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Recovering metals from obsolete automobiles, home appliances, and other metal-containing obsolete durables and other scrap involves shredding these objects and separating the reusable metals from the shredded material by using magnets, eddy current separators, and metal detectors. Over 12 million automobiles are shredded annually in the United States alone, and almost all of the 4.5 million metric tonnes (5 million short tons) of the shredder residue produced in the United States annually is disposed of in landfills. Over 13.6 million tonnes (15 million tons) of shredder residue is generated worldwide every year. The rise in disposal costs is further exacerbated in that the percentage of shredder residue that must be disposed of, in comparison with the percentage of marketable recovered metals, is increasing because of the increasing content of polymers in automobiles and in home appliances.

Argonne National Laboratory (Argonne) and Idaho National Laboratory (INL), working with the FreedomCAR and Fuels Partnership, lead activities in vehicle dynamometer and fleet testing as well as in modeling activities. By using Argonne’s Advanced Powertrain Research Facility (APRF), the General Motors (GM) Tahoe 2-mode was instrumented and tested in the 4-wheel-drive test facility. Measurements included both sensors and controller area network (CAN) messages. In this paper, we describe the vehicle instrumentation as well as the test results. On the basis of the analysis performed, we discuss the vehicle model developed in Argonne’s vehicle simulation tool, the Powertrain System Analysis Toolkit (PSAT), and its comparison with test data. Finally, on-road vehicle data, performed by INL, is discussed and compared with the dynamometer results.

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.

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 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.

Vehicle emissions and fuel economy testing applications rely on accurate sensors to track power flow and measure component efficiencies. A non-intrusive in-vehicle torque sensor has been implemented in a hybrid powertrain to directly measure engine torque. Previously used off-the-shelf torque sensors required additional mechanical space, and so chassis modifications were needed to accommodate the sensor, which potentially limited the vehicle to only dynamometer testing. The challenges in implementing this type of sensor in automotive environments are described in detail, as are sensor capabilities and test results.

Modern vehicles use automated driving assistance systems (ADAS) products to automate certain aspects of driving, which improves operational safety. In the U.S. in 2020, 38,824 fatalities occurred due to automotive accidents, and typically about 25% of these are associated with inclement weather. ADAS features have been shown to reduce potential collisions by up to 21%, thus reducing overall accidents. But ADAS typically utilize camera sensors that rely on lane visibility and the absence of obstructions in order to function, rendering them ineffective in inclement weather. To address this research gap, we propose a new technique to estimate snow coverage so that existing and new ADAS features can be used during inclement weather. In this study, we use a single camera sensor and historical weather data to estimate snow coverage on the road. Camera data was collected over 6 miles of arterial roadways in Kalamazoo, MI.

The Prius is a major achievement by Toyota: it is the first mass-produced HEV with the first available HEV-optimized engine. Argonne National Laboratory's Advanced Powertrain Test Facility has been testing the Prius for model validation and technology performance and assessment. A significant part of the Prius test program is focused on testing and mapping the engine. A short-length torque sensor was installed in the powertrain in-situ. The torque sensor data allow insight into vehicle operational strategy, engine utilization, engine efficiency, and specific emissions. This paper describes the design and process necessary to install a torque sensor in a vehicle and shows the high-fidelity data measured during chassis dynamometer testing. The engine was found to have a maximum thermodynamic efficiency of 36.4%. Emissions and catalyst efficiency maps were also produced.

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.

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.

Perception in adverse weather conditions is one of the most prominent challenges for automated driving features. The sensors used for mid-to-long range perception most impacted by weather (i.e., camera and LiDAR) are susceptible to data degradation, causing potential system failures. This research series aims to better understand sensor data degradation characteristics in real-world, dynamic environmental conditions, focusing on adverse weather. To achieve this, a dataset containing LiDAR (Velodyne VLP-16) and camera (Mako G-507) data was gathered under static scenarios using a single vehicle target to quantify the sensor detection performance. The relative position between the sensors and the target vehicle varied longitudinally and laterally. The longitudinal position was varied from 10m to 175m at 25m increments and the lateral position was adjusted by moving the sensor set angle between 0 degrees (left position), 4.5 degrees (center position), and 9 degrees (right position).