In this work, the problem of battery state of charge estimation is investigated using a model based approach. An experimentally validated model of a battery developed by AllCell Technologies, specific for light electric vehicles (electric scooter or bicycles) is used. Two state of charge estimation algorithms are developed: an extended Kalman filter and an adaptive extended Kalman filter. The adaptive version of Kalman filter is designed in order to adaptively set a proper value of the model noise covariance, using the information coming from the on-line innovation analysis. A comparison between the two approaches is conducted that shows that the adaptive Kalman filter can deal with the problem of incorrect value of the model noise covariance matrix producing lower estimation error.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    State of charge estimation using extended Kalman filters for battery management system


    Contributors:


    Publication date :

    2014-12-01


    Size :

    691709 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Improved extended Kalman filter for state of charge estimation of battery pack

    Sepasi, Saeed / Ghorbani, Reza / Liaw, Bor-Yann | Tema Archive | 2014


    Auto-tuning extended Kalman filters to improve state estimation

    Boulkroune, Boulaid / Geebelen, Kurt / Wan, Jia et al. | IEEE | 2023



    State of Charge Estimation of Li-ion Battery using Extended Kalman Filter and Combined Battery Model

    Mandhana, Abhishek / Gambhir, Ameya V / Bagade, Aniket C | SAE Technical Papers | 2022


    State of Charge Estimation of Li-ion Battery using Extended Kalman Filter and Combined Battery Model

    Bagade, Aniket C / Gambhir, Ameya V / Mandhana, Abhishek | British Library Conference Proceedings | 2022