Electric Motor Surrogate Model Combining Subdomain Method and Neural Network

This paper proposes a surrogate model for the rapid evaluation of electric machine designs, based on a neural network combined with a semi-analytical subdomain model. Although both physical-based analytical approaches and data-driven approaches have been proposed to construct surrogate models, which can be significantly faster than numerical finite-element simulations, issues still remain. On one hand, simplifications in analytical approaches often cause inaccuracy, especially in the prediction of highly nonlinear phenomena such as cogging torque of permanent magnet synchronous motors; on the other hand, data-driven approaches often require a large amount of training data to achieve high accuracy. In our proposed method, the performance of the electric machine is initially approximated by using a semi-analytical subdomain method, and this initial prediction is used as an input of a neural network, together with other design variables, to obtain the final prediction. We test the method to predict the cogging torque of surface-mounted permanent magnet motors. With this physics-assisted neural network (PANN) approach, we can predict cogging torque with good accuracy, which cannot be achieved with only physical-based model; the prediction accuracy is also much improved compared with conventional neural networks, especially when the size of the training dataset is small.