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The porous structures of the catalyst layer (CL) for proton exchange membrane (PEM) fuel cells were reconstructed using the sphere-based simulated annealing (SA) method, in which different carbon sphere diameters, carbon phase fractions, ionomer loadings, and porosities were achieved to simulate different samples. The pore-scale simulation model based on the random walk algorithm was developed to evaluate the tortuosity in the CL. The results show that the carbon phase fraction, particularly the carbon sphere diameter, significantly influences the tortuosities in both pores and ionomer. Larger carbon spheres contribute to tightening the solid phase and enlarging the pore size within the CL, thereby affecting the pore structure and the ionomer distribution. The tortuosity obtained from the random walk algorithm aligns well with those from pore-scale diffusion and flow simulations using the Lattice–Boltzmann method (LBM). The random walk algorithm has the highest efficiency of the three methods in calculating the tortuosity of the CL when the porosity is less than 0.2, demonstrating its wider applicability. This study further proposes formulas for predicting the tortuosities in both pores and ionomer, which are closer to actual CLs than empirical equations. In addition, the present work confirms that the structural randomness resulting from the reconstruction method used in this work has little effect on the tortuosity, indicating that a small number of reconstructed samples suffice for investigating transport processes in the CL. Overall, our research contributes to the prediction of the properties of real porous media in energy engineering. Practical Applications: This manuscript reports on the reconstruction of the catalyst layer in proton exchange membrane fuel cells using the simulated annealing method under some appropriate assumptions. A pore-scale simulation using a random walk algorithm was performed to compute the tortuosity both in the pores and in the ionomer, which is compared with pore-scale diffusion and flow simulations using the Lattice–Boltzmann method. A parametric study for several geometric parameters was carried out. Some conclusions are drawn from the results of the pore-scale simulations, and several formulas are proposed accordingly. This study helps to predict the tortuosity in the porous structure closer to the actual porous medium than the empirical equations. Meanwhile, the proposed tortuosity formulas improve the accuracy of the macroscopic models compared to using the assumed transmission parameter values. In addition, structural randomness analysis is explored, which will further reduce the workload in actual engineering calculations and improve computational efficiency. [ABSTRACT FROM AUTHOR]

We present a pore-scale simulation of the capillary condensation of water in the cathode catalyst layer (CCL) of proton exchange membrane fuel cells by the lattice Boltzmann method. Based on the reconstructed CCL, the capillary condensation process in CCL is simulated under different humidity conditions, and the effects of porosity and especially wettability on the liquid water distribution in CCL are studied. The influence of liquid water on the void pore size distribution and pore connectivity in CCL is evaluated, and the results show that the hydrophilic CCL is more prone to be flooded. Subsequently, the effective transport coefficients of oxygen and proton in partially saturated CCL are investigated. The results reveal that the hydrophobic CCL is beneficial for reducing the gas transport tortuosity but simultaneously causes a higher Knudsen diffusion resistance. By comprehensively considering the changes in tortuosity and Knudsen resistance caused by liquid water, a more practical correlation of effective diffusivity for the partially saturated CCL is proposed. Moreover, this work proves the vital role of liquid water in the proton conduction in CCL. The simulated effective proton conductivity in CCL is more agree with the measurements if the contribution of liquid water to proton transport is considered. [Display omitted] • Capillary condensation in the CCL is simulated at the pore scale. • A correlation of effective diffusivity to porosity and saturation is proposed. • Liquid water significantly increases the effective proton conductivity in the CCL. [ABSTRACT FROM AUTHOR]

• Gas crossover significantly affects the cold start process of PEMFCs. • Gas crossover is more important at lower current densities. • The ice formation rate may increase by 54.5 % by gas crossover at 0.04 A cm−2. • Anode pressure affects the cold start process more significantly. • Water increases faster in thinner membranes by considering the gas crossover. This study develops a transient three-dimensional model to investigate the effect of reactant gas crossover through the membrane on the cold start process of PEM fuel cells. Key operating and structure parameters such as current density, anode/cathode inlet pressure, and membrane thickness are explored. The results reveal that gas crossover enhances ice formation rate and cell temperature rise due to additional hydrogen–oxygen side reactions, increasing both heat and water generations. The dominance of the ice formation rate over the temperature rise rate by gas crossover adds complexity to successful cold starts. Notably, the influence of gas crossover is more pronounced at a lower current density. As the operating current density increases from 0.04 A cm−2 to 0.12 A cm−2, the contribution of gas crossover to the temperature rise rate and ice formation rate decrease from 31.6 % to 11.3 % and from 54.5 % to 16.8 %, respectively. The increase of anode inlet pressure accelerates ice formation and temperature rise, whereas cathode inlet pressure has minimal effect. A thicker membrane mitigates gas crossover and is beneficial to cold start success, especially under a wet initial condition. This study emphasizes the significant role of gas crossover in the PEM fuel cell cold start process, highlighting the imperative consideration of gas crossover in both cold start models and strategy developments. [ABSTRACT FROM AUTHOR]

