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Microsimulation of electric vehicle energy consumption and driving range

Author

Listed:
  • Xie, Yunkun
  • Li, Yangyang
  • Zhao, Zhichao
  • Dong, Hao
  • Wang, Shuqian
  • Liu, Jingping
  • Guan, Jinhuan
  • Duan, Xiongbo

Abstract

In order to predict and study the effects of different parameters on performance characteristics of electric vehicles. A vehicle simulation model of pure battery electric vehicles equipped with single pedal control system is established and calibrated by the experimental data based on vehicle energy flow and driving range analysis, the simulation doesn’t include thermal aspect of the battery/vehicle. Next, the effects of different environmental and control parameters on energy consumption and driving range of pure electric vehicles are analyzed. The main findings are: (1) for the single driving cycle, the relative error of battery power and current is below 5%, and the absolute error of battery voltage is below 2.5 V. For the whole driving range, the absolute error of driving range is only about 5.75 km. (2) The main factors influencing energy consumption and driving range are average vehicle speed, running time and the frequency distribution of braking process, besides, the energy consumption of congested traffic with/without regenerative brake control system are 46.07 kW·h/100 km and 47.19 kW·h/100 km, respectively, meanwhile, vehicle with regenerative braking saves 2.43% energy under congested traffic. (3) The threshold of quitting the working condition of energy recovery for the motor can be set in a certain value based on the safety of driver in the emergencies and energy conversion. Further, the model and data in the paper can be applied to evaluate and optimize the energy consumption and driving range by changing different technologies or strategies in the future.

Suggested Citation

  • 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).
  • Handle: RePEc:eee:appene:v:267:y:2020:i:c:s0306261920305936
    DOI: 10.1016/j.apenergy.2020.115081
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    References listed on IDEAS

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    1. Hsu, Chia-Yu & Yang, Chin-Sheng & Yu, Liang-Chih & Lin, Chi-Fang & Yao, Hsiu-Hsen & Chen, Duan-Yu & Robert Lai, K. & Chang, Pei-Chann, 2015. "Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system," International Journal of Production Economics, Elsevier, vol. 164(C), pages 454-461.
    2. Liu, Kai & Wang, Jiangbo & Yamamoto, Toshiyuki & Morikawa, Takayuki, 2018. "Exploring the interactive effects of ambient temperature and vehicle auxiliary loads on electric vehicle energy consumption," Applied Energy, Elsevier, vol. 227(C), pages 324-331.
    3. Feng, Sida & Magee, Christopher L., 2020. "Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees," Applied Energy, Elsevier, vol. 260(C).
    4. Ballinger, Benjamin & Stringer, Martin & Schmeda-Lopez, Diego R. & Kefford, Benjamin & Parkinson, Brett & Greig, Chris & Smart, Simon, 2019. "The vulnerability of electric vehicle deployment to critical mineral supply," Applied Energy, Elsevier, vol. 255(C).
    5. Li, Zhenhe & Khajepour, Amir & Song, Jinchun, 2019. "A comprehensive review of the key technologies for pure electric vehicles," Energy, Elsevier, vol. 182(C), pages 824-839.
    6. Luin, Blaž & Petelin, Stojan & Al-Mansour, Fouad, 2019. "Microsimulation of electric vehicle energy consumption," Energy, Elsevier, vol. 174(C), pages 24-32.
    7. Carbot-Rojas, D.A. & Escobar-Jiménez, R.F. & Gómez-Aguilar, J.F. & Téllez-Anguiano, A.C., 2017. "A survey on modeling, biofuels, control and supervision systems applied in internal combustion engines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1070-1085.
    8. Chen, Zheng & Zhang, Fan & Xu, Boya & Zhang, Quanchang & Liu, Jingping, 2017. "Influence of methane content on a LNG heavy-duty engine with high compression ratio," Energy, Elsevier, vol. 128(C), pages 329-336.
    9. Li, Yangyang & Duan, Xiongbo & Fu, Jianqin & Liu, Jingping & Wang, Shuqian & Dong, Hao & Xie, Yunkun, 2019. "Development of a method for on-board measurement of instant engine torque and fuel consumption rate based on direct signal measurement and RGF modelling under vehicle transient operating conditions," Energy, Elsevier, vol. 189(C).
    10. Fiori, Chiara & Ahn, Kyoungho & Rakha, Hesham A., 2016. "Power-based electric vehicle energy consumption model: Model development and validation," Applied Energy, Elsevier, vol. 168(C), pages 257-268.
    11. Hong, Jichao & Wang, Zhenpo & Chen, Wen & Yao, Yongtao, 2019. "Synchronous multi-parameter prediction of battery systems on electric vehicles using long short-term memory networks," Applied Energy, Elsevier, vol. 254(C).
    12. Koltsaklis, Nikolaos E. & Dagoumas, Athanasios S., 2018. "State-of-the-art generation expansion planning: A review," Applied Energy, Elsevier, vol. 230(C), pages 563-589.
    13. Qu, Xiaobo & Yu, Yang & Zhou, Mofan & Lin, Chin-Teng & Wang, Xiangyu, 2020. "Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach," Applied Energy, Elsevier, vol. 257(C).
    14. Khan, Muhammad Imran & Yasmin, Tabassum & Shakoor, Abdul, 2015. "Technical overview of compressed natural gas (CNG) as a transportation fuel," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 785-797.
    15. Li, Lifu & Liu, Qin, 2019. "Acceleration curve optimization for electric vehicle based on energy consumption and battery life," Energy, Elsevier, vol. 169(C), pages 1039-1053.
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