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International audience; A novel Fuzzy rule value reinforcement learning based energy management strategy is proposed for the fuel cell hybrid electric vehicle. The optimization target of the proposed method is to reduce hydrogen consumption and maintain the continuous operation of the battery. Fuzzy rule value reinforcement learning uses a fuzzy inference system to approximate the state-action value function, which enables the possible implementation of continuous state and/or action space. It does not rely on data or experience to set rules, but learns and adjusts rules by itself through interacting with the environment. Therefore, the proposed method can adapt to the changes in models or operating conditions, such as fuel cell degradation and changes in driving conditions. Simulation tests verify the effectiveness of the proposed method in solving energy management problems. Faster and smoother convergence and powerful adaptability to the environment changes are also verified.

The series-parallel architecture is the most interesting for hybrid electric vehicles, allowing the lowest fuel consumption. Unlike passenger cars, this architecture is not commercially available on the heavy-duty vehicles market. This is due to technical limitations associated with unsufficient load capability of the geartrain. To address this issue, new transmissions, such as the electrical variable transmission, have been developed. The novelty of this paper relies on the hybridization of a long-haul truck using the electrical variable transmission. This study aims to investigate the potential of using this new transmission for trucks. For that aim, fuel consumption benchmarking of three powertrain topologies is performed, considering: (a) a gearless topology; (b) a geared topology that uses one gearbox inserted between the engine and the mechanical input port of the electrical variable transmission; (c) a geared topology similar to the second one, but, with an additional multi-stage gearbox inserted to the mechanical output port of the electrical variable transmission. For a fair comparison between the different topologies, a bi-level optimization process has been used, incorporating the optimization of both components sizing and control. Results show that the fuel consumption of the gearless powertrain is higher than the engine-powered truck due to higher losses in the electrical variable transmission. While maximum fuel reduction of 14.2% was obtained by a geared topology that uses two gearboxes. Furthermore, emphasis is given to understand the effect of the powertrain component sizing on fuel consumption. Depending on the defined sizing, a possible fuel reduction is achieved from 3.3% to 14.2% for the two geared topologies. The reduction of CO2 emissions is found to be proportional to the fuel savings. Considering a long-haul mission, the last findings prove that the electrical variable transmission exhibits potential to reduce fuel consumption, if an adequate powertrain topology and its sizing are well defined.

International audience; Retrofit electrification of engine-powered vehicles to plug-in hybrid electric vehicles is growing. Different hybrid solutions exist, but all of them must compromise between integration constraints, cost and efficiency. Full vehicle simulation, including the powertrain subsystems is thus mandatory. During the pre-design step, some sub-systems are not available. Use a simplified model for some subsystems is then common, to initiate the sizing process. In this paper, two electric drive models are compared: one using a torque and speed dependent loss map and another with a constant efficiency. Using Dynamic Programming, the fuel consumption is estimated for both models. The simple average efficiency leads to only 3% of errors on fuel saving. Such simple model can thus be relevant for the comparison of solutions in terms of fuel saving.

International audience; In the paper, a self-learning energy management strategy is proposed for fuel cell hybrid electric vehicles (FCHEV). The studied energy system for FCHEV is composed of fuel cells and lithium batteries. A reinforcement learning (RL) based energy management strategy (EMS) for FCHEV is studied to achieve the power allocation of the two energy sources. The objective is to learn a satisfactory EMS from scratch and only through the interaction of environments. Specifically, Q-Learning, one of the RL methods, is applied to minimize fuel consumption and ensure battery sustainability. Compare with Dynamic Programming (DP), which can reach the best performance of sequential decision problems theoretically, Q-Learning based EMS can achieve results close to DP based EMS. During the process, different objective functions are optimized to be suitable for Q-Learning. Finally, the simulation results with python verify the effectiveness of the method proposed in this paper.

International audience; This paper aims at presenting an energy management strategy (EMS) based upon optimal control theory for a battery–supercapacitor hybrid power system. The hybrid power system consists of a lithium-ion battery and a supercapacitor with associated bidirectional DC/DC converters. The proposed EMS aims at computing adaptive gains using the salp swarm algorithm and load following control technique to assign the power reference for both the supercapacitor and the battery while achieving optimal performance and stable voltage. The DC/DC converter model is derived utilizing the first-principles method and computes the required gains to achieve the desired power. The fact that the developed algorithm takes disturbances into account increases the power elements’ life expectancies and supplies the power system with the required power.

The transportation sector is a major contributor to both air pollution and greenhouse gas emissions. Hybrid electric vehicles can reduce fuel consumption and CO2 emissions by optimizing the energy management of the powertrain. This study examines the trade-off between regulated pollutant emissions and hybrid powertrain efficiency. In the first part of the work, three-dimensional dynamic programming is used with a weighted objective function to determine the optimal distribution of a power request between the thermal engine and the electric motor. In the second part, we modify the control variable in order to take into account the engine actuators (intake pressure, air fuel ratio, and ignition timing). The purpose of this second control problem is to identify the potential differences between conventional solutions and optimal engine control in the context of hybridization. The thermal dynamics of the three-way catalyst are taken into account in order to model the aftertreatment efficiency. Experimental campaigns are conducted on spark-ignition engines to introduce simplified models for emissions, exhaust gas temperature, catalyst heat transfers and efficiency. The convergence of the results is analyzed with respect to the discretization parameters. Parametric studies evaluate the influence of the weighting factor, driving cycle (NEDC, WLTC), electric motor and battery sizing, as well as emission and catalyst modeling parameters. We assess the impact of including the sparkignition engine’s actuators as control variables, focusing on the interdepence between the three parameters (intake pressure, air fuel ratio, and ignition timing). We discuss the difference between the optimal engine control that we calculated for a hybrid vehicle and the standard approach in a conventionnal powertrain., Le secteur des transports est un des principaux contributeurs aux émissions de gaz à effet de serre et à la pollution de l’air. Les véhicules hybrides électriques ont été introduits pour réduire la consommation de ressources fossiles et les émissions de CO2 en optimisant l’efficacité énergétique de la motorisation. Cette thèse s’intéresse au compromis qui peut exister entre cet objectif et la réduction des émissions de polluants réglementés. Dans la première partie du travail, la programmation dynamique à trois dimensions est utilisée avec une fonction objectif qui pondère les deux critères pour déterminer la répartition optimale de puissance entre le moteur thermique et la machine électrique. La seconde partie consiste à introduire les paramètres de contrôle rapproché du moteur thermique (pression d’admission, richesse et avance à l’allumage) dans la méthode d’optimisation. L’objectif est d’identifier les différences potentielles entre la commande optimale du moteur hybridé et les stratégies habituellement utilisées pour les véhicules conventionnels. La dynamique thermique du catalyseur trois voies est modélisée pour évaluer l’efficacité du système de dépollution. Des campagnes de mesures sur deux moteurs à allumage commandé permettent de formuler des modèles simplifiés des émissions de la température des gaz, des coefficients de transferts thermiques et des efficacités du catalyseur. La convergence des résultats est validée au regard des paramètres d’optimisation (finesse de la discrétisation en particulier). Des études paramétriques permettent d’évaluer l’influence sur les résultats du facteur de pondération, du cycle de conduite (NEDC, WLTC), du dimensionnement des composants, et de certains paramètres de modélisation. L’optimisation du contrôle rapproché du moteur est analysée, en particulier concernant l’interdépendance entre les trois variables considérées. La différence entre les stratégies conventionnelles de contrôle du moteur et les résultats obtenus est discutée.

