Moth flame-random search optimization of a zero-dimensional model of a proton exchange membrane fuel cell
Original scientific paper
DOI:
https://doi.org/10.5599/jese.1991Keywords:
Fuel cell system, parameters estimation, global optimization algorithm, chemical energy, hybrid algorithm
Abstract
Modelling of proton exchange membrane fuel cell (PEMFC) is important for better understanding, simulation, and design of high-efficiency fuel cell systems. PEMFC models are often multivariate with several nonlinear elements. Metaheuristic algorithms that are successful in solving nonlinear optimization problems are good candidates for this purpose. This study proposes a new metaheuristic algorithm called MFORS that uses the advantages of the moth-flame optimization algorithm in global search and the non-deterministic properties of the random search algorithm to identify the optimal parameters of the PEMFC model. The sum of squared errors between the estimated and measured voltage is a quality of fit criterion. To show the effectiveness of the proposed algorithm, two case studies of zero-dimensional models of SR-12 Modular PEM Generator and Ballard Mark V fuel cell are considered. The sum of squared errors for the parameter estimated of electrical PEMFCs by the proposed MFORS algorithm is compared with recent works. The results showed that by the MFORS algorithm, the minimum sum of absolute errors of the actual stack voltage and the simulated stack voltage in the two PEMFC are 0.095037 and 0.018019, compared with the second-best algorithm results giving 0.09681 and 0.8092, respectively.
Downloads
References
M. K. Singla, P. Nijhawan, A. S. Oberoi, Hydrogen fuel and fuel cell technology for cleaner future: a review, Environmental Science and Pollution Research 28 (2021) 15607-15626. https://doi.org/10.1007/s11356-020-12231-8
S. Turkdogan, Design and optimization of a solely renewable based hybrid energy system for residential electrical load and fuel cell electric vehicle, Engineering Science and Technology, an International Journal 24 (2021) 397-404. https://doi.org/10.1016/j.jestch.2020.08.017
Z. Zakaria, S. K. Kamarudin, K. A. Abd Wahid, S. H. A. Hassan, The progress of fuel cell for malaysian residential consumption: Energy status and prospects to introduction as a renewable power generation system, Renewable and Sustainable Energy Reviews 144 (2021) 110984. https://doi.org/10.1016/j.rser.2021.110984
Z. Zakaria, S. K. Kamarudin, K. A. A. Wahid, Fuel cells as an advanced alternative energy source for the residential sector applications in Malaysia, International Journal of Energy Research 45 (2021) 5032-5057. https://doi.org/10.1002/er.6252
E. El-Hay, M. El-Hameed, A. El-Fergany, Optimized parameters of SOFC for steady state and transient simulations using interior search algorithm, Energy 166 (2019) 451-461. https://doi.org/10.1016/j.energy.2018.10.038
A. A. El‐Fergany, Electrical characterisation of proton exchange membrane fuel cells stack using grasshopper optimiser, IET Renewable Power Generation 12 (2018) 9-17. https://doi.org/10.1049/iet-rpg.2017.0232
X. Cai, D. Wu, J. Sun, B. Chen, The effect of cathode channel blockages on the enhanced mass transfer and performance of PEMFC, Energy 222 (2021) 119951. https://doi.org/10.1016/j.energy.2021.119951
A. Fathy, H. Rezk, H. S. M. Ramadan, Recent moth-flame optimizer for enhanced solid oxide fuel cell output power via optimal parameters extraction process, Energy 207 (2020) 118326. https://doi.org/10.1016/j.energy.2020.118326
A. Abdullah, H. Rezk, A. Hadad, M. Hassan, A. Mohamed, Optimal parameter estimation of proton exchange membrane fuel cells, Intelligent Automation & Soft Computing 29 (2021) 619-631. https://doi.org/10.32604/iasc.2021.018289
R. Ben Messaoud, Parameters determination of proton exchange membrane fuel cell stack electrical model by employing the hybrid water cycle moth‐flame optimization algorithm, International Journal of Energy Research 45 (2021) 4694-4708. https://doi.org/10.1002/er.6065
R. B. Messaoud, A. Midouni, S. Hajji, PEM fuel cell model parameters extraction based on moth-flame optimization, Chemical Engineering Science 229 (2021) 116100. https://doi.org/10.1016/j.ces.2020.116100
