Optimal parameter identification of solid oxide fuel cell using modified fire Hawk algorithm.
Modified fire Hawk algorithm
Parameter identification
Polarization curves
Solid oxide fuel cell
Statistical analysis
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
28 Sep 2024
28 Sep 2024
Historique:
received:
19
07
2024
accepted:
09
09
2024
medline:
29
9
2024
pubmed:
29
9
2024
entrez:
28
9
2024
Statut:
epublish
Résumé
An accurate and efficient approach is required to identify the unknown parameters of solid oxide fuel cell (SOFC) mathematical model for a robust design of any energy system considering SOFC. This research study proposes a modified fire hawk algorithm (MFHA) to determine the values of SOFC model parameters. The performance evaluation of MFHA is tested on two case studies. Firstly, the performance of MFHA is tested on commercially available cylindrical cell developed by Siemens at four temperatures. Results reveal that the least value of sum of squared error (SSE) is 1.04E-05, 2.30E-05, 1.03E-05, and 1.60E-05 at 1073 K, 1173 K, 1213 K, and 1273 K respectively. Results obtained using MFHA have been compared with original fire hawk algorithm (FHA) and other well established and recent algorithms. Secondly, MFHA is implemented for estimating unknown parameters of a 5 kW dynamic tabular stack of 96 cells at various pressures and temperatures. The obtained value of SSE at different temperatures of 873 K, 923 K, 973 K, 1023 K and 1073 K is 1.18E-03, 6.12E-03, 2.21E-02, 5.18E-02, and 6.00E-02, respectively whereas, SSE at different pressures of 1 atm, 2 atm, 3 atm, 4 atm, and 5 atm is 6.05E-02, 6.11E-02, 5.53E-02, 5.11E-02, and 6.64E-02 respectively.
Identifiants
pubmed: 39341887
doi: 10.1038/s41598-024-72541-6
pii: 10.1038/s41598-024-72541-6
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
22469Informations de copyright
© 2024. The Author(s).
Références
Alam, M., Kumar, K., Verma, S. & Dutta, V. Renewable sources based DC microgrid using hydrogen energy storage: Modelling and experimental analysis. Sustain. Energy Technol. Assess. 42, 100840 (2020).
Verma, S., Kumar, K., Das, L. & Kaushik, S. Effect of hydrogen enrichment strategy on performance and emission features of biodiesel-biogas dual fuel engine using simulation and experimental analyses. J. Energy Res. Technol. 143, 092301 (2021).
doi: 10.1115/1.4049179
Verma, S., Suman, A., Das, L., Kaushik, S. & Tyagi, S. A renewable pathway towards increased utilization of hydrogen in diesel engines. Int. J. Hydrogen Energy 45, 5577–5587 (2020).
doi: 10.1016/j.ijhydene.2019.05.213
Khajuria, R., Yelisetti, S., Lamba, R. & Kumar, R. Optimal model parameter estimation and performance analysis of PEM electrolyzer using modified honey badger algorithm. Int. J. Hydrogen Energy 49, 238–259 (2024).
doi: 10.1016/j.ijhydene.2023.07.172
Kumar, K., Alam, M. & Dutta, V. Energy management strategy for integration of fuel cell-electrolyzer technologies in microgrid. Int. J. Hydrogen Energy 46, 33738–33755 (2021).
doi: 10.1016/j.ijhydene.2021.07.203
Ćalasan, M., Aleem, S. H. A., Hasanien, H. M., Alaas, Z. M. & Ali, Z. M. An innovative approach for mathematical modeling and parameter estimation of PEM fuel cells based on iterative lambert w function. Energy 264, 126165 (2023).
doi: 10.1016/j.energy.2022.126165
Khajuria, R., Lamba, R. & Kumar, R. Optimal parameter extraction and performance analysis of proton exchange membrane fuel cell. In 2022 IEEE International Conference on Power Electronics, Drives and Energy Systems (PEDES) 1–6 (IEEE, 2022).
Zhang, B. et al. Parameter identification of proton exchange membrane fuel cell based on swarm intelligence algorithm. Energy 128935 (2023).
Khajuria, R., Yelisetti, S., Lamba, R. & Kumar, R. Optimal model parameter estimation and performance analysis of PEM electrolyzer using modified honey badger algorithm. Int. J. Hydrogen Energy (2023).
Fathy, A. & Rezk, H. Political optimizer based approach for estimating SOFC optimal parameters for static and dynamic models. Energy 238, 122031 (2022).
doi: 10.1016/j.energy.2021.122031
Khajuria, R., Lamba, R., Kumar, R. & Yelisetti, S. Application of metaheuristic techniques in optimal parameter estimation of solid oxide fuel cell. In International Conference on Advances in Energy Research 605–613 (Springer, 2022).
