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An Analytical Planning Model to Estimate the Optimal Density of Charging Stations for Electric Vehicles

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  • Yongjun Ahn
  • Hwasoo Yeo

Abstract

The charging infrastructure location problem is becoming more significant due to the extensive adoption of electric vehicles. Efficient charging station planning can solve deeply rooted problems, such as driving-range anxiety and the stagnation of new electric vehicle consumers. In the initial stage of introducing electric vehicles, the allocation of charging stations is difficult to determine due to the uncertainty of candidate sites and unidentified charging demands, which are determined by diverse variables. This paper introduces the Estimating the Required Density of EV Charging (ERDEC) stations model, which is an analytical approach to estimating the optimal density of charging stations for certain urban areas, which are subsequently aggregated to city level planning. The optimal charging station’s density is derived to minimize the total cost. A numerical study is conducted to obtain the correlations among the various parameters in the proposed model, such as regional parameters, technological parameters and coefficient factors. To investigate the effect of technological advances, the corresponding changes in the optimal density and total cost are also examined by various combinations of technological parameters. Daejeon city in South Korea is selected for the case study to examine the applicability of the model to real-world problems. With real taxi trajectory data, the optimal density map of charging stations is generated. These results can provide the optimal number of chargers for driving without driving-range anxiety. In the initial planning phase of installing charging infrastructure, the proposed model can be applied to a relatively extensive area to encourage the usage of electric vehicles, especially areas that lack information, such as exact candidate sites for charging stations and other data related with electric vehicles. The methods and results of this paper can serve as a planning guideline to facilitate the extensive adoption of electric vehicles.

Suggested Citation

  • Yongjun Ahn & Hwasoo Yeo, 2015. "An Analytical Planning Model to Estimate the Optimal Density of Charging Stations for Electric Vehicles," PLOS ONE, Public Library of Science, vol. 10(11), pages 1-26, November.
    • Handle: RePEc:plo:pone00:0141307
    DOI: 10.1371/journal.pone.0141307
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    References listed on IDEAS

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    2. Vazifeh, Mohammad M. & Zhang, Hongmou & Santi, Paolo & Ratti, Carlo, 2019. "Optimizing the deployment of electric vehicle charging stations using pervasive mobility data," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 75-91.
    3. Fekete, Patrick & Lim, Sirirat & Martin, Steve & Kuhn, Katja & Wright, Nick, 2016. "Improved energy supply for non-road electric vehicles by occasional charging station location modelling," Energy, Elsevier, vol. 114(C), pages 1033-1040.
    4. Yong, Jin Yi & Tan, Wen Shan & Khorasany, Mohsen & Razzaghi, Reza, 2023. "Electric vehicles destination charging: An overview of charging tariffs, business models and coordination strategies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    5. Mo, Dong & Yu, Jingru & Chen, Xiqun Michael, 2020. "Modeling and managing heterogeneous ride-sourcing platforms with government subsidies on electric vehicles," Transportation Research Part B: Methodological, Elsevier, vol. 139(C), pages 447-472.
    6. Irfan Ullah & Muhammad Safdar & Jianfeng Zheng & Alessandro Severino & Arshad Jamal, 2023. "Employing Bibliometric Analysis to Identify the Current State of the Art and Future Prospects of Electric Vehicles," Energies, MDPI, vol. 16(5), pages 1-24, February.

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