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It is well known that the ride quality of trucks is much harsher than that of automobiles. Additionally, truck drivers typically drive trucks for much longer duration than automobile drivers. These two factors contribute to the fatigue that a truck driver typically experiences during long haul deliveries. Fatigue reduces driver alertness and increases reaction times, increasing the possibility of an accident. One may conclude that better ride quality contributes to safer operation. The secondary suspensions of a tractor have been an area of particular interest because of the considerable ride comfort improvements they provide. A gap exists in the current engineering domain of an easily configurable high fidelity low computational cost simulation tool to analyze the ride of a tractor semi-trailer. For a preliminary design study, a 15 d.o.f. model of the tractor semi-trailer was developed to simulate in the Matlab/Simulink environment.

The research objective was to measure and understand the preferred seat position of older drivers and younger drivers within their personal vehicles to influence recommended practices and meet the increased safety needs of all drivers. Improper selection of driver’s seat position may impact safety during a crash event and affect one’s capacity to see the roadway and reach the vehicle’s controls, such as steering wheel, accelerator, brake, clutch, and gear selector lever. Because of the stature changes associated with ageing and the fact that stature is normally distributed for both males and females, it was hypothesized that the SAE J4004 linear regression would be improved with the inclusion of gender and age terms that would provide a more accurate model to predict the seat track position of older drivers. Participants included 97 older drivers over the age of 60 and 20 younger drivers between the ages of 30 to 39.

In this paper, a soft computing approach to a model-free vehicle stability control (VSC) algorithm is presented. The objective is to create a fuzzy inference system (FIS) that is robust enough to operate in a multitude of vehicle conditions (load, tire wear, alignment), and road conditions while at the same time providing optimal vehicle stability by detecting and minimizing loss of traction. In this approach, an adaptive neuro-fuzzy inference system (ANFIS) is generated using previously collected data to train and optimize the performance of the fuzzy logic VSC algorithm. This paper outlines the FIS detection algorithm and its benefits over a model-based approach. The performance of the FIS-based VSC is evaluated via a co-simulation of MATLAB/Simulink and CarSim model of the vehicle under various road and load conditions. The results showed that the proposed algorithm is capable of accurately indicating unstable vehicle behavior for two different types of vehicles (SUV and Sedan).

This paper presents the application of a proposed fuzzy inference system as part of a stability control design scheme implemented with active steering actuator sets. The fuzzy inference system is used to detect the level of overseer/understeer at the high level and a speed-adaptive activation module determines whether an active front steering, active rear steering, or active 4 wheel steering is suited to improve vehicle handling stability. The resulting model-free system is capable of minimizing the amount of model calibration during the vehicle stability control development process as well as improving vehicle performance and stability over a wide range of vehicle and road conditions. A simulation study will be presented that evaluates the proposed scheme and compares the effectiveness of active front steer (AFS) and active rear steer (ARS) in enhancing the vehicle performance. Both time and frequency domain results are presented.

This set consists of three books, Design of Automotive Composites, CAE Design and Failure Analysis of Automotive Composites, and Biocomposites in Automotive Applications all developed by Dr. Charles Lu and Dr. Srikanth Pilla. Design of Automotive Composites reports successful designs of automotive composites occurred recently in this arena, CAE Design and Failure Analysis of Automotive Composites focuses on the latest use of CAE (Computer-Aided Engineering) methods in design and failure analysis of composite materials and structures, and Biocomposites in Automotive Applications, focuses on processing and characterization of biocomposites, their application in the automotive industry and new perspectives on automotive sustainability. Together, they are a focused collection providing the reader with must-read technical papers, hand-picked by the editors, supporting the growing importance of the use of composites in the ground vehicle industry. Dr. Charles Lu is H.E.

The automotive sector has taken a keen interest in lightweighting as new required performance standards for fuel economy come into place. This strategy includes parts consolidation, design optimization, and material substitution, with sustainable polymers playing a major role in reducing a vehicle’s weight. Sustainable polymers are largely biodegradable, biocompatible, and sourced from renewable plant and agricultural stocks. A facile way to enhance their properties, so they can indeed replace the ones made from fossil fuels, is by reinforcing them with fibers to make composites. Natural fibers are gaining more acceptance in the industry due to their renewable nature, low cost, low density, low energy consumption, high specific strength and stiffness, CO2 sequestration potential, biodegradability, and less wear imposed on machinery. Biocomposites then become a very feasible way to help address the fuel consumption challenge ahead of us.

