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To improve the electric vehicle remaining driving range estimation, this paper investigates the vehicle driving energy consumption estimation method based on advanced technologies including vehicle, infrastructure, transport and cloud. Based on these technologies, real-time vehicle mass, future road information and driving condition are obtained. Thus, the vehicle future driving energy consumption and remaining driving range can be predicted. From the simulation, the proposed predictive estimation method achieves higher accuracy than the conventional proportional estimation method which is based on the standard driving cycles and real-time battery SOC condition. The proposed predictive estimation method also outperforms the dynamic estimation method which considers recent driver behaviours and driving condition characteristics.

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Electric Vehicle Remaining Driving Range Estimation with Predictive Driving Condition

  • 会议论文

  • 发表日期：

    2025年

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  • Shilei Zhou,
  • Lanwen Zhang,
  • Rina Wu,
  • Beichen Wang

摘要

To improve the electric vehicle remaining driving range estimation, this paper investigates the vehicle driving energy consumption estimation method based on advanced technologies including vehicle, infrastructure, transport and cloud. Based on these technologies, real-time vehicle mass, future road information and driving condition are obtained. Thus, the vehicle future driving energy consumption and remaining driving range can be predicted. From the simulation, the proposed predictive estimation method achieves higher accuracy than the conventional proportional estimation method which is based on the standard driving cycles and real-time battery SOC condition. The proposed predictive estimation method also outperforms the dynamic estimation method which considers recent driver behaviours and driving condition characteristics.