Optimisation sous contraintes et incertitudes de la commande du conducteur pour réduire la consommation énergétique des trains à grande vitesse à l'aide de la dynamique stochastique non linéaire et des statistiques

Le monde ferroviaire est en pleine mutation. L'avènement de nouvelles technologies permet de repenser le système du train mais aussi de faire face à de nouveaux enjeux. Le train autonome est une avancée notable dans le domaine mais elle ne doit pas oublier l'ensemble des contraintes écologiques qui sont aujourd'hui accentuées par l'augmentation des coûts de l'énergie. Ces problématiques soulèvent une question: comment faire rouler un train de façon autonome tout en réduisant sa consommation énergétique ? Plusieurs pistes peuvent être explorées pour répondre à cette interrogation. Ce travail de thèse se penche sur l'optimisation de la commande du conducteur pour économiser l'énergie consommée par les trains. Ce problème est difficile à résoudre à cause de la complexité du système ferroviaire, de la grande amplitude d'incertitudes attribuée aux différentes grandeurs du modèle, ou encore de l'importance des contraintes dans le problème d'optimisation. Ce travail de thèse s'articule autour de ces trois axes. Dans un premier temps, le train est un système complexe dont le comportement dynamique peut s'avérer difficile à prévoir. La construction d'un modèle de corps rigides permet de représenter les éléments constituant le train et l'ensemble de leurs interactions mais celui-ci est coûteux à résoudre pour des trajets de grandes distances. Pour cette raison, la dynamique longitudinale est souvent privilégiée lorsque celle-ci est suffisante. L'énergie consommée par le train doit être estimée avec attention comme elle constitue un élément clé de cette recherche. Le deuxième point se focalise sur l'identification des paramètres du modèle. Ceux-ci couvent à la fois des grandeurs décrivant la dynamique, mais aussi la consommation énergétique. Mais les trains ne se comportent pas tous de la même façon. Aussi, l'utilisation du cadre probabiliste permet de représenter autant que possible l'ensemble de ces comportements. L'utilisation de l'inférence Bayésienne sur un ensemble de mesure réalisée sur des trains commerciaux rapproche le modèle de la réalité physique. Enfin, le problème d'optimisation est complexe à résoudre. Les variables d'optimisation ainsi que le domaine de recherche doivent être définis avec attention pour respecter le cadre physique. Un ensemble de contraintes assure la sécurité, la ponctualité et le confort des passagers. La fonction coût doit s'approcher de l'objectif industriel. Cependant, toutes ces grandeurs sont des variables aléatoires. Pour cette raison, une stratégie robuste a été mise en place pour être capable de tenir compte de l'ensemble des incertitudes liées au système. Les solutions optimales obtenues sont comparées avec des mesures de trains commerciauxThe railway world is undergoing major changes. The advent of new technologies allows us to rethink the train system, but also face new challenges. The autonomous train is a significant advance in the field, but one must not forget all the ecological constraints that are now accentuated by the increase in energy costs. These issues raise a question: how can one make a train run autonomously while reducing its energy consumption? Several ideas can be explored to answer this question. This thesis work focuses on the optimization of the driver's command to limit the energy consumption of the trains. This problem is difficult to solve mainly because of the complexity of the railway system, the large amplitude of uncertainties attributed to the different model parameters, likewise, the importance of the constraints in the optimization problem. This thesis work is based on these three axes. Firstly, the train is a complex system whose dynamic behavior can be difficult to predict. The construction of a rigid body model allows for describing the elements constituting the train and all their interactions, but it is expensive to solve for long journeys. For this reason, the longitudinal dynamics is often preferred when it is sufficient. The energy consumed by the train must carefully be estimated as it constitutes a key element of this work.The second point focuses on the identification of the model parameters. This covers both models describing the dynamics and the energy consumption. But all trains do not behave in the same way. Therefore, the use of the probabilistic framework allows us to depict all these behaviors as much as is possible. The use of the Bayesian inference on a set of measurements performed on commercial trains brings the model closer to physical reality.Finally, the optimization problem is complex to solve. The optimization variables and the search domain must be carefully defined with respect to the physical framework. A set of constraints ensures safety, punctuality, as well as passenger comfort. The cost function must be close to the industrial objective. However, all these quantities are random variables. For this reason, a robust strategy has been set up to be able to take into account all the uncertainty related to the train system. The optimal solutions obtained are compared with measurements from commercial train

