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Kettering University's entry in the 2002 Clean Snowmobile Challenge involves the installation of a fuel injected four-stroke engine into a conventional snowmobile chassis. Exhaust emissions are minimized through the use of a catalytic converter and an electronically controlled closed-loop fuel injection system, which also maximizes fuel economy. Noise emissions are minimized by the use of a specifically designed engine silencing system and several chassis treatments. Emissions tests run during the SAE collegiate design event revealed that a snowmobile designed by Kettering University produces lower unburned hydrocarbon (1.5 to 7 times less), carbon monoxide (1.5 to 7 times less), and oxides of nitrogen (and 5 to 23 times less) levels than the average automobile driven in Yellowstone National Park. The Kettering University entry also boasted acceleration performance better than the late-model 500 cc two-stroke snowmobile used as a control snowmobile in the Clean Snowmobile testing.

Vehicle Dynamics play an important role in responsiveness of a vehicle. The performance of a vehicle depends on its ride and handling characteristics [1]. Handling is a measure of the directional response of a vehicle and one of the important characteristics from the vehicle dynamics point of view. The directional response of a vehicle depends on the dynamics of the steering system. A good steering control provides an accurate feedback about how the vehicle reacts to the road. In this paper, the powerful techniques of Bond graphs and state equations [2] are used to design and analyze the dynamics of a manual rack and pinion steering system. The author obtains the transfer function between the Angle of rotation of front tire and the Angle of rotation of steering wheel. The overall steering ratio of the bond graph modeled steering system is compared with the overall ratio of a similar vehicle to validate the model.

Bond graph modeling is a powerful technique to study the complex interactions occurring between various components in a system. A few investigations were carried out to study vehicle dynamics using Bondgraphs, but are limited to 2 degree of freedom systems [1,2&3]. In this work, a 4-DOF-vehicle model was developed using bond graphs. A frequency response analysis was also carried out to study the natural frequencies. This model was later validated using Lagrangian principles. The results correlated well for a typical passenger car using the manufacturer supplied information available in the public domain.

Much development in the automotive industry relates to the use of high-content ethanol blended fuels to reduce the emissions produced by on-road engines/vehicles. However, less research has been done on the effect of operating small off-road engines (SORE) on high-blend ethanol fuels without substantial modifications. Most manufacturers of such engines only certify proper operation on low content ethanol blends such as E10 (10% ethanol, 90% gasoline by volume). This paper focuses on the use of E77 fuel in a small off-road engine which is speed-governed. Such engines are commonly used in lawn mowers, small recreational vehicles, or other equipment. The exhaust emissions and performance of the engine were evaluated using the EPA 6-mode duty cycle for small recreational engines where testing and analysis followed the recommendations of SAE J1088. This test cycle consisted of operating the engine at steady state load points using a fixed engine speed.

Kettering University's Clean Snowmobile Challenge student design team has developed a new robust and innovative snowmobile for the 2004 competition. Switching from the previous years four-stroke automotive engine, Kettering University has utilized a modified snowmobile in-line four cylinder, four-stroke, fuel- injected engine. This engine has been installed into a 2003 Yamaha RX-1 snowmobile chassis. Exhaust emissions have been minimized through the use of a customized catalytic converter and an electronically controlled closed-loop fuel injection system. A newly designed and tuned exhaust as well as several chassis treatments have aided in minimizing noise emissions.

Kettering University's Clean Snowmobile Challenge student design team has developed a new robust and innovative snowmobile for the 2005 competition. This snowmobile dramatically reduces exhaust and noise emissions and improves fuel economy compared with a conventional snowmobile. Kettering University has utilized a modified snowmobile in-line four cylinder, four-stroke, engine. The team added an electronically-controlled fuel-injection system with oxygen sensor feedback to this engine. This engine has been installed into a 2003 Yamaha RX-1 snowmobile chassis. Exhaust emissions have been further minimized through the use of a customized catalytic converter and an electronically controlled closed-loop fuel injection system. A newly designed and tuned exhaust as well as several chassis treatments have aided in minimizing noise emissions.

A Task and Message Schedule for a FlexCAN-based Hybrid-Electric vehicle (HEV) functional unit is described. The resulting schedule is a component of an incremental message and task scheduling approach based on a time-driven message schedule and priority-driven task schedule. The HEV functional unit involves the combined control and monitoring functions of an internal combustion engine working in parallel with a permanent magnet synchronous motor. The control algorithm for the synchronous motor has been simulated using VHDL-AMS. The global message system is supported by FlexCAN and the task scheduler system is supported by a priority based OS (e.g., OSEK or AUTOSAR).

