Current Status of Fuel Cell Fault Diagnosis and Lifetime Prediction

Abstract

Fuel cells (FCs), as an efficient and clean renewable energy technology, face commercialization challenges primarily due to their limited operational lifetime. Prognostics and Health Management (PHM) techniques offer a solution by monitoring FC states and estimating their Remaining Useful Life (RUL), thereby providing decision support for proactive maintenance and extending system lifespan. Current PHM challenges include reliable and rapid fault localization, developing accurate degradation models, and adopting precise, reliable, and cost-effective life prediction methods. This review describes the key steps of PHM for fuel cells, enumerates various fault diagnosis and life prediction methods, and provides a comparative analysis of hybrid prediction approaches. It also summarizes data-driven life prediction methods, including those based on machine learning and signal processing. Finally, future research directions are proposed, encompassing enhancing online fault diagnosis capabilities and improving the accuracy of PHM systems.