-current density [A.m-2]': <function nmc_LGM50_electrolyte_exchange_current_density_Chen2020 at 0x7f1ddcb471c0>,\n",
       " 'Positive electrode porosity': 0.335,\n",
       " 'Positive electrode reaction-driven LAM factor [m3.mol-1]': 0.0,\n",
       " 'Positive electrode specific heat capacity [J.kg-1.K-1]': 700.0,\n",
       " 'Positive electrode thermal conductivity [W.m-1.K-1]': 2.1,\n",
       " 'Positive electrode thickness [m]': 7.56e-05,\n",
       " 'Positive particle radius [m]': 5.22e-06,\n",
       " 'Ratio of lithium moles to SEI moles': 2.0,\n",
       " 'Reference temperature [K]': 298.15,\n",
       " 'SEI growth activation energy [J.mol-1]': 0.0,\n",
       " 'SEI kinetic rate constant [m.s-1]': 1e-12,\n",
       " 'SEI open-circuit potential [V]': 0.4,\n",
       " 'SEI reaction exchange current density [A.m-2]': 1.5e-07,\n",
       " 'SEI resistivity [Ohm.m]': 200000.0,\n",
       " 'Separator Bruggeman coefficient (electrolyte)': 1.5,\n",
       " 'Separator density [kg.m-3]': 397.0,\n",
       " 'Separator porosity': 0.47,\n",
       " 'Separator specific heat capacity [J.kg-1.K-1]': 700.0,\n",
       " 'Separator thermal conductivity [W.m-1.K-1]': 0.16,\n",
       " 'Separator thickness [m]': 1.2e-05,\n",
       " 'Thermodynamic factor': 1.0,\n",
       " 'Total heat transfer coefficient [W.m-2.K-1]': 10.0,\n",
       " 'Upper voltage cut-off [V]': 4.2,\n",
       " 'citations': ['Chen2020']}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_SPMe = pybamm.ParameterValues(\"Chen2020\")\n",
    "param_SPMe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8e418aa3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Solve time: 153.219 ms seconds\n",
      "Setup time: 45.254 ms seconds\n"
     ]
    }
   ],
   "source": [
    "# Runnung SPMe in PyBaMM\n",
    "param_SPMe = pybamm.ParameterValues(\"Chen2020\")\n",
    "param_SPMe['Initial inner SEI thickness [m]'] = 5e-13\n",
    "param_SPMe['Initial outer SEI thickness [m]'] = 5e-13\n",
    "\n",
    "c_rate = 50  # Charging and discharging rate\n",
    "cycle_number = 1\n",
    "exp_CCCV_CC = pybamm.Experiment(\n",
    "    [(\"Charge at C/{} until 4.2 V\".format(c_rate),\n",
    "      #    \"Rest for 10 hours\",\n",
    "      # \"Discharge at C/{} until 2.5 V\".format(c_rate),\n",
    "      )] * cycle_number\n",
    "\n",
    "\n",
    ")\n",
    "\n",
    "var_pts = {\n",
    "    \"x_n\": 20,  # negative electrode\n",
    "    \"x_s\": 20,  # separator\n",
    "    \"x_p\": 20,  # positive electrode\n",
    "    \"r_n\": 20,  # negative particle\n",
    "    \"r_p\": 20,  # positive particle\n",
    "}\n",
    "SEIModels = [\"reaction limited\"]\n",
    "SEIModels = [\"interstitial-diffusion limited\"]\n",
    "models = []\n",
    "solutions_SPMe = []\n",
    "for index in range(len(SEIModels)):\n",
    "    models.append(pybamm.lithium_ion.SPM(\n",
    "        {\n",
    "            \"SEI\": SEIModels[index],\n",
    "            \"SEI porosity change\": \"true\",\n",
    "            \"SEI film resistance\": \"average\"\n",
    "        }))\n",
    "    # \"x-average side reactions\": \"true\"\n",
    "    # sim = pybamm.Simulation(models[index], parameter_values=param, experiment=exp, var_pts=var_pts)\n",
    "    # solver = pybamm.CasadiSolver(mode=\"safe\", atol=1e-6, rtol=1e-6)\n",
    "    solver = pybamm.IDAKLUSolver()\n",
    "    sim = pybamm.Simulation(models[index], parameter_values=param_SPMe,\n",
