volume | PIER Journals

Abstract

In order to achieve the optimization objectives of low torque ripple, high torque and high efficiency, this paper proposes a multi-objective optimization strategy based on genetic algorithms optimization BP neural network (GA-BP) combined with non-dominated sorting genetic algorithm (NSGA-II) and applies it to the multi-objective optimization design of an external rotor ferrite-assisted synchronous reluctance motor (ERFa-SynRM). Firstly, the preliminary design and selection of ERFa-SynRM structure are carried out. Secondly, a comprehensive sensitivity analysis is presented on the extent to which the design variables affect the optimization objectives. Following this, a high-precision prediction model is constructed by GA-BP neural network, and NSGA-II is applied to global optimization of the prediction model. Finally, the electromagnetic performances of the motor before and after the optimization are compared by the finite element analysis (FEA) software. Compared with the initial motor, the average torque and efficiency of the optimized motor are improved, and the torque ripple is reduced by 54.9%, which verifies the effectiveness of the multi-objective optimization design method.

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Dr. Chaozhi Huang

Ms. Haiwen Li

Dr. Siying Li

Dr. Yanwen Sun