Optimal Structure Design of Ferromagnetic Cores in Wireless Power Transfer by Reinforcement Learning

Abstract

In this paper, a reinforcement learning algorithm is applied for the first time to find a ferromagnetic core structure with optimal coupling coefficient between transmitting (Tx) and receiving (Rx) coils of a wireless power transfer (WPT) system. Since formula-based theoretical design is not available due to the non-linear magnetic field distortion stems from the presence of the ferromagnetic core in a WPT system, the proposed design has been achieved through finite element analysis (FEA) simulation-based data learning. The proposed design methods are so general that they can be applied to any conventional WPT coil types. We applied the proposed algorithm to the ferromagnetic core structure design of a simple dipole coil first. By training only 2.3 % data out of total possible cases, it is experimentally verified that the core structure obtained by the proposed method has a coupling coefficient 7 % higher than that of the example design level in the case of 98 cm distance between Tx and Rx coils.

Keywords

• Ferromagnetic core
• wireless power transfer (WPT)
• coil design
• reinforcement learning
• neural network