Simulating Custom Model.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulating Physics Based Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pybamm\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def my_current(t):\n",
    "    current = pybamm.sin(2 * np.pi * t / 60)\n",
    "    return current"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = {\"thermal\": \"x-full\"}\n",
    "model = pybamm.lithium_ion.SPMe(options=options) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1 + dlnf/dlnc': 1.0,\n",
       " 'Ambient temperature [K]': 298.15,\n",
       " 'Bulk solvent concentration [mol.m-3]': 2636.0,\n",
       " 'Cation transference number': 0.4,\n",
       " 'Cell cooling surface area [m2]': 0.0569,\n",
       " 'Cell volume [m3]': 7.8e-06,\n",
       " 'Current function [A]': 0.680616,\n",
       " 'EC diffusivity [m2.s-1]': 2e-18,\n",
       " 'EC initial concentration in electrolyte [mol.m-3]': 4541.0,\n",
       " 'Edge heat transfer coefficient [W.m-2.K-1]': 0.3,\n",
       " 'Electrode height [m]': 0.13699999999999998,\n",
       " 'Electrode width [m]': 0.207,\n",
       " 'Electrolyte conductivity [S.m-1]': <function electrolyte_conductivity_Capiglia1999 at 0x00000146CE5FD280>,\n",
       " 'Electrolyte diffusivity [m2.s-1]': <function electrolyte_diffusivity_Capiglia1999 at 0x00000146CE5FD3A0>,\n",
       " 'Initial concentration in electrolyte [mol.m-3]': 1000.0,\n",
       " 'Initial concentration in negative electrode [mol.m-3]': 19986.609595075,\n",
       " 'Initial concentration in positive electrode [mol.m-3]': 30730.755438556498,\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]': 1.0000000000000001e-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]': 3.105,\n",
       " 'Maximum concentration in negative electrode [mol.m-3]': 24983.2619938437,\n",
       " 'Maximum concentration in positive electrode [mol.m-3]': 51217.9257309275,\n",
       " 'Negative current collector conductivity [S.m-1]': 59600000.0,\n",
       " 'Negative current collector density [kg.m-3]': 8954.0,\n",
       " 'Negative current collector specific heat capacity [J.kg-1.K-1]': 385.0,\n",
       " 'Negative current collector surface heat transfer coefficient [W.m-2.K-1]': 0.0,\n",
       " 'Negative current collector thermal conductivity [W.m-1.K-1]': 401.0,\n",
       " 'Negative current collector thickness [m]': 2.5e-05,\n",
       " 'Negative electrode Bruggeman coefficient (electrode)': 1.5,\n",
       " 'Negative electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
       " 'Negative electrode OCP [V]': <function graphite_mcmb2528_ocp_Dualfoil1998 at 0x00000146CE5EBDC0>,\n",
       " 'Negative electrode OCP entropic change [V.K-1]': <function graphite_entropic_change_Moura2016 at 0x00000146CE5FD040>,\n",
       " 'Negative electrode active material volume fraction': 0.6,\n",
       " 'Negative electrode cation signed stoichiometry': -1.0,\n",
       " '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]': <function graphite_mcmb2528_diffusivity_Dualfoil1998 at 0x00000146CE5EB9D0>,\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]': <function graphite_electrolyte_exchange_current_density_Dualfoil1998 at 0x00000146CE5EBEE0>,\n",
       " 'Negative electrode porosity': 0.3,\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]': 0.0001,\n",
       " 'Negative particle radius [m]': 1e-05,\n",
       " 'Negative tab centre y-coordinate [m]': 0.06,\n",
       " 'Negative tab centre z-coordinate [m]': 0.13699999999999998,\n",
       " 'Negative tab heat transfer coefficient [W.m-2.K-1]': 10.0,\n",
       " 'Negative tab width [m]': 0.04,\n",
       " 'Nominal cell capacity [A.h]': 0.680616,\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]': 35500000.0,\n",
       " 'Positive current collector density [kg.m-3]': 2707.0,\n",
       " 'Positive current collector specific heat capacity [J.kg-1.K-1]': 897.0,\n",
       " 'Positive current collector surface heat transfer coefficient [W.m-2.K-1]': 0.0,\n",
       " 'Positive current collector thermal conductivity [W.m-1.K-1]': 237.0,\n",
       " 'Positive current collector thickness [m]': 2.5e-05,\n",
       " 'Positive electrode Bruggeman coefficient (electrode)': 1.5,\n",
