Modeling the Output Power of Direct Methanol Fuel Cells Using SVR

Jiang Ling Tang; Xing Jian Zhu

doi:10.4028/www.scientific.net/AMM.904.1

Paper Titles

Preface

Modeling the Output Power of Direct Methanol Fuel Cells Using SVR
p.1

The Impact of 3D Coordinate Technology Using Nanomaterials on Architecture Engineering Industry
p.7

Large Torsion Deformation: Centrosymmetric Reentrant Honeycomb
p.17

Evaluation of the Effect of the Difference between the Real Attachment Unit and the Sealing in the Study of the Stress-Strain State of the Solar Panel of a Small Spacecraft as a Result of a Temperature Shock
p.27

Numerical Calculation and Prediction of the Water Temperature in Reservoir of a Hydropower Station in Laos
p.35

Research on the Denoising Method of Intelligent Ultrasonic Fuel Consumption Testing in Automobiles
p.43

Vibration Analysis of Cylindrical with Splitter Plates under Different Deflection Angles at High Reynolds Number
p.51

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 904Modeling the Output Power of Direct Methanol Fuel...

Modeling the Output Power of Direct Methanol Fuel Cells Using SVR

Abstract:

The output power of Direct Methanol Fuel Cells (DMFC) is one of the most important elements which limit the performance of DMFC. In order to enhance performance of DMFC, it is necessary to have model to modeling the output power of DMFC. In this paper, a novel model base on Support Vector Regression (SVR) to modeling the output power of DMFC base on output current (I) and operating temperature (T). The test result is shown that the generalization ability of SVR model is high accuracy. This investigation suggests that SVR is quite satisfied used to developing a DMFC model and can be used for controlling, optimal designing and feasibility study of the DMFC system.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volume 904)

Pages:

1-5

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.904.1

Citation:

Cite this paper

Online since:

January 2022

Authors:

Jiang Ling Tang*, Xing Jian Zhu

Keywords:

Direct Methanol Fuel Cells, Model, Output Power, Regression Analysis, Support Vector Regression

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] C.K. Dyer, Fuel cells for portable applications [J]. Journal of Power Sources, 2002, 106: pp.31-34.

Google Scholar

[2] V. Vapnik, The nature of statistical learning theory, New York: Springer, (1995).

Google Scholar

[3] K. Veropoulos, et al. Controlling the sensitivity of support vector machines [C]. Proceedings of the International Joint Conference on AI, 1999: pp.55-60.

Google Scholar

[4] C.Z. Cai, et al. Predicting the superconducting transition temperature Tc of BiPbSrCaCuOF superconductors by using support vector regression. Journal of Superconductivity and Novel Magnetism, 2010, 23(5): pp.737-740.

DOI: 10.1007/s10948-010-0732-x

Google Scholar

[5] J.L. Tang, et al. Modeling and Predicting the Central Magnetic Flux Density of the Superconducting Solenoid Surrounded with Iron Yoke via SVR. Journal of Superconductivity and Novel Magnetism 2012(25): pp.1747-1751.

DOI: 10.1007/s10948-012-1527-z

Google Scholar

[6] M. Ordonez, et al. Modeling and optimization of direct methanol fuel cells using statistical design of experiment methodology [C]. IEEE Transactions on CCECE/CCGEI, 2006: pp.1120-1124.

Google Scholar