    "                            experiment=exp_CCCV_CC, var_pts=var_pts,  solver=solver)\n",
    "    # start_time = time.time()\n",
    "    sol = sim.solve(initial_soc=0)\n",
    "    # solutions_SPMe.append(sim.solve(initial_soc = 0))\n",
    "    # end_time = time.time()\n",
    "    solutions_SPMe.append(sol)\n",
    "    # execution_time = end_time - start_time\n",
    "\n",
    "# print(f\"Execution time: {execution_time} seconds\")\n",
    "print(f\"Solve time: {solutions_SPMe[0].solve_time} seconds\")\n",
    "print(f\"Setup time: {solutions_SPMe[0].set_up_time} seconds\")\n",
    "# solutions_SPMe[0].cycles[0].plot([\"Current [A]\",\"Voltage [V]\"])\n",
    "\n",
    "sol_SPMe = []\n",
    "# sol_SPMe.append(solutions_SPMe[0]['Negative electrode volume-averaged concentration [mol.m-3]'].entries)\n",
    "# sol_SPMe.append(solutions_SPMe[0]['Positive electrode volume-averaged concentration [mol.m-3]'].entries)\n",
    "sol_SPMe.append(\n",
    "    solutions_SPMe[0]['X-averaged negative particle surface concentration [mol.m-3]'].entries)\n",
    "sol_SPMe.append(\n",
    "    solutions_SPMe[0]['X-averaged positive particle surface concentration [mol.m-3]'].entries)\n",
    "\n",
    "sol_SPMe.append(-solutions_SPMe[0]\n",
    "                [\"Negative electrolyte potential [V]\"].entries[0])\n",
    "sol_SPMe.append(\n",
    "    solutions_SPMe[0]['X-averaged negative electrode porosity'].entries)\n",
    "sol_SPMe.append(solutions_SPMe[0]['X-averaged SEI thickness [m]'].entries)\n",
    "# sol_SPMe.append(solutions_SPMe[0]['X-averaged negative electrode potential [V]'].entries)\n",
    "# sol_SPMe.append(solutions_SPMe[0]['X-averaged SEI interfacial current density [A.m-2]'].entries)\n",
    "sol_SPMe.append(solutions_SPMe[0][\"Voltage [V]\"].entries)\n",
    "\n",
    "# # sol_SPMe.append(solutions_SPMe[0]['X-averaged negative electrode SEI interfacial current density [A.m-2]'].entries),\n",
    "# # sol_SPMe.append(solutions_SPMe[0]['X-averaged SEI film overpotential [V]'].entries ),\n",
    "\n",
    "\n",
    "# sol_SPMe.append(solutions_SPMe[0][\"Voltage [V]\"].entries)\n",
    "t = solutions_SPMe[0][\"Time [s]\"].entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7a975d44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Execution time: 64.204 ms seconds\n"
     ]
    }
   ],
   "source": [
    "# Simplified Model\n",
    "# Defininig variables\n",
    "from pybamm import exp\n",
    "from pybamm import tanh\n",
    "cn_av = pybamm.Variable(\"Negative concentration [mol.m-3]\")\n",
    "cp_av = pybamm.Variable(\"Positive concentration [mol.m-3]\")\n",
    "phi_n = pybamm.Variable(\"Negative electrode potential [V]\")\n",
    "phi_p = pybamm.Variable(\"Positive electrode potential [V]\")\n",
    "phi_e_n = pybamm.Variable(\"Negative electrolyte potential [V]\")\n",
    "E_n = pybamm.Variable(\"Negative electrode porosity\")\n",
    "L_SEI = pybamm.Variable(\"Thickness of SEI [m]\")\n",
    "J_SEI = pybamm.Variable(\"SEI interfacial current density [A.m-2]\")\n",
    "\n",
    "\n",
    "x_n = pybamm.SpatialVariable(\n",
    "    \"x_n\", domain=[\"negative electrode\"], coord_sys=\"cartesian\")\n",
    "\n",
    "\n",
    "# dimensional pybamm.Parameter(eters)\n",
    "R_n = pybamm.Parameter('Negative particle radius [m]')\n",
    "R_p = pybamm.Parameter('Positive particle radius [m]')\n",
