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

Author

Listed:
  • 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)

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%.

Suggested Citation

  • Yifei Zhao & Hang Liu & Jinsong Li & Hongli Liu & Bin Li, 2025. "Data-Driven Energy Consumption Analysis and Prediction of Real-World Electric Vehicles at Low Temperatures: A Case Study Under Dynamic Driving Cycles," Energies, MDPI, vol. 18(5), pages 1-14, March.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1239-:d:1604564
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    References listed on IDEAS

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    1. Ke, Wenwei & Zhang, Shaojun & He, Xiaoyi & Wu, Ye & Hao, Jiming, 2017. "Well-to-wheels energy consumption and emissions of electric vehicles: Mid-term implications from real-world features and air pollution control progress," Applied Energy, Elsevier, vol. 188(C), pages 367-377.
    2. Xie, Yunkun & Li, Yangyang & Zhao, Zhichao & Dong, Hao & Wang, Shuqian & Liu, Jingping & Guan, Jinhuan & Duan, Xiongbo, 2020. "Microsimulation of electric vehicle energy consumption and driving range," Applied Energy, Elsevier, vol. 267(C).
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