Optimized Wireless Power Transfer for Dynamic EV Charging Using a Hybrid Binary Waterwheel Plant Optimization Algorithm and Finite Element Interpolated Neural Network

Wireless Power Transfer (WPT) systems have emerged as a promising solution for electric vehicle (EV) charging by allowing contactless energy transfer between stationary coils. Despite their potential, conventional WPT systems often face challenges such as suboptimal power transfer efficiency, energy losses and sensitivity to coil misalignment. To address these issues, this paper presents a novel hybrid optimization framework to improve the performance of WPT systems for dynamic EV charging.The proposed framework integrates the Binary Waterwheel Plant Optimization Algorithm (BWPOA) with a Finite Element Interpolated Neural Network (FEINN), forming the BWPOA-FEINN approach. The objective is to optimize power transmission and accurately predict the coupling coefficient between transmitter and receiver coils, thus improving power transfer efficiency and reducing energy losses.BWPOA is used to determine optimal operating parameters, while FEINN enhances predictive performance under varying conditions. MATLAB platform is used to simulate the proposed method in a WPT environment and to benchmark against existing methods like Particle Swarm Algorithm (PSA), Improved Simulated Annealing Algorithm (ISAA) and Adaptive Path Generation and Solution Algorithm (APGSA).The proposed method achieves a power transfer efficiency of 95%, low electricity loss of 2% and a prediction accuracy of 97%. These findings confirm the efficiency of the proposed method in enhancing energy transfer performance while ensuring accurate coupling prediction in WPT systems.