Machine Learning Assisted Characteristics Prediction for Wireless Power Transfer Systems

Abstract

One of the main challenges in wireless power transfer (WPT) devices is performance degradation when the receiver’s position and characteristics vary. Therefore, the load resistance and receiver position must be monitored to ensure proper optimization of power transfer. This study proposes a machine learning (ML) assisted method that estimates the power delivered to the receiver by only using measurements at the transmitter side. Based on the delivered power estimation, we also propose a method to identify if the system efficiency is too low, so that the transmitter should be turned off. This activation control method can be useful in multi-transmitter WPT systems. In addition, we propose an ML method to estimate the load resistance and the coupling coefficient. Using the proposed method, the characteristics of an inductor-capacitor-capacitor (LCC)-Series tuned WPT system are successfully predicted only using the measured root-mean-square and the harmonic contents of the input current. The proposed approach is experimentally validated using a laboratory prototype.

Keywords

• Wireless power transfer
• machine learning
• coupling strength estimation
• load resistance estimation