ANFIS-based FOPID controller to enhanced stability of cascaded converter for electric vehicle (EV) charger

Abstract

In an electric vehicle (EV) charger system, a two-stage conversion process is integrated and cascaded with a voltage source converter (VSC) and a Dual active bridge converter (DAB). These impedances VSC outputs impedance (ZoutVSC(s)) and DAB have input impedance (ZinDAB(s)) interact, leading to voltage fluctuation and power loss. To address the cascaded converter’s impedance interaction, the modified controller that combines an adaptive Neuro-fuzzy inference system (ANFIS) based fractional order proportional integral derivative (FOPID) controller is used with effective active damping (AD). The modified controller (ANFIS + FOPID + AD) uses ANFIS’s skills to capture and predict the system’s nonlinear behaviour, using its trained data to guide the FOPID’s parameter. The presence of AD ensures the sorting out of impedance interaction problems. Compared to the PID controller, the FOPID controller includes two more degrees of freedom and offers superior adaptability and performance. The effectiveness of the modified controller is tested via frequency response analysis and time domain simulations. Time domain simulations underscore the advantage of the modified controller (ANFIS + FOPID + AD), revealing a remarkable 30% settling time and a 25% overshoot compared to the (FOPID + AD) controller. It is better regarding flexibility, faster response time, and improved system stability. The system performance has been validated and compared by simulation process and HIL technique OPAL RT OP44512.