Multi-objective optimisation design of two-phase excitation switched reluctance motor for electric vehicles

作者：Zhu, Yueying^*; Wei, Weiyan; Yang, Chuantian; Zhang, Yan

来源：IET Electric Power Applications, 2018, 12(7): 929-937.

DOI：10.1049/iet-epa.2018.0046

• reluctance motors
• electric vehicles
• finite element analysis
• optimisation
• two phase excitation switched reluctance motor
• multiobjective simultaneous optimisation strategy
• four-phase 8-6 SRM
• sensitivity index
• orthogonal experiment design method
• static electromagnetic field finite element analysis
• geometrical multi-objective optimisation strategy
• static torque curve performance
• single-phase excitation mode
• electric vehicle

Compared with the single-phase excitation mode of the switched reluctance motor (SRM), the static torque curves of the SRM with two-phase excitation mode are more flexuous and wide flat regions are absent from the curves. To improve the static torque performance of the SRM with two-phase mode, geometrical multi-objective optimisation strategy is developed in this paper. Considering the requirements of the electric vehicles, four indicators are presented and defined to evaluate the development of the SRM. Six main independent variables are determined and a novel multi-objective optimisation function was proposed to simultaneously improve static performance of the SRM. Furthermore, to simplify the optimisation, a sensitivity index that reflects the relative importance of each variable is developed to eliminate insignificant variables. The four-phase 8/6 SRM with two-phase mode is optimised by means of the proposed multi-objective optimisation method based on effects and sensitivity analysis of the design variables on indicators. The static and dynamic torque performance of the optimised SRM is evaluated and compared with those of the initial motor. The comparison results show that the multi-objective simultaneous optimisation strategy can improve the static average torque, static average torque per mass, and global index by 18.09, 12.08, and 6.57%, respectively.

• 出版日期2018-8
• 单位天津科技大学