A Review of Design Optimization Methods for Electrical Machines

Gang Lei, Jianguo Zhu, Youguang Guo, Chengcheng Liu and Bo Ma
Additional contact information
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

Energies, 2017, vol. 10, issue 12, 1-31

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.

Keywords: electrical machines; multi-level optimization; multi-objective optimization; system-level optimization; manufacturing variations; manufacturing quality; robust optimization; industrial big data; cloud computing (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: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (28)

Downloads: (external link)
https://www.mdpi.com/1996-1073/10/12/1962/pdf (application/pdf)
https://www.mdpi.com/1996-1073/10/12/1962/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:10:y:2017:i:12:p:1962-:d:120308

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().