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A variety of methodologies to use embedded multicore controllers efficiently has been discussed in the last years. Several assumptions are usually made in the automotive domain, such as static assignment of tasks to the cores. This paper shows an approach for efficient task allocation depending on different system modes. An engine management system (EMS) is used as application example, and the performance improvement compared to static allocation is assessed. The paper is structured as follows: First the control algorithms for the EMS will be classified according to operating modes. The classified algorithms will be allocated to the cores, depending on the operating mode. We identify mode transition points, allowing a reliable switch without neglecting timing requirements. As a next step, it will be shown that a load distribution by mode-dependent task allocation would be better balanced than a static task allocation.

Recently, vehicle networks have increased complexity due to the demand for autonomous driving or connected devices. This increasing complexity requires high bandwidth. As a result, vehicle manufacturers have begun using Ethernet-based communication for high-speed links. In order to deal with the heterogeneity of such networks where legacy automotive buses have to coexist with high-speed Ethernet links vehicle manufacturers introduced a vehicle gateway system. The system uses Ethernet as a backbone between domain controllers and CAN buses for communication between internal controllers. As a central point in the vehicle, the gateway is constantly exchanging vehicle data in a heterogeneous communication environment between the existing CAN and Ethernet networks. In an in-vehicle network context where the communications are strictly time-constrained, it is necessary to measure the delay for such routing task.

In terms of modern technical systems, the automotive sector is characterized by escalating complexity and functionality requirements. The development of embedded control systems has to meet highest demands regarding process-, time- and cost-optimization. Hence, the efficiency of software development becomes a crucial competitive advantage. Systems design engineers need effective tools and methods to achieve exemplary speed and productivity within the development phase. To obtain such tools and methods, semiconductor manufacturers and tool manufacturers must work closely together. Within the joint efforts of ETAS and Infineon, the software tool suite ASCET-SD was enhanced to generate efficient C code for Infineon's TriCore architecture mapped on ETAS's real-time operating system ERCOSEK. The processor interface to application & calibration tools was realized using the ETK probe based on a JTAG/Nexus link at very high bandwidth.

The recent improvements in automotive electronics have had a tremendous impact on safety, comfort and emissions. But the continuous increase of the volume of electronic equipment in cars (representing more than 25% of purchasing volume) as well as the increasing system complexity represent a new challenge to quality, post-sales customer support and maintenance. Identifying a fault in a complex network of ECUs, where the different functions are getting more and more intricate, is not an easy task. It can be shown that with the levels of reliability common in 1980, an upper-range automobile of today could never function fault-free. On-Board-Diagnostics (OBD) concepts are emerging to assist the maintenance personnel in localizing the source of a problem with high accuracy, reducing the vehicle repair time, repair costs and costs of warranty claims.

A mechatronic approach to implementing a BLDC motor drive control system is described. The partitioning method used allows the motor power to be scaled from around 100 watts to 1 kilowatt. The chosen approach maps the required electronic functionality to different existing front-end technologies. By drawing on vast experience with back-end technologies, especially chip-on-chip assembly, it is possible to implement a system in a one-package solution. The advantages of each technology are used to achieve a cost-effective, space-saving solution.

A cooperation of several research partners supported by the German Federal Ministry of Research and Education proposes a new active matrix LED light source. A multi pixel flip chip LED array is directly mounted to an active driver IC. A total of 1024 pixel can be individually addressed through a serial data bus. Several of these units are integrated in a prototype headlamp to enable advanced light distribution patterns in an evaluation vehicle.

In times when automated driving is becoming increasingly relevant, dynamic simulators present an appropriate simulation environment to faithfully reproduce driving scenarios. A realistic replication of driving dynamics is an important criterion to immerse persons in the virtual environments provided by the simulator. Motion Cueing Algorithms (MCAs) compute the simulator’s control input, based on the motions of the simulated vehicle. The technical restrictions of the simulator’s actuators form the main limitation in the execution of these input commands. Typical dynamic simulators consist of a hexapod with six degrees of freedom (DoF) to reproduce the vehicle motion in all dimensions. Since its workspace dimensions are limited, significant improvements in motion capabilities can be achieved by expanding the simulator with redundant DoF by means of additional actuators.

Among the issues affecting the design process of a vehicle, there is the lack of multidisciplinary knowledge among the different teams involved. This often leads to the risk of loosing important key points from the initial concept idea of designers to the final vehicle package definition made by engineers. Therefore, this study builds up a method based on parameters defining exterior aesthetic priorities according to car segment to support engineers involved in the automotive design process. In particular, during the early design-engineering phase, this method should help them to understand better vehicle proportions defined by designers. This work is currently used in the course of design and simulation of road vehicles (Chair of Automotive Technology, TUM) to explain fundamentals of automotive design.

Existing electronic combustion engine control systems only guarantee a desired air-to-fuel-ratio λ in stationary operation. In order to achieve the desired λ also in in-stationary use of the engine, it is necessary to use new-technology-based control systems. Artificial Intelligence provides methods to cope with difficulties like wide operation range, unknown nonlinearities and time delay. We will propose a strategy for control of a Spark Ignition Engine to determine the mass of air inside the combustion chambers with the highest accuracy. Since Neural Networks are universal approximators for multidimensional nonlinear static functions they can be used effectively for identification and compensation purposes of unknown nonlinearities in closed control loops.

