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A major driving force for change in light-duty vehicle design and technology is the National Highway Traffic Safety Administration (NHTSA) and the U.S. Environmental Protection Agency (EPA) joint final rules concerning Corporate Average Fuel Economy (CAFE) and greenhouse gas (GHG) emissions for model years 2017 (MY17) through 2025 (MY25) passenger cars and light trucks. The chief goal of this current study is to compare the already rapid pace of fuel economy improvement and technological change over the previous decade to the required rate of change to meet regulations over the next decade. EPA and NHTSA comparisons of the model year 2005 (MY05) US light-duty vehicle fleet to the model year 2015 (MY15) fleet shows improved fuel economy (FE) of approximately 26% using the same FE estimating method mandated for CAFE regulations. Future predictions by EPA and NHTSA concerning ensemble fleet fuel economy are examined as an indicator of required vehicle rate-of-change.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

Accurately predicting the fuel savings that can be achieved with the implementation of various technologies developed for fuel efficiency can be very challenging, particularly when considering combinations of technologies. Differences in the usage of highway vehicles can strongly influence the benefits realized with any given technology, which makes generalizations about fuel savings inappropriate for different vehicle applications. A model has been developed to estimate the potential for reducing fuel consumption when advanced efficiency technologies, or combinations of these technologies, are employed on highway vehicles, particularly medium- and heavy-duty trucks. The approach is based on a tractive energy analysis applied to drive cycles representative of the vehicle usage, and the analysis specifically accounts for individual energy loss factors that characterize the technologies of interest.

The global energy situation, the dependence of the transportation sector on fossil fuels, and a need for rapid response to the global warming challenge, provide a strong impetus for development of fuel efficient vehicle propulsion. The task is particularly challenging in the case of trucks due to severe weight/size constraints. Hybridization is the only approach offering significant breakthroughs in near and mid-term. In particular, the series configuration decouples the engine from the wheels and allows full flexibility in controlling the engine operation, while the hydraulic energy conversion and storage provides exceptional power density and efficiency. The challenge stems from a relatively low energy density of the hydraulic accumulator, and this provides part of the motivation for a simulation-based approach to development of the system power management. The vehicle is based on the HMMWV platform, a 4×4 off-road truck weighing 5112 kg.

The objective of this project was to model and study the interior noise in an Off-Highway Truck cab using Statistical Energy Analysis (SEA). The analysis was performed using two different modeling techniques. In the first method, the structural members of the cab were modeled along with the panels and the interior cavity. In the second method, the structural members were not modeled and only the acoustic cavity and panels were modeled. Comparison was done between the model with structural members and without structural members to evaluate the necessity of modeling the structure. Correlation between model prediction of interior sound pressure and test data was performed for eight different load conditions. Power contribution analysis was performed to find dominant paths and 1/3rd octave band frequencies.

The Office of Heavy Vehicle Technologies supports research to enable high-efficiency diesel engines to meet future emissions regulations, thus clearing the way for their use in light trucks as well as continuing as the most efficient powerplant for freight-haulers. Compliance with Tier 2 emission regulations for light-duty vehicles will require effective exhaust emission controls (aftertreatment) for diesels in these applications. Diesel-powered heavy trucks face a similar situation for the 2007 regulations announced by EPA in December 2000. DOE laboratories are working with industry to improve emission control technologies in projects ranging from application of new diagnostics for elucidating key mechanisms, to development and evaluation of prototype devices. This paper provides an overview of these R&D efforts, with examples of key findings and developments.

Developing a new technology requires decision-makers to understand the technology's implications on an organization's objectives, which depend on user needs targeted by the technology. If these needs are common between two organizations, collaboration could result in more efficient technology development. For hybrid truck design, both commercial manufacturers and the military have similar performance needs. As the new technology penetrates the truck market, the commercial enterprise must quantify how the hybrid's superior fuel efficiency will impact consumer purchasing and, thus, future enterprise profits. The Army is also interested in hybrid technology as it continues its transformation to a more fuel-efficient force. Despite having different objectives, maximizing profit and battlefield performance, respectively, the commercial enterprise and Army can take advantage of their mutual needs.

