Approach to proton exchange membrane fuel cell modeling based on dynamic neural networks

被引：0

作者：

Cao, Zheng-Cai ^{[1
,2
]}

Li, Bo ^{[3
]}

Liu, Min ^{[4
]}

Zhang, Jie ^{[1
]}

机构：

[1] College of Information Science and Technology, Beijing University of Chemical Technology

[2] Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Southeast University, Nanjing

[3] Technical Institute of Physics and Chemistry of the Chinese Academy of Sciences

[4] Department of Automation, Tsinghua University

来源：

Tien Tzu Hsueh Pao/Acta Electronica Sinica | 2014年 / 42卷 / 01期

关键词：

Dynamic neural network; Modeling; Proton exchange membrane fuel cell (PEMFC); Sensitivity analysis;

D O I：

10.3969/j.issn.0372-2112.2014.01.016

中图分类号：

学科分类号：

摘要：

An innovative approach of proton exchange membrane fuel cell (PEMFC) modeling based on dynamic neural networks is proposed to improve approximating and self-adaptive ability of the existing PEMFC models. To evaluate the rationality of networks structure, sensitivity analysis (SA) of the model output was introduced. The hidden nodes were pruned or inserted according to the result of SA to optimize the networks structure and parameters, so that the networks could adapt the PEMFC data processing automatically. The approach was validated using operation data from a commercial dual-system fuel cell test platform. The result shows the proposed PEMFC model with more compact structure, higher accuracy and faster convergence rate compared with the common models, have the capability to be applied to engineering simulation applications.

引用

页码：102 / 106

页数：4

共 15 条

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