A Novel Battery Inconsistency Analysis Scheme for Fault Diagnosis of Real-World Electric Vehicles Based on Blending of Entropy Theory and Clustering Method

Battery fault diagnosis is crucial to ensure the safe and reliable operation of electric vehicles (EVs). Cell inconsistency within a battery pack can trigger battery faults during long-term usage, and thus cell consistency evaluation is vital to identify battery faults in the early usage stages. This paper proposes a battery inconsistency evaluation method that combines the Shannon entropy and Cluster in QUEst (CLIQUE) algorithm to diagnose faulty cells. First, the charging fragments extracted from real-world EV operating data are used to calculate the normalized Shannon entropy (NSE) for each battery cell. Then, the CLIQUE clustering algorithm is utilized to identify the NSE outliers in a charging fragment. Finally, the NSE outlier proportion of each charging fragment is extracted and employed as an indicator for battery fault diagnosis. The real-world EV datasets with two different types of battery faults are applied to examine the effectiveness of the proposed scheme. The results show that the proposed method can effectually perform battery fault diagnosis and identify faulty battery cells before thermal runaway.