Symbolic Regression Method for Estimating Distance Between Two Coils of an Inductive Wireless Power Transfer System

Abstract

Symbolic regression (SR) has emerged as a powerful tool for the characterization of Wireless Power Transfer (WPT) systems, estimating the distance between coils and finding the relationship between frequency and phase so as to find the best frequency to increase the power factor. This study explores the application of SR on both simulated and experimental data, demonstrating its effectiveness with low prediction errors. SR employs a genetic algorithm to identify the analytical formula that best represents the input–output relationship, combining the strengths of traditional machine learning and analytical modeling. The results, with prediction errors of less than 1%, indicate that SR not only enhances predictive accuracy but also provides insights into the underlying physical principles governing WPT systems. This dual advantage positions SR as a valuable method for optimizing WPT applications, paving the way for further research and development in this field