Abstract: For the performance maximization and maintenance of lithium-ion batteries, accurate remaining useful life(RUL) predictions are essential. To accurately predict the RUL of lithium-ion batteries, a novel double Gaussian model is proposed to describe the aging process of lithium-ion batteries. Specifically, several popular empirical models for battery capacity degradation are analyzed and evaluated, and a double Gaussian model with better performance is proposed. Afterward, a double Gaussian aging model is established utilizing the particle filter(PF) technique, based on the historical capacity data. The fitted correlation coefficient and root mean square error are also introduced to assess the model. Finally, the RUL prediction experiments are conducted to verify the verification of the proposed aging model based on the battery aging data from the laboratory’s battery cells and the National Aeronautics and Space Administration(NASA) Ames Prognostics Center of Excellence. The experimental results demonstrate that the proposed aging model can predict the RUL accurately, and the prediction error is significantly improved compared to other models.