Optimisation sous contraintes et incertitudes de la commande du conducteur pour réduire la consommation énergétique des trains à grande vitesse à l'aide de la dynamique stochastique non linéaire et des statistiques

The railway world is undergoing major changes. The advent of new technologies allows us to rethink the train system, but also face new challenges. The autonomous train is a significant advance in the field, but one must not forget all the ecological constraints that are now accentuated by the increase in energy costs. These issues raise a question: how can one make a train run autonomously while reducing its energy consumption? Several ideas can be explored to answer this question. This thesis work focuses on the optimization of the driver's command to limit the energy consumption of the trains. This problem is difficult to solve mainly because of the complexity of the railway system, the large amplitude of uncertainties attributed to the different model parameters, likewise, the importance of the constraints in the optimization problem. This thesis work is based on these three axes. Firstly, the train is a complex system whose dynamic behavior can be difficult to predict. The construction of a rigid body model allows for describing the elements constituting the train and all their interactions, but it is expensive to solve for long journeys. For this reason, the longitudinal dynamics is often preferred when it is sufficient. The energy consumed by the train must carefully be estimated as it constitutes a key element of this work.The second point focuses on the identification of the model parameters. This covers both models describing the dynamics and the energy consumption. But all trains do not behave in the same way. Therefore, the use of the probabilistic framework allows us to depict all these behaviors as much as is possible. The use of the Bayesian inference on a set of measurements performed on commercial trains brings the model closer to physical reality.Finally, the optimization problem is complex to solve. The optimization variables and the search domain must be carefully defined with respect to the physical framework. A set of constraints ensures safety, punctuality, as well as passenger comfort. The cost function must be close to the industrial objective. However, all these quantities are random variables. For this reason, a robust strategy has been set up to be able to take into account all the uncertainty related to the train system. The optimal solutions obtained are compared with measurements from commercial trainsLe monde ferroviaire est en pleine mutation. L'avènement de nouvelles technologies permet de repenser le système du train mais aussi de faire face à de nouveaux enjeux. Le train autonome est une avancée notable dans le domaine mais elle ne doit pas oublier l'ensemble des contraintes écologiques qui sont aujourd'hui accentuées par l'augmentation des coûts de l'énergie. Ces problématiques soulèvent une question: comment faire rouler un train de façon autonome tout en réduisant sa consommation énergétique ? Plusieurs pistes peuvent être explorées pour répondre à cette interrogation. Ce travail de thèse se penche sur l'optimisation de la commande du conducteur pour économiser l'énergie consommée par les trains. Ce problème est difficile à résoudre à cause de la complexité du système ferroviaire, de la grande amplitude d'incertitudes attribuée aux différentes grandeurs du modèle, ou encore de l'importance des contraintes dans le problème d'optimisation. Ce travail de thèse s'articule autour de ces trois axes. Dans un premier temps, le train est un système complexe dont le comportement dynamique peut s'avérer difficile à prévoir. La construction d'un modèle de corps rigides permet de représenter les éléments constituant le train et l'ensemble de leurs interactions mais celui-ci est coûteux à résoudre pour des trajets de grandes distances. Pour cette raison, la dynamique longitudinale est souvent privilégiée lorsque celle-ci est suffisante. L'énergie consommée par le train doit être estimée avec attention comme elle constitue un élément clé de cette recherche. Le deuxième point se focalise sur l'identification des paramètres du modèle. Ceux-ci couvent à la fois des grandeurs décrivant la dynamique, mais aussi la consommation énergétique. Mais les trains ne se comportent pas tous de la même façon. Aussi, l'utilisation du cadre probabiliste permet de représenter autant que possible l'ensemble de ces comportements. L'utilisation de l'inférence Bayésienne sur un ensemble de mesure réalisée sur des trains commerciaux rapproche le modèle de la réalité physique. Enfin, le problème d'optimisation est complexe à résoudre. Les variables d'optimisation ainsi que le domaine de recherche doivent être définis avec attention pour respecter le cadre physique. Un ensemble de contraintes assure la sécurité, la ponctualité et le confort des passagers. La fonction coût doit s'approcher de l'objectif industriel. Cependant, toutes ces grandeurs sont des variables aléatoires. Pour cette raison, une stratégie robuste a été mise en place pour être capable de tenir compte de l'ensemble des incertitudes liées au système. Les solutions optimales obtenues sont comparées avec des mesures de trains commerciau

