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A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. An integral part of this System is an Intervention Strategy that uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. Through this strategy, the driver's commands are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority. Real-time implementation requires the development of computationally efficient predictive vehicle models. This work develops one means to alter the future vehicle states: modulating the driver's brake commands. This control strategy must be considered in relationship to changes in the throttle commands. Three key elements of this strategy are developed in this work.

A Location-Aware Adaptive Vehicle Dynamics System (LAAVDS) is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. An Intervention Strategy uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. The driver's commands are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority. Real-time implementation requires the development of computationally efficient predictive vehicle models which is the focus of this work. This work develops one means to alter the future vehicle states: modulating the driver's throttle commands. First, changes to the longitudinal force are translated to changes in engine torque based on the current operating state (torque and speed) of the engine.

One seminal question that faces a vehicle's driver (either human or computer) is predicting the capability of the vehicle as it encounters upcoming terrain. A Location-Aware Adaptive Vehicle Dynamics (LAAVD) System is developed to assist the driver in maintaining vehicle handling capabilities through various driving maneuvers. In contrast to current active safety systems, this system is predictive rather than reactive. This work provides the conceptual groundwork for the proposed system. The LAAVD System employs a predictor-corrector method in which the driver's input commands (throttle, brake, steering) and upcoming driving environment (terrain, traffic, weather) are predicted. An Intervention Strategy uses a novel measure of handling capability, the Performance Margin, to assess the need to intervene. The driver's throttle and brake control are modulated to affect desired changes to the Performance Margin in a manner that is minimally intrusive to the driver's control authority.

The Cummins M-11 high soot diesel engine test is a key tool in evaluating lubricants for the new PC-7 (CH-4) performance category. M-11 rocker arms and crossheads from tests with a wide range of lubricant performance were studied by surface analytical techniques. Abrasive wear by primary soot particles is supported by the predominant appearance of parallel grooves on the worn parts with their widths matching closely the primary soot particle sizes. Soot abrasive action appears to be responsible for removing the protective antiwear film and, thus, abrades against metal parts as well. Subsequent to the removal of the antiwear film, carbide particles, graphite nodules, and other wear debris are abraded, either by soot particles or sliding metal-metal contact, from the crosshead and rocker arm metal surfaces. These particles further accelerate abrasive wear. In addition to abrasive wear, fatigue wear was evident on the engine parts.

One Dimensional models for front end air flows through the cooling system package are very useful for evaluating the effects of component and front end geometry changes. To solve such models for the air flow requires a robust iterative process that involves a number of non-linear sub-models. The cooling fan (s) constitute a major part of the difficulty, especially when they employ a viscous or “thermal” fan drive. This drive varies the torque coupling between the input and output shafts based on the radiator outlet air temperature. The coupling is achieved by viscous shear between two grooved disks and is regulated by a bimetal strip valve that varies the amount of fluid between the disks. This paper presents a mathematical model by which the input/output speed ratio may be determined as a function of the air temperature and input speed. Coefficients in the model are estimated from standard supplier performance information.

The Hybrid Electric Vehicle Team of Virginia Tech (HEVT) is participating in the 2009 - 2011 EcoCAR: The NeXt Challenge Advanced Vehicle Technology Competition series organized by Argonne National Lab (ANL), and sponsored by General Motors Corporation (GM), and the U.S. Department of Energy (DOE). Following GM's Vehicle Development Process (VDP), HEVT established team goals that meet or exceed the competition requirements for EcoCAR in the design of a plug-in extended-range hybrid electric vehicle. The competition requires participating teams to improve and redesign a stock Vue XE donated by GM. The result of this design process is an Extended-Range Electric Vehicle (E-REV) that uses grid electric energy and E85 fuel for propulsion. The vehicle design is predicted to achieve an SAE J1711 utility factor corrected fuel consumption of 2.9 L(ge)/100 km (82 mpgge) with an estimated all electric range of 69 km (43 miles) [1].

