In this paper, a time-partitioned piecewise affine output error (PWA-OE) model for batch processes is proposed, in which time index is used to simplify the partition of regression domain by utilizing the repetitive nature of batch processes. The identification problem involves both continuous and discrete variables, and the derivative information on discrete variables is unavailable. Thus, an identification algorithm based on separable nonlinear least-squares is developed to reduce the complexity of nonlinear minimization. Furthermore, the consistent information criterion (CIC) is used to determine model structure. Results show that the time-partitioned PWA-OE model can effectively describe batch processes.