- Category Technical paper
- Related event Congrès : L'IA pour les Nouvelles Mobilités - événement digital - Matinees des 21 & 22 septembre 2022
- Edition SIA
- Date 10/04/2022
- Author Nicolas LAMARQUE, VITESCO TECHNOLOGIES
- Language English
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Type PDF file (354.78 Ko)
(Downloadable immediately on receipt of online payment) - Number of pages 8
- Code R-2022-04-01
- Fee Free
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.