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This paper analyzes the timetable impact on the energy consumption of a subway line. In most timetable studies, simplified models are used and can lead to misestimation of the braking energy and thus the energy transfer between braking vehicles and accelerating vehicles. In this paper, specific attention is paid to the models of the vehicles, the traction power substation, and the rail supply network to enable an accurate estimation of the energy consumption. The energetic macroscopic representation formalism is used to organize the models of the subsystems so they have the right interactions. The developed model is validated by experimental tests on a real subway line. The error on the global energy consumption is lower than 2.2%. The model is then employed to examine the influence of the vehicle time interval on energy consumption. A 10-second adjustment in this time interval can result in a substantial 22% decrease in energy consumption for the analyzed real subway line.

Urbanization has led to a surge in demand for efficient urban mobility. As cities grow, the need for sustainable transport solutions becomes vital. Subways, characterized by their high capacity and relatively low emissions, have emerged as a favored solution to address this growing interest. However, while they offer numerous advantages, they also present challenges, especially in the scope of energy efficiency and management. The MEL (Métropole Européenne de Lille) is committed to exploring the potential energy savings on its transport network, underscoring the importance of evaluating adapted solutions in specific urban contexts. This PhD thesis explores the energy efficiency challenges of subway systems, particularly, solutions for reduction of energy consumption. A dynamical model and a flexible simulation tool for subway carousel energy studies is developed. They are validated by experimental results on a real track, as well by a dedicated reduced-scale and flexible experimental platform using the Hardware-In-the-Loop principle. This platform enables the test of specific cases, which are difficult to measure on the real track. Simplified models are then proposed to reduce computational demands without sacrificing accuracy. Different simulation scenarios have been studied to highlight the main factors to reduce global energy consumption. For line 1 of MEL, reducing the maximal velocity by 10% leads to a reduction of energy consumption by 6.7% while increasing the travel time by less than 2 minutes. A slight change of headway (time between two cars) can also lead to a reduction of global energy up to 12%. The potential braking energy recovery has been analyzed showing the use case to supply electric cars or electric buses.