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Current developments in automotive industry such as hybrid powertrains and the continuously increasing demands on emission control systems, are pushing complexity still further. Validation of such systems lead to a huge amount of test cases and hence extreme testing efforts on the road. At the same time the pressure to reduce costs and minimize development time is creating challenging boundaries on development teams. Therefore, it is of utmost importance to utilize testing and validation prototypes in the most efficient way. It is necessary to apply high levels of instrumentation and collect as much data as possible. And a streamlined data pipeline allows the fleet managers to get new insights from the raw data and control the validation vehicles as well as the development team in the most efficient way. In this paper we will demonstrate a data-driven approach for validation testing.

This paper makes a contribution toward a more efficient chassis durability process for the development of passenger cars, in which the simulation of relevant load data is a time-consuming part. This is especially due to the full vehicle model complexity which is usually determined by the demands of rough road simulations. However, for the load calculation on a racetrack, time saving model approaches that are more simplified might be sufficient. Our investigation comprises two levels of vehicle model complexity: one with all chassis parts modeled in a multibody system environment and one characteristic curve based model in an internal simulation environment. Both approaches consider an original chassis control system as a Software-in-the-Loop model. By the evaluation of real-world experiments the main influence factors in terms of durability are demonstrated. With the help of those highly sensitive durability criteria the measurement and simulation results are then compared.

In the last years automotive industry has shown a growing interest in exploring the field of vehicle dynamic control, improving handling performances and safety of the vehicle, and actuating devices able to optimize the driving torque distribution to the wheels. These techniques are defined as torque vectoring. The potentiality of these systems relies on the strong coupling between longitudinal and lateral vehicle dynamics established by tires and powertrain. Due to this fact the detailed (and correct) simulation of the dynamic behaviour of the driveline has a strong importance in the development of these control systems, which aim is to optimize the contact forces distribution. The aim of this work is to build an integrated vehicle and powertrain model in order to provide a proper instrument to be used in the development of such systems, able to reproduce the dynamic interaction between vehicle and driveline and its effects on the handling performances.

While the market share for diesel engines for LD vehicles in Europe has grown continuously in the past years, the market share in North America is still negligible. Until now, it has been possible to fulfill the limits for nitrogen oxides (NOx) both in Europe and in North America by engine measures alone, without using an active NOx aftertreatment system. With the introduction of Tier II Bin 8 and Tier II Bin 5 emissions legislation in the US in 2007, most new diesel applications will now require NOx aftertreatment. One of the possible technologies for the reduction of nitrogen oxides in lean exhaust gas is the NOx storage catalyst which has become the generally-accepted choice for engines with gasoline direct injection systems and which is also utilized in the current diesel Bluetec I systems from Daimler. For heavier applications urea-SCR is the preferred technology to fulfill NOx legislation limits.

The economic challenges and environmental imperatives facing the trucking and automobile industries today all point to a pressing need to improve fuel efficiency. Due to increasing volatility of fuel supplies, prices and a growing interest in reducing greenhouse gas emissions, fuel efficiency has taken on new urgency. In the long-haul trucking industry this is especially important given the fact that fuel accounts for a significant share of fleet operating costs. To this end Daimler and NAVTEQ have developed a system to improve fuel economy and reduce CO₂ emissions through the integration of digital map data into Advanced Driver Assistance Systems or ADAS. Digital road map attributes, especially road slope have been demonstrated to enable powertrain controls to anticipate road inclination changes and use this information to predictively enhance load management optimization versus the reactive approach afforded by current technology.

Tire-road interaction is one of the main concerns in the design of control strategies for active/semi-active differentials oriented to improve handling performances of a vehicle. In particular, the knowledge of the friction coefficient at the tire-road interface is crucial for achieving the best performance in any working condition. State observers and estimators have been developed at the purpose, based on the measurements traditionally carried out on board vehicle (steer angle, lateral acceleration, yaw rate, wheels speed). However, until today, the problem of tire-road friction coefficient estimation (and especially of its maximum value) has not completely been solved. Thus, active control systems developed so far rely on a driver manual selection of the road adherence condition (anyway characterized by a rough and imprecise quality) or on a conservative tuning of the control logic in order to ensure vehicle safety among different tire-road friction coefficients.

Future demands regarding emissions, fuel consumption and driveability lead to complex engine and power train control systems. The calibration of the increasing number of free parameters in the ECU's contradicts the demand for reduced time in the power train development cycle. This paper will focus on the automatic, unmanned closed loop optimization of driveability quality on a high dynamic engine test bed. The collaboration of three advanced methods will be presented: Objective real time driveability assessment, to predict the expected feelings of the buyers of the car Automatic computer assisted variation of ECU parameters on the basis of statistical methods like design of experiments (DoE). Thus data are measured in an automated process allowing an optimization based on models (e.g. neural networks).

