This chapter mainly presents model‐based state of charge (SOC) and state of energy (SOE) estimation methods using the extended Kalman filter (EKF) and H infinity filter (HIF) algorithms. There are four major groups of battery SOC estimation methods, including a look‐up‐table‐based method, an ampere‐hour (Ah) integral method, data‐driven estimation methods, and model‐based estimation methods. Battery performances vary with battery temperature, discharge/charge current and health state. To further improve the accuracy of SOC estimation, battery model parameters are identified by the recursive least squares (RLS) algorithm in real‐time to reflect such variation. Then, the EKF algorithm is implemented on these battery models to provide more accurate SOC estimation. The SOC calculation only considers charge flow into or out of a battery, which neglects energy losses caused by electrochemical reactions and internal resistances inside the battery. The chapter introduces the model‐based method with identified model parameters in real‐time to estimate the SOE.


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    Titel :

    Battery State of Charge and State of Energy Estimation


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


    Erscheinungsdatum :

    19.02.2019


    Format / Umfang :

    27 pages




    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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