Addressing Key Challenges in Dual Active Bridge (DAB) Converters: A Review of Voltage Gain Flexibility, Voltage Imbalance and Fault Detection

Dual Active Bridge (DAB) converters are widely employed in isolated bidirectional DC–DC power conversion for applications such as electric vehicles, renewable energy systems, aerospace, and solid-state transformers because of their high-power density, galvanic isolation, and soft-switching capability. However, their practical deployment remains constrained by several operational challenges, including restricted voltage gain flexibility, voltage imbalance in multilevel and modular configurations, and vulnerability to fault conditions during high-speed operation. Although numerous topological, control, and modulation-based solutions have been reported, most existing review papers classify these approaches according to methodology rather than the operational challenges they address, limiting direct comparison between different solution domains. To address this gap, this paper presents a challenge-oriented review of three major operational challenges in DAB converters: limited voltage gain flexibility, voltage imbalance in multilevel and modular systems, and fault detection and protection in high-speed operation. The reviewed solutions are systematically categorized into topology, control, and modulation domains and compared in terms of effectiveness, implementation complexity, computational burden, hardware requirements, and practical applicability. The comparative analysis shows that multilevel and modular topologies, such as Triple Active Bridge and modular high-frequency-transformer architectures, provide the greatest improvements in voltage gain flexibility, while active voltage balancing strategies that combine real-time control with modulation outperform passive balancing methods in multilevel and modular DAB systems. For fault detection, observer-based and sensorless topological methods deliver fast, low-cost diagnosis, whereas AI-driven and modulation-based approaches, such as pulse-injection and spread-spectrum monitoring, extend detection sensitivity at the cost of additional signal-processing complexity. Model Predictive Control emerges consistently across all three challenges as a strategy offering strong dynamic performance, though at higher computational cost. This review identifies current research gaps and outlines future research directions toward integrated, adaptive, and intelligent DAB converter architectures for next-generation power electronic systems.