As lithium-ion batteries are widely used in mobile electronics and electric vehicles, accurate prediction of their remaining useful life (RUL) is of great importance for health management of lithium batteries. A prediction method based on particle filter (PF) and gated recurrent unit (GRU) neural network fusion method (PF-GRU) is proposed to predict the RUL of lithium batteries. This fusion method combines the advantages of PF in estimating the probability distribution of RUL and the ability of GRU to perform long-term prediction of time series to obtain the fusion prediction result. Using the capacity prediction results of each prediction period, the GRU model is retrained by iteratively updating the training dataset with a sliding window, which improves long-term prediction performance of GRU. The above prediction steps are iteratively performed until the capacity decays below the lifetime threshold. Finally, prediction results represented by particles are extrapolated to the lifetime threshold, and RUL distribution histogram of the battery is obtained. The lithium battery data provided by the NASA prognostics center of excellence (PCoE) laboratory is adopted to verify the proposed method, which compares the proposed fusion method with GRU, PF and fusion method without sliding window for RUL prediction. Experimental results show that the proposed fusion method has good performance, which is obviously better than PF and fusion method without sliding window in terms of state and parameter estimation and RUL prediction accuracy.