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This paper proposes an approach that characterizes a driver's driving behavior and style in real-time during car-following drives. It uses an online learning of the evolving Takagi-Sugeno fuzzy model combined with the Markov model. The inputs fed into the proposed algorithm are from the measured signals of on-board sensors equipped with current vehicles, including the relative distance sensors for Adaptive Cruise Control feature and the accelerometer for Electronic Stability Control feature. The approach is verified using data collected using a test vehicle from several car-following test trips. The effectiveness of the proposed approach has been shown in the paper.

This paper proposes an approach to use ABS wheel speed sensor signals together with other vehicle state information from a brake control module to detect an unbalanced tire or tires in real-time. The proposed approach consists of two-stage algorithms that mix a qualitative method using band-pass filtering with a quantitative parameter identification using conditional least squares. This two-stage approach can improve the robustness of tire imbalance or imbalances. The proposed approach is verified through vehicle testing and the test results show the effectiveness of the approach.

In an attempt to predict the responses of side crash pressure sensors, the Corpuscular Particle Method (CPM) was adopted and enhanced in this research. Acceleration-based crash sensors have traditionally been used extensively in automotive industry to determine the air bag firing time in the event of a vehicle accident. The prediction of crash pulses obtained from the acceleration-based crash sensors by using computer simulations has been very challenging due to the high frequency and noisy responses obtained from the sensors, especially those installed in crash zones. As a result, the sensor algorithm developments for acceleration-based sensors are largely based on prototype testing. With the latest advancement in the crash sensor technology, side crash pressure sensors have emerged recently and are gradually replacing acceleration-based sensor for side impact applications.

An Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this study to predict the responses of side crash pressure sensors in an attempt to assist pressure sensor algorithm development by using computer simulations. Acceleration-based crash sensors have traditionally been used to deploy restraint devises (e.g., airbags, air curtains, and seat belts) in vehicle crashes. The crash pulses recorded by acceleration-based crash sensors usually exhibit high frequency and noisy responses depending on the vehicle's structural design. As a result, it is very challenging to predict the responses of acceleration-based crash sensors by using computer simulations, especially those installed in crush zones. Therefore, the sensor algorithm developments for acceleration-based sensors are mostly based on physical testing.

With a goal to help develop pressure sensor calibration and deployment algorithms using computer simulations, an Arbitrary Lagrangian Eulerian (ALE) approach was adopted in this research to predict the responses of side crash pressure sensors for unitized vehicles. For occupant protection, acceleration-based crash sensors have been used in the automotive industry to deploy restraint devices when vehicle crashes occur. With improvements in the crash sensor technology, pressure sensors that detect pressure changes in door cavities have been developed recently for vehicle crash safety applications. Instead of using acceleration (or deceleration) in the acceleration-based crash sensors, the pressure sensors utilize pressure change in a door structure to determine the deployment of restraint devices. The crash pulses recorded by the acceleration-based crash sensors usually exhibit high frequency and noisy responses.

Controlled Auto Ignition combustion systems have a high potential for fuel consumption and emissions reduction for gasoline engines in part load operation. Controlled auto ignition is initiated by reaching thermal ignition conditions at the end of compression. Combustion of the CAI process is controlled essentially by chemical kinetics, and thus differs significantly from conventional premixed combustion. Consequently, the CAI combustion process is determined by the thermodynamic state, and can be controlled by a high amount of residual gas and stratification of air, residual gas and fuel. In this paper both fundamental and application relevant aspects are investigated in a combined approach. Fundamental knowledge about the auto-ignition process and its dependency on engine operating conditions are required to efficiently develop an application strategy for CAI combustion.

A model of Ford's current FTP based OBD-II catalyst monitor has been developed and used in determining the optimal monitored catalyst volume for several LEV applications. The model predictions were found to agree reasonably well with the available experimental data. Furthermore, the results of this study indicate that the optimal monitored catalyst volume for meeting LEV requirements is vehicle application specific. As a result, it is concluded that a general guideline for sizing of the monitored catalyst volume for LEVs will most likely be inadequate and could result in grossly suboptimal catalyst monitor function for some applications. The model which is described in this paper offers a potentially more effective means of determining the best monitored catalyst volume for a given vehicle application. It should be possible to utilize this model during the early phase of a vehicle program in order to provide for the optimal packaging of the catalyst monitor sensor (CMS).

