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Emissions of particulate matter, or PM, due to brake wear, are not well quantified in current air pollutant emission inventories. Current emission factor models need to be updated to reflect new technologies and materials and to incorporate the effects of changing driving habits and speeds. While emission regulations drive technical innovations that are significantly reducing PM emissions in vehicle exhaust, non-exhaust automotive emissions remain unregulated. Current emission factor models need to be updated to reflect the changes caused by new technologies, materials, and speed-dependent vehicle usage. Most research regarding brake emissions relies on a laboratory setting. Laboratory testing has allowed researchers, application engineers, data modeling engineers, and environmental agencies to generate large datasets for multiple vehicle configurations and friction couple designs.

This article presents an approach for comparing alternative repairable systems and calculating the value of information obtained by testing a specified number of such systems. More specifically, an approach is presented to determine the value of information that comes from field testing a specified number of systems in order to appropriately estimate the reliability metric associated with each of the respective repairable systems. Here the reliability of a repairable system will be measured by its failure rate. In support of the decision-making effort, the failure rate is translated into an expected utility based on a utility curve that represents the risk tolerance of the decision-maker. The algorithm calculates the change of the expected value of the decision with the sample size. The change in the value of the decision represents the value of information obtained from testing.

Design optimization occurs through a series of decisions that are a standard part of the product development process. Decisions are made anywhere from concept selection to the design of the assembly and manufacturing processes. The effectiveness of these decisions is based on the information available to the decision maker. Decision analysis provides a structured approach for quantifying the value of information that may be provided to the decision maker. This paper presents a process for determining the value of information that can be gained by evaluating linearly correlated design alternatives. A unique approach to the application of Bayesian Inference is used to provide simulated estimates in the expected utility with increasing observations sizes. The results provide insight into the optimum observation size that maximizes the expected utility when assessing correlated decision alternatives.

This paper covers work performed for the California Air Resources Board and US Environmental Protection Agency by Southwest Research Institute. Emission measurements were made on four in-use off-road two-stroke motorcycles and all-terrain vehicles utilizing oxygenated and non-oxygenated fuels. Emission data was produced to augment ARB and EPA's off-road emission inventory. It was intended that this program provide ARB and EPA with emission test results they require for atmospheric modeling. The paper describes the equipment and engines tested, test procedures, emissions sampling methodologies, and emissions analytical techniques. Fuels used in the study are described, along with the emissions characterization results. The fuel effects on exhaust emissions and operation due to ethanol content and fuel components is compared.

Importance Sampling is a popular method for reliability assessment. Although it is significantly more efficient than standard Monte Carlo simulation if a suitable sampling distribution is used, in many design problems it is too expensive. The authors have previously proposed a method to manage the computational cost in standard Monte Carlo simulation that views design as a choice among alternatives with uncertain reliabilities. Information from simulation has value only if it helps the designer make a better choice among the alternatives. This paper extends their method to Importance Sampling. First, the designer estimates the prior probability density functions of the reliabilities of the alternative designs and calculates the expected utility of the choice of the best design. Subsequently, the designer estimates the likelihood function of the probability of failure by performing an initial simulation with Importance Sampling.

The impact of biodiesel and new generation biofuels on emissions from heavy-duty diesel engines was investigated using a California Air Resources Board (CARB) certified diesel fuel as a base fuel. This study was performed on two heavy-duty diesel engines, a 2006 engine and a diesel particle filter (DPF) equipped 2007 engine, on an engine dynamometer over four different test cycles. Emissions from soy-based and animal-based biodiesel, renewable diesel fuel, and gas-to-liquid (GTL) diesel fuel were evaluated at blend levels ranging from 5 to 100%. Consistent with previous studies, particulate matter (PM), hydrocarbons (HC), and carbon monoxide (CO) emissions generally showed increasing reductions with increasing biodiesel and renewable/GTL diesel fuel blend levels for the non-DPF equipped engine. The levels of these reductions were generally comparable to those found in previous studies performed using more typical Federal diesel fuels.

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.

