Hybrid electric vehicles are considered to be an important part of the future vehicle industry, since they decrease fuel consumption without decreasing the performance compared to a conventional vehicle. They use two or more power sources to propel the vehicle, normally one combustion engine and one electric machine. These power sources can be arranged in different topologies and can cooporate in different ways. In this thesis, dynamic models of parallel and series hybrid powertrains are developed, and different strategies for how to control them are compared.An optimization algorithm for decreasing fuel consumption and utilize the battery storage capacity as much as possible is also developed, implemented and tested.

Hybrid electric vehicles have two power sources - an internal combustion engine and an electric motor. These vehicles are of great interest because they contribute to a decreasing fuel consumption and air pollution and still maintain the performance of a conventional car. Different topologies are described in this thesis and especially the series and parallel hybrid electric vehicle and Toyota Prius have been studied. This thesis also depicts modelling of a reference car and a series hybrid electric vehicle in Modelica. When appropriate, models from the Modelica standard library have been used. Models for a manual gearbox, final drive, wheel, chassis, air drag and a driver have been developed for the reference car. For the hybrid electric vehicle a continuously variable transmission, battery, an electric motor, fuel cut-off function for the internal combustion engine and a converter that distributes the current between generator, electric motor and internal combustion engine have been designed. These models have been put together with models from the Modelica standard library to a reference car and a series hybrid electric vehicle which follows the NEDC driving cycle. A sketch for the parallel hybrid electric vehicle and Toyota Prius have also been made in Modelica. Developed models have been introduced into the Modelica library VehProLib, which is a vehicle propulsion library under development by Vehicular Systems, Linköpings universitet.

The main objective of this research is the development of a flexible test-bench for evaluation of hybrid electric powertrain controllers. As a case study, a real-time near-optimal powertrain controller for a series hybrid electric vehicle (HEV) has been designed and tests. The designed controller, like many other optimal controllers, is based on a simple model. This control-oriented model aims to be as simple as possible in order to minimize the controller computational effort. However, a simple model may not be able to capture the vehicle's dynamics accurately, and the designed controller may fail to deliver the anticipated behavior. Therefore, it is crucial that the controller be tested in a realistic environment. To evaluate the performance of the designed model-based controller, it is first applied to a high-fidelity series HEV model that includes physics-based component models and low-level controllers. After successfully passing this model-in-the-loop test, the controller is programmed into a rapid-prototyping controller unit for hardware-in-the-loop simulations. This type of simulation is mostly intended to consider controller computational resources, as well as the communication issues between the controller and the plant (model solver). As the battery pack is one of the most critical components in a hybrid electric powertrain, the component-in-the-loop simulation setup is used to include a physical battery in the simulations in order to further enhance simulation accuracy. Finally, the driver-in-the-loop setup enables us to receive the inputs from a human driver instead of a fixed drive cycle, which allows us to study the effects of the unpredictable driver behavior. The developed powertrain controller itself is a real-time, drive cycle-independent controller for a series HEV, and is designed using a control-oriented model and Pontryagin's Minimum Principle. Like other proposed controllers in the literature, this controller still requires some information about future driving conditions; however, the amount of information is reduced. Although the controller design procedure is based on a series HEV with NiMH battery as the electric energy storage, the same procedure can be used to obtain the supervisory controller for a series HEV with an ultra-capacitor. By testing the designed optimal controller with the prescribed simulation setups, it is shown that the controller can ensure optimal behavior of the powertrain, as the dominant system behavior is very close to what is being predicted by the control-oriented model. It is also shown that the controller is able to handle small uncertainties in the driver behavior.

A plug-in hybrid electric vehicle (PHEV) is a vehicle powered by a combination of an internal combustion engine and an electric motor with a battery pack. The battery pack can be charged by plugging the vehicle into the electric grid or from using excess engine power. A PHEV allows for all electric operation for limited distances, while having the operation and range of a conventional hybrid electric vehicle on longer trips. A PHEV design with design parameters electric motor size, engine size, battery capacity, and battery chemistry type, is optimized with minimum cost as a figure of merit. The PHEV is required to meet a fixed set of performance constraints consisting of 0-60 mph acceleration, 50-70 mph acceleration, 0-30 mph acceleration in all electric operation, top speed, grade ability, and all electric range. The optimization is carried out for values of all electric range of 10, 20, and 40 miles. The social and economic impacts of the optimum designs in terms of reduced gasoline consumption and carbon emissions reduction are calculated. Argonne National Laboratorys Powertrain Systems Analysis Toolkit is used to simulate the performance and fuel economy of the PHEV designs. The costs of different PHEV components and the present value of battery replacements over the vehicles life are used to determine the designs drivetrain cost. The resulting optimum PHEVs are designs using lead acid battery type. The optimum design parameter values are all determined by a single controlling performance constraint. The PHEV designs show a 63% to 80% reduction in gasoline consumption and a 53% to 47% reduction in CO2 emissions. The PHEV designs have an annual gas savings of $696 to $643 per year over the average sedan meeting the 27.5 mpg CAFE standards.

