A Thermal-Aware Digital Twin Model of Permanent Magnet Synchronous Motors (PMSM) Based on BP Neural Networks.

Estimating accurate torque and speed is critical to control the operation of permanent magnet synchronous motors (PMSM). But the temperature factors are usually neglected in existing studies, which degrades estimation accuracy. In this paper, a thermal-aware digital twin model is proposed for PMSM to estimate motor torque and speed with the motor temperature and d-q axis current and voltage. Firstly, the motor parameters related to torque and speed are extracted by the Spearman correlation coefficients. Moreover, the stator winding temperature is selected as the input feature. Secondly, a digital model based on BP neural networks (BPNN) is established to estimate torque and speed. Thirdly, the parameters of the BPNN model are optimized by the whale optimization algorithm to accelerate the convergence speed and avoid local optima. Finally, experimental results show that the mean square error (MSE) of the BPNN model considering the temperature factors is reduced by 8.3%, which verifies that there is an effect of temperature on the torque and speed estimation. The MSE of the proposed method is reduced by 11.7% on average, which confirmed the higher accuracy of the proposed method compared with the classical BPNN model.