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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.

In this work, a dynamically loaded hydrodynamic journal bearing test rig is developed and introduced. The rig is a novel design, using a hydraulic actuator with fast acting spool valves to apply load to a connecting rod. This force is transmitted through the connecting rod to the large end bearing which is mounted on a spinning shaft. The hydraulic actuator allows for fully variable control and can be used to apply either static load in compression or tension, or dynamic loading to simulate engine operation. A variable speed electric motor controls shaft speed and is synchronized to the hydraulic actuator to accurately simulate loading to represent all four engine strokes. A high precision torque meter enables direct measurements of friction torque, while shaft position is measured via a high precision encoder.

Autonomous vehicle operation is dependent upon accurate position estimation and thus a major concern of implementing the autonomous navigation is obtaining robust and accurate data from sensors. This is especially true, in case of Inertial Measurement Unit (IMU) sensor data. The IMU consists of a 3-axis gyro, 3-axis accelerometer, and 3-axis magnetometer. The IMU provides vehicle orientation in 3D space in terms of yaw, roll and pitch. Out of which, yaw is a major parameter to control the ground vehicle’s lateral position during navigation. The accelerometer is responsible for attitude (roll-pitch) estimates and magnetometer is responsible for yaw estimates. However, the magnetometer is prone to environmental magnetic disturbances which induce errors in the measurement.

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.

This paper summarizes the Heavy-Duty In-Use Testing (HDUIT) measurement allowance program for Particulate Matter Portable Emissions Measurement Systems (PM-PEMS). The measurement allowance program was designed to determine the incremental error between PM measurements using the laboratory constant volume sampler (CVS) filter method and in-use testing with a PEMS. Two independent PM-PEMS that included the Sensors Portable Particulate Measuring Device (PPMD) and the Horiba Transient Particulate Matter (TRPM) were used in this program. An additional instrument that included the AVL Micro Soot Sensor (MSS) was used in conjunction with the Sensors PPMD to be considered a PM-PEMS. A series of steady state and transient tests were performed in a 40 CFR Part 1065 compliant engine dynamometer test cell using a 2007 on-highway heavy-duty diesel engine to quantify the accuracy and precision of the PEMS in comparison with the CVS filter-based method.

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.

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.

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.

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.

The presented manuscript discusses the implementation of the pulsed-thermography technique for the non-intrusive evaluation of automotive parts. The study discusses the fundamentals of static and dynamic thermography through examples and case studies. Furthermore, the proposed pulse thermography system is analyzed in terms of hardware calibration i.e. pulse duration and intensity and the detector effect on the time and the spatial resolutions. Current thermography processing codes and techniques are also described and critiqued, with new processing subroutines proposed; one based on self-referencing thersholding. Additionally, new trends in infrared and visible sensors fusion are presented.

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.

The number of control actuators available on spark-ignition engines is rapidly increasing to meet demand for improved fuel economy and reduced exhaust emissions. The added complexity greatly complicates control strategy development because there can be a wide range of potential actuator settings at each engine operating condition, and map-based actuator calibration becomes challenging as the number of control degrees of freedom expand significantly. Many engine actuators, such as variable valve actuation and flow control valves, directly influence in-cylinder combustion through changes in gas exchange, mixture preparation, and charge motion. The addition of these types of actuators makes it difficult to predict the influences of individual actuator positioning on in-cylinder combustion without substantial experimental complexity.

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.

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.

The NOx reduction performance under lean conditions over γ-alumina was evaluated using a micro-reactor system and a non-thermal plasma-equipped bench test system. Various alumina samples were obtained from alumina manufacturers to assess commercial alumina materials. In addition, γ-alumina samples were synthesized at Caterpillar with a sol-gel technique in order to control alumina properties. The deNOx performances of the alumina samples were compared. The alumina samples were characterized with analytical techniques such as inductively coupled plasma (ICP) emission spectroscopy, temperature programmed desorption (TPD) and surface area measurements (BET) to understand physical and chemical properties. The information derived from these techniques was correlated with the NOx reduction performance to identify key parameters of γ-alumina for optimizing materials for lean-NOx and plasma assisted catalysis.

Atmospheric corrosion of steels, aluminum alloys, and Al-clad aluminum alloys is a problem for many civil engineering structures, commercial and military vehicles, and aircraft. Paint is usually the primary means to prevent the corrosion of steel bridge components, automobiles, trucks, and aircraft. Under ideal conditions, the coating provides a continuous layer that is impervious to moisture. At present, maintenance cycles for commercial and military aircraft and ground vehicles, as well as engineered structures, is based on experience and appearance rather than a quantitative determination of coating integrity. To improve the maintenance process and reduce costs, sensors are often used to monitor corrosion. The present suite of sensors designed to detect corrosion and marketed to predict the lifetime of the engineered components, however, are not useful for determining the condition of the protective paint coatings.