Machine Learning Based Design of Magnetic Coupler for Wireless Power Transfer

This paper presents a magnetic coupler design method for wireless power transfer (WPT) systems, which is based on machine-learning algorithms. A synthetic dataset generated by ANSYS-Maxwell is used for training and evaluating machine-learning models. The trained model can obtain the optimal values of the coil inner radius R-i and number of turns N, when other coil design parameters such as the coil outer radius R-o, the wire diameter R-w, and the coil inductance L, are given based on the application environment. The proposed method provides a practical solution to design of magnetic couplers with ferrite cores and also accelerates the design compared with conventional methods.