Optimization under uncertainties of high-speed train speed to limit energy consumption

International audienceThe speed profile of a train plays an important role in energy consumption and resulting costs. The industrial objective of this work is to develop a method, which optimizes the train speed under constraints, in order to reduce the energy consumed over a journey. Some articles have presented similar problems solved with other methods [1, 2]. In this work, we choose to describe the dynamic problem thanks to a rigid body approach (Lagrangian formalism), which is projected onto the longitudinal axis. The aerodynamic, traction, and braking forces applied to the train are taken into account. The pneumatic braking is dissociated from dynamic braking that can recover energy.The aim is to reduce the energy consumed (cost function) playing on the driver commands (traction and braking forces) respecting comfort, security, and punctuality constraints. As the track topology is an important parameter, we describe track slopes and curves to fit with industrial needs. The railway system depends on uncertainties derived from weather and from the power available at the catenary. As speed is a functional feature of the curvilinear abscissa of the track, a discretization strategy is used. The optimization problem under uncertainties is solved using a CMA-ES method where the constraints are implemented using an augmented Lagrangian. The method is applied to a real high-speed line and the model has been validated with experimental measurements. The optimal trajectory is compared to the in-line trains.REFERENCES[1] P. Wang, R. Goverde, Multiple-phase train trajectory optimization with signaling and operational constraints. Transportation Research Part C: Emerging Technologies, 192, 913 – 922, 2016.[2] G. Scheepmaker, R. Goverde, L. Kroon, Review of energy-efficient train control and timetabling. European Journal of Operational Research, 257, 355 – 376, 2017

Uncertainty quantification for high-speed train dynamics modeling and optimization under uncertainties to limit energy consumption

International audienceControlling 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 andphysical 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)

Bayesian inference for high-speed train dynamics and speed optimization under uncertainty for energy saving

International audienceThe train is a complex nonlinear system, whose dynamic behavior is difficult to predict accurately because of its environmental sensitivity. Indeed, in spite of a relative fine modeling of the vehicle and its rolling environment (track and wind), the slightest uncontrolled disturbance can modify the dynamic comportment of the train. For this reason, uncertainty must be considered in the physical models. The industrial objective of this work is twofold. Firstly, the construction of a longitudinal dynamic model for high-speed trains able to take into account the fluctuations inherent to the system. Secondly, the optimization under uncertainty of the driver's command with the objective of reducing the energy consumed by the train, under a set of punctuality and physical nonlinear constraints (speed limitation, final speed, and final position constraints)

Driver’s control optimization under uncertainties to reduce energy consumption of high-speed trains

International audienceControlling 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.On the scientific point of view, this work has enabled the development of original methods to introduce non-linear physical and punctuality constraints in a probabilistic framework by means of order relations. The driver's command is chosen as the optimization variable instead of the train speed, as it is often the case in literature. It facilitates the transposition of the developments to real systems. In addition, many energy measurements are used to calibrate and validate the models. The rolling environment and the vehicle characteristics are carefully defined from existing case study. To conclude, algorithms are developed for the robust optimization of the problem including uncertainties on both objective function and constraints

Optimisation of train speed to limit energy consumption

International audienceThe 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

Robust adaptation of the train speed for energy saving under punctaulity and security constraints

