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Energies
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Advances in Permanent Magnet Motor and Motor Control
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Advances in Permanent Magnet Motor and Motor Control

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F: Electrical Engineering".

Deadline for manuscript submissions: 28 November 2024 | Viewed by 3513

Special Issue Editors

Dr. Kai Liu

E-Mail Website
Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: permanent magnet motor design and control; servo system; flywheel/CMG system
Dr. Haiwei Cai

E-Mail Website
Guest Editor
School of Electrical Engineering, Southeast University, Nanjing 210096, China
Interests: design; simulation; modeling and control of permanent magnet motors

Special Issue Information

Dear Colleagues,

Permanent magnet motors are extensively utilized in various fields, such as aerospace, new energy vehicle drives, robotics, and household appliances, due to their high power density, torque density, reliability, and efficiency. The increasing demands in application fields continuously drive the performance of permanent magnet motors to new levels. Consequently, research on the design and control of permanent magnet motors has become a hot topic in both academia and industry. The implementation of novel theories, materials, and techniques enables the rapid development of permanent magnet motors, leading to research topics including multi-phase motors, multi-harmonic motors, motors with different types of permanent magnets, sensorless control, model predictive control, fault-tolerant design and control, NVH study, and high-speed motor applications.

This Special Issue aims to provide the latest and most innovative research on all theoretical and practical aspects of permanent magnet motors.

Topics of interest for publication include, but are not limited to:

  • Permanent Magnet Synchronous Motors;
  • Flux Modulation Motors;
  • Multiphase Permanent Magnet Motors;
  • Permanent Magnet DC Motors;
  • Superconducting Machine;
  • High-Speed Motors;
  • Special Machines;
  • Modeling and Simulation of Permanent Magnet Motors;
  • Magnetics and Field Analysis;
  • Noise, Vibration, and Harshness;
  • Loss, Thermal, and Cooling;
  • Field-Oriented Control;
  • Direct Torque Control;
  • Sensorless Control;
  • Model Predictive Control;
  • Sliding Mode Control;
  • Motion Control and Servo Systems;
  • Reliability, Diagnostics, and Tolerance;
  • Other Areas in Permanent Magnet Motors.

Dr. Kai Liu
Dr. Haiwei Cai
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. 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

  • permanent magnet
  • motor
  • design
  • control

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Published Papers (4 papers)

