Modeling and predicting the membrane water content of proton exchange membrane fuel cell by using support vector regression
- https://doi.org/10.2991/icaemt-15.2015.141How to use a DOI?
- Modeling; predicting; membrane water content; proton exchange membrane fuel cell; support vector regression.
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.
- © 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 - 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 - https://doi.org/10.2991/icaemt-15.2015.141 ID - Tang2015/08 ER -