Hierarchical Parameter Calibration of Digital Twins at Ford Motor Company

Ford Motor Company has developed a virtual digital twin model as an integrated vehicle analysis tool to simulate various driving scenarios of its electric vehicles. To ensure that the digital twin model functions accurately as a twin of the actual vehicle, the simulator output must closely match driving data collected during real-world trips. Achieving this requires tuning of the parameters used within the simulator, a process known as parameter calibration. One unique challenge in this calibration process is the multilevel hierarchical structure, where some parameters must be universally calibrated using data from all trips, whereas others are specific to certain trips. This study devises a new calibration procedure by accounting for this hierarchical structure and demonstrates its superiority using real-world vehicle trip data compared with other alternative methods. Our approach enabled Ford Motor Company to identify key vehicle attributes and understand how trip-specific parameters vary under different driving conditions. With this capability, we also demonstrate the estimation of trip-specific parameters for future trips, which can be used to manage vehicle conditions, such as cabin temperature.

History: This paper was refereed.

Funding: This work was supported by the National Science Foundation Division of Civil, Mechanical and Manufacturing Innovation [“Collaborative Research: Calibrating Digital Twins in the Era of Big Data with Stochastic Optimization,” Grant CMMI-2226348] and the Ford Motor Company (“Digital Twin Calibration for In-Service Vehicles”).