The state of power (SOP) estimation of battery systems is indispensable to ensure the safe and reliable operation of electric vehicles (EVs). This chapter discusses instantaneous SOP estimation methods, including the hybrid pulse power characterization (HPPC) method, the state of charge (SOC)‐limited method, the voltage‐limited method, and the multi‐constrained dynamic (MCD) method. The SOC‐limited method, the voltage‐limited method, and the MCD method are extended to continuous SOP estimation. The adaptive extended Kalman filter (AEKF) and recursive least squares (RLS) are utilized to jointly estimate SOC, SOP, and model parameters in the presence of uncertainties of battery states and model parameters. The AEKF is used to estimate the SOC while RLS is used to estimate the model parameters. The uncertainty of the SOC is taken into account in the SOP estimation by introducing the joint estimation for the SOC and SOP based on the AEKF.


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    Titel :

    Battery State of Power Estimation


    Beteiligte:
    Xiong, Rui (Herausgeber:in) / Shen, Weixiang (Herausgeber:in)


    Erscheinungsdatum :

    2019-02-19


    Format / Umfang :

    24 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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