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Joint Optimal Policy for Subsidy on Electric Vehicles and Infrastructure Construction in Highway Network

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  • Yue Wang

    (Science and Technology on Information Systems Engineering Laboratory, College of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Zhong Liu

    (Science and Technology on Information Systems Engineering Laboratory, College of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Jianmai Shi

    (Science and Technology on Information Systems Engineering Laboratory, College of System Engineering, National University of Defense Technology, Changsha 410073, China)

  • Guohua Wu

    (School of Traffic and Transportation Engineering, Central South University, Changsha 410075, China)

  • Rui Wang

    (Science and Technology on Information Systems Engineering Laboratory, College of System Engineering, National University of Defense Technology, Changsha 410073, China)

Abstract

The promotion of the battery electric vehicle has become a worldwide problem for governments due to its short endurance range and slow charging rate. Besides an appropriate network of charging facilities, a subsidy has proved to be an effective way to increase the market share of battery electric vehicles. In this paper, we investigate the joint optimal policy for a subsidy on electric vehicles and infrastructure construction in a highway network, where the impact of siting and sizing of fast charging stations and the impact of subsidy on the potential electric vehicle flows is considered. A new specified local search (LS)-based algorithm is developed to maximize the overall number of available battery electric vehicles in the network, which can get provide better solutions in most situations when compared with existed algorithms. Moreover, we firstly combined the existing algorithms to establish a multi-stage optimization method, which can obtain better solutions than all existed algorithms. A practical case from the highway network in Hunan, China, is studied to analyze the factors that impact the choice of subsidy and the deployment of charging stations. The results prove that the joint policy for subsidy and infrastructure construction can be effectively improved with the optimization model and the algorithms we developed. The managerial analysis indicates that the improvement on the capacity of charging facility can increase the proportion of construction fees in the total budget, while the improvement in the endurance range of battery electric vehicles is more efficient in expanding battery electric vehicle adoption in the highway network. A more detailed formulation of the battery electric vehicle flow demand and equilibrium situation will be studied in the future.

Suggested Citation

  • Yue Wang & Zhong Liu & Jianmai Shi & Guohua Wu & Rui Wang, 2018. "Joint Optimal Policy for Subsidy on Electric Vehicles and Infrastructure Construction in Highway Network," Energies, MDPI, vol. 11(9), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:9:p:2479-:d:170495
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    References listed on IDEAS

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    Cited by:

    1. Hong Gao & Kai Liu & Xinchao Peng & Cheng Li, 2020. "Optimal Location of Fast Charging Stations for Mixed Traffic of Electric Vehicles and Gasoline Vehicles Subject to Elastic Demands," Energies, MDPI, vol. 13(8), pages 1-16, April.
    2. Gabriela D. Oliveira & Luis C. Dias, 2019. "Influence of Demographics on Consumer Preferences for Alternative Fuel Vehicles: A Review of Choice Modelling Studies and a Study in Portugal," Energies, MDPI, vol. 12(2), pages 1-33, January.
    3. Tianwei Lu & Enjian Yao & Fanglei Jin & Long Pan, 2020. "Alternative Incentive Policies against Purchase Subsidy Decrease for Battery Electric Vehicle (BEV) Adoption," Energies, MDPI, vol. 13(7), pages 1-19, April.

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