Digital-Twin Modeling and Parameter Monitoring Method of Dual Active Bridge DC–DC Converters With Improved Minimum Current Stress Optimization

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Abstract
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.
Funding Information
  • Sichuan Youth Science and Technology Innovation Research Team (22CXTD0055)
  • National Natural Science Foundation of China-Joint Key Program for Railway Basic Research (U2368206)

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