190
Views
5
CrossRef citations to date
0
Altmetric
Research Article

Optimal parameters estimation of the proton exchange membrane fuel cell stacks using a combined owl search algorithm

, , , ORCID Icon & ORCID Icon
Pages 11712-11732 | Received 04 Apr 2023, Accepted 28 Jun 2023, Published online: 03 Oct 2023

References

  • Abdullah, N., R. Saidur, A.M. Zainoodin, and N. Aslfattahi. 2020. Optimization of electrocatalyst performance of platinum–ruthenium induced with MXene by response surface methodology for clean energy application. Journal of Cleaner Production 277:123395. doi:10.1016/j.jclepro.2020.123395.
  • Akbary, P., et al. 2019. Extracting appropriate nodal marginal prices for all types of committed reserve. Computational Economics. 53(1):1–26. doi:10.1007/s10614-017-9716-2.
  • Alsaidan, I., M.A. Shaheen, H.M. Hasanien, M. Alaraj, and A.S. Alnafisah. 2022. A PEMFC model optimization using the enhanced bald eagle algorithm. Ain Shams Engineering Journal. 13(6):101749. doi:10.1016/j.asej.2022.101749.
  • Amali, D., and M. Dinakaran. 2019. Wildebeest herd optimization: A new global optimization algorithm inspired by wildebeest herding behaviour. Journal of Intelligent & Fuzzy Systems. 37 (6):1–14. (Preprint). doi:10.3233/JIFS-190495.
  • Barbir, F. 2012. PEM fuel cells: Theory and practice. Academic press, elsevier.
  • Biedrzycki, R. 2017. A version of IPOP-CMA-ES algorithm with midpoint for CEC 2017 single objective bound constrained problems. 2017 IEEE Congress on Evolutionary Computation (CEC) Donostia, Spain, IEEE.
  • Cai, W., et al. 2019. Optimal bidding and offering strategies of compressed air energy storage: A hybrid robust-stochastic approach. Renewable Energy 143:1–8. doi:10.1016/j.renene.2019.05.008.
  • Cao, Y., Li, Y., Zhang, G., Jermsittiparsert, K. and Razmjooy, N. 2019a. Experimental modeling of PEM fuel cells using a new improved seagull optimization algorithm. Energy Reports 5:1616–25. doi:10.1016/j.egyr.2019.11.013.
  • Cao, Y., Y. Wu, L. Fu, K. Jermsittiparsert, and N. Razmjooy. 2019b. Multi-objective optimization of a PEMFC based CCHP system by meta-heuristics. Energy Reports 5:1551–59. doi:10.1016/j.egyr.2019.10.029.
  • Corrêa, J. M., Farret, F. A., Canha, L. N. and Simoes, M. G. 2004. An electrochemical-based fuel-cell model suitable for electrical engineering automation approach. IEEE Transactions on Industrial Electronics. 51(5):1103–12. doi:10.1109/TIE.2004.834972.
  • Dehghani, M., M. Ghiasi, T. Niknam, A. Kavousi-Fard, M. Shasadeghi, N. Ghadimi, and F. Taghizadeh-Hesary. 2021. Blockchain-based securing of data exchange in a power transmission system considering congestion management and social welfare. Sustainability 13 (1):90. doi:10.3390/su13010090.
  • Deng, M., Zhang, Q., Huang, Y. and Zhang, X. 2021. Integration and optimization for a PEMFC and PSA oxygen production combined system. Energy Conversion and Management 236:114062. doi:10.1016/j.enconman.2021.114062.
  • Dhiman, G., and V. Kumar. 2019. Seagull optimization algorithm: Theory and its applications for large-scale industrial engineering problems. Knowledge-Based Systems 165:169–96. doi:10.1016/j.knosys.2018.11.024.
  • Diab, A. A. Z., M. A. Tolba, A. G. A. El-Magd, M. M. Zaky, and A. M. El-Rifaie. 2020. Fuel cell parameters estimation via marine predators and political optimizers. IEEE Access 8:166998–7018. doi:10.1109/ACCESS.2020.3021754.