       " 'Positive electrode Bruggeman coefficient (electrolyte)': 1.5,\n",
       " 'Positive electrode OCP [V]': <function lico2_ocp_Dualfoil1998 at 0x00000146CE5FD0D0>,\n",
       " 'Positive electrode OCP entropic change [V.K-1]': <function lico2_entropic_change_Moura2016 at 0x00000146CE5FD1F0>,\n",
       " 'Positive electrode active material volume fraction': 0.5,\n",
       " 'Positive electrode cation signed stoichiometry': -1.0,\n",
       " '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]': <function lico2_diffusivity_Dualfoil1998 at 0x00000146C8AFAF70>,\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]': <function lico2_electrolyte_exchange_current_density_Dualfoil1998 at 0x00000146CE5FD160>,\n",
       " 'Positive electrode porosity': 0.3,\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]': 0.0001,\n",
       " 'Positive particle radius [m]': 1e-05,\n",
       " 'Positive tab centre y-coordinate [m]': 0.147,\n",
       " 'Positive tab centre z-coordinate [m]': 0.13699999999999998,\n",
       " 'Positive tab heat transfer coefficient [W.m-2.K-1]': 10.0,\n",
       " 'Positive tab width [m]': 0.04,\n",
       " 'Ratio of inner and outer SEI exchange current densities': 1.0,\n",
       " 'Reference temperature [K]': 298.15,\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': 1.0,\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]': 2.5e-05,\n",
       " 'Total heat transfer coefficient [W.m-2.K-1]': 10.0,\n",
       " 'Typical current [A]': 0.680616,\n",
       " 'Typical electrolyte concentration [mol.m-3]': 1000.0,\n",
       " 'Upper voltage cut-off [V]': 4.1}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "parameter_values = model.default_parameter_values\n",
    "parameter_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining RMSE\n",
    "import math\n",
    "\n",
    "def RMSE(true_voltage, model_voltage):\n",
    "    res = 0\n",
    "    n = len(true_voltage)\n",
    "    for i in range(n):\n",
    "        res+= (true_voltage[i]-model_voltage[i])**2\n",
    "    res = res/n\n",
    "    return math.sqrt(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# defining array of C_rates\n",
    "C_Rates = [0.1, 0.2, 0.4, 0.8, 1, 2, 4, 6, 8, 10]\n",
    "\n",
    "\n",
    "NSOC = []\n",
    "Temp = []\n",
    "Voltage = []\n",
    "OCV = []\n",
    "C_Rate = []\n",
    "\n",
    "SPMT_RMSE = []\n",
    "\n",
    "n = len(C_Rates)\n",
    "\n",
    "\n",
    "for i in range (n):\n",
    "    sim = pybamm.Simulation(model, parameter_values=parameter_values, C_rate = C_Rates[i])\n",
    "    sim.solve([0,3600])\n",
    "    nsoc = sim.solution[\"Negative electrode SOC\"].entries\n",
    "    temp = sim.solution[\"Negative electrode temperature [K]\"].entries[-1]\n",
    "    voltage = sim.solution[\"Terminal voltage [V]\"].entries\n",
    "    ocv = sim.solution[\"Measured open circuit voltage [V]\"].entries\n",
    "    \n",
    "    rmse = RMSE(ocv,voltage) \n",
    "    SPMT_RMSE.append(rmse)\n",
    "    \n",
    "    for j in range(len(nsoc)):\n",
    "        C_Rate.append(C_Rates[i])\n",
    "        NSOC.append(nsoc[j])\n",
    "        Temp.append(temp[j])\n",
    "        Voltage.append(voltage[j])\n",
    "        OCV.append(ocv[j])\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating a dataframe and storing the results\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.DataFrame()\n",
    "df['C_Rate'] = C_Rate\n",
    "df['NSOC'] = NSOC\n",
    "df['Temp'] = Temp\n",
    "df['Voltage'] = Voltage\n",
    "df['OCV'] = OCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C_Rate</th>\n",
       "      <th>NSOC</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>OCV</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>298.150000</td>\n",
       "      <td>3.841727</td>\n",
       "      <td>3.851824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.799397</td>\n",
       "      <td>298.153985</td>\n",
       "      <td>3.840125</td>\n",
       "      <td>3.850778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.798793</td>\n",
       "      <td>298.158054</td>\n",
       "      <td>3.839417</td>\n",
       "      <td>3.850216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.798190</td>\n",
       "      <td>298.162147</td>\n",
