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Highway and roadway surface measurement is a practice that has been ongoing for decades now. This sort of measurement is intended to ensure a safe level of road perturbances. The measurement may be conducted by a slow moving apparatus directly measuring the elevation of the road, at varying distance intervals, to obtain a road profile, with varying degrees of resolution. An alternate means is to measure the surface roughness at highway speeds using accelerometers coupled with high speed distance measurements, such as laser sensors. Vehicles out rigged with such a system are termed inertial profilers. This type of inertial measurement provides a sort of filtered roadway profile. Much research has been conducted on the analysis of highway roughness, and the associated metrics involved. In many instances, it is desirable to maintain an off-road course such that the course will provide sufficient challenges to a vehicle during durability testing.

Fault diagnosis for automotive systems is driven by government regulations, vehicle repairability, and customer satisfaction. Several methods have been developed to detect and isolate faults in automotive systems, subsystems and components with special emphasis on those faults that affect the exhaust gas emission levels. Limit checks, model-based, and knowledge-based methods are applied for diagnosing malfunctions in emission control systems. Incipient and partial faults may be hard to detect when using a detection scheme that implements any of the previously mentioned methods individually; the integration of model-based and knowledge-based diagnostic methods may provide a more robust approach. In the present paper, use is made of fuzzy residual evaluation and of a fuzzy expert system to improve the performance of a fault detection method based on a mathematical model of the engine.

The use of mathematical models derived from physical principles is gaining more widespread acceptance for automotive control and diagnostic applications. A suitable mathematical model may reduce, though not eliminate, the need for empirical calibrations, and may help in accommodating changes in operating conditions, external disturbances, vehicle to vehicle variability, aging etc. Recent studies have shown that model based approaches for both control and diagnostic design offer a viable alternative to empirical methods for industrial applications. However, until recently, model-based control and diagnostic algorithms have been designed separately, without considering their interactions explicitly. As a consequence, the performance of these algorithms may be limited, and even deteriorated in the presence of modeling uncertainty and disturbance.

Over the years numerous researchers have suggested that the ionization current signal carries within it combustion relevant information. The possibility of using this signal for diagnostics and control provides motivation for continued research in this area. To be able to use the ion current signal for feedback control a reliable estimate of some combustion related parameter is necessary and therein lies the difficulty. Given the nature of the ion current signal this is not a trivial task. Fei An et al. [1] employed PCA for feature extraction and then used these feature vectors to design a neural network based classifier for the estimation of air to fuel ratio (AFR). Although the classifier predicted AFR with sufficient reliability, a major draw back was that the ion current signals used for prediction were averaged signals thus precluding a cycle to cycle estimate of AFR.

There is increasing interest in the use of alternative fuels for transportation, due to the increasing cost of petroleum based fuels. One possible alternative to the use of petroleum for transportation is to use electric grid power. This paper explores a possible design solution based on a plug-in fuel cell hybrid. A scaled down version of FC-HEV that is applicable to this concept, has been implemented as a proof of concept with fast prototyping toolkits, including a 32 bit micro processor, Matlab/Simulink software and an embedded system development kit. The resulting prototype vehicle demonstrated a high gasoline equivalent MPG as well as a successful functionality of micro grid power generation.

This paper presents an all-wheel-drive (AWD) hybrid electric vehicle (HEV) design approach for extreme off-road applications. The paper focuses on the powertrain design, modeling, simulation, and performance analysis. Since this project focuses on a military-type application, the powertrain is designed to enhance crew survivability and provide several different modes of limp-home operation by utilizing a new vehicle topology -herein referred to as the island topology. This topology consists of designing the vehicle such that the powertrain and other equipment and subsystems surround the crew compartment to provide a high level of protection against munitions and other harmful ordnance. Thus, in the event of an external shield penetration, the crew compartment remains protected by the surrounding equipment - which serves as a secondary shield.

This paper discusses the use of intelligent control techniques for the control of a parallel hybrid electric vehicle powertrain. Artificial neural networks and fuzzy logic are used to implement a load leveling strategy. The resulting vehicle control unit, a supervisory controller, coordinates the powertrain components. The presented controller has the ability to adapt to different drivers and driving cycles. This allows a control strategy which includes both fuel-economy and performance modes. The strategy was implemented on the Ohio State University FutureCar.

Crankshaft angular position measurements are fundamental to all modern automotive engines. These measurements are required to control fuel injection timing as well as ignition timing. However, many other functions can be performed from such measurements through the use of advanced signal processing. These additional functions are essentially diagnostic in nature although there is potential for substitution of primary fuel and ignition control functions. This paper illustrates the application of crankshaft angular position measurement to the estimation of individual cylinder indicated and/or brake torque in IC engines from measurement of crankshaft position/velocity.

The introduction of advanced electronic controls in passenger vehicles over the last decade has made traditional diagnostic methods inadequate to satisfy on- and off-board diagnostic needs. Due to the complexity of today's automotive control systems, it is imperative that appropriate diagnostic tools be developed that are capable of satisfying current and projected service and on-board requirements. The performance of available diagnostic and test equipment is still amenable to further improvement, especially as it pertains to the diagnosis of incipient and intermittent faults. It is our contention that significant improvement is possible in these areas. This paper briefly summarizes the evolution of on- and off-board diagnostic tools documented in the published literature, with the aim of giving the reader an understanding of their capabilities and limitations, and it further proposes alternative solutions that may be adopted as a basis for an advanced diagnostic instrument.

