IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i13p3295-d1685862.html
   My bibliography  Save this article

Literature Reviews of Topology Optimal Design Methods and Applications in Magnetic Devices

Author

Listed:
  • Jiaqi Wu

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Ziyan Ren

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Dianhai Zhang

    (School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China)

Abstract

With the evolution of magnetic devices toward structural innovation and high reliability, the traditional design methods, such as size optimization and shape optimization, are limited by preset structural forms, making it challenging to generate novel structures and topologies. Topology-optimized design methods can achieve an optimal distribution of constituent materials of magnetic devices by optimizing the objective performance subject to certain constraints, and can provide greater freedom for designers. Based on the above background, this paper firstly investigates the principles of deterministic topology optimization methods, and introduces the latest specific applications in magnetic devices. It also demonstrates the advantages of topology optimization technology in enhancing operating performance, fostering structural innovation, and improving material utilization in magnetic devices. To manage uncertainties in design and manufacturing processes of magnetic devices, this paper analyzes uncertainty topology optimization methods, respectively, reliability and robustness-based topology optimization algorithms. To facilitate manufacturing, this paper summarizes the filter strategy for the new structure obtained by topology optimization. Finally, the problems faced by the topology optimization method in the field of magnetic devices are discussed, and a future development direction is projected.

Suggested Citation

  • Jiaqi Wu & Ziyan Ren & Dianhai Zhang, 2025. "Literature Reviews of Topology Optimal Design Methods and Applications in Magnetic Devices," Energies, MDPI, vol. 18(13), pages 1-27, June.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3295-:d:1685862
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/13/3295/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/13/3295/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Adarsh Vaderobli & Dev Parikh & Urmila Diwekar, 2020. "Optimization under Uncertainty to Reduce the Cost of Energy for Parabolic Trough Solar Power Plants for Different Weather Conditions," Energies, MDPI, vol. 13(12), pages 1-17, June.
    2. Susana Costa & Jorge Ferreira & Nelson Martins, 2025. "Geometry Optimisation of a Wave Energy Converter," Energies, MDPI, vol. 18(1), pages 1-15, January.
    3. Marwa Al-Saidi & Abdullah Al-Badi & Ahmet Onen & Abdelsalam Elhaffar, 2023. "Optimal Location and Size of Static Var Compensators (SVC) to Enhance the Voltage Profile on the Main Interconnected System in Oman," Energies, MDPI, vol. 16(19), pages 1-15, September.
    4. Guo, Shiliang & Li, Pengpeng & Ma, Kai & Yang, Bo & Yang, Jie, 2022. "Robust energy management for industrial microgrid considering charging and discharging pressure of electric vehicles," Applied Energy, Elsevier, vol. 325(C).
    5. Lu, Xinhui & Liu, Zhaoxi & Ma, Li & Wang, Lingfeng & Zhou, Kaile & Feng, Nanping, 2020. "A robust optimization approach for optimal load dispatch of community energy hub," Applied Energy, Elsevier, vol. 259(C).
    6. Shahid Hussain & Ants Kallaste & Toomas Vaimann, 2023. "Recent Trends in Additive Manufacturing and Topology Optimization of Reluctance Machines," Energies, MDPI, vol. 16(9), pages 1-19, April.
    7. Gyeong Uk Jang & Seunghyeon Cho & Jaemin Moon & Kyunghun Jeon & Chang-wan Kim, 2021. "Topology Optimization to Reduce Electromagnetic Force Induced Vibration for the Specific Frequency of PMSM Motor Using Electromagnetic-Structural Coupled Analysis," Energies, MDPI, vol. 14(2), pages 1-13, January.
    8. Jin-Cheol Park & Soo-Hwan Park & Jae-Hyun Kim & Soo-Gyung Lee & Geun-Ho Lee & Myung-Seop Lim, 2021. "Diagnosis and Robust Design Optimization of SPMSM Considering Back EMF and Cogging Torque due to Static Eccentricity," Energies, MDPI, vol. 14(10), pages 1-19, May.
    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. Emrani-Rahaghi, Pouria & Hashemi-Dezaki, Hamed & Ketabi, Abbas, 2023. "Efficient voltage control of low voltage distribution networks using integrated optimized energy management of networked residential multi-energy microgrids," Applied Energy, Elsevier, vol. 349(C).
    2. Henghui Li & Zi-Qiang Zhu & Ziad Azar & Richard Clark & Zhanyuan Wu, 2025. "Fault Detection of Permanent Magnet Synchronous Machines: An Overview," Energies, MDPI, vol. 18(3), pages 1-44, January.
