Optimized Parameters of SOFC for steady state and transient simulations using interior search algorithm

El-Hay, Enas A.; El‐Hameed, Mohamed A.; El‐Fergany, Attia A.

doi:10.1016/j.energy.2018.10.038

Cited by 67 publications

(23 citation statements)

References 37 publications

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“…The use of the fuel cell is due to its advantages over other renewable energy sources, e.g., the electrical efficacies of FC range between 50% and 70%, rich reserves, high utilization efficiency, versatility, high safety, and flexibility in the use of the necessary fuels. Thus, it has become a genuinely competitive power source of energy [2,3]. Generally, two types of fuel can be distinguished in industries; they are: (i) Proton Exchange Membrane FCs (PEMFCs) and Solid-Oxide FCs (SOFCs).…”

Section: Introductionmentioning

confidence: 99%

Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique

2020

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Accurate modelling of the fuel cell characteristics curve is essential for the simulation analysis, control management, performance evaluation, and fault detection of fuel cell power systems. However, the big challenge in fuel cell modelling is the multi-variable complexity of the characteristic curves. In this paper, we propose the implementation of a computer graphic technique called Bézier curve to approximate the characteristics curves of the fuel cell. Four different case studies are examined as follows: Ballard Systems, Horizon H-12 W stack, NedStackPS6, and 250 W proton exchange membrane fuel cells (PEMFC). The main objective is to minimize the absolute errors between experimental and calculated data by using the control points of the Bernstein–Bézier function and de Casteljau’s algorithm. The application of this technique entails subdividing the fuel cell curve to some segments, where each segment is approximated by a Bézier curve so that the approximation error is minimized. Further, the performance and accuracy of the proposed techniques are compared with recent results obtained by different metaheuristic algorithms and analytical methods. The comparison is carried out in terms of various statistical error indicators, such as Individual Absolute Error (IAE), Relative Error (RE), Root Mean Square Error (RMSE), Mean Bias Errors (MBE), and Autocorrelation Function (ACF). The results obtained by the Bézier curve technique show an excellent agreement with experimental data and are more accurate than those obtained by other comparative techniques.

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Section: Introductionmentioning

confidence: 99%

Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique

2020

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“…5,14,20 To better design of PEMFCs, a reliable model should be achieved for them. 21,22 Several types of research have been for modeling PEMFC in the literature. For instance, Springer et al 23 introduced a simple model of a PEMFC by considering the steady-state analysis based on isothermal parameterization.…”

Section: Introductionmentioning

confidence: 99%

Application of the meta‐heuristics for optimizing exergy of a HT‐PEMFC

Han

Khodaei

2020

Int J Energy Res

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Summary The aim of this study is to analyze the exergy efficiency of a high‐temperature proton exchange membrane fuel cell. To do this purpose, meta‐heuristic technique has been used. First, the model of a membrane fuel cell is simulated and the polarization diagram shows a potent agreement with empirical data. Then, a new improved version of collective animal behavior algorithm is utilized for evaluating and optimizing the thermodynamic irreversibility, exergy efficiency, and work of the fuel cell. The algorithm uses opposition‐based learning and Lévy flight for improving the algorithm's premature convergence shortcoming. The result of this study shows that by comparison with standard collective animal behavior algorithm, genetic algorithm, and empirical results, the proposed algorithm has better achievements for both terms of optimal value finding and convergence strength.

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“…[1][2][3] The fuel cell (FC) is one of the important recently developed clean energy technologies with high efficiency and fuel flexibility. [1][2][3] The fuel cell (FC) is one of the important recently developed clean energy technologies with high efficiency and fuel flexibility.…”

Section: Introductionmentioning

confidence: 99%

“…The contribution of renewable energy resources became mandatory because of the of scarcity of fossil fuel, price inflation, and environmental pollution. [1][2][3] The fuel cell (FC) is one of the important recently developed clean energy technologies with high efficiency and fuel flexibility. [4][5][6] The -electrolyte membranes (PEM) have sufficient properties to achieve high performance and resilience required for FCs.…”

Section: Introductionmentioning

confidence: 99%

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

2019

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Summary I/V polarization curves of proton‐exchange membrane fuel cells (PEMFCs) are used to characterize the performance of single cells and stacks. Numerous semi‐empirical models are presented to predict such polarization curves by determining the unknown parameters of mathematical model of the PEMFCs stack. In this paper, a novel optimization approach, namely neural network algorithm (NNA) is applied for an estimation of the unknown PEMFC model parameters. The NNA is employed to minimize adopted objective function, which is formulated as the sum of squared deviations (SSD) between the actual data and estimated voltage points subjects to set of inequality constraints are satisfied. Three commercial types of PEMFCs stack namely Ballard Mark V, BCS‐500 W, and Nedstack PS6 are numerically simulated to show the effectiveness of the proposed NNA‐based tool for parameter identification. The minimum values of SSD are 0.8536 V2 for Ballard Mark V, 0.011698 V2 for BCS‐500 W stack, and 2.14487 V2 for Nedstack PS6, respectively. The obtained results of the NNA are compared with other optimizers recently published in the literature such as flower pollination algorithm, slap swarm optimizer, grey wolf algorithm, grasshopper optimization algorithm, and shark smell algorithm under the same conditions. The comparisons and other performance tests indicate the robustness and the competition of the adopted NNA‐based method for producing accurate I/V polarization curves under different operating scenarios.

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Optimized Parameters of SOFC for steady state and transient simulations using interior search algorithm

Cited by 67 publications

References 37 publications

Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique

Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique

Application of the meta‐heuristics for optimizing exergy of a HT‐PEMFC

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

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