Smart power distribution in hybrid DC–DC converters via physics-guided machine learning

Purpose This study aims to enhance the efficiency and dynamic performance of hybrid resonant and nonresonant DC–DC converters used in photovoltaic (PV) microinverters (MIs). By addressing the challenge of real-time power path control under varying solar input conditions, the work seeks to enable intelligent and adaptive control. This approach optimizes power distribution while ensuring soft-switching and reduced losses. Design/methodology/approach A physics-guided machine learning (PGML) model is developed using time-domain simulation data from a hybrid current-fed converter. The model is trained to predict optimal power split ratios based on real-time operating conditions such as input voltage, load, switching frequency and phase shift. The control strategy minimizes conduction losses, turn-off current and power backflow while meeting design constraints. Findings The machine learning-assisted control approach delivers up to 5.5% higher efficiency and reduces switching stress compared to traditional static control methods. It ensures optimal soft-switching across varying PV conditions. At the same time, it eliminates the need for computationally intensive real-time optimization or lookup tables, improving response time and overall system performance. Originality/value This work pioneers the integration of physics-informed machine learning into hybrid power converter control, offering a scalable and adaptive solution for next-generation PV systems. Unlike conventional lookup table-based or heuristic control methods, the proposed PGML approach achieves sub-35 µs real-time inference on embedded hardware while maintaining high zero-voltage switching compliance (>97%) across the tested operating envelope and low current ripple. It enables up to 5.5% higher conversion efficiency and improved dynamic response across a wide input-voltage and load range. This demonstrates a scalable, adaptive control paradigm for next-generation PV MIs.