Hybrid dynamic model of polymer electrolyte membrane fuel cell stack using variable neural network

Peng Li*, Jie Chen, Tao Cai, Guang Hui Wang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

The polymer electrolyte membrane (PEM) fuel cell has been regarded as a potential alternative power source, and a model is necessary for its design, control and power management. A hybrid dynamic model of PEM fuel cell, which combines the advantages of mechanism model and black-box model, is proposed in this paper. To improve the performance, the static neural network and variable neural network are used to build the black-box model. The static neural network can significantly improve the static performance of the hybrid model, and the variable neural network makes the hybrid dynamic model predict the real PEM fuel cell behavior with required accuracy. Finally, the hybrid dynamic model is validated with a 500 W PEM fuel cell. The static and transient experiment results show that the hybrid dynamic model can predict the behavior of the fuel cell stack accurately and therefore can be effectively utilized in practical application.

Original languageEnglish
Pages (from-to)354-361
Number of pages8
JournalJournal of Beijing Institute of Technology (English Edition)
Volume21
Issue number3
Publication statusPublished - Sept 2012

Keywords

  • Fuel cell
  • Hybrid dynamic model
  • PEM
  • Variable neural network

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