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Multi-Objective Optimal Allocation of Wireless Bus Charging Stations Considering Costs and the Environmental Impact

Author

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  • Oren E. Nahum

    (Faculty of Economics, The Economics and Logistics Track, Ashkelon Academic College, Ashkelon 78211, Israel
    Department of Management, Faculty of Social Sciences, Bar-Ilan University, Ramat Gan 5290002, Israel)

  • Yuval Hadas

    (Department of Management, Faculty of Social Sciences, Bar-Ilan University, Ramat Gan 5290002, Israel)

Abstract

In recent years, due to environmental concerns, there has been an increasing desire to develop alternative solutions to traditional energy sources. Since transportation is a significant fossil-fuel consumer, the development of electric vehicles, especially buses, has the potential to reduce fossil-fuel use and thus provide a better living environment. The aim of the current work was to develop an optimal allocation model for designing a system-wide network of wireless bus charging stations. The main advantages of wireless charging are the need for a much smaller battery and the fact that the charging process may occur under both static and dynamic (in-motion) conditions. The suggested approach consisted of a multi-objective model that selected the locations for the charging stations while (a) minimizing the costs, (b) maximizing the environmental benefit, and (c) minimizing the number of charging stations. The problem was formulated as a multi-objective non-linear optimization model with both deterministic and stochastic variations. An efficient genetic algorithm was introduced to solve the problem. A test case was used to demonstrate the model; accordingly, the decision-maker was provided with a solution set from which the best fit solution could be selected.

Suggested Citation

  • Oren E. Nahum & Yuval Hadas, 2020. "Multi-Objective Optimal Allocation of Wireless Bus Charging Stations Considering Costs and the Environmental Impact," Sustainability, MDPI, vol. 12(6), pages 1-20, March.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:6:p:2318-:d:333121
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    References listed on IDEAS

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    1. P. L. Yu, 1973. "A Class of Solutions for Group Decision Problems," Management Science, INFORMS, vol. 19(8), pages 936-946, April.
    2. Kristoffersen, Trine Krogh & Capion, Karsten & Meibom, Peter, 2011. "Optimal charging of electric drive vehicles in a market environment," Applied Energy, Elsevier, vol. 88(5), pages 1940-1948, May.
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    Cited by:

    1. Benoliel, Peter & Taylor, Margaret & Coburn, Timothy & Desai, Ranjit R. & Schey, Stephen & Gerdes, Mindy & Peng, Peng, 2025. "Soft costs and EVSE – Knowledge gaps as a barrier to successful projects," Applied Energy, Elsevier, vol. 389(C).
    2. Mahmoudi, Reza & Saidi, Saeid & Emrouznejad, Ali, 2025. "Mathematical programming in public bus transit design and operations: Emerging technologies and sustainability – A review," Socio-Economic Planning Sciences, Elsevier, vol. 98(C).
    3. Foda, Ahmed & Abdelaty, Hatem & Mohamed, Moataz & El-Saadany, Ehab, 2023. "A generic cost-utility-emission optimization for electric bus transit infrastructure planning and charging scheduling," Energy, Elsevier, vol. 277(C).
    4. Ji, Jinhua & Wang, Linhong & Yang, Menglin & Bie, Yiming & Hao, Mingjie, 2024. "Optimal deployment of dynamic wireless charging facilities for electric bus route considering stochastic travel times," Energy, Elsevier, vol. 289(C).
    5. Shehabeldeen, Ali & Foda, Ahmed & Mohamed, Moataz, 2024. "A multi-stage optimization of battery electric bus transit with battery degradation," Energy, Elsevier, vol. 299(C).
    6. Pengxiang Song & Wenchuan Song & Ao Meng & Hongxue Li, 2024. "Adaptive Equivalent Factor-Based Energy Management Strategy for Plug-In Hybrid Electric Buses Considering Passenger Load Variations," Energies, MDPI, vol. 17(6), pages 1-30, March.
    7. Xiaotong Guo & Lingyan Li & Haiyan Xie & Wei Shi, 2020. "Improved Multi-Objective Optimization Model for Policy Design of Rental Housing Market," Sustainability, MDPI, vol. 12(14), pages 1-23, July.

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