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Design and Control of Electrical Machines for Electric Vehicles
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Prof. Dr. Jinhua Du
The State Key Laboratory of Electrical Insulation and Power Equipment, Xi’an Jiaotong University, Xi’an 710049, China
Prof. Dr. Xuan Wu
School of Electrical and Electronic Engineering, Hunan University, Changsha 410082, China
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Design and Control of Electrical Machines for Electric Vehicles

Abstract submission deadline
31 January 2026
Manuscript submission deadline
31 March 2026
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Topic Information

Dear Colleagues,

With the global attention to environmental protection and sustainable energy development, the market demand for electric vehicles (EVs) is gradually increasing. As one of the key components, the electrical machines involved electric vehicles have achieved substantial levels of development and are poised to become integral to further technological innovations. The main directions of innovation and development include being more efficient, lighter, higher-power, smarter and more diversified, and comfort and safety have also become integral elements. The achievement of these objectives may involve the next-generation machine topology, innovative research on control strategy, new techniques in magnetics and field analysis, as well as advanced technologies for power electronic converters and inverters. This special section aims to present current state-of-the-art research addressing the design and control of electrical machines within the context of electric vehicle applications. We encourage researchers in this field to contribute their original papers to share their technical achievements with the readers. The subjects include, but are not limited to:

  1. New machine topology of electric machines in EV;
  2. Innovative methods of machine design and analysis in EV;
  3. Multi-objective optimization of electric machines in EV;
  4. Vibration and noise suppression technology of electric machines in EV;
  5. Efficient and reliable control of the power train in EV;
  6. Power electronic devices in the application of EV machine;
  7. New technologies related to motor and drive integration in EV;
  8. Applications for hub motors or multi-motor systems in EV;
  9. Other advanced technologies related to modeling, design, and control of electric machines in EV.

The application of AI to the electric machines in EV. We look forward to receiving your contributions.

Prof. Dr. Jinhua Du
Prof. Dr. Xuan Wu
Topic Editors

Keywords

  • electrical machines
  • high speed motor
  • hybrid/electric vehicles
  • magnetics and field analysis
  • motor control
  • optimization design
  • permanent magnet machines
  • PWM modulation
  • sensorless control
  • thermal and cooling design

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci 		2.5 5.3 2011 18.4 Days CHF 2400 Submit
Electricity
electricity 		- 4.8 2020 27.9 Days CHF 1000 Submit
Electronics
electronics 		2.6 5.3 2012 16.4 Days CHF 2400 Submit
Energies
energies 		3.0 6.2 2008 16.8 Days CHF 2600 Submit
Machines
machines 		2.1 3.0 2013 15.5 Days CHF 2400 Submit
Smart Cities
smartcities 		7.0 11.2 2018 28.4 Days CHF 2000 Submit
Vehicles
vehicles 		2.4 4.1 2019 19.9 Days CHF 1600 Submit
World Electric Vehicle Journal
wevj 		2.6 4.5 2007 16.2 Days CHF 1400 Submit

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Published Papers (2 papers)

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26 pages, 8588 KiB  
Article
A High-Precision Torque Control Method for New Energy Vehicle Motors Based on Virtual Signal Injection
by Zhiqiang Wang, Weihao Wang, Wei Chen, Chen Li and Zhichen Lin
Electronics 2025, 14(7), 1443; https://doi.org/10.3390/electronics14071443 - 2 Apr 2025
Viewed by 213
Abstract
The operating temperature of new energy vehicles fluctuates significantly, and variations in motor temperature lead to changes in parameters. These changes introduce errors into the motor’s mathematical model, reducing torque accuracy and causing deviations in the Maximum Torque Per Ampere (MTPA). This paper [...] Read more.
The operating temperature of new energy vehicles fluctuates significantly, and variations in motor temperature lead to changes in parameters. These changes introduce errors into the motor’s mathematical model, reducing torque accuracy and causing deviations in the Maximum Torque Per Ampere (MTPA). This paper proposes a Gated Recurrent Unit (GRU) neural network-based torque observer that employs virtual signal injection. Specifically, this method innovatively injects a virtual constant signal into the d-q axis current inputs processed by the neural network to derive the partial derivatives of torque concerning the d-axis and q-axis currents. Subsequently, it calculates the derivative of torque concerning the current vector angle (β) using the total differential equation. By leveraging these partial derivatives, the motor parameters are identified online, and the MTPA current reference value is dynamically adjusted based on the identified parameters. Additionally, the GRU’s internal parameters are fine-tuned in real time using the least mean square (LMS) algorithm, which adjusts based on the derivative of torque concerning the current angle and the error between the observed and actual values, thereby enhancing the accuracy of torque observation, and bringing results closer to the true shaft-end torque. Finally, experimental validation confirms the effectiveness and superiority of the proposed method. Full article
(This article belongs to the Topic Design and Control of Electrical Machines for Electric Vehicles)
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13 pages, 9723 KiB  
Article
Demagnetization Fault Diagnosis for PMSM Drive System with Dual Extended Kalman Filter
by Jiahan Wang, Chen Li and Zhanqing Zhou
World Electr. Veh. J. 2025, 16(2), 112; https://doi.org/10.3390/wevj16020112 - 18 Feb 2025
Viewed by 460
Abstract
Aiming at the irreversible demagnetization of permanent magnet synchronous motors (PMSMs) under extreme working conditions, a fault diagnosis method for permanent magnet demagnetization based on multi-parameter estimation is proposed in this paper. This scheme aims to provide technical support for enhancing the safety [...] Read more.
Aiming at the irreversible demagnetization of permanent magnet synchronous motors (PMSMs) under extreme working conditions, a fault diagnosis method for permanent magnet demagnetization based on multi-parameter estimation is proposed in this paper. This scheme aims to provide technical support for enhancing the safety and reliability of permanent magnet motor drive systems. In the proposed scheme, multiple operating states of the motor are acquired by injecting sinusoidal current signals into the d-axis, ensuring that the parameter estimation equation satisfies the full rank condition. Furthermore, the accurate dq-axis inductance parameters are obtained based on a recursive least square method. Subsequently, a dual extended Kalman filter is employed to acquire real-time permanent magnet flux linkage data of PMSMs, and the estimation data between the two algorithms are transferred to each other to eliminate the bias of permanent magnet flux estimation caused by a parameter mismatch. Finally, accurate evaluation of the remanence level of the rotor permanent magnet and demagnetization fault diagnosis can be achieved based on the obtained permanent magnet flux linkage parameters. The experimental results show that the relative estimation errors of the dq-axis inductance and permanent magnet flux linkage are within 5%, which can realize the effective diagnosis of demagnetization fault and high-precision condition monitoring of a permanent magnet health. Full article
(This article belongs to the Topic Design and Control of Electrical Machines for Electric Vehicles)
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