In the state of charge (SOC) estimation problem, when some outliers appear in the data measured by the sensor, the estimation effect will become worse, and the heavy-tailed distribution is more suitable to describe this complex interference noise with outliers than the Gaussian distribution. To solve the SOC estimation problem in this case, the SOC estimation scheme based on Student’s-T distribution is proposed, and the Taylor series expansion method is utilized to make the Student’s-T filter suitable for the nonlinear system of battery. Then, the SOC estimation experiment of batteries affected by different degrees of outlier noise in high and low temperature conditions are carried out by using the extended Student’s-T filter (ESTF), robust extended Student’s-T filter (RESTF), and Kullback–Leibler divergence extended Student’s-T filter (KLDESTF). The experiment results demonstrate that the proposed scheme can effectively improve the estimation effect of SOC under various working conditions when the system is polluted by different degrees of outliers, which shows that the proposed scheme has good robustness and is more suitable for SOC estimation under actual complex interference conditions.