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Article Navigation> JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY> 2012> 21(3): 354-361
LI Peng, CHEN Jie, CAI Tao, WANG Guang-hui. Hybrid dynamic model of polymer electrolyte membrane fuel cell stack using variable neural network[J]. JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2012, 21(3): 354-361.
Citation: LI Peng, CHEN Jie, CAI Tao, WANG Guang-hui. Hybrid dynamic model of polymer electrolyte membrane fuel cell stack using variable neural network[J].JOURNAL OF BEIJING INSTITUTE OF TECHNOLOGY, 2012, 21(3): 354-361.
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Hybrid dynamic model of polymer electrolyte membrane fuel cell stack using variable neural network

  • School of Automation, Beijing Institute of Technology,Beijing 100081, China;Key Laboratory of Complex System Intelligent Control and Decision, Ministry of Education, Beijing Institute of Technology, Beijing 100081, China

  • Received Date:2011-07-18
    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.
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    • References (17)
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