Abstract

Automotive OEMs have established, over the years, standardized processes to coordinate and optimize the development schedule, with clear roles and responsibilities in each function. This constant refinement of requirements and processes has proven effective to develop attractive products for the customers. However, the increasing complexity of product development scenarios, such as Connected, Autonomous, Shared and Electrified (CASE) poses new challenges: the requirements of the systems and their interactions grow to a size which is challenging to manage with traditional approaches. New mobility business models require thinking about a vehicle not just as a product, but rather as a system within a complex system-of-systems. Model Based Development provides the framework to develop the vehicle using holistic systems thinking and to better manage the risks from that complexity using simulation for continuous exploration and validation. Recently, there has been a growing interest in technologies like co-simulation and engineering data management. However, practical industrial implementation still needs to deal with fundamental issues such as domain and subsystem "silos". Often, it is difficult to maintain up-to-date models with high quality data, validating the simulation scenarios against realistic operating conditions of the vehicle. Silos are a barrier to adopting Systems Thinking and to reusing knowledge across the enterprise. The authors propose approaches and platform characteristics that increase the smoothness of the information flow across domains, and between system designers and simulation engineers, through the realization of a custom, domain-independent, system-centric digital thread. This flexible data model provides confidence in up to date system specification data and it enables the simulation engineers to generate simulation models automatically, including variants; it can be further expanded to connect related data and processes; for example, it can trace the link between new requirements, affected systems, their evolutions, and overall product performance. Such digital thread not only enables re-use of previous data; it also provides the foundation for future data mining and AI applications to further accelerate the development process.