Data-Driven Output Prediction and Efficiency Optimization Control for Wireless Power Transfer Systems

Existing research on efficiency optimization for wireless power transfer (WPT) systems is limited by complicated models and low control accuracy. In this paper, a data-driven efficiency optimization control method is proposed for phase-shift controlled WPT systems. This method integrates an experimental data-driven output prediction and efficiency optimization approach, driven by a multilayer perceptron (MLP) and particle swarm optimization (PSO). First, the MLP model establishes a nonlinear mapping between phase shift angles and key performance indicators, including output power and system efficiency. Subsequently, the PSO algorithm determines the optimal phase angle combination that maximizes efficiency for a given reference power. In addition, the PSO-generated control parameters are regressed using lightweight artificial neural networks (ANN), enabling real-time deployment onto micro-controllers. To verify the accuracy of the proposed data-driven efficiency optimization control WPT method with MLP and PSO, an experimental 430 W/85 kHz prototype is built. The measured results show that the mean absolute percentage error (MAPE) of the MLP predictive model is less than 3.5% across all selected load conditions. Meanwhile, the efficiency tracking algorithm driven by PSO ensures high-efficiency operation under variation of the reference power, load resistance, and mutual inductance with a maximum efficiency of 93.5%.