Privacy-preserving collaborative battery fault warning for massive electric vehicles by heterogeneous data from charging stations.

Timely warning of battery faults can improve the safety of electric vehicles, aiding the decarbonization of both transportation and power sectors. Although machine learning holds great promise for accurate fault detection, its practical deployment is hindered by the need to gather adequate data, which generally belongs to various owners and exhibits significant heterogeneity. Here, we propose a personalized federated learning framework that allows diverse data owners to cooperatively develop fault detection models without sharing data. In addition, our approach empowers data owners to customize their detection models, ensuring robust performance across diverse data distributions. A large real-world battery charging dataset is gathered for the validation, encompassing charging sequences collected from over 10,000 vehicles across 30 authentic charging stations. The dataset spans a wide range of vehicle and battery types, as well as voltage and power levels. Results indicate that our method outperforms state-of-the-art federated learning approaches in detection performance. Moreover, it exhibits robust generalization, facilitating swift adaptation to new participants. Additional validations confirm its robustness to data heterogeneity and variations in the input data window length. This work underscores the potential for privacy-preserving cooperation among data owners to improve battery safety management, which can result in significant economic and social advantages.