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A prototype 2007 ISL Cummins diesel engine equipped with a diesel oxidation catalyst (DOC), diesel particle filter (DPF), variable geometry turbocharger (VGT), and cooled exhaust gas recirculation (EGR) was tested at Southwest Research Institute (SwRI) under a high-load accelerated durability cycle for 1000 hours with B20 soy-based biodiesel blends and ultra-low sulfur diesel (ULSD) fuel to determine the impact of B20 on engine durability, performance, emissions, and fuel consumption. At the completion of the 1000-hour test, a thorough engine teardown evaluation of the overhead, power transfer, cylinder, cooling, lube, air handling, gaskets, aftertreatment, and fuel system parts was performed. The engine operated successfully with no biodiesel-related failures. Results indicate that engine performance was essentially the same when tested at 125 and 1000 hours of accumulated durability operation.

The use of virtual prototyping early in the design stage of a product has gained popularity due to reduced cost and time to market. The state of the art in vehicle simulation has reached a level where full vehicles are analyzed through simulation but major difficulties continue to be present in interfacing the vehicle model with accurate powertrain models and in developing adequate formulations for the contact between tire and terrain (specifically, scenarios such as tire sliding on ice and rolling on sand or other very deformable surfaces). The proposed work focuses on developing a ground vehicle simulation capability by combining several third party packages for vehicle simulation, tire simulation, and powertrain simulation. The long-term goal of this project consists in promoting the Digital Car idea through the development of a reliable and robust simulation capability that will enhance the understanding and control of off-road vehicle performance.

In order to reduce fuel consumption, companies have been looking at hybridizing vehicles. So far, two main hybridization options have been considered: electric and hydraulic hybrids. Because of light duty vehicle operating conditions and the high energy density of batteries, electric hybrids are being widely used for cars. However, companies are still evaluating both hybridization options for medium and heavy duty vehicles. Trucks generally demand very large regenerative power and frequent stop-and-go. In that situation, hydraulic systems could offer an advantage over electric drive systems because the hydraulic motor and accumulator can handle high power with small volume capacity. This study compares the fuel displacement of class 6 trucks using a hydraulic system compared to conventional and hybrid electric vehicles. The paper will describe the component technology and sizes of each powertrain as well as their overall vehicle level control strategies.

Acoustical materials are widely used in automotive vehicles and other industrial applications. Two important parameters namely Sound Transmission Loss (STL) and absorption coefficient are commonly used to evaluate the acoustical performance of these materials. Other parameters, such as insertion loss, noise reduction, and loss factors are also used to judge their performance depending on the application of these materials. A systematic comparative study of STL and absorption coefficient was conducted on various porous acoustical materials. Several dozen materials including needled cotton fiber (shoddy) and foam materials with or without barrier/scrim were investigated. The results of STL and absorption coefficient are presented and compared. As expected, it was found that most of materials are either good in STL or good in absorption. However, some combinations can achieve a balance of performance in both categories.

Modeling plume interaction and collapse for direct-injection gasoline sprays is important because of its impact on fuel-air mixing and engine performance. Nevertheless, the aerodynamic interaction between plumes and the complicated two-phase coupling of the evaporating spray has shown to be notoriously difficult to predict. With the availability of high-speed (100 kHz) Particle Image Velocimetry (PIV) experimental data, we compare velocity field predictions between plumes to observe the full temporal evolution leading up to plume merging and complete spray collapse. The target “Spray G” operating conditions of the Engine Combustion Network (ECN) is the focus of the work, including parametric variations in ambient gas temperature. We apply both LES and RANS spray models in different CFD platforms, outlining features of the spray that are most critical to model in order to predict the correct aerodynamics and fuel-air mixing.

In this work, the effects of ozone, hydrogen, carbon monoxide, and exhaust gas recirculation (EGR) addition to Haltermann gasoline combustion were investigated. For these additives, laminar and turbulent flame speeds were experimentally determined using spherically propagating premixed flames in a constant volume combustion vessel. Two initial mixture pressures of Po = 1 and 5 bar, two initial mixture temperatures of 358 and 373 K and a range of equivalence ratios (Ф) from 0.5 to 1 were investigated. The additives were added as single, binary and ternary mixtures to Haltermann gasoline over a wide range of concentrations. For the stoichiometric mixture, the addition of 10% H2, 5% CO and 1000 ppm O3 shows remarkable enhancement (80%) in SL0compared to neat Haltermann gasoline. In addition, for this same blend, increasing the mixture initial temperature and pressure results in a significant increase in SL0compared to the neat gasoline.

