This thesis deals with aerodynamic forces and their influence on body position and ride comfort due changes in wheel loads. Simulation was made in computer program ADAMS (MSC Software TM).

Electric mobility, E-mobility, will play a central role in a sustainable future transport system. The potential of curbing climate change in both short and long term are significant. Emobility will also offer the possibility to leapfrog the Internal Combustion car, IC-car, economy for developing countries. The low dependence of oil will be a benefit but E-mobility will demand a well functional electricity grid. Development of this grid will be beneficial for the developing world. For the European society E-mobility will in long term offer lower operating costs, decreased dependence of oil and lower emission of pollutants and Green House Gases, GHGs. All these factors are beneficial for the European society. The transition to E-mobility will depend upon a set of different factors and will call for different actions in order to overcome the barriers of E-mobility. A well developed charging infrastructure will be important in order to offer the full potential of E-mobility. The infrastructure will develop along with the market introduction of Electrical Vehicles, EVs. It is important that there are existing charging alternatives in the early introduction phase of EV in order to avoid the stagnation in the transition toward E-mobility. In order to provide the proper conditions for E-mobility, the determining factors must be investigated and evaluated. The four main factors are economical, social, R&D and infrastructure. The European driving patterns meet the offered operating range of an EV with ease. This means that EV has the potential to become an inner city vehicle under existing conditions. The investigation of the four determining factors leads to a base from which an implementation plan is suggested. The implementation plan is directed toward governments, energy utilities and other active participants in the development. The key factors of the implementation plan are to actively engage in the market, see E-mobility as a disruptive technology, use spin-off companies and social transparency. In order to gain the most from the implementation plan it is important that the correct actions are taken at the correct time. Therefore the transition period is divided into three phases; the introduction phase, the commercial phase and the re-development phase. The introduction phase will create the basic conditions for E-mobility. Government’s main action will be to invest in EVs and offer subsidies and other incentives to major companies that will equip their vehicle fleets with EVs. These actions will send signals toward vehicle Original Equipment Manufactures, OEMs, and other actors that the market of EVs is worth investing in. During the introduction phase try-out sessions, demonstrations and hearings will be important in order to communicate the advantages of E-mobility to society. Energy utilities will work to create roaming deals and standardization of important components and characteristics. The commercial phase is the most important phase for social adoption of E-mobility. During this phase commercial businesses will use EV charging a competitive advantage. New business models will be one of the keys to fully adoption of E-mobility. Cross industry alliances will reduce the initial cost, offer the service of a vehicle without owning it and leasing deals. The perception of travelling will shift and reduction of operating cost will be evaluated against travel time and planning. The re-development phase is based on a society that has adopted E-mobility. The development will proceed in order to offer more advantages to drivers but also to increase efficiency and to use the full potential of E-mobility.

