Uncertainty Quantification and Optimal Design of EV-WPT System Efficiency based on Adaptive Gaussian Process Regression

Abstract

Wireless power transfer (WPT) is a safe, convenient, and intelligent charging solution for electric vehicles. To address the problem of the susceptibility of transmission efficiency to large uncertainties owing to differences in coil and circuit element processing and actual driving levels, this study proposes the use of adaptive Gaussian process regression (aGPR) for the uncertainty quantification of efficiency. A WPT system efficiency aGPR surrogate model is constructed with a set of selected small-sample data, and the confidence interval and probability density function of the transmission efficiency are predicted. Finally, the reptile search algorithm is used to optimize the structure of the WPT system to improve efficiency.