Data centers, factories housing thousands of computer servers that work permanently to exchange, store, process data and make it accessible via the Internet. With the digital sector development, their energy consumption, which is largely fossil fuel-based, has grown continuously over the last decade, posing a real threat to the environment. The use of renewable energy is a promising way to limit the ecological footprint of data centers. Nevertheless, the intermittent nature of these sources hinders their integration into a system requiring a high reliability degree. The hybridization of several technologies for green electricity production, coupled with storage devices, is currently an effective solution to this problem. As a result, this research work studies a multi-source system, integrating tidal turbines, photovoltaic panels, batteries and a hydrogen storage system to power an MW-scale data center. The main objective of this thesis is the optimization of a data center power supply, both for isolated sites and grid-connected ones. The first axis of this work is the modeling of the system components using the energetic macroscopic representation (EMR). Energy management strategy based on the frequency separation principle is first adopted to share power between storage devices with different dynamic characteristics. The second axis concerns the optimal sizing of the proposed system, in order to find the best configuration that meets the technical constraints imposed at minimum cost, using particle swarm optimization (PSO) and genetic algorithm (GA). Here, a rules-based energy management technique is used for simplicity and reduced computing time purposes. The last axis focuses on the energy management optimization through GA, taking into account the storage systems degradation in order to reduce their operating costs and extend their lifetime. It should be noted that each axis previously discussed has been the subject of a specific sensitivity analysis, which aims to evaluate the performance of the hybrid system under different operating conditions., Les data centers, des usines abritant des milliers de serveurs informatiques, fonctionnent en permanence pour échanger, stocker, traiter des données et les rendre accessibles via l'internet. Avec le développement du secteur numérique, leur consommation énergétique, en grande partie d’origine fossile, n’a cessé de croitre au cours de la dernière décennie, représentant une réelle menace pour l’environnement. Le recours aux énergies renouvelables constitue un levier prometteur pour limiter l’empreinte écologique des data centers. Néanmoins, le caractère intermittent de ces sources freine leur intégration dans un système nécessitant un degré de fiabilité élevée. L’hybridation de plusieurs technologies pour la production d’électricité verte, couplée à des dispositifs de stockage est actuellement une solution efficace pour pallier ce problème. De ce fait, ce travail de recherche étudie un système multi-sources, intégrant des hydroliennes, des panneaux photovoltaïques, des batteries et un système de stockage d’hydrogène pour alimenter un data center à l’échelle du MW. L'objectif principal de cette thèse est l’optimisation de l'alimentation électrique d'un data center, aussi bien pour des sites isolés que des installations raccordées au réseau. Le premier axe de ce travail est la modélisation des différents composants du système à l’aide de la représentation énergétique macroscopique (REM). Une gestion d’énergie reposant sur le principe de séparation fréquentielle est adoptée dans un premier temps pour répartir l’énergie entre des organes de stockage présentant des caractéristiques dynamiques différentes. Le deuxième axe concerne le dimensionnement optimal du système proposé afin de trouver la meilleure configuration qui satisfasse les contraintes techniques imposées à un coût minimum, en utilisant l’optimisation par essaims particulaires (PSO) et l’algorithme génétique (AG). Ici, une technique de gestion d’énergie basée sur des règles simples est utilisée pour des raisons de simplicité et de réduction de temps de calcul. Le dernier axe se focalise sur l’optimisation de la gestion d’énergie via l’AG, en tenant compte des problèmes de dégradation des systèmes de stockage en vue de réduire leur coût d’exploitation et de prolonger leur durée de vie. Il est bien entendu que chaque axe précédemment abordé a fait l’objet d’une analyse de sensibilité spécifique, afin d’évaluer les performances du système hybride dans différentes conditions de fonctionnement.

