Merge branch 'develop' into issue-2760-step

Author: valentinsulzer

.pre-commit-config.yaml

@@ -9,7 +9,7 @@ repos:
       - id: black
 
   - repo: https://github.com/charliermarsh/ruff-pre-commit
-    rev: "v0.0.254"
+    rev: "v0.0.255"
     hooks:
       - id: ruff
         args: [--ignore=E741, --exclude=__init__.py]

CHANGELOG.md

@@ -9,6 +9,7 @@
 
 ## Bug fixes
 
+- Fixed electrolyte conservation in the case of concentration-dependent transference number ([#2758](https://github.com/pybamm-team/PyBaMM/pull/2758))
 - Fixed `plot_voltage_components` so that the sum of overpotentials is now equal to the voltage ([#2740](https://github.com/pybamm-team/PyBaMM/pull/2740))
 
 ## Optimizations
@@ -30,6 +31,7 @@
 - Added an option for using a banded jacobian and sundials banded solvers for the IDAKLU solve ([#2677](https://github.com/pybamm-team/PyBaMM/pull/2677))
 - The "particle size" option can now be a tuple to allow different behaviour in each electrode ([#2672](https://github.com/pybamm-team/PyBaMM/pull/2672)).
 - Added temperature control to experiment class. ([#2518](https://github.com/pybamm-team/PyBaMM/pull/2518))
+- Added method to calculate maximum theoretical energy. ([#2777](https://github.com/pybamm-team/PyBaMM/pull/2777))
 
 ## Bug fixes
 

examples/notebooks/Getting Started/Tutorial 7 - Model options.ipynb

@@ -59,7 +59,7 @@
     {
      "data": {
       "text/plain": [
-       "<pybamm.solvers.solution.Solution at 0x7f29f1333e50>"
+       "<pybamm.solvers.solution.Solution at 0x139461520>"
       ]
      },
      "execution_count": 3,
@@ -70,10 +70,7 @@
    "source": [
     "model = pybamm.lithium_ion.SPMe(options=options) # loading in options\n",
     "\n",
-    "parameter_values = model.default_parameter_values\n",
-    "parameter_values[\"Current function [A]\"] = 3\n",
-    "\n",
-    "sim = pybamm.Simulation(model, parameter_values=parameter_values)\n",
+    "sim = pybamm.Simulation(model)\n",
     "sim.solve([0, 3600])"
    ]
   },
@@ -92,16 +89,26 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c00d126a3fc543829596dd24f8b9d626",
+       "model_id": "0fc5db759e804d3fb2793222ddbec5d4",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "interactive(children=(FloatSlider(value=0.0, description='t', max=800.0000000000001, step=8.000000000000002), …"
+       "interactive(children=(FloatSlider(value=0.0, description='t', max=1.0, step=0.01), Output()), _dom_classes=('w…"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<pybamm.plotting.quick_plot.QuickPlot at 0x139461fd0>"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
@@ -139,7 +146,8 @@
       "[1] 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",
       "[2] 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",
       "[3] Scott G. Marquis, Valentin Sulzer, Robert Timms, Colin P. Please, and S. Jon Chapman. An asymptotic derivation of a single particle model with electrolyte. Journal of The Electrochemical Society, 166(15):A3693–A3706, 2019. doi:10.1149/2.0341915jes.\n",
-      "[4] 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",
+      "[4] Valentin Sulzer, Scott G. Marquis, Robert Timms, Martin Robinson, and S. Jon Chapman. Python Battery Mathematical Modelling (PyBaMM). Journal of Open Research Software, 9(1):14, 2021. doi:10.5334/jors.309.\n",
+      "[5] Robert Timms, Scott G Marquis, Valentin Sulzer, Colin P. Please, and S Jonathan Chapman. Asymptotic Reduction of a Lithium-ion Pouch Cell Model. SIAM Journal on Applied Mathematics, 81(3):765–788, 2021. doi:10.1137/20M1336898.\n",
       "\n"
      ]
     }
@@ -165,7 +173,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.11.1"
+   "version": "3.9.16"
   },
   "vscode": {
    "interpreter": {

pybamm/models/full_battery_models/lithium_ion/electrode_soh.py

@@ -557,3 +557,47 @@ def get_min_max_stoichiometries(
     """
     esoh_solver = ElectrodeSOHSolver(parameter_values, param, known_value)
     return esoh_solver.get_min_max_stoichiometries()
+
+
+def calculate_theoretical_energy(
+    parameter_values, initial_soc=1.0, final_soc=0.0, points=100
+):
+    """
+    Calculate maximum energy possible from a cell given OCV, initial soc, and final soc
+    given voltage limits, open-circuit potentials, etc defined by parameter_values
+
+    Parameters
+    ----------
+    parameter_values : :class:`pybamm.ParameterValues`
+        The parameter values class that will be used for the simulation.
+    initial_soc : float
+        The soc at begining of discharge, default 1.0
+    final_soc : float
+        The soc at end of discharge, default 1.0
+    points : int
+        The number of points at which to calculate voltage.
+
+    Returns
+    -------
+    E
+        The total energy of the cell in Wh
+    """
+    # Get initial and final stoichiometric values.
+    n_i, p_i = get_initial_stoichiometries(initial_soc, parameter_values)
+    n_f, p_f = get_initial_stoichiometries(final_soc, parameter_values)
+    n_vals = np.linspace(n_i, n_f, num=points)
+    p_vals = np.linspace(p_i, p_f, num=points)
+    # Calculate OCV at each stoichiometry
+    param = pybamm.LithiumIonParameters()
+    T = param.T_amb(0)
+    Vs = np.empty(n_vals.shape)
+    for i in range(n_vals.size):
+        Vs[i] = parameter_values.evaluate(
+            param.p.prim.U(p_vals[i], T)
+        ) - parameter_values.evaluate(param.n.prim.U(n_vals[i], T))
+    # Calculate dQ
+    Q_p = parameter_values.evaluate(param.p.prim.Q_init) * (p_f - p_i)
+    dQ = Q_p / (points - 1)
+    # Integrate and convert to W-h
+    E = np.trapz(Vs, dx=dQ)
+    return E

