Modelling and control PEMFC using fuzzy neural networks

被引：29
作者：
Sun T. [1 ]
Yan S.-J. [2 ]
Cao G.-Y. [1 ]
Zhu X.-J. [1 ]
机构：
[1] Fuel Cell Institute, Department of Automation, Shanghai Jiaotong University
[2] Department of Applied Chemistry, Shanghai Normal University
来源：
Journal of Zhejiang University-SCIENCE A | 2005年 / 6卷 / 10期
关键词：
Adaptive neural-networks fuzzy infer system; Modeling; Neural network; Proton exchange membrane fuel cell;
D O I：
10.1631/jzus.2005.A1084
中图分类号：
学科分类号：
摘要：
Proton exchange membrane generation technology is highly efficient, clean and considered as the most hopeful green power technology. The operating principles of proton exchange membrane fuel cell (PEMFC) system involve thermodynamics, electrochemistry, hydrodynamics and mass transfer theory, which comprise a complex nonlinear system, for which it is difficult to establish a mathematical model and control online. This paper first simply analyzes the characters of the PEMFC; and then uses the approach and self-study ability of artificial neural networks to build the model of the nonlinear system, and uses the adaptive neural-networks fuzzy infer system (ANFIS) to build the temperature model of PEMFC which is used as the reference model of the control system, and adjusts the model parameters to control it online. The model and control are implemented in SIMULINK environment. Simulation results showed that the test data and model agreed well, so it will be very useful for optimal and real-time control of PEMFC system.
引用
收藏
页码：1084 / 1089
页数：5
相关论文
共 14 条
  • [1] Arriaqada J., Olausson P., Selimoric A., Artificial neural network simulator for SOFC performance prediction, Journal of Power Sources, 112, pp. 54-60, (2002)
  • [2] Baschuk J.J., Li X., Modelling of polymer electrolyte membrane fuel cells with variable degrees of water flooding, Journal of Power Sources, 86, pp. 181-196, (2000)
  • [3] Bender G., Wilson M.S., Zawodzinski T.A., Further refinements in the segmented cell approach to diagnosing performance in polymer membrane fuel cells, Journal of Power Sources, 123, pp. 163-171, (2003)
  • [4] Berning T., Djilali N., Three-dimensional computational analysis of transport phenomena in a PEM fuel cell - A parametric study, Journal of Power Sources, 124, pp. 440-452, (2003)
  • [5] Berning T., Lu D.M., Djilali N., Three-dimensional computational analysis of transport phenomena in a PEM fuel cell, Journal of Power Sources, 106, pp. 284-294, (2002)
  • [6] Chen S., Bilings S.A., Neural networks for nonlinear dynamic system modeling and identification, International Journal of Control, 56, 2, pp. 319-346, (1992)
  • [7] Efe M.O., Kaynak O., Neuro-fuzzy approaches for identification and control of nonlinear systems, Proceedings of the IEEE International Symposium on Industrial Electronics, (1999)
  • [8] Fowler M.W., Mann R.F., Amphlett J.C., Peppley B.A., Roberge P.R., Incorporation of voltage degradation into a generalized steady state electrochemical model for a PEM fuel cell, Journal of Power Sources, 106, pp. 274-283, (2002)
  • [9] Kim Y.H., Kim S.S., An electrical modeling and fuzzy logic control of a fuel cell generation system, IEEE Transactions on Energy Conversion, 14, 2, pp. 239-244, (1999)
  • [10] Rowe A., Li X., Mathematical modeling of proton exchange membrane fuel cells, Journal of Power Sources, 102, pp. 82-96, (2001)
12