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Optimizing Electric Vehicle Charging Station Location on Highways: A Decision Model for Meeting Intercity Travel Demand

Author

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  • Ibrahim Tumay Gulbahar

    (Industrial Engineering Department, Abdullah Gul University, Sumer Campus, Barbaros Boulevard, Kocasinan, 38080 Kayseri, Türkiye)

  • Muhammed Sutcu

    (Engineering Management Department, College of Engineering & Architecture, Gulf University for Science & Technology, Mishref 32093, Kuwait)

  • Abedalmuhdi Almomany

    (Electrical & Computer Engineering Department, College of Engineering & Architecture, Gulf University for Science & Technology, Mishref 32093, Kuwait
    Department of Computer Engineering, Hijjawi Faculty for Engineering Technology, Yarmouk University, Irbid 21163, Jordan)

  • Babul Salam KSM Kader Ibrahim

    (Electrical & Computer Engineering Department, College of Engineering & Architecture, Gulf University for Science & Technology, Mishref 32093, Kuwait)

Abstract

Electric vehicles have emerged as one of the top environmentally friendly alternatives to traditional internal combustion engine vehicles. The development of a comprehensive charging infrastructure, particularly determining the optimal locations for charging stations, is essential for the widespread adoption of electric vehicles. Most research on this subject focuses on popular areas such as city centers, shopping centers, and airports. With numerous charging stations available, these locations typically satisfy daily charging needs in routine life. However, the availability of charging stations for intercity travel, particularly on highways, remains insufficient. In this study, a decision model has been proposed to determine the optimal placement of electric vehicle charging stations along highways. To ensure a practical approach to the location of charging stations, the projected number of electric vehicles in Türkiye over the next few years is estimated by using a novel approach and the outcomes are used as crucial input in the facility location model. An optimization technique is employed to identify the ideal locations for charging stations on national highways to meet customer demand. The proposed model selects the most appropriate locations for charging stations and the required number of chargers to be installed, ensuring that electric vehicle drivers on highways do not encounter charging problems.

Suggested Citation

  • Ibrahim Tumay Gulbahar & Muhammed Sutcu & Abedalmuhdi Almomany & Babul Salam KSM Kader Ibrahim, 2023. "Optimizing Electric Vehicle Charging Station Location on Highways: A Decision Model for Meeting Intercity Travel Demand," Sustainability, MDPI, vol. 15(24), pages 1-17, December.
  • Handle: RePEc:gam:jsusta:v:15:y:2023:i:24:p:16716-:d:1297518
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    References listed on IDEAS

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    1. Lim, Seow & Kuby, Michael, 2010. "Heuristic algorithms for siting alternative-fuel stations using the Flow-Refueling Location Model," European Journal of Operational Research, Elsevier, vol. 204(1), pages 51-61, July.
    2. Chung, Sung Hoon & Kwon, Changhyun, 2015. "Multi-period planning for electric car charging station locations: A case of Korean Expressways," European Journal of Operational Research, Elsevier, vol. 242(2), pages 677-687.
    3. Tehseen Mazhar & Rizwana Naz Asif & Muhammad Amir Malik & Muhammad Asgher Nadeem & Inayatul Haq & Muhammad Iqbal & Muhammad Kamran & Shahzad Ashraf, 2023. "Electric Vehicle Charging System in the Smart Grid Using Different Machine Learning Methods," Sustainability, MDPI, vol. 15(3), pages 1-26, February.
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