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System-Level Design Optimization Methods for Electrical Drive Systems: Deterministic Approach
被引:135
作者:
Lei, Gang
[1
]
Wang, Tianshi
[1
]
Guo, Youguang
[1
]
Zhu, Jianguo
[1
]
Wang, Shuhong
[2
]
机构:
[1] Univ Technol Sydney, Sch Elect Mech & Mechatron Syst, Sydney, NSW 2007, Australia
[2] Xi An Jiao Tong Univ, Fac Elect Engn, State Key Lab Elect Insulat & Power Equipment, Xian 710049, Peoples R China
关键词:
Approximate models;
finite-element methods (FEMs);
model predictive control (MPC);
multilevel design optimization;
system-level design optimization;
transverse flux machine (TFM);
MODEL-PREDICTIVE CONTROL;
ELECTROMAGNETIC DEVICES;
THERMAL OPTIMIZATION;
MOTOR;
HYBRID;
D O I:
10.1109/TIE.2014.2321338
中图分类号:
TP [自动化技术、计算机技术];
学科分类号:
0812 ;
摘要:
Electrical drive systems are key components in modern appliances, industry equipment, and systems, e.g., hybrid electric vehicles. To obtain the best performance of these drive systems, the motors and their control systems should be designed and optimized at the system level rather than the component level. This paper presents an effort to develop system-level design and optimization methods for electrical drive systems. Two system-level design optimization methods are presented in this paper: 1) single-level method (only at system level); and 2) multilevel method. Meanwhile, the approximate models, the design of experiments technique, and the sequential subspace optimization method are presented to improve the optimization efficiency. Finally, a drive system consisting of a permanent-magnet transverse flux machine with a soft magnetic composite core is investigated, and detailed results are presented and discussed. This is a high-dimensional optimization problem with 14 parameters mixed with both discrete and continuous variables. The finite-element analysis model and method are verified by the experimental results on the motor prototype. From the discussion, it can be found that the proposed multilevel method can increase the performance of the whole drive system, such as bigger output power and lower material cost, and decrease the computation cost significantly compared with those of single-level design optimization method.
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页码:6591 / 6602
页数:12
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