International audience; This paper presents an energy management strategy for a multi-source system composed of a fuel cell (FC) as the primary source and supercapacitors (SC) as secondary source. Isolated LLC resonant converter is used to connect the fuel cell to the DC-link, while the supercapacitors are connected through a bidirectional resonant converter. The resonant operation ensures high efficiency and high power density by means of soft switching and high frequency operation. The aim of this paper is the control of the DC-DC resonant converters to share the requested power between the SC and FC according to their dynamic response. So, the Fuel cell supplies the nominal power, while the supercapacitors ensure the mitigation of the fluctuations from the load power. The performances of the proposed topology are evaluated through some simulations.; Cet article présente une stratégie de gestion de l'énergie pour un système multi-sources composé d'une pile à combustible (FC) comme source principale et de supercondensateurs (SC) comme source secondaire. Un convertisseur résonant LLC isolé est utilisé pour connecter la pile à combustible à la liaison CC, tandis que les supercondensateurs sont connectés via un convertisseur résonnant bidirectionnel. Le fonctionnement résonnant garantit un rendement élevé et une densité de puissance élevée au moyen d'une commutation douce et d'un fonctionnement à haute fréquence. Le but de cet article est le contrôle des convertisseurs résonants DC-DC pour partager la puissance demandée entre le SC et le FC en fonction de leur réponse dynamique. Ainsi, la pile à combustible fournit la puissance nominale, tandis que les supercondensateurs assurent l'atténuation des fluctuations de la puissance de charge. Les performances de la topologie proposée sont évaluées à travers quelques simulations.

International audience; Gas turbine systems are among potential energy converters to substitute the internal combustion engine as auxiliary power unit in future series hybrid electric vehicle powertrains. Fuel consumption of these auxiliary power units in the series hybrid electric vehicle strongly relies on the energy converter efficiency and power-to-weight ratio as well as on the energy management strategy deployed on-board. This paper presents a technological analysis and investigates the potential of fuel consumption savings of a series hybrid electric vehicle using different gas turbine–system thermodynamic configurations. These include a simple gas turbine, a regenerative gas turbine, an intercooler regenerative gas turbine, and an intercooler regenerative reheat gas turbine. An energetic and technological analysis is conducted to identify the systems’ efficiency and power-to-weight ratio for different operating temperatures. A series hybrid electric vehicle model is developed and the different gas turbine–system configurations are integrated as auxiliary power units. A bi-level optimization method is proposed to optimize the powertrain. It consists of coupling the non-dominated sorting genetic algorithm to the dynamic programming to minimize the fuel consumption and the number of switching ON/OFF of the auxiliary power unit, which impacts its durability. Fuel consumption simulations are performed on the worldwide-harmonized light vehicles test cycle while considering the electric and thermal comfort vehicle energetic needs. Results show that the intercooler regenerative reheat gas turbine–auxiliary power unit presents an improved fuel consumption compared with the other investigated gas turbine systems and a good potential for implementation in series hybrid electric vehicles.

International audience; The transportation sector is a major contributor to both air pollution and greenhouse gas emissions. Hybrid electric vehicles can reduce fuel consumption and CO2 emissions by optimizing the energy management of the powertrain. The purpose of this study is to examine the trade-off between regulated pollutant emissions and hybrid powertrain efficiency. The thermal dynamics of the three-way catalyst are taken into account in order to optimize the light-off. Experimental campaigns are conducted on a spark-ignition engine to introduce simplified models for emissions, exhaust gas temperature, catalyst heat transfers and efficiency. These models are used to determine the optimal distribution of a power request between the thermal engine and the electric motor with three-dimensional dynamic programming and a weighted objective function. A pollution-centered scenario is compared with a consumption-centered scenario for various driving cycles. The optimal torque distribution for the emissions-centered scenario on the world harmonized light-duty vehicles test cycle shows an 8–33% decrease in pollutant emissions while the consumption remains stable (0.1% increase). The consistency of the results is analyzed with respect to the discretization parameters, driving cycle, electric motor and battery sizing, as well as emission and catalyst models. The control strategies are promising but will have to be adapted to online engine control where the driving cycle and the catalyst efficiency are uncertain.

The transportation sector is a major contributor to both air pollution and greenhouse gas emissions. Hybrid electric vehicles can reduce fuel consumption and CO2 emissions by optimizing the energy management of the powertrain. The purpose of this study is to examine the trade-off between regulated pollutant emissions and hybrid powertrain efficiency. The thermal dynamics of the three-way catalyst are taken into account in order to optimize the light-off. Experimental campaigns are conducted on a spark-ignition engine to introduce simplified models for emissions, exhaust gas temperature, catalyst heat transfers and efficiency. These models are used to determine the optimal distribution of a power request between the thermal engine and the electric motor with three-dimensional dynamic programming and a weighted objective function. A pollution-centered scenario is compared with a consumption-centered scenario for various driving cycles. The optimal torque distribution for the emissions-centered scenario on the world harmonized light-duty vehicles test cycle shows an 8–33% decrease in pollutant emissions while the consumption remains stable (0.1% increase). The consistency of the results is analyzed with respect to the discretization parameters, driving cycle, electric motor and battery sizing, as well as emission and catalyst models. The control strategies are promising but will have to be adapted to online engine control where the driving cycle and the catalyst efficiency are uncertain.

International audience; This paper presents the modeling and control of an Electric Vehicle (EV) driven by a 5-phase Permanent Magnet Synchronous Machine (PMSM). The machine in an open-end winding configuration is supplied by two isolated 5-leg Voltage Source Inverters (VSIs). The inverters are fed by a Hybrid Energy Storage System (HESS) consisting of a battery and Supercapacitors (SCs).To increase the battery lifetime, the SCs are used to provide the high-power requirement during driving operations. In addition, an Energy Management Strategy (EMS) is proposed to take advantages of this EV architecture. Numerical results are derived to verify workability of the EV system.

