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.

Installation

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.

Using PyBOP

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.

Jupyter Notebooks

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

Python Scripts

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)

Supported Methods

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

Battery Models	Optimization Algorithms	Cost Functions
Single Particle Model (SPM)	Covariance Matrix Adaptation Evolution Strategy (CMA-ES)	Sum of Squared Errors (SSE)
Single Particle Model with Electrolyte (SPMe)	Particle Swarm Optimization (PSO)	Root Mean Squared Error (RMSE)
Doyle-Fuller-Newman (DFN)	Exponential Natural Evolution Strategy (xNES)	Minkowski
Many Particle Model (MPM)	Separable Natural Evolution Strategy (sNES)	Sum of Power
Multi-Species Multi-Reactants (MSMR)	Adaptive Moment Estimation with Weight Decay (AdamW)	Gaussian Log Likelihood
Weppner-Huggins	Improved Resilient Backpropagation (iRProp-)	Log Posterior
Equivalent Circuit Models (ECM)	SciPy Minimize & Differential Evolution	Unscented Kalman Filter (UKF)
	Cuckoo Search	Gravimetric Energy Density
	Gradient Descent	Volumetric Energy Density
	Nelder-Mead

Code of Conduct

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)

License

PyBOP is released under the BSD 3-Clause License.

Contributors ✨

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 💡

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