New energy vehicle battery state of charge prediction based on XGBoost algorithm and RF fusion

Abstract

As the most important component of new energy electric vehicles, lithium-ion batteries may suffer irreversible damage to the battery due to an abnormal state of charge. Nevertheless, the extant research on charge prediction predominantly employs a single model or an enhanced single model. However, these approaches do not fully account for the intricacies and variability of the actual driving road conditions of the vehicle. Furthermore, the prediction accuracy of the charge state in the latter phase of discharge remains suboptimal. To further improve the accuracy of predicting the state of charge, the study utilizes actual operating data of new energy vehicles and combines two proposed algorithms to build a first layer learner of a fusion prediction model. The second layer learner integrates various prediction results. The fusion model can enhance its adaptability to complex data structures by combining the gradient boosting ability of XGBoost algorithm and the diversity of Random Forest when dealing with nonlinear problems. This fusion method modifies the input features of the second layer of the fusion model, enhances the complexity of the second layer learner, effectively circumvents overfitting, and exhibits reduced error rates relative to traditional single-chip prediction models. As a result, the performance of the prediction model is significantly enhanced. The tests showed that when using the fusion model for state of charge prediction, the prediction accuracy could reach 97.6%, and the prediction accuracy was higher than the other four comparison models. When the car was driving in a 25 ℃ highway environment, the root mean square error of the fusion model was 1.3%, and the average absolute error was 1.5%. In urban road environments, the root mean square error of the fusion model was 1.5%, and the average absolute error was 1%. The experiment demonstrates that the proposed fusion prediction model can accurately predict the charging status, thereby enabling the battery to be fully utilized while simultaneously reducing energy consumption. In comparison to the traditional single model or enhanced single model, the proposed fusion model has demonstrated a notable enhancement in both prediction accuracy and computational efficiency.