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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.

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 Honda Particulate Matter Index (PMI) is a very helpful tool which provides an indication of a fuel’s sooting tendency. Currently, the index is being used by various laboratories and vehicle OEMs as a metric to understand a fuels impact on automotive engine sooting, in preparation for new global emissions regulations. The calculation of the index involves generating detailed hydrocarbon analysis (hydrocarbon molecular speciation) using gas chromatography laboratory equipment and the PMI calculation requires the exact list of compounds and correct naming conventions to work properly. The analytical methodology can be cumbersome, when the gas chromatography methodology has to be adjusted for new compounds that are not in the method, or if the compounds are not matching the list for quantification. Also, the method itself is relatively expensive, and not easily transferrable between labs.

Simultaneous measurements of the radial and the tangential components of velocity are obtained in a high-speed, direct-injection diesel engine typical of automotive applications. Results are presented for engine operation with fuel injection, but without combustion, for three different swirl ratios and four injection pressures. With the mean and fluctuating velocities, the r-θ plane shear stress and the mean flow gradients are obtained. Longitudinal and transverse length scales are also estimated via Taylor's hypothesis. The flow is shown to be sufficiently homogeneous and stationary to obtain meaningful length scale estimates. Concurrently, the flow and injection processes are simulated with KIVA-3V employing a RNG k-ε turbulence model. The measured turbulent kinetic energy k, r-θ plane mean strain rates ( 〈Srθ〉, 〈Srr〉, and 〈Sθθ〉 ), deviatoric turbulent stresses , and the r-θ plane turbulence production terms are compared directly to the simulated results.

This paper investigates the effects of manufacturing variations in fuel injectors on the engine performance with emphasis on emissions. The variations are taken into consideration within a Reliability-Based Design Optimization (RBDO) framework. A reduced version of Multi-Zone Diesel engine Simulation (MZDS), MZDS-lite, is used to enable the optimization study. The numerical noise of MZDS-lite prohibits the use of gradient-based optimization methods. Therefore, surrogate models are developed to filter out the noise and to reduce computational cost. Three multi-objective optimization problems are formulated, solved and compared: deterministic optimization using MZDS-lite, deterministic optimization using surrogate models and RBDO using surrogate models. The obtained results confirm that manufacturing variation effects must be taken into account in the early product development stages.

In FOA (First Order Analysis) any vehicle body structure might be interpreted as a collective simple structure that can be decomposed into 3 fundamental structure types. The first structure is the “BEAM”, whose cross sectional properties as well as its material dominates the mechanical behavior, the second is the “PANEL (shear panel, plate, and shell)”, whose mechanical behavior can be varied by changing its geometrical properties in the thickness direction, i.e. adding beads or flanges. The third structure is the “JOINT”, which connects the proceeding structures, and transfer complex three-dimensional loads with three-dimensional deformation. In the present work, we shall propose a methodology to identify a portion of an arbitrary FE model of an automotive body structure, with a “BEAM” structure in the FOA approach. In the latter chapter of this paper, cross section loads will be related with cross sectional properties in the aspect of the element strain energy concept.

APPLICATION of nuclear energy for civilian automotive uses has possibilities, these authors say. Nuclear power for automotive applications, they feel, is technically feasible now where size and weight are not prime considerations; where size and weight are major parameters, discoveries of new materials for construction of nuclear-power reactors must be made. New materials are needed for reactor fuels, heat extractants, neutron reflectors, reactor construction materials, controls, and radiation shields which must have unique nuclear properties in addition to conventional engineering properties. This paper presents nuclear automotive propulsion devices in terms of technologies now available. The necessary radiation-shielding mass and weight requirements are presented for an ideal point-source nuclear-heat-power engine.

The need for improved understanding of new magnesium alloys for the automotive industry continues to grow as the application for these lightweight alloys expands to more demanding environments, particularly in drivetrain components. Their use at elevated temperatures, such as in transmission cases, presents a challenge because magnesium alloys generally have lower creep resistance than aluminum alloys currently employed for such applications. In this study, a new die cast magnesium alloy, MEZ, containing rare earth (RE) elements and zinc as principal alloying constituents, was examined for its bolt-load retention (BLR) properties. Preloads varied from 14 to 28 kN and test temperatures ranged from 125 to 175°C. At all test temperatures and preloads, MEZ retained the greatest fraction of the initial imposed preload when compared to the magnesium alloys AZ91D, AE42, AM50, and the AM50+Ca series alloys.

