• Energy Research

This study focuses on investigating the performance change of a high temperature proton exchange membrane fuel cell (HT-PEMFCs) stack at different operation modes. A HT-PEMFC stack consisting of 30 single cells was tested both at constant load (0.4 A cm−2) and dynamic load (0.05–0.4 A cm−2) conditions at a temperature of 160 ℃ and hydrogen as anode fuel. Besides, the effect of impurities on the stack was also investigated by feeding a methanol reformate mixture to the stack anode as fuel for both constant and dynamic operation. The results reveal that the stack performance was stable after 120 h of both constant and dynamic operation with hydrogen, while the stack performance decreased greatly when the stack was fed with dry reformate on the anode. Significant degradation rates of 94.4 µV h−1 for constant operation, while the degradation was 200 times higher in dynamic operation with reformate gas.

This paper focuses on proton exchange membrane fuel cell (PEMFC) operation at current densities in the order of 6 A/cm2. Such high current densities are conceivable when the traditional carbon fiber papers are replaced with perforated metal plates as the gas diffusion layer to enhance waste heat removal, and at the same time the relative humidity inside the fuel cell is kept below 100% by applying appropriate operating conditions as was found in previous one-dimensional modeling work. In the current paper, we applied a three-dimensional, multi-phase computational fluid dynamics model based on Ansys-CFX to obtain additional insight into the underlying physics. The calculated pressure drops are in very good agreement with previous one-dimensional modeling work, and the current densities in all case studies are in the order of 5–6 A/cm2, but different from the previous one-dimensional study, the results suggest that the relative humidity is very close to 100% throughout the entire channel length when the inlet relative humidity is 100%, ensuring best hydration cell conditions and hence best performance. Importantly, the model results suggest that fuel cell performance at a high current density in conjunction with relatively low stoichiometric flow ratios around 1.5–2 is possible.

A single-channel proton exchange membrane fuel cell model (anode side) based on computational fluid dynamics is used to investigate the possibility of operating a fuel cell at low stoichiometric flow ratios using completely dry inlet hydrogen. A case study of three different stoichiometric flow ratios (ξ = 1.01, ξ = 1.03, ξ = 1.05), three different operating temperatures (343.15 K, 347.15 K, 353.15 K), and three different operating pressures (1 atm, 1.2 atm and 1.5 atm) are presented. It is found that the predicted hydrogen concentration and relative humidity (RH) in the catalyst layer (CL) have opposite trends: the RH in CL decreases with increasing stoichiometric flow ratios, but it reaches 100% at the outlet. While hydrogen concentration in CL increases with increasing stoichiometric flow ratios and the largest difference is at the inlet, with a maximum of 3.6%. The results also suggest that PEM fuel cells may be operated in a stoichiometric flow ratio as low as ξ = 1.01 at the anode side. This cell operation would allow open-ended anode operation without a recirculation system, thus significantly reducing system complexity and cost. The CFD code is disclosed to provide a starting point for more complex model development.

The cryogenic carbon capture (CCC) process is a promising post-combustion CO2 removal method. This method is very novel compared with conventional and well-developed methods. However, cryogenic carbon capture is not yet commercially available despite its techno-economic benefits. Thus, a model-based design approach for this process can provide valuable information. This paper will first introduce the cryogenic carbon capture process. Then, a comprehensive literature overview that focuses on different methods for modeling the process at the component level will be given. The modelling methods which are deemed most effective are presented more in depth for each of the key system components. These methods are compared with each other in terms of complexity and accuracy and the simplest methods with an acceptable level of precision for modelling a specific component in the CCC process are recommended. Furthermore, potential research areas in modeling and simulation of the CCC process are also highlighted.

Polymer electrolyte membrane (PEM) fuel cells require good hydration in order to deliver high performance and ensure long life operation. Water is essential for proton conductivity in the membrane which increases by nearly six orders of magnitude from dry to fully hydrated. Adequate water management in PEM fuel cell is crucial in order to avoid an imbalance between water production and water removal from the fuel cell.In the present study, a novel mathematical zero-dimensional model has been formulated for the water mass balance and hydration of a polymer electrolyte membrane. This model incorporates all the essential fundamental physical and electrochemical processes occurring in the membrane electrolyte and considers the water adsorption/desorption phenomena in the membrane. The effect of diffusivity model, surface roughness and water content driving force is considered. The model is validated against experimental data. In the results it is shown that the fuel cell water balance calculated by this model shows better fit with experimental data-points compared with model where only steady state operation were considered. We conclude that this discrepancy is due a different rate of water transport when membrane absorption/ desorption is considered in the model. The model becomes useful in system modelling when studying fuel cells in dynamic conditions.

In this paper a solid oxide fuel cell (SOFC) system for cogeneration of heat and power integrated with a stratified heat storage tank is studied. The use of a storage tank with thermal stratification allows one to increase the annual operating hours of CHP: heat can be produced when the request is low (for instance during the night), taking advantage of thermal stratification to increases the heat recovery performance. A model of the SOFC system is presented to estimate the energy required to meet the average electric energy demand of the residence. Two fuels are considered, namely syngas produced by gasification and natural gas. The tank model considers the temperature gradients over the tank height. The results of the numerical simulation are used to size the SOFC system and storage heat tank to provide energy for a small household using two different fuels. In particular it was shown that in the case of syngas, due to larger system heat output, a larger tank volume was required in order to accumulate unused heat over the night. The detailed description of the tank model will be useful to energy system modelers when sizing hot water tanks. Problem formulation is reported also using a Matlab script.

Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy; (2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults; (3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation; (2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.