Modeling of a 5-cell direct methanol fuel cell using adaptive-network-based fuzzy inference systems

被引：31

作者：

Wang, Rongrong

Qi, Liang ^{[2
]}

Xie, Xiaofeng ^{[2
]}

Ding, Qingqing ^{[3
]}

Li, Chunwen ^{[1
]}

Ma, ChenChi M. ^{[4
]}

机构：

[1] Tsinghua Univ, Dept Automat, INET, Beijing 100084, Peoples R China

[2] Tsinghua Univ, Inst Nucl & New Energy Technol, Beijing 100084, Peoples R China

[3] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China

[4] Natl Tsing Hua Univ, Hsinchu 300, Taiwan

来源：

JOURNAL OF POWER SOURCES | 2008年 / 185卷 / 02期

关键词：

Direct methanol fuel cell (DMFC); Adaptive-network-based fuzzy inference system (ANFIS); Artificial neural network (ANN); Polynomial-based model; Modeling;

D O I：

10.1016/j.jpowsour.2008.06.090

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

The methanol concentrations, temperature and current were considered as inputs. the cell voltage was taken as output. and the performance of a direct methanol fuel cell (DMFC) was modeled by adaptive-network-based fuzzy inference systems (ANFIS). The artificial neural network (ANN) and polynomial-based models were selected to be compared with the ANFIS in respect of quality and accuracy. Based on the ANFIS model obtained, the characteristics Of the DMFC were studied. The results show that temperature and methanol concentration greatly affect the performance of the DMFC. Within a restricted current range, the methanol concentration does not greatly affect the stack voltage. In order to obtain higher fuel utilization efficiency, the methanol concentrations and temperatures should be adjusted according to the load on the system. (C) 2008 Published by Elsevier B.V.

引用

页码：1201 / 1208

页数：8

共 30 条

[1] A neuro fuzzy logic approach to material processing [J].