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This study pertains to motion control algorithms using statistical calculations based on relative displacement measurements, in particular where the rattle space is strictly limited by fixed end-stops and a load leveling system that allows for roll to go undetected by the sensors. One such application is the cab suspension of semi trucks that use widely-spaced springs and dampers and a load leveling system that is placed between the suspensions, near the center line of the cab. In such systems it is possible for the suspension on the two sides of the vehicle to settle at different ride heights due to uneven loading or the crown of the road. This paper will compare the use of two moving average signals (one positive and one negative) to the use of one root mean square (RMS) signal, all calculated based on the relative displacement measurement.

This paper presents a unified modeling environment to simulate vehicle driving and powertrain operations within the context of the surrounding environment, including interactions between vehicles and between vehicles and the road. The goal of this framework is to facilitate the analysis of the energy impacts of vehicle connectivity and automation, as well as the development of eco-driving algorithms. Connectivity and automation indeed provide the potential to use information about the environment and future driving to minimize energy consumption. To achieve this goal, the designers of eco-driving control strategies need to simulate a wide range of driving situations, including the interactions with other vehicles and the infrastructure in a closed-loop fashion.

Animal-vehicle collision (AVC) is a significant safety issue on American roads. Each year approximately 1.5 million AVCs occur in the U.S., the majority of them involving deer. The increasing use of cameras and radar on vehicles provides opportunities for prevention or mitigation of AVCs, particularly those involving deer or other large animals. Developers of such AVC avoidance/mitigation systems require information on the behavior of encountered animals, setting characteristics, and driver response in order to design effective countermeasures. As part of a larger study, naturalistic driving data were collected in high AVC incidence areas using 48 participant-owned vehicles equipped with data acquisition systems (DAS). Continuous driving data including forward video, location information, and vehicle kinematics were recorded. The respective 11TB dataset contains 35k trips covering 360K driving miles.

The Unmanned Ground Vehicle Competition is jointly sponsored by the SAE, the Association for Unmanned Vehicle Systems (AUVS), and Oakland University. College teams, composed of both undergraduate and graduate students, build autonomous vehicles that compete by navigating a 139 meter outdoor obstacle course. The course, which includes a sand pit and a ramp, is defined by painted continuous or dashed boundary lines on grass and pavement. The obstacles are arbitrarily placed, multi-colored plastic-wrapped hay bales. The vehicles must be between 0.9 and 2.7 meters long and less than 1.5 meters wide. They must be either electric-motor or combustion-engine driven and must carry a 9 kilogram payload. All computational power, sensing and control equipment must be carried on board the vehicle. The technologies employed are applicable in Intelligent Transportation Systems (ITS).

This research investigates the energy savings achieved through eco-driving controls in connected and automated vehicles (CAVs), with a specific focus on the influence of powertrain characteristics. Eco-driving strategies have emerged as a promising approach to enhance efficiency and reduce environmental impact in CAVs. However, uncertainty remains about how the optimal strategy developed for a specific CAV applies to CAVs with different powertrain technologies, particularly concerning energy aspects. To address this gap, on-track demonstrations were conducted using a Chrysler Pacifica CAV equipped with an internal combustion engine (ICE), advanced sensors, and vehicle-to-infrastructure (V2I) communication systems, compared with another CAV, a previously studied Chevrolet Bolt electric vehicle (EV) equipped with an electric motor and battery.

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 availability of active safety systems, such as Lane Departure Warning (LDW), has recently been added as a rating factor in the U.S. New Car Assessment Program (NCAP). The objective of this study is to determine the relevance of the NCAP LDW confirmation test to real-world road departure crashes. This study is based on data collected as part of supplemental crash reconstructions performed on 890 road departure collisions from the National Automotive Sampling System, Crashworthiness Data System (NASS/CDS). Scene diagrams and photographs were examined to determine the lane departure and lane marking characteristics not available in the original data. The results suggest that the LDW confirmation test captures many of the conditions observed in real-world road departures. For example, 40% of all single vehicle collisions in the dataset involved a drift-out-of-lane type of departures represented by the test.

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.

Climate change due to greenhouse gas emissions has led to new vehicle emissions standards which in turn have led to a call for vehicle technologies to meet these standards. Modeling of vehicle fuel consumption and emissions emerged as an effective tool to help in developing and assessing such technologies, to help in predicting aggregate vehicle fuel consumption and emissions, and to complement traffic simulation models. The paper identifies the current state of the art on vehicle fuel consumption and emissions modeling and its utilization to test the environmental impact of the Intelligent Transportation Systems (ITS)’ measures and to evaluate transportation network improvements. The study presents the relevant models to ITS in the key classifications of models in this research area. It demonstrates that the trends of vehicle fuel consumption and emissions provided by current models generally do satisfactorily replicate field data trends.

A sustainable activity is one that is economically attractive, environmentally friendly and provides a beneficial service to society in a safe and responsible manner. Having a sustainable operation is a target that today’s industries are striving to attain. The automotive industry and its products are major users of natural resources and a source of greenhouse emissions. In order to reduce its energy consumption and greenhouse emissions the industry is using more lightweighting materials in manufacturing its products. These materials include polymers, composites, aluminum and magnesium. The increased interest in hybrid vehicles will increase the need for new materials such as lithium, cobalt and nickel. At the same time, regulations are calling for recycling more of the obsolete vehicles. Replacing the steel, which is recyclable, with lighter materials will result in a reduction in the recycling rate of vehicles unless the lightweighting materials are recycled.

