Feasibility Assessment of Fast Numerical Simulations for Real-Time Monitoring and Control of PEM Fuel Cells

Computational models that ensure accurate and fast responses to the variations in operating conditions,such as the cell tem-perature and relative humidity(RH),are essential monitoring tools for the real-time control of proton exchange membrane(PEM)fuel cells.To this end,fast cell-area-averaged numerical simulations are developed and verifi ed against the present experiments under various RH levels.The present simulations and measurements are found to agree well based on the cell voltage(polarization curve)and power density under variable RH conditions(RH=40%,RH=70%,and RH=100%),which verifi es the model accuracy in predicting PEM fuel cell performance.In addition,computationally feasible reduced-order models are found to deliver a fast output dataset to evaluate the charge/heat/mass transfer phenomena as well as water production and two-phase fl ow transport.Such fast and accurate evaluations of the overall fuel cell operation can be used to inform the real-time control systems that allow for the improved optimization of PEM fuel cell performance.

Efficient recovery of group-sparse signals with truncated and reweighted l_(2,1)-regularization

The l2, 1-norm regularization can efficiently recover group-sparse signals whose non-zero coefficients occur in a few groups. It is well known that the l2, 1-norm regularization based on the classic alternating direction method shows strong stability and robustness in many applications. However, the l2, 1-norm regularization requires more measurements. In order to recover group-sparse signals with a better sparsity-measurement tradeoff, the truncated l2, 1-norm regularization and reweighted l2, 1-norm regularization are proposed for the recovery of group-sparse signals based on the iterative support detection. The proposed algorithms are tested and compared with the l2, 1-norm model on a series of synthetic signals and the Shepp-Logan phantom. Experimental results demonstrate the performance of the proposed algorithms, especially at a low sample rate and high sparsity level. ? 1990-2011 Beijing Institute of Aerospace Information.

A facile synthesis of high activity cube-like Pt/carbon composites for fuel cell application

High activity catalyst with simple low-cost synthesis is essential for fuel cell commercialization. In this study, a facile procedure for the synthesis of cube-like Pt nanoparticle （PtCube） composites with high surface area carbon supports is developed by mixing precursor of Pt with carbon supports in organic batches, hence, one pot synthesis. The PtCube grow with Vulcan XC-72 or Ketjen black, respectively, and then treated for 5.5 h at 185℃ （i.e., Ptcube5.5/V and PtcubeS.5/K）. The resulting particle sizes and shapes are similar; however, Ptcube5.5/K has a larger electrochemical active surface area （EASA） and a remarkably better formic acid （FA） oxidation performance. Optimization of the PtCube/K composites leads to Ptcubelo/K that has been treated for 10 h at 185℃. With a larger EASA, Ptcubelo/K is also more active in FA oxidation than the other Ptcube/K composites. Impedance spectroscopy analysis of the temperature treated and asprepared （i.e., untreated） Ptcube/K composites indicates that PtCube10/K is less resistive, and has the highest limiting capacitance among the PtCube/K electrodes. Consistently, the voltammetric EASA is the largest for Ptcube 10/K. Furthermore, PtCube10/K is compared with two commercial Pt/C catalysts, Tanaka Kikinzoku Kogyo （TKK）, and Johnson Matthey （JM）Pt/C catalysts. The TKK Pt/C has a higher EASA than Ptcube10/K, as expected from their relative particles sizes （3-4 nm vs. 6-7 nm for PtcubelO/K）, however, Ptcube10/K has a significantly better FA oxidation activity.

A modeling and experimental study of capacity fade for lithium-ion batteries

Lithium-ion batteries are extensively used in electric vehicles,however,their significant degradation over dis-charge and charge cycles results in severe capacity fade,limiting driving ranges of electric vehicles over time and useful lifetime of batteries.In this study,capacity fade for lithium-ion battery has been investigated through modeling and experiment.A predictive model is developed based on first principles incorporating degradation mechanisms.The mechanisms of degradation considered include solid-electrolyte interface(SEI)growth and ac-tive material loss at both negative and positive electrodes.Battery performance including capacity is measured experimentally under discharge and charge cycling with battery operation temperature controlled.It is shown that battery capacity is reduced over battery discharge/charge cycling at a given battery operation temperature,and the model predicted battery performance,including capacity fade,agrees well with the experimental results.As the number of discharge/charge cycles are increased,battery capacity is reduced significantly;battery ca-pacity fade is increased substantially when battery operation temperature is increased,indicating significantly accelerated aging of the battery at elevated operation temperatures and hence the importance of battery thermal management in the control of battery operation temperature for practical applications such as electric vehicles.Battery capacity fade is mainly caused by SEI film growth at the negative electrode,which is the largest contribut-ing factor to the capacity fade,and the active material isolation at the negative electrode,which is the second largest influencing aging factor.

