A Primer on Bayesian Parameter Estimation and Model Selection for Battery Simulators

TL;DR

This paper introduces two new algorithms, SOBER and BASQ, that significantly enhance Bayesian inference for battery model parameterization and comparison, facilitating better model development and selection using experimental data.

Contribution

It presents novel algorithms SOBER and BASQ that accelerate Bayesian inference in battery modelling, addressing data observability and model identifiability issues.

Findings

SOBER and BASQ algorithms improve inference speed and accuracy.

Bayesian model selection helps address data and model challenges.

Approach aids in discovering models for new battery materials.

Abstract

Physics-based battery modelling has emerged to accelerate battery materials discovery and performance assessment. Its success, however, is still hindered by difficulties in aligning models to experimental data. Bayesian approaches are a valuable tool to overcome these challenges, since they enable prior assumptions and observations to be combined in a principled manner that improves numerical conditioning. Here we introduce two new algorithms to the battery community, SOBER and BASQ, that greatly speed up Bayesian inference for parameterisation and model comparison. We showcase how Bayesian model selection allows us to tackle data observability, model identifiability, and data-informed model development together. We propose this approach for the search for battery models of novel materials.