Xing, Y. Adaptive parameter estimation. In Modeling and Control Strategies for a Fuel Cell System 83–133 (Springer, 2023).
Khajuria, R., Lamba, R. & Kumar, R. Model parameter extraction for PEM electrolyzer using honey badger algorithm. In 2023 IEEE 3rd International Conference on Sustainable Energy and Future Electric Transportation (SEFET) 1–6 (IEEE, 2023).
Wang, J., Xu, Y.-P., She, C., Xu, P. & Bagal, H. A. Optimal parameter identification of SOFC model using modified gray wolf optimization algorithm. Energy 240, 122800 (2022).
doi: 10.1016/j.energy.2021.122800
Guo, H., Gu, W., Khayatnezhad, M. & Ghadimi, N. Parameter extraction of the SOFC mathematical model based on fractional order version of dragonfly algorithm. Int. J. Hydrogen Energy 47, 24059–24068 (2022).
doi: 10.1016/j.ijhydene.2022.05.190
Hao, P. & Sobhani, B. Application of the improved chaotic grey wolf optimization algorithm as a novel and efficient method for parameter estimation of solid oxide fuel cells model. Int. J. Hydrogen Energy 46, 36454–36465 (2021).
doi: 10.1016/j.ijhydene.2021.08.174
Bai, Q. & Li, H. The application of hybrid cuckoo search-grey wolf optimization algorithm in optimal parameters identification of solid oxide fuel cell. Int. J. Hydrogen Energy 47, 6200–6216 (2022).
doi: 10.1016/j.ijhydene.2021.11.216
Kele, C., Xinmei, W. & Youssefi, N. Model parameter estimation of SOFCS using a modified cat optimization algorithm. Sustain. Energy Technol. Assess. 52, 102176 (2022).
Zhang, M. et al. An optimal model identification for solid oxide fuel cell based on extreme learning machines optimized by improved red fox optimization algorithm. Int. J. Hydrogen Energy 46, 28270–28281 (2021).
doi: 10.1016/j.ijhydene.2021.06.046
Xiong, G., Zhang, J., Shi, D., Zhu, L. & Yuan, X. Optimal identification of solid oxide fuel cell parameters using a competitive hybrid differential evolution and jaya algorithm. Int. J. Hydrogen Energy 46, 6720–6733 (2021).
doi: 10.1016/j.ijhydene.2020.11.119
Yousri, D., Hasanien, H. M. & Fathy, A. Parameters identification of solid oxide fuel cell for static and dynamic simulation using comprehensive learning dynamic multi-swarm marine predators algorithm. Energy Convers. Manag. 228, 113692 (2021).
doi: 10.1016/j.enconman.2020.113692
Ba, S., Xia, D. & Gibbons, E. M. Model identification and strategy application for solid oxide fuel cell using rotor hopfield neural network based on a novel optimization method. Int. J. Hydrogen Energy 45, 27694–27704 (2020).
doi: 10.1016/j.ijhydene.2020.07.127
Xiong, G., Zhang, J., Shi, D. & Yuan, X. A simplified competitive swarm optimizer for parameter identification of solid oxide fuel cells. Energy Convers. Manag. 203, 112204 (2020).
doi: 10.1016/j.enconman.2019.112204
Yang, B. et al. A state-of-the-art survey of solid oxide fuel cell parameter identification: Modelling, methodology, and perspectives. Energy Convers. Manag. 213, 112856 (2020).
doi: 10.1016/j.enconman.2020.112856
Wei, Y. & Stanford, R. J. Parameter identification of solid oxide fuel cell by chaotic binary shark smell optimization method. Energy 188, 115770 (2019).
doi: 10.1016/j.energy.2019.07.100
Wang, N., Wang, D., Xing, Y., Shao, L. & Afzal, S. Application of co-evolution RNA genetic algorithm for obtaining optimal parameters of SOFC model. Renew. Energy 150, 221–233 (2020).
doi: 10.1016/j.renene.2019.12.105
El-Hay, E., El-Hameed, M. & El-Fergany, A. Optimized parameters of SOFC for steady state and transient simulations using interior search algorithm. Energy 166, 451–461 (2019).
doi: 10.1016/j.energy.2018.10.038
Yang, B. et al. Extreme learning machine based meta-heuristic algorithms for parameter extraction of solid oxide fuel cells. Appl. Energy 303, 117630 (2021).
doi: 10.1016/j.apenergy.2021.117630
Shi, H., Li, J. & Zafetti, N. New optimized technique for unknown parameters selection of SOFC using converged grass fibrous root optimization algorithm. Energy Rep. 6, 1428–1437 (2020).
doi: 10.1016/j.egyr.2020.05.024
Abaza, A., El Sehiemy, R. A. & Bayoumi, A. S. A. Optimal parameter estimation of solid oxide fuel cell model using coyote optimization algorithm. In Recent Advances in Engineering Mathematics and Physics: Proceedings of the International Conference RAEMP 2019 135–149 (Springer, 2020).
