| Abstrak/Abstract |
Fuel cells are increasingly recognized as a cornerstone technology for sustainable, decarbonized, and intelligent
energy infrastructures. They offer high energy efficiency, zero carbon emissions with green hydrogen, and
operational flexibility—making them well suited for transportation, distributed generation, and backup power
applications. This review presents a comprehensive and systematic analysis of recent advances in the modeling,
control, and optimization of fuel cell systems. First, the review outlines the research background, technological
significance, fundamental principles, and potential applications for high-performance fuel cell systems. Second, it
categorizes and compares existing modeling methods, including mechanistic, empirical, semi-empirical, and
data-driven models, highlighting quantitative metrices such as computational efficiency, accuracy, and suit
ability for real-time deployment. Third, the evolution of control strategies is further systematically discussed,
from conventional proportional integral differential controller to advanced adaptive, robust, and artificial
intelligence-based schemes, with special attention to tracking error and robustness under dynamic operating
conditions. Fourth, multi-objective optimization frameworks are examined for balancing efficiency, cost, dura
bility, and fuel utilization. Finally, the review identifies key challenges and future research directions for
enhancing modeling fidelity, real-time control performance, and intelligence optimization. This work provides
valuable insights for researchers and practitioners aiming to enhance the intelligence, efficiency, and reliability
of next-generation fuel cell systems. |
