Shark Smell Optimizer applied to identify the optimal parameters of the proton exchange membrane fuel cell model

Yong, Rao; Shao, Zehui; Ahangarnejad, Arash Hosseinian; Gholamalizadeh, Ehsan; Sobhani, Behnam

doi:10.1016/j.enconman.2018.12.057

Cited by 136 publications

(95 citation statements)

References 28 publications

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“…This fitness value is minimized by the MRFO to ensure good matching among theoretical and actual measurements. The SSD is itemized in Equation (19) which is regularly used in literatures . The same is used for fair evaluations to other optimizers' results.…”

Section: Procedures Of Mrfo and Problem Statementmentioning

confidence: 99%

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Parameters extraction of PEMFC's model using manta rays foraging optimizer

2020

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Summary In this article, a recently developed bio‐inspired based manta rays foraging optimizer (MRFO) is attempted for reliable and accurate extraction of the model uncertain parameters of proton exchange membrane fuel cells (PEMFCs). The parameter estimation is formulated as a non‐linear optimization problem subject to set of restrictions. The great development and tremendous revolution of computation heuristic‐based algorithms are the impetus of the authors to apply the MRFO to solve this constrained optimization problem resulting in a precise PEMFC model. Three case studies of typical field PEMFC stacks namely Ballard type Mark V, NedStack type PS6, and Horizon type H‐12. Various I to V datasets are demonstrated to appraise the performance of MRFO among other recent optimizers available in the literature. To be objective and for sake of quantifications, the best scores of minimum fitness values are 0.8533, 2.1360, and 0.0966 for the later said PEMFC stacks, correspondingly. At a later stage, production of various characteristics under varying operating conditions such as changeable cell temperature and regulating pressures are established using the generated best values of PEMFCs model. Further calculations of statistical indices are performed to validate the robustness of obtained results by the MRFO. Through comprehensive performance assessments, it can be confirmed that MRFO is very promising tool for the effective extraction of PEMFCs' model and suggested to be applied for solving other engineering problems.

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Section: Procedures Of Mrfo and Problem Statementmentioning

confidence: 99%

“…In this issue, various forms of meta‐heuristic frameworks are used to solve this optimization problem achieving an accurate model. These algorithms involve NNA, atom search, SSA, harmony search, JAYA, SSO, satin bowerbird optimizer, adaptive GA, DE, cuckoo search, hybrid bee colony, and GWO …”

Section: Introductionmentioning

confidence: 99%

Parameters extraction of PEMFC's model using manta rays foraging optimizer

2020

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“…25,29,30,35,38 This stack has 35 cells connected in series with 70-A maximum thermal current. 25,29,30,35,38 This stack has 35 cells connected in series with 70-A maximum thermal current.…”

Section: Case Study (1): Ballard Mark V 5 Kwmentioning

confidence: 99%

“…22,24,25,29,34,38 As can be seen, the thermal rated current of this system is 30 A. The main characteristics of this PEMFCs stack are extracted from the literatures.…”

Section: Case Study (2): 500 W Bcs Stackmentioning

confidence: 99%

“…24 Moreover, flower pollination algorithm has been adopted to estimate unknown PEMFC parameters. 38 Some researchers have been concerned with the PEMFC controller design in addition to parameters to enhance its performance and characteristics. 26 Another grouping-based harmony-search procedure is introduced to define a PEMFC model and to evaluate PEMFC parameters.…”

Section: Introductionmentioning

confidence: 99%

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Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

2019

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Summary I/V polarization curves of proton‐exchange membrane fuel cells (PEMFCs) are used to characterize the performance of single cells and stacks. Numerous semi‐empirical models are presented to predict such polarization curves by determining the unknown parameters of mathematical model of the PEMFCs stack. In this paper, a novel optimization approach, namely neural network algorithm (NNA) is applied for an estimation of the unknown PEMFC model parameters. The NNA is employed to minimize adopted objective function, which is formulated as the sum of squared deviations (SSD) between the actual data and estimated voltage points subjects to set of inequality constraints are satisfied. Three commercial types of PEMFCs stack namely Ballard Mark V, BCS‐500 W, and Nedstack PS6 are numerically simulated to show the effectiveness of the proposed NNA‐based tool for parameter identification. The minimum values of SSD are 0.8536 V2 for Ballard Mark V, 0.011698 V2 for BCS‐500 W stack, and 2.14487 V2 for Nedstack PS6, respectively. The obtained results of the NNA are compared with other optimizers recently published in the literature such as flower pollination algorithm, slap swarm optimizer, grey wolf algorithm, grasshopper optimization algorithm, and shark smell algorithm under the same conditions. The comparisons and other performance tests indicate the robustness and the competition of the adopted NNA‐based method for producing accurate I/V polarization curves under different operating scenarios.

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Parametric Optimization of Proton Exchange Membrane Fuel Cell Using Chaotic Swarm Intelligence Technique

Kaura,

Narang,

Singh

et al. 2024

Chem Eng & Technol

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The proton exchange membrane fuel cell (PEMFC) converts chemical energy into electricity without pollution or noise. PEMFC has a stable electrolyte, reliable, fast start‐up, and lightweight portability. Optimal PEMFC parameter prediction is difficult. Due to inaccurate estimation, many meta‐heuristic models are inaccurate. Particle swarm, Ant colony, and grey wolf algorithms are tested for fuel cell parameter estimation. These traditional algorithms benefit from chaotic maps for exploitation and exploration. Analyzing V–I and P–I polarization curves, hybrid approaches estimate unknown parameters. Results are compared with others to support the identifying strategy. This project examined SR‐12 and NedSstack PS‐6 PEMFC stacks. The objective function was shown by the sum of square errors between the experiments, and once an appropriate map is identified, the single chaotic map inclusion method can perform better than the multiple chaotic map scheme, and the “Iterative” approach in chaotic swarm is the most promising for multidimensional challenges.

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Shark Smell Optimizer applied to identify the optimal parameters of the proton exchange membrane fuel cell model

Cited by 136 publications

References 28 publications

Parameters extraction of PEMFC's model using manta rays foraging optimizer

Parameters extraction of PEMFC's model using manta rays foraging optimizer

Effective methodology based on neural network optimizer for extracting model parameters of PEM fuel cells

Parametric Optimization of Proton Exchange Membrane Fuel Cell Using Chaotic Swarm Intelligence Technique

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