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<p>A robust and efficient charging infrastructure is necessary due to the growing popularity of electric vehicles (EVs). A solar photovoltaic (PV)-based EV charging station's energy-efficient multi-level inverter technology is suggested by this manuscript. The Improved Spider Wasp Optimizer (ISWO), an optimization method enhanced by Beluga Whale Optimization (BWO), and the Evolutionary Gravity Neocognitron Neural Network (EGravity-NCNN) are integrated into the system. The goals are to lower switching losses, limit harmonics, and enhance the inverter system's efficiency and power quality. To minimize reliance on the grid, the ISWO algorithm is used to optimize solar energy usage and schedule EV charging sessions. A deep learning-based prediction model is created to estimate solar energy generation and EV charging demands. MATLAB simulations are used to assess critical performance indicators, including total harmonic distortion (THD), charging time, and operating efficiency. The simulation results proved that efficiency improvement in EV charging using this system was around 25%, reducing dependency on grid supply by around 20%, and lower THD compared to other conventional and PSO-based techniques.</p>

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Optimization of energy-efficient multi-level inverter for solar PV-based EV charging and scheduling in multi-EV charging stations

  • 期刊论文

  • 发表日期：

    2026年8月3日

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  • P. Meenalochini,
  • Santosh Mani,
  • F. X. Edwin Deepak,
  • Rajesh G. Bodkhe,
  • Ramesh Chandra Khamari

摘要

A robust and efficient charging infrastructure is necessary due to the growing popularity of electric vehicles (EVs). A solar photovoltaic (PV)-based EV charging station's energy-efficient multi-level inverter technology is suggested by this manuscript. The Improved Spider Wasp Optimizer (ISWO), an optimization method enhanced by Beluga Whale Optimization (BWO), and the Evolutionary Gravity Neocognitron Neural Network (EGravity-NCNN) are integrated into the system. The goals are to lower switching losses, limit harmonics, and enhance the inverter system's efficiency and power quality. To minimize reliance on the grid, the ISWO algorithm is used to optimize solar energy usage and schedule EV charging sessions. A deep learning-based prediction model is created to estimate solar energy generation and EV charging demands. MATLAB simulations are used to assess critical performance indicators, including total harmonic distortion (THD), charging time, and operating efficiency. The simulation results proved that efficiency improvement in EV charging using this system was around 25%, reducing dependency on grid supply by around 20%, and lower THD compared to other conventional and PSO-based techniques.