Search Results

Traditional autonomous vehicle perception subsystems that use onboard sensors have the drawbacks of high computational load and data duplication. Infrastructure-based sensors, which can provide high quality information without the computational burden and data duplication, are an alternative to traditional autonomous vehicle perception subsystems. However, these technologies are still in the early stages of development and have not been extensively evaluated for lane detection system performance. Therefore, there is a lack of quantitative data on their performance relative to traditional perception methods, especially during hazardous scenarios, such as lane line occlusion, sensor failure, and environmental obstructions.

Five different commercially available high-temperature martensitic steels were evaluated for use in a heavy-duty diesel engine piston application and compared to existing piston alloys 4140 and microalloyed steel 38MnSiVS5 (MAS). Finite element analyses (FEA) were performed to predict the temperature and stress distributions for severe engine operating conditions of interest, and thus aid in the selection of the candidate steels. Complementary material testing was conducted to evaluate the properties relevant to the material performance in a piston. The elevated temperature strength, strength evolution during thermal aging, and thermal property data were used as inputs into the FEA piston models. Additionally, the long-term oxidation performance was assessed relative to the predicted maximum operating temperature for each material using coupon samples in a controlled-atmosphere cyclic-oxidation test rig.

In support of the Federal Motor Carrier Safety Administration’s (FMCSA’s) ongoing interest in connected and automated commercial vehicles, this report summarizes analyses conducted to quantify variability in stopping distance tests conducted on commercial truck tractors. The data used were retrieved from tests performed under the controlled conditions specified for FMVSS-121 air brake system compliance testing. The report explores factors affecting the variability of the service brake stopping distance as defined by 49 CFR 571.121, S5.3.1 Stopping Distance—trucks and buses stopping distance. Variables examined in this analysis include brake type, weight, wheelbase, and tractor antilock braking system (ABS). This analysis uses existing test data collected between 2010 and 2019. Several of the examined parameters affected both tractor stopping distance and stopping distance variability.

A new analytical method to aid in the understanding of the organic carbon (OC) phase of particulate matter (PM) from advanced compression ignition (ACI) operating modes, is presented. The presence of NO2 and unburned fuel aromatics in ACI emissions, and the low exhaust temperatures that result from this low temperature combustion strategy, provide the right conditions for the formation of carboxylic acids and nitroaromatic compounds. These polar compounds contribute to OC in the PM and are not typically measured using nonpolar solvent extraction methods such as the soluble organic fraction (SOF) method. The new extraction and detection method employs capillary electrophoresis with electrospray ionization mass spectrometry (CE-ESI MS) and was specifically developed to determine polar organic compounds in the ACI PM emissions. The new method identified both nitrophenols and aromatic carboxylic acids in the ACI PM.

Current artificial intelligence techniques for end to end driving of autonomous vehicles typically rely on a single form of learning or training processes along with a corresponding dataset or simulation environment. Relatively speaking, success has been shown for a variety of learning modalities in which it can be shown that the machine can successfully “drive” a vehicle. However, the realm of real-world driving extends significantly beyond the realm of limited test environments for machine training. This creates an enormous gap in capability between these two realms. With their superior neural network structures and learning capabilities, humans can be easily trained within a short period of time to proceed from limited test environments to real world driving.

Metal additive manufacturing has high potential to produce automobile parts, due to its shape flexibility and unique material properties. On the other hand, residual stress which is generated by rapid solidification causes deformation, cracks and failure under building process. To avoid these problems, understanding of internal residual stress distribution is necessary. However, from the view point of measureable area, conventional residual stress measurement methods such as strain gages and X-ray diffractometers, is limited to only the surface layer of the parts. Therefore, neutron which has a high penetration capability was chosen as a probe to measure internal residual stress in this research. By using time of flight neutron diffraction facility VULCAN at Oak Ridge National Laboratory, residual stress for mono-cylinder head, which were made of aluminum alloy, was measured non-distractively. From the result of precise measurement, interior stress distribution was visualized.

As the fuel economy regulations increase in stringency, many manufacturers are implementing start-stop operation to enhance vehicle fuel economy. During start-stop operation, the engine shuts off when the vehicle is stationary for more than a few seconds. When the brake is released by the driver, the engine restarts. Depending on traffic conditions, start-stop operation can result in fuel savings from a few percent to close to 10%. Gasoline direct injection (GDI) engines are also increasingly available on light-duty vehicles. While GDI engines offer fuel economy advantages over port fuel injected (PFI) engines, they also tend to have higher PM emissions, particularly during start-up transients. Thus, there is interest in evaluating the effect of start-stop operation on PM emissions. In this study, a 2.5L GDI vehicle was operated over the FTP75 drive cycle.

