Optimal parameters estimation of the PEMFC using a balanced version of water strider algorithm

Recently, much attention was paid to the application of renewable energy in environmental issues. Meanwhile, the fuel cell industry, which is considered an environmentally friendly industry, is one of the important components of this project. They are in fact devices for the direct conversion of che...

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Autores:
Syah, Rahmad
Lawal, Adedoyin Isola
Grimaldo Guerrero, John William
Suksatan, Wanich
Sunarsi, Denok
Elveny, Marischa
Alkaim, Ayad
Thangavelu, Lakshmi
Aravindhan, Surendar
Tipo de recurso:
Article of journal
Fecha de publicación:
2021
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/8990
Acceso en línea:
https://hdl.handle.net/11323/8990
https://doi.org/10.1016/j.egyr.2021.10.057
https://repositorio.cuc.edu.co/
Palabra clave:
Proton exchange membrane fuel cell
Model parameters estimation
Balanced Water Strider optimizer
A total of squared error
Terminal voltage
Practical test case
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_2876ddab2ba70246522246f5a8a2f176
oai_identifier_str oai:repositorio.cuc.edu.co:11323/8990
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Optimal parameters estimation of the PEMFC using a balanced version of water strider algorithm
title Optimal parameters estimation of the PEMFC using a balanced version of water strider algorithm
spellingShingle Optimal parameters estimation of the PEMFC using a balanced version of water strider algorithm
Proton exchange membrane fuel cell
Model parameters estimation
Balanced Water Strider optimizer
A total of squared error
Terminal voltage
Practical test case
title_short Optimal parameters estimation of the PEMFC using a balanced version of water strider algorithm
title_full Optimal parameters estimation of the PEMFC using a balanced version of water strider algorithm
title_fullStr Optimal parameters estimation of the PEMFC using a balanced version of water strider algorithm
title_full_unstemmed Optimal parameters estimation of the PEMFC using a balanced version of water strider algorithm
title_sort Optimal parameters estimation of the PEMFC using a balanced version of water strider algorithm
dc.creator.fl_str_mv Syah, Rahmad
Lawal, Adedoyin Isola
Grimaldo Guerrero, John William
Suksatan, Wanich
Sunarsi, Denok
Elveny, Marischa
Alkaim, Ayad
Thangavelu, Lakshmi
Aravindhan, Surendar
dc.contributor.author.spa.fl_str_mv Syah, Rahmad
Lawal, Adedoyin Isola
Grimaldo Guerrero, John William
Suksatan, Wanich
Sunarsi, Denok
Elveny, Marischa
Alkaim, Ayad
Thangavelu, Lakshmi
Aravindhan, Surendar
dc.subject.spa.fl_str_mv Proton exchange membrane fuel cell
Model parameters estimation
Balanced Water Strider optimizer
A total of squared error
Terminal voltage
Practical test case
topic Proton exchange membrane fuel cell
Model parameters estimation
Balanced Water Strider optimizer
A total of squared error
Terminal voltage
Practical test case
description Recently, much attention was paid to the application of renewable energy in environmental issues. Meanwhile, the fuel cell industry, which is considered an environmentally friendly industry, is one of the important components of this project. They are in fact devices for the direct conversion of chemical energy into electrical energy by an electrochemical reaction without the need for any mechanical parts. In this study, it is attempted to model one of their important types, called proton exchange membrane fuel cells, so that it can be used in predicting the behavior of the fuel cell and examining various parameters affecting the performance of the cell. The main idea is to optimal parameters estimation for the proton exchange membrane fuel cells by minimizing the total Squared Error value between the empirical output voltage and the approximated output voltage. For giving better results in terms of accuracy and reliability, a new design of a metaheuristic called the balanced Water Strider Algorithm is utilized. The results of the suggested method are finally validated by comparison with several latest optimizers applied on a practical test case. After running all of the optimizers 30 times independently, the proposed method with minimum absolute error equals 3.4831e−4 shows the best results toward the others.
