Digital twins for automotive development: Two wheelers application

Abstract

Shortening product lifecycles and increasing customer demands are forcing manufacturers to increase the efficiency and effectiveness of their product development process to stay competitive in an increasingly global setting. The effectiveness of current day product development is hampered by the availability of real-world data that allows the deduction of comprehensive customer requirements. Key to solving this is a tight connection between the manufacturer and the vehicle in-use. Founded by technological advances put forward by Industry 4.0, the digital twin provides the tools needed to strengthen this connection. Driven by the difficulties automotive manufacturers face in generating insights in customer usage of the vehicle, the present work develops a general digital twin framework for the automotive industry, characterizing the digital twin as a holistic representation of a physical vehicle with the appropriate fidelity throughout its lifecycle. Further highlighting that this holistic portrayal requires models which depict the human interaction with the vehicle. To illustrate this, a digital twin model capable of capturing human interaction with automotive products, more specifically motorcycles, is presented. Data generated by this digital twin model provides a data-based foundation for the development of customer requirements, increasing the effectiveness of next-generation vehicle development.