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Two-stage turbochargers are a recent solution to improve engine performance. The large flexibility of these systems, able to operate in different modes, can determine a reduction of the turbo-lag phenomenon and improve the engine tuning. However, the presence of two turbochargers that can be in part operated independently requires effort in terms of analysis and optimization to maximize the benefits of this technology. In addition, the design and calibration of the control system is particularly complex. The transitioning between single stage and two-stage operations poses further control issues. In this scenario a model-based approach could be a convenient and effective solution to investigate optimization, calibration and control issues, provided the developed models retain high accuracy, limited calibration effort and the ability to run in real time.

The definition of the energy management strategy for a hybrid electric vehicle is a key element to ensure maximum energy efficiency. The ability to optimally manage the on-board energy sources, i.e., fuel and electricity, greatly affects the final energy consumption of hybrid powertrains. In the case of plug-in series-hybrid architectures, such as Range-Extender Electric Vehicles (REEVs), fuel efficiency optimization alone can result in a stressful operation of the range-extender engine with an excessively high number of start/stops. Nonetheless, reducing the number of start/stops can lead to long periods in which the engine is off, resulting in the after-treatment system temperature to drop and higher emissions to be produced at the next engine start.

When developing a new engine control strategy, some of the important issues are cost, resource minimization, and quality improvement. This paper outlines how a model based approach was used to develop an engine control strategy for an Extended Range Electric Vehicle (EREV). The outlined approach allowed the development team to minimize the required number of experiments and to complete much of the control development and calibration before implementing the control strategy in the vehicle. It will be shown how models of different fidelity, from map-based models, to mean value models, to 1-D gas dynamics models were generated and used to develop the engine control system. The application of real time capable models for Hardware-in-the-Loop testing will also be shown.

Homogeneous Charge Compression Ignition (HCCI) is considered a very promising concept to achieve low NOx and Particulate Matter emissions in traditional spark ignition and Diesel engines. However, controlling the complex mechanisms which govern the combustion process and finding a proper method for the fuel introduction for Diesel HCCI engines have proven to still be a challenge. In addition, the well known IMEP limitations of HCCI combustion restrict the benefits on emissions to low engine load conditions. The current work attempts to extend the benefits of HCCI combustion to a broader range of engine operating conditions by blending the conventional Direct Injection (DI) with the external fuel atomization. A dual combustion system could potentially overcome the limits of low-load operations and allow for a gradual transition between the conventional DI mode at high load and the HCCI external mixture formation at idle and low load.

Electronic throttle control is increasingly being considered as a viable alternative to conventional air management systems in modern spark-ignition engines. In such a scheme, driver throttle commands are interpreted by the powertrain control module together with many other inputs; rather than directly commanding throttle position, the driver is now simply requesting torque - a request that needs to be appropriately interpreted by the control module. Engine management under these conditions will require optimal control of the engine torque required by the various vehicle subsystems, ranging from HVAC, to electrical and hydraulic accessories, to the vehicle itself. In this context, the real-time estimation of engine and load torque can play a very important role, especially if this estimation can be performed using the same signals already available to the powertrain control module.

The integrity of the exhaust emission system in a passenger vehicle can best be maintained by monitoring its performance continuously on board the vehicle. It is with the intent of monitoring emission system performance that the California Air Resources Board has proposed regulations which will require vehicles to be equipped with on-board monitoring systems. These proposed regulations are known as OBDII and will probably be followed by similar Federal EPA regulations.This paper discusses a method of monitoring engine misfire as part of the OBDII requirements for passenger vehicle on-board diagnostics. The method is relatively inexpensive in that it uses an existing sensor for measuring instantaneous crankshaft angular position, and utilizes electronic signal processing which can be implemented in relatively inexpensive custom integrated circuits.

An appropriate fault diagnosis and Isolation (FDI) strategy is very useful to prevent system failure. In this paper, a model-based fault diagnosis strategy is developed for an internal combustion engine (ICE) under speed control. Engine throttle fault and the manifold pressure sensor fault are detected and isolated. A nonlinear observer based residual generation approach is proposed. Manifold pressure and throttle are observed. Fault codes are designed with redundancy to prevent bit error. Performance of fault diagnosis strategy has been evaluated with simulations.

