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

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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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  -