Already today, car manufacturers are designing E/E architectures using so-called component platforms. Such a platform comprises the superset of all components that are required to build all acquirable variants of a certain or even multiple car models. To find and optimize such component platforms, each candidate platform has to be evaluated by (a) determining a number of design objectives (monetary cost, etc.) of each car variant when derived from the candidate platform and then (b) approximating the platform's design objectives themselves, e. g., by a weighted sum that includes the expected sales of each variant. But typically, since this optimization has to take place in early design stages, important parameters like the number of expected sales numbers per car variant can only be projected and are, thus, uncertain. To investigate the susceptibility of the optimization to such uncertain parameters, this paper proposes a Monte-Carlo simulation-based method that enables to evaluate the uncertainty of a combined multi-variant objective wrt. parameter variations. By treating the minimization of uncertainty as an additional design objective, not only can the robustness of the derived component platforms be improved but also the confidence of the manufacturer. Moreover, we also propose to treat uncertainty not as a conventional design objective, but to use uncertain objectives: Here, not a single (e. g., mean) value but an interval given by observed upper and lower objective values is used. Experimental results show that the design objectives of an E/E architecture component platform are relatively robust wrt. parameter variations (here expected sales numbers of car variants). Moreover, it will be shown that the difference in expected overall costs between different non-dominated solutions is often much higher than the expected variation in cost as a result of parameter uncertainty

This paper proposes a novel design method for modern automotive electrical and electronic (E/E) architecture component platforms. The addressed challenge is to derive an optimized component platform termed Baukasten where components, i. e., different manifestations of Electronic Control Units (ECUs), are reused across different car configurations, models, or even OEM companies. The proposed approach derives an efficient graph-based exploration model from defined functional variants. From this, a novel symbolic formulation of multi-variant resource allocation, task binding, and message routing serves as input for a state-of-the-art hybrid optimization technique to derive the individual architecture for each functional variant and the resulting Baukasten at once. For the first time, this enables a concurrent analysis and optimization of individual variants and the Baukasten. Given each manifestation of a component in the Baukasten induces production, storage, and maintenance overhead, we particularly investigate the trade-off between the number of different hardware variants and other established design objectives like monetary cost. We apply the proposed technique to a real-world automotive use case, i. e., a subsystem within the safety domain, to illustrate the advantages of the multi-variant-based design space exploration approach.

This paper proposes a design method for electrical and electronic (E/E) architecture component platforms, with a focus on different manifestations of the (re-)used hardware components. The addressed challenge is to derive an optimized component platform where various manifestations of observed components are reused across different car configurations, models, or even OEM companies. This enables to ponder between the number of component manifestations and other design objectives in a multi-objective fashion. The proposed approach integrates component manifestations' reuse in a state-of-the-art hybrid optimization technique, allowing the optimization to directly influence the number of manifestations and, thus, enabling a holistic optimization of the component platform. The proposed technique is applied to a real-world automotive use case.

Due to ever-increasing bandwidth requirements of modern automotive applications, Ethernet AVB is becoming a standard high-speed bus in automotive E/E architectures. Since Ethernet AVB is tailored to audio and video entertainment, existing analysis approaches neglect the specific requirements and features of heterogeneous E/E architectures and their applications. This paper presents a timing analysis technique based on Real Time Calculus to consider Ethernet AVB in complex E/E architectures, reflecting key features such as static routing and stream reservation, fixed topology, and real-time applications. A comparison with a simulation on case studies from the automotive domain gives evidence that the proposed technique delivers valuable bounds for complete sensor-to-actuator chains, enabling automatic system synthesis and design space exploration approaches.