• Energy Research

Abstract Cathode catalyst layer (CCL) design is critically important for improved performance, durability and stability of proton exchange membrane (PEM) fuel cells. In this study, CCL is designed to consist of two sub-layers with different loadings of short-side chain (SSC) ionomer and platinum (Pt) catalyst for each sub-layer; and such CCLs are manufactured and characterized through morphological, microstructural, physical, and electrochemical characterizations, and the performance of single scaled-up cells containing such CCL designs is measured. The results show that the two-layer design results in a higher porosity and mean pore size, and hence higher air permeability, and provides distinctively higher catalyst activity and Pt utilization for the CCL than the conventional single-layer design under the same overall loadings of the ionomer and Pt for the entire CCL. It is also found that a higher ionomer loading in the first sub-layer (faced to the membrane) is beneficial to cell performance, and the Pt loading in this sub-layer plays a considerably important role as well. Overall, it is demonstrated that with a proper ionomer/Pt-gradient design, it is possible to achieve favorable morphological and microstructural characteristics that lessen ionic resistance while improving mass transport capability, catalyst activity, and Pt utilization, hence overall cell performance.

The final publication is available at Elsevier via https://dx.doi.org/10.1016/j.apenergy.2018.08.125 © 2018. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/

Proton exchange membrane fuel cell (PEMFC) is considered essential for climate change mitigation, and a fast and accurate model is necessary for its control and operation in practical applications. In this study, various machine learning methods are used to develop data-based models for PEMFC performance attributes and internal states. Techniques such as Artificial Neural Network (ANN) and Support Vector Machine Regressor (SVR) are used to predict the cell voltage, membrane resistance, and membrane hydration level for various operating conditions. Varying input features such as cell current, temperature, reactant pressures, and humidity are introduced to evaluate the accuracy of the model, especially under extreme conditions. Two different sets of data are considered in this study, which are acquired from, a physics-based semiempirical model and a 1-D reduced-dimension Computational Fluid Dynamics model, respectively. The aspect of data preprocessing and hyperparameter tuning procedures are investigated that are extensively used to calibrate the artificial neural network layers and support vector regressor to predict the fuel cell attributes. ANN clearly shows an advantage in comparison with SVR, especially on a multivariable output regression. However, the SVR is advantageous to model simple regressions as it greatly reduces the level of computation without sacrificing accuracy. Data-based models for PEMFC are successfully developed on both the data sets by adapting advanced modeling techniques and calibration procedures such as ANN incorporating the dropout technique, resulting in an R2 ≥ 0.99 for all the predicted variables, demonstrating the ability to build accurate data-based models solely on data from validated physics-based models, reducing the dependency on extensive experimentation.

Lithium-ion battery (LIB) has been deployed for the electrification of the transport sector as a key strategy for climate change mitigation and adaptation. However, it has significant technical challenges such as thermal runaway, requiring a good understanding and accurate prediction of the LIB thermal behavior (heat generation rate). In this study, a novel hybrid approach using an Artificial Neural Network (ANN) is developed for predicting the heat generation rate with discharge current, output voltage, ambient temperature, cell surface temperature, and Depth of Discharge (DOD) as the feature vectors (inputs); where the DOD is estimated with an Extended Kalman Filter (EKF), and direct Coulomb Counting (CC) method, respectively. A shallow neural network utilizing the Marquette-Levenberg algorithm is designed and calibrated using over 8000 cases of the testing data. It is shown that the predicted heat generation rate of LIB agrees well with the experimental results with an accuracy of R > 0.995. Further potential of this hybrid data-based model is evaluated by simulating a thermal management system control and by introducing voltage and current sensor faults for diagnostic purposes. It is shown that, when compared to the experimental value, the relative error of the total heat output generated is less than 2% when there is no sensor fault, and greater than 50% and 25%, respectively, with an induced failure of the current and voltage sensor, demonstrating the ability to build accurate models relying solely on LIB discharge data for sensor diagnostics. This study highlights the combination of using battery thermal behavior with machine learning for real time battery system monitoring, controls and field diagnostics.

