Artificial Neural Network-Based Parameter Identification Method for Wireless Power Transfer Systems

In this paper, a Wireless Power Transfer (WPT) system parameter identification method that combines an artificial neural network and system modeling is presented. During wireless charging, there are two critical parameters; specifically, mutual inductance and load resistance, which change due to the movement of the transmitter/receiver and battery conditions. The identification of these two uncertain parameters is an essential prerequisite for the implementation of feedback control. The proposed method utilizes an Artificial Neural Network (ANN) to acquire a mutual inductance value. A succinct system model is formulated to calculate the load resistance of the remote receiver. The maximum error of the mutual inductance estimation is 2.93%, and the maximum error of the load resistance estimation is 7.4%. Compared to traditional methods, the proposed method provides an alternative way to obtain mutual inductance and load resistance using only primary-side information. Experimental results were provided to validate the effectiveness of the proposed method.

Automated summary

This paper presents a Wireless Power Transfer(WPT) system parameter identification method using an artificial neural network and system modeling. The proposed method is able to estimate the values of mutual inductance and load resistance accurately, and it provides an alternative way to obtain these parameters using only primary-side information.