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Permanent Magnet Motors and Driving Control for Electric Vehicles
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Permanent Magnet Motors and Driving Control for Electric Vehicles

A special issue of World Electric Vehicle Journal (ISSN 2032-6653).

Deadline for manuscript submissions: 30 September 2025 | Viewed by 13538

Special Issue Editor

Dr. Ming Yao

E-Mail Website
Guest Editor
School of Automobile and Traffic Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: motor model predictive control; electric vehicle safety technology

Special Issue Information

Dear Colleagues,

With global warming and increasingly serious environmental pollution, the development of electric vehicles (EV) has become an inevitable trend. At present, the efficiency, comfort and reliability of EVs have attracted more and more attention, and the corresponding higher requirements are put forward for the power transmission system of EVs.

Motor as a key component of EV power transmission, not only determines the performance of EVs, but also needs to meet the comfort and economic performance requirements. Permanent magnet motor has become the preferred driving motor for EVs because of its high efficiency, high power density and compact structure. However, the drive control methods of the permanent magnet motor have a great influence on the output torque ripple and robustness, different drive control methods have different characteristics and scope of application, it is necessary to choose the appropriate method according to the actual situation.

It is clear that advances in permanent magnet motors and drive methods will contribute to the development of EVs, and this Special Issue invites original papers and review articles on all aspects of EVs, permanent magnet motors and drive control.

Dr. Ming Yao
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. World Electric Vehicle Journal is an international peer-reviewed open access monthly 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 1400 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 DC motor
  • permanent magnet AC motor
  • bidirectional synchronous motor
  • permanent magnet synchronous motor
  • moving component motor
  • model predictive flux control
  • model predictive current control
  • continuous-control-set model predictive control

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Published Papers (8 papers)

