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An Optimal Integral Fast Terminal Synergetic Control Scheme for a Grid-to-Vehicle and Vehicle-to-Grid Battery Electric Vehicle Charger Based on the Black-Winged Kite Algorithm

Author

Listed:
  • Ishak Aris

    (Department of Automatic, Faculty of Electrical Engineering, University of Sciences and Technology Houari Boumediene, El Alia-Bab Ezzouar, Algiers 16111, Algeria)

  • Yanis Sadou

    (Department of Automatic, Faculty of Electrical Engineering, University of Sciences and Technology Houari Boumediene, El Alia-Bab Ezzouar, Algiers 16111, Algeria)

  • Abdelbaset Laib

    (Department of Automatic, Faculty of Electrical Engineering, University of Sciences and Technology Houari Boumediene, El Alia-Bab Ezzouar, Algiers 16111, Algeria)

Abstract

The utilization of electric vehicles (EVs) has grown significantly and continuously in recent years, encouraging the creation of new implementation opportunities. The battery electric vehicle (BEV) charging system can be effectively used during peak load periods, for voltage regulation, and for the improvement of power system stability within the smart grid. It provides an efficient bidirectional interface for charging the battery from the grid and discharging the battery into the grid. These two operation modes are referred to as grid-to-vehicle (G2V) and vehicle-to-grid (V2G), respectively. The management of power flow in both directions is highly complex and sensitive, which requires employing a robust control scheme. In this paper, an Integral Fast Terminal Synergetic Control Scheme (IFTSC) is designed to control the BEV charger system through accurately tracking the required current and voltage in both G2V and V2G system modes. Moreover, the Black-Winged Kite Algorithm is introduced to select the optimal gains of the proposed IFTS control scheme. The system stability is checked using the Lyapunov stability method. Comprehensive simulations using MATLAB/Simulink are conducted to assess the safety and efficacy of the suggested optimal IFTSC in comparison with IFTSC, optimal integral synergetic, and conventional PID controllers. Furthermore, processor-in-the-loop (PIL) co-simulation is carried out for the studied system using the C2000 launchxl-f28379d digital signal processing (DSP) board to confirm the practicability and effectiveness of the proposed OIFTS. The analysis of the obtained quantitative comparison proves that the proposed optimal IFTSC provides higher control performance under several critical testing scenarios.

Suggested Citation

  • Ishak Aris & Yanis Sadou & Abdelbaset Laib, 2025. "An Optimal Integral Fast Terminal Synergetic Control Scheme for a Grid-to-Vehicle and Vehicle-to-Grid Battery Electric Vehicle Charger Based on the Black-Winged Kite Algorithm," Energies, MDPI, vol. 18(13), pages 1-31, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3397-:d:1689363
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    References listed on IDEAS

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    1. 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.
    2. Rubino, Luigi & Capasso, Clemente & Veneri, Ottorino, 2017. "Review on plug-in electric vehicle charging architectures integrated with distributed energy sources for sustainable mobility," Applied Energy, Elsevier, vol. 207(C), pages 438-464.
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    4. Zhai, Xiangyu & Li, Zening & Li, Zhengmao & Xue, Yixun & Chang, Xinyue & Su, Jia & Jin, Xiaolong & Wang, Peng & Sun, Hongbin, 2025. "Risk-averse energy management for integrated electricity and heat systems considering building heating vertical imbalance: An asynchronous decentralized approach," Applied Energy, Elsevier, vol. 383(C).
    5. Jia, Chunchun & Liu, Wei & He, Hongwen & Chau, K.T., 2025. "Superior energy management for fuel cell vehicles guided by improved DDPG algorithm: Integrating driving intention speed prediction and health-aware control," Applied Energy, Elsevier, vol. 394(C).
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