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With the inception of model-based design and automatic code generation, many organizations are developing controls and diagnostics algorithms in model-based development tools to meet customer and regulatory requirements. Advances in model-based design have made it easier to generate C code from models and help software engineers streamline their workflow. Typically, after the software has been developed, the models are handed over to a calibration team responsible for calibrating the features to meet specified customer and regulatory requirements. However, once the models are handed over to the calibration team, the calibration engineers are unaware of how to calibrate the features because documentation is not available. Typically, model documentation trails behind the software process because it is created manually, most of this time is spent on formatting. As a result, lack of model documentation or up-to date documentation causes a lot of pain for OEM’s and Tier 1 suppliers.

In a CAS system, the distance and relative velocity between front and host vehicles are estimated to calculate time-to-collision (TTC). The distance estimates by different methods will certainly include noise which should be removed to ensure the accuracy of TTC calculations. Kalman filter is a good tool to filter such type of noise. Nevertheless, Kalman filter is a model based filter, which means a correct model is important to get the good filtering results. Usually, a vehicle is either moving with a constant velocity (CV) or constant acceleration (CA) maneuvers. This means the distance data between front and host vehicles can be described by either constant velocity or constant acceleration model. In this paper, first, CV and CA models are used to design two Kalman filters and an interacting multiple model (IMM) is used to dynamically combine the outputs from two filters.

The paper describes a new strategy for real-time sensor diagnostics that is based on the statistical correlation of various sensor signal pairs. During normal fault-free operation there is a certain correlation between the sensor signals which is lost in the event of a fault. The proposed algorithm quantifies the correlation between sensor signal pairs using real-time scalar metrics based on the Mahalanobis-distance concept. During normal operation all metrics follow a similar pattern, however in the event of a fault; metrics involving the faulty sensor would increase in proportion to the magnitude of the fault. Thus, by monitoring this pattern and using a suitable fault-signature table it is possible to isolate the faulty sensor in real-time. Preliminary simulation results suggest that the strategy can mitigate the false-alarms experienced by most model-based diagnostic algorithms due to an intrinsic ability to distinguish nonlinear vehicle behavior from actual sensor faults.

A study was conducted to investigate the effects of commercial E-85 fuel properties on Port Fuel Injector (PFI) durability performance. E-85 corrosivity, not lubricity, was identified as the primary property affecting injector performance. Relatively high levels of water, chloride and organic acid contamination, detected in commercial E-85 fuels sampled in the U.S. in 2006, were the focus of the study. Analysis results and analytical techniques for determining contaminant levels in and corrosivity of commercial E-85 fuels are discussed. Studies were conducted with E-85 fuels formulated to represent worst-case field fuels. In addition to contamination with water, chloride and organic acids, fuels with various levels of a typical ethanol corrosion inhibitor were tested in the laboratory to measure the effects on E-85 corrosivity. The effects of these E-85 contaminants on injector durability performance were also evaluated.

The paper describes the development of a vehicle stability indicator based on the correlation between various current vehicle chassis sensors such as hand wheel angle, yaw rate and lateral acceleration. In general, there is a correlation between various pairs of sensor signals when the vehicle operation is linear and stable and a lack of correlation when the vehicle is becoming unstable or operating in a nonlinear region. The paper outlines one potential embodiment of the technology that makes use of the Mahalanobis distance metric to assess the degree of correlation among the sensor signals. With this approach a single scalar metric provides an accurate indication of vehicle stability.

This study examines the effectiveness of gaseous oxygen at preventing embrittlement in steel associated with exposure to gaseous hydrogen under static loading conditions. Notched C-ring samples machined from 4340 steel and heat treated to HRC 51-53 were used to test the neutrality of an oxygen-hydrogen gas mixture similar to that which may be used as a generant in an air bag inflator. The 29 percent oxygen to hydrogen gas ratio of the gas mixture was found to be sufficient to protect the steel from hydrogen embrittlement under static loading conditions. This would indicate that any steel with a hardness of HRC 51 or lower would be safe to use in gas-based air bag inflators containing a oxygen to hydrogen gas ratio of 29 percent or higher.

