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Heavy trucks are produced with a great variety of vehicle configurations, operate over a wide range of gross vehicle weight and sometimes function in extreme duty environments. Frontal crashes of heavy trucks can pose a threat to truck occupants when the vehicle strikes another large object such as bridge works, large natural features or another heavy-duty vehicle. Investigations of heavy truck frontal crashes indicate that the factors listed above all affect the outcome for the driver and the resulting damage to the truck Recently, a new chassis was introduced for on-highway heavy truck models that feature frontal airbag occupant protection. This introduction presented an opportunity to incorporate the knowledge gained from crash investigation into the process for developing the crash sensor's parameter settings.

Accident statistics show that rollover accidents contribute to a large proportion of fatal traffic accidents in the U.S.. In the past it has been documented that some light passenger cars showed tendencies to roll over in evasive lane change maneuvers. In 1997, a newly developed mini van rolled over in a severe double lane change test called “moose-test”. Recently (2001), a new SUV showed similar tendencies in the Consumers Union Short Course test. It is not immediately clear why these evasive test maneuvers are so strongly related to untripped rollover of light passenger vehicles. Therefore, the goal of current research is to understand the circumstances and effects causing modern passenger vehicles to roll over in evasive maneuvers on the road. This paper discusses research activities concerning the following questions: How do critical steering strategies lead to untripped rollover? Are resonant frequencies excited during maneuvers leading to rollover?

The aim of the project is to reliably identify the set of constructive features responsible for the highest noise levels in the interior of motor vehicles. A simulation environment based on artificial intelligence techniques such as neural networks and genetic algorithms has been implemented. We used a system identification approach in order to approximate the functional relationship between the target noise series and the sets of constructive parameters corresponding to the cars. The noise levels were measured with a microphone positioned on the driver''s chair, and corresponded to a variation of the engine rotation of 600-900 rot/min. The database includes 45 different cars, each described by vectors of 67 constructive features.

A local Gaussian process regression approach is presented, which allows to model nonlinearities of internal combustion engines more accurate than global Gaussian process regression. By building smaller models, the prediction of local system behavior improves significantly. In order to predict a value, the algorithm chooses the nearest training points. The number of chosen training points depends on the intensity of estimated nonlinearity. After determining the training points, a model is built, the prediction performed and the model discarded. The approach is demonstrated with a benchmark system and air charge test bed measurements. The measurements are taken from a turbocharged SI gasoline engine with both variable inlet valve lift and variable inlet and exhaust valve opening angle. The results show how local Gaussian process regression outmatches global Gaussian process regression concerning model quality and nonlinearities in particular.

Air charge calibration of turbocharged SI gasoline engines with both variable inlet valve lift and variable inlet and exhaust valve opening angle has to be very accurate and needs a high number of measurements. In particular, the modeling of the transition area from unthrottled, inlet valve controlled resp. throttled mode to turbocharged mode, suffers from small number of measurements (e.g. when applying Design of Experiments (DoE)). This is due to the strong impact of residual gas respectively scavenging dominating locally in this area. In this article, a virtual residual gas sensor in order to enable black-box-modeling of the air charge is presented. The sensor is a multilayer perceptron artificial neural network. Amongst others, the physically calculated air mass is used as training data for the artificial neural network.

Nowadays, a continually growing system complexity due to the development of an increasing number of vehicle concepts in a steadily decreasing development time forces the engineering departments in the automotive industry to a deepened system understanding. The virtual design and validation of individual components from subsystems up to full vehicles becomes an even more significant role. As an answer to the challenge of reducing complete hardware prototypes, the virtual competence in NVH, among other methods, has been improved significantly in the last years. At first, the virtual design and validation of objectified phenomena in analogy to hardware tests via standardized test rigs, e.g. four poster test rig, have been conceived and validated with the so called MBS (Multi Body Systems).

Handling characteristics, ride comfort and active safety are customer relevant attributes of modern premium vehicles. Electronic control units offer new possibilities to optimize vehicle performance with respect to these goals. The integration of multiple control systems, each with its own focus, leads to a high complexity. BMW and ITK Engineering have created a tool to tackle this challenge. A simulation environment to cover all development stages has been developed. Various levels of complexity are addressed by a scalable simulation model and functionality, which grows step-by-step with increasing requirements. The simulation environment ensures the coherence of the vehicle data and simulation method for development of the electronic systems. The article describes both the process of the electronic control unit (ECU) development and positive impact of an integrated tool on the entire vehicle development process.

Development times in the automotive industry are becoming increasingly shorter. For this reason, design decisions based on simulation results must be made at an early development stage. The dynamic simulation of an automotive refrigeration cycle with Dymola/Modelica as part of the design process will be described in the following paper. The component supplier's expertise as well as the automotive manufacturer's knowledge of vehicle parameters in one simulation platform will also be discussed.

In this paper a method for the identification of most significant vehicle parameters influencing the behavior of a lateral control system of autonomous car is presented. Requirements for the design stage of the controller need to consider many uncertainties in the plant. While most vehicle properties can be compensated by an appropriate tuning of the control parameters, other vehicle properties can change significantly during usage. The control system is evaluated based on performance measures. Analyzed parameters comprise functional tire characteristics, mass of the vehicle and position of its center of gravity. Since the parameters are correlated, but Sobol’ sensitivity analysis assumes decorrelated inputs, random variation yields no reasonable results. Furthermore, the variation of each parameter or set of parameters is not applicable since the numbers of required simulations is increased significantly according to input dimension.