Reductions in vehicle drive losses are as important to improving fuel economy as increases in powertrain efficiencies. In order to measure vehicle fuel economy, chassis dynamometer testing relies on accurate road load determinations. Road load is currently determined (with some exceptions) using established test track coastdown testing procedures. Because new vehicle technologies and usage cases challenge the accuracy and applicability of these procedures, on-road experiments were conducted using axle torque sensors to address the suitability of the test procedures in determining vehicle road loads in specific cases. Whereas coastdown testing can use vehicle deceleration to determine load, steady-state testing can offer advantages in validating road load coefficients for vehicles with no mechanical neutral gear (such as plug-in hybrid and electric vehicles).

This paper investigates the aerodynamic influence of multiple on-highway trucks in different platooning configurations. Complex pressure fields are generated on the highways due to interference of multiple vehicles. This pressure field causes an aerodynamic drag to be different than the aerodynamic drag of a vehicle in a no-traffic condition. In order to study the effect of platooning, three-dimensional modeling and numerical simulations were performed using STAR-CCM+® commercial Computational Fluid Dynamics (CFD) tool. The aerodynamic characteristics of vehicles were analyzed in five different platooning configurations with two and three vehicles in single and multiple lanes. A significant Yaw Averaged Aerodynamic Drag (YAD) reduction was observed in both leading and trailing vehicles. YAD was based on the average result of three different yaw angles at 0°, −6° and 6°. In single-lane traffic, YAD reduction was up to 8% and 38% in leading and trailing vehicles, respectively.

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.

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.

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.

The process of shredding end-of-life vehicles to recover metals results in a byproduct commonly referred to as shredder residue. The four and a half million metric tons of shredder residue produced annually in the United States is presently land filled. To meet the challenges of automotive materials recycling, the U.S. Department of Energy is supporting research at Argonne National Laboratory in cooperation with the Vehicle Recycling Partnership (VRP) of the United States Council for Automotive Research (USCAR) and the American Plastics Council. This paper presents the results of a study that was conducted by Argonne to determine variations in the composition of shredder residue from different shredders. Over 90 metric tons of shredder residues were processed through the Argonne pilot plant. The contents of the various separated streams were quantitatively analyzed to determine their composition and to identify materials that should be targeted for recovery.

Connected and automated technologies poised to change the way vehicles operate are starting to enter the mainstream market. Methods to accurately evaluate these technologies, in particular for their impact on safety and energy, are complex due to the influence of static and environmental factors, such as road environment and traffic scenarios. Therefore, it is important to develop modeling and testing frameworks that can support the development of complex vehicle functionalities in a realistic environment. Microscopic traffic simulations have been increasingly used to assess the performance of connected and automated vehicle technologies in traffic networks. In this paper, we propose and apply an evaluation method based on a combination of microscopic traffic simulation (AIMSUN) and a chassis dynamometer-based vehicle-in-the-loop environment, developed at Argonne National Laboratory.

This paper presents the energy savings of an automated driving control applied to an electric vehicle based on the on-track testing results. The control is a universal speed planner that analytically solves the eco-driving optimal control problem, within a receding horizon framework and coupled with trajectory tracking lower-level controls. The automated eco-driving control can take advantage of signal phase and timing (SPaT) provided by approaching traffic lights via vehicle-to-infrastructure (V2I) communications. At each time step, the controller calculates the accelerator and brake pedal position (APP/BPP) based on the current state of the vehicle and the current and future information about the surrounding environment (e.g., speed limits, traffic light phase).

As emerging automated vehicle technology is making advances in safety and reliability, engineers are also exploring improvements in energy efficiency with this new paradigm. Powertrain efficiency receives due attention, but also impactful is finding ways to reduce driving losses in coordinated-driving scenarios. Efforts focused on simulation to quantify road load improvements require a sufficient amount of background validation work to support them. This study uses a practical approach to directly quantify road load changes by testing the coordinated driving of two vehicles on a test track at various speeds (64, 88, 113 km/h) and vehicle time gaps (0.3 to 1.3 s). Axle torque sensors were used to directly measure the load required to maintain steady-state speeds while following a lead vehicle at various gap distances.

Over 250 million vehicles are operating on United States roads and highways and over 12 million of them reach the end of their useful lives annually. These end-of-life vehicles (ELVs) contain over 24 million tons (21.8 million metric tonnes) of materials including ferrous and non-ferrous metals, polymers, glass, and automotive fluids. They also contain many parts and components that are still useable and some that could be economically rebuilt or remanufactured. Dismantlers acquire the ELVs and recover from them parts for resale “as-is” or after remanufacturing. The dismantler then sells what remains of the vehicle, the “hulk”, to a shredder who shreds it to recover and sell the metals. Presently, the remaining non-metallic materials, commonly known as shredder residue, are mostly landfilled. The vehicle manufacturers, now more than ever, are working hard to build more energy efficient and safer, more affordable vehicles.