Using hybrid MCDM methods to assess fuel cell technology for the next generation of hybrid power automobiles

Chi-Yo Huang, Yi-xuan Hong, Gwo Hshiung Tzeng

Research output: Contribution to journalArticle

9 Citations (Scopus)

Abstract

With their huge consumption of petroleum and creation of a large number of pollutants, traditional vehicles have become one of the major creators of pollution in the world. To save energy and reduce carbon dioxide emissions, in recent years national governments have aggressively planned and promoted energy-saving vehicles that use green energy. Thus, hybrid electric vehicles have already become the frontrunners for future vehicles while fuel cells are considered the most suitable energy storage devices for future hybrid electric vehicles. However, various competing fuel cell technologies do exist. Furthermore, very few scholars have tried to investigate how the development of future fuel cells for hybrid electric vehicles can be assessed so that the results can serve as a fundation for the next generation of hybrid electric vehicle developments. Thus, how to assess various fuel cells is one the most critical issues in the designing of hybrid electric vehicles. This research intends to adopt a framework based on HybridMultiple-Criteria Decision Making (MCDM) for the assessment of the development in fuel cells for future hybrid electric vehicles. The analytic framework can be used for selecting the most suitable fuel cell technology for future hybrid electric vehicles. The results of the analysis can also be used for designing the next generation of hybrid electric vehicles.

Original languageEnglish
Pages (from-to)406-417
Number of pages12
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume15
Issue number4
Publication statusPublished - 2011 Jun 1

Keywords

  • Analytic network process (ANP)
  • Decision making trial and evaluation laboratory (DEMATEL)
  • Fuel cells
  • Grey relation analysis (GRA)
  • Multiple criteria decision making (MCDM) mixed electrical energy vehicles

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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