Digital Twin of an Electric Motor to Predict the Temperature over a Drive Cycle

In recent years, simulation-based performance of the models is a highly effective way to finalize the model at design stage itself. But simulation-based models are complex owing to more parameters involved hence resulting in more computational time. With the increasing demand for electric vehicles, the development time for electric vehicle (EV) powertrain is reduced, thereby increasing pressure on original equipment manufacturers (OEMs) to develop products faster. Digital twin is a platform where replication of physical models is made possible with extremely limited data to predict the performance of the model hence providing the most accurate results in a short time. Electric vehicles are the best alternatives for reducing emissions. An Electric vehicle is run by an electric motor which in turn is powered by a battery. Interior permanent magnet synchronous motors (IPMSMs) are the conventional type of motors in electric vehicles because of their high-power density and efficiency. This paper shows the method of developing a digital twin of an IPMSM. Electromagnetic, thermal and drive cycle analysis are performed on permanent magnet motors. Electromagnetic and thermal reduced order models (ROMs) have been extracted from the analysis performed. Losses have been transferred from electromagnetic ROM to thermal ROM to calculate temperatures of motor components. This coupled ROM analysis enables us to predict thermal characteristics of a motor during a drive cycle. The losses and temperature profiles from coupled ROM analysis were compared to original electromagnetic and thermal simulation results.