Achieving High-Performance Dynamic Wireless Charging through Model-Free Predictive Control: Theoretical Insights and Practical Implementations

Abstract

Dynamic Wireless Charging (DWC) for Electric Vehicles (EVs) is constrained by a critical, dual-sided control challenge. A receiver-side voltage regulation bottleneck, caused by severe disturbances, severely hampers the transmitter's ability to achieve Maximum Energy Efficiency Tracking (MEET) on a non-monotonic landscape, especially at high speeds. This thesis develops a systematic solution using Model-Free Predictive Control (MFPC), advancing both its theory and practical implementation for DWC systems. First, a novel unified modelling framework is developed to analyze and generalize MFPC strategies. This systematic tool predicts controller behavior, expedites the design process, and reveals a new structural design criterion for enhancing performance robustness. Second, a specific MFPC controller, MFC1, is proposed for the power receiver to eliminate the voltage regulation bottleneck. Small-signal analysis and comprehensive experiments validate that MFC1’s cascaded control structure inherently enhances disturbance rejection compared to Proportional-Integral (PI) control. Finally, a multi-layered adaptive MFPC (ad-MFPC) is developed for ultra-fast MEET. It overcomes the fundamental limitations of standard MFPC for non-monotonic tasks by integrating two key innovations: an adapted Ultra-Local Model (ULM) with gradient-sign detection and a dual-factor adaptive step-size mechanism. Experimental validation demonstrates the superiority of the ad-MFPC in achieving both rapid transient and robust performance.