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.

This paper presents a new control strategy that combines classical control and an optimization scheme to regulate the output voltage of the bidirectional converter under the presence of matched and mismatched disturbances. In detail, a control-oriented modeling method is presented first to capture the system dynamics in a common canonical form, allowing different disturbances to be considered. To estimate and compensate for unknown disturbances, an extended state observer (ESO)-based continuous sliding mode control is then proposed, which can guarantee high tracking precision, fast disturbance rejection, and chattering reduction. Next, an extremum seeking (ES)-based adaptive scheme is introduced to ensure system robustness as well as optimal control effort under different working scenarios. Finally, comparative simulations with classical proportional-integral-derivative (PID) control and constant switching gains are conducted to verify the effectiveness of the proposed adaptive control methodology through three case studies of load resistance variations, buck/boost mode switching, and input voltage variation.

This paper proposes a sensor-reduction control in order to control the output voltage of the dual active bridge (DAB) converter under dual-phase-shift (DPS) modulation. The deadbeat control-based Lagrange multiplier method (LMM) is adopted to achieve current stress optimization (CSO). A first-order extended state observer (ESO) is established to observe the load current based on the basis of the dynamic equation of the output capacitor. Compared to the existing ESO for the DAB converter, the proposed observer is able to reject disturbances even when output capacitor mismatches occur, resulting in better observer performance. Besides, the proposed method exhibits good steady-state performance without using any compensating controller. Consequently, the proposed method enables current sensorless control and significantly decreases the number of control parameters, resulting in increased simplicity and cost-saving. The simulation and experimental results obtained with a 300 W prototype demonstrate that the proposed method outperforms the existing methods under changing load current and input voltage conditions.