Robust design of E/E architecture component platforms

Abstract

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

Functional feasibility analysis of variability-intensive data flow-oriented applications over highly-configurable platforms

“…al. [13,14] manages some variability in both platform and functional specifications. On the platform variability side, resource components can be selected or not, while optional and mutually exclusive task groups are managed on the functional part.…”

Architecture exploration for distributed embedded systems: a gap analysis in automotive domain

“…Performance is usually defined in terms of End-to-End (E2E) timing [10]- [14], [26]. Considerations regarding material and development cost can be found in [17], [18], [20], [22], [23].…”

“…One key concern regarding variability is to control the architectural differences over all the variants so as to reduce the effort for verification, manufacture and maintenance. Graf et al [18]- [20] proposed a DSE method to maximize the number of ECUs that can be reused over multiple variants. To increase the reusability of one ECU, they assumed that when the ECU is used for different variants, different subsets of its SW-Cs are activated.…”

“…The schedule of tasks on ECUs with multiple manifestations can be designed by the method introduced in [38]. In addition, when optimizing the whole product line, estimated sales volumes of different variants should also be considered together with the uncertainty of the estimation [20]. 7) Operational Modes: Vehicle operational modes (e.g., parking or driving) are in the found literature linked to energy saving in DSE methods [6], [7].…”

“…Some of these attributes are also considered by the DSE methods. For example, reusability [18]- [20] and flexibility [12], [13], [37]. Cost related heuristics have also been considered by DSE methods, such as to reduce the number of ECUs, [17], [18], [20] and ASIL decomposition to reduce verification cost [22], [23].…”