Yaru Deng

Dual-bridge series resonant converters (DBSRCs) are promising solutions to interface energy storage modules with DC-bus flexibly. To compensate for the defect that the fundamental harmonic approximation (FHA) model has high accuracy only near the resonant frequency point, a generalized time-domain model (the G-TDA model) is proposed for DBSRCs, which can accurately describe operating characteristics and transmission power of DBSRCs. In addition, the evolving time-domain models of other symmetric phase-shift modulations are derived from the G-TDA model. Compared with the FHA model, the G-TDA model can predict the resonant current behavior and zero-voltage-switching (ZVS) conditions with higher accuracy. Then, the relationship between backflow power and resonant current is discussed, the backflow power optimization performance is closely related to the prediction accuracy of the resonant current behavior. Besides, dead time effects are analyzed when switching devices achieve/lose ZVS, which is closely related to ZVS conditions. Finally, comparative experiments of optimization methods based on the FHA model and the G-TDA model are carried out, better control performance of the backflow power optimization and the dead time compensation can be achieved due to the higher accuracy of the G-TDA model, such as zero-backflow power optimization, all switching devices ZVS, and efficiency improvement of DBSRCs.

To achieve a comprehensive state monitoring and guarantee the reliability of dual active bridge DC-DC (DAB) converters, a modeling and parameter monitoring method based on digital-twin technology is proposed. The proposed method only requires non-intrusive sampling of the input and output voltage as well as the inductor current, without the need for an additional sampling circuit. Initially, the discrete mathematical model for the topological circuit of DAB converters has been developed. Here, a load current feedforward minimum current stress optimization (LCF-CSO) method is proposed to reduce the power losses and improve the transient performance of DAB converters. Secondly, the external operating data of the physical DAB converter are collected to drive the digital-twin model, thereby reproducing the characteristics of the real system. Thirdly, an objective evaluation function is constructed, and the key parameters are identified iteratively by particle swarm optimization (PSO) algorithm. As a result, an off-line digital-twin model is developed to enable reliable and non-invasive state monitoring of the capacitors, inductors, and power MOSFETs. Finally, the reliability of the proposed LCF-CSO and, the digital-twin model condition monitoring method is verified by experiments, and the monitoring error is within 5 %, which contributes to effective component state monitoring.