    "T = pybamm.Parameter('Initial temperature [K]')\n",
    "R = pybamm.Parameter('Ideal gas constant [J.K-1.mol-1]')\n",
    "F = pybamm.Parameter(\"Faraday constant [C.mol-1]\")\n",
    "cn0 = pybamm.Parameter('Initial concentration in negative electrode [mol.m-3]')\n",
    "cp0 = pybamm.Parameter('Initial concentration in positive electrode [mol.m-3]')\n",
    "c_e = pybamm.Parameter('Initial concentration in electrolyte [mol.m-3]')\n",
    "L_n = pybamm.Parameter(\"Negative electrode thickness [m]\")\n",
    "L_p = pybamm.Parameter(\"Positive electrode thickness [m]\")\n",
    "I_app = pybamm.Parameter(\"Applied current [A]\")\n",
    "j_SEI_0 = pybamm.Parameter(\n",
    "    'Initial SEI reaction exchange current density [A.m-2]')\n",
    "an = pybamm.Parameter('Negative particle surface area to volume ratio [m-1]')\n",
    "ap = pybamm.Parameter('Positive particle surface area to volume ratio [m-1]')\n",
    "U_SEI = pybamm.Parameter('SEI open-circuit potential [V]')\n",
    "s_SEI = pybamm.Parameter(\"SEI conductivity [S.m-1]\")\n",
    "L_SEI_0 = pybamm.Parameter(\"Initial SEI thickness [m]\")\n",
    "alpha_SEI = pybamm.Parameter(\"alpha_SEI [-]\")\n",
    "sigma_e = pybamm.Parameter('Electrolyte conductivity [S.m-1]')\n",
    "B = pybamm.Parameter('Negative electrode Bruggeman coefficient (electrolyte)')\n",
    "E_init = pybamm.Parameter('Initial negative electrode porosity')\n",
    "M_SEI = pybamm.Parameter(\"Molar weight of SEI material [kg.mol-1]\")\n",
    "rho_SEI = pybamm.Parameter(\"Density of SEI material [kg.m-3]\")\n",
    "n_SEI = pybamm.Parameter(\"electrone number [-]\")\n",
    "c_n_max = pybamm.Parameter(\n",
    "    \"Maximum concentration in negative electrode [mol.m-3]\")\n",
    "c_p_max = pybamm.Parameter(\n",
    "    \"Maximum concentration in positive electrode [mol.m-3]\")\n",
    "\n",
    "L_Z = pybamm.Parameter('Electrode height [m]')\n",
    "L_Y = pybamm.Parameter('Electrode width [m]')\n",
    "\n",
    "\n",
    "D_Lii = pybamm.Parameter('SEI lithium interstitial diffusivity [m2.s-1]')\n",
    "C_Lii = pybamm.Parameter(\n",
    "    'Lithium interstitial reference concentration [mol.m-3]')\n",
    "\n",
    "\n",
    "def Un(x):\n",
    "    return pybamm.FunctionParameter(\n",
    "        \"Negative electrode OCP [V]\",\n",
    "        {\"Negative particle stoichiometry\": x},\n",
    "    )\n",
    "\n",
    "\n",
    "def Up(x):\n",
    "    return pybamm.FunctionParameter(\n",
    "        \"Positive electrode OCP [V]\",\n",
    "        {\"Positive particle stoichiometry\": x},\n",
    "    )\n",
    "\n",
    "\n",
    "def ch_dch(x):\n",
    "    return pybamm.FunctionParameter(\n",
    "        \"ch_dch_I_app\",\n",
    "        {\"Positive particle stoichiometry\": x},\n",
    "    )\n",
    "\n",
    "\n",
    "def OCP_n(sto):\n",
    "    stretch = 1.00\n",
    "    # sto = stretch * cc / c_n_max\n",
    "    u_eq = (\n",
    "        1.9793 * exp(-39.3631 * sto)\n",
    "        + 0.2482\n",
    "        - 0.0909 * tanh(29.8538 * (sto - 0.1234))\n",
    "        - 0.04478 * tanh(14.9159 * (sto - 0.2769))\n",
    "        - 0.0205 * tanh(30.4444 * (sto - 0.6103))\n",
    "    )\n",
    "    return u_eq\n",
    "\n",
    "\n",
    "def OCP_P(sto):\n",
    "    u_eq = (\n",
    "        -0.8090 * sto\n",
    "        + 4.4875\n",
    "        - 0.0428 * tanh(18.5138 * (sto - 0.5542))\n",
    "        - 17.7326 * tanh(15.7890 * (sto - 0.3117))\n",