pybamm/models/submodels/electrolyte_diffusion/full_diffusion.py

@@ -89,17 +89,12 @@ def get_coupled_variables(self, variables):
     def set_rhs(self, variables):
         eps_c_e = variables["Porosity times concentration [mol.m-3]"]
         c_e = variables["Electrolyte concentration [mol.m-3]"]
-        T = variables["Cell temperature [K]"]
-        N_e_diffusion = variables["Electrolyte diffusion flux [mol.m-2.s-1]"]
-        N_e_convection = variables["Electrolyte convection flux [mol.m-2.s-1]"]
+        N_e = variables["Electrolyte flux [mol.m-2.s-1]"]
         div_Vbox = variables["Transverse volume-averaged acceleration [m.s-2]"]
 
         sum_s_a_j = variables["Sum of electrolyte reaction source terms [A.m-3]"]
-        sum_a_j = variables["Sum of volumetric interfacial current densities [A.m-3]"]
         sum_s_a_j.print_name = "aj"
-        source_terms = (sum_s_a_j - self.param.t_plus(c_e, T) * sum_a_j) / self.param.F
-
-        N_e = N_e_diffusion + N_e_convection
+        source_terms = sum_s_a_j / self.param.F
 
         self.rhs = {eps_c_e: -pybamm.div(N_e) + source_terms - c_e * div_Vbox}
 

tests/integration/test_models/standard_output_comparison.py

@@ -128,13 +128,13 @@ def test_all(self):
         self.compare("X-averaged positive electrode open-circuit potential [V]")
         self.compare("Voltage [V]")
         self.compare("X-averaged solid phase ohmic losses [V]")
-        self.compare("Negative electrode reaction overpotential [V]")
+        self.compare("Negative electrode reaction overpotential [V]", atol=1e-4)
         self.compare("Positive electrode reaction overpotential [V]")
         self.compare("Negative electrode potential [V]", atol=1e-5)
         self.compare("Positive electrode potential [V]")
         self.compare("Electrolyte potential [V]")
         # Currents
-        self.compare("Exchange current density [A.m-2]")
+        self.compare("Exchange current density [A.m-2]", atol=2e-3)
         self.compare("Negative electrode current density [A.m-2]", atol=1e-10)
         self.compare("Positive electrode current density [A.m-2]", atol=1e-10)
 

tests/integration/test_models/standard_output_tests.py

@@ -545,7 +545,10 @@ def test_conservation(self):
         diff = (
             self.c_e_tot(self.solution.t[1:]) - self.c_e_tot(self.solution.t[:-1])
         ) / self.c_e_tot(self.solution.t[:-1])
-        if self.model.options["surface form"] == "differential":
+        if self.model.options["surface form"] == "differential" or (
+            isinstance(self.model, pybamm.lithium_ion.DFN)
+            and self.model.options["surface form"] == "algebraic"
+        ):
             np.testing.assert_allclose(0, diff, atol=1e-4, rtol=1e-6)
         else:
             np.testing.assert_allclose(0, diff, atol=1e-14, rtol=1e-14)

tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_electrode_soh.py

@@ -143,6 +143,25 @@ def test_known_solution(self):
         self.assertAlmostEqual(sol["Uw(x_0)"].data[0], V_min, places=5)
 
 
+class TestCalculateTheoreticalEnergy(unittest.TestCase):
+    def test_efficiency(self):
+        model = pybamm.lithium_ion.DFN(options={"calculate discharge energy": "true"})
+        parameter_values = pybamm.ParameterValues("Chen2020")
+        sim = pybamm.Simulation(model, parameter_values=parameter_values)
+        sol = sim.solve([0, 3600], initial_soc=1.0)
+        discharge_energy = sol["Discharge energy [W.h]"].entries[-1]
+        theoretical_energy = (
+            pybamm.lithium_ion.electrode_soh.calculate_theoretical_energy(
+                parameter_values
+            )
+        )
+        # Real energy should be less than discharge energy,
+        #  and both should be greater than 0
+        self.assertLess(discharge_energy, theoretical_energy)
+        self.assertLess(0, discharge_energy)
+        self.assertLess(0, theoretical_energy)
+
+
 class TestGetInitialSOC(unittest.TestCase):
     def test_initial_soc(self):
         param = pybamm.LithiumIonParameters()