This PhD thesis proposes Energy Management Strategies conceived for a hybrid electrical urban bus. The hybrid control system should create an efficient strategy of coordinating the flow of energy between the heat engine, battery, electrical and hydraulic motors. Firstly, a Deterministic Dynamic Programming (DDP) based approach has been proposed: simultaneous speed and powersplit optimization algorithm for a given trip (constrained by the traveled distance and time limit). This algorithm turned out to be highly time consuming so it cannot be used in real-time. To overcome this drawback, an Optimal Profiles Database based on DP (OPD-DP) has been constructed for real-time application. Afterwards, a Stochastic Dynamic Programming (SDP) technique is used to simultaneously generate an optimal speed profile and related powersplit strategy. This approach takes into account a stochastic nature of the driving behavior and urban conditions. The formulated energy optimization problem, being intrinsically multi-objective problem, has been transformed into several single-objective ones with constraints using an ε-constraint method to determine a set of optimal solutions (the Pareto Front).In urban environment, due to traffic conditions, traffic lights, a bus encounters frequent Stop&Go situations. This results in increased energy consumption during the starts. In this sense, a relevant Eco Adaptive Cruise Control with Stop&Go (eACCwSG) strategy brings the undeniable benefit. The algorithm smooths speed profile during acceleration and braking phases. One more important feature of this algorithm is the safety aspect, as eACCwSG permits to maintain a safety distance in order to avoid collision and apply a smooth braking. As it was mentioned before, smooth braking ensures passengers comfort., Cette thèse propose des stratégies de gestion de l'énergie conçues pour un bus urbain électrique hybride. Le système de commande hybride devrait créer une stratégie efficace de coordination du flux d’énergie entre le moteur thermique, la batterie, les moteurs électriques et hydrauliques. Tout d'abord, une approche basée sur la programmation dynamique déterministe (DDP) a été proposée : algorithme d'optimisation simultanée de la vitesse et de la puissance pour un trajet donné (limité par la distance parcourue et le temps de parcours). Cet algorithme s’avère être gourmand en temps de calcul, il n’a pas été donc possible de l’utiliser en temps réel. Pour remédier à cet inconvénient, une base de données de profils optimaux basée sur DP (OPD-DP) a été construite pour une application en temps réel. Ensuite, une technique de programmation dynamique stochastique (SDP) a été utilisée pour générer simultanément et d’une manière optimale un profil approprié de la vitesse du Bus ainsi que sa stratégie de partage de puissance correspondante. Cette approche prend en compte à la fois la nature stochastique du comportement de conduite et les conditions de circulations urbaines (soumises à de multiples aléas). Le problème d’optimisation énergétique formulé, en tant que problème intrinsèquement multi-objectif, a été transformé en plusieurs problèmes à objectif unique avec contraintes utilisant une méthode ε-constraint afin de déterminer un ensemble de solutions optimales (le front de Pareto).En milieu urbain, en raison des conditions de circulation, des feux de circulation, un bus rencontre fréquemment des situations Stop&Go. Cela se traduit par une consommation d'énergie accrue lors notamment des démarrages. En ce sens, une stratégie de régulation de vitesse adaptative adaptée avec Stop&Go (eACCwSG) apporte un avantage indéniable. L'algorithme lisse le profil de vitesse pendant les phases d'accélération et de freinage du Bus. Une autre caractéristique importante de cet algorithme est l’aspect sécurité, étant donné que l’ACCwSG permet de maintenir une distance de sécurité afin d’éviter les collisions et d’appliquer un freinage en douceur. Comme il a été mentionné précédemment, un freinage en douceur assure le confort des passagers.

In this paper, a hybrid ship powered by diesel generator sets and power batteries in series is considered. By analyzing the characteristics of hybrid ship cycle operating conditions, the load power of the hybrid ship under load uncertainty is firstly predicted. Then, considering the economy, emissions and continuous navigation time (endurance) of the hybrid ship, an energy optimization strategy based on the predicted load power is proposed to achieve the goal of minimum fuel consumption, minimum emissions and maximum endurance of ship operation. The experimental results show that, compared with the fuzzy logic rules based strategy, the fuel economy of the ship is increased by 9.6% and the ship&rsquo, s endurance is increased by 24% for the proposed strategy.

International audience; An analytical method is proposed to solve the optimization problem of energy management for a parallel hybrid electric vehicle. This method is based on Pontryagin's Maximum Principle (PMP) for a class of Hybrid Dynamic Systems (HDS). Therefore, the analytical models are used, which are an approximation of the reference models. A numerical method based on the reference models is also used in order to validate the analytical solution by comparing their results. In this paper, two types of optimization variables are considered: continuous and discrete. The first type is the power split between the Internal Combustion Engine (ICE) and the Electric Machine (EM). The second one is the transmission ratio, which includes the ICE On/Off decision. The results show that the analytical and the numerical solutions are almost the same. In addition, the analytical approach requires less computing time and requires less memory space than the numerical method.

International audience; This paper presents a comparison between two optimization methods for the energy management of a parallel hybrid electric powertrain: convex programming and Pontryagin's Minimum Principle (PMP). The objective of this comparison is to validate the analytical solution by comparing the results with the ones obtained on the original model with Dynamic Programming (DP). Therefore, before its application, some necessary approximations and convexification were made on the original nonlinear and non-convex model. The validation of the simplified model was also carried out. In this paper, two cases are studied. In the first case, the supervisory control considers only the torque split between the Internal Combustion Engine (ICE) and the Electric Machine. In the second case, a binary decision ICE On/Off is included in the optimization problem. In order to solve the problem of the binary decision, which makes the problem non-convex, a analytical solution based on PMP is then proposed. The results show that the analytical solution is close to the optimal solution given by DP.

