Dead time compensation of IPMSM based on neural network filter and current polarity detection

Abstract

Aiming at the problem of increased current harmonics and low efficiency of permanent magnet synchronous motors for electric vehicles due to dead time effect, a dead time compensation method based on neural network filter and current polarity detection is proposed. First, the DC components and the higher harmonic components of the motor currents are obtained by virtue of what the neural network filters and the extracted harmonic currents are adjusted to the required compensation voltages by virtue of what the neural network filters. Then, the extracted DC components are used for current polarity dead time compensation control to avert the false compensation when currents approach zero. The neural network filter method extracts the required compensation voltages from the speed component and the current polarity detection compensation method obtains the required compensation voltages by discriminating the current polarity. The combination of the two methods can more precisely compensate the dead time effect of the control system to improve the control performance. Finally, the experiments of dead time compensation are conducted on the experimental platform, and the current waveform, harmonic amplitude, and voltage loss caused by different dead time compensation methods are analyzed to corroborate the validity of the proposed method. Improving the efficiency of electric drive systems by reducing voltage losses and increasing their range is of great importance for the lasting and stable operation of electric vehicles.