Abstract:

Recently, there are a lot efforts in developing machine learning and deep learning methods for the prediction of electric motor performances. In particular, torque profile for a given motor design, including cogging torque and torque ripple, are challenging for surrogate models to achieve high prediction accuracy. One promising approach is to represent a motor design as a 2d image, and utilize deep learning models that found success in image recognition and classification tasks for motor performance prediction. A number of deep convolutional neural networks (CNNs) have been adopted for motor applications previously, while more recently Vision Transformer (ViT) models are gaining interests. In this paper, we evaluate multiple deep CNN and ViT models on two datasets of interior permanent magnet motors, and show that ViT based models can achieve superior accuracy for cogging torque prediction compared with CNN based models, and can be jointly trained on the two different datasets and still provides prediction with low error.