The capacity degradation of Lithium battery is influenced by excessive factors and the mechanism is complex, and the remaining useful life (RUL) is very difficult to predict. The particle filter (PF) is one of the main stream methods in the current RUL prediction research because of its excellent non-linear non-Gaussian characteristics. However, the PF has a problem of particle degeneration in nature, which weakens the precision especially when the model has a dramatic trend of changes. In order to overcome the above problems, the PF is improved by introducing the renewal philosophy of particle swarm optimism (PSO) to reassign particle weight by optimizing the global position of particle. The simulation results reveal that compared with the original PF, a more precise prediction of the RUL can be obtained with the 33.6% reduction of error, and an alleviation of particle degeneration is reached for the 18.3% reduction of resampling rate.