Machine learning in hydrogen fuel cell system: Fault diagnosis, degradation prediction and energy management

Proton exchange membrane fuel cell (PEMFC), characterized by high efficiency, low noise, and zero emissions, has become an important approach for hydrogen energy utilization. However, the continuous degradation and frequent faults of fuel cells pose severe challenges to efficient, reliable and long-lifespan operation. Machine learning (ML) exhibits remarkable advantages in addressing the above-mentioned problems by virtue of its powerful data-driven capability. targeting the latest applied research on fault diagnosis, degradation prediction and energy management of fuel cell systems, this review constructs a unified framework for the application of traditional ML, deep learning, transfer learning and reinforcement learning. By horizontally comparing the applicability and challenges of different methods in various tasks, this paper further summarizes the key issues existing in current research, such as data scarcity, insufficient model generalization ability and limited interpretability, and prospects future research directions including integrating multi-source data, developing physics-informed learning methods and improving online application capabilities.