Uncertainty Quantification of Transmission Efficiency in EV-WPT System Based on Gaussian Process Regression

The power transfer efficiency of electric vehicle wireless power transmission (EV-WPT) systems is susceptible to differences in the processing of coils and circuit components as well as the driver’s operating level. In order to quantify the uncertainty and save the computational cost, this paper adopts the Gaussian process regression (GPR) agent model to obtain predicted confidence intervals and transmission efficiency probability density function and calculates the response surface based on the agent model, and finally analyzes the degree of the influence of each variable on transmission efficiency by using the Morris one-at-a-time (MOAT) method. The computational time cost of the GPR agent-based model uncertainty quantification method obtained through simulation experiments is 9 hours and 21 minutes, which improves the computational time by 94.5% compared to the Monte Carlo (MC) method. The prediction error of the predicted values of the GPR agent model is only 1.0294% of the measured values, and its variance error is only 3.5587% of the measured values, so that the GPR agent model is able to realize uncertainty quantification (UQ) accurately and efficiently. Results show that the offset between the coupling mechanism and the diameter of the transmitting coil cross-section are the main factors affecting transmission efficiency.