Zhongtian Sun
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Zhongtian Sun has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Nian Cai
School of Information Engineering, Guangdong University of Technology, Guangzhou, China
2 shared publications - Weiguo Feng
School of Information Engineering, Guangdong University of Technology, Guangzhou, China
2 shared publications - Yilin Han
School of Information Engineering, Guangdong University of Technology, Guangzhou, China
2 shared publications - Yinghong Zhou
School of Information Engineering, Guangdong University of Technology, Guangzhou 510006, China
2 shared publications
Latest Publications
- 20 August 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. PP (00189456) , 1
A Bimodal Large-Small Model Collaborative Network for Joint Prediction of State of Health and Remaining Useful Life of Lithium-Ion Batteries
Accurate predictions of the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are crucial to ensure their efficient and safe operations. Existing deep learning methods are hindered by the limited quantity and diversity of datasets, leading to model overfitting and poor generalization. To this end, a bimodal large-small model collaborative network (BLSCN) is designed in this paper, which seamlessly combines a pretrained large vision model (LVM) with a designed small model (SM) for joint prediction of SOH and RUL of lithium-ion batteries. Specifically, the BLSCN utilizes the LVM to extract features from the bimodal images generated by the battery data, followed by a SM for feature fusion and prediction. To promote the generalization ability of LVM on the battery data, an attention mask strategy is proposed to guide the LVM to focus on more features of interest in the images. In the SM, an elaborately-designed cross fusion module (CFM) is employed to interactively fuse bimodal image features for subsequent joint prediction of SOH and RUL. Experimental results on the public dataset demonstrate the superiority of the proposed BLSCN on joint prediction of SOH and RUL, with coefficients of determination (R²) of 0.983 and 0.942 for SOH and RUL prediction tasks, respectively.
- 20 August 2025
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Instrumentation and Measurement
- Vol. PP (00189456) , 1
A Bimodal Large-Small Model Collaborative Network for Joint Prediction of State of Health and Remaining Useful Life of Lithium-Ion Batteries
Accurate predictions of the state of health (SOH) and remaining useful life (RUL) of lithium-ion batteries are crucial to ensure their efficient and safe operations. Existing deep learning methods are hindered by the limited quantity and diversity of datasets, leading to model overfitting and poor generalization. To this end, a bimodal large-small model collaborative network (BLSCN) is designed in this paper, which seamlessly combines a pretrained large vision model (LVM) with a designed small model (SM) for joint prediction of SOH and RUL of lithium-ion batteries. Specifically, the BLSCN utilizes the LVM to extract features from the bimodal images generated by the battery data, followed by a SM for feature fusion and prediction. To promote the generalization ability of LVM on the battery data, an attention mask strategy is proposed to guide the LVM to focus on more features of interest in the images. In the SM, an elaborately-designed cross fusion module (CFM) is employed to interactively fuse bimodal image features for subsequent joint prediction of SOH and RUL. Experimental results on the public dataset demonstrate the superiority of the proposed BLSCN on joint prediction of SOH and RUL, with coefficients of determination (R²) of 0.983 and 0.942 for SOH and RUL prediction tasks, respectively.