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Adaptive cruise control (ACC) systems allow a safe and reliable driving by adapting the velocity of the vehicle to velocity setpoints and the distance from preceding vehicles. This substantially reduces the effort of the driver especially in heavy traffic conditions. However, standard ACC systems do not necessarily take in account comfort and fuel efficiency. Recently some work has been done of the latter aspect. This paper extends previous works for CI engines by incorporating a prediction model of the surrounding traffic and a simplified control law capable for real time use in experiments. The prediction model itself uses sinusoidal functions as the traffic measurements often show periodic behavior and is adapted in every sample instant with respect to the predecessor's velocity. Furthermore, the controlled vehicle is forced to stay within a specific inter-vehicle distance corridor to avoid collisions and ensure safe driving.

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 focus in the development of modern exhaust after treatment systems, like the Diesel Oxidation Catalyst (DOC), the Diesel Particulate Filter (DPF) and Selective Catalytic Reduction (SCR), is to increase on one hand the oxidation rates of Carbon monoxide (CO), HC (Hydro Carbons) and NO (Nitrogen Oxide) and on the other hand the reduction rates of Particulate Matter (PM) and the NOx emissions to fulfill the more and more restricting requirements of the exhaust emission legislation. The simplest, practical most relevant way to obtain such a dosing strategy of a SCR system is the use of a nonlinear map, which has to be determined by extensive calibration efforts. This feedforward action has the advantage of not requiring a downstream NOx sensor and can achieve high conversion efficiency under steady-state operating conditions for nominal systems.

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.

Higher initial load is known to allow faster catalyst heating and lower cold-start emission values. The generally expected change of the board net voltage to 42 Volts allows the use of additional or improved electric systems for increased vehicle comfort and safety. The resulting configuration offers as well new degrees of freedom in terms of board net energy management, which can be used to provide additional features, one of them the optimization of the engine load operation in the cost-start phase. In this paper, a case study is used to show how a sensible choice of the operating point of the starter/generator in the cold-start phase allows to reduce very strongly emissions with no additional exhaust aftertreatment equipment.

The Laser Induced Incandescence technique (LII) is a sensitive optical method for reliable spatially and temporally resolved measurement of particulate matter (PM) concentration. This technique appears to be suitable for measurement of fast transient PM emissions, from diesel engines, which forms the main fraction of total emissions during standardized test cycles. However, the existing commercial LII devices require modifications in the exhaust gas flow, dilution, sampling cell, or it measure only in a partial stream. This article presents the development of a laser based optical setup - LII for rapid in-situ measurement of PM concentrations during the combustion process of a diesel production engine. The presented LII setup is suitable for direct in-situ, full stream, measurements of soot emissions without needs of dilution or a sampling cell.

Measurements of transient emissions become more important due to the increasing contribution of transient operation to the total tail pipe emissions. While for many quantities measurement devices with response time in the range of few milliseconds exist, the same is not true for particulate matter(PM). Pulsed Laser Induced Incandescence (LII) is widely used in experimental setups and may offer a viable approach also for production engines, but the specific nature of LII raises doubts on the quantitative precision achievable by the method, especially in transient operation. Indeed, there are two main problems in particular for dynamic measurements. On one side, the emitted laser power must be high enough to excite a sufficiently large number of particles within the observed area, but not as high to destroy them, and varying engine operating conditions imply changes in the number and size distribution of the particles as well.

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.

This paper presents a methodology which is able to calculate emissions over crank angle (CA) using the measured cylinder pressure. The models are based on a 2-zone Diesel combustion process calculation combined with data based methods to fulfill the objective of a wide operating range, for a later possible use in transients — differently from the standard methods, which usually work accurate only in a small range. The resulting hybrid or grey box oriented model structure offers many advantages when purely physical modeling is too complicated and the combination with data based methods allow to obtain better results. The workflow of CA based emission models on the basis of a 2-zone calculation is presented. The focus of this paper is to explain the development process of a nitric oxide (NO) formation model, which is accurate over a wide operating range. For future similar data based particulate matter (PM) models the shown workflow could be used too.

Pollutant immissions must be kept below some threshold values to prevent health and environmental damage. At the moment, the problem is usually met by constant emission limits for each vehicle independently from specific conditions - in particular, without any relation to the actual immission situation. This approach offers the advantage of simplicity, but offers no guarantee that the immission levels will be kept. New developments, in particular the expected diffusion of i2v methods, allows suggesting context specific emission levels so that the total emission roughly corresponding to the local immissions - can be limited to the target values. To meet this goal, emission-oriented control will be needed. This paper proposes a robust control system which allows tracking a time-varying NOx profile, based on the sliding mode concept.

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.

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.

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 for nitric oxides (NOx) and particulate matter (PM) emissions, by online optimization. It is based on a former work [1] in which alternative target quantities for engine control were proposed, namely in-cylinder oxygen concentrations before (O2,BC) and after combustion (O2,AC). A generic nonlinear optimization is applied to provide a systematic determination for the optimal trajectories of these oxygen target quantities during a transient torque maneuver. The proposed method was implemented on a dynamic engine test bed using a production passenger car Diesel engine for the objective function evaluation. Torque response could be maintained unchanged while NOx as well as PM emission peaks were reduced significantly.

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.

Complexity and nonlinearity of engines makes precise first principle engine models often difficult to obtain, as for instance for emissions. System identification is a well known possible alternative, successfully used in several automotive applications. In most cases system identification is concerned with the estimation of the unknown parameters of a known set of equations. Unfortunately, for many engine subsystems, there is no sufficiently precise or real time suitable model. This paper presents a sequential algorithm which allows to derive real time suitable models on line by a combination of model structure hypothesis of increasing complexity and an associated optimal input design and selection process. This paper introduces the method and shows its use both for a rather simple and a very difficult engine identification task, a dynamical model of the airpath of a Diesel engine and a dynamical model of nitrogen oxides and particulate matter.

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.

As a physical description of the emissions of a specific engine is seldom possible, we present here a method to design an online dynamic estimator for PM and NOx based on data. The design method is based on a systematic search of function candidates performed using genetic programming after data have been pre-treated in an adequate fashion. While data and a simple data pretreatment prove enough for NOx, some basic physical understanding is necessary to preset the method and obtain the required precision in the case of PM. The method has been applied for raw emissions of a production DI diesel engine and shows a remarkable prediction performance.