Multiphysical field collaborative optimization of premium induction motor based on GA

The premium induction motor has received worldwide attention in recent years due to its high efficiency. However, the high efficiency benefits from the high cost. In the paper, a multiphysical field collaborative optimization method of the premium induction motor based on the genetic algorithm (GA) is presented to improve the utilization of the materials and reduce the cost. First, the cost and efficiency are taken as bi-objective; the magnetic circuit optimization based on GA is established to get the initial electromagnetic scheme. Then, the parameters of the premium induction motor are automatically transferred to a 2-D transient electromagnetic field calculation model so that the operation performances of the premium motor can be determined. The results will be sent to the magnetic circuit optimization to compare with the optimization objectives. The electromagnetic scheme will adjust until the errors between the transient operation performances and optimization objectives are within the convergence errors. Based on the results above, the transient results will be transferred to the 2-D temperature field calculation model to determine the thermal distributions of the premium induction motor. The structures of the premium induction motor will also adjust until the thermal distributions meet the requirements. The presented multiphysical field collaborative optimization method is applied to optimize several premium motors, and the accuracy is validated by the experimental data.