    3. Paulo M. De Oliveira-De Jesus & Jose M. Yusta, 2024. "Optimal Power Dispatch for Maximum Energy Community Welfare by Considering Closed Distribution Systems and Renewable Sources," Energies, MDPI, vol. 17(18), pages 1-21, September.
    4. Mansour-Saatloo, Amin & Pezhmani, Yasin & Mirzaei, Mohammad Amin & Mohammadi-Ivatloo, Behnam & Zare, Kazem & Marzband, Mousa & Anvari-Moghaddam, Amjad, 2021. "Robust decentralized optimization of Multi-Microgrids integrated with Power-to-X technologies," Applied Energy, Elsevier, vol. 304(C).
    5. Yang, Ting & Zhao, Liyuan & Li, Wei & Zomaya, Albert Y., 2021. "Dynamic energy dispatch strategy for integrated energy system based on improved deep reinforcement learning," Energy, Elsevier, vol. 235(C).
    6. Tan, Bifei & Chen, Simin & Liang, Zipeng & Zheng, Xiaodong & Zhu, Yanjin & Chen, Haoyong, 2024. "An iteration-free hierarchical method for the energy management of multiple-microgrid systems with renewable energy sources and electric vehicles," Applied Energy, Elsevier, vol. 356(C).
    7. Tan, Bifei & Lin, Zhenjia & Zheng, Xiaodong & Xiao, Fu & Wu, Qiuwei & Yan, Jinyue, 2023. "Distributionally robust energy management for multi-microgrids with grid-interactive EVs considering the multi-period coupling effect of user behaviors," Applied Energy, Elsevier, vol. 350(C).
    8. Najafi, Arsalan & Jasiński, Michał & Leonowicz, Zbigniew, 2022. "A hybrid distributed framework for optimal coordination of electric vehicle aggregators problem," Energy, Elsevier, vol. 249(C).
    9. Tang, Bao-Jun & Cao, Xi-Lin & Li, Ru & Xiang, Zhi-Bo & Zhang, Sen, 2024. "Economic and low-carbon planning for interconnected integrated energy systems considering emerging technologies and future development trends," Energy, Elsevier, vol. 302(C).
    10. Seyfi, Mohammad & Mehdinejad, Mehdi & Mohammadi-Ivatloo, Behnam & Shayanfar, Heidarali, 2022. "Deep learning-based scheduling of virtual energy hubs with plug-in hybrid compressed natural gas-electric vehicles," Applied Energy, Elsevier, vol. 321(C).
    11. Ge, Haotian & Zhu, Yu & Zhong, Jiuming & Wu, Liang, 2024. "Day-ahead optimization for smart energy management of multi-microgrid using a stochastic-robust model," Energy, Elsevier, vol. 313(C).
    12. Li, Yuming & Wang, Tingyu & Li, Xinxi & Zhang, Guoqing & Chen, Kai & Yang, Wensheng, 2022. "Experimental investigation on thermal management system with flame retardant flexible phase change material for retired battery module," Applied Energy, Elsevier, vol. 327(C).
    13. Li, Jinpeng & Xu, Yinliang & Zhang, Junxiao & Gao, Chong & Sun, Hongbin, 2025. "Distributed EV scheduling in distribution networks with reserve market participation under ambiguous probability distribution," Applied Energy, Elsevier, vol. 383(C).
    14. Barone, G. & Buonomano, A. & Cipolla, G. & Forzano, C. & Giuzio, G.F. & Russo, G., 2024. "Designing aggregation criteria for end-users integration in energy communities: Energy and economic optimisation based on hybrid neural networks models," Applied Energy, Elsevier, vol. 371(C).
    15. Zhu, Dafeng & Yang, Bo & Liu, Yuxiang & Wang, Zhaojian & Ma, Kai & Guan, Xinping, 2022. "Energy management based on multi-agent deep reinforcement learning for a multi-energy industrial park," Applied Energy, Elsevier, vol. 311(C).
    16. Mohammadpour Shotorbani, Amin & Zeinal-Kheiri, Sevda & Chhipi-Shrestha, Gyan & Mohammadi-Ivatloo, Behnam & Sadiq, Rehan & Hewage, Kasun, 2021. "Enhanced real-time scheduling algorithm for energy management in a renewable-integrated microgrid," Applied Energy, Elsevier, vol. 304(C).
    17. Yubo Wang & Weiqing Sun, 2024. "A Two-Stage Robust Pricing Strategy for Electric Vehicle Aggregators Considering Dual Uncertainty in Electricity Demand and Real-Time Electricity Prices," Sustainability, MDPI, vol. 16(9), pages 1-19, April.
    18. Ming, Fangzhu & Gao, Feng & Liu, Kun & Li, Xingqi, 2023. "A constrained DRL-based bi-level coordinated method for large-scale EVs charging," Applied Energy, Elsevier, vol. 331(C).
    19. Paweł Węgierek & Justyna Pastuszak & Kamil Dziadosz & Marcin Turek, 2020. "Influence of Substrate Type and Dose of Implanted Ions on the Electrical Parameters of Silicon in Terms of Improving the Efficiency of Photovoltaic Cells," Energies, MDPI, vol. 13(24), pages 1-17, December.
    20. Jiao, Feixiang & Ji, Chengda & Zou, Yuan & Zhang, Xudong, 2021. "Tri-stage optimal dispatch for a microgrid in the presence of uncertainties introduced by EVs and PV," Applied Energy, Elsevier, vol. 304(C).

    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:gam:jeners:v:18:y:2025:i:13:p:3295-:d:1685862. 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.