Bayesian Parameterization of Continuum Battery Models from Featurized Electrochemical Measurements Considering Noise

Abstract

Physico-chemical continuum ba ery models can predict the performance of new materials and cell geometries. The physical model parameters are typically determined by manually fi ing to experimental data. However, this process relies on the individual expertise of researchers. In this article, we introduce a computer algorithm that directly utilizes the experience of ba ery 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 ba ery 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 one to two orders of magnitude less evaluations of the simulations. This enables the fi ing of complex models that take up to a few minutes to simulate. For validation, we process full-cell GITT measurements to characterize the di usivities 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.