Ultra-Fast and Efficient Design Method Using Deep Learning for Capacitive Coupling WPT System

TL;DR

This paper introduces a deep learning-based design method for capacitive coupling wireless power transfer systems, significantly reducing design time and computational resources compared to traditional electromagnetic simulation-based optimization.

Contribution

It replaces electromagnetic simulation with a ResNet-18 neural network for rapid design optimization of pixelated microstrip plates in CCWPT systems.

Findings

Design process is 3629 times faster than traditional CST-based methods.

The AI-based method achieves MAE of 110 MHz for resonance frequency prediction.

Prototype measurements validate the accuracy of the AI-driven design approach.

Abstract

Capacitive coupling wireless power transfer (CCWPT) is one of the pervasive methods to transfer power in the reactive near-field zone. In this paper, a flexible design methodology based on Binary Particle Swarm Optimization (BPSO) algorithm is proposed for a pixelated microstrip structure. The pixel configuration of each parallel plate (43x43 pixels) determines the frequency response of the system (S-parameters) and by changing this configuration, we can achieve the dedicated operating frequency (resonance frequency) and its related |S21| value. Due to the large number of pixels, iterative optimization algorithm (BPSO) is the solution for designing a CCWPT system. However, the output of each iteration should be simulated in electromagnetic simulators (e.g., CST, HFSS, etc.), hence, the whole optimization process is time-consuming. This paper develops a rapid, agile and efficient method for designing two parallel pixelated microstrip plates of a CCWPT system based on deep neural networks. In the proposed method, CST-based BPSO algorithm is replaced with an AI-based method using ResNet-18. Advantages of the AI-based iterative method are automatic design process, more efficient, less time-consuming, less computational resource-consuming and less background EM knowledge requirements compared to the conventional techniques. Finally, the prototype of the proposed simulated structure is fabricated and measured. The simulation and measurement results validate the design procedure accuracy, using AI-based BPSO algorithm. The MAE (Mean Absolute Error) of prediction for the main resonance frequency and related |S21| are 110 MHz and 0.18 dB, respectively and according to the simulation results, the whole design process is 3629 times faster than the CST-based BPSO algorithm.