Effect of water distribution in gas diffusion layer on proton exchange membrane fuel cell performance

Water management of proton exchange membrane fuel cells remains a prominent issue in research concerning fuel cells. In this study, the gas diffusion layer (GDL) of a fuel cell is partially treated with a hydrophobic agent, and the effect of GDL hydrophobicity on the water distribution in the fuel cell is examined. First, the effect of the position of the cathode GDL hydrophobic area relative to the channel on the fuel cell performance is investigated. Then, the water distribution in the fuel cell cathode GDL is observed using X-ray imaging. The experimental results indicate that when the hybrid GDL's hydrophobic area lies on the channel, water tends to accumulate under the rib, and the water content in the channel is low; this improves the fuel cell performance. When the hydrophobic area is under the rib, the water distribution is more uniform, but the performance deteriorates.     

Efficient fault diagnosis method of PEMFC thermal management system for various current densities

The temperature of a fuel cell has a considerable impact on the saturation of a membrane, electrochemical reaction speed, and durability. So thermal management is considered one of the critical issues in polymer electrolyte membrane fuel cells. Therefore, the reliability of the thermal management system is also crucial for the performance and durability of a fuel cell system. In this work, a methodology for component-level fault diagnosis of polymer electrolyte membrane fuel cell thermal management system for various current densities is proposed. Specifically, this study suggests fault diagnosis using limited data, based on an experimental approach. Normal and five component-level fault states are diagnosed with a support vector machine model using temperature, pressure, and fan control signal data. The effects of training data at different operating current densities on fault diagnosis are analyzed. The effects of data preprocessing method are investigated, and the cause of misdiagnosis is analyzed. On this basis, diagnosis results show that the proposed methodology can realize efficient component-level fault diagnosis using limited data. The diagnosis accuracy is over 92% when the residual basis scaling method is used, and data at the highest operating current density is used to train the support vector machine.     

Predicting elastic modulus of porous La0.6Sr0.4Co0.2Fe0.8O3-δ cathodes from microstructures via FEM and deep learning

In this work, a deep learning accelerated homogenization framework is developed for prediction of elastic modulus of porous materials directly from their inner microstructures. The finite element method (FEM) and the homogenization theory are used to obtain the macroscopic properties of materials based on their microstructures. Based on a large dataset consisting of various microstructures and corresponding elastic properties via FEM, a deep convolutional neural network (CNN) is trained to capture the nonlinear functional relationship between the microstructure features and their macroscopic elastic properties. The deep learning model is finally well validated against extra new samples with excellent predictive performances. This demonstrates that the CNN deep learning model can be trusted as a surrogate model for the FEM based homogenization method, with the computation time being reduced by several orders of magnitude. The proposed deep learning framework is highly extendable for prediction of various macroscopic properties from microstructures.     

Effect of fiber orientation on Liquid–Gas flow in the gas diffusion layer of a polymer electrolyte membrane fuel cell

In this study, the lattice Boltzmann method was used to simulate the three-dimensional intrusion process of liquid water in the gas diffusion layer (GDL) of a polymer electrolyte membrane fuel cell (PEMFC). The GDL was reconstructed by the stochastic method and used to investigate fiber orientation's influence on liquid water transport in the GDL of a PEMFC. The fiber orientation can be described by the angle between a single fiber and the in-plane direction; three different samples were simulated for three different fiber orientation ranges. The simulated permeability correlated well with the anisotropic characteristics of reconstructed carbon papers. It was concluded that the fiber orientation had a significant effect on the liquid invasion pattern in the GDL by changing the pore shape and distribution of the GDL. The results indicated that the stochastically reconstructed GDL, taking into account the fiber orientation, better demonstrates the mass transport properties of the GDL.     

Chromium evaporation and oxidation characteristics of alumina-forming austenitic stainless steels for balance of plant applications in solid oxide fuel cells

The chromium (Cr) evaporation behavior of several different types of iron (Fe)-based AFA alloys and benchmark Cr₂O₃-forming Fe-based 310 and Ni-based 625 alloys was investigated for 500 h exposures at 800 °C to 900 °C in air with 10% H₂O. The Cr evaporation rates from alumina-forming austenitic (AFA) alloys were ~5 to 35 times lower than that of the Cr₂O₃-forming alloys depending on alloy and temperature. The Cr evaporation behavior was correlated with extensive characterization of the chemistry and microstructure of the oxide scales, which also revealed a degree of quartz tube Si contamination during the test. Long-term oxidation kinetics were also assessed at 800 to 1000 °C for up to 10,000 h in air with 10% H₂O to provide further guidance for SOFC BOP component alloy selection.     

