Genetic Algorithm-based Modeling of PEM Fuel Cells Suitable for Integration in DC Microgrids

Kumar, Priya; Kannaiah, Sathish Kumar; Choudhury, Sashang Roy; Rajasekar, N.

doi:10.1080/15325008.2017.1318980

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…Perhaps, material property identification using genetic algorithm optimisation technique for PEMFC performance enhancement is reported in [38]. But the work fails to report the credibility of this technique on different vol% concentration, material selection and its experimental validation.…”

Section: Pemfc Heat Transfer and Nanofluidsmentioning

confidence: 99%

An integrated approach on proton exchange membrane fuel cell performance enhancement combining flower pollination algorithm and nanofluids

Kumar

Gnanasekar

Andrews

et al. 2021

IET Renewable Power Gen

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Proton exchange membrane fuel cell (PEMFC) is widely popular for its inherent advantages like low operating temperature, high efficiency, durability, and reliability. However, modelling its characteristics is often restricted by virtue of its shortage of data and strongly coupled multivariate behaviour. At the same time, when operated within its temperature limits, PEMFC systems exhibit maximum efficiency; otherwise, they cause membrane dryness, poor ionic conductivity and less efficiency. Therefore, PEMFC modelling alongside thermal management is of interest in recent years. With the benefits rendered by the metaheuristics algorithm, an integrated approach is emphasised in this work for achieving twin objectives of modelling PEMFC together with thermal management. An aspiration to enhance PEMFC performance via mathematical modelling, flower pollination algorithm for parameter identification as well as thermal conductivity property computation is outlined. Simulated values are experimentally matched using copper-based nanofluids. The selection of copper (Cu) is justified by its thermal conductivity property and cost. For brevity, synthesis procedure, characterisation and estimation of thermal conductivity property is expounded in methodology. Numerous experimentations on various vol% fractions combinations lying between 0.05% and 0.5% indicate that Cu nanoparticle when dispersed in base fluids such as water and ethylene glycol (EG) substantially improves thermal conductivity property. Especially, among different combinations, nanofluid with EG and water mixture achieves the highest thermal conductivity value of 1.4255. 1Perhaps, fuel cell modelling is extremely helpful in performance prediction, simulation analysis, design, and developmentThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Section: Pemfc Heat Transfer and Nanofluidsmentioning

confidence: 99%

An integrated approach on proton exchange membrane fuel cell performance enhancement combining flower pollination algorithm and nanofluids

Kumar

Gnanasekar

Andrews

et al. 2021

IET Renewable Power Gen

Self Cite

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“…Especially since the contributions by Mo et al [18] and Ohenoja and Leiviskä [19] authors of this review were unable to access. In addition, some studies handle the parameter estimation problem from a different perspective, either utilizing simulated data [20][21][22], more detailed [23,24] or simpler [25][26][27] model structures, or in conjunction with simulation studies [28][29][30]. These contributions are therefore excluded from the review tables.…”

Section: Review Of Parameter Optimization For the Electrochemical Modelmentioning

confidence: 99%

Observations on the Parameter Estimation Problem of Polymer Electrolyte Membrane Fuel Cell Polarization Curves

Ohenoja

Leiviskä

2020

Fuel Cells

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The optimal operation of fuel cells in changing environmental and variable load conditions requires mathematical modeling. The electrochemical behavior of polymer electrolyte membrane fuel cells (PEMFC) is commonly described with a semi-empirical model requiring fuel cell specific model parameter values. A large number of different nature inspired, heuristic optimization methods have been proposed for this PEMFC parameter estimation problem. In this study, those studies are listed and critically reviewed. In particular, the aim is to elaborate the generalization ability of the results and discuss the fair comparison of the algorithms used for the parameter estimation of the polarization curve. The observations made in this review could further increase the quality of future contributions in this particular area, as well as applications of heuristic optimization methods in other related problems in fuel cell systems.

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“…With the widespread diffusion of approaches based on artificial intelligence (AI), scientific production in the DC-MG field has recently achieved important results thanks to the strong peculiarity of these techniques in the processing of large quantities of data for the process of decision making. In fact, machine learning techniques have opened wide frontiers, especially in the study of stability (regression [26,46,47], random forest tree [26,48,49], convolutional neural networks [26,50,51] and others [52][53][54]). However, these techniques, although promising in performance and results obtained, have the flaw of being black-box type procedures so, on the one hand, they are difficult to understand by non-experts and, on the other, they do not allow updates except in the case of substantial interventions.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Approach for Managing Renewable Energy Flows for DC-Microgrid with Composite PV-WT Generators and Energy Storage System

Versaci,

La Foresta

2024

Energies

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Recently, the implementation of software/hardware systems based on advanced artificial intelligence techniques for continuous monitoring of the electrical parameters of intelligent networks aimed at managing and controlling energy consumption has been of great interest. The contribution of this paper, starting from a recently studied DC-MG, fits into this context by proposing an intuitionistic fuzzy Takagi–Sugeno approach optimized for the energy management of isolated direct current microgrid systems consisting of a photovoltaic and a wind source. Furthermore, a lead-acid battery guarantees the stability of the DC bus while a hydrogen cell ensures the reliability of the system by avoiding blackout conditions and increasing interaction with the loads. The fuzzy rule bank, initially built using the expert’s knowledge, is optimized with the aforementioned procedure, maximizing external energy and minimizing consumption. The complete scheme, modeled using MatLab/Simulink, highlighted performance comparable to fuzzy Takagi–Sugeno systems optimized using a hybrid approach based on particle swarm optimization (to structure the antecedents of the rules) and minimum batch squares (to optimize the output).

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Genetic Algorithm-based Modeling of PEM Fuel Cells Suitable for Integration in DC Microgrids

Cited by 14 publications

References 15 publications

An integrated approach on proton exchange membrane fuel cell performance enhancement combining flower pollination algorithm and nanofluids

An integrated approach on proton exchange membrane fuel cell performance enhancement combining flower pollination algorithm and nanofluids

Observations on the Parameter Estimation Problem of Polymer Electrolyte Membrane Fuel Cell Polarization Curves

Fuzzy Approach for Managing Renewable Energy Flows for DC-Microgrid with Composite PV-WT Generators and Energy Storage System

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