Object avoidance for autonomous driving is a vital factor in safe driving. When a vehicle travels from any random start places to any target positions in the milieu, an appropriate route must prevent static and moving obstacles. Having the accurate depth of each barrier in the scene can contribute to obstacle prevention. In recent years, precise depth estimation systems can be attributed to notable advances in Deep Neural Networks and hardware facilities/equipment. Several depth estimation methods for autonomous vehicles usually utilize lasers, structured light, and other reflections on the object surface to capture depth point clouds, complete surface modeling, and estimate scene depth maps. However, estimating precise depth maps is still challenging due to the computational complexity and time-consuming process issues. On the contrary, image-based depth estimation approaches have recently come to attention and can be applied for a broad range of applications.

As automated driving becomes more common, simulation of vehicle dynamics and control scenarios are increasingly important for investigating motion control approaches. In this work, a study of the differences between the Pure Pursuit and Stanley autonomous vehicle controllers, based on vehicle dynamics responses, is presented. Both are geometric controllers that use only immediate vehicle states, along with waypoint data, to control a vehicle’s future direction as it proceeds from point to point, and both are among the most popular lateral controllers in use today. The MATLAB Automated Driving Toolbox is employed to implement and virtually test the Pure Pursuit and Stanley lateral controllers in different driving scenarios. These include low intensity scenarios such as city driving, and emergency maneuvers such as the moose test.

Safety is considered one of the most important parameters when designing a ground vehicle. The adverse effect of weather on a vehicle can lead to a surge in safety issues and accidents. Several safety assistance systems are available in modern vehicles, which are designed to lessen the negative effects of weather hazards. Although these safety systems can intervene during crucial conditions to avoid accidents, driving a vehicle on snowy or icy terrain can still be a challenging task. Road conditions with the least tire-road friction often results in poor vehicle handling, and without any kind of safety system it can lead to mishaps. With the use of Adams Car software and vehicle dynamics modeling, a realistic relationship between the vehicle and road surface may be established. The simulation can be used to have a better understanding of vehicle handling in snowy and icy conditions, tire-ice interaction, and tire modeling.

Aerodynamics of trucks and other high sided vehicles is of significant interest in reducing road side accidents due to wind loading and in improving fuel economy. Recognizing the limitations of conventional wind tunnel testing, considerable efforts have been invested in the last decade to study vehicle aerodynamics computationally. In this paper, a three-dimensional near field flow analysis has been performed for axial and cross wind loading to understand the airflow characteristics surrounding a truck-like bluff body. Results provide associated drag for the truck geometry including the exterior rearview mirror. Modifying truck geometry can reduce drag, improving fuel economy.

Lane marks are an important aspect for autonomous driving. Autonomous vehicles rely on lane mark information to determine a safe and legal path to drive. In this paper an approach to estimate lane lines on straight or slightly curved roads using a LiDAR unit for autonomous vehicles is presented. By comparing the difference in elevation of LiDAR channels, a drivable region is defined. The presented approach used in this paper differs from previous LiDAR lane line detection methods by reducing the drivable region from three to two dimensions exploring only the x-y trace. In addition, potential lane markings are extracted by filtering a range of intensity values as opposed to the traditional approach of comparing neighboring intensity values. Further, by calculating the standard deviation of the potential lane markings in the y-axis, the data can be further refined to specific points of interest.

The safety of ground vehicles is a matter of critical importance. Vehicle safety is enhanced with the use of control systems that mitigate the effect of unachievable demands from the driver, especially demands for tire forces that cannot be developed. This paper presents the results of a smart tire prototyping and validation study, which is an investigation of a smart tire system that can be used as part of these mitigation efforts. The smart tire can monitor itself using in-tire sensors and provide information regarding its own tire forces and moments, which can be transmitted to a vehicle control system for improved safety. The smart tire is designed to estimate the three orthogonal tire forces and the tire aligning moment at least once per wheel revolution during all modes of vehicle operation, with high accuracy. The prototype includes two in-tire piezoelectric deformation sensors and a rotary encoder.

