Multi-dimension statistical analysis and selection of safety-representing features for battery pack in real-world electric vehicles

Battery safety issues have been raising anxiety of electric vehicle (EV) consumers and threatening to the occupant's life. Many battery safety management/monitoring methods and features have been reported, but which features can better represent battery safety in real-world EVs has been rarely discussed and compared in the unified level. In this study, the multi-feature and multi-dimension statistical analysis for battery pack safety in numerous real-world electric EVs is deployed. Firstly, an EV state distinction scheme is proposed to cope with the different characteristics of EV driving and EV charging, and twenty statistical features are constructed to extract the characteristics of battery voltages. Then, a scheme is proposed to select the feature which can effectively represent battery safety in real-world EVs. A multiple iteration Gaussian mixture model is proposed in the scheme to construct the actual feature distribution and cope with the random sensor error and data outlier. Finally, a model is constructed to analyze the statistical dimensions of selected battery safety-representing feature, and statistical analysis from different seasons, mileages, state-of-charges, and EV states is deployed. Results showcase that the selected feature is competent to distinguish between normal and unsafe batteries in real-world EV packs of different conditions. Various interesting conclusions based on the selected feature are also summarized to explore some common laws of battery safety in real-world EV application and support for the establishment of battery safety management method.