SRSG UNIVERSE-HPC Training

Parameter Sets

PyBaMM comes with 12 ready-made parameter sets for lithium-ion batteries. To select one, pass the name of one of them as an input argument to the ParameterValues class. For now, select Marquis2019, which is the default parameter set for lithium-ion models.

import pybamm
parameter_values = pybamm.ParameterValues("Marquis2019")
model = pybamm.lithium_ion.DFN()

There are over 100 parameter values! Fortunately, ParameterValues objects are dictionaries so you can look up the parameters you're interested in:

print(parameter_values["Upper voltage cut-off [V]"])
print(parameter_values["Lower voltage cut-off [V]"])

Recall experiment3 from the morning session:

experiment3 = pybamm.Experiment(
    # Initialize
    [
        "Hold at 4.2 V until C/100",
        "Rest for 4 hours",
    ]
    # Capacity check
    + [(
        "Discharge at C/10 until 2.5 V",
        "Charge at C/10 until 4.2 V",
        "Hold at 4.2 V until C/100"
    )]
    # Ageing cycles

    + [(
        "Discharge at 1C until 2.5 V",
        "Charge at 0.3C until 4.2 V",
        "Hold at 4.2 V until C/100",
    )] * 10 +
    # Capacity check
    + [(
        "Discharge at C/10 until 2.5 V",
        "Charge at C/10 until 4.2 V",
        "Hold at 4.2 V until C/100"
    )]
)

The above experiment3 will not work with the default parameters, because it was designed for a different cell with different voltage limits. Marquis et al. studied the Kokam SLPB78205130H 16 Ah prismatic cell, whereas experiment3 is designed for the LG M50 5 Ah cylindrical cell. Four of PyBaMM's built-in parameter sets correspond to the LG M50:

Chen2020 comes from the first study, published in 2020.
Chen2020_composite is an upgrade of Chen2020 designed to work with PyBaMM's composite electrode model
OKane2022 is a superset of Chen2020 designed to work with PyBaMM's various degradation models
ORegan2022 comes from a follow-up paper to Chen2020 that addressed some questions the first paper didn't answer

Like Experiment objects, ParameterValues objects are an optional argument to the Simulation class:

simulation3 = pybamm.Simulation(
    model,
    experiment=experiment3,
    parameter_values=parameter_values
)

If you use suitable parameter values, the simulation will run, but the results won't be very interesting. Try changing the model options to enable one or more degradation mechanisms. This example notebook shows you how to do this, and how to interpret the results. However, 10 cycles isn't enough for the battery to degrade very much. Fortunately, ParameterValues objects are dictionaries and can therefore be updated easily:

parameter_values.update({
    "Outer SEI solvent diffusivity [m2.s-1]": 1.25e-20,
    "Lithium plating kinetic rate constant [m.s-1]": 1e-8,
    "Dead lithium decay constant [s-1]": 4e-6,
    "Negative electrode cracking rate": 1.95e-18,
    "Negative electrode LAM constant proportional term [s-1]": 5.5556e-6,
    "Positive electrode LAM constant proportional term [s-1]": 5.5556e-6,
})

The updated parameter values will lead to faster degradation.

You can also create, import and use your own parameter sets:

from your_parameter_set import get_parameter_values
parameter_values = get_parameter_values()

Create your own parameter set!

Try creating your own parameter set and running a simulation with it! Take one of PyBaMM's existing parameter sets and change some of the function parameters to make it your own!

Input parameters

Pybamm's parameter sets contain all the parameters needed to run a simulation, but often you want to see how the solution changes with respect to a single parameter or a small subset of parameters. Of course, you could create a new parameter set with the desired parameters changed, like so:

parameter_values = pybamm.ParameterValues("Marquis2019")
parameter_values.update({
    "Current function [A]": 2,
})

You would then need to create a new Simulation object with the updated parameter values.

simulation = pybamm.Simulation(
    model,
    parameter_values=parameter_values
)

This is a lot of extra overhead for changing a single parameter, as the simulation needs to be recreated from scratch. Instead, you can use an input parameter, which tells the simulation that this parameter does not yet have a specific value, but one will be provided when the model is solved.

An input parameter can be created by setting its value to the string [input], like so:

parameter_values = pybamm.ParameterValues("Marquis2019")
parameter_values.update({
    "Current function [A]": "[input]",
})

When the model is solved, you can provide a value for the input parameter

simulation = pybamm.Simulation(
    model,
    parameter_values=parameter_values
)
solution = simulation.solve([0, 3600], inputs={"Current function [A]": 2})

Input parameters

Try creating a new parameter set with an input parameter, and then solve the model with a value for the input parameter. Vary the value of the input parameter and see how the solution changes.

Hint: To plot multiple solutions, you can pass a list of solutions to pybamm.dynamic_plot

model = pybamm.lithium_ion.DFN()
parameter_values = pybamm.ParameterValues("Marquis2019")
parameter_values.update({
    "Current function [A]": "[input]",
})
simulation = pybamm.Simulation(
    model,
    parameter_values=parameter_values
)

solutions = []
labels = []
for value in [1, 2, 3]:
    solution = simulation.solve([0, 3600], inputs={"Current function [A]": value})
    solutions.append(solution)
    labels.append(f"Current function [A] = {value}")

pybamm.dynamic_plot(solutions, labels=labels)

Parameter sets

Parameter Sets

Create your own parameter set!

Input parameters

Input parameters