A new method based on adaptive unscented Kalman filter (AUKF) is proposed to improve the SOC estimation accuracy of lithium-ion battery in this paper. The noise covariance in AUKF is adaptively adjusted. To improve the accuracy of the AUKF-based method, least squares support vector machine (LSSVM) is used to establish measurement equation. A comparison with unsented Kalman filter shows that the proposed method has a better accuracy. Simulation data indicates a better SOC estimation result and a faster convergence can be obtained by using the AUKF-based method.


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    Titel :

    State-of-charge estimation for lithium-ion battery using AUKF and LSSVM


    Beteiligte:
    Jinhao Meng (Autor:in) / Guangzhao Luo (Autor:in) / Fei Gao (Autor:in)


    Erscheinungsdatum :

    01.08.2014


    Format / Umfang :

    538111 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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