A novel battery abnormality diagnosis method using multi-scale normalized coefficient of variation in real-world vehicles.

Accurate and efficient diagnosis of battery voltage abnormality is crucial for the safe operation of electric vehicles. This paper proposes an innovative battery voltage abnormality diagnosis method based on a normalized coefficient of variation in real-world electric vehicles. Vehicle and laboratory data are collected and analyzed, with joint preprocessing to improve data quality, and battery voltages are log-transformed to improve the contribution of anomalous voltage fluctuations. The normalized coefficient of variation is proposed to detect the fluctuation inconsistency of cell voltage, and the risk coefficient rule is formulated by Z-score and normalization. Furthermore, the validity and robustness are verified by laboratory and real-world battery faults. The results demonstrate that the optimal slide step and calculation window for real-world under-voltage fault are 10 and 40, and those for laboratory lithium plating and real-world thermal runaway are both 10 and 50, respectively. More importantly, this study introduces a battery abnormality diagnosis strategy based on the vehicle T-box, anticipated to be widely implemented to ensure the safety of real-vehicle operations. This method not only enhances the accuracy and efficiency of detecting electric vehicle battery abnormalities, but also offers a practical solution to prevent battery related faults. • The abnormal diagnosis of battery voltage is crucial to electric vehicle safety. • A innovative diagnosis method based on the multi-scale NCOV is proposed. • The validity and robustness are verified by experimental and real-vehicle data. • A actual battery thermal runaway process is discussed and analyzed in detail. • A real-time diagnosis scheme based on vehicle-end edge computing is presented. [ABSTRACT FROM AUTHOR]