A. A. Z. Diab, H. Ali, H. Abdul-Ghaffar, H. A. Abdelsalam, M. Abd El Sattar, Accurate parameters extraction of PEMFC model based on metaheuristics algorithms, Energy Reports 7 (2021) 6854-6867. https://doi.org/10.1016/j.egyr.2021.09.145
E. H. Houssein, B. E. Helmy, H. Rezk, A. M. Nassef, An enhanced Archimedes optimization algorithm based on Local escaping operator and Orthogonal learning for PEM fuel cell parameter identification, Engineering Applications of Artificial Intelligence 103 (2021) 104309. https://doi.org/10.1016/j.engappai.2021.104309
H. Rezk, S. Ferahtia, A. Djeroui, A. Chouder, A. Houari, M. Machmoum, M. A. Abdelkareem, Optimal parameter estimation strategy of PEM fuel cell using gradient-based optimizer, Energy 239 (2022) 122096. https://doi.org/10.1016/j.energy.2021.122096
M. Kandidayeni, A. Macias, A. Khalatbarisoltani, L. Boulon, S. Kelouwani, Benchmark of proton exchange membrane fuel cell parameters extraction with metaheuristic optimization algorithms, Energy 183 (2019) 912-925. https://doi.org/10.1016/j.energy.2019.06.152
K. Priya, N. Rajasekar, Application of flower pollination algorithm for enhanced proton exchange membrane fuel cell modelling, International Journal of Hydrogen Energy 44 (2019) 18438-18439. https://doi.org/10.1016/j.ijhydene.2019.05.022
S. Xu, X. Wang, Z. Wang, Parameter estimation of proton exchange membrane fuel cells using eagle strategy based on JAYA algorithm and Nelder-Mead simplex method, Energy 173 (2019) 457-467. https://doi.org/10.1016/j.energy.2019.02.106
A. Fathy, M. A. Elaziz, A. G. Alharbi, A novel approach based on hybrid vortex search algorithm and differential evolution for identifying the optimal parameters of PEM fuel cell, Renewable Energy 146 (2020) 1833-1845. https://doi.org/10.1016/j.renene.2019.08.046
X. Chen, N. Wang, Cuckoo search algorithm with explosion operator for modeling proton exchange membrane fuel cells, International Journal of Hydrogen Energy 44 (2019) 3075-3087. https://doi.org/10.1016/j.ijhydene.2018.11.140
H. M. Hasanien, M. A. Shaheen, R. A. Turky, M. H. Qais, S. Alghuwainem, S. Kamel, M. Tostado-Véliz, F. Jurado, Precise modeling of PEM fuel cell using a novel Enhanced Transient Search Optimization algorithm, Energy 247 (2022) 123530. https://doi.org/10.1016/j.energy.2022.123530
M. Abdel-Basset, R. Mohamed, A. El-Fergany, R. K. Chakrabortty, M. J. Ryan, Adaptive and efficient optimization model for optimal parameters of proton exchange membrane fuel cells: A comprehensive analysis, Energy 233 (2021) 121096. https://doi.org/10.1016/j.energy.2021.121096
S. Mirjalili, Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm, Knowledge-Based Systems 89 (2015) 228-249. https://doi.org/10.1016/j.knosys.2015.07.006
P. Singh, S. Bishnoi, Modified moth-Flame optimization for strategic integration of fuel cell in renewable active distribution network, Electric Power Systems Research 197 (2021) 107323. https://doi.org/10.1016/j.epsr.2021.107323
M. Talaat, A. Alblawi, M. Tayseer, M. Elkholy, FPGA control system technology for integrating the PV/wave/FC hybrid system using ANN optimized by MFO techniques, Sustainable Cities and Society 80 (2022) 103825. https://doi.org/10.1016/j.scs.2022.103825
X. Zhang, P. Wang, H. Yang, Q. Cui, Optimal dispatching of microgrid based on improved moth-flame optimization algorithm based on sine mapping and Gaussian mutation, Systems Science & Control Engineering 10 (2022) 115-125. https://doi.org/10.1080/21642583.2022.2042424
A. A. Zhigljavsky, Theory of global random search, Springer Science & Business Media, 2012, 9401134367. https://doi.org/10.1007/978-94-011-3436-1
M. Abdel-Basset, R. Mohamed, M. Elhoseny, R. K. Chakrabortty, M. J. Ryan, An efficient heap-based optimization algorithm for parameters identification of proton exchange membrane fuel cells model: Analysis and case studies, International Journal of Hydrogen Energy 46 (2021) 11908-11925. https://doi.org/10.1016/j.ijhydene.2021.01.076
Q. Niu, H. Zhang, K. Li, An improved TLBO with elite strategy for parameters identification of PEM fuel cell and solar cell models, International Journal of Hydrogen Energy 39 (2014) 3837-3854. https://doi.org/10.1016/j.ijhydene.2013.12.110
