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Battery-electric vehicles (BEVs) require new chassis components, which are realized as mechatronic systems mainly and support more and more by-wire functionality. Besides better controllability, it eases the implementation of integrated control strategies to combine different domains of vehicle dynamics. Especially powertrain layouts based on electric in-wheel machines (IWMs) require such an integrated approach to unfold their full potential. The present study describes an integrated, longitudinal vehicle dynamics control strategy for a battery electric sport utility vehicle (SUV) with an electric rear axle based on in-wheel propulsion. Especially the influence of electronic brake force distribution (EBD) and torque blending control on the overall performance are discussed and demonstrated through experiments and driving cycles on public road and benchmarked to results of previous studies derived from [1].

The current automotive electronic and electrical (EE) architecture has reached a scalability limit and in order to adapt to the new and upcoming requirements, novel automotive EE architectures are currently being investigated to support: a) an Ethernet backbone, b) consolidation of hardware capabilities leading to a centralized architecture from an existing distributed architecture, c) optimization of wiring to reduce cost, and d) adaptation of service-oriented software architectures. These requirements lead to the development of Zonal EE architectures as a possible solution that require appropriate adaptation of used security mechanisms and the corresponding utilized hardware trust anchors. 1 The current architecture approaches (ECU internal and in-vehicle networking) are being pushed to their limits, simultaneously, the current embedded security solutions also seem to reveal their limitations due to an increase in connectivity.

This paper explains why software for efficient model-based development is needed to improve the efficiency of automakers and suppliers when implementing solutions with next generation automotive embedded systems. The resulting synergies are an important contribution for the automotive industry to develop safer, smarter, and more eco-friendly cars. To achieve this, it requires implementations of algorithms for machine learning, deep learning and model predictive control within embedded environments. The algorithms’ performance requirements often exceed the capabilities of traditional embedded systems with a homogeneous multicore architecture and, therefore, additional computing resources are introduced. The resulting embedded systems with heterogeneous computing architectures enable a next level of safe and secure real-time performance for innovative use cases in automotive applications such as domain controllers, e-mobility, and advanced driver assistance systems (ADAS).

With the increasing complexity of electronic vehicle systems, one particular “gap” between function development and ECU integration becomes more and more apparent, and critical; albeit not new. The core of the problem is: as more functions are integrated and share the same E/E resources, they increasingly mutually influence and disturb each other in terms of memory, peripherals, and also timing and performance. This has two consequences: The amount of timing-related errors increases (because of the disturbance) and it becomes more difficult to find root causes of timing errors (because of the mutual influences). This calls for more systematic methods to deal with timing requirements in general and their transformation from function timing requirements to software architecture timing requirements in particular.

Future automotive systems will be connected with other vehicles and information systems for improved road safety, mobility and comfort. This new connectivity establishes data and command channels between the internal automotive system and arbitrary external entities. One significant issue of this paradigm shift is that formerly closed automotive systems now become open systems that can be maliciously influenced through their communication interfaces. This introduces a new class of security challenges for automotive design. It also indirectly impacts the safety mechanisms that rely on a closed-world assumption for the vehicle. We present a new security analysis approach that helps to identify and prioritize security issues in automotive architectures. The methodology incorporates a new threat classification for data flows in connected vehicle systems.

The infrastructure in modern cars is a heterogeneous and historically grown network of different field buses coupling different electronic control units (ECUs) from different sources. In the past years, the amount of ECUs in the network has rapidly grown due to the mushrooming of new functions which historically were mostly implemented on a one-ECU-per-function basis resulting in up to a hundred ECUs in fully equipped luxury cars. Additionally, new functions like parking assist systems or advanced chassis control functions are getting increasingly complex and require more computing power. These two facts add up to a complex challenge in development. The current trend to host several functions in single ECUs as integration platforms is one attempt to address this challenge. This trend is supported by the increased computing power of current and upcoming multi-core microcontrollers.

In current research projects such as "Vehicle to Grid" (V2G), "Vehicle to Building" (V2B) or "Vehicle to Home" (V2H), plug-in vehicles are integrated into stationary energy systems. V2B or V2H therefore stands for intelligent networking between vehicles and buildings. However, in these projects the objective is mostly from a pure electric point of view, to smooth the load profile on a household level by optimized charging and discharging of electric vehicles. In the present paper a small energy system of this kind, consisting of a building and a vehicle, is investigated from a holistic point of view. Thermal as well as electrical system components are taken into account and there is a focus on reduction of overall energy consumption and CO₂ emissions. A predictive energy management is presented that coordinates the integration of a plug-in hybrid electric vehicle into the energy systems of a building. System operation is optimized in terms of energy consumption and CO₂ emissions.

More electronic vehicle functions lead to an exponentially growing degree of software integration in automotive ECUs. We are seeing an increasing number of ECUs with mixed criticality software. ISO26262 describes different safety requirements, including freedom from interference and absence from error propagation for the software. These requirements mandate particular attention for mixed-criticality ECUs. In this paper we investigate the ability to guarantee that these safety requirements will be fulfilled by using established (deadline monitoring) and new error detection mechanisms (execution time monitoring). We also show how these methods can be used to build up safe and efficient schedules for today's and future automotive embedded real time systems with mixed criticality software.

It is the beginning of a new age: multicore technology from the PC desktop market is now also hitting the automotive domain after several years of maturation. New microcontrollers with two or more main processing cores have been announced to provide the next step change in available computing power while keeping costs and power consumption at a reasonable level. These new multicore devices should not be confused with the specialized safety microcontrollers using two redundant cores to detect possible hardware failures which are already available. Nor should they be confused with the heterogeneous multicore solutions employing an additional support core to offload a single main processing core from real-time tasks (e.g. handling peripherals).

Future applications in the automotive domain such as distributed control functions need a highly dependable communication system. The current FlexRay standard already provides high transmission speeds and addresses deterministic data communication. This paper shows how to enhance the safety properties for handling a new set of applications and speeding up the communication even more. The concept of Layered FlexRay is based on the FlexRay protocol and addresses the requirements of safety-relevant applications in a distributed communication network. An implementation of this approach is depicted with a Safety Core hardware chip. It is designed to handle the communication between the FlexRay system beneath and the application on the host CPU above, providing highly efficient data management and execution of safety functions which otherwise would have to be executed in software on the host CPU.

The term X-by-Wire is commonly used in the automotive industry to describe the notion of replacing current mechanical or hydraulic chassis and powertrain systems with pure electro-mechanical systems. The paper describes the current trends and the architecture of future chassis electronics systems. The first part of the paper covers the systems architecture of x-by-wire electronics systems. We describe the network and the software architecture in more detail. The paper also explains some of the software components, in particular the operating system and the communication layer. The second part of the paper gives a description of the current state of the development process for software intended for safety-relevant systems. A possible tool chain for this development process, current possibilities as well as limitations and challenges are described.