Prototype and in-production vehicles are collecting more and more data. It is crucial to be able to feed this data back into the development process effectively and beneficially. A methodology developed at FKFS uses this Big Data to generate AI-based digital twin instances from each individual vehicle in a fleet. In particular, these digital twins of the real vehicles can also model dynamic inertias in different time scales. Such dynamic effects and inertias are often of high relevance in the powertrain system.
With our digital twins, for example, aging effects can be identified via virtual test drives or outlier vehicles in fleets can be detected in their dynamic behavior and analyzed in detail.
We would be pleased to present our FKFS methodology and discuss possible use cases with you in a personal meeting.