Electric vehicles are receiving considerable attention because they offer a more efficient and sustainable transportation alternative compared to conventional fossil-fuel powered vehicles. Since the battery pack represents the primary energy storage component in an electric vehicle powertrain, it requires accurate monitoring and control. In order to effectively estimate the battery pack critical parameters such as the battery state of charge (SOC), state of health (SOH), and remaining capacity, a high-fidelity battery model is needed as part of a robust SOC estimation strategy. As the battery degrades, model parameters significantly change, and this model needs to account for all operating conditions throughout the battery's lifespan. For effective battery management system design, it is critical that the physical model adapts to parameter changes due to aging.

The components in a hybrid electric vehicle (HEV) powertrain include the battery pack, an internal combustion engine, and the electric machines such as motors and possibly a generator. These components generate a considerable amount of heat during driving cycles. A robust thermal management system with advanced controller, designed for temperature tracking, is required for vehicle safety and energy efficiency. In this study, a hybridized mid-size truck for military application is investigated. The paper examines the integration of advanced control algorithms to the cooling system featuring an electric-mechanical compressor, coolant pump and radiator fans. Mathematical models are developed to numerically describe the thermal behavior of these powertrain elements. A series of controllers are designed to effectively manage the battery pack, electric motors, and the internal combustion engine temperatures.

According to the National Highway Traffic Safety Administration (NHTSA), motor collisions account for nearly 2.4 million injuries and 37 thousand fatalities each year in the United States. A great deal of research has been done in the area of vehicular safety, but very little has been completed to ensure licensed drivers are properly trained. Given the inherent risks in driving itself, the test for licensure should be uniform and consistent. To address this issue, an inexpensive, portable data acquisition and analysis system has been developed for the evaluation of driver performance. A study was performed to evaluate the system, and each participant was given a normalized driver rating. The average driver rating was μ=55.6, with a standard deviation of σ=12.3. All but 3 drivers fell into the so-called “Target Zone”, defined by a Driver Rating of μ± 1σ.

Driver modeling is essential to both vehicle design and control unit development. It can improve the understanding of human driving behavior and decrease the cost and risk of vehicle system verification and validation. In this paper, three driver models were implemented to simulate the behavior of drivers subject to a run-off-road recovery event. Target path planning, pursuit behavior, compensate behavior, physical limitations, and neuromuscular modeling were taken into consideration in the feedforward/feedback driver model. A transfer function driver model and a cost function based driver model from a popular vehicle simulation software were also simulated and a comparison of these three models was made. The feedforward/feedback driver model exhibited the best balance of performance with smallest overshoot (0.226m), medium settling time (1.20s) and recovery time (4.30s).

Driving skills and driving experience develop differently between a civilian and a military service member. Since 2000, the Department of Defense reports that two-thirds of non-related to war fatalities among active duty service members were due to transportation-related incidents. In addition, vehicle crashes are the leading non-related to war cause of both fatalities and serious injuries among active duty Marines. A pilot safe driving program for Marines was jointly developed by the Richard Petty Driving Experience and Clemson University Automotive Safety Research Institute. The pilot program includes four modules based on leading causes of vehicle crashes, and uses classroom and behind the wheel components to improve and reinforce safe driving skills and knowledge. The assessment results of this pilot program conducted with 192 Marines in September 2011 at Camp LeJeune, NC are presented and discussed.

Composites are now extensively used in applications where outstanding mechanical properties are necessary in combination with weight savings, due to their highly tunable microstructure and mechanical properties. These properties present great potential for part integration, which results in lower manufacturing costs and faster time to market. Composites also have a high level of styling flexibility in terms of deep drawn panel, which goes beyond what can be achieved with metal stampings. The so-called multifunctional or smart composites provide significant benefits to the vehicles as compared to the traditional materials that only have monotonic properties.

Motor vehicle crashes involving novice drivers are significantly higher than matured driver incidents as reported by the National Highway Traffic Safety Administration Fatality Analysis Reporting System (NHTSA-FARS). Researchers around the world and the United States are focused on how to decrease crashes for this driver demographic. Novice drivers usually complete driver education classes as a pre-requisite for full licensure to improve overall knowledge and safety. However, compiled statistics still indicate a need for more in-depth training after full licensure. An opportunity exists to supplement in-vehicle driving with focused learning modules using automotive simulators. In this paper, a training program for “Following Etiquette” and “Situational Awareness” was developed to introduce these key driving techniques and to complete a feasibility study using a driving simulator as the training tool.

