On-line identification of fuel cell model with variable neural network

Peng Li; Jie Chen; Tao Cai; Guoping Liu; Peng Li

On-line identification of fuel cell model with variable neural network

Peng Li^*, Jie Chen, Tao Cai, Guoping Liu, Peng Li^*

^*Corresponding author for this work

School of Automation

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

5 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	Proceedings of the 29th Chinese Control Conference, CCC'10
Pages	1417-1421
Number of pages	5
Publication status	Published - 2010
Event	29th Chinese Control Conference, CCC'10 - Beijing, China Duration: 29 Jul 2010 → 31 Jul 2010

Publication series

Name	Proceedings of the 29th Chinese Control Conference, CCC'10

Conference

Conference	29th Chinese Control Conference, CCC'10
Country/Territory	China
City	Beijing
Period	29/07/10 → 31/07/10

Keywords

GRBF
PEM fuel cell
Variable neural network

Cite this

@inproceedings{1dc38fb6945b4164ae8d185c892a3aaa,

title = "On-line identification of fuel cell model with variable neural network",

abstract = "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.",

keywords = "GRBF, PEM fuel cell, Variable neural network",

author = "Peng Li and Jie Chen and Tao Cai and Guoping Liu and Peng Li",

year = "2010",

language = "English",

isbn = "9787894631046",

series = "Proceedings of the 29th Chinese Control Conference, CCC'10",

pages = "1417--1421",

booktitle = "Proceedings of the 29th Chinese Control Conference, CCC'10",

note = "29th Chinese Control Conference, CCC'10 ; Conference date: 29-07-2010 Through 31-07-2010",

}

TY - GEN

T1 - On-line identification of fuel cell model with variable neural network

AU - Li, Peng

AU - Chen, Jie

AU - Cai, Tao

AU - Liu, Guoping

AU - Li, Peng

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

KW - GRBF

KW - PEM fuel cell

KW - Variable neural network

UR - http://www.scopus.com/inward/record.url?scp=78650235999&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:78650235999

SN - 9787894631046

T3 - Proceedings of the 29th Chinese Control Conference, CCC'10

SP - 1417

EP - 1421

BT - Proceedings of the 29th Chinese Control Conference, CCC'10

T2 - 29th Chinese Control Conference, CCC'10

Y2 - 29 July 2010 through 31 July 2010

ER -

On-line identification of fuel cell model with variable neural network

Abstract

Publication series

Conference

Keywords

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