In order to improve the operating environment of the mine, it will become an inevitable trend for the future development to develop the transportation equipment under the new energy mine. As the main energy supply source of new energy electric vehicles at present, lithium-ion battery capacity will gradually decline with the increase of the number of lithium-ion battery cycles. Therefore, in order to improve the safety and reliability of lithium-ion battery in the process of recycling, it is necessary to predict its Remaining Useful Life (RUL). In this paper, based on the application background of cable retracting and retracting vehicle, a double exponential model matching battery aging was selected to simulate the capacity decline during battery use, and the battery capacity was selected as the evaluation index of battery. Particle filtering(PF) algorithm, as the main method to solve the nonlinear system parameter estimation and state filtering, has a wide range of applications in the field of target tracking and prediction. In order to ensure the diversity of particles, the butterfly optimization algorithm was introduced to optimize the particle filtering algorithm, and a new particle set was generated after each particle filtering calculation. By re-weighting the particles in the particle concentration to ensure the diversity of particles, the particle degradation problem in the particle filtering algorithm can be overcome, and users can make maintenance decisions based on the predicted results.