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Research

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19 pages, 4643 KiB  
Article
Fault Diagnosis of Permanent Magnet Synchronous Motor Based on Wavelet Packet Transform and Genetic Algorithm-Optimized Back Propagation Neural Network
by Ming Ye, Run Gong, Wanjun Wu, Zhiyuan Peng and Kelin Jia
World Electr. Veh. J. 2025, 16(4), 238; https://doi.org/10.3390/wevj16040238 - 18 Apr 2025
Viewed by 264
Abstract
In this paper, a fault diagnosis method for permanent magnet synchronous motors is proposed, combining wavelet packet transform (WPT) energy feature extraction and a genetic algorithm (GA)-optimized back propagation (BP) neural network. Firstly, for the common types of motor faults (turn-to-turn short-circuit, phase-to-phase [...] Read more.
In this paper, a fault diagnosis method for permanent magnet synchronous motors is proposed, combining wavelet packet transform (WPT) energy feature extraction and a genetic algorithm (GA)-optimized back propagation (BP) neural network. Firstly, for the common types of motor faults (turn-to-turn short-circuit, phase-to-phase short-circuit, loss of magnetism, inverter open-circuit, rotor eccentricity), a corresponding motor fault model is established. The stator current signals during motor operation are analyzed using wavelet packet transform, and energy features are extracted from them as feature vectors for fault diagnosis. Then, a BP neural network is constructed, and a genetic algorithm is used to optimize its initial weights and thresholds, thereby improving the network’s classification accuracy. The results show that the GA-BP model outperforms the SSA-PNN diagnostic model in terms of fault classification accuracy. In particular, for the diagnosis of normal operation, inverter open-circuit, and demagnetization faults, the accuracy rate reaches 100%. This method demonstrates high diagnostic accuracy and practical application value. Full article
(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)
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36 pages, 2524 KiB  
Article
Compensating PI Controller’s Transients with Tiny Neural Network for Vector Control of Permanent Magnet Synchronous Motors
by Martin Joel Mouk Elele, Danilo Pau, Shixin Zhuang and Tullio Facchinetti
World Electr. Veh. J. 2025, 16(4), 236; https://doi.org/10.3390/wevj16040236 - 18 Apr 2025
Viewed by 278
Abstract
Recent advancements in neural networks (NNs) have underscored their potential for deployment in domains that demand computationally intensive operations, including applications on resource-constrained edge devices. This study investigates the integration of a compact neural network, TinyFC, within the Field-Oriented Control (FOC) framework of [...] Read more.
Recent advancements in neural networks (NNs) have underscored their potential for deployment in domains that demand computationally intensive operations, including applications on resource-constrained edge devices. This study investigates the integration of a compact neural network, TinyFC, within the Field-Oriented Control (FOC) framework of a Permanent Magnet Synchronous Motor (PMSM). While proportional–integral (PI) controllers remain a widely adopted choice for FOC due to their simplicity, their performance can degrade significantly under high-frequency speed transitions, where nonlinear dynamics introduce notable inaccuracies. The TinyFC model complements the PI controller by learning the intrinsic dependencies within the control loops and generating corrective signals to alleviate these inaccuracies. To ensure practical implementation, TinyFC underwent extensive optimization procedures, incorporating advanced techniques such as hyperparameter tuning, pruning, and 8-bit quantization. These measures successfully reduced the model’s computational overhead while preserving predictive accuracy. Simulation results demonstrated that embedding TinyFC within the FOC framework substantially reduced overshoot, with the pruned TinyFC entirely eliminating overshoot when integrated into the speed control unit. These findings highlight the feasibility of employing lightweight neural networks for real-time motor control applications, establishing a foundation for more efficient and precise control strategies in edge automotive and industrial systems. Full article
(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)
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23 pages, 6849 KiB  
Article
Fault Diagnosis Method of Permanent Magnet Synchronous Motor Demagnetization and Eccentricity Based on Branch Current
by Zhiqiang Wang, Shangru Shi, Xin Gu, Zhezhun Xu, Huimin Wang and Zhen Zhang
World Electr. Veh. J. 2025, 16(4), 223; https://doi.org/10.3390/wevj16040223 - 9 Apr 2025
Viewed by 374
Abstract
Since permanent magnets and rotors are core components of electric vehicle drive motors, accurate diagnosis of demagnetization and eccentricity faults is crucial for ensuring the safe operation of electric vehicles. Currently, intelligent diagnostic methods based on three-phase current signals have been widely adopted [...] Read more.
Since permanent magnets and rotors are core components of electric vehicle drive motors, accurate diagnosis of demagnetization and eccentricity faults is crucial for ensuring the safe operation of electric vehicles. Currently, intelligent diagnostic methods based on three-phase current signals have been widely adopted due to their advantages of easy acquisition, low cost, and non-invasiveness. However, in practical applications, the fault characteristics in current signals are relatively weak, leading to diagnostic performance that falls short of expected standards. To address this issue and improve diagnostic accuracy, this paper proposes a novel diagnostic method. First, branch current is utilized as the data source for diagnosis to enhance the fault characteristics of the diagnostic signal. Next, a dual-modal feature extraction module is constructed, employing Variational Mode Decomposition (VMD) and Fast Fourier Transform (FFT) to concatenate the input branch current along the feature dimension in both the time and frequency domains, achieving nonlinear coupling of time–frequency features. Finally, to further improve diagnostic accuracy, a cascaded convolutional neural network based on dilated convolutional layers and multi-scale convolutional layers is designed as the diagnostic model. Experimental results show that the method proposed in this paper achieves a diagnostic accuracy of 98.6%, with a misjudgment rate of only about 2% and no overlapping feature results. Compared with existing methods, the method proposed in this paper can extract higher-quality fault features, has better diagnostic accuracy, a lower misjudgment rate, and more excellent feature separation ability, demonstrating great potential in intelligent fault diagnosis and maintenance of electric vehicles. Full article
(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)
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14 pages, 17824 KiB  
Article
The Multiphysics Analysis and Suppression Method for the Electromagnetic Noise of Permanent-Magnet Motors Used in Electric Vehicle
by Junhong Dong, Hongbin Yin, Guohao Li, Xiaojun Wang and Mingyang Luo
World Electr. Veh. J. 2025, 16(3), 136; https://doi.org/10.3390/wevj16030136 - 1 Mar 2025
Viewed by 557
Abstract
A method for predicting the electromagnetic noise of a permanent-magnet motor based on the coupling of electromagnetic force and modal is proposed. Firstly, a theoretical analysis and finite element method are combined to establish an electromagnetic force analysis model for a 6-pole 36-slot [...] Read more.
A method for predicting the electromagnetic noise of a permanent-magnet motor based on the coupling of electromagnetic force and modal is proposed. Firstly, a theoretical analysis and finite element method are combined to establish an electromagnetic force analysis model for a 6-pole 36-slot permanent-magnet motor used in vehicles. The spatial order and frequency characteristics of the electromagnetic force are analyzed. Then, the modal array of the motor is calculated using the finite element method, and the main sources of the motor vibration noise are predicted by combining the electromagnetic force with the modal frequency array of each order. Finally, a vibration noise multiphysics simulation analysis model is established using the finite element method, and the electromagnetic noise is calculated. The simulation results are consistent with the predicted results, verifying the effectiveness of the analysis method. Full article