publishDate 2021
dc.date.issued.none.fl_str_mv 2021
dc.date.accessioned.none.fl_str_mv 2022-01-21T15:00:23Z
dc.date.available.none.fl_str_mv 2022-01-21T15:00:23Z
dc.type.spa.fl_str_mv Artículo de revista
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.type.coar.spa.fl_str_mv http://purl.org/coar/resource_type/c_6501
dc.type.content.spa.fl_str_mv Text
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.type.version.spa.fl_str_mv info:eu-repo/semantics/acceptedVersion
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status_str acceptedVersion
dc.identifier.issn.spa.fl_str_mv 2352-4847
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/8990
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1016/j.egyr.2021.10.057
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
identifier_str_mv 2352-4847
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/8990
https://doi.org/10.1016/j.egyr.2021.10.057
https://repositorio.cuc.edu.co/
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv Aghajani and Ghadimi, 2018 Aghajani G., Ghadimi N. Multi-objective energy management in a micro-grid Energy Rep., 4 (2018), pp. 218-225
Amali and Dinakaran, 2019 Amali D., Dinakaran M. Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour J. Intell. Fuzzy Systems (Preprint) (2019), pp. 1-14
Bagheri et al., 2018 Bagheri M., et al. A novel wind power forecasting based feature selection and hybrid forecast engine bundled with honey bee mating optimization 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I & CPS Europe, IEEE (2018)
Cai et al., 2019 Cai W., et al. Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach Renew. Energy, 143 (2019), pp. 1-8
Choi and Lee, 1998 Choi C., Lee J.-J. Chaotic local search algorithm Artif. Life Robot., 2 (1) (1998), pp. 41-47
Cuevas et al., 2020 Cuevas E., Fausto F., González A. The locust swarm optimization algorithm New Advancements in Swarm Algorithms: Operators and Applications, Springer (2020), pp. 139-159
Dhiman and Kumar, 2017 Dhiman G., Kumar V. Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications Adv. Eng. Softw., 114 (2017), pp. 48-70
Ener Fuel Inc, 2020 Ener fuel inc (2020) Available from: https://www.enerfuel.com/humid2.aspx
Fan et al., 2020a Fan X., et al. High voltage gain DC/DC converter using coupled inductor and VM techniques IEEE Access, 8 (2020), Article 131975-131987
Fan et al., 2020b Fan X., et al. Multi-objective optimization for the proper selection of the best heat pump technology in a fuel cell-heat pump micro-CHP system Energy Rep., 6 (2020), pp. 325-335
Fei et al., 2019 Fei X., Xuejun R., Razmjooy N. Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran Energy Sources A (2019), pp. 1-21
Ghiasi et al., 2019 Ghiasi M., Ghadimi N., Ahmadinia E. An analytical methodology for reliability assessment and failure analysis in distributed power system SN Appl. Sci., 1 (1) (2019), p. 44
Guo et al., 2020a Guo Y., et al. An optimal configuration for a battery and PEM fuel cell-based hybrid energy system using developed Krill herd optimization algorithm for locomotive application Energy Rep., 6 (2020), pp. 885-894
Guo et al., 2020b Guo H., et al. Optimized parameter estimation of a PEMFC model based on improved grass Fibrous root optimization algorithm Energy Rep., 6 (2020), pp. 1510-1519
Hamian et al., 2018 Hamian M., et al. A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm Eng. Appl. Artif. Intell., 72 (2018), pp. 203-212
Hosseini Firouz and Ghadimi, 2016 Hosseini Firouz M., Ghadimi N. Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods Complexity, 21 (6) (2016), pp. 70-88
Jia et al., 2009 Jia J., et al. Modeling and dynamic characteristic simulation of a proton exchange membrane fuel cell IEEE Trans. Energy Convers., 24 (1) (2009), pp. 283-291
Kaveh et al., 2020 Kaveh A., Eslamlou A.D., Khodadadi N. Dynamic water strider algorithm for optimal design of skeletal structures Period. Polytech. Civ. Eng., 64 (3) (2020), pp. 904-916
