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

被引：0
|
作者：
Shang, Xinlei [1 ]
Xu, Linlin [1 ]
Yu, Quanyi [1 ]
Li, Bo [1 ]
Lv, Gang [2 ]
Chi, Yaodan [3 ]
Wang, Tianhao [1 ]
机构：
[1] Jilin Univ, Coll Instrument Sci & Elect Engn, Changchun 130026, Peoples R China
[2] Natl Automot Qual Supervis & Inspect Ctr, EMC Dept, Changchun 130011, Peoples R China
[3] Jilin Jianzhu Univ Changchun, Jilin Prov Key Lab Architectural Elect & Comprehen, Changchun 130118, Peoples R China
来源：
APPLIED COMPUTATIONAL ELECTROMAGNETICS SOCIETY JOURNAL | 2023年 / 38卷 / 12期
关键词：
Adaptive Gaussian process regres- sion (aGPR); electric vehicle (EV); optimal design; uncertainty quantification (UQ); wireless power transfer (WPT); POWER TRANSFER SYSTEM; WIRELESS POWER;
D O I：
10.13052/2023.ACES.J.381202
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
- 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.
引用
收藏
页码：929 / 940
页数：12
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