Abstract:

Studies on the estimation of the battery power state of charge (SOC) are primarily based on the charge and discharge data of a single cell under ideal experimental conditions, which may not correspond to the actual complex and variable driving conditions. In response to this problem, relying on the National Big Data Alliance of New Energy Vehicles and using data-driven methods, the transient, dynamic, and driving behavior and aging, dynamic, time-varying, and mileage features were derived. Using the method of stacking fusion and integration learning based on feature combination, an SOC estimation of the battery power discharge process under actual complex and variable working conditions was conducted. A reference XGBoost Model 0 based on transient features, five XGBoost Models (1, 2, 3, 5, and 6) based on feature combination, and a Linear Model 4 were constructed for comparison and analysis. Comparing Models 1-6 with the reference Model 0, the mean absolute error (MAE) and mean square error (MSE) of the stacked fusion model (stacked model) based on feature combination were the smallest, 0.39 and 0.32, respectively, which compared to Model 0, decreased by 38% and 46%, respectively; meanwhile, the coefficient of determination was the highest, reaching 0.9995, which was an increase of 2% compared to Model 0. The generalization ability of the stacked model also performed well, and the average and standard deviation of its accuracy reached 98.89% and 0.03%, respectively. Comparing Models 5 and 6 and the reference Model 0, it can be seen that, as the feature dimension increases, the MAE and MSE of the model decreases, the coefficient of determination increases, and the performance of the model improves. This study helps promote the application of data-driven methods in the estimation of the SOC of power batteries and provides guidance and a reference for the estimation of the SOC of actual electric vehicles.

Key words: power battery, Xgboost, SOC estimation, ensemble learning, feature combination