This work presents insights from a vehicle battery life modeling project launched at Stellantis in pursuit of its strategic Dare Forward 2030 goal which, among others, includes full powertrain electrification. The developed solution aims to predict a broad spectrum of battery failures weeks in advance to reduce the risk of sudden vehicle immobilization, as well as to cut down on the maintenance and warranty costs. The underlying classifier is built upon a recurrent neural network architecture with roots in computer vision. It is fed by temporal sequences of window-aggregated multidimensional signals composed of raw measurements that vehicles continuously emit and send over the air. Prediction results obtained using the above setup in two scenarios involving high-voltage traction battery and 12V battery are presented and discussed. Practical tips on deploying and using this type of predictive model, as well as quantify its business value are also given.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Preventive Vehicle Battery Maintenance Using Recurrent Neural Networks


    Beteiligte:


    Erscheinungsdatum :

    24.09.2024


    Format / Umfang :

    1060492 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Preventive maintenance

    Wanamaker, E. | Engineering Index Backfile | 1937


    PREVENTIVE MAINTENANCE

    Bernhardt, I.M. | SAE Technical Papers | 1935


    Preventive maintenance

    Millar, A.E. | Engineering Index Backfile | 1962


    Preventive maintenance records

    Greiner, H. | Engineering Index Backfile | 1953


    Preventive maintenance methods

    Rose, L. | Engineering Index Backfile | 1929