Assessment and validation of liquid breakup models for high-pressure dense diesel sprays

Liquid breakup in fuel spray and atomization significantly affects the consequent mixture formation, combustion behavior, and emission formation processes in a direct injection diesel engine. In this paper, different models for liquid breakup processes in high-pressure dense diesel sprays and its impact on multi-dimensional diesel engine simulation have been evaluated against experi- mental observations, along with the influence of the liquid breakup models and the sensitivity of model parameters on diesel sprays and diesel engine simulations. It is found that the modified Kelvin-Helmholtz （KH）-Rayleigh-Taylor （RT） breakup model gives the most reasonable predicted results in both engine simulation and high-pressure diesel spray simulation. For the standard KH-RT model, the model constant Cbl for the breakup length has a significant effect on the predictability of the model, and a fixed value of the constant Cbl cannot provide a satisfactory result for different operation conditions. The Taylor-analogy- breakup （TAB） based models and the RT model do not provide reasonable predictions for the characteristics of high-pressure sprays and simulated engine performance and emissions.

Hybrid data-based modeling for the prediction and diagnostics of Li-ion battery thermal behaviors

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.

Machine learning modeling for proton exchange membrane fuel cell performance

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.

Structure,Property,and Performance of Catalyst Layers in Proton Exchange Membrane Fuel Cells

Catalyst layer(CL)is the core component of proton exchange membrane(PEM)fuel cells,which determines the performance,durability,and cost.However,difficulties remain for a thorough understanding of the CLs’inhomogeneous structure,and its impact on the physicochemical and electrochemical properties,operating performance,and durability.The inhomogeneous structure of the CLs is formed during the manufacturing process,which is sensitive to the associated materials,composi-tion,fabrication methods,procedures,and conditions.The state-of-the-art visualization and characterization techniques are crucial to examine the CL structure.The structure-dependent physicochemical and electrochemical properties are then thoroughly scrutinized in terms of fundamental concepts,theories,and recent progress in advanced experimental techniques.The relation between the CL structure and the associated effective properties is also examined based on experimental and theoretical findings.Recent studies indicated that the CL inhomogeneous structure also strongly affects the performance and degradation of the whole fuel cell,and thus,the interconnection between the fuel cell performance,failure modes,and CL structure is comprehensively reviewed.An analytical model is established to understand the effect of the CL structure on the effective properties,performance,and durability of the PEM fuel cells.Finally,the challenges and prospects of the CL structure-associated studies are highlighted for the development of high-performing PEM fuel cells.

Copper nanowires decorated with TiO_(2−x) from MXene for enhanced electrocatalytic nitrogen oxidation into nitrate under vacuum assistance

The green synthesis of nitrate(NO_(3)^(−))via electrocatalytic nitrogen oxidation reaction(NOR)is a promising strategy for artificial nitrogen fixation,which shows great advantages than traditional nitrate synthesis based on Haber–Bosch and Ostwald processes.But the poor N_(2)absorption,high bond energy of N≡N(941 kJ·mol^(−1)),and competing multi-electron-transfer oxygen evolution reaction(OER)limit the activity and selectivity.Herein,we fabricated MXene-derived irregular TiO_(2)−x nanoparticles anchored Cu nanowires(Cu-NWs)electrode for efficient electrocatalytic nitrogen oxidation,which exhibits a NO_(3)−yield of 62.50μg·h^(−1)·mgcat^(−1)and a Faradaic efficiency(FE)of 22.04%,and a significantly enhanced NO_(3)−yield of 92.63μg·h^(−1)·mgcat^(−1),and a FE of 40.58%under vacuum assistance.The TiO_(2)−x/Cu-NWs electrode also shows excellent reproducibility and stability under optimal experimental conditions.Moreover,a Zn-N_(2)reaction device was assembled with TiO_(2−x)/Cu-NWs as an anode and Zn plate as a cathode,obtaining an extremely high NO_(3)−yield of 156.25μg·h^(−1)·mgcat^(−1).The Zn-nitrate battery shows an open circuit voltage(OCV)of 1.35 V.This work provides novel strategies for enhancing the performance of ambient N_(2)oxidation to obtain higher NO_(3)^(−)yield.