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Research

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20 pages, 10025 KiB  
Article
Research on Markov Decision Model Predictive Control of Interior Permanent Magnet Synchronous Motor Based on Lumped Disturbances Compensation
by Yongxiao Teng, Qiang Gao, Xuehan Chen and Dianguo Xu
Energies 2024, 17(17), 4469; https://doi.org/10.3390/en17174469 - 5 Sep 2024
Viewed by 527
Abstract
To improve the performance of the interior permanent magnet synchronous motor control system, a Markov decision model predictive current control strategy based on a lumped disturbances sliding mode disturbance observer is proposed in this paper. A fast terminal sliding mode disturbance observer based [...] Read more.
To improve the performance of the interior permanent magnet synchronous motor control system, a Markov decision model predictive current control strategy based on a lumped disturbances sliding mode disturbance observer is proposed in this paper. A fast terminal sliding mode disturbance observer based on a recursive integral sliding surface is designed to observe and compensate the unideal factors in the motor control system unified as lumped disturbances. Then, according to the characteristic of model predictive control where only the first vector in the optimal control sequence is selected and applied to the system during rolling optimization, the discounted cost criterion based on the Markov decision process is introduced to enhance the control performance of the system. The compensation of lumped disturbances can eliminate the impact of unideal factors, enhance the dynamic performance of the speed loop, and eliminate the static errors in the current loop. The introduction of the discounted cost criterion can reduce the fluctuations in system states without affecting the system’s dynamic performance. Moreover, the proposed control strategy does not require the original control structure of the system to be changed. Experiments are carried out to verify the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Advances in Permanent Magnet Motor and Motor Control)
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20 pages, 12034 KiB  
Article
Convolutional Neural Networks Based on Resonance Demodulation of Vibration Signal for Rolling Bearing Fault Diagnosis in Permanent Magnet Synchronous Motors
by Li Ding, Haotian Guo and Liqiang Bian
Energies 2024, 17(17), 4334; https://doi.org/10.3390/en17174334 - 29 Aug 2024
Cited by 1 | Viewed by 694
Abstract
Permanent magnet synchronous motors (PMSMs) are widely used due to their unique advantages. Their transmission system mainly relies on rolling bearings; therefore, monitoring the motor’s working status and fault diagnosis for the rolling bearings are the key focuses. Traditional resonance demodulation methods analyze [...] Read more.
Permanent magnet synchronous motors (PMSMs) are widely used due to their unique advantages. Their transmission system mainly relies on rolling bearings; therefore, monitoring the motor’s working status and fault diagnosis for the rolling bearings are the key focuses. Traditional resonance demodulation methods analyze the vibration signals of bearings to achieve bearing fault diagnosis, but the limiting condition is that the inherent frequency needs to be known. Based on the resonance demodulation method, deep learning methods, such as the convolutional neural network (CNN) model designed in this article, have improved the practicality and effectiveness of diagnosis. A physical explanation of the deep learning model for bearing fault diagnosis is presented in this article, the relationship between resonance demodulation and the 1D CNN is analyzed, and the model is trained and validated. The experimental results show that the CNN model can identify different types of bearing faults. The analysis results of the trained CNN model and the intermediate results indicate that the CNN model is consistent with the resonance demodulation method. The optimized method is verified, proving that the model can achieve the classification and diagnosis of fault bearing data collected under different environments after the optimized training method is adopted. Full article
(This article belongs to the Special Issue Advances in Permanent Magnet Motor and Motor Control)
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16 pages, 19054 KiB  
Article
Active Disturbance Rejection Control of Permanent Magnet Synchronous Motor Based on RPLESO
by Chengpeng Zhou, Bo Wang, Kai Liu and Kaixuan Ren
Energies 2024, 17(12), 3025; https://doi.org/10.3390/en17123025 - 19 Jun 2024
Cited by 1 | Viewed by 678
Abstract
In view of the problem of the low-speed jitter of household lawn mowers driven by a permanent magnet synchronous motor (PMSM) at low speeds and high torque, and the complicated parameters of traditional non-linear active disturbance rejection controllers, a partially optimized linear active [...] Read more.
In view of the problem of the low-speed jitter of household lawn mowers driven by a permanent magnet synchronous motor (PMSM) at low speeds and high torque, and the complicated parameters of traditional non-linear active disturbance rejection controllers, a partially optimized linear active disturbance rejection control (LADRC) driving PMSM strategy is proposed. First, the linear extended state observer (LESO), which bears a significant burden in terms of speed and load estimation in active disturbance rejection control, is optimized by reducing its order to improve the anti-disturbance performance of the active disturbance rejection controller within a limited bandwidth. Secondly, the reduced-order parallel linear extended state observer (RPLESO) is obtained by optimizing the parallel structure of the order-reduced LESO, which improves the control precision and robustness of the system. Through a simulation and experimental verification, the optimized LADRC control of the PMSM system is shown to improve the parameter adjustability, speed estimation precision and system robustness. Full article
(This article belongs to the Special Issue Advances in Permanent Magnet Motor and Motor Control)
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Review

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27 pages, 2897 KiB  
Review
Essential Features and Torque Minimization Techniques for Brushless Direct Current Motor Controllers in Electric Vehicles
by Arti Aniqa Tabassum, Haeng Muk Cho and Md. Iqbal Mahmud
Energies 2024, 17(18), 4562; https://doi.org/10.3390/en17184562 - 12 Sep 2024
Cited by 1 | Viewed by 822
Abstract
The use of electric automobiles, or EVs, is essential to environmentally conscious transportation. Battery EVs (BEVs) are predicted to become increasingly accepted for passenger vehicle transportation within the next 10 years. Although enthusiasm for EVs for environmentally friendly transportation is on the rise, [...] Read more.
The use of electric automobiles, or EVs, is essential to environmentally conscious transportation. Battery EVs (BEVs) are predicted to become increasingly accepted for passenger vehicle transportation within the next 10 years. Although enthusiasm for EVs for environmentally friendly transportation is on the rise, there remain significant concerns and unanswered research concerns regarding the possible future of EV power transmission. Numerous motor drive control algorithms struggle to deliver efficient management when ripples in torque minimization and improved dependability control approaches in motors are taken into account. Control techniques involving direct torque control (DTC), field orientation control (FOC), sliding mode control (SMC), intelligent control (IC), and model predictive control (MPC) are implemented in electric motor drive control algorithms to successfully deal with this problem. The present study analyses only sophisticated control strategies for frequently utilized EV motors, such as the brushless direct current (BLDC) motor, and possible solutions to reduce torque fluctuations. This study additionally explores the history of EV motors, the operational method between EM and PEC, and EV motor design techniques and development. The future prospects for EV design include a vital selection of motors and control approaches for lowering torque ripple, as well as additional research possibilities to improve EV functionality. Full article
(This article belongs to the Special Issue Advances in Permanent Magnet Motor and Motor Control)
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