Deep-Learning-Aided Closed-Loop Synchronous Rectification for Isolated Bidirectional DC/DC Converter Based on Temperature Gradient Descent

Synchronous rectification (SR) reduces conduction loss and improves efficiency by replacing anti-parallel diodes with switching devices for rectification. However, determining SR signals for CLLC converters is challenging. Existing SR strategies are open-loop and categorized into diode on-state detection and model-based calculation. The diode on-state detection requires high-speed, sophisticated sensors, and performance is sensitive to parasitic elements. The model-based calculation is robust, but current mathematical models are only effective for specific operating conditions. For more reliable and accurate SR signals, this paper proposes a deep-learning-aided closed-loop SR strategy. It includes an analytically solvable deep-learning model and a closed-loop optimization algorithm. The deep-learning model is a compact, computing-friendly neural network trained with fast-generated training datasets. It gives initial SR signals under all possible operating conditions accurately. The closed-loop optimization is gradient descent on the heat-sink temperature measured by low-cost thermocouples against SR phase shifts. It eliminates model deviations caused by ideal-to-actual differences in circuit parameters and achieves the ideal SR state, which minimizes conduction loss and heat-sink temperature. Experiment results on the 400V/300V, 2.4kW SiC-based CLLC prototype indicate that the proposed SR strategy consistently achieves the design purpose under various operating conditions with efficiency improvement of at least 1.74 percentage points.