Steady-state and dynamic models of solid oxide fuel cells based on Satin Bowerbird Optimizer

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

doi:10.1016/j.ijhydene.2018.06.032

Cited by 61 publications

(27 citation statements)

References 40 publications

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“…7,8 Among them, proton-exchange membrane fuel cell (PEMFC) has low operating pressure, low operating temperature, low relative humidity, high efficiency, green source, no contamination, and waste less other than other types. 7 The identification of the best utilized properties of the FC is a big challenge for the simple and accurate modeling 6 . 9 It can be utilized for different power applications.…”

Section: Introductionmentioning

confidence: 99%

“…[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. The FCs can be categorized into a number of major types as presented in Liu et al and Kim and Lee.…”

Section: Introductionmentioning

confidence: 99%

“…The steadiness of electrochemical, mechanical, and thermal process of PEM using a thinner membranes leads to produce effective generation. 7 The identification of the best utilized properties of the FC is a big challenge for the simple and accurate modeling 6 . Many factors affect PEMFC selection and performance such as stack life, startup speed, efficiency, temperature, and modularity.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2019

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“…[9][10][11][12][13][14][15][16] The significant aim of the present work is to provide an efficient PEMFC model according to Amphlett equation model that simulates the practical performance of the cell at various operational circumstances. [31][32][33][34][35][36] So in this article, SBO is used to establish variables of PEMFC model. In the earlier decade, metaheuristic approaches developing as optimization issue have applied by many researchers.…”

Section: Introductionmentioning

confidence: 99%

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

2019

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Summary This study proposes precise modeling for the proton exchange membrane fuel cells which present desirable advantages compared with other energy management systems. The presented model can be applied for the simulation of the actual behavior of the proton exchange membrane fuel cells such as the electrical, electrochemical, and mechanical. In the present literature, a newly presented optimizer namely Satin Bowerbird is implemented for the evaluation of the proton exchange membrane fuel cell performance criteria. The Satin Bowerbird optimizer is an evolutionary algorithm that imitates the mating process of the Bowerbirds in the mating season. The Satin Bowerbird optimizer is applied to the different commercial benchmark of proton exchange membrane fuel cell stack to assess the performance of the proposed algorithm. The statistical study is also carried out to show the superiority of the proposed method compared with other schemes. The standard deviation for the Satin Bowerbird optimizer is obtained 0.0941 which is the lowest value amongst the other well‐known approaches. Also, the lowest sum of squared error is calculated for the proposed algorithm. Moreover, the validation of the presented method is done with the experimental data which shows good agreement between the experimental and modeling data.

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“…chemical energy is converted into electrical energy through some chemical reactions under environmental conditions. 3,4 Different types of FCs are incorporated in the industry such as PEMFCs, 5,6 SOFCs, 4,7 and others. 8 Among these different types, PEMFC has several salient features like operating conditions including temperature and pressure, high performance, and no waste material.…”

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confidence: 99%

Parameters extraction of PEMFC's model using manta rays foraging optimizer

2020

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Summary In this article, a recently developed bio‐inspired based manta rays foraging optimizer (MRFO) is attempted for reliable and accurate extraction of the model uncertain parameters of proton exchange membrane fuel cells (PEMFCs). The parameter estimation is formulated as a non‐linear optimization problem subject to set of restrictions. The great development and tremendous revolution of computation heuristic‐based algorithms are the impetus of the authors to apply the MRFO to solve this constrained optimization problem resulting in a precise PEMFC model. Three case studies of typical field PEMFC stacks namely Ballard type Mark V, NedStack type PS6, and Horizon type H‐12. Various I to V datasets are demonstrated to appraise the performance of MRFO among other recent optimizers available in the literature. To be objective and for sake of quantifications, the best scores of minimum fitness values are 0.8533, 2.1360, and 0.0966 for the later said PEMFC stacks, correspondingly. At a later stage, production of various characteristics under varying operating conditions such as changeable cell temperature and regulating pressures are established using the generated best values of PEMFCs model. Further calculations of statistical indices are performed to validate the robustness of obtained results by the MRFO. Through comprehensive performance assessments, it can be confirmed that MRFO is very promising tool for the effective extraction of PEMFCs' model and suggested to be applied for solving other engineering problems.

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Steady-state and dynamic models of solid oxide fuel cells based on Satin Bowerbird Optimizer

Cited by 61 publications

References 40 publications

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

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

Optimal parameter identification for the proton exchange membrane fuel cell using Satin Bowerbird optimizer

Parameters extraction of PEMFC's model using manta rays foraging optimizer

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