IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v10y2017i12p1962-d120308.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/10/12/1962/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/10/12/1962/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Paul Waide & Conrad U. Brunner, 2011. "Energy-Efficiency Policy Opportunities for Electric Motor-Driven Systems," IEA Energy Papers 2011/7, OECD Publishing.
    3. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sauer, Ildo L. & Tatizawa, Hédio & Salotti, Francisco A.M. & Mercedes, Sonia S., 2015. "A comparative assessment of Brazilian electric motors performance with minimum efficiency standards," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 308-318.
    2. Paramonova, Svetlana & Nehler, Therese & Thollander, Patrik, 2021. "Technological change or process innovation – An empirical study of implemented energy efficiency measures from a Swedish industrial voluntary agreements program," Energy Policy, Elsevier, vol. 156(C).
    3. Hosain, Md Lokman & Bel Fdhila, Rebei & Rönnberg, Kristian, 2017. "Taylor-Couette flow and transient heat transfer inside the annulus air-gap of rotating electrical machines," Applied Energy, Elsevier, vol. 207(C), pages 624-633.
    4. Danilo Ferreira de Souza & Francisco Antônio Marino Salotti & Ildo Luís Sauer & Hédio Tatizawa & Aníbal Traça de Almeida & Arnaldo Gakiya Kanashiro, 2022. "A Performance Evaluation of Three-Phase Induction Electric Motors between 1945 and 2020," Energies, MDPI, vol. 15(6), pages 1-31, March.
    5. Zuberi, M. Jibran S. & Tijdink, Anton & Patel, Martin K., 2017. "Techno-economic analysis of energy efficiency improvement in electric motor driven systems in Swiss industry," Applied Energy, Elsevier, vol. 205(C), pages 85-104.
    6. Xinmei Wang & Yifei Wang & Tao Wu, 2022. "The Review of Electromagnetic Field Modeling Methods for Permanent-Magnet Linear Motors," Energies, MDPI, vol. 15(10), pages 1-18, May.
    7. Accordini, D. & Cagno, E. & Trianni, A., 2021. "Identification and characterization of decision-making factors over industrial energy efficiency measures in electric motor systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    8. Burgos Payán, Manuel & Roldan Fernandez, Juan Manuel & Maza Ortega, Jose Maria & Riquelme Santos, Jesus Manuel, 2019. "Techno-economic optimal power rating of induction motors," Applied Energy, Elsevier, vol. 240(C), pages 1031-1048.
    9. Armenia Androniceanu & Ioana-Catalina Enache & Elena-Narcisa Valter & Florin-Felix Raduica, 2023. "Increasing Energy Efficiency Based on the Kaizen Approach," Energies, MDPI, vol. 16(4), pages 1-24, February.
    10. Gómez, Julio R. & Sousa, Vladimir & Cabello Eras, Juan J. & Sagastume Gutiérrez, Alexis & Viego, Percy R. & Quispe, Enrique C. & de León, Gabriel, 2022. "Assessment criteria of the feasibility of replacement standard efficiency electric motors with high-efficiency motors," Energy, Elsevier, vol. 239(PA).
    11. Bortoni, Edson C. & Magalhães, Leonardo P. & Nogueira, Luiz A.H. & Bajay, Sérgio V. & Cassula, Agnelo M., 2020. "An assessment of energy efficient motors application by scenarios evaluation," Energy Policy, Elsevier, vol. 140(C).
    12. Primitivo Díaz & Marco Pérez-Cisneros & Erik Cuevas & Omar Avalos & Jorge Gálvez & Salvador Hinojosa & Daniel Zaldivar, 2018. "An Improved Crow Search Algorithm Applied to Energy Problems," Energies, MDPI, vol. 11(3), pages 1-22, March.
    13. Dingfeng Dong & Wenxin Huang & Feifei Bu & Qi Wang & Wen Jiang & Xiaogang Lin, 2017. "Modeling and Static Analysis of Primary Consequent-Pole Tubular Transverse-Flux Flux-Reversal Linear Machine," Energies, MDPI, vol. 10(10), pages 1-16, September.
    14. Fernández Oro, J.M. & Barrio Perotti, R. & Galdo Vega, M. & González, J., 2023. "Effect of the radial gap size on the deterministic flow in a centrifugal pump due to impeller-tongue interactions," Energy, Elsevier, vol. 278(PA).
    15. Jianfei Zhao & Minqi Hua & Tingzhang Liu, 2018. "Research on a Sliding Mode Vector Control System Based on Collaborative Optimization of an Axial Flux Permanent Magnet Synchronous Motor for an Electric Vehicle," Energies, MDPI, vol. 11(11), pages 1-16, November.
    16. Yoon, Hae-Sung & Kim, Eun-Seob & Kim, Min-Soo & Lee, Jang-Yeob & Lee, Gyu-Bong & Ahn, Sung-Hoon, 2015. "Towards greener machine tools – A review on energy saving strategies and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 870-891.
    17. Louback, Eduardo & Biswas, Atriya & Machado, Fabricio & Emadi, Ali, 2024. "A review of the design process of energy management systems for dual-motor battery electric vehicles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 193(C).
    18. Miguel Castro Oliveira & Muriel Iten & Pedro L. Cruz & Helena Monteiro, 2020. "Review on Energy Efficiency Progresses, Technologies and Strategies in the Ceramic Sector Focusing on Waste Heat Recovery," Energies, MDPI, vol. 13(22), pages 1-24, November.
    19. Md Junayed Hasan & Jong-Myon Kim, 2019. "Fault Detection of a Spherical Tank Using a Genetic Algorithm-Based Hybrid Feature Pool and k-Nearest Neighbor Algorithm," Energies, MDPI, vol. 12(6), pages 1-14, March.
    20. Nogueira Vilanova, Mateus Ricardo & Perrella Balestieri, José Antônio, 2014. "Energy and hydraulic efficiency in conventional water supply systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 30(C), pages 701-714.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:10:y:2017:i:12:p:1962-:d:120308. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.