Aiming at the complex situation where the parameters of powertrain mounting system (PMS) of electric