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Digital twin describes data aggregation along a product lifecycle. This data can be classified, analyzed, and used to predict events within the product lifecycle and recommend appropriate actions. As virtual replicas of physical objects or systems, digital twins outlive the physical lifecycle, so they can be used to sustainably generate value for decisions and features. Digital twins can be used to reduce costs, optimize processes, and develop new features.
Why do we need them? Vehicle data is generated by various sources, and it is used globally by dozens of systems, covering the complete lifecycle of our cars: R&D, production plants, over-the-air (OTA) updates, connected services and many more.
For example, we get Predictive Maintenance Protocols (PMP) from the vehicles or other systems and merge them with the rest of the vehicle data. This allows us to give other systems an overview of the vehicle and the current wear and tear. Thus, errors and possible problems in components can be detected at an early stage before a defect occurs.Another use case are synthetic vehicles. To test vehicle development, IT systems need to have access to their data. Instead of using real vehicles to the projects for tests, we can create synthetic vehicles (as a digital vehicle), with which the systems can test various scenarios.