The energy, exergy and economic aspects are analyzed of a cycle consisting of a polymer fuel cell, a burner, a reformer and a heat exchanger. Water is used for cooling the fuel cell, and the heated water is used for domestic consumption. The exergy and energy efficiencies of the cycle are calculated and the effects of various cycle parameters on the exergy and energy efficiencies are investigated. To maximize the exergy efficiency while minimizing the cost of electricity generation by the fuel cell, the PSO algorithm is utilized. The results show that increasing the cooling water flow rate has the greatest effect on increasing the energy efficiency of the cycle, while increasing the burner temperature has the greatest effect on increasing the exergy efficiency of the cycle. Moreover, it is shown via multiobjective optimization of the proposed cycle that the exergy efficiency of the cycle increases by 38% and the cost of electricity generation decreases by 18% by applying optimized parameters.

The sodium-ion batteries have low energy density, low capacity and inferior cycling performance as compared to Li-ion batteries. However, lithium depletion poses a serious problem for production and cost of Li-ion batteries. In the present work, a NaNi1/3Mn1/3Co1/3O2 was synthesized as cathode material for Na-ion batteries using the sol-gel method. The conventional cathode material used in Na-ion batteries had been replaced with the synthesized cathode material and the data had been collected by performing charging/discharging experiments. The support vector regression synchronized cross validation – Simplex Algorithm cluster was then used for predictive modelling and optimization of fabrication process of positive electrode material of sodium-ion batteries. The stable normal distribution without any skewness validated the robustness of the model for better accuracy and stability of the Na-ion batteries. The optimized value of capacity 176 mAh/g obtained for 99 cycles was found which is better than those of conventional batteries used for commercial storage purposes.

A multiple model adaptive control (MMAC) methodology is used to control the critical parameters of a solid oxide fuel cell gas turbine (SOFC-GT) cyberphysical simulator, capable of characterizing 300 kW hybrid plants. The SOFC system is composed of a hardware balance of plant (BoP) component, and a high fidelity FC model implemented in software. This study utilizes empirically derived transfer functions (TFs) of the BoP facility to derive the MMAC gains for the BoP system, based on an estimation algorithm which identifies current operating points. The MMAC technique is useful for systems having a wide operating envelope with nonlinear dynamics. The practical implementation of the adaptive methodology is presented through simulation in the matlab/simulink environment.

Operating points of a 300 kW solid oxide fuel cell gas turbine (SOFC-GT) power plant simulator are estimated with the use of a multiple model adaptive estimation (MMAE) algorithm. This algorithm aims to improve the flexibility of controlling the system to changing operating conditions. Through a set of empirical transfer functions (TFs) derived at two distinct operating points of a wide operating envelope, the method demonstrates the efficacy of estimating online the probability that the system behaves according to a predetermined dynamic model. By identifying which model the plant is operating under, appropriate control strategies can be switched and implemented. These strategies come into effect upon changes in critical parameters of the SOFC-GT system—most notably, the load bank (LB) disturbance and fuel cell (FC) cathode airflow parameters. The SOFC-GT simulator allows the testing of various FC models under a cyber-physical configuration that incorporates a 120 kW auxiliary power unit and balance-of-plant (Bop) components. These components exist in hardware, whereas the FC model in software. The adaptation technique is beneficial to plants having a wide range of operation, as is the case for SOFC-GT systems. The practical implementation of the adaptive methodology is presented through simulation in the matlab / simulink environment.

