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Energies
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Next-Generation Electric Machines: Design, Control, and Fault Diagnosis
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Next-Generation Electric Machines: Design, Control, and Fault Diagnosis

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 1583

Special Issue Editor

Dr. Yannis L. Karnavas

E-Mail Website
Guest Editor
Department of Electrical & Computer Engineering, Democritus University of Thrace, Xanthi, Greece
Interests: electrical machines design, analysis, modeling and optimization; controller design and application to electrical machines
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

The rapid electrification of transportation, industry, and renewable energy systems is driving unprecedented advances in electric machine technology. Next-generation electric machines must meet increasingly stringent efficiency, reliability, power density, cost, and sustainability requirements. This calls for the use of innovative approaches across the entire machine development lifecycle, in areas from conceptual design and advanced materials to control strategies and fault-tolerant operation.

This Special Issue aims to bring together cutting-edge research that addresses the design, modeling, control, and condition monitoring of emerging electric machine technologies. The topics of interest include, but are not limited to, the following:

  • Novel topologies and advanced electromagnetic/thermal designs.
  • The use of new materials, including wide-bandgap devices and magnetic composites.
  • Digital twin approaches and high-fidelity multiphysics modeling.
  • Intelligent control strategies, including model predictive control, AI-driven optimization, and energy management.
  • Fault detection, diagnosis, and prognosis methods for enhanced reliability.
  • The integration of electric machines in electrified transport, renewable energy, and industrial applications.

By covering both theoretical advancements and practical applications, this Special Issue seeks to provide a comprehensive perspective on the state of the art and future trends in electric machine research. Contributions are expected to include innovative methodologies, experimental validation, and cross-disciplinary insights that can accelerate the transition toward more sustainable and resilient electrical systems.

Dr. Yannis L. Karnavas
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
  • electromagnetic/thermal design
  • new materials and magnetic composites
  • multiphysics modeling approaches
  • electrical machine control
  • electric propulsion
  • electric machines in transportation electrification
  • fault diagnosis
  • fault-tolerant methods

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Published Papers (2 papers)

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Research

19 pages, 4245 KB  
Article
Multi-Objective Collaborative Optimization of Magnetic Gear Compound Machines Using Parameter Grouping and Kriging Surrogate Models
by Bin Zhang, Jinghong Zhao, Yihui Xia, Xiang Peng, Xiaohua Shi, Xuedong Zhu, Baozhong Qu and Keke Yang
Energies 2025, 18(23), 6153; https://doi.org/10.3390/en18236153 - 24 Nov 2025
Cited by 2 | Viewed by 388
Abstract
This paper proposes a novel optimization framework for Magnetic Gear Compound Machines (MGCMs) that integrates parameter grouping and surrogate modeling to address challenges of high-dimensional design spaces and conflicting objectives. The core methodological contribution is a new parameter grouping strategy employing sensitivity analysis [...] Read more.
This paper proposes a novel optimization framework for Magnetic Gear Compound Machines (MGCMs) that integrates parameter grouping and surrogate modeling to address challenges of high-dimensional design spaces and conflicting objectives. The core methodological contribution is a new parameter grouping strategy employing sensitivity analysis and partial correlation coefficients, which systematically classifies design parameters into high-, medium-, and low-impact groups. This approach achieves a 60% reduction in optimization dimensionality while preserving essential electromagnetic relationships. Latin Hypercube Sampling (LHS) is coupled with high-fidelity Maxwell 2D transient simulations to construct an accurate Kriging surrogate model, which is then integrated with the NSGA-III algorithm for efficient Pareto front identification. Comprehensive simulations demonstrate the framework’s exceptional performance. The sensitivity-based optimized design achieves an 85.5% reduction in inner rotor torque ripple (0.091), maintains 90.3% of the original torque output (475.100 N·m), and preserves 94.8% of the induced electromotive force (399.578 V), yielding an optimal objective function value of −0.901 that indicates superior overall performance improvement. In comparison, the correlation-based approach provides an 84.5% torque ripple reduction (0.097) with 97.7% torque retention (514.166 N·m) and 86.0% voltage preservation (362.739 V), corresponding to an objective function value of −0.841. Both grouping strategies significantly reduce computational cost by approximately 60% compared to conventional single-stage optimization methods. This research establishes an effective optimization paradigm for MGCMs, successfully resolving the fundamental trade-off between power density maximization and operational stability, with promising applications in electric propulsion and renewable energy systems. Full article
(This article belongs to the Special Issue Next-Generation Electric Machines: Design, Control, and Fault Diagnosis)
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34 pages, 42005 KB  
Article
Adaptive Microprocessor-Based Interval Type-2 Fuzzy Logic Controller Design for DC Micro-Motor Control Considering Hardware Limitations
by Nikolaos V. Chatzipapas and Yannis L. Karnavas
Energies 2025, 18(21), 5781; https://doi.org/10.3390/en18215781 - 2 Nov 2025
Viewed by 903
Abstract
The increasing adoption of high-performance DC motor control in embedded systems has driven the development of cost-effective solutions that extend beyond traditional software-based optimization techniques. This work presents a refined hardware-centric approach implementing real-time particle swarm optimization (PSO) directly executed on STM32 microcontroller [...] Read more.
The increasing adoption of high-performance DC motor control in embedded systems has driven the development of cost-effective solutions that extend beyond traditional software-based optimization techniques. This work presents a refined hardware-centric approach implementing real-time particle swarm optimization (PSO) directly executed on STM32 microcontroller for DC motor speed control, departing from conventional simulation-based parameter-tuning methods. Novel hardware-optimized composition of an interval type-2 fuzzy logic controller (FLC) and a PID controller is developed, designed for resource-constrained embedded systems and accounting for processing delays, memory limitations, and real-time execution constraints typically overlooked in non-experimental studies. The hardware-in-the-loop implementation enables real-time parameter optimization while managing actual system uncertainties in controlling DC micro-motors. Comprehensive experimental validation against conventional PI, PID, and PIDF controllers, all optimized using the same embedded PSO methodology, reveals that the proposed FT2-PID controller achieves superior performance with 28.3% and 56.7% faster settling times compared to PIDF and PI controllers, respectively, with significantly lower overshoot at higher reference speeds. The proposed hardware-oriented methodology bridges the critical gap between theoretical controller design and practical embedded implementation, providing detailed analysis of hardware–software co-design trade-offs through experimental testing that uncovers constraints of the low-cost microcontroller platform. Full article
(This article belongs to the Special Issue Next-Generation Electric Machines: Design, Control, and Fault Diagnosis)
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