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Merge pull request pybamm-team#4330 from parkec3/ocvr_ecm
ECM with split OCV
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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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@@ -12,3 +12,4 @@ Lithium-ion Models | |
msmr | ||
yang2017 | ||
electrode_soh | ||
ecm_split_ocv |
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100 changes: 100 additions & 0 deletions
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src/pybamm/models/full_battery_models/lithium_ion/basic_splitOCVR.py
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# | ||
# 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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tests/integration/test_models/test_full_battery_models/test_lithium_ion/test_splitOCVR.py
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# | ||
# Test that the model works with an example parameter set | ||
# | ||
import pybamm | ||
import numpy as np | ||
import tests | ||
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class TestSplitOCVR: | ||
def test_basic_processing(self): | ||
# example parameters | ||
qp0 = 8.73231852 | ||
qn0 = 5.82761507 | ||
theta0_n = 0.9013973983641687 * 0.9 | ||
theta0_p = 0.5142305254580026 * 0.83 | ||
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# OCV functions | ||
def Un(theta_n): | ||
Un = ( | ||
0.1493 | ||
+ 0.8493 * np.exp(-61.79 * theta_n) | ||
+ 0.3824 * np.exp(-665.8 * theta_n) | ||
- np.exp(39.42 * theta_n - 41.92) | ||
- 0.03131 * np.arctan(25.59 * theta_n - 4.099) | ||
- 0.009434 * np.arctan(32.49 * theta_n - 15.74) | ||
) | ||
return Un | ||
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def Up(theta_p): | ||
Up = ( | ||
-10.72 * theta_p**4 | ||
+ 23.88 * theta_p**3 | ||
- 16.77 * theta_p**2 | ||
+ 2.595 * theta_p | ||
+ 4.563 | ||
) | ||
return Up | ||
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pars = pybamm.ParameterValues( | ||
{ | ||
"Positive electrode capacity [A.h]": qp0, | ||
"Ohmic resistance [Ohm]": 0.001, | ||
"Negative electrode initial stoichiometry": theta0_n, | ||
"Lower voltage cut-off [V]": 2.8, | ||
"Positive electrode initial stoichiometry": theta0_p, | ||
"Upper voltage cut-off [V]": 4.2, | ||
"Negative electrode capacity [A.h]": qn0, | ||
"Current function [A]": 5, | ||
"Positive electrode OCP [V]": Up, | ||
"Negative electrode OCP [V]": Un, | ||
"Nominal cell capacity [A.h]": 5, | ||
} | ||
) | ||
model = pybamm.lithium_ion.SplitOCVR() | ||
modeltest = tests.StandardModelTest(model) | ||
modeltest.test_all(param=pars) |
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tests/unit/test_models/test_full_battery_models/test_lithium_ion/test_splitOCVR.py
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# | ||
# Test for the ecm split-OCV model | ||
# | ||
import pybamm | ||
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class TestSplitOCVR: | ||
def test_ecmsplitocv_well_posed(self): | ||
model = pybamm.lithium_ion.SplitOCVR() | ||
model.check_well_posedness() | ||
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def test_get_default_quick_plot_variables(self): | ||
model = pybamm.lithium_ion.SplitOCVR() | ||
variables = model.default_quick_plot_variables | ||
assert "Current [A]" in variables |