Proton exchange membrane fuel cells modeling based on artificial neural networks

Yudong Tian; Xinjian Zhu; Guangyi Cao

Volume 12 Issue 1

Feb. 2005

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文章导航 > 矿物冶金与材料学报（英文） > 2005 > 12(1): 72-77

Yudong Tian, Xinjian Zhu, and Guangyi Cao, Proton exchange membrane fuel cells modeling based on artificial neural networks, J. Univ. Sci. Technol. Beijing, 12(2005), No. 1, pp. 72-77.

Cite this article as:

Yudong Tian, Xinjian Zhu, and Guangyi Cao, Proton exchange membrane fuel cells modeling based on artificial neural networks, J. Univ. Sci. Technol. Beijing, 12(2005), No. 1, pp. 72-77.

Yudong Tian, Xinjian Zhu, and Guangyi Cao, Proton exchange membrane fuel cells modeling based on artificial neural networks, J. Univ. Sci. Technol. Beijing, 12(2005), No. 1, pp. 72-77.

Citation:

Yudong Tian, Xinjian Zhu, and Guangyi Cao, Proton exchange membrane fuel cells modeling based on artificial neural networks, J. Univ. Sci. Technol. Beijing, 12(2005), No. 1, pp. 72-77.

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Materials

Proton exchange membrane fuel cells modeling based on artificial neural networks

Fuel Cell Research Institute, Shanghai Jiaotong University, Shanghai 200030, China

通讯作者:
Yudong Tian E-mail: tyd228@sjtu.edu.cn

中文摘要

To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.

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Materials

Proton exchange membrane fuel cells modeling based on artificial neural networks

+ Author Affiliations

Abstract

Abstract

To understand the complexity of the mathematical models of a proton exchange membrane fuel cell (PEMFC) and their shortage of practical PEMFC control, the PEMFC complex mechanism and the existing PEMFC models are analyzed, and artificial neural networks based PEMFC modeling is advanced. The structure, algorithm, training and simulation of PEMFC modeling based on improved BP networks are given out in detail. The computer simulation and conducted experiment verify that this model is fast and accurate, and can be used as a suitable operational model for PEMFC real-time control.
- fuel cells;
- proton exchange membrane;
- artificial neural networks;
- improved BP algorithm;
- modeling