Explainable Functional Relation Discovery for Battery State-of-Health Using Kolmogorov-Arnold Network

TL;DR

This paper introduces a Kolmogorov-Arnold Network-based method to derive an explicit analytical formula for battery State-of-Health degradation from temperature data, enhancing interpretability and accuracy.

Contribution

It presents a novel data-driven pipeline using KAN to establish a functional relationship for battery SoH degradation, improving interpretability over existing methods.

Findings

Achieved high-accuracy analytical formula for SoH degradation.

Validated the pipeline with real-world battery data.

Provided a closed-form solution for battery health monitoring.

Abstract

Battery health management is heavily dependent on reliable State-of-Health (SoH) estimation to ensure battery safety with maximized energy utilization. Although SoH estimation can effectively track battery degradation, it requires continuous battery data acquisition. In addition, model-based SoH estimation methods rely on accurate battery model knowledge, whereas data-driven approaches often suffer from limited interpretability. In contrast, analytical characterization of SoH will offer a direct and tractable handle on battery performance degradation, while also establishing a foundation for further analytical studies toward effective battery health management. Thus, in this work, we propose a Kolmogorov Arnold Network (KAN)-based data-driven pipeline to establish a functional relationship for SoH degradation using battery temperature data. Specifically, we learn long-term battery thermal dynamics and battery heat generation via learnable activation functions of our KAN model. We utilize the learned mapping to obtain an explicit functional relationship between SoH degradation and cycle number. The proposed pipeline was validated using real-world data, yielding a closed-form analytical formula of SoH degradation with high accuracy.