Estimation of lithium-ion battery remaining useful life (RUL) is the key to lithium-ion battery health. Achieving accurate and reliable remaining useful life prediction of lithium-ion batteries is very vital for the normal operation of the battery system. Proposes a lithium-ion battery RUL prediction method based on the combination of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and support vector machine-regression (SVR). First, a measurable health factor is extracted during the discharge process, and the correlation between health factor and capacity is analyzed by Pearson and Spearman methods. Then, the health factor is decomposed by CEEMDAN to obtain a series of the relatively stable components. Finally, the health factor decomposed by CEEMDAN is used as the input of SVR prediction model, and the capacity is used as the output, so as to realize lithium-ion RUL prediction. The lithium-ion battery data published by NASA PcoE is used to carry out simulation experiments, and compare it with the standard SVR model, the experimental results show that the proposed method can effectively verify the effectiveness of the proposed RUL prediction model, and the prediction error is controlled below 2%.