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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,7 @@ | ||
| Equivalent Circuit Model with Split OCV (SplitOCVR) | ||
| ===================================================== | ||
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| .. autoclass:: pybamm.lithium_ion.SplitOCVR | ||
| :members: | ||
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| .. footbibliography:: |
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| Original file line number | Diff line number | Diff line change |
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@@ -12,3 +12,4 @@ Lithium-ion Models | |
| msmr | ||
| yang2017 | ||
| electrode_soh | ||
| ecm_split_ocv | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,57 @@ | ||
| import pybamm | ||
| import time | ||
| import numpy as np | ||
|
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| # This script provides an example for massively vectorised | ||
| # model solves using the JAX BDF solver. First, | ||
| # we set up the model and process parameters | ||
| model = pybamm.lithium_ion.SPM() | ||
| model.convert_to_format = "jax" | ||
| model.events = [] # remove events (not supported in jax) | ||
| geometry = model.default_geometry | ||
| param = pybamm.ParameterValues("Chen2020") | ||
| param.update({"Current function [A]": "[input]"}) | ||
| param.process_geometry(geometry) | ||
| param.process_model(model) | ||
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| # Discretise and setup solver | ||
| mesh = pybamm.Mesh(geometry, model.default_submesh_types, model.default_var_pts) | ||
| disc = pybamm.Discretisation(mesh, model.default_spatial_methods) | ||
| disc.process_model(model) | ||
| t_eval = np.linspace(0, 3600, 100) | ||
| solver = pybamm.JaxSolver(atol=1e-6, rtol=1e-6, method="BDF") | ||
|
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| # Set number of vectorised solves | ||
| values = np.linspace(0.01, 1.0, 1000) | ||
| inputs = [{"Current function [A]": value} for value in values] | ||
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| # Run solve for all inputs, with a just-in-time compilation | ||
| # occurring on the first solve. All sequential solves will | ||
| # use the compiled code, with a large performance improvement. | ||
| start_time = time.time() | ||
| sol = solver.solve(model, t_eval, inputs=inputs) | ||
| print(f"Time taken: {time.time() - start_time}") # 1.3s | ||
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| # Rerun the vectorised solve, showing performance improvement | ||
| start_time = time.time() | ||
| compiled_sol = solver.solve(model, t_eval, inputs=inputs) | ||
| print(f"Compiled time taken: {time.time() - start_time}") # 0.42s | ||
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| # Plot one of the solves | ||
| plot = pybamm.QuickPlot( | ||
| sol[5], | ||
| [ | ||
| "Negative particle concentration [mol.m-3]", | ||
| "Electrolyte concentration [mol.m-3]", | ||
| "Positive particle concentration [mol.m-3]", | ||
| "Current [A]", | ||
| "Negative electrode potential [V]", | ||
| "Electrolyte potential [V]", | ||
| "Positive electrode potential [V]", | ||
| "Voltage [V]", | ||
| ], | ||
| time_unit="seconds", | ||
| spatial_unit="um", | ||
| ) | ||
| plot.dynamic_plot() |
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100 changes: 100 additions & 0 deletions
100
src/pybamm/models/full_battery_models/lithium_ion/basic_splitOCVR.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,100 @@ | ||
| # | ||
| # Equivalent Circuit Model with split OCV | ||
| # | ||
| import pybamm | ||
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| class SplitOCVR(pybamm.BaseModel): | ||
| """Basic Equivalent Circuit Model that uses two OCV functions | ||
| for each electrode. This model is easily parameterizable with minimal parameters. | ||
| This class differs from the :class: pybamm.equivalent_circuit.Thevenin() due | ||
| to dual OCV functions to make up the voltage from each electrode. | ||
