Range-adaptive Impedance Matching of Wireless Power Transfer System Using a Machine Learning Strategy Based on Neural Networks

This work describes the implementation of a machine learning (ML) strategy based on the neural network for real-time range-adaptive automatic impedance matching of Wireless Power Transfer (WPT) applications. This approach for the effective prediction of the optimal parameters of the tunable matching network and classification range-adaptive transmitter coils (Tx) is introduced in this paper aiming to achieve an effective automatic impedance matching over a wide range of relative distances. We propose a WPT system consisting of a tunable matching circuit and 3 Tx coils which have different radius controlled by trained neural network models. The feedforward neural network algorithm was trained using 220 data and classifier's in pattern recognition accuracy were characterized. The proposed approach achieves a Power transfer efficiency (PTE) around 90% for ranges within 10 to 25cm, is reported.