Power Density Analysis and Optimization of SMPMSM Based on FEM, DE Algorithm and Response Surface Methodology

Jinshun Hao, Shuangfu Suo, Yiyong Yang, Yang Wang and Wenjie Wang
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Jinshun Hao: School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China
Shuangfu Suo: State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China
Yiyong Yang: School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China
Yang Wang: School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100083, China
Wenjie Wang: State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, China

Energies, 2019, vol. 12, issue 19, 1-9

Abstract: Surface-mounted permanent magnet synchronous motors (SMPMSM) with high power density and good speed regulation are widely used in industrial applications. In order to further improve its power density, this paper studied the relationship between the thickness of the stator yoke, the thickness of the rotor yoke, the relative magnet span of the motor and the motor power density using the finite element simulation method. On this basis, a response surface model between the three parameters and the power density of the motor was established. Based on this model and a differential evolution algorithm, the motor was optimized and the power density was improve; finally, the optimization results were verified using the finite element simulation method. In addition, the optimization results showed that, when other structure parameters remain unchanged, there is an optimal combination of parameters that can maximize the motor’s power density, including the thickness of the stator yoke, the thickness of the rotor yoke and relative magnet span of the motor.

Keywords: response surface model; permanent magnet synchronous motor; power density; differential evolution algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
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