The global greenhouse gas CO2 emission from the transportation sector is very significant. To reduce this gas emission, EU has set an average target of not more than 95 CO2/km for new passenger cars by the year 2020. A great reduction is still required to achieve the CO2 emission target in 2020, and many different approaches are being considered. This thesis focuses on the thermal management of the engine as an area that promise significant improvement of fuel efficiency with relatively small changes. The review of the literature shows that thermal management can improve engine efficiency through the friction reduction, improved air-fuel mixing, reduced heat loss, increased engine volumetric efficiency, suppressed knock, reduce radiator fan speed and reduction of other toxic emissions such as CO, HC and NOx. Like heat loss and friction, most emissions can be reduced in high temperature condition, but this may lead to poor volumetric efficiency and make the engine more prone to knock. The temperature trade-off study is conducted in simulation using a GT-SUITE engine model coupled with the FE in-cylinder wall structure and cooling system. The result is a map of the best operating temperature over engine speed and load. To quantify the benefit of this map, eight driving styles from the legislative and research test cycles are being compared using an immediate application of the optimal temperature, and significant improvements are found for urban style driving, while operation at higher load (motorway style driving) shows only small efficiency gains. The fuel consumption saving predicted in the urban style of driving is more than 4%. This assess the chance of following the temperature set point over a cycle, the temperature reference is analysed for all eight types of drive cycles using autocorrelation, lag plot and power spectral density. The analysis consistently shows that the highest volatility is recorded in the Artemis Urban Drive Cycle: the autocorrelation disappears after only 5.4 seconds, while the power spectral density shows a drop off around 0.09Hz. This means fast control action is required to implement the optimal temperature before it changes again. Model Predictive Control (MPC) is an optimal controller with a receding horizon, and it is well known for its ability to handle multivariable control problems for linear systems with input and state limits. The MPC controller can anticipate future events and can take control actions accordingly, especially if disturbances are known in advance. The main difficulty when applying MPC to thermal management is the non-linearity caused by changes in flow rate. Manipulating both the water pump and valve improves the control authority, but it also amplifies the nonlinearity of the system. Common linearization approaches like Jacobian Linearization around one or several operating points are tested, by found to be only moderately successful. Instead, a novel approach is pursued using feedback linearization of the plant model. This uses an algebraic transformation of the plant inputs to turn the nonlinear systems dynamics into a fully or predominantly linear system. The MPC controller can work with the linear model, while the actual control inputs are found using an inverse transformation. The Feedback Linearization MPC of the cooling system model is implemented and testing using MathWork Simulink®. The process includes the model transformation approach, model fitting, the transformation of the constraints and the tuning of the MPC controller. The simulation shows good temperature tracking performance, and this demonstrates that a MPC controller with feedback linearization is a suitable approach to thermal management. The controller strategy is then validated in a test rig replicating an actual engine cooling system. The new MPC controller is again evaluated over the eight driving cycles. The average water pump speed is reduced by 9.1% compared to the conventional cooling system, while maintaining good temperature tracking. The controller performance further improves with future disturbance anticipation by 20.5% for the temperature tracking (calculated by RMSE), 6.8% reduction of the average water pump speed, 47.3% reduction of the average valve movement and 34.0% reduction of the average radiator fan speed.

Fuel consumption is a major economical component of vehicles, particularly for heavy-duty vehicles. It is dependent on many factors, such as driver and environment, and control over some factors is present, e.g. route, and we can try to optimize others, e.g. driver. The driver is responsible for around 30% of the operational cost for the fleet operator and is therefore important to have efficient drivers as they also inuence fuel consumption which is another major cost, amounting to around 40% of vehicle operation. The difference between good and bad drivers can be substantial, depending on the environment, experience and other factors. In this thesis, two methods are proposed that aim at quantifying and qualifying driver performance of heavy duty vehicles with respect to fuel consumption. The first method, Fuel under Predefined Conditions (FPC), makes use of domain knowledge in order to incorporate effect of factors which are not measured. Due to the complexity of the vehicles, many factors cannot be quantified precisely or even measured, e.g. wind speed and direction, tire pressure. For FPC to be feasible, several assumptions need to be made regarding unmeasured variables. The effect of said unmeasured variables has to be quantified, which is done by defining specific conditions that enable their estimation. Having calculated the effect of unmeasured variables, the contribution of measured variables can be estimated. All the steps are required to be able to calculate the influence of the driver. The second method, Accelerator Pedal Position - Engine Speed (APPES) seeks to qualify driver performance irrespective of the external factors by analyzing driver intention. APPES is a 2D histogram build from the two mentioned signals. Driver performance is expressed, in this case, using features calculated from APPES. The focus of first method is to quantify fuel consumption, giving us the possibility to estimate driver performance. The second method is more skewed towards qualitative analysis allowing a better understanding of driver decisions and how they affect fuel consumption. Both methods have the ability to give transferable knowledge that can be used to improve driver's performance or automatic driving systems. Throughout the thesis and attached articles we show that both methods are able to operate within the specified conditions and achieve the set goal.