Modified La0.6Sr0.4Co0.2Fe0.8O3-δ cathodes with the infiltration of Er0.4Bi1.6O3 for intermediate-temperature solid oxide fuel cells

Aiming to lower the activation energy and expedite the oxygen reduction reaction (ORR) process of La_0.6Sr_0.4Co_0.2Fe_0.8O_3-δ (LSCF) cathodes for application in intermediate-temperature solid oxide fuel cells (IT-SOFCs), Er_0.4Bi_1.6O₃ (ESB) modified LSCF was prepared by infiltrating using organic solvents. The infiltration of ESB dramatically reduces the polarization resistances of LSCF cathodes (from 0.27 to 0.11 Ω cm² at 700 °C, from 0.58 to 0.25 Ω cm² at 650 °C), and lowers their activation energy (from 100.28 to 97.15 kJ mol^?1). Also, ESB makes the rate-limiting step of LSCF cathodes at high frequency change from the charge transfer process on the cathode to the adsorption and diffusion of oxygen on cathode surface. The single cell with ESB infiltrated LSCF cathodes shows a peak power density of 469 mW cm^?2 at 700 °C using humid hydrogen and air as fuels and oxidants, respectively, as well as a good short-term stability for 50 h.     

A subtle review on the challenges of photocatalytic fuel cell for sustainable power production

The revolution in the arena of functional materials for the development of well advanced engineered photocatalyst can efficiently harness photon energy from a wide spectrum of electromagnetic radiation. These next-generation smart materials would be a spectacular approach in designing devices such as photovoltaic cells, photoelectrochemical cells, and photocatalytic fuel cells. Photocatalytic oxidation of water or wastewater for concurrent production of hydrogen and electric current has turned out as a principal concept for the construction of modern photocatalytic fuel cells (PFCs). Such PFCs mimics reverse photosynthesis process where electrical energy is generated from organic pollutants. In recent years many reviews on focusing the design, fabrication, and theoretical efficiency of the PFCs have been published. Hence the present review is aimed to unveil the wall-to-wall information starting from fundamentals spanning to working principles, structural configuration, electrochemical degradation of pollutants and photoelectrochemical properties, electron transport, thermodynamic behavior and columbic efficiency of studied PFCs.     

Perovskite materials for highly efficient catalytic CH4 fuel reforming in solid oxide fuel cell

SOFC (solid oxide fuel cell, SOFC) is recognized to be efficient green energy technology in the 21st century. However, when hydrocarbons are directly used as fuel, carbon deposition is easy to occur in Ni-based anode, thus losing electrochemical catalytic activity. Fuel pre-reforming is also called on-cell reforming of hydrocarbons, which has been a promising solution for alleviating the carbon deposition problem in cermet anodes to varying degrees. And the key factor is to find an efficient and stable fuel reforming catalyst. Perovskite oxides have stable structure, highly catalytic activity and adjustable thermal expansion coefficient for using on the cells, showing great potentials of application for fuel reforming. In this paper, we summarize the application of perovskite catalyst in CH₄ fuel reforming based on the research of our group and other scholars, and puts forward the corresponding views and perspective, especially in perovskite catalyst with Ni exsolution.     

Parameter identification of proton exchange membrane fuel cell via Levenberg-Marquardt backpropagation algorithm

It is essential to develop an accurate model of proton exchange membrane fuel cell (PEMFC) for a reliable operation and analysis, in which unknown parameters usually need to be determined. The inherent nonlinear, strong coupling, and diversification of PEMFC model seriously hinder traditional methods to identify the parameters. For the sake of overcoming these thorny obstacles, Levenberg-Marquardt backpropagation (LMBP) algorithm based on artificial neural networks (ANNs) is proposed for PEMFC parameter identification. Furthermore, the performance of LMBP is thoroughly evaluated and compared with four typical meta-heuristic algorithms under three cases. Simulation results indicate that LMBP performs a higher accuracy and faster speed for parameter identification. In particular, accuracy and convergence speed can achieve as much as 99.8% and 95.9% growth via LMBP, respectively.     

Performance improvement of microbial fuel cell through artificial intelligence

The current work introduces an enhancement in the performance of the microbial fuel cell through estimating the optimal set of controlling parameters. The maximization of both power density (PD) and the percentage of chemical oxygen demand (COD) removal were considered as the enhancement in the cell's performance. Three main parameters in terms of performance as well as commercialization are the system's inputs; the Pt which takes the range of 0.1‐0.5 mg/cm², the degree of sulphonation in sulfonated‐poly‐ether‐ether‐ketone that changes in the range of 20‐80%, and the rate of aeration of cathode which varies between 10 and 150 mL/min. From the experimental dataset, two robust adaptive neuro‐fuzzy inference system models based on the fuzzy logic technique have been constructed. The comparisons between the models' outputs and the experimental data showed well‐fitting in both training and testing datasets. The mean squared errors of the PD model, for testing and whole datasets, were found 2.575 and 0.909 while for the COD model it showed 19.242 and 6.791, respectively. Then, based on the two fuzzy models, a Particle Swarm Optimization algorithm has been used to determine the best parameters that maximize both of the PD and the COD removal of the cell. The optimization process was utilized for single and multi‐object optimization processes. In the single optimization, the resulting maximums of the PD and the COD removal were found 62.844 (mW/m²) and 99.99 (%), respectively. Whereas, in the multi‐object optimization, the values of 61.787 (mW/m²) and 96.21 (%) were reached as the maximums for the PD and COD, respectively. This implies that, in both cases of optimization processes, the adopted methodology can efficiently enhance the microbial fuel cell performances than the previous work.