The dual active bridge (DAB) converter is a popular candidate for DC-DC converter applications in industrial and automotive applications. While single phase shift (SPS) control is the conventional control technique for DAB converters, different phase shift control techniques have been proposed with multiple control variables. Extended phase shift (EPS) control is one such control technique with an extra degree of freedom to minimize the backflow power (BFP) and inductor peak current, which claims to be superior to SPS control. In this analysis, functions for BFP and peak current in SPS and EPS control for both buck and boost operations, considering all possible cases of the inductor current waveform are derived. The two control variables of EPS control are reduced to a single control variable to minimize BFP and peak current. To identify the most efficient control technique between SPS and EPS control, efficiency analysis is carried out over a wide voltage and load range.

System reliability and design optimization are the main concerns in power electronic converters of more-electric aircraft (MEA). The dual active bridge (DAB) is the topology usually considered to link the HVDC bus to the LV network onboard the aircraft. This paper proposes a design and control algorithm that maximizes the power transmission efficiency by minimizing the transformer RMS current while considering the converter parasitic elements in the process. The optimization methodology includes two layers of optimization through a genetic algorithm (GA); optimization of transformer turns ratio and DAB inductor and phase angle optimization by using triple phase shift (TPS) control mode. The high-turn ratio of the isolating transformer utilized in this application results in a considerable effect on the converter performance. The parasitics effect is discussed and FEA simulations are carried out in Ansys Q3D from which the stray inductances are extracted and fed to the optimization algorithm.

Reliability, efficiency, and control optimization are the key features of modernized aircraft. This paper proposes a control algorithm across various voltages and load conditions that maximizes the power transmission efficiency between the high voltage DC (HVDC) link and the low voltage (LV) network aboard the aircraft. The algorithm is developed for a Gallium Nitride (GaN)-based dual active bridge (DAB) converter, for more electric aircraft (MEA). GaN is considered for maximized efficiency, weight reduction and improved thermal performance. The dual phase shift (DPS) and extended phase shift (EPS) modulation techniques are optimized using Genetic Algorithm (GA) and verified through simulation. The optimization algorithm aims at minimizing the backflow power, peak current, and converter losses. Efficiency results of the DAB converter are presented and compared under different modulation techniques. The results are validated on a 4 kW GaN-Silicon (Si) DAB converter.

This paper reviews zero-voltage switching (ZVS) possibilities with multiple phase shift modulations for the more electric aircraft (MEA). Three additional triple-phase shift modes are proposed and analyzed in detail based on ZVS conditions beyond the traditional six modes. A minimum peak current stress closed-loop hybrid multiple phase shift (HMPS) modulation scheme based on the proposed modes of operation is presented. Multiple phase shift angles are optimized based on accurate pre-calculation of output current, providing the minimum peak current and decreasing the switching loss, enabling the design of power-dense converters. The proposed HMPS modulation shows higher efficiency at light load and boundary operating points than the existing SPS and unified TPS (UTPS) methods. Finally, a 10kW SiC DAB converter hardware setup for the MEA application is implemented. The efficiency of the setup is up to 96.5%. At boundary operating points, the converter obtains a peak efficiency of 94.7% with the proposed HMPS method. The converter shows peak efficiency improvements of 1%-2% compared to traditional modulation.