Modeling and predicting the membrane water content of proton exchange membrane fuel cell by using support vector regression

DOI

10.2991/icaemt-15.2015.141How to use a DOI?

Keywords

Modeling; predicting; membrane water content; proton exchange membrane fuel cell; support vector regression.

Abstract

This study examines the use of the support vector regression (SVR) approach in modeling and predicting the membrane water content of Proton Exchange Membrane Fuel Cell (PEMFC) under two influence factors, including the impedance of single-PEM-chip and operating temperature. The leave-one-out cross validation (LOOCV) test results by SVR strongly support that the generalization ability of SVR model is high enough: mean absolute error (MAE) is 0.01, mean absolute percentage error (MAPE) is 0.15% and multiple correlation coefficients (R2) is 1.00. This investigation suggests that the SVR approach is a promising and practical methodology to simulate the properties of fuel cell system.

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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TY  - CONF
AU  - Jiang.Ling. Tang
AU  - Hong.Yu. He
AU  - Hai.Ying. Liu
PY  - 2015/08
DA  - 2015/08
TI  - Modeling and predicting the membrane water content of proton exchange membrane fuel cell by using support vector regression
BT  - Proceedings of the 2015 International Conference on Advanced Engineering Materials and Technology
PB  - Atlantis Press
SP  - 742
EP  - 748
SN  - 2352-5401
UR  - https://doi.org/10.2991/icaemt-15.2015.141
DO  - 10.2991/icaemt-15.2015.141
ID  - Tang2015/08
ER  -