Estimation of equivalent internal-resistance of PEM fuel cell using artificial neural networks

  • Li Wei  (李炜)Email author
  • Zhu Xin-jian  (朱新坚)
  • Mo Zhi-jun  (莫志军)


A practical method of estimation for the internal-resistance of polymer electrolyte membrane fuel cell (PEMFC) stack was adopted based on radial basis function (RBF) neural networks. In the training process, k-means clustering algorithm was applied to select the network centers of the input training data. Furthermore, an equivalent electrical-circuit model with this internal-resistance was developed for investigation on the stack. Finally using the neural networks model of the equivalent resistance in the PEMFC stack, the simulation results of the estimation of equivalent internal-resistance of PEMFC were presented. The results show that this electrical PEMFC model is effective and is suitable for the study of control scheme, fault detection and the engineering analysis of electrical circuits.

Key words

polymer electrolyte membrane fuel cell(PEMFC) equivalent internal-resistance radial basis function neural networks 


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  1. [1]
    SAKHARE A, DAVARI A, FELIACHI A. Fuzzy logic control of fuel cell for stand-alone and grid connection[J]. Journal of Power Sources, 2004, 135(1/2): 165–176.CrossRefGoogle Scholar
  2. [2]
    JEMEI S, HISSEL D, PERA M C, et al. On-board fuel cell power supply modeling on the basis of neural network methodology[J]. Journal of Power Sources, 2003, 124(2): 479–486.CrossRefGoogle Scholar
  3. [3]
    ROWE A, LI X. Mathematical modeling of proton exchange membrane fuel cells[J]. Journal of Power Sources, 2001, 102(1/2): 82–96.CrossRefGoogle Scholar
  4. [4]
    AMPHLETT J C, BAUMERT R M, MANN R F, et al. Performance modeling of the Ballard Mark IV solid polymer electrolyte fuel cell II. Empirical model development[J]. Journal of the Electrochemical Society, 1995, 142(1): 9–15.CrossRefGoogle Scholar
  5. [5]
    MANN R F, AMPHLETT J C, HOOPER A I, et al. Development and application of a generalized steady-state electrochemical model for a PEM fuel cell[J]. Journal of Power Sources, 2000, 86(1/2): 173–180.CrossRefGoogle Scholar
  6. [6]
    LARMINIE J, DICKS A. Fuel Cell Systems Explained[M]. London: Wiley, 2001.Google Scholar
  7. [7]
    Al-AMOUDI A, ZHANG L, Application of radial basis function networks for solar-array modelling and maximum power-point prediction[J]. IEE Proceedings: Generation, Transmission and Distribution, 2000, 147(5): 310–316.Google Scholar
  8. [8]
    GOMM J B, YU D L. Selecting radial basis function network centers with recursive orthogonal least squares training[J]. IEEE Transactions on Neural Networks, 2000, 11(2): 306–314.CrossRefGoogle Scholar
  9. [9]
    MOODY J, DARKEN C. Fast learning in networks of locally-tuned processing units[J]. Neural Comput, 1989, 1(2): 281–294.CrossRefGoogle Scholar
  10. [10]
    LJUNG L. System Identification (Theory for the User)[M]. New Jersey: Prentice Hall PTR, 1999.Google Scholar

Copyright information

© Central South University Press, Sole distributor outside Mainland China: Springer 2007

Authors and Affiliations

  • Li Wei  (李炜)
    • 1
    Email author
  • Zhu Xin-jian  (朱新坚)
    • 1
  • Mo Zhi-jun  (莫志军)
    • 1
  1. 1.Department of Automation, Fuel Cell Research InstituteShanghai Jiaotong UniversityShanghaiChina

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