Python Battery Optimisation and Parameterisation

PyBOP provides a complete set of tools for parameterisation and optimisation of battery models, using both Bayesian and frequentist approaches, with example workflows to assist the user. PyBOP can be used to parameterise various battery models, including electrochemical and equivalent circuit models available in PyBaMM. PyBOP prioritises clear and informative diagnostics for the user, while also allowing for advanced probabilistic methods.

The diagram below shows the conceptual framework of PyBOP. This package is currently under development, so users can expect the API to evolve with future releases.

Within your virtual environment, install PyBOP:

pip install pybop

To install the most recent state of PyBOP, install from the develop branch,

pip install git+https://github.com/pybop-team/PyBOP.git@develop

To install a previous version of PyBOP, use the following template and replace the version number:

pip install pybop==v24.3

To check that PyBOP is installed correctly, run one of the examples in the following section. For a development installation, see the Contribution Guide. More installation information is available in our documentation and the extended installation instructions for PyBaMM.

PyBOP has two intended uses:

1. Parameter inference from battery test data.

2. Design optimisation under battery manufacturing/use constraints.

These include a wide variety of optimisation problems that require careful consideration due to the choice of battery model, data availability and/or the choice of design parameters.

Explore our example notebooks for hands-on demonstrations:

• Gravimetric design optimisation (SPMe)
• Non-linear constrained ECM parameter identification
• Optimiser comparison for parameter identification
• Parameter identification for spatial pouch cell model
• Estimating ECM parameters from HPPC pulse

Find additional script-based examples in the examples directory:

• UKF parameter identification (SPM)
• BPX format parameter import/export
• Electrochemical Impendence Spectroscopy (EIS) parameter identification
• Maximum a Posteriori parameter identification (SPM)
• Gradient-based parameter identification (SPM)

The table below lists the currently supported models, optimisers, and cost functions in PyBOP.

PyBOP aims to foster a broad consortium of developers and users, building on and learning from the success of the PyBaMM community. Our values are:

• Inclusivity and fairness (those who wish to contribute may do so, and their input is appropriately recognised)

• Interoperability (modularity for maximum impact and inclusivity)

• User-friendliness (putting user requirements first via user-assistance & workflows)

PyBOP is released under the BSD 3-Clause License.

Thanks goes to these wonderful people (emoji key):

Brady Planden 🚇 ⚠️ 💻 💡 👀	NicolaCourtier 💻 👀 💡 ⚠️	David Howey 🤔 🧑‍🏫	Martin Robinson 🤔 🧑‍🏫 👀 💻 ⚠️	Ferran Brosa Planella 👀 💻 💡	Agriya Khetarpal 💻 🚇 👀	Faraday Institution 💵
UK Research and Innovation 💵	Horizon Europe IntelLiGent Consortium 💵	Muhammed Nedim Sogut 💻	MarkBlyth 💻	f-g-r-i-m-m 💡	Dibyendu-IITKGP 💡 ⚠️ 💻	Noël Hallemans 💡
Pip Liggins 💻

This project follows the all-contributors specifications. Contributions of any kind are welcome! See CONTRIBUTING.md for ways to get started.