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.
Highlights
In 2016, people of the world were astonished at the result of AlphaGo versus Lee Sedol, known as the Google DeepMind Challenge Match [1]
COMPARISON WITH GENETIC ALGORITHM The goal of this study is to find out the ferromagnetic core structure having a high coupling coefficient between the
An optimal structure design of ferromagnetic cores in wireless power transfer (WPT) by reinforcement learning algorithm has been proposed in this paper for the first time
Summary
In 2016, people of the world were astonished at the result of AlphaGo versus Lee Sedol, known as the Google DeepMind Challenge Match [1]. In the conventional method for developing the ferromagnetic core layout design of a WPT system, the initial structure design for obtaining high coupling coefficient was mostly designed by the experience, creativity, and intuition of skilled WPT designers. After this initial step, many times of simulation and experiment were conducted for the detailed design until specific criteria are satisfied such as maximum efficiency or load power. The machine learning can be used to learn the characteristic of non-linear magnetic field distortion caused by ferromagnetic core in a WPT system, and to determine the optimal layout of ferromagnetic core having high performance. With application of the machine learning algorithm to the core structure design of a WPT system, because it is possible to find an innovative core structure of Tx and Rx coils that transcends the existing knowledge and to establish an opportunity to generalize it, it is expected that this will open a new chapter in future studies of WPT technology
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