PyBOP: A Python Framework for Battery Model Optimisation and Parameterisation

To maximise the benefits of battery models, we need to parameterise them accurately from materials and electrochemical data. However, this remains a significant challenge [1] due to the need for expert knowledge in experimental data acquisition, processing, and system identification. In response, we introduce a systematic software framework, PyBOP [2], to establish and formalise novel parameterisation workflows for various continuum battery models. We present initial results from synthetic open-source battery datasets for benchmarking our model parameter identification efforts, comparing traditional deterministic methods with advanced Bayesian inference techniques. We will also highlight PyBOP's capabilities for optimising cell design by discussing an electrode optimisation study on a high-voltage LNMO cathode and silicon-graphite composite anode cell. Throughout this work, we demonstrate PyBOP's ability to accommodate different levels of proficiency in model parameterisation and optimisation, including llustrative workflows tailored towards researchers or industrial users.<br/><br/>[1] E. Miguel, G. L. Plett, M. S. Trimboli, L. Oca, U. Iraola, and E. Bekaert, “Review of computational parameter estimation methods for electrochemical models,” Journal of Energy Storage, vol. 44, p. 103388, 2021. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2352152X2101077X<br/>[2] B. Planden, N. Courtier, and D. Howey, “Python Battery Optimisation and Parameterisation (PyBOP).” [Online]. Available: https://www.github.com/pybop-team/pybop