Bayesian–Kalman Fusion Framework for Thermal Fault Diagnosis of Battery Energy Storage Systems
Year: 2025
Type: article
Abstract: Fault diagnosis of battery energy storage systems (BESSs) in dynamic operating conditions presents significant challenges due to complex spatiotemporal patterns and measurement noise. This research proposes a novel thermal fault diagnosis framework for BESSs based on Bayesian inference and a Kalman filter. Firstly, PLS-based spatiotemporal feature extraction is designed to capture temporal dependencies. Based on Bayesian global exploration and Kalman real-time weight adaptation, a dual-stage optimization strategy is proposed to derive a multiscale detection index with the dominant statistic, the residual statistic, and the module voltage similarity. A time window-based cumulative contribution strategy is constructed for precise cell localization. Finally, the experimental validation on a Li-ion battery pack demonstrates the proposed method’s superior performance: 96.92–99.90% anomaly detection rate, false alarm rate ranging from 0.10% to 7.22%, detection delays of 1–27 s, and 100% accuracy in fault localization. The proposed framework provides a comprehensive solution for safety management of BESSs and is significant for battery life and energy sustainability.
Source: Sustainability
DOI: 10.3390/su172210092
Authors: Peng Wei, Jinze Tao, Changjun Xie, Yang Yang, Wenchao Zhu, Yunhui Huang
Institution: Wuhan University of Technology
Language: English
Subfield: Automotive Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Awards: G1137224851, G620650270
OpenAlex ID: W4416129870