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

Abstract

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.