Repository: pybamm-team/liionpack (language: python)

Issue:
Make initial_soc work here instead of using init_funcs

Make initial_soc work here instead of using init_funcs



Discussion / hints:
The pybamm function `pybamm.lithium_ion.get_initial_stoichiometries` does a lot of the same thing as the `sim_utils.py` file and could act as a drop in replacement.  I really like the idea of optionally giving an initial OCV instead though, we should add that to pybamm
The pybamm function `pybamm.lithium_ion.get_initial_stoichiometries` does a lot of the same thing as the `sim_utils.py` file and could act as a drop in replacement.  I really like the idea of optionally giving an initial OCV instead though, we should add that to pybamm

Fix patch:
diff --git a/examples/notebooks/04 Initial SoC.ipynb b/examples/notebooks/04 Initial SoC.ipynb
new file mode 100644
index 00000000..612100eb
--- /dev/null
+++ b/examples/notebooks/04 Initial SoC.ipynb
@@ -0,0 +1,288 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 4 Setting the initial SoC"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Setting the initial SoC for your pack is performed with an argument passed to the solve algorithm. Currently the same value is applied to each battery but in future it will be possible to vary the SoC across the pack."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\tom\\code\\pybamm\\pybamm\\expression_tree\\functions.py:204: RuntimeWarning: invalid value encountered in sign\n",
+      "  return self.function(*evaluated_children)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import liionpack as lp\n",
+    "import pybamm\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Lets set up the most simple pack possible with 1 battery and very low busbar resistance to compare to a pure PyBaMM simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "netlist = lp.setup_circuit(Np=1, Ns=1, Rb=1.0e-8, Rc=1.0e-8, Ri=5e-2, V=4.0, I=1.0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Heat transfer coefficients\n",
+    "htc = np.ones(1) * 10\n",
+    "# PyBaMM parameters\n",
+    "chemistry = pybamm.parameter_sets.Chen2020\n",
+    "parameter_values = pybamm.ParameterValues(chemistry=chemistry)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Cycling experiment\n",
+    "experiment = pybamm.Experiment(\n",
+    "    [\n",
+    "        (\n",
+    "            \"Discharge at 1 A for 1000 s or until 3.3 V\",\n",
+    "            \"Rest for 1000 s\",\n",
+    "            \"Charge at 1 A for 1000 s or until 4.0 V\",\n",
+    "            \"Rest for 1000 s\",\n",
+    "        )\n",
+    "    ]\n",
+    "    * 3, period=\"10 s\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "SoC = 0.5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Solving Pack: 100%|███████████████████████████████████████████████████████████████| 1200/1200 [00:06<00:00, 189.00it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Solve pack\n",
+    "output = lp.solve(netlist=netlist,\n",
+    "                  parameter_values=parameter_values,\n",
+    "                  experiment=experiment,\n",
+    "                  output_variables=output_variables,\n",
+    "                  htc=htc, initial_soc=SoC)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Let's compare to the PyBaMM simulation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "parameter_values = pybamm.ParameterValues(chemistry=chemistry)\n",
+    "parameter_values.update({\"Total heat transfer coefficient [W.m-2.K-1]\": 10.0})\n",
+    "sim = lp.create_simulation(parameter_values, experiment, make_inputs=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sol = sim.solve(initial_soc=SoC)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Single PyBaMM Simulation')"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1080x720 with 4 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Get pack level results\n",
+    "time = sol[\"Time [s]\"].entries\n",
+    "v_pack = output[\"Pack terminal voltage [V]\"]\n",
+    "i_pack = output[\"Pack current [A]\"]\n",
+    "v_batt = sol[\"Terminal voltage [V]\"].entries\n",
+    "i_batt = sol[\"Current [A]\"].entries\n",
+    "\n",
+    "# Plot pack voltage and current\n",
+    "_, (axl, axr) = plt.subplots(1, 2, tight_layout=True, figsize=(15, 10), sharex=True, sharey=True)\n",
+    "axl.plot(time[1:], v_pack, color=\"green\", label=\"simulation\")\n",
+    "axl.set_xlabel(\"Time [s]\")\n",
+    "axl.set_ylabel(\"Pack terminal voltage [V]\", color=\"green\")\n",
+    "axl2 = axl.twinx()\n",
+    "axl2.plot(time[1:], i_pack, color=\"black\", label=\"simulation\")\n",
+    "axl2.set_ylabel(\"Pack current [A]\", color=\"black\")\n",
+    "axl2.set_title(\"Liionpack Simulation\")\n",
+    "axr.plot(time, v_batt, color=\"red\", label=\"simulation\")\n",
+    "axr.set_xlabel(\"Time [s]\")\n",
+    "axr.set_ylabel(\"Battery terminal voltage [V]\", color=\"red\")\n",
+    "axr2 = axr.twinx()\n",
+    "axr2.plot(time, i_batt, color=\"blue\", label=\"simulation\")\n",
+    "axr2.set_ylabel(\"Battery current [A]\", color=\"blue\")\n",
+    "axr2.set_title(\"Single PyBaMM Simulation\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['Time [s]', 'Pack current [A]', 'Pack terminal voltage [V]', 'Cell current [A]', 'Terminal voltage [V]', 'Measured battery open circuit voltage [V]', 'Local ECM resistance [Ohm]', 'X-averaged negative particle surface concentration [mol.m-3]', 'X-averaged positive particle surface concentration [mol.m-3]'])"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.597551957691098"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output['Terminal voltage [V]'].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.6823172075752413"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "output['Pack terminal voltage [V]'].min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/liionpack/init_funcs/init_funcs.pickle b/liionpack/init_funcs/init_funcs.pickle
index db9126e4..dfbaa6ec 100644
Binary files a/liionpack/init_funcs/init_funcs.pickle and b/liionpack/init_funcs/init_funcs.pickle differ
diff --git a/liionpack/sim_utils.py b/liionpack/sim_utils.py
index c358b0c3..e35c3a6c 100644
--- a/liionpack/sim_utils.py
+++ b/liionpack/sim_utils.py
@@ -1,9 +1,6 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Thu Sep 23 10:41:16 2021
-
-@author: Tom
-"""
+#
+# Simulation utilities
+#
 import pickle
 import pybamm
 import liionpack as lp
@@ -15,58 +12,84 @@
 init_fname = os.path.join(lp.INIT_FUNCS, "init_funcs.pickle")
 
