Accurate estimation of the state of charge ( SOC) of lithium-ion batteries is one of the key technologies in the battery management system, which has a vital impact on the service efficiency and safety of power battery pack. Lithium-ion batteries have complicated characteristics and SOC cannot be directly measured which are greatly affected by the current and temperature. Therefore, combining a gated recurrent unit (GRU) neural network with an unscented Kalman filter (UKF) algorithm is presented. The method uses GRU neural network to obtain the nonlinear relationship between the SOC and measurements, including the current, voltage, temperature. The relationship is used as the observation equation of UKF, and the SOC is estimated by the UKF to improve the accuracy and stability of estimation algorithm. Experimental results show that under different temperatures and different working conditions the root mean square error and the mean absolute error of the SOC estimate are less than 0. 51% and 0. 46%, respectively, which can improve the accuracy of SOC estimation.