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

ABSTRACT: 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 col

SUBMITTER: Yang H 

PROVIDER: S-EPMC12847864 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature