AI-Enabled Control Architectures for Dynamic Wireless Power Transfer Systems in Electric Vehicles: An Analytical Review

The rapid growth of the Electric Vehicle (EV)market has intensified the need for efficient, reliable, andintelligent charging technologies. Among various EV chargingapproaches, Dynamic Wireless Power Transfer (DWPT) hasemerged as a promising solution to mitigate range anxiety andreduce long charging durations, especially for highwayapplications. However, accurately modelling, controlling, andanalysing DWPT systems remains highly challenging due totheir complex interaction between fast-changing transportationdynamics and underlying power electronic systems. In thiscontext, the integration of advanced control techniques—particularly those driven by Artificial Intelligence (AI)—hasbecome increasingly essential for enhancing power transferefficiency, maintaining system stability, and ensuringoperational safety.This review systematically categorizes and evaluatestraditional and AI-enabled control strategies used in DWPTsystems, including frequency-tuned control, phase-shiftcontrol, amplitude modulation, adaptive hybrid methods, andintelligent AI-based controllers such as machine learningpredictors, reinforcement learning optimizers, and digitaltwin-assisted control frameworks. It further analysesmathematical models and analytical tools used to representsystem dynamics, offering a comparative assessment ofcontroller performance based on efficiency, misalignmenttolerance, power transfer capability, and robustness undervehicle motion.The review also highlights practical implementationchallenges, such as real-time parameter estimation, mutualinductance variability, foreign object detection, andcommunication delays, while identifying emergingopportunities where AI can significantly improve predictivecontrol, autonomous tuning, and system-wide optimization. Bysynthesizing recent technological advancements andaddressing existing research gaps, this review aims to providea comprehensive foundation for future research anddevelopment in AI-assisted DWPT systems for next-generationelectric mobility.