Active controls for braking dynamics are widely investigated in literature [1]-[8] as one of the way to improve vehicle safety and avoid collisions. Active systems commonly mounted on passenger cars like ABS/EBD, have achieved a high level of robustness towards possible changes in the tires' characteristics due to multiple causes such as: under-inflation, wear and also replacement of tires with new ones different from the first equipment series. Although these electronic control systems have been designed to be robust and no case-sensitive to such variations in tire conditions, a further improvement of their performance could be achieved by means of a continuous adaptive control.

Digital or virtual prototyping by means of a multibody simulation model (MBS) is a standard part of the automotive design process. A high-fidelity model is built and often correlated against test data to increase its accuracy. Once built the MBS model can then be used for high fidelity analysis in ride comfort, handling as well as durability. Next to the MBS model, current industry practice is to develop a reduced degree of freedom model for the design and validation of control or intelligent systems. The models used in the control system design are required to execute in hardware-in-the-loop (HIL) simulations where it is necessary to run real-time. The reason for the creation of the reduced degree of freedom models so far has been that the high-fidelity or off-line model does not execute fast enough to be used in an HIL simulation.

In order to meet current and future emission and CO2 targets, an efficient vehicle thermal management system is one of the key factors in conventional as well as in electrified powertrains. Furthermore the increasing number of vehicle configurations leads to a high variability and degrees of freedom in possible system designs and the control thereof, which can only be handled by a comprehensive tool chain of vehicle system simulation and a generic control system architecture. The required model must comprise all relevant systems of the vehicle (control functionality, cooling system, lubrication system, engine, drive train, HV components etc.). For proper prediction with respect to energy consumption all interactions and interdependencies of those systems have to be taken into consideration, i.e. all energy fluxes (mechanical, hydraulically, electrical, thermal) have to be exchanged among the system boundaries accordingly.

The correct information about legal demands of the On-Board-Diagnostic (OBD) system in a vehicle project is required throughout the entire development process. Usually, the main obstacle in succeeding is to provide the company's expertise of some few experts for all employees who work in OBD related projects. The paper describes the AVL solution for knowledge management and tool supported control system design and calibration: OBD System Development Database. The software enables the user to access the regulatory requirements for a specific application and legislation from past, present and future (proposed rule-making) point of view. Information concerning already available and stored monitoring concepts is linked to the requirements in order to re-use potentially suitable concepts and to enable an efficient knowledge exchange within the company.

Most stringent emission legislations have been implemented in all major markets to improve air quality across the past years. This effects the product cost of the vehicles which is considered being critical and needs to be minimized. India suffers from bad air quality and countermeasures have been defined. One being the implementation of similar emission standards than EU VI. By doing so, India takes a large step going from the currently effective BS IV directly to BS VI. Emission reduction is currently mainly handled by the usage of EGR, thus no engine aftertreatment system has been applied. BS VI will require an aftertreatment (EAS) concept with several catalysts and corresponding control system. India is a very cost sensitive market and a carry over of solutions from the EU needs to be evaluated carefully and new approaches need to be found.

Tire mark analysis is an important factor in accident reconstruction. A precise determination of pre- and postcrash speeds as well as longitudinal and lateral accelerations from tire marks contributes significantly to a reliable accident reconstruction. Continuous advancements in tire and vehicle technology – in particular with respect to modern control systems such as anti-lock braking systems (ABS) – raises the question what role tire marks play in accident reconstruction today. Moreover, this accompanies the question to what extent potential interventions by vehicle control systems such as the electronic stability program (ESP®) resp. the electronic stability control (ESC) can be identified in a tire mark. The widespread use of these systems today makes them increasingly important in accident reconstruction.

Given the growing interest in improving the efficiency of the bus fleet in public transportation systems, this paper presents an analysis of the energy consumption of a battery electric bus. During the experimental campaign, a battery electric bus was loaded using sand payloads to simulate the passenger load on board and followed another bus during regular service. Data related to the energy consumed by various bus utilities were published on the vehicle’s CAN network using the FMS standard and sampled at a frequency of 1 Hz. The collected experimental data were initially analyzed on a daily basis and then on a per-route basis. The results reveal the breakdown of energy consumption among various utilities over the course of each day of the experiment, highlighting those responsible for the highest energy consumption.