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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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    References listed on IDEAS

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    Cited by:

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    6. Sun, Xilei & Fu, Jianqin, 2024. "Many-objective optimization of BEV design parameters based on gradient boosting decision tree models and the NSGA-III algorithm considering the ambient temperature," Energy, Elsevier, vol. 288(C).
    7. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    8. 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).
    9. Yuan, Hong & Ma, Minda & Zhou, Nan & Xie, Hui & Ma, Zhili & Xiang, Xiwang & Ma, Xin, 2024. "Battery electric vehicle charging in China: Energy demand and emissions trends in the 2020s," Applied Energy, Elsevier, vol. 365(C).
    10. Al-Wreikat, Yazan & Serrano, Clara & Sodré, José Ricardo, 2021. "Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving," Applied Energy, Elsevier, vol. 297(C).
    11. Leandro do C. Martins & Rafael D. Tordecilla & Juliana Castaneda & Angel A. Juan & Javier Faulin, 2021. "Electric Vehicle Routing, Arc Routing, and Team Orienteering Problems in Sustainable Transportation," Energies, MDPI, vol. 14(16), pages 1-30, August.
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    13. Huang, Hai-chao & He, Hong-di & Peng, Zhong-ren, 2024. "Urban-scale estimation model of carbon emissions for ride-hailing electric vehicles during operational phase," Energy, Elsevier, vol. 293(C).
    14. Yongxing Wang & Jun Bi & Chaoru Lu & Cong Ding, 2020. "Route Guidance Strategies for Electric Vehicles by Considering Stochastic Charging Demands in a Time-Varying Road Network," Energies, MDPI, vol. 13(9), pages 1-24, May.
    15. Seo, Minhwan & Song, Youngbin & Kim, Jake & Paek, Sung Wook & Kim, Gi-Heon & Kim, Sang Woo, 2021. "Innovative lumped-battery model for state of charge estimation of lithium-ion batteries under various ambient temperatures," Energy, Elsevier, vol. 226(C).
    16. 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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