Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Multi-objective parameter optimization of multi-shaft ring-plate magnetic gear

  • Yanjun Ge (School of Mechanical Engineering, Dalian Jiaotong University) ;
  • Dongning Liu (School of Mechanical Engineering, Dalian Jiaotong University) ;
  • Junyue Yang (School of Mechanical Engineering, Dalian Jiaotong University) ;
  • Daming Wang (School of Mechanical Engineering, Dalian Jiaotong University)
  • Received : 2022.07.07
  • Accepted : 2022.10.17
  • Published : 2023.03.20
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Abstract

Parameter setting and optimization are key steps to improving the transmission performance of eccentric permanent magnet gear. Studying this step makes it possible to analyze the transmission relationship between the design parameters, and improve both the operation stability and the carrying capacity of eccentric permanent magnet gear. Taking multi-shaft ring-plate magnet gear (MRMG) as the research object, this paper analyzes the influence of actual structural parameters on torque performance through the comprehensive sensitivity method, and selects the parameters with the greatest influence as the main optimization parameters. Then using the BSO algorithm and the BP neural network, a prediction model of MRMG electromagnetic torque and torque density is established, the relationship between each of the parameters is analyzed, and the optimization parameter points are determined. By comparison with finite element simulations and experiments, the built model and its measured results and FEM analysis are shown to have good accuracy (error<6%). Moreover, the model can quickly and accurately obtain optimization parameters, and greatly improve the transmission performance of the structure (the maximum torque increases by 15%, and the torque density increases by 36%).

Keywords

Acknowledgement

This work was funded by the Innovative Research Group Project of the National Natural Science Foundation of China (Grant No.51375063) and also sponsored by the Natural Science Foundation of Liaoning Province (Grant No. JDL2020001).

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