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A 2-D computational model was developed to describe the flow and filtration processes, in a honeycomb structured ceramic diesel particulate trap. This model describes the steady state trap loading, as well as the transient behavior of the flow and filtration processes. The theoretical model includes the effect of a copper fuel additive on trap loading and transient operation. The convective terms were based on a 2-D analytical flow field solution derived from the conservation of mass and momentum equations. The filtration theory incorporated in the time dependent numerical code included the diffusion, inertia, and direct interception mechanisms. Based on a measured upstream particle size distribution, using the filtration theory, the downstream particle size distribution was calculated. The theoretical filtration efficiency, based on particle size distribution, agreed very well (within 1%) with experimental data for a number of different cases.

A 2-D CFD model was developed to describe the heat transfer, and reaction kinetics in a honeycomb structured ceramic diesel particulate trap. This model describes the steady state as well as the transient behavior of the flow and heat transfer during the trap regeneration processes. The trap temperature profile was determined by numerically solving the 2-D unsteady energy equation including the convective, heat conduction and viscous dissipation terms. The convective terms were based on a 2-D analytical flow field solution derived from the conservation of mass and momentum equations (Opris, 1997). The reaction kinetics were described using a discretized first order Arrhenius function. The 2-D term describing the reaction kinetics and particulate matter conservation of mass was added to the energy equation as a source term in order to represent the particulate matter oxidation. The filtration model describes the particulate matter accumulation in the trap.

Power split in Fuel Cell Hybrid Electric Vehicles (FCHEVs) has been controlled using different strategies ranging from rule-based to optimal control. Dynamic Programming (DP) and Model Predictive Control (MPC) are two common optimal control strategies used in optimization of the power split in FCHEVs with a trade-off between global optimality of the solution and online implementation of the controller. In this paper, both control strategies are developed and tested on a FC/battery vehicle model, and the results are compared in terms of total energy consumption. In addition, the effects of the MPC prediction horizon length on the controller performance are studied. Results show that by using the DP strategy, up to 12% less total energy consumption is achieved compared to MPC for a charge sustaining mode in the Urban Dynamometer Driving Schedule (UDDS) drive cycle.

Damping measurements on vehicle subsystems are rarely straightforward due to the complexity of the dynamic interaction of system joints, trim, and geometry. Various experimental techniques can be used for damping estimation, such as frequency domain modal analysis curve-fitting methods, time domain decay-rate methods, and other methods based on energy and wave propagation. Each method has its own set of advantages and drawbacks. This paper describes an analytical and an experimental comparison between two, widely used loss factor estimation techniques frequently used in Statistical Energy Analysis (SEA). The single subsystem Power Injection Method (PIM) and the Impulse Response Decay Method (IRDM) were compared using analytical models of a variety of simulated simple spring-mass-damper systems. Frequency averaged loss factor values were estimated from both methods for comparison.

As part of an ongoing technical collaboration between Ford and Rouge Steel Company, a comprehensive study of door slam event was undertaken. The experimental phase of the project involved measurements of accelerations at eight locations on the outer panel and strains on six locations of the inner panel. Although slam tests were conducted with window up and window down, results of only one test is presented in this paper. The CAE phase of the project involved the development of suitable “math” model of the door assembly and analysis methodology to capture the dynamics of the event. The predictability of the CAE method is examined through detailed comparison of accelerations and strains. While excellent agreement between CAE and test results of accelerations on the outer panel is obtained, the analysis predicts higher strains on the inner panel than the test. In addition, the tendency of outer panel to elastically buckle is examined.

A methodology for predicting the fatigue initiation life in metals experiencing multiaxial non-proportional loading is presented. The methodology utilizes nonlinear finite-element analysis to determine the stress distribution of the loaded component. This distribution is used in conjunction with a physically based damage law to determine the cycles to failure. The damage law is based on the fatigue prediction method introduced by Dang Van [1], and further developed by Papadopoulos [2] and Morel [3]. The fatigue damage initiation is treated as the persistent crystalline slip phenomenon taking place on the order of a grain or few grains. The damage variable is chosen to be the accumulated plastic strain at this scale. The initiation life is determined when the damage variable reaches a critical value. The developed methodology is applicable to both in-phase and out-of-phase loading, without any empirical adjustment parameter.

