Dr. Chi-Young Jung's research team at the Korea Institute of Energy Research has made a breakthrough in fuel cell maintenance by developing a method that uses AI to analyze the microstructure of carbon fiber paper in hydrogen fuel cells at a remarkable speed. This advancement is achieved through a combination of digital twin technology, AI learning, and X-ray diagnostics. The new method eliminates the need for time-consuming and destructive electron microscope examinations, providing near real-time condition diagnosis of fuel cells. By training a machine learning algorithm with thousands of images, the team achieved over 98% accuracy in predicting the distribution of key components in the carbon fiber paper. This allows for quick identification of performance degradation causes. The research also identified how design factors impact fuel cell performance and proposed optimal parameters for improved efficiency. Dr. Jung highlighted the significance of the study in enhancing energy material analysis, foreseeing its broad application in various energy sectors beyond hydrogen fuel cells. The study, supported by KIER, was published in Applied Energy, showcasing the potential of AI in revolutionizing fuel cell maintenance and energy material analysis.