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Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

One of the major challenges in multiobjective, multidisciplinary design optimization (MDO) is the long computational time required in evaluating the new designs' performances. To shorten the cycle time of product design, a data mining-based strategy is developed to improve the efficiency of heuristic optimization algorithms. Based on the historical information of the optimization process, clustering and classification techniques are employed to identify and eliminate the low quality and repetitive designs before operating the time-consuming design evaluations. The proposed method improves design performances within the same computation budget. Two case studies, one mathematical benchmark problem and one vehicle side impact design problem, are conducted as demonstration.

Since their inception, the design of airbag sensing systems has continued to evolve. The evolution of air bag sensing system design has been rapid. Electromechanical sensors used in earlier front air bag applications have been replaced by multi-point electronic sensors used to discriminate collision mechanics for potential air bag deployment in front, side and rollover accidents. In addition to multipoint electronic sensors, advanced air bag systems incorporate a variety of state sensors such as seat belt use status, seat track location, and occupant size classification that are taken into consideration by air bag system algorithms and occupant protection deployment strategies. Electronic sensing systems have allowed for the advent of event data recorders (EDRs), which over the past decade, have provided increasingly more information related to air bag deployment events in the field.

Numerous studies describe the fuel consumption benefits of changing the powertrain temperature based on vehicle operating conditions. Actuators such as electric water pumps and active thermostats now provide more flexibility to change powertrain operating temperature than traditional mechanical-only systems did. Various control strategies have been proposed for powertrain temperature set-point regulation. A characteristic of powertrain thermal management systems is that the operating conditions (speed, load etc) change continuously to meet the driver demand and in most cases, the optimal conditions lie on the edge of the constraint envelope. Control strategies for set-point regulation which rely purely on feedback for disturbance rejection, without knowledge of future disturbances, might not provide the full fuel consumption benefits due to the slow thermal inertia of the system.

Robustness/Reliability Assessment and Optimization (RRAO) is often computationally expensive because obtaining accurate Uncertainty Quantification (UQ) may require a large number of design samples. This is especially true where computationally expensive high fidelity CAE simulations are involved. Approximation methods such as the Polynomial Chaos Expansion (PCE) and other Response Surface Methods (RSM) have been used to reduce the number of time-consuming design samples needed. However, for certain types of problems require the RRAO, one of the first question to consider is which method can provide an accurate and affordable UQ for a given problem. To answer the question, this paper tests the PCE, RSM and pure sampling based approaches on each of the three selected test problems: the Ursem Waves mathematical function, an automotive muffler optimization problem, and a vehicle restraint system optimization problem.

As vehicle fuel economy continues to grow in importance, the ability to accurately measure the level of efficiency on all driveline components is required. A standardized test procedure enables manufacturers and suppliers to measure component losses consistently and provides data to make comparisons. In addition, the procedure offers a reliable process to assess enablers for efficiency improvements. Previous published studies have outlined the development of a comprehensive test procedure to measure transfer case speed-dependent parasitic losses at key speed, load, and environmental conditions. This paper will take the same basic approach for the Power Transfer Units (PTUs) used on Front Wheel Drive (FWD) based All Wheel Drive (AWD) vehicles. Factors included in the assessment include single and multi-stage PTUs, fluid levels, break-in process, and temperature effects.

For spark-ignition gasoline engines operating under the wide speed and load conditions required for light duty vehicles, ignition quality limits the ability to minimize fuel consumption and NOx emissions via dilution under light and part load conditions. In addition, during transients including tip-outs, high levels of dilution can occur for multiple combustion events before either the external exhaust gas can be adjusted and cleared from the intake or cam phasing can be adjusted for correct internal dilution. Further improvement and a thorough understanding of the impact of the ignition system on combustion near the dilution limit will enable reduced fuel consumption and robust transient operation. To determine and isolate the effects of multiple parameters, a variable output ignition system (VOIS) was developed and tested on a 3.5L turbocharged V6 homogeneous charge direct-injection gasoline engine with two spark plug gaps and three ignition settings.