This paper presents an overview of the connected controls and optimization system for vehicle dynamics and powertrain operation on a light-duty plug-in multi-mode hybrid electric vehicle developed as part of the DOE ARPA-E NEXTCAR program by Michigan Technological University in partnership with General Motors Co. The objective is to enable a 20% reduction in overall energy consumption and a 6% increase in electric vehicle range of a plug-in hybrid electric vehicle through the utilization of connected and automated vehicle technologies. Technologies developed to achieve this goal were developed in two categories, the vehicle control level and the powertrain control level. Tools at the vehicle control level include Eco Routing, Speed Harmonization, Eco Approach and Departure and in-situ vehicle parameter characterization.

Studies in an Angular Stretch Bend Test (ASBT) have demonstrated that the failure location moves from the side wall to punch nose area. This occurs as the R/T ratio decreases below a certain limit and applies to most low carbon steels with the exception of Dual Phase (DP) steels. Such behavior in DP steels indicates that bending effects have a severe impact on the formability of DP materials. Therefore, the traditional criterion using the forming limit curve (FLC) is not suitable to assess the formability at punch radius areas for DP steels due in part to its uniqueness of unconventional microstructures. In this paper, a new failure criterion, ‘Bending-modified’ FLC (BFLC), is proposed by extending the traditional FLC using the “Stretch Bendability Index” (SBI) concept for the stretch bendability assessment.

An automobile seat’s thermal performance can be challenging to quantify since it requires comprehensive human subject testing. Seat manufacturers must rely on subjective ratings to understand how the construction of a seat and its underlying heating and cooling technology may compare to other seats. Other factors may influence seat ratings published by global marketing information services companies (e.g., JD Power and Associates). In particular, occupants may be biased by the vehicle class in which a seat is installed and by how much the contribution of a specific vehicle’s HVAC system performance affects the perception of seat thermal comfort. Therefore, there is a need for an objective testing methodology that does not rely on human participants but is still capable of producing a thermal performance rating in terms of established thermal comfort scales.

A simple strategy for building lightweight automobile body-in-whites (BIWs) is developed and discussed herein. Because cost is a critical factor, expensive advanced materials, such as carbon fiber composites and magnesium, must only be used where they will be most effective. Constitutive laws for mass savings under various loading conditions indicate that these materials afford greater opportunity for mass saving when used in bending, buckling or torsion than in tensile, shear or compression. Consequently, it is recommended that these advanced materials be used in BIW components subject to bending and torsion such as rails, sills, “A-B-C” pillars, etc. Furthermore, BIW components primarily subject to tension, compression, or shear, such as floor pans, roofs, shock towers, etc., should be made from lower cost steel. Recommendations for future research that are consistent with this strategy are included.

The successful implementation of a clean, quiet, high-performance four-stroke motorcycle engine into an existing snowmobile chassis has been achieved. The snowmobile is easy to start, easy to drive, and environmentally friendly. The following paper describes the conversion process in detail with actual dynamometer and field test data. The vehicle meets the proposed 2010 EPA snowmobile emissions regulations and is quieter than a stock snowmobile. The snowmobile not only addresses environmental concerns, it is economical as well, with an approximate cost of $5874.

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.

This report explores the relationship of different failure criteria - specifically, surface cracks, stiffness changes, and two-piece failures - on rolled, ductile, cast-iron crankshafts. Crankshaft samples were closely monitored throughout resonant bending fatigue testing and were taken to near complete fracture. By monitoring resonance shifts of the samples during testing, stiffness changes and cracks were monitored. These data showed that an accelerating frequency shift was sufficient to indicate imminent two-piece failure and that this condition can be used as a failure criterion. Fatigue studies on two different crankshafts using this failure criterion were compared to those using a surface crack failure criterion. This comparison showed that using the surface crack failure criterion erroneously decreased the apparent fatigue life of the crankshaft significantly.

Low Temperature Combustion (LTC) engines are promising to improve powertrain fuel economy and reduce NOx and soot emissions by improving the in-cylinder combustion process. However, the narrow operating range of LTC engines limits the use of these engines in conventional powertrains. The engine’s limited operating range can be improved by taking advantage of electrification in the powertrain. In this study, a multi-mode LTC-SI engine is integrated with a parallel hybrid electric configuration, where the engine operation modes include Homogeneous Charge Compression Ignition (HCCI), Reactivity Controlled Compression Ignition (RCCI), and conventional Spark Ignition (SI). The powertrain controller is designed to enable switching among different modes, with minimum fuel penalty for transient engine operations.

