Robust parameter estimation of a PEMFC via optimization based on probabilistic model building

Botello-Rionda, Salvador; Ordóñez, L. C.; Valdez, S. Ivvan

doi:10.1016/j.matcom.2020.12.021

Cited by 17 publications

(8 citation statements)

References 35 publications

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“…Moreover, the PEMFC's model is high nonlinear, multi-variant and its variables are strongly coupled and severely affected by the operating conditions. Hence, the dynamics and accurate modelling have become more complicated and time-consuming when employed by such conventional techniques [36][37][38][39][40][41][42]. Therefore, there is an imperative demand to derive robust and feasible techniques to tackle such issue.…”

Section: C) Power Rating (Kw) Meritsmentioning

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: C) Power Rating (Kw) Meritsmentioning

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 Univariate Marginal Distribution Algorithm with Gaussian models (U M DA G ) is a kind of EDA [63,64,65,66,67,21] that assumes that the probabilistic distribution of the populations is a l-dimensional joint probability function that is factored as a product of l univariate and independent probability functions. Then, at each iteration, the algorithm compute the join probability given by…”

Section: The Univariate Marginal Distribution Algorithm With Gaussian...mentioning

confidence: 99%

“…The stop criteria is a function of the norm of the normalized variance of the optimized parameters [21],…”

Section: The Univariate Marginal Distribution Algorithm With Gaussian...mentioning

confidence: 99%

“…In addition, they can optimize parameters to improve the energetic performance in a PEMFC. The parameter estimation problem has been tackled by nature inspired algorithms mainly for the semi-empirical model [18,19,20,21], for instance, parameters as maximum current density, active area, equivalent contact resistance to electron conduction and parametric coefficients have been estimated using a genetic algorithm in [2,22,23,24], simulated annealing in [2,25], particle swarm optimization in [26,27], artificial bee swarm in [28], artificial immune system in [29], seeker optimization algorithm in [30], harmonic search in [31], optimization algorithms based on psystems in [32], differential evolution algorithm in [33].…”

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confidence: 99%

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Design optimization and parameter estimation of a PEMFC using nature-inspired algorithms

2022

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Numerical simulation of a Proton-Exchange Membrane Fuel Cell (PEMFC) requires an adequate model and precise parameters for reproducing their operational performance quantified by the polarization curve. Bio-inspired algorithms are well-suited for estimating unknown parameters; nevertheless, the simulator is stressed by inputting thousands of randomly-generated parameters. As a consequence, they require a robust numerical model. Once the model with the proper parameters reproduces the experimental data, it is applicable for optimizing the PEMFC. This article proposes a reformulation of a macro-homogeneous mathematical model by analyzing its relation with singularly perturbed differential equations to provide higher numerical stability to the solutions. This formulation introduces a boundary value problem instead of the usual initial value problem. Hence, we propose an assembly of the proper numerical methods for solving this version of the problem. Then, we introduce optimization problems for parameter estimation and design optimization; three bio-inspired algorithms solve the first. The most consistent of them is applied to optimize the PEMFC design by maximizing its performance or minimizing the platinum mass loading, highly related to the cost. To the best of our knowledge, there is no other research for optimizing the cell design, neither its performance nor its cost, using numerical simulation and optimization. The results are validated as follows: a) Comparing experimental polarization curves with the simulation with the parameters estimated by bio-inspired algorithms. b) Comparing a base design's performance with the optimized for maximum performance, and c) comparing a base design with the optimized for minimum platinum mass loading.

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“…Many optimisation algorithms have been applied with the purpose of drawing out the parameters of the PEMFCs [6,8,9,11,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29]. Moreover, the application of recent optimisa-tion algorithms is very interesting by many researchers for modelling the components of energy systems, such as PV cells [30,31].…”

Section: Literature Review and Research Gapmentioning

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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Robust parameter estimation of a PEMFC via optimization based on probabilistic model building

Cited by 17 publications

References 35 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

Design optimization and parameter estimation of a PEMFC using nature-inspired algorithms

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

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