Journal of Energy Storage, Volume 58, 2023, 106437, ISSN 2352-152X, DOI: 10.1016/j.est.2022.106437.

A Novel Graph-based Framework for State of Health Prediction of Lithium-ion Battery

Xing-Yan Yao1, Guolin Chen1, Michael Pecht2, and Bin Chen3
1Chongqing Engineering Laboratory for Detection Control and Integrated System, Chongqing Technology and Business University, Chongqing 400067, China
2Center for Advanced Life Cycle Engineering, University of Maryland, College Park, MD 20742, USA
3China Merchants Testing Vehicle Technology Research Institute Co., Ltd, Chongqing 400067, China

For more information about this article and related research, please contact Prof. Michael Pecht


State of health (SOH) plays a vital role in lithium-ion batteries (LIBs) safety, reliability and lifetime. Health indicators (HIs) are a powerful approach to predict battery SOH. The existing methods for battery SOH prediction according to HIs only consider the temporal features of HIs. The spatial features of interdependence between HIs enrich predicational information especially for the limited data. This paper proposes a novel framework CL-GraphSAGE to predict battery SOH based on graph neural network (GNN), which takes into both temporal and spatial features of HIs. Firstly, the Pearson's correlation coefficients between HIs and SOH are obtained to extract highly correlated HIs to build a graph. Subsequently, the temporal features are extracted by convolutional neural network (CNN) and long short-term memory neural network (LSTM). Finally, the spatial features are obtained by the graph sample aggregate (GraphSAGE) to propagate messages on a pre-defined graph structure, which uncovers the deep information among HIs. The effectiveness of the proposed approach in predicting battery SOH is verified by MIT, NASA datasets and the experimental datasets, and compared with CNN, LSTM and graph convolutional network and graph attention network. The results show that the root mean square error of the proposed approach CL-GraphSAGE can achieve 0.2 %, and the different datasets verify its feasibility.

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