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<p>This paper introduces the design of a bidirectional grid-connected solar power electric vehicles (EVs) charging station (CS) with a focus on optimizing operational efficiency by minimizing grid power consumption. In this paper, a feed-forward Artificial Neural Network (ANN) is first employed to forecast the maximum power point (MPP) of a photovoltaic (PV) array using an extensive training dataset. To optimize the ANN training process, an adaptive Particle Swarm Optimization (PSO) algorithm is applied, achieving a tracking efficiency of 99.47% at a solar irradiance of 800 W/m<sup>2</sup>. Furthermore, to enhance the overall performance of the charging station, a Grey Wolf Optimization (GWO) algorithm is proposed for operational optimization. The GWO strategy minimizes grid power consumption while maximizing the utilization of solar energy, energy storage systems (ESS), and inter-vehicle power sharing. The proposed design incorporates full bridge bidirectional AC to DC converter (BDAC) with fewer switching devices and small inductive filter is employed to work as a rectifier in vehicles charging mode and inverter in vehicles discharging mode. To control the charge in EVs half-bridge bidirectional DC to DC (BDC) converter with only two switching devices and small inductive filter is employed. The overall performance of charging station at 800&#xa0;W/m<sup>2</sup> solar irradiation is 97.11% in mode-1, 97.62% in mode-2 and 97.17% in mode-3. The design of proposed charging station is formulated and validated in MATLAB/Simulink and simulation results are compared with the other PV based charging station in the literature.</p>

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Design and optimization of grid-connected solar powered electric vehicle charging station with novel adaptive ANN-PSO and GWO algorithm

  • 期刊论文

  • 发表日期：

    2026年7月21日

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  • Md Inayat Ali,
  • Rajib Kumar Mandal,
  • Amitesh Kumar

摘要

This paper introduces the design of a bidirectional grid-connected solar power electric vehicles (EVs) charging station (CS) with a focus on optimizing operational efficiency by minimizing grid power consumption. In this paper, a feed-forward Artificial Neural Network (ANN) is first employed to forecast the maximum power point (MPP) of a photovoltaic (PV) array using an extensive training dataset. To optimize the ANN training process, an adaptive Particle Swarm Optimization (PSO) algorithm is applied, achieving a tracking efficiency of 99.47% at a solar irradiance of 800 W/m2. Furthermore, to enhance the overall performance of the charging station, a Grey Wolf Optimization (GWO) algorithm is proposed for operational optimization. The GWO strategy minimizes grid power consumption while maximizing the utilization of solar energy, energy storage systems (ESS), and inter-vehicle power sharing. The proposed design incorporates full bridge bidirectional AC to DC converter (BDAC) with fewer switching devices and small inductive filter is employed to work as a rectifier in vehicles charging mode and inverter in vehicles discharging mode. To control the charge in EVs half-bridge bidirectional DC to DC (BDC) converter with only two switching devices and small inductive filter is employed. The overall performance of charging station at 800 W/m2 solar irradiation is 97.11% in mode-1, 97.62% in mode-2 and 97.17% in mode-3. The design of proposed charging station is formulated and validated in MATLAB/Simulink and simulation results are compared with the other PV based charging station in the literature.