A modified farmland fertility optimizer for parameters estimation of fuel cell models

Menesy, Ahmed S.; Sultan, Hamdy M.; Korashy, Ahmed; Kamel, Salah

doi:10.1007/s00521-021-05821-1

Cited by 23 publications

(4 citation statements)

References 49 publications

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“…Moreover, the PEMFC's model is high nonlinear, multi-variant and its variables are strongly coupled and severely affected by the operating conditions. Hence, the dynamics and accurate modelling have become more complicated and time-consuming when employed by such conventional techniques [36][37][38][39][40][41][42]. Therefore, there is an imperative demand to derive robust and feasible techniques to tackle such issue.…”

Section: C) Power Rating (Kw) Meritsmentioning

confidence: 99%

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Ashraf

Abdellatif

Elkholy

et al. 2022

Arch Computat Methods Eng

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For the sake of precise simulation, and proper controlling of the performance of the proton exchange membrane fuel cells (PEMFCs) generating systems, robust and neat mathematical modelling is crucially needed. Principally, the robustness and precision of modelling strategy depend on the accurate identification of PEMFC’s uncertain parameters. Hence, in the last decade, with the noteworthy computational development, plenty of meta-heuristic algorithms (MHAs) are applied to tackle such problem, which have attained very positive results. Thus, this review paper aims at announcing novel inclusive survey of the most up-to-date MHAs that are utilized for PEMFCs stack’s parameter identifications. More specifically, these MHAs are categorized into swarm-based, nature-based, physics-based and evolutionary-based. In which, more than 350 articles are allocated to attain the same goal and among them only 167 papers are addressed in this effort. Definitely, 15 swarm-based, 7 nature-based, 6 physics-based, 2 evolutionary-based and 4 others-based approaches are touched with comprehensive illustrations. Wherein, an overall summary is undertaken to methodically guide the reader to comprehend the main features of these algorithms. Therefore, the reader can systematically utilize these techniques to investigate PEMFCs’ parameter estimation. In addition, various categories of PEMFC’s models, several assessment criteria and many PEMFC commercial types are also thoroughly covered. In addition to that, 27 models are gathered and summarized in an attractive manner. Eventually, some insights and suggestions are presented in the conclusion for future research and for further room of improvements and investigations.

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Section: C) Power Rating (Kw) Meritsmentioning

confidence: 99%

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Ashraf

Abdellatif

Elkholy

et al. 2022

Arch Computat Methods Eng

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“…The experiments affirmed the superiority of the outcomes obtained by BWO when compared to those obtained by the compared algorithms. A modified variant, namely MFFA, of farmland fertility optimization algorithm (FFA) has been recently adopted by Menesy, A.S., et al [15] for finding the seven unknown parameters of the PEMFCs. MFFA was modified based on two-folds: the former employs the levy-flight operator to promote both the exploration and exploitation operators of the standard algorithm, while the latter employs the sine-cosine functions to search widely around the best-sofar solution in order to find better solutions while avoiding getting stuck in local minima.…”

Section: Introductionmentioning

confidence: 99%

Improved Metaheuristic Algorithms for Optimal Parameters Selection of Proton Exchange Membrane Fuel Cells: A Comparative Study

et al. 2023

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As a new attempt to design a precise mathematical model for the proton exchange membrane fuel cell (PEMFC), in this paper, three recently-proposed well-established optimizers: horse herding optimization algorithm (HOA), seagull optimization algorithm (SOA) and gradient-based optimizer (GBO) integrated with two newly-proposed effective strategies, namely self-adaptive strategy, and ranking-based updating strategy, have been extensively investigated to accurately estimate the unknown parameters of this model for accomplishing a better output voltage of the simulated PEMFC stacks. Those hybridized algorithms were briefly named HHOA, HSOA, and HGBO. To assess the performance of those proposed algorithms, six common PEMFC stacks were used and their outcomes were extensively compared with the standard algorithms and some of the state-of-the-arts under various performance metrics and the Wilcoxon rank-sum test. The experimental findings show the effectiveness of both HGBO and HSOA in terms of convergence speed and final accuracy; However, HSOA could be more stable. The source code of this study is publicly available at https://drive.matlab.com/sharing/d9263036-9f80-4a40-bad9-ad476ed19c69.

