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Parameter Optimization of PEMFC with Genetic Algorithm

Author

Listed:
  • Puja Bhatt

    (Department of Mathematics and Computer Science, University of Missouri, Saint Louis, MO 63121, USA)

  • Neha Agarwal

    (Department of Mathematics and Computer Science, University of Missouri, Saint Louis, MO 63121, USA)

  • Uday K. Chakraborty

    (Department of Mathematics and Computer Science, University of Missouri, Saint Louis, MO 63121, USA)

Abstract

This paper provides a review of recent research in the application of genetic algorithms to proton exchange membrane fuel cell parameter optimization.

Suggested Citation

  • Puja Bhatt & Neha Agarwal & Uday K. Chakraborty, 2016. "Parameter Optimization of PEMFC with Genetic Algorithm," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 12(03), pages 241-249, November.
    • Handle: RePEc:wsi:nmncxx:v:12:y:2016:i:03:n:s1793005716500162
    DOI: 10.1142/S1793005716500162
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    References listed on IDEAS

    as
    1. Chakraborty, Uday Kumar, 2009. "Static and dynamic modeling of solid oxide fuel cell using genetic programming," Energy, Elsevier, vol. 34(6), pages 740-751.
    2. Chakraborty, Uday K. & Abbott, Travis E. & Das, Sajal K., 2012. "PEM fuel cell modeling using differential evolution," Energy, Elsevier, vol. 40(1), pages 387-399.
    3. Cheng, Shan-Jen & Miao, Jr-Ming & Wu, Sheng-Ju, 2013. "Use of metamodeling optimal approach promotes the performance of proton exchange membrane fuel cell (PEMFC)," Applied Energy, Elsevier, vol. 105(C), pages 161-169.
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