Selectively reinforced, squeeze cast automotive pistons contain a boundary between the reinforced and unreinforced regions. This boundary is known as the macro-interface. Due to the difference in CTE between the composite and unreinforced matrix, the macro-interface can be the site of residual stress formation during cooling from the casting or heat treatment temperature. Subsequent thermal exposure, particularly thermal cycling, may produce cyclic stress at this interface causing it to experience fatigue. It has been found that matrix precipitates at the macro-interface and the aging behavior of the matrix also may play a role in defining the strength of the macro-interface during thermal cycling conditions.

The response and risk of injury for occupants in frontal crashes are more severe when structural deformation occurs in the vehicle interior. To reproduce this impact environment in the laboratory, a sled system capable of producing structural intrusion in the footwell region has been developed. The system couples the hydraulic decelerator of the sled to actuator pistons attached to the toepan and floorpan structure of the buck. Characterization of the footwell intrusion event is based on developing a toepan pulse analogous to the acceleration pulse used to characterize sled and vehicle decelerations. Preliminary sled tests with the system indicate that it is capable of simulating a complex sequence of toepan/floorpan translations and rotations.

Two sled systems capable of producing structural intrusion in the footwell region of an automobile have been developed. The first, System A, provides translational toepan intrusion using actuator pistons to drive the footwell structure of the test buck. These actuator pistons are coupled to the hydraulic decelerator of the test sled and are powered by hydraulic energy from the impact event. Resulting footwell intrusion is characterized using a toepan pulse analogous to the acceleration pulse used to characterize sled and vehicle decelerations. Sled tests with System A indicate that it is capable of accurately and repeatably simulating toepan/floorpan intrusion into the occupant footwell. Test results, including a comparison of lower extremity response between intrusion sled tests and no intrusion sled tests, indicate that this system is capable of repeatable, controlled structural intrusion during a sled test impact.

This paper examines an originally designed airbag deployment system for use in static experimental testing. It consists of a pressure vessel and valve arrangement with pneumatic and electric controls. A piston functions like a valve when operated and is activated pneumatically to release the air in the tank. Once released, the air fills the attached airbag. The leading edge velocity can be controlled by the initial pressure in the tank, which can range up to 960 kPa. Three different test configurations were studied, which resulted in leading edge deployment speeds of approximately 20 m/s, 40 m/s, and 60 m/s. In experiments using this system, seven types of airbags were tested that differed in their material, coating, and presence of a tether. Data for each series of tests is provided. High speed video and film were used to record the deployments, and a pressure transducer measured the airbag's internal pressure.

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 the energy consumption of a vehicle is equally as important. This paper presents a methodology that has been packaged into a tool, called VTool (short for Vehicle Tool), which 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 and losses while still providing reasonably accurate results. This paper briefly explains the methodology of VTool and demonstrates the capability of VTool as a design tool by presenting 4 example exercises.

Advanced design of modern engine cooling and vehicle HVAC components involves sophisticated simulation. In particular, front end air flow models must be able to cover the complete range of conditions from idle to high road speeds involving multiple fans of varying types both powered and unpowered. This paper presents a model for electric radiator cooling fans which covers the complete range of powered and unpowered (freewheel) operation. The model applies equally well to mechanical drive fans.

The pre-prototype development of a simulated rule-based hybrid energy management strategy for a 2019 Chevrolet Blazer RS converted parallel P4 full hybrid is presented. A vehicle simulation model is developed using component bench data and validated using EPA-reported dynamometer fuel economy test data. A combined Willans line model is proposed for the engine and transmission, with hybrid control rules based on efficiency-derived engine power thresholds. Algorithms are proposed for battery state of charge (SOC) management including engine loading and one pedal strategies, with battery SOC maintained within 20% to 80% safe limits and charge balanced behavior achieved. The simulated rule-based hybrid control strategy for the hybrid vehicle has an energy consumption reduction of 20% for the Hot 505, 3.6% for the HwFET, and 12% for the US06 compared to the stock vehicle.

The influence of driver modeling and drive cycle target speed trace modification on vehicle dynamics within energy consumption simulations is studied. EPA dynamometer speed error criteria and the SAE J2951 Drive Quality Evaluation for Chassis Dynamometer Testing standard are applied to simulation outputs as proposed components of simulation validation, providing guidelines for acceptable vehicle speed outputs and allowing comparison of simulation results to reported EPA dynamometer test statistics. The combined effect of driver model tuning and drive cycle interpolation methods is investigated for the UDDS, HwFET and US06 drive cycles, with EPA-specified linearly interpolated speed trace and a PI controller driver as a baseline result.

