• English
    • العربية
  • العربية
  • Login
  • QU
  • QU Library
  •  Home
  • Communities & Collections
  • Help
    • Item Submission
    • Publisher policies
    • User guides
    • FAQs
  • About QSpace
    • Vision & Mission
View Item 
  •   Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Electrical Engineering
  • View Item
  • Qatar University Digital Hub
  • Qatar University Institutional Repository
  • Academic
  • Faculty Contributions
  • College of Engineering
  • Electrical Engineering
  • View Item
  •      
  •  
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    A novel aging prediction method of fuel cell based on empirical mode decomposition and complexity threshold quantitative criterion

    Thumbnail
    View/Open
    Publisher version (You have accessOpen AccessIcon)
    Publisher version (Check access options)
    Check access options
    1-s2.0-S0378775323004950-main.pdf (10.28Mb)
    Date
    2023-05-10
    Author
    Zhuang, Tian
    Wang, Jinhui
    Al-Durra, Ahmed
    Muyeen, S.M.
    Zhou, Daming
    Hua, Shiyang
    ...show more authors ...show less authors
    Metadata
    Show full item record
    Abstract
    Data-driven methods have been widely applied to fault diagnosis and aging predictions to assist fuel cell Prognostic and Health Management (PHM) system, in order to achieve early maintenance management and corrective measures for fuel cell systems. This paper proposes a novel fuel cell aging prediction method considering the applicability of data and algorithm. This method first adopts empirical mode decomposition (EMD) to split the aging data into several intrinsic mode functions (IMFs), and each IMF represents a different characteristic. Then the sample entropy (SE) is used as the quantitative criterion for complexity threshold. Furthermore, the nonlinear autoregressive neural network (NARNN) and the Long Short-Term Memory (LSTM) recurrent neural network are combined to ensure the applicability of data and algorithm. The results show that EMD can split the various data types of the aging data and weaken or even eliminate the excessive mutation phenomenon that occurs at the beginning of each experimental fuel cell. In addition, the targeted selection of data-driven methods can ensure the applicability of the data and algorithm. Finally, by comparing different prediction methods, the proposed method shows higher accuracy in the prediction of each experimental dataset, and good generality for different fuel cell types.
    URI
    https://www.sciencedirect.com/science/article/pii/S0378775323004950
    DOI/handle
    http://dx.doi.org/10.1016/j.jpowsour.2023.233120
    http://hdl.handle.net/10576/62103
    Collections
    • Electrical Engineering [‎2821‎ items ]

    entitlement


    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Home

    Submit your QU affiliated work

    Browse

    All of Digital Hub
      Communities & Collections Publication Date Author Title Subject Type Language Publisher
    This Collection
      Publication Date Author Title Subject Type Language Publisher

    My Account

    Login

    Statistics

    View Usage Statistics

    About QSpace

    Vision & Mission

    Help

    Item Submission Publisher policiesUser guides FAQs

    Qatar University Digital Hub is a digital collection operated and maintained by the Qatar University Library and supported by the ITS department

    Contact Us | Send Feedback
    Contact Us | Send Feedback | QU

     

     

    Video