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Fuzzy-Based Efficient Control of DC Microgrid Configuration for PV-Energized EV Charging Station

Author

Listed:
  • Dominic Savio Abraham

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai 603203, India)

  • Balaji Chandrasekar

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai 603203, India)

  • Narayanamoorthi Rajamanickam

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai 603203, India)

  • Pradeep Vishnuram

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai 603203, India)

  • Venkatesan Ramakrishnan

    (Department of Electrical and Electronics Engineering, SRM Institute of Science and Technology, Chennai 603203, India)

  • Mohit Bajaj

    (Department of Electrical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India
    Graphic Era Hill University, Dehradun 248002, India
    Applied Science Research Center, Applied Science Private University, Amman 11931, Jordan)

  • Marian Piecha

    (Ministry of Industry and Trade, 11015 Prague, Czech Republic)

  • Vojtech Blazek

    (ENET Centre, VSB—Technical University of Ostrava, 70800 Ostrava, Czech Republic)

  • Lukas Prokop

    (ENET Centre, VSB—Technical University of Ostrava, 70800 Ostrava, Czech Republic)

Abstract

Electric vehicles (EVs) are considered as the leading-edge form of mobility. However, the integration of electric vehicles with charging stations is a contentious issue. Managing the available grid power and bus voltage regulation is addressed through renewable energy. This work proposes a grid-connected photovoltaic (PV)-powered EV charging station with converter control technique. The controller unit is interfaced with the renewable energy source, bidirectional converter, and local energy storage unit (ESU). The bidirectional converter provides a regulated output with a fuzzy logic controller (FLC) during charging and discharging. The fuzzy control is implemented to maintain a decentralized power distribution between the microgrid DC-link and ESU. The PV coupled to the DC microgrid of the charging station is variable in nature. Hence, the microgrid-based charging is examined under a range of realistic scenarios, including low, total PV power output and different state of charge (SOC) levels of ESU. In order to accomplish the effective charging of EV, a decentralized energy management system is created to control the energy flow among the PV system, the battery, and the grid. The proposed controller’s effectiveness is validated using a simulation have been analyzed using MATLAB under various microgrid situations. Additionally, the experimental results are validated under various modes of operation.

Suggested Citation

  • Dominic Savio Abraham & Balaji Chandrasekar & Narayanamoorthi Rajamanickam & Pradeep Vishnuram & Venkatesan Ramakrishnan & Mohit Bajaj & Marian Piecha & Vojtech Blazek & Lukas Prokop, 2023. "Fuzzy-Based Efficient Control of DC Microgrid Configuration for PV-Energized EV Charging Station," Energies, MDPI, vol. 16(6), pages 1-17, March.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:6:p:2753-:d:1098412
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    References listed on IDEAS

    as
    1. Khaizaran Abdulhussein Al Sumarmad & Nasri Sulaiman & Noor Izzri Abdul Wahab & Hashim Hizam, 2022. "Energy Management and Voltage Control in Microgrids Using Artificial Neural Networks, PID, and Fuzzy Logic Controllers," Energies, MDPI, vol. 15(1), pages 1-22, January.
    2. Dominic A. Savio & Vimala A. Juliet & Bharatiraja Chokkalingam & Sanjeevikumar Padmanaban & Jens Bo Holm-Nielsen & Frede Blaabjerg, 2019. "Photovoltaic Integrated Hybrid Microgrid Structured Electric Vehicle Charging Station and Its Energy Management Approach," Energies, MDPI, vol. 12(1), pages 1-28, January.
    3. Fabrice Locment & Manuela Sechilariu, 2015. "Modeling and Simulation of DC Microgrids for Electric Vehicle Charging Stations," Energies, MDPI, vol. 8(5), pages 1-22, May.
    4. Khaizaran Abdulhussein Al Sumarmad & Nasri Sulaiman & Noor Izzri Abdul Wahab & Hashim Hizam, 2022. "Microgrid Energy Management System Based on Fuzzy Logic and Monitoring Platform for Data Analysis," Energies, MDPI, vol. 15(11), pages 1-19, June.
    5. Nagaraju Dharavat & Suresh Kumar Sudabattula & Suresh Velamuri & Sachin Mishra & Naveen Kumar Sharma & Mohit Bajaj & Elmazeg Elgamli & Mokhtar Shouran & Salah Kamel, 2022. "Optimal Allocation of Renewable Distributed Generators and Electric Vehicles in a Distribution System Using the Political Optimization Algorithm," Energies, MDPI, vol. 15(18), pages 1-25, September.
    6. Md. Rayid Hasan Mojumder & Fahmida Ahmed Antara & Md. Hasanuzzaman & Basem Alamri & Mohammad Alsharef, 2022. "Electric Vehicle-to-Grid (V2G) Technologies: Impact on the Power Grid and Battery," Sustainability, MDPI, vol. 14(21), pages 1-53, October.
    7. Shahid Hussain & Mohamed A. Ahmed & Ki-Beom Lee & Young-Chon Kim, 2020. "Fuzzy Logic Weight Based Charging Scheme for Optimal Distribution of Charging Power among Electric Vehicles in a Parking Lot," Energies, MDPI, vol. 13(12), pages 1-27, June.
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    Cited by:

    1. Tawfiq Aljohani, 2024. "Intelligent Type-2 Fuzzy Logic Controller for Hybrid Microgrid Energy Management with Different Modes of EVs Integration," Energies, MDPI, vol. 17(12), pages 1-26, June.
    2. Matej Tkac & Martina Kajanova & Peter Bracinik, 2023. "A Review of Advanced Control Strategies of Microgrids with Charging Stations," Energies, MDPI, vol. 16(18), pages 1-25, September.

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