A two-stage PPCI-diffusion combustion process recently showed good potential to enable clean and fuel-efficient gasoline compression ignition (GCI) combustion at medium-to-high loads. By conducting closed-cycle 3-D CFD combustion analysis, a further step was undertaken in this work to evaluate and optimize the PPCI-diffusion combustion strategy at a full load operating point (2000rpm-23.5 bar IMEPcc) while keeping engine-out NOx below 1 g/kWh. The light-duty GCI engine used in this investigation featured a custom-designed piston bowl geometry at a 17.0 compression ratio (CR), a high pressure diesel fuel injection system, and advanced single-stage turbocharging. A split fuel injection strategy was used to enable the two-stage PPCI-diffusion combustion process. First, the injector spray pattern and swirl ratio effects were evaluated. In-cylinder air utilization and the PPCI-diffusion combustion process were notably influenced by the closed-cycle combustion system design.

Available methodologies for model bias identification are mainly regression-based approaches, such as Gaussian process, Bayesian inference-based models and so on. Accuracy and efficiency of these methodologies may degrade for characterizing the model bias when more system inputs are considered in the prediction model due to the curse of dimensionality for regression-based approaches. This paper proposes a copula-based approach for model bias identification without suffering the curse of dimensionality. The main idea is to build general statistical relationships between the model bias and the model prediction including all system inputs using copulas so that possible model bias distributions can be effectively identified at any new design configurations of the system. Two engineering case studies whose dimensionalities range from medium to high will be employed to demonstrate the effectiveness of the copula-based approach.

Accurately characterizing vehicle drive cycles plays a fundamental role in assessing the performance of new vehicle technologies. Repeatable, short duration representative drive cycles facilitate more informed decision making, resulting in improved test procedures and more successful vehicle designs. With continued growth in the deployment of onboard telematics systems employing global positioning systems (GPS), large scale, low cost collection of real-world vehicle drive cycle data has become a reality. As a result of these technological advances, researchers, designers, and engineers are no longer constrained by lack of operating data when developing and optimizing technology, but rather by resources available for testing and simulation. Experimental testing is expensive and time consuming, therefore the need exists for a fast and accurate means of generating representative cycles from large volumes of real-world driving data.

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 fidelity of the hybrid electric vehicle simulation is increased with the integration of a computationally-efficient finite-element based electric machine model, in order to address optimization of component design for system level goals. In-wheel electric motors are considered because of the off-road military application which differs significantly from commercial HEV applications. Optimization framework is setup by coupling the vehicle simulation to the constrained optimization solver. Utilizing the increased design flexibility afforded by the model, the solver is able to reshape the electric machine's efficiency map to better match the vehicle operation points. As the result, the favorable design of the e-machine is selected to improve vehicle fuel economy and reduce cost, while satisfying performance constraints.

A global, systems-level model which characterizes the thermal behavior of internal combustion engines is described in this paper. Based on resistor-capacitor thermal networks, either steady-state or transient thermal simulations can be performed. A two-zone, quasi-dimensional spark-ignition engine simulation is used to determine in-cylinder gas temperature and convection coefficients. Engine heat fluxes and component temperatures can subsequently be predicted from specification of general engine dimensions, materials, and operating conditions. Emphasis has been placed on minimizing the number of model inputs and keeping them as simple as possible to make the model practical and useful as an early design tool. The success of the global model depends on properly scaling the general engine inputs to accurately model engine heat flow paths across families of engine designs. The development and validation of suitable, scalable submodels is described in detail in this paper.

The components in a hybrid electric vehicle (HEV) powertrain include the battery pack, an internal combustion engine, and the electric machines such as motors and possibly a generator. These components generate a considerable amount of heat during driving cycles. A robust thermal management system with advanced controller, designed for temperature tracking, is required for vehicle safety and energy efficiency. In this study, a hybridized mid-size truck for military application is investigated. The paper examines the integration of advanced control algorithms to the cooling system featuring an electric-mechanical compressor, coolant pump and radiator fans. Mathematical models are developed to numerically describe the thermal behavior of these powertrain elements. A series of controllers are designed to effectively manage the battery pack, electric motors, and the internal combustion engine temperatures.

