Temperature Prediction of PMSMs Using Pseudo-Siamese Nested LSTM

Abstract

Permanent Magnet Synchronous Motors (PMSMs) are widely used in electric vehicles due to their simple structure, small size, and high power-density. The research on the temperature monitoring of the PMSMs, which is one of the critical technologies to ensure the operation of PMSMs, has been the focus. A Pseudo-Siamese Nested LSTM (PSNLSTM) model is proposed to predict the temperature of the PMSMs. It takes the features closely related to the temperature of PMSMs as input and realizes the temperature prediction of stator yoke, stator tooth, and stator winding. An optimization algorithm of learning rate combined with gradual warmup and decay is proposed to accelerate the convergence during the training and improve the training performance of the model. Experimental results reveal the proposed method and Nested LSTM (NLSTM) achieves high accuracy by comparing with other intelligent prediction methods. Moreover, the proposed method is slightly better than NLSTM in temperature prediction of PMSMS.