Production of hydrogen from fossil fuel:A review

Production of hydrogen,one of the most promising alternative clean fuels,through catalytic conversion from fossil fuel is the most technically and economically feasible technology.Catalytic conversion of natural gas into hydrogen and carbon is thermodynami-cally favorable under atmospheric conditions.However,using noble metals as a catalyst is costly for hydrogen production,thus mandating non-noble metal-based catalysts such as Ni,Co,and Cu-based alloys.This paper reviews the various hydrogen production methods from fossil fuels through pyrolysis,partial oxidation,autother-mal,and steam reforming,emphasizing the catalytic production of hydrogen via steam reforming of methane.The multicomponent catalysts composed of several non-noble materials have been summarized.Of the Ni,Co,and Cu-based catalysts investigated in the literature,Ni/Al_(2)O_(3)catalyst is the most economical and performs best because it suppresses the coke formation on the catalyst.To avoid carbon emission,this method of hydrogen production from methane should be integrated with carbon capture,utilization,and storage(CCUS).Carbon capture can be accomplished by absorption,adsorption,and membrane separation processes.The remaining challenges,prospects,and future research and development directions are described.

A Review of physics-based and data-driven models for real-time control of polymer electrolyte membrane fuel cells

The real-time model-based control of polymer electrolyte membrane(PEM)fuel cells requires a computationally efficient and sufficiently accurate model to predict the transient and long-term performance under various operational conditions,involving the pressure,temperature,humidity,and stoichiometry ratio.In this article,recent progress on the development of PEM fuel cell models that can be used for real-time control is reviewed.The major operational principles of PEM fuel cells and the associated mathematical description of the transport and electrochemical phenomena are described.The reduced-dimensional physics-based models(pseudo-twodimensional,one-dimensional numerical and zero dimensional analytical models)and the non-physics-based models(zero-dimensional empirical and data-driven models)have been systematically examined,and the comparison of these models has been performed.It is found that the current trends for the real-time control models are(i)to couple the single cell model with balance of plants to investigate the system performance,(ii)to incorporate aging effects to enable long-term performance prediction,(iii)to increase the computational speed(especially for one-dimensional numerical models),and(iv)to develop data-driven models with artificial intelligence/machine learning algorithms.This review will be beneficial for the development of physics or nonphysics based models with sufficient accuracy and computational speed to ensure the real-time control of PEM fuel cells.

Controlled Synthesis of Carbon-Supported Pt-Based Electrocatalysts for Proton Exchange Membrane Fuel Cells

Proton exchange membrane fuel cells are playing an increasing role in postpandemic economic recovery and climate action plans.However,their performance,cost,and durability are significantly related to Pt-based electrocatalysts,hampering their large-scale commercial application.Hence,considerable efforts have been devoted to improving the activity and durability of Pt-based electrocatalysts by controlled synthesis in recent years as an effective method for decreasing Pt use,and consequently,the cost.Therefore,this review article focuses on the synthesis processes of carbon-supported Pt-based electrocatalysts,which significantly affect the nanoparticle size,shape,and dispersion on supports and thus the activity and durability of the prepared electrocatalysts.The reviewed processes include(i)the functionalization of a commercial carbon support for enhanced catalyst-support interaction and additional catalytic effects,(ii)the methods for loading Pt-based electrocatalysts onto a carbon support that impact the manufacturing costs of electrocatalysts,(iii)the preparation of spheri-cal and nonspherical Pt-based electrocatalysts(polyhedrons,nanocages,nanoframes,one-and two-dimensional nanostruc-tures),and(iv)the postsynthesis treatments of supported electrocatalysts.The influences of the supports,key experimental parameters,and postsynthesis treatments on Pt-based electrocatalysts are scrutinized in detail.Future research directions are outlined,including(i)the full exploitation of the potential functionalization of commercial carbon supports,(ii)scaled-up one-pot synthesis of carbon-supported Pt-based electrocatalysts,and(iii)simplification of postsynthesis treatments.One-pot synthesis in aqueous instead of organic reaction systems and the minimal use of organic ligands are preferred to simplify the synthesis and postsynthesis treatment processes and to promote the mass production of commercial carbon-supported Pt-based electrocatalysts.