This paper assesses the technical potential, costs and benefits of improving the fuel economy of light-duty trucks over the next five to ten years in the United States using conventional technologies. We offer an in-depth analysis of several technology packages based on a detailed vehicle system modeling approach. Results are provided for fuel economy, cost, oil savings and reductions in greenhouse gas emissions. We examine a range of refinements to body, powertrain and electrical systems, reflecting current best practice and emerging technologies such as lightweight materials, high-efficiency IC engines, integrated starter-generator, 42 volt electrical system and advanced transmission. In this paper, multiple technological pathways are identified to significantly improve fleet average light-duty-truck fuel economy to 27.0 MPG or higher with net savings to consumers.

Carbon fiber composite use in automobiles and light trucks could dramatically reduce energy use and engine-out emissions. However, worldwide capacity of 28,000 tonnes per year of carbon fiber from polyacrylonitrile (PAN) and petroleum pitch could support limited automotive use. Production of high-volume, industrial-grade fiber from renewable and recycled polymers (lignin, recycled plastics, regenerated cellulosics) could meet automotive demand. Profiles of material volumes, carbon content, and melting points indicate several attractive candidates for production melt-spun carbon fiber feedstocks. Effects on the carbon fiber production cycle and its integration into automotive production are discussed.

The results of an experimental investigation of the flow over a pickup truck are presented. The main objectives of the study are to gain a better understanding of the flow structure in near wake region, and to obtain a detailed quantitative data set for validation of numerical simulations of this flow. Experiments were conducted at moderate Reynolds numbers (∼3×105) in the open return tunnel at the University of Michigan. Measured quantities include: the mean pressure on the symmetry plane, unsteady pressure in the bed, and Particle Image Velocimetry (PIV) measurements of the flow in the near wake. The unsteady pressure results show that pressure fluctuations in the forward section of the bed are small and increase significantly at the edge of the tailgate. Pressure fluctuation spectra at the edge of the tailgate show a spectral peak at a Strouhal number of 0.07 and large energy content at very low frequency.

Automotive seats are commonly described by one-dimensional measurements, including those documented in SAE J2732. However, 1-D measurements provide minimal information on seat shape. The goal of this work was to develop a statistical framework to analyze and model the surface shapes of seats by using techniques similar to those that have been used for modeling human body shapes. The 3-D contour of twelve driver seats of a pickup truck and sedans were scanned and aligned, and 408 landmarks were identified using a semi-automatic process. A template mesh of 18,306 vertices was morphed to match the scan at the landmark positions, and the remaining nodes were automatically adjusted to match the scanned surface. A principal component (PC) analysis was performed on the resulting homologous meshes. Each seat was uniquely represented by a set of PC scores; 10 PC scores explained 95% of the total variance. This new shape description has many applications.

A systems-level modeling framework developed to estimate the life cycle cost of medium- and heavy-duty trucks is discussed in this paper. Costs are estimated at a resolution of five major subsystems and 30+ subsystems, each representing a specific manufacturing technology. Interrelationships among various subsystems affecting cost are accounted for. Results of a specific Class 8 truck are finally discussed to demonstrate the modeling framework's capability, including the analysis of cost-effectiveness of some of the competing alternative system design options being considered by the industry today.

Automated clutches for vehicle startup is being increasingly deployed in commercial trucks for benefits, which include driver comfort, gradient performance, improved clutch life, emissions and driveline vibration reduction potential. The process of designing the controller is divided into 2 parts. Firstly, the parameter estimation of previously developed driveline models is carried out. The procedure involves an off-line minimization technique based on measured and estimated speeds. Secondly, the nominal plant model is used to develop LQR based optimal control strategy, which takes into account the slip time, dissipated power and slip acceleration. Mathematical expression of the performance index is clearly developed. A variety of clutch lock up profiles can be incorporated by changing a single tuning parameter, thus providing the driver the ability to select a launch profile based on specific driving objectives.

This paper presents a simple method of using Voronoi partitions for estimating vehicle fuel economy from a limited set of engine operating conditions. While one of the overarching goals of engine research is to continually improve vehicle fuel economy, evaluating the impact of a change in engine operating efficiency on the resulting fuel economy is a non-trivial task and typically requires drive cycle simulations with experimental data or engine model predictions and a full suite of engine controllers over a wide range of engine speeds and loads. To avoid the cost of collecting such extensive data, proprietary methods exist to estimate fuel economy from a limited set of engine operating conditions. This study demonstrates the use of Voronoi partitions to cluster and quantize the fuel consumed along a complex trajectory in speed and load to generate fuel consumption estimates based on limited simulation or experimental results.

