On-line Identification of Fuel Cell Model with Variable Neural Network

摘要：

It is important to predict fuel cells behaviors for fuel cell control, power management and other practical applications. In this paper, a Gaussian radial basis function (GRBF) variable neural network is used to on-line identify the PEM (Polymer Electrolyte Membrane) fuel cell model. The structure of the neural network changes over time according to the required accuracy and complexity. Finally, a real test data of fuel cell power system is used to illustrate the effectiveness of the variable neural network for online identification of the fuel cell model. The result shows that this method guarantees the output of the predictive model attains the required accuracy.

关键词： PEM Fuel Cell Variable Neural Network GRBF

作者: LI Peng CHEN Jie CAI Tao LIU Guoping LI Peng

作者单位: School of Automation, Beijing Institute of Technology, Beijing 100081, P. R. China Faculty of Advanced Technology, University of Glamorgan, Pontypridd CF37 1DL, UK Beijing Research & Design Institute of Rubber Industry, Beijing 100039, P. R. China

会议类型: 国际会议

会议名称: The 29th Chinese Control Conference(第二十九届中国控制会议)

会议地点: 北京

会议语种:英文

页码: 1-5

在线出版日期: 2010-07-29（万方平台首次上网日期，不代表论文的发表时间）