Online Prediction of Operating Temperature for Permanent Magnet Motor Used in EVs Based on Parameter Identification

Abstract

The online prediction of operating temperature in permanent magnet motor (PM motor) is crucial for preventing motor damage due to excessive heat and minimizing the risk of irreversible demagnetization of the permanent magnet (PM). In this paper, an online temperature prediction method for PM motor used in electric vehicles (EVs) has been developed in relatively high-speed operating conditions, which is based on parameter identification. First of all, utilizing the model reference adaptive system (MRAS), the feedback gain matrix and adaptive law are formulated. Preliminary designs for stator winding resistance recognizer and PM flux linkage recognizer are developed. Then, to mitigate the impact of changing operating conditions on the convergence performance of parameter recognizer, the proportional-integral (PI) gains are adjusted. The parameter estimation accuracy of the parameter recognizer is guaranteed, which lays a good foundation for realizing the accurate estimation of stator-rotor operation temperature. Finally, a stator-rotor temperature observer is constructed, leveraging the approximately linear relationship between the above two parameters and temperature. Simulation results demonstrate that the proposed method is relatively effective at relatively high speeds. Under relatively high-speed operating conditions, the stator temperature estimation error is within 2°C, while the absolute value of the rotor temperature estimation error remains within 5°C, confirming the accuracy and effectiveness of the proposed method. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.