Energy demands are steadily increasing as the world's population continues to grow. Automobiles are primary transportation means in a large portion of the world. The combination of fuel consumption by automobiles along with the shrinking fossil fuel reserves makes the development of new more energy efficient technologies crucial. Electric vehicle technologies have been studied and are still being studied today as a means of improving fuel efficiency. To that end, this work studies the effect of switching frequency on the efficiency of a hybrid electric vehicle traction drive, which contains both an internal combustion engine as well as electric motor. Therefore improving the efficiency of the electric motor and its drive will help improve the viability of alternative vehicle technologies. Automobiles spend the majority of their operational time in the lower speed, lower torque region. This work focuses on efficiency improvements in that region. To estimate the efficiency trend, the system is modeled and then tested both electrically and thermally. The efficiency is shown to increase at lower switching frequencies. The experimental results show that there are some exceptions, but the basic trend is the same. / Master of Science

There are various technologies used for reducing fuel consumption of automobiles. Hybrid electric vehicles is one approach that has been used, which can reduce fuel consumption by 10-30% compared to conventional vehicles. In this master thesis the minimization of fuel consumption of a power-split type HEV along a given route is considered, where the vehicle speed has been assumed to be known a priori. This minimization was made by first deriving a model of the HEV powertrain, followed by creating a Dynamical programming based program for finding the optimal distribution of torques. The performance was evaluated through the commercial software GT-Suite. The resulting control from the Dynamic program could follow the reference speed in many situations. However the battery state-of-charge calculated in the Dynamic program did not update properly, resulting in a depleted battery in some cases. The model derived could follow the dynamics of the vehicle, but there are some parts which could be improved. One of them is the dynamical model of the rotational speed for the engine <img src="http://www.diva-portal.org/cgi-bin/mimetex.cgi?%5Comega_%7Be%7D" />.  The Dynamic program works for finding the controller, and can be modified to work with improved state-equations. / Det finns olika sätt att minska bränsleförbrukningen hos bilar, men ett sätt som använts är el-hybrider. Dessa kan minska bränsleförbrukningen med 10-30% jämfört med konventionella bilar. I det här examensarbetet undersöks optimering av bränsleförbrukning för en el-hybrid, där hastigheten antas vara känd i förväg. Optimeringen skedde genom att först härleda en modell för drivlinan, och därefter skapades ett Dynamisk programerings baserat program för att hitta den optimal kombinationen av moment. Bränsleförbrukning och prestanda jämfördes genom programvaran GT-Suite. Dynamiska programmeringen gav lovande resultat som följde referenshastigheten i många fall. Däremot uppdaterades inte laddningen för batteriet lika bra, vilket ledde till att batteriet i vissa fall blev urladdat. Modellen som härleddes visade i många fall liknande respons som GT-Suite, men viss förbättring kan ske. En utav dessa förbättringar är rotationsekvationen för bränslemotorn, <img src="http://www.diva-portal.org/cgi-bin/mimetex.cgi?%5Comega_%7Be%7D" />. Den Dynamiska programmeringen som skapades fungerade, och kan modifieras för förbättrade tillståndsekvationer

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is one of 16 university teams participating in EcoCAR 3, the latest competition in the Advanced Vehicle Technology Competitions (AVTC) organized by Argonne National Labs. EcoCAR 3 tasks teams with converting a 2016 Chevrolet Camaro into a hybrid electric vehicle with 5 main goals: reducing petroleum energy use and greenhouse gas emissions while maintaining safety, performance, and consumer acceptability. Over the last 4 years, HEVT has designed and built a plugin parallel hybrid electric vehicle with a unique powertrain architecture. This work deals with utilizing the unique powertrain layout of the HEVT Camaro to improve drive quality, a key component in consumer acceptability. Although there are many ways to approach drive quality, most aspects can be analyzed in the smoothness of the vehicle longitudinal acceleration response. This research is focused on improving the drive quality of the vehicle developed for EcoCAR 3. Multiple algorithms are developed to address specific aspects of drive quality that can only be done with the powertrain developed. This begins by researching the control strategies used in modern automatic transmissions, and moves into the modeling strategy used to begin algorithm development. Two main strategies are developed and calibrated in the vehicle. The first being a strategy for reducing jerk in pure electric mode by limiting motor torque response. The second strategy aims to improve transmission shift quality by using the electric motor to reduce torque fluctuations at the driveshaft. The energy consumption impact of both of these strategies is also analyzed to ensure that drive quality does not come at the large expense of energy consumption. / Master of Science / The Hybrid electric vehicle team (HEVT) of Virginia Tech is one of 16 university teams participating in EcoCAR 3, the latest competition in the Advanced Vehicle Technology Competitions (AVTC) organized by Argonne National Labs. EcoCAR 3 tasks teams with converting a 2016 Chevrolet Camaro into a hybrid electric vehicle with 5 main goals: reducing petroleum energy use and greenhouse gas emissions while maintaining safety, performance, and consumer acceptability. Over the last 4 years, HEVT has designed and built a plugin parallel hybrid electric vehicle with a unique powertrain architecture. This work deals with utilizing the unique powertrain layout of the HEVT Camaro to improve drive quality, a key component in consumer acceptability. Multiple strategies were examined and implemented for different driving conditions, and ultimately an improvement was made. However, new challenges are introduced by having some components remain stock that limit the success of smoothing gear shifts.

