As data emerges as the most valuable resource in the world, the evolution of the related data industry is progressing faster. In this study, we tried to discover effective factors for fuel cell durability by using big data analysis techniques with accumulated vehicle actual road data (de-identified Blue Link Data). Basic analysis is performed assuming factors that are expected to have a significant impact on the fuel cell durability performance, and durability factor modeling according to the clustering between driving patterns and durability performance is used to determine. Now can see the change in durability performance. By analyzing the correlation between each driving pattern and durability performance, it is possible to know the weight of the effective factor affecting the durability. If the effective factor with high weight is improved in the actual vehicle unit, the durability performance is expected to increase, and the effect will be verified through real road operation.


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    Titel :

    Discovering Effective Factors for Big Data-Based Fuel Cell Durability


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Jin, Youngpin (Autor:in)

    Kongress:

    WCX SAE World Congress Experience ; 2022



    Erscheinungsdatum :

    2022-03-29




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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