Artificial Neural Network Based Improved Modulation Strategy for GaN–based Inverter in EV

Abstract

Wide–bandgap (WBG) device based high–frequency inverters using Gallium Nitride (GaN) switches are gaining significant research attention in the field of electric vehicles (EVs) due to their potential to operate at higher switching frequencies with improved efficiency as compared to the available power devices. However, the computation time of the control algorithm plays a significant role in the effective control and operation of such high–frequency converters. This paper presents an advanced artificial neural network (ANN) based improved space vector pulse width modulation (SVPWM) control for Gallium Nitride based inverter in EV application. The proposed neural–network (NN) based control technique has two core objectives: the first objective is to overcome the processing speed of the complex algorithm during high switching frequency operation and hence reduce the computation time which is the major challenge in WBG device–based inverter control. The second objective is to minimize the GaN inverter switching losses and to improve the overall performance of the inverter. The NN based SVPWM is trained using the reference voltage to get the modulated signal for pulse generation, thereby reducing the computation time and improving the performance of the inverter. The proposed ANN–based improved switching strategy has been validated experimentally using a GaN inverter and a comparative performance analysis with a conventional SVPWM technique is presented in this paper.