Data-Driven Energy Consumption Analysis and Prediction of Real-World Electric Vehicles at Low Temperatures: A Case Study Under Dynamic Driving Cycles

Yifei Zhao, Hang Liu, Jinsong Li, Hongli Liu () and Bin Li ()
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Yifei Zhao: School of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
Hang Liu: CATARC Automotive Test Center Co., Ltd., Changzhou 213100, China
Jinsong Li: School of Mechanical and Electrical Engineering, Hainan University, Haikou 570228, China
Hongli Liu: School of Automobile, Chang’an University, Xi’an 710018, China
Bin Li: School of Automobile, Chang’an University, Xi’an 710018, China

Energies, 2025, vol. 18, issue 5, 1-14

Abstract: Accurate analysis and prediction of low-temperature energy consumption in pure electric vehicles can provide a reliable reference for energy optimization strategies, thereby alleviating range anxiety. Here, we propose a data-driven energy consumption analysis and prediction approach for real-world electric vehicles in cold conditions. Specifically, the dataset was divided into multiple kinematic segments by the fixed-step intercept method, and principal component analysis was applied on segment parameters, showing the average speed and acceleration time had the greatest impact on energy consumption at −7 °C. Then, a Bayesian optimized XGBoost model, with the two factors above as input, was constructed to predict the cumulative driving and total energy consumption. This method was validated with two different types of pure electric vehicles under different dynamic driving cycles. The results demonstrated that the model could predict low-temperature energy consumption accurately, with all mean relative errors less than 3%.

Keywords: electric vehicle; low temperature; energy consumption prediction; machine learning; Bayesian optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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