As a renewable energy source, the ethanol fuel was employed with a diesel fuel in this study to improve the cylinder charge homogeneity for high load operations, targeting on ultra-low nitrogen oxides (NOx) and smoke emissions. A light-duty diesel engine is configured to adapt intake port fuelling of the ethanol fuel while keeping all other original engine components intact. High load experiments are performed to investigate the combustion control and low emission enabling without sacrificing the high compression ratio (18.2:1). The intake boost, exhaust gas recirculation (EGR) and injection pressure are independently controlled, and thus their effects on combustion and emission characteristics of the high load operation are investigated individually. The low temperature combustion is accomplished at high engine load (16~17 bar IMEP) with regulation compatible NOx and soot emissions.

Diesel fuels are complex mixtures of thousands of hydrocarbons. Since modeling their combustion characteristics with the inclusion of all hydrocarbon species is not feasible, a hybrid surrogate model approach is used in the present work to represent the physical and chemical properties of three different diesel fuels by using up to 13 and 4 separate hydrocarbon species, respectively. The surrogates are arrived at by matching their distillation profiles and important properties with the real fuel, while the chemistry surrogates are arrived at by using a Group Chemistry Representation (GCR) method wherein the hydrocarbon species in the physical property surrogates are grouped based on their chemical classes, and the chemistry of each class is represented by using up to two hydrocarbon species.

Alkali and alkaline earth metal impurities found in diesel fuels are potential poisons for diesel exhaust catalysts. Using an accelerated aging procedure, a set of production exhaust systems from a 2011 Ford F250 equipped with a 6.7L diesel engine have been aged to an equivalent of 150,000 miles of thermal aging and metal exposure. These exhaust systems included a diesel oxidation catalyst (DOC), selective catalytic reduction (SCR) catalyst, and diesel particulate filter (DPF). Four separate exhaust systems were aged, each with a different fuel: ULSD containing no measureable metals, B20 containing sodium, B20 containing potassium and B20 containing calcium. Metals levels were selected to simulate the maximum allowable levels in B100 according to the ASTM D6751 standard. Analysis of the aged catalysts included Federal Test Procedure emissions testing with the systems installed on a Ford F250 pickup, bench flow reactor testing of catalyst cores, and electron probe microanalysis (EPMA).

Chrysler, Ford, General Motors, the U.S. Environmental Protection Agency (EPA) and the California Air Resources Board (CARB) have collaborated over the past two years to develop an efficiency test for mobile air conditioner (MAC) systems. Because the effect of efficiency differences between different MAC systems and different technologies is relatively small compared to overall vehicle fuel consumption, quantifying these differences has been challenging. The objective of this program was to develop a single dynamic test procedure that is capable of discerning small efficiency differences, and is generally representative of mobile air conditioner usage in the United States. The test was designed to be conducted in existing test facilities, using existing equipment, and within a sufficiently short time to fit standard test facility scheduling. Representative ambient climate conditions for the U.S. were chosen, as well as other test parameters, and a solar load was included.

In-line hydrocarbon (HC) traps are not widely used to reduce HC emissions due to their limited durability, high platinum group metal (PGM) concentrations, complicated processing, and insufficient hydrocarbon (HC) retention temperatures required for efficient conversion by the three-way catalyst component. New trapping materials and system architectures were developed utilizing an engine dynamometer test equipped with dual Fourier Transform Infrared (FTIR) spectrometers for tracking the adsorption and desorption of various HC species during the light-off period. Parallel laboratory reactor studies were conducted which show that the new HC trap formulations extend the traditional adsorption processes (i.e., based on physic-sorption and/or adsorption at acid sites) to chemical reaction mechanisms resulting in oligomerized, dehydro-cyclization, and partial coke formation.

Advanced high strength steels (AHSS) have been widely accepted as a material of choice in the automotive industry to balance overall vehicle weight and stringent vehicle crash test performance targets. Combined with efficient use of geometry and load paths through shape and topology optimization, AHSS has enabled vehicle manufacturers to obtain the highest possible ratings in safety evaluations by the Insurance Institute for Highway Safety (IIHS) and the National Highway Traffic Safety Administration (NHTSA). In this study, vehicle CAE side impact models were used to evaluate three side impact crash test conditions (IIHS side impact, NHTSA LINCAP and FMVSS 214 side pole) and the IIHS roof strength test condition and to identify several key components affecting the side impact test performance. HyperStudy® optimization software and LS-DYNA® nonlinear finite element software were utilized for shape and gauge optimization.