    "        + 17.5842 * tanh(15.9308 * (sto - 0.3120))\n",
    "    )\n",
    "    return u_eq\n",
    "\n",
    "\n",
    "def my_current(t):\n",
    "    return pybamm.sign(180000-t)\n",
    "\n",
    "\n",
    "param_simplified = pybamm.ParameterValues(\n",
    "    {'Negative particle radius [m]': 5.86e-06,  # from Chen2020\n",
    "     'Positive particle radius [m]': 5.22e-06,\n",
    "     'Initial temperature [K]': 298.15,  # from Chen2020\n",
    "     'Ideal gas constant [J.K-1.mol-1]': 8.314462618,   # from Chen2020\n",
    "     \"Faraday constant [C.mol-1]\": 96485.33212,  # from Chen2020\n",
    "     'Initial concentration in negative electrode [mol.m-3]': solutions_SPMe[0]['Negative electrode volume-averaged concentration [mol.m-3]'].entries[0],\n",
    "     'Initial concentration in positive electrode [mol.m-3]': solutions_SPMe[0]['Positive electrode volume-averaged concentration [mol.m-3]'].entries[0],\n",
    "     'Initial concentration in electrolyte [mol.m-3]': 1000,\n",
    "     \"Negative electrode thickness [m]\": 8.52e-05,  # from Chen2020\n",
    "     'Positive electrode thickness [m]': 7.56e-05,\n",
    "     \"Applied current [A]\": 0,  # managed during running the code\n",
    "     # from Chen2020\n",
    "     'Initial SEI reaction exchange current density [A.m-2]': 1.5e-07,\n",
    "     # From Ferran AMM 2023 checked with chen2020\n",
    "     'Positive particle surface area to volume ratio [m-1]': 3.82e5,\n",
    "     # From Ferran AMM 2023 checked with chen2020\n",
    "     'Negative particle surface area to volume ratio [m-1]': 3.84e5,\n",
    "     'SEI open-circuit potential [V]':  0.4,  # from Chen2020\n",
    "     # From Ferran AMM 2023 same as in Chen2020 (Resistivity)\n",
    "     \"SEI conductivity [S.m-1]\": 5e-6,\n",
    "     \"Initial SEI thickness [m]\": 1e-12,\n",
    "     \"alpha_SEI [-]\": 0.5,\n",
    "     'Electrolyte conductivity [S.m-1]': 0.9486,\n",
    "     # from Chen2020\n",
    "     'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
    "     'Initial negative electrode porosity': 0.25,  # from Chen2020\n",
    "     \"Molar weight of SEI material [kg.mol-1]\": 0.162,  # From Ferran AMM 2023\n",
    "     # From Ferran AMM 2023   M/rho=Volume = Chen 2020\n",
    "     \"Density of SEI material [kg.m-3]\": 1690,\n",
    "     \"electrone number [-]\": 2,  # from Chen2020\n",
    "     # from Chen2020\n",
    "     \"Maximum concentration in negative electrode [mol.m-3]\": 33133.0,\n",
    "     \"Maximum concentration in positive electrode [mol.m-3]\": 63104.0,\n",
    "     'Electrode height [m]': 0.065,  # Chen2020\n",
    "     'Electrode width [m]': 1.58,  # Chen2020\n",
    "     \"Negative electrode OCP [V]\": OCP_n,\n",
    "     \"Positive electrode OCP [V]\": OCP_P,\n",
    "     # \"electrolyte_conductivity [S m-1]\": sigma_e\n",
    "\n",
    "\n",
    "     'SEI lithium interstitial diffusivity [m2.s-1]': 1e-20,\n",
    "     'Lithium interstitial reference concentration [mol.m-3]': 15.0,\n",
    "\n",
    "\n",
    "     \"ch_dch_I_app\": ch_dch,\n",
    "\n",
    "\n",
    "\n",
    "     }\n",
    ")\n",
    "\n",
    "model_simplified = pybamm.lithium_ion.BaseModel()\n",
    "param_simplified['Applied current [A]'] = solutions_SPMe[0]['Current [A]'].entries[0] / \\\n",
    "    (L_Y*L_Z)\n",
    "\n",
    "if SEIModels[0] == \"reaction limited\":\n",
