点击次数：

影响因子：10.7

DOI码：10.1016/j.est.2026.124389

发表刊物：Journal of Energy Storage

关键字：Lithium-ion battery; Fault diagnosis; Physics-guided; Multi-source fusion; Domain adaptation

摘要：Precise fault diagnosis for real-world lithium-ion batteries is critical to guaranteeing safe and reliable operation. To overcome the difficulties in early fault recognition, unreliable assessment, and limited generalization capability of existing methods, we propose a physics-guided multi-source collaborative diagnostic framework for lithium-ion batteries. First, we design a physics-guided cell-level healthy reference voltage reconstruction model to address the scarcity of fault samples and to provide a foundation for subsequent feature generation. Simultaneously, the model decouples time-varying implicit electrochemical parameters that reflect the health evolution of the battery pack. Then, we establish a physics residual driven dual-view representation enhancement strategy to improve the feature separability of different faults. Furthermore, we integrate the residual representations, implicit parameters, and statistical features through an adaptive gating mechanism to obtain a more comprehensive fault representation. At last, a cross-scenario multi-source diagnostic scheme is constructed to achieve highly accurate cross-scenario diagnosis with low-cost fine-tuning. Compared with existing algorithms, the proposed method demonstrates superior performance (with the Macro-F1 score consistently between 97.8% and 98.9%). Moreover, extensive experiments on four real-world electric vehicle datasets thoroughly validate its effectiveness and practical applicability, contributing to enhanced active safety capabilities in battery operation.

备注：中科院2区Top

合写作者：Li Wang,Lei Bao,Qilong Xie,Zhannan Wang

第一作者：Muxing Li

论文类型：论文集

通讯作者：Muyao Wu

论文编号：124389

学科门类：工学

文献类型：J

卷号：181

ISSN号：2352-152X

是否译文：否

发表时间：2026-08-22

收录刊物：SCI、EI

发布期刊链接：https://www.sciencedirect.com/science/article/pii/S2352152X26040533