This paper presents an algorithm based on Artificial Neural Networks (ANNs) for the estimation of the State of Health (SOH) in Lithium batteries. The method exploits a feed-forward pattern recognition classifier trained with datasets collected at different temperatures and at a predefined current mean value of the discharging profile. During the real implementation, the algorithm scans the time history of the battery load and analyses it on buffers of 60 seconds. Whenever the same conditions of the training dataset are encountered during the scanning (i.e. the same temperature and current mean value), the designed algorithm is enabled and provides an estimation of the SOH. The classifier acquires as input a set of predictors extracted from the direct measurement of characteristic parameters of the battery, namely voltage, temperature, capacity, energy and estimated State of Charge (SOC). The networks are trained and validated by means of a battery model based on look-up tables and previously characterized in a laboratory environment.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    State of Health Estimation of Lithium Batteries for Automotive Applications with Artificial Neural Networks


    Contributors:


    Publication date :

    2019-07-01


    Size :

    991027 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English






    LiBaMobil: new lithium-ion batteries for automotive applications

    Schuster,M. / Kuempers,J. / Degussa,Hanau,DE et al. | Automotive engineering | 2007


    Neural Networks in Automotive Applications

    Prokhorov, Danil | Springer Verlag | 2008