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

TL;DR

This paper introduces a Bayesian optimization algorithm that uses physics-based features from electrochemical measurements to efficiently parameterize continuum battery models, reducing the need for extensive simulations.

Contribution

The authors develop a novel Bayesian optimization method incorporating Expectation Propagation that leverages features from experimental data for more efficient battery model parameterization.

Findings

Reduces simulation count by using physics-based features

Successfully characterizes electrode diffusivities non-destructively

Provides a reproducible, accessible tool for experimental data analysis

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.