During the charging and discharging of a parallel-configured battery pack， the temperature distribution of each cell in the battery pack is inconsistent. There is a big difference between core and surface temperature for each cell， which directly affects the thermal safety evaluation of the battery pack. To solve the problem that the surface temperature of the battery measured traditionally cannot reflect the core temperature distribution， a battery core temperature estimation algorithm based on the combination of Rauch-Tung-Striebel （RTS） and unscented Kalman filtering （UKF） is proposed. Based on the data processing method of RTS， the future information is combined with the UKF algorithm. The results of the UKF algorithm are corrected using the future information Value to improve the accuracy and stability of the estimation. The hybrid pulse power characteristic （HPPC） experiment at different temperatures is used to identify the parameters of the equivalent circuit model. The current distribution model and the lumped thermal model of the parallel-configured battery pack are established. The model of the parallel-configured battery pack is experimentally verified. Under the dynamic stress test （DST）， the accuracy and stability of battery core temperature estimation by the RTS-UKF algorithm are improved compared with the UKF algorithm， and its estimation standard deviation is 4.2%.