Search Results

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.

One goal of modern power train control systems in heavy trucks is to damp driveline oscillations using appropriate controllers. Modern control algorithms like state-space controllers are based on a state-space model, which should accurately characterize the real process behavior. Otherwise, optimal control can not be guaranteed. These state-space models include a huge number of parameters, which have to be identified by an identification process. However, existing driveline models contain two serious problems: an increasing offset over time between measured and simulated data and an inadequate detection of the longitudinal dynamics of the truck. Therefore, this article deals with two goals: to optimize the offline identification process for the special use in driveline systems and to establish an online adaptation of the model parameters to guarantee an optimal model fit.

The reproduction of the vehicle motion is a crucial element of accident reconstruction. Apart from the position of the center of gravity in an inertial coordinate system, the vehicle heading plays an important role. The heading is the sum of the yaw angle and the vehicle body side slip angle. In standard vehicles, the yaw angle can be determined using the yaw rate sensor and the wheel speeds. However, the yaw rate sensor is often subject to temperature drift. The wheel speed signals are forged at low speeds or due to slip. These errors result in significant deviations of reconstructed and real vehicle heading. Therefore, an intelligent combination of these signals is required. This paper describes a fuzzy system which is capable to increase the accuracy of yaw angle calculation by means of fuzzy logic. Before the data is applied to the fuzzy system, it is preprocessed to ensure the accuracy of the fuzzy system inputs.

Future vehicle electrical systems will differ substantially from current ones due to rising requirements. For example driver-assistance and drive-by-wire systems will lead to novel and demanding electrical load profiles which in turn will pose new requirements on the electrical system. Furthermore safety concepts, reliability, availability and diagnosis are getting increasingly important in such systems and thus also in the vehicle's electrical system. In order to meet the upcoming requirements new concepts for future vehicle electrical systems have to be developed such that the new powernet is able to adapt flexibly to different situations or failures by routing the energy through different channels. For efficiency the corresponding development process should be based on modeling and simulation techniques. Depending on the design or analysis task, the powernet is represented through different modeling descriptions.

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.

Today, in many passenger cars and light trucks, the conventional driveline is extended by a dual mass flywheel (DMF). The DMF reduces driveline oscillations by mechanically decoupling the crankshaft and the transmission. Existing engine control systems are designed for conventional single mass flywheel (SMF) systems. In the future, to facilitate the optimal control of engines equipped with advanced DMF systems, such conventional control systems may require adaptation, modification or even replacement. The design and testing of appropriate new control systems has required the development of various types of engine models. In this paper, various engine modeling techniques are introduced and compared in respect to their capabilities for both driveline simulation and control system development.

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.

In this paper we present the results of a project that concentrates on the design of distributed embedded systems for control-related applications. The OPTMAP (Optimal Mapping of Virtual Control Functions to a Distributed Architecture) framework supports the function allocation based on given constrains involving a feasible solution. The control systems we will consider use a time-triggered paradigm for sensor reading and event-driven behavior for inter-processor communication. Sensor values are read at fixed periods in time and data processing occurs after the control unit receives the proper message. The aim of the project is to get an optimized mapping which minimizes information traffic on the network and guarantees that all processing units are able to handle the distributed control functions in real time.

Coordination and concentration of different electronic functions within a car with the objective of functional cooperation and, if possible, incorporation into a single package to reduce costs and improve reliability is discussed. The alternatives of a Special Purpose Computer or a General Purpose Realtime Computer are described with regard to available sensor technology.