The lithium ion battery pack, which is filled with cells, is an important part in electric vehicles (EVs), also the main fault source. The inconsistent cells or the design and assembly fail of the pack could affect its performance and life or even endanger vehicles security in extreme situation, which makes the early fault diagnosis is essential. For further analysis, we introduce an equivalent circuit model (ECM) to identify the cell characteristics parameters, which supports the fault diagnosis by simulating the fault battery performance in dynamic cycle. According the battery working mechanism and the practical experience, via collecting data and preprocessing the typical data, a diagnostic method and model based on fuzzy neural network is proposed to discover the battery pack fault related to irreversible or reversible capacity loss.


    Access

    Access via TIB

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Capacity Fade Diagnosis of Lithium Ion Battery Pack in Electric Vehicle Base on Fuzzy Neural Network


    Contributors:
    Li, Junqiu (author) / Tan, Fei (author) / Zhang, Chengning (author) / Sun, Fengchun (author)


    Publication date :

    2014


    Size :

    5 Seiten, 7 Quellen




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




    Capacity fade modeling of a Lithium-ion battery for electric vehicles

    Baek, K. W. / Hong, E. S. / Cha, S. W. | Online Contents | 2015


    Capacity fade modeling of a Lithium-ion battery for electric vehicles

    Baek, K. W. / Hong, E. S. / Cha, S. W. | British Library Online Contents | 2015


    Capacity fade modeling of a Lithium-ion battery for electric vehicles

    Baek, K. W. / Hong, E. S. / Cha, S. W. | Springer Verlag | 2015


    Analysis of capacity fade in a lithium ion battery

    Stamps, Andrew T. | Online Contents | 2006