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Statistical Analysis of Electric Vehicle Charging Based on AC Slow Chargers

Author

Listed:
  • Dong Sik Kim

    (Deparment of Electronics Engineering, Hankuk University of Foreign Studies, Yongin-si 17035, Republic of Korea)

  • Young Mo Chung

    (Department of Electronics and Information Engineering, Hansung University, Seoul 02876, Republic of Korea)

  • Beom Jin Chung

    (Research Center for Electrical and Information Technology, Seoul National University of Science & Technology, Seoul 01811, Republic of Korea)

Abstract

Regarding DC fast chargers, various studies, such as the charge scheduling, have been conducted. On the other hand, research on AC slow chargers has rarely been conducted due to the predictable and simple usage pattern. Despite the long charging times of AC slow chargers, which use the existing electric outlets with relatively low supplied power, these chargers are suitable for daily home charging of electric vehicles (EVs) during the night. Due to their low installation costs, they are likely to be the dominant type of charging equipment. In this paper, the EV charging process based on AC slow chargers, which supply a maximum power of 3 kW from an AC 220 V outlet, is analyzed by constructing a simple charging model. The charging time and fees are statistically derived and investigated. Furthermore, power load curves for charging EVs with the 3 kW charger are observed. From the statistical analyses, we conclude that daily charging of EVs can be an appropriate scenario in using the AC slow chargers, and the power load can be spread without employing any demand response schemes.

Suggested Citation

  • Dong Sik Kim & Young Mo Chung & Beom Jin Chung, 2023. "Statistical Analysis of Electric Vehicle Charging Based on AC Slow Chargers," Energies, MDPI, vol. 16(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2735-:d:1097897
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    References listed on IDEAS

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    1. Nanaki, Evanthia A. & Koroneos, Christopher J., 2016. "Climate change mitigation and deployment of electric vehicles in urban areas," Renewable Energy, Elsevier, vol. 99(C), pages 1153-1160.
    2. Edoardo Locorotondo & Fabio Corti & Luca Pugi & Lorenzo Berzi & Alberto Reatti & Giovanni Lutzemberger, 2021. "Design of a Wireless Charging System for Online Battery Spectroscopy," Energies, MDPI, vol. 14(1), pages 1-17, January.
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    Cited by:

    1. Ruengwit Khwanrit & Saher Javaid & Yuto Lim & Chalie Charoenlarpnopparut & Yasuo Tan, 2024. "Optimal Vehicle-to-Grid Strategies for Energy Sharing Management Using Electric School Buses," Energies, MDPI, vol. 17(16), pages 1-25, August.

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