Fault diagnosis for lithium-ion batteries in electric vehicles based on signal decomposition and two-dimensional feature clustering

Abstract

Battery fault diagnosis is essential for ensuring the reliability and safety of electric vehicles (EVs). The existing battery fault diagnosis methods are difficult to detect faults at an early stage based on the real-world vehicle data since lithium-ion battery systems are usually accompanied by inconsistencies, which are difficult to distinguish from faults. A fault diagnosis method based on signal decomposition and two-dimensional feature clustering is introduced in this paper. Symplectic geometry mode decomposition (SGMD) is introduced to obtain the components characterizing battery states, and distance-based similarity measures with the normalized extended average voltage and dynamic time warping distances are established to evaluate the state of batteries. The 2-dimensional feature clustering based on DBSCAN is developed to reduce the number of feature thresholds and differentiate flaw cells from the battery pack with only one parameter under a wide range of values. The proposed method can achieve fault diagnosis and voltage anomaly identification as early as 43 days ahead of the thermal runaway. And the results of four electric vehicles and the comparison with other traditional methods validated the proposed method with strong robustness, high reliability, and long time scale warning, and the method is easy to implement online.