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Innovations in Electric Motor Drives: Exploring the Future of High-Performance, Energy Efficiency and Reliability

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

Deadline for manuscript submissions: closed (20 May 2026) | Viewed by 2989

Special Issue Editor


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Guest Editor
Department of Electrical and Computer Engineering, Baylor University, Waco, TX 76798, USA
Interests: electric machine design; power electronics; motor drives; diagnostics; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As industries worldwide accelerate toward electrification and sustainable technologies, advanced electric machine topologies and motor drives have remained as the core enabling technologies driving the advancements in new applications. Innovations in topologies including superconducting machines; materials and components, including the additive manufacturing of components, intelligent control techniques, and modulation strategies; motor-drive integration; and the application of machine learning and AI techniques in motor drives have seen significant progress over the past few decades. These innovations are redefining the benchmarks for energy efficiency, high performance, and reliability across applications in electrified transportation, industrial automation, and renewable energy systems.

This Special Issue seeks to explore cutting-edge innovations and emerging trends that are shaping the future of electric machines and motor drives. The Guest Editor is inviting research scientists/engineers and industry experts to contribute original work that addresses both fundamental challenges in the design and control of electric motor drives, as well as reliability and diagnostic issues related to their application. Submissions that explore novel topologies, novel materials and manufacturing methods, advanced control strategies, modeling techniques, diagnostics, and the reliability of high-specific-power electric machines are particularly encouraged, with the aim of highlighting practical challenges and setting new directions for future developments in this rapidly evolving field.

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

  • Design of high-specific-power electric machines, novel topologies, superconducting machines, high-speed machines, opportunities in additively manufactured machines, advancements in motor drive topologies, high-efficiency power converter design, modulation strategies, wide-bandgap semiconductor applications, hybrid switching topologies, integrated motor drive design and challenges, co-design of power electronics and electric machine, thermal management, and efficiency.
  • Motor drive robust control strategies, adaptive algorithms, and artificial intelligence techniques in motor control.
  • Multi-physics modeling and simulation techniques, including real-time, hardware-in-the-loop, and power-in-the-loop testing, as well as digital twin development.
  • Reliability and fault tolerance, fault detection and diagnostics, health monitoring, lifetime prediction, remaining useful life estimation of machine and components such as windings and bearings, demagnetization mitigation techniques, and demagnetization risk assessment.

Dr. Emmanuel Agamloh
Guest Editor

Manuscript Submission Information

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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

  • high-specific-power electric machines
  • superconducting machines
  • diagnostics and reliability

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Published Papers (2 papers)

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Research

22 pages, 3981 KB  
Article
Rotating Electric Machine Fault Diagnosis with Magnetic Flux Measurement Using Deep Learning Models
by Obinna Onodugo, Innocent Enyekwe and Emmanuel Agamloh
Energies 2026, 19(4), 1106; https://doi.org/10.3390/en19041106 - 22 Feb 2026
Viewed by 1213
Abstract
This paper presents new techniques for electric machine diagnostics that combine advanced signal processing and artificial intelligence (AI)-based techniques using magnetic flux measurements acquired under various operating conditions. Developing an effective electric machine diagnostics tool is paramount for increased industrial productivity and extending [...] Read more.
This paper presents new techniques for electric machine diagnostics that combine advanced signal processing and artificial intelligence (AI)-based techniques using magnetic flux measurements acquired under various operating conditions. Developing an effective electric machine diagnostics tool is paramount for increased industrial productivity and extending the service life of the machine. The existing diagnostic tools face issues, including false indication of faults using classical methods, and the proposed data-driven methods based on machine learning lack transferability of model knowledge on an unseen dataset from different motor types or power ratings due to structural differences. To overcome these diagnostic drawbacks of statistical ML classifiers and classical approaches, innovative feature selection methods were employed in this work to preprocess the measured magnetic flux into a spectrogram image, and the transfer learning (TL) technique was applied to fine-tune convolution neural networks (CNNs) ImageNet pretrained models. The experimental results show the trained statistical ML classifiers and traditional CNN performance on unseen BU data and on the external data, and the performance demonstrated a lack of generalization on external datasets of different power ratings or structures. Models with such drawbacks cannot be used for developing effective diagnostic systems. The TL technique was employed on different deep CNN ImageNet pretrained models with spectrogram images as inputs to the deep CN network. This approach demonstrated an advanced and improved electric machine diagnostic system that addresses the drawbacks of the current ML-based diagnostic systems. The generalized model developed using CNN ResNet50 outperformed other deep CNN ImageNet models in correctly diagnosing faults on both the dataset generated from the authors’ lab and on an external dataset of a different machine from another research lab. Full article
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13 pages, 2738 KB  
Article
Maximum Torque per Ampere Control of IPMSM Based on Current Angle Searching with Sliding-Mode Extremum Seeking
by Ziqing Zhang, Xiang Wu and Bo Yang
Energies 2025, 18(21), 5613; https://doi.org/10.3390/en18215613 - 25 Oct 2025
Cited by 2 | Viewed by 1310
Abstract
Model-based maximum torque per ampere (MTPA) control methods of interior permanent magnet synchronous motors (IPMSM) often suffer from poor robustness. To address this issue, a new MTPA control method based on current angle searching with sliding-mode extremum seeking is proposed. Based on Lyapunov’s [...] Read more.
Model-based maximum torque per ampere (MTPA) control methods of interior permanent magnet synchronous motors (IPMSM) often suffer from poor robustness. To address this issue, a new MTPA control method based on current angle searching with sliding-mode extremum seeking is proposed. Based on Lyapunov’s criterion, the stability of the proposed MTPA method is proven. By analyzing the formation and switching process of a sliding-mode surface, the convergence speed and control accuracy of the proposed MTPA are derived. Compared with the conventional MTPA method, based on the sinusoidal excitation extremum search algorithm, the proposed method does not require either a sinusoidal excitation signal or high-pass and low-pass filters. The effectiveness of the proposed method is verified by experiment. Full article
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