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Optimizing Wireless Charging Locations for Battery Electric Bus Transit with a Genetic Algorithm

Author

Listed:
  • Gang Chen

    (School of Transportation Engineering, Chang’an University, 710054 Xi’an, China)

  • Dawei Hu

    (School of Transportation Engineering, Chang’an University, 710054 Xi’an, China)

  • Steven Chien

    (School of Transportation Engineering, Chang’an University, 710054 Xi’an, China
    John A. Reif, Jr. Department of Civil and Environmental Engineering, New Jersey Institute of Technology, Newark, NJ 07102, USA)

  • Lei Guo

    (Systems Engineering Institute, AMS, 100071 Beijing, China)

  • Mingzheng Liu

    (Systems Engineering Institute, AMS, 100071 Beijing, China)

Abstract

Electrifying bus transit has been deemed as an effective way to reduce the emissions of transit vehicles. However, some concerns about on-board battery hinder its further development. Recently, dynamic wireless power transfer (DWPT) technologies have been developed, which enable buses to charge in-motion and overcome the drawback (short service range) with opportunity charging. This paper proposes a mathematic model which optimizes the locations for DWPT devices deployed at stops and size of battery capacity for battery electric buses (BEB) in a multi-route network, which considers the battery’s service life, depth of discharge and weight. A tangible solution algorithm based on a genetic algorithm (GA) is developed to find the optimal solution. A case study based on the bus network from Xi’an China is conducted to investigate the relationship among optimized costs, greenhouse gas (GHG) emissions, battery service life, size of the battery capacity and the number of DWPT devices. The results demonstrated that a bus network powered by DWPT shows better performance in both costs (a 43.3% reduction) and emissions (a 14.4% reduction) compared to that with stationary charging at bus terminals.

Suggested Citation

  • Gang Chen & Dawei Hu & Steven Chien & Lei Guo & Mingzheng Liu, 2020. "Optimizing Wireless Charging Locations for Battery Electric Bus Transit with a Genetic Algorithm," Sustainability, MDPI, vol. 12(21), pages 1-20, October.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:21:p:8971-:d:436479
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    References listed on IDEAS

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    Cited by:

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    2. Boud Verbrugge & Mohammed Mahedi Hasan & Haaris Rasool & Thomas Geury & Mohamed El Baghdadi & Omar Hegazy, 2021. "Smart Integration of Electric Buses in Cities: A Technological Review," Sustainability, MDPI, vol. 13(21), pages 1-23, November.
    3. Ande Chang & Yuan Cong & Chunguang Wang & Yiming Bie, 2024. "Optimal Vehicle Scheduling and Charging Infrastructure Planning for Autonomous Modular Transit System," Sustainability, MDPI, vol. 16(8), pages 1-16, April.
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    5. Yueqiu Wu & Liping Wang & Yanke Zhang & Jiajie Wu & Qiumei Ma & Lisha Yue, 2021. "Application of Marginal Rate of Transformation in Decision Making of Multi-Objective Reservoir Optimal Operation Scheme," Sustainability, MDPI, vol. 13(3), pages 1-17, February.
    6. Hanhee Kim & Niklas Hartmann & Maxime Zeller & Renato Luise & Tamer Soylu, 2021. "Comparative TCO Analysis of Battery Electric and Hydrogen Fuel Cell Buses for Public Transport System in Small to Midsize Cities," Energies, MDPI, vol. 14(14), pages 1-31, July.
    7. Manzolli, Jônatas Augusto & Trovão, João Pedro & Antunes, Carlos Henggeler, 2022. "A review of electric bus vehicles research topics – Methods and trends," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
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    9. Yiming Bie & Mingjie Hao & Mengzhu Guo, 2021. "Optimal Electric Bus Scheduling Based on the Combination of All-Stop and Short-Turning Strategies," Sustainability, MDPI, vol. 13(4), pages 1-21, February.

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