Increasing regulatory demand to reduce CO2 emissions has led to an industry focus on electrified vehicles while limiting the development of conventional internal combustion engine (ICE) and hybrid powertrains. Hybrid electric vehicle (HEV) powertrains rely on conventional SI mode IC engines that are optimized for a narrow operating range. Advanced combustion strategies such as Gasoline Compression Ignition (GCI) have been demonstrated by several others including the authors to improve brake thermal efficiency compared to both gasoline SI and Diesel CI modes. Soot and NOx emissions are also reduced significantly by using gasoline instead of diesel in GCI engines due to differences in composition, fuel properties, and reactivity. In this work, an HEV system was proposed utilizing a multi-mode GCI based ICE combined with a HEV components (e-motor, battery, and invertor).

Hydrogen is widely considered a promising fuel for future transportation applications for both, internal combustion engines and fuel cells. Due to their advanced stage of development and immediate availability hydrogen combustion engines could act as a bridging technology towards a wide-spread hydrogen infrastructure. Although fuel cell vehicles are expected to surpass hydrogen combustion engine vehicles in terms of efficiency, the difference in efficiency might not be as significant as widely anticipated [1]. Hydrogen combustion engines have been shown capable of achieving efficiencies of up to 45 % [2]. One of the remaining challenges is the reduction of nitric oxide emissions while achieving peak engine efficiencies. This paper summarizes research work performed on a single-cylinder hydrogen direct injection engine at Argonne National Laboratory.

This paper describes a multi-sensor data set, suitable for testing algorithms to detect and track pedestrians and cyclists, with an autonomous vehicle’s sensor suite. The data set can be used to evaluate the benefit of fused sensing algorithms, and provides ground truth trajectories of pedestrians, cyclists, and other vehicles for objective evaluation of track accuracy. One of the principal bottlenecks for sensing and perception algorithm development is the ability to evaluate tracking algorithms against ground truth data. By ground truth we mean independent knowledge of the position, size, speed, heading, and class of objects of interest in complex operational environments. Our goal was to execute a data collection campaign at an urban test track in which trajectories of moving objects of interest are measured with auxiliary instrumentation, in conjunction with several autonomous vehicles (AV) with a full sensor suite of radar, lidar, and cameras.

Sunroof is placed in certain high-end vehicles to give user a better driving experience. All automakers are searching alternatives to reduce weight and cost in the vehicle, in which sunroofs are also impacted. Some alternatives are already applied, as a honeycomb paper used in some sunshades that presents benefits, as less weight and with a good cost reduction. Although, due the reduced weight for this part produced in this material, it shows more susceptibility to reproduce the vibration that vehicle propagates in movement, especially in bad condition roads. The sunroof assembly is dependent of the roof reinforcement and roof skin, but in this special case, the validation could be done in the components itself because the interaction of the sunshades is directly dependent of the other sunroof parts, as rails and front frame.

The growing competition of the automotive market makes more and more necessary the reduction of development time and consequently, the increase of the capacity to quickly respond to the launching of the competitors. One of the most costly phases on the vehicle development process is the field durability test, both in function of the number of prototypes employed and the time needed to its execution. More and more diffused, the fatigue life prediction methods have played an important part in the durability analysis via CAE. Nevertheless, in order they can be reliable and really being able to reduce the development time and cost, they need to be provided with load cases that can accurately represent the field durability tests. This work presents a CAE approach used for light trucks in order to get a reasonable understanding of component durability behavior due to payload increase. In general, road load data is not available for a new payload condition.

The durability of fuel tank straps is essential for vehicle safety. Extensive physical tests are conducted to verify designs for durability. Due to the complexity of the loads and the fuel-to-tank interaction, computer-aided-engineering (CAE) simulation has had limited application in this area. This paper presents a CAE method for fuel tank strap durability prediction. It discusses the analytical loads, modeling of fuel-to-tank interaction, dynamic analysis methods, and fatigue analysis methods. Analysis results are compared to physical test results. This method can be used in either a fuel-tank-system model or a full vehicle model. It can give directional design guidance for fuel tank strap durability in the early stages of product development to reduce vehicle development costs.