Publication Date
3-15-2024
Document Type
Article
Publication Title
Expert Systems with Applications
Volume
238
DOI
10.1016/j.eswa.2023.122041
Abstract
Graph convolutional networks (GCNs) have been increasingly used to predict the state of health (SOH) and remaining useful life (RUL) of batteries. However, conventional GCNs have limitations. Firstly, the correlation between features and the SOH or RUL is not considered. Secondly, temporal relationships among features are not considered when projecting aggregated temporal features into another dimensional space. To address these issues, two types of undirected graphs are introduced to simultaneously consider the correlation among features and the correlation between features and the SOH or RUL. A conditional GCN is built to analyze these graphs. A dual spectral graph convolutional operation is introduced to analyze the topological structures of these graphs. Additionally, a dilated convolutional operation is integrated with the conditional GCN to consider the temporal correlation among the aggregated features. Two battery datasets are used to evaluate the effectiveness of the proposed method. Experimental results show that the proposed method outperforms other machine learning methods reported in the literature.
Funding Number
2131619
Funding Sponsor
National Science Foundation
Keywords
Graph convolutional network, Lithium-ion battery, Remaining useful life, State of health, Temporal convolutional network
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Department
Industrial and Systems Engineering
Recommended Citation
Yupeng Wei and Dazhong Wu. "State of health and remaining useful life prediction of lithium-ion batteries with conditional graph convolutional network" Expert Systems with Applications (2024). https://doi.org/10.1016/j.eswa.2023.122041
