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This paper describes a novel approach for modeling an automotive HVAC unit. The model consists of black-box models trained with experimental data from a self-developed measurement setup. It is capable of predicting the temperature and mass flow of the air entering the vehicle cabin at the various air vents. A combination of temperature and velocity sensors is the basis of the measurement setup. A measurement fault analysis is conducted to validate the accuracy of the measurement system. As the data collection is done under fluctuating ambient conditions, a review of the impact of various ambient conditions on the HVAC unit is performed. Correction models that account for the different ambient conditions incorporate these results. Numerous types of black-box models are compared to identify the best-suited type for this approach. Moreover, the accuracy of the model is validated using test drive data.

Highly automated driving (HAD) is under rapid development and will be available for customers within the next years. However the evidence that HAD is at least as safe as human driving has still not been produced. The challenge is to drive hundreds of millions of test kilometers without incidents to show that statistically HAD is significantly safer. One approach is to let a HAD function run in parallel with human drivers in customer cars to utilize a fraction of the billions of kilometers driven every year. To guarantee safety, the function under test (FUT) has access to sensors but its output is not executed, which results in an open loop problem. To overcome this shortcoming, the proposed method consists of four steps to close the loop for the FUT. First, sensor data from real driving scenarios is fused in a world model and enhanced by incorporating future time steps into original measurements.

Wheel slip control system have found a remarkable penetration in all car segments. The information on the wheel behavior has lead to further developments which control the brake performance as well as the driving of the car in general. Latest systems introduced especially on luxury cars use wheel individual brake intervention to ensure vehicle stability under various driving maneuvers within the physical limits. Such systems use vehicle dynamic sensors and special hydraulics which serve as energy source for the automatic brake application. The technical effort of such systems like the Dynamic Stability Control DSC has limited the installation to upper class cars so far. New approaches are required to allow for a more wide spread penetration. Optimized hydraulics together with a rational design of the electronics seems to offer a basis for a more cost effective design.

a practice-oriented approach for an accelerated product development and product design process for mechatronic systems is presented. The handling of complex and versatile product data to perform this process is shown in the area of electrical drives and actuators in cars. It is discussed, how the coordination of all the necessary disciplines as development, design, testing field, specification and release management should be software supported and PDM integrated. The advantages and benefits of the presented methods are shown on particular examples. The necessary software modules are introduced, showing that the realized solution gives both opportunities - the integration into a PDM backbone and at the same time an independent communication within department and/or company. The practical way, to realize the expert-specific needs of the development department, which is not possible with a general PDM system is pointed out.

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.

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.

The permanently increasing number of convenience and safety functions leads to higher complexity of in-car electronics and the rapidly growing amount of sensors, actuators and electronic control units places higher demands on high- speed data communication protocols. Safety-critical systems need deterministic protocols with fault-tolerant behavior. The need for on-board diagnosis calls for flexible use of bandwidth and an ever-increasing number of functions necessitates a flexible means of extending the system. None of the communication solutions available on the market until now (like CAN or TTP) have been able to fulfill all these demands. To solve these problems, BMW together with several semiconductor companies has developed a new protocol for safety-critical applications in automotive vehicles.

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.