New high-temperature Mg alloys are being considered to replace 380 Al in transmission cases, wherein bolt-load retention, and creep, is of prime concern. One of these alloys is die cast AE42, which has much better creep properties than does AZ91D but is still not as creep resistant as 380 Al. It is thus important to investigate bolt-load retention and creep of AE42 as an initial step in assessing its suitability as a material for transmission housings. To that end, the bolt-load retention behavior of die-cast AE42, AZ91D and 380 Al have been examined using standard M10 bolts specially instrumented with stable high-temperature strain gages. The bolt-load retention test pieces were die cast in geometries approximating the flange and boss regions in typical bolted joints. Bolt-load retention properties were examined as a function of time (at least 100 hours), temperature (150 and 175 °C) and initial bolt preload (14 to 34 kN).

Degreening is crucial in obtaining a stable catalyst prior to assessing its performance characteristics. This paper characterizes the light-off behavior and conversion efficiency of a Diesel Oxidation Catalyst (DOC) during the degreening process. A platinum DOC is degreened for 16 hours in the presence of actual diesel engine exhaust at 650°C and 10% water (H2O) concentration. The DOC's activity for carbon monoxide (CO) and for total hydrocarbons (THC) conversion is checked at 0, 1, 2, 3, 4, 6, 8, 10, 12, and 16 hours of degreening. Pre-and post-catalyst hydrocarbon species are analyzed via gas chromatography at 0, 4, 8, and 16 hours of degreening. It is found that both light-off temperature and species-resolved conversion efficiencies change rapidly during the first 8 hours of degreening and then stabilize to a large degree. T50, the temperature where the catalyst is 50% active towards a particular species, increases by 14°C for CO and by 11°C for THC through the degreening process.

Federal standards that mandate improved fuel economy have resulted in the increased use of lightweight materials in automotive applications. However, the environmental burdens associated with a product extend well beyond the use phase. Life cycle assessment is the science of determining the environmental burdens associated with the entire life cycle of a given product from cradle-to-grave. This report documents the environmental burdens associated with every phase of the life cycle of two fuel tanks utilized in full-sized 1996 GM vans. These vans are manufactured in two configurations, one which utilizes a steel fuel tank, and the other a multi-layered plastic fuel tank consisting primarily of high density polyethylene (HDPE). This study was a collaborative effort between GM and the University of Michigan's National Pollution Prevention Center, which received funding from EPA's National Risk Management Research Laboratory.

A numerical study has been conducted to investigate possible extension of the low load limit of the HCCI operating range by charge stratification using direct injection. A wide range of SOI timings at a low load HCCI engine operating condition were numerically examined to investigate the effect of DI. A multidimensional CFD code KIVA3v with a turbulent combustion model based on a modified flamelet approach was used for the numerical study. The CFD code was validated against experimental data by comparing pressure traces at different SOI’s. A parametric study on the effect of SOI on combustion has been carried out using the validated code. Two parameters, the combustion efficiency and CO emissions, were chosen to examine the effect of SOI on combustion, which showed good agreement between numerical results and experiments. Analysis of the in-cylinder flow field was carried out to identify the source of CO emissions at various SOI’s.

The power management control system development and vehicle test results for a medium-duty hybrid electric truck are reported in this paper. The design procedure adopted is a model-based approach, and is based on the dynamic programming technique. A vehicle model is first developed, and the optimal control actions to maximize fuel economy are then obtained by the dynamic programming method. A near-optimal control strategy is subsequently extracted and implemented using a rapid-prototyping control development system, which provides a convenient environment to adjust the control algorithms and accommodate various I/O configurations. Dynamometer-testing results confirm that the proposed algorithm helps the prototype hybrid truck to achieve a 45% fuel economy improvement on the benchmark (non-hybrid) vehicle. It also compares favorably to a conventional rule-based control method, which only achieves a 31% fuel economy improvement on the same hybrid vehicle.

