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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    Title :

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


    Contributors:
    Jinhao Meng (author) / Guangzhao Luo (author) / Fei Gao (author)


    Publication date :

    2014-08-01


    Size :

    538111 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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