Add SPDiffusion to model tests

pybop-team · NicolaCourtier · Jan 28, 2025 · Jan 28, 2025 · Jan 28, 2025 · Jan 28, 2025
commit 72005d3f3d9ee83bbaef207d04cf011bce0b6f57
diff --git a/tests/unit/test_models.py b/tests/unit/test_models.py
@@ -30,6 +30,7 @@ class TestModels:
                 {"number of MSMR reactions": ("6", "4")},
             ),
             (pybop.lithium_ion.WeppnerHuggins, "Weppner & Huggins Model", None),
+            (pybop.lithium_ion.SPDiffusion, "Single Particle Diffusion Model", None),
             (
                 pybop.lithium_ion.GroupedSPMe,
                 "Grouped Single Particle Model with Electrolyte",
@@ -58,6 +59,7 @@ def test_model_classes(self, model_class, expected_name, options):
             pybop.lithium_ion.MPM(),
             pybop.lithium_ion.MSMR(options={"number of MSMR reactions": ("6", "4")}),
             pybop.lithium_ion.WeppnerHuggins(),
+            pybop.lithium_ion.SPDiffusion(),
             pybop.lithium_ion.GroupedSPMe(),
             pybop.lithium_ion.GroupedSPMe(options={"surface form": "differential"}),
             pybop.empirical.Thevenin(),
@@ -103,7 +105,9 @@ def test_predict_with_inputs(self, model):
                 "Negative electrode relative porosity": 0.52,
                 "Positive electrode relative porosity": 0.63,
             }
-        elif isinstance(model, (pybop.lithium_ion.WeppnerHuggins)):
+        elif isinstance(
+            model, (pybop.lithium_ion.WeppnerHuggins, pybop.lithium_ion.SPDiffusion)
+        ):
             inputs = {
                 "Theoretical electrode capacity [A.s]": 5000,
             }

diff --git a/pybop/models/lithium_ion/basic_SP_diffusion.py b/pybop/models/lithium_ion/basic_SP_diffusion.py
        unused_keys = []
        for key in model_kwargs.keys():
            if key not in ["build", "parameter_set", "options"]:
                unused_keys.append(key)
        if model_kwargs.get("build", True) is False:
            unused_keys.append("build")
        options = {"working electrode": "positive"}
        if model_kwargs.get("options", None) is not None:
            for key, value in model_kwargs["options"].items():
                if key in ["working electrode"]:
                    options[key] = value
                else:
                    unused_keys.append("options[" + key + "]")
        if any(unused_keys):
            unused_kwargs_warning = "The input model_kwargs are not currently used by the SP Diffusion Model."
            warnings.warn(unused_kwargs_warning, UserWarning, stacklevel=2)
        # Unpack model options
        electrode = self.options["working electrode"]
        if electrode not in ["positive", "negative"]:
            raise ValueError(
                f"Unrecognised electrode type: {electrode}, "
                'expecting either "positive" or "negative".'
            )
        if electrode == "positive":
            j = -I / (3 * Q_th)
        else:
            j = I / (3 * Q_th)

        self.boundary_conditions[sto] = {
            "left": (Scalar(0), "Neumann"),
        if electrode == "positive":
            U = self.U(sto_surf, electrode)
        else:
            U = -self.U(sto_surf, electrode)
        V = U - R0 * I

        # Save the initial OCV
        out = u_ref + 1e-6 * (1 / sto + 1 / (sto - 1))

        if domain == "negative":
            out.print_name = r"U_\mathrm{n}(c^\mathrm{surf}_\mathrm{s,n})"
        elif domain == "positive":
            out.print_name = r"U_\mathrm{p}(c^\mathrm{surf}_\mathrm{s,p})"
        return out
        Build model variables and equations
        Credit: PyBaMM
        """
        self._build_model()

        self._built = True
        pybamm.logger.info(f"Finish building {self.name}")

    @property
    def default_parameter_values(self):

    @property
    def default_quick_plot_variables(self):
        return [
            "Particle stoichiometry",
            "Particle surface stoichiometry",
            "Current [A]",
    dict
        A dictionary of the grouped parameters.
    """
    parameter_set = ParameterSet.to_pybamm(parameter_set)

