Research on permanent magnet switch design based on simulated annealing and structure optimization algorithm

The permanent magnet switch structure plays a crucial role in enhancing the efficient operation of permanent magnet linear motors.Based on this, this paper constructs a finite element simulation model of the permanent magnet switch structure based on the working principle of the permanent magnet switch, on the basis of analyzing the stray parameter and magnetic circuit model of the permanent magnet switch, and combining with Infolytica MagNet simulation software.Then from the multi-objective optimization problem of the permanent magnet switch structure, the multi-objective optimization model of the permanent magnet switch is established based on the optimization objectives and constraints.The genetic algorithm is introduced to optimize the parameters of the simulated annealing algorithm, and the multi-objective optimization model of permanent magnet switch is solved by GA-SA algorithm.Simulation shows that the GA-SA algorithm is relatively efficient, and the sinusoidal distortion rates of the cogging torque and the no-load air-gap magnetic density waveform are 0.32N-m and 21.75%, respectively, after solving the multi-objective optimization model.The optimized permanent magnet switch opening speed and opening coil energy conversion rate are increased by 21.92% and 20.80%, respectively.In conclusion, the permanent magnet switch design obtained by the multi-objective optimization algorithm meets the requirements of the permanent magnet linear motor and can significantly enhance the operating efficiency of the permanent magnet linear motor.

Publication Details

Journal
International journal for housing science and its applications.
Published
2025-08-15
DOI
https://doi.org/10.70517/ijhsa463415
Primary Topic
Electric Motor Design and Analysis
Type
article
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Research on permanent magnet switch design based on simulated annealing and structure optimization algorithm

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International journal for housing science and its applications.
Electric Motor Design and Analysis
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Research on permanent magnet switch design based on simulated annealing and structure optimization algorithm

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Abstract

The permanent magnet switch structure plays a crucial role in enhancing the efficient operation of permanent magnet linear motors.Based on this, this paper constructs a finite element simulation model of the permanent magnet switch structure based on the working principle of the permanent magnet switch, on the basis of analyzing the stray parameter and magnetic circuit model of the permanent magnet switch, and combining with Infolytica MagNet simulation software.Then from the multi-objective optimization problem of the permanent magnet switch structure, the multi-objective optimization model of the permanent magnet switch is established based on the optimization objectives and constraints.The genetic algorithm is introduced to optimize the parameters of the simulated annealing algorithm, and the multi-objective optimization model of permanent magnet switch is solved by GA-SA algorithm.Simulation shows that the GA-SA algorithm is relatively efficient, and the sinusoidal distortion rates of the cogging torque and the no-load air-gap magnetic density waveform are 0.32N-m and 21.75%, respectively, after solving the multi-objective optimization model.The optimized permanent magnet switch opening speed and opening coil energy conversion rate are increased by 21.92% and 20.80%, respectively.In conclusion, the permanent magnet switch design obtained by the multi-objective optimization algorithm meets the requirements of the permanent magnet linear motor and can significantly enhance the operating efficiency of the permanent magnet linear motor.

International journal for housing science and its applications.Vol. 46(3)
Openalex Percentile: Top 79%
Electric Motor Design and Analysis
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Research on permanent magnet switch design based on simulated annealing and structure optimization algorithm · International journal for housing science and its applications. (2025) | TGRS Research Map | TGRS