Weihong Tang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Weihong Tang has a total of 4 co-authors. Here are the top co-authors by number of shared publications:
- Jifei Zhao
College of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300457, China
1 shared publication - Shaofang Wu
School of Intelligent Manufacturing, Guangzhou Maritime University, Guangzhou 510725, China
1 shared publication - Xuhui Liu
School of Intelligent Manufacturing, Guangzhou Maritime University, Guangzhou 510725, 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
- Open Access
Bagging-PiFormer: An Ensemble Transformer Framework with Cross-Channel Attention for Lithium-Ion Battery State-of-Health Estimation
Accurate estimation of lithium-ion battery (LIB) state of health (SOH) is critical for prolonging battery life and ensuring safe operation. To address the limitations of existing data-driven models in robustness and feature coupling, this paper presents a new Bagging-PiFormer framework for SOH estimation. The framework integrates ensemble learning with an improved Transformer architecture to achieve accurate and stable performance across various degradation conditions. Specifically, multiple PiFormer base models are trained independently under the Bagging strategy to enhance generalization. Each PiFormer consists of a stack of PiFormer layers, which combines a cross-channel attention mechanism to model voltage–current interactions and a local convolutional feed-forward network (LocalConvFFN) to extract local degradation patterns from charging curves. Residual connections and layer normalization stabilize gradient propagation in deep layers, while a purely linear output head enables precise regression of the continuous SOH values. Experimental results on three datasets demonstrate that the proposed method achieves the lowest MAE, RMSE, and MAXE values among all compared models, reducing overall error by 10–33% relative to mainstream deep-learning methods such as Transformer, CNN-LSTM, and GCN-BiLSTM. These results confirm that the Bagging-PiFormer framework significantly improves both the accuracy and robustness of battery SOH estimation.
Open AccessBagging-PiFormer: An Ensemble Transformer Framework with Cross-Channel Attention for Lithium-Ion Battery State-of-Health Estimation
Accurate estimation of lithium-ion battery (LIB) state of health (SOH) is critical for prolonging battery life and ensuring safe operation. To address the limitations of existing data-driven models in robustness and feature coupling, this paper presents a new Bagging-PiFormer framework for SOH estimation. The framework integrates ensemble learning with an improved Transformer architecture to achieve accurate and stable performance across various degradation conditions. Specifically, multiple PiFormer base models are trained independently under the Bagging strategy to enhance generalization. Each PiFormer consists of a stack of PiFormer layers, which combines a cross-channel attention mechanism to model voltage–current interactions and a local convolutional feed-forward network (LocalConvFFN) to extract local degradation patterns from charging curves. Residual connections and layer normalization stabilize gradient propagation in deep layers, while a purely linear output head enables precise regression of the continuous SOH values. Experimental results on three datasets demonstrate that the proposed method achieves the lowest MAE, RMSE, and MAXE values among all compared models, reducing overall error by 10–33% relative to mainstream deep-learning methods such as Transformer, CNN-LSTM, and GCN-BiLSTM. These results confirm that the Bagging-PiFormer framework significantly improves both the accuracy and robustness of battery SOH estimation.