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

Authors

  • T. H. Wang College of Instrumentation & Electrical Engineering Jilin University, Changchun 130026, China
  • K. F. Zhao College of Instrumentation & Electrical Engineering Jilin University, Changchun 130026, China
  • H. W. Duan College of Instrumentation & Electrical Engineering Jilin University, Changchun 130026, China
  • Q. Y. Yu College of Instrumentation & Electrical Engineering Jilin University, Changchun 130026, China
  • L. L. Xu College of Instrumentation & Electrical Engineering Jilin University, Changchun 130026, China
  • S. S. Guan College of Instrumentation & Electrical Engineering Jilin University, Changchun 130026, China

DOI:

https://doi.org/10.13052/2025.ACES.J.400307

Keywords:

Gaussian process regression, power transmission efficiency, uncertainty quantification, vehicle engineering, wireless power transfer

Abstract

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.

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Author Biographies

T. H. Wang, College of Instrumentation & Electrical Engineering Jilin University, Changchun 130026, China

Tianhao Wang received the B.S. degree in electrical engineering and the Ph.D. degree in vehicle engineering from Jilin University, Changchun, Jilin, China, in 2010 and 2016, respectively. From 2016 to 2019, he was a Post-Doctoral Researcher at the Department of Science and Technology of Instrument, Jilin University, where he is currently an Associate Professor with the College of Instrumentation and Electrical Engineering. His research interests include the uncertainty quantification of wireless power transfer of EVs and human electromagnetic exposure safety.

K. F. Zhao, College of Instrumentation & Electrical Engineering Jilin University, Changchun 130026, China

Kaifeng Zhao received the B.S. degree in electrical engineering and automation from the College of Instrumentation and Electrical Engineering, Jilin University, Changchun, Jilin, China, in 2023, where he is currently pursuing the M.S. degree in electrical engineering. His research interests include human electromagnetic safety protection and electromagnetic compatibility of EVs.

H. W. Duan, College of Instrumentation & Electrical Engineering Jilin University, Changchun 130026, China

Hongwei Duan received the B.S. degree in electrical engineering and automation from the College of Electrical Engineering, Northeast Electric Power University, Jilin, China, in 2024. He’s currently working on his M.S. degree at Jilin University, Jilin, China. Since 2024, he has worked on the operation and the application of micro-grid systems.

Q. Y. Yu, College of Instrumentation & Electrical Engineering Jilin University, Changchun 130026, China

Quanyi Yu received the B.S. and the M.S. degrees from the College of Communication Engineering, Jilin University, Changchun, Jilin, China, in 2016 and 2020, respectively, where he is pursuing the Ph.D. degree. His research interests include uncertainty quantification and electromagnetic compatibility of EVs.

L. L. Xu, College of Instrumentation & Electrical Engineering Jilin University, Changchun 130026, China

Linlin Xu received the B.S. degree and M.S. degree in electrical engineering and automation from the College of Instrumentation and Electrical Engineering, Jilin University, Changchun, Jilin, China, in 2020 and 2024, respectively. Her research interests include electromagnetic safety.

S. S. Guan, College of Instrumentation & Electrical Engineering Jilin University, Changchun 130026, China

Shanshan Guan received the B.S. degree in precision instruments and machinery and the Ph.D. degree in measurement technology and instruments from Jilin University, Changchun, Jilin, China, in 2008 and 2012, respectively. In 2019, she was a Visiting Scholar at the Southern University of Science and Technology, Shenzhen, China. She is currently an Associate Professor with the College of Instrumentation and Electrical Engineering, Jilin University. Her research interests include forward modeling and inverse algorithms of EM fields, and the development of electromagnetic instruments.

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Published

2025-03-30

How to Cite

[1]
T. H. . Wang, K. F. . Zhao, H. W. . Duan, . Q. Y. . Yu, L. L. . Xu, and S. S. . Guan, “Uncertainty Quantification of Transmission Efficiency in EV-WPT System Based on Gaussian Process Regression”, ACES Journal, vol. 40, no. 03, pp. 226–236, Mar. 2025.

Issue

2025: Vol 40 Iss 3

Section

General Submission

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