WIRELESS POWER TRANSFER SYSTEM EMBEDDED AI LOAD MANAGEMENT: SIMULATION AND EVALUATION

Abstract

^{Wireless Power Transfer (WPT) technology has emerged as a transformative solution for eliminating the physical constraints of wired power delivery in consumer electronics, electric vehicles, biomedical devices, and industrial automation. However, the dynamic and unpredictable nature of electrical loads in real-world applications often results in suboptimal energy efficiency, increased transmission losses, and reduced system lifespan. This paper proposes an Embedded Artificial Intelligence (AI) Load Management System integrated with a WPT platform to autonomously monitor, predict, and optimize power delivery in real time. The proposed system employs a hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) model to forecast load variations, dynamically adjust transmitter coil power levels, and maintain optimal efficiency across varying operating conditions. The architecture is designed for bidirectional data exchange between the power transmitter and receiver through an Internet of Things (IoT) communication layer, enabling remote diagnostics and adaptive control. Simulation experiments were conducted in MATLAB/Simulink to evaluate performance metrics, including power transfer efficiency, load balancing accuracy, and system response time. Results demonstrate an average 15–20% improvement in energy efficiency compared to traditional WPT systems without AI-based management. The integration of embedded AI into WPT not only enhances operational performance but also offers scalability for applications in smart grids, electric vehicle charging infrastructure, and autonomous IoT devices.}