    # Unpack physical parameters
    F = parameter_set["Faraday constant [C.mol-1]"]
    if electrode == "positive":
        alpha = parameter_set["Positive electrode active material volume fraction"]
        c_max = parameter_set["Maximum concentration in positive electrode [mol.m-3]"]
        L = parameter_set["Positive electrode thickness [m]"]
        R = parameter_set["Positive particle radius [m]"]
        D = parameter_set["Positive particle diffusivity [m2.s-1]"]
        sto_init = (
            parameter_set["Initial concentration in positive electrode [mol.m-3]"]
            / c_max
        )
        ocp = parameter_set["Positive electrode OCP [V]"]
    elif electrode == "negative":
        alpha = parameter_set["Negative electrode active material volume fraction"]
        c_max = parameter_set["Maximum concentration in negative electrode [mol.m-3]"]
        L = parameter_set["Negative electrode thickness [m]"]
        R = parameter_set["Negative particle radius [m]"]
        D = parameter_set["Negative particle diffusivity [m2.s-1]"]
        sto_init = (
            parameter_set["Initial concentration in negative electrode [mol.m-3]"]
            / c_max
        )
        ocp = parameter_set["Negative electrode OCP [V]"]
    else:
        raise ValueError(
            f"Unrecognised electrode type: {electrode}, "
            'expecting either "positive" or "negative".'
        )

    # Compute the cell area
    A = parameter_set["Electrode height [m]"] * parameter_set["Electrode width [m]"]

    # Grouped parameters
    Q_th = F * alpha * c_max * L * A
    tau_d = R**2 / D

    return {
        "Nominal cell capacity [A.h]": parameter_set["Nominal cell capacity [A.h]"],
        "Current function [A]": parameter_set["Current function [A]"],
        "Initial stoichiometry": sto_init,
diff --git a/pybop/models/lithium_ion/weppner_huggins.py b/pybop/models/lithium_ion/weppner_huggins.py
        unused_keys = []
        for key in model_kwargs.keys():
            if key not in ["build", "parameter_set", "options"]:
                unused_keys.append(key)
        options = {"working electrode": "positive"}
        if model_kwargs.get("options", None) is not None:
            for key, value in model_kwargs["options"].items():
                if key in ["working electrode"]:
                    options[key] = value
                else:
                    unused_keys.append("options[" + key + "]")
        if any(unused_keys):
            unused_kwargs_warning = "The input model_kwargs are not currently used by the Weppner & Huggins model."
            warnings.warn(unused_kwargs_warning, UserWarning, stacklevel=2)
    dict
        A dictionary of the grouped parameters.
    """
    parameter_set = ParameterSet.to_pybamm(parameter_set)

    # Unpack physical parameters
    F = parameter_set["Faraday constant [C.mol-1]"]
    if electrode == "positive":
        alpha = parameter_set["Positive electrode active material volume fraction"]
        c_max = parameter_set["Maximum concentration in positive electrode [mol.m-3]"]
        L = parameter_set["Positive electrode thickness [m]"]
        R = parameter_set["Positive particle radius [m]"]
        D = parameter_set["Positive particle diffusivity [m2.s-1]"]
    elif electrode == "negative":
        alpha = parameter_set["Negative electrode active material volume fraction"]
        c_max = parameter_set["Maximum concentration in negative electrode [mol.m-3]"]
        L = parameter_set["Negative electrode thickness [m]"]
        R = parameter_set["Negative particle radius [m]"]
        D = parameter_set["Negative particle diffusivity [m2.s-1]"]
    else:
        raise ValueError(
            f"Unrecognised electrode type: {electrode}, "
            'expecting either "positive" or "negative".'
        )

