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<p>This manuscript proposes a hybrid approach to analyze the efficiency of an electric vehicle (EV) system integrated with a solar photovoltaic (PV)-based switched reluctance motor (SRM). The proposed hybrid approach combines both the giant trevally optimizer(GTO) and the dilated residual neural network (DRNN) technique, commonly referred to as the GTO-DRNN approach. GTO provides control among the PI controller and DRNN predicts the optimal control of the PI controller. Electric vehicle employing the solar powered PV involves the maximum power point tracking (MPPT) for regulating the output of the sources. The major objective of the proposed technique is to lessen the cost and enhance the system's efficiency. The proposed model is done in MATLAB software and compared to the different existing techniques like seagull optimization algorithm, grasshopper optimization algorithm, and wild horse optimizer methods. The proposed GTO-DRNN method obtains higher efficiency than existing approaches.</p>

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Efficiency assessment of EV system integrated with solar PV based SRM using hybrid approach

  • 期刊论文

  • 发表日期：

    2025年2月23日

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  • B. Devi,
  • S. Edwin Jose

摘要

This manuscript proposes a hybrid approach to analyze the efficiency of an electric vehicle (EV) system integrated with a solar photovoltaic (PV)-based switched reluctance motor (SRM). The proposed hybrid approach combines both the giant trevally optimizer(GTO) and the dilated residual neural network (DRNN) technique, commonly referred to as the GTO-DRNN approach. GTO provides control among the PI controller and DRNN predicts the optimal control of the PI controller. Electric vehicle employing the solar powered PV involves the maximum power point tracking (MPPT) for regulating the output of the sources. The major objective of the proposed technique is to lessen the cost and enhance the system's efficiency. The proposed model is done in MATLAB software and compared to the different existing techniques like seagull optimization algorithm, grasshopper optimization algorithm, and wild horse optimizer methods. The proposed GTO-DRNN method obtains higher efficiency than existing approaches.