The prediction of Remaining useful life (RUL) and the estimation of State of health (SOH) are extremely important issues for operating performance of Lithium-ion (Li-ion) batteries in the Battery management system (BMS). A multi-scale prediction approach of RUL and SOH is presented, which combines Wavelet neural network (WNN) with Unscented particle filter (UPF) model. The capacity degradation data of Li-ion batteries are decomposed into the low-frequency degradation trend and high-frequency fluctuation components by Discrete wavelet transform (DWT). Based on the WNN-UPF model, the long-term RUL of Li-ion batteries is predicted with the low-frequency degradation trend data. The high-frequency fluctuation data and RUL prediction results are integrated effectively to estimate the short-term SOH of Li-ion batteries. The experimental results show that the proposed method achieves high accuracy and strong robustness, even if the prediction starting point is set to the early stage of Li-ion batteries' lifespan.