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Should Charging Stations Provide Service for Plug-In Hybrid Electric Vehicles During Holidays?

Author

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  • Tianhua Zhang

    (College of Business Administration, Capital University of Economics and Business, Beijing 100070, China)

  • Xin Li

    (School of Management Engineering, Shandong Jianzhu University, Jinan 250101, China)

  • Yiwen Zhang

    (College of Business Administration, Capital University of Economics and Business, Beijing 100070, China)

  • Chenhui Shu

    (China Unicom Digital Technology Corporation, Ltd., Beijing 100032, China)

Abstract

The development of the new energy vehicle (NEV) market in China has promoted the sustainability of the automotive industry, but has also brought pressures to NEV charging infrastructure. This paper aims to determine the strategic role of charging stations, particularly on whether they should provide service for plug-in hybrid electric vehicles (PHEVs) in the highway service area during peak holidays. Firstly, the charging service resource allocation for a charging station that provides services for both electronic vehicles (EVs) and PHEVs is studied. Secondly, different queueing disciplines are compared. At last, a comparison between scenarios where charging services are limited to EVs and those where services extend to both EVs and PHEVs is conducted. A queueing system considering customer balking and reneging is developed. The impacts of parameters, such as the NEV arrival rate and patience degree of different NEV drivers, on the optimal allocation plan, profit, and comparison results are discussed. The main conclusions are as follows: (1) If the EV arrival rate is greater than the charging service rate, the charging station should not provide charging services for PHEVs. Providing service only for EVs derives more revenues and profits and results in a shorter waiting queue. Conversely, if the total arrival rate of NEVs (including EVs and PHEVs) is lower than the charging service rate, then the charging station should also serve PHEVs. (2) If providing service for PHEVs, a mixed queueing discipline should be applied when the total arrival rate approximates the service rate. When the total NEV arrival rate is significantly lower than the charging service rate, the separate queueing discipline should be adopted. (3) When applying a separate queueing discipline, if a certain type of NEV has a higher arrival rate and the drivers exhibit greater patience, then more charging resources should be allocated to this type of NEV. If the charging service is less busy, the more patient the drivers are, the less service resources should be allocated to them, whereas, during peak times, the more patient the drivers are, the more service resources should be allocated to them.

Suggested Citation

  • Tianhua Zhang & Xin Li & Yiwen Zhang & Chenhui Shu, 2025. "Should Charging Stations Provide Service for Plug-In Hybrid Electric Vehicles During Holidays?," Sustainability, MDPI, vol. 17(1), pages 1-21, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:1:p:336-:d:1560299
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    References listed on IDEAS

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    1. Tao Yi & Xiao-bin Cheng & Hao Zheng & Jin-peng Liu, 2019. "Research on Location and Capacity Optimization Method for Electric Vehicle Charging Stations Considering User’s Comprehensive Satisfaction," Energies, MDPI, vol. 12(10), pages 1-17, May.
    2. Wu, Yan & Aziz, Syed Mahfuzul & Haque, Mohammed H., 2023. "Techno-economic modelling for energy cost minimisation of a university campus to support electric vehicle charging with photovoltaic capacity optimisation," Renewable Energy, Elsevier, vol. 219(P1).
    3. Pourvaziri, H. & Sarhadi, H. & Azad, N. & Afshari, H. & Taghavi, M., 2024. "Planning of electric vehicle charging stations: An integrated deep learning and queueing theory approach," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 186(C).
    4. Yin, Wanjun & Ji, Jianbo & Qin, Xuan, 2023. "Study on optimal configuration of EV charging stations based on second-order cone," Energy, Elsevier, vol. 284(C).
    5. Bolong Yun & Daniel (Jian) Sun & Yingjie Zhang & Siwen Deng & Jing Xiong, 2019. "A Charging Location Choice Model for Plug-In Hybrid Electric Vehicle Users," Sustainability, MDPI, vol. 11(20), pages 1-23, October.
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