A Data-Driven Model to Predict Mutual Inductance Between Planar Coils With Arbitrary Specifications and Positions

Abstract

The accurate prediction of mutual inductance in inductive planar coils is a critical challenge in advancing wireless power transfer (WPT) systems, particularly as traditional analytical methods struggle to balance precision and computational speed in complex, real-world scenarios. This study addresses these limitations by exploring data-driven algorithms for predicting mutual inductance. Additionally, the study offers a robust solution to handle the nonlinearities and dynamic requirements of three-dimensional coil configurations. Seven regression algorithms—linear, polynomial, kernel ridge, decision tree, random forest, support vector and neural network—are evaluated to identify the most effective approach. Key results reveal the superior performance of kernel ridge, support vector and neural network regression models, achieving R² scores of 0.995, 0.987 and 0.992, respectively. Kernel ridge regression demonstrated the lowest error metrics, with an MAE of 49.624 nH and an RMSE of 86.174 nH, whereas support vector and neural network regression followed closely with slightly higher errors. Conversely, traditional models such as linear regression and decision tree showed significantly higher MAEs and RMSEs, highlighting their inadequacy for handling the complexities of WPT datasets. This research establishes a scalable and accurate framework for mutual inductance prediction, paving the way for improved efficiency in WPT systems.