Special Issue

AI-based Power Electronics and Electric Drives for Enhanced Reliability

Submission Deadline: January 31, 2026 (Open) Submit Now

Guest Editor

Acacio Amaral, PhD

1. Assistant Professor, Department of Informatics and Systems, Polytechnic Institute of Coimbra, Coimbra Institute of Engineering, Coimbra, Portugal

2. CISE - Electromechatronic Systems Research Centre, University of Beira Interior, Calçada Fonte do Lameiro, P – 6201-001 Covilhã, Portugal

Website1 | Website2 | E-Mail

Research Interests: Fault diagnosis in electronic systems; internet of things (IoT); machine learning; signal processing; business intelligence

About This Topic

Power converters and electric motor drives are essential elements in our daily lives, playing a crucial role in combating climate change. Their widespread use in industry, automation, transport and household appliances has enabled nations to progressively achieve a more efficient and greener environment.

The continuous evolution of these systems has driven the development of new converter topologies and increasingly sophisticated control strategies. However, this increasing complexity also introduces significant challenges, especially with regard to the reliability and robustness of these systems.

A key tool for achieving this goal is the use of artificial intelligence, both in the design process and in the development of solutions that make these systems more resilient and reliable.

This special issue encourages and invites original contributions on the development of solutions that contribute to increasing the reliability and robustness of power converters and electric motor drives. Topics of interest include the use of artificial intelligence in the implementation of fault-tolerant control strategies, in the design of innovative power converter topologies and in the design of advanced techniques for fault detection and diagnosis.

Keywords

Design optimization; system-level design; hardware-in-the-loop (HIL) testing; modeling and simulation; condition monitoring; fault diagnosis; predictive maintenance; fault-tolerant operation; power converters; electric motor drives; converter failure modes; electrical drives failure modes; machine learning; smart electric drives; digital twin technology; data-driven control

Manuscript Submission Information

Manuscripts should be submitted through the LIDSEN Submission System. Detailed information on manuscript preparation and submission is available in the Instructions for Authors. All submitted articles will be thoroughly refereed through a single-blind peer-review process and will be processed following the Editorial Process and Quality Control policy. Upon acceptance, the article will be immediately published in a regular issue of the journal and will be listed together on the special issue website, with a label that the article belongs to the Special Issue. LIDSEN distributes articles under the Creative Commons Attribution (CC BY 4.0) License in an open-access model. The authors own the copyright to the article, and the article can be free to access, distribute, and reuse provided that the original work is correctly cited.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). Research articles and review articles are highly invited. Authors are encouraged to send the tentative title and abstract of the planned paper to the Editorial Office (rpse@lidsen.com) for record. If you have any questions, please do not hesitate to contact the Editorial Office.

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