Parameter estimation of PEMFC model based on Harris Hawks’ optimization and atom search optimization algorithms

Mossa, Mahmoud A.; Kamel, Omar Makram; Sultan, Hamdy M.; Diab, Ahmed A. Zaki

doi:10.1007/s00521-020-05333-4

Cited by 46 publications

(47 citation statements)

References 26 publications

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“…In the exploration phase, Harris hawks employ two tactics to discover the prey where the first tactic supposes that the hawks' position is near to the group members and the prey. On the other hand, the second one assumes that the hawks allocate on stochastic trees [123,124]. Furthermore, an improved HHO using chaotic equations, called chaotic HHO (CHHO), is presented in [86] where the convergence trend is enhanced.…”

Section: Harris Hawk's Optimizer (Hho)mentioning

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: Harris Hawk's Optimizer (Hho)mentioning

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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“…Among them,

{E_{{\rm{Nernst}}}}

is the reversible voltage of the fuel cell in open‐circuit electrodynamic equilibrium, which is calculated using () [9, 23].

{V_{{\rm{act}}\ }}

is the activation voltage drop caused by the chemical reaction kinetics around the electrode surface, which causes the I‐V curve of the fuel cell stack to drop sharply when the current is low.…”

Section: Problem Formulationmentioning

confidence: 99%

An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

Diab

Abdul-Ghaffar²,

Ahmed

et al. 2021

IET Renewable Power Gen

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This paper introduces the application of the variants of the Grey Wolf Optimisation algorithm for the sake of assessing unknown parameters of Proton Exchange Membrane Fuel Cells models. Three versions of Grey Wolf Optimisation algorithm are applied: Conventional Grey Wolf Optimisation, Improved Grey Wolf Optimisation based on dimension learning-based hunting, and Selective Opposition-based Grey Wolf Optimisation. Moreover, Optimisation algorithms of Ant Lion Optimiser, Atom search optimisation, Dragonfly algorithm, and Multi-Verse Optimiser have been applied to estimate the parameters of Proton Exchange Membrane Fuel Cells models for the validation purpose. These algorithms are applied to three commercial Proton Exchange Membrane Fuel Cells stacks: BCS 500W-PEM, 500W-SR-12PEM and 250W-stack. The analyses are executed considering several operational circumstances. Sum of square errors value of the results based on parameters estimation and those experimentally tested are calculated. The objective function is chosen as sum of square errors value. The results are compared with those obtained using well-known methods in the literature to validate the effectiveness of the proposed methods. It is noticeable that the simulated I-V curves momentously match the datasheet curves for all the studied cases. In addition, considering the accuracy of the solution and the convergence speed, the Proton Exchange Membrane Fuel Cells model based on the Improved Grey Wolf Optimisation algorithm excels all other algorithms. Based on the simulation results, the Improved Grey Wolf Optimisation algorithm can improve the optimisation efficiency to 99.97 for the 250 W stack while the efficiency with Grey Wolf Optimisation and Selective Opposition-based Grey Wolf Optimisation are 98.83 and 98.84, respectively, for the 250 W stack case study.

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“…Both models were applied for the problem of features selection, and their analysis showed great improvements in tackling feature selection challenges. Very recent hybrid studies which integrate HHO with other metaheuristic optimization algorithms were discussed by Abba et al [ 27 ], Ebrahim et al [ 28 ], Bandyopadhyay et al [ 29 ], Suresh et al [ 30 ], and Mossa et al [ 31 ]. In [ 27 ], the hybrid of PSO with HHO for renewable energy load demand forecasting was presented.…”

Section: Introductionmentioning

confidence: 99%

“…The hybrid of chaotic multi-verse optimizer with HHO was given in [ 30 ] for the problem of medical diagnosis. The issue of parameters estimation of proton exchange membrane fuel cell using a hybrid atom optimizer with HHO was investigated in [ 31 ]. Nevertheless, the main challenge of hybrid-based methods is related to the increase of fitness evaluation cost needed for each optimizer.…”

Section: Introductionmentioning

confidence: 99%

Rules embedded harris hawks optimizer for large-scale optimization problems

Samma

Sama

2022

Neural Comput & Applic

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Harris Hawks Optimizer (HHO) is a recent optimizer that was successfully applied for various real-world problems. However, working under large-scale problems requires an efficient exploration/exploitation balancing scheme that helps HHO to escape from possible local optima stagnation. To achieve this objective and boost the search efficiency of HHO, this study develops embedded rules used to make adaptive switching between exploration/exploitation based on search performances. These embedded rules were formulated based on several parameters such as population status, success rate, and the number of consumed search iterations. To verify the effectiveness of these embedded rules in improving HHO performances, a total of six standard high-dimensional functions ranging from 1000-D to 10,000-D and CEC’2010 large-scale benchmark were employed in this study. In addition, the proposed Rules Embedded Harris Hawks Optimizer (REHHO) applied for one real-world high dimensional wavelength selection problem. Conducted experiments showed that these embedded rules significantly improve HHO in terms of accuracy and convergence curve. In particular, REHHO was able to achieve superior performances against HHO in all conducted benchmark problems. Besides that, results showed that faster convergence was obtained from the embedded rules. Furthermore, REHHO was able to outperform several recent and state-of-the-art optimization algorithms.

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Parameter estimation of PEMFC model based on Harris Hawks’ optimization and atom search optimization algorithms

Cited by 46 publications

References 26 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

An effective model parameter estimation of PEMFCs using GWO algorithm and its variants

Rules embedded harris hawks optimizer for large-scale optimization problems

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