Deep Learning-Based Predictive Control of Vehicle-to-Grid Onboard Charger Back Stage Models

To address the insufficient modeling accuracy and slow dynamic response of the Capacitor-Inductor-Inductor-Capacitor (CLLC) resonant converter in Vehicle-to-Grid (V2G) automotive onboard charger, a deep learning-based model predictive control method for the post-stage V2G onboard charger is proposed. This approach utilizes deep learning techniques to perform steady-state implicit modeling of the CLLC resonant converter, enabling accurate calculation of voltage gain across a diverse array of switching frequencies and varying load conditions. Based on this model, a deep learning model predictive control (DL-MPC) strategy is developed. The DL-MPC method leverages deep learning-based voltage gain predictions for direct multi-step control sequence prediction. Simulations demonstrate that the proposed DL-MPC strategy outperforms conventional proportional-integral (PI) and model predictive control (MPC) control, effectively suppressing voltage overshoot and undershoot, achieving reductions of up to 60.9%, and reducing recovery time by up to 55.6% under load transients and reference voltage step changes, thereby enhancing dynamic response and stability. Experimental validation that DL-MPC achieves a 65.21% reduction in voltage dips and a 31.25% faster recovery time compared to PI control under load transients. Simulation and experimental results consistently show that the deep-learning-based modeling attains high fidelity, while the proposed DL-MPC strategy delivers superior control performance for the CLLC resonant converter, yielding a markedly improved dynamic response.