Scholarlink

Photovoltaic (PV)-based electric vehicle (EV) systems are gaining traction due to their potential for sustainable energy solutions. One of the key challenges in this system is efficiently harnessing solar energy from the PV array, particularly under changing environmental conditions such as varying weather conditions. This novel presents an advanced PV-based EV motor system, integrating a hybrid quadratic boost SEPIC (QBS) converter and a flower pollination-optimized artificial neural network-based maximum power point tracking (FPOANN-MPPT) controller. The hybrid quadratic boost SEPIC converter combines the advantages of both converters effectively handle a wide input voltage range while stabilizing and boosting the voltage, thereby ensuring efficient energy utilization. The FPOANN-based MPPT controller managed in real-time variations in environmental conditions, significantly optimizing the power extraction process. As a result, the output power directed to the permanent magnet synchronous motor (PMSM), ensuring efficient operation and improved performance. To evaluate the effectiveness of this approach, simulations are carried out using MATLAB. The results reveal that the FPOANN-MPPT strategy, when paired with the hybrid converter, substantially enhances voltage gain, improves tracking precision, and boosts the overall efficiency of PV system relative to conventional methods. This innovative method utilized maximizes power and also ensures a more reliable and effective operation for the EV motor system.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing Electric Vehicle Performance Through Advanced Metaheuristic MPPT-Based PV System and PMSM Control

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • C. Ebbie Selva Kumar,
  • D. Karthikeyan,
  • E. Immanuelbright,
  • G. W. Martin,
  • K. S. Kavin,
  • Thirupathi Allam

摘要

Photovoltaic (PV)-based electric vehicle (EV) systems are gaining traction due to their potential for sustainable energy solutions. One of the key challenges in this system is efficiently harnessing solar energy from the PV array, particularly under changing environmental conditions such as varying weather conditions. This novel presents an advanced PV-based EV motor system, integrating a hybrid quadratic boost SEPIC (QBS) converter and a flower pollination-optimized artificial neural network-based maximum power point tracking (FPOANN-MPPT) controller. The hybrid quadratic boost SEPIC converter combines the advantages of both converters effectively handle a wide input voltage range while stabilizing and boosting the voltage, thereby ensuring efficient energy utilization. The FPOANN-based MPPT controller managed in real-time variations in environmental conditions, significantly optimizing the power extraction process. As a result, the output power directed to the permanent magnet synchronous motor (PMSM), ensuring efficient operation and improved performance. To evaluate the effectiveness of this approach, simulations are carried out using MATLAB. The results reveal that the FPOANN-MPPT strategy, when paired with the hybrid converter, substantially enhances voltage gain, improves tracking precision, and boosts the overall efficiency of PV system relative to conventional methods. This innovative method utilized maximizes power and also ensures a more reliable and effective operation for the EV motor system.