Intelligent Optimization and Real-Time Control of Wireless Power Transfer for Electric Vehicles

Abstract

Wireless Power Transfer (WPT) for Electric Vehicles (EVs) offers a promising solution for convenient and efficient charging. However, misalignments, sensor noise, and parameter variability can significantly degrade Power Transfer Efficiency (PTE). This study proposes a novel unified artificial intelligence (AI)-driven optimization and control framework that integrates Genetic Algorithm (GA)-based static optimization, Artificial Neural Network (ANN) surrogate modeling, and Reinforcement Learning (RL) dynamic control using the Proximal Policy Optimization (PPO) algorithm. This unified design bridges the gap between previous static-only optimization methods and dynamic adaptive controllers, enabling both peak efficiency and verified robustness within a single digital twin simulation environment. A high-fidelity MATLAB/Simulink model of the WPT system was developed and validated using an ANN surrogate model (Test MSE: 7.87×10−13). The GA-optimized configuration achieved a peak PTE of 85.47%, representing a 2.11 percentage-point improvement over the baseline. The RL controller, based on PPO, maintained a mean efficiency of approximately 80% under unseen trajectories, ±10% hardware parameter variations, and Gaussian sensor noise (σ=0.56%), demonstrating superior adaptability. Comparative analysis with state-of-the-art studies confirms that the proposed approach not only matches or exceeds the reported efficiency gains, but also uniquely integrates robustness validation and generalization testing. The results suggest that combining offline GA optimization with online RL adaptation provides a scalable, real-time control strategy for practical WPT deployments.