A Comprehensive Review of Parameter Estimation and Modeling Approaches for Proton Exchange Membrane Fuel Cells: Challenges, Methods, and Future Directions

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Proton Exchange Membrane Fuel Cells (PEMFCs) have emerged as promising clean-energy-conversion devices for various applications, including transportation and stationary power generation. The accurate modeling and parameter estimation of PEMFCs are fundamental to optimizing their performance, extending their operational lifespan, and facilitating their widespread commercialization. However, their inherent nonlinear characteristics and complex electrochemical processes present significant challenges in developing accurate mathematical models and reliable parameter-estimation methods. This review systematically examines the state of the art in PEMFC parameter estimation and modeling, with particular emphasis on the critical challenges posed by nonlinearities, the evolution of analytical and computational methods, and the role of sensitivity analysis in model refinement. The paper synthesizes findings from recent studies employing evolutionary algorithms, metaheuristic techniques, reinforcement learning, and hybrid approaches, evaluating their relative merits in terms of accuracy, convergence speed, and robustness. Furthermore, it explores how sensitivity analysis provides valuable insights into parameter influence under varying operational conditions, guiding model optimization efforts. The review identifies persistent gaps in current methodologies, particularly regarding generalizability across different fuel cell types and real-time applicability, while highlighting promising directions for future research, including hybrid algorithms and advanced data-preprocessing techniques.

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