The transportation sector is the dominant source of U.S. fuel consumption and emissions. Specifically, highway travel accounts for nearly 75 percent of total transportation energy use and slightly more than 33 percent of national emissions of EPA's six Criteria pollutants. Enactment of the Clean Air Act Amendment of 1990 (CAAA) and the Intermodal Surface Transportation Efficiency Act of 1991 (ISTEA) have changed the ways that most states and local governments deal with transportation problems. Transportation planning is geared to improve air quality as well as mobility. It is required that each transportation activity be analyzed in advance using the most recent mobile emission estimate model to ensure not to violate the Conformity Regulation. Several types of energy and emission models have been developed to capture the impact of a number of factors on vehicle fuel consumption and emissions. Specifically, the current state-of-practice in emission modeling (i.e. Mobile5 and EMFAC7) uses the average speed as a single explanatory variable. However, up to date there has not been a systematic attempt to quantify the impact of various travel and driver-related factors on vehicle fuel consumption and emissions. This thesis first systematically quantifies the impact of various travel-related and driver-related factors on vehicle fuel consumption and emissions. The analysis indicates that vehicle fuel consumption and emission rates increase considerably as the number of vehicle stops increases especially at high cruise speed. However, vehicle fuel consumption is more sensitive to the cruise speed level than to vehicle stops. The aggressiveness of a vehicle stop, which represents a vehicle's acceleration and deceleration level, does have an impact on vehicle fuel consumption and emissions. Specifically, the HC and CO emission rates are highly sensitive to the level of acceleration when compared to cruise speed in the range of 0 to 120 km/h. The impact of the deceleration level on all MOEs is relatively small. At high speeds the introduction of vehicle stops that involve extremely mild acceleration levels can actually reduce vehicle emission rates. Consequently, the thesis demonstrated that the use of average speed as a sole explanatory variable is inadequate for estimating vehicle fuel consumption and emissions, and the addition of speed variability as an explanatory variable results in better models. Second, the thesis identifies a number of critical variables as potential explanatory variables for estimating vehicle fuel consumption and emission rates. These explanatory variables include the average speed, the speed variance, the number of vehicle stops, the acceleration noise associated with positive acceleration and negative acceleration noise, the kinetic energy, and the power exerted. Statistical models are developed using these critical variables. The statistical models predict the vehicle fuel consumption rate and emission rates of HC, CO, and NOx (per unit of distance) within an accuracy of 88%-96% when compared to instantaneous microscopic models (Ahn and Rakha, 1999), and predict emission rates of HC, CO, and NOx within 95 percentile confidence limits of chassis dynamometer tests conducted by EPA. Comparing with the current state-of-practice, the proposed statistical models provide better estimates for vehicle fuel consumption and emissions because speed variances about the average speed along a trip are considered in these models. On the other hand, the statistical models only require several aggregate trip variables as input while generating reasonable estimates that are consistent with microscopic model estimates. Therefore, these models could be used with transportation planning models for conformity analysis. / Master of Science

Safety in transportation systems is of paramount concern to society; many improvements have been made in recent decades and yet thousands of fatalities still occur annually. Work zones in particular are areas with increased safety risks in transit networks. Advances in electronics have now allowed engineers to merge powerful computing and communication technologies with modern automotive and vehicular technology, known as connected vehicle. Connected vehicle will allow vehicles to exchange data wirelessly with each other and infrastructure to improve safety, mobility and sustainability. This thesis presents a paper that focuses on evaluating the impact of connected vehicle on work zone traffic safety. A dynamic route guidance system based on decaying average-travel-time and shortest path routing was developed and tested in a microscopic traffic simulation environment to avoid routes with work zones. To account for the unpredictable behaviour and psychology of driver’s response to information, three behaviour models, in the form of multinomial distributions, are proposed and studied in this research. The surrogate safety measure improved Time to Collision was used to gauge network safety at various market penetrations of connected vehicles. Results show that higher market penetrations of connected vehicles decrease network safety due to increased average travel distance, while the safest conditions, 5%-10% reduction in critical Time to Collision events, were observed at market penetrations of 20%-40% connected vehicle, with network safety strongly influenced by behaviour model. / Thesis / Master of Applied Science (MASc)

