Parameter estimation of PEMFC model based on Harris Hawks’ optimization and atom search optimization algorithms
Proton exchange membrane fuel cell (PEMFC) is considered as propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and in-depth understanding of all its operating characteristics attracted the interest of many...
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| Published in | Neural computing & applications Vol. 33; no. 11; pp. 5555 - 5570 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
London
Springer London
01.06.2021
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0941-0643 1433-3058 |
| DOI | 10.1007/s00521-020-05333-4 |
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| Abstract | Proton exchange membrane fuel cell (PEMFC) is considered as propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and in-depth understanding of all its operating characteristics attracted the interest of many researchers. In this paper, novel meta-heuristic optimization methods have been successfully applied to evaluate the unknown parameters of PEMFC models, particularly Harris Hawks’ optimization (HHO) and atom search optimization (ASO) techniques. The proposed optimization algorithms have been tested on three different commercial PEMFC stacks, namely BCS 500-W PEM, 500W SR-12PEM and 250W stack, under various operating conditions. The sum of square errors (SSE) between the results obtained by the application of the estimated parameters and the experimentally measured results of the fuel cell stacks was considered as the objective function of the optimization problem. In order to validate the effectiveness of the proposed methods, the results are compared with that obtained in studies. Moreover, the
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curves obtained by the application of HHO and ASO showed a clear matching with data sheet curves for all the studied cases. Finally, PEMFC model based on HHO technique surpasses all compared algorithms in terms of the solution accuracy and the convergence speed. |
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| AbstractList | Proton exchange membrane fuel cell (PEMFC) is considered as propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and in-depth understanding of all its operating characteristics attracted the interest of many researchers. In this paper, novel meta-heuristic optimization methods have been successfully applied to evaluate the unknown parameters of PEMFC models, particularly Harris Hawks’ optimization (HHO) and atom search optimization (ASO) techniques. The proposed optimization algorithms have been tested on three different commercial PEMFC stacks, namely BCS 500-W PEM, 500W SR-12PEM and 250W stack, under various operating conditions. The sum of square errors (SSE) between the results obtained by the application of the estimated parameters and the experimentally measured results of the fuel cell stacks was considered as the objective function of the optimization problem. In order to validate the effectiveness of the proposed methods, the results are compared with that obtained in studies. Moreover, the
I
/
V
curves obtained by the application of HHO and ASO showed a clear matching with data sheet curves for all the studied cases. Finally, PEMFC model based on HHO technique surpasses all compared algorithms in terms of the solution accuracy and the convergence speed. Proton exchange membrane fuel cell (PEMFC) is considered as propitious solution for an environmentally friendly energy source. A precise model of PEMFC for accurate identification of its polarization curve and in-depth understanding of all its operating characteristics attracted the interest of many researchers. In this paper, novel meta-heuristic optimization methods have been successfully applied to evaluate the unknown parameters of PEMFC models, particularly Harris Hawks’ optimization (HHO) and atom search optimization (ASO) techniques. The proposed optimization algorithms have been tested on three different commercial PEMFC stacks, namely BCS 500-W PEM, 500W SR-12PEM and 250W stack, under various operating conditions. The sum of square errors (SSE) between the results obtained by the application of the estimated parameters and the experimentally measured results of the fuel cell stacks was considered as the objective function of the optimization problem. In order to validate the effectiveness of the proposed methods, the results are compared with that obtained in studies. Moreover, the I/V curves obtained by the application of HHO and ASO showed a clear matching with data sheet curves for all the studied cases. Finally, PEMFC model based on HHO technique surpasses all compared algorithms in terms of the solution accuracy and the convergence speed. |
| Author | Diab, Ahmed A. Zaki Kamel, Omar Makram Sultan, Hamdy M. Mossa, Mahmoud A. |
| Author_xml | – sequence: 1 givenname: Mahmoud A. surname: Mossa fullname: Mossa, Mahmoud A. organization: Electrical Engineering Department, Faculty of Engineering, Minia University – sequence: 2 givenname: Omar Makram surname: Kamel fullname: Kamel, Omar Makram organization: Electrical and Computer Department, El Minia High Institute of Engineering and Technology – sequence: 3 givenname: Hamdy M. surname: Sultan fullname: Sultan, Hamdy M. organization: Electrical Engineering Department, Faculty of Engineering, Minia University – sequence: 4 givenname: Ahmed A. Zaki orcidid: 0000-0002-8598-9983 surname: Diab fullname: Diab, Ahmed A. Zaki email: a.diab@mu.edu.eg organization: Electrical Engineering Department, Faculty of Engineering, Minia University, Department of Electrical and Electronic Engineering, Kyushu University |