Today’s automotive industry is witnessing increasing applications of advanced high strength steels (AHSS) combined with innovative manufacturing techniques to satisfy fuel economy requirements of stringent environmental regulations. The integration of AHSS in novel automotive structure design has introduced huge advantages in mass reduction while maintaining their structural performances, yet several concerns have been raised for this relatively new family of steels. One of those concerns is their potentially high springback after forming, which can lead to geometrical deviation of the final product from its designed geometry and cause difficulties during assembly. From the perspective of accurate prediction, control and compensation of springback, further understanding on the effect of residual stress in AHSS parts is urged. In this work, the residual stress distribution in a 980GEN3 steel part after hydroforming is investigated via experimental and numerical approaches.

Reliable means for on-board detection of particulate filter failures or malfunctions are needed to meet diagnostics (OBD) requirements. Detecting these failures, which result in tailpipe particulate matter (PM) emissions exceeding the OBD limit, over all operating conditions is challenging. Current approaches employ differential pressure sensors and downstream PM sensors, in combination with particulate filter and engine-out soot models. These conventional monitors typically operate over narrowly-defined time windows and do not provide a direct measure of the filter’s state of health. In contrast, radio frequency (RF) sensors, which transmit a wireless signal through the filter substrate provide a direct means for interrogating the condition of the filter itself.

The increasing use of diesel and gasoline particulate filters requires advanced on-board diagnostics (OBD) to prevent and detect filter failures and malfunctions. Early detection of upstream (engine-out) malfunctions is paramount to preventing irreversible damage to downstream aftertreatment system components. Such early detection can mitigate the failure of the particulate filter resulting in the escape of emissions exceeding permissible limits and extend the component life. However, despite best efforts at early detection and filter failure prevention, the OBD system must also be able to detect filter failures when they occur. In this study, radio frequency (RF) sensors were used to directly monitor the particulate filter state of health for both gasoline particulate filter (GPF) and diesel particulate filter (DPF) applications.

Advances in engine controls and sensor technology are making advanced, direct, high-speed control of engine combustion more feasible. Control of combustion rate and phasing in low-temperature combustion regimes and active control of cyclic variability in dilute SI combustion are being pursued in laboratory environments with high-quality data acquisition systems, using metrics calculated from in-cylinder pressure. In order to implement these advanced combustion controls in production, lower-quality data will need to be tolerated even if indicated pressure sensors become available. This paper examines the effects of several data quality issues, including phase shifting (incorrect TDC location), reduced data resolution, pressure pegging errors, and random noise on calculated combustion metrics that are used for control feedback.

Driver is a key component in vehicle simulation. An ideal driver model simulates driving patterns a human driver may perform to negotiate road profiles. There are simulation packages having the capability to simulate driver behavior. However, it is rarely documented how they work with road profiles. This paper proposes a new truck driver model for vehicle simulation to imitate actual driving behavior in negotiating road grade and curvature. The proposed model is developed based upon Gipps' car-following model. Road grade and curvature were not considered in the original Gipps' model although it is based directly on driver behavior and expectancy for vehicles in a stream of traffic. New parameters are introduced to capture drivers' choice of desired speeds that they intend to use in order to negotiating road grade and curvature simultaneously. With the new parameters, the proposed model can emulate behaviors like uphill preparation for different truck drivers.

Failure mode and fatigue behavior of friction stir spot welds made with convex and concave tools in lap-shear specimens of dissimilar high strength dual phase steel (DP780GA) and hot stamped boron steel (HSBS) sheets are investigated based on experiments and a kinked fatigue crack growth model. Lap-shear specimens with the welds were tested under both quasistatic and cyclic loading conditions. Optical micrographs indicate that under both quasi-static and cyclic loading conditions, the welds mainly fail from cracks growing through the upper DP780GA sheets where the tools were plunged in during the welding processes. Based on the observed failure mode, a kinked fatigue crack growth model is adopted to estimate fatigue lives of the welds. In the kinked crack fatigue crack growth model, the stress intensity factor solutions for fatigue life estimations are based on the closed-form solutions for idealized spot welds in lap-shear specimens.

Development of reliable magnesium (Mg) to steel joining methods is one of the critical issues in broader applications of Mg in automotive body construction. Ultrasonic spot welding (USW) has been demonstrated successfully to join Mg to steel and to achieve strong joints. In this study, corrosion test of ultrasonic spot welds between 1.6 mm thick Mg AZ31B-H24 and 0.8 mm thick galvanized mild steel, without and with adhesive, was conducted. Adhesive used was a one-component, heat-cured epoxy material, and was applied between overlapped sheets before USW. Corrosion test was conducted with an automotive cyclic corrosion test, which includes cyclic exposures of dipping in the 0.5% sodium chloride (NaCl) bath, a constant humidity environment, and a drying period. Lap shear strength of the joints decreased with the cycles of corrosion exposure. Good joint strengths were retained at the end of 30-cycle test.

