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Optimal Charging Navigation Strategy Design for Rapid Charging Electric Vehicles

Author

Listed:
  • Wangyi Mo

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Chao Yang

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Xin Chen

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Kangjie Lin

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

  • Shuaiqi Duan

    (School of Automation, Guangdong University of Technology, Guangzhou 510006, China)

Abstract

Electric vehicles (EVs) have become an efficient solution to making a transportation system environmentally friendly. However, as the number of EVs grows, the power demand from charging vehicles increases greatly. An unordered charging strategy for huge EVs affects the stability of a local power grid, especially during peak times. It becomes serious under the rapid charging mode, in which the EVs will be charged fully within a shorter time. In contrast to regular charging, the power quality (e.g.,voltages deviation, harmonic distortion) is affected when multiple EVs perform rapid charging at the same station simultaneously. To reduce the impacts on a power grid system caused by rapid charging, we propose an optimal EV rapid charging navigation strategy based on the internet of things network. The rapid charging price is designed based on the charging power regulation scheme. Both power grid operation and real-time traffic information are considered. The formulated objective of the navigation strategy is proposed to minimize the synthetic costs of EVs, including the traveling time and the charging costs. Simulation results demonstrate the effectiveness of the proposed strategy.

Suggested Citation

  • Wangyi Mo & Chao Yang & Xin Chen & Kangjie Lin & Shuaiqi Duan, 2019. "Optimal Charging Navigation Strategy Design for Rapid Charging Electric Vehicles," Energies, MDPI, vol. 12(6), pages 1-18, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:6:p:962-:d:213355
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    References listed on IDEAS

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    1. Hannan, M.A. & Hoque, M.M. & Mohamed, A. & Ayob, A., 2017. "Review of energy storage systems for electric vehicle applications: Issues and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 771-789.
    2. Wang, Yusheng & Huang, Yongxi & Xu, Jiuping & Barclay, Nicole, 2017. "Optimal recharging scheduling for urban electric buses: A case study in Davis," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 100(C), pages 115-132.
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    Cited by:

    1. Kabir A. Mamun & F. R. Islam & R. Haque & Aneesh A. Chand & Kushal A. Prasad & Krishneel K. Goundar & Krishneel Prakash & Sidharth Maharaj, 2022. "Systematic Modeling and Analysis of On-Board Vehicle Integrated Novel Hybrid Renewable Energy System with Storage for Electric Vehicles," Sustainability, MDPI, vol. 14(5), pages 1-33, February.
    2. Ki-Beom Lee & Mohamed A. Ahmed & Dong-Ki Kang & Young-Chon Kim, 2020. "Deep Reinforcement Learning Based Optimal Route and Charging Station Selection," Energies, MDPI, vol. 13(23), pages 1-22, November.
    3. Luis B. Elvas & Joao C Ferreira, 2021. "Intelligent Transportation Systems for Electric Vehicles," Energies, MDPI, vol. 14(17), pages 1-9, September.

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