Self-Tuning Wireless Power Transfer System Using Reinforcement Learning for Dynamic Electric Vehicle Charging

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2026 | Volume : 04 | 02 | Page :
By

Bibhu Prasad Ganthia,

Subash Ranjan Kabat,

  1. Assistant Professor, Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India
  2. Associate Professor, Electrical Engineering, Indira Gandhi Institute of Technology, Sarang, Dhenkanal, Odisha, India

Abstract

The rapid growth of electric vehicles (EVs) has intensified the demand for efficient, reliable, and user-friendly wireless power transfer (WPT) systems capable of supporting dynamic charging under varying operating conditions. This paper presents a self-tuning wireless power transfer system employing deep reinforcement learning (DRL) to optimize power transfer efficiency in real time. Unlike conventional WPT controllers that rely on fixed compensation parameters or predefined control rules, the proposed framework continuously learns the optimal operating strategy by monitoring coil misalignment, air-gap variation, vehicle speed, battery state-of-charge, and load fluctuations. A DRL agent adaptively regulates the inverter switching frequency, compensation network, and output voltage to maximize transmission efficiency while minimizing power losses and electromagnetic stress. The proposed architecture integrates high-frequency resonant converters, intelligent sensing modules, and an edge-computing controller for low-latency decision-making during dynamic EV charging. Simulation studies performed under diverse driving and environmental conditions demonstrate that the proposed self-tuning approach achieves higher power transfer efficiency, improved charging stability, faster transient response, and superior tolerance to coil misalignment compared with conventional proportional–integral and model predictive controllers. The framework also reduces switching losses and enhances system reliability, making it suitable for next-generation intelligent transportation infrastructure. The proposed reinforcement learning-enabled WPT system offers a scalable, adaptive, and energy-efficient solution for future autonomous electric vehicle charging applications.

Keywords: Wireless Power Transfer (WPT); Deep Reinforcement Learning; Dynamic Electric Vehicle Charging; Resonant Power Converter; Adaptive Control; Coil Misalignment Compensation.

How to cite this article: Bibhu Prasad Ganthia, Subash Ranjan Kabat. Self-Tuning Wireless Power Transfer System Using Reinforcement Learning for Dynamic Electric Vehicle Charging. International Journal of Advanced Control and System Engineering. 2026; 04(02):-.
How to cite this URL: Bibhu Prasad Ganthia, Subash Ranjan Kabat. Self-Tuning Wireless Power Transfer System Using Reinforcement Learning for Dynamic Electric Vehicle Charging. International Journal of Advanced Control and System Engineering. 2026; 04(02):-. Available from: https://journals.stmjournals.com/ijacse/article=2026/view=253126

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Ahead of Print Subscription Review Article
Volume 04
02
Received 01/08/2026
Accepted 06/08/2026
Published 22/08/2026
Publication Time 21 Days

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