 
-def update_init_conc(sim, SoC=-1, OCV=-1):
-    r"""
-
+def update_init_conc(sim, SoC=-1, OCV=-1, method="calculation"):
+    """
+    Update initial concentration parameters
 
     Parameters
     ----------
-    sim : TYPE
-        DESCRIPTION.
-    SoC : TYPE, optional
-        DESCRIPTION. The default is -1.
-    OCV : TYPE, optional
-        DESCRIPTION. The default is -1.
-
-    Returns
-    -------
-    None.
-
+    sim : :class:`pybamm.Simulation`
+        The battery simulation.
+    SoC : float, optional
+        Target initial SoC. Must be between 0 and 1. Default is -1, in which
+        case the initial concentrations are set using the target OCV.
+    OCV : float, optional
+        Target initial OCV. Must be between 0 and 1. Default is -1, in which
+        case the initial concentrations are set using the target SoC. This option is
+        only used if method is "experiment".
+    method : str, optional
+        The method used to compute the initial concentrations. Can be "calculation",
+        in which case `pybamm.get_initial_stoichiometries` is used to compute initial
+        stoichiometries that give the desired initial state of charge, or "experiment",
+        in which case a slow discharge between voltage limits is used to compute the initial
+        concentrations given a target SoC or OCV. Default is "calculation".
     """
     param = sim.parameter_values
-    c_s_n_init, c_s_p_init = initial_conditions(SoC=SoC, OCV=OCV)
+
+    if method == "calculation":
+        c_n_max = param["Maximum concentration in negative electrode [mol.m-3]"]
+        c_p_max = param["Maximum concentration in positive electrode [mol.m-3]"]
+        x, y = pybamm.lithium_ion.get_initial_stoichiometries(SoC, param)
+        c_s_n_init, c_s_p_init = x * c_n_max, y * c_p_max
+    elif method == "experiment":
+        c_s_n_init, c_s_p_init = initial_conditions_from_experiment(
+            param, SoC=SoC, OCV=OCV
+        )
+    else:
+        raise ValueError("'method' must be 'calculation' or 'experiment'.")
+
     param.update(
         {
             "Initial concentration in negative electrode [mol.m-3]": c_s_n_init,
             "Initial concentration in positive electrode [mol.m-3]": c_s_p_init,
         }
     )
-    # print(param["Initial concentration in negative electrode [mol.m-3]"])
-    # print(param["Initial concentration in positive electrode [mol.m-3]"])
 
 
-def initial_conditions(SoC=-1, OCV=-1):
-    r"""
+def initial_conditions_from_experiment(parameter_values, SoC=-1, OCV=-1):
+    """
+    Update initial concentration parameters using an experiment between voltage limits.
 
