Edge-compatible SOH estimation for Li-ion batteries via hybrid knowledge distillation and model compression

Abstract

Accurate and efficient State of Health (SOH) estimation is essential for the reliability of lithium-ion batteries in electric vehicles (EVs). However, deploying deep learning models on a real-world Battery Management System (BMS) remains challenging due to edge device constraints. In this study, we propose a lightweight SOH estimation framework integrating knowledge distillation (KD), structured pruning, and dynamic quantization. Our KD approach employs a hybrid strategy, combining response-based loss with two relation-based losses (pairwise squared Euclidean distance and cosine similarity) in the latent feature space. This ensures the student model mimics not only the teacher’s outputs but also its internal data representation structure. Comprehensive experiments on the NASA and CALCE datasets demonstrate the framework’s effectiveness. Our compressed models achieve over 99% model compression while consistently outperforming a representative MobileNetV1-based lightweight baseline in both accuracy and compactness. The practical feasibility of our framework is further validated through on-device performance tests on a Raspberry Pi 4B, robustness analysis under various noise conditions, and an investigation showing a strong correlation between the learned latent space and physical degradation indicators. These results confirm that our framework produces highly efficient, robust, and physically meaningful models suitable for real-time, on-device battery health monitoring.