battmo 0.2.2

Battery simulation with BattMo in Python.

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PyBattMo

PyBattMo is a Python wrapper around the Julia-based BattMo.jl. The Battery Modelling Toolbox (BattMo) is a resource for continuum modelling of electrochemical devices. The initial development features a pseudo X-dimensional (PXD) framework for the Doyle-Fuller-Newman model of lithium-ion battery cells. This is currently a early release that implements a subset of features from the MATLAB version of BattMo.

BattMo Family

BattMo can be used through several interfaces:

• MATLAB
• Julia
• Python
• Web app

Installation

To install PyBattMo, use the following pip command:

pip install battmo

Quick start

Run examples using our internal library, for example the cell parameters from Chen et al.

Important tip: run the examples within a notebook or using cells in VSCode to make use of the high performance of Julia. Julia compiles the functions and objects that you use when you first run a code. Because of this, the second time you run the same code it is super fast! But to make use of this, you need to have a kernel that keeps running in between code executions. Therefore, it does work with jupytor notebooks.

Note the BattMo plotting functions in PyBattMo are experimental and not very stable yet.

P2D example

Here are a few examples to get you started:

from battmo import *
import plotly.express as px
import pandas as pd
import numpy as np

# Initialize the model
model = LithiumIonBattery()

# Load cell parameters and cycling protocol
cell_parameters = load_cell_parameters(from_default_set = "chen_2020")
cycling_protocol = load_cycling_protocol(from_default_set = "cc_discharge")

# Have a quick look into what kind of cell we're dealing with
quick_cell_check(cell_parameters)

# Set up the simulation
sim = Simulation(model, cell_parameters, cycling_protocol)

# Run the simulation
output = solve(sim)

# Have a look into which output quantities are available
print_info(output)

# Plotting using Plotly
df = to_pandas(output.time_series)
fig = px.line(df, x="Time", y="Voltage", title="Voltage curve")
fig.show()

# Use BattMo internal plotting functions
plot_dashboard(output, plot_type="contour")

Run a 3D simulation

from battmo import *

# Load parameter sets and settings
cell_parameters = load_cell_parameters(from_default_set="chen_2020")
cycling_protocol = load_cycling_protocol(from_default_set="cc_discharge")
model_settings = load_model_settings(from_default_set="p4d_cylindrical")
simulation_settings = load_model_settings(from_default_set="p4d_cylindrical")

# We adjust the parameters so that the simulation in this example is not too long

cell_parameters["Cell"]["OuterRadius"]                                   = 0.004
cell_parameters["NegativeElectrode"]["CurrentCollector"]["TabFractions"] = [0.5]
cell_parameters["PositiveElectrode"]["CurrentCollector"]["TabFractions"] = [0.5]
cell_parameters["NegativeElectrode"]["CurrentCollector"]["TabWidth"]     = 0.002
cell_parameters["PositiveElectrode"]["CurrentCollector"]["TabWidth"]     = 0.002
simulation_settings["AngularGridPoints"]                                 = 8

# Setup model and simulation
model = LithiumIonBattery(model_settings=model_settings)
sim = Simulation(model, cell_parameters, cycling_protocol, simulation_settings = simulation_settings)
output = solve(sim)

# Plot interative 3D results
plot_interactive_3d(output)

Documentation

Some additional examples are be found in the BattMo.jl documentation as well as a comprehensive documentation on the API and architecture of BattMo.jl.

Acknowledgements

BattMo has received funding from the European Union’s Horizon 2020 innovation program under grant agreement numbers:

• 875527 HYDRA
• 957189 BIG-MAP
• 101104013 BATMAX
• 101103997 DigiBatt