Variable Dead-Time Based Novel Soft-Start Method for Dual Active Bridge Converters
Sachith Wijesooriya, Sandun S. Kuruppu
- Year
- 2026
- Access
- Open access
Abstract
Effective startup control is critical for the safe and reliable operation of Dual Active Bridge (DAB) converters. Unlike traditional soft-start techniques that rely solely on phase-shift control or fixed dead-time settings, the proposed approach gradually reduces the dead time from a value close to one switching period to the hardware-defined minimum. This enables a smooth buildup of the secondary-side voltage while effectively minimizing voltage overshoot and suppressing inrush current during startup. As a result, the leakage inductor current rises in a controlled manner, ensuring safe and predictable startup behavior. Simulation results demonstrate that conventional startup methods lead to severe voltage overshoot and high inrush currents, whereas the proposed method achieves a gradual voltage rise with well-regulated current profiles. Experimental validation using a 15 kW hardware platform confirms the effectiveness and robustness of the approach under different operating conditions. The proposed technique is simple, hardware-friendly, easily implementable on standard microcontrollers, and applicable to nth - order DAB architecture, making it a versatile solution for enhancing the reliability and safety of DAB converters in practical applications.
Keywords
Related papers
A dual-loop framework for manufacturability-aware topology optimization of electric vehicle structures via wire arc additive manufacturing
Qiang Cui, Chuan Yu, Daoqian Yang +2 more
Robotics and Computer-Integrated Manufacturing · 2026
Geometric digital twin: A digital and intelligent model for aero-engine assembly accuracy prediction
Ke Shang, Xin Jin, Teli Xu +4 more
Robotics and Computer-Integrated Manufacturing · 2026
Revolutionizing Industries Through AI-Driven Robotics
Aryan Chaudhary
Recent Advances in Computer Science and Communications · 2026
Design and dynamic performance prediction of a novel large-aperture offset-feed deployable antenna
Chuang Shi, Tianming Liu, Ning Xue +6 more
Aerospace Science and Technology · 2026