Understanding the transport mechanism in catalyst layer (CL) agglomerates is crucial to enhance Pt utilization and overall fuel cell performance. In this study, high-resolution porous agglomerates are stochastically reconstructed, and capillary condensation of water in agglomerates is directly simulated using a lattice Boltzmann pseudopotential model. On this basis, a pore-scale electrochemical model is developed to investigate the effects of agglomerate structure and operating humidity on local transport resistance and agglomerate effectiveness at different Pt loadings. The results reveal that liquid water formed above 70%RH improves the electrochemical surface area (ECSA) of agglomerates with low ionomer content. An optimum I/C of 0.4 in agglomerates is determined by balancing the activated ECSA and oxygen transport resistance. Through the comparison of local transport resistance with limiting current data, an additional oxygen dissolution resistance of 60–350 s m−1 at the pore/ionomer or ionomer/Pt interface is quantified. The lowest local transport resistance of the agglomerate is achieved at about 70%RH, while condensed water at higher humidities hampers oxygen diffusion within the agglomerate, leading to reduced Pt utilization. Agglomerate size significantly affects the local transport resistance only when excessive ionomer or liquid water severely blocks primary pores. Finally, a novel multiscale coupling strategy integrating the porous agglomerate sub-model with a continuous-scale CL model is proposed, offering innovative insights into comprehending the relationship between CL structure and the performance of proton exchange membrane fuel cells. • High-resolution porous structures of the agglomerate are reconstructed. • Quantify the interfacial dissolute resistance and permeability reduction of ionomer. • Condensed water formed above 70%RH degrades the oxygen diffusion process. • Agglomerate size affects the transport resistance when primary pores are blocked. • A multiscale coupling strategy for the pore-scale agglomerate model is proposed. [ABSTRACT FROM AUTHOR]

An in-depth understanding of the structure-performance relationship of the catalyst layer is crucial for its optimal design. In this work, the effects of carbon aggregation and ionomer distribution morphologies on the performance of the catalyst layer are investigated by pore-scale simulation. First, an optimized stochastic algorithm that can control the carbon aggregation degree and ionomer morphology is proposed to reconstruct realistic catalyst layer structures. The structural characteristics of the catalyst layer, such as pore size distribution, agglomerate size, and ionomer connectivity, are analyzed. A pore-scale model based on the lattice Boltzmann method is developed to simulate the reactive transport processes in the catalyst layer. The results indicate that the reasonable aggregation degree of carbon supports can balance the pore and cross-ionomer transport resistances of the oxygen to achieve optimal catalyst layer performance. The uniform ionomer coating in the catalyst layer can further improve the performance, and the optimal ionomer content is determined by balancing the electrochemical active surface area and the oxygen transport resistance. Based on the simulation results, a key principle for the optimal design of the catalyst layer is also proposed. [ABSTRACT FROM AUTHOR]

A 1D transient model was proposed to investigate the effects of the current density operating modes on the cold-start process of proton exchange membrane (PEM) fuel cells. The temperature evolution of the cell is solved by considering the reversible heat, reaction heat, Joule heat, and phase change heat. Four current density operating modes were studied based on this model: constant mode, gradually increasing mode, stepwise increasing mode, and zigzag mode. The results show that there is a threshold for the initial current density that influences the relationship between the successful cold start and current density. The cell can successfully start at a lower operating temperature by increasing the initial current density when the initial current density is below this threshold value. However, the lowest temperature for successful cold start increases if the initial current density exceeds the threshold value. Finally, a chart was plotted to indicate the regions of successful and failed self-start processes at different operating temperatures for the studied current density operating modes, respectively. The chart suggests that the cells with gradually and stepwise increasing current density modes have nearly equivalent cold-start performance, and are both better than the constant and zigzag modes. [ABSTRACT FROM AUTHOR]

