Computational Technology Resources - CCC

R. Jorge Do Marco^1,2,3, G. Perrin¹, C. Funfschilling² and C. Soize³

¹COSYS, Université Gustave Eiffel, France
²Direction Technologies, Innovation et Projets Groupe, SNCF, France
³MSME UMR 8208, Université Gustave Eiffel, CNRS, Marne-La-Vallée, France

doi:10.4203/ccc.7.21.3

R. Jorge Do Marco, G. Perrin, C. Funfschilling, C. Soize, "Constrained Optimization of Driver Control to Limit Energy Consumption", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 21.3, 2024, doi:10.4203/ccc.7.21.3

Keywords: optimization, Bayesian calibration, energy economy, driving assistance, automatic-train operation, model predictive control.

Abstract

This work aims to optimize the driver control along a track to ensure minimal energy consumption. The focus is on optimizing a control system, which requires a dynamic model to feed an energy model. This will enable the linking of control and consumption while checking operational constraints such as punctuality and safety that apply to the dynamics of the train. In both models it is necessary to determine parameters that are not directly measurable and potentially variable from one trip to another (such as the mass of the passengers). As we have both expert knowledge and real measurements, this work focuses on Bayesian calibration to deduce an a posterior distribution, from this distribution, we will extract the maximum from this a posteriori distribution in order to perform deterministic optimization. The conclusion of this work is that energy can be reduced. However, the robustness of the model is not sufficient, since a small variation of variable parameters (passenger mass or wind) could cause the operational constraints to be violated.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 7 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 21.3 Constrained Optimization of Driver Control to Limit Energy Consumption R. Jorge Do Marco^1,2,3, G. Perrin¹, C. Funfschilling² and C. Soize³ ¹COSYS, Université Gustave Eiffel, France ²Direction Technologies, Innovation et Projets Groupe, SNCF, France ³MSME UMR 8208, Université Gustave Eiffel, CNRS, Marne-La-Vallée, France doi:10.4203/ccc.7.21.3 Full Bibliographic Reference for this paper R. Jorge Do Marco, G. Perrin, C. Funfschilling, C. Soize, "Constrained Optimization of Driver Control to Limit Energy Consumption", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 21.3, 2024, doi:10.4203/ccc.7.21.3 Keywords: optimization, Bayesian calibration, energy economy, driving assistance, automatic-train operation, model predictive control. Abstract This work aims to optimize the driver control along a track to ensure minimal energy consumption. The focus is on optimizing a control system, which requires a dynamic model to feed an energy model. This will enable the linking of control and consumption while checking operational constraints such as punctuality and safety that apply to the dynamics of the train. In both models it is necessary to determine parameters that are not directly measurable and potentially variable from one trip to another (such as the mass of the passengers). As we have both expert knowledge and real measurements, this work focuses on Bayesian calibration to deduce an a posterior distribution, from this distribution, we will extract the maximum from this a posteriori distribution in order to perform deterministic optimization. The conclusion of this work is that energy can be reduced. However, the robustness of the model is not sufficient, since a small variation of variable parameters (passenger mass or wind) could cause the operational constraints to be violated. download the full-text of this paper (PDF, 12 pages, 1203 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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