    # Compute the cell area
    A = parameter_set["Electrode height [m]"] * parameter_set["Electrode width [m]"]

    # Grouped parameters
    Q_th = F * alpha * c_max * L * A
    tau_d = R**2 / D

    return {
        "Current function [A]": parameter_set["Current function [A]"],
        "Reference voltage [V]": 4,
        "Derivative of the OCP wrt stoichiometry [V]": -1,
	unused_keys = []
	for key in model_kwargs.keys():
	if key not in ["build", "parameter_set", "options"]:
	unused_keys.append(key)
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	if model_kwargs.get("build", True) is False:
	unused_keys.append("build")
	options = {"working electrode": "positive"}
	if model_kwargs.get("options", None) is not None:
	for key, value in model_kwargs["options"].items():
	if key in ["working electrode"]:
	options[key] = value
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	else:
	unused_keys.append("options[" + key + "]")
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	if any(unused_keys):
	unused_kwargs_warning = "The input model_kwargs are not currently used by the SP Diffusion Model."
	warnings.warn(unused_kwargs_warning, UserWarning, stacklevel=2)
	# Unpack model options
	electrode = self.options["working electrode"]
	if electrode not in ["positive", "negative"]:
	raise ValueError(
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	f"Unrecognised electrode type: {electrode}, "
	'expecting either "positive" or "negative".'
	)
	if electrode == "positive":
	j = -I / (3 * Q_th)
	else:
	j = I / (3 * Q_th)
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	self.boundary_conditions[sto] = {
	"left": (Scalar(0), "Neumann"),
	if electrode == "positive":
	U = self.U(sto_surf, electrode)
	else:
	U = -self.U(sto_surf, electrode)
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	V = U - R0 * I

	# Save the initial OCV
	out = u_ref + 1e-6 * (1 / sto + 1 / (sto - 1))

	if domain == "negative":
	out.print_name = r"U_\mathrm{n}(c^\mathrm{surf}_\mathrm{s,n})"
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	elif domain == "positive":
	out.print_name = r"U_\mathrm{p}(c^\mathrm{surf}_\mathrm{s,p})"
	return out
	Build model variables and equations
	Credit: PyBaMM
	"""
	self._build_model()
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	self._built = True
	pybamm.logger.info(f"Finish building {self.name}")
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	@property
	def default_parameter_values(self):

	@property
	def default_quick_plot_variables(self):
	return [
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	"Particle stoichiometry",
	"Particle surface stoichiometry",
	"Current [A]",
	dict
	A dictionary of the grouped parameters.
	"""
	parameter_set = ParameterSet.to_pybamm(parameter_set)
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	# Unpack physical parameters
	F = parameter_set["Faraday constant [C.mol-1]"]
	if electrode == "positive":
	alpha = parameter_set["Positive electrode active material volume fraction"]
	c_max = parameter_set["Maximum concentration in positive electrode [mol.m-3]"]
	L = parameter_set["Positive electrode thickness [m]"]
	R = parameter_set["Positive particle radius [m]"]
	D = parameter_set["Positive particle diffusivity [m2.s-1]"]
	sto_init = (
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	parameter_set["Initial concentration in positive electrode [mol.m-3]"]
	/ c_max
	)
	ocp = parameter_set["Positive electrode OCP [V]"]
	elif electrode == "negative":
	alpha = parameter_set["Negative electrode active material volume fraction"]
	c_max = parameter_set["Maximum concentration in negative electrode [mol.m-3]"]
	L = parameter_set["Negative electrode thickness [m]"]
	R = parameter_set["Negative particle radius [m]"]
	D = parameter_set["Negative particle diffusivity [m2.s-1]"]
	sto_init = (
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	parameter_set["Initial concentration in negative electrode [mol.m-3]"]
	/ c_max
	)
	ocp = parameter_set["Negative electrode OCP [V]"]
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	else:
	raise ValueError(
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	f"Unrecognised electrode type: {electrode}, "
	'expecting either "positive" or "negative".'
	)

	# Compute the cell area
	A = parameter_set["Electrode height [m]"] * parameter_set["Electrode width [m]"]
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	# Grouped parameters
	Q_th = F * alpha * c_max * L * A
	tau_d = R**2 / D
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	return {
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	"Nominal cell capacity [A.h]": parameter_set["Nominal cell capacity [A.h]"],
	"Current function [A]": parameter_set["Current function [A]"],
	"Initial stoichiometry": sto_init,