Membrane electrolyte assembly (MEA) aging is a major concern for deployed proton exchange membrane (PEM) fuel cell stacks. Studies have shown that working conditions, such as the operating temperature, humidity, and open circuit voltage (OCV), have a major effect on degradation rates and also vary significantly from cell to cell. Individual cell health estimations would be very beneficial to maintenance and control schemes. Ideally, estimations would occur in response to the applied load to avoid service interruptions. To this end, this paper presents the use of an extended Kalman filter (EKF) to estimate the effective membrane surface area (EMSA) of each cell using cell voltage measurements taken during operation. The EKF method has a low computational cost and can be applied in real time to estimate the EMSA of each cell in the stack. This yields quantifiable data regarding cell degradation. The EKF algorithm was applied to experimental data taken on a 23-cell stack. The load profiles for the experiments were based on the FTP-75 and highway fuel economy test (HWFET) standard drive cycle tests to test the ability of the algorithm to perform in realistic load scenarios. To confirm the results of the EKF method, low performing cells and an additional “healthy” cell were selected for scanning electron microscope (SEM) analysis. The images taken of the cells confirm that the EKF accurately identified problematic cells in the stack. The results of this study could be used to formulate online sate of health estimators for each cell in the stack that can operate during normal operation.

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 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.

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.

Research into reduced-order models (ROM) for Lithium-ion batteries is motivated by the need for a real-time embedded model possessing the accuracy of physics-based models, while retaining computational simplicity comparable to equivalent-circuit models. The discrete-time realization algorithm (DRA) proposed by Lee et al. (2012, “One-Dimensional Physics-Based Reduced-Order Model of Lithium-Ion Dynamics,” J. Power Sources, 220 , pp. 430–448) can be used to obtain a physics-based ROM in standard state-space form, the time-domain simulation of which yields the evolution of all the electrochemical variables of the standard pseudo-2D porous-electrode battery model. An unresolved issue with this approach is the high computation requirement associated with the DRA, which needs to be repeated across multiple SoC and temperatures. In this paper, we analyze the computational bottleneck in the existing DRA and propose an improved scheme. Our analysis of the existing DRA reveals that singular value decomposition (SVD) of the large Block–Hankel matrix formed by the system's Markov parameters is a key inefficient step. A streamlined DRA approach that bypasses the redundant Block–Hankel matrix formation is presented as a drop-in replacement. Comparisons with existing DRA scheme highlight the significant reduction in computation time and memory usage brought about by the new method. Improved modeling accuracy afforded by our proposed scheme when deployed in a resource-constrained computing environment is also demonstrated.

A 300 kW solid oxide fuel cell gas turbine (SOFC-GT) power plant simulator is evaluated with the use of a model reference adaptive control (MRAC) scheme, implemented for a set of nonlinear empirical transfer functions. The SOFC-GT simulator allows testing of various fuel cell models under a hardware-in-the-loop configuration that incorporates a 120 kW auxiliary power unit and balance-of-plant components in hardware, and a fuel cell model in software. The adaptation technique is beneficial to plants having a wide range of operation, and strong coupling interaction. The practical implementation of the adaptive methodology is presented through simulation in the Matlab/Simulink environment.

As fuel cells continue to improve in performance and power densities levels rise, potential applications ensue. System-level performance modeling tools are needed to further the investigation of future applications. One such application is small-scale aircraft propulsion. Both piloted and unmanned fuel cell aircrafts have been successfully demonstrated suggesting the near-term viability of revolutionizing small-scale aviation. Nearly all of the flight demonstrations and modeling efforts are conducted with low temperature fuel cells; however, the solid oxide fuel cell (SOFC) should not be overlooked. Attributing to their durability and popularity in stationary applications, which require continuous operation, SOFCs are attractive options for long endurance flights. This study presents the optimization of an integrated solid oxide fuel cell-fuel processing system model for performance evaluation in aircraft propulsion. System parameters corresponding to maximum steady state thermal efficiencies for various flight phase power levels were obtained through implementation of the particle swarm optimization (PSO) algorithm. Optimal values for fuel utilization, air stoichiometric ratio, air bypass ratio, and burner ratio, a four-dimensional optimization problem, were obtained while constraining the SOFC operating temperature to 650–1000 °C. The PSO swarm size was set to 35 particles, and the number of iterations performed for each case flight power level was set at 40. Results indicate the maximum thermal efficiency of the integrated fuel cell-fuel processing system remains in the range of 44–46% throughout descend, loitering, and cruise conditions. This paper discusses a system-level model of an integrated fuel cell-fuel processing system, and presents a methodology for system optimization through the particle swarm algorithm.

