IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v215y2025ics1364032125002825.html
   My bibliography  Save this article

Review of surrogate model assisted multi-objective design optimization of electrical machines: New opportunities and challenges

Author

Listed:
  • Liu, Liyang
  • Li, Zequan
  • Kang, Haoyu
  • Xiao, Yang
  • Sun, Lu
  • Zhao, Hang
  • Zhu, Z.Q.
  • Ma, Yiming

Abstract

This paper overviews surrogate model-assisted multi-objective design optimization techniques of electrical machines for efficient, accurate, and robust design optimization to ease design issues due to unprecedentedly increasing machine performance requirements. Firstly, the mechanism of surrogate-assisted modeling is introduced by comparing it with conventional physical modeling approaches. The relevant techniques are then categorized and subsequently reviewed in terms of the design of experiments, surrogate model construction, and multi-objective optimization algorithms. The potential application prospects for machine design optimization are highlighted. Finally, three surrogate-assisted modeling methods, i.e., transfer learning-based models, gradient sampling-based multi-fidelity models, and search space decay-based surrogate models, are quantitively compared by applying them to the design optimization of a five-phase permanent magnet synchronous machine.

Suggested Citation

  • Liu, Liyang & Li, Zequan & Kang, Haoyu & Xiao, Yang & Sun, Lu & Zhao, Hang & Zhu, Z.Q. & Ma, Yiming, 2025. "Review of surrogate model assisted multi-objective design optimization of electrical machines: New opportunities and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 215(C).
  • Handle: RePEc:eee:rensus:v:215:y:2025:i:c:s1364032125002825
    DOI: 10.1016/j.rser.2025.115609
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032125002825
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2025.115609?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Qian Xiao & Hongquan Xu, 2017. "Construction of maximin distance Latin squares and related Latin hypercube designs," Biometrika, Biometrika Trust, vol. 104(2), pages 455-464.
    2. Felipe Viana & Raphael Haftka & Layne Watson, 2013. "Efficient global optimization algorithm assisted by multiple surrogate techniques," Journal of Global Optimization, Springer, vol. 56(2), pages 669-689, June.
    3. Riba, Jordi-Roger & López-Torres, Carlos & Romeral, Luís & Garcia, Antoni, 2016. "Rare-earth-free propulsion motors for electric vehicles: A technology review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 367-379.
    4. Kleijnen, Jack P.C., 2009. "Kriging metamodeling in simulation: A review," European Journal of Operational Research, Elsevier, vol. 192(3), pages 707-716, February.
    5. Husslage, B.G.M. & Rennen, G. & van Dam, E.R. & den Hertog, D., 2011. "Space-filling Latin hypercube designs for computer experiments," Other publications TiSEM 694f73df-a373-46a7-aa4d-1, Tilburg University, School of Economics and Management.
    6. Chi T. P. Nguyen & Bảo-Huy Nguyễn & Minh C. Ta & João Pedro F. Trovão, 2023. "Dual-Motor Dual-Source High Performance EV: A Comprehensive Review," Energies, MDPI, vol. 16(20), pages 1-28, October.
    7. 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.
    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. Mehdad, E. & Kleijnen, Jack P.C., 2014. "Classic Kriging versus Kriging with Bootstrapping or Conditional Simulation : Classic Kriging's Robust Confidence Intervals and Optimization (Revised version of CentER DP 2013-038)," Other publications TiSEM 4915047b-afe4-4fc7-8a1c-4, Tilburg University, School of Economics and Management.
    2. Liuqing Yang & Yongdao Zhou & Min-Qian Liu, 2021. "Maximin distance designs based on densest packings," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 615-634, July.
    3. Dawei Zhan & Huanlai Xing, 2020. "Expected improvement for expensive optimization: a review," Journal of Global Optimization, Springer, vol. 78(3), pages 507-544, November.
