A Comparative Review of Modeling and Metaheuristic Parameter Identification Strategies for Zero-Dimensional PEMFC Polarization Models

Proton exchange membrane fuel cells (PEMFCs) are promising energy conversion de-vices owing to high efficiency and zero local emissions. Accurate PEMFC performance assessment and control require well-posed models, whose predictive accuracy is largely determined by the correct calibration of key parameters. Metaheuristic algorithms (MHAs) have therefore been widely applied to PEMFC stack parameter estimation, but their rapid proliferation calls for a more systematic and fine-grained synthesis. This review refines the taxonomy of PEMFC mathematical modeling approaches and summarizes Zero-Dimensional PEMFC modeling methods, key parameters, and representative improvement directions aimed at reducing identification difficulty while retaining physical meaning. Newly developed MHAs and enhanced variants of existing methods are then surveyed, and over 40 distinctive optimization approaches are selected for systematic comparison. Modeling approaches and parameter identification methodologies are summarized. In addition, an algorithm selection guide and 26 representative algorithms with their variants are compiled and benchmarked across the five most widely used commercial PEMFC models to enable cross-model comparison.