Advertisement

Modeling of Nexa-1.2kW Proton Exchange Membrane Fuel Cell Power Supply Using Swarm Intelligence

  • Tata Venkat DixitEmail author
  • Anamika Yadav
  • Shubhrata Gupta
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 711)

Abstract

The heuristic approach of simulator design based on swarm intelligence of Nexa-1.2kW Ballard proton exchange membrane fuel cell (PEMFC) has been presented. The parameters of the Nexa-1.2kW PEMFC simulator are determined using particle swarm optimization (PSO) algorithm. The results of PEMFC simulator are experimentally verified. Further, the discrete PI controlled SEPIC converter has been used for interconnecting a fuel cell to a load. The fuel cell simulator, converter integration, and its control are implemented in MATLAB/SIMULINK environment. Finally, the effect of load variation and stack temperature on fuel cell power conditioning unit has been investigated. The rise in stack temperature results in slight reduction in cell current and considerable rise in terminal voltage of the fuel cell.

Keywords

Nexa-1.2kW PEM fuel cell SEPIC PSO 

References

  1. 1.
    Kanhu Charan Bhuyan, ‘Development of Controllers Using FPGA for Fuel Cells in Standalone and Utility Applications Kanhu Charan Bhuyan’, National Institute of Technology Rourkela, 2014.Google Scholar
  2. 2.
    T. Lajnef, S. Abid, and A. Ammous, ‘Modeling, control, and simulation of a solar hydrogen/fuel cell hybrid energy system for grid-connected applications’, Adv. Power Electron., vol. 2013, p. 352765 (9 pp.), 2013.Google Scholar
  3. 3.
    I. Soltani, ‘An Intelligent, Fast and Robust Maximum Power Point Tracking for Proton Exchange Membrane Fuel Cell’, World Appl. Program., vol. 3, no. July, pp. 264–281, 2013.Google Scholar
  4. 4.
    C. Ziogou, E. N. Pistikopoulos, M. C. Georgiadis, S. Voutetakis, and S. Papadopoulou, ‘Empowering the performance of advanced NMPC by multiparametric programming - An application to a PEM fuel cell system’, Ind. Eng. Chem. Res., vol. 52, no. 13, pp. 4863–4873, 2013.CrossRefGoogle Scholar
  5. 5.
    J. M. Corrêa, F. A. Farret, L. N. Canha, and M. G. Simoes, ‘An electrochemical-based fuel-cell model suitable for electrical engineering automation approach’, IEEE Trans. Ind. Electron., vol. 51, no. 5, pp. 1103–1112, 2004.CrossRefGoogle Scholar
  6. 6.
    R. I. Salim, H. Noura, M. Nabag, and A. Fardoun, ‘Modeling and Temperature Analysis of the Nexa 1.2 kW Fuel Cell System’, J. Fuel Cell Sci. Technol., vol. 12, no. 6, pp. 1–9, 2015.CrossRefGoogle Scholar
  7. 7.
    A. Gebregergis and P. Pillay, ‘Implementation of fuel cell emulation on DSP and dSPACE controllers in the design of power electronic converters’, IEEE Trans. Ind. Appl., vol. 46, no. 1, pp. 285–294, 2010.CrossRefGoogle Scholar
  8. 8.
    N. Benchouia and A. Hadjadj, ‘Modeling and validation of fuel cell PEMFC’, Rev. des Energies …, vol. 16, pp. 365–377, 2013.Google Scholar
  9. 9.
    M. T. Outeiro, R. Chibante, A. S. Carvalho, and A. T. de Almeida, ‘A parameter optimized model of a Proton Exchange Membrane fuel cell including temperature effects’, J. Power Sources, vol. 185, no. 2, pp. 952–960, 2008.CrossRefGoogle Scholar
  10. 10.
    U. K. Chakraborty, T. E. Abbott, and S. K. Das, ‘PEM fuel cell modeling using differential evolution’, Energy, vol. 40, no. 1, pp. 387–399, 2012.CrossRefGoogle Scholar
  11. 11.
    W. Gong and Z. Cai, ‘Accelerating parameter identification of proton exchange membrane fuel cell model with ranking-based differential evolution’, Energy, vol. 59, pp. 356–364, 2013.CrossRefGoogle Scholar
  12. 12.
    Q. Li, W. Chen, Y. Wang, S. Liu, and J. Jia, ‘Parameter Identification for PEM Fuel-Cell Mechanism Model Based on Effective Informed Adaptive Particle Swarm Optimization’, IEEE Trans. Ind. Electron., vol. 58, no. 6, pp. 2410–2419, 2011.CrossRefGoogle Scholar
  13. 13.
    J. L. Tang, C. Z. Cai, T. T. Xiao, and S. J. Huang, ‘Support vector regression model for direct methanol fuel cell’, Int. J. Morden Phys. C, vol. 23, no. 7, pp. 1–8, 2012.CrossRefGoogle Scholar
  14. 14.
    E. Durán, M. B. Ferrera, J. M. Andújar, and M. S. Mesa, ‘I-V and P-V Curves Measuring System for PV Modules based on DC-DC Converters and Portable Graphical Environment’, in Industrial Electronics (ISIE), 2010 IEEE International Symposium on, 2010, pp. 3323–3328.Google Scholar
  15. 15.
    S. K. Sahu and D. D. Neema, ‘A robust speed sensorless vector control of multilevel inverter fed induction motor using particle swarm optimization’, Int. J. Innov. Res. Electr. Electron. Instrum. Control Eng., vol. 3, no. 1, pp. 23–32, 2015.Google Scholar
  16. 16.
    C. C. Kuo, ‘A novel coding scheme for practical economic dispatch by modified particle swarm approach’, IEEE Trans. Power Syst., vol. 23, no. 4, pp. 1825–1835, 2008.CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Tata Venkat Dixit
    • 1
    Email author
  • Anamika Yadav
    • 1
  • Shubhrata Gupta
    • 1
  1. 1.Department of Electrical EngineeringNational Institute of TechnologyRaipurIndia

Personalised recommendations