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 pybopTo install the most recent state of PyBOP, install from the develop branch,
pip install git+https://github.com/pybop-team/PyBOP.git@developTo install a previous version of PyBOP, use the following template and replace the version number:
pip install pybop==v24.3To 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:
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Parameter estimation from battery test data.
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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.
PyBOP comes with a number of example notebooks, which can be found in the examples folder. A few noteworthy ones are listed below.
- Gravimetric design optimisation of a single particle model
- Experimental GITT fitting of an ECM for an LG M50
- Compare PyBOP optimisers for parameter identification
- Parameter identification for a spatial pouch cell model
Additional script-based examples can be found in the examples directory. Some notable scripts are listed below.
- Unscented Kalman filter parameter identification of a SPM
- Import and export parameters using Faraday's BPX format
- Maximum a posteriori parameter identification of a SPM
- Gradient based parameter identification of a SPM
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) | Gaussian Log Likelihood |
| Many Particle Model (MPM) | Separable Natural Evolution Strategy (sNES) | Gaussian Log Likelihood w/ known variance |
| Multi-Species Multi-Reactants (MSMR) | Adaptive Moment Estimation with Weight Decay (AdamW) | Maximum a Posteriori (MAP) |
| Equivalent Circuit Models (ECM) | Improved Resilient Backpropagation (iRProp-) | Unscented Kalman Filter (UKF) |
| SciPy Minimize & Differential Evolution | Gravimetric Energy Density | |
| Gradient Descent | Volumetric Energy Density | |
| Nelder-Mead | ||
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:
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Inclusivity and fairness (those who wish to contribute may do so, and their input is appropriately recognised)
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Interoperability (modularity for maximum impact and inclusivity)
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User-friendliness (putting user requirements first via user-assistance & workflows)
Thanks goes to these wonderful people (emoji key):
This project follows the all-contributors specifications. Contributions of any kind are welcome! See CONTRIBUTING.md for ways to get started.
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