Fuel cells are emerging as alternate green power producers for both large power production and for use in automobiles. Hydrogen is seen as the best option as a fuel; however, hydrogen fuel cells require recirculation of unspent hydrogen. A supersonic ejector is an apt device for recirculation in the operating regimes of a hydrogen fuel cell. Optimal ejectors have to be designed to achieve best performances. The use of the vector evaluated particle swarm optimization technique to optimize supersonic ejectors with a focus on its application for hydrogen recirculation in fuel cells is presented here. Two parameters, compression ratio and efficiency, have been identified as the objective functions to be optimized. Their relation to operating and design parameters of ejector is obtained by control volume based analysis using a constant area mixing approximation. The independent parameters considered are the area ratio and the exit Mach number of the nozzle. The optimization is carried out at a particular entrainment ratio and results in a set of nondominated solutions, the Pareto front. A set of such curves can be used for choosing the optimal design parameters of the ejector.

This paper presents a systematic approach to the multivariable robust control of a hybrid fuel cell gas turbine plant. The hybrid configuration under investigation comprises a physical simulation of a 300 kW fuel cell coupled to a 120 kW auxiliary power unit single spool gas turbine. The facility provides for the testing and simulation of different fuel cell models that in turn help identify the key issues encountered in the transient operation of such systems. An empirical model of the facility consisting of a simulated fuel cell cathode volume and balance of plant components is derived via frequency response data. Through the modulation of various airflow bypass valves within the hybrid configuration, Bode plots are used to derive key input/output interactions in transfer function format. A multivariate system is then built from individual transfer functions, creating a matrix that serves as the nominal plant in an H-infinity robust control algorithm. The controller’s main objective is to track and maintain hybrid operational constraints in the fuel cell’s cathode airflow and the turbo machinery states of temperature and speed under transient disturbances. This algorithm is then tested on a SIMULINK/MATLAB platform for various perturbations of load and fuel cell heat effluence.

High-fidelity dynamic models of solid oxide fuel cells (SOFCs) capture the spatial distribution of key performance variables by considering the cells as distributed parameter systems. As such, they are often complex and require extensive computational resources. In this paper, driven by the need to support the control strategy development and system optimization, we develop a low-order SOFC model by approximating the mass and energy balance dynamics in the fuel and air bulk flows using quasi-static relations. However, due to the coupling between the quasi-static mass balance and current distribution, this approximation leads to a large set of coupled nonlinear algebraic equations that have to be solved online using iterative computation. In order to mitigate the computational cost involved, an efficient iterative algorithm is proposed to solve these equations. The new algorithm requires to iterate on only one variable—the cell voltage—to determine the current and flow compositions and their distributions. The low-order model with 16 states is compared to the baseline model, which has 160 states that incorporates fully the mass and energy balance dynamics. Simulations are performed to evaluate the model performance for both steady-state and transient operations, and to assess the computational cost associated with the low-order and full order models. It is shown that the low-order model closely matches the original baseline model, while the computation time is reduced by more than 50% compared to the baseline model.

A three-dimensional steady-state electrochemical mathematical model is developed where the mass, fluid, and thermal transport processes are considered, as well as the electrochemical reaction phenomena. The influences of the parameters of interest, which include porosity, permeability, and the thickness of the gas diffusion layer, and the inlet gas stoichiometric ratio on the performance of fuel cells are identified. By applying the Powell algorithm, the optimum values of multiple parameters are obtained while optimizing the potential of the electrolyte phase at the membrane/cathode interface at a typical value of the cell voltage. Compared with the reference case, the optimized results, such as the oxygen mole fraction and the local current density distribution, provide useful information for a better design of fuel cells.