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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.

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 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 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 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.

In this paper the problem of fault detection in distributed systems is addressed. Due to the trend that these systems are incorporating an increasing number of subsystems from different suppliers fault detection is becoming an essential part of distributed system design. While meeting the typical constraints of the automotive industry there is the demand on increased safety and improved availability. Because of the connection of different subsystems, errors propagate through the system and may affect other subsystems where they can be detected. The key task which is dealt with in this paper is to detect the origin of these errors. Therefore, Hierarchical Colored Bayesian Petri-Nets are introduced to fulfill fault detection according to Bayesian networks. To reduce calculation efforts, the principle of clustering is included.

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 general designed for use with conventional single mass flywheel (SMF) systems. In the future, to facilitate the optimal control of engines equipped with advanced DMF systems, these conventional control systems may require adaptation, modification or even replacement. In the past, misfire detection has been done by expensive dedicated sensors; seismic, ion current measurement at the spark plugs or even by measuring in-cylinder pressures directly. Typically misfire detection is performed using signals derived from the crankshaft position sensor, which works well for engines with a limited number of cylinders and which are connected to relatively simply drivelines.

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 approach a nonlinear controller for the lateral vehicle dynamics is designed. The basis for the design is a nonlinear model of the lateral vehicle dynamics in state space representation consisting of three states: The vehicle velocity, the yaw rate as well as the vehicle body sideslip angle (VBSSA). As control variables the yaw rate and the VBSSA are chosen. To assure the vehicle follows the driver's directional intent, the yaw rate is adapted to a desired reference value determined by means of a linear single track model. The second control variable -the VBSSA- is utilized to reduce the lateral forces. Incorporating the VBSSA, the controller's behavior can be significantly improved. Thus, a nonlinear controller is designed which is capable to stabilize the vehicle in critical driving situations. This nonlinear controller is based on an adaptation of a quality function for the nonlinear model to the one for a linear reference system.