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Control, Operation and Stability of PMSM for Electric Vehicles

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "E: Electric Vehicles".

Deadline for manuscript submissions: 5 August 2026 | Viewed by 842

Special Issue Editor

Electric Powertran R&D Center, Korea Automotive Technology Institute, Cheonan 31214, Republic of Korea
Interests: testing and control of motor/generator; power conversion systems; applications of motor drives; electric vehicles; industrial servo drives; robot applications; HVAC systems; home appliance; renewable energy systems

Special Issue Information

Dear Colleagues,

The Guest Editor is inviting submissions to an Energies Special Issue titled “Control, Operation and Stability of PMSM for Electric Vehicles”. This issue will focus on the testing and control of motors/generators, power conversion systems, and the applications of PMSM (permanent magnet synchronous motor) drives, such as in electric vehicles.

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

  • All aspects of induction machines, permanent magnet synchronous machines, synchronous reluctance machines, switched reluctance machines, brushless DC machines, and emerging PM machines, among others;
  • Electric motor/generator technologies for more electric aircraft, electric vehicles, and wind energy conversion systems;
  • Machines for safety-critical applications;
  • Novel applications of electric machines;
  • Multiphase machines and drives;
  • Modular machines;
  • Fault-tolerant machines;
  • Optimal design methodologies.

Dr. Suyeon Cho
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 250 words) can be sent to the Editorial Office for assessment.

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
  • motor
  • inverter
  • drive
  • electric vehicle
  • xEV
  • control
  • design

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Published Papers (1 paper)

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Research

15 pages, 2836 KB  
Article
Digital-Twin-Driven PMSM Inter-Turn Short-Circuit Fault Diagnosis Method
by Renxiang Chen and Shaojun Lin
Energies 2026, 19(5), 1152; https://doi.org/10.3390/en19051152 - 26 Feb 2026
Viewed by 546
Abstract
Under practical operating conditions, intelligent fault diagnosis of permanent magnet synchronous motors (PMSMs) is often hindered by the shortage of effective fault samples. To address this issue, this paper proposes a twin-data-driven transfer learning-based diagnostic method for PMSM inter-turn short-circuit faults. First, a [...] Read more.
Under practical operating conditions, intelligent fault diagnosis of permanent magnet synchronous motors (PMSMs) is often hindered by the shortage of effective fault samples. To address this issue, this paper proposes a twin-data-driven transfer learning-based diagnostic method for PMSM inter-turn short-circuit faults. First, a finite element model of the motor is established in Ansys to generate inter-turn short-circuit twin data, thereby enriching the source-domain samples. Second, continuous wavelet transform (CWT) is employed to convert stator current signals into multi-scale time–frequency feature maps, which are then fed into a feature extraction network constructed by integrating a residual network (ResNet) into an efficient channel attention mechanism (ECA) to achieve effective fusion of local and global time–frequency features. Finally, a joint loss function combining multi-kernel maximum mean discrepancy (MK-MMD) and a domain-adversarial neural network (DANN) is introduced to align feature distributions and perform adversarial optimization, enhancing cross-domain invariance and improving fault recognition capability. Experimental results demonstrate that the proposed REDM method achieves higher diagnostic accuracy and robustness than several existing intelligent fault diagnosis approaches. Full article
(This article belongs to the Special Issue Control, Operation and Stability of PMSM for Electric Vehicles)
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