Accurately estimating the SOC of a battery during the electric vehicle drive cycle is a vital issue that currently remains unresolved. A support vector regression algorithm (SVR), which has good nonlinear approximation ability, a quick convergence rate and global optimal solution, is proposed to estimate the battery SOC. First, the training data and the test data required in the estimation operation are collected using the ADVISOR software, followed by normalization of the data above. Then, cross validation and grid search methodologies are used to determine the parameters in the $ u$-SVR model. Finally, simulation experiments have been carried out in the LIBSVM simulator. The simulation results show that, compared to the BP neural network algorithm, the $ u$-Support Vector Regression algorithm performs better in estimating the battery SOC.