State of health and remaining useful life prediction of lithium-ion batteries with conditional graph convolutional network

Graph convolutional networks have been increasingly used to estimate the state of health and predict the remaining useful life of batteries. However, there are two issues with conventional graph convolutional networks. Firstly, they ignore the correlation between features and the state of health or remaining useful life. Secondly, they do not consider temporal relationships among features 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 state of health or remaining useful life. A conditional graph convolution network is built to handle these graphs, incorporating a dual spectral graph convolutional operation to analyze the topological structures of these graphs. Additionally, the dilated convolutional operation is integrated with the proposed conditional graph convolution network to account for the temporal correlation among the aggregated features. Two battery datasets were used to evaluate the effectiveness of the presented method, resulting in a minimum mean absolute remaining useful life prediction error of 3.219. Moreover, the proposed method outperforms methods reported in the literature, such as Gaussian processes and other deep learning methods.