Fault Diagnosis and Health Management of Fuel Cell Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Industrial Electronics".
Deadline for manuscript submissions: 15 June 2026 | Viewed by 193
Special Issue Editors
2. Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, China
Interests: fuel cell impedance study; fuel cell system control; fuel cell fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Interests: modeling, optimization, and control of fuel cell systems; energy management of fuel cell
2. Clean Energy Automotive Engineering Center, Tongji University, Shanghai 201804, China
Interests: fuel cell cold start; system integration; CFD simulation; optimization algorithm
Interests: fuel cell; durability control; fault diagnosis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Proton Exchange Membrane Fuel Cells (PEMFCs) hold significant application potential in future transportation and distributed power generation due to their advantages, such as high power generation efficiency, low startup temperature, and good transient response characteristics. However, the durability of fuel cells remains a major bottleneck hindering their large-scale commercial adoption. The primary reason lies in the tendency of internal moisture and heat states to deviate from the optimal range during operation, leading to faults such as membrane drying, flooding, and gas starvation. Prolonged accumulation of these conditions can cause irreversible damage and accelerate performance degradation. Therefore, the real-time fault diagnosis and health management of fuel cells are essential to extend their service life.
Addressing the aforementioned needs, this Special Issue focuses on the design and development of health management systems for PEMFCs, including, but not limited to, the following topics:
- Modeling and state estimation;
- Fault feature analysis and diagnosis;
- Degradation and lifetime prediction;
- System fault-tolerant control;
- AI-enabled state recognition and control.
We look forward to receiving your contributions.
Dr. Hao Yuan
Dr. Kai Ou
Dr. Lei Shi
Dr. Xingwang Tang
Guest Editors
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Keywords
- fuel cell
- state estimation
- fault diagnosis
- degradation prediction
- fault-tolerant control
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