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

Abstract

AbstractPhysico‐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.