Optimized PI control of dual active bridge converter for high-efficiency PV-integrated EV charging

Dependable and efficient Electric Vehicle (EV) charging is essential to support the increasing adoption of EVs and the integration of Renewable Energy Sources (RES). An optimized Proportional-Integral (PI)-controlled Dual Active Bridge (DAB) converter for high-efficiency Photovoltaic (PV)-integrated EV charging applications is proposed in this work. An Extended Gain (X-Gain) Step boost converter is deployed to boost the low PV output voltage to the required DC-link voltage level, thereby ensuring stable operation of the charging system. Moreover, a Radial Basis Function Neural Network (RBFNN)-based Maximum Power Point Tracking (MPPT) model is utilized to determine the maximum power extracted from the PV system under varying environmental conditions. The DAB converter provides effective power transfer and galvanic isolation between the PV source and EV battery system. The Red Panda Optimization (RPO) algorithm is utilized to optimize the PI control parameters, thereby improving voltage regulation and dynamic performance. In addition, the optimized control model reduces steady-state error and enhances transient response under variations in load and solar irradiance. The synchronous rectifier integrated into the DAB converter enhances rectification efficiency, while the optimized PI controller maintains a stable output voltage during EV charging. The proposed model is validated using MATLAB/Simulink simulations and a hardware prototype, which shows a converter efficiency of 95.40% under Simulink and 90% of hardware prototype. From a sustainable development perspective, the proposed model supports SDG 7 (Affordable and Clean Energy) by improving power conversion efficiency in EV charging.