       "      <td>3.838888</td>\n",
       "      <td>3.849725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.797586</td>\n",
       "      <td>298.166249</td>\n",
       "      <td>3.838417</td>\n",
       "      <td>3.849262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C_Rate      NSOC        Temp   Voltage       OCV\n",
       "0     0.1  0.800000  298.150000  3.841727  3.851824\n",
       "1     0.1  0.799397  298.153985  3.840125  3.850778\n",
       "2     0.1  0.798793  298.158054  3.839417  3.850216\n",
       "3     0.1  0.798190  298.162147  3.838888  3.849725\n",
       "4     0.1  0.797586  298.166249  3.838417  3.849262"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing result into spmt.csv file\n",
    "df.to_csv(r\"spmt_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>C_Rate</th>\n",
       "      <th>SPMT_RMSE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.1</td>\n",
       "      <td>0.010783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.2</td>\n",
       "      <td>0.021097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.4</td>\n",
       "      <td>0.038985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.8</td>\n",
       "      <td>0.063912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.073439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2.0</td>\n",
       "      <td>0.107727</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4.0</td>\n",
       "      <td>0.150256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.0</td>\n",
       "      <td>0.179473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.0</td>\n",
       "      <td>0.206541</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10.0</td>\n",
       "      <td>0.226304</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C_Rate  SPMT_RMSE\n",
       "0     0.1   0.010783\n",
       "1     0.2   0.021097\n",
       "2     0.4   0.038985\n",
       "3     0.8   0.063912\n",
       "4     1.0   0.073439\n",
       "5     2.0   0.107727\n",
       "6     4.0   0.150256\n",
       "7     6.0   0.179473\n",
       "8     8.0   0.206541\n",
       "9    10.0   0.226304"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating C_Rate VS RMSE csv\n",
    "df2 = pd.DataFrame()\n",
    "df2['C_Rate'] = C_Rates\n",
    "df2['SPMT_RMSE'] = SPMT_RMSE\n",
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing result into spmt_rmse.csv\n",
    "df2.to_csv(r\"spmt_rmse.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simulating Hybrid Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0  C_Rate      NSOC\n",
      "0           0     0.1  0.800000\n",
      "1           1     0.1  0.799397\n",
      "2           2     0.1  0.798793\n",
      "3           3     0.1  0.798190\n",
      "4           4     0.1  0.797586\n",
      "        OCV\n",
      "0  3.851824\n",
      "1  3.850778\n",
      "2  3.850216\n",
      "3  3.849725\n",
      "4  3.849262\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "data = pd.read_csv(r\"spmt_data.csv\")\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "# Split data\n",
    "x = df.drop(df.columns[[3,4,5]], axis = 1)\n",
    "y = df.drop(df.columns[[0,1,2,3,4]], axis = 1)\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y,test_size = 0.2, random_state = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "16/16 [==============================] - 1s 1ms/step - loss: 8864.8834 - mean_squared_error: 8864.8834\n",
      "Epoch 2/250\n",
      "16/16 [==============================] - 0s 1ms/step - loss: 721.3788 - mean_squared_error: 721.3788\n",
      "Epoch 3/250\n",
      "16/16 [==============================] - 0s 533us/step - loss: 97.8886 - mean_squared_error: 97.8886\n",
      "Epoch 4/250\n",
      "16/16 [==============================] - 0s 533us/step - loss: 39.9510 - mean_squared_error: 39.9510\n",
      "Epoch 5/250\n",
      "16/16 [==============================] - 0s 533us/step - loss: 7.8772 - mean_squared_error: 7.8772\n",
      "Epoch 6/250\n",
      "16/16 [==============================] - 0s 1ms/step - loss: 5.2978 - mean_squared_error: 5.2978\n",
      "Epoch 7/250\n",
      "16/16 [==============================] - 0s 1ms/step - loss: 4.1662 - mean_squared_error: 4.1662\n",
