Accepted Manuscripts

Jinwei Chen, Huisheng Zhang and Shilie Weng
J. Electrochem. En. Conv. Stor.   doi: 10.1115/1.4036805
A nonlinear autoregressive network with exogenous inputs (NARX) identification model is employed for predicting the Solid oxide fuel cell (SOFC) operating temperature dynamics fast and accurately. At the same time, the least squares support vector regression (LSSVR) method with radial basis kernel function (RBF) which uses particle swarm optimization (PSO) to optimize the LSSVR’s parameters is applied to establish the NARX model. With the training data sampled from the mechanism model which is derived from conservation laws, a SOFC temperature the NARX model based on the LSSVR is established. Investigations are conducted to analyze the effects of training data size and fitness function of PSO on the accuracy of the NARX model. And by comparing the temperature behaviors with the results collected form the mechanism model, the accuracy of the NARX model based on the LSSVR is verified with enough accuracy in predicting the dynamic performance of the SOFC temperature. Furthermore, in the aspect of simulation speed, the NARX model is much faster than the mechanism model because the NARX model avoids the internal complex computation process. For large size training data, the training time of the NARX model is only about 1.2s. For running all 20,000s of simulation, the predicting time of the NARX model is only about 0.2s, while the mechanism model is about 36s. In consideration of the high speed and accuracy of the NARX model, it can be applied to design valid multivariable model predictive control (MPC) schemes with high reputation.
TOPICS: Temperature, Particulate matter, Optimization, Solid oxide fuel cells, Support vector machines, Particle swarm optimization, Simulation, Design, Predictive control, Computation, Operating temperature, Dynamics (Mechanics)
Valentina Zaccaria, Zachary Branum and David Tucker
J. Electrochem. En. Conv. Stor.   doi: 10.1115/1.4036809
The use of high temperature fuel cells, such as Solid Oxide Fuel Cells (SOFCs), for power generation is considered a very efficient and clean solution to conservation of energy resources. When the SOFC is coupled with a gas turbine, the global system efficiency can go beyond 70% on natural gas LHV. However, durability of the ceramic material and system operability can be significantly penalized by thermal stresses due to temperature fluctuations and non-even temperature distributions. Thermal management of the cell during load following is therefore essential. The purpose of this work is to develop and test a pre-combustor model for real-time applications in hardware-based simulations, and to implement a control strategy to keep constant cathode inlet temperature during different operative conditions. The real-time model of the pre-combustor was incorporated into the existing SOFC model and tested in a hybrid system facility, where a physical gas turbine and hardware components were coupled with a cyber-physical fuel cell for flexible, accurate, and cost-reduced simulations. The control of the fuel flow to the pre-combustor was proven to be effective in maintaining a constant cathode inlet temperature during a step change in fuel cell load. With a 20 A load variation, the maximum temperature deviation from the nominal value was below 0.3% (3K). Temperature gradients along the cell were maintained below 10 K/cm. An efficiency analysis was performed in order to evaluate the impact of the pre-combustor on the overall system efficiency.
TOPICS: Temperature control, Stress, Combustion chambers, Fuel cells, Gas turbines, Solid oxide fuel cells, Temperature, Simulation, Hardware, Engineering simulation, System efficiency, High temperature, Temperature gradient, Temperature distribution, Thermal management, Flow (Dynamics), Natural gas, Durability, Energy conservation, Energy generation, Fluctuations (Physics), Thermal stresses, Ceramics, Fuels
Shian Li, Jinliang Yuan, Dr. Martin Andersson, Gongnan Xie and Bengt Sunden
J. Electrochem. En. Conv. Stor.   doi: 10.1115/1.4036810
The flow field design of current collectors is a significant issue, which greatly affects the mass transport processes of reactants/products inside fuel cells. Especially for Proton Exchange Membrane (PEM) fuel cells, an appropriate flow field design is very important due to the water balance problem. In this paper, a wavy surface is employed at the cathode flow channel to improve the oxygen mass transport process. The effects of wavy surface on transport processes are numerically investigated by using a three-dimensional anisotropic model including a water phase change model and a spherical agglomerate model. It is found that the wavy configurations enhance the oxygen transport and decrease the water saturation level. It is concluded that the predicted results and findings provide the guideline for the design and manufacture of fuel cells.
TOPICS: Gas flow, Proton exchange membrane fuel cells, Transport processes, Water, Design, Fuel cells, Flow (Dynamics), Oxygen, Proton exchange membranes, Anisotropy
Mohammad A. Rafe Biswas and Melvin Robinson
J. Electrochem. En. Conv. Stor.   doi: 10.1115/1.4036811
A direct methanol fuel cell (DMFC) converts liquid fuel into electricity to power devices, while operating at relatively low temperatures and producing virtually no greenhouse gases. Since DMFC performance characteristics are inherently complex, it can be postulated that artificial neural networks (NN) represent a marked improvement in prediction capabilities. In this work, an artificial NN is employed to predict the performance of a DMFC under various operating conditions. Input variables for the analysis consist of methanol concentration, temperature, current density, number of cells and anode flow rate. The addition of the two latter variables allows for a more distinctive model when compared to prior NN models. The key performance indicator of our NN model is cell voltage, which is an average voltage across the stack and ranges from 0 to 0.8 V. Experimental studies were conducted using DMFC stacks with membrane electrode assemblies consisting of an additional unique liquid barrier layer to minimize water loss to atmosphere. To determine best fit to the experimental data, the model is trained using two second order training algorithms: OWO-Newton and Levenberg-Marquardt (LM). The topology of OWO-Newton algorithm is slightly different from that of LM algorithm by employing bypass weights. The application of NN shows rapid construction of a predictive model of cell voltage for varying operating conditions with an accuracy on the order of 10^-4 , which can be comparable to literature. The coefficient of determination of the optimal model results using either algorithm were greater than 0.998.
