Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis

Gouda, Eid Abdelbaki; Kotb, Mohamed F.; El‐Fergany, Attia A.

doi:10.1016/j.energy.2021.119836

Cited by 62 publications

(15 citation statements)

References 61 publications

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“…(b) Jellyfish are interested towards the locations which contains large amount of food. (c) The food quantity is assigned, and its corresponding cost fitness value is determined accordingly [135,136]. Fewer control parameter and lesser computational efforts and random trials are such the JSO merits.…”

Section: Jellyfish Search Optimizer (Jso)mentioning

confidence: 99%

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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: Jellyfish Search Optimizer (Jso)mentioning

confidence: 99%

“…Fewer control parameter and lesser computational efforts and random trials are such the JSO merits. Hence, the authors in [136] has applied JSO in the field of estimating the PEMFCs unknown parameters, while the outcomes are encapsulated in Table 7 in the fourteenth row.…”

Section: Jellyfish Search Optimizer (Jso)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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“…In 2021, Gouda et al propose an experimental study presenting an application of the JS algorithm for extracting unknown parameters of PEM fuel cell models [8]. Performance of JS is employed to determine PEM model parameters where three different models are used in experiments.…”

Section: Jellyfish Search Optimisermentioning

confidence: 99%

Three Steps to Improve Jellyfish Search Optimiser

Bujok

2021

mendel

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This paper describes three different mechanisms used in Jellyfish Search (JS) optimiser. At first, an archive of good old solutions is used to prevent getting stuck in the local-optima area. Further, a distribution coefficient beta is adapted during the search process to control population diversity. Finally, an Eigen transformation of individuals in the reproduction process is used occasionally to cope with rotated functions. Three proposed variants of the JS optimiser are compared with the original JS algorithm and nine various well-known Nature-inspired optimisation methods when solving real-world problems of CEC 2011. Provided results achieved by statistical comparison show efficiency of the individual newly employed mechanisms.

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“…The authors in Yousri et al (2021) have introduced a hybrid algorithm of marine predator algorithm (MPA) and slime mould algorithm to enhance the MPA exploitation phase for the triple diode PV model and the results obtained are compared based on RMSE and standard deviation. In Gouda et al (2021), the Jellyfish search algorithm is used for the identification problem of the polymer exchange membrane fuel cells model. Three test cases are considered and results are compared based on sum of squared errors and the response of PV modules is recorded under varying temperature and pressure.…”

Section: Introductionmentioning

confidence: 99%

A novel way of parameter estimation of solar photovoltaic system

Bisht

Sikander

2021

COMPEL

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Purpose This paper aims to achieve accurate maximum power from solar photovoltaic (PV), its five parameters need to be estimated. This study proposes a novel optimization technique for parameter estimation of solar PV. Design/methodology/approach To extract optimal parameters of solar PV new optimization technique based on the Jellyfish search optimizer (JSO). The objective function is defined based on two unknown variables and the proposed technique is used to estimate the two unknown variables and the rest three unknown variables are estimated analytically. Findings In this paper, JSO is used to estimate the parameters of a single diode PV model. In this study, eight different PV panels are considered. In addition, various performance indices, such as PV characteristics, such as power-voltage and current-voltage curves, relative error (RE), root mean square error (RMSE), mean absolute error (MAE) and normalized mean absolute error (NMAE) are determined using the proposed algorithm and existing algorithms. The results for different solar panels have been obtained under varying environmental conditions such as changing temperature and constant irradiance or changing irradiance and constant temperature. Originality/value The proposed technique is new and provides better results with minimum RE, RMSE, NMAE, MAE and converges fast, as depicted by the fitness graph presented in this paper.

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Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis

Cited by 62 publications

References 61 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

Three Steps to Improve Jellyfish Search Optimiser

A novel way of parameter estimation of solar photovoltaic system

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