Generalized Data-Driven Model to Minimize Current Ringing in DAB Power Converter

The present article identifies the root causes of current ringing in a dual-active-bridge (DAB) power converter and proposes a novel methodology to design the magnetic tank (transformer, primary, and secondary inductances) configuration to minimize it. In particular, two metrics proportional to the current harmonic content on each transformer side are correspondingly proposed based on a steady-state frequency analysis of primary and secondary ac voltages and impedance of the magnetic tank. To get the metrics, different combinations of dv/dt, switching frequency, primary and secondary inductance configuration, and stray capacitance of the magnetic tank (model inputs) are simulated to generate primary and secondary harmonic current indicators (model outputs). Then, a machine-learning model is trained to predict the harmonic current metrics for each given operating setpoint. The model is able to make predictions beyond its training data, therefore generalizing to unseen inputs and enabling rapid evaluation of various designs. Experimental results are presented to evaluate the performance of the metric model for four different magnetic tank configurations. Designers can use the methodology to rapidly analyze the generation of current ringing under multiple operating parameters, identifying operating zones with low harmonic content and reduced sensitivity to parameter shifts for the converter's operation.