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.
Preventive Vehicle Battery Maintenance Using Recurrent Neural Networks
24.09.2024
1060492 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
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