    4. Fani Boukouvala & M. M. Faruque Hasan & Christodoulos A. Floudas, 2017. "Global optimization of general constrained grey-box models: new method and its application to constrained PDEs for pressure swing adsorption," Journal of Global Optimization, Springer, vol. 67(1), pages 3-42, January.
    5. Nicolas Bernard & Linh Dang & Luc Moreau & Salvy Bourguet, 2022. "A Pre-Sizing Method for Salient Pole Synchronous Reluctance Machines with Loss Minimization Control for a Small Urban Electrical Vehicle Considering the Driving Cycle," Energies, MDPI, vol. 15(23), pages 1-19, December.
    6. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    7. Zitrou, Athena & Bedford, Tim & Walls, Lesley, 2016. "A model for availability growth with application to new generation offshore wind farms," Reliability Engineering and System Safety, Elsevier, vol. 152(C), pages 83-94.
    8. Zhang, Wei & (Ato) Xu, Wangtu, 2017. "Simulation-based robust optimization for the schedule of single-direction bus transit route: The design of experiment," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 106(C), pages 203-230.
    9. WoongHee Jung & Alexandros A. Taflanidis & Norberto C. Nadal-Caraballo & Madison C. Yawn & Luke A. Aucoin, 2024. "Regional storm surge hazard quantification using Gaussian process metamodeling techniques," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(1), pages 755-783, January.
    10. Xuefei Lu & Alessandro Rudi & Emanuele Borgonovo & Lorenzo Rosasco, 2020. "Faster Kriging: Facing High-Dimensional Simulators," Operations Research, INFORMS, vol. 68(1), pages 233-249, January.
    11. Xin Wang & Xinchao Jiang & Hu Wang & Guangyao Li, 2025. "Manifold learning-assisted uncertainty quantification of system parameters in the fiber metal laminates hot forming process," Journal of Intelligent Manufacturing, Springer, vol. 36(3), pages 2193-2219, March.
    12. Wang, Zequn & Wang, Pingfeng, 2015. "A double-loop adaptive sampling approach for sensitivity-free dynamic reliability analysis," Reliability Engineering and System Safety, Elsevier, vol. 142(C), pages 346-356.
    13. Janssen, Hans, 2013. "Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence," Reliability Engineering and System Safety, Elsevier, vol. 109(C), pages 123-132.
    14. López, I. & Ibarra, E. & Matallana, A. & Andreu, J. & Kortabarria, I., 2019. "Next generation electric drives for HEV/EV propulsion systems: Technology, trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 114(C), pages 1-1.
    15. Song, Zhouzhou & Zhang, Hanyu & Liu, Zhao & Zhu, Ping, 2023. "A two-stage Kriging estimation variance reduction method for efficient time-variant reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    16. Puppo, L. & Pedroni, N. & Maio, F. Di & Bersano, A. & Bertani, C. & Zio, E., 2021. "A Framework based on Finite Mixture Models and Adaptive Kriging for Characterizing Non-Smooth and Multimodal Failure Regions in a Nuclear Passive Safety System," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    17. Menafoglio, Alessandra & Secchi, Piercesare, 2017. "Statistical analysis of complex and spatially dependent data: A review of Object Oriented Spatial Statistics," European Journal of Operational Research, Elsevier, vol. 258(2), pages 401-410.
    18. Stephen Ntiri Asomani & Jianping Yuan & Longyan Wang & Desmond Appiah & Kofi Asamoah Adu-Poku, 2020. "The Impact of Surrogate Models on the Multi-Objective Optimization of Pump-As-Turbine (PAT)," Energies, MDPI, vol. 13(9), pages 1-29, May.
    19. Liqin Wu & Hao Chen & Tingyue Yu & Chengzhi Sun & Lin Wang & Xuerong Ye & Guofu Zhai, 2023. "Robust Design Optimization of the Cogging Torque for a PMSM Based on Manufacturing Uncertainties Analysis and Approximate Modeling," Energies, MDPI, vol. 16(2), pages 1-24, January.
    20. Zio, E., 2018. "The future of risk assessment," Reliability Engineering and System Safety, Elsevier, vol. 177(C), pages 176-190.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:eee:rensus:v:215:y:2025:i:c:s1364032125002825. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.