Defining an operation strategy for a Split Parallel Architecture (SPA) Hybrid Electric Vehicle (HEV) is accomplished through calculating powertrain component losses. The results of these calculations define how the vehicle can decrease fuel consumption while maintaining low vehicle emissions. For a HEV, simply operating the vehicle's engine in its regions of high efficiency does not guarantee the most efficient vehicle operation. The results presented are meant only to define a literal strategy; that is, an understanding as to why the vehicle should operate in a certain way under the given conditions. The literature review gives a background of hybrid vehicle control publications, and without the SPA HEV addressed or a hybrid analysis based on loss calculations between engine only and hybrid modes, there is a need for this paper. Once the REVLSE architecture and components are understood, the hybrid modes are explained. Then the losses for each hybrid mode are calculated, and both the conversion and assist efficiencies are detailed. The conversion efficiency represents the amount of additional fuel required to store a certain amount of energy in the battery, and this marginal efficiency can be higher than peak engine efficiency itself. This allows electric only propulsion to be evaluated against the engine only mode, and at low torques the electric motor is more efficient despite the roundtrip losses of the hybrid system. / Master of Science

The goals of the research documented in this thesis were the design, construction, modeling, and validation of a fuel cell hybrid electric vehicle based a conversion of a five-passenger production sedan. Over 60 engineering students working together as the Hybrid Electric Vehicle Team of Virginia Tech (HEVT), integrated a proton exchange membrane fuel cell system into a series hybrid electric vehicle. This design produced an efficient and truly zero-emission vehicle. This 1997 Chevrolet Lumina sedan, renamed ANIMUL H2, carries this advanced powertrain, using an efficient AC induction drivetrain, regenerative braking, compressed hydrogen fuel storage, and an advanced lead-acid battery pack for peak power load leveling. The vehicle weighed 2000 kg (4400 lb) and achieved a combined city/highway fuel economy of 9L/100 km or 26 mpgge (miles per gallon gasoline equivalent, charge depleting, state of charge corrected). A model of the vehicle was developed using ADVISOR, an Advanced Vehicle Simulator that tracks energy flow and fuel usage within the vehicle drivetrain and energy conversion components. The vehicle was tested using the Environmental Protection Agency city and highway driving cycles to provide data for validation of the model. Vehicle data and model results show good correlation at all levels and show that ADVISOR has the capability to model fuel cell hybrid electric vehicles. To make techniques proven by this work more versatile for real world application, VT worked with engineers at the National Renewable Energy Laboratory to develop a 'generic' version of this fuel cell system model that was released to the public in ADVISOR 2.2. This generic model correlates well to test data and incorporates both fuel cell stack and subsystem models. This feature allowed HEVT to predict the benefits of load following subsystem control, showing a 40% fuel economy improvement. / Master of Science

Modern hybrid electric and pure electric vehicles are highly dependent on control algorithms to provide seamless safe and reliable operation under any driving condition, regardless of driver behavior. Three unique and independently operating supervisory control algorithms are introduced to improve reliability and vehicle performance on a series-hybrid electric vehicle with an all-wheel drive all-electric drivetrain. All three algorithms dynamically control or limit the amount of torque that can be delivered to the wheels through an all-electric drivetrain, consisting of two independently controlled brushless-direct current (BLDC) electric machines. Each algorithm was developed and validated following a standard iterative engineering development process which places a heavy emphasis on modeling and simulation to validate the algorithms before they are tested on the physical system. A comparison of simulated and in-vehicle test results is presented, emphasizing the importance of modeling and simulation in the design process.

A Hybrid Electric Vehicle (HEV) is a complex electro-mechanical-chemical system that involves two or more energy sources. The inherent advantages of HEVs are their increased fuel economy, reduced harmful emissions and better vehicle performance. The extent of improvement in fuel economy and vehicle performance greatly depends on selecting optimal component sizes. The complex interaction between the various components makes it difficult to size specific components manually or analytically. So, simulation-based multi-variable design optimization is a possible solution for such kind of system level design problems. The multi-modal, noisy and discontinuous nature of the Hybrid Vehicle design requires the use of derivativeree global algorithms because the derivative-based local algorithms work poorly with such design problems. In this thesis, a Hybrid Vehicle is optimized using various Global Algorithms ? DIviding RECTangles (DIRECT), Simulated Annealing (SA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO). The objective of this study is to increase the overall fuel economy on a composite of city and highway driving cycle and to improve the vehicle performance. The performance of each algorithm is compared on a six variable hybrid electric vehicle design problem. Powertrain System Analysis Tool (PSAT), a state-of-the-art powertrain simulator, developed in MATLAB/Simulink environment by Argonne National Laboratory is used as the vehicle simulator. Further, a Hybrid algorithm that is a combination of global and local algorithm is developed to improve the convergence of the global algorithms. The hybrid algorithm is tested on two simple mathematical functions to check its efficiency.