Hydrostatic (hydraulic hybrid) drives have demonstrated energy efficiency and emissions reduction benefits. This paper investigates the potential of an independent hydrostatic wheel drive system for implementing a traction-based vehicle lateral stability control system. The system allows an upper level vehicle stability controller to produce a desired corrective yaw moment via a differential distribution of torque to the independent wheel motors. In cornering maneuvers that require braking on any one wheel of the vehicle, the motors can be operated as pumps for re-generating energy into an on-board accumulator. This approach avoids or reduces activation of the friction brakes, thereby reducing energy waste as heat in the brake pads and offering potential savings in brake maintenance costs. For this study, a model of a 4×4 hydrostatic independent wheel drive system is constructed in a causal and modular fashion and is coupled to a 7 DOF vehicle handling dynamics model.

In an effort to build a shear band of a lunar rover wheel which operates at lunar surface temperatures (40 to 400K), the design of a metallic cellular shear band is suggested. Six representative honeycombs with aluminum alloy (7075-T6) are tailored to have a shear modulus of 6.5MPa which is a shear modulus of an elastomer by changing cell wall thickness, cell angles, cell heights and cell lengths at meso-scale. The designed cellular solids are used for a ring typed shear band of a wheel structure at macro-scale. A structural performance such as contact pressure at the outer layer of the wheel is investigated with the honeycomb shear bands when a vertical force is applied at the center of the wheel. Cellular Materials Theory (CMT) is used to obtain in-plane effective properties of a honeycomb structure at meso-scale. Finite Element Analysis (FEA) with commercial software ABAQUS is employed to investigate the structural behavior of a wheel at macro-scale.

Recent research at Clemson University has focused on the development of an advanced non-pneumatic, non-elastomeric lunar wheel for NASA with superior traction. This paper reports on several concepts for tread materials and geometries that have been explored for tire-on-sand use. Specifically, fourteen concepts, involving the use of metal meshes, textile carpet materials, soft grousers, foams, and screens, were physically tested in an on-vehicle environment. Prototypes for each concept and formal test procedures to quantify traction were developed. This paper presents the results of the tests for several different concepts and the comparison between the concepts that were developed. Students developed their own testing environment through which these test procedures are implemented, an inclined hill 45 ft. in length and 8 ft. wide will approximately 6 inches deep filled with sand.

Three-dimensional simulation has become an indispensable approach to develop improved understanding of ring rolling technology, with validity as the basic requirement of the ring rolling simulation. Cold ring rolling is simple conceptually, however complex to analyze as the metal forming process is subject to coupled effects with multiple influencing factors such as sizes of rolls and ring blank, form geometry, material, process parameters, and frictional effects. Investigating the coupled thermal and plastic deformation behavior (the plastic deformation state and its development) in the deformation zone during the process is significant for predicting metal flow in order to control the geometric and tensile residual stress quality of deformed rings, and to provide for cycle time optimization of the cold ring rolling process.

To facilitate the design of a non-pneumatic tire for NASA's new Moon mission, the authors used the Finite Element Method (FEM) to investigate the interaction between soil and non-pneumatic tire made of different cellular shear bands. Cellular shear bands, made of an aluminum alloy (AL7075-T6), are designed to have the same effective shear modulus of 6.5E+6 Pa, which is the shear modulus of an elastomer. The Lebanon sand of New Hampshire is used in the model. This sand has a complete set of material properties in the literature and Drucker-Prager/Cap plasticity constitutive law with hardening is employed to model the sand. The tires are treated as deformable bodies, and the authors used the penalty contact algorithm to model the tangential behavior of the contact. The friction between tire and sand is considered by using Coulomb's law. Numerical results show deformation of sand and tire.

This research examines a Discrete Element Method (DEM) for modeling the behavior of sand under various loading conditions as a critical first step in developing computational tools to aid in designing new sand-tire interaction systems for improved traction and mobility. Sand as a material is challenging to model computationally due to its unusual behavior: sometimes resembling a fluid and sometimes behaving more like a solid, yet never exactly replicating either. This behavior arises from the particulate nature of sand which, in contrast to the systems typically modeled in continuum mechanics, is not readily represented by continuum models. In sand, elements (i.e. particles) do not have permanent associations with neighboring elements as they do in most continua, but rather are free to migrate anywhere in the domain according to their interactions with other elements.

The presented manuscript discusses the implementation of the pulsed-thermography technique for the non-intrusive evaluation of automotive parts. The study discusses the fundamentals of static and dynamic thermography through examples and case studies. Furthermore, the proposed pulse thermography system is analyzed in terms of hardware calibration i.e. pulse duration and intensity and the detector effect on the time and the spatial resolutions. Current thermography processing codes and techniques are also described and critiqued, with new processing subroutines proposed; one based on self-referencing thersholding. Additionally, new trends in infrared and visible sensors fusion are presented.