The hybrid electric vehicle uses two different energy sources to propel itself: fuel as well as a reversible electric storage system. The energy management strategy aims at supervising the power flows inside the powertrain by choosing the operating points of the different components so as to optimize a given criterion. The energy management strategy is formulated as an optimal control problem where the criterion to be minimized takes into account the total fuel consumption of the vehicle on the considered trip. The optimal solution can be calculated off-line when the vehicle’s mission is perfectly known, an assumption no longer admissible for an embedded strategy whose main objective is to get as close as possible to the optimal result. The work presented in this manuscript highlights the potential of multi-objective optimal control to handle the features’ trade-offs inherent to the development of production vehicle. An energy management strategy taking into account the trade-off between fuel consumption and drivability, as well as one dealing with the trade-off between fuel consumption and battery state of health, are proposed. The presented strategies share a modular approach following the transversal solution of the Equivalent Consumption Minimization Strategy (ECMS). As a result, the control policy of the plug-in hybrid electric vehicle, the Mild-Hybrid, together with complex hybrid architectures provided with an automated transmission, two electric machines or two electric storage systems, is tackled through a common base. This approach allows to reduce the development period of the energy management strategies which shares a maximum of common elements., Le véhicule hybride électrique dispose de deux sources d’énergie distinctes pour se mouvoir : le carburant, ainsi qu’un système de stockage électrique ayant la particularité d’être réversible. La loi de gestion d’énergie a pour objectif de superviser les flux de puissance dans le groupe motopropulseur en intervenant sur le point de fonctionnement des organes de celui-ci, et ce dans le but d’optimiser un critère donné. La loi de gestion d’énergie se formalise donc par un problème de commande optimale dont le critère à minimiser tient compte de la consommation de carburant du véhicule sur un trajet donné. La solution de ce problème peut se calculer hors ligne lorsque toutes les données du trajet sont parfaitement connues à l’avance, hypothèse qui n’est plus admissible pour une stratégie embarquée sur véhicule dont l’objectif est alors de s’approcher au maximum du résultat optimal. Les travaux présentés dans ce manuscrit mettent en avant la commande optimale orientée multi-objectif pour répondre à la problématique du compromis inter-prestations au coeur du développement d’un véhicule de série. Une loi de gestion d’énergie tenant compte du compromis entre consommation et agrément de conduite, ainsi qu’une autre traitant le compromis entre consommation et vieillissement batterie sont proposées. Les stratégies présentées s’inscrivent également dans une approche modulaire tirée de la solution de nature transversale issue de l’Equivalent Consumption Minimization strategy (ECMS). Ainsi, la commande du véhicule hybride rechargeable, du Mild-Hybride, ainsi que d’architectures hybrides complexes disposant d’une transmission automatique, de deux machines électriques ou deux systèmes de stockage électriques, est ici traitée à travers un socle commun. Cette approche permet de réduire le temps de développement des stratégies qui partagent un maximum d’éléments communs.

The energy systems describe a class of systems whose from structural and functional characteristics raise the problem of the energy distribution to satisfy the services in real time. The solution of this multi-objectives problem, namely energetic, is the energy management strategy, whose design is still an open problem. The solutions studied in this thesis are incorporated in the framework of an industrial partnership and particularly in those systemic design approaches. The first contribution is a methodology of modular and generic design of the energy management strategy, for the multi- clients and multi-sources systems. It defines two types of functional elements: the clients and the sources, interacting through a node, which is the carrier of thestrategy. The second contribution deals with thegeneric formulation of the strategy and itssimplification by means of decomposition in accordance with two problems: the hybridization of sources and the competition of clients, which a real ready known in the literature. The third contribution is partial to the selection of innovative or existing algorithms, which are compatible with a real-time target to execute the strategy. Finally, the energy strategy of a refrigerated truck with a hybrid energy architecture is designed by the proposed modular approach, and the algorithm feasibility is validated by the simulation.; Les systèmes énergétiques désignent une classe de systèmes dont les spécificités structurelles et fonctionnelles posent la question de la distribution de l’énergie, en temps réel, pour satisfaire des services. Cette problématique multi-objectifs, nommée énergétique, a pour solution une stratégie de gestion,dont la conception représente un problème ouvert. Les verrous étudiés dans cette thèse s’inscrivent dans le cadre d’un partenariat industriel et en particulier celui des démarches de conception systémique. Trois contributions sont apportées. La première est une méthodologie de conception modulaire et générique de la stratégie énergétique, pour les systèmes multi-clients et multi-sources. Elle définit deux types d’éléments fonctionnels : les clients et les sources, interagissant par le biais d’un nœud, porteur de la stratégie. La seconde traite la simplification de la stratégie par une décomposition selon deux problématiques déjà connues de la littérature : l’hybridation de sources et la concurrence de clients. La troisième porte sur la sélection d’algorithmes novateurs ou existants, compatibles avec une cible temps réel, pour exécuter la stratégie. Enfin, la stratégie énergétique d’un camion frigorifique disposant d’une architecture énergétique hybride série est conçue par notre approche modulaire, et la faisabilité algorithmique est validée en simulation.

International audience; This paper addresses the energy management problem of a Mild hybrid electric vehicle (HEV) provided with a singular dual electric storage system. The latter is the combination of a Li-ion battery, a double layer capacitor (DLC) pack and a DC/DC converter. The energy management of the hybrid powertrain is formulated as an optimal control problem with two dimensional control and state variables. The optimal solution is computed using deterministic dynamic programming (DDP), and an on-line oriented solution based on the equivalent consumption minimization strategy (ECMS) is proposed. Simulation results of both strategies are presented and discussed, highlighting the minor sub-optimality of the on-line energy management strategy (EMS).

International audience; This paper presents modeling and control strategies of 5MW Permanent Magnet Synchronous Generator (PMSG) for offshore wind energy conversion application. The PMSG is connected to electrical grid of 20kV phase to phase RMS voltage using a back-to-back converter. The contribution of this paper is focused on energy management strategies using “grid code”, where the required power profile is fixed by electrical grid manager without any consideration for wind speed variations. For this study, it is assumed that the available power is higher than required power for electrical grid. The proposed control strategies include a Maximum Power Tracking (MPPT) for PMSG speed control, active/reactive power control, and DC-bus voltage management. To show the performances of the control, some simulation results are presented and analyzed using Matlab/Simulink software.

International audience; The paper proposes a generic methodology to incorporate constraints (pollutant emission, battery health, drivability) into on-line energy management strategies (EMSs) for hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs). The integration of each constraint into the EMS, made with the Pontryagin maximum principle, shows a tradeoff between the fuel consumption and the constraint introduced. As state dynamics come into play (catalyst temperature, battery cell temperature, etc.), the optimization problem becomes more complex. Simulation results are presented to highlight the contribution of this generic strategy, including constraints compared to the standard approach. These results show that it is possible to find an energy management strategy that takes into account an increasing number of constraints (drivability, pollution, aging, environment, etc.). However, taking these constraints into account increases fuel consumption (the existence of a trade-off curve). This trade-off can be sometimes difficult to find, and the tools developed in this paper should help to find an acceptable solution quickly.

