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Intelligent energy consumption prediction for battery electric vehicles: A hybrid approach integrating driving behavior and environmental factors

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  • Jiang, Yu
  • Guo, Jianhua
  • Zhao, Di
  • Li, Yue

Abstract

The precise prediction of energy usage in Battery Electric Vehicles (BEVs) effectively mitigates drivers’ concerns over “mileage anxiety”. However, the conventional approach to predicting energy consumption, which relies solely on historical data and a single model, exhibits significant limitations in terms of accuracy and applicability. These limitations are particularly evident in scenarios lacking traffic information, where uncertainty about velocity and driving patterns can result in suboptimal predictions. As a result, a hybrid method based on driving style and route information recognition is proposed in this paper to accurately predict future energy consumption. This method relies on multi-source information and achieves its objective through a driving cycle prediction and residual fitting model. Simulation results indicate that the framework exhibits acceptable predictive performance in urban, motorway, and suburban settings, with Terminal Relative Errors (TRE) of 5.40%, 5.60%, and 4.26%, respectively.

Suggested Citation

  • Jiang, Yu & Guo, Jianhua & Zhao, Di & Li, Yue, 2024. "Intelligent energy consumption prediction for battery electric vehicles: A hybrid approach integrating driving behavior and environmental factors," Energy, Elsevier, vol. 308(C).
    • Handle: RePEc:eee:energy:v:308:y:2024:i:c:s0360544224025489
    DOI: 10.1016/j.energy.2024.132774
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    References listed on IDEAS

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    1. Fang, Baling & Zhang, Qifei & Luo, Zhaoxu & Zhao, Kaihui & Jiang, Chengyuan & Zhang, Jiawei & Liu, Kangjin, 2025. "Charging decision modelling and load collaborative simulation of electric vehicles based on multi-source fusion: Adaptive Huff-LSTM method," Energy, Elsevier, vol. 340(C).
    2. Yuan, Wei & Han, Yaxi & Lu, Yibin & Zhang, Yali & Ge, Zhenzhen & Pan, Yingjiu, 2025. "Prediction of driving energy consumption for pure electric buses using dynamic driving style recognition and speed forecasting," Energy, Elsevier, vol. 329(C).
    3. Xie, Dong & Jiang, Yu & Guo, Jianhua & Wang, Yanbo, 2025. "Full-scene energy consumption prediction for electric vehicles: A knowledge-enhanced hybrid-driven framework," Energy, Elsevier, vol. 333(C).
    4. Hu, Lipeng & Tang, Jinjun & Xu, Fuqiao & Liang, Xiao, 2025. "SOC prediction for electric buses based on interpretable transformer model: Impact of traffic conditions and feature importance," Energy, Elsevier, vol. 324(C).
    5. Gurusamy, Azhaganathan & Bokdia, Akshat & Kumar, Harsh & Ashok, Bragadeshwaran & Gunavathi, Chellamuthu, 2025. "Appositeness of automated machine learning libraries on prediction of energy consumption for electric two-wheelers based on micro-trip approach," Energy, Elsevier, vol. 320(C).

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