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Parameter Estimation of Fuel Cells Using a Hybrid Optimization Algorithm

Author

Listed:
  • Manish Kumar Singla

    (Department of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering & Technology, Chitkara University, Rajpura 140401, India)

  • Jyoti Gupta

    (Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, India)

  • Beant Singh

    (Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, India)

  • Parag Nijhawan

    (Electrical and Instrumentation Engineering Department, Thapar Institute of Engineering and Technology, Patiala 147004, India)

  • Almoataz Y. Abdelaziz

    (Faculty of Engineering and Technology, Future University in Egypt, Cairo 11835, Egypt)

  • Adel El-Shahat

    (Energy Technology Program, School of Engineering Technology, Purdue University, West Lafayette, IN 47907, USA)

Abstract

Because of the current increase in energy requirement, reduction in fossil fuels, and global warming, as well as pollution, a suitable and promising alternative to the non-renewable energy sources is proton exchange membrane fuel cells. Hence, the efficiency of the renewable energy source can be increased by extracting the precise values for each of the parameters of the renewable mathematical model. Various optimization algorithms have been proposed and developed in order to estimate the parameters of proton exchange membrane fuel cells. In this manuscript, a novel hybrid algorithm, i.e., Hybrid Particle Swarm Optimization Puffer Fish (HPSOPF), based on the Particle Swarm Optimization and Puffer Fish algorithms, was proposed to estimate the proton exchange membrane fuel cell parameters. The two models were taken for the parameter estimation of proton exchange membrane fuel cells, i.e., Ballard Mark V and Avista SR-12 model. Firstly, justification of the proposed algorithm was achieved by benchmarking it on 10 functions and then a comparison of the parameter estimation results obtained using the Hybrid Particle Swarm Optimization Puffer Fish algorithm was done with other meta-heuristic algorithms, i.e., Particle Swarm Optimization, Puffer Fish algorithm, Grey Wolf Optimization, Grey Wolf Optimization Cuckoo Search, and Particle Swarm Optimization Grey Wolf Optimization. The sum of the square error was used as an evaluation metric for the performance evaluation and efficiency of the proposed algorithm. The results obtained show that the value of the sum of square error was smallest in the case of the proposed HPSOPF, while for the Ballard Mark V model it was 6.621 × 10 −9 and for the Avista SR-12 model it was 5.65 × 10 −8 . To check the superiority and robustness of the proposed algorithm computation time, voltage–current (V–I) curve, power–current (P–I) curve, convergence curve, different operating temperature conditions, and different pressure results were obtained. From these results, it is concluded that the Hybrid Particle Swarm Optimization Puffer Fish algorithm had a better performance in comparison with the other compared algorithms. Furthermore, a non-parametric test, i.e., the Friedman Ranking Test, was performed and the results demonstrate that the efficiency and robustness of the proposed hybrid algorithm was superior.

Suggested Citation

  • Manish Kumar Singla & Jyoti Gupta & Beant Singh & Parag Nijhawan & Almoataz Y. Abdelaziz & Adel El-Shahat, 2023. "Parameter Estimation of Fuel Cells Using a Hybrid Optimization Algorithm," Sustainability, MDPI, vol. 15(8), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6676-:d:1123833
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    References listed on IDEAS

    as
    1. Narinder Singh & S. B. Singh, 2017. "Hybrid Algorithm of Particle Swarm Optimization and Grey Wolf Optimizer for Improving Convergence Performance," Journal of Applied Mathematics, Hindawi, vol. 2017, pages 1-15, November.
    2. Sun, Zhe & Wang, Ning & Bi, Yunrui & Srinivasan, Dipti, 2015. "Parameter identification of PEMFC model based on hybrid adaptive differential evolution algorithm," Energy, Elsevier, vol. 90(P2), pages 1334-1341.
    3. El-Fergany, Attia A., 2018. "Extracting optimal parameters of PEM fuel cells using Salp Swarm Optimizer," Renewable Energy, Elsevier, vol. 119(C), pages 641-648.
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