Battery Management System (BMS) is a critical component in EV (Electric Vehicle) powertrains. The precise knowledge of the battery's state of health and capacity impacts the estimation and control strategies of many other EV components. Current battery aging models are physics-based and complex, with limited capability to run in real-time. In this paper, we apply deep learning techniques to design an estimator of battery capacity using a combination of virtual and real battery data, and which can be run in real-time on the EV ECU. The estimator is implemented in an Amesim model of the EV powertrain and experimental results of its performance with standard drive cycles are demonstrated.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Battery aging estimation with deep learning


    Beteiligte:


    Erscheinungsdatum :

    01.12.2017


    Format / Umfang :

    679941 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Battery life estimation method based on AI deep learning

    YAN QUANZHONG / JIANG YUNBAO / ZHANG LIANXIN | Europäisches Patentamt | 2023

    Freier Zugriff


    SYSTEM AND METHOD FOR BATTERY ACCUMULATED AGING ESTIMATION

    LIU YIQI / TONG NIANNIAN | Europäisches Patentamt | 2023

    Freier Zugriff

    BATTERY SYSTEM IN VEHICLE AND AGING DETERIORATION ESTIMATION METHOD FOR BATTERY

    TAKAHASHI KENJI / AKAMATSU KAZUSHI / MACHIDA KIYOHITO | Europäisches Patentamt | 2018

    Freier Zugriff