Table of Contents
ISRN Chemical Engineering
Volume 2012 (2012), Article ID 191308, 8 pages
Research Article

The Assessment of Hydrogen Energy Systems for Fuel Cell Vehicles Using Principal Component Analysis and Cluster Analysis

1School of Chemistry and Chemical Engineering, Chongqing University, Shapingba, Chongqing 400044, China
2(Quality and Environmental Research Centre) CESQA, Department of Industrial Engineering, University of Padova, Via Marzolo 9, 35131 Padova, Italy

Received 3 October 2012; Accepted 31 October 2012

Academic Editors: V. Baglio, R. Mariscal, Y. Otsubo, and E. Van Steen

Copyright © 2012 Jing-Zheng Ren et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Hydrogen energy which has been recognized as an alternative instead of fossil fuel has been developed rapidly in fuel cell vehicles. Different hydrogen energy systems have different performances on environmental, economic, and energy aspects. A methodology for the quantitative evaluation and analysis of the hydrogen systems is meaningful for decision makers to select the best scenario. principal component analysis (PCA) has been used to evaluate the integrated performance of different hydrogen energy systems and select the best scenario, and hierarchical cluster analysis (CA) has been used to verify the correctness and accuracy of the principal components (PCs) determined by PCA in this paper. A case including 11 different hydrogen energy systems for fuel cell vehicles has been studied in this paper, and the system using steam reforming of natural gas for hydrogen production, pipeline for transportation of hydrogen, hydrogen gas tank for the storage of hydrogen at refueling stations, and gaseous hydrogen as power energy for fuel cell vehicles has been recognized as the best scenario. Also, the clustering results calculated by CA are consistent with those determined by PCA, denoting that the results calculated by PCA are scientific and accurate.