Failure modes and fatigue behaviors of ultrasonic spot welds in lap-shear specimens of magnesium AZ31B-H24 and hot-dipped-galvanized mild steel sheets with and without adhesive are investigated. Ultrasonic spot welded, adhesive-bonded, and weld-bonded lap-shear specimens were made. These lap-shear specimens were tested under quasi-static and cyclic loading conditions. The ultrasonic spot weld appears not to provide extra strength to the weld-bonded lap-shear specimen under quasi-static and cyclic loading conditions. The quasi-static and fatigue strengths of adhesive-bonded and weld-bonded lap-shear specimens appear to be the same. For the ultrasonic spot welded lap-shear specimens, the optical micrographs indicate that failure mode changes from the partial nugget pullout mode under quasi-static and low-cycle loading conditions to the kinked crack growth mode under high-cycle loading conditions.

We summarize results from comparative simulations of hybrid electric vehicles with either stoichiometric gasoline or diesel engines. Our simulations utilize previously published models of transient engine-out emissions and models of aftertreatment devices for both stoichiometric and lean exhaust. Fuel consumption and emissions were estimated for comparable gasoline and diesel light-duty hybrid electric vehicles operating over single and multiple urban drive cycles. Comparisons between the gasoline and diesel vehicle fuel consumptions and emissions were used to identify potential advantages and technical barriers for diesel hybrids.

Failure modes of friction stir spot welds in lap-shear specimens of dissimilar high strength dual phase steel (DP780GA) and hot stamped boron steel (HSBS) sheets are investigated under quasi-static and cyclic loading conditions based on experimental observations. Optical micrographs of dissimilar DP780GA/HSBS friction stir spot welds made by a concave tool before and after failure are examined. The micrographs indicate that the failure modes of the welds under quasi-static and cyclic loading conditions are quite similar. The micrographs show that the DP780GA/HSBS welds mainly fail from cracks growing through the upper DP780GA sheets where the concave tool was plunged into during the welding process. Based on the observed failure modes, a kinked fatigue crack growth model is adopted to estimate fatigue lives.

Friction stir spot welding (FSSW) is applied to join advanced high strength steels (AHSS): galvannealed dual phase 780 MPa steel (DP780GA), transformation induced plasticity 780 MPa steel (TRIP780), and hot-stamped boron steel (HSBS). A low-cost Si₃N₄ ceramic tool was developed and used for making welds in this study instead of polycrystalline cubic boron nitride (PCBN) material used in earlier studies. FSSW has the advantages of solid-state, low-temperature process, and the ability of joining dissimilar grade of steels and thicknesses. Two different tool shoulder geometries, concave with smooth surface and convex with spiral pattern, were used in the study. Welds were made by a 2-step displacement control process with weld time of 4, 6, and 10 seconds. Static tensile lap-shear strength achieved 16.4 kN for DP780GA-HSBS and 13.2 kN for TRIP780-HSBS, above the spot weld strength requirements by AWS. Nugget pull-out was the failure mode of the joint.

We utilize physically-based models for diesel exhaust catalytic oxidation and urea-based selective catalytic NOx reduction to study their impact on drive cycle performance of hypothetical light-duty diesel-powered hybrid and plug-in hybrid vehicles (HEVs and PHEVs). The models have been implemented as highly flexible SIMULINK block modules that can be used to study multiple engine-aftertreatment system configurations. The parameters of the NOx reduction model have been adjusted to reflect the characteristics of commercially available Cu-zeolite catalysts, which are of widespread current interest. We demonstrate application of these models using the Powertrain System Analysis Toolkit (PSAT) software for vehicle simulations, along with a previously published methodology that accounts for emissions and temperature transients in the engine exhaust.

Fatigue behavior of dissimilar ultrasonic spot welds in lap-shear specimens of magnesium AZ31B-H24 and hot-dipped-galvanized mild steel sheets is investigated based on experimental observations, closed-form stress intensity factor solutions, and a fatigue life estimation model. Fatigue tests were conducted under different load ranges with two load ratios of 0.1 and 0.2. Optical micrographs of the welds after the tests were examined to understand the failure modes of the welds. The micrographs show that the welds mainly fail from kinked fatigue cracks growing through the magnesium sheets. The optical micrographs also indicate that failure mode changes from the partial nugget pullout mode under low-cycle loading conditions to the transverse crack growth mode under high-cycle loading conditions. The closed-form stress intensity factor solutions at the critical locations of the welds are used to explain the locations of fatigue crack initiation and growth.