Model-constrained deep learning for online fault diagnosis in Li-ion batteries over stochastic conditions.

ABSTRACT: For the intricate and infrequent safety issues of batteries, online safety fault diagnosis over stochastic working conditions is indispensable. In this work, we employ deep learning methods to develop an online fault diagnosis network for lithium-ion batteries operating under unpredictable conditions. The network integrates battery model constraints and employs a framework designed to manage the evolution of stochastic systems, thereby enabling fault real-time determination. We evaluate the performance using a dataset of 18.2 million valid entries from 515 vehicles. The results demonstrate our proposed algorithm outperforms other relevant approaches, enhancing the true positive rate by over 46.5% within a false positive rate range of 0 to 0.2. Meanwhile, we identify the trigger probability

SUBMITTER: Cao R 

PROVIDER: S-EPMC11829048 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature