Search Results

During the last few years numerous innovations in advanced driveline control have improved the performance of commercial vehicles. In this context a major goal of driveline control is the enhancement of dynamical behavior and driving comfort. However, fast engine torque changes during Tipin and Tipout operations improve the dynamical behavior but induce unintentional driveline jerking at the same time. Due to this fact that comfort is contradictory to dynamic, a control strategy is necessary, which can handle with both targets at the same time. Based on a simple model of the driveline two Linear Quadratic (LQ) controllers are developed: A comfort controller, which damps the driveline oscillations, and a dynamic controller, which guarantees a high dynamical performance. However, as both controllers have different targets it is not possible to activate both at the same time.

As automotive systems are becoming increasingly distributed, communication between their components is becoming even more eminent. In safety-relevant distributed systems, the reliability of communication between nodes is crucial for the safety of a system. To guarantee such reliability, it is prerequisite that all nodes in the system have a consistent view of which nodes are functioning correctly and which are not (group membership). In this paper existing algorithms for ensuring group membership are presented and possible solutions for communication systems without such functionality, for example FlexRay, as well as a solution for a network based approach are outlined.

In this paper two new approaches are presented how to partition an amount of functions distributed in automotive electronic systems. In contrast to common partitioning algorithms as Kernighan-Lin, Best-Gain-First, Simulated-Annealing, a.s.o., these algorithms are real multi-partitioning ones. With respect to ECU (electronic control unit) characteristics, the software functions to be partitioned will be allocated automatically onto the available hardware. Main motivation is the reduction of the resulting bus-load which is provoked by the communication between such functions. Moreover these algorithms optimize the final partitioning solution to achieve a reduced number of ECUs. Reducing bus-load and the number of ECUs can lead to significant cost reduction. In order to validate partitioning results, a CAN as well as a FlexRay model was developed in Matlab/Simulink determining the bus-load over time.

This paper treats the complete robust H∞ controllers design. The whole synthesis is exemplified by the idle speed control problem at passenger cars and light trucks. Subsequently the robustness of the designed controller is tested and compared to conventional P and PI controllers. The main steps of controller synthesis are described detailed in this work. First, a closed loop structure has to be chosen. For this purpose, basic principles will be introduced. After this, the weighting matrices for the cost functions have to be defined. Finally, the choice of the calculation algorithm is important. In this approach, the idle speed control is done with the Mixed-Sensitivity design and a derivation of the Doyle-Glover (DGKF) algorithm. The choice for the weighting matrices is depicted clearly in the frequency domain. Finally, a comparison between conventional P(I)- controllers and the introduced H∞ - method is demonstrated and discussed.

In this paper an approach is presented to determine an adequate number of clusters automatically in case of clustering a distributed automotive electronic system. Hereby, this approach is based on the ISODATA clustering algorithm. Its advantages are its flexibility and less computational effort in comparison to normally used partitioning algorithms. In order to cluster a distributed automotive electronic system with respect to a reduced external communication the input data normally used for partitioning algorithms has to be adapted. Besides, a new overall quality criterion is introduced to validate the results of clustering in reference to the busload before test stage.

In this paper an approach to clustering of complex electronic systems using Self-Ordering Maps (SOMs) is presented. SOMs are neural networks which learn through a competitive learning algorithm. In order to use SOMs for the clustering of electronic networks, a representation of the communication behavior in n-dimensional space is developed. The SOM is then used as a nonlinear projection of this space onto a two-dimensional plane. Two examples of clustering are given. The more complex of the two is verified by comparing the behavior of the clustered system and the unclustered system on a simple model of the CAN bus. It is shown that SOMs can be used to effectively cluster complex electronic systems.

There are a number of tools available to assist the engineer during the automotive electronics design process, for example when transferring a graphical specification to a real time rapid prototyping environment. One step in this tool chain however is largely ignored by automated design tools: mapping a large monolithic model to a distributed system, more specifically the mapping of several functions on only a few electronic control units (ECUs) which are connected by a bus. In this paper we will present a method to analyze the underlying functional structure of a given model, partition it using a heuristic algorithm and verify the results with a model of the CAN bus. Based on a given functional model, we will show how to extract an algebraic representation of the communication behavior, the adjacency matrix. Using the adjacency matrix, the heuristic algorithm Best Gain First can be applied to map functions to ECUs.

The increasing linkage of route guidance servers within the recent years leads to numerous efforts to split traffic assignment algorithms in an efficient way on these distributed computers. Especially in the field of intermodal services, i.e. calculating the fastest paths of certain origin-destination pairs with respect to different individual and public traffic services, solutions are required to implement the routing models in a fast, reliable way. Unfortunately, analysis of different realizations is commonly done by comparing the amount of necessary instructions O(·) in different net topologies. However, as computing power is in the meanwhile at a fairly high level, delay in a distributed environment can mainly be expected due to communication time. Dynamic calculations demand to transmit actual traffic conditions during several time periods, thus this paper examines the different routing strategies by evaluating the occuring message transmission time in common graph classes.