Multi-verse optimizer for identifying the optimal parameters of PEMFC model

Fathy, Ahmed; Rezk, Hegazy

doi:10.1016/j.energy.2017.11.014

Cited by 163 publications

(98 citation statements)

References 26 publications

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“…For comparative parameter extraction performance analysis, results of HISA are compared with basic ISA and six recent prominent literature methods, including SSA, GOA, GWO, MVO, BSABCM, and MPSO . The parameter settings utilised for all the different optimisation methods used in this comparative study for all test cases is listed in Table .…”

Section: Pemfc/sofc Parameter Identification: Results and Discussionmentioning

confidence: 99%

“…Experimental data that have been cited as input to optimisation techniques have been extracted from graphical points of a few papers . However, extracted data points that have been finally used for comparison were never fully revealed . This leads to loss of objectivity since different graphical methods of data points extraction, performed by different researchers, will have a high chance of variability .…”

Section: Introductionmentioning

confidence: 99%

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Parameter extraction of fuel cells using hybrid interior search algorithm

2019

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Summary Precise modelling of fuel cells is very important for understanding their functioning. In this work, an application of hybrid interior search algorithm (HISA) is proposed to extract the parameters of fuel cells for their electromechanical equations based on nonlinear current‐voltage characteristics. Proposed hybridised algorithm has been developed using evolutionary mutation and crossover operators so as to enhance the modelling capability of interior search algorithm (ISA). To assess the modelling performance of HISA, parameter extraction of two types of fuel cell models, namely, proton exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) have been considered. Modelling performance of HISA, assessed using mean squared error between computed and experimental data, is found to be superior to ISA and several other recently reported prominent optimisation methods. Based on the presented intensive simulation investigations, it is concluded that HISA improves the performance of the basic ISA in terms of fitter solutions, robustness, and convergence rate and therefore offers a promising optimisation technique for parameter extraction of fuel cells.

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Section: Pemfc/sofc Parameter Identification: Results and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Parameter extraction of fuel cells using hybrid interior search algorithm

2019

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“…This fitness value is minimized by the MRFO to ensure good matching among theoretical and actual measurements. The SSD is itemized in Equation (19) which is regularly used in literatures . The same is used for fair evaluations to other optimizers' results.…”

Section: Procedures Of Mrfo and Problem Statementmentioning

confidence: 99%

“…Added to the aforesaid, number of heuristic‐based approaches, namely SFLA, firefly, and colonial competitive methods, backtracking framework, teaching‐learning, modified particle swarm optimization, multi‐verse optimizer, GHO, VSA and DE, and flower pollination for the PEMFC's model calibration and precise representation are reported in the published literature.…”

Section: Introductionmentioning

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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“…[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%

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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Multi-verse optimizer for identifying the optimal parameters of PEMFC model

Cited by 163 publications

References 26 publications

Parameter extraction of fuel cells using hybrid interior search algorithm

Parameter extraction of fuel cells using hybrid interior search algorithm

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

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

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