Modeling, optimization, and thermal management strategies of hydrogen fuel cell systems

The transition to sustainable energy has intensified the focus on Proton Exchange Membrane Fuel Cells (PEMFCs) due to their high efficiency, zero emissions, and flexibility for both mobile and stationary uses. However, widespread adoption is limited by technical challenges, including efficiency losses from activation and mass transport overpotentials, thermal instability under transient loads, and high system and hydrogen production costs. Traditional static modeling approaches struggle to capture the real-time, coupled dynamics of PEMFCs in practical scenarios. This review synthesizes over one hundred studies published between 2015 and 2025, highlighting advancements in steady-state and dynamic modeling, AI-driven multi-objective optimization, and integrated thermal management. Optimization algorithms such as PSO, WOA, MIGA, and NSGA-II have shown promising results, including up to 15 % reduction in hydrogen consumption and 20 to 30 % improvement in thermal uniformity. The review also explores hybrid physical-AI models, CFD-based surrogate models, and predictive machine-learning methods like LSTM and CNN. Emphasis is placed on AI-enhanced energy management systems (EMS) capable of real-time control by integrating stress, degradation, and load conditions. Economic modeling for green hydrogen production is also included. The paper concludes by offering a unified framework and identifying current limitations, paving the way for scalable, intelligent PEMFC deployment in real-world energy systems.