Pouch Cell Model Parameter Identification¶

In this notebook, we present the single particle model with a two dimensional current collector. This is achieved via the potential-pair models introduced in Marquis et al. [1] as implemented in PyBaMM. At a high-level this is accomplished as a potential-pair model which is resolved across the discretised spatial locations.

Setting up the Environment¶

Before we begin, we need to ensure that we have all the necessary tools. We will install PyBOP and upgrade dependencies:

In [ ]:

%pip install --upgrade pip ipywidgets -q
%pip install pybop -q

/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip
Note: you may need to restart the kernel to use updated packages.
/Users/engs2510/Documents/Git/PyBOP/.nox/notebooks-overwrite/bin/python3: No module named pip
Note: you may need to restart the kernel to use updated packages.

Importing Libraries¶

With the environment set up, we can now import PyBOP alongside other libraries we will need:

In [ ]:

import numpy as np

import pybop

go = pybop.PlotlyManager().go
pybop.PlotlyManager().pio.renderers.default = "notebook_connected"

Generating Synthetic Data¶

To demonstrate parameter estimation, we first need some data. We will generate synthetic data using a forward model, which requires defining a parameter set and the model itself.

Defining Parameters and Model¶

We start by creating an example parameter set and then instantiate the single-particle model (SPM):

In [ ]:

parameter_set = pybop.ParameterSet.pybamm("Marquis2019")
parameter_set.update(
    {
        "Negative electrode active material volume fraction": 0.495,
        "Positive electrode active material volume fraction": 0.612,
    }
)
model = pybop.lithium_ion.SPM(
    parameter_set=parameter_set,
    options={"current collector": "potential pair", "dimensionality": 2},
)

Updating the Spatial Grid¶

Next, we update the number of spatial locations to solve the potential-pair model,

In [ ]:

model.var_pts["y"] = 5
model.var_pts["z"] = 5

Simulating the Forward Model¶

We can then simulate the model using the predict method, with a default constant current to generate voltage data.

In [ ]:

t_eval = np.arange(0, 900, 3)
values = model.predict(t_eval=t_eval)

Adding Noise to Voltage Data¶

To make the parameter estimation more realistic, we add Gaussian noise to the data.

In [ ]:

sigma = 0.001
corrupt_values = values["Voltage [V]"].data + np.random.normal(0, sigma, len(t_eval))

Identifying the Parameters¶

We will now set up the parameter estimation process by defining the datasets for optimisation and selecting the model parameters we wish to estimate.

Creating a Dataset¶

The dataset for optimisation is composed of time, current, and the noisy voltage data:

In [ ]:

dataset = pybop.Dataset(
    {
        "Time [s]": t_eval,
        "Current function [A]": values["Current [A]"].data,
        "Voltage [V]": corrupt_values,
    }
)

Defining Parameters to Estimate¶

We select the parameters for estimation and set up their prior distributions and bounds:

In [ ]:

parameters = pybop.Parameters(
    pybop.Parameter(
        "Negative electrode active material volume fraction",
        prior=pybop.Gaussian(0.7, 0.05),
        bounds=[0.45, 0.9],
    ),
    pybop.Parameter(
        "Positive electrode active material volume fraction",
        prior=pybop.Gaussian(0.58, 0.05),
        bounds=[0.5, 0.8],
    ),
)

For plotting purposes, we want additional variables to be stored in the problem class. These are defined as,

In [ ]:

additional_variables = [
    "Negative current collector potential [V]",
    "Positive current collector potential [V]",
]

Setting up the Optimisation Problem¶

With the datasets and parameters defined, we can set up the optimisation problem, its cost function, and the optimiser.

In [ ]:

problem = pybop.FittingProblem(
    model, parameters, dataset, additional_variables=additional_variables
)
cost = pybop.SumSquaredError(problem)
optim = pybop.CMAES(cost, max_iterations=30)

Running the Optimisation¶

We proceed to run the CMA-ES optimisation algorithm to estimate the parameters:

In [ ]:

x, final_cost = optim.run()

Viewing the Estimated Parameters¶

After the optimisation, we can examine the estimated parameter values:

In [ ]:

x  # This will output the estimated parameters

Out[ ]:

array([0.49949096, 0.60907043])

Plotting and Visualisation¶

PyBOP provides various plotting utilities to visualise the results of the optimisation.

Comparing System Response¶

We can quickly plot the system's response using the estimated parameters compared to the target:

In [ ]:

pybop.quick_plot(problem, problem_inputs=x, title="Optimised Comparison");

Spatial Plotting¶

We can now plot the spatial variables from the solution object. First, the final negative current collector potential can be displayed. In this example, this is just a reference variable, but could be used for fitting or optimisation in the correct workflows.

In [ ]:

sol = problem.evaluate(parameters.as_dict(x))

go.Figure(
    [
        go.Contour(
            x=np.arange(0, model.var_pts["y"] - 1, 1),
            y=np.arange(0, model.var_pts["z"] - 1, 1),
            z=sol["Negative current collector potential [V]"][:, :, -1],
            colorscale="Viridis",
        )
    ],
    layout=dict(
        title="Negative current collector potential [V]",
        xaxis_title="x node",
        yaxis_title="y node",
        width=600,
        height=600,
    ),
)

We plot can then plot the positive current collector potential,

In [ ]:

go.Figure(
    [
        go.Contour(
            x=np.arange(0, model.var_pts["y"] - 1, 1),
            y=np.arange(0, model.var_pts["z"] - 1, 1),
            z=sol["Positive current collector potential [V]"][:, :, -1],
            colorscale="Viridis",
        )
    ],
    layout=dict(
        title="Positive current collector potential [V]",
        xaxis_title="x node",
        yaxis_title="y node",
        width=600,
        height=600,
    ),
)

Convergence and Parameter Trajectories¶

To assess the optimisation process, we can plot the convergence of the cost function and the trajectories of the parameters:

In [ ]:

pybop.plot_convergence(optim)
pybop.plot_parameters(optim);

Cost Landscape¶

Finally, we can visualise the cost landscape and the path taken by the optimiser:

In [ ]:

pybop.plot2d(optim, steps=10);