Deep Learning-Based Rotor Temperature Estimation for Rare-Earth-Free Motors

Abstract

This paper presents a deep learning-based approach for online rotor temperature estimation in electrically excited synchronous motors (EESMs). Accurate rotor temperature estimation is critical for ensuring safe operation, improving performance, and enabling reliable thermal management of electric traction motors. Recurrent neural network (RNN) architectures, including gated recurrent unit (GRU) and long short-term memory (LSTM) networks, are investigated to develop a data-driven thermal virtual sensor capable of capturing the temporal dynamics of motor operation. Experimental data collected from a 190 kW EESM prototype are used to train and evaluate the proposed models. A systematic training, testing, and 10-fold cross-validation framework is employed to assess prediction accuracy and generalization capability. The results demonstrate that the GRU-based model achieves higher prediction accuracy than the LSTM model while maintaining comparable inference latency. The proposed approach provides an efficient and lightweight solution for real-time rotor temperature estimation suitable for embedded motor control applications.