Abstract: Accurate and fast prediction of the remaining useful life(RUL) of lithium-ion batteries is crucial for safe and stable system operation. However, the complex internal degradation mechanism and the changeable external operating conditions of the battery bring great challenges to RUL prediction. Therefore, a RUL prediction method based on battery expansion stress is proposed in this paper. The battery expansion stress information is extracted, the relationship between reversible expansion as well as irreversible expansion and capacity is analyzed respectively, and the correlation is calculated. The reversible expansion and irreversible expansion are used as feature parameters, and long short-term memory(LSTM) neural network is constructed and trained to achieve accurate and fast RUL prediction. Through the verification on UMBL public dataset, the use of expansion stress features enables better learning of the battery aging state and captures the battery capacity degradation trend. The results show that the RMSE and MAE are within 0.82% and 0.70%, respectively, under different cycle starting points and various aging conditions. The proposed method can predict RUL with strong robustness accurately and quickly.