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“…Modified farmland fertility optimization algorithm (MFFA) [18] • Proposing two stages-based modifications to the standard farmland fertility algorithm: the first is employing the levy flight strategy to improve its exploration and exploitation operators and the other integrates a sine-cosine function to generate various solutions around the best-so-far solution.…”

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confidence: 99%

“…There are several other parameter estimation PEMFC techniques based on metaheuristic algorithms: improved chaotic grey wolf optimization algorithm [26], modified farmland fertility optimizer [18], hunger games search algorithm [27], improved version of the Archimedes optimization algorithm [28], moth-flame optimization [19], Levenberg-Marquardt backpropagation algorithm [29], whale optimization algorithm [30], marine predator algorithm optimizer [31], pathfinder algorithm [32], hybrid water cycle moth-flame optimization algorithm [33], improved fluid search optimization algorithm [34], Seeker optimization algorithm [35], improved grass fibrous root optimization algorithm [36], developed coyote optimization algorithm [37], improved TLBO with elite strategy [38], developed owl search algorithm [39], modified artificial electric field algo-Energies 2021, 14, 7115 5 of 23 rithm [40], Supply-Demand-Based Optimization Algorithm [41], convolutional neural network optimized by balanced deer hunting optimization algorithm [42], and chaos game optimization technique [43].…”

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confidence: 99%

An Efficient Parameter Estimation Algorithm for Proton Exchange Membrane Fuel Cells

2021

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The proton exchange membrane fuel cell (PEMFC) is a favorable renewable energy source to overcome environmental pollution and save electricity. However, the mathematical model of the PEMFC contains some unknown parameters which have to be accurately estimated to build an accurate PEMFC model; this problem is known as the parameter estimation of PEMFC and belongs to the optimization problem. Although this problem belongs to the optimization problem, not all optimization algorithms are suitable to solve it because it is a nonlinear and complex problem. Therefore, in this paper, a new optimization algorithm known as the artificial gorilla troops optimizer (GTO), which simulates the collective intelligence of gorilla troops in nature, is adapted for estimating this problem. However, the GTO is suffering from local optima and low convergence speed problems, so a modification based on replacing its exploitation operator with a new one, relating the exploration and exploitation according to the population diversity in the current iteration, has been performed to improve the exploitation operator in addition to the exploration one. This modified variant, named the modified GTO (MGTO), has been applied for estimating the unknown parameters of three PEMFC stacks, 250 W stack, BCS-500W stack, and SR-12 stack, used widely in the literature, based on minimizing the error between the measured and estimated data points as the objective function. The outcomes obtained by applying the GTO and MGTO on those PEMFC stacks have been extensively compared with those of eight well-known optimization algorithms using various performance analyses, best, average, worst, standard deviation (SD), CPU time, mean absolute percentage error (MAPE), and mean absolute error (MAE), in addition to the Wilcoxon rank-sum test, to show which one is the best for solving this problem. The experimental findings show that MGTO is the best for all performance metrics, but CPU time is competitive among all algorithms.

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A modified farmland fertility optimizer for parameters estimation of fuel cell models

Cited by 23 publications

References 49 publications

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Computational Techniques Based on Artificial Intelligence for Extracting Optimal Parameters of PEMFCs: Survey and Insights

Improved Metaheuristic Algorithms for Optimal Parameters Selection of Proton Exchange Membrane Fuel Cells: A Comparative Study

An Efficient Parameter Estimation Algorithm for Proton Exchange Membrane Fuel Cells

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