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

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.

In this thesis, several design methodologies are presented to improve the reliability of dual traction inverters in hybrid electric vehicles (HEVs). Several power inverter topologies including the two-level voltage-source inverter, the boost voltage-source inverter, the Z-source inverter, and reduced-parts inverters are compared in terms of power ratings, volume, and efficiency. The comparison results show that the two-level voltage-source inverter presents higher efficiency, higher power density, and lower cost. Therefore, the back-to-back two-level voltage-source inverter is selected. DC-link capacitor and power modules are the most vulnerable components in dual traction inverters. The lifetime of capacitor is mainly determined by the core temperature. In this thesis, an interleaving control scheme is proposed to reduce the capacitor power loss by decreasing the total DC-link current harmonics. With reduced capacitor power loss, the core temperature of capacitor is reduced. Therefore, the lifetime of capacitor is improved. In addition, a fast electro-thermal model of traction inverters is proposed to estimate the junction temperatures of power devices. Practical switching losses are measured and thermal coupling effects between multiple devices are considered. The calculation rate of junction temperature is reduced by considering both power loss profiles and properties of the thermal impedance. With this model, over-temperature protection and lifetime evaluation can be implemented to enhance the reliability of traction inverters. Finally, a current sensor fault-tolerant operation scheme with six-phase current reconstruction technique is proposed to improve the reliability of dual inverters. In order to get the missing phase currents, the PWM signals are phase shifted to create the reconstruction conditions. With measured DC-link current, all phase-currents of dual inverters are obtained at the expense of slight degradation of maximum allowable modulation index. Therefore, when some or all of the phase current sensors are failed, the dual traction inverters can operate normally. / Thesis / Doctor of Philosophy (PhD)

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)

There has been an increased necessity to search for alternative transportation methods, mainly driven by limited fuel availability and the negative impacts of climate change and exhaust emissions. These factors have lead to increased regulations and a societal shift towards a cleaner and more e cient transportation system. Automotive and technology companies need to be looking for ways to reshape mobility, enhance safety, increase accessibility, and eliminate the ine ciencies of the current transportation system in order to address such a shift. Hybrid vehicles are a popular solution that address many of these goals. In order to fully realize the bene ts of hybrid vehicle technology, the power distribution decision needs to be optimized. In the past, global optimization techniques have been dismissed because they require knowledge of the journey of the vehicle in advance, and are generally computationally extensive. Recent advancements in technologies, like sensors, cameras, lidar, GPS, Internet of Things, and computing processors, have changed the basic assumptions that were made during the vehicle design process. In particular, it is becoming increasingly possible to know future driving conditions. In addition to this, autonomous vehicle technology is addressing many safety and e ciency concerns. This thesis considers and integrates recent technologies when de ning a new approach to hybrid vehicle supervisory controller design and optimization. The dynamic programming algorithm has been systematically applied to an autonomous vehicle with a power-split hybrid electric powertrain. First, a more realistic driving cycle, the Journey Mapping cycle, is introduced to test the performance of the proposed control strategy under more appropriate conditions. Techniques such as vectorization and partitioning are applied to improve the computational e ciency of the dynamic programming algorithm, as it is applied to the hybrid vehicle energy management problem. The dynamic programming control algorithm is benchmarked against rule-based algorithms to substantively measure its bene ts. It is proven that the DP solution improves vehicle performance by at least 9 to 17% when simulated over standard drive cycles. In addition, the dynamic programming solution improves vehicle performance by at least 32 to 39% when simulated over more realistic conditions. The results emphasize the bene ts of optimal hybrid supervisory control and the need to design and test vehicles over realistic driving conditions. Finally, the dynamic programming solution is applied to the process of adaptive control calibration. The particle swarm optimization algorithm is used to calibrate control variables to match an existing controller's operation to the dynamic programming solution. / Thesis / Master of Applied Science (MASc)

EcoRouting refers to the determination of a route that minimizes vehicle energy consumption compared to traditional routing methods, which usually attempt to minimize travel time. EcoRoutes typically increase travel time and in some cases this increase is constrained for a viable route. While significant research on EcoRouting exists for conventional vehicles, incorporating the novel aspects of plug-in hybrids opens new areas to be explored. A prototype EcoRouting system has been developed on the MATLAB platform that takes in map information and converts it to a graph of nodes containing route information such as speed and grade. Various routes between the origin and destination of the vehicle are selected and the total energy consumption and travel time for each route are estimated using a vehicle model. The route with the minimum energy consumption will be selected as the EcoRoute unless there is a significant difference between the minimum time route and the EcoRoute. In this case, selecting a sub-optimal route as the EcoRoute will increase the probability that the driver uses a lower fuel consumption route. EcoRouting has the potential to increase the fuel efficiency for powertrains designed mainly for performance, and we examine the sensitivity of the increased efficiency to various vehicle and terrain features. The reduction in energy consumption can be achieved independent of powertrain modifications and can be scaled using publicly available parameters. / 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

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

EPA testing methods for calculation of fuel economy label ratings, which were revised beginning in 2008, use equations that weight the contributions of fuel consumption results from multiple dynamometer tests to synthesize city and highway estimates that reflect average U.S. driving patterns. The equations incorporate effects with varying weightings into the final fuel consumption, which are explained in this thesis paper, including illustrations from testing. Some of the test results used in the computation come from individual phases within the certification driving cycles. This methodology causes additional complexities for hybrid electric vehicles, because although they are required to have charge-balanced batteries over the course of a full drive cycle, they may have net charge or discharge within the individual phases. The fundamentals of studying battery charge-balance are discussed in this paper, followed by a detailed investigation of the implications of per-phase charge correction that was undertaken through testing of a 2010 Toyota Prius at Argonne National Laboratory's vehicle dynamometer test facility. Using the charge-correction curves obtained through testing shows that phase fuel economy can be significantly skewed by natural charge imbalance, although the end effect on the fuel economy label is not as large. Finally, the characteristics of the current 5-cycle fuel economy testing method are compared to previous methods through a vehicle simulation study which shows that the magnitude of impact from mass and aerodynamic parameters vary between labeling methods and vehicle types. / Master of Science