(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)
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19 pages, 5788 KiB  
Article
Mutual Inductance Identification and Bilateral Cooperation Control Strategy for MCR-BE System
by Ke Li, Yuanmeng Liu, Xiaodong Sun and Xiang Tian
World Electr. Veh. J. 2024, 15(5), 196; https://doi.org/10.3390/wevj15050196 - 2 May 2024
Viewed by 1212
Abstract
Considering that the excitation method of an electric excitation synchronous motor has the disadvantages of the brush and slip ring, this article proposes a new brushless excitation system, which includes two parts: a wireless charging system and a motor. To meet the requirements [...] Read more.
Considering that the excitation method of an electric excitation synchronous motor has the disadvantages of the brush and slip ring, this article proposes a new brushless excitation system, which includes two parts: a wireless charging system and a motor. To meet the requirements of maximum transmission efficiency and constant voltage output of the system, a bilateral cooperation control strategy is proposed. For the strategy, the buck converter in the receiving side of the system can maintain maximum transmission efficiency through impedance matching, while the inverter in the transmitting side can keep the output voltage constant through phase shift modulation. In the control process, considering that the offset of coupling coils will affect the control results, a grey wolf optimization–particle swarm optimization algorithm is proposed to identify mutual inductance. Simulation and experimental results show that this identification algorithm can improve the identification accuracy and maximize the avoidance of falling into local optima. The final experimental result shows that the bilateral cooperation control strategy can maintain the output voltage around 48 V and the transmission efficiency around 84.5%, which meets the expected requirements. Full article
(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)
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17 pages, 5418 KiB  
Article
Design and Implementation of Improved Gate Driver Circuit for Sensorless Permanent Magnet Synchronous Motor Control
by Indra Ferdiansyah and Tsuyoshi Hanamoto
World Electr. Veh. J. 2024, 15(3), 106; https://doi.org/10.3390/wevj15030106 - 9 Mar 2024
Cited by 2 | Viewed by 2265
Abstract
Reliable motor control is important for electric vehicle applications. The control process requires accurate measurements of the current and rotor position information to establish correct motor control design, particularly in sensorless permanent magnet synchronous motor control systems. Practical issues regarding the motor control [...] Read more.
Reliable motor control is important for electric vehicle applications. The control process requires accurate measurements of the current and rotor position information to establish correct motor control design, particularly in sensorless permanent magnet synchronous motor control systems. Practical issues regarding the motor control circuit, such as the effects of parasitic element behavior on the switching components in the insulated gate bipolar transistor-driven inverter, were discussed in this study. It analyzed the effects of parasitic elements that can cause the ringing of switching losses and affect the spike of the signal in the motor current, which must be avoided in the implementation of motor control. The gate driver circuit topology was improved to reduce this effect in motor control devices. The proposed gate driver circuit design with the ringing suppression circuit configuration achieved good performance by keeping the signal spike at less than 10% in the motor current. Furthermore, a signal spike or noise was not observed in the estimation results of rotor position when using current information as the parameter control process. Both conditions were verified by experiments on the designed motor control devices. Under these conditions, signal precision can be achieved in motor control. Full article
(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)
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20 pages, 7383 KiB  
Article
Parameter Compensation for the Predictive Control System of a Permanent Magnet Synchronous Motor Based on Bacterial Foraging Optimization Algorithm
by Jiali Yang, Yanxia Shen and Yongqiang Tan
World Electr. Veh. J. 2024, 15(1), 23; https://doi.org/10.3390/wevj15010023 - 9 Jan 2024
Cited by 3 | Viewed by 2068
Abstract
The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the foundation for high-performance driving in predictive control systems. The traditional PMSM multi-parameter identification method suffers from insufficient rank of the identification equation and is prone to getting stuck in local optimal [...] Read more.
The accurate identification of permanent magnet synchronous motor (PMSM) parameters is the foundation for high-performance driving in predictive control systems. The traditional PMSM multi-parameter identification method suffers from insufficient rank of the identification equation and is prone to getting stuck in local optimal solutions. This article combines the bacterial foraging optimization algorithm (BFOA) to establish a built-in PMSM predictive control parameter compensation model. Firstly, we analyzed the reasons why the distortion of PMSM motor parameters affects the actual speed and calculated the deviation of d-axis and q-axis currents caused by the distortion. Secondly, parameter compensation was applied to the prediction model, and BFOA was combined to optimize the compensation parameters. This algorithm does not use the traditional voltage equation as the fitness function but instead uses a brand-new set of four equations for parameter iteration optimization. The optimized compensation parameters can reduce current deviation and improve the robustness of the PMSM predictive control system. The proposed model can cover four kinds of motor distortion parameters, including stator resistance, D-axis inductance, Q-axis inductance, and permanent magnet flux linkage. Finally, the traditional PMSM predictive control model is compared with the predictive control model combined with BFOA. The simulation results show that the dynamic and static performance of the compensated system is improved when single or multiple parameters are distorted. Full article
(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)
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Review

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21 pages, 2522 KiB  
Review
Overview of Position-Sensorless Technology for Permanent Magnet Synchronous Motor Systems
by Yulei Xu, Ming Yao and Xiaodong Sun
World Electr. Veh. J. 2023, 14(8), 212; https://doi.org/10.3390/wevj14080212 - 10 Aug 2023
Cited by 8 | Viewed by 4911
Abstract
In recent years, permanent magnet synchronous motors (PMSMs) have been widely used in industry. Position-sensorless control has the advantages of reducing costs and improving reliability, and is becoming one of the most promising technologies for permanent magnet synchronous motors. This article reviews the [...] Read more.
In recent years, permanent magnet synchronous motors (PMSMs) have been widely used in industry. Position-sensorless control has the advantages of reducing costs and improving reliability, and is becoming one of the most promising technologies for permanent magnet synchronous motors. This article reviews the main position-sensorless technologies. The advantages and disadvantages of model-based and saliency-based techniques were summarized and compared. Finally, the developmental trends and research directions of position-sensorless technology were discussed. Full article
(This article belongs to the Special Issue Permanent Magnet Motors and Driving Control for Electric Vehicles)
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