Precise Modeling of the Self-Inductance of Circular Coils with Deep Neural Networks

The circular coil is extensively employed in different wireless power transfer (WPT) applications due to its simple structure and satisfactory magnetic coupling capabilities. Different analytical models have been proposed to calculate the inductance of circular coils and some of them have achieved relatively high accuracies for coreless circular coils. However, the influence of ferrite plates is not fully considered in these studies, thus limiting their application potential. Therefore, a new analytical model and practical calculation method of circular coil's inductance are proposed in this paper. First, the parametric structure model for circular coils is derived, and the analytical inductance model is proposed based on Neumann's Formula. Second, the image current method is adopted to calculate the inductances of circular coils when ferrite plates are added to WPT systems. Then, a new Feedforward Neural Netweork (FNN) model is designed and trained to improve the computation accuracy of the proposed analytical inductance model for different circular coils in WPT systems. The proposed model exhibits exceptional precision and stability, which makes it promising in coil designing and optimization for diverse applications.