examples/notebooks/Validating_mechanical_models_Enertech_DFN.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# Test the parameter set of the Enertech cells\n",
    "In this notebook, we show how to use pybamm to reproduce the experimental results for the Enertech cells (LCO-G). To see all of the models and submodels available in PyBaMM, please take a look at the documentation [here](https://pybamm.readthedocs.io/en/latest/source/models/index.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "\u001b[33mWARNING: You are using pip version 21.0.1; however, version 21.1.1 is available.\n",
      "You should consider upgrading via the '/Users/vsulzer/Documents/Energy_storage/PyBaMM/.tox/dev/bin/python -m pip install --upgrade pip' command.\u001b[0m\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install pybamm -q    # install PyBaMM if it is not installed\n",
    "import pybamm\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "os.chdir(pybamm.__path__[0]+'/..')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When you load a model in PyBaMM it builds by default. Building the model sets all of the model variables and sets up any variables which are coupled between different submodels: this is the process which couples the submodels together and allows one submodel to access variables from another. If you would like to swap out a submodel in an exisitng battery model you need to load it without building it by passing the keyword `build=False`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = pybamm.lithium_ion.DFN(\n",
    "    options = {\n",
    "        \"particle\": \"Fickian diffusion\", \n",
    "        \"cell geometry\": \"arbitrary\", \n",
    "        \"thermal\": \"lumped\", \n",
    "        \"particle mechanics\": \"swelling only\",\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can get the parameter set `Ai2020` for the model, which includes the mechanical properties required by the mechanical model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "0.5 C\n",
      "1 C\n",
      "2 C\n"
     ]
    }
   ],
   "source": [
    "chemistry = pybamm.parameter_sets.Ai2020\n",
    "param = pybamm.ParameterValues(chemistry=chemistry)\n",
    "capacity = param[\"Nominal cell capacity [A.h]\"]\n",
    "param.update({\n",
    "    \"Current function [A]\": capacity * pybamm.InputParameter(\"C-rate\")\n",
    "})\n",
    "# experiment05C = pybamm.Experiment([\"Discharge at 0.5C until 3 V\"])\n",
    "# experiment1C = pybamm.Experiment([\"Discharge at 1C until 3 V\"])\n",
    "# experiment2C = pybamm.Experiment([\"Discharge at 2C until 3 V\"])\n",
    "var = pybamm.standard_spatial_vars\n",
    "var_pts = {\n",
    "  var.x_n: 50,\n",
    "  var.x_s: 50,\n",
    "  var.x_p: 50,\n",
    "  var.r_n: 20,\n",
    "  var.r_p: 20,\n",
    "}\n",
    "\n",
    "sim = pybamm.Simulation(\n",
    "        model,\n",
    "        var_pts=var_pts,\n",
    "        parameter_values=param,\n",
    "        solver=pybamm.CasadiSolver(dt_max=600)\n",
    "    )\n",
    "Crates = [0.5, 1, 2]\n",
    "solutions = []\n",
    "\n",
    "for Crate in Crates:\n",
    "    print(f\"{Crate} C\")\n",
    "    sol = sim.solve(t_eval=[0, 3600/Crate*1.05], inputs={\"C-rate\": Crate})\n",
    "    solutions.append(sol)\n",
    "\n",
    "\n",
    "solution05C, solution1C, solution2C = solutions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load experimental results of the Enertech cells (see [[1]](#References))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load experimental results\n",
    "import pandas as pd\n",
    "path = \"pybamm/input/discharge_data/Enertech_cells/\"\n",
    "data_Disp_01C=pd.read_csv (path + \"0.1C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
    "data_Disp_05C=pd.read_csv (path + \"0.5C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
    "data_Disp_1C=pd.read_csv (path + \"1C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
