Cette thèse aborde la conception d'un Système de Gestion Énergétique (EMS), prenant en compte les contraintes de trafic, pour un véhicule hybride électrique. Actuellement, les EMS sont habituellement classé en deux catégories ceux proposant une architecture en temps réel cherchant un optimum local, et ceux qui recherchent un optimum global, plus coûteux en temps de calcul et donc plus approprié à un usage hors ligne. Cette thèse repose sur le fait que la consommation énergétique peut être modélisée précisément à l'aide de distributions de probabilité sur la vitesse et l'accélération. Dans le but de réduire la taille des données, une classification est proposé, basé sur la distance de Wasserstein, les barycentres des classes pouvant être calculés grâce aux itérations de Sinkhorn ou la méthode du Gradient Stochastique Alterné. Cette modélisation trafic a permis à une optimisation hors ligne de déterminer le contrôle optimal (le couple du moteur électrique) qui minimise la consommation de carburant du véhicule hybride sur un segment routier. Dans la continuité, un algorithme bi-niveau tirant avantage de cette information afin d'optimiser la consommation sur l'ensemble du trajet. Le niveau supérieur d'optimisation, étant déterministe, est suffisamment rapide pour une implémentation en temps réel. La pertinence du modèle de trafic et de la méthode bi-niveau est illustré à l'aide de données trafic générées par un simulateur, mais aussi grâce à des données réelles collectées prés de Lyon (France). Enfin, une extension de la méthode bi-niveau au problème d'éco-routage est envisagé, utilisant un graphe augmenté pour déterminer l'état de charge lors du chemin optimal. / The focus of this PhD thesis is to design an optimal Energy Management System (EMS) for a Hybrid Electric Vehicle (HEV) following traffic constraints.In the current state of the art, EMS are typically divided between real-time designs relying on local optimization methods, and global optimization that is only suitable for off-line use due to computational constraints.The starting point of the thesis is that in terms of energy consumption, the stochastic aspect of the traffic conditions can be accurately modelled thanks to (speed,acceleration) probability distributions.In order to reduce the data size of the model, we use clustering techniques based on the Wasserstein distance, the corresponding barycenters being computed by either a Sinkhorn or Stochastic Alternate Gradient method.Thanks to this stochastic traffic model, an off-line optimization can be performed to determine the optimal control (electric motor torque) that minimizes the fuel consumption of the HEV over a certain road segment.Then, a bi-level algorithm takes advantage of this information to optimize the consumption over a whole travel, the upper level optimization being deterministic and therefore fast enough for real-time implementation.We illustrate the relevance of the traffic model and the bi-level optimization, using both traffic data generated by a simulator, as well as some actual traffic data recorded near Lyon (France).Finally, we investigate the extension of the bi-level algorithm to the eco-routing problem, using an augmented graph to track the state of charge information over the road network.

<p>Alternate power sources in automotive class-8 trucking industry is a major focus of research in recent days. Green house gasses, oxides of Nitrogen(NOx), Oxides of Sulphur(SOx), hydrocarbons and particulate matter are major concerns contributing to the shift in alternate fuel strategies. Another direct relation to move to an alternate power strategy is the reduction in net fuel consumption which in turn implicitly improves the emission components.</p> <p>A holistic approach is needed while designing a modern class-8 vehicle. A variety of system architecture, control algorithms, diagnostic levers are needed to be manipulated to achieve the best of blends amongst Total Cost of Ownership (TCO), Drivability, Fuel</p> <p>Economy, Emissions Compliant, Hauling Capacity, etc. The control and system levers are not mutually exclusive and there is a strong correlation amongst all these control and system components. In order to achieve a consensus amongst all these levers to achieve a common objective, is a challenging and complex problem to solve. It is often required to shift the algorithm strategy to predictive information based rather than reactive logic. Predictively modulating and manipulating control logic can help with better fuel efficient solution along with emissions improvement. A further addition to the above challenge is when we add a fleet of vehicle to the problem. So, the problem now is to optimize a control action for a fleet</p> <p>of vehicles and design/select the correct component size. A lot of research has been done and is still underway to use a 48V hybrid system with a small battery using a simple charge sustaining SOC control strategy. This will make the system light enough not to compromise on the freight carrying capacity as well as give some extra boost during the high torque requirement sections in the route for a better fuel and emissions efficient solution. In this work a P2 type 48V hybrid system is used which is side mounted to the transmission via a gear system. The selection of