Personal vehicles have made great contributions to our life and satisfy our daily mobility needs. However, they have also caused societal issues, such as air pollution and global warming. Further to the recent attention to low-carbon energy technologies and environmentally friendly mobility, hybrid electric vehicles play an important role in the current automotive industry. As a leading center and an educational institution in Canada, McMaster University wants to build a Hybrid Electric Vehicle Powertrain Laboratory for introducing undergraduate students to hybrid powertrain architectures, instrumentation and control. A phased development of the hybrid powertrain teaching laboratory is being pursued. The first phase is to design a electric motor laboratory, as a platform for demonstrating motor characteristics. A LabVIEW based interface is designed to enable electric motor characterization tests. This laboratory set-up is still under construction. Real experiments would be implemented, once finishing the utility connections. For the hybrid powertrain laboratory, an innovative design architecture is proposed to enable different hybrid architectures, such as series, parallel, and power-split modes to be investigated. Instead of a planetary gearbox, bevel gearboxes with a continuous variable transmission (CVT) are used for making the laboratory more compact and flexible for demonstrating hybrid functionalities. The additional generator provides the ability of input power-split for allowing the engine to operate at a narrow high efficiency region. After designing the hybrid laboratory, a novel rule-based energy management strategy is applied to a simplified simulation model. / Thesis / Master of Applied Science (MASc)

Today, hybrid electric propulsion technology provides a promising and practical solution for improving vehicle performance, increasing energy efficiency, and reducing harmful emissions, due to the additional flexibility that the technology has provided in the optimal power control and energy management, which are the keys to its success. In this work, a systematic approach for real-time optimal energy management of hybrid electric vehicles (HEVs) and plug-in hybrid electric vehicles (PHEVs) has been introduced and validated through two HEV/PHEV case studies. Firstly, a new analytical model of the optimal control problem for the Toyota Prius HEV with both offline and real-time solutions was presented and validated through Hardware-in-Loop (HIL) real-time simulation. Secondly, the new online or real-time optimal control algorithm was extended to a multi-regime PHEV by modifying the optimal control objective function and introducing a real-time implementable control algorithm with an adaptive coefficient tuning strategy. A number of practical issues in vehicle control, including drivability, controller integration, etc. are also investigated. The new algorithm was also validated on various driving cycles using both Model-in-Loop (MIL) and HIL environment. This research better utilizes the energy efficiency and emissions reduction potentials of hybrid electric powertrain systems, and forms the foundation for development of the next generation HEVs and PHEVs. / Graduate / laindeece@gmail.com

Throughout the past several years, a major push has been made for the automotive industry to provide vehicles with lower environmental impacts while maintaining safety, performance, and overall appeal. Various legislation has been put into place to establish guidelines for these improvements and serve as a challenge for automakers all over the world. In light of these changes, hybrid technologies have been growing immensely on the market today as customers are seeing the benefits with lower fuel consumption and higher efficiency vehicles. With the need for hybrids rising, it is vital for the engineers of this age to understand the importance of advanced vehicle technologies and learn how and why these vehicles can change the world as we know it. To help in the education process, this thesis seeks to define a powertrain model created and developed to help users understand the basics behind hybrid vehicles and the effects of these advanced technologies. One of the main goals of this research is to maintain a simplified approach to model development. There are very complex vehicle simulation models in the market today, however these can be hard to manipulate and even more difficult to understand. The 1 Hz model described within this work aims to allow energy to be simply and understandable traced through a hybrid powertrain. Through the use of a 'backwards' energy tracking method, demand for a drive cycle is found using a drive cycle and vehicle parameters. This demand is then used to determine what amount of energy would be required at each component within the powertrain all the way from the wheels to the fuel source, taking into account component losses and accessory loads on the vehicle. Various energy management strategies are developed and explained including controls for regenerative braking, Battery Electric Vehicles, and Thermostatic and Load-following Series Hybrid Electric Vehicles. These strategies can be easily compared and manipulated to understand the tradeoffs and limitations of each. After validating this model, several studies are completed. First, an example of using this model to design a hybrid powertrain is conducted. This study moves from defining system requirements to component selection, and then finding the best powertrain to accomplish the given constraints. Next, a parameter known as Power Split Fraction is studied to provide insight on how it affects overall powertrain efficiency. Since the goal with advanced vehicle powertrains is to increase overall system efficiency and reduce overall energy consumption, it is important to understand how all of the factors involved affect the system as a whole. After completing these studies, this thesis moves on to discussing future work which will continue refining this model and making it more applicable for design. Overall, this work seeks to provide an educational tool and aid in the development of the automotive engineers of tomorrow. / Master of Science