Ensuring the reliable operation of the emissions control system is a critical factor in complying with increasingly stringent exhaust emissions standards. In spite of significant advances, the performance of available diagnostic and test equipment is still amenable to further improvement, especially as it pertains to the diagnosis of incipient and intermittent faults. This paper presents experimental results pertaining to the diagnosis of complete, partial and intermittent faults in various components of the engine emissions control system. The instrumentation used in the study permitted simultaneous and essentially continuous analysis of the exhaust gases and of engine variables. Tests were conducted using a section of the EPA urban driving cycle (I/M 240), simulated by means of a throttle/dynamometer controller.

A prototype CO sensor based on anatase TiO2 was fabricated and tested in a Ford V6 engine. Fuel combustion was programmed to be near stoichiometric conditions, and emissions were monitored with an FT-IR analytical instrument. The sensor, positioned near the oxygen sensor in the exhaust manifold, was successfully tested for 50 cycles of revving and idling, and was observed to respond quickly and reproducibly. The sensor response was correlated to the CO concentration at specific engine temperatures and was found to vary systematically with increasing concentrations. This sensor has promising potentials to monitor the efficiency of the catalytic converter.

Legislation pertaining to automobile emissions has caused an increased focus on the cold-start performance of internal combustion engines. Of particular concern is the period of time before all available sensors become active. Present engine control strategies must rely on methods other than feedback control while these sensors are not active. Without feedback control during this critical period, engine emissions performance is not optimized. These conditions cause difficulty in performing comprehensive cold-start experiments. For these reasons, we have developed several methods for feedback control during cold-start to aid in laboratory investigations of engine emissions phenomena.

Engine knock has been recognized as a major problem limiting the development of fuel efficient spark-ignition engines. Detection methods employed in current knock control systems for spark ignition engines use a measurement of engine block vibration tuned to one or more resonance frequencies to extract knock-related information from the engine structural vibration. A major problem in the detection of knock (especially at higher engine speed) in commercial engines is the isolation of the desired signal from the contributions of the components other than those associated with the phenomenon under investigation. This is generally referred to as background noise. It is known that the engine knock resonance frequencies vary due to changes in combustion chamber volume and temperature during the expansion phase. Therefore, we propose an improved knock detection method using joint time-frequency analysis of engine block vibration and pressure signals.

An approach to fault diagnosis for internal combustion engines is considered. It is based on the estimation of cylinder indicated torque by means of sliding mode observers. Instead of measuring indicated pressure in cylinders directly, crankshaft speed is measured as the input of observers, which estimate the indicated torque. Several engine models are considered with different levels of complexity. The indicated torque estimation using sliding mode observers is based on the equivalent control method. The estimation technique is validated experimently on a research engine.

In recent years considerable interest has been placed on the detection of engine misfire. As part of the California Air Resources Board on-board diagnostics regulations for 1994 model year vehicles, misfire should be monitored continuously by the engine diagnostic system. It is expected that the next generation of on-board diagnostics regulations will demand monitoring of partial misfire as well. Several solutions to the misfire detection problem have been proposed and demonstrated for the detection of complete misfires. However, the performance of these methods in the presence of partial misfire is not altogether clear. The aim of this paper is to evaluate the performance of various misfire detection indices, all based on a measurement of crankshaft angular velocity, in the presence of partial misfire. The proposed algorithms are compared to a standard based on a measurement of indicated pressure.

One of the gray areas in the implementation of regulations limiting the generation of pollutants from mobile sources is the actual effectiveness of the exhaust gas emissions control strategy in vehicles that have been in use for some time. While it is possible today to conduct limited diagnostics with the on-board engine computer by performing periodic checks to verify the validity of the signals measured by the on-board sensors, and to measure tailpipe emissions during routine inspection and maintenance, the task of correlating these measurements with each other to provide an on-line, accurate diagnosis of critical malfunctions has thus far proven to be a very challenging task, especially in the case of misfire.

Three engine friction models of increasing complexity were developed in order to determine which type of model most effectively captured transient engine operation. Empirical constants for these models were determined through an optimization procedure using experimental data. These constants were then used with a simple dynamic model to produce overall simulations of the engine reciprocating and rotating dynamics. All three friction models appeared to provide useful results, however the two simpler models were much easier to implement. The most complex model presented some implementation problems, but promises to provide a more detailed picture of engine friction. The models were tested on a single cylinder research engine.

We have performed an extensive study of cycle-to-cycle variation of engine knock and its occurrence in order to have a better understanding of engine knock. Experimental studies show the randomness of knock as well as its frequency varying nature. We propose using time-frequency based signal detection method to improve knock detection since this method can track frequency variations in the measured signals. The fundamental idea behind time-frequency analysis is to be able to understand and describe how the spectral content of a signal is changing in time. Classical signal analysis has traditionally dealt with time and frequency separately. Such individual descriptions are good in situation where spectral content of signals do not change with time; however, there are often signals that have time-varying spectral content such as engine knock. It is shown that one gains more information about knock signals using time-frequency analysis method.

A number of automotive diagnostic equipment and procedures have evolved over the last two decades, leading to two generations of on-board diagnostic requirements (OBDI and OBDII), increasing the number of components and systems to be monitored by the diagnostic tools. The goal of On-Board Diagnostic is to alert the driver to the presence of a malfunction of the emission control system, and to identify the location of the problem in order to assist mechanics in properly performing repairs. The aim of this paper is to suggest a methodology for the development of an Integrated Powertrain Diagnostic System (EPDS) that can combine the information supplied by conventional tailpipe inspection programs with onboard diagnostics to provide fast and reliable diagnosis of malfunctions.