Optimal parameters of PEM fuel cells using chaotic binary shark smell optimizer

Han, Weijian; Li, Dezhi; Yu, Dongmin; Ebrahimian, Homayoun

doi:10.1080/15567036.2019.1676845

Cited by 15 publications

(9 citation statements)

References 29 publications

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“…(ii) The blood is uninterruptedly flowed out from the prey to the sea and the propagation of the prey smell particles isn't affected by the flow of the seawater. (iii) Only one prey (seeking environment) is existed in the search domain [115][116][117]. (15)…”

Section: Shark Smell Optimizer (Sso)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: Shark Smell Optimizer (Sso)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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“…The great advantages of using metaheuristic algorithms are the simplicity, ease of implementation, and robustness [15]. The broad applications of metaheuristic algorithms in solving the parameter identification problems of PEMFC models, such as the genetic algorithm (GA) [16], particle swarm optimization (PSO) [17], firefly optimization (FFO) [18], grey wolf optimization (GWO) [19], simulated annealing (SA) [20], harmony search (HS) [21], artificial bee swarm (ABS) optimization [22], flower pollination algorithm (FPA) [23], artificial bee colony (ABC) algorithm [24], big bang-big crunch (BBBC) algorithm [25], salp swarm optimizer (SSA) [26], shark smell optimizer (SSO) [27], multiverse optimizer (MVO) [28], teaching learning-based algorithm (TLBO) [29], backtracking search algorithm (BSA) [30], differential evolution algorithm (DEA) [31], biogeographybased optimization (BBO) [32], imperialist competitive algorithm (ICA) [33], grasshopper optimization algorithm (GOA) [34], bird mating optimizer (BMO) [35], flower pollination algorithm (FPA) [23], whale optimization algorithm (WOA) [36], satin bowerbird optimizer (SBO) [37], seagull optimization algorithm (SOA) [38], shuffled frog-leaping algorithm (SFLA) [33], vortex search algorithm (VSA) [39], bat algorithm (BA) [40], owl search algorithm (OSA) [18], tree growth algorithm (TGA) [41], Harris hawks optimization (HHO) [42], atom search optimizer (ASO) [43], dragonfly algorithm (DA) [44], ant lion optimizer (ALO) [44], cuckoo search algorithm (CS) [45], artificial ...…”

Section: Introductionmentioning

confidence: 99%

Proton Exchange Membrane Fuel Cell Parameter Extraction Using a Supply–Demand-Based Optimization Algorithm

et al. 2021

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For proton exchange membrane fuel cells (PEMFCs), the parameter extraction issue is among the most widely studied problems in the field of energy storage systems, since the precise identification of such parameters plays an important role in increasing the PEMFC performance and life span. The optimization process is intended to adjust the performance of PEMFCs by appraising the optimal parameters that produce a good estimation of the current–voltage (I–V) curve. In order to build an accurate equivalent circuit model for PEMFCs, a reliable and effective parameter extraction algorithm, termed a supply–demand-based optimization (SDO) algorithm, is proposed in this paper. Nine parameters (ξ1, ξ2, ξ3, ξ4, Rc, β, λ, l, and Jmax) are evaluated, to minimize the sum squared deviation (SSE) between the experimental and simulated I–V curves. To validate the feasibility and effectiveness of the SDO algorithm, four sets of experimental data with diverse characteristics and two well-known PEMFC stacks (BSC500W and 500W Horizon) are employed. Comparison of the simulated and experimental results clearly demonstrates the superiority/competitiveness of the SDO algorithm over five well-established parameter extraction algorithms, i.e., the whale optimization algorithm (WOA), grey wolf optimization (GWO), Harris hawks optimization (HHO), and genetic algorithm (GA). Several evaluation criteria, including best SSE, worst SSE, mean SSE, and standard deviation, show that the SDO algorithm has merits in terms of PEMFC modeling.

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“…The applied methods may be divided into two groups; the first is based on only one algorithm, while the second is based on combining two techniques or more [8, 17, 26, 27]. The first group of single algorithms such as adaptive differential evolution algorithm (ADE) [25, 26], particle swarm optimisation, seeker optimisation algorithm (SOA) and genetic algorithm (GA) [18, 21, 27], Grey Wolf Optimisation (GWO) [8], Antlion Optimiser (ALO) [33] and Dragonfly Algorithm (DA) [34], Grasshopper Optimisation Algorithm (GOH) [16], Slap Swarm Optimiser (SSO) [17], Shark Smell Optimiser (SHSO) [35], JAYA algorithm [24], and Cuckoo Search (CS) [13] have been proposed to resolve the issue of parameter estimation of PEMFCs. The main objective of applying the reported methods is to obtain an accurate model of the PEMFC.…”

Section: Introductionmentioning

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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Optimal parameters of PEM fuel cells using chaotic binary shark smell optimizer

Cited by 15 publications

References 29 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

Proton Exchange Membrane Fuel Cell Parameter Extraction Using a Supply–Demand-Based Optimization Algorithm

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

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