      "Epoch 8/250\n",
      "16/16 [==============================] - 0s 1ms/step - loss: 3.8590 - mean_squared_error: 3.8590\n",
      "Epoch 9/250\n",
      "16/16 [==============================] - 0s 1ms/step - loss: 3.9700 - mean_squared_error: 3.9700\n",
      "Epoch 10/250\n",
      "16/16 [==============================] - 0s 533us/step - loss: 3.7824 - mean_squared_error: 3.7824\n",
      "Epoch 11/250\n",
      "16/16 [==============================] - 0s 533us/step - loss: 3.8646 - mean_squared_error: 3.8646\n",
      "Epoch 12/250\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 150/250\n",
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      "16/16 [==============================] - 0s 3ms/step - loss: 0.0207 - mean_squared_error: 0.0207\n",
      "Epoch 174/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0161 - mean_squared_error: 0.0161\n",
      "Epoch 175/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0293 - mean_squared_error: 0.0293\n",
      "Epoch 176/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0137 - mean_squared_error: 0.0137\n",
      "Epoch 177/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0216 - mean_squared_error: 0.0216\n",
      "Epoch 178/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0262 - mean_squared_error: 0.0262\n",
      "Epoch 179/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0105 - mean_squared_error: 0.0105\n",
      "Epoch 180/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0139 - mean_squared_error: 0.0139\n",
      "Epoch 181/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0153 - mean_squared_error: 0.0153\n",
      "Epoch 182/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0173 - mean_squared_error: 0.0173\n",
      "Epoch 183/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0144 - mean_squared_error: 0.0144\n",
      "Epoch 184/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0153 - mean_squared_error: 0.0153\n",
      "Epoch 185/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0131 - mean_squared_error: 0.0131\n",
      "Epoch 186/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0138 - mean_squared_error: 0.0138\n",
      "Epoch 187/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0128 - mean_squared_error: 0.0128\n",
      "Epoch 188/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0124 - mean_squared_error: 0.0124\n",
      "Epoch 189/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0105 - mean_squared_error: 0.0105\n",
      "Epoch 190/250\n",
      "16/16 [==============================] - 0s 4ms/step - loss: 0.0110 - mean_squared_error: 0.0110\n",
      "Epoch 191/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0194 - mean_squared_error: 0.0194\n",
      "Epoch 192/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0128 - mean_squared_error: 0.0128\n",
      "Epoch 193/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0163 - mean_squared_error: 0.0163\n",
      "Epoch 194/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0212 - mean_squared_error: 0.0212\n",
      "Epoch 195/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0198 - mean_squared_error: 0.0198\n",
      "Epoch 196/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 197/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0126 - mean_squared_error: 0.0126\n",
      "Epoch 198/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0106 - mean_squared_error: 0.0106\n",
      "Epoch 199/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0135 - mean_squared_error: 0.0135\n",
      "Epoch 200/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0181 - mean_squared_error: 0.0181\n",
      "Epoch 201/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0340 - mean_squared_error: 0.0340\n",
      "Epoch 202/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0110 - mean_squared_error: 0.0110\n",
      "Epoch 203/250\n",
      "16/16 [==============================] - 0s 2ms/step - loss: 0.0100 - mean_squared_error: 0.0100\n",
      "Epoch 204/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0128 - mean_squared_error: 0.0128\n",
      "Epoch 205/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0104 - mean_squared_error: 0.0104\n",
