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The Environmental Protection Agency (EPA), in partnership with Research Triangle Institute (RTI International) and Auto Research Center (ARC-Indy), have created digital geometries of commercially available heavy-duty tractor-trailers. The goal of this effort was to improve the agency’s understanding of aerodynamic modeling of modern trucks and to provide opportunities for more consistent engagement on computational fluid dynamics (CFD) analyses. Sleeper and day cab tractors with aerodynamic features and a 53-foot box trailer with aerodynamic technology options were scanned to create high-resolution geometries. The scanning process consisted of a combination of physical scanning with a handheld device, along with digital post-processing. The completed truck geometries are compatible with most commercial CFD software and are publicly available for modeling and analyses.

Mobility in the automotive and transportation sectors has been experiencing a period of unprecedented evolution. A growing need for efficient, clean and safe mobility has increased momentum toward sustainable technologies in these sectors. Toward this end, battery electric vehicles have drawn keen interest and their market share is expected to grow significantly in the coming years, especially in light-duty applications such as passenger cars. Although the battery electric vehicles feature high performance and zero tailpipe emission characteristics, economic and technical issues such as battery cost, driving range, recharging time and infrastructure remain main hurdles that need to be fully addressed. In particular, the low power density of the battery limits its broad adoption in heavy-duty applications such as class 8 semi-trailer trucks due to the required size and weight of the battery and electric motor.

Tanker trucks are commonly used for transporting liquid material including chemical and petroleum products. On the one hand, tanker trucks are susceptible to rollover accidents due to the high center of gravity when they are loaded and due to the liquid sloshing effects when the tank is partially filled. On the other hand, tanker truck rollover accidents are among the most dangerous vehicle crashes, frequently resulting in serious to fatal driver injuries and significant property damage, because the liquid cargo is often hazardous and flammable. Therefore, effective schemes for tanker truck rollover avoidance are highly desirable and can bring a considerable amount of societal benefit. Yet, the development of such schemes is challenging, as tanker trucks can operate in various environments and be affected by manufacturing variability, aging, degradation, etc. This paper considers the use of Learning Reference Governor (LRG) for tanker truck rollover avoidance.

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.

Nearly a third of the fuel energy is wasted through the exhaust of a vehicle. An efficient waste heat recovery process will undoubtedly lead to improved fuel efficiency and reduced greenhouse gas (GHG) emissions. Currently, there are multiple waste heat recovery technologies that are being investigated in the auto industry. One innovative waste heat recovery approach uses Thermoacoustic Converter (TAC) technology. Thermoacoustics is the field of physics related to the interaction of acoustic waves (sonic power) with heat flows. As in a heat engine, the TAC produces electric power where a temperature differential exists, which can be generated with engine exhaust (hot side) and coolant (cold side). Essentially, the TAC converts exhaust waste heat into electricity in two steps: 1) the exhaust waste heat is converted to acoustic energy (mechanical) and 2) the acoustic energy is converted to electrical energy.

Commercial, class-8 tractor-trailers were tested to develop a relationship between vehicle speed and fuel savings associated with trailer aerodynamic technologies representative of typical long-haul freight applications. This research seeks to address a concern that many long-distance U.S. freight companies hold that, as vehicle speed is reduced, the fuel savings benefits of aerodynamic technologies are not realized. In this paper, the reductions in fuel consumption were measured using the SAE J1231 test method and thru-engine fueling rates recorded from the vehicle’s electronic data stream. Constant speed testing was conducted on road at different speeds and corresponding testing was conducted on track to confirm results. Data was collected at four (4) vehicle speeds: 35, 45, 55, and 62 miles per hour. Two different trailer aerodynamic configurations were evaluated relative to a baseline tractor trailer.

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.

This paper examines truck driver injury and loss of life in truck crashes related to cab crashworthiness. The paper provides analysis of truck driver fatality and injury in crashes to provide a better understanding of how injury occurs and industry initiatives focused on reducing the number of truck occupant fatalities and the severity of injuries. The commercial vehicle focus is on truck-tractors and single unit trucks in the Class 7 and 8 weight range. The analysis used UMTRI's Trucks Involved in Fatal Accidents (TIFA) survey file and NHTSA's General Estimates System (GES) file for categorical analysis and the Large Truck Crash Causation Study (LTCCS) for a supplemental clinical review of cab performance in frontal and rollover crash types. The paper includes analysis of crashes producing truck driver fatalities or injuries, a review of regulatory development and industry safety initiatives including barriers to implementation.

