Digital Twins for Magnetic Devices

A special issue of Magnetism (ISSN 2673-8724).

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 4478

Special Issue Editors

School of Electrical and Data Engineering, University of Technology Sydney, Ultimo, NSW 2007, Australia
Interests: computational electromagnetics; advanced electrical machines and drive systems for electric vehicles; optimal energy management systems for microgrids and virtual power plants; multidisciplinary design optimization methods based on AI and cloud services
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Guest Editor
State Key Lab of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin 300130, China
Interests: computational electromagnetics; measurement and modeling of magnetic properties of materials; power electronics; electromagnetic devices

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Guest Editor
School of Electrical Engineering and Automation, Hefei University of Technology, Hefei 230009, China
Interests: electromagnetic field; numerical analysis; multi-physics coupling and its application in engineering
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia
Interests: measurement and modeling of magnetic properties of magnetic materials; electrical machine design and optimization; electric motor drives and control
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Guest Editor
School of Electrical and Information Engineering, The University of Sydney, Camperdown, NSW 2006, Australia
Interests: computational electromagnetics; measurement and modeling of magnetic properties of materials; electrical machines and drives; power electronics; renewable energy systems; smart microgrids; digital energy systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Magnetic devices have a long history of usage and play a crucial role in modern technology. Various advanced magnetic materials have been developed to improve their performance, such as soft magnetic composites, amorphous materials, nanocrystalline materials, grain-oriented SiFe, and 6.5% SiFe. Meanwhile, many new application-oriented design methods (such as novel topologies for electrical machines) and manufacturing methods (such as 3D printing) have been studied with the aim to make full use of these materials. Recently, digital twin technology has been used to visualize and monitor the operating conditions and evaluate the lifetime reliability of these devices. There are many challenges in developing efficient digital twin models, such as accurate lifetime models for magnetic materials and efficient reduced-order models.

This Special Issue aims to present a collection of scientific manuscripts covering the theoretical and practical aspects associated with digital twin models and their applications for magnetic devices. Contributions focusing on state-of-the-art and emerging developments in this field are welcome. Topics may include, but are not limited to, the following:

  • Measurement and modeling of magnetic materials under different conditions;
  • Multiphysics modeling and analysis;
  • Reduced-order models for magnetic devices;
  • Development of digital twin models for magnetic devices, including AI methods;
  • Digital twins for the design, optimization, and manufacturing of magnetic devices;
  • Digital twins for the condition monitoring, control, and reliability analysis of magnetic devices.

Dr. Gang Lei
Prof. Dr. Yongjian Li
Prof. Dr. Yujiao Zhang
Prof. Dr. Youguang Guo
Prof. Dr. Jianguo Zhu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Magnetism is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • magnetic materials
  • magnetic devices
  • digital twin
  • design optimization
  • condition monitoring
  • reliability analysis

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Published Papers (1 paper)

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Research

16 pages, 7738 KiB  
Article
Estimation of Iron Loss in Permanent Magnet Synchronous Motors Based on Particle Swarm Optimization and a Recurrent Neural Network
by Kai Xu, Youguang Guo, Gang Lei and Jianguo Zhu
Magnetism 2023, 3(4), 327-342; https://doi.org/10.3390/magnetism3040025 - 11 Dec 2023
Cited by 4 | Viewed by 3294
Abstract
The popularity of permanent magnet synchronous motors (PMSMs) has increased in recent years due to their high efficiency, compact size, and low maintenance needs. Calculating iron loss in PMSMs is crucial for designing and optimizing PMSMs to achieve high efficiency and a long [...] Read more.
The popularity of permanent magnet synchronous motors (PMSMs) has increased in recent years due to their high efficiency, compact size, and low maintenance needs. Calculating iron loss in PMSMs is crucial for designing and optimizing PMSMs to achieve high efficiency and a long lifespan, as this can significantly affect motor performance. However, multiple factors influence the accuracy of iron loss calculations in PMSMs, including the intricate magnetic behavior of the motor under different operating conditions, as well as the influence of the motor’s dynamic behavior during the operation process. This paper proposes a method based on particle swarm optimization (PSO) and a recurrent neural network (RNN) to estimate the iron loss in PMSMs, independent of the empirical iron loss formula. This method establishes an iron loss calculation model considering high-order harmonics, rotating magnetization, and temperature factors. Accounting for the multifactor influence, the model studies the law of loss change under different magnetic flux densities, frequencies, and temperature conditions. To avoid the deviation problem caused by conventional polynomial fitting, a multilayer RNN and PSO are used to train and optimize the neural network. Iron loss in complex cases beyond the measurement range can be accurately estimated. The proposed method helps achieve a PMSM iron loss calculation model with broad applicability and high accuracy. Full article
(This article belongs to the Special Issue Digital Twins for Magnetic Devices)
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