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

页码：1084 / 1089

页数：5

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