Magnetic field estimation using Gaussian process regression for interactive wireless power system design

TL;DR

This paper introduces a Gaussian Process Regression-based method for rapid magnetic field and efficiency estimation in wireless power transfer systems, enabling interactive design with high accuracy and low latency.

Contribution

It presents the first application of GPR for fast magnetic field estimation in wireless power systems, incorporating adaptive grids and active learning for improved accuracy.

Findings

Achieves sub-second magnetic field estimation latency.

Maintains less than 6% average error compared to electromagnetic simulations.

Enables interactive system design with high computational efficiency.

Abstract

Wireless power transfer (WPT) with coupled resonators offers a promising solution for the seamless powering of electronic devices. Interactive design approaches that visualize the magnetic field and power transfer efficiency based on system geometry adjustments can facilitate the understanding and exploration of the behavior of these systems for dynamic applications. However, typical electromagnetic field simulation methods, such as the Method of Moments (MoM), require significant computational resources, limiting the rate at which computation can be performed for acceptable interactivity. Furthermore, the system's sensitivity to positional and geometrical changes necessitates a large number of simulations, and structures such as ferromagnetic shields further complicate these simulations. Here, we introduce a machine learning approach using Gaussian Process Regression (GPR), demonstrating for the first time the rapid estimation of the entire magnetic field and power transfer efficiency for near-field coupled systems. To achieve quick and accurate estimation, we develop 3D adaptive grid systems and an active learning strategy to effectively capture the nonlinear interactions between complex system geometries and magnetic fields. By training a regression model, our approach achieves magnetic field computation with sub-second latency and with an average error of less than 6% when validated against independent electromagnetic simulation results.