It is important to predict the capacity of lithium-ion battery for future cycles to assess to estimate its health condition and remaining useful life (RUL). Particle filter approaches are widely applied into the estimation of battery capacity. However, after several iterations, the degeneracy and impoverishment of particles can cause prediction results unreliable and inaccurate in particle filter (PF). In this paper, a fusion method is proposed by integrating unscented kalman filter (UKF) and minimum sampling variance resampling (MSVR) into the standard PF to RUL prediction of batteries. The UKF is employed to generate the proposal distribution of particles, which is used by PF to calculate the weights of particles. Next, the MSVR algorithm is introduced for performing resampling procedure to improve the performance. Finally, the performance of the proposed method is validated and compared to other predictors with four different batteries data from NASA. According to the results, the integrated method has high reliability and prediction accuracy.