International audience; This paper aims at comparing three different configurations of energy storage systems (ESS) for a fuel cell hybrid vehicle. This study is applied on an off-road vehicle, a new concept of hybrid electric mini-excavator. The power supply system (PSS) is composed of a predefined fuel cell system and one or two energy storage systems. In this paper, the sizing and the configuration of energy storage components is explored and cost considerations are taken into account for optimal design.

To face the increasing demand of electrical power in compliance with the liberalization of the electricity market and the need of reducing CO2 emissions, many distributed energy resources have emerged and especially the generation systems that utilize renewable energy sources. In the nearfuture, the grid could be described as an aggregation of several microgrids both consumer and producer. For those "prosumers", a classical strategy consists in selling all the highly subsidized production at important prices while all consumed energy is purchased. Smarter operations now become possible with developments of energy storage technologies and evolving prices policies. The microgrid considered in the thesis is composed of an industrial load and a photovoltaic generator associated to an energy storage. Two technologies are considered with high speed flywheels on one hand and a Li-ion electrochemical battery on the other. The common study referring to such systems allude to the optimal scheduling, the real-time management and the sizing methodology. Firstly in the thesis, the optimal power flow dispatching is performed using various algorithms. Those operations aim at reducing the electrical bill taking account of consumption and production forecasts as well as the different fares and possible constraints imposed by the power supplier. Then the design strategy is investigated. The approach consists in simultaneously integrating the energy management and the sizing of the system. We particularly underline the complexity of the resulting optimization problem and how it can be solved using suitable optimization methods in compliance with relevant models of the microgrid. We specifically show the reduction of the computational time allowing the microgrid simulation over long time durations in the optimization process in order to take seasonal variations into account. In the last part a cost analysis is performed, and different design are computed depending on the prices policies. The goal is to determine a financial context that would encourage the deployment of storage systems that are necessary to favor the development of intermittent renewable energy sources., L‘augmentation de la consommation pour soutenir la croissance, le souci de réduction des gaz à effet de serre et les avancées technologiques ont favorisé le développement des sources d‘énergie renouvelables depuis les années 90. L‘implantation de ces générateurs décentralisés a progressivement modifié l‘architecture du réseau en passant d‘un modèle vertical à une situation davantage clusterisée. Ce réseau maillé voit ainsi apparaitre de nouveaux acteurs, à la fois producteurs et consommateurs (en anglais, les « prosumers»). Pour ce type de structures, la stratégie classique actuelle consiste à acheter l'ensemble de l'énergie consommée alors que la totalité de la production est vendue séparément à des tarifs intéressants. Avec les progrès réalisés sur les différentes technologies de stockage, de nouveaux degrés de liberté apparaissent et des opérations plus intelligentes deviennent possibles. L‘objet de l‘étude est un microréseau comprenant un générateur photovoltaïque et un consommateur tertiaire associés à un moyen de stockage. Deux technologies sont envisagées avec des volants d‘inertie dans un premier temps et une batterie électrochimique (Li-ion) par la suite. Les domaines d‘étude relatifs à ce type de système sont la gestion énergétique par planification, la commande temps réel et le dimensionnement. Les travaux de cette thèse se concentrent d‘abord sur la problématique de gestion par optimisation des flux d‘énergie. Différents algorithmes sont ainsi utilisés et comparés pour planifier le fonctionnement du microréseau. L‘objectif est de diminuer la facture énergétique en tenant compte des données de consommation et production mais également des politiques tarifaires en vigueur et d‘éventuelles contraintes de fonctionnement imposées par le fournisseur d‘énergie. Dans un second temps la problématique de dimensionnement est abordée avec une démarche de conception optimale intégrant la boucle de gestion dès la phase de design. Nous montrons plus particulièrement comment l‘adéquation entre les méthodes d‘optimisationutilisées et le modèle du microréseau employé peut permettre la réduction significative des temps de calcul. Une configuration optimale du microréseau, valable sur des horizons temporels longs intégrant les alternances saisonnières, peut finalement se dégager. Les travaux se concluent sur une phase d‘analyse avec des dimensionnements établis pour différents contextes tarifaires. Le but est de dégager des domaines permettant de valoriser et justifier l‘installation d‘un moyen de stockage qui s‘avère indispensable pour soutenir le développement des sources d‘énergies renouvelables et assurer la transition énergétique.

This thesis addresses the development of some advanced control design tools for a class of nonlinear systems in general and for automotive systems in particular.Motivated by automotive applications, Part I proposes some novel theoretical results on control design for nonlinear systems under Takagi-Sugeno form subject to the control input saturation. The input saturation is dealt with by using its polytopic representation or an anti-windup strategy.Part II deals with our automotive application concerning the control of a turbocharged air system of a spark ignition engine. To this end, two novel control approaches are proposed in this part. For the first one, the theoretical design tool on switching Takagi-Sugeno controller developed in Part I is directly applied. The second one is based on a robust feedback linearization control technique. The originality of these MIMO approaches consist in their simplicity and effectiveness compared to other ones existing in the literature.Part III aims at developing the strategies, which are based on the Pontryagin's Minimum Principle in optimal control theory, for the energy management of the vehicular electric power systems in a hybrid engine configuration. To this end, both offline optimization approach using the future information of driving conditions and online implementable one have been developed and evaluated in an advanced simulator.; Cette thèse est consacrée au développement de techniques de commande avancées pour des classes de systèmes non linéaires en général et pour des applications automobiles en particulier.Pour répondre au besoin du contrôle moteur, la première partie propose des nouveaux résultats théoriques sur la technique de commande non linéaire à base de modèles de type Takagi-Sugeno soumis à la saturation de la commande. La saturation de la commande est traitée en utilisant sa représentation polytopique ou une stratégie anti-windup.La deuxième partie porte sur la commande du système d'air d'un moteur turbocompressé à allumage commandé. Deux approches originales sont proposées. Dans la première, l'outil théorique concernant les modèles Takagi-Sugeno à commutation développé dans la première partie est directement appliqué. La seconde approche est basée sur une commande linéarisante robuste. L'originalité de ces approches multivariables consiste dans sa simplicité de mise en œuvre et son efficacité par rapport à celles qui existent dans la littérature.La dernière partie vise à développer des stratégies pour la gestion énergétique des systèmes électriques d'un véhicule obtenues en se basant sur le Principe du Minimum de Pontryagin. À cet effet, deux approches sont considérées : l'approche hors ligne d'optimisation utilisant les informations du futur concernant les conditions de roulage et l'approche en ligne qui est adaptée de la précédente. Ensuite, ces deux approches sont implémentées et évaluées dans un simulateur avancé.