  • Duan, F., et al. 2022. Model parameters identification of the PEMFCs using an improved design of crow search algorithm. International Journal of Hydrogen Energy. 47(79):33839–49. doi:10.1016/j.ijhydene.2022.07.251.
  • Eslami, M., et al., 2018. A new formulation to reduce the number of variables and constraints to Expedite SCUC in Bulky power Systems. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 1–11.
  • Fan, X., Sun, H., Yuan, Z., Li, Z., Shi, R. and Ghadimi, N. 2020a. High voltage gain DC/DC converter using coupled inductor and VM techniques. IEEE Access 8:131975–87. doi:10.1109/ACCESS.2020.3002902.
  • Fan, X., H. Sun, Z. Yuan, Z. Li, R. Shi, and N. Razmjooy. 2020b. Multi-objective optimization for the proper selection of the best heat pump technology in a fuel cell-heat pump micro-CHP system. Energy Reports 6:325–35. doi:10.1016/j.egyr.2020.01.009.
  • Firouz, M. H., and N. Ghadimi. 2016. Concordant controllers based on FACTS and FPSS for solving wide-area in multi-machine power system. Journal of Intelligent & Fuzzy Systems 30 (2):845–59. doi:10.3233/IFS-151807.
  • Gao, W., Darvishan, A., Toghani, M., Mohammadi, M., Abedinia, O. and Ghadimi, N. 2019. Different states of multi-block based forecast engine for price and load prediction. International Journal of Electrical Power & Energy Systems 104:423–35. doi:10.1016/j.ijepes.2018.07.014.
  • Ghadimi, N., et al. 2023. An innovative technique for optimization and sensitivity analysis of a PV/DG/BESS based on converged Henry gas solubility optimizer: A case study. IET Generation, Transmission and Distribution. doi:10.1049/gtd2.12773.
  • Ghadimi, N., E. Yasoubi, E. Akbari, M. H. Sabzalian, H. A. Alkhazaleh, and M. Ghadamyari. 2023. SqueezeNet for the forecasting of the energy demand using a combined version of the sewing training-based optimization algorithm. Heliyon 9 (6):e16827. doi:10.1016/j.heliyon.2023.e16827.
  • Gheydi, M., A. Nouri, and N. Ghadimi. 2016. Planning in microgrids with conservation of voltage reduction. IEEE Systems Journal 12 (3):2782–90. doi:10.1109/JSYST.2016.2633512.
  • Ghiasi, M., Niknam, T., Wang, Z., Mehrandezh, M., Dehghani, M. and Ghadimi, N. 2023a. A comprehensive review of cyber-attacks and defense mechanisms for improving security in smart grid energy systems: Past, present and future. Electric Power Systems Research 215:108975. doi:10.1016/j.epsr.2022.108975.
  • Ghiasi, M., Wang, Z., Mehrandezh, M., Jalilian, S. and Ghadimi, N 2023b. Evolution of smart grids towards the Internet of energy: Concept and essential components for deep decarbonisation. IET Smart Grid. 6(1):86–102. doi:10.1049/stg2.12095.
  • Gollou, A. R., and N. Ghadimi. 2017. A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets. Journal of Intelligent & Fuzzy Systems 32 (6):4031–45. doi:10.3233/JIFS-152073.
  • Guo, H., Gu, W., Khayatnezhad, M., and Ghadimi, N. 2022. Parameter extraction of the SOFC mathematical model based on fractional order version of dragonfly algorithm. International Journal of Hydrogen Energy. 47(57):24059–68. doi:10.1016/j.ijhydene.2022.05.190.
  • Hadi, A. A., A. W. Mohamed, and K. M. Jambi. 2021. Single-objective real-parameter optimization: Enhanced LSHADE-SPACMA algorithm. In Heuristics for optimization and learning, 103–21. Springer International Publishing. doi:10.1007/978-3-030-58930-1_7.
