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Estimating remaining driving range of battery electric vehicles based on real-world data: A case study of Beijing, China

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  • Bi, Jun
  • Wang, Yongxing
  • Sai, Qiuyue
  • Ding, Cong

Abstract

Battery electric vehicles (BEVs) have positive effects on the reduction of petroleum dependence and vehicle emissions. However, limited driving range of BEVs contributes to the range anxiety of drivers. Therefore, accurately estimating remaining driving range is a critical issue for BEV manufacturers to help drivers alleviating range anxiety. In this paper, by using the real-world data collected from a BEV operating in Beijing, China, the nonlinear estimation models for remaining driving range under different temperature conditions are established based on the data-driven method. The models consider the State of Charge (SOC), speed and temperature conditions as the impacting factors for remaining driving range. The significant nonlinear relationship between speed and driving distance per SOC is explored and considered in the model. The robust nonlinear regression method is used to determine the parameters of the models. Model verification results confirm the accuracy of the model. Moreover, the models are used to explore the economical driving speeds for the BEV under different temperature conditions. The results indicate that the economical driving speeds have an increasing trend as temperature increases. The economical driving speeds under low, moderate and high temperate conditions equal to 48.97 km/h, 50.89 km/h and 51.37 km/h, respectively.

Suggested Citation

  • Bi, Jun & Wang, Yongxing & Sai, Qiuyue & Ding, Cong, 2019. "Estimating remaining driving range of battery electric vehicles based on real-world data: A case study of Beijing, China," Energy, Elsevier, vol. 169(C), pages 833-843.
    • Handle: RePEc:eee:energy:v:169:y:2019:i:c:p:833-843
    DOI: 10.1016/j.energy.2018.12.061
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    Cited by:

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    7. Afaq Ahmad & Muhammad Khalid & Zahid Ullah & Naveed Ahmad & Mohammad Aljaidi & Faheem Ahmed Malik & Umar Manzoor, 2022. "Electric Vehicle Charging Modes, Technologies and Applications of Smart Charging," Energies, MDPI, vol. 15(24), pages 1-32, December.
    8. Ye Yang & Zhongfu Tan & Yilong Ren, 2020. "Research on Factors That Influence the Fast Charging Behavior of Private Battery Electric Vehicles," Sustainability, MDPI, vol. 12(8), pages 1-19, April.
    9. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    10. Sun, Tao & Xu, Yuwen & Feng, Lihong & Xu, Bowen & Chen, Dizuo & Zhang, Fang & Han, Xuebing & Zhao, Lihui & Zheng, Yuejiu, 2022. "A vehicle-cloud collaboration strategy for remaining driving range estimation based on online traffic route information and future operation condition prediction," Energy, Elsevier, vol. 248(C).
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    13. Cui, Shaohua & Yao, Baozhen & Chen, Gang & Zhu, Chao & Yu, Bin, 2020. "The multi-mode mobile charging service based on electric vehicle spatiotemporal distribution," Energy, Elsevier, vol. 198(C).

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