This thesis addresses the development of some advanced control design tools for a class of nonlinear systems in general and for automotive systems in particular.Motivated by automotive applications, Part I proposes some novel theoretical results on control design for nonlinear systems under Takagi-Sugeno form subject to the control input saturation. The input saturation is dealt with by using its polytopic representation or an anti-windup strategy.Part II deals with our automotive application concerning the control of a turbocharged air system of a spark ignition engine. To this end, two novel control approaches are proposed in this part. For the first one, the theoretical design tool on switching Takagi-Sugeno controller developed in Part I is directly applied. The second one is based on a robust feedback linearization control technique. The originality of these MIMO approaches consist in their simplicity and effectiveness compared to other ones existing in the literature.Part III aims at developing the strategies, which are based on the Pontryagin's Minimum Principle in optimal control theory, for the energy management of the vehicular electric power systems in a hybrid engine configuration. To this end, both offline optimization approach using the future information of driving conditions and online implementable one have been developed and evaluated in an advanced simulator.; Cette thèse est consacrée au développement de techniques de commande avancées pour des classes de systèmes non linéaires en général et pour des applications automobiles en particulier.Pour répondre au besoin du contrôle moteur, la première partie propose des nouveaux résultats théoriques sur la technique de commande non linéaire à base de modèles de type Takagi-Sugeno soumis à la saturation de la commande. La saturation de la commande est traitée en utilisant sa représentation polytopique ou une stratégie anti-windup.La deuxième partie porte sur la commande du système d'air d'un moteur turbocompressé à allumage commandé. Deux approches originales sont proposées. Dans la première, l'outil théorique concernant les modèles Takagi-Sugeno à commutation développé dans la première partie est directement appliqué. La seconde approche est basée sur une commande linéarisante robuste. L'originalité de ces approches multivariables consiste dans sa simplicité de mise en œuvre et son efficacité par rapport à celles qui existent dans la littérature.La dernière partie vise à développer des stratégies pour la gestion énergétique des systèmes électriques d'un véhicule obtenues en se basant sur le Principe du Minimum de Pontryagin. À cet effet, deux approches sont considérées : l'approche hors ligne d'optimisation utilisant les informations du futur concernant les conditions de roulage et l'approche en ligne qui est adaptée de la précédente. Ensuite, ces deux approches sont implémentées et évaluées dans un simulateur avancé.

To achieve a "greener" freight transport, efforts are still needed to overcome some technological barriers. New improvements must be carried at to limit the shunting locomotives' use intended for the goods' load/unload. Considering this aim, FEMTO-ST and Alstom Transport decided to conceive and develop an energy management strategy system for hybrid electric locomotives. Such locomotives include a diesel driven generator set which is coupled with batteries and ultra-capacitors. The architecture aims at improving the flexibility, the effectiveness of the electrical railway transport and at reducing the environmental impact of these activities again. Firstly, the study consists in implementing a macroscopic modelling of the hybrid electric powertrain. Then, the control is optimally designed by identifying the hardware and software sensors of the powertrain. The dynamics and the behavior of the secondary sources' models are improved thanks to their experimental characterizations. Secondly, the use of Type-2 Fuzzy Logic (interval and general) controllers permits to study their efficiency in the control of a very simple industrial system: the output voltage of a buck converter. Lastly, based on the obtained results, an innovative management of the system's energy flows is developed in the case of the hybrid electrical locomotive. The use of the type-2 fuzzy logic and evolutionary algorithms permit to optimally perform a real time energy management strategy without a priori knowledge of the duty cycle. The frequency approach and the secondary sources' state of charge control contribute to their efficient use.; Dans le cadre du transport de marchandises par voie ferroviaire, un certain nombre de verrous limitent les efforts consentis pour un fret plus "propre". Des améliorations doivent être faites afin de limiter le nombre de locotracteurs assurant les charges/décharges de marchandises en bout de ligne. Dans cette perspective, FEMTO-ST et Alstom Transport ont pour objectif de concevoir et développer un système de gestion d'énergie pour locomotive électrique hybride. Composée d'un groupe électrogène couplé à des accumulateurs électrochimiques et des super-condensateurs, la locomotive électrique hybride permet d'accroître la souplesse et l'efficacité du transport ferroviaire électrique, tout en réduisant encore son impact environnemental. Cette étude a consisté dans un premier temps à développer une modélisation macroscopique de la chaîne de traction électrique hybride et à proposer une structuration de la commande avec l'identification des capteurs matériels et logiciels nécessaires au contrôle optimal de la chaîne de traction. La caractérisation expérimentale des moyens de stockage a permis l'amélioration comportementale et dynamique des modèles correspondant. Dans un second temps, le contrôle de la tension de sortie d'un hacheur dévolteur a permis d'étudier l'application de la logique floue de type-2 (intervalle et générale) dans le cas d'applications industrielles (relativement simples). Enfin, une gestion innovante des flux d'énergie au sein d'un système plus complexe: locomotive électrique hybride, a été développée. Mettant en œuvre la logique floue de type-2 et des algorithmes s'inspirant de la théorie de l'évolution, cette gestion d'énergie optimale opère en temps réel et sans connaissance à priori du cycle de conduite. La gestion fréquentielle ainsi que le contrôle des états de charge des sources secondaires du véhicule contribuent également à leur bon fonctionnement.

