An efficient method for battery Remaining Useful Life(RUL) prediction would greatly improve the reliability of systems. A novel Autoregressive Integrated Moving Average Model-Particle Filter(ARIMA-PF) fusion prognostic framework is developed to improve the performance of battery RUL prediction. It is composed of ARIMA algorithm and PF algorithm. ARIMA is employed for short-term estimation of system state, while Particle Filter for long-term estimation of system state. Firstly, the lithium ion battery is monitored online; then the corresponding algorithms are employed according to short-term forecasts or long-term forecasts requirements; the forecast maps are obtained with the transverse and longitudinal coordinates standing for the cycle and capacity respectively. The experimental results indicate that the proposed prognostic framework can predict lithium ion battery RUL accurately and fast.