the system and components enables the usage of different control strategies such as neutral coasting and Engine off coasting. This architecture with a traditional 12-15L Internal combustion engine along with a mild 48V hybrid system provides the most viable selection for a long haul class-8 application and is used in this work. It is also possible to identify other component sizes along with architectures for new configurations. The framework in this research work can help develop the study for different component sizing. While this research work is focused towards building a framework for achieving predictive control in a 3 truck platooning system using multi-agent based control, the other supporting work done also helps understand the optimal behavior of the interacting multiple controls when the corridor information such as road grade and route speed limit are known a-priori, in a single vehicle. The build up of this work analyzes an offline simulation of a 4 control optimal solution for a single hybrid truck and then extend the optimal controls to a 3 truck platoon. In the single truck, this research will help identify the interacting zones in the route where the various control actions will provide the best cost benefits which is fuel economy. These benefits are associated as a function of exogenous look ahead information such as grade and speed limit. Further it is also possible to identify the optimal behavior and the look ahead horizon required for achieving that. In other words the optimal behavior and benefits associated with the global solution can be accomplished by implementing rule based control system with a look ahead horizon of 2-5 km. If this would not have been the case then it is almost impossible to design a predictive controller based on the entire route information which can stretch up to hundreds of kilometers. Optimal algorithms of such prediction horizon are not feasible to be implemented in real time controllers. This research work will also help understand the interaction between different active control actions such as predictive speed modulation, gear shift, coasting and power split with passive control levers such as slow down due to hybrid regeneration, hybrid boost during coasting, etc. This will help in architecting a system involving component specifications, active optimal control, look ahead information, hybrid system strength, etc, working in close interaction with each other. Though we analyze these predictive behavior for a single vehicle as a supporting work the prime objective is to include these predictive levers in a platooning system using an agent based method. This multi-agent based technique will help analyze the behavior of multiple trucks in a platoon in terms of fuel efficient safe operation. The focus of this research work is to not directly come up with a controller or strategy but rather to understand the optimality of this control levers for a multi-vehicle platoon system given a look ahead information is available. The research shows that predictive information will help in gaining fuel economy for a platoon of class-8 mild hybrid trucks. It also highlights the challenges in doing so and what needs to be traded off in order to achieve the net fuel benefit.</p>

“GreenPeace” es una tendencia que hoy en día las personas siguen, la cual tiene como objetivo poner fin a los abusos en contra del medio ambiente y disfrutar de un futuro “verde”. Es así que varios países se están inclinando en poner reglas estrictas respecto al cuidado del ecosistema, una de estas son eliminar o reducir la emisión de gases contaminantes por parte de los autos a motor. En varios países como Brasil, Suiza, Holanda entre otros; ya se pusieron limitaciones al funcionamiento de los autos a motor. Es así que las empresas empezaron a ofrecer autos eléctricos e híbridos. En el Perú está sucediendo algo parecido, ya que el estado ha empezado a brindar incentivos como la eliminación del ISC; asimismo, tienen planeado ofrecer bonos y otro tipo de ayuda con el fin de incentivar el ingreso de los autos eléctricos e híbridos. Por tal motivo a futuro, las empresas automotoras que no se unan a este cambio, no serán sostenibles y estarán destinadas a quebrar. El presente trabajo de investigación profesional ha sido realizado con la finalidad de evaluar el impacto financiero que tendrán las empresas importadoras del sector automotriz a razón de la renovación del parque automotor en Lima con autos híbridos. Por lo tanto, ha sido desarrollado en relación a los conceptos financieros, principalmente, los costos, ya que, es la variable más influyente del tema investigado. Este trabajo de investigación se centra en el estudio de las empresas importadoras del sector automotriz en Lima. Es importante que las empresas en general del sector y sus principales gerencias, comprendan el impacto financiero que tendrán en el futuro próximo a razón de importar autos híbridos y la verdadera importancia de hacerlo, ya que, la escasez del combustible va en ascenso con el transcurso de los años y del consumo. En consecuencia, se espera que el presente trabajo de investigación sirva como base para lograr el entendimiento del sector y como se verá impactado financieramente a razón de la importación de estos vehículos. Para validar las