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Advanced driver assistance systems like cooperative adaptive cruise control (CACC) are designed to exploit information provided by vehicle-to-vehicle (V2V) and/or infrastructure-to-vehicle (I2V) communication systems to achieve desired objectives such as safety, traffic fluidity or fuel economy. In a day to day traffic scenario, the presence of unknown disturbances complicates achieving these objectives. In particular, CACC benefits in terms of fuel economy require the prediction of the behavior of a preceding vehicle during a finite time horizon. This paper suggests an estimation method based on actual and past inter-vehicle distance data as well as on traffic and upcoming traffic lights. This information is used to train a set of nonlinear, autoregressive (NARX) models. Two scenarios are investigated, one of them assumes a V2V communication with the predecessor, the other uses only data acquired by on-board vehicle sensors.

Modern cars feature a variety of different driving assistance systems, which aim to improve driving comfort and safety as well as fuel consumption. Due to the technical advances and the possibility to consider vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication, cooperative adaptive cruise control (CACC) strategies have received significant attention from both research and industrial communities. The performance of such systems can be enhanced if the future velocity of the surrounding traffic can be predicted. Generally, human driving behavior is a complex process and influenced by several environmental impacts. In this work a stochastic model of the velocity of a preceding vehicle based on the incorporation of available information sources such as V2I, V2V and radar information is presented. The main influences on the velocity prediction considered in this approach are current and previous velocity measurements and traffic light signals.

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.

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.

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.

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.

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.

It is well known that driver behavior can affect fuel consumption to a large extent hence modifying it can lead to reasonable reduction in the magnitude of 10 to 20%. However, it is also known that effects of training are short lived and therefore many authors and companies suggest the use of monitoring systems, sometimes called eco-driver, which allow recognizing opportunities for reduction. V2V is an emerging technology which has been widely studied especially for safety applications. In terms of fuel consumption, there has also been a significant effort for methods directed to coordinate the movement of vehicles, especially of trucks, to improve fuel consumption by platooning [1].

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.

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.

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.

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.

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.