A Hybrid Signal-Based Fault Diagnosis Method for Lithium-Ion Batteries in Electric Vehicles

A large proportion of electric vehicle accidents are attributed to lithium-ion battery failure recently, which demands the time-efficient diagnosis and safety warning in advance of severe fault occurrence to ensure reliable operation of electric vehicles. However, serious battery system faults are o...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.19175-19186
Main Authors: Jiang, Jiuchun, Cong, Xinwei, Li, Shuowei, Zhang, Caiping, Zhang, Weige, Jiang, Yan
Format: Article
Language:English
Subjects:
Anomaly detection
Circuit faults
Clustering
Data analysis
dimensionless indicator
Electric vehicles
False alarms
Fault diagnosis
Feature extraction
Integrated circuit modeling
Lithium
Lithium-ion batteries
lithium-ion battery
Machine learning
Management systems
manifold learning
Manifolds (mathematics)
Outliers (statistics)
Power management
Product safety
Rechargeable batteries
Signal analysis
Signal resolution
Thermal runaway
Time series analysis
variational mode decomposition
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Summary:A large proportion of electric vehicle accidents are attributed to lithium-ion battery failure recently, which demands the time-efficient diagnosis and safety warning in advance of severe fault occurrence to ensure reliable operation of electric vehicles. However, serious battery system faults are often not caused by easily-observed cell state inconsistency, but derived from a certain cell failure with precursory signals untended, or occasional abuse, thus eventually thermal runaway. In this paper, a signal-based fault diagnosis method is presented, including signal analysis to eliminate the impact of state inconsistency on time-series feature extraction, feature fusion, and dimensionality reduction by manifold learning, with clustering-based outlier detection to identify abnormal signal features. The challenges in threshold determination of fused features can be effectively resolved by supplementary correction to largely reduce the amount of false alarms. Compared with the judgments from actual battery management systems, and other signal-based methods with single features, earlier detections can be achieved with robustness, verified by real-world pre-fault operation data of electric vehicles that suffered thermal runaway.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3052866