Microstructure changes of the catalyst layers in proton exchange membrane fuel cells (PEMFCs) lead to significant performance degradation and durability limitations, especially under dynamic loading condition; and they are investigated experimentally in this study simulating the effect of wet-dry cycles through water intrusion-evaporation and water flow-through-dehydration experiments, respectively. It is found that the cycling of water intrusion-evaporation processes significantly contributes to the growth of agglomerates as well as the formation of pinholes and cracks, causing irreversible losses of active surface areas and catalytic activity; in contrast, the cycling of water flow-through-dehydration experiments enlarges the large pores but changes very little the agglomerate sizes. This is because water tends to flow through the path of least resistance, that is, the large pores in the catalyst layers, leaving the other parts of the catalyst layers with relatively small pores less affected. These observations provide experimental evidence of microstructure changes and their forms for a better understanding of degradation in PEMFCs, especially under dynamic operating conditions.

Abstract Durability is one of the most significant technical barriers to successful commercialization of polymer electrolyte membrane (PEM) fuel cells for practical vehicular applications. It is determined by the aging (degradation) and malfunction of various components during the long-term operation. Therefore, understanding the mechanisms of degradation modes in different components is crucial to the development of high-performing and long-lasting PEM fuel cells. In this review article, the critical degradation modes in major cell components, including membranes, catalyst layers, gas diffusion layers, and distribution plates, are comprehensively reviewed and analyzed, and the potential causes are described. Advanced experimental techniques to investigate the PEM fuel cell degradation modes reported in literature include steady-state durability tests and accelerated stress tests (ASTs). The steady-state durability test is straightforward but time-consuming and costly; therefore, ASTs are often applied to accelerate durability testing. For comparable results among different research studies, the experimental protocols and conditions have to be consistent, and the details of these experimental techniques are systematically reviewed in this article. The experimental results with a focus on the degradation modes, degradation rate, and test time of the PEM fuel cells have been reported. Finally, in order to understand the root causes of degradation modes and to develop the mitigation strategies, ex-situ ASTs in literature have been reviewed, including the effects of cyclic temperature, humidity, water wet-dry, freeze-thaw, clamping stress, and vibration operations.

Abstract Air-assisted atomization of the sprays produced by injecting different types of highly viscous pyrolysis oils are studied. Different breakup and atomization regimes are characterized using the long distance microscopic imaging technique. The high resolution imaging reveals detailed underlying two-phase interactions in pyrolysis oil injection that are found unlike other conventional liquid fuels. Several cases of droplet size characterization are carried out using the Malvern’s laser diffraction measurements under variable liquid flow rates, atomizing air flow rates and oil preheating temperatures. The time-averaged and high resolution instantaneous images reveal that the highly viscous nature of pyrolysis oils prevent the instability mechanisms from efficiently atomizing the liquid. The present broad set of measured data suggest that the atomization quality of pyrolysis oils can be optimized by applying a combination of liquid preheating and air-assisted atomization. The flow visualizations reveal the transition from ligament-dominated two-phase flow field into fully atomized sprays when proper oil preheating temperatures and sufficient atomizing air flow rates are provided. Visualized observations are quantitatively verified using the droplet size distribution measurements as well as the Sauter mean diameter (SMD) values for several measurement conditions.

Abstract Current state-of-the-art proton exchange membrane (PEM) fuel cell electrodes are typically comprised of either short-side-chain (SSC) or long-side-chain (LSC) ionomers, owing to their proven success in the electrode performance and durability under regular cell operation. However, the electrodes based on these two prominent ionomers have not been sufficiently investigated under sub-freezing conditions. In this study, the impact of ionomer type on the degradations of the surface wettability and gas permeability characteristics has been investigated for PEM fuel cell electrodes under freeze-thaw (F-T) cycles between 30 °C and −40 °C. The electrodes comprised of either SSC or LSC ionomers are manufactured with different catalyst loadings. It is found that the F-T cycles induce severe degradations in the electrodes, and the resulting surface morphologies differ greatly, depending on the ionomer type and catalyst loading. For a given catalyst loading, the SSC electrodes degrade more heavily than the LSC ones. Further, independent of the ionomer type, the high catalyst loading electrodes tend to degrade slower than their low catalyst loading counterparts. The SSC catalyst layers peel off from the electrodes virtually completely with the microporous layer largely degraded, inducing a highly corroded and heterogeneous surface morphology. The LSC electrodes experience relatively less degradations, thus the resulting surface morphologies are less corroded and more homogeneous. For all the electrodes, the morphological degradations cause a substantial increase in the gas permeability coefficients, but a decrease in the static contact angles. These increments and decrements correlate well with the severity of the surface degradations, and they are rapid and more substantial for the SSC electrodes.