    "data_Disp_2C=pd.read_csv (path + \"2C_discharge_displacement.txt\", delimiter= '\\s+',header=None)\n",
    "data_V_01C=pd.read_csv (path + \"0.1C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
    "data_V_05C=pd.read_csv (path + \"0.5C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
    "data_V_1C=pd.read_csv (path + \"1C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
    "data_V_2C=pd.read_csv (path + \"2C_discharge_U.txt\", delimiter= '\\s+',header=None)\n",
    "data_T_05C=pd.read_csv (path + \"0.5C_discharge_T.txt\", delimiter= '\\s+',header=None)\n",
    "data_T_1C=pd.read_csv (path + \"1C_discharge_T.txt\", delimiter= '\\s+',header=None)\n",
    "data_T_2C=pd.read_csv (path + \"2C_discharge_T.txt\", delimiter= '\\s+',header=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "t_all2C = solution2C[\"Time [h]\"].entries\n",
    "V_n2C = solution2C[\"Terminal voltage [V]\"].entries\n",
    "T_n2C = solution2C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n",
    "L_x2C = solution2C[\"Cell thickness change [m]\"].entries\n",
    "\n",
    "t_all1C = solution1C[\"Time [h]\"].entries\n",
    "V_n1C = solution1C[\"Terminal voltage [V]\"].entries\n",
    "T_n1C = solution1C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n",
    "L_x1C = solution1C[\"Cell thickness change [m]\"].entries\n",
    "\n",
    "t_all05C = solution05C[\"Time [h]\"].entries\n",
    "V_n05C = solution05C[\"Terminal voltage [V]\"].entries\n",
    "T_n05C = solution05C[\"Volume-averaged cell temperature [K]\"].entries - param[\"Initial temperature [K]\"]\n",
    "L_x05C = solution05C[\"Cell thickness change [m]\"].entries\n",
    "f, (ax1, ax2,ax3) = plt.subplots(1, 3 ,figsize=(14,4))\n",
    "ax1.plot(t_all2C, V_n2C,'r-',label=\"Simulation\")\n",
    "ax1.plot(data_V_2C.values[::30,0]/3600, data_V_2C.values[::30,1],'ro',markerfacecolor='none',label=\"Experiment\")\n",
    "ax1.plot(t_all05C, V_n05C,'g-')\n",
    "ax1.plot(data_V_05C.values[::100,0]/3600, data_V_05C.values[::100,1],'go',markerfacecolor='none')\n",
    "ax1.plot(t_all1C, V_n1C,'b-')\n",
    "ax1.plot(data_V_1C.values[::50,0]/3600, data_V_1C.values[::50,1],'bo',markerfacecolor='none')\n",
    "ax1.legend()\n",
    "#plt.xlim(0, 3600);\n",
    "ax1.set_xlabel(\"Time [h]\")\n",
    "ax1.set_ylabel(\"Terminal voltage [V]\")\n",
    "ax1.text(0.1, 3.2, r'2 C', {'color': 'r', 'fontsize': 14})\n",
    "ax1.text(1.1, 3.2, r'1 C', {'color': 'b', 'fontsize': 14})\n",
    "ax1.text(1.6, 3.2, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
    "\n",
    "ax2.plot(t_all2C, T_n2C,'r-',label=\"Simulation\")\n",
    "ax2.plot(data_T_2C.values[0:1754:50,0]/3600, data_T_2C.values[0:1754:50,1],'ro',markerfacecolor='none',label=\"Experiment\")\n",
    "ax2.plot(t_all05C, T_n05C,'g-')\n",
    "ax2.plot(data_T_05C.values[0:7301:200,0]/3600, data_T_05C.values[0:7301:200,1],'go',markerfacecolor='none')\n",
    "ax2.plot(t_all1C, T_n1C,'b-')\n",
    "ax2.plot(data_T_1C.values[0:3598:100,0]/3600, data_T_1C.values[0:3598:100,1],'bo',markerfacecolor='none')\n",
    "ax2.legend()\n",
    "ax2.set_xlabel(\"Time [h]\")\n",
    "ax2.set_ylabel(\"Temperature rise [K]\")\n",
    "#plt.xlim(0, 3600);\n",
    "ax2.text(0.5, 8, r'2 C', {'color': 'r', 'fontsize': 14})\n",
    "ax2.text(0.8, 4.4, r'1 C', {'color': 'b', 'fontsize': 14})\n",
    "ax2.text(1.5, 2, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
    "\n",
    "ax3.plot(t_all2C, L_x2C,'r-',label=\"Simulation\")\n",
    "ax3.plot(data_Disp_2C.values[0:1754:5,0]/3600, data_Disp_2C.values[0:1754:5,1]-data_Disp_2C.values[0,1],'ro',markerfacecolor='none',label=\"Experiment\")\n",
    "ax3.plot(t_all05C, L_x05C,'g-')\n",
    "ax3.plot(data_Disp_05C.values[0:1754:10,0]/3600, data_Disp_05C.values[0:1754:10,1]-data_Disp_05C.values[0,1],'go',markerfacecolor='none')\n",
    "ax3.plot(t_all1C, L_x1C,'b-')\n",
    "ax3.plot(data_Disp_1C.values[0:1754:10,0]/3600, data_Disp_1C.values[0:1754:10,1]-data_Disp_1C.values[0,1],'bo',markerfacecolor='none')\n",
