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Although the application of cylinder pressure sensors to gain insight into the combustion process is not a novel topic itself, the recent availability of inexpensive in-cylinder pressure sensors has again prompted an upcoming interest for the utilization of the cylinder pressure signal within engine control and monitoring. Besides the use of the in-cylinder pressure signal for combustion analysis and control the information can also be used to determine related quantities in the exhaust or intake manifold. Within this work two different methods to estimate the pressure inside the exhaust manifold are proposed and compared. In contrary to first principle based approaches, which may require time extensive parameterization, alternative data driven approaches were pursued. In the first method a Principle Component Analysis (PCA) is applied to extract the cylinder pressure information and combined with a polynomial model approach.

At the moment, no equipment is available for fast measurements of particulate matter (PM) from production CI engines, especially during transients. Against this background, virtual sensors may be an option, provided their precision can be validated. This paper presents a new approach to estimate PM emission based only on in-cylinder pressure data. To this end, an in-cylinder pressure trace is measured with a high resolution (0.5 CAD) and every trace is divided into 8 segments according to critical cylinder events (e.g. opening of the valves or the beginning of injection). A piecewise principle component analysis (PCA) is used to compress the information. This information is then used for PM estimation via a second order polynomial model structure. The key element is the separate use of pressure trace information before and during the early stages of combustion. The model is parameterized by steady points and transient experiments which include parts of the FTP and the NEDC.

NOx and PM are the critical emissions to meet the legislation limits for diesel engines. Often a value for these emissions is needed online for on-board diagnostics, engine control, exhaust aftertreatment control, model-based controller design or model-in-the-loop simulations. Besides the obvious method of measuring these emissions, a sensible alternative is to estimate them with virtual sensors. A lot of literature can be found presenting different modeling approaches for NOx emissions. Some are very close to the physics and the chemical reactions taking place inside the combustion chamber, others are only given by adapting general functions to measurement data. Hence, generally speaking, there is not a certain method which is seen as the solution for modeling emissions. Finding the best model approach is not straightforward and depends on the model application, the available measurement channels and the available data set for calibration.

Modern Diesel engines have become complex systems with a high number of available sensor information and degrees of freedom in control. Due to recent developments in production type in-cylinder pressure sensors, there is again an upcoming interest for in-cylinder pressure based applications. Besides the standard approaches, like to use it for closed loop combustion control, also estimation and on-board diagnostics have become important topics. Not surprising in general the trend is to utilize those sensors for as many tasks as possible. Consequently this work focuses on the estimation of the injection parameters based on the indicated pressure signal information which can be seen as first step of a combustion control based on desirable indicated pressure characteristics which may be utilized for e.g. the minimization of NOx emissions. Currently the acquisition of the cylinder pressure traces can be done in real-time by fast FPGA (Field Programmable Gate Array) based systems.

Drag torque compensation is a part of the control units of modern gasoline and diesel engines. To achieve it, a characteristic drag torque curve as a function of the engine speed is usually saved in the ECU. Since the drag torque will not be constant during an engine's lifetime, this curve must be adapted. This paper proposes an approach to adapt the drag torque curve. The goal is achieved using a high gain observer known as a Kalman filter. The proposed method combines detection of drag torque curve errors and adaptation of the drag torque curve in one step. The effects of variable geometry turbochargers are included in the overall curve by an extension of the basic algorithm. The performance of the method is shown using data and measurements on a BMW M47D engine. As the measurements confirm, the proposed method works consistently and correctly.

Modern ECUs are usually torque orientated. As a consequence, a good estimation of the real mean value output torque of the engine is needed. As torque measurement is mostly too expensive, the ECUs usually include torque estimation algorithms, which, however, are usually quite simple and give a poor estimate of dynamic effects. In this paper we present a simple but effective method to estimate the engine torque based on an extended Kalman filter used in combination with a polynomial engine model and a simple friction model. Using only standard measurements or ECU internal variables, like fuel mass, spark advance for gasoline engines and injection timing for diesel engines, pressure of the intake manifold and speed are enough to get a good estimation value for the mean value torque of the engine. In this paper we also discuss the algorithm of estimating the mean value torque of the engine that is mounted in a vehicle, where usually the load torque is not known.

Computation of combustion, in particular of emissions over crank angle, relies on chemical oriented models. In some cases, chemical equilibrium can be assumed, as chemical reaction time scales tend to be fast compared to the crank rotation, so the rather complex reaction kinetics can be neglected. For engine process calculation based on the measured cylinder pressure chemical equilibrium concentrations are needed for every crank angle or calculation time step. On the one hand the equilibrium concentrations are necessary for estimating the thermodynamic properties of the working gas (internal energy and specific gas constant) which are needed for deriving the energy release (burn rate) and on the other hand the obtained concentrations are inputs for crank angle based soot and nitric oxygen emission models which depends also on the engine process calculation results.

This paper presents a detailed optical and thermodynamic analysis of effects which influences the soot formation and oxidation process during Diesel combustion. To measure the actual soot concentration over crank angle an optical sensor was installed on the engine. In combination with a thermodynamic engine process calculation, based on the measured cylinder pressure, several important effects are analyzed and described in detail. The main focus of the paper is to produce knowledge on how soot dynamics is influenced by changed engine control unit (ECU) calibration parameters. A modern 4 cylinder production car Diesel engine was used for the studies, which offers a lot of opportunities to influence combustion by varying injection timing and air path ECU parameters. As a consequence discussion is done on how the analyzed effects are treated by published 0-dimensional simulation models with focus on later control and optimization application.