Affiliations: [a] School of Instrument Science and Optoelectronics
Engineering, Beihang University, Beijing, China | [b] Science and Technology on Inertial Laboratory,
Beijing, China
Correspondence:
[*]
Corresponding author: Bangcheng Han, School of Instrument
Science and Optoelectronics Engineering, Beihang University, Beijing 100191,
China. E-mail: [email protected]
Abstract: In order to find an algorithm to get a better optimization result of
high-speed rotor supported by magnetic bearing in BLDCM, we presented a
multiple objective optimization results which included three algorithms' in
this paper. They are the local Sequential Quadratic Program (SQP) algorithm,
the global Genetic Algorithm (GA) and the combined optimization strategy
algorithm which combines GA and SQP. The parametric optimization model of a 100
kW BLDCM supported by magnetic bearings was constituted with software ANSYS and
an effective connection between software ANSYS and iSIGHT was used to execute
the whole optimization process. To insure the best performance, mass and
strength were chosen as the optimization goals, meanwhile, the static strength,
dynamic modal, shape and magnetic force of the rotor subassembly were used as
the main constrains. Six main dimensions of the subassembly were optimized. The
optimization results indicated that the GA can get a higher optimization
precision than the other two algorithms and the SQP was not effective in the
optimization of magnetic suspended motor rotor subassembly. The GA's
optimization result made the mass decrease 7.62 percent with the safe factor is
3.15. The 100 kW BLDCM supported by magnetic bearings was designed and
fabricated; the multiple objective optimization results were verified by the
prototype.
Keywords: Magnetic suspension motor, optimization, genetic algorithm, combined optimization strategy, static strength and dynamic modal