Since the mid-1990s, light-duty vehicles equipped with gasoline direct injection (GDI) engines have been added to the vehicle fleet in increasing numbers. Compared to conventional port fuel injection (PFI) engines, GDI engines provide higher power output for the same size engine, higher fuel efficiency, and lower carbon dioxide (CO₂) emissions. Due to the paucity of particulate matter (PM) emission data for light-duty gasoline vehicles in general and the increasing interest in these emissions relative to climate and air quality concerns, it is important to investigate PM emissions from current-generation GDI technologies. In this study, nine 2007-2010 light-duty GDI vehicles equipped with either wall-guided or spray-guided fuel injection systems were tested using California commercial gasoline fuel containing six percent ethanol by volume. Criteria pollutants including gaseous and PM emissions were measured over the Federal Test Procedure (FTP) transient test cycle.

The introduction of hybrid and plug-in hybrid electric vehicles has resulted in the introduction of battery systems into the realm of OBD II diagnostics. After a high-level overview of battery systems, general battery system fault responses are discussed, as well as which of these might be OBD faults. The alignment of the OBD regulations and DTC assignment in systems with large numbers of similar/identical components is discussed, along with apparent conflicts between existing OBD regulations and the physical realities of battery systems in HEVs and PHEVs. Presenter Dyche Anderson, Ford Motor Co.

Update on light-duty regulations. Presenter Michael J. McCarthy, California Air Resources Board

In recognizing the potential for large, damaging impacts from climate change, California enacted Executive Order S-03-05, requiring a reduction in statewide greenhouse gas (GHG) emissions to 80% below 1990 levels by 2050. Given that the transportation light-duty vehicle (LDV) segment accounts for 28% of the state's GHG emissions today, it will be difficult to meet the 2050 goal unless a portfolio of near-zero carbon transportation solutions is pursued. Because it takes decades for a new propulsion system to capture a large fraction of the passenger vehicle market due to vehicle fleet turn-over rates, it is important to accelerate the introduction of these alternatives to ensure markets enter into early commercial volumes (10,000s) between 2015 and 2020. This report summarizes the results and conclusions of a modeling exercise that simulated GHG emissions from the LDV sector to 2050 in California.

A complete probabilistic model of uncertainty in probabilistic analysis and design problems is the joint probability distribution of the random variables. Often, it is impractical to estimate this joint probability distribution because the mechanism of the dependence of the variables is not completely understood. This paper proposes modeling dependence by using copulas and demonstrates their representational power. It also compares this representation with a Monte-Carlo simulation using dispersive sampling.

In order to demonstrate the performance of a retrofitted selective catalytic reduction (SCR) system while also addressing the issues associated with greater use of biodiesel, a 2005 International 9200i tractor owned by the City of Santa Monica was retrofitted with a titania-vanadia-tungsten catalyst and a urea dosing system supplied by Extengine Systems, Inc. This tractor was operated under normal service conditions within the City of Santa Monica refuse collection and transportation fleet. An on-board emissions measurement system supplied by Engine, Fuel, and Emissions Engineering, Inc. was installed on the vehicle; it measured the emissions and fuel use of the vehicle while it operated on ultra-low-sulfur diesel (ULSD), 20% biodiesel (B20), and 99% biodiesel (B99) on consecutive days.

A novel in situ method was performed for measuring emissions and fuel consumption of transport refrigeration units (TRUs). The test matrix included two fuels, two exhaust configurations, and two TRU engine operating speeds. The test fuels were California ultra low sulfur diesel and gas-to-liquid (GTL) diesel. The exhaust configurations were a stock original equipment manufacturer (OEM) muffler and a Thermo King pDPF™ diesel particulate filter. The two TRU engine operating speeds were high and low, as controlled by the TRU user interface. Test results indicate that GTL diesel fuel reduces all regulated emissions at high and low engine operating speeds. Separately, the application of a Thermo King pDPF reduced regulated emissions, in some cases almost entirely. Finally, the application of both GTL diesel and a Thermo King pDPF reduced regulated emissions at high engine operating speed, but with an increase in oxides of nitrogen (NOx) at low engine speed.

