A novel approach for
            <scp>PEM</scp>
            fuel cell parameter estimation using
            <scp>LSHADE</scp>
            ‐
            <scp>EpSin</scp>
            optimization algorithm

Fathy, Ahmed; Aleem, Shady H. E. Abdel; Rezk, Hegazy

doi:10.1002/er.6282

Cited by 33 publications

(17 citation statements)

References 64 publications

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“…Differential Evolution (DE) is a stochastic population-based optimization algorithm, starting with randomly generated individuals that evolve through probabilistic operators such as recombination and mutation [30][31][32]. The efficiency of DE depends heavily on population size (Np), the chosen mutation and/or crossover strategy, the mutation-associated scale factor (F), and the recombination-associated crossover rate (Cr).…”

Section: Lshade With Semi-parameter Adaptation Hybrid With Cma-esmentioning

confidence: 99%

Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA

et al. 2021

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This paper introduces a multi-stage dynamic transmission network expansion planning (MSDTNEP) model considering the N-1 reliability constraint. The integrated planning problem of N-1 security and transmission expansion planning is essential because a single line outage could be a triggering event to rolling blackouts. Two suggested scenarios were developed to obtain the optimal configuration of the Egyptian West Delta Network’s realistic transmission (WDN) to meet the demand of the potential load growth and ensure the system reliability up to the year 2040. The size of a blackout, based on the amount of expected energy not supplied, was calculated to evaluate both scenarios. The load forecasting (up to 2040) was obtained based on an adaptive neuro-fuzzy inference system because it gives excellent results compared to conventional methods. The linear population size reduction—Success-History-based Differential Evolution with semi-parameter adaptation (LSHADE-SPA) hybrid—covariance matrix adaptation evolution strategy (CMA-ES) algorithm (LSHADE-SPACMA)—is proposed to solve the problem. The semi-adaptive nature of LSHADE-SPACMA and the hybridization between LSHADE and CMA-ES are able to solve complex optimization problems. The performance of LSHADE-SPACMA in solving the problem is compared to other well-established methods using three testing systems to validate its superiority. Then, the MSDTNEP of the Egyptian West Delta Network is presented, and the numerical results of the two scenarios are compared to obtain an economic plan and avoid a partial or total blackout.

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Section: Lshade With Semi-parameter Adaptation Hybrid With Cma-esmentioning

confidence: 99%

Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA

et al. 2021

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“…Case-II: 250 W fuel cell stack 250 W fuel cell stack is commonly used in parameter estimation by researchers. 36,[48][49][50] For this fuel cell stack, there are four sets of experimental data divided into two groups. The first group of experimental data is used for parameter estimation.…”

Section: Case-i: Nedstack Ps6 Fuel Cellmentioning

confidence: 99%

Parameter estimation study of polymer electrolyte membrane fuel cell using artificial hummingbird algorithm

Çelik

Soylu

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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This study represents a comprehensive investigation of the performance of Artificial Hummingbird Algorithm for parameter estimation for Polymer Electrolyte Membrane Fuel Cell. With this purpose, four commercial fuel cell systems which were widely preferred in the literature such as NedStack PS6 (Case-I), 250 W fuel cell stack (Case-II), Horizon 500 W (Case-III), and BCS 500 W (Case-IV) were chosen. In order to compare the performance of this algorithm, seven well-known optimization techniques including Artificial Bee Colony, Salp Swarm Optimization, Particle Swarm Optimization, Gray Wolf Optimization, Genetic Algorithm, Harris Hawks Optimization, and Whale Optimization Algorithm were used. The sum of the squared errors, computational speed, and statistical measurements were calculated for the performance comparison. In this context, the best SSE values were found as 2.06556, 5.25017, 0.02477, 0.01170 for Case-I, Case-II, Case-III, and Case-IV, respectively. The best standard deviation value was found as 1 e−6 for the Case-III. Based on the obtained results, the Artificial Hummingbird Algorithm established itself as a competitive optimization technique for parameter estimation study of PEMFC in terms of computational speed and robustness.

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“…In the last decades, the necessities of using clean energy sources are rapidly increasing due to the ecological devastating impacts of the fossil fuels [1][2][3][4]. As a result, renewable energy sources (RESs) are targeted to somehow replace the conventional ones due to their advantageous characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…Virtually, no emissions, static nature of most RESs types, higher efficiency, availability upon wide range of output power (from mW to MW's) and convenient for all applications (portable, transportation and stationery). Amongst the several alternatives of RESs; fuel cells (FCs), solar and wind have attracted decision-makers and industry-stakeholders to utilize them as prime energy supplies [1][2][3]5].…”

Section: Introductionmentioning

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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A novel approach for PEM fuel cell parameter estimation using LSHADE ‐ EpSin optimization algorithm

Cited by 33 publications

References 64 publications

Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA

Multi-Stage Dynamic Transmission Network Expansion Planning Using LSHADE-SPACMA

Parameter estimation study of polymer electrolyte membrane fuel cell using artificial hummingbird algorithm

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

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