• DocumentCode
    694526
  • Title

    Research of BP network based solid oxide fuel cell stack temperature model

  • Author

    Zhang Ying ; Zhang Yingying ; Hou Guangli ; Lv Jing

  • Author_Institution
    Shandong Provincial Key Lab. of Ocean Environ. Monitoring Technol., Inst. of Oceanogr. Instrum., Qingdao, China
  • fYear
    2013
  • fDate
    12-13 Oct. 2013
  • Firstpage
    1037
  • Lastpage
    1040
  • Abstract
    The working temperature environment of the stack plays the key role in maintaining the high efficient, stable and secure operation of the system. This paper based on BP network model for the prediction of reactor temperature of SOFC electric power system, so as to implement thermal administration. Through repeated training on the model, selection of proper parameters and the test of the trained model, the error between test results and actual data is within ±1°C, which proves the effectiveness of this model and shows that this model can be applied in the thermal management of SOFC electricity supply system.
  • Keywords
    backpropagation; neural nets; power engineering computing; solid oxide fuel cells; BP network; SOFC; electric power system; electricity supply system; reactor temperature; solid oxide fuel cell; stack temperature model; temperature environment; thermal administration; thermal management; trained model; Fuel cells; Fuels; Heating; Neurons; Ocean temperature; Thermal management; Training; BP network model; Solid oxide fuel cell; stack; temperature;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Science and Network Technology (ICCSNT), 2013 3rd International Conference on
  • Conference_Location
    Dalian
  • Type

    conf

  • DOI
    10.1109/ICCSNT.2013.6967281
  • Filename
    6967281