A transient numerical model was developed to investigate the effect of the pore structure in the cathode catalyst layer on the cold start process of polymer electrolyte fuel cells. The results prove the necessity of considering the subcooled water at subzero temperatures when simulating the cold start of fuel cells based on the Gibbs-Thomson effect. At ambient temperatures lower than −13 °C, the ice blockage is found to occur inside the cathode catalyst layer (CCL). However, due to the generation and flow of the subcooled water, the blockage occurs at the CCL and cathode microporous layer (CMPL) interface at a temperature higher than −13 °C. The pore size of the CCL also plays an important role in the cold start of PEM fuel cells, and there exists a critical pore size to shift the blockage location from the interior of the CCL to the CCL/CMPL interface. The CCL with this critical pore size gives the fuel cell the best cold start capability. It is also worth noting that the actual pore size distribution of the CCL complicates the blocking mechanisms in the fuel cell and remarkably influences the cold start simulation, especially at an operating temperature higher than −12 °C. • Subcooled water is considered based on the Gibbs-Thomson effect. • Transport of subcooled water affects the ice distribution and cold start process. • A suitable pore size of CCL contributes to cold-start. • CCL's pore size distribution complicates the blocking mechanisms. [ABSTRACT FROM AUTHOR]

• Convolutional neural network (CNN) applies for neutron radiography analysis; • CNN results of the water contents are favorably compared with literature data; • CNN model takes less than 0.1 s to analyze an image; • Various current densities, relative humidity, and flow fields are analyzed. Polymer electrolyte membrane (PEM) fuel cells produce water as a byproduct, which, if not properly managed, will cause electrode "flooding" and consequently performance loss. Nonintrusive methods, such as neutron or X-ray radiography, have been employed to obtain in-situ water images in PEM fuel cell. This study presents one of the first studies developing a machine learning approach to analyze neutron radiography images using the convolutional neural network (CNN), a deep learning model, to quantify liquid water content in PEM fuel cell. The CNN model is trained using the labeled radiography images constructed from the contour legend, which contains the information of the water areal mass density. Image enhancement is carried out to generate additional data for CNN training. The properly-trained CNN model is subsequently applied to the radiography images to obtain the average water areal mass densities under various current densities, relative humidity (RH), and flow fields. The results show that the water content can be significantly reduced by using low RH inlet flow and the counter-flow configuration renders the fuel cell a higher water content than the co-flow one. In addition, the quad-serpentine flow field increases the water content in the current density range of 0.4–0.8 A/cm2 compared with the single-serpentine one. The CNN model takes less than 0.1 s to analyze an image and its results agree well with the literature data from the conventional image processing method with an accuracy of 91%. The CNN method is applicable to other radiography methods and is suitable for the online/rapid monitoring of liquid water in PEM fuel cell. [ABSTRACT FROM AUTHOR]

Abstract: Capillary pressures in a carbon paper gas diffusion layer (GDL) having hydrophilic and hydrophobic pores of a polymer electrolyte membrane fuel cell (PEMFC) are investigated by both lattice Boltzmann simulations and experimental measurements. The simulated and measured capillary pressures as a function of water saturation for water drainage and imbibition processes in the GDL are presented and compared. It is shown that the pore-scale simulated drainage and imbibition capillary pressure curves are in good agreement with that obtained by experiment, both indicating the coexistence of hydrophilic and hydrophobic properties in the polytetrafluoroethylene (PTFE) treated carbon paper GDLs. The fitted capillary pressure curves, obtained from this paper, can provide more accurate predictions of the capillary pressure in carbon paper GDLs with non-uniform porosity and wettability than the standard Leverett–Udell relationship which was obtained for soil with more uniform porosity and wettability. [Copyright &y& Elsevier]

