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Virtual testing of advanced driver assistance systems (ADAS) using a simulation environment provides great potential in reducing real world testing and therefore currently much effort is spent on the development of such tools. This work proposes a simulation and hardware-in-the-loop (HIL) framework, which helps to create a virtual test environment for ADAS based on real world test drive. The idea is to reproduce environmental conditions obtained on a test drive within a simulation environment. For this purpose, a production standard BMW 320d is equipped with a radar sensor to capture surrounding traffic objects and used as vehicle for test drives. Post processing of recorded GPS raw data from the navigation system using an open source map service and the radar data allows an exact reproduction of the driven road including other traffic participants.

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.

Eliminating toxic exhaust emissions, amongst them particulate matter (PM), is one of the driving factors behind the increasing use of electrified vehicles. However, it is frequently overseen that PM arise not only from combustion, but from non-exhaust traffic related causes as well; in particular from the vehicle brakes, tires and the road surface. Furthermore, as electrified vehicles weigh more and typically exhibit higher torques at low speeds, their non-exhaust emissions tend to be higher than for comparable conventional vehicles, especially those generated by tires. Fortunately, tire related emissions are directly related to tire wear, so that limiting tire wear can reduce these emissions as well. This can be accomplished by intelligently modulating the vehicle torque profile in real time, to limit the operation in conditions of higher tire wear.

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.

Abatement and control of emissions from passenger car combustion engines have been in the focus for a long time. Nevertheless, to address upcoming real-world driving emission targets, knowledge of current engine emissions is crucial. Still, adequate sensors for transient emissions are seldom available in production engines. One way to target this issue is by applying virtual sensors which utilize available sensor information in an engine control unit (ECU) and provide estimates of the not measured emissions. For real-world application it is important that the virtual sensor has low complexity and works under varying conditions. Naturally, the choice of suitable inputs from all available candidates will have a strong impact on these factors. In this work a method to set up virtual sensors by means of design of experiments (DOE) and iterative identification of polynomial models is augmented with a novel input candidate selection strategy.

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.

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.

Especially in view of more and more stringent emission legislation in passenger cars it is required to reduce the amount of pollutants. In the case of Diesel engines mainly NOx and PM are emitted during engine operation. The main influence factors for these pollutants are the in-cylinder oxygen concentration and the injected fuel amount. Typically the engine control task can be divided into two separate main parts, the fuel and the air system. Commonly air system control, consisting of a turbocharger and exhaust gas recirculation control, is used to provide the required amount of oxygen and address the emission targets, whereas the fuel is used to provide the desired torque. Especially in transient maneuvers the different time scales of both systems can lead to emission peaks which are not desired. Against this background in this work instead of the common way to address the air system, the fuel system is considered to reduce emission peaks during transients.

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.

This paper shows the potential of a multicalibration approach for reducing fuel consumption while keeping pollutant immissions. The paper demonstrates that the current engine control approach with a single fixed calibration involves important fuel penalties in areas with low vehicle densities where local pollution is not an issue, while the NOx emissions in urban areas are usually too high to fulfill air quality standards. The proposed strategy is based on using information about the vehicle location and the NOx concentrations in the ambient to choose a suitable calibration amongst a set of possibilities. To assess the potential of such a strategy experimental tests have been done with a state-of-art turbocharged Diesel engine. First, a design of experiments is used to obtain three different calibrations.

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.

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.

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.

The emissions of modern Diesel engines, which are known to have various health effects, are beside the drivers torque demands and low fuel consumptions one of the most challenging issues for combustion and after treatment control. To comply with legal requirements, emission control for heavy duty engines is not feasible without additional hardware, usually consisting of a Diesel oxidation catalyst (DOC), a Diesel particulate filter (DPF) and a selective catalytic reduction (SCR) system. In contrast to other NOx reduction systems, e.g. lean NOx traps, the SCR system requires an additional ingredient, namely ammonia (NH3), to reduce the NOx emissions to non harmful components. Consequently, the correct amount of NH3 dosing in the SCR catalyst is one of the critical components to reach high conversion rates and avoid ammonia slip.

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.