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| Cites_doi | 10.4324/9781315769431 10.1016/j.asej.2015.05.003 10.1016/j.ijheatmasstransfer.2017.03.120 10.1016/j.future.2019.02.028 10.1109/TEC.2003.821821 10.1016/j.tsep.2018.12.008 10.1016/S0378-7753(99)00484-X 10.1016/j.knosys.2018.08.030 10.1109/access.2019.2930831 10.1016/j.renene.2017.04.036 10.1016/j.renene.2017.12.051 10.1016/j.ijhydene.2012.10.026 10.1038/nature06518 10.1016/j.enconman.2019.04.005 10.1002/er.1170 10.1016/j.energy.2019.02.106 10.1016/j.energy.2015.06.081 10.1049/iet-rpg.2017.0232 10.1016/j.future.2018.05.037 10.1016/j.enconman.2018.12.057 10.1109/ACCESS.2019.2921545 10.1016/j.ijhydene.2018.11.140 10.1016/j.ijhydene.2011.01.070 10.1038/381413a0 10.1149/1.2043959 10.1016/j.ijepes.2014.04.043 |
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| References | MannRFAmphlettJCHooperMAIJensenHMPeppleyBARobergePRDevelopment and application of a generalized steady-state electrochemical model for a PEM fuel cellJ Power Sour2000861–217318010.1016/S0378-7753(99)00484-X ArshadAAliHMHabibABashirMAJabbalMYanYEnergy and exergy analysis of fuel cells: a reviewTherm Sci Eng Progr2019930832110.1016/j.tsep.2018.12.008 LiuZZengXGeYShenJLiuWMulti-objective optimization of operating conditions and channel structure for a proton exchange membrane fuel cellInt J Heat Mass Transf201711128929810.1016/j.ijheatmasstransfer.2017.03.120 Lennard-JonesJEOn the determination of molecular fieldsProc R Soc Lond A Math Phys Eng Sci R Soc1924106738463477 ZhangLWangNAn adaptive RNA genetic algorithm for modeling of proton exchange membrane fuel cellsInt J Hydrogen Energy201338121922810.1016/j.ijhydene.2012.10.026 SunZWangNBiYSrinivasanDParameter identification of PEMFC model based on hybrid adaptive differential evolution algorithmEnergy2015901334134110.1016/j.energy.2015.06.081 MoZZhuXWeiLCaoGParameter optimization for a PEMFC model with a hybrid genetic algorithmInt J Energy Res20063058559710.1002/er.1170 ViswanathanGMAfanasyevVBuldyrevSMurphyEPrincePStanleyHEL´evy flight search patterns of wandering albatrossesNature199638141310.1038/381413a0 AmphlettJCBaumertRMMannRFPeppleyBARobergePRPerformance modeling of the Ballard mark IV solid polymer electrolyte fuel cell: empirical model developmentJ Electrochem Soc199514291510.1149/1.2043959 SimsDWSouthallEJHumphriesNEHaysGCBradshawCJPitchfordJWJamesAAhmedMZBrierleyASHindellMAScaling laws of marine predator search behaviourNature20084511098110210.1038/nature06518 El-FerganyAAElectrical characterisation of proton exchange membrane fuel cells stack using grasshopper optimizerIET Renew Power Gener201812191710.1049/iet-rpg.2017.0232 BaoXJiaHLangCA novel hybrid harris hawks optimization for color image multilevel thresholding segmentationIEEE Access201910.1109/ACCESS.2019.2921545 QuaschningVUnderstanding Renewable Energy Systems20162MunichHan ser. 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| References_xml | – reference: QuaschningVUnderstanding Renewable Energy Systems20162MunichHan ser. Verlag10.4324/9781315769431 – reference: ChenYWangNCuckoo search algorithm with explosion operator for modeling proton exchange membrane fuel cellsInt J Hydrogen Energy201910.1016/j.ijhydene.2018.11.140 – reference: ViswanathanGMAfanasyevVBuldyrevSMurphyEPrincePStanleyHEL´evy flight search patterns of wandering albatrossesNature199638141310.1038/381413a0 – reference: HeidariAAMirjaliliSFarisHAljarahIMafarjaMChenHHarris hawks optimization: Algorithm and applicationsFuture Gener Computer Syst20199784987210.1016/j.future.2019.02.028 – reference: El-FerganyAAElectrical characterisation of proton exchange membrane fuel cells stack using grasshopper optimizerIET Renew Power Gener201812191710.1049/iet-rpg.2017.0232 – reference: SimsDWSouthallEJHumphriesNEHaysGCBradshawCJPitchfordJWJamesAAhmedMZBrierleyASHindellMAScaling laws of marine predator search behaviourNature20084511098110210.1038/nature06518 – reference: AliMEl-HameedMAFarahatMAEffective parameters’ identification for polymer electrolyte membrane fuel cell models using grey wolf optimizerRenew Energy201711145546210.1016/j.renene.2017.04.036 – reference: ChengJZhangGParameter fitting of PEMFC models based on adaptive differential evolutionInt J Electr Power Energy Syst20146218919810.1016/j.ijepes.2014.04.043 – reference: ZhangLWangNAn adaptive RNA genetic algorithm for modeling of proton exchange membrane fuel cellsInt J Hydrogen Energy201338121922810.1016/j.ijhydene.2012.10.026 – reference: ZhaoWWangLZhangZA novel atom search optimization for dispersion coeffcient estimation in groundwaterFuture Gener Comput Syst20199160161010.1016/j.future.2018.05.037 – reference: MannRFAmphlettJCHooperMAIJensenHMPeppleyBARobergePRDevelopment and application of a generalized steady-state electrochemical model for a PEM fuel cellJ Power Sour2000861–217318010.1016/S0378-7753(99)00484-X – reference: SohaniANaderiSTorabiFComprehensive comparative evaluation of different possible optimization scenarios for a polymer electrolyte 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