Data-Driven Prognostic Health Monitoring of Key Components in Hydrogen Fuel Cells

Hydrogen fuel cells are emerging as a key technology in transportation electrification, offering high energy density, rapid refueling, and lower environmental impact compared to conventional batteries. However, ensuring their reliability through real-time monitoring and predictive maintenance remains a challenge due to the lack of standardized, cost-effective sensors and the complexity of internal electrochemical reactions. Degradation of key components is influenced by multiple factors, making accurate health assessment challenging. To address these issues, this paper proposes a deep learning-based Prognostic Health Monitoring System (PHMS) designed to predict maintenance needs and improve fuel cell longevity. The proposed framework begins by downsizing parameters to enhance efficiency, selects key status indicators, and then concatenates the data for model training. This approach emphasizes adapting servicedeveloped data to accommodate machine learning methods, ensuring both accurate and scalable deployment in industrial settings. The PHMS is then validated using real-world hydrogen fuel cell data from Plug Power, demonstrating high accuracy in forecasting maintenance requirements. This approach enhances operational reliability, reduces downtime, and contributes to the broader adoption of hydrogen fuel cells in sustainable transportation.