Machine learning-based MPPT integration with quadratic double-extended DC-DC converter for grid-connected PV-powered BLDC electric vehicles

Abstract To mitigate the environmental challenges posed by pollution and climate change, researchers are increasingly focused on developing highly efficient, emission-free transportation and energy systems. Electric Vehicles (EVs) implementation resolves the need for emission-free transportation, contributing to reduced air pollution and greenhouse gas emissions. Evs are utilized with advanced Brushless Direct Current (BLDC) motor for their efficiency and reliability, offering improved performance. For the purpose of decarbonized energy production, to power-up the BLDC motor, Renewable Energy Source (RES) based solar Photovoltaic (PV) system is equipped. However, to meet load demand due to insufficient DC output voltage attained from PV panels, this paper proposes a novel converter with an advanced Machine Learning (ML) based Maximum Power Point Tracking (MPPT) controller. A Quadratic Double Extended (QDE) DC-DC converter is proposed to improve voltage engendered from PV system, resulting with reduced voltage stress and improved gain. Moreover, for tracking of maximum power from PV, Sea Turtle Foraging optimized Radial Bias Function Neural Network (STFO-RBFNN) is introduced, ensuring tracking of most available power. The system incorporates supplementary sources including grid and battery to power BLDC motor of EV. The bidirectional converter together with battery enables flexible energy management by allowing for charging/discharging operations. A 3-phase Voltage Source Inverter (VSI) performs AC-DC conversion to drive the BLDC motor of EV. The simulation is done by MATLAB/Simulink and the results validate that introduced methodology significantly enhances performance, achieving improved converter efficiency (95.43%) with reduced Total Harmonic Distortion (THD) (1.14%) compared to state of art topologies.