id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
ajst-18568	Shi, Yun	Remaining Useful Life Prediction of Lithium-Ion Batteries Based on Transfer Learning and DAE-LSTM	2024	8	.pdf	application/pdf	5100	245	49	[10] proposed an LSTM-based prediction model for battery life prediction, using capacity data at 50% and 70% of the lifecycle for model training and subsequent remaining life prediction. Finally, the fine-tuned LSTM pre-training model is applied to the task of predicting the capacity of target domain batteries, thereby completing the RUL prediction.	cache/ajst-18568.pdf	txt/ajst-18568.txt