Enhanced wireless power transfer for electric vehicles: A 7.2 kW ANN-based MPPT approach with LCC-LCC compensation topology

Abstract

Optimizing electric vehicle charging via wireless power transfer is achievable through various control strategies. One effective method involves integrating an Artificial Neural Network based Maximum Power Point Tracking controller with a double-sided LCC compensation topology. This system, designed for a 7.2 kW inductive power transfer operating at 80 kHz, harnesses solar energy as its primary input. The ANN-based MPPT controller is trained to establish the optimal duty cycle for the DC-DC converter. It accomplishes this by learning the intricate relationship between photovoltaic voltage, current, and the maximum power point. This approach ensures robust MPP tracking performance even in diverse environmental conditions, including partial shading and noise. Furthermore, the double-sided LCC compensation network significantly boosts the system's power transfer capability and overall efficiency. The proposed system was meticulously modeled and simulated using MATLAB/Simulink. Simulation outcomes confirm that the combined ANN-based MPPT controller and double-sided LCC compensation deliver superior power transfer efficiency and enhanced output performance when compared to conventional WPT systems used for EV charging.