Battery models in PyBaMM are widely used to predict performance, but identifying accurate model parameters is a significant challenge. Various parameter sets have been reported in literature [1,2,3], however, their consistency and reproducibility is lacking due to the need for specialist, costly equipment and extensive testing times, plus non-standardised procedures and lack of published data. Furthermore, parameter uniqueness is often not considered, or reported using widely varying methods. The lack of a consistent, standardised parameterisation toolbox causes significant time to be wasted by researchers in finding (often inappropriate) parameters from literature or ‘reinventing the wheel’ by writing fitting algorithms from scratch, which is difficult and error prone.

Addressing these issues, we present the Python Battery Optimisation and Parameterisation (PyBOP) software package which provides parameter fitting and design optimisation methods, as well as workflows for battery researchers and engineers. PyBOP is built upon PyBaMM, offering identification and optimisation of electrochemical and equivalent circuit model structures. PyBOP offers users significant flexibility in choosing various cost functions and optimisers, and features a host of visualisation options for comparing results, as well as methods to check the identifiability (uniqueness) of parameters. Furthermore, PyBOP provides distribution-based sampling methods for posteriors construction, enabling uncertainty quantification on the identified parameters alongside encapsulating prior knowledge into the process. In this work, we will present PyBOP’s capabilities across a range of parameter fitting and design optimisation tasks, specifically from the perspective of identifying and exploiting physics-based continuum models.

[1] C.-H. Chen, F. Brosa Planella, K. O’Regan, D. Gastol, W. D. Widanage, and E. Kendrick, ‘Development of Experimental Techniques for Parameterization of Multi-scale Lithium-ion Battery Models’, J. Electrochem. Soc., vol. 167, no. 8, p. 080534, Jan. 2020, doi: 10.1149/1945-7111/ab9050.

[2] C. Schmitt, M. Gerle, D. Kopljar, and K. A. Friedrich, ‘Full Parameterization Study of a High-Energy and High-Power Li-Ion Cell for Physicochemical Models’, J. Electrochem. Soc., vol. 170, no. 7, p. 070509, Jul. 2023, doi: 10.1149/1945-7111/ace1a7.

[3] M. Ecker, T. K. D. Tran, P. Dechent, S. Käbitz, A. Warnecke, and D. U. Sauer, ‘Parameterization of a Physico-Chemical Model of a Lithium-Ion Battery: I. Determination of Parameters’, J. Electrochem. Soc., vol. 162, no. 9, pp. A1836–A1848, 2015, doi: 10.1149/2.0551509jes.