The thermal management of traction batteries for electric-drive vehicles greatly affects the battery performance and life time. It is important that the maximum cell temperature is maintained below the allowable maximum temperature and the temperature difference in the battery system is as small as possible. A spatial-resolution lumped-capacitance thermal model was developed to predict the battery temperatures of core, surface and averaged for high cell Biot numbers (Bi ≻ 0.1) to which the classical lumped-capacitance model is inapplicable because of the significant spatial distribution of the cell temperature. The results of the spatial-resolution lumped-capacitance model were compared with the numerical results using ANSYS, which is referred as a benchmark solution. It was found that the spatial-resolution lumped-capacitance thermal model accurately predicts the temperatures of core and surface and is fast enough for real-time temperature forecast and control for smart battery thermal management. The spatial-resolution lumped-capacitance thermal model could be very suitable for battery lifetime prediction and analysis using dynamic battery duty cycles with long time duration.