High-torque and low-noise IPMSM multi-objective collaborative
optimization based on multi-layer surrogate model
Abstract
To achieve efficient and rapid optimization for high torque and low
noise permanent magnet synchronous motors, this paper proposes a
multi-layer surrogate model-based optimization method for IPMSM
(Interior Permanent Magnet Synchronous Motor) based on sensitivity
classification of structural parameters. Firstly, using a hybrid model
of “FEM + Unit Force Wave Response,” the key order electromagnetic
forces causing electromagnetic noise in various operating conditions of
the motor are obtained. Their amplitudes, along with the motor’s average
output torque and torque ripple, are taken as optimization objectives.
By analyzing the sensitivity of structural parameters using the random
forest algorithm, the selection and classification of structural
parameters are achieved. A hierarchical optimization is then performed
using a combination of a multi-island genetic algorithm, a
multi-objective particle swarm optimization algorithm, and parameterized
scanning. Compared with traditional multi-field coupled optimization
methods, this method saves computational resources while reducing
calculation time by 54.9%. After optimization, the average output
torque is increased by 34.6% compared to before optimization, the
amplitude of key order electromagnetic forces of the motor is reduced by
13.7%, and torque ripple is reduced by 67.8%.