Bagal, H. A. et al. SOFC model parameter identification by means of modified African vulture optimization algorithm. Energy Rep. 7, 7251–7260 (2021).
doi: 10.1016/j.egyr.2021.10.073
Jia, H. & Taheri, B. Model identification of solid oxide fuel cell using hybrid Elman neural network/quantum pathfinder algorithm. Energy Rep. 7, 3328–3337 (2021).
doi: 10.1016/j.egyr.2021.05.070
Hachana, O. & El-Fergany, A. A. Efficient PEM fuel cells parameters identification using hybrid artificial bee colony differential evolution optimizer. Energy 250, 123830 (2022).
doi: 10.1016/j.energy.2022.123830
Khajuria, R., Lamba, R. & Kumar, R. Parameters extraction of PEMFC model using evolutionary based optimization algorithms. In International Conference on Advances in Energy Research 443–451 (Springer, 2022).
Fathy, A., Rezk, H., Alharbi, A. G. & Yousri, D. Proton exchange membrane fuel cell model parameters identification using chaotically based-bonobo optimizer. Energy 268, 126705 (2023).
doi: 10.1016/j.energy.2023.126705
Vaze, R., Deshmukh, N., Kumar, R. & Saxena, A. Development and application of quantum entanglement inspired particle swarm optimization. Knowl.-Based Syst. 219, 106859 (2021).
doi: 10.1016/j.knosys.2021.106859
Deshmukh, N., Vaze, R., Kumar, R. & Saxena, A. Quantum entanglement inspired grey wolf optimization algorithm and its application. Evol. Intel. 16, 1097–1114 (2023).
doi: 10.1007/s12065-022-00721-2
Abdollahzadeh, B. et al. Puma optimizer (PO): A novel metaheuristic optimization algorithm and its application in machine learning. Cluster Comput. 1–49 (2024).
Barua, S. & Merabet, A. Lévy arithmetic algorithm: An enhanced metaheuristic algorithm and its application to engineering optimization. Expert Syst. Appl. 241, 122335 (2024).
doi: 10.1016/j.eswa.2023.122335
Tian, Z. & Gai, M. Football team training algorithm: A novel sport-inspired meta-heuristic optimization algorithm for global optimization. Expert Syst. Appl. 245, 123088 (2024).
doi: 10.1016/j.eswa.2023.123088
Oladejo, S. O., Ekwe, S. O. & Mirjalili, S. The hiking optimization algorithm: A novel human-based metaheuristic approach. Knowl.-Based Syst. 296, 111880 (2024).
doi: 10.1016/j.knosys.2024.111880
Zhang, J., Wu, W. & Mobayen, S. System identification of solid oxide fuel cell models using improved version of cat and mouse optimizer. Energy Sources Part A Recovery Util. Environ. Eff. 45, 2553–2571 (2023).
Ebrahimi, S. M., Hasanzadeh, S. & Khatibi, S. Parameter identification of fuel cell using repairable grey wolf optimization algorithm. Appl. Soft Comput. 147, 110791 (2023).
doi: 10.1016/j.asoc.2023.110791
Ismael, I., El-Fergany, A. A., Gouda, E. A. & Kotb, M. F. Cooperation search algorithm for optimal parameters identification of SOFCS feeding electric vehicle at steady and dynamic modes. Int. J. Hydrogen Energy 50, 1395–1407 (2024).
doi: 10.1016/j.ijhydene.2023.07.027
Azizi, M., Talatahari, S. & Gandomi, A. H. Fire hawk optimizer: A novel metaheuristic algorithm. Artif. Intell. Rev. 56, 287–363 (2023).
doi: 10.1007/s10462-022-10173-w
Shishehgarkhaneh, M. B., Azizi, M., Basiri, M. & Moehler, R. C. BIM-based resource tradeoff in project scheduling using fire hawk optimizer (FHO). Buildings 12, 1472 (2022).
