A double-layer fault diagnosis strategy for electric vehicle batteries based on Gaussian mixture model

Battery fault diagnosis is essential to ensure the safe operation of electric vehicles (EVs). In this paper, due to the complexity of EVs’ battery thermal runaway tracing investigation and the limited capacity of on-board computing system, a double-layer fault diagnosis strategy for abnormal cells is proposed. The method bases on probability distribution, which can accurately trace a faulty cell and avoid misinterpreting a normal cell. In this method, unified statistical features are extracted from the big data during vehicle charging, and the corresponding statistical values are analyzed based on Gaussian mixture model and abnormal alarm is made based on the risk accumulation in double-layer diagnostics. The electric vehicles with thermal runaway accident are taken as examples to verify the method, and based on the data of normal-running vehicles, the false alarm tests are carried out. The verification results show that the proposed method can not only successfully identify the outlier cells, but also not generate false alarm, which is conducive to the practical application of fault diagnosis in the on-board battery management system. •Gaussian mixture model is used for battery fault diagnosis.•The unified statistical values are extracted to reduce the requirement of data.•Thresholds by using risk accumulation is proposed to detect the abnormal cells.•The false alarm test is performed as an auxiliary verification for the algorithm.