TOPICS: Artificial neural networks, Direct methanol fuel cells, Algorithms, Low temperature, Current density, Flow (Dynamics), Temperature, Gases, Anodes, Fuels, Construction, Topology, Water, Methanol, Performance characterization, Membrane electrode assemblies
Yufeng Qiu, Jian Pu, Jian Li, Yihui Liu and Bin Hua
J. Electrochem. En. Conv. Stor.   doi: 10.1115/1.4036812
The chemical stability of La1-xSrxCo0.2Fe0.8O3-d (x=0, 0.4, 0.6, 1) oxides before and after annealing at 750 °C in air is investigated by field emission scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), auger electron spectroscopy (AES) and environmental transmission electron microscopy (TEM). Results indicate that Sr surface segregation has initially occurred at the sintering stage, and then the secondary-phase particles are formed with increasing time during annealing at 750 °C in air. Increasing Sr content accelerates Sr segregation on the surface, because of two driving forces including elastic/electrostatic stresses in the crystal lattice and thermal activation. AES and XPS results reveal that Sr and Co segregations toward the surface have great contributions to the chemical instability of LSCF related to annealing.
TOPICS: Intermediate temperature solid oxide fuel cells, Chemical stability, Annealing, Sintering, Stress, Augers, Crystal lattices, Electron spectroscopy, Photoelectron spectroscopy, Scanning electron microscopy, Electron field emission, Transmission electron microscopy, X-rays, Particulate matter
Khaliq Ahmed and Karl Foger
J. Electrochem. En. Conv. Stor.   doi: 10.1115/1.4036762
The fuel cell technology has undergone extensive research and development in the past 20 years. Even though it has not yet made a commercial breakthrough, it is still seen as a promising enabling technology for emissions reduction. The high electrical efficiency [1,2] of an SOFC-based fuel cell system, and the ability to operate on renewable fuels makes it an ideal platform for transition from fossil-fuel dependency to a sustainable world relying on renewable energy, by reducing emissions during the transition period where fossil fuels including natural gas remain a major source of energy. The key hurdles to commercialization are cost, life and reliability. Despite significant advances in all areas of the technology cost and durability targets [3] have not been met. The major contribution to cost comes from tailor-made BoP components as SOFC-based systems cannot be optimised functionally with off-the shelf commercial items, and cost targets for BoP and stack cannot be met without volume manufacturing [4]. Reliability issues range from stack degradation and mechanical failure and BoP component failure to grid-interface issues in a grid-connected distributed generation system. Resolving some of these issues are a key to the commercial viability of SOFC-based micro-combined heat and power (CHP) systems. This articles discusses some of the technical and practical i.e. real life challenges facing developers of this product.
TOPICS: Heat, Power systems (Machinery), Solid oxide fuel cells, Failure, Fossil fuels, Emissions, Reliability, Industrial research, Durability, Fuel cell technology, Fuel cells, Natural gas, Fuels, Manufacturing, Electrical efficiency, Renewable energy, Sustainability, Combined heat and power, Distributed power generation
Adriana Marinoiu, Mircea Raceanu, Elena Carcadea, Mihai Varlam, Danut Balan, Daniela Ebrasu, Ioan Stefanescu and Marius Enachescu
J. Electrochem. En. Conv. Stor.   doi: 10.1115/1.4036684
We prepared iodine-doped graphenes by several techniques (electrophilic substitution and nucleophilic substitution methods) in order to incorporate iodine atoms onto the graphene base materials. The physical characterization of prepared samples was performed by using an array of different techniques, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS) and electrochemical methods. A series of cathodes using I-doped graphene were prepared and evaluated. Electrochemical performances of the cathodes with and without I-doped graphene indicated an effective improvement, resulting a better mass transport in the catalyst layer.
TOPICS: Graphene, Oxygen, Proton exchange membrane fuel cells, Transmission electron microscopy, X-rays, Atoms, Photoelectron spectroscopy, Scanning electron microscopy, Catalysts
Akeel A. Shah
J. Electrochem. En. Conv. Stor.   doi: 10.1115/1.4036491
Detailed physics-based computer models of fuel cells can be computationally prohibitive for certain applications, including optimization, sensitivity analysis, uncertainty quantification and real-time control. Such applications can require an enormous number of runs in order to extract reliable results. Approximate models based on spatial homogeneity or data-driven techniques can serve as surrogates when scalar quantities such as the cell voltage are of interest. When more detailed information is required, e.g., the potential or temperature field, computationally inexpensive surrogate models are difficult to construct. In this paper we use dimensionality reduction to develop a surrogate model approach for high-fidelity fuel cell codes in cases where the target is a field. A detailed 3-d model of a high-temperature PEM fuel cell is used to test the approach. We develop a framework for using such surrogate models to quantify the uncertainty in a scalar/functional output, using the field output results. We propose a number of alternative methods including a semi-analytical approach requiring only limited computational resources.
TOPICS: Fuel cells, Modeling, Uncertainty quantification, Scalars, Physics, Temperature, Optimization, Real-time control, Computers, Proton exchange membrane fuel cells, Sensitivity analysis, Three-dimensional models, High temperature, Uncertainty

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