Characteristics and Optimization of Bilateral Linear Permanent-Magnet-Assisted Synchronous Reluctance Motor with Compact Secondary Structure

Yu, Haitao; Huang, Xianhe; Zhang, Zhuoran; Liang, Jin

doi:10.1109/icems.2019.8921969

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…The multi-objective quadratic fitting equation can be obtained by analysing the BBD test results. The quadratic term fitting equations for three objectives are shown in Equations ( 6)- (8).…”

Section: Response Surface Modelmentioning

confidence: 99%

“…The double-sided permanent magnet synchronous linear motor (PMSLM) is a more suitable driving source for ropeless elevator due to the advantages of high thrust density per unit length, and cancellation of double-sided normal force, and better thrust stability against air gap fluctuation [5][6][7][8][9]. While, limited by the installation accuracy, the air gap of the doublesided PMSLM will deviate during operation, resulting in wheel-rail wear and vibration noise problems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Multi‐objective optimisation design of a suspension‐guided permanent magnet synchronous linear motor for ropeless elevator

Zhang

Feng

et al. 2022

IET Electric Power Appl

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In this article, a suspension‐guided permanent magnet synchronous linear motor (SG‐PMSLM) for ropeless elevator is proposed, which can meet the requirements of high thrust and high thrust density for ropeless elevator, and reduce vibration noise and wheel‐rail wear during motor operation. The structure, working principle and electromagnetic characteristics of SG‐PMSLM are analysed. In order to improve the overall performance of SG‐PMSLM, this article aims at improving the average thrust and thrust density, reducing the thrust fluctuation of SG‐PMSLM. Firstly, the sensitivity analysis of the key structural parameters of SG‐PMSLM is conducted, and the parameters that have significant influence on the three optimisation objectives are selected as the optimisation variables. Secondly, the response surface model of three optimisation objectives is built, and the Particle Swarm Optimisation algorithm is combined to conduct the multi‐objective optimisation design for SG‐PMSLM. Thirdly, the optimised solution set is obtained for three objectives. Finite Element Analysis is used to verify that the optimised solution set has better diversity and can effectively improve SG‐PMSLM overall performance compared with the initial design. Finally, the electromagnetic performance of SG‐PMSLM is verified by experiments.

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Section: Response Surface Modelmentioning

confidence: 99%