The effect of fuel sulfur on the dual HEGO sensor OBD-II catalyst monitor has been investigated. Laboratory studies revealed two competing effects of the fuel sulfur on the operation of the catalyst monitor: 1. the loss of catalyst oxygen storage capacity, and 2. the degradation in the response rate of the rear HEGO sensor. The magnitude of the loss in catalyst OSC relative to the increase in rear HEGO sensor response time determined whether the rear HEGO sensor index increased, decreased, or remained constant as the fuel sulfur level was increased. The effect of fuel sulfur on tailpipe emissions and the catalyst monitor was also measured for two LEVs equipped with Pd-only catalyst technology (a 1997MY Escort and a 1998MY Crown Victoria). For both vehicles, the effect of the fuel sulfur on the rear HEGO sensor response characteristics dominated; as the fuel sulfur level was increased, tailpipe emissions increased, but the rear HEGO sensor index decreased.

This report is a comprehensive investigation into the use of resin transfer molded glass fiber reinforced plastics in a structural application. A pickup truck cab structure is an ideal application for plastic composites. The cab is designed to fit a production Ranger pickup truck and uses carryover frame and front end structure. The cab concept consists primarily of two molded pieces. This design demonstrates extensive parts integration and allows for low-cost tooling, along with automated assembly.

Experimental forms of two different types of engine oil viscosity sensors have been tested that use uniformly poled disks of piezoelectric PZT ceramic. In both cases, the disks were used to form electromechanical resonators functioning as the frequency-controlling element in a transistor oscillator circuit. The simpler type of sensor used only one disk, vibrating in a radial-longitudinal mode of vibration. In this mode, a disk 2.54 cm in diameter and 0.127 cm thick had a resonant frequency of approximately 90 kHz. The second type of sensor used two such disks bonded together by a conducting epoxy, with poling directions oriented in opposite directions. This composite resonator vibrated in a radially-symmetrical, flexural mode of vibration, with the lowest resonant frequency at approximately 20 kHz. The presence of tangential components of motion on the major faces of both resonators made them sensitive to the viscosity of fluids in which they were immersed.

A piezoelectrically driven vibrating cantilever blade is damped by a number of mechanisms including viscous damping in a still fluid and aerodynamic damping in a flow. By measuring the damping of devices operating at resonance in the 1 to 5 kHz region, one can measure such properties as mass flow, absolute pressure or the product of molecualar mass and viscosity. In the case of the mass flow measurement, the device offers a mechanical alternative to hotwire and hot film devices for the automotive application.

Rapid Prototyping, Fabrication and Tooling is a process that blends a series of technologies (machines, tools, and methods) capable of generating physical objects directly from a CAD database. The process dramatically reduces the time spent during product development by allowing for fast visualization, verification, iteration, optimization, and fabrication of parts and tools. Many new techniques of tooling have been and are being developed by using rapid fabricated parts. These are having a dramatic impact on both timing and costs throughout the automotive industry. One area that these methods can be utilized to their full potential is motorsports. Of particular interest is the growing use of bridge tooling to provide first article through production intent parts that promote cost effective changes.

Lookup tables and functions are widely used in real-time embedded automotive applications to conserve scarce processor resources. To minimize the resource utilization, these lookup tables (LUTs) commonly use custom data structures. The lookup function code is optimized to process these custom data structures. The legacy routines for these lookup functions are very efficient and have been in production for many years. These lookup functions and the corresponding data structures are typically used for calibration tables. The third-party calibration tools are specifically tailored to support these custom data structures. These tools assist the calibrators in optimizing the control algorithm performance for the targeted environment for production. Application software typically contains a mix of both automatically generated software and manually developed code. Some of the same calibration tables may be used in both auto generated and hand-code [ 1 ] [ 2 ].

