Condition Monitoring and Fault Diagnosis in Power Electronics and Energy Storage Systems: 2nd Edition
A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "D: Energy Storage and Application".
Deadline for manuscript submissions: 30 December 2025 | Viewed by 44
Special Issue Editors
Interests: condition monitoring and fault diagnosis in power electronics systems; energy storage system components; AC machines
Special Issues, Collections and Topics in MDPI journals
2. School of Electrical and Information, Zhengzhou University, Zhengzhou, China
Interests: application of real-time simulation and digital twin technology in power electronics-dominated systems
Interests: multilevel converters: design, operation, optimization and control strategies; novel standardized design approaches for power converters based on multilevel technologies; power electronic systems for renewable energy integration and electric vehicles
Special Issue Information
Dear Colleagues,
In today’s rapidly evolving energy landscape, power electronics and energy storage systems (ESSs) have become indispensable in applications such as electric vehicles (EVs), modern integrated grids, and renewable energy systems. The integration of ESSs with advanced power electronic technologies, including multilevel converters, has significantly enhanced the efficient use of power, supporting the advancement of next-generation energy storage solutions and the electrification of transportation. However, these systems are routinely exposed to mechanical and electrical stresses, temperature variations, and vibrations, all of which increase the likelihood of equipment failures. Such failures can lead to unexpected interruptions, severe safety hazards, and significant operational losses. Moreover, ESSs face challenges in meeting the demands for cost-competitiveness, compactness, efficiency, safety, reliability, and long operational lifespans.
To address these challenges and minimize unplanned failures, it is crucial to implement early fault detection and diagnostic strategies. This requires a deep understanding of the degradation and failure processes, achieved through methods such as accelerated aging testing, multiphysics modeling, and physics-of-failure analyses. Online real-time condition monitoring and early warning technologies, developed using insights from these assessments, play a vital role in improving the reliability and longevity of power electronics components and ESSs.
This Special Issue aims to provide a platform for scientists, researchers, and engineers to present and discuss their latest findings and innovations in this critical field. We invite original research and review articles that highlight emerging trends, novel methodologies, and advanced solutions for condition monitoring and fault diagnosis in power electronics and energy storage systems.
Topics of interest include, but are not limited to, the following areas:
- Fault detection and fault tolerance in energy storage systems;
- Advancements in semiconductor and packaging reliability;
- Failure modes, mechanisms, and physics-of-failure analysis;
- Fault diagnosis and failure prognosis tools;
- Battery management systems (BMS);
- Lifetime diagnostics of supercapacitors;
- State-of-charge (SoC) and state-of-health (SoH) estimation;
- Lifetime modeling and remaining useful life (RUL) estimation;
- Thermal performance analysis of energy storage systems;
- Fault diagnosis of power electronics in energy storage systems;
- Multilevel converters and their reliability and fault diagnosis;
- Condition monitoring and diagnostics for EV applications;
- Applications of artificial intelligence and machine learning in performance analysis, diagnosis, and management of power electronics and ESSs.
We look forward to your valuable contributions and to advancing the state of the art in this vital area.
Best regards,
Dr. Khaled Laadjal
Dr. Chen Liu
Dr. Àlber Filbà-Martínez
Guest Editors
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Keywords
- condition monitoring
- fault diagnosis
- power electronics
- energy storage systems (ESSs)
- multilevel converters
- physics-of-failure analysis
- battery management systems (BMS)
- state-of-health (SoH) estimation
- remaining useful life (RUL) estimation
- artificial intelligence and machine learning in diagnostics
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