hipótesis planteadas, se emplearon instrumentos de investigación cualitativos y cuantitativos. El instrumento cualitativo utilizado fue entrevista a profundidad a expertos del sector y los instrumentos cuantitativos fueron las encuestas realizadas. Todo el trabajo de investigación realizado tuvo como finalidad recabar información cualitativa y cuantitativa para corroborar las hipótesis planteadas. Finalmente, se desarrollará un caso práctico en el que se plantearán tres escenarios que comparan información financiera respecto a los vehículos de combustible convencionales que se encuentran en el mercado y a los vehículos híbridos. / "GreenPeace" is a trend that people nowadays follow, which aims to end abuses against the environment and enjoy a "green" future. Thus, several countries are inclined to set strict rules regarding the care of the ecosystem, one of these is to eliminate or reduce the emission of polluting gases by motor cars. In several countries like Brazil, Switzerland, Holland among others; limitations were already placed on the operation of motor cars. Thus, companies began offering electric and hybrid cars. Something similar is happening in Peru, since the state has begun to provide incentives such as the elimination of the ISC; They also plan to offer bonuses and other assistance in order to encourage the entry of electric and hybrid cars. For this reason in the future, automotive companies that do not join this change will not be sustainable and will be destined to fail. This professional research work has been carried out with the purpose of evaluating the financial impact that the importing companies of the automotive sector will have due to the renewal of the automotive fleet in Lima with hybrid cars. Therefore, it has been developed in relation to financial concepts, mainly costs, since it is the most influential variable of the subject under investigation. This research work focuses on the study of import companies in the automotive sector in Lima. It is important that companies in general in the sector and their main management understand the financial impact they will have in the near future due to the import of hybrid cars and the true importance of doing so, since fuel shortages are on the rise with the course of the years and consumption. Consequently, this research work is expected to serve as a basis for understanding the sector and how it will be financially impacted due to the importation of these vehicles. To validate the hypotheses raised, qualitative and quantitative research instruments were used. The qualitative instrument used was an in-depth interview with experts from the sector and the quantitative instruments were the surveys carried out. All the research work carried out was aimed at gathering qualitative and quantitative information to corroborate the hypotheses. Finally, a case study will be developed in which two scenarios that compare financial information regarding conventional fuel vehicles on the market and hybrid vehicles will be considered. / Tesis

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The market segment of hybrid-electric and full function electric vehicles is growing within the automotive transportation sector. While many papers exist concerning fuel economy or fuel consumption and the limitations of conventional powertrains, little published work is available for vehicles which use grid electricity as an energy source for propulsion. Generally, the emphasis is put solely on the average drive cycle efficiency for the vehicle with very little thought given to propelling and braking powertrain losses for individual components. The modeling section of this paper will take basic energy loss equations for vehicle speed and acceleration, along with component efficiency information to predict the grid energy consumption in AC Wh/km for a given drive cycle. This paper explains how to calculate the forces experienced by a vehicle while completing a drive cycle in three different ways: using vehicle characteristics, United States Environmental Protection Agency's (EPA) Dynamometer "target" coefficients, and an adaptation of the Sovran parameters. Once the vehicle forces are determined, power and energy demands at the wheels are determined. The vehicle power demands are split into propelling, braking, and idle to aide in the understanding of what it takes to move a vehicle and to identify possible areas for improvement. Then, using component efficiency data for various parameters of interest, the energy consumption of the vehicle as a pure EV is supplied in both DC (at the battery terminals) and AC (from the electric grid) Wh/km. The energy that flows into and out of each component while the vehicle is driving along with the losses at each step along the way of the energy path are detailed and explained. The final goal is to make the results of the model match the vehicle for any driving schedule. Validation work is performed in order to take the model estimates for efficiencies and correlate them against real world data. By using the Virginia Tech Range Extended Crossover (VTREX) and collecting data from testing, the parameters that the model is based on will be correlated with real world test data. The paper presents a propelling, braking, and net energy weighted drive cycle averaged efficiency that can be used to calculate the losses for a given cycle. In understanding the losses at each component, not just the individual efficiency, areas for future vehicle improvement can be identified to reduce petroleum energy use and greenhouse gases. The electric range of the vehicle factors heavily into the Utility Weighted fuel economy of a plug-in hybrid electric vehicle, which will also be addressed. / Master of Science

