Conference: 2022 한국자동차공학회 추계학술대회
Authors: 이지현, 박수환, 김재현, 성무현, 채승희, 임명섭
DOI:
This paper, the prediction method of parameter for interior permanent magnet synchronous motor using Deep neural network (DNN) is proposed to exactly predict the motor parameter. For the DNN surrogate model to prediction the parameters well and to minimize the computational cost, the experimental points should be evenly distributed within the design area. However, as the design variable increases, the number of experimental points must be increased. Thus, a high computational cost is required. Therefore, in this paper, to reduce the calculation cost, the motor parameters according to the shape change of the motor are predicted through the following two steps. First, the motor parameters were predicted using the DNN surrogate model for the change in the stator outer diameter and split ratio. Then, the motor parameters were calculated mathematically for the changes in the stack length and the number of series turns per phase.