Research Papers

J. Electrochem. En. Conv. Stor.. 2017;14(3):031001-031001-9. 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 in a better mass transport in the catalyst layer.

Commentary by Dr. Valentin Fuster
J. Electrochem. En. Conv. Stor.. 2017;14(3):031002-031002-6. doi:10.1115/1.4036812.

The chemical stability of La1−xSrxCo0.2Fe0.8O3−δ (x = 0, 0.4, 0.6, and 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 the heat-treatment time at 750 °C in air. Increasing Sr content accelerates Sr segregation on the surface, because of two driving forces including interaction forces 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 La1−xSrxCo1−yFeyO3−δ (LSCF) during annealing.

Commentary by Dr. Valentin Fuster
J. Electrochem. En. Conv. Stor.. 2017;14(3):031003-031003-10. doi:10.1115/1.4036805.

In order to facilitate valid solid oxide fuel cell (SOFC) temperature control scheme, a nonlinear identification method of SOFC temperature dynamic behaviors is proposed using an autoregressive network with exogenous inputs (NARX) model, whose nonlinear function is described by a least-squares support vector regression (LSSVR) method with radial basis kernel function (RBF). During the identifying process, a particle swarm optimization (PSO) algorithm is introduced to optimize the parameters of LSSVR. On the other hand, a mechanism model is developed to sample the training data to regress the NARX model. Investigations are conducted to analyze the effects of training data size and PSO fitness function on the accuracy of the NARX model. The results demonstrate that the NARX model with tenfold cross-validation fitness function and large size data is precise enough in predicting the SOFC temperature dynamic behaviors. The maximum errors of cathode and anode outlet temperature are 0.3081 K and 0.3293 K, respectively. Furthermore, the simulation speed of NARX model is much faster than the mechanism model because NARX model avoids the internal complex computation process. The training time of the NARX model with large size data is about 1.2 s. For a 20,000 s simulation, the predicting time of the NARX model is about 0.2 s, while the mechanism model is about 36 s. In consideration of its high computational speed and accuracy, NARX model is a powerful candidate for valid multivariable model predictive control (MPC) schemes.

Commentary by Dr. Valentin Fuster
J. Electrochem. En. Conv. Stor.. 2017;14(3):031004-031004-16. doi:10.1115/1.4036944.

A model predictive control (MPC) strategy has been suggested and simulated with the empirical dynamic data collected on the hybrid performance (HyPer) project facility installed at the National Energy Technology Laboratory (NETL), U.S. Department of Energy, in Morgantown, WV. The excursion dynamic data collected between the setup changes of the actuators on the cathode side of the HyPer facility were processed offline to determine the feasibility of applying an adaptive model predictive control strategy. Bypass valves along with electric load (EL) of the system were manipulated, and variables such as turbine speed, mass flow, temperature, pressure of the cathode side, among others were recorded for analysis. The three main phases of the MPC, identification of the models, control design, and control tuning have been described. Two identification structures, autoregressive exogenous (ARX) and a state-space model, were used to fit the measured data to dynamic models of the facility. The system identification ARX model required around 0.12 s of computer time. The state-space identification algorithm spent around 0.65 s, which was relatively high considering that the sample time of the sensors was 0.4 s. Visual inspection of the tracking accuracy showed that the ARX approach was approximately as accurate as the state-space structure in its ability to reproduce measured data. However, by comparing the loss function and the final prediction error (FPE) parameters, the state-space approach gives better results. For the ARX/state-space models, the MPC was robust in tracking setpoint variations. The MPC strategy described here offers potential to be the way to control the HyPer facility. One of the strengths of MPC is that it can allow the designer to impose strict limits on inputs and outputs in order to keep the system within known safe bounds. Constraints are highly present in the HyPer facility. The constraint airflow valves and the electric load were used in the simulation to control the constraint turbine speed and the cathode airflow (CAF). The MPC also displayed good disturbance rejection on the output variables when the fuel flow was set to simulate fuel cell (FC) heat effluent disturbances. Different off-design scenarios of operation were tested to confirm the estimated implementation behavior of the plant-controller dynamics. One drawback in MPC implementation is the computational time consuming between calculations and will be considered for future studies.

Commentary by Dr. Valentin Fuster
J. Electrochem. En. Conv. Stor.. 2017;14(3):031005-031005-12. doi:10.1115/1.4036762.

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 (Powell et al., 2012, “Demonstration of a Highly Efficient Solid Oxide Fuel Cell Power System Using Adiabatic Steam Reforming and Anode Gas Recirculation,” J. Power Sources, 205, pp. 377–384; Föger and Payne, 2014, “Ceramic Fuel Cells BlueGen—Market Introduction Experience,” 11th European SOFC & SOE Forum 2014, Lucerne, Switzerland, Paper No. A0503; and Payne et al., 2009, “Generating Electricity at 60% Electrical Efficiency From 1-2 kWe SOFC Products,” ECS Trans., 25(2), pp. 231–240) of an solid oxide fuel cell (SOFC)-based fuel cell system and the ability to operate on renewable fuels make 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. Key technical hurdles to commercialization are cost, life, and reliability. Despite significant advances in all areas of the technology cost and durability targets (Papageorgopoulos, 2012, “Fuel Cells, 2012 Annual Merit Review and Peer Evaluation Meeting,” U.S. Department of Energy, Washington, DC, accessed May 14, 2012, http://www.hydrogen.energy.gov/pdfs/review12/fc_plenary_papageorgopoulos_2012_o.pdf) have not been met. The major contribution to cost comes from tailor-made balance of plant (BoP) components as SOFC-based systems cannot be optimized functionally with off-the shelf commercial items, and cost targets for BoP and stack cannot be met without volume manufacturing (Föger, 2008, “Materials Basics for Fuel Cells,” Materials for Fuel Cells, M. Gasik ed., Woodhead Publishing, Cambridge, UK, pp. 6–63). 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 microcombined heat and power (CHP) systems. This paper highlights some of the technical and practical challenges facing developers of SOFC-based products.

Commentary by Dr. Valentin Fuster
J. Electrochem. En. Conv. Stor.. 2017;14(3):031006-031006-8. 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 for conservation of energy resources. When the SOFC is coupled with a gas turbine, the global system efficiency can go beyond 70% on natural gas lower heating value (LHV). However, durability of the ceramic material and system operability can be significantly penalized by thermal stresses due to temperature fluctuations and noneven temperature distributions. Thermal management of the cell during load following is therefore essential. The purpose of this work is to develop and test a precombustor 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 precombustor 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 precombustor 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% (3 K). Temperature gradients along the cell were maintained below 10 K/cm. An efficiency analysis was performed in order to evaluate the impact of the precombustor on the overall system efficiency.

Commentary by Dr. Valentin Fuster
J. Electrochem. En. Conv. Stor.. 2017;14(3):031007-031007-10. 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.

Commentary by Dr. Valentin Fuster
J. Electrochem. En. Conv. Stor.. 2017;14(3):031008-031008-7. 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 the 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 104, which can be comparable to literature. The coefficient of determination of the optimal model results using either algorithm were greater than 0.998.

Commentary by Dr. Valentin Fuster

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