Design Optimization of an Automotive Permanent-Magnet Synchronous Motor by Combining DOE and NMGWO
This study proposes an optimization methodology for automotive permanent-magnet synchronous motors (PMSMs) to achieve maximum efficiency, maximum average torque, and minimum torque ripple. Many geometrical parameters can be used to define the PMSM of an automobile. To identify the most significant p...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-12-01
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| Series: | Electronics |
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| Online Access: | https://www.mdpi.com/2079-9292/12/24/5024 |
| _version_ | 1797381326886666240 |
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| author | Junguo Cui Fanqiang Cui Jun Zhang Hongsheng Huang Liping Tan Wensheng Xiao |
| author_facet | Junguo Cui Fanqiang Cui Jun Zhang Hongsheng Huang Liping Tan Wensheng Xiao |
| author_sort | Junguo Cui |
| collection | DOAJ |
| description | This study proposes an optimization methodology for automotive permanent-magnet synchronous motors (PMSMs) to achieve maximum efficiency, maximum average torque, and minimum torque ripple. Many geometrical parameters can be used to define the PMSM of an automobile. To identify the most significant parameters for optimization, the fractional factorial design of the design of experiment (DOE) was employed for screening, considering the interaction effects. The central composite design was used to construct the proxy model between the optimization target and optimization variable, and the effectiveness of the model was judged. Aiming at the multi-objective optimization problem of a motor, a new mechanism for grey wolf optimizer (NMGWO) algorithm combining an elite reverse learning strategy, a local search strategy, and a nonlinear control parameter strategy is innovatively proposed. This algorithm was applied to solve the multi-objective optimization model. The numerical calculation results show that this is an effective optimization design method that can improve the performance of automotive PMSMs. The effectiveness of the NMGWO algorithm on the optimization results of permanent-magnet synchronous motors is verified by the experimental results. |
| first_indexed | 2024-03-08T20:49:50Z |
| format | Article |
| id | doaj.art-70e4e77756f44485970db9390645f34a |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-08T20:49:50Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-70e4e77756f44485970db9390645f34a2023-12-22T14:05:16ZengMDPI AGElectronics2079-92922023-12-011224502410.3390/electronics12245024Design Optimization of an Automotive Permanent-Magnet Synchronous Motor by Combining DOE and NMGWOJunguo Cui0Fanqiang Cui1Jun Zhang2Hongsheng Huang3Liping Tan4Wensheng Xiao5College of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, ChinaCollege of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, ChinaSAIC Volkswagen Automotive Co., Ltd., Shanghai 201800, ChinaWuhan Alliance Electrical Technology Co., Ltd., Wuhan 432900, ChinaCollege of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, ChinaCollege of Mechanical and Electrical Engineering, China University of Petroleum, Qingdao 266580, ChinaThis study proposes an optimization methodology for automotive permanent-magnet synchronous motors (PMSMs) to achieve maximum efficiency, maximum average torque, and minimum torque ripple. Many geometrical parameters can be used to define the PMSM of an automobile. To identify the most significant parameters for optimization, the fractional factorial design of the design of experiment (DOE) was employed for screening, considering the interaction effects. The central composite design was used to construct the proxy model between the optimization target and optimization variable, and the effectiveness of the model was judged. Aiming at the multi-objective optimization problem of a motor, a new mechanism for grey wolf optimizer (NMGWO) algorithm combining an elite reverse learning strategy, a local search strategy, and a nonlinear control parameter strategy is innovatively proposed. This algorithm was applied to solve the multi-objective optimization model. The numerical calculation results show that this is an effective optimization design method that can improve the performance of automotive PMSMs. The effectiveness of the NMGWO algorithm on the optimization results of permanent-magnet synchronous motors is verified by the experimental results.https://www.mdpi.com/2079-9292/12/24/5024permanent-magnet synchronous motorDOENMGWOmotor design |
| spellingShingle | Junguo Cui Fanqiang Cui Jun Zhang Hongsheng Huang Liping Tan Wensheng Xiao Design Optimization of an Automotive Permanent-Magnet Synchronous Motor by Combining DOE and NMGWO Electronics permanent-magnet synchronous motor DOE NMGWO motor design |
| title | Design Optimization of an Automotive Permanent-Magnet Synchronous Motor by Combining DOE and NMGWO |
| title_full | Design Optimization of an Automotive Permanent-Magnet Synchronous Motor by Combining DOE and NMGWO |
| title_fullStr | Design Optimization of an Automotive Permanent-Magnet Synchronous Motor by Combining DOE and NMGWO |
| title_full_unstemmed | Design Optimization of an Automotive Permanent-Magnet Synchronous Motor by Combining DOE and NMGWO |
| title_short | Design Optimization of an Automotive Permanent-Magnet Synchronous Motor by Combining DOE and NMGWO |
| title_sort | design optimization of an automotive permanent magnet synchronous motor by combining doe and nmgwo |
| topic | permanent-magnet synchronous motor DOE NMGWO motor design |
| url | https://www.mdpi.com/2079-9292/12/24/5024 |
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