Controlling the energy consumption is an important stake in today’s world. In the railway field, the energy consumed by high-speed trains depends on many variables such as the driver behaviour. Significant variations have been noticed for different drivers on the same journey. To help drivers, crossing points are defined along the journey, but differences still exist. The industrial objective of this work is to define a model, able to describe the train dynamics and to propose an optimization method, which aims to minimize the energy consumption. This work is composed of two parts. First, a deterministic model is defined to describe the train longitudinal dynamics based on a Lagrangian approach [1]. This model is calibrated based on commercial trains measurements. Afterwards, the optimization of the command is performed using the CMA-ES method [2] to minimize the energy consumed while punctuality, security, and comfort constraints are respected. Nevertheless, the high-speed train system is complex, and taking into account the uncertainties of the model parameters is necessary. Therefore, a Bayesian inference method [3] is applied in order to include uncertainties in the previous deterministic model. Finally, an optimization under uncertainty method is used to find the optimal command. The originality of this work lies on its transposability to real train systems. Indeed, pneumatic braking is distinguished from dynamic braking (able to recover a part of the energy consumed). The optimization method is applied to the driver command and it combines both punctuality and physical constraints. Many energy measurements are used to calibrate and validate the models and verify the quality of the optimal solution. Finally, the rolling environment of the train is determined carefully by the use of wind predictions, track declivity and curvature measurements. REFERENCES [1] Nespoulous J., Soize C., Funfschilling C., and Perrin G. Optimization of train speed to limit energy consumption. Vehicle System Dynamics. 2021. [2] Hansen N. The CMA evolution strategy: a tutorial. 2016. ArXiv e-prints, arXiv:1604.00772v1 [cs.LG], 4 April 2016 and https://hal.inria.fr/hal-01297037. [3] Box G.E.P. and Tiao G.C. Bayesian inference in statistical analysis. John Wiley & Sons. 2011 (40).