      "Epoch 206/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0103 - mean_squared_error: 0.0103\n",
      "Epoch 207/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0149 - mean_squared_error: 0.0149\n",
      "Epoch 208/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0111 - mean_squared_error: 0.0111\n",
      "Epoch 209/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0122 - mean_squared_error: 0.0122\n",
      "Epoch 210/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0118 - mean_squared_error: 0.0118\n",
      "Epoch 211/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0119 - mean_squared_error: 0.0119\n",
      "Epoch 212/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0114 - mean_squared_error: 0.0114\n",
      "Epoch 213/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0116 - mean_squared_error: 0.0116\n",
      "Epoch 214/250\n",
      "16/16 [==============================] - ETA: 0s - loss: 0.0056 - mean_squared_error: 0.00 - 0s 2ms/step - loss: 0.0139 - mean_squared_error: 0.0139\n",
      "Epoch 215/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0095 - mean_squared_error: 0.0095\n",
      "Epoch 216/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0144 - mean_squared_error: 0.0144\n",
      "Epoch 217/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0172 - mean_squared_error: 0.0172\n",
      "Epoch 218/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0125 - mean_squared_error: 0.0125\n",
      "Epoch 219/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0153 - mean_squared_error: 0.0153\n",
      "Epoch 220/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0110 - mean_squared_error: 0.0110\n",
      "Epoch 221/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0113 - mean_squared_error: 0.0113\n",
      "Epoch 222/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0156 - mean_squared_error: 0.0156\n",
      "Epoch 223/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0101 - mean_squared_error: 0.0101\n",
      "Epoch 224/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0082 - mean_squared_error: 0.0082\n",
      "Epoch 225/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0144 - mean_squared_error: 0.0144\n",
      "Epoch 226/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0163 - mean_squared_error: 0.0163\n",
      "Epoch 227/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0171 - mean_squared_error: 0.0171\n",
      "Epoch 228/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0099 - mean_squared_error: 0.0099\n",
      "Epoch 229/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0219 - mean_squared_error: 0.0219\n",
      "Epoch 230/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0159 - mean_squared_error: 0.0159\n",
      "Epoch 231/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0107 - mean_squared_error: 0.0107\n",
      "Epoch 232/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0140 - mean_squared_error: 0.0140\n",
      "Epoch 233/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0184 - mean_squared_error: 0.0184\n",
      "Epoch 234/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0088 - mean_squared_error: 0.0088\n",
      "Epoch 235/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0094 - mean_squared_error: 0.0094\n",
      "Epoch 236/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0104 - mean_squared_error: 0.0104\n",
      "Epoch 237/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0081 - mean_squared_error: 0.0081\n",
      "Epoch 238/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0129 - mean_squared_error: 0.0129\n",
      "Epoch 239/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0266 - mean_squared_error: 0.0266\n",
      "Epoch 240/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0089 - mean_squared_error: 0.0089\n",
      "Epoch 241/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0122 - mean_squared_error: 0.0122\n",
      "Epoch 242/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0104 - mean_squared_error: 0.0104\n",
      "Epoch 243/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0192 - mean_squared_error: 0.0192\n",
      "Epoch 244/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0094 - mean_squared_error: 0.0094\n",
