Search Results

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.

Modeling the soot emissions of a Diesel engine is a challenge. Although it was part of many works before, it is still not a solved issue and has a substantial potential for improvement. A major problem is the presence of two competing effects during combustion, soot formation and soot oxidation, whereas only the cumulative difference of these effects can be measured in the exhaust. There is a wide consensus that it is sensible to design crank angle resolved models for both effects. Indeed, many authors propose crank angle based soot models which are mostly based on detailed first principles based structures, e.g. spray models, engine process calculations etc. Although these models are appealing from a theoretical point of view, they are all lacking of the required measurement information to validate all the complex model parts. Finally, most parts of the model remain at their assumed values and only a few parameters are used for calibration.

Transient emission peaks have become an important fraction of the total emissions during the standardized test cycles for passenger car Diesel engines. To this end this paper is concerned with the challenge of measuring emissions during transients. The importance of this topic is increasing due to strict regulation on pollutant emissions. Hence, suitably accurate and fast measurement devices for PM emission detection are required. Thus, we present a comparison between different measurement techniques for Particulate matter (PM) emissions from a Diesel engine, in particular during transients. The compared equipments include AVL Micro soot sensor, AVL Opacimeter, Differential mobility spectrometer and Laser induced incandescence. The goal of this paper is to reveal the most accurate device in the sense of sensitivity and dynamics for fast measurements of PM from a Diesel engine.

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.

Transient emission peaks have become an important fraction of the total emissions during the standardized test cycles for passenger car Diesel engines. This paper is concerned with their reduction, in particular of nitric oxides (NOx) and particulate matter (PM) emissions, by online receding horizon optimal control. It is based on former works in which alternative target quantities for engine control were proposed, namely in-cylinder oxygen concentrations before (O2,BC) and after combustion (O2,AC). The actual work is concerned with testing an in-cylinder oxygen concentrations based control in simulation as well as by a real-time implementation on a turbocharged common rail passenger car production Diesel engine. The promising results confirm the choice of these concentrations as sensible control references and the feasibility of a real-time use in a model predictive control implementation.

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.

Nitrogen oxide (NOx) sensing is required both for on-board diagnosis and optimal selective catalytic reduction (SCR)-catalyst control in heavy duty diesel engines. This can be accomplished either by physical solid-state sensors, or by so-called virtual sensors, which estimate the value of the target quantity using other states by means of a model. Both approaches have advantages and disadvantages. This paper resumes the derivation and the identification of a virtual sensor based on a polynomial structure and optimal experimental design methods and compares its performance to the one of a production physical solid state sensor. The virtual sensor is compared with a commercially available solid-state sensor in terms of accuracy (stationary as well as dynamic) and operation limits.

Due to the advancements in passenger car Diesel engine design, the contribution of transient emission spikes has become an important fraction of the total emissions during the standardized test cycles, hence the interest of this work on dynamical engine operation, in particular on the improvement of NOX and PM emissions. This paper proposes to use a UEGO sensor (universal exhaust gas oxygen sensor) in the upstream of the turbine in combination with a Kalman filter to estimate the target quantities, namely in-cylinder oxygen concentration before and after combustion. This information is used to define the fuel injection as well as the values of the air path actuators. Test bench measurements with a production Diesel engine are presented, where the oxygen based approach is compared to the standard calibration during a fast load increase. It is shown that the torque response could be maintained while NOX as well as PM emission peaks were reduced significantly.

Emission abatement is one of the main targets in engine development and design today. Modern turbocharged CRDI Diesel engines with variable turbine geometry (VTG) and exhaust gas recirculation (EGR) provide new degrees of freedom for air path control with enormous effects on emissions. Exploiting these degrees of freedom usually involves a huge calibration work, as sensors are available only for few quantities and dynamical models are mostly not available, so feedback or model based optimization is hardly possible. This paper presents a time efficient data based strategy to obtain such models yielding an accurate as well as robust emission model for nitrogen oxides (NOx) and particulate matter (PM) by means of design of experiment. The model output is generated by smoothly switching between local models, representing different engine operating points. An adapted D-optimal design of experiments strategy provides optimal data for model identification.

The trend to reduced development times represents a strong motivation for an extended use of dynamical engine test benches, which are used to reproduce driving conditions typically measured in the vehicle. To reproduce the engine load conditions a complex control system is used, which has an inherently different nature in comparison with the vehicle driving conditions and consequently also limitations. As a consequence, the set of possible transients is limited by the actuator limitations and the bandwidth of the control loops. Outside the admissible transients it is necessary to do a trade-off between goals, mainly between the precision of the reproduction of the engine speed and the precision of the load torque tracking. In this paper we discuss how to calculate the admissible set and how to do a trade off. Based on these cognitions we present a feedforward control algorithm which allows best performance transients simulation along the performance limits.

Two-wheel vehicles are becoming continuously more important in Europe, but their spread is accompanied by an increase in security concerns due a number of reasons. These include stability problems during braking, and in particular curve braking, which is much more critical than in 4-wheel vehicles. These stability problems are strongly influenced by the behavior of the driver, in particular by his braking and steering activity. In this work we present a curve-safe ABS control, and analyze the role of the driver by a simulation model. It turns out that the demands on the driver in terms of stability control vary strongly with the braking behavior.

The plausibility of measurements is a key question for many kinds of large complex systems. In this paper we present a method of automatic measurement data plausibility assessment. Even the focus here was put on an automotive engine test bench application, the presented method can cope also with other complex systems with a large number of measurement devices included. The presented method is still in the development phase, thus its performance still did not reach the optimum. But as for the time being the obtained experimental results seams to be quite acceptable.

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.

On-line measurement of engine NOx emissions is the object of a substantial effort, as it would strongly improve the control of CI engines. Many efforts have been directed towards hardware solutions, in particular to physical sensors, which have already reached a certain degree of maturity. In this paper, we are concerned with an alternative approach, a virtual sensor, which is essentially a software code able to estimate the correct value of an unmeasured variable, thus including in some sense an input/output model of the process. Most virtual sensors are either derived by fitting data to a generic structure (like an artificial neural network, ANN) or by physical principles. In both cases, the quality of the sensor tends to be poor outside the measured values.

The proposed vehicle structure with an integrated-starter-generator (ISG) and the possible change of the net voltage offers substantial opportunities for energy and thus fuel saving at several levels or increased dynamical performance. However, the arrangement of two power sources - combustion engine and the ISG - brings more complexity in the system and makes it necessary to control the power flow of both sources, which has to be done by an energy management system. This paper describes possible changes in the powertrain setting and a systematic approach for the design of an energy management system without using heuristic design rules. Measurement results on a dynamical test bench for the FTP-75 emission test cycle confirm the increased fuel economy and an adequate battery charge level.

Torque control is a basic element of engine control systems, in particular since it has become a standard interface for different functionalities. Torque control is also a critical requirement emission test cycle simulation on test benches. This torque control is usually reached by extensive, physical based modeling of the vehicle. This paper presents an approach to avoid this effort and to obtain a dramatic reduction of the parametrization work, by first determining an approximated model and then updating it online during operation. This model is than used for a stable inverse control. To handle model uncertainties and perturbation a correction feedback, with robustifying effect, is added to the control structure. This approach is detailed using data and measurements on a BMW M47D production diesel engine on a dynamic test bench.