A complete scheme for motion prediction based on motion capture data is presented. The scheme rests on three main components: a special posture representation, a diverse motion capture database and prediction method. Most prior motion prediction schemes have been based on posture representations based on well-known local or global angles. Difficulties have arisen when trying to satisfy constraints, such as placing a hand on a target or scaling the posture for a subject of different stature. Inverse kinematic methods based on such angles require optimization that become increasingly complex and computationally intensive for longer linkages. A different representation called stretch pivot coordinates is presented that avoids these difficulties. The representation allows for easy rescaling for stature and other linkage length variations and satisfaction of endpoint constraints, all without optimization allowing for rapid real time use.

Model Predictive Control (MPC) design methods are becoming popular among automotive control researchers because they explicitly address an important challenge faced by today’s control designers: How does one realize the full performance potential of complex multi-input, multi-output automotive systems while satisfying critical output, state and actuator constraints? Nonlinear MPC (NMPC) offers the potential to further improve performance and streamline the development for those systems in which the dynamics are strongly nonlinear. These benefits are achieved in the MPC framework by using an on-line model of the controlled system to generate the control sequence that is the solution of a constrained optimization problem over a receding horizon.

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.

This study presents an efficient process to optimize the transmission loss of a vehicle muffler by using both experimental and analytical methods. Two production mufflers were selected for this study. Both mufflers have complex partitions and one of them was filled with absorbent fiberglass. CAD files of the mufflers were established for developing FEA models in ANSYS and another commercial software program (CFEA). FEA models were validated by experimental measurements using a two-source method. After the models were verified, sensitivity studies of design parameters were performed to optimize the transmission loss (TL) of both mufflers. The sensitivity study includes the perforated hole variations, partition variations and absorbent material insertion. The experimental and sensitivity analysis results are included in the paper.

A new method for crashworthiness optimization of vehicle structures is presented, where an early design exploration is done by the optimization of an equivalent mechanism approximating a vehicle structure. An equivalent mechanism (EM) is a network of rigid bodies connected by prismatic and revolute joints with special nonlinear springs. These springs are designed to mimic the force-displacement characteristics of thin-walled beams often found in the vehicle body structures. A computer software is implemented that allows the designer to quickly construct an equivalent mechanism model of a structure using a graphical user interface (GUI) to optimize the model for given objectives prior to final tuning using finite element (FE) models. A case study of a vehicle front substructure consisting of mid and lower rails is presented, which demonstrates that the new approach can obtain a better design with less computational resources than the direct optimization of a FE model.

This paper proposes a framework to perform design optimization of a series PHEV and investigates the impact of using real-world driving inputs on final design. Real-World driving is characterized from a database of naturalistic driving generated in Field Operational Tests. The procedure utilizes Markov chains to generate synthetic drive cycles representative of real-world driving. Subsequently, PHEV optimization is performed in two steps. First the optimal battery and motor sizes to most efficiently achieve a desired All Electric Range (AER) are determined. A synthetic cycle representative of driving over a given range is used for function evaluations. Then, the optimal engine size is obtained by considering fuel economy in the charge sustaining (CS) mode. The higher power/energy demands of real-world cycles lead to PHEV designs with substantially larger batteries and engines than those developed using repetitions of the federal urban cycle (UDDS).

The investigation and measurement of particle emissions from foundation brakes require the use of a special adaptation of inertia dynamometer test systems. To have proper measurements for particle mass and particle number, the sampling system needs to minimize transport losses and reduce residence times inside the brake enclosure. Existing models and spreadsheets estimate key transport losses (diffusion, turbophoretic, contractions, gravitational, bends, and sampling isokinetics). A significant limitation of such models is that they cannot assess the turbulent flow and associated particle dynamics inside the brake enclosure; which are anticipated to be important. This paper presents a Design of Experiments (DOE) approach using Computational Fluid Dynamics (CFD) to predict the flow within a dynamometer enclosure under relevant operating conditions. The systematic approach allows the quantification of turbulence intensity, mean velocity profiles, and residence times.