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Design and Analysis of Permanent Magnet Machines for Electric Propulsion Applications

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A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F3: Power Electronics".

Deadline for manuscript submissions: closed (20 August 2023) | Viewed by 9255

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Special Issue Editor

Dr. Gilsu Choi

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Guest Editor

Department of Electrical Engineering, Inha University, Incheon 22212, Republic of Korea
Interests: electrical machines and drives for electric propulsion systems; design, analysis, control, and optimization of electrical machines and drives; transportation electrification; multiphysics analysis of electric machines

Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions in the form of research and review papers to a Special Issue of Energies titled "Recent Trends in Design and Analysis of Permanent Magnet Synchronous Machines for Electric Propulsion Systems".

As the global demand to reduce greenhouse gas emissions increases, manufacturers of automobiles, ships, and aircraft are facing increasing challenges related to the electrification of propulsion systems. Among the various types of traction machines, permanent magnet synchronous machines have received considerable attention in the past few decades due to their superior properties in terms of efficiency and torque density compared to their counterparts.

The aim of this Special Issue is to promote ground-breaking methods and approaches for the design and analysis of permanent magnet synchronous machines and their implementation in the harsh environments of electric propulsion systems.

Topics of interest include (but are not limited to) the following:

Design and optimization of permanent magnet machines;
Surrogate-model-based multi-objective design optimization;
Analytical and numerical techniques;
Multiphysics aspects of permanent magnet machine design;
Application of new materials and design configurations;
Multiphase design configurations and topologies;
Design without heavy rare-earth magnets;
Fault-mode analysis and diagnostic strategies;
The impact of tolerances and uncertainties in machine geometry;
Correlation of experimental results with simulation;
Application-specific issues (e.g., traction, naval, and aerospace applications).

We would like to invite you to submit your outstanding research works to this Special Issue, and we look forward to receiving your contributions. Please send a tentative title and a short summary of the manuscript to Energies Editor Ms. Stephanie Sang ([email protected]).

Dr. Gilsu Choi
Guest Editor

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. Energies is an international peer-reviewed open access semimonthly 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 2600 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

electric machines
permanent magnet synchronous machines
design optimization
multiphysics analysis
manufacturing effects
new materials
experimental verification
novel configurations and topologies
fault analysis and diagnostics

Published Papers (4 papers)

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Research

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19 pages, 9016 KiB

Open AccessArticle

Rapid Prototyping of Three-Phase AC Machine Drive System with Subtractive and Additive Manufacturing

by Ye Gu Kang, Kang Min Lee and Gilsu Choi

Energies 2023, 16(5), 2266; https://doi.org/10.3390/en16052266 - 27 Feb 2023

Cited by 1 | Viewed by 1623

Abstract

We propose a method for rapid prototyping of a three-phase AC drive system for educational purposes. The proposed method allows college students to design and manufacture a drive system comprising three-phase inverters, a permanent-magnet (PM) machine, a controller, and sensors within a semester. The rapid prototyping process, which requires less than a day, enables efficient iteration and testing during the development process. In addition to addressing the electrical design considerations, this study also addresses the mechanical aspects of the drive system, including the use of coreless PM machines fabricated, using additive manufacturing technology, and the inverter manufacturing process, utilizing an auto-milling machine. Finally, we provide details of the rapid prototyping of closed-loop control, based on sensor feedback to regulate the rotating magnetic fields and output torque. Full article

(This article belongs to the Special Issue Design and Analysis of Permanent Magnet Machines for Electric Propulsion Applications)

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18 pages, 5913 KiB

Open AccessArticle

Design of Stator Winding Turn Number of Tap-Change PMSM for EVs According to Driving Cycles

