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Optimizing Electric Vehicle Charging Infrastructure on Highways: A Multi-Agent-Based Planning Approach

Author

Listed:
  • Yongzhong Wu

    (School of Business Administration, South China University of Technology, 381 Wushan Road, Guangzhou 510641, China)

  • Yikuan Lu

    (School of Business Administration, South China University of Technology, 381 Wushan Road, Guangzhou 510641, China)

  • Zhijie Zhu

    (School of Business Administration, South China University of Technology, 381 Wushan Road, Guangzhou 510641, China)

  • José Holguín-Veras

    (Department of Civil Engineering, Rensselaer Polytechnic Institute, 110 8th St., Troy, NY 12180, USA)

Abstract

The lack of sufficient charging infrastructure for long-haul transportation is a significant barrier preventing the widespread adoption of electric vehicles (EVs). Planning EV charging facilities in this context requires considerations distinct from those in urban environments, accounting for factors such as traffic patterns and charging behaviors. This research paper presents a multi-agent simulation model designed to assess travel and charging activities, specifically on highways. By utilizing this model, the effectiveness of EV charging facility planning is evaluated. Empirical data from a real highway section in China are employed for analysis purposes. The findings reveal that the concentration of charging facilities significantly impacts both travel time and queue time for vehicles, demonstrating the potential for optimization through the proposed model. These established models hold practical value for both greenfield development and the expansion of existing charging networks, with the goal of minimizing total social costs.

Suggested Citation

  • Yongzhong Wu & Yikuan Lu & Zhijie Zhu & José Holguín-Veras, 2023. "Optimizing Electric Vehicle Charging Infrastructure on Highways: A Multi-Agent-Based Planning Approach," Sustainability, MDPI, vol. 15(18), pages 1-14, September.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:18:p:13634-:d:1238283
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    References listed on IDEAS

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    1. Awasthi, Abhishek & Venkitusamy, Karthikeyan & Padmanaban, Sanjeevikumar & Selvamuthukumaran, Rajasekar & Blaabjerg, Frede & Singh, Asheesh K., 2017. "Optimal planning of electric vehicle charging station at the distribution system using hybrid optimization algorithm," Energy, Elsevier, vol. 133(C), pages 70-78.
    2. Emilia M. Szumska, 2023. "Electric Vehicle Charging Infrastructure along Highways in the EU," Energies, MDPI, vol. 16(2), pages 1-18, January.
    3. John Hodgson, M. & Rosing, K. E. & Leontien, A. & Storrier, G., 1996. "Applying the flow-capturing location-allocation model to an authentic network: Edmonton, Canada," European Journal of Operational Research, Elsevier, vol. 90(3), pages 427-443, May.
    4. Ren, Xianqiang & Zhang, Huiming & Hu, Ruohan & Qiu, Yueming, 2019. "Location of electric vehicle charging stations: A perspective using the grey decision-making model," Energy, Elsevier, vol. 173(C), pages 548-553.
    5. Wang, Ying-Wei & Lin, Chuah-Chih, 2009. "Locating road-vehicle refueling stations," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 45(5), pages 821-829, September.
    6. Alberto Danese & Michele Garau & Andreas Sumper & Bendik Nybakk Torsæter, 2021. "Electrical Infrastructure Design Methodology of Dynamic and Static Charging for Heavy and Light Duty Electric Vehicles," Energies, MDPI, vol. 14(12), pages 1-15, June.
    7. Sadeghi-Barzani, Payam & Rajabi-Ghahnavieh, Abbas & Kazemi-Karegar, Hosein, 2014. "Optimal fast charging station placing and sizing," Applied Energy, Elsevier, vol. 125(C), pages 289-299.
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