- Catégorie Article technique
- Évènement lié Congrès : L'IA pour les Nouvelles Mobilités - événement digital - Matinees des 21 & 22 septembre 2022
- Édition SIA
- Date 04/10/2022
- Auteur Nicolas LAMARQUE, VITESCO TECHNOLOGIES
- Langue Anglais
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Format Fichier PDF (354.78 Ko)
(livraison exclusivement par téléchargement) - Nombre de pages 8
- Code R-2022-04-01
- Prix Gratuit
The present work describes the different steps of the development of an embedded machine learning algorithm, which enables to predict the real temperature of an automotive component. This methodology is proposed as an alternative to classical modelling approaches based on simplified physical laws, most often used in engine controls. The present study describes the different steps from data pre-processing to tests on a vehicle, including the model design and the embedding. Two different neural networks are evaluated and prove to be very good and relevant alternatives to classical modelling.