Digital Twins as Electric Motor Soft-Sensors in the Automotive Industry
Francesco Toso, Riccardo Torchio, Andrea Favato, Paolo Gherardo Carlet, Silverio Bolognani, Piergiorgio Alotto
Abstract
Digital Twins can be defined as virtual representation of physical assets enabled through data and simulators, for real time prediction, optimization, control, and decision making improvements. In this paper, the concept of digital twin is applied to electric motors. In particular, the use of this technology is shown to solve general problems related to the application of electric motors in the automotive industry, such as estimation of the driving torque and the internal rotor temperature to improve cooling control. Proof-of-concept results are shown, showing the validity of the adopted methodology and the effectiveness of the proposed solution.