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<p>Environmental reasons are contributing to the growing popularity of electric vehicles (EVs) in automotive industry. Since EVs rely on electrical energy for propulsion, a dependable, cost-effective, and efficient charging system is required to provide the stable power needed for EV motor operation. A novel converter is proposed that works with an Artificial Intelligence (AI) Maximum Power Point Tracking (MPPT) controller to efficiently track PV power while achieving the desired output voltage. The proposed Soft Switching Two Winding (S<sup>2</sup>TW) converter, particulary designed for boosting of PV voltage to power the Brushless DC (BLDC) Motor of EV, offers increased voltage gain, continuous input current and reduced switching losses. The integration of Genghis Khan Shark Optimization (GKSO) -based Recurrent Neural Network (RNN) MPPT system enhances the efficiency of energy harvesting from the PV panels, ensuring that the EV receives maximum power under dynamic conditions.The system is integrated with grid and batteries to achieve efficient power management. This energy storage solution supports in improving EV performance ensuring uninterrupted operation even in low-sunlight conditions or during grid outages. Finally, the DC link voltage is efficiently utilized to drive the three-phase BLDC motor of the EV through a three-phase Voltage Source Inverter (VSI) controlled by a Proportional-Integral (PI) controller. MATLAB simulation outcomes using the developed system reveals efficiency of 97.6%, a 1.46% Total Harmonic Distortion (THD) and effective power management capability.</p>

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Effective Power Management and Advancement in PV Based EV Charging System Using S2TW Converter with Optimized MPPT

  • 期刊论文

  • 发表日期：

    2026年6月30日

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  • E. Maheswari,
  • K. Natarajan,
  • K. Sakthidhasan,
  • R. K. Padmashini

摘要

Environmental reasons are contributing to the growing popularity of electric vehicles (EVs) in automotive industry. Since EVs rely on electrical energy for propulsion, a dependable, cost-effective, and efficient charging system is required to provide the stable power needed for EV motor operation. A novel converter is proposed that works with an Artificial Intelligence (AI) Maximum Power Point Tracking (MPPT) controller to efficiently track PV power while achieving the desired output voltage. The proposed Soft Switching Two Winding (S2TW) converter, particulary designed for boosting of PV voltage to power the Brushless DC (BLDC) Motor of EV, offers increased voltage gain, continuous input current and reduced switching losses. The integration of Genghis Khan Shark Optimization (GKSO) -based Recurrent Neural Network (RNN) MPPT system enhances the efficiency of energy harvesting from the PV panels, ensuring that the EV receives maximum power under dynamic conditions.The system is integrated with grid and batteries to achieve efficient power management. This energy storage solution supports in improving EV performance ensuring uninterrupted operation even in low-sunlight conditions or during grid outages. Finally, the DC link voltage is efficiently utilized to drive the three-phase BLDC motor of the EV through a three-phase Voltage Source Inverter (VSI) controlled by a Proportional-Integral (PI) controller. MATLAB simulation outcomes using the developed system reveals efficiency of 97.6%, a 1.46% Total Harmonic Distortion (THD) and effective power management capability.