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A Review of Design Optimization Methods for Electrical Machines

Author

Listed:
  • Gang Lei

    (School of Electrical and Data Engineering, University of Technology Sydney, Ultimo 2007, Australia)

  • Jianguo Zhu

    (School of Electrical and Data Engineering, University of Technology Sydney, Ultimo 2007, Australia)

  • Youguang Guo

    (School of Electrical and Data Engineering, University of Technology Sydney, Ultimo 2007, Australia)

  • Chengcheng Liu

    (State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300131, China)

  • Bo Ma

    (School of Electrical and Data Engineering, University of Technology Sydney, Ultimo 2007, Australia)

Abstract

Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their design optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models, algorithms and methods/strategies. Several efficient optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel optimization methods are introduced for advanced design optimization of electrical machines. First, a system-level design optimization method is introduced for the development of advanced electric drive systems. Second, a robust design optimization method based on the design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust design optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart design optimization method for future intelligent design and production of electrical machines.

Suggested Citation

  • Gang Lei & Jianguo Zhu & Youguang Guo & Chengcheng Liu & Bo Ma, 2017. "A Review of Design Optimization Methods for Electrical Machines," Energies, MDPI, vol. 10(12), pages 1-31, November.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:1962-:d:120308
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    References listed on IDEAS

    as
    1. Saidur, R., 2010. "A review on electrical motors energy use and energy savings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(3), pages 877-898, April.
    2. Juncai Song & Fei Dong & Jiwen Zhao & Siliang Lu & Le Li & Zhenbao Pan, 2016. "A New Design Optimization Method for Permanent Magnet Synchronous Linear Motors," Energies, MDPI, vol. 9(12), pages 1-15, November.
    3. Paul Waide & Conrad U. Brunner, 2011. "Energy-Efficiency Policy Opportunities for Electric Motor-Driven Systems," IEA Energy Papers 2011/7, OECD Publishing.
    4. Yi Li & Feng Chai & Zaixin Song & Zongyang Li, 2017. "Analysis of Vibrations in Interior Permanent Magnet Synchronous Motors Considering Air-Gap Deformation," Energies, MDPI, vol. 10(9), pages 1-18, August.
    5. Yee Pien Yang & Guan Yu Shih, 2016. "Optimal Design of an Axial-Flux Permanent-Magnet Motor for an Electric Vehicle Based on Driving Scenarios," Energies, MDPI, vol. 9(4), pages 1-18, April.
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