Data-driven fault early warning of electric vehicle power battery based on ensemble learning from multiple dimensions
Aiming at the problems such as poor performance of single algorithm on unbalanced data set and insufficient analysis dimension in electric vehicle power battery risk prediction,a data-driven multi-model ensemble learning method based on multi-dimensional characteristics was proposed to realize battery fault warning.According to internal and external factors,the features from battery information,driving condition,historical situation and time environment was extracted to simulate real application scenarios.To complete feature filtering,redundant information was removed by Filter-Wrapper method,which improved the robustness.The heterogeneous Stacking integrated model was constructed with SVM,LightGBM and XGBoost as primary learners and LR as secondary learners based on Bayesian optimization with grid search to optimize hyperparameters.The results show that the ensemble model with the addition of external features has the best comprehensive performance in the evaluation index,among which the recall rate in the medium and high level fault warning reaches 85.81%and 88.92%,respectively.Therefore,the method has better prediction accuracy and generalization ability than the single model that only considers the internal characteristics of the battery.
vehicle power batteryearly warning of faultStacking ensemble learningmulti-dimensional feature
NETL
NSTL
万方数据
