Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Anti-misalignment capability optimization for laminated magnetic couplers in wireless charging systems using balanced particle swarm optimization method

  • Hao Liu (College of Mechanical and Electrical Engineering, Northeast Forestry University) ;
  • Zhenjie Li (College of Mechanical and Electrical Engineering, Northeast Forestry University) ;
  • Yuhong Tian (College of Mechanical and Electrical Engineering, Northeast Forestry University) ;
  • Yiqi Liu (College of Mechanical and Electrical Engineering, Northeast Forestry University) ;
  • Wenlong Song (College of Mechanical and Electrical Engineering, Northeast Forestry University)
  • Received : 2022.05.28
  • Accepted : 2022.09.15
  • Published : 2023.02.20
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Abstract

To desensitize wireless charging systems (WCSs) to misalignment and to achieve a stable output voltage without using additional control methods, a laminated magnetic coupler (LMC) optimized by the balanced particle swarm optimization (BPSO) method is proposed to improve the performance of WCSs. First, the output characteristics of the LCC-S compensated WCS are analyzed to illustrate the working principle of the LMC. Next, the misalignment characteristics of the LMC, which employs the magnetic integration technique and coil self-decoupling method, are analyzed. Then, the operating principle of the BPSO method is analyzed and used to optimize the LMC in terms of the mutual inductance and anti-misalignment range. Finally, experimental results indicate that the optimized LMC achieves high-efficiency constant voltage charging within a reasonable horizontal misalignment range. When compared to commonly used methods (such as the exhaustive method) for optimizing magnetic couplers, the BPSO method avoids complex algorithms and a heavy computation burden.

Keywords

Acknowledgement

This work was supported by the National Natural Science Foundation of China under Grant No. 52107001, in part by China Postdoctoral Science Foundation under Grant No. 2022M710641, and in part by Fundamental Research Funds for the Central Universities under Grant No. 2572021BF04.

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