    "    J_SEI = -an * j_SEI_0 * \\\n",
    "        exp(-alpha_SEI*F/(R*T) * (phi_n - phi_e_n -\n",
    "            U_SEI - (I_app*L_SEI/s_SEI)))\n",
    "    J_SEI_AV = pybamm.x_average(J_SEI)\n",
    "elif SEIModels[0] == \"interstitial-diffusion limited\":\n",
    "    J_SEI = -F*D_Lii*C_Lii/(.5*L_SEI) * exp(-F/(R*T) * (phi_n - phi_e_n))*an\n",
    "    J_SEI_AV = pybamm.x_average(J_SEI)\n",
    "\n",
    "\n",
    "model_simplified.variables = {\n",
    "    \"Negative concentration [mol.m-3]\": cn_av,\n",
    "    \"Positive concentration [mol.m-3]\": cp_av,\n",
    "    \"Negative electrode potential [V]\": phi_n,\n",
    "    \"Positive electrode potential [V]\": phi_p,\n",
    "    \"Negative electrolyte potential [V]\":  phi_e_n,\n",
    "    \"Negative electrode porosity\": E_n,\n",
    "    \"Thickness of SEI [m]\": L_SEI,\n",
    "    \"SEI interfacial current density [A.m-2]\": I_app,\n",
    "\n",
    "\n",
    "}\n",
    "\n",
    "\n",
    "model_simplified.algebraic = {\n",
    "    phi_n: phi_n - Un(cn_av/c_n_max),\n",
    "    phi_p: phi_p - Up(cp_av/c_p_max) + Un(cn_av/c_n_max),\n",
    "    phi_e_n: phi_e_n + I_app/(2.0*sigma_e*B*L_n)*L_n**2,\n",
    "    L_SEI: L_SEI - L_SEI_0 + 1/an*(E_n-E_init),\n",
    "}\n",
    "\n",
    "dcndt = -3/(an*R_n*F)*(I_app/L_n - J_SEI)\n",
    "dcpdt = 3/(ap*R_p*F)*(I_app/L_p)\n",
    "dEndt = M_SEI/(n_SEI*rho_SEI*F)*J_SEI\n",
    "\n",
    "\n",
    "model_simplified.rhs = {cn_av: dcndt, E_n: dEndt, cp_av: dcpdt}\n",
    "\n",
    "\n",
    "model_simplified.initial_conditions = {\n",
    "    phi_n: Un(cn0/c_n_max),\n",
    "    phi_p: Un(cp0/c_p_max)-Un(cn0/c_n_max),\n",
    "    phi_e_n: 1e-7,\n",
    "    L_SEI: L_SEI_0,\n",
    "    cn_av: cn0,\n",
    "    cp_av: cp0,\n",
    "    E_n: E_init,\n",
    "}\n",
    "\n",
    "geometry = {\n",
    "    \"negative electrode\": {x_n: {\"min\": 0, \"max\": L_n}},\n",
    "}\n",
    "\n",
    "param_simplified.process_model(model_simplified)\n",
    "param_simplified.process_geometry(geometry)\n",
    "\n",
    "\n",
    "submesh_types = {\n",
    "    \"negative electrode\": pybamm.Uniform1DSubMesh,\n",
    "}\n",
    "var_pts = {x_n: 20}\n",
    "mesh = pybamm.Mesh(geometry, submesh_types, var_pts)\n",
    "\n",
    "spatial_methods = {\n",
    "    \"negative electrode\": pybamm.FiniteVolume(),\n",
    "}\n",
    "disc = pybamm.Discretisation(mesh, spatial_methods)\n",
    "disc.process_model(model_simplified)\n",
    "\n",
    "\n",
    "# solver = pybamm.CasadiSolver()\n",
    "solver = pybamm.IDAKLUSolver()\n",
    "# t = np.linspace(0, 80000, 20000)\n",
    "\n",
    "\n",
    "# start_time = time.time()\n",
    "sim_k = pybamm.Simulation(\n",
    "    model_simplified, parameter_values=param_simplified, solver=solver)\n",
    "solution_simplified = sim_k.solve(t)\n",
    "# end_time = time.time()\n",
    "# execution_time = end_time - start_time\n",
    "\n",
    "# print(f\"Execution time: {execution_time} seconds\")\n",
    "print(f\"Execution time: {solution_simplified.solve_time} seconds\")\n",
    "\n",
    "# print(np.sum(solution_simplified[\"SEI interfacial current density [A.m-2]\"].entries))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4bd9ec5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ch_dch =  solution_simplified[\"SEI interfacial current density [A.m-2]\"].entries\n",
    "# cc = solution_simplified[\"Negative concentration [mol.m-3]\"].entries\n",