 
     Parameters
     ----------
-    SoC : TYPE, optional
-        DESCRIPTION. The default is -1.
-    OCV : TYPE, optional
-        DESCRIPTION. The default is -1.
+    parameter_values : :class:`pybamm.ParamaterValues`
+        The parameter values used in the simulation.
+    SoC : float, optional
+        Target initial SoC. Must be between 0 and 1. Default is -1, in which
+        case the initial concentrations are set using the target OCV.
+    OCV : float, optional
+        Target initial OCV. Must be between 0 and 1. Default is -1, in which
+        case the initial concentrations are set using the target SoC. This option is
+        only used if method is "experiment".
 
     Returns
     -------
-    c_s_n_init : TYPE
-        DESCRIPTION.
-    c_s_p_init : TYPE
-        DESCRIPTION.
-
+    c_s_n_init : float
+        The initial concentration in the negative electrode.
+    c_s_p_init : float
+        The initial concentration in the positive electrode.
     """
 
-    with open(init_fname, "rb") as handle:
-        init_funcs = pickle.load(handle)
+    # TODO: what if someone changes parameters? We should make this every time,
+    # but I see why you want to pickle it for speed.
+    try:
+        with open(init_fname, "rb") as handle:
+            init_funcs = pickle.load(handle)
+    except FileNotFoundError:
+        init_funcs = create_init_funcs(parameter_values)
+        with open(init_fname, "rb") as handle:
+            init_funcs = pickle.load(handle)
 
     x_n_SoC = init_funcs["x_n_SoC"]
     x_p_SoC = init_funcs["x_p_SoC"]
@@ -90,19 +113,16 @@ def initial_conditions(SoC=-1, OCV=-1):
     return c_s_n_init, c_s_p_init
 
 
-def create_init_funcs(parameter_values=None):
-    r"""
-
+def create_init_funcs(parameter_values):
+    """
+    Run an experiment which can be used to create interpolants to determine the
+    initial concentrations corresponding to a given initial state of charge or
+    open circuit voltage.
 
     Parameters
     ----------
-    parameter_values : TYPE, optional
-        DESCRIPTION. The default is None.
-
-    Returns
-    -------
-    None.
-
+    parameter_values : :class:`pybamm.ParamaterValues`
+        The parameter values used in the simulation.
     """
     V_upper_limit = parameter_values["Upper voltage cut-off [V]"]
     V_lower_limit = parameter_values["Lower voltage cut-off [V]"]
@@ -116,16 +136,12 @@ def create_init_funcs(parameter_values=None):
             f"Discharge at C/100 until {V_lower_limit}V (1 minute period)",
         ]
     )
-    # sim.set_up_experiment(model, experiment)
-    # sim = pybamm.Simulation(model=model, parameter_values=param, experiment=experiment)
+
+    # Set up and solve simulation
     sim = lp.create_simulation(parameter_values=parameter_values, experiment=experiment)
     sim.solve()
-    # sim.plot()
-
-    # param = sim.parameter_values
 
     # Save concentrations for initial conditions
-
     neg_surf = "X-averaged negative particle surface concentration [mol.m-3]"
     pos_surf = "X-averaged positive particle surface concentration [mol.m-3]"
     sol_charged = sim.solution.cycles[2]
@@ -137,7 +153,7 @@ def create_init_funcs(parameter_values=None):
     v_lower_lim_neg = sol_dischg[neg_surf].entries[-1]
     v_lower_lim_pos = sol_dischg[pos_surf].entries[-1]
 
-    # Use max conc. for normalization
+    # Use max concentration for normalization
     c_s_n_max = parameter_values[
         "Maximum concentration in negative electrode [mol.m-3]"
     ]
@@ -145,14 +161,13 @@ def create_init_funcs(parameter_values=None):
         "Maximum concentration in positive electrode [mol.m-3]"
     ]
 
-    # These are now the min and max concs for full range of SoC
+    # These are now the min and max concentrations for full range of SoC
     x_n_max = v_upper_lim_neg / c_s_n_max
     x_p_min = v_upper_lim_pos / c_s_p_max
     x_n_min = v_lower_lim_neg / c_s_n_max
     x_p_max = v_lower_lim_pos / c_s_p_max
 
