Yurui Xiang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Yurui Xiang has a total of 7 co-authors. Here are the top co-authors by number of shared publications:
- Hongzhang Xu
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
1 shared publication - Liangliang Wei
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
1 shared publication - Mengtang Li
Shenzhen Campus of Sun Yat-sen University
1 shared publication - Tianpei Liu
School of Intelligent Systems Engineering, Sun Yat-sen University, Shenzhen, China
1 shared publication - Xiaojun Tan
School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China
1 shared publication - Yuqian Fan
School of Computer Science and Technology, Henan Institute of Science and Technology, Xinxiang, China
1 shared publication
Latest Publications
- 4 March 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industrial Electronics
- Vol. 73 (8) , 12401-12413
A SOH Estimation Method for Lithium-Ion Battery Based on Partial Charging Data Reconstruction With Physical Information Constraints
State of health (SOH) estimation is critical for the safe and efficient operation of lithium-ion batteries. However, obtaining complete charge-discharge data in real-world applications remains challenging. This article proposes an SOH estimation framework based on physical information constraints and partial charging data reconstruction. A convolutional neural network (CNN) is designed to reconstruct partial voltage curves into the peak region of the incremental capacity (IC) curve, capturing critical aging information. Features extracted from the reconstructed IC curve are then used to estimate SOH, with physics-based constraints from reconstructed IC peak incorporated into the loss function to enhance model interpretability and accuracy. Experimental validation on three different datasets demonstrates that the proposed method achieves high estimation accuracy, with errors under 2% using limited voltage segments. The results verify the effectiveness and generalization capability of the approach, providing a practical solution for the limited data availability in real scenarios.
- 4 March 2026
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Industrial Electronics
- Vol. 73 (8) , 12401-12413
A SOH Estimation Method for Lithium-Ion Battery Based on Partial Charging Data Reconstruction With Physical Information Constraints
State of health (SOH) estimation is critical for the safe and efficient operation of lithium-ion batteries. However, obtaining complete charge-discharge data in real-world applications remains challenging. This article proposes an SOH estimation framework based on physical information constraints and partial charging data reconstruction. A convolutional neural network (CNN) is designed to reconstruct partial voltage curves into the peak region of the incremental capacity (IC) curve, capturing critical aging information. Features extracted from the reconstructed IC curve are then used to estimate SOH, with physics-based constraints from reconstructed IC peak incorporated into the loss function to enhance model interpretability and accuracy. Experimental validation on three different datasets demonstrates that the proposed method achieves high estimation accuracy, with errors under 2% using limited voltage segments. The results verify the effectiveness and generalization capability of the approach, providing a practical solution for the limited data availability in real scenarios.