The state of health (SOH) is an indicator to quantify aging level of a battery in terms of capacity fade and/or power fade. The SOH can be generally estimated by experimental and model‐based methods. This chapter considers all of these methods and their strengths and weaknesses for use in online battery management system (BMS) applications and discuss a potential and promising method to estimate the SOH in electric vehicle (EV) applications. The experimental methods monitor battery behaviors by analyzing the full cycle of experimental data of battery voltage, current, and temperature. Model‐based estimation methods can be further divided into adaptive state estimation methods and data‐driven methods. Generally, system parameters change slowly over time while system states are prone to change fast over time; a multi‐time scale adaptive extended Kalman filter (AEKF) algorithm is used to estimate the system parameters in the macro time scale and the system state in the micro time scale.


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    Title :

    Battery State of Health Estimation


    Contributors:
    Xiong, Rui (editor) / Shen, Weixiang (editor)


    Publication date :

    2019-02-19


    Size :

    36 pages




    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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