by Jong-Kyong Lim, Bon-Gwan Gu, Seong-Hwan Im and Rae-Young Kim

Energies 2023, 16(1), 412; https://doi.org/10.3390/en16010412 - 29 Dec 2022

Viewed by 1606

Abstract

Driving cycles are commonly used to evaluate the fuel efficiency and mileage of internal combustion engine vehicles and electric vehicles (EVs). To improve the mileage of EVs, efficient propulsion motors and inverters must be employed, particularly at frequently used speed and torque points of driving cycles. In this study, the number of the stator winding turns of a tap-change permanent magnet synchronous motor (PMSM) used for EV propulsion was investigated to increase the motor and inverter energy efficiencies under an urban dynamometer driving schedule (UDDS), a highway fuel economy test (HWFET), and combined UDDS/HWFET driving cycles. The motor core loss due to the carrier harmonics, copper loss, and inverter loss were analyzed for various winding turn number combinations, and the most efficient combination was selected. Based on the analysis results, the tap-change motor winding turns that exhibited the least loss were rewound on a PMSM of a mass-produced EV. The demand energy of the proposed tap-change PMSM and that of a mass-produced PMSM in the UDDS, HWFET, and combined UDDS/HWFET driving cycles were compared through experimentation. The proposed PMSM exhibited energy improvements of 1.50%, 1.84%, and 1.65%, respectively. Full article

(This article belongs to the Special Issue Design and Analysis of Permanent Magnet Machines for Electric Propulsion Applications)

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19 pages, 4881 KiB

Open AccessArticle

Systematic Development of a Multi-Objective Design Optimization Process Based on a Surrogate-Assisted Evolutionary Algorithm for Electric Machine Applications

by Mingyu Choi, Gilsu Choi, Gerd Bramerdorfer and Edmund Marth

Energies 2023, 16(1), 392; https://doi.org/10.3390/en16010392 - 29 Dec 2022

Cited by 2 | Viewed by 1595

Abstract

Surrogate model (SM)-based optimization approaches have gained significant attention in recent years due to their ability to find optimal solutions faster than finite element (FE)-based methods. However, there is limited previous literature available on the detailed process of constructing SM-based approaches for multi-parameter, multi-objective design optimization of electric machines. This paper aims to present a systematic design optimization process for an interior permanent magnet synchronous machine (IPMSM), including a thorough examination of the construction of the SM and the adjustment of its parameters, which are crucial for reducing computation time. The performances of SM candidates such as Kriging, artificial neural networks (ANNs), and support vector regression (SVR) are analyzed, and it is found that Kriging exhibits relatively better performance. The hyperparameters of each SM are fine-tuned using Bayesian optimization to avoid manual and empirical tuning. In addition, the convergence criteria for determining the number of FE computations needed to construct an SM are discussed in detail. Finally, the validity of the proposed design process is verified by comparing the Pareto fronts obtained from the SM-based and conventional FE-based methods. The results show that the proposed procedure can significantly reduce the total computation time by approximately 93% without sacrificing accuracy compared to the conventional FE-based method. Full article

(This article belongs to the Special Issue Design and Analysis of Permanent Magnet Machines for Electric Propulsion Applications)

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Review

Jump to: Research

54 pages, 20326 KiB

Open AccessReview

The Bearing Faults Detection Methods for Electrical Machines—The State of the Art

by Muhammad Amir Khan, Bilal Asad, Karolina Kudelina, Toomas Vaimann and Ants Kallaste

Energies 2023, 16(1), 296; https://doi.org/10.3390/en16010296 - 27 Dec 2022

Cited by 17 | Viewed by 3735

Abstract

Electrical machines are prone to faults and failures and demand incessant monitoring for their confined and reliable operations. A failure in electrical machines may cause unexpected interruptions and require a timely inspection of abnormal conditions in rotating electric machines. This article aims to summarize an up-to-date overview of all types of bearing faults diagnostic techniques by subdividing them into different categories. Different fault detection and diagnosis (FDD) techniques are discussed briefly for prognosis of numerous bearing faults that frequently occur in rotating machines. Conventional approaches, statistical approaches, and artificial intelligence-based architectures such as machine learning and deep learning are discussed summarily for the diagnosis of bearing faults that frequently arise in revolving electrical machines. The most advanced trends for diagnoses of frequent bearing faults based on intelligence and novel applications are reviewed. Future research directions that are helpful to enhance the performance of conventional, statistical, and artificial intelligence (machine learning, deep learning) and novel approaches are well addressed and provide hints for future work. Full article

(This article belongs to the Special Issue Design and Analysis of Permanent Magnet Machines for Electric Propulsion Applications)

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