State of Charge Prediction for Electric Vehicles Based on Integrated Model Architecture

Min Wei, Yuhang Liu, Haojie Wang, Siquan Yuan and Jie Hu ()
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Min Wei: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Yuhang Liu: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Haojie Wang: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Siquan Yuan: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Jie Hu: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China

Mathematics, 2025, vol. 13, issue 13, 1-22

Abstract: To enhance the accuracy of SOC prediction in EVs, which often suffers from significant discrepancies between displayed and actual driving ranges, this study proposes a data-driven model guided by an energy consumption framework. The approach addresses the problem of inaccurate remaining range prediction, improving drivers’ travel planning and vehicle efficiency. A PCA-GA-K-Means-based driving cycle clustering method is introduced, followed by driving style feature extraction using a GMM to capture behavioral differences. A coupled library of twelve typical driving cycle style combinations is constructed to handle complex correlations among driving style, operating conditions, and range. To mitigate multicollinearity and nonlinear feature redundancies, a Pearson-DII-based feature extraction method is proposed. A stacking ensemble model, integrating Random Forest, CatBoost, XGBoost, and SVR as base models with ElasticNet as the meta model, is developed for robust prediction. Validated with real-world vehicle data across −21 °C to 39 °C and four driving cycles, the model significantly improves SOC prediction accuracy, offering a reliable solution for EV range estimation and enhancing user trust in EV technology.

Keywords: pure electric vehicle; battery state estimation; data-driven approach; remaining driving range prediction; stacking model (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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