  • Han, E., and N. Ghadimi. 2022. Model identification of proton-exchange membrane fuel cells based on a hybrid convolutional neural network and extreme learning machine optimized by improved honey badger algorithm. Sustainable Energy Technologies and Assessments 52:102005. doi:10.1016/j.seta.2022.102005.
  • Hansen, N. 2009. Benchmarking a BI-population CMA-ES on the BBOB-2009 function testbed. Proceedings of the 11th annual conference companion on genetic and evolutionary computation conference: late breaking papers, GECCO09: Genetic and Evolutionary Computation Conference Montreal Québec Canada, July 8–12, 2009.
  • Hatamlou, A. 2013. Black hole: A new heuristic optimization approach for data clustering. Information Sciences 222:175–84. doi:10.1016/j.ins.2012.08.023.
  • Hou, M., Y. Li, F. Peng, and B. Daneshvar Rouyendegh. 2023. A new optimum technique for parameter identification of the proton exchange membrane fuel cells based on improved remora optimizer. Energy Sources, Part A: Recovery, Utilization, & Environmental Effects 45 (1):3019–40. doi:10.1080/15567036.2023.2192011.
  • Jain, M., Maurya, S., Rani, A., and Singh, V. 2018. Owl search algorithm: A novel nature-inspired heuristic paradigm for global optimization. Journal of Intelligent & Fuzzy Systems. 34(3):1573–82. doi:10.3233/JIFS-169452.
  • Kandidayeni, M., Macias, A., Khalatbarisoltani, A., Boulon, L., and Kelouwani, S. 2019. Benchmark of proton exchange membrane fuel cell parameters extraction with metaheuristic optimization algorithms. Energy 183:912–25. doi:10.1016/j.energy.2019.06.152.
  • Khishe, M., and M. R. Mosavi. 2020. Chimp optimization algorithm. Expert Systems with applications. Expert Systems with Applications 149:113338. doi:10.1016/j.eswa.2020.113338.
  • Leng, H., et al. 2018. A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting. Advanced Engineering Informatics 36:20–30. doi:10.1016/j.aei.2018.02.006.
  • Li, D., L. Deng, Q. Su, and Y. Song. 2020. Providing a guaranteed power for the BTS in telecom tower based on improved balanced owl search algorithm. Energy Reports 6:297–307. doi:10.1016/j.egyr.2020.01.006.
  • Liu, J., et al. 2020. An IGDT-based risk-involved optimal bidding strategy for hydrogen storage-based intelligent parking lot of electric vehicles. Journal of Energy Storage 27:101057. doi:10.1016/j.est.2019.101057.
  • Mahdinia, S., et al. 2021. Optimization of PEMFC model parameters using meta-Heuristics. Sustainability. 13(22):12771. doi:10.3390/su132212771.
  • Nanadegani, F. S., E.N. Lay, A. Iranzo, J.A. Salva, and B. Sunden. 2020. On neural network modeling to maximize the power output of PEMFCs. Electrochimica Acta 348:136345. doi:10.1016/j.electacta.2020.136345.
  • Rao, R. V., V. J. Savsani, and D. Vakharia. 2011. Teaching–learning-based optimization: A novel method for constrained mechanical design optimization problems. Computer-Aided Design 43 (3):303–15. doi:10.1016/j.cad.2010.12.015.
  • Razmjooy, N., M. Ashourian, and Z. Foroozandeh. Lecture Notes in Electrical Engineering. Metaheuristics and Optimization in Computer and Electrical Engineering, ed.R. Navid, A. Mohsen, and F. Zahra, Cham: Springer. doi:10.1007/978-3-030-56689-0.
  • Razmjooy, N., V.V. Estrela, H.J. Loschi, and W. Fanfan. 2019. A comprehensive survey of new meta-heuristic algorithms. recent Advances in hybrid Metaheuristics for data Clustering. Wiley Publishing.
  • Rezaie, M., et al. 2022. Model parameters estimation of the proton exchange membrane fuel cell by a Modified Golden Jackal optimization. Sustainable Energy Technologies and Assessments 53:102657. doi:10.1016/j.seta.2022.102657.
