Explainable Functional Relation Discovery for Battery State-of-Health Using Kolmogorov-Arnold Network
This work addresses battery health management for safety and energy utilization by providing an interpretable data-driven method, though it appears incremental as it builds on existing KAN frameworks for a specific domain.
The paper tackled the problem of estimating battery State-of-Health (SoH) by proposing a Kolmogorov-Arnold Network-based pipeline to derive an explicit functional relationship between SoH degradation and cycle number using temperature data, achieving high accuracy with a closed-form analytical formula validated on real-world data.
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.