This thesis evaluates emerging electric vehicle technology and estimates what effect it might have on how an oil company decides on its gas station network. It is conducted using data from South Korea, a country poised for a fast adoption of electric vehicles. The study first reviews the literature to gather reasonable cases of electric vehicle penetration. Also, after researching technology-diffusion theories, the study selects a model that can well explain the literature review data. The scenarios induced by this function are utilized as the main uncertainties confronting an oil company’s network decision model. Based on a probabilistic simulation, the study finds that the effects of technology diffusion alter the priority order of an oil company’s network decision alternatives. Namely, after the overall uncertainty level rises, directly owning gas station, with its heavy initial investment, is not preferred for an oil company’s network strategy. From the result, the study also estimates the scale of the new technology’s effect. Such effect is found to be significant enough to alter a part of an oil company’s retail strategy. Nevertheless, such effect cannot be shown to be so great as to change the current retail oil market structures. / text

Today uncertainties of petroleum supply and concerns over global warming call for further advancement of green vehicles with higher energy efficiency and lower green house gas (GHG) emissions. Development of advanced hybrid electric powertrain technology plays an important role in the green vehicle transformation with continuously improved energy efficiency and diversified energy sources. The added complexity of the multi-discipline based, advanced hybrid powertrain systems make traditional powertrain design method obsolete, inefficient, and ineffective. This research follows the industrial leading model-based design approach for hybrid electric vehicle powertrain development and introduces the optimization based methods to address several key design challenges in hybrid electric powertrain and its control system design. Several advanced optimization methods are applied to identify the proper hybrid powertrain architecture and design its control strategies for better energy efficiency. The newly introduced optimization based methods can considerably alleviate the design challenges, avoid unnecessary design iterations, and improve the quality and efficiency of the powertrain design. The proposed method is tested through the design and development of a prototype extended range electric vehicle (EREV), UVic EcoCAR. Developments of this advanced hybrid vehicle provide a valuable platform for verifying the new design method and obtaining feedbacks to guide the fundamental research on new hybrid powertrain design methodology. / Graduate

The main objective of this research is the development of a flexible test-bench for evaluation of hybrid electric powertrain controllers. As a case study, a real-time near-optimal powertrain controller for a series hybrid electric vehicle (HEV) has been designed and tests. The designed controller, like many other optimal controllers, is based on a simple model. This control-oriented model aims to be as simple as possible in order to minimize the controller computational effort. However, a simple model may not be able to capture the vehicle's dynamics accurately, and the designed controller may fail to deliver the anticipated behavior. Therefore, it is crucial that the controller be tested in a realistic environment. To evaluate the performance of the designed model-based controller, it is first applied to a high-fidelity series HEV model that includes physics-based component models and low-level controllers. After successfully passing this model-in-the-loop test, the controller is programmed into a rapid-prototyping controller unit for hardware-in-the-loop simulations. This type of simulation is mostly intended to consider controller computational resources, as well as the communication issues between the controller and the plant (model solver). As the battery pack is one of the most critical components in a hybrid electric powertrain, the component-in-the-loop simulation setup is used to include a physical battery in the simulations in order to further enhance simulation accuracy. Finally, the driver-in-the-loop setup enables us to receive the inputs from a human driver instead of a fixed drive cycle, which allows us to study the effects of the unpredictable driver behavior. The developed powertrain controller itself is a real-time, drive cycle-independent controller for a series HEV, and is designed using a control-oriented model and Pontryagin's Minimum Principle. Like other proposed controllers in the literature, this controller still requires some information about future driving conditions; however, the amount of information is reduced. Although the controller design procedure is based on a series HEV with NiMH battery as the electric energy storage, the same procedure can be used to obtain the supervisory controller for a series HEV with an ultra-capacitor. By testing the designed optimal controller with the prescribed simulation setups, it is shown that the controller can ensure optimal behavior of the powertrain, as the dominant system behavior is very close to what is being predicted by the control-oriented model. It is also shown that the controller is able to handle small uncertainties in the driver behavior.

