Design and Analysis of Three-Phase Bidirectional Active Front End Converter for V2G Application

Abstract

The rapid growth of electric vehicles (EVs) and their increasing integration with power systems highlight the significant potential for EVs to support the grid through Vehicle-to-Grid (V2G) technology. By enabling bidirectional power flow, EVs can function as distributed energy resources, balancing grid demands, stabilizing voltage levels, and facilitating the integration of renewable energy. However, the development of high-power bidirectional chargers presents challenges such as maintaining power quality, ensuring dynamic control, and providing rapid response capabilities. This research addresses these challenges through a comprehensive framework involving the design of a 15 kW Active Front-End (AFE) converter, adaptive control strategies, and an ad- vanced non-linear stability analysis for closed-loop system dynamics. These components col- lectively enhance grid interaction, improve system reliability, and enable efficient power flow between the grid and EV batteries. The proposed solutions aim to advance V2G technology for high-power applications, contributing to sustainable and resilient energy systems. The first approach focuses on the design of a three-phase bidirectional grid-connected AFE converter optimized for high power quality and stable operation under varying grid conditions. The use of an adaptive synchronous reference frame (SRF) model-based controller ensures seamless transitions between constant current (CC) and constant voltage (CV) charging modes. Additionally, the system integrates a bidirectional DC/DC converter, minimizing total harmonic distortion (THD) and reducing DC voltage ripple. Experimental results validate the system’s robust performance, demonstrating compliance with industry standards. In the second work, an adaptive control mechanism is introduced for a three-phase bidi- rectional battery charging system that incorporates an interleaved buck-boost converter with an optimized voltage-oriented control (OVOC) model-based approach. This system employs feed- forward decoupled current control and pulse width modulation to maintain unity power factor (UPF) and minimize voltage ripple. Validated through a 12.5 kW experimental setup, the con- trol scheme achieves low THD and reliable performance under dynamic conditions, making it suitable for real-world V2G applications where reliability is paramount. The third study presents an advanced Continuous Control Set Model Predictive Control (CCS-MPC) strategy tailored for the three-phase bidirectional AFE converter with an inte- grated interleaved buck-boost DC/DC converter. This control framework enhances operational quality by maintaining fixed switching frequencies and minimizing THD, achieving energy ef- ficiency in both Grid-to-Vehicle (G2V) and V2G operations. It is particularly effective under unstable grid conditions, as the CCS-MPC ensures fast transitions and accurate state estima- tion, enabling robust system stability and adaptability. Experimental validation on a 12.5 kW hardware prototype demonstrates THD levels below 2%, stable DC-link voltage (DLV), and unity power factor, underscoring its viability for high-performance EV charging stations. The condensed approach reduces system complexity while maintaining its ability to respond to fluctuating grid scenarios and external disturbances. Finally, a Lyapunov function-based advanced non-linear stability control strategy is also developed to address robustness in closed-loop dynamics, ensuring stability under parame- ter variations and external disturbances such as voltage sags and reactive power fluctuations. Simulation studies confirm the effectiveness of this method in maintaining power quality and stability, reinforcing the potential of V2G systems as reliable grid components. Together, these advancements represent a scalable, high-performance solution for next- generation bidirectional V2G chargers. By combining adaptive control, optimized converter designs, and robust stability measures, this research supports sustainable energy goals and en- hances grid resilience through efficient EV-grid interactions.