Multi-fault diagnosis for series-connected lithium-ion battery pack with reconstruction-based contribution based on parallel PCA-KPCA. (15th October 2022)

• Record Type: Journal Article
• Authors: Ma, Mina; Li, Xiaoyu; Gao, Wei; Sun, Jinhua; Wang, Qingsong; Mi, Chris
• Is Part Of: Applied energy. Volume 324(2022)
• Publication Date: 2022-10-15
• Subjects: Lithium-ion battery safety -- Inconsistency -- Connection fault -- External short circuit -- Fault diagnosis; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.119678
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A multi-fault diagnostic strategy for the series-connected lithium-ion battery pack is proposed. The contribution-based PCA is adopted to detect the fault of the battery. The reconstruction-based parallel PCA-KPCA is used to estimate the fault waveform. Inconsistency, connection fault, and external short circuit are comprehensively diagnosed. Algorithm matrix and experiment demonstrate the validity and accuracy. Abstract: Various faults of the lithium-ion battery threaten the safety and performance of the battery system. The early faults are difficult to detect and isolate owing to unobvious abnormality and the nonlinear time-varying characteristics of the battery. Herein, a multi-fault diagnosis strategy is proposed that focuses on detecting and isolating different types of faults, and estimating fault waveforms of the battery, including inconsistency evaluation, virtual connection fault, and external short circuit. First, the principal component analysis (PCA) model of the battery is established and the contribution is employed to detect the abnormity in the battery pack. Once the fault is detected, the parallel kernel principal component analysis (KPCA) technology is adopted to reconstruct the fault waveform of the battery parameters, including ohmic resistance, terminal voltage, and open-circuit voltage. These parameters are jointly taken as fault indexes improving the reliability of fault diagnosis. Finally, the proposed method is verified using amounts of tested data of eight cells in series. The results indicate that the contribution-based PCA method can accurately detect the fault. Furthermore, the reconstruction-based parallel PCA-KPCA can accurately estimate the fault waveform of the faulty battery, which helps investigate the fault degree and causes.