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The training data of the widely adopted lithium battery state-of-charge (SOC) prediction method needs to be obtained through plenty of simulation experiments, while the large amount of charging record data generated by the electric vehicle during the charging process is not properly utilized. In order to effectively utilize these data, a multiple linear regression algorithm is applied to the SOC prediction. The multivariate robust regression method uses the physical quantities such as voltage, current, and capacitance of the lithium battery which directly related to the SOC as input variables to perform regression prediction on the SOC. Due to the timing characteristics of the SOC, the prediction of SOC is divided into multiple sub-prediction processes, and the iterative calculation is continuously performed to cyclically predict the next output value of SOC. At the same time, two common robust regression algorithms (Theil-sen algorithm and RANSAC algorithm) are used to predict SOC in order to overcome the influence of abnormal samples on SOC prediction accuracy. The experimental results show that the robust regression algorithm and the multiple linear regression algorithm can well capture the growth law of SOC. In contrast, Theil-sen algorithm has higher precision and the error is about 1.398%, which can satisfy the actual needs of SOC prediction well.