Work

Power-quality-enhanced control scheme applied in V2G converters: Unified gains optimization for previewable reference feedforward, repetitive learning, and state feedback

Year: 2025

Type: article

Abstract: The rapid proliferation of electric vehicles (EVs) faces challenges due to diversified battery voltage levels and power quality degradation in vehicle-to-grid (V2G) chargers under distorted grid conditions. This paper proposes a hybrid hierarchical control scheme for two-stage V2G converters, offering two primary advantages: the front-stage dual-active-bridge (DAB) converter could stabilize the DC-link voltage and accommodate wide-ranging battery voltages; meanwhile, a unified linear matrix inequality (LMI) framework is established for the rear-stage DC–AC converter to systematically synthesize all control gains, which integrates a resonant-suppressing state feedback controller to reduce parameter sensitivity, repetitive learning to attenuate multi-order harmonics in the charger current, and previewable reference current feedforward to enhance tracking precision. Experimental results demonstrate superior performance over conventional methods: total harmonic distortion is reduced by 55% (from 8.72% to 3.97% in V2L mode), zero steady-state error is achieved under distorted grid voltages (V2G mode), and tracking accuracy improves by over 86% under exceeding 30% parameter deviations (V2L/V2G mode), alongside enhanced grid harmonic suppression.

Source: International Journal of Electrical Power & Energy Systems

DOI: 10.1016/j.ijepes.2025.111274