A multi-body simulation study was undertaken to investigate the running behavior of a rail grinder vehicle with newly developed Ganz bogies. The main purpose of the simulations was to forecast and support the vehicle acceptance tests and support the ongoing development. A multi-body model was built in Simpack and the most critical force elements were isolated and validated by tests. Derailment safety was assessed based on both European and Australian standards. The vehicle's running stability was carried out for both standard (1435mm) and broad gauge (1676mm) versions. Vehicle sway characteristics were determined through the calculation of flexibility coefficient both numerically and analytically. A parametric study for primary vertical damper was undertaken to assess the empirically selected dampers and optimize the performance. The results show that the damping coefficient can be greatly reduced while maintaining acceptable running behavior, thus the lifetime of dampers can be increased. Based on stability investigations, yaw damper installation is not necessary, though the bracketry for the possibility of later installation will remain on the prototype bogie. / En flerkroppssimulering genomfördes med hjälp av Simpack med syfte att undersöka gångegenskaperna hos ett järnvägsslipfordon, med boggi som nyligen utvecklats av Ganz. Huvudsyftet bakom simuleringarna var att förutspå - och underlätta godkännandet av fordonets acceptanskrav samt underlätta pågående utveckling.  En flerkroppsmodell konstruerades och de mest kritiska kraftbärande elementen undersöktes - och verifierades separat med hjälp av tester. Säkerhetsaspekterna kring urspårning undersöktes utifrån både europeiska och australiska standarder. Fordonets gångstabilitet bedömdes med både standard - samt bred spårviddsvarianter. Fordonets svängningsegenskaper bestämdes genom att genomföra beräkningar av flexibilitetskonstanten både numeriskt och analytiskt.  En parameterstudie genomfördes för fordonets primära vertikala dämpare med avsikt att evaluera empiriskt valda dämpare samt optimera prestanda. Resultaten visar att dämpningskoefficienten avsevärt kan minskas samtidigt som acceptabel rullningsförmåga behålls, vilket därmed förlänger dämparens livstid. Installation av girdämpare är inte nödvändigt, baserat på stabilitetsundersökningar, dock behålls fästen på boggi-prototypen för att möjliggöra framtida monteringar av sådana element.

The development of autonomous vehicles is progressing rapidly through extensive efforts by the automotive industry and researchers. One of the key factors for the adoption of autonomous driving technology is motion comfort and the ability to engage in non-driving tasks such as reading, socialising, and relaxing without experiencing motion sickness while travelling. Therefore, for the full success of autonomous vehicles, it is necessary to learn how to design and control the vehicles to mitigate motion sickness for the passengers.  This thesis aims to investigate methods for prediction of motion sickness in autonomous vehicles and how to mitigate it using vehicle dynamics based solutions, with an emphasis on trajectory planning. As a first step, a review and evaluation of existing motion sickness prediction methods were performed. The review highlighted the importance of accurate motion sickness assessment in the early phases of autonomous vehicle design. Two chosen methods (ISO 2631-based and sensory conflict theory-based) were evaluated to estimate individual motion sickness feelings using measured data and subjective assessment ratings from field tests. It can be concluded that the methods can be adjusted to predict individual motion sickness feelings, as shown by the comparison with the experimental data. To continue the work, a review of vehicle dynamics based motion sickness mitigation methods for autonomous vehicles was performed. Several chassis control strategies in literature like active suspension, rear-wheel steering and torque distribution have demonstrated the potential help to reduce motion sickness. Another effective approach to mitigate motion sickness in autonomous vehicles is to regulate vehicle speed and path using trajectory planning which was chosen to be further investigated. The trajectory planning was constructed as an optimisation problem where there is a trade-off between motion sickness and manoeuvre time. The impact of the trajectory planning algorithm to reduce motion sickness was analysed by simulating two different vehicle models in specific test manoeuvres. The results indicate that driving style has a significant influence on motion sickness and trajectory planning algorithms should be carefully designed to find a good balance between journey time and motion sickness. The research presented in this thesis contributes to the development of methodologies for predicting and mitigating motion sickness in autonomous vehicles, helping to achieve the goal of ensuring their overall success. / Utvecklingen av autonoma fordon går snabbt framåt tack vare omfattande insatser från fordonsindustrin och forskare. En av de viktigaste faktorerna för införandet av teknik för autonom körning är åkkomfort och möjligheten att ägna sig åt andra saker än körning, som att läsa, umgås och koppla av, utan att drabbas av åksjuka under resan. För att autonoma fordon ska lyckas fullt ut är det därför nödvändigt att förstå hur man utformar och styr fordonen för att minska risken för att passagerarna drabbas av åksjuka.  Denna licentiatuppsats syftar till att undersöka hur åksjuka kan förutsägas i vägfordon och hur den kan reduceras med hjälp av fordonsdynamikbaserade lösningar, med tonvikt på trajektorieplanering. Som ett första steg genomfördes en granskning och utvärdering av befintliga metoder för åksjukeprediktion. Granskningen belyste vikten av en korrekt bedömning av åksjuka i de tidiga faserna av autonom fordonsdesign. Två valda metoder (ISO 2631-baserad och sensorisk konfliktbaserad) utvärderades för att uppskatta individuell åksjuka med hjälp av uppmätta data och subjektiva bedömningar från fälttester. Slutsatsen är att metoderna kan justeras för att förutsäga individuell åksjuka, vilket framgår av jämförelsen med experimentella data. För att fortsätta arbetet gjordes en genomgång av fordonsdynamikbaserade metoder för att minska åksjuka i autonoma fordon. Flera chassireglerstrategier i litteraturen, såsom aktiv fjädring, bakhjulsstyrning och drivmomentfördelning, har visat sig kunna bidra till att minska åksjuka. En annan effektiv metod för att minska åksjuka i autonoma fordon är att reglera fordonets hastighet och bana med hjälp av trajektorieplanering, vilket valdes att undersökas ytterligare. Trajektorieplaneringen konstruerades som ett optimeringsproblem där det finns en avvägning mellan åksjuka och manövertid. Effekten av trajektorieplaneringsalgoritmen för att minska åksjuka analyserades genom att simulera två olika fordonsmodeller i specifika testmanövrar. Resultaten indikerar att körstil har en betydande inverkan på åksjuka och att algoritmer för trajektorieplanering bör utformas noggrant för att hitta en bra balans mellan restid och åksjuka. Forskningen som presenteras i denna uppsats bidrar till utvecklingen av metoder för att förutsäga och mildra åksjuka i autonoma fordon, vilket hjälper till att uppnå målet att säkerställa deras framgång.

