The gas diffusion layer (GDL) is an important component of proton exchange membrane fuel cells (PEMFCs), and its porosity distribution has considerable effects on the transport properties and durability of PEMFCs. A 3-D two-phase flow computation fluid dynamics model was developed in this study, to numerically investigate the effects of three different porosity distributions in a cathode GDL: gradient-increasing (Case 1), gradient-decreasing (Case 3), and uniform constant (Case 2), on the gas–liquid transport and performance of PEMFCs; the novelty lies in the porosity gradient being along the channel direction, and the physical properties of the GDL related to porosity were modified accordingly. The results showed that at a high current density (2400 mA·cm−2), the GDL of Case 1 had a gas velocity of up to 0.5 cm·s−1 along the channel direction. The liquid water in the membrane electrode assembly could be easily removed because of the larger gas velocity and capillary pressure, resulting in a higher oxygen concentration in the GDL and the catalyst layer. Therefore, the cell performance increased. The voltage in Case 1 increased by 8% and 71% compared to Cases 2 and 3, respectively. In addition, this could ameliorate the distribution uniformity of the dissolved water and the current density in the membrane along the channel direction, which was beneficial for the durability of the PEMFC. The distribution of the GDL porosity at lower current densities had a less significant effect on the cell performance. The findings of this study may provide significant guidance for the design and optimization of the GDL in PEMFCs.

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Durability and remaining useful life (RUL) prediction techniques are ones of the key issues for proton exchange membrane fuel cell (FC) commercialization. Herein, the performance degradation of an FC is analyzed based on the whole lifetime experimental data (up to 6500 h). The voltage model with different patterns is developed based on the voltage data, which can be easily measured. The mechanism model is developed based on the evolution of degradation indices reflecting the degradation state. However, the former is sensitive to the local and periodic changes in the voltage curve, leading to a large prediction error, and the latter requires aging data from complex and high-cost characterization, limiting the practical applications. Therefore, a hybrid prediction model combining the voltage and mechanism model is proposed where the respective weight of each model is dynamically determined based on their local prediction errors. The results reveal that the maximum errors in RUL prediction are 9.72%, 3.90% and 2.01% for the voltage, mechanism and hybrid model, respectively, and the RUL prediction results of the hybrid model are close to actual RUL when those of the voltage model are far from the accuracy zone, indicating that the hybrid model provides credible RUL predictions with the highest accuracy.

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