Optimisation of train speed to limit energy consumption

Abstract

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.

Measurements-based constrained control optimization in presence of uncertainties with application to the driver commands for high-speed trains

“…to-evaluate models This section briefly presents the mathematical/physical models that have been constructed in order to represent the train behavior, as well as the way the parameters on which they depend are estimated. Only the most useful elements are presented in this section and more details following the works presented in [19] and [20] can be found in Appendix A.…”

“…In our case, we do not have access to these commands. We therefore propose to use the dynamic measurements to identify in inverse the "experimental" commands before identifying the uncertain parameters from the energy measurements (see [19] for more details). The likelihood function is thus defined from two distinct sources of model error:…”

A Literature Review on Train Motion Model Calibration

“…The braking distance of trams is also estimated in [65] using coasting operational data. Traction and brake efficiencies and auxiliary power are also estimated in [66], targeting to match energy consumption records, finding that the parameters that are most related to energy consumption are r 2 and the traction efficiency. Tractive effort measurements are used in [67] to generate speed profiles and calibrate r 0 first at low speeds, and then r 2 at high speeds.…”

Energy-Saving Optimization Method of Urban Rail Transit Based on Improved Differential Evolution Algorithm

“…For this reason, exploring an optimization strategy to reduce the energy consumption of trains without changing the existing equipment and infrastructure has become an important research topic in recent years [ 2 , 3 ]. Due to the fact that the energy consumption of urban rail accounts for a large proportion of the public transport system, it is of great significance to realize energy savings in the urban rail [ 4 , 5 ], which will improve its own economic and social benefits. Based on the analysis of the existing research, a new traction energy-saving optimization based on the combination of train inertia motion and energy optimization is proposed in this study.…”