Coil Parameter Optimization Method for Wireless Power Transfer System Based on Crowding Distance Division and Adaptive Genetic Operators

Hua Zhang, Xin Sui (), Peng Sui, Lili Wei, Yuanchun Huang, Zhenglong Yang and Haidong Yang
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Hua Zhang: School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China
Xin Sui: School of Public Utilities and Road and Bridge Engineering, Shanghai Communications Polytechnic, Shanghai 200030, China
Peng Sui: China Mobile Group Heilongjiang Company Limited, Harbin 150028, China
Lili Wei: School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China
Yuanchun Huang: School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China
Zhenglong Yang: School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China
Haidong Yang: School of Urban Railway Transportation, Shanghai University of Engineering Science, Shanghai 201620, China

Energies, 2024, vol. 17, issue 13, 1-19

Abstract: In a Magnetically Coupled Resonant Wireless Power Transfer (MCR-WPT) system, the magnetic coupling coil is one of the key factors that determines the system’s output power, transmission efficiency, anti-offset capability, and so on. This article proposes a coil parameter optimization method for a wireless power transfer system based on crowding distance division and adaptive genetic operators. Through optimizing the design of decision variables, such as the numbers of transmitting and receiving coil turns, the spacings between transmitting and receiving coil turns, the inner radii of the transmitting and receiving coils, and the vertical distance of the coil, the best transmission performance can be achieved. This study improves the NSGA-II algorithm through proposing a genetic operator algorithm for average crowding and high crowding populations based on adaptive operators, as well as a genetic operator algorithm for low crowding populations based on information entropy. These improved algorithms avoid problems inherent to traditional genetic operators such as fixed genetic proportions, do not easily cause the algorithm to fall into a local optimal solution, and show better convergence in the ZDT1–ZDT3 test functions. The optimization design method in this article is not only independent of commercial software such as ANSYS Maxwell 2021 R1, but can also significantly improve the calculation speed compared with traditional simulation software.

Keywords: MCR-WPT; parameter optimization of magnetic coupling coil; NSGA-II algorithm; crowding distance calculation; adaptive operator; information entropy (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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