Intelligent Forecasting, Advanced Control, Multi-objective Optimization, and Health Management of Hydrogen and Fuel Cell Energy Systems: A Comprehensive Review of Recent Advances, Challenges, and Future Directions

Hydrogen and fuel cell energy systems are increasingly being integrated with renewable energy resources, creating new requirements for coordinated forecasting, control, optimisation, and equipment health management. This review examines recent developments in intelligent forecasting, advanced control, multi-objective optimisation, and prognostics and health management for hydrogen production, storage, utilisation, and fuel cell applications. Forecasting methods ranging from statistical and machine-learning approaches to deep-learning, hybrid, and probabilistic models are considered in relation to renewable generation, hydrogen demand, equipment performance, and operational uncertainty. Advanced control strategies, including adaptive, robust, model predictive, and learning-assisted approaches, are assessed according to their ability to manage nonlinear dynamics, constraints, disturbances, and changing operating conditions. Multi-objective optimisation methods are examined with respect to economic performance, efficiency, reliability, environmental objectives, hydrogen availability, and equipment degradation. The review also evaluates condition monitoring, fault diagnosis, state-of-health estimation, degradation modelling, remaining useful life prediction, and maintenance decision support. Across these areas, a persistent limitation is their frequent treatment as separate functions despite strong operational interactions. Accordingly, an integrated architecture is outlined in which forecasting, equipment health assessment, optimisation, supervisory control, local protection, and model updating operate within a coordinated framework. Key research needs include improved uncertainty representation, representative operational datasets, computationally efficient real-time implementation, standardised benchmarking, experimental validation, lifecycle-aware decision-making, and validated digital-twin integration.