A comprehensive investigation into why gasoline fuel injectors fail in the field due to deposit formation has led to the development of a robust fuel injector design. Analysis of field failures provided critical clues as to why fuel injectors form deposits. The development of a repeatable test and a repeatable deposit forming fuel allowed the confirmation of these clues and the testing of design improvements. This combination of test cycle and fuel allowed for a reduced test time while providing sufficient sensitivity to differentiate between injector design improvements. Confirmation of design improvements was completed on a stationary vehicle using both commercially available gasoline and a formulated deposit forming fuel.

Close-coupled or manifold catalysts have been extensively employed to reduce emissions during cold start by achieving quick catalyst light-off. These catalysts must have good thermal durability, high intrinsic light-off activity and high HC/CO/NOx conversions at high temperature and flow conditions. A number of studies have been dedicated to engine control, manifold design and converter optimization to reduce cold start emissions. The current paper focuses on the effect of catalyst design parameters and their performance response to different engine operating conditions. Key design parameters such as catalyst formulation (CeO2 vs. non CeO2), precious metal loading and composition (Pd vs. Pd/Rh), washcoat loading, catalyst thermal mass, substrate properties and key application (in use) parameters such as catalyst aging, exhaust A/F ratio, A/F ratio modulation, exhaust temperature, temperature rise rate and exhaust flow rate were studied on engine dynamometers in a systematic manner.

The focus of this paper is to understand, from experimental data, the R134a refrigerant emission rates of various hose materials due to permeation. This paper focuses on four main points for hose assembly emission of R134a: (1) characteristics of hose permeation in response to the effect of oil in R134a and the characteristics of hose permeation of vapor vs. liquid refrigerant; (2) conditioning of the hose material over time to reach steady state R134a emission; (3) the relative contribution of hose permeation and coupling emission to the overall hose assembly refrigerant emission; (4) transient emission rates due to transient temperature and pressure conditions. Studies include hoses with different materials and constructions resulting in various levels of R134a permeation.

Slush molding is a unique processing operation that was developed originally for polyvinyl chloride (PVC) based materials. It has been utilized to produce a variety of automotive interior products, including instrument panel skins, where relatively intricate designs are required. PVC becomes brittle upon aging, while thermoplastic polyolefin (TPO) doesn’t lose its ductility upon aging. TPOs have made significant inroads into interior applications in the form of thermoformed extruded sheet. However, when multiple grains, geometric (technical) grains, deep profile lettering, and logos are needed, slush molding is the preferred process. Currently, there is an increased demand for non-PVC slush moldable materials, such as TPO, that can meet these demanding aggressive styling requirements. The semi-crystalline nature of TPO compositions renders them more difficult to process than PVC in slush molding.

As automotive electronic control systems continue to increase in usage and complexity, the challenges for developing automotive diagnostics also increase. Reduced development cycle times, the increased significance of diagnostics for safety critical systems, and the integration of vehicle systems across multiple control systems all add to the tasks of developing diagnostics for the automobiles of today and tomorrow. Addressing automotive diagnostics now requires the Tier 1 supplier to utilize a formal diagnostic development methodology. There are also opportunities for Tier 1 suppliers to add value by developing vehicle-level supervisory diagnostic strategies, in addition to subsystem and system-level diagnostic strategies. There is also a prospect to provide strategies and tools to enhance service at the vehicle level. This paper proposes an approach for Tier 1 suppliers to address diagnostic and service issues at the component, system, and vehicle level.

The perceived interior noise has been one of the major driving factors in the design of automotive interior assemblies. Buzz, Squeak and Rattle (BSR) issues are one of the major contributors toward the perceived quality in a vehicle. Traditionally BSR issues have been identified and rectified through extensive hardware testing. In order to reduce the product development cycle and minimize the number of costly hardware builds, however, one must rely on engineering analysis and simulation upfront in the design cycle. In this paper, an analytical and experimental study to identify potential BSR locations in a cockpit assembly is presented. The analytical investigation utilizes a novel and practical methodology, implemented in the software tool Nhance.BSR, for identification and ranking of potential BSR issues. The emphasis here is to evaluate the software for the BSR predictions and the identification of modeling issues, rather than to evaluate the cockpit design itself for BSR issues.

This paper presents a methodology to predict component and system reliability and durability. The methodology is illustrated with an electronic diesel fuel injector case study that integrates customer usage data, component failure distribution, system failure criteria, manufacturing variation, and variation in customer severity. Extension to the vehicle system level enables correlation between component and system requirements. Further, this analysis provides the basis to establish a knowledge-based test option for a success test validation program to demonstrate reliability.