abstract: For years the automotive industry has been shifting towards hybridization and electrification of conventional powertrains due to increase in fossil fuel cost and environmental impact due heavy emission of Green House Gases (GHG) and various pollutants into atmosphere by combustion engine powered vehicles. Hybrid Electric Vehicles (HEV) have proved to achieve superior fuel economy and reduced emissions. Supervisory control strategies determining the power split among various onboard power sources are evolving with time, providing better fuel economies. With increasing complexity of control systems driving HEV’s, mathematical modeling and simulation tools have become extremely advanced and have derived whole industry into adopting Model Based Design (MBD) and Hardware-in-the-loop (HIL) techniques to validate the performance of HEV systems in real world. This report will present a systematic mythology where MBD techniques are used to develop hybrid powertrain, supervisory control strategies and control systems. To validate the effectiveness of various energy management strategies for HEV energy management in a real world scenario, Conventional rule-based power split strategies are compared against advanced Equivalent Consumption Minimization Strategy (ECMS), in software and HIL environment. Since effective utilization of the fuel reduction potential of a HEV powertrain requires a careful design of the energy management control methodology, an advanced ECMS strategy involving implementation with Fuzzy Logic to reduce computational overload has been proposed. Conventional real-time implementation of ECMS based strategy is difficult due to the involvement of heavy computation. Methods like Fuzzy Logic based estimation can be used to reduce this computational overload. Real-time energy management is obtained by adding a Fuzzy Logic based on-the-fly algorithm for the estimation of driving profile and adaptive equivalent consumption minimization strategy (A-ECMS) framework. The control strategy is implemented to function without any prior knowledge of the future driving conditions. The idea is to periodically refresh the energy management strategy according to the estimated driving pattern, so that the Battery State of Charge (SOC) is maintained within the boundaries and the equivalent fuel consumption is minimized. The performance of the presented Fuzzy Logic based adaptive control strategy utilizing driving pattern recognition is benchmarked using a Dynamic Programming based global optimization approach. / Dissertation/Thesis / Masters Thesis Engineering 2015

Um problema que as empresas distribuidoras de energia elétrica convivem são as quedas repentinas no fornecimento, causando inúmeros prejuízos tanto para essas empresas quanto para seus consumidores. Essa dissertação apresentará uma ferramenta que utilizará conhecimentos de sistemas de informações geográficas junto com o uso de inferência nebulosa para orientar a disposição de veículos híbridos (elétricos e à combustão) que podem operar como mini-usinas elétricas no abastecimento de localidades que esteja necessitando de energia em um determinado momento. Para isso, será levantada uma base de dados com características dos veículos híbridos e locais necessitados, dados esses que alimentarão um sistema nebuloso agregado à ferramenta MapServer e a um SIG (Sistema de Informações Geográficas) para, dessa forma, mostrar como saída do sistema qual veículo estará mais apto naquele instante para abastecer o local da demanda de energia. / A problem that electric energy companies have to face is the power outage, which causes innumerable damages for both companies and consumers. This dissertation describes a tool that joins Geographic Information Systems knowledge to Fuzzy Logic knowledge to guide the distribution of hybrid vehicles (electric and combustion) that can operate as mini electric power plants to supply places that need electrical power. For this, a database will be built with the characteristics of vehicles and places in need. The data will supply a Fuzzy system joined to a MapServer tool and a GIS (Geographic Information System), showing which vehicle will be more capable in that moment to supply the place in energy demand.

