Double-layer Diagnosis Model for Electric Vehicle Power Battery Faults Based on LightGBM

Power battery is the energy source for electric vehicles. It is of great significance to ensure the safety and reliability of electric vehicles by accurately predicting power battery failures and identifying their fault types. Based on the 6-month actual vehicle monitoring data of 10 pure electric vehicles, 16 feature data were extracted as input and the battery fault type was used as the output. A double-layer diagnosis model for electric vehicle power battery faults based on LightGBM was established through model training and parameter tuning. The upper-layer model was used to determine whether the vehicle power battery is fault. The lower-layer model diagnosed and analyzed the specific fault type. Results show that whether the electric vehicle power battery will be fault can be correctly predicted. The accuracy of diagnosing the fault type is 94. 05%. Meanwhile, the main features that affect the failure of the power battery are screened out according to the ranking of the eigenvalues of the model results. This study provides an approach for identifying the state of electric vehicle power battery, analyzing the fault type and diagnosing the cause of the fault.