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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.

As part of a general EGR system model, an adiabatic thermodynamic vacuum EGR valve actuator model was developed and validated. The long term goal of the work is improved system operation by correctly specifying and allocating EGR system component requirements.

This paper describes two types of dielectric-effect sensors that may be used as alternatives to a dielectric-effect sensor using a single capacitor. In the first type, three capacitors are mounted in a compact module inserted into a vehicle fuel line. The three capacitors are connected together to form an electrical pi-filter network. This approach provides a large variation of output signal as the fuel changes from gasoline to methanol. The sensor can be designed to operate in the 1 to 20 MHz frequency range. The second type of sensor investigated uses a resonant-cavity structure. Ordinarily, sensors based on resonant cavities are useful only if the operating frequency is several hundred MHz or higher. The high relative dielectric constant of methanol allows useful sensors to be built using relatively short lengths of metal tubing for the cavities. For example, a sensor built using a fuel rail only 38.7 cm long operated in a frequency range from 31 to 52 MHz.

A new estimator for IC engine A/F ratio is described. A/F ratio is important for engine operation since it determines the quantities of engine emissions, such as HC, CO, NOx, the conversion efficiency of catalyst systems, and the engine combustion stability. The A/F ratio estimator described in this paper is based on a fundamental metric that relies on inducing and detecting crankshaft speed fluctuations caused by modulating the engine's fuel injection pulse widths. Fuel pulse width modulation varies the instantaneous combustion A/F ratio crankshaft velocity. Synchronous measurement of crankshaft velocity provides a metric that, when used with other engine state variables as inputs to a conventional neural network, can accurately estimate A/F ratio. The estimator provides A/F information when a physical sensor is not available.

The change in the resistance of titanium dioxide with oxygen partial pressure is utilized to obtain an air-to-fuel ratio sensor. TiO2 material properties, sensor components and performance characteristics are discussed. Some results of engine dynamometer and vehicle tests of sensor performance and durability are presented.

The paper describes the use of strain gauge goniometers to measure dummy leg joint angles in impact tests. The instruments have been developed based on regular goniometers used for human gait analysis. Specific modifications enhanced the mechanical stability and the electrical insulation of the sensors. They are now compatible with standard crash data acquisition systems. Several vehicle crash tests have been analyzed using the goniometers as a supplementary measurement device. Due to its low weight, the device does not significantly alter the dummy behavior. Further areas of application are outlined in the paper.

The effect of MMT on the OBD-II catalyst efficiency monitor has been investigated. The results conclusively show that manganese which is deposited onto the catalyst during the combustion of MMT- containing fuel provides for an increased level of catalyst oxygen storage capacity. This added oxygen storage was found to result in a reduced rear EGO sensor response and caused malfunctioning catalysts to be incorrectly diagnosed by the OBD-II catalyst efficiency monitor.

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.

The development of the AUTO TEMP II Temperature Control System used in Chrysler Corp. vehicles is summarized. A description of the design, development, function, and manufacturing aspects of the control system is presented, with emphasis on unique control parameters, reliability, serviceability, and check-out of production assemblies. Auto Temp II was developed by Chrysler in conjunction with Ranco Incorporated. The servo-controlled, closed-loop system, which has a sensitivity of 0.5 F, utilizes a water-flow control valve for temperature control, along with a cold engine lockout. The basic components are: sensor string, servo, and amplifier. All automatic functions involving control of mass flow rate, temperature, and distribution of the air entering the vehicle, are encompassed in one control unit. All components are mechanically linked through the gear train and are responsive to the amplifier through the feedback potentiometer.

Results of laboratory studies of the static characteristics of several different commercially available heated exhaust gas oxygen sensors are described. In these studies, the emf of the sensors was measured as a function of temperature and of the composition of calibrated gas mixtures. Several different binary gas mixtures (H2/N2, CO/N2, C3H6/N2, C3H8/N2, and CH4/N2) were used together with a variable amount of O2. In addition to laboratory studies, the same sensors were also studied in the exhaust gas of an engine. Whereas at high temperatures thermodynamic equilibrium appears to prevail, clear departures from thermodynamic equilibrium are observed at some lower temperatures (the value of which depends on the specific sensor and the specific gas mixture used). This behavior is manifested by shifts of the emf step away from stoichiometry, broadening of the step, abnormally high emf values in excess oxygen mixtures, and abnormally low emf values in reducing gas mixtures.

We review the advantages of silicon micromachining in manufacturing low-cost, high-volume, mechanical sensors. The characteristics of the technology are discussed and contrasted with those of typical milling operations. We describe the fabrication of simple mechanical elements to explain how micromachined parts can be manufactured in large numbers with a high degree of dimensional control. These parts are the key components of cost-effective, high performance pressure, flow, and acceleration sensors that are gradually penetrating the automotive market.

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.

With appropriate signal conditioning, it is possible to estimate Distance-to-Empty utilizing only fuel level and distance sensors. The relationship of speed of response to changing driving conditions and the accuracy of the calculated value is a function of fuel tank/fuel sensor design and driving conditions.

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.

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.

There is a clearly demonstrated need to develop “real-time” methods for the measurement of diesel vehicle particulate emissions. Optical techniques provide One alternative for such methodology because of the rapid data acquisition times involved and the relatively simple sampling methods that can be used. This report describes two different approaches to this problem. The first, the spectrophone, measures light absorption by the diluted exhaust plume using photoacoustic spectroscopy, and the second, the long pathlength smokemeter, determines total light extinction across the diluted exhaust plume. For the measurement of total mass emissions, both techniques show estimated errors of ±15% for specific vehicles, while for a multi-vehicle diesel fleet the estimated errors are ±20% and ±30% for the long pathlength smokemeter and the spectrophone, respectively.

During the fall of 1992, the Michigan Roadside Study was conducted. During this study IM240 tests were conducted on vehicles that had also been emissions tested during on-road operation via two remote sensors that were separated by 100 feet. The use of two remote sensors provided an indication of the short-term real-world emissions variability of a large number of on-road vehicles. This data was used to determine the frequency of flippers, i.e. vehicles that are sometimes high emitters (>4% CO) and at other times low emitters (<2% CO). The data show that the flipper frequency increases for older model year vehicles. Also, the correlations between remote sensor readings of emissions concentrations and IM240 mass emissions rates were determined. The data show that the correlation between remote sensing and IM240 improves with increasing numbers of remote sensing readings. For three remote sensor readings, CO correlates with an r2 of 0.69 and HC correlates with an r2 of 0.54

This paper presents the real time implementation of detection filters for the diagnosis of incipient failures in electronically controlled internal combustion (IC) engines. The detection filters are implemented in a production vehicle. Recent results [1] have demonstrated the feasibility of a model-based failure detection and isolation (FDI) methodology for detecting partially failed components in electronically controlled vehicle subsystems. The present paper describes the real time application of the FDI concept to the detection of faults in sensors associated with the engine/controller In a detection filter, the performance of the engine/controller system is continuously compared to a prediction based on sensor measurements and an analytical model (typically a control model) of the system. Any discrepancy between actual and predicted performance is analyzed to identify the unique failure signatures related to specific system components.