In this paper the design of a robust control system for a parallel hybrid drivetrain is presented The drivetram is based on a continuously variable transmission (CVT) and is therefore a highly nonlinear multiple-input-multiple-output system (MIMO-System) Input-Output-Linearization offers the possibility of linearizing and of decoupling the system Since for example the vehicle mass varies with the load and the efficiency of the gearbox depends strongly on the actual working point, an exact linearization of the plant will mostly fail Therefore a robust control algorithm based on sliding mode is used to control the drivetrain

In order to reduce emissions of spare ignition engines using a three way catalyst, a stoichiometric air-fuel ratio must be guaranteed in stationary and transient operation of the engine. This aim can be reached by using a specific feed-forward structure for the control of the paths of air and fuel based on identification abilities of Artificial Intelligence. As approximators for multidimensional nonlinear static functions we will use specific Neural Networks (NN) together with sophisticated stability-proven learning structures. The acquired knowledge within the NN determines our control action mainly through using feed-forward structures. Our investigations are based on the so-called mean-value-modelling approach of SI engines; it is our aim to present this strategy.

The signal delivery and quality of sensor data is of growing importance for modern automotive control applications. Sensors tend to be calibrated subsystems that are designed to stay in a defined tolerance and thus can easily be modeled. Compared to this deterministic behavior the transmission channel is time variant due to EMC and aging of contacts for example. The use of analog signaling, which is the actual state of realization in many cases, is sensitive to the time variant effects mentioned before. This time variance is hard to consider for the control system development. In this paper we will analyze the role of the sensor in the signal supply chain and discuss approaches for digital sensor-ECU communication and their potential to establish a link, which allows neglecting low level effects of the channel.

The increasing complexity of automotive functions which are necessary for improved driving assistance systems and automated driving require a change of common vehicle architectures. This includes new concepts for E/E architectures such as a domain-oriented vehicle network based on powerful Domain Control Units (DCUs). These highly integrated controllers consolidate several applications on different safety levels on the same ECU. Hence, the functions depend on a strictly separated and isolated implementation to guarantee a correct behavior. This requires middleware layers which guarantee task isolation and Quality of Service (QoS) communication have to provide several new features, depending on the domain the corresponding control unit is used for. In a first step we identify requirements for a middleware in automotive DCUs. Our goal is to reuse legacy AUTOSAR based code in a multicore domain controller.

Glare-free high beams are a consistent enhancement of adaptive headlight systems for vehicles with advanced driver assist systems. A prerequisite for these are camera-based systems with the ability to recognize and classify objects such as vehicles in front or oncoming vehicles when driving at night. These objects can then be dynamically masked out of the high beam of the specially designed headlights. Since we are talking about moving objects, it is essential for the high beam to be continuously and dynamically adapted. This paper describes a modular LED matrix system for dynamically adjusting a glare-free and continuously active high beam. The main focus was on the modularity of the system and the optimization of the thermal properties of an LED matrix in order to ensure that operation was reliable under the harsh environmental conditions inside a headlight. Specific control electronics and different interconnection methods were examined.

Bicycle-drawn cargo trailers with an electric drive to enable the transportation of high cargo loads are used as part of the last-mile logistics. Depending on the load, the total mass of a trailer can vary between approx. 50 and 250 kg, potentially more than the mass of the towing bicycle. This can result in major changes in acceleration and braking behavior of the overall system. While existing systems are designed primarily to provide sufficient power, improvements are needed in the powertrain control system in terms of driver safety and comfort. Hence, we propose a novel prototype that allows measurement of the tensile force in the drawbar which can subsequently be used to design a superior control system. In this context, a sinusoidal force input from the cyclist to the trailer according to the cadence of the cyclist is observed. The novelty of this research is to analyze whether torque impulses of the cyclist can be reduced with the help of Model Predictive Control (MPC).

In order to predict reality as accurately as possible leads to the fact that numerical models in automotive vibroacoustic problems become increasingly high dimensional. This makes applications with a large number of model evaluations, e.g. optimization tasks or uncertainty quantification hard to solve, as they become computationally very expensive. Engineers are thus faced with the challenge of making decisions based on a limited number of model evaluations, which increases the need for data-efficient methods and reduced order models. In this contribution, variational autoencoders (VAEs) are used to reduce the dimensionality of the vibroacoustic model of a vehicle body and to find a low-dimensional latent representation of the system.

Software complexity of vehicles is constantly growing especially with additional autonomous driving features being introduced. This increases the risk for bugs in the system, when the car is delivered. According to a car manufacturer, more than 90% of availability problems corresponding to Electronic Control Unit (ECU) functionality are either caused by software bugs or they can be resolved by applying software updates to overcome hardware issues. The main concern are sporadic errors which are not caught during the development phase since their trigger condition is too unlikely to occur or is not covered by the tests. For such systems, there is a need of safe and secure infield diagnosis. In this paper we present a tool software architecture with remote access, which facilitates standard read/write access, an efficient channel interface for communication and file I/O, and continuous trace.