Proceedings of the 5th International Conference on Information Engineering for Mechanics and Materials

Modeling of Proton Exchange Membrane Fuel Cell Using Support Vector Regression Machine

Authors
Jiangling Tang, Jian Huang
Corresponding Author
Jiangling Tang
Available Online July 2015.
DOI
10.2991/icimm-15.2015.72How to use a DOI?
Keywords
Support Vector Regression Machines; Voltage; Proton Exchange Membrane Fuel Cell; Modeling
Abstract

In this paper, a nonlinear offline model of proton exchange membrane fuel cell (PEMFC) is built by using a support vector regression machine (SVRM) based on particle swarm optimization (PSO) algorithm. During the process of modeling, the PSO aims to optimize the parameters of SVRM. Compared with the artificial neural network (ANN) approach, the prediction results show that the SVRM approach is superior to the conventional ANN in predicting the stack voltage with different hydrogen pressure. The mean absolute percentage error (MAPE) of 36 test samples is 0.73%, such that prediction result was provided by leave-one-out cross validation (LOOCV) test of SVRM. So it is feasible to establish the prediction model of PEMFC system by using SVRM identification based on the PSO.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 5th International Conference on Information Engineering for Mechanics and Materials
Series
Advances in Engineering Research
Publication Date
July 2015
ISBN
978-94-62520-88-2
ISSN
2352-5401
DOI
10.2991/icimm-15.2015.72How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Jiangling Tang
AU  - Jian Huang
PY  - 2015/07
DA  - 2015/07
TI  - Modeling of Proton Exchange Membrane Fuel Cell Using Support Vector Regression Machine
BT  - Proceedings of the 5th International Conference on Information Engineering for Mechanics and Materials
PB  - Atlantis Press
SP  - 380
EP  - 385
SN  - 2352-5401
UR  - https://doi.org/10.2991/icimm-15.2015.72
DO  - 10.2991/icimm-15.2015.72
ID  - Tang2015/07
ER  -