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Storage","sub_category":"Battery","monthly_downloads":2331,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"\u003cdiv align=\"center\"\u003e\n\n  \u003cimg src=\"https://raw.githubusercontent.com/pybop-team/PyBOP/develop/assets/logo/PyBOP_logo.svg\" alt=\"The PyBOP logo.\" width=\"600\" /\u003e\n\n  # Python Battery Optimisation and Parameterisation\n\n\n  [![Scheduled](https://github.com/pybop-team/PyBOP/actions/workflows/scheduled_tests.yaml/badge.svg)](https://github.com/pybop-team/PyBOP/actions/workflows/scheduled_tests.yaml)\n  [![Contributors](https://img.shields.io/github/contributors/pybop-team/PyBOP)](https://github.com/pybop-team/PyBOP/graphs/contributors)\n  [![Python Versions from PEP 621 TOML](https://img.shields.io/python/required-version-toml?tomlFilePath=https%3A%2F%2Fraw.githubusercontent.com%2Fpybop-team%2FPyBOP%2Fdevelop%2Fpyproject.toml\u0026label=Python)](https://pypi.org/project/pybop/)\n  [![Codecov](https://codecov.io/gh/pybop-team/PyBOP/branch/develop/graph/badge.svg)](https://app.codecov.io/gh/pybop-team/PyBOP)\n  [![License](https://img.shields.io/github/license/pybop-team/PyBOP?color=blue)](https://github.com/pybop-team/PyBOP/blob/develop/LICENSE)\n  [![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/pybop-team/PyBOP/blob/develop/)\n  [![nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/pybop-team/PyBOP/tree/develop/examples/notebooks/)\n  [![Releases](https://img.shields.io/github/v/release/pybop-team/PyBOP?color=gold)](https://github.com/pybop-team/PyBOP/releases)\n  [![DOI](https://joss.theoj.org/papers/10.21105/joss.07874/status.svg)](https://doi.org/10.21105/joss.07874)\n\n[\u003cins\u003eMain Branch Examples\u003c/ins\u003e](https://github.com/pybop-team/PyBOP/tree/main/examples) [\u003cins\u003eDevelop Branch Examples\u003c/ins\u003e](https://github.com/pybop-team/PyBOP/tree/develop/examples)\n\n\u003c/div\u003e\n\nPyBOP provides tools for the parameterisation and optimisation of battery models, using both Bayesian and frequentist approaches, with [example workflows](https://github.com/pybop-team/PyBOP/tree/main/examples/) to assist the user. PyBOP can be used to parameterise various battery models, including the electrochemical and equivalent circuit models available in [PyBaMM](https://pybamm.org/).\n\n📌 PyBOP v25.10 presents a [major restructure](https://github.com/pybop-team/PyBOP/blob/main/CHANGELOG.md) of PyBOP's base classes. We move from setting up\na model, problem, cost, then optimiser to defining a simulator, cost, problem, and then optimiser. A `pybop.pybamm.Simulator` is designed to simulate a\n`pybamm.BaseModel`. Optimisation parameters can be passed through a `pybamm.ParameterValues` class.\nTo understand how to update your use of PyBOP, please take a look at the example notebooks and scripts.\n\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"https://raw.githubusercontent.com/pybop-team/PyBOP/develop/assets/PyBOP-high-level.svg\" alt=\"A diagram showing the PyBOP logo surrounded by connections to experimental data as an input, forward simulations provided by PyBaMM, results for parameteriation and design optimisation, as well as funding bodies (the Faraday Institution, EU IntelLiGent and DigiBatt projects).\" width=\"700\" /\u003e\n\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\n    \u003cimg src=\"assets/PyBOP_components.svg\" alt=\"A diagram showing the main PyBOP classes: the Simulator and Cost, which together define the optimisation Problem, and the Optimiser or Sampler which generates the Result.\" width=\"700\" /\u003e\n\u003c/p\u003e\n\n## 💻 Installation\n\nWithin your virtual environment, install PyBOP:\n\n```bash\npip install pybop\n```\n\nTo install the most recent state of PyBOP, install from the `develop` branch,\n\n```bash\npip install git+https://github.com/pybop-team/PyBOP.git@develop\n```\n\nTo install a previous version of PyBOP, use the following template and replace the version number:\n\n```bash\npip install pybop==v24.3\n```\n\nTo check that PyBOP is installed correctly, run one of the examples in the following section. For a development installation, see the [Contribution Guide](https://github.com/pybop-team/PyBOP/blob/develop/CONTRIBUTING.md#Installation). More installation information is available in our [documentation](https://pybop-docs.readthedocs.io/en/latest/installation.html) and the [extended installation instructions](https://docs.pybamm.org/en/latest/source/user_guide/installation/gnu-linux-mac.html) for PyBaMM.\n\n## 📖 Citing PyBOP\n\nIf you use PyBOP in your work, please cite our paper according to the [CITATION.cff](https://github.com/pybop-team/PyBOP/blob/develop/CITATION.cff) using\n\n\u003e Planden, B., Courtier, N. E., Robinson, M., Khetarpal, A., Planella, F. B., \u0026 Howey, D. A. (2025). PyBOP: A Python package for battery model optimisation and parameterisation. _Journal of Open Source Software, 10(116), 7874_. https://doi.org/10.21105/joss.07874\n\nor the BibTeX entry\n\n```bib\n@article{Planden_PyBOP_A_Python_2025,\n    author = {Planden, Brady and Courtier, Nicola E. and Robinson, Martin and Khetarpal, Agriya and Planella, Ferran Brosa and Howey, David A.