La crise du pétrole et les contraintes écologiques obligent de nombreux constructeurs automobiles à développer des programmes de recherche importants dans le développement des véhicules électriques et hybrides électriques. Dans ce contexte, cette thèse a pour but de vérifier la faisabilité d’une chaine de traction hybride innovante consistant à partir d’unvéhicule thermique existant et à réduire la puissance du moteur thermique tout en ajoutant des moteurs intégrés dans les roues du train arrière. Ce travail a été réalisé dans le cadre d’un projet financé par l’ADEME et en collaboration notamment avec le constructeur automobile AIXAM-MEGA.Plus précisément, le travail de thèse a donc porté sur le dimensionnement des sources énergétiques, la modélisation énergétique et fonctionnelle du véhicule et enfin la réalisation et la caractérisation expérimentale du véhicule.Dans le premier chapitre, l’auteur développe une revue bibliographique relative aux véhicules hybrides électriques existants. Cela permet ensuite d’introduire le concept innovant de chaine de traction hybride décrit ci-dessus, reposant en quelque sorte sur un couplage par la route des puissances de propulsion thermiques et électriques.Dans le deuxième chapitre l’auteur aborde le dimensionnement des sources énergétiques en se focalisant sur les super-condensateurs. Il propose une approche analytique simple de calcul reposant sur les missions définies par le constructeur AIXAM-MEGA. Les modules de supercondensateurs retenus sont ensuite caractérisés expérimentalement (capacité, résistance interne, rendement de stockage…) en prenant en compte l’effet de la température.Les troisième et quatrième chapitres sont consacrés à la modélisation du véhicule. En premier lieu, le troisième chapitre aborde la modélisation énergétique du véhicule. Le véhicule a entièrement été modélisé en utilisant le formalisme de représentation énergétique macroscopique développée initialement au Laboratoire d’Électrotechnique et d’Électroniquede Puissance de Lille. Ce modèle a permis de développer le contrôle du véhicule. Ensuite, dans le quatrième chapitre, l’auteur présente la modélisation fonctionnelle du véhicule par machine d’état. Cela permet de prévoir le comportement du véhicule dans ses différentes phases de vie et de définir les transitions entre ces différentes phases. Cette étape deprototypage virtuel est essentielle afin de vérifier en amont la fonctionnalité du véhicule et sa sécurité.Enfin, le cinquième et dernier chapitre est entièrement consacré à la caractérisation expérimentale du véhicule. Les différents fonctionnements thermiques, électriques et hybrides sont testés lors de vrais essais de roulage.En conclusion, le travail de thèse a abouti à la réalisation d’un véhicule hybride. Les approches de dimensionnement des sources et de modélisation sont ainsi validées, tout en faisant également la preuve de la faisabilité d’une chaine cinématique hybride électrique avec couplage par la route. / The exhaustion, increased cost and location of fossil fuels on the one hand, and the environmental problems caused by emissions of CO2 in the atmosphere on the other hand, are forcing many automotive manufactures to develop major research programs in the designof electric vehicles and hybrid electric. In this context, this thesis aims to test the feasibility ofan innovative hybrid drivetrain consisting of a vehicle from existing heat and reduce engine power while adding motors integrated into the wheels of the rear axle. This work was conducted as part of a project funded by ADEME and also in collaboration with the car manufacturer Aixam-MEGA.More specifically, the thesis has focused on the design of energy sources, energy modeling and functional vehicle and finally the implementation and experimental characterization of the vehicle.In the first chapter, the author develops a literature review on the existing hybrid electric vehicles. This allows then to introduce the innovative concept of hybrid drivetrain described above, based somewhat on a road coupling powers of thermal and electric propulsion.In the second chapter the author discusses the design of energy sources focusing on ultracapacitors. It offers an analytical approach simple calculation based on the tasks set by the manufacturer Aixam-MEGA. Modules selected ultracapacitors are then characterized experimentally (capacity, internal resistance, storage efficiency ...) taking into account the effect of temperature.The third and fourth chapters are devoted to the modeling of the vehicle. First, the third chapter discusses the modeling efficiency of the vehicle. The vehicle has been fully modeled using the formalism of Energetic Macroscopic Representation initially developed at the Laboratory of Electrical and Power Electronics of Lille. This model has led to the development of vehicle control. Then, in the fourth chapter, the author presents the functional modeling of the vehicle state machine. This allows predicting the behavior of the vehicle in its different life phases and defining the transitions between these phases. This stage of virtual prototyping is essential to verify the functionality of the upstream and vehicle safety.Finally, the fifth and final chapter is devoted to the experimental characterization of the vehicle. The different operations thermal, electric and hybrid are tested in real taxi trials.In conclusion, the thesis has led to the realization of a hybrid vehicle. The design approaches and modeling of sources and are validated, while also demonstrated the feasibility of a hybrid electric powertrain coupling the road.