A Practitioner's Guide to Automatic Kernel Search for Gaussian Processes in Battery Applications

TL;DR

This paper introduces an automated kernel search method for Gaussian Process models in battery applications, improving model performance and simplifying kernel selection for practitioners dealing with complex data.

Contribution

It extends existing GP kernel search methods with a new base kernel and model selection criteria, demonstrating improved results in battery-related tasks.

Findings

Composite kernels outperform baseline kernels in battery tasks

Bayesian Information Criterion offers a good balance of performance and complexity

Automated kernel search benefits battery data modeling

Abstract

Gaussian process (GP) models have been used in a wide range of battery applications, in which different kernels were manually selected with considerable expertise. However, to capture complex relationships in the ever-growing amount of real-world data, selecting a suitable kernel for the GP model in battery applications is increasingly challenging. In this work, we first review existing GP kernels used in battery applications and then extend an automatic kernel search method with a new base kernel and model selection criteria. The GP models with composite kernels outperform the baseline kernel in two numerical examples of battery applications, i.e., battery capacity estimation and residual load prediction. Particularly, the results indicate that the Bayesian Information Criterion may be the best model selection criterion as it achieves a good trade-off between kernel performance and computational complexity. This work should, therefore, be of value to practitioners wishing to automate their kernel search process in battery applications.