      "Epoch 245/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0135 - mean_squared_error: 0.0135\n",
      "Epoch 246/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0110 - mean_squared_error: 0.0110\n",
      "Epoch 247/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0149 - mean_squared_error: 0.0149\n",
      "Epoch 248/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0117 - mean_squared_error: 0.0117\n",
      "Epoch 249/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0146 - mean_squared_error: 0.0146\n",
      "Epoch 250/250\n",
      "16/16 [==============================] - 0s 3ms/step - loss: 0.0127 - mean_squared_error: 0.0127\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x227d19843d0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras\n",
    "\n",
    "# defining model\n",
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(32, activation = 'relu', input_dim = 3),\n",
    "    keras.layers.Dense(32, activation = 'relu'),\n",
    "    keras.layers.Dense(1, activation = 'linear')\n",
    "])\n",
    "\n",
    "# opt = keras.optimizers.Adam(learning_rate=0.01)\n",
    "\n",
    "# compiling model\n",
    "model.compile(optimizer = 'adam',\n",
    "             loss = 'mean_squared_error',\n",
    "             metrics = ['mean_squared_error'])\n",
    "\n",
    "# fitting model\n",
    "model.fit(x_train,y_train, epochs = 250)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s 4ms/step - loss: 0.0100 - mean_squared_error: 0.0100\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.010027347132563591, 0.010027347132563591]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test)\n",
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>C_Rate</th>\n",
       "      <th>NSOC</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Voltage</th>\n",
       "      <th>OCV</th>\n",
       "      <th>Hybrid_Predicted_Voltage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>298.150000</td>\n",
       "      <td>3.841727</td>\n",
       "      <td>3.851824</td>\n",
       "      <td>3.860100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.799397</td>\n",
       "      <td>298.153985</td>\n",
       "      <td>3.840125</td>\n",
       "      <td>3.850778</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.798793</td>\n",
       "      <td>298.158054</td>\n",
       "      <td>3.839417</td>\n",
       "      <td>3.850216</td>\n",
       "      <td>3.843900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.798190</td>\n",
       "      <td>298.162147</td>\n",
       "      <td>3.838888</td>\n",
       "      <td>3.849725</td>\n",
       "      <td>3.869452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.797586</td>\n",
       "      <td>298.166249</td>\n",
       "      <td>3.838417</td>\n",
       "      <td>3.849262</td>\n",
       "      <td>3.851907</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  C_Rate      NSOC        Temp   Voltage       OCV  \\\n",
       "0           0     0.1  0.800000  298.150000  3.841727  3.851824   \n",
       "1           1     0.1  0.799397  298.153985  3.840125  3.850778   \n",
       "2           2     0.1  0.798793  298.158054  3.839417  3.850216   \n",
       "3           3     0.1  0.798190  298.162147  3.838888  3.849725   \n",
       "4           4     0.1  0.797586  298.166249  3.838417  3.849262   \n",
       "\n",
       "   Hybrid_Predicted_Voltage  \n",
       "0                  3.860100  \n",
       "1                  3.782901  \n",
       "2                  3.843900  \n",
       "3                  3.869452  \n",
       "4                  3.851907  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predicted = model.predict(x)\n",
    "df['Hybrid_Predicted_Voltage'] = y_predicted\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing result into hybrid.csv file\n",
    "df.to_csv(r\"hybrid_data.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "Hybrid_RMSE = []\n",
    "\n",
    "m = len(df['C_Rate'])\n",
    "\n",
    "for i in range(n):\n",
    "    c_rate = C_Rates[i]\n",
    "    ocv = []\n",
    "    hybrid = []\n",
    "    for j in range(m):\n",
    "        if df['C_Rate'][j] == c_rate:\n",
    "            ocv.append(df['OCV'][j])\n",