International audienc

High-speed train speed optimization for limiting energy consumption

International audienc

Acute aphasia after right hemisphere stroke

Abstract Right hemispheric stroke aphasia (RHSA) rarely occurs in right- or left-handed patients with their language representation in right hemisphere (RH). For right-handers, the term crossed aphasia is used. Single cases, multiple cases reports, and reviews suggest more variable anatomo-clinical correlations. We included retrospectively from our stroke data bank 16 patients (right- and left-handed, and ambidextrous) with aphasia after a single first-ever ischemic RH stroke. A control group was composed of 25 successive patients with left hemispheric stroke and aphasia (LHSA). For each patient, we analyzed four modalities of language (spontaneous fluency, naming, repetition, and comprehension) and recorded eventual impairment: (1) on admission (hyperacute) and (2) between day 3 and 14 (acute). Lesion volume and location as measured on computed tomography (CT) and magnetic resonance imaging (MRI) were transformed into Talairach stereotaxic space. Nonparametric statistics were used to compare impaired/nonimpaired patients. Comprehension and repetition were less frequently impaired after RHSA (respectively, 56% and 50%) than after LHSA (respectively, 84% and 80%, P = 0.05 and 0.04) only at hyperacute phase. Among RHSA, fewer left-handers/ambidextrous than right-handers had comprehension disorders at second evaluation (P = 0.013). Mean infarct size was similar in RHSA and LHSA with less posterior RHSA lesions (caudal to the posterior commissure). Comprehension and repetition impairments were more often associated with anterior lesions in RHSA (Fisher's exact test, P < 0.05). Despite the small size of the cohort, our findings suggest increased atypical anatomo-functional correlations of RH language representation, particularly in non-right-handed patients. Rapport de synthèse : Des aphasies secondaires à un accident vasculaire ischémique cérébral (AVC) hémisphérique droit sont rarement rencontrées chez des patients droitiers ou gauchers avec une représentation du langage dans l'hémisphère droit. Chez les droitiers, on parle d'aphasie croisée. Plusieurs études sur le sujet ont suggéré des corrélations anatomocliniques plus variables. Dans notre étude, nous avons inclus rétrospectivement, à partir d'une base de données de patients avec un AVC, seize patients (droitiers, gauchers et ambidextres) souffrant d'une aphasie suite à un premier et unique AVC ischémique hémisphérique droit. Un groupe contrôle est composé de vingt-cinq patients successifs avec une aphasie suite à un AVC ischémique hémisphérique gauche. Pour chaque patient, nous avons analysé quatre modalités de langage, à savoir la fluence spontanée, la dénomination, la répétition et la compréhension et leur éventuelle atteinte à deux moments distincts : 1) à l'admission (phase hyperaiguë) et 2) entre le 3e et le 14e jour (phase aiguë). Le volume et la localisation de la lésion mesurés, soit sur un CT-scanner soit sur une imagerie par résonance magnétique cérébrale, ont été analysés à l'aide de l'échelle stéréotaxique de Talairach. Des statistiques non paramétriques ont été utilisées pour comparer les patients atteints et non atteints. . La compréhension et la répétition étaient moins souvent atteintes, seulement en phase hyperaiguë, après une aphasie suite à un AVC hémisphérique droit (resp. 56% et 50%) plutôt que gauche (resp. 84 % et 80%, p= 0.05 et 0.04). Parmi les aphasies suite à un AVC ischémique hémisphérique droit, moins de gauchers et d'ambidextres que de droitiers avaient des troubles de la compréhension lors de la seconde évaluation (p=0.013}. La .taille moyenne de la zone infarcie était semblable entre les aphasies droites et gauches, avec moins de lésions postérieures (caudale à la commissure postérieure) lors des aphasies droites. Les troubles de la répétition et de la compréhension étaient plus souvent associés à des lésions antérieures lors d'aphasie droite. (Fischer's exact test, p>0.05). Malgré la petite taille de notre cohorte de patients, ces résultats suggèrent une augmentation des corrélations anatomocliniques atypiques lors d'une représentation du langage dans l'hémisphère droit, surtout chez les patients non droitiers