中国电气工程学报（英文） ›› 2021, Vol. 7 ›› Issue (3): 73-87.doi: 10.23919/CJEE.2021.000027

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  • 收稿日期:2021-03-31 修回日期:2021-05-06 接受日期:2021-07-20 出版日期:2021-09-25 发布日期:2021-09-17

Multi-objective Optimization Design of Inset-surface Permanent Magnet Machine Considering Deterministic and Robust Performances*

Gaohong Xu, Zexin Jia, Wenxiang Zhao*, Qian Chen, Guohai Liu   

  1. School of Electrical and Information Engineering, Jiangsu University, Zhenjiang 212013, China
  • Received:2021-03-31 Revised:2021-05-06 Accepted:2021-07-20 Online:2021-09-25 Published:2021-09-17
  • Contact: * E-mail: zwx@ujs.edu.cn
  • About author:Gaohong Xu received B.Sc., M.Sc., and Ph.D. degrees from Jiangsu University, Zhenjiang, China, in 2009, 2011 and 2018, respectively, in Electrical Engineering. She has been with Jiangsu University since 2018, where she is currently a Lecturer in School of Electrical Information Engineering. Her current research interests include computation of electromagnetic fields for permanent-magnet machine and electric machine design.
    Zexin Jia received his B.S. degree in electrical engineering from Changshu Institute of Technology, Suzhou, China, in 2020. And he is currently working toward the M.Sc. degree in Electrical Engineering, Jiangsu University, Zhenjiang, China. His research interests include optimization design and vibration noise simulation of permanent-magnet machine.
    Wenxiang Zhao (M’08-SM’14) received the B.Sc. and M.Sc. degrees from Jiangsu University, Zhenjiang, China, in 1999 and 2003, respectively, and the Ph.D. degree from Southeast University, Nanjing, China, in 2010, all in Electrical Engineering. He has been with Jiangsu University since 2003, where he is currently a Professor with the School of Electrical Information Engineering. From 2008 to 2009, he was a Research Assistant with the Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, China. From 2013 to 2014, he was a Visiting Professor with the Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, UK. His current research interests include electric machine design, modeling, fault analysis, and intelligent control. He has authored and co-authored over 200 technical papers in these areas.
    Qian Chen (M’16-SM’20) received the B.Sc. and Ph.D. degrees from Jiangsu University, Zhenjiang, China, in 2009 and 2015, respectively, in Electrical Engineering and Control Engineering.He has been with Jiangsu University since 2015, where he is currently an Associate Professor in the School of Electrical Information Engineering. His current research interests include electric machine design, modeling, fault analysis, and intelligent control.
    Guohai Liu (M’07-SM’15) received the B.Sc. degree from Jiangsu University, Zhenjiang, China, in 1985, and the M.Sc. and Ph.D. degrees from Southeast University, Nanjing, China, in 1988 and 2002, respectively, in Electrical Engineering and Control Engineering. He has been with Jiangsu University since 1988, where he is currently a Professor, the Dean of the School of Electrical Information Engineering. From 2003 to 2004, he was a Visiting Professor with the Department of Electronic and Electrical Engineering, University of Sheffield, Sheffield, UK. His teaching and research interests include electrical machines, motor drives for electric vehicles and intelligent control. He has authored or co-authored over 200 technical papers and 4 textbooks, and holds 30 patents in these areas.
  • Supported by:
    * National Natural Science Foundation of China (51907080), by the Natural Science Foundation of Jiangsu Province (BK20190848), and by the China Postdoctoral Science Foundation (2019M661746).

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Abstract: The inset-surface permanent magnet (ISPM) machine can achieve the desired electromagnetic performance according to the traditional deterministic design. However, the reliability and quality of the machine may be affected by the essential manufacturing tolerances and unavoidable noise factors in mass production. To address this weakness, a comprehensive multi-objective optimization design method is proposed, in which robust optimization is performed after the deterministic design. The response surface method is first adopted to establish the optimization objective equation. Afterward, the sample points are obtained via Monte Carlo simulation considering the design-variable uncertainty. The Design for Six Sigma approach is adopted to ensure the robustness of the design model. Furthermore, the barebones multi-objective particle swarm optimization algorithm is used to obtain a compromise solution. A prototype is manufactured to evaluate the effectiveness of the proposed method. According to the finite-element analysis and experimental tests, the electromagnetic performance and reliability of the machine are significantly enhanced with the proposed method.

Key words: Multi-objective optimization design, robust design, Design for Six Sigma, Monte Carlo simulation, barebones multi-objective particle swarm optimization