Li et al., 2018 Li X., Niu P., Liu J. Combustion optimization of a boiler based on the chaos and levy flight vortex search algorithm Appl. Math. Model., 58 (2018), pp. 3-18
Liu et al., 2020 Liu J., et al. An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles J. Energy Storage, 27 (2020), Article 101057
Mani et al., 2018 Mani M., Bozorg-Haddad O., Chu X. Ant lion optimizer (ALO) algorithm Advanced Optimization By Nature-Inspired Algorithms, Springer (2018), pp. 105-116
Meng et al., 2020 Meng Q., et al. A single-phase transformer-less grid-tied inverter based on switched capacitor for PV application J. Control Autom. Electr. Syst., 31 (1) (2020), pp. 257-270
Mir et al., 2020 Mir M., et al. Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction Evol. Syst., 11 (4) (2020), pp. 559-573
Mirjalili et al., 2016 Mirjalili S., Mirjalili S.M., Hatamlou A. Multi-verse optimizer: a nature-inspired algorithm for global optimization Neural Comput. Appl., 27 (2) (2016), pp. 495-513
Navid Razmjooy and Ghadimi, 2018 Navid Razmjooy F.R.S., Ghadimi Noradin A hybrid neural network – world cup optimization algorithm for melanoma detection Open Med., 13 (2018), pp. 9-16
Ramezani et al., 2020 Ramezani M., Bahmanyar D., Razmjooy N. A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home SN Appl. Sci., 2 (12) (2020), pp. 1-17
Razmjooy et al., 2020 Razmjooy N., Estrela V.V., Loschi H.J. Entropy-based breast cancer detection in digital mammograms using world cup optimization algorithm Int. J. Swarm Intell. Res. (IJSIR), 11 (3) (2020), pp. 1-18
Razmjooy et al., 2016 Razmjooy N., Khalilpour M., Ramezani M. A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system J. Control Autom. Electr. Syst., 27 (4) (2016), pp. 419-440
Razmjooy et al., 2017 Razmjooy N., Ramezani M., Ghadimi N. Imperialist competitive algorithm-based optimization of neuro-fuzzy system parameters for automatic red-eye removal Int. J. Fuzzy Syst., 19 (4) (2017), pp. 1144-1156
Saeedi et al., 2019 Saeedi M., et al. Robust optimization based optimal chiller loading under cooling demand uncertainty Appl. Therm. Eng., 148 (2019), pp. 1081-1091
Shabani et al., 2020 Shabani A., et al. Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems Expert Syst. Appl., 161 (2020), Article 113698
Tejani et al., 2016 Tejani G.G., Savsani V.J., Patel V.K. Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization J. Comput. Des. Eng., 3 (3) (2016), pp. 226-249
Tejani et al., 2017 Tejani G.G., et al. Topology, shape, and size optimization of truss structures using modified teaching-learning based optimization Adv. Comput. Des., 2 (4) (2017), pp. 313-331
Tejani et al., 2018a Tejani G.G., et al. Size, shape, and topology optimization of planar and space trusses using mutation-based improved metaheuristics J. Comput. Des. Eng., 5 (2) (2018), pp. 198-214
Tejani et al., 2018b Tejani G.G., et al. Truss optimization with natural frequency bounds using improved symbiotic organisms search Knowl.-Based Syst., 143 (2018), pp. 162-178
Tejani et al., 2019 Tejani G.G., et al. Topology optimization of truss subjected to static and dynamic constraints by integrating simulated annealing into passing vehicle search algorithms Eng. Comput., 35 (2) (2019), pp. 499-517
Tian et al., 2020 Tian M.-W., et al. New optimal design for a hybrid solar chimney, solid oxide electrolysis and fuel cell based on improved deer hunting optimization algorithm J. Cleaner Prod., 249 (2020), Article 119414
Wang et al., 2015 Wang G.-G., Deb S., Coelho L.d.S. Elephant herding optimization 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), IEEE (2015)
Xu et al., 2020 Xu H., et al. Exergy analysis and optimization of a HT-PEMFC using developed manta ray foraging optimization algorithm Int. J. Hydrogen Energy, 45 (55) (2020), pp. 30932-30941
Yanda et al., 2020 Yanda L., Yuwei Z., Razmjooy N. Optimal arrangement of a micro-CHP system in the presence of fuel cell-heat pump based on metaheuristics Int. J. Ambient Energy (2020), pp. 1-12
Yang, 2008 Yang X.-S. Firefly algorithm (2008)
