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Proton Exchange Membrane Fuel Cell Stack Design Optimization Using an Improved Jaya Algorithm

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  • Uday K. Chakraborty

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

Abstract

Fuel cell stack configuration optimization is known to be a problem that, in addition to presenting engineering challenges, is computationally hard. This paper presents an improved computational heuristic for solving the problem. The problem addressed in this paper is one of constrained optimization, where the goal is to seek optimal (or near-optimal) values of (i) the number of proton exchange membrane fuel cells (PEMFCs) to be connected in series to form a group, (ii) the number of such groups to be connected in parallel, and (iii) the cell area, such that the PEMFC assembly delivers the rated voltage at the rated power while the cost of building the assembly is as low as possible. Simulation results show that the proposed method outperforms four of the best-known methods in the literature. The improvement in performance afforded by the proposed algorithm is validated with statistical tests of significance.

Suggested Citation

  • Uday K. Chakraborty, 2019. "Proton Exchange Membrane Fuel Cell Stack Design Optimization Using an Improved Jaya Algorithm," Energies, MDPI, vol. 12(16), pages 1-26, August.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:16:p:3176-:d:258874
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    References listed on IDEAS

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    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, Uttara, 2016. "Fuel crossover and internal current in proton exchange membrane fuel cell modeling," Applied Energy, Elsevier, vol. 163(C), pages 60-62.
    3. Julian Kates-Harbeck & Alexey Svyatkovskiy & William Tang, 2019. "Predicting disruptive instabilities in controlled fusion plasmas through deep learning," Nature, Nature, vol. 568(7753), pages 526-531, April.
    4. Gregory D. Merkel & Richard J. Povinelli & Ronald H. Brown, 2018. "Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression †," Energies, MDPI, vol. 11(8), pages 1-12, August.
    5. 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.
    6. Besseris, George J., 2014. "Using qualimetric engineering and extremal analysis to optimize a proton exchange membrane fuel cell stack," Applied Energy, Elsevier, vol. 128(C), pages 15-26.
    7. Uday K. Chakraborty, 2018. "Reversible and Irreversible Potentials and an Inaccuracy in Popular Models in the Fuel Cell Literature," Energies, MDPI, vol. 11(7), pages 1-11, July.
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    Cited by:

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    2. Walter Zamboni & Giovanni Petrone & Giovanni Spagnuolo & Davide Beretta, 2019. "An Evolutionary Computation Approach for the Online/On-Board Identification of PEM Fuel Cell Impedance Parameters with A Diagnostic Perspective," Energies, MDPI, vol. 12(22), pages 1-19, November.
    3. Behzad Najafi & Paolo Bonomi & Andrea Casalegno & Fabio Rinaldi & Andrea Baricci, 2020. "Rapid Fault Diagnosis of PEM Fuel Cells through Optimal Electrochemical Impedance Spectroscopy Tests," Energies, MDPI, vol. 13(14), pages 1-19, July.

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