     # Work out total capacity between voltage lims
-
     current = sol_dischg["Current [A]"].entries
     time = sol_dischg["Time [h]"].entries
     dt = time[1:] - time[:-1]
@@ -167,7 +182,7 @@ def create_init_funcs(parameter_values=None):
     x_n = x_n_min + (x_n_max - x_n_min) * SoC
     x_p = x_p_max - (x_p_max - x_p_min) * SoC
 
-    # Ocp are functions
+    # OCP are functions
     U_n = parameter_values["Negative electrode OCP [V]"]
     U_p = parameter_values["Positive electrode OCP [V]"]
     U_n_eval = parameter_values.evaluate(U_n(pybamm.Array(x_n)))
@@ -202,5 +217,6 @@ def create_init_funcs(parameter_values=None):
         "vmin": vmin,
         "vmax": vmax,
     }
+
     with open(init_fname, "wb") as handle:
         pickle.dump(init_funcs, handle)


Test patch:
diff --git a/tests/unit/test_sim_utils.py b/tests/unit/test_sim_utils.py
index a71808ac..18c3dc65 100644
--- a/tests/unit/test_sim_utils.py
+++ b/tests/unit/test_sim_utils.py
@@ -15,7 +15,9 @@ def setUpClass(self):
     def test_create_init_funcs(self):
         if os.path.isfile(self.fname):
             os.remove(self.fname)
-        lp.create_init_funcs(self.param)
+        # initial_conditions_from_experiment calls create_init_funcs when the pkl file is not found
+        # doing it this way tests the try/except block in initial_conditions_from_experiment
+        lp.initial_conditions_from_experiment(self.param, SoC=0.0)
         assert os.path.isfile(self.fname)
         plt.close("all")
 
@@ -27,16 +29,45 @@ def test_update_init_conc(self):
         b = param["Initial concentration in negative electrode [mol.m-3]"]
         assert a > b
 
-    def test_initial_conditions(self):
-        a_n, a_p = lp.initial_conditions(SoC=0.0)
-        b_n, b_p = lp.initial_conditions(SoC=1.0)
+    def test_initial_conditions_from_experiment(self):
+        a_n, a_p = lp.initial_conditions_from_experiment(self.param, SoC=0.0)
+        b_n, b_p = lp.initial_conditions_from_experiment(self.param, SoC=1.0)
         assert a_n < b_n
         assert a_p > b_p
-        a_n, a_p = lp.initial_conditions(OCV=3.0)
-        b_n, b_p = lp.initial_conditions(OCV=4.0)
+        a_n, a_p = lp.initial_conditions_from_experiment(self.param, OCV=3.0)
+        b_n, b_p = lp.initial_conditions_from_experiment(self.param, OCV=4.0)
         assert a_n < b_n
         assert a_p > b_p
 
+    def test_initial_conditions_method(self):
+        sim = lp.create_simulation(self.param)
+
+        lp.update_init_conc(sim, SoC=1.0, method="calculation")
+        param = sim.parameter_values
+        c_n_1 = param["Initial concentration in negative electrode [mol.m-3]"]
+        c_p_1 = param["Initial concentration in positive electrode [mol.m-3]"]
+
+        lp.update_init_conc(sim, SoC=1.0, method="experiment")
+        param = sim.parameter_values
+        c_n_2 = param["Initial concentration in negative electrode [mol.m-3]"]
+        c_p_2 = param["Initial concentration in positive electrode [mol.m-3]"]
+
+        c_n_max = param["Maximum concentration in negative electrode [mol.m-3]"]
+        c_p_max = param["Maximum concentration in positive electrode [mol.m-3]"]
+
+        self.assertAlmostEqual(c_n_1 / c_n_max, c_n_2 / c_n_max, 4)
+        self.assertAlmostEqual(c_p_1 / c_p_max, c_p_2 / c_p_max, 4)
+
+    def test_bad_method(self):
+        with self.assertRaises(ValueError):
+            sim = lp.create_simulation(self.param)
+            lp.update_init_conc(sim, SoC=1.0, method="bad method")
+
+    def test_bad_soc(self):
+        with self.assertRaises(ValueError):
+            sim = lp.create_simulation(self.param)
+            lp.update_init_conc(sim, SoC=10.0)
+
 
 if __name__ == "__main__":
     unittest.main()