2024-08-28-active-material-distribution.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-28 15:26:41.890 - [WARNING] citations.read_citations(76): Citations could not be read because the 'pybtex' library is not installed. Install 'pybamm[cite]' to enable citation reading.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3c14bdc93af24544b76f6c6b180795bb",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "interactive(children=(FloatSlider(value=0.0, description='t', max=1629.967524837035, step=16.29967524837035), …"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<pybamm.plotting.quick_plot.QuickPlot at 0x16914ca10>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pybamm\n",
    "\n",
    "model = pybamm.lithium_ion.DFN()\n",
    "parameter_values = pybamm.ParameterValues(\"Chen2020\")  # 5 Ah NMC/Graphite cell\n",
    "\n",
    "\n",
    "def eps_e_n(x):\n",
    "    L_n = pybamm.Parameter(\"Negative electrode thickness [m]\")\n",
    "    a = pybamm.InputParameter(\"a\")\n",
    "    b = pybamm.InputParameter(\"b\")\n",
    "    return a + (b - a) * x / L_n\n",
    "\n",
    "\n",
    "def eps_s_n(x):\n",
    "    # 95% of the active material volume fraction\n",
    "    return 0.95 * (1 - eps_e_n(x))\n",
    "\n",
    "\n",
    "parameter_values.update(\n",
    "    {\n",
    "        \"Negative electrode porosity\": eps_e_n,\n",
    "        \"Negative electrode active material volume fraction\": eps_s_n,\n",
    "        \"Current function [A]\": 10,  # 2C discharge for 5 Ah cell\n",
    "    }\n",
    ")\n",
    "\n",
    "solver = pybamm.IDAKLUSolver()\n",
    "sim = pybamm.Simulation(model, parameter_values=parameter_values, solver=solver)\n",
    "sols = {}\n",
    "designs = {\n",
    "    \"More porosity near current collector\": {\"a\": 0.35, \"b\": 0.15},\n",
    "    \"More porosity near separator\": {\"a\": 0.15, \"b\": 0.35},\n",
    "    \"Constant porosity\": {\"a\": 0.25, \"b\": 0.25},\n",
    "}\n",
    "for name, design in designs.items():\n",
    "    sols[name] = sim.solve([0, 1800], inputs=design)\n",
    "\n",
    "pybamm.dynamic_plot(\n",
    "    list(sols.values()),\n",
    "    [\n",
    "        \"Voltage [V]\",\n",
    "        \"Negative electrode porosity\",\n",
    "        \"Negative electrode active material volume fraction\",\n",
    "        \"Electrolyte concentration [mol.m-3]\",\n",
    "        \"Electrolyte potential [V]\",\n",
    "        \"Negative electrode interfacial current density [A.m-2]\",\n",
    "    ],\n",
    "    labels=sols.keys(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-08-28 15:06:08.151 - [ERROR] callbacks.on_experiment_error(233): Simulation error: idaklu solver failed\n",
      "\n",
      "[IDAS ERROR]  IDASolve\n",
      "  At t = 96508.3, , mxstep steps taken before reaching tout.\n",
      "\n"
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "def cycle(c_rate):\n",
    "    return [\n",
    "        (\n",
    "            pybamm.step.c_rate(c_rate, termination=\"2.5V\"),\n",
    "            pybamm.step.c_rate(0, duration=60*30),\n",
    "            pybamm.step.c_rate(-c_rate, termination=\"4.2V\"),\n",
    "            pybamm.step.voltage(4.2, termination=\"0.05C\"),\n",
    "            pybamm.step.c_rate(0, duration=60*30),\n",
    "        )\n",
    "    ]\n",
    "\n",
    "experiment = []\n",
    "for c_rate in [1/2, 1, 2]:\n",
    "    experiment += cycle(c_rate) * 3\n",
    "\n",
    "experiment = pybamm.Experiment(experiment)\n",
    "sim = pybamm.Simulation(model, parameter_values=parameter_values, experiment=experiment, solver=solver)\n",
    "\n",
    "capacities = {}\n",
    "for name, design in designs.items():\n",
    "    sol = sim.solve(inputs=design)\n",
    "    for cycle in sol.cycles:\n",
    "        step = cycle.steps[0]\n",
    "        q = step[\"Discharge capacity [A.h]\"].data[-1]\n",
    "        capacities.setdefault(name, []).append(q)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "for name, q in capacities.items():\n",
    "    ax.plot(q, \"-s\", label=name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    },
    {
     "ename": "",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31mnotebook controller is DISPOSED. \n",
      "\u001b[1;31mView Jupyter <a href='command:jupyter.viewOutput'>log</a> for further details."
     ]
    }
   ],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pybamm",
   "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": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}