Deadbeat Control With Bivariate Online Parameter Identification for SPS-Modulated DAB Converters

Abstract

Deadbeat control is considered an efficient method of controlling dual active bridge (DAB) converters among the different control methods presented in recent years. The conventional deadbeat control is heavily reliant on the precise values of the system model parameters. However, in DAB converters, system model parameters such as series inductance and output capacitance suffer from mismatches due to operating conditions, manufacturing tolerance, and aging. Thus, the inevitable result is degradation in the steady-state and dynamic performance of the output voltage. In order to compensate for this drawback of deadbeat control, this study proposes an adaptive online parameter identification approach for DAB converters operating under single phase-shift (SPS) modulation. From the matrix form of linear equations in deadbeat control, the least-squares analysis (LSA) approach is utilized to solve the solution by a simple 2-by-2 matrix inverse calculation. Thus, series inductance and output capacitance are identified straightforwardly. Meanwhile, the predicted value of the phase-shift ratio is updated using sampled measurement values in deadbeat control after every sampling step, which can control the output voltage. The benefits of the proposed algorithm are demonstrated by theoretical analysis, simulation, and experimental results under a variety of parameter mismatches and operational circumstances.