Yang et al., 2020 Yang Z., et al. Model parameter estimation of the PEMFCs using improved barnacles mating optimization algorithm Energy (2020), Article 118738
Ye et al., 2020 Ye H., et al. High step-up interleaved dc/dc converter with high efficiency Energy Sources A (2020), pp. 1-20
Yin and Razmjooy, 2020 Yin Z., Razmjooy N. PEMFC identification Using deep learning developed by improved deer hunting optimization algorithm Int. J. Power Energy Syst., 40 (2) (2020)
Yu and Ghadimi, 2019 Yu D., Ghadimi N. Reliability constraint stochastic UC by considering the correlation of random variables with copula theory IET Renew. Power Gener., 13 (14) (2019), pp. 2587-2593
Yu et al., 2019 Yu D., et al. System identification of PEM fuel cells using an improved elman neural network and a new hybrid optimization algorithm Energy Rep., 5 (2019), pp. 1365-1374
Yu et al., 2020 Yu D., et al. Energy management of wind-PV-storage-grid based large electricity consumer using robust optimization technique J. Energy Storage, 27 (2020), Article 101054
Yuan et al., 2020 Yuan Z., et al. A new technique for optimal estimation of the circuit-based PEMFCs using developed sunflower optimization algorithm Energy Rep., 6 (2020), pp. 662-671
Zhang et al., 2020a Zhang G., Xiao C., Razmjooy N. Optimal operational strategy of hybrid PV/Wind renewable energy system using homer: A case study Int. J. Ambient Energy (2020), pp. 1-33
Zhang et al., 2020b Zhang G., Xiao C., Razmjooy N. Optimal parameter extraction of PEM fuel cells by meta-heuristics Int. J. Ambient Energy (2020), pp. 1-10
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spelling Syah, RahmadLawal, Adedoyin IsolaGrimaldo Guerrero, John WilliamSuksatan, WanichSunarsi, DenokElveny, MarischaAlkaim, AyadThangavelu, LakshmiAravindhan, Surendar2022-01-21T15:00:23Z2022-01-21T15:00:23Z20212352-4847https://hdl.handle.net/11323/8990https://doi.org/10.1016/j.egyr.2021.10.057Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Recently, much attention was paid to the application of renewable energy in environmental issues. Meanwhile, the fuel cell industry, which is considered an environmentally friendly industry, is one of the important components of this project. They are in fact devices for the direct conversion of chemical energy into electrical energy by an electrochemical reaction without the need for any mechanical parts. In this study, it is attempted to model one of their important types, called proton exchange membrane fuel cells, so that it can be used in predicting the behavior of the fuel cell and examining various parameters affecting the performance of the cell. The main idea is to optimal parameters estimation for the proton exchange membrane fuel cells by minimizing the total Squared Error value between the empirical output voltage and the approximated output voltage. For giving better results in terms of accuracy and reliability, a new design of a metaheuristic called the balanced Water Strider Algorithm is utilized. The results of the suggested method are finally validated by comparison with several latest optimizers applied on a practical test case. After running all of the optimizers 30 times independently, the proposed method with minimum absolute error equals 3.4831e−4 shows the best results toward the others.Syah, Rahmad-will be generated-orcid-0000-0003-2232-7189-600Lawal, Adedoyin Isola-will be generated-orcid-0000-0001-8295-1560-600Grimaldo Guerrero, John William-will be generated-orcid-0000-0002-1632-5374-600Suksatan, Wanich-will be generated-orcid-0000-0003-1797-1260-600Sunarsi, Denok-will be generated-orcid-0000-0001-6876-0143-600Elveny, MarischaAlkaim, Ayad-will be generated-orcid-0000-0003-3459-4583-600Thangavelu, LakshmiAravindhan, Surendarapplication/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Energy Reportshttps://www.sciencedirect.com/science/article/pii/S235248472101074XProton exchange membrane fuel cellModel parameters estimationBalanced Water Strider optimizerA total of squared errorTerminal voltagePractical test caseOptimal parameters estimation of the PEMFC using a balanced version of water strider algorithmArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionAghajani and Ghadimi, 2018 Aghajani G., Ghadimi N. Multi-objective energy management in a micro-grid Energy Rep., 4 (2018), pp. 218-225Amali and Dinakaran, 2019 Amali D., Dinakaran M. Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour J. Intell. Fuzzy Systems (Preprint) (2019), pp. 1-14Bagheri et al., 2018 Bagheri M., et al. A novel wind power forecasting based feature selection and hybrid forecast engine bundled with honey bee mating optimization 2018 IEEE International Conference on Environment and Electrical Engineering and 2018 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I & CPS Europe, IEEE (2018)Cai et al., 2019 Cai W., et al. Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach Renew. Energy, 143 (2019), pp. 1-8Choi and Lee, 1998 Choi C., Lee J.-J. Chaotic local search algorithm Artif. Life Robot., 2 (1) (1998), pp. 41-47Cuevas et al., 2020 Cuevas E., Fausto F., González A. The locust swarm optimization algorithm New Advancements in Swarm Algorithms: Operators and Applications, Springer (2020), pp. 139-159Dhiman and Kumar, 2017 Dhiman G., Kumar V. Spotted hyena optimizer: a novel bio-inspired based metaheuristic technique for engineering applications Adv. Eng. Softw., 114 (2017), pp. 48-70Ener Fuel Inc, 2020 Ener fuel inc (2020) Available from: https://www.enerfuel.com/humid2.aspxFan et al., 2020a Fan X., et al. High voltage gain DC/DC converter using coupled inductor and VM techniques IEEE Access, 8 (2020), Article 131975-131987Fan et al., 2020b Fan X., et al. Multi-objective optimization for the proper selection of the best heat pump technology in a fuel cell-heat pump micro-CHP system Energy Rep., 6 (2020), pp. 325-335Fei et al., 2019 Fei X., Xuejun R., Razmjooy N. Optimal configuration and energy management for combined solar chimney, solid oxide electrolysis, and fuel cell: a case study in Iran Energy Sources A (2019), pp. 1-21Ghiasi et al., 2019 Ghiasi M., Ghadimi N., Ahmadinia E. An analytical methodology for reliability assessment and failure analysis in distributed power system SN Appl. Sci., 1 (1) (2019), p. 44Guo et al., 2020a Guo Y., et al. An optimal configuration for a battery and PEM fuel cell-based hybrid energy system using developed Krill herd optimization algorithm for locomotive application Energy Rep., 6 (2020), pp. 885-894Guo et al., 2020b Guo H., et al. Optimized parameter estimation of a PEMFC model based on improved grass Fibrous root optimization algorithm Energy Rep., 6 (2020), pp. 1510-1519Hamian et al., 2018 Hamian M., et al. A framework to expedite joint energy-reserve payment cost minimization using a custom-designed method based on mixed integer genetic algorithm Eng. Appl. Artif. Intell., 72 (2018), pp. 203-212Hosseini Firouz and Ghadimi, 2016 Hosseini Firouz M., Ghadimi N. Optimal preventive maintenance policy for electric power distribution systems based on the fuzzy AHP methods Complexity, 21 (6) (2016), pp. 70-88Jia et al., 2009 Jia J., et al. Modeling and dynamic characteristic simulation of a proton exchange membrane fuel cell IEEE Trans. Energy Convers., 24 (1) (2009), pp. 283-291Kaveh et al., 2020 Kaveh A., Eslamlou A.D., Khodadadi N. Dynamic water strider algorithm for optimal design of skeletal structures Period. Polytech. Civ. Eng., 64 (3) (2020), pp. 904-916Li et al., 2018 Li X., Niu P., Liu J. Combustion optimization of a boiler based on the chaos and levy flight vortex search algorithm Appl. Math. Model., 58 (2018), pp. 3-18Liu et al., 2020 Liu J., et al. An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles J. Energy Storage, 27 (2020), Article 101057Mani et al., 2018 Mani M., Bozorg-Haddad O., Chu X. Ant lion optimizer (ALO) algorithm Advanced Optimization By Nature-Inspired Algorithms, Springer (2018), pp. 105-116Meng et al., 2020 Meng Q., et al. A single-phase transformer-less grid-tied inverter based on switched capacitor for PV application J. Control Autom. Electr. Syst., 31 (1) (2020), pp. 257-270Mir et al., 2020 Mir M., et al. Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction Evol. Syst., 11 (4) (2020), pp. 559-573Mirjalili et al., 2016 Mirjalili S., Mirjalili S.M., Hatamlou A. Multi-verse optimizer: a nature-inspired algorithm for global optimization Neural Comput. Appl., 27 (2) (2016), pp. 495-513Navid Razmjooy and Ghadimi, 2018 Navid Razmjooy F.R.S., Ghadimi Noradin A hybrid neural network – world cup optimization algorithm for melanoma detection Open Med., 13 (2018), pp. 9-16Ramezani et al., 2020 Ramezani M., Bahmanyar D., Razmjooy N. A new optimal energy management strategy based on improved multi-objective antlion optimization algorithm: applications in smart home SN Appl. Sci., 2 (12) (2020), pp. 1-17Razmjooy et al., 2020 Razmjooy N., Estrela V.V., Loschi H.J. Entropy-based breast cancer detection in digital mammograms using world cup optimization algorithm Int. J. Swarm Intell. Res. (IJSIR), 11 (3) (2020), pp. 1-18Razmjooy et al., 2016 Razmjooy N., Khalilpour M., Ramezani M. A new meta-heuristic optimization algorithm inspired by FIFA world cup competitions: theory and its application in PID designing for AVR system J. Control Autom. Electr. Syst., 27 (4) (2016), pp. 419-440Razmjooy et al., 2017 Razmjooy N., Ramezani M., Ghadimi N. Imperialist competitive algorithm-based optimization of neuro-fuzzy system parameters for automatic red-eye removal Int. J. Fuzzy Syst., 19 (4) (2017), pp. 1144-1156Saeedi et al., 2019 Saeedi M., et al. Robust optimization based optimal chiller loading under cooling demand uncertainty Appl. Therm. Eng., 148 (2019), pp. 1081-1091Shabani et al., 2020 Shabani A., et al. Search and rescue optimization algorithm: A new optimization method for solving constrained engineering optimization problems Expert Syst. Appl., 161 (2020), Article 113698Tejani et al., 2016 Tejani G.G., Savsani V.J., Patel V.K. Adaptive symbiotic organisms search (SOS) algorithm for structural design optimization J. Comput. Des. Eng., 3 (3) (2016), pp. 226-249Tejani et al., 2017 Tejani G.G., et al. Topology, shape, and size optimization of truss structures using modified teaching-learning based optimization Adv. Comput. Des., 2 (4) (2017), pp. 313-331Tejani et al., 2018a Tejani G.G., et al. Size, shape, and topology optimization of planar and space trusses using mutation-based improved metaheuristics J. Comput. Des. Eng., 5 (2) (2018), pp. 198-214Tejani et al., 2018b Tejani G.G., et al. Truss optimization with natural frequency bounds using improved symbiotic organisms search Knowl.-Based Syst., 143 (2018), pp. 162-178Tejani et al., 2019 Tejani G.G., et al. Topology optimization of truss subjected to static and dynamic constraints by integrating simulated annealing into passing vehicle search algorithms Eng. Comput., 35 (2) (2019), pp. 499-517Tian et al., 2020 Tian M.-W., et al. New optimal design for a hybrid solar chimney, solid oxide electrolysis and fuel cell based on improved deer hunting optimization algorithm J. Cleaner Prod., 249 (2020), Article 119414Wang et al., 2015 Wang G.-G., Deb S., Coelho L.d.S. Elephant herding optimization 2015 3rd International Symposium on Computational and Business Intelligence (ISCBI), IEEE (2015)Xu et al., 2020 Xu H., et al. 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Ambient Energy (2020), pp. 1-10PublicationORIGINALOptimal parameters estimation of the PEMFC using a balanced version.pdfOptimal parameters estimation of the PEMFC using a balanced version.pdfapplication/pdf1150834https://repositorio.cuc.edu.co/bitstreams/943acfe5-3b8f-4583-9817-8d9f8b3b3dd9/download8dc0cad2fa5f42470d67963701320e13MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/914c3ea9-209e-4182-b5c2-6f46d1a1222f/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/f742bcfc-3723-4404-b17f-40a4d8ade432/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILOptimal parameters estimation of the PEMFC using a balanced version.pdf.jpgOptimal parameters estimation of the PEMFC using a balanced 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