Overview
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 presenters 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 provides a single source of truth for the specifications of all systems of interest and related data. With a flexible data model, it is the basis for: (a) managing and validating the accuracy of the core engineering data, (b) allowing the simulation engineers to streamline the automatic generation of simulation models, including for all vehicle variants, and use, with confidence, up-to-date system data; and (c) enabling the entire product organization to trace the history of changes and the links between requirements, related systems and their evolution, and overall product performance.
Such a digital thread enables, the reuse of previous data and knowledge, without the burden of complex manual searches and exclusive dependence on expert knowledge and experience, and provides a highly-customizable data management infrastructure for structured data accumulation that forms the basis of Machine Learning and AI applications to further accelerate the vehicle development process.
Ernesto Mottola is the Sr Manager, Model Based Development, Toyota Motor Europe.