<p>The optimization of the electrical system voltage range of a mild hybrid electric vehicle is examined in this research study. The objective is to evaluate and propose the optimized vehicle voltage level for the mild hybrid electric vehicle from both technical and economic aspects. The approach is to evaluate the fuel economy improvement from the mild hybrid electric vehicle of various voltage level for the cost benefit study. The evaluation is conducted from the vehicle system level with discussions of components selection for system optimization. Autonomie, a simulation tool widely used by academic and automotive industry, is used for the vehicle simulation and fuel economy evaluation. The cost analysis is based on the system cost factoring in the component cost based forecasted production volume. </p> <p>The driver for this study is to propose an optimized voltage for the mild hybrid electric vehicle for the vehicle manufacturers and suppliers to standardize the implementation to meet the fuel economy and emission requirements and vehicle power demand. The standardization of the vehicle voltage level can improve design and development efficiency, reusability and reduce cost in developing non-standard voltage levels of the mild hybrid vehicle. The synergy in standardized voltage level for the mild hybrid vehicle can accelerate technology implementation toward mass production to meet regulatory emission and fuel economy requirements. </p>

<p dir="ltr">An EGR Pump and High-Efficiency turbo were installed on a 13.6 L John Deere diesel engine. The main objective of this study was to examine how a high-efficiency turbocharger and an electric EGR pump can work together to improve fuel efficiency in engines without increasing the emission of NOx. The focus is on finding a balance, between enhancing efficiency and controlling emissions which will ultimately contribute to making vehicles eco-friendly and fuel-efficient.</p>

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Our society is heavily and intrinsically dependent on energy transformation and usage. In a world scenario where resources are being depleted while their demand is increasing, it is crucial to optimize every process. During the last decade the concept of energy efficiency has become a leitmotif in several fields and has directly influenced our everyday life: from light bulbs to airplane turbines, there has been a general shift from pure performance to better efficiency. In this vein, we focus on the mobility and tractive efficiency of off-road vehicles. These vehicles are adopted in military, agriculture, construction, exploration, recreation, and mining applications and are intended to operate on soft, deformable terrain. The performance of off-road vehicles is deeply influenced by the tire-soil interaction mechanism. Soft soil can drastically reduce the traction performance of tires up to the point of making motion impossible. In this study, a tire model able to predict the performance of rigid wheels and flexible tires is developed. The model follows a semi-empirical approach for steady-state conditions and predicts basic features, such as the drawbar pull, the driving torque and the lateral force, as well as complex behaviors, such as the slip-sinkage phenomenon and the multi-pass effect. The tractive efficiency of different tire-soil configurations is simulated and discussed. To investigate the handling and the traction efficiency, the tire model is implemented into a four-wheel vehicle model. Several tire geometries, vehicle configurations (FWD, RWD, AWD), soil types, and terrain profiles are considered to evaluate the performance under different simulation scenarios. The simulation environment represents an effective tool to realistically analyze the impact of tire parameters (size, inflation pressure) and torque distribution on the energy efficiency. It is verified that larger tires and decreased inflation pressure generally provide better traction and energy efficiency (under steady-state working conditions). The torque distribution strategy between the axles deeply affects the traction and the efficiency: the two variables can't clearly be maximized at the same time and a trade-off has to be found. / Ph. D.

