Physics Informed Neural Network—Estimated Circuit Parameter Adaptive Modulation of DAB

This article presents the development, implementation, and validation of a loss-optimized and circuit parameter-sensitive triple-phase-shift (TPS) modulation scheme for a dual-active-bridge (DAB) DC-DC converter. The proposed approach dynamically adjusts control parameters based on circuit parameters estimated using a physics-informed neural network (PINN). For this purpose, first, a precise frequency-domain model for the DAB converter is developed, incorporating resistive and capacitive non-idealities, deadtime effects, and switching network losses. This model closely mimics real hardware behavior with a power flow absolute error margin of within 5%. Secondly, such an analytical model is utilized to synthesize overall system loss optimized TPS control variables as polynomial fitted functions of the converter's output voltage, power, and parameters that are subject to uncertainty such as power transfer inductances and device on-state resistances. Following this, combining a datadriven neural net (NN) and analytical DAB physics model, a PINN architecture is constructed with the purpose of estimating DAB circuit parameters with inputs of DC side voltages and currents, and modulation variables. Notably, the data-light PINN is trained with a mixed dataset collected from real hardware and the analytical DAB model. Further, the trained NN is implemented in a Python environment, with serial peripheral interface (SPI) communication established with the DAB controller enabling realtime updates of the TPS modulation variables based on NNpredicted circuit parameters. The proposed NN-in-loop, parameter-sensitive TPS modulation is validated and benchmarked in a 2kW/100kHz GaN-based DAB converter. With an experimentally emulated 30% reduction in series inductance, the converter achieves a peak efficiency of 98.3%, representing an average efficiency improvement of approximately 1.4% compared to conventional non-adaptive TPS modulation.