A Study on Optimization of Noise Reduction of Powered Vehicle Seat Movement Using Brushless Direct-Current Motor

Abstract

In this paper, an optimal design model was developed to reduce noise and secure the torque performance of a brushless direct-current motor used in the seat of an autonomous vehicle. An acoustic model using finite elements was developed and verified through the noise test of the brushless direct-current motor. In order to reduce noise in the brushless direct-current motor and obtain a reliable optimization geometry of noiseless seat motion, parametric analysis was performed through the design of experiments and Monte Carlo statistical analysis. The slot depth, stator tooth width, slot opening, radial depth, and undercut angle of the brushless direct-current motor were selected as design parameters for design parameter analysis. Then, a non-linear prediction model was used to determine the optimal slot depth and stator tooth width to maintain the drive torque and minimize the sound pressure level at 23.26 dB or lower. The Monte Carlo statistical method was used to minimize the deviation of the sound pressure level caused by the production deviation of the design parameters. The result is that the SPL was 23.00–23.50 dB with a confidence level of approximately 99.76% when the level of production quality control was set at 3σ.

Measurement of Magnetic and Magnetostrictive Characteristics of Transformer Core Based on Triaxial Strain Gauge and B-H Vector Sensor

“…Suawa et al used vibration and sound data sets from sensors to study the example of coordinating the optimal combination of sensors with a deep learning sensor fusion algorithm, to achieve predictive maintenance of the BLDC motor [7]. In order to reduce noise in the brushless direct-current motor and obtain a reliable optimization geometry of noiseless seat motion, Lee et al conducted parametric analysis of a brushless DC motor through the design of experiments and Monte Carlo statistical analysis, and determined the optimal slot depth and stator tooth width by using a non-linear prediction model [8]. Martinez et al detected the occurrence of iron resonance events by utilizing noise caused by the magnetostrictive force in the core of the inductor voltage transformer [9].…”