Global Optimization of a Complementary and Modular Linear Flux-Switching Permanent Magnet Motor Applied for Urban Rail Transit to Reduce Normal Force

Wang, Qi; Wang, Ke; Hu, Yadong; Zeng, Dihui; Wang, Jin

doi:10.1109/ldia59564.2023.10297513

Cited by 1 publication

(1 citation statement)

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“…In [3], in order to address the problems of parameter sensitivity and high thrust fluctuation of double-side linear vernier permanent magnet motors (DS-LVPM), RSM with an improved differential evolutionary algorithm is used to form a multi-objective optimization framework to optimize the parameters of the motors. In [4], sensitivity analysis and multiobjective genetic algorithm achieve the global optimization of a complementary and modular linear flux-switching permanent magnet motor (CMLFSPM), reducing detent force. In [5], Kriging models and improved genetic algorithm (GA) optimize parameters for a doubly-fed linear motor (DFLM) subsection, and FEA verifies the improved method.…”

Section: Introductionmentioning

confidence: 99%

Co-optimization of Long Secondary Double-sided Linear Flux Switching Permanent Magnet Motors

Wen,

Cui,

et al. 2024

PIER C

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This study aims to achieve the co-optimization of thrust force and thrust fluctuation using a long secondary double-sided linear flux switching permanent magnet motor (LSDLFSPM). Firstly, the motor model is constructed and derived using a theoretical approach. Subsequently, the motor parameters are subjected to sensitivity analysis using the Taguchi method to identify the significant influencing factors. Based on the screening results, the Response Surface Method (RSM) is employed to construct the test space and derive regression equations for thrust force and thrust fluctuation. The Multi-Objective Grasshopper Optimization Algorithm (MOGOA) is then utilized to iteratively optimize the regression equation for optimal parameter sizes. Finally, the optimized results are validated through finite element analysis (FEA) and compared with the original motor performance to demonstrate the effectiveness of the optimization approach proposed in this paper.

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Section: Introductionmentioning

confidence: 99%