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Remaining useful life (RUL) prediction of lithium-ion batteries can reduce the risk of battery failure by predicting the end of life. In this paper, we propose novel RUL prediction techniques based on long short-term memory (LSTM). To estimate RUL even in the presence of capacity regeneration phenomenon, we consider multiple measurable data from battery management system such as voltage, current and temperature charging profiles whose patterns vary as aging. Unlike the traditional LSTMdoi:10.1109/access.2020.2968939 fatcat:gailoal7yvft7l5logf3kq4lse