Publication

Abstract : Electric vehicles (EVs) are quickly becoming a promising solution for competent and sustainable transportation. However, the increasing use of EVs and their associated charging infrastructure has raised significant concerns about harmonic pollution and power quality issues in the power systems. To tackle these challenges, this paper suggests an Artificial Neural Network (ANN)-based EV charger to improve power quality. The proposed charger combines a canonical switching converter and a buck-boost converter to improve power quality. The proposed charger is simulated in MATLAB/Simulink under static and transient conditions. The observed supply current Total Harmonic Distortion (THD) is 1.85 % and 2.26 % during static and transient conditions. Experimental validation is conducted using a 500 W, 230 V, 50 Hz laboratory prototype under static loading conditions. The measured THD was 2 %, which complies with the IEEE 519–2014 standard, which sets the limit for current THD at below 5 %. Additionally, a comparison was made with a conventional two-wheeler charger, which recorded a THD of 67.7 %, as measured by a KRYKARD ALM-35 Power Quality Analyzer. These results confirm that the proposed ANN-based charger, utilizing a canonical switching converter, significantly reduces harmonic distortion and enhances power quality in EV charging systems.