Early stage internal short circuit fault diagnosis for lithium-ion batteries based on local-outlier detection

Abstract

Internal short circuit (ISC) is considered to be one of the main causes of battery thermal runaway, which is a critical obstacle to the application of lithium-ion batteries for energy storage. Aiming at inconspicuous characteristics and slow detection speed of early stage ISC faults, this paper proposes a fast diagnostic method for ISC based on local-gravitation outlier detection. In the serial battery module, the cell terminal voltages are normalized to characterize voltage trends that are more sensitive to faults than voltage magnitudes, which improves the speed of fault diagnosis. The normalized voltages are then evaluated by a local gravity outlier detection algorithm to detect faults, by which the anomalies caused by the faults are amplified to achieve the ability to diagnose early ISC faults. The performance of the method is validated under the Urban Dynamometer Driving Schedule test, where several sets of experimental results for ISC faults of varying severity showed that the proposed method could detect them accurately and rapidly even when the fault characteristics are not obvious.