Multi-fault detection and diagnosis method for battery packs based on statistical analysis

Hanxiao Liu, Liwei Li, Bin Duan, Yongzhe Kang and Chenghui Zhang

Energy, 2024, vol. 293, issue C

Abstract: Rapid and accurate battery fault diagnosis and distinction is of great importance in electrical vehicles and electrochemical energy storage system. However, misdiagnosis and missed diagnosis happened occasionally. In this paper, a statistical analysis-based multi-fault diagnosis method is proposed to detect and localize short circuit faults, electrical connection faults and voltage sensor faults in LFP battery packs. This method uses non-redundant interleaved voltage measurement topology to detect battery voltages, where every voltage sensor measures the sum of two neighboring batteries and one connection resistor between them. The statistical analysis method sets detection thresholds based on the battery operating data, and captures fault characteristics by analyzing abnormal changes in battery voltage unrelated to current. Theoretical analysis and tests verified that this method can diagnose these three kinds of faults. Sensor faults of excessive error and data sticking can also be distinguished.

Keywords: Battery; Multi-fault diagnosis; Statistical analysis; Eigenvalue of covariance (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:293:y:2024:i:c:s0360544224002366

DOI: 10.1016/j.energy.2024.130465

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