Enhancing temperature and torque prediction in permanent magnet synchronous motors using deep learning neural networks and BiLSTM RNNs

Abstract

This study aims to develop an effective method for predicting the temperature and torque of Permanent Magnet Synchronous Motors (PMSMs) using deep learning techniques, which is crucial for optimizing motor performance and ensuring longevity, particularly in the automotive industry. Various Neural Network (NN) architectures, including a Recurrent Neural Network (RNN) with a Bidirectional Long Short-Term Memory (BiLSTM) unit, were employed to model the complex relationships between motor parameters, such as stator winding, current, torque, and permanent magnet temperature. The findings demonstrate that an NN with two hidden layers (64 and 32 neurons) achieved an R2 score of 0.99 for both torque and temperature prediction, while the BiLSTM network effectively modeled temporal dynamics, leading to high-fidelity rotor temperature predictions. This research provides a novel application of BiLSTM RNNs in accurately predicting PMSM temperatures, offering valuable insights for industries reliant on these motors. Integrating these models into motor control systems can enhance operational efficiency, reduce overheating risks, and extend motor lifespan, contributing to energy savings and environmental sustainability by lowering energy consumption and reducing waste.