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Advancing Electric Vehicle Infrastructure: A Review and Exploration of Battery-Assisted DC Fast Charging Stations

Author

Listed:
  • Ahmet Aksoz

    (MOBILERS Team, Sivas Cumhuriyet University, 58350 Sivas, Türkiye)

  • Burçak Asal

    (Department of Computer Engineering, Hacettepe University, 06800 Ankara, Türkiye)

  • Emre Biçer

    (Battery Research Laboratory, Faculty of Engineering and Natural Sciences, Sivas University of Science and Technology, 58010 Sivas, Türkiye)

  • Saadin Oyucu

    (Department of Computer Engineering, Faculty of Engineering, Adıyaman University, 02040 Adıyaman, Türkiye)

  • Merve Gençtürk

    (Battery Research Laboratory, Faculty of Engineering and Natural Sciences, Sivas University of Science and Technology, 58010 Sivas, Türkiye)

  • Saeed Golestan

    (AAU Energy, Aalborg University, 9220 Aalborg, Denmark)

Abstract

Concerns over fossil fuel depletion, fluctuating fuel prices, and CO 2 emissions have accelerated the development of electric vehicle (EV) technologies. This article reviews advancements in EV fast charging technology and explores the development of battery-assisted DC fast charging stations to address the limitations of traditional chargers. Our proposed approach integrates battery storage, allowing chargers to operate independently of the electric grid by storing electrical energy during off-peak hours and releasing it during peak times. This reduces dependence on grid power and enhances grid stability. Moreover, the transformer-less, modular design of the proposed solution offers greater flexibility, scalability, and reduced installation costs. Additionally, the use of smart energy management systems, incorporating artificial intelligence and machine learning techniques to dynamically adjust charging rates, will be discussed to optimize efficiency and cost-effectiveness.

Suggested Citation

  • Ahmet Aksoz & Burçak Asal & Emre Biçer & Saadin Oyucu & Merve Gençtürk & Saeed Golestan, 2024. "Advancing Electric Vehicle Infrastructure: A Review and Exploration of Battery-Assisted DC Fast Charging Stations," Energies, MDPI, vol. 17(13), pages 1-23, June.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:13:p:3117-:d:1421511
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    References listed on IDEAS

    as
    1. Tehseen Mazhar & Rizwana Naz Asif & Muhammad Amir Malik & Muhammad Asgher Nadeem & Inayatul Haq & Muhammad Iqbal & Muhammad Kamran & Shahzad Ashraf, 2023. "Electric Vehicle Charging System in the Smart Grid Using Different Machine Learning Methods," Sustainability, MDPI, vol. 15(3), pages 1-26, February.
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