Traditionally in the United States, Diesel engines have negative connotations, primarily due to their association with heavy duty trucks, which are wrongly characterized as “dirty.” Diesel engines are more energy efficient and produce less carbon dioxide than gasoline engines, but their particulate and NOx emissions are more difficult to reduce than spark ignition engines. To tackle this problem, a number of after-treatment technologies are available, such as Diesel Lean NOx Traps (LNTs)), which reduces oxides of nitrogen, and the Diesel particulate filter (DPF), which reduces particulate matter. Sophisticated control techniques are at the heart of these technologies, thus making Diesel engines run cleaner. Another potentially unattractive aspect of Diesel engines is noise.

Model-based design is a collection of practices in which a system model is at the center of the development process, from requirements definition and system design to implementation and testing. This approach provides a number of benefits such as reducing development time and cost, improving product quality, and generating a more reliable final product through the use of computer models for system verification and testing. Model-based design is particularly useful in automotive control applications where ease of calibration and reliability are critical parameters. A novel application of the model-based design approach is demonstrated by The Ohio State University (OSU) student team as part of the Challenge X advanced vehicle development competition. In 2008, the team participated in the final year of the competition with a highly refined hybrid-electric vehicle (HEV) that uses a through-the-road parallel architecture.

Two-stage turbochargers are a recent solution to improve engine performance, reducing the turbo-lag phenomenon and improving the matching. However, the definition of the control system is particularly complex, as the presence of two turbochargers that can be in part operated independently requires effort in terms of analysis and optimization. This work documents a characterization study of two-stage turbocharger systems. The study relies on a mean-value model of a Diesel engine equipped with a two-stage turbocharger, validated on experimental data. The turbocharger is characterized by a VGT actuator and a bypass valve (BPV), both located on the high-pressure turbine. This model structure is representative of a “virtual engine”, which can be effectively utilized for applications related to analysis and control. Using this tool, a complete characterization was conducted considering key operating conditions representative of FTP driving cycle operations.

In recent years, the increasing interest and requirements for improved engine diagnostics and control has led to the implementation of several different sensing and signal processing technologies. In order to optimize the performance and emission of an engine, detailed and specified knowledge of the combustion process inside the engine cylinder is required. In that sense, the torque generated by each combustion event in an IC engine is one of the most important variables related to the combustion process and engine performance. This paper introduces torque estimation techniques in the real-time basis for engine control applications using the measurement of crankshaft speed variation. The torque estimation scheme presented in this paper consists of two entirely different approaches, “Stochastic Analysis” and “Frequency Analysis”.

The stochastic Fault Detection and Isolation (FDI) algorithm, known as the statistical local approach, is applied in a model-based framework to the diagnosis of component faults in the air-intake system of an automotive engine. The FDI scheme is first presented as a general methodology that permits the detection of faults in complex nonlinear systems without the need for building inverse models or numerous observers. Although sensor and actuator faults can be detected by this FDI methodology, component faults are generally more difficult to diagnose. Hence, this paper focuses on the detection and isolation of component faults for which the local approach is especially suitable. The challenge is to provide robust on-board diagnostics regardless of the inherent nonlinearities in a system and the random noise present.

This year, in the third year of FutureTruck competition, the Ohio State University team has taken the challenge to convert a 2002 Ford Explorer into a more fuel efficient and environmentally friendly SUV. This goal was achieved by use of a post-transmission, charge sustaining, parallel hybrid diesel-electric drivetrain. The main power source is a 2.5-liter, 103 kW advanced CIDI engine manufactured by VM Motori. A 55 kW Ecostar AC induction electric motor provides the supplemental power. The powertrain is managed by a state of the art supervisory control system which optimizes powertrain characteristics using advanced energy management and emission control algorithms. A unique driver interface implementing advanced telematics, and an interior designed specifically to reduce weight and be more environmentally friendly add to the utility of the vehicle as well as the consumer appeal.

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.

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.

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.