doi: 10.3390/buildings12091472
Hosseinzadeh, M. et al. A cluster-based trusted routing method using fire hawk optimizer (FHO) in wireless sensor networks (WSNS). Sci. Rep. 13, 13046 (2023).
pubmed: 37567984
pmcid: 10421948
doi: 10.1038/s41598-023-40273-8
Mudhsh, M. et al. Modelling of thermo-hydraulic behavior of a helical heat exchanger using machine learning model and fire hawk optimizer. Case Stud. Therm. Eng. 49, 103294 (2023).
doi: 10.1016/j.csite.2023.103294
Jasmine, G. S., Stanislaus, R., Kumar, N. M. & Logeswaran, T. Optimal location for an EVPL and capacitors in grid for voltage profile and power loss: FHO-SNN approach. Expert Syst. Appl. 239, 121980 (2024).
doi: 10.1016/j.eswa.2023.121980
Kumari, K. A., Soujanya, T., Alsalami, Z., Rohini, I. et al. Fire hawk optimization based multi-objective dynamic load balancing in cloud computing. In 2024 International Conference on Distributed Computing and Optimization Techniques (ICDCOT) 1–4 (IEEE, 2024).
Hosseinzadeh, M. et al. DCFH: A dynamic clustering approach based on fire hawk optimizer in flying ad hoc networks. Veh. Commun. 47, 100778 (2024).
Alsattar, H. A., Zaidan, A. & Zaidan, B. Novel meta-heuristic bald eagle search optimisation algorithm. Artif. Intell. Rev. 53, 2237–2264 (2020).
doi: 10.1007/s10462-019-09732-5
Kennedy, J. & Eberhart, R. Particle swarm optimization. In Proceedings of ICNN’95-International Conference on Neural Networks, vol. 4, 1942–1948 (IEEE, 1995).
Karaboğa, D. & Ökdem, S. A simple and global optimization algorithm for engineering problems: Differential evolution algorithm. Turk. J. Electr. Eng. Comput. Sci. 12, 53–60 (2004).
Mirjalili, S., Mirjalili, S. M. & Lewis, A. Grey wolf optimizer. Adv. Eng. Softw. 69, 46–61 (2014).
doi: 10.1016/j.advengsoft.2013.12.007
Mirjalili, S. & Lewis, A. The whale optimization algorithm. Adv. Eng. Softw. 95, 51–67 (2016).
doi: 10.1016/j.advengsoft.2016.01.008
Zhao, W., Zhang, Z. & Wang, L. Manta ray foraging optimization: An effective bio-inspired optimizer for engineering applications. Eng. Appl. Artif. Intell. 87, 103300 (2020).
doi: 10.1016/j.engappai.2019.103300
Zhao, W., Wang, L. & Zhang, Z. Artificial ecosystem-based optimization: A novel nature-inspired meta-heuristic algorithm. Neural Comput. Appl. 32, 9383–9425 (2020).
doi: 10.1007/s00521-019-04452-x
Hashim, F. A., Houssein, E. H., Hussain, K., Mabrouk, M. S. & Al-Atabany, W. Honey badger algorithm: New metaheuristic algorithm for solving optimization problems. Math. Comput. Simul. 192, 84–110 (2022).
doi: 10.1016/j.matcom.2021.08.013
Obadina, O. O., Thaha, M. A., Mohamed, Z. & Shaheed, M. H. Grey-box modelling and fuzzy logic control of a leader-follower robot manipulator system: A hybrid grey wolf-whale optimisation approach. ISA Trans. 129, 572–593 (2022).
pubmed: 35277266
doi: 10.1016/j.isatra.2022.02.023
Alhumade, H., Fathy, A., Al-Zahrani, A., Rawa, M. J. & Rezk, H. Optimal parameter estimation methodology of solid oxide fuel cell using modern optimization. Mathematics 9, 1066 (2021).
doi: 10.3390/math9091066
Wang, C. & Nehrir, M. H. A physically based dynamic model for solid oxide fuel cells. IEEE Trans. Energy Convers. 22, 887–897 (2007).
doi: 10.1109/TEC.2007.895468
Luo, R. & Shafiee, M. The application of metaheuristics in optimal parameter identification of solid oxide fuel cell. Energy Rep. 7, 2563–2573 (2021).
doi: 10.1016/j.egyr.2021.04.052
El-Hay, E., El-Hameed, M. & El-Fergany, A. Steady-state and dynamic models of solid oxide fuel cells based on satin bowerbird optimizer. Int. J. Hydrogen Energy 43, 14751–14761 (2018).
doi: 10.1016/j.ijhydene.2018.06.032