| Parameters | ||
| ---------- | ||
| name: str, optional | ||
| The name of the model. | ||
| """ | ||
|
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| def __init__(self, name="ECM with split OCV"): | ||
| super().__init__(name) | ||
|
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| ###################### | ||
| # Variables | ||
| ###################### | ||
| # All variables are only time-dependent | ||
| # No domain definition needed | ||
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| theta_n = pybamm.Variable("Negative particle stoichiometry") | ||
| theta_p = pybamm.Variable("Positive particle stoichiometry") | ||
| Q = pybamm.Variable("Discharge capacity [A.h]") | ||
| V = pybamm.Variable("Voltage [V]") | ||
|
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| # model is isothermal | ||
| I = pybamm.FunctionParameter("Current function [A]", {"Time [s]": pybamm.t}) | ||
|
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| # Capacity equation | ||
| self.rhs[Q] = I / 3600 | ||
| self.initial_conditions[Q] = pybamm.Scalar(0) | ||
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| # Capacity in each electrode | ||
| Q_n = pybamm.Parameter("Negative electrode capacity [A.h]") | ||
| Q_p = pybamm.Parameter("Positive electrode capacity [A.h]") | ||
|
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| # State of charge electrode equations | ||
| theta_n_0 = pybamm.Parameter("Negative electrode initial stoichiometry") | ||
| theta_p_0 = pybamm.Parameter("Positive electrode initial stoichiometry") | ||
| self.rhs[theta_n] = -I / Q_n / 3600 | ||
| self.rhs[theta_p] = I / Q_p / 3600 | ||
| self.initial_conditions[theta_n] = theta_n_0 | ||
| self.initial_conditions[theta_p] = theta_p_0 | ||
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| # Resistance for IR expression | ||
| R = pybamm.Parameter("Ohmic resistance [Ohm]") | ||
|
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| # Open-circuit potential for each electrode | ||
| Un = pybamm.FunctionParameter( | ||
| "Negative electrode OCP [V]", {"Negative particle stoichiometry": theta_n} | ||
| ) | ||
| Up = pybamm.FunctionParameter( | ||
| "Positive electrode OCP [V]", {"Positive particle stoichiometry": theta_p} | ||
| ) | ||
|
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| # Voltage expression | ||
| V = Up - Un - I * R | ||
|
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| # Parameters for Voltage cutoff | ||
| voltage_high_cut = pybamm.Parameter("Upper voltage cut-off [V]") | ||
| voltage_low_cut = pybamm.Parameter("Lower voltage cut-off [V]") | ||
|
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| self.variables = { | ||
| "Negative particle stoichiometry": theta_n, | ||
| "Positive particle stoichiometry": theta_p, | ||
| "Current [A]": I, | ||
| "Discharge capacity [A.h]": Q, | ||
| "Voltage [V]": V, | ||
| "Times [s]": pybamm.t, | ||
| "Positive electrode OCP [V]": Up, | ||
| "Negative electrode OCP [V]": Un, | ||
| "Current function [A]": I, | ||
| } | ||
|
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| # Events specify points at which a solution should terminate | ||
| self.events += [ | ||
| pybamm.Event("Minimum voltage [V]", V - voltage_low_cut), | ||
| pybamm.Event("Maximum voltage [V]", voltage_high_cut - V), | ||
| pybamm.Event("Maximum Negative Electrode stoichiometry", 0.999 - theta_n), | ||
| pybamm.Event("Maximum Positive Electrode stoichiometry", 0.999 - theta_p), | ||
| pybamm.Event("Minimum Negative Electrode stoichiometry", theta_n - 0.0001), | ||
| pybamm.Event("Minimum Positive Electrode stoichiometry", theta_p - 0.0001), | ||
| ] | ||
|
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| @property | ||
| def default_quick_plot_variables(self): | ||
| return [ | ||
| "Voltage [V]", | ||
| ["Negative particle stoichiometry", "Positive particle stoichiometry"], | ||
| "Negative electrode OCP [V]", | ||
| "Positive electrode OCP [V]", | ||
| "Current [A]", | ||
| ] |
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