A comparison of statistical learning approaches for engine torque estimationReport as inadecuate

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1 LITIS - Laboratoire d-Informatique, de Traitement de l-Information et des Systèmes 2 CROCUS-ENSMSE - Equipe : Calcul de Risque, Optimisation et Calage par Utilisation de Simulateurs 3 3MI-ENSMSE - Département Méthodes et Modèles Mathématiques pour l-Industrie 4 LCG-ENSMSE - UMR 5146 - Laboratoire Claude Goux

Abstract : Engine torque estimation has important applications in the automotive industry: for example, automatically setting gears, optimizing engine perfor- mance, reducing emissions and designing drivelines. A methodology is described for the on-line calculation of torque values from the gear, the accelerator pedal position and the engine rotational speed. It is based on the availability of input-torque experimental signals that are pre- processed resampled, filtered and segmented and then learned by a statistical machine-learning method. Four methods, spanning the main learning principles, are reviewed in a uni- fied framework and compared using the torque estimation problem: linear least squares, linear and non-linear neural networks and support vector machines. It is found that a non-linear model structure is necessary for accurate torque estimation. The most efficient torque model built is a non-linear neural net- work that achieves about 2% test normalized mean square error in nominal conditions.

Author: A. Rakotomamonjy - Rodolphe Le Riche - David Gualandris - Zaid Harchaoui -

Source: https://hal.archives-ouvertes.fr/


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