정지형 전력 시스템용 고분자 전해질 연료전지의 장기 내구성과 진단 분석
Long-Term Durability and Diagnostic Analysis of Proton Exchange Membrane Fuel Cells for Stationary Power Applications
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Proton Exchange Membrane Fuel Cells (PEMFCs) are emerging as reliable energy conversion devices for stationary power generation due to their high efficiency and environmental benefits. However, achieving long-term durability remains a critical challenge for commercialization. This study investigates the performance and degradation behavior of a PEMFC stack under 360 hours of constant current operation at approximately 0.22-0.23 A/cm², delivering a stack output of 1.5 kW. Electrochemical Impedance Spectroscopy (EIS) was employed to diagnose cell degradation, revealing increases in ohmic and charge transfer resistances over time. The results highlight the importance of uniform cell performance within the stack to prevent output limitations. Furthermore, we propose a framework for Remaining Useful Life (RUL) prediction to enhance system reliability. Future work will focus on applying these diagnostic techniques under varied operating conditions and integrating machine learning for advanced predictive models, aiming to support the development of stable, long-life PEMFC systems for stationary energy applications.
Abstract
Proton Exchange Membrane Fuel Cells (PEMFCs) are emerging as reliable energy conversion devices for stationary power generation due to their high efficiency and environmental benefits. However, achieving long-term durability remains a critical challenge for commercialization. This study investigates the performance and degradation behavior of a PEMFC stack under 360 hours of constant current operation at approximately 0.22-0.23 A/cm², delivering a stack output of 1.5 kW. Electrochemical Impedance Spectroscopy (EIS) was employed to diagnose cell degradation, revealing increases in ohmic and charge transfer resistances over time. The results highlight the importance of uniform cell performance within the stack to prevent output limitations. Furthermore, we propose a framework for Remaining Useful Life (RUL) prediction to enhance system reliability. Future work will focus on applying these diagnostic techniques under varied operating conditions and integrating machine learning for advanced predictive models, aiming to support the development of stable, long-life PEMFC systems for stationary energy applications.