Chaojie Guan
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Chaojie Guan has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Chenwei Ma
Southwest Jiaotong University
1 shared publication - Sihui Zhang
School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
1 shared publication - Wensheng Song
Southwest Jiaotong University
1 shared publication - Yaru Deng
School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
1 shared publication - Zhiwei Zhang
School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
1 shared publication
Latest Publications
- 15 December 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 74 (00189456) , 1-12
Digital-Twin Modeling and Parameter Monitoring Method of Dual Active Bridge DC–DC Converters With Improved Minimum Current Stress Optimization
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.
- 15 December 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. 74 (00189456) , 1-12
Digital-Twin Modeling and Parameter Monitoring Method of Dual Active Bridge DC–DC Converters With Improved Minimum Current Stress Optimization
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.