Conveniently Identify Coils in Inductive Power Transfer System Using Machine Learning

TL;DR

This paper introduces a machine learning method that quickly identifies coil parameters in inductive power transfer systems using images and frequency data, eliminating the need for costly equipment or disassembly.

Contribution

It presents a novel, image-based machine learning approach for rapid coil parameter identification in IPT systems, bypassing traditional measurement and simulation limitations.

Findings

Accurate coil parameter identification using images and frequency data.

Rapid and cost-effective alternative to traditional measurement methods.

Applicable to complex coil designs without disassembly or extensive simulation.

Abstract

High-frequency inductive power transfer (IPT) has garnered significant attention in recent years due to its long transmission distance and high efficiency. The inductance values L and quality factors Q of the transmitting and receiving coils greatly influence the system's operation. Traditional methods involved impedance analyzers or network analyzers for measurement, which required bulky and costly equipment. Moreover, disassembling it for re-measurement is impractical once the product is packaged. Alternatively, simulation software such as HYSS can serve for the identification. Nevertheless, in the case of very high frequencies, the simulation process consumes a significant amount of time due to the skin and proximity effects. More importantly, obtaining parameters through simulation software becomes impractical when the coil design is more complex. This paper firstly employs a machine learning approach for the identification task. We simply input images of the coils and operating frequency into a well-trained model. This method enables rapid identification of the coil's L and Q values anytime and anywhere, without the need for expensive machinery or coil disassembly.