Abstract:

In order to accurately predict the battery safety risk， a multi-index battery safety risk prediction method based on vehicle-weather-driver is proposed in this paper. Firstly， multi-dimensional information inside and outside the vehicle is extracted， i.e. multi-index characteristics such as weather condition， driving conditions and driving style are extracted by data mining to simulate the actual battery application scenario. Then， features are filtered by random forest and SHAP combination model， which improves the generalization and robustness of the model. Finally， the battery safety risk prediction problem is decoupled into machine learning prediction and time series prediction problems， and XGBoost and random forest models are selected to predict respectively. On this basis， a new Stacking integrated model is established to predict the battery safety risk. According to the predictive effect of the final model and the results of data experiment， the scheme can make a more accurate prediction of the battery safety risk of EV and provide decision-making information for safe and intelligent battery management system.

Key words: battery safety, multi-index feature, Stacking integration, data experiment