The EPAct/V2/E-89 gasoline fuel effects program collected emissions data for 27 test fuels using a fleet of 15 high-sales cars and light trucks from the 2008 model year (all with port fuel injection). The test fuel matrix covered values of T50, T90, vapor pressure, ethanol content, and total aromatic content spanning ranges typical of market gasolines. Emission measurements were made over the LA92 cycle at a nominal temperature of 24°C (75°F). The resulting emissions database of 956 tests includes a particulate matter (PM) mass measurement for each. Emission models for PM fuel effects were fit based on terms for which the fuel matrix was originally optimized, with results published by EPA in a 2013 analysis report. This paper presents results of a subsequent modeling analysis of this PM data using the PM Index fuel parameter, and compares these models to the original versions.

The U.S. Army currently uses JP-8 for global operations according to the "one fuel forward policy" that was enacted almost twenty years ago in order to help reduce the logistics burden of supplying a variety of fuels for given Department of Defense vehicle and base applications. One particular challenge with using global JP-8 is the lack of or too broad a range of specified combustion and fuel system affecting properties including ignition quality, high temperature viscosity, and lubricity. In addition to these challenges, the JP-8 fuel specification currently allows the use of blending with certain types of synthetic jet fuels up to 50% by volume. This blended fuel also doesn't include an ignition quality or high temperature viscosity specification, but does include a lubricity specification that is much less restrictive than DF-2.

The U.S. Environmental Protection Agency, U.S. Department of Transportation's National Highway and Traffic Safety Administration, and the California Air Resources Board have recently released proposed new regulations for greenhouse gas emissions and fuel economy for light-duty vehicles and trucks in model years 2017-2025. These proposed regulations intend to significantly reduce greenhouse gas emissions and increase fleet fuel economy from current levels. At the fleet level, these rules the proposed regulations represent a 50% reduction in greenhouse gas emissions by new vehicles in 2025 compared to current fleet levels. At the same time, global growth, especially in developing economies, should continue to drive demand for crude oil and may lead to further fuel price increases. Both of these trends will therefore require light duty vehicles (LDV) to significantly improve their greenhouse gas emissions over the next 5-15 years to meet regulatory requirements and customer demand.

This paper describes the author's experiences in the design, validation and field-testing of a low cost, online oil condition monitor for diesel driven Army ground vehicles. This online oil condition monitor utilizes a multi-frequency approach to electrochemical impedance spectroscopy to interrogate and evaluate fluid health in near real time. A dual microcontroller processing architecture embedded in the sensor itself executes an oil-health evaluation algorithm and provides estimates of lubricant remaining useful life, as well as identification of the primary mode of degradation of the fluid. These data are transmitted off the sensor via J1939 compliant CAN messages. In this paper the unique application requirements, which formed the foundation of the development process, are discussed, and the technical and design challenges associated with producing a military grade smart-sensor at a sufficiently low price point for widespread adoption in the ground vehicle market are detailed.

The power-plant of the truck is one of main vibration and noise sources which often result in drives' uncomfortable, steering wheel and rear view mirror vibration and so on, because the diesel engine of the higher vibration and noise operation is used. The rubber mounting system had been used to isolating the power-plant vibration in truck. But, the conventional design optimization may not always satisfy the designed targets duo to the significant uncertainties in the rubber mounting system. In the paper, the method of robust design methods is used to minimize the quality effect of uncertainties on the power-plant isolating system. The 6-DOF dynamical model of the power-plant isolating of the truck is established for calculating the performance and robustness.

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.

In order to improve the isolating performance of the power-train mount system of one light truck with the uncontrollable parameters of the stiffness and location, a robust optimization design problem based on the 6-DOF dynamical equation of the power-train mount system is presented in the paper. For the robust optimization design of the power-train mount system with the uncontrollable parameter variations, a deterministic, non-gradient approach for objective robust and constraint robust optimization is discussed based on the design of orthogonal experiment and Genetic Algorithm.

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.

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.

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.

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.

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.