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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.

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.

Bus systems like CAN or FlexRay allowed great advances in automotive electronics over the last 20 years. In order to function in an environment which requires the communication medium to tolerate one safety-relevant fault, these bus systems require a second, redundant bus to act as a backup for the original unit. With the network approach presented in this paper (SafeNet) it is possible to use the network intrinsic redundancy to keep the network fail-safe after at least one safety relevant fault in the network. To ensure this, messages are relayed to every node in the network. Even though the message delivery times in the network are not deterministic, it is shown that it is suitable for safety-relevant applications like drive-by-wire. Due to the simple point-to-point connections used to connect the nodes, high speeds can be achieved. The network approach is compared to both CAN and FlexRay under different aspects.

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.

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.

Over more than 20 years 50 million LuK dual mass flywheels (DMF) have been produced for use in passenger cars and light trucks. A typical DMF consists of two flywheels connected by long travel arc-springs. It is located between the combustion engine and the clutch or automatic transmission. The DMF reduces driveline oscillations by mechanically decoupling the transmission from the periodic combustion events that excite the engine crankshaft. Existing engine control systems are generally designed for conventional single mass flywheel (SMF) systems. In the future, to facilitate the best possible control of engines equipped with DMF systems, these conventional control systems may require modification or even replacement. With the integration of the highly non-linear DMF, the complexity, and thus the order of the powertrain system increase.

The new aerial communication protocol “Controller Area Network” (CAN) efficiently supports distributed realtime control in automotive applications. In order to unload CPUs from high-speed message transfer, dedicated CAN hardware handles messages up to the communication object level. In multiplex wiring message rates are one to two orders of magnitude lower, allowing to implement the upper communication level more cost-effectively in software. This reduces CAN interface hardware to bitwise protocol handling only. It may be incorporated even into low-end microcontrollers without significantly increasing chip size. Thus the same CAN protocol supports the entire range of serial automotive communication, matching implementation costs to requirements at each performance level.

Today's vehicle networks are mainly based on the event-triggered CAN-bus. In future FlexRay, which is a TDMA-based bus, will more and more be used for the implementation of safety-relevant real-time systems due to its determinism. In order to configure a CAN-based network the priorities of the messages sending via the external bus have to be defined. In this paper an approach will be presented allowing automated priority determination. Subsequently it will be shown how to adapt this method to automated cycle configuration in case of a FlexRay-based system. In order to ensure determinism not only in TDMA-based but also in event-triggered networks, a method will be presented adapting priorities of messages intending to exceed their deadline. This can be easily realized without changing the CAN protocol.

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.

A high speed serial communication link has been developed for interconnecting electronic control units within automobiles. The incorporation of object oriented communication in conjunction with acceptance filtering introduces a new level of message handling efficiency and flexibility. Powerful error handling techniques guarantee safe operation in noisy automotive environments.

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.

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.

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 integration of a Dual Mass Flywheel (DMF) in the conventional vehicle driveline leads to various benefits, and hence today it has established its position in many passenger cars and light trucks. Transmission and driveline oscillations are reduced by mechanically decoupling the transmission from the periodic combustion events that excite the engine crankshaft, improving driving comfort and reducing transmission stresses. For systems with conventional single mass flywheel (SMF) reliable engine control systems have already been developed. However, the complexity of the driveline increases with the integration of a DMF. Hence, in the future conventional engine control systems may require adaptation, modification or even replacement, in order to guarantee the optimal control of engines equipped with advanced DMF systems.

In this paper the vehicle body side slip angle (VBSSA) is determined by means of non-linear state space observers. First, an adaptive non-linear double track model is presented. Validation with real measurement data shows that the model accuracy is sufficient for observer design. On basis of this model two observers are derived. One observer is based on a linearization of the vehicle model around the currently estimated state vector. The other observer adapts the dynamics of the non-linear estimation error to the one of a linear reference model. As this observer is restricted to systems of a specific structure, the adaptive non-linear double track model has to be restructured accordingly. The presented observers are validated with real measurement data. They provide an accurate estimation of the VBSSA up to the stability limit of the vehicle.

The formulation of software tasks as parallel processes allows their implementation within distributed microcontrollers. The requirements for Automotive Networks to support these applications are discussed. By introduction of a locality measure, a classification of networks can be made either into interactive distributed realtime processing or into classical communication. Given a sufficiantly small locality, the physical network extension does not have an impact on the implementation. A concept i presented how to integrate process dispachting and synchronization. Based upon this concept, functions may be formulated independant of their location in a specific microcontroller.

As future drive-by-wire systems have no mechanical fallback level, the increased safety requirements need to be met by software-based solutions. The task of the software is to provide services in the field of fault detection and compensation as well as control of redundant hardware structures. Particularly the implementation of fault detection and error correction avoids fatal output of drive-by-wire control units caused by erroneous input signals. This article describes the implementation of a module compensating faults in the input signals of a vehicle function, which controls the longitudinal dynamics of a truck. The error correction is achieved by means of data fusion. Sensing units consisting of the sensor as well as the preprocessing unit often are provided by external suppliers. In some cases information regarding the characteristics of their output data written on the CAN bus is not available.

A significant portion of communication in Automotive Networks consists of signals, which are vital to the safety of the vehicle. In addition to requirements resulting from the actual transfer of information an Automotive Communication Protocol has to incorporate properties which ensure operational safety even in presence of errors. Based upon a discrimination into reversible errors and irreversible failures, defect nodes have to be determined and subsequently disconnected from the network. In this paper proper schemes for error detection, report, recovery and confinement are presented.

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.

The vehicle body sideslip angle (VBSSA) is a key variable in vehicle dynamics indicating critical driving situations. It is, e.g., essential in vehicle dynamics control concepts. Since it cannot be measured with standard sensors, it has to be determined via a model based approach. Thereto an Extended Kalman Filter will be presented that is capable of describing the VBSSA with high accuracy. The filter design is based on a nonlinear double track model combining the longitudinal and lateral dynamics. Starting point is a double track model with three state variables, that are the velocity in the center of gravity, the VBSSA and the yaw rate. Then, the longitudinal dynamics are incorporated, yielding the velocity and the longitudinal forces at the individual wheels. The resulting nonlinear state space model only requires information that is provided by the standard sensors available in series production vehicles. On basis of this nonlinear model an Extended Kalman Filter is derived.