},\n    doi = {10.21105/joss.07874},\n    journal = {Journal of Open Source Software},\n    month = dec,\n    number = {116},\n    pages = {7874},\n    title = {{PyBOP: A Python package for battery model optimisation and parameterisation}},\n    url = {https://joss.theoj.org/papers/10.21105/joss.07874},\n    volume = {10},\n    year = {2025}\n}\n```\n\nDepending on which models and methods you use, please also check the [PyBaMM citation guidelines](https://github.com/pybamm-team/PyBaMM?tab=readme-ov-file#-citing-pybamm) and include the following command at the end of your script to print the relevant BibTeX information.\n\n```python\npybamm.print_citations()\n```\n\n## 💡 Use Cases\n\nPyBOP has two intended uses:\n\n1. Parameter inference from battery test data.\n\n2. Design optimisation under battery manufacturing/use constraints.\n\nThese 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.\n\n### Publications\n\nPlease take inspiration from the following journal articles which show how PyBOP is being used for research:\n\n- \"Physics-based battery model parametrisation from impedance data\" by [Hallemans et al. (2025)](https://doi.org/10.1149/1945-7111/add41b) with [open-source code and data](https://github.com/Battery-Intelligence-Lab/Hallemans-2025-JES)\n- \"Transport limitations in single-crystal NCM cathode electrodes\" by [Jackowska et al. (2025)](https://doi.org/10.1016/j.jpowsour.2025.238881) with [open-source code](https://github.com/Battery-Intelligence-Lab/Jackowska-2025-JPS) and [original data](https://edata.bham.ac.uk/)\n\n### Jupyter Notebooks\n\nExplore our [example notebooks](https://github.com/pybop-team/PyBOP/tree/develop/examples) for hands-on demonstrations:\n\n- [Getting started with gradient-based optimisation](https://nbviewer.org/github/pybop-team/PyBOP/blob/develop/examples/notebooks/getting_started/optimising_with_adamw.ipynb)\n- [Estimating ECM parameters from a HPPC pulse](https://nbviewer.org/github/pybop-team/PyBOP/blob/develop/examples/notebooks/battery_parameterisation/ecm_multipulse_identification.ipynb)\n- [Identifying ECM parameters with nonlinear constraints](https://nbviewer.org/github/pybop-team/PyBOP/blob/develop/examples/notebooks/battery_parameterisation/ecm_scipy_constraints.ipynb)\n- [Parameter identification for a spatial pouch cell model](https://nbviewer.org/github/pybop-team/PyBOP/blob/develop/examples/notebooks/battery_parameterisation/pouch_cell_identification.ipynb)\n- [Energy-based electrode design optimisation](https://nbviewer.org/github/pybop-team/PyBOP/blob/develop/examples/notebooks/design_optimisation/energy_based_electrode_design.ipynb)\n\n### Python Scripts\n\nFind additional script-based examples in the [examples directory](https://github.com/pybop-team/PyBOP/tree/develop/examples/scripts):\n\n- [Getting started with SciPy minimize](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/getting_started/optimising_with_scipy_minimize.py)\n- [Estimating diffusivity from GITT data](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/battery_parameterisation/gitt_fitting.py)\n- [Maximum a Posteriori parameter identification](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/getting_started/maximum_a_posteriori.py)\n- [Using electrochemical impedance spectroscopy (EIS) data](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/battery_parameterisation/simple_eis.py)\n- [Getting started with MCMC samplers](https://github.com/pybop-team/PyBOP/blob/develop/examples/scripts/getting_started/monte_carlo_sampling.py)\n\n\n### Grouped Models\nIn addition to the models available in PyBaMM, PyBOP currently hosts some grouped-parameter versions of common battery models which are\npurpose-built for parameter esimation and can be found under [models](https://github.com/pybop-team/PyBOP/tree/develop/pybop/models).\n\n\n## 👉 Code of Conduct\n\nPyBOP aims to foster a broad consortium of developers and users, building on and learning from the success of the [PyBaMM](https://pybamm.org/) community. Our values are:\n\n-   Inclusivity and fairness (those who wish to contribute may do so, and their input is appropriately recognised)\n\n-   Interoperability (modularity for maximum impact and inclusivity)\n\n-   User-friendliness (putting user requirements first via user-assistance \u0026 workflows)\n\n## 📃 License\n\nPyBOP is released under the [BSD 3-Clause License](https://github.com/pybop-team/PyBOP/blob/develop/LICENSE).\n\n## 🌟 Contributing\n\nWe would like to thank all contributors to PyBOP. Contributions are welcome! See [CONTRIBUTING.md](https://github.com/pybop-team/PyBOP/blob/develop/CONTRIBUTING.md) for ways to get started.\n\nThe original PyBOP developers were supported by research funding from the [Faraday Institution](https://www.faraday.ac.uk/),\n[UK Research and Innovation](https://www.ukri.org/),\nand [Horizon Europe IntelLiGent Consortium](https://heuintelligent.eu).\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.21105/joss.07874","https://doi.org/10.1149/1945-7111/add41b","https://doi.org/10.1016/j.jpowsour.2025.238881"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["batteries","battery-models","pybamm","solvers"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/95213","html_url":"https://ost.ecosyste.ms/projects/95213"}