A Ford 2.4-liter 115PS light-duty diesel engine was modified to allow solenoid control of the oil feed to the piston cooling jets, enabling these to be switched on or off on demand. The influence of the jets on piston temperatures, engine thermal state, gaseous emissions and fuel economy has been investigated. With the jets switched off, piston temperatures were measured to be between 23 and 88°C higher. Across a range of speed-load points, switching off the jets increased engine-out emissions of NOx typically by 3%, and reduced emissions of CO by 5-10%. Changes in HC were of the same order and were reductions at most conditions. Fuel consumption increased at low-speed, high-load conditions and decreased at high-speed, low-load conditions. Applying the results to the NEDC drive cycle suggests active on/off control of the jets could reduce engine-out emissions of CO by 6%, at the expense of a 1% increase in NOx, compared to the case when the jets are on continuously.

Analytical models (math or computer simulation models) are typically built on the basis of many assumptions and simplifications and hence model prediction could be inaccurate in intended applications. Model validation is thus critical to quantify and improve the degree of accuracy of these models. So far, little work considers model validation for various design configurations so that model prediction is accurate in the intended design space. Furthermore, there is a lack of effective approaches that can be used to quantify model accuracy considering different number of experimental data. To overcome these limitations, objective of this paper is to develop a model validation approach for various design configurations with a reference metric for model accuracy check considering different number of experimental data.

The effect of compression ratio on sensitivity to changes in start of injection and air-fuel ratio has been investigated on a single-cylinder DI diesel engine at fixed low and medium speeds and loads. Compression ratio was set to 17.9:1 or 13.7:1 by using pistons with different bowl sizes. Injection timing and air-to-fuel ratio were swept around a nominal map point at which gross IMEP and NOx values were matched for the two compression ratios. It was found that CO, HC and ISFC were higher at low compression ratio, but the soot/NOx trade-off improved and this could be exploited to reduce the fuel economy penalty. Sensitivity to inputs is generally similar, but high compression ratio tended to have steeper response gradients. Reducing compression ratio to 13.7 gave rise to a marked degradation of performance at light load, producing high CO emissions and a fall in combustion efficiency. This could be eased by reducing rail pressure, but the advantage in smoke emission was lost.

This paper reports a 2010 study undertaken to determine generic acceleration pulses for testing and evaluating advanced batteries for application in electric passenger vehicles. These were based on characterizing vehicle acceleration time histories from standard laboratory vehicle crash tests. Crash tested passenger vehicles in the United States vehicle fleet of the model years 2005-2009 were used. The crash test data were gathered from the following test modes and sources: 1 Frontal rigid flat barrier test at 35 mph (NHTSA NCAP) 2 Frontal 40% offset deformable barrier test at 40 mph (IIHS) 3 Side moving deformable barrier test at 38 mph (NHTSA side NCAP) 4 Side oblique pole test at 20 mph (US FMVSS 214/NHTSA side NCAP) 5 Rear 70% offset moving deformable barrier impact at 50 mph (US FMVSS 301). The accelerometers used were from locations in the vehicle where deformation is minor or non-existent, so that the acceleration represents the “rigid-body” motion of the vehicle.

This study demonstrates the use of pressure sensing technology to predict the crash severity of frontal impacts. It presents an investigation of the pressure change in the front structural elements (bumper, crush cans, rails) during crash events. A series of subsystem tests were conducted in the laboratory that represent a typical frontal crash development series and provided empirical data to support the analysis of the concept. The pressure signal energy at different sensor mounting locations was studied and design concepts were developed for amplifying the pressure signal. In addition, a pressure signal processing methodology was developed that relies on the analysis of the air flow behavior by normalizing and integrating the pressure changes. The processed signal from the pressure sensor is combined with the restraint control module (RCM) signals to define the crash severity, discriminate between the frontal crash modes and deploy the required restraint devices.

The SAE Fuel Cell Vehicle (FCV) Safety Working Group has been addressing FCV safety for over 11 years. In the past couple of years, significant attention has been directed toward a revision to the standard for vehicular hydrogen systems, SAE J2579(1). In addition to streamlining test methodologies for verification of Compressed Hydrogen Storage Systems (CHSSs) as discussed last year,(2) the working group has been considering the effect of vehicle fires, with the major focus on a small or localized fire that could damage the container in the CHSS and allow a burst before the Pressure Relief Device (PRD) can activate and safely vent the compressed hydrogen stored from the container.