Optimization of Wireless Charging System Parameters via Intelligent Algorithms

Abstract

Against the backdrop of rapid digitalization and intelligent development, wireless charging has emerged as a vital power supply method for modern electronic devices due to its non-contact and convenient advantages. However, it remains constrained by issues such as efficiency degradation with distance and angle, interference from complex electromagnetic environments, and resource conflicts during simultaneous charging of multiple devices. Centered on the theme of “Optimizing Wireless Charging System Parameters Using Intelligent Algorithms,” this study first systematically reviews the current state of research worldwide in system modeling, magnetic coupling mechanisms, compensation topologies, anti-offset techniques, and electromagnetic compatibility. It identifies gaps in addressing dynamic complex operating conditions and multi-objective cooperative optimization. Subsequently, it proposes a multi-objective dynamic optimization approach centered on an enhanced genetic algorithm, supplemented by machine learning-based interference identification and resource allocation strategies. This approach targets critical parameters including coil spacing, transmission frequency, power, and charging sequence. A simulation platform was constructed using MATLAB/Simulink, and validation was conducted on a self-built hardware prototype under four typical operating conditions: single-load operation, offset tolerance, simultaneous multi-device power delivery, and complex electromagnetic interference. Results demonstrate: the optimized system exhibits significantly enhanced efficiency, concurrently improved offset tolerance and interference resistance. Power distribution remains balanced across multi-device scenarios while maintaining robust overall system performance. All trends consistently persist through multiple iterations and hardware upgrades. This research provides theoretical foundations and engineering pathways for scaling wireless charging technology in smart home and electric vehicle applications, offering significant academic value and broad practical prospects.