To achieve a "greener" freight transport, efforts are still needed to overcome some technological barriers. New improvements must be carried at to limit the shunting locomotives' use intended for the goods' load/unload. Considering this aim, FEMTO-ST and Alstom Transport decided to conceive and develop an energy management strategy system for hybrid electric locomotives. Such locomotives include a diesel driven generator set which is coupled with batteries and ultra-capacitors. The architecture aims at improving the flexibility, the effectiveness of the electrical railway transport and at reducing the environmental impact of these activities again. Firstly, the study consists in implementing a macroscopic modelling of the hybrid electric powertrain. Then, the control is optimally designed by identifying the hardware and software sensors of the powertrain. The dynamics and the behavior of the secondary sources' models are improved thanks to their experimental characterizations. Secondly, the use of Type-2 Fuzzy Logic (interval and general) controllers permits to study their efficiency in the control of a very simple industrial system: the output voltage of a buck converter. Lastly, based on the obtained results, an innovative management of the system's energy flows is developed in the case of the hybrid electrical locomotive. The use of the type-2 fuzzy logic and evolutionary algorithms permit to optimally perform a real time energy management strategy without a priori knowledge of the duty cycle. The frequency approach and the secondary sources' state of charge control contribute to their efficient use.; Dans le cadre du transport de marchandises par voie ferroviaire, un certain nombre de verrous limitent les efforts consentis pour un fret plus "propre". Des améliorations doivent être faites afin de limiter le nombre de locotracteurs assurant les charges/décharges de marchandises en bout de ligne. Dans cette perspective, FEMTO-ST et Alstom Transport ont pour objectif de concevoir et développer un système de gestion d'énergie pour locomotive électrique hybride. Composée d'un groupe électrogène couplé à des accumulateurs électrochimiques et des super-condensateurs, la locomotive électrique hybride permet d'accroître la souplesse et l'efficacité du transport ferroviaire électrique, tout en réduisant encore son impact environnemental. Cette étude a consisté dans un premier temps à développer une modélisation macroscopique de la chaîne de traction électrique hybride et à proposer une structuration de la commande avec l'identification des capteurs matériels et logiciels nécessaires au contrôle optimal de la chaîne de traction. La caractérisation expérimentale des moyens de stockage a permis l'amélioration comportementale et dynamique des modèles correspondant. Dans un second temps, le contrôle de la tension de sortie d'un hacheur dévolteur a permis d'étudier l'application de la logique floue de type-2 (intervalle et générale) dans le cas d'applications industrielles (relativement simples). Enfin, une gestion innovante des flux d'énergie au sein d'un système plus complexe: locomotive électrique hybride, a été développée. Mettant en œuvre la logique floue de type-2 et des algorithmes s'inspirant de la théorie de l'évolution, cette gestion d'énergie optimale opère en temps réel et sans connaissance à priori du cycle de conduite. La gestion fréquentielle ainsi que le contrôle des états de charge des sources secondaires du véhicule contribuent également à leur bon fonctionnement.

International audience; A gasoline-Hybrid Electric Vehicle (HEV) Energy Management Strategy (EMS) is the computation of the distribution between electric and gasoline propulsion. The historical objective of EMS was to minimize fuel consumption. However, decreasing the fuel consumption does not directly minimize the pollutant emissions, as the operation of the 3 Way Catalytic Converter (3WCC) needs to be taken into account. This paper therefore proposes to integrate pollutant emission into the EMS with the Pontryagin Minimum Principle. A tradeoff between pollution and fuel consumption is then introduced. The integration of the 3WCC temperature into EMS is discussed and finally a simplification is proposed.

International audience; Among the general problematic of the HEV power trains, the most critical point is the determination of the power-split ratio between the mechanical and the electrical paths, known as the energy management strategy (EMS). Many EMS are proposed in the literature, and can be grouped in two categories: the local optimization EMS and the global optimization EMS. The local optimization category corresponds to the EMS based on human expertise and the knowledge of the power train components efficiency maps. Thus, the local optimization EMS manages the power train operations by referring to predefined rules. The drawback of such strategies is that it brings an instantaneous fuel consumption optimization, and does not fully optimize the fuel consumption over the whole trip. Therefore, additional fuel savings are still possible. This paper presents an overall optimized predictive EMS for the Toyota Hybrid System (THS-II) power train of the Prius. The proposed EMS is based on Dynamic Programming (DP), where the prior knowledge of the route is required in order to predetermine the power-split ratio and optimize the fuel consumption for the whole predicted route. The DP EMS proposed for the THS-II power train is designed with a very short computation time, intended to be implemented in real-time applications. The potential of this DP-controller in reducing fuel consumption on regulatory cycles are computed and compared to a rule-based controller and to the Prius published fuel consumption results. Finally, the fuel reduction enhancements of the DP-controller are computed for real road tests achieved on a MY06 Prius in Ile-de-France, by comparing to the associated observed consumption measurements.

International audience; Various energy management strategies for a hybrid pneumatic engine are reviewed and a real time neural control strategy proposed. This Neural Network strategy learns off line the optimal control given by Dynamic Programming and the resulting control model is applied on line. The dierent strategies are simulated with a backward vehicle model for various driving cycles and their fuel consumptions compared. The results show that the Neural Network strategy is better than a classical Equivalent Consumption Minimization Strategy (ECMS) and equivalent to a Variable Penalty Coecient Strategy with Driving Pattern Recognition.

International audience; In this paper, the authors propose an approach to the problem of the energy management in ECCE laboratory hybrid vehicle project. ECCE is a series hybrid vehicle, which currently has three sources of energy: two diesel motors each coupled with an alternator and a battery module of rated voltage DC 540V. This contribution is focused on the energy coupling between batteries and two supercapacitors modules. The authors present a strategy for coupling these power sources with the batteries in order to find the best compromise between sizes of the on-board devices, dynamics of the supply and efficiency of the energy storage. The target is to provide 200kW power during 20s from the supercapacitors modules.