Air corridors are an integral part of the advanced air mobility infrastructure. They are the virtual highways in the sky for transportation of people and cargo in the controlled airspace at an altitude of around 1000 ft. to 2000 ft. above the ground level. This paper presents fundamental insights into the design of air corridors with high operational efficiency as well as zero collisions. It begins with the definitions of air cube, skylane or track, intersection, vertiport, gate, and air corridor. Then, a multi-layered air corridor model is proposed. Traffic at intersections is analyzed in detail with examples of vehicles turning in different directions. The concept of capacity of an air corridor is introduced along with the nature of distribution of locations of vehicles in the air corridor and collision probability inside the corridor are discussed. Finally, the results of simulations of traffic flows are presented.

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

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

With the development of technology, people's viewpoints of the automobile have shifted; instead of merely a means of transportation, the automobile has become a space in which a driver can still perform daily activities besides driving, such as communicating with other people, interacting with electronic devices, and receiving information. In the meantime, different ways of interaction have been explored. Among all the modalities, gestures have been considered as a feasible way for performing in-car secondary tasks because of their intuitiveness. However, few researches have been conducted in terms of subjects' cognitive load. This thesis has examined four gesture interfaces (air swipe, air tap, screen swipe, and screen tap), in terms of their effects on drivers' driving performance, secondary task performance, perceived cognitive load, and eye glance behavior. The result demonstrated that air gestures are generally slower than screen gestures with regard to secondary performance. Screen swipe gesture requires the lowest cognitive load while air swipe and screen tap gesture remain the same. Subjects in this study tend to prefer screen swipe gesture the most while prefer air tap gesture the least. However, there is no significant difference between air swipe and screen tap gesture. Although air tap gesture and screen tap gesture generated the largest amount of dwell times, no variance among the four gesture interfaces in driving performance has been found. The result indicated that even though air gestures are not limited by space, screen swipe in this study still seemed to be the most ideal way for performing in-car secondary task of music selection.