S. Yang, R. Chellali, X. Lu, L. Li, C. Bo, Modeling and optimization for proton exchange membrane fuel cell stack using aging and challenging P systems based optimization algorithm, Energy 109 (2016) 569-577. https://doi.org/10.1016/j.energy.2016.04.093
D. Yousri, S. Mirjalili, J. T. Machado, S. B. Thanikanti, A. Fathy, Efficient fractional-order modified Harris hawks optimizer for proton exchange membrane fuel cell modeling, Engineering Applications of Artificial Intelligence 100 (2021) 104193. https://doi.org/10.1016/j.engappai.2021.104193
H. Rezk, A. Olabi, S. Ferahtia, E. T. Sayed, Accurate parameter estimation methodology applied to model proton exchange membrane fuel cell, Energy 255 (2022) 124454. https://doi.org/10.1016/j.energy.2022.124454
E. A. Gouda, M. F. Kotb, A. A. El-Fergany, Investigating dynamic performances of fuel cells using pathfinder algorithm, Energy Conversion and Management 237 (2021) 114099. https://doi.org/10.1016/j.enconman.2021.114099
H. M. Sultan, A. S. Menesy, S. Kamel, A. Selim, F. Jurado, Parameter identification of proton exchange membrane fuel cells using an improved salp swarm algorithm, Energy Conversion and Management 224 (2020) 113341. https://doi.org/10.1016/j.enconman.2020.113341
X. Zhu, N. Yousefi, Optimal parameter identification of PEMFC stacks using adaptive sparrow search algorithm, International Journal of Hydrogen Energy 46 (2021) 9541-9552. https://doi.org/10.1016/j.ijhydene.2020.12.107
I. Alsaidan, M. A. Shaheen, H. M. Hasanien, M. Alaraj, A. S. Alnafisah, A PEMFC model optimization using the enhanced bald eagle algorithm, Ain Shams Engineering Journal 13 (2022) 101749. https://doi.org/10.1016/j.asej.2022.101749
M. K. Singla, P. Nijhawan, A. S. Oberoi, Parameter estimation of proton exchange membrane fuel cell using a novel meta-heuristic algorithm, Environmental Science and Pollution Research 28 (2021) 34511-34526. https://doi.org/10.1007/s11356-021-13097-0
A. A. El-Fergany, H. M. Hasanien, A. M. Agwa, Semi-empirical PEM fuel cells model using whale optimization algorithm, Energy Conversion and Management 201 (2019) 112197. https://doi.org/10.1016/j.enconman.2019.112197
O. Hachana, Accurate PEM fuel cells parameters estimation using hybrid artificial bee colony differential evolution shuffled complex optimizer, International Journal of Energy Research 46 (2022) 6383-6405. https://doi.org/10.1002/er.7576
O. Hachana, A. A. El-Fergany, Efficient PEM fuel cells parameters identification using hybrid artificial bee colony differential evolution optimizer, Energy 250 (2022) 123830. https://doi.org/10.1016/j.energy.2022.123830
H. M. Sultan, A. S. Menesy, S. Kamel, M. Tostado-Véliz, F. Jurado, Parameter Identification of Proton Exchange Membrane Fuel Cell Stacks Using Bonobo Optimizer, 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I&CPS Europe), 2020, pp. 1-7. https://doi.org/10.1109/EEEIC/ICPSEurope49358.2020.9160597
E. A. Gouda, M. F. Kotb, A. A. El-Fergany, Jellyfish search algorithm for extracting unknown parameters of PEM fuel cell models: Steady-state performance and analysis, Energy 221 (2021) 119836. https://doi.org/10.1016/j.energy.2021.119836
A. S. Menesy, H. M. Sultan, A. Korashy, F. A. Banakhr, M. G. Ashmawy, S. Kamel, Effective parameter extraction of different polymer electrolyte membrane fuel cell stack models using a modified artificial ecosystem optimization algorithm, IEEE Access 8 (2020) 31892-31909. https://doi.org/10.1109/ACCESS.2020.2973351
A. A. Z. Diab, M. A. Tolba, A. G. A. El-Magd, M. M. Zaky, A. M. El-Rifaie, Fuel cell parameters estimation via marine predators and political optimizers, IEEE Access 8 (2020) 166998-167018. https://doi.org/10.1109/ACCESS.2020.3021754
X. Rao, Z. Shao, A. H. Ahangarnejad, E. Gholamalizadeh, B. Sobhani, Shark Smell Optimizer applied to identify the optimal parameters of the proton exchange membrane fuel cell model, Energy Conversion and Management 182 (2019) 1-8. https://doi.org/10.1016/j.enconman.2018.12.057
A. A. Z. Diab, H. I. Abdul‐Ghaffar, A. A. Ahmed, H. A. Ramadan, An effective model parameter estimation of PEMFCs using GWO algorithm and its variants, IET Renewable Power Generation 16 (2022) 1380-1400. https://doi.org/10.1049/rpg2.12359