Abstract: The effect of wettability on water transport dynamics in gas diffusion layer (GDL) is investigated by simulating water invasion in an initially gas-filled GDL using the multiphase free-energy lattice Boltzmann method (LBM). The results show that wettability plays a significant role on water saturation distribution in two-phase flow in the uniform wetting GDL. For highly hydrophobicity, the water transport falls in the regime of capillary fingering, while for neutral wettability, water transport exhibits the characteristic of stable displacement, although both processes are capillary force dominated flow with same capillary numbers. In addition, the introduction of hydrophilic paths in the GDL leads the water to flow through the hydrophilic pores preferentially. The resulting water saturation distributions show that the saturation in the GDL has little change after water breaks through the GDL, and further confirm that the selective introduction of hydrophilic passages in the GDL would facilitate the removal of liquid water more effectively, thus alleviating the flooding in catalyst layer (CL) and GDL. The LBM approach presented in this study provides an effective tool to investigate water transport phenomenon in the GDL at pore-scale level with wettability distribution taken into consideration. [Copyright &y& Elsevier]

Abstract: Using the multiphase free-energy lattice Boltzmann method (LBM), the formation of a water droplet emerging through a micro-pore on the hydrophobic gas diffusion layer (GDL) surface in a proton exchange membrane fuel cell (PEMFC) and its subsequent movement on the GDL surface under the action of gas shear are simulated. The dynamic behavior of the water droplet emergence, growth, detachment and movement in the gas flow channel is presented. The size of the detached droplet and the time of the droplet removing out of the channel under the influence of gas flow velocity and GDL surface wettability are investigated. The results show that water droplet removal is facilitated by a high gas flow velocity on a more hydrophobic GDL surface. A highly hydrophobic surface is shown to be capable of lifting the water droplet from the GDL surface, resulting in more GDL surface available for gas reactant transport. Furthermore, an analytical model based on force balance is presented to predict the droplet detachment size, and the predicted results are in good agreement with the simulation results. It is shown that the LBM approach is an effective tool to investigate water transport phenomena in the gas flow channel of PEMFCs with surface wettability taken into consideration. [Copyright &y& Elsevier]

• A homemade microsensor measures CCL dynamic temperature. • An improved transient thermal model is developed to analyze the process. • Both experiment and simulation indicate 0–7 °C temperature variation. • Temperature variation is dramatically affected by cathode operation. • Underlying mechanisms for temperature variation are analyzed. Temperature variation inside the cathode catalyst layer (CCL) is significant, which could greatly damage fuel cell performance and durability. However, limited by CCL microstructure and complex physicochemical processes, the knowledge regarding CCL dynamic temperature is indeed insufficient. Thus, this work integrates a pre-prepared thin-film microsensor into the fuel cell to measure CCL surface temperature under dynamic load. At the same time, an improved transient thermal model considering local mass transport and liquid effect on thermal contact resistance (TCR) is established for the first time to analyze the internal process. Results show that CCL temperature is greatly affected by local resistance and interface accumulated water. And, as confirmed by both experiment and simulation, the CCL surface exhibits 0–7 °C temperature variation, whose profile changes sharply at the beginning and then evolutes slowly, eventually taking tens or hundreds of seconds to reach a new steady state due to thermal inertia. Moreover, the temperature is further increased by ∼1 °C as cathode humidity increases or stoichiometric ratio decreases, but only slight changes with the anode condition. The significant role of cathode operation is attributed to the greater mass transfer resistance and more sensitive intrinsic oxygen reduction reaction to reactant change. This study is expected to provide a deeper understanding of CCL dynamic temperature, thus improving fuel cell thermal management. [ABSTRACT FROM AUTHOR]

Abstract: Validation of computational models for polymer electrolyte membrane fuel cell (PEMFC) performance is crucial for understanding the limits of the model predictions. We compare predictions from a multiphase PEMFC computational model with experimental data collected under various current density, temperature and humidification conditions from a single 50 cm2 PEMFC with a 10 × 10 segmented current collector. Both cell voltage and current distribution measurements are used to quantify the predictive capability of the computational model. Several quantitative measures are used to quantify the error in the model predictions for current distribution, including root mean square error, maximum/minimum local error, and local error averaged from inlet to outlet. The cell voltage predictions were within 15 mV of the experimental data in the current range from 0.1 to 1.2 A cm−2, and the current distributions were acceptable (less than 30% local error) except for the low temperature case, where the model overpredicted the current distribution. Particular attention was paid to incorporating experimental variability into the model validation process. [Copyright &y& Elsevier]