The problem of predicting the quality of weld is critical to manufacturing. A great deal of data is collected under multiple conditions to predict the quality. The data generated at Daimler Chrysler has been used to develop a model based on grammatical evolution. Grammatical Evolution Technique is based on Genetic Algorithms and generates rules from the data which fit the data. This paper describes the development of a software tool that enables the user to choose input variables such as the metal types of top and bottom layers and their thickness, intensity and speed of laser beam, to generate a three dimensional map showing weld quality. A 3D weld quality surface can be generated in response to any of the two input variables picked from the set of defining input parameters. This tool will enable the user to pick the right set of input conditions to get an optimal weld quality. The tool is developed in Matlab with Graphical User Interface for the ease of operation.

This paper presents a computer generated model of a hydraulic circuit that is typically seen in hybrid hydraulic vehicles (HHV's). HHV's have shown considerable potential for increasing fuel economy and decreasing emissions for mid-size and commercial trucks that exhibit urban driving habits. The aim of the present model is to aid in further optimizing the performance of the hybrid powertrain and serve as a baseline for future studies into the noise and vibration (NV) associated with the system. The model simulates the regenerative braking process of a parallel type hybrid hydraulic propulsion system. Regenerative braking captures and stores otherwise lost energy into an accumulator. This stored energy can then later be used to propel the vehicle, thus reducing the vehicle's reliance on a conventional internal combustion engine (ICE). This model will serve as a baseline for future developments and will be expanded and validated experimentally in ensuing research.

Low frequency torsional vibrations can be a significant source of objectionable vehicle vibrations and in-vehicle boom, especially with changes in engine operation required for improved fuel economy. These changes include lower torque converter lock-up speeds and cylinder deactivation. This paper has two objectives: 1) Examine the effect of increased torsional vibrations on vehicle NVH performance and ways to improve this performance early in the program using test and simulation techniques. The important design parameters affecting vehicle NVH performance will be identified, and the trade-offs required to produce an optimized design will be examined. Also, the relationship between torsional vibrations and mount excursions, will be examined. 2) Investigate the ability of simulation techniques to predict and improve torsional vibration NVH performance. Evaluate the accuracy of the analytical models by comparison to test results.

Brake torque variation is a source of objectionable NVH body/chassis response. Such input commonly results from brake disk thickness variation. The NVH dynamic characteristics of a vehicle can be assessed and quantified through experimental modal testing for determination of mode resonance frequency, damping property, and shape. Standard full vehicle modal testing typically utilizes a random input excitation into the vehicle frame or underbody structure. An alternative methodology was sought to quantify and predict body/chassis sensitivity to brake torque variation. This paper presents a review of experimental modal test methodologies investigated for the reproduction of vehicle response to brake torque variation in a static laboratory environment. Brake caliper adapter random and sine sweep excitation input as well as body sine sweep excitation in tandem with an intentionally locked brake will be detailed.

This paper describes the application of the modal compliance method to a complex structure such as a vehicle body in white, and the extension of the method from normal modes to the complex modes of a complete vehicle. In addition to the usual bending and torsion calculations, the paper also describes the application of the method to less usual tests such as second torsion, match-boxing and breathing. We also show how the method can be used to investigate the distribution of compliance throughout the structure.

NVH refinement of passenger vehicles is crucial to customer acceptance of contemporary vehicles. This paper describes the vehicle NVH development process, with specific examples from a Diesel sedan application that was derived from gasoline engine-based vehicle architecture. Using an early prototype Diesel vehicle as a starting point, this paper examines the application of a Vehicle Interior Noise Simulation (VINS) technique in the development process. Accordingly, structureborne and airborne noise shares are analyzed in the time-domain under both steady-state and transient test conditions. The results are used to drive countermeasure development to address structureborne and airborne noise refinement. Examples are provided to highlight the refinement process for “Diesel knocking” under idle as well as transient test conditions. Specifically, the application of VINS to understanding the influence of high frequency dynamic stiffness of hydro-mounts on Diesel clatter noise is examined.