Bayesian inference for high-speed train dynamics and speed optimization under uncertainty for energy saving

Julien Nespoulous (1, 2) , Christian Soize (1) , Christine Fünfschilling (2) , Guillaume Perrin (3)

1 MSME - Laboratoire Modélisation et Simulation Multi-Echelle
2 DTIPG, SNCF
3 COSYS - Département Composants et Systèmes

The train is a complex nonlinear system, whose dynamic behavior is difficult to predict accurately because of its environmental sensitivity. Indeed, in spite of a relative fine modeling of the vehicle and its rolling environment (track and wind), the slightest uncontrolled disturbance can modify the dynamic comportment of the train. For this reason, uncertainty must be considered in the physical models. The industrial objective of this work is twofold. Firstly, the construction of a longitudinal dynamic model for high-speed trains able to take into account the fluctuations inherent to the system. Secondly, the optimization under uncertainty of the driver's command with the objective of reducing the energy consumed by the train, under a set of punctuality and physical nonlinear constraints (speed limitation, final speed, and final position constraints).

Mots clés: Bayesian inference; optimization under uncertainties; Uncertainty quantification; UQ; High-speed train; Energy saving

Domaines: Statistiques [math.ST]; Sciences de l'ingénieur [physics]; Machine Learning [stat.ML]

Julien Nespoulous, Christian Soize, Christine Fünfschilling, Guillaume Perrin. Bayesian inference for high-speed train dynamics and speed optimization under uncertainty for energy saving. The 30th edition of the biennial ISMA conference on Noise and Vibration Engineering (ISMA 2022) and The 9th International Conference on Uncertainty in Structural Dynamics, USD 2022, KU Leuven, Sep 2022, Leuven, Belgium. pp.1-6. ⟨hal-03799277⟩