Neural Network for Both Metal Object Detection and Coil Misalignment Prediction in Wireless Power Transfer
Yunyi Gong, Yoshitsugu Otomo, Hajime Igarashi
Abstract
This study proposes a method for wireless power transfer (WPT) systems to identify the existence of foreign metal objects and simultaneously predict the misalignment distance between the primary and secondary coils. The proposed method is based on a neural network (NN) trained using electromagnetic field simulations. The training data for the NN consist of differential voltages in the detection coils, together with the input voltage of the primary coil. Although the metallic objects and coil misalignment induce confusing voltages, the trained NN exhibits over 90% accuracy for the validation dataset and mean prediction errors of less than 1 mm for the misalignment distance and ground clearance variance.