Optimizing proton exchange membrane fuel cell parameter identification using enhanced hummingbird algorithm

Fuel cells (FCs) have attracted significant interest due to their versatile applications, but modeling their nonlinear behavior is challenging. This research proposes an Enhanced Artificial Hummingbird Algorithm (EAHA) to identify the seven unknown parameters of proton exchange membrane fuel cell (PEMFC) stacks using their experimental data. The goal is to accurately predict the current/voltage (I/V) curves by minimizing a cost function defined as the sum of squared differences between measured data points and model estimates. The EAHA combines several territorial foraging techniques with a linear regulation mechanism. Its performance is compared to the conventional Artificial Hummingbird Algorithm (AHA) using three common PEMFC modules. Additionally, a comparative analysis is performed against previously published methods and newly developed optimizers like Particle Swarm Optimizer (PSO), Grasshopper Optimization Algorithm (GOA), Atom Search Optimization (ASO), Grey Wolf Optimizer (GWO), and parental algorithm i.e., Artificial Hummingbird Algorithm (AHA). The findings showcase the proposed approach’s efficacy relative to existing methods and state-of-the-art optimizers. The two models are taken for the checking of reliability and performance of the PEMFC. The results are also compared with the Non-Parametric tests and it is concluded that the proposed algorithm is far better than the rest of the compared algorithms in both the models.

1. Sultan, H. M., Menesy, A. S., Hassan, M. S., Jurado, F., & Kamel, S. (2023). Standard and Quasi Oppositional bonobo optimizers for parameter extraction of PEM fuel cell stacks. Fuel, 340, 127586.

2. Rezk, H., Olabi, A. G., Abdelkareem, M. A., & Sayed, E. T. (2022). Boosting the power density of two‐chamber microbial fuel cell: Modeling and optimization. International Journal of Energy Research, 46(15), 20975-20984.

3. Abd Elaziz, M., Abualigah, L., Issa, M., & Abd El-Latif, A. A. (2023). Optimal parameters extracting of fuel cell based on Gorilla Troops Optimizer. Fuel, 332, 126162.

4. Samara, G., & Aljaidi, M. (2018). Aware-routing protocol using best first search algorithm in wireless sensor. Int. Arab J. Inf. Technol., 15(3A), 592-598.

5. Özdemir, M. T. (2021). Optimal parameter estimation of polymer electrolyte membrane fuel cells model with chaos embedded particle swarm optimization. International Journal of Hydrogen Energy, 46(30), 16465-16480.

6. Shaheen, A., El-Sehiemy, R., El-Fergany, A., & Ginidi, A. (2023). Fuel-cell parameter estimation based on improved gorilla troops technique. Scientific Reports, 13(1), 8685.

7. Yang, S., & Wang, N. (2012). A novel P systems based optimization algorithm for parameter estimation of proton exchange membrane fuel cell model. International Journal of Hydrogen Energy, 37(10), 8465-8476.

8. Chang, L., Li, M., Qian, L., & de Oliveira, G. G. (2024). Developed multi-objective honey badger optimizer: Application to optimize proton exchange membrane fuel cells-based combined cooling, heating, and power system. International Journal of Hydrogen Energy, 50, 592-605.

9. Houssein, E. H., Hashim, F. A., Ferahtia, S., & Rezk, H. (2021). An efficient modified artificial electric field algorithm for solving optimization problems and parameter estimation of fuel cell. International Journal of Energy Research, 45(14), 20199-20218.

10. Kandidayeni, M., Macias, A., Khalatbarisoltani, A., Boulon, L., & Kelouwani, S. (2019). Benchmark of proton exchange membrane fuel cell parameters extraction with metaheuristic optimization algorithms. Energy, 183, 912-925.

11. Bai, Q., & Li, H. (2022). The application of hybrid cuckoo search-grey wolf optimization algorithm in optimal parameters identification of solid oxide fuel cell. International Journal of Hydrogen Energy, 47(9), 6200-6216.

12. Aljaidi, M., Aslam, N., Samara, G., Almatarneh, S., Khaled, A. Q., & Alqammaz, A. (2022, November). EV charging station placement and sizing techniques: survey, challenges and directions for future work. In 2022 International Arab Conference on Information Technology (ACIT) (pp. 1-6). IEEE.

13. Aly, M., & Rezk, H. (2022). An improved fuzzy logic control-based MPPT method to enhance the performance of PEM fuel cell system. Neural Computing and Applications, 1-12.

14. Kheirandish, A., Shafiabady, N., Dahari, M., Kazemi, M. S., & Isa, D. (2016). Modeling of commercial proton exchange membrane fuel cell using support vector machine. International journal of hydrogen energy, 41(26), 11351-11358.

15. Wilberforce, T., & Biswas, M. (2022). A study into proton exchange membrane fuel cell power and voltage prediction using artificial neural network. Energy Reports, 8, 12843-12852.

16. Mostafaeipour, A., Qolipour, M., Goudarzi, H., Jahangiri, M., Golmohammadi, A. M., Rezaei, M., & Khalifeh Soltani, S. R. (2019). Implementation of adaptive neuro-fuzzy inference system (ANFIS) for performance prediction of fuel cell parameters. Journal of Renewable Energy and Environment, 6(3), 7-15.