  • Sharifi, P., V. Jain, M. Arab Poshtkohi, E. Seyyedi, and V. Aghapour. 2021. Banks Credit Risk prediction with optimized ANN based on improved owl search algorithm. Mathematical Problems in Engineering 2021:1–10. doi:10.1155/2021/8458501.
  • Simon, D. 2008. Biogeography-based optimization. IEEE transactions on evolutionary computation. IEEE Transactions on Evolutionary Computation 12 (6):702–13. doi:10.1109/TEVC.2008.919004.
  • Sun, L., X.F. Han, Y.P. Xu, and N. Razmjooy. 2021. Exergy analysis of a fuel cell power system and optimizing it with Fractional-order Coyote optimization algorithm. Energy Reports 7:7424–33. doi:10.1016/j.egyr.2021.10.098.
  • Sun, S., Y. Su, C. Yin, and K. Jermsittiparsert. 2020. Optimal parameters estimation of PEMFCs model using converged moth search algorithm. Energy Reports 6:1501–09. doi:10.1016/j.egyr.2020.06.002.
  • Tan, K.H., L. Samylingam, N. Aslfattahi, R. Saidur, and K. Kadirgama. 2021. Optical and conductivity studies of polyvinyl alcohol-MXene (PVA-MXene) nanocomposite thin films for electronic applications Vol. 136: 106772. Optics & Laser Technology.
  • Tian, M.-W., S.R. Yan, S.Z. Han, S. Nojavan, K. Jermsittiparsert, and N. Razmjooy. 2020. New optimal design for a hybrid solar chimney, solid oxide electrolysis and fuel cell based on improved deer hunting optimization algorithm. Journal of Cleaner Production 249:119414. doi:10.1016/j.jclepro.2019.119414.
  • Wang, T., H. Huang, X. Li, X. Guo, M. Liu, and H. Lei. 2023. Optimal estimation of proton exchange membrane fuel cell model parameters based on an improved chicken swarm optimization algorithm. International Journal of Green Energy. 20(9):946–65. doi:10.1080/15435075.2022.2131432.
  • Wu, G., R. Mallipeddi, and P. N. Suganthan. 2017. Problem definitions and evaluation criteria for the CEC 2017 competition on constrained real-parameter optimization. National University of Defense Technology, Changsha, Hunan. PR China and Kyungpook National University, Daegu, South Korea and Nanyang Technological University, Singapore, Technical Report.
  • Yang, Z., Q. Liu, L. Zhang, J. Dai, and N. Razmjooy. 2020. Model parameter estimation of the PEMFCs using improved Barnacles Mating optimization algorithm. Energy 212:118738. doi:10.1016/j.energy.2020.118738.
  • Yin, Z., and N. Razmjooy. 2020. PEMFC identification using deep learning developed by improved deer hunting optimization algorithm. International Journal of Power and Energy Systems 40 (2). doi:10.2316/J.2020.203-0189.
  • Yuan, Z., W. Wang, H. Wang, and N. Razmjooy. 2020. A new technique for optimal estimation of the circuit-based PEMFCs using developed sunflower optimization algorithm. Energy Reports 6:662–71. doi:10.1016/j.egyr.2020.03.010.
  • Zhang, J., M. Khayatnezhad, and N. Ghadimi. 2022. Optimal model evaluation of the proton-exchange membrane fuel cells based on deep learning and modified African Vulture optimization algorithm. Energy Sources, Part A: Recovery, Utilization, & Environmental Effects 44 (1):287–305. doi:10.1080/15567036.2022.2043956.
  • Zhu, L., F. Zhang, Q. Zhang, Y. Chen, M. Khayatnezhad, and N. Ghadimi. 2023. Multi-criteria evaluation and optimization of a novel thermodynamic cycle based on a wind farm, Kalina cycle and storage system: An effort to improve efficiency and sustainability. Sustainable Cities and Society 96:104718. doi:10.1016/j.scs.2023.104718.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.