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Reliability Modeling and Evaluation of Electric Vehicle Motor by Using Fault Tree and Extended Stochastic Petri Nets

Author

Listed:
  • Bing Wang
  • Guangdong Tian
  • Yanping Liang
  • Tiangang Qiang

Abstract

Performing reliability analysis of electric vehicle motor has an important impact on its safety. To do so, this paper proposes its reliability modeling and evaluation issues of electric vehicle motor by using fault tree (FT) and extended stochastic Petri nets (ESPN). Based on the concepts of FT and ESPN, an FT based ESPN model for reliability analysis is obtained. In addition, the reliability calculation method is introduced and this work designs a hybrid intelligent algorithm integrating stochastic simulation and NN, namely, NN based simulation algorithm, to solve it. Finally, taking an electric vehicle motor as an example, its reliability modeling and evaluation issues are analyzed. The results illustrate the proposed models and the effectiveness of proposed algorithms. Moreover, the results reported in this work could be useful for the designers of electric vehicle motor, particularly, in the process of redesigning the electric vehicle motor and scheduling its reliability growth plan.

Suggested Citation

  • Bing Wang & Guangdong Tian & Yanping Liang & Tiangang Qiang, 2014. "Reliability Modeling and Evaluation of Electric Vehicle Motor by Using Fault Tree and Extended Stochastic Petri Nets," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).
  • Handle: RePEc:wly:jnljam:v:2014:y:2014:i:1:n:638013
    DOI: 10.1155/2014/638013
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    References listed on IDEAS

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    1. González Palencia, Juan C. & Furubayashi, Takaaki & Nakata, Toshihiko, 2012. "Energy use and CO2 emissions reduction potential in passenger car fleet using zero emission vehicles and lightweight materials," Energy, Elsevier, vol. 48(1), pages 548-565.
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    Cited by:

    1. Lei Yu & Bingchang Zhou, 2014. "The Burgers Equation for a New Continuum Model with Consideration of Driver’s Forecast Effect," Journal of Applied Mathematics, John Wiley & Sons, vol. 2014(1).

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