Striving for sustainable transportation solutions, hydrogen is often identified as a promising energy carrier and internal combustion engines are seen as a cost effective consumer of hydrogen to facilitate the development of a large-scale hydrogen infrastructure. Driven by efficiency and emissions targets defined by the U.S. Department of Energy, a research team at Argonne National Laboratory has worked on optimizing a spark-ignited direct injection engine for hydrogen. Using direct injection improves volumetric efficiency and provides the opportunity to properly stratify the fuel-air mixture in-cylinder. Collaborative 3D-CFD and experimental efforts have focused on optimizing the mixture stratification and have demonstrated the potential for high engine efficiency with low NOx emissions. Performance of the hydrogen engine is evaluated in this paper over a speed range from 1000 to 3000 RPM and a load range from 1.7 to 14.3 bar BMEP.

A methodology has been developed for identifying the combination of battery characteristics which lead to least-cost electric vehicles. Battery interrelationships include specific power vs, specific energy, peak power vs. specific energy and DOD, cycle life vs. DOD, cost vs. specific energy and peak power, and volumetric and battery size effects. The method is illustrated for the “second car” mission assuming lead/acid batteries. Reductions in life-cycle costs associated with future battery research breakthroughs are estimated using a sensitivity technique. A research prioritization system is described.

Vehicle structure crashworthiness design is one of the most challenging problems in product development and it has been studied for decades. Challenges still remain, which include developing a reliable and systematic approach for general crashworthiness design problems, which can be used to design an optimum vehicle structure in terms of topology, shape, and size, and for both structural layout and material layout. In this paper, an advanced and systematic approach is presented, which is called Magic Cube (MQ) approach for crashworthiness design. The proposed MQ approach consists of three major dimensions: Decomposition, Design Methodology, and General Considerations. The Decomposition dimension is related to the major approaches developed for the crashworthiness design problem, which has three layers: Time (Process) Decomposition, Space Decomposition, and Scale Decomposition.

The pulleys employed in automotive accessory drive systems very often consist of a two piece assembly; a multitude of fastening techniques are used in completing the assembly. There are numerous assembly methods and a variety of distinct pulley configurations dictated by the various automobile manufacturers in accordance with individual accessory drive needs. An expert system is being developed to evaluate the merit of multiple assembly alternatives for a specific pulley application. The expert system provides a consistent evaluation tool for assembly alternatives, balancing the influence of product cost, strength and quality considerations. The knowledge-based system is implemented in an expert system shell called AGNESS (A Generalized Network-based Expert System Shell). The expert system judges the acceptability of various pulley assembly techniques, assigning a high “merit value” to the better designs and proportionately lower values to less desirable designs.

The reach capability of drivers is currently represented in vehicle design practice in two ways. The SAE Recommended Practice J287 presents maximum reach capability surfaces for selected percentiles of a generic driving population. Driver reach is also simulated using digital human figure models. In typical applications, a family of figure models that span a large range of the target driver population with respect to body dimensions is positioned within a digital mockup of the driver's workstation. The articulated segments of the figure model are exercised to simulate reaching motions and driver capabilities are calculated from the constraints of the kinematic model. Both of these current methods for representing driver reach are substantially limited. The J287 surfaces are not configurable for population characteristics, do not provide the user with the ability to adjust accommodation percentiles, and do not provide any guidance on the difficulty of reaches that are attainable.

Accurate evaluation of vehicles' transient total power requirement helps achieving further improvements in vehicle fuel efficiency. When operated, the air-conditioning (A/C) system is the largest auxiliary load on a vehicle, therefore accurate evaluation of the load it places on the vehicle's engine and/or energy storage system is especially important. Vehicle simulation models, such as "Autonomie," have been used by OEMs to evaluate vehicles' energy performance. However, the load from the A/C system on the engine or on the energy storage system has not always been modeled in sufficient detail. A transient A/C simulation tool incorporated into vehicle simulation models would also provide a tool for developing more efficient A/C systems through a thorough consideration of the transient A/C system performance. The dynamic system simulation software MATLAB/Simulink® is frequently used by vehicle controls engineers to develop new and more efficient vehicle energy system controls.

Traditional Lagrangian spray modeling approaches for internal combustion engines are highly grid-dependent due to insufficient resolution in the near nozzle region. This is primarily because of inherent restrictions of volume fraction with the Lagrangian assumption together with high computational costs associated with small grid sizes. A state-of-the-art grid-convergent spray modeling approach was recently developed and implemented by Senecal et al., (ASME-ICEF2012-92043) in the CONVERGE software. The key features of the methodology include Adaptive Mesh Refinement (AMR), advanced liquid-gas momentum coupling, and improved distribution of the liquid phase, which enables use of cell sizes smaller than the nozzle diameter. This modeling approach was rigorously validated against non-evaporating, evaporating, and reacting data from the literature.