Physico-chemical continuum battery models are typically parameterized by manual fits, relying on the individual expertise of researchers. In this article, we introduce a computer algorithm that directly utilizes the experience of battery researchers to extract information from experimental data reproducibly. We extend Bayesian Optimization (BOLFI) with Expectation Propagation (EP) to create a black-box optimizer suited for modular continuum battery models. Standard approaches compare the experimental data in its raw entirety to the model simulations. By dividing the data into physics-based features, our data-driven approach uses orders of magnitude less simulations. For validation, we process full-cell GITT measurements to characterize the diffusivities of both electrodes non-destructively. Our algorithm enables experimentators and theoreticians to investigate, verify, and record their insights. We intend this algorithm to be a tool for the accessible evaluation of experimental databases.
@article{arxiv.2208.03289,
title = {Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise},
author = {Yannick Kuhn and Hannes Wolf and Arnulf Latz and Birger Horstmann},
journal= {arXiv preprint arXiv:2208.03289},
year = {2023}
}