With the recent advances in rapid modeling and rapid prototyping, accurate simulation models for tires are very desirable. Selection of a tire slip model depends on the required frequency range and nonlinearity associated with the dynamics of the vehicle. This paper presents a brief overview of three major slip concepts including “Stationary slip”, “Physical transient slip”, and “Pragmatic transient slip”; tire models use these slip concepts to incorporate tire slip behavior. The review illustrates that there can be no single accurate slip model which could be ideally used for all modes of vehicle dynamics simulations. For this study, a rigid ring based semi-analytical tire model for intermediate frequency (up to 100 Hz) is used.

Vehicles are in contact with the road surface through tires, and the interaction at the tire-road interface is usually the major source of vibrations that is experienced by the passengers in the vehicle. Thus, it is critical to measure the vibrational characteristics of the tires in order to improve the safety and comfort of the passengers and also to make the vehicle quieter. The measurement results can also be used to validate numerical models. In this paper, Digital Image Correlation (DIC) as a non-contact technique is used to measure the dynamics of a racing tire in static and rolling conditions. The Kettering University FSAE car is placed on the dynamometer machine for this experiment. A pair of high-speed cameras is used to capture high-resolution images of the tire in a close-up view. The images are processed using DIC to obtain strain and displacement of the sidewall of the tire during rolling. The experiment is performed for various testing speeds.

For virtual simulation of the vehicle attributes such as handling, durability, and ride, an accurate representation of pneumatic tire behavior is very crucial. With the advancement in autonomous vehicles as well as the development of Driver Assisted Systems (DAS), the need for an Intelligent Tire Model is even more on the increase. Integrating sensors into the inner liner of a tire has proved to be the most promising way in extracting the real-time tire patch-road interface data which serves as a crucial zone in developing control algorithms for an automobile. The model under development in Kettering University (KU-iTire), can predict the subsequent braking-traction requirement to avoid slip condition at the interface by implementing new algorithms to process the acceleration signals perceived from an accelerometer installed in the inner liner on the tire.

The automotive and aerospace industries are increasingly using the light-weight material to improve the vehicle performance. However, using light-weight material can increase the airborne and structure-borne noise. A special attention needs to be paid in designing the structures and measuring their dynamics. Conventionally, the structure is excited using an impulse hammer or a mechanical shaker and the response is measured using uniaxial or multi-axial accelerometers to obtain the dynamics of the structure. However, using contact-based transducers can mass load the structure and provide data at a few discrete points. Hence, obtaining the true dynamics of the structure conventionally can be challenging. In the past few years, stereo-photogrammetry and three-dimensional digital image correlation have received special attention in collecting operating data for structural analysis. These non-contact optical techniques provide a wealth of distributed data over the entire structure.

This paper aims to study the NVH and acoustic performance of a 3-phase AC induction motor in order to develop an approach to reduce the magnetic component of noise from an electric motor in an electric vehicle (EV). The final goal of this project is to reduce the magnetic component of sound from the motor by making modifications to the end bracket of the motor housing. EVs are being considered the future of mobility mainly due to the fact that they are environment-friendly. As many companies are already investing in this technology, electric drives are set to become extremely popular in the years to come. The heart of an EV is its motor. Modern electric vehicles are quiet, furthermore with the lack of an IC engine to mask most sounds from other components, the sound from the electric motor and other auxiliary parts become more prominent.

Automakers looking to remake their traditional vehicle line-up into autonomous vehicles, Noise, Vibration, and Harshness (NVH) considerations for autonomous vehicles are soon to follow. While traditional NVH considerations still must be applied to carry-over systems, additional components are required for an autonomous vehicle to operate. These additional components needed for autonomy also require NVH analysis and optimization. Autonomous vehicles rely on a suite of sensors, including Light Detection and Ranging (LiDAR) and cameras placed at optimal points on the vehicle for maximum coverage and utilization. In this study, the NVH considerations of autonomous vehicles are examined, focusing on the additional perception equipment installed in autonomous vehicles.

Kettering University has developed a cleaner and quieter snowmobile using technologies and innovative methods which can be applied in the real world with a minimal increase in cost. Specifically, a commercially available snowmobile using a two cylinder, four-stroke engine has been modified to run on high-blend ethanol (E-85) fuel. Further, a new exhaust system which features customized catalytic converters and mufflers to minimize engine noise and exhaust emissions has developed. A number of additional improvements have been made to the track to reduce friction and diminish noise. This paper provides details of the snowmobile development the results of these efforts on performance and emissions. Specifically, the Kettering University snowmobile achieved reductions of approximately 72% in CO, and 98% in HC+NOx when compared with the 2012 standard. Further, the snowmobile achieved a drive by noise level of 73 dbA while operating on hard packed snow.