    "            hybrid.append(df['Hybrid_Predicted_Voltage'][j])\n",
    "            \n",
    "    rmse = RMSE(ocv , hybrid)\n",
    "    Hybrid_RMSE.append(rmse)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>C_Rate</th>\n",
       "      <th>SPMT_RMSE</th>\n",
       "      <th>Hybrid_RMSE</th>\n",
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      ],
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       "   Unnamed: 0  C_Rate  SPMT_RMSE  Hybrid_RMSE\n",
       "0           0     0.1   0.010783     0.014332\n",
       "1           1     0.2   0.021097     0.020271\n",
       "2           2     0.4   0.038985     0.025802\n",
       "3           3     0.8   0.063912     0.094791\n",
       "4           4     1.0   0.073439     0.160272\n",
       "5           5     2.0   0.107727     0.165108\n",
       "6           6     4.0   0.150256     0.110694\n",
       "7           7     6.0   0.179473     0.121411\n",
       "8           8     8.0   0.206541     0.135617\n",
       "9           9    10.0   0.226304     0.140963"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r\"spmt_rmse.csv\")\n",
    "df2 = pd.DataFrame(data)\n",
    "df2['Hybrid_RMSE'] = Hybrid_RMSE\n",
    "df2.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def RER(RMSE_SPMT,RMSE_Hybrid):\n",
    "    rer = ((RMSE_SPMT - RMSE_Hybrid)/RMSE_SPMT) * 100\n",
    "    return rer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "ReR = []\n",
    "\n",
    "m = len(df2['SPMT_RMSE'])\n",
    "for i in range(m):\n",
    "    rer = RER(df2['SPMT_RMSE'][i],df2['Hybrid_RMSE'][i])\n",
    "    ReR.append(rer)\n",
    "    \n",
    "df2['RER']=ReR "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing result into hybrid_rmse.csv\n",
    "df2.to_csv(r\"hybrid_rmse.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>C_Rate</th>\n",
       "      <th>SPMT_RMSE</th>\n",
       "      <th>Hybrid_RMSE</th>\n",
       "      <th>RER</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0.1</td>\n",
       "      <td>0.010783</td>\n",
       "      <td>0.014332</td>\n",
       "      <td>-32.912043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.021097</td>\n",
       "      <td>0.020271</td>\n",
       "      <td>3.914582</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.038985</td>\n",
       "      <td>0.025802</td>\n",
       "      <td>33.814043</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.063912</td>\n",
       "      <td>0.094791</td>\n",
       "      <td>-48.314656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.073439</td>\n",
       "      <td>0.160272</td>\n",
       "      <td>-118.238872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.107727</td>\n",
       "      <td>0.165108</td>\n",
       "      <td>-53.265368</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.150256</td>\n",
       "      <td>0.110694</td>\n",
       "      <td>26.329824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.179473</td>\n",
       "      <td>0.121411</td>\n",
       "      <td>32.351405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0.206541</td>\n",
       "      <td>0.135617</td>\n",
       "      <td>34.338912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.226304</td>\n",
       "      <td>0.140963</td>\n",
       "      <td>37.710943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  C_Rate  SPMT_RMSE  Hybrid_RMSE         RER\n",
       "0           0     0.1   0.010783     0.014332  -32.912043\n",
       "1           1     0.2   0.021097     0.020271    3.914582\n",
       "2           2     0.4   0.038985     0.025802   33.814043\n",
       "3           3     0.8   0.063912     0.094791  -48.314656\n",
       "4           4     1.0   0.073439     0.160272 -118.238872\n",
       "5           5     2.0   0.107727     0.165108  -53.265368\n",
       "6           6     4.0   0.150256     0.110694   26.329824\n",
       "7           7     6.0   0.179473     0.121411   32.351405\n",
       "8           8     8.0   0.206541     0.135617   34.338912\n",
       "9           9    10.0   0.226304     0.140963   37.710943"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head(10)"
   ]
  }
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