Parameter estimation of PEMFC based on Improved Fluid Search Optimization Algorithm

Qin, Fuzhen; Liu, Peixue; Niu, Haichun; Song, Haiyan; Yousefi, Nasser

doi:10.1016/j.egyr.2020.05.006

Cited by 46 publications

(19 citation statements)

References 41 publications

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“…Where, improving the fluid speed, diminishes the fluid pressure and the potential energy. Herein, the fluid pressure is considered as the fitness function value where improving the fluid pressure, reduces its velocity [156,157]. Despite all the afore-stated procedures, FSO has an obvious drawback which is the premature convergence.…”

Section: Fluid Search Optimizer (Fso)mentioning

confidence: 99%

“…Despite all the afore-stated procedures, FSO has an obvious drawback which is the premature convergence. Hence, an improved FSO (IFSO) is proposed in [157] to tackle this problem. Particularly, IFSO is composed of two enhancement techniques, that have been integrated with the basic FSO, which are quasi-oppositional based learning and chaotic concept.…”

Section: Fluid Search Optimizer (Fso)mentioning

confidence: 99%

“…Particularly, IFSO is composed of two enhancement techniques, that have been integrated with the basic FSO, which are quasi-oppositional based learning and chaotic concept. IFSO has been applied for tackling the parameter estimation problem of the PEMFCs, while the results are indicated in Table 11 in the fifth row [157].…”

Section: Fluid Search Optimizer (Fso)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: Fluid Search Optimizer (Fso)mentioning

confidence: 99%

Section: Fluid Search Optimizer (Fso)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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“…( 6), (7), and (8) Update x m k uing 6, 7 Update parameters ρ 1 and ρ 2 using Eqs. ( 12), ( 13), (14), and (15) Update Δx using (16 and 21) Update X1 m n using Eq: 11 ð Þ Update yp n and yq n using Eqs: 19 ð Þand 20 ð Þ Update X2 m n using Eq: 18 ð Þ Calculate the position x mþ1 n,i using Eq. ( 9) End for…”

Section: End Formentioning

confidence: 99%

“…Among these approaches, modern optimization-based methods are employed to solve the PEMFC parameter identification problem due to their consistency, robustness, and simplicity. For example, Qin et al 19 proposed an improved fluid search optimization algorithm to solve the problem of PEMFC. Additionally, Yuan et al 20 suggested an optimized technique to estimate PEMFC parameters using an enhanced edition of the sunflower optimization algorithm.…”

Section: Introductionmentioning

confidence: 99%

An efficient modified artificial electric field algorithm for solving optimization problems and parameter estimation of fuel cell

et al. 2021

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The artificial electric field algorithm (AEFA) is a recent physics populationbased optimization approach inspired by Coulomb's law of electrostatic force and Newton's law of motion. In this paper, an alternative version of AEFA called mAEFA is proposed to boost the searchability and the balance between the explorations to the exploitation of the original AEFA. To escape dropping on the local points in the mAEFA, three efficient strategies for instance; modified local escaping operator (MLEO), levy flight (LF), and opposition-based learning (OBL), are in conjunction with the original AEFA. The convergence rate will be improved when the best agent is identified; thus, stagnation at a local solution can be efficiently avoided. To assess the performance of the proposed mAEFA, it has been evaluated over the CEC'2020 test functions. Furthermore, a robust methodology based on mAEEA is proposed to identify the best parameters of PEM fuel cell (PEMFC). The model of the PEMFC includes nonlinear characteristics that involve several unknown design variables. Thus, it is challenging to develop an accurate model. There are seven design variables to be tuned to reach the targeted dependable model. Two different types of PEMFCs: NedStack PS6 and SR-12 500 W were used to demonstrate the superiority of the mAEEA. Throughout the optimization process, the unidentified parameters of PEMFC are appointed to be decision variables. But the objective function, which necessary to be least is represented by the SSE between the calculated PEMFC voltage and the experimental one. Nine recent optimizers are used in the comparison with the proposed mAEEA. According to the main findings, the advantage of the proposed mAEEA in determining the best PEMFC parameters is verified compared to the other optimizers. Lowest SSE, lowest RMSE, minimum stranded deviation, maximum efficiency, and high coefficient of determination are achieved by the proposed mAEEA.

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Estimation of PEMFC design parameters with social learning-based optimization

Celtek

2024

Electr Eng

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Parameter estimation of PEMFC based on Improved Fluid Search Optimization Algorithm

Cited by 46 publications

References 41 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 efficient modified artificial electric field algorithm for solving optimization problems and parameter estimation of fuel cell

Estimation of PEMFC design parameters with social learning-based optimization

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