17. Ashraf, H., Abdellatif, S. O., Elkholy, M. M., & El-Fergany, A. A. (2022). Computational techniques based on artificial intelligence for extracting optimal parameters of PEMFCs: Survey and insights. Archives of Computational Methods in Engineering, 29(6), 3943-3972.

18. El-Fergany, A. A., Hasanien, H. M., & Agwa, A. M. (2019). Semi-empirical PEM fuel cells model using whale optimization algorithm. Energy Conversion and Management, 201, 112197.

19. Ali, Z. M., Ahmed, A. M., Hasanien, H. M., & Aleem, S. H. A. (2023). Optimal Design of Fractional-Order PID Controllers for a Nonlinear AWS Wave Energy Converter Using Hybrid Jellyfish Search and Particle Swarm Optimization. Fractal and Fractional, 8(1), 6.

20. Ali, Z. M., Al-Dhaifallah, M., Al-Gahtani, S. F., & Muranaka, T. (2023). A new maximum power point tracking method for PEM fuel cell power system based on ANFIS with modified manta ray foraging algorithm. Control Engineering Practice, 134, 105481.

21. Rizk-Allah, R. M., & El-Fergany, A. A. (2021). Artificial ecosystem optimizer for parameters identification of proton exchange membrane fuel cells model. International Journal of Hydrogen Energy, 46(75), 37612-37627.

22. Gupta, J., Nijhawan, P., & Ganguli, S. (2021). Optimal parameter estimation of PEM fuel cell using slime mould algorithm. International Journal of Energy Research, 45(10), 14732-14744.

23. Priya, K., & Rajasekar, N. (2019). Application of flower pollination algorithm for enhanced proton exchange membrane fuel cell modelling. International Journal of Hydrogen Energy, 44(33), 18438-18449.

24. Diab, A. A. Z., Tolba, M. A., El-Magd, A. G. A., Zaky, M. M., & El-Rifaie, A. M. (2020). Fuel cell parameters estimation via marine predators and political optimizers. IEEE Access, 8, 166998-167018.

25. Jiang, B., Wang, N., & Wang, L. (2014). Parameter identification for solid oxide fuel cells using cooperative barebone particle swarm optimization with hybrid learning. International journal of hydrogen energy, 39(1), 532-542.

26. Hao, P., & Sobhani, B. (2021). Application of the improved chaotic grey wolf optimization algorithm as a novel and efficient method for parameter estimation of solid oxide fuel cells model. International Journal of Hydrogen Energy, 46(73), 36454-36465.

27. Guo, C., Lu, J., Tian, Z., Guo, W., & Darvishan, A. (2019). Optimization of critical parameters of PEM fuel cell using TLBO-DE based on Elman neural network. Energy Conversion and Management, 183, 149-158.

28. Li, J., Gao, X., Cui, Y., Hu, J., Xu, G., & Zhang, Z. (2020). Accurate, efficient and reliable parameter extraction of PEM fuel cells using shuffled multi-simplexes search algorithm. Energy conversion and management, 206, 112501.

29. Zhao, W., Wang, L., & Mirjalili, S. (2022). Artificial hummingbird algorithm: A new bio-inspired optimizer with its engineering applications. Computer Methods in Applied Mechanics and Engineering, 388, 114194.

30. Hamida, M. A., El-Sehiemy, R. A., Ginidi, A. R., Elattar, E., & Shaheen, A. M. (2022). Parameter identification and state of charge estimation of Li-Ion batteries used in electric vehicles using artificial hummingbird optimizer. Journal of Energy Storage, 51, 104535.

31. Sarhana, S., Shaheen, A., El-Sehiemy, R., & Gafar, M. (2023). Optimal multi-dimension operation in power systems by an improved artificial hummingbird optimizer. Hum.-Centric Comput. Inf. Sci, 13, 13.

32. El-Sehiemy, R., Shaheen, A., El-Fergany, A., & Ginidi, A. (2023). Electrical parameters extraction of PV modules using artificial hummingbird optimizer. Scientific Reports, 13(1), 9240.

33. Moustafa, G., Ginidi, A. R., Elshahed, M., & Shaheen, A. M. (2023). Economic environmental operation in bulk AC/DC hybrid interconnected systems via enhanced artificial hummingbird optimizer. Electric Power Systems Research, 222, 109503.

34. Mann, R. F., Amphlett, J. C., Hooper, M. A., Jensen, H. M., Peppley, B. A., & Roberge, P. R. (2000). Development and application of a generalised steadystate electrochemical model for a PEM fuel cell. Journal of power sources, 86(1-2), 173-180.

35. Mahato, D. P., Sandhu, J. K., Singh, N. P., & Kaushal, V. On scheduling transaction in grid computing using cuckoo search-ant colony optimization considering load. Cluster Computing, 2020, 23, 1483-1504.

36. Singla, M. K., Nijhawan, P., & Oberoi, A. S. (2022). Parameter estimation of three diode solar PV cell using chaotic dragonfly algorithm. Soft Computing, 26(21), 11567-11598.

37. Rani, S., Babbar, H., Kaur, P., Alshehri, M. D., & Shah, S. H. A. An optimized approach of dynamic target nodes in wireless sensor network using bio inspired algorithms for maritime rescue. IEEE Transactions on Intelligent Transportation Systems, 2022.