Ambient temperature is a significant factor that influences the accuracy of battery SOC estimation, which is critical for remaining driving range prediction of electric vehicles (EVs) and optimal charge/discharge control of batteries. A widely used method to estimate SOC is based on an online inference of open-circuit voltage (OCV). However, the fact that the OCV–SOC is dependent on ambient temperature can result in errors in battery SOC estimation. To address this problem, this paper presents an SOC estimation approach based on a temperature-based model incorporated with an OCV–SOC–temperature table. The unscented Kalman filtering (UKF) was applied to tune the model parameters at each sampling step to cope with various uncertainties arising from the operation environment, cell-to-cell variation, and modeling inaccuracy. Two dynamic tests, the dynamic stress test (DST) and the federal urban driving schedule (FUDS), were used to test batteries at different temperatures. Then, DST was used to identify the model parameters while FUDS was used to validate the performance of the SOC estimation. The estimation was made covering the major working range from 25% to 85% SOC. The results indicated that our method can provide accurate SOC estimation with smaller root mean squared errors than the method that does not take into account ambient temperature. Thus, our approach is effective and accurate when battery operates at different ambient temperatures. Since the developed method takes into account the temperature factor as well as the complexity of the model, it could be effectively applied in battery management systems for EVs.