The application of Computational Fluid Dynamics (CFD) for three-dimensional (3D) combustion analysis coupled with detailed chemistry in engine development is hindered by its expensive computational cost. Chemistry computation may occupy as much as 90% of the total computational cost. In the present paper, a new two-step iso-octane combustion model was developed for spark-ignited (SI) engine to maximize computational efficiency while maintaining acceptable accuracy. Starting from the model constants of an existing global combustion model, the new model was developed using an approach based on sensitivity analysis to approximate the results of a reference skeletal mechanism. The present model involves only five species and two reactions and utilizes only one uniform set of model constants. The validation of the new model was performed using shock tube and real SI engine cases.

An unconventional conceptual design of truck chassis with multi-functional cross-members is proposed, and an optimization framework is developed to optimize its structure to minimize mass while satisfying stiffness and modal frequency constraints. The side rails are C-sectional channels of variable height and were divided into six sections, each with different thickness distribution for the flanges and the web. The gearbox cross-member and the intermediate cross-members are compressed-air cylinders, and hence they act as multi-functional components. The dimensions and thickness of the side rails and the air-tank cross members are defined by a set of parameters which are considered as design variables in the optimization problem. The structure consists of three additional fixed cross-members which are modeled using beam elements. The limits of the design variables are decided while considering manufacturing limits.

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is excited about the opportunity to apply for participation in the next Advanced Vehicle Technology Competition. EcoCAR 3 is a new four year competition sponsored by the Department of Energy and General Motors with the intention of promoting sustainable energy in the automotive sector. The goal of the competition is to guide students from universities in North America to create new and innovative technologies to reduce the environmental impact of modern day transportation. EcoCAR 3, like its predecessors, will give students hands-on experience in designing and implementing advanced technologies in a setting similar to that of current production vehicles.

Due to increased urgency regarding environmental concerns within the transportation industry, sustainable solutions for combating climate change are in high demand. One solution is a widespread transition from internal combustion engine vehicles (ICEVs) to battery electric vehicles (BEVs). To facilitate this transition, reliable energy consumption modeling is desired for providing quick, high-level estimations for a BEV without requiring extensive vehicle and computational resources. Therefore, the goal of this paper is to create a simple, yet reliable vehicle model, that can estimate the energy consumption of most electric vehicles on the market by using parameter normalization techniques. These vehicle parameters include the vehicle test weight and performance to obtain a unified net Willans line to describe the input/output power using a linear relationship.

Pedal misapplication (PM) crashes, i.e., crashes caused by a driver pressing one pedal while intending to press another pedal, have historically been identified by searching unstructured crash narratives for keywords and verified via labor-intensive manual inspection. This study proposes an alternative method to identify PM crashes using event data recorders (EDRs). Since drivers in emergency braking situations are motivated to hit the brake hard, it follows that drivers in emergency braking situations that commit a PM would likewise hit the accelerator hard, likely harder than accelerator pedal application during normal driving. Thus, the time-series accelerator pedal position and the derived accelerator pedal application rate were used to isolate accelerator misapplications. Additional strategic filters were applied based on characteristics observed from previous PM analyses to reduce false positive PM identifications.

The rise of Software-Defined Vehicles (SDV) has rapidly advanced the development of Advanced Driver Assistance Systems (ADAS), Autonomous Vehicle (AV), and Battery Electric Vehicle (BEV) technology. While AVs need power to compute data from perception to controls, BEVs need the efficiency to optimize their electric driving range and stand out compared to traditional Internal Combustion Engine (ICE) vehicles. AVs possess certain shortcomings in the current world, but SAE Level 2+ (L2+) Automated Vehicles are the focus of all major Original Equipment Manufacturers (OEMs). The most common form of an SDV today is the amalgamation of AV and BEV technology on the same platform which is prominently available in most OEM’s lineups. As the compute and sensing architectures for L2+ automated vehicles lean towards a computationally expensive centralized design, it may hamper the most important purchasing factor of a BEV, the electric driving range.