As energy storage system (ESS) technology advances, vehicle testing in both laboratory and on-road settings is needed to characterize the performance of state-of-the-art technology and also identify areas for future improvement. The Idaho National Laboratory (INL), through its support of the U.S. Department of Energy's (DOE) Advanced Vehicle Testing Activity (AVTA), is collaborating with ECOtality North America and Oak Ridge National Laboratory (ORNL) to conduct on-road testing of advanced ESSs for the Electric Drive Advanced Battery (EDAB) project. The project objective is to test a variety of advanced ESSs that are close to commercialization in a controlled environment that simulates usage within the intended application with the variability of on-road driving to quantify the ESS capabilities, limitations, and performance fade over cycling of the ESS.

Existing methods for design optimization under uncertainty assume that a high level of information is available, typically in the form of data. In reality, however, insufficient data prevents correct inference of probability distributions, membership functions, or interval ranges. In this article we use an engine design example to show that optimal design decisions and reliability estimations depend strongly on uncertainty characterization. We contrast the reliability-based optimal designs to the ones obtained using worst-case optimization, and ask the question of how to obtain non-conservative designs with incomplete uncertainty information. We propose an answer to this question through the use of Bayesian statistics. We estimate the truck's engine reliability based only on available samples, and demonstrate that the accuracy of our estimates increases as more samples become available.

As part of a multi-agency study concerning emissions and fuel consumption from heavy-duty diesel truck idling, Oak Ridge National Laboratory personnel measured CO, HC, NOx, CO2, O2, particulate matter (PM), aldehyde and ketone emissions from truck idle exhaust. Two methods of quantifying PM were employed: conventional filters and a Tapered Element Oscillating Microbalance (TEOM). A partial flow micro-dilution tunnel was used to dilute the sampled exhaust to make the PM and aldehyde measurements. The work was performed at the U.S. Army's Aberdeen Test Center's (ATC) climate controlled chamber. ATC performed 37 tests on five class-8 trucks (model years ranging from 1992 to 2001). One was equipped with an 11 hp diesel auxiliary power unit (APU), and another with a diesel direct-fired heater (DFH). The APU powers electrical accessories, heating, and air conditioning, whereas a DFH heats the cab in cold weather. Both devices offer an alternative to extended truck-engine idling.

Simulations of humans performing seated reaches require accurate descriptions of the movements of the body segments that make up the torso. Data to generate such simulations were obtained in a laboratory study using industrial, auto, and truck seats. Twelve men and women reached to push-button targets located throughout their right-hand reach envelopes as their movements were recorded using an electromagnetic tracking system. The data illustrate complex patterns of motion that depend on target location and shoulder range of motion. Pelvis motion contributes substantially to seated reach capability. On padded seats, the effective center of rotation of the pelvis is often within the seat cushion below the pelvis rather than at the hips. Lumbar spine motions differ markedly depending on the location of the target. A categorization of reach targets into four zones differentiated by torso kinematics is proposed.

On-road emission measurements of 23 VN-trucks on a randomly chosen driving cycle, consisting of 10 miles two-lane and 8 miles four-lane road, showed tailpipe NOx emissions on fleet average of 0.96 g/bhp-hr, or 1.06 g/bhp-hr when including the time the exhaust gas temperature was below 200°C. Complementary measurements in a SET-cycle (13 point OICA -cycle) on a chassis dynamometer showed a tailpipe emission of 0.008 g PM per bhp-hr. Moreover, cost analysis show that the diesel fuel consumption remains unchanged whether the truck running on ULSD is equipped with a Combined Exhaust gas AfterTreatment System (CEATS) installed or not.

Rolling resistance measurements were made on a sample of twenty-four retreaded and four new truck tires spanning a variety of constructions, tread patterns and tread depths. Examination of the results showed some relationship between three construction features and the resulting rolling resistance values. These are (a) Radial or bias carcass construction, (b) Tread pattern, and (c) Tread depth. The latter two characteristics can over-ride radial or bias construction effects. The effect of tread compound could not be analyzed in this limited test program. Good quality retreaded truck tires can exhibit comparable rolling resistance with new truck tires.