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<p>In this paper, a brain-inspired learning (BIL) controller for grid-connected electric vehicle (EV) charging junction is developed and analyzed. The system configuration combines a utility grid, nonlinear loads, a voltage source converter (VSC), and a bidirectional EV charger. The system is controlled and analyzed to study the smooth operation of battery charge and discharge of the EV. Further, a VSC is realized as a custom power device (CPD) that improves power quality (PQ) problems caused by nonlinear loads, providing reactive power support and correcting unbalances in the grid current. The paper focuses on designing a biologically inspired intelligent control technique for controlling the VSC. The approach mimics the mammalian brain behavior and inspires the development of brain-inspired learning (BIL) techniques for controlling VSC. The developed controller is further synchronized with a bidirectional EV charger to regulate smooth operation in both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) conditions. The designed BIL technique offers the advantages of quick learning, minimum error, and fast response with medium computational complexity. The response of the designed controller under grid variations is also presented. A suitable experimental setup is developed and investigated for its performance evaluation under steady-state and dynamic load situations. The comparative analysis of the BIL control algorithm with synchronous reference frame (SRF) and least mean square (LMS) techniques during load change is presented. The paper also presents a detailed analysis using Bode plots.</p>

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Brain-Inspired Learning Controller for Grid-Connected Electric Vehicle Charging Junction with Improved Power Quality

  • 期刊论文

  • 发表日期：

    2025年5月22日

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  • Amarendra Pandey,
  • Alka Singh,
  • Sudhanshu Mittal

摘要

In this paper, a brain-inspired learning (BIL) controller for grid-connected electric vehicle (EV) charging junction is developed and analyzed. The system configuration combines a utility grid, nonlinear loads, a voltage source converter (VSC), and a bidirectional EV charger. The system is controlled and analyzed to study the smooth operation of battery charge and discharge of the EV. Further, a VSC is realized as a custom power device (CPD) that improves power quality (PQ) problems caused by nonlinear loads, providing reactive power support and correcting unbalances in the grid current. The paper focuses on designing a biologically inspired intelligent control technique for controlling the VSC. The approach mimics the mammalian brain behavior and inspires the development of brain-inspired learning (BIL) techniques for controlling VSC. The developed controller is further synchronized with a bidirectional EV charger to regulate smooth operation in both grid-to-vehicle (G2V) and vehicle-to-grid (V2G) conditions. The designed BIL technique offers the advantages of quick learning, minimum error, and fast response with medium computational complexity. The response of the designed controller under grid variations is also presented. A suitable experimental setup is developed and investigated for its performance evaluation under steady-state and dynamic load situations. The comparative analysis of the BIL control algorithm with synchronous reference frame (SRF) and least mean square (LMS) techniques during load change is presented. The paper also presents a detailed analysis using Bode plots.