Prediction of state of health and remaining useful life of lithium-ion battery using graph convolutional network with dual attention mechanisms

Prediction of state-of-health and remaining useful life is crucial to the safety of lithium-ion batteries. Existing state-of-health and remaining useful life prediction methods are not effective in revealing the correlation among features. Establishing the correlation can help identify features with high similarities and aggregate them to improve the accuracy of predictive models. Moreover, existing methods, such as recurrent neural networks and long short-term memory, have limitations in state-of-health and remaining useful life predictions as they are not capable of using the most relevant part of time-series data to make predictions. To address these issues, a two-stage optimization model is introduced to construct an undirected graph with optimal graph entropy. Based on the graph, the graph convolutional networks with different attention mechanisms are developed to predict the state-of-health and remaining useful life of a battery, where the attention mechanisms enable the neural network to use the most relevant part of time series data to make predictions. Experimental results have shown that the proposed method can accurately predict the state-of-health and remaining useful life with a minimum root-mean-squared-error of 0.0104 and 5.80, respectively. The proposed method also outperforms existing data-driven methods, such as gradient-boosting decision trees, long short-term memory, and Gaussian process.