Towards high-performance fuel cell systems: Comprehensive review of methods for modeling, control, and optimization

Zhihua Deng, Bin Miao, Yunjia Cui, Jian Chen, Zehua Pan, Hao Liu, Deendarlianto Deendarlianto, Suwarno Suwarno and Siew Hwa Chan

Renewable and Sustainable Energy Reviews, 2025, vol. 224, issue C

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 suitability 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, durability, 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.

Keywords: Fuel cell systems; Modeling methods; Intelligent control; Optimization methods; Artificial intelligence (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1016/j.rser.2025.116122

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