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Accurate prediction of the remaining useful life of lithium-ion (Li-ion) batteries is particularly important for their prognosis and health management. Therefore, a new feature extraction technique for extracting health indicators (HIs) characterizing the battery aging and a new improved extreme learning machine (ELM) algorithm for model training and prediction are proposed in this paper. Firstly, based on the measurable parameters, singular value decomposition (SVD) is used to extract the respective singular value as HIs, and then the Pearson correlation coefficient between each HI and capacity are calculated. Next, several HIs with high correlation coefficients are selected as the input of the model. Finally, the relationship model between HIs and capacity is constructed by using the improved ELM (OS-PELM) algorithm, and the final prediction results are obtained. Li-ion battery data from three different research institutions are adopted to verify the feasibility and reliability of the proposed method. Experiment results show that feature extraction technique and improved algorithm can not only extract features highly related to capacity, but also ensure the accuracy of prediction. The comparison with other algorithms further shows that the novel method is more accurate and competitive. • A technique called SVD is used to extract features. • OS-PELM is added online sequential learning based on PELM. • Battery data sets from three different institutions are considered. • The proposed method demonstrates high prediction accuracy, real-time and robustness. [ABSTRACT FROM AUTHOR]