TY - GEN
T1 - Optimization on Magnetization-Regulation Performance of a Variable-Flux Machine with Parallel Permanent Magnets
AU - Wang, Mingqiao
AU - Yu, Bin
AU - Tong, Chengde
AU - Oiao, Guangyuan
AU - Liu, Faliang
AU - Yang, Shijie
AU - Zheng, Ping
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - Variable-flux machine (VFM) is a promising candidate for wide-speed-range applications, such as electric vehicle, numerical control machine and railway traction. Magnetization-regulation range, which is the ratio of back electromotive forces at forward and reverse magnetization states, is an important performance of VFM. In this paper, the magnetization-regulation performance of a parallel VFM with flux barrier is analyzed, and the influences of magnetization current, split ratio, the geometries of PM pole, and the position and shape of flux barrier on the magnetization-regulation range of VFM are investigated. The best magnetization angle of VFM is explored, and a control method of forward magnetization is proposed. With the sample data obtained by finite element method, Kriging surrogate model of VFM is established to save optimization time, which is proven with good accuracy. The particle swarm optimization algorithm is utilized for optimizing the forward magnetization effect of VFM, and the optimal scheme is obtained, whose average flux density of AlNiCo PM at forward magnetization state is increased to 1.151T. The improvement measures and optimization method applied in this paper are proven effective in improving magnetization-regulation performance.
AB - Variable-flux machine (VFM) is a promising candidate for wide-speed-range applications, such as electric vehicle, numerical control machine and railway traction. Magnetization-regulation range, which is the ratio of back electromotive forces at forward and reverse magnetization states, is an important performance of VFM. In this paper, the magnetization-regulation performance of a parallel VFM with flux barrier is analyzed, and the influences of magnetization current, split ratio, the geometries of PM pole, and the position and shape of flux barrier on the magnetization-regulation range of VFM are investigated. The best magnetization angle of VFM is explored, and a control method of forward magnetization is proposed. With the sample data obtained by finite element method, Kriging surrogate model of VFM is established to save optimization time, which is proven with good accuracy. The particle swarm optimization algorithm is utilized for optimizing the forward magnetization effect of VFM, and the optimal scheme is obtained, whose average flux density of AlNiCo PM at forward magnetization state is increased to 1.151T. The improvement measures and optimization method applied in this paper are proven effective in improving magnetization-regulation performance.
KW - Kriging surrogate model
KW - magnetization regulation
KW - parallel permanent magnets
KW - variable-flux machine
UR - https://www.scopus.com/pages/publications/85113372324
U2 - 10.1109/CEFC46938.2020.9451409
DO - 10.1109/CEFC46938.2020.9451409
M3 - 会议稿件
AN - SCOPUS:85113372324
T3 - CEFC 2020 - Selected Papers from the 19th Biennial IEEE Conference on Electromagnetic Field Computation
BT - CEFC 2020 - Selected Papers from the 19th Biennial IEEE Conference on Electromagnetic Field Computation
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 19th Biennial IEEE Conference on Electromagnetic Field Computation, CEFC 2020
Y2 - 16 November 2020 through 18 November 2020
ER -