    "ax3.legend()\n",
    "ax3.set_xlabel(\"Time [h]\")\n",
    "ax3.set_ylabel(\"Thickness change [m]\")\n",
    "ax3.text(0.1, -0.0001, r'2 C', {'color': 'r', 'fontsize': 14})\n",
    "ax3.text(0.9, -0.0001, r'1 C', {'color': 'b', 'fontsize': 14})\n",
    "ax3.text(1.8, -0.0001, r'0.5 C', {'color': 'g', 'fontsize': 14})\n",
    "#plt.xlim(0, 3600);\n",
    "f.tight_layout()\n",
    "f.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Stress data below are from Fig. 6 in [[1]](#References)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x252 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "cs_n_xav = solution2C[\"X-averaged negative particle concentration [mol.m-3]\"].entries\n",
    "cs_p_xav = solution2C[\"X-averaged positive particle concentration [mol.m-3]\"].entries\n",
    "st_surf_n =  solution2C[\"Negative particle surface tangential stress\"].entries\n",
    "st_surf_p =  solution2C[\"Positive particle surface tangential stress\"].entries\n",
    "\n",
    "data_st_n_2C=pd.read_csv (path + \"stn_2C.txt\", delimiter= ',',header=3)\n",
    "data_st_p_2C=pd.read_csv (path + \"stp_2C.txt\", delimiter= ',',header=3)\n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(1, 2 ,figsize=(10,3.5))\n",
    "ax1.plot(t_all2C, st_surf_n[-1,:],'ro',markerfacecolor='none',label=\"Current\")\n",
    "ax1.plot(data_st_n_2C.values[:,0]/3600, data_st_n_2C.values[:,1],'r-',label=\"Ai et al. 2020\")\n",
    "ax1.legend()\n",
    "#plt.xlim(0, 3600);\n",
    "ax1.set_xlabel(\"Time [h]\")\n",
    "ax1.set_ylabel(\"$\\sigma_{t,n}/E_n$\")\n",
    "\n",
    "ax2.plot(t_all2C, st_surf_p[0,:],'ro',markerfacecolor='none',label=\"Current\")\n",
    "ax2.plot(data_st_p_2C.values[0:3601,0]/3600, data_st_p_2C.values[0:3601,1],'r-',label=\"Ai et al. 2020\")\n",
    "ax2.legend()\n",
    "ax2.set_xlabel(\"Time [h]\")\n",
    "ax2.set_ylabel(\"$\\sigma_{t,p}/E_p$\")\n",
    "#plt.xlim(0, 3600);\n",
    "f.tight_layout()\n",
    "#f.set_title(\"particle surface tangential stress close to the separator\")\n",
    "f.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## References\n",
    "\n",
    "The relevant papers for this notebook are:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[1] Weilong Ai, Ludwig Kraft, Johannes Sturm, Andreas Jossen, and Billy Wu. Electrochemical thermal-mechanical modelling of stress inhomogeneity in lithium-ion pouch cells. Journal of The Electrochemical Society, 167(1):013512, 2019. doi:10.1149/2.0122001JES.\n[2] Joel A. E. Andersson, Joris Gillis, Greg Horn, James B. Rawlings, and Moritz Diehl. CasADi – A software framework for nonlinear optimization and optimal control. Mathematical Programming Computation, 11(1):1–36, 2019. doi:10.1007/s12532-018-0139-4.\n[3] Rutooj Deshpande, Mark Verbrugge, Yang-Tse Cheng, John Wang, and Ping Liu. Battery cycle life prediction with coupled chemical degradation and fatigue mechanics. Journal of the Electrochemical Society, 159(10):A1730, 2012. doi:10.1149/2.049210jes.\n[4] Marc Doyle, Thomas F. Fuller, and John Newman. Modeling of galvanostatic charge and discharge of the lithium/polymer/insertion cell. Journal of the Electrochemical society, 140(6):1526–1533, 1993. doi:10.1149/1.2221597.\n[5] Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, Ralf Gommers, Pauli Virtanen, David Cournapeau, Eric Wieser, Julian Taylor, Sebastian Berg, Nathaniel J. Smith, and others. Array programming with NumPy. Nature, 585(7825):357–362, 2020. doi:10.1038/s41586-020-2649-2.\n[6] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). ECSarXiv. February, 2020. doi:10.1149/osf.io/67ckj.\n[7] Robert Timms, Scott G. Marquis, Valentin Sulzer, Colin P. Please, and S. Jon Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. Submitted for publication, 2020. arXiv:2005.05127.\n\n"
     ]
    }
   ],
   "source": [
    "pybamm.print_citations()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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