J. M. Corrêa, F. A. Farret, L. . Canha, M. G. Simoes, An electrochemical-based fuel-cell model suitable for electrical engineering automation approach, IEEE Transactions on industrial electronics 51 (2004) 1103-1112. https://doi.org/10.1109/TIE.2004.834972
A. Askarzadeh, L. dos Santos Coelho, A backtracking search algorithm combined with Burger's chaotic map for parameter estimation of PEMFC electrochemical model, International journal of hydrogen energy 39 (2014) 11165-11174. https://doi.org/10.1016/j.ijhydene.2014.05.052
J. M. Correa, F. A. Farret, V. A. Popov, M. G. Simoes, Sensitivity analysis of the modeling parameters used in simulation of proton exchange membrane fuel cells, IEEE Transactions on Energy Conversion 20 (2005) 211-218. https://doi.org/10.1109/TEC.2004.842382
R. F. Mann, J. C. Amphlett, M. A. Hooper, H. M. Jensen, B. A. Peppley, P. R. Roberge, Development and application of a generalised steady-state electrochemical model for a PEM fuel cell, Journal of Power Sources 86 (2000) 173-180. https://doi.org/10.1016/S0378-7753(99)00484-X
H. Saleem, S. Karmalkar, An Analytical Method to Extract the Physical Parameters of a Solar Cell From Four Points on the Illuminated J-V Curve, IEEE Electron Device Letters 30 (2009) 349-352. https://doi.org/10.1109/LED.2009.2013882
Z. B. Zabinsky, Stochastic adaptive search for global optimization, Springer Science & Business Media, 2013, 1441991824. https://doi.org/10.1007/978-1-4419-9182-9
J. Feng, W. Z. Shen, Optimization of Wind Farm Layout: A Refinement Method by Random Search, International Conference on aerodynamics of Offshore Wind Energy Systems and wakes (ICOWES 2013), 2013, pp. 624-633.
S. Z. Ramadan, A Hybrid Global Optimization Method Based on Genetic Algorithm and Shrinking Box, Modern Applied Science 10 (2016) 67-82. https://doi.org/10.5539/mas.v10n2p67
M. Anfal, H. Abdelhafid, Optimal Placement of PMUs in Algerian Network using a Hybrid Particle Swarm–Moth Flame Optimizer (PSO-MFO), Electrotehnica, Electronica, Automatica 65 (2017) 191-196.
H. Zhao, H. Zhao, S. Guo, Using GM (1, 1) Optimized by MFO with rolling mechanism to forecast the electricity consumption of Inner Mongolia, Applied Sciences 6 (2016) 20. https://doi.org/10.3390/app6010020
L. Zhang, K. Mistry, S. C. Neoh, C. P. Lim, Intelligent facial emotion recognition using moth-firefly optimization, Knowledge-Based Systems 111 (2016) 248-267. https://doi.org/10.1016/j.knosys.2016.08.018
X. Wang, Modeling of two-phase transport in the diffusion media of polymer electrolyte fuel cells, Journal of Power Sources 185 (2008) 261-271. https://doi.org/10.1016/j.jpowsour.2008.07.007
A. Askarzadeh, Parameter estimation of fuel cell polarization curve using BMO algorithm, International Journal of Hydrogen Energy 38 (2013) 15405-15413. https://doi.org/10.1016/j.ijhydene.2013.09.047
K. Priya, . Rajasekar, Application of flower pollination algorithm for enhanced proton exchange membrane fuel cell modelling, International Journal of Hydrogen Energy 44 (2019) 18438-18449. https://doi.org/10.1016/j.ijhydene.2019.05.022
B. Mohanty, R. Madurai Elavarasan, H. M. Hasanien, E. Devaraj, R. A. Turky, R. Pugazhendhi, Parameters Identification of Proton Exchange Membrane Fuel Cell Model Based on the Lightning Search Algorithm, Energies 15 (2022) 7893. https://doi.org/10.3390/en15217893
S. A. Mujeer, X. Chandrasekhar, M. S. Kumari, S. R. Salkuti, An accurate method for parameter estimation of proton exchange membrane fuel cell using Dandelion optimizer, International Journal of Emerging Electric Power Systems (2023). https://doi.org/10.1515/ijeeps-2023-0025
A. J. Riad, H. M. Hasanien, R. A. Turky, A. H. Yakout, Identifying the PEM Fuel Cell Parameters Using Artificial Rabbits Optimization Algorithm, Sustainability 15 (2023) 4625. https://doi.org/10.3390/su15054625a
Downloads
Published
How to Cite
Issue
Section
License
Articles are published under the terms and conditions of the
Creative Commons Attribution license 4.0 International.
Funding data
-
Torbat Heydariyeh University of Medical Sciences
Grant numbers UTH1401/10/05147