Increasing electronics content, growing computing power, and proliferation of opportunities for information connectivity (through improved sensors, GPS, road and traffic information systems, wireless internet access, vehicle-to-vehicle communication, etc.) are technology trends which can significantly transform and impact future automotive vehicle's control and diagnostic strategies. One aspect of the increasing vehicle connectivity is access to ambient and road condition information, such as ambient temperature, ambient pressure, humidity, % cloudiness, visibility, cloud ceiling, precipitation, rain droplet size, wind speed, and wind direction based on wireless internet access. The paper discusses the potential opportunities made available through wireless communication between the vehicle and the internet.

Exhaust gas temperature is often measured with a device such as thermocouple or RTD (Resistance Temperature Detector). An alternative method to determine the gas temperature would be to use an existing gas sensor heating mechanism to perform as a temperature sensor. A planar type FLOH (Fast Light Off HEGO-Heated Exhausted Gas Oxygen) sensor under transient vehicle speed/load conditions is suited to this function and was modeled to predict the exhaust gas temperature. The numerical input to the model includes exhaust flow rate, heater voltage, and heater current. Laboratory experiments have been performed to produce an equation relating the resistance of the heater and the temperature of the sensor (heater), which provides a method to indirectly determine HEGO sensor temperature.

In 1999, the first generation NOx sensor from NGK Spark Plug, Co., Ltd. was commercialized for use in gasoline LNT NOx after-treatment systems [ 1 ]. Since then, as emissions regulations and OBD requirements have become more stringent, the demand for a high-accuracy NOx sensor with fast light-off has increased, particularly for diesel after-treatment systems. To meet such market demands, NGK Spark Plug, Co., Ltd. has developed, in collaboration with Ford Motor Company, a second generation NOx sensor.

This paper proposes a novel algorithm. This algorithm is called Self-Organizing Fuzzy Neural Network (SOFNN). SOFNN revolutionizes how researchers apply control theories, image/signal processing on control systems and other applications. In general, SOFNN is an identification technique that automatically initiates, builds and fine-tunes the required network parameters. SOFNN evaluates required structures without predefined parameters or expressions regarding systems. SOFNN sets out to learn and configure a system's characteristics. Self-constructing and self-tuning features enable SOFNN to handle complex, non-linear, and time-varying systems with higher accuracy, making systems identification easier. SOFNN constructs and fine-tunes the system parameter through two phases. The two phases are the construction and the parameter-tuning phase. The two phases run concurrently allowing SOFNN to identify systems on-line.

Several shortcomings of mechanical door checks are overcome using a magnetorheological damper. Because the damper is electrically actuated, it can check in any desired position. The logical decision to activate or release the door check can be made either by passive circuitry based on input signals from switches attached to door handles or under microprocessor control, in which case the decision can take into account a variety of unconventional input factors, including the magnitude of the force applied to the door, the rate of change of the applied force, and the angle of door opening. With the addition of an appropriate proximity sensor, the controllable damper can prevent the door from inadvertently hitting a nearby obstacle. Details of the damper mechanism are described, and several implemented control strategies, both passive and microprocessor based, are discussed.

A series of tests have been performed on a production vehicle to determine the characteristics of the external turbulent flow field in wind tunnel and road conditions. Empirical formulas are developed to use the measured data as source levels for a Statistical Energy Analysis (SEA) model of the vehicle structural and acoustical responses. Exterior turbulent flow and acoustical subsystems are used to receive power from the source excitations. This allows for both the magnitudes and wavelengths of the exterior excitations to be taken into account - a necessary condition for consistently accurate results. Comparisons of measured and calculated interior sound levels show good correlation.

Due to today's demands to reduce cost and product time to market, engineering procedures are increasingly using more sophisticated simulation techniques, instead of validation testing. Early implementation of CAE methods yield higher quality products, even with first prototypes, reducing the design iterations required to reach production quality. The strategy is to conduct specific evaluations of a realistic representation of the product while focusing on the key boundary conditions necessary to extract fatigue effects. Discussed in this paper are adequate CAE methods for early identification, evaluation and removal of conceptual and local structural weaknesses. Possible solutions gained from a computational optimization process are discussed for highly loaded HSDI diesel cylinder heads as a representative example.