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In state estimation of batch process, it is a challenge to make full use of the multi-batch repetitive characteristic information. An iterative learning Kalman filter method uses Kalman filter to estimate the state errors between adjacent batches along the time direction, and iteratively updates the current state estimation along the batch direction, taking into account both time and batch two-dimensional characteristics. However, this method only applies to linear systems. An iterative learning quasilinear Kalman filter (ILQKF) method is proposed to estimate the state of nonlinear batch process. Based on the nominal model of batch process, ILQKF takes the error between the real state and the nominal state as a new state. Then a linearized model related to the error is established. The estimation of the error is obtained by referring to the method of the iterative learning Kalman filter. The state trajectory is equal to the sum of the error trajectory and the nominal trajectory, so as to estimate the state of the nonlinear batch process. The beer fermentation simulation is used to verify the performance of the ILQKF method.