Optimal Design of IPMSM for EV Using Subdivided Kriging Multi-Objective Optimization

Processes

卷:9

Jong-Min Ahn¹  Dong-Kuk Lim¹  Sang-Hun Park²  Myung-Ki Baek² 

[1] Department of Electrical, Electronic and Computer Engineering, University of Ulsan, Ulsan 44610, Korea;

[2] Korea Electrotechnology Research Institute, Changwon-si 51543, Korea;

关键词: electric vehicle;    fill blank;    interior permanent magnet synchronous motor;    kriging;    multi-objective optimization;   

DOI  :  10.3390/pr9091490

来源: DOAJ

【 摘 要 】

In this paper, subdivided kriging multi-objective optimization (SKMOO) is proposed for the optimal design of interior permanent magnet synchronous motor (IPMSM). The SKMOO with surrogate kriging model can obtain a uniform and accurate pareto front set with a reduced computation cost compared to conventional algorithms which directly adds the solution in the objective function area. In other words, the proposed algorithm uses a kriging surrogate model, so it is possible to know which design variables have the value of the objective function on the blank space. Therefore, the solution can be added directly in the objective function area. In the SKMOO algorithm, a non-dominated sorting method is used to find the pareto front set and the fill blank method is applied to prevent premature convergence. In addition, the subdivided kriging grid is proposed to make a well-distributed and more precise pareto front set. Superior performance of the SKMOO is confirmed by compared conventional multi objective optimization (MOO) algorithms with test functions and are applied to the optimal design of IPMSM for electric vehicle.

【 授权许可】

Unknown