As the global demand for energy increases, the people of the United States are increasingly subject to high and ever-rising oil prices. Additionally, the U.S. transportation sector accounts for 27% of total nationwide Greenhouse Gas (GHG) emissions. In the U.S. transportation sector, light-duty passenger vehicles account for about 58% of energy use. Therefore incremental improvements in light-duty vehicle efficiency and energy use will significantly impact the overall landscape of energy use in America. A crucial step to designing and building more efficient vehicles is modeling powertrain energy consumption. While accurate modeling is indeed key to effective and efficient design, a fundamental understanding of the powertrain and auxiliary systems that contribute to energy consumption for a vehicle is equally as important if not more important. This thesis presents a methodology that has been packaged into a tool, called VTool, that can be used to estimate the energy consumption of a vehicle powertrain. The method is intrinsically designed to foster understanding of the vehicle powertrain as it relates to energy consumption while still providing reasonably accurate results. VTool explicitly calculates the energy required at the wheels of the vehicle to complete a prescribed drive cycle and then explicitly applies component efficiencies to find component losses and the overall energy consumption for the drive cycle. In calculating component efficiencies and losses, VTool offers several tunable parameters that can be used to calibrate the tool for a particular vehicle, compare powertrain architectures, or simply explore the tradeoffs and sensitivities of certain parameters. In this paper, the method is fully and explicitly developed to model Electric Vehicles (EVs), Series Hybrid Electric Vehicles (HEVs) and Parallel HEVs for various different drive cycles. VTool has also been validated for use in UDDS and HwFET cycles using on-road test results from the 2011 EcoCAR competition. By extension, the method could easily be extended for use in other cycles. The end result is a tool that can predict fuel consumption to a reasonable degree of accuracy for a variety of powertrains, calculate J1711 Utility Factor weighted energy consumption for Extended Range Electric Vehicles (EREVs) and determine the Well-to-Wheel impact of a given powertrain or fuel. VTool does all of this while performing all calculations explicitly and calculating all component losses to allow the user maximum access which promotes understanding and comprehension of the fundamental dynamics of automotive fuel economy and the powertrain as a system. / Master of Science

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

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is participating in the EcoCAR 3 Advanced Vehicle Technology Competition series organized by Argonne National Labs (ANL), and sponsored by General Motors (GM) and the U.S. Department of Energy (DOE). EcoCAR 3 is a 4-year collegiate competition that challenges student with redesigning a 2016 Chevrolet Camaro into a hybrid. The five main goals of EcoCAR 3 are to reduce petroleum energy use (PEU) and green house gas (GHG) emissions while maintaining safety, consumer acceptability, and performance, with an increased focus on cost and innovation. HEVT selected a P3 Plug-in Parallel hybrid electric vehicle (PHEV) to meet design goals and competition requirements. This study presents different stages of the vehicle development process (VDP) followed to integrate the HEVT Camaro. This work documents the control system development process up to Year 2 of EcoCAR 3. The modeling process to select a powertrain is the first stage in this research. Several viable powertrains and the respective vehicle technical specifications (VTS) are evaluated. The P3 parallel configuration with a V8 engine is chosen because it generated the set of VTS that best meet design goals and EcoCAR 3 requirements. The V8 engine also preserves the heritage of the Camaro, which is attractive to the established target market. In addition, E85 is chosen as the fuel for the powertrain because of the increased impact it has on GHG emissions compared to E10 and gasoline. The use of advanced methods and techniques like model based design (MBD), and rapid control prototyping (RCP) allow for faster development of engineering products in industry. Using advanced engineering techniques has a tremendous educational value, and these techniques can assist the development of a functional and safe hybrid control system. HEVT has developed models of the selected hybrid powertrain to test the control code developed in software. The strategy developed is a Fuzzy controller for torque management in charge depleting (CD) and charge sustaining (CS) modes. The developed strategy proves to be functional without having a negative impact of the energy consumption characteristics of the hybrid powertrain. Bench testing activities with the V8 engine, a low voltage (LV) motor, and high voltage (HV) battery facilitated learning about communication, safety, and functionality requirements for the three components. Finally, the process for parallel development of models and control code is presented as a way to implement more effective team dynamics. / Master of Science