In this research work, we demonstrate state-of-charge (SoC) estimation using support vector regression (SVR) approach for a high capacity Lithium Ferro Phosphate (LiFePO4) battery module. The proposed SoC estimator in this work is extracted from open circuit voltage (OCV)-SoC lookup table which is obtained from the battery module discharging and charging testing cycles, using voltage and current as independent variables. The SoC estimation based on SVR gives a perfectly linear curve fitting with its reference within the range of 37.5% to 90% while the rest hysteresis due to the discharging and charging process is compensated using OCV-SoC curve as the training data set. The SVR estimates the battery module SoC with RMSE of 2.3% over the whole test and the maximum positive and negative error is 4%, which means that it shows good accuracy.


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    Titel :

    State of charge (SoC) estimation of LiFePO4 battery module using support vector regression


    Beteiligte:
    Haq, Irsyad Nashirul (Autor:in) / Saputra, Riza Hadi (Autor:in) / Edison, Frans (Autor:in) / Kurniadi, Deddy (Autor:in) / Leksono, Edi (Autor:in) / Yuliarto, Brian (Autor:in)


    Erscheinungsdatum :

    01.11.2015


    Format / Umfang :

    646908 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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    Estimation of battery state-of-charge using ν-support vector regression algorithm

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    Shi, Q. S. / Zhang, C. H. / Cui, N. X. | British Library Online Contents | 2008


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    Shi, Q. -S. / Zhang, C. -H. / Cui, N. -X. | Online Contents | 2008