    "# plt.plot(cc, ch_dch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a1f9f63c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x1600 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sol = []\n",
    "tt = (solution_simplified.t/3600)\n",
    "an = param_simplified['Negative particle surface area to volume ratio [m-1]']\n",
    "sol.append(solution_simplified[\"Negative concentration [mol.m-3]\"].entries)\n",
    "sol.append(solution_simplified[\"Positive concentration [mol.m-3]\"].entries)\n",
    "sol.append(solution_simplified[\"Negative electrode potential [V]\"].entries)\n",
    "sol.append(solution_simplified[\"Negative electrode porosity\"].entries)\n",
    "sol.append(solution_simplified[\"Thickness of SEI [m]\"].entries)\n",
    "# sol.append(solution_simplified[\"Negative electrolyte potential [V]\"].entries)\n",
    "# sol.append(solution_simplified[\"SEI interfacial current density [A.m-2]\"].entries / an)\n",
    "sol.append(solution_simplified[\"Positive electrode potential [V]\"].entries)\n",
    "\n",
    "\n",
    "# print(np.max(sol[0]))\n",
    "# sol.append(solution_simplified['X-averaged SEI film overpotential [V]'].entries[0])\n",
    "# sol.append(solution_simplified[\"Voltage [V]\"].entries)\n",
    "\n",
    "\n",
    "Label = [\"cn_AV\", \"cp_AV\", \"phi_n\", \"Porosity\", \"SEI L\", \"Voltage\"]\n",
    "\n",
    "fig, axes = plt.subplots(4, 2, figsize=(12, 16))\n",
    "\n",
    "for i in range(6):  # Loop through the data to plot in subplots\n",
    "    row = i // 2\n",
    "    col = i % 2\n",
    "    ax = axes[row, col]\n",
    "\n",
    "    ax.plot(tt,\n",
    "            sol[i],\n",
    "            label=Label[i]+\" Simplfied\",\n",
    "            linestyle=\"-\",\n",
    "            color=\"black\",\n",
    "            linewidth=1.75)\n",
    "\n",
    "    ax.plot(tt,\n",
    "            sol_SPMe[i],\n",
    "            label=Label[i]+\" SPMe\",\n",
    "            linestyle=\"-\",\n",
    "            color=\"red\",\n",
    "            linewidth=0.75)\n",
    "    ax.set_title(\"Subplot \" + str(i+1))\n",
    "    ax.set_xlabel(\"Time (h)\")\n",
    "    ax.set_ylabel(Label[i])\n",
    "    ax.legend()\n",
    "    ax.minorticks_on()\n",
    "    ax.tick_params(which='both', direction='in')\n",
    "\n",
    "    # Add minor ticks\n",
    "\n",
    "# ax.grid(which='minor', linestyle='--', linewidth=0.5)\n",
    "# Adjust the layout to prevent overlapping labels\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plots\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e7931d6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tt = solution_simplified.t\n",
    "phi_e = solutions_SPMe[0][\"Negative electrolyte potential [V]\"].entries[0]\n",
    "OCP_n = solutions_SPMe[0]['Negative electrode open-circuit potential [V]'].entries[0]\n",
    "\n",
    "# En=solution[\"Concentration [mol.m-3]\"].entries\n",
    "# # L_SEI=solution[\"Thickness of SEI [m]\"].entries\n",
    "# # L_SEI: L_SEI_0-1/an*(En-E_init)\n",
    "# plt.plot(tt,cc)\n",
    "# v=phin - phie\n",
    "\n",
    "\n",
    "plt.plot(tt, phi_e, label=\"phi_e SPMe\")\n",
    "plt.plot(tt, -OCP_n, label=\"Un in SPMe\")\n",
    "plt.legend()\n",
    "plt.show()\n",
    "# # plt.plot(tt,L_SEI[0,:])"
   ]
  }
 ],
 "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": "3.10.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}