Abstract
In the prediction task of remaining battery life (RUL), processes such as data cleaning and feature selection are often used. In this paper, an end-to-end prediction model, named Serial Informer (SI), is designed based on a deep learning approach. A serial self-attention mechanism is used in the model to calculate sequence correlations as weights for the collected data under different cycle counts of battery charging and discharging experiments, which makes the SI fully use the correlation information between sequences during the model training and learning process to achieve the end-to-end long sequence prediction task. The SI was evaluated using lithium-ion battery data from the NASA Ames Prognostics Data Repository. The experimental results show that SI has good prediction results, and it provides a new solution to the battery RUL prediction problem.
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