Parameter Optimization of Wireless Power Transfer Based on Machine Learning

Heng Zhang, Manwen Liao, Liangxi He, Chi-Kwan Lee

Wireless power transfer (WPT) has become a crucial feature in numerous electronic devices, electric appliances, and electric vehicles. However, traditional design methods for WPT suffer from numerous drawbacks, such as time-consuming computations and high error counts due to inaccurate model parameters. As artificial intelligence (AI) continues to gain traction across industries, its ability to provide quick decisions and solutions makes it highly attractive for system optimizations. In this paper, a method for optimizing WPT parameters based on machine learning is proposed. The convolutional neural network is adapted for training and predicting the performance of a pair of coupled coils under a set of input parameters. The performance parameters include the spatial magnetic field distribution map, quality factor, inductance value, and mutual inductance value, which are critical in determining the efficiency and selecting optimal coil parameters such as the number of turns and wire diameter. Moreover, the spatial magnetic field distribution map is also helpful for identifying design compliance with the electromagnetic field safety standards. The training results reveal that the proposed method takes an average of 3.2 ms with a normalized image prediction error of 0.0034 to calculate the results to calculate one set of parameters, compared to an average of 23.74 s via COMSOL. This represents significant computational time savings while still maintaining acceptable computational accuracy.