State Estimation and Fault Diagnosis of Proton Exchange Membrane Fuel Cells Based on Artificial Intelligence

Objectives The proton exchange membrane fuel cell (PEMFC), as a highly promising clean energy technology, has attracted much attention in the field of energy conversion. However, the high complexity and operational uncertainties of PEMFC systems pose significant challenges to state estimation and fault diagnosis, seriously affecting system reliability and safety. To effectively address these challenges, the application strategies and effectiveness of artificial intelligence (AI) technology in PEMFC state estimation and fault diagnosis are studied. Methods Current research progress on PEMFC state estimation and fault diagnosis is analyzed. In the field of state estimation, the nonlinear model characteristics of PEMFC are analyzed, AI-based state estimation technologies are introduced, and the application principles and advantages of different algorithms for PEMFC state estimation are analyzed. In the field of fault diagnosis, common fault types of PEMFC are summarized, their fault manifestations and internal causes are analyzed, and AI-based fault diagnosis technologies are introduced. Finally, the future prospects for AI-based PEMFC state estimation and fault diagnosis technologies are discussed. Conclusions With its powerful data processing and pattern recognition capabilities, AI technology can accurately estimate the state of PEMFC and effectively diagnose potential system faults, thereby significantly improving the the operational efficiency and stability of PEMFC systems and enhancing their reliability and safety. Future research can focus on areas such as AI algorithm innovation, optimization of state estimation and fault diagnosis, intelligent system development, and collaboration with other technologies.

Source

Huadian Electric Power Research Institute Co., Ltd., and the Thermal Power Professional Committee of the Chinese Society of Electrical Engineering. Power Generation Technology, Volume 46 , Issue 3. ISSN: 2096-4528 (Print). CN :33-1405/TK