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A dynamic charging strategy with hybrid fast charging station for electric vehicles

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  • Elma, Onur

Abstract

The popularity of electric vehicles (EV) have been on the rise with technological advancements and environmental concerns. The charging time and charging demand are important challenges for EV adaptation. In order to address these challenges, a DC fast charging technology with a dynamic energy management system is proposed in this study. However, DC fast chargers require high power demand periods to reduce the charging time. This, in turn, will cause negative effects on the grid such as stability, resilience, and efficiency problems. The purpose of the study is to evaluate a hybrid DC fast charging station with the aim of reducing peak demand during charging periods. The proposed energy management algorithm together with the dynamic data use provides more reliable results on such systems operations. With the proposed control algorithm, both peak demand from the grid is substantially reduced by 45% and the battery life span is extended thanks to more controlled charge/discharge coordination.

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  • Elma, Onur, 2020. "A dynamic charging strategy with hybrid fast charging station for electric vehicles," Energy, Elsevier, vol. 202(C).
    • Handle: RePEc:eee:energy:v:202:y:2020:i:c:s0360544220307878
    DOI: 10.1016/j.energy.2020.117680
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    References listed on IDEAS

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    2. Jin, Ruiyang & Zhou, Yuke & Lu, Chao & Song, Jie, 2022. "Deep reinforcement learning-based strategy for charging station participating in demand response," Applied Energy, Elsevier, vol. 328(C).
    3. Makeen, Peter & Ghali, Hani A. & Memon, Saim & Duan, Fang, 2023. "Smart techno-economic operation of electric vehicle charging station in Egypt," Energy, Elsevier, vol. 264(C).
    4. Hemmatpour, Mohammad Hasan & Rezaeian Koochi, Mohammad Hossein & Dehghanian, Pooria & Dehghanian, Payman, 2022. "Voltage and energy control in distribution systems in the presence of flexible loads considering coordinated charging of electric vehicles," Energy, Elsevier, vol. 239(PA).
    5. Fu, Zhengtang & Dong, Peiwu & Ju, Yanbing & Gan, Zhenkun & Zhu, Min, 2022. "An intelligent green vehicle management system for urban food reliably delivery:A case study of Shanghai, China," Energy, Elsevier, vol. 257(C).
    6. Sami M. Alshareef, 2022. "A Novel Fairness-Based Cost Model for Adopting Smart Charging at Fast Charging Stations," Sustainability, MDPI, vol. 14(11), pages 1-28, May.
    7. Tiande Mo & Yu Li & Kin-tak Lau & Chi Kin Poon & Yinghong Wu & Yang Luo, 2022. "Trends and Emerging Technologies for the Development of Electric Vehicles," Energies, MDPI, vol. 15(17), pages 1-34, August.
    8. Morro-Mello, Igoor & Padilha-Feltrin, Antonio & Melo, Joel D. & Heymann, Fabian, 2021. "Spatial connection cost minimization of EV fast charging stations in electric distribution networks using local search and graph theory," Energy, Elsevier, vol. 235(C).
    9. Luo, Lizi & He, Pinquan & Gu, Wei & Sheng, Wanxing & Liu, Keyan & Bai, Muke, 2022. "Temporal-spatial scheduling of electric vehicles in AC/DC distribution networks," Energy, Elsevier, vol. 255(C).
    10. Aree Wangsupphaphol & Surachai Chaitusaney & Mohamed Salem, 2023. "A Techno-Economic Assessment of a Second-Life Battery and Photovoltaics Hybrid Power Source for Sustainable Electric Vehicle Home Charging," Sustainability, MDPI, vol. 15(7), pages 1-19, March.
    11. Pegah Alaee & Julius Bems & Amjad Anvari-Moghaddam, 2023. "A Review of the Latest Trends in Technical and Economic Aspects of EV Charging Management," Energies, MDPI, vol. 16(9), pages 1-28, April.
    12. Natascia Andrenacci & Mauro Di Monaco & Giuseppe Tomasso, 2022. "Influence of Battery Aging on the Operation of a Charging Infrastructure," Energies, MDPI, vol. 15(24), pages 1-18, December.
    13. Yap, Kah Yung & Chin, Hon Huin & Klemeš, Jiří Jaromír, 2022. "Solar Energy-Powered Battery Electric Vehicle charging stations: Current development and future prospect review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).

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