Le travail présenté traite la question quelle architecture hybride est la plus adapté pour quel type de véhicule. Une approche multi-architecture/multi-application capable d’identifier l’architecture hybride plus efficace en énergie qui considère à la fois les components clés (batterie, moteur électrique, moteur à combustion interne) et la commande optimale, est présenté. La base de cette modélisation est la représentation énergétique macroscopique (REM), qui est combiné avec la programmation orienté objet (POO) afin d’améliorer la modularité et la réutilisation. Les résultats obtenus montrent que des architectures hybrides différents sont le plus adaptés pour différentes applications. De plus, la robustesse des résultats en utilisant des approches de contrôle en temps réel sont étudiés, montrant l’importance de la stratégie de commande. Les résultats obtenus contribuent à la simplification et l’harmonisation de la conception des solutions hybrides pour des applications multiples. / The presented work deals with the question which hybrid architecture is most adapted for which type of vehicle. Therefore, a multi-architecture/multi-application approach capable to identify the most energy efficient hybrid architecture considering both the dimensions of key components (battery, electric motor, internal combustion engine) and the optimal control is presented. Basis of the model is the energetic macroscopic representation (EMR), which has been combined with object oriented programming (OOP) in order to enhance its modularity and reuse capabilities. The obtained results show, that different hybrid architectures are most adapted for different applications. Moreover, the robustness of the results using real time control algorithms are studied, showing that control strategy matters. The obtained results contribute to simplify and harmonize the design of hybrid solutions for multiple applications.

Electrified vehicles (EVs) with a large electric energy storage system (ESS), including Plug-in Hybrid Electric Vehicles (PHEVs) and Pure Electric Vehicles (PEVs), provide a promising solution to utilize clean grid energy that can be generated from renewable sources and to address the increasing environmental concerns. Effectively extending the operation life of the large and costly ESS, thus lowering the lifecycle cost of EVs presents a major technical challenge at present. A hybrid energy storage system (HESS) that combines batteries and ultracapacitors (UCs) presents unique energy storage capability over traditional ESS made of pure batteries or UCs. With optimal energy management system (EMS) techniques, the HESS can considerably reduce the frequent charges and discharges on the batteries, extending their life, and fully utilizing their high energy density advantage. In this work, an intelligent energy allocation (IEA) algorithm that is based on Q-learning has been introduced. The new IEA method dynamically generate sub-optimal energy allocation strategy for the HESS based on each recognized trip of the EV. In each repeated trip, the self-learning IEA algorithm generates the optimal control schemes to distribute required current between the batteries and UCs according to the learned Q values. A RBF neural networks is trained and updated to approximate the Q values during the trip. This new method provides continuously improved energy sharing solutions better suited to each trip made by the EV, outperforming the present passive HESS and fixed-cutoff-frequency method. To efficiently recognize the repeated trips, an extended Support Vector Machine (e-SVM) method has been developed to extract significant features for classification. Comparing with the standard 2-norm SVM and linear 1-norm SVM, the new e-SVM provides a better balance between quality of classification and feature numbers, and measures feature observability. The e-SVM method is thus able to replace features with bad observability with other more observable features. Moreover, a novel pattern classification algorithm, Inertial Matching Pursuit Classification (IMPC), has been introduced for recognizing vehicle driving patterns within a shorter period of time, allowing timely update of energy management strategies, leading to improved Driver Performance Record (DPR) system resolution and accuracy. Simulation results proved that the new IMPC method is able to correctly recognize driving patterns with incomplete and inaccurate vehicle signal sample data. The combination of intelligent energy allocation (IEA) with improved e-SVM feature extraction and IMPC pattern classification techniques allowed the best characteristics of batteries and UCs in the integrated HESS to be fully utilized, while overcoming their inherent drawbacks, leading to optimal EMS for EVs with improved energy efficiency, performance, battery life, and lifecycle cost. / Graduate