Multi-Objective Optimal Design of 200 kW Permanent Magnet Synchronous Motor Based on NSGA-II
Interior permanent magnet synchronous motors (IPMSMs) are widely applied as drive motors in electric vehicles because they have the advantages of high power density, high efficiency, and excellent dynamic performance. This paper introduces a framework for multi-objective optimization, tailored for t...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/16/6/299 |
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| author | Chengxu Sun Qi Li Tao Fan Xuhui Wen Ye Li Hongyang Li |
| author_facet | Chengxu Sun Qi Li Tao Fan Xuhui Wen Ye Li Hongyang Li |
| author_sort | Chengxu Sun |
| collection | DOAJ |
| description | Interior permanent magnet synchronous motors (IPMSMs) are widely applied as drive motors in electric vehicles because they have the advantages of high power density, high efficiency, and excellent dynamic performance. This paper introduces a framework for multi-objective optimization, tailored for the demands of V-Shaped IPMSMs, which involves high-dimensional variables. The framework is divided into three parts. Firstly, a proportional parametric finite element analysis (FEA) model for V-Shaped IPMSMs was established to reduce the probability of size interference among motor design parameters. Secondly, a surrogate model was trained using the design of experiments (DOE) approach and was utilized to substitute the FEA model. The accuracy of the surrogate model was then verified. Thirdly, the surrogate model was used as a fitness function, and a non-dominated sorting genetic algorithm II (NSGA-II) was employed as the optimization method to acquire the optimal goals rapidly. Based on the optimal design parameters, a prototype of the electrical motor was fabricated. Finally, the effectiveness of optimization was proven by experimental testing. |
| format | Article |
| id | doaj-art-a5f0b0a9dc6541bb91af616e26c1dde1 |
| institution | Kabale University |
| issn | 2032-6653 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-a5f0b0a9dc6541bb91af616e26c1dde12025-08-20T03:32:31ZengMDPI AGWorld Electric Vehicle Journal2032-66532025-05-0116629910.3390/wevj16060299Multi-Objective Optimal Design of 200 kW Permanent Magnet Synchronous Motor Based on NSGA-IIChengxu Sun0Qi Li1Tao Fan2Xuhui Wen3Ye Li4Hongyang Li5State Key Laboratory of High Density Electromagnetic Power and Systems, Institute of Electrical Engineering, Chinese Academy of Sciences, Haidian District, Beijing 100190, ChinaState Key Laboratory of High Density Electromagnetic Power and Systems, Institute of Electrical Engineering, Chinese Academy of Sciences, Haidian District, Beijing 100190, ChinaState Key Laboratory of High Density Electromagnetic Power and Systems, Institute of Electrical Engineering, Chinese Academy of Sciences, Haidian District, Beijing 100190, ChinaState Key Laboratory of High Density Electromagnetic Power and Systems, Institute of Electrical Engineering, Chinese Academy of Sciences, Haidian District, Beijing 100190, ChinaState Key Laboratory of High Density Electromagnetic Power and Systems, Institute of Electrical Engineering, Chinese Academy of Sciences, Haidian District, Beijing 100190, ChinaState Key Laboratory of High Density Electromagnetic Power and Systems, Institute of Electrical Engineering, Chinese Academy of Sciences, Haidian District, Beijing 100190, ChinaInterior permanent magnet synchronous motors (IPMSMs) are widely applied as drive motors in electric vehicles because they have the advantages of high power density, high efficiency, and excellent dynamic performance. This paper introduces a framework for multi-objective optimization, tailored for the demands of V-Shaped IPMSMs, which involves high-dimensional variables. The framework is divided into three parts. Firstly, a proportional parametric finite element analysis (FEA) model for V-Shaped IPMSMs was established to reduce the probability of size interference among motor design parameters. Secondly, a surrogate model was trained using the design of experiments (DOE) approach and was utilized to substitute the FEA model. The accuracy of the surrogate model was then verified. Thirdly, the surrogate model was used as a fitness function, and a non-dominated sorting genetic algorithm II (NSGA-II) was employed as the optimization method to acquire the optimal goals rapidly. Based on the optimal design parameters, a prototype of the electrical motor was fabricated. Finally, the effectiveness of optimization was proven by experimental testing.https://www.mdpi.com/2032-6653/16/6/299interior permanent magnet motorBP neural networkfinite element analysissurrogate modelmulti-objective optimization |
| spellingShingle | Chengxu Sun Qi Li Tao Fan Xuhui Wen Ye Li Hongyang Li Multi-Objective Optimal Design of 200 kW Permanent Magnet Synchronous Motor Based on NSGA-II World Electric Vehicle Journal interior permanent magnet motor BP neural network finite element analysis surrogate model multi-objective optimization |
| title | Multi-Objective Optimal Design of 200 kW Permanent Magnet Synchronous Motor Based on NSGA-II |
| title_full | Multi-Objective Optimal Design of 200 kW Permanent Magnet Synchronous Motor Based on NSGA-II |
| title_fullStr | Multi-Objective Optimal Design of 200 kW Permanent Magnet Synchronous Motor Based on NSGA-II |
| title_full_unstemmed | Multi-Objective Optimal Design of 200 kW Permanent Magnet Synchronous Motor Based on NSGA-II |
| title_short | Multi-Objective Optimal Design of 200 kW Permanent Magnet Synchronous Motor Based on NSGA-II |
| title_sort | multi objective optimal design of 200 kw permanent magnet synchronous motor based on nsga ii |
| topic | interior permanent magnet motor BP neural network finite element analysis surrogate model multi-objective optimization |
| url | https://www.mdpi.com/2032-6653/16/6/299 |
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