The speed profile of a train plays an important role in energy consumption and resulting costs. The industrial objective of this work is thus to develop a method to reduce the energy consumed by a train over a journey by playing on the driver commands (traction and braking forces) while respecting punctuality constraints. A coupling between measured data and simulation is proposed to solve this optimization problem. First, a rigid body approach (Lagrangian formalism) is introduced to characterize the dynamics of each element of the train and their interactions with their environment. In particular, the aerodynamic (including the wind effect), traction, and braking forces are taken into account, and a special attention is paid to the vertical and lateral characteristics of the track as they play a key role on the train dynamics. Secondly, a model for energy consumption and recovery (thanks to dynamic braking) is introduced. Experimental measurements of a high-speed line are then used to estimate the parameters on which the two previous models are based and to validate their predictive capacities. The optimization problem under constraints is finally solved using an evolutionary algorithm where the constraints are implemented using an augmented Lagrangian formalism. The performance of the proposed method in terms of speed optimization and energy consumption reduction is compared to measurements associated with commercial trains.

Controlling the energy consumed by our systems has turned to be an important stake in today's world and especially in the railway domain, since transports constitute one of the largest energy consumers. In the railway sector, the energy consumed by highspeed trains depends on many variables such as the vehicle characteristics, the rolling environment of the train, or its speed profile. To limit the impact of the latter, drivers are asked to follow a target trajectory defined by crossing points along the journey. Nevertheless, we can remark that important differences in energy consumption 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 under uncertainties. This work is composed of two parts. First of all, two probabilistic models are defined to describe the train longitudinal dynamics (based on a Lagrangian approach) and its energy consumption. This model is fitted using a Bayesian calibration from measurements carried out on commercial trains. Particular attention is paid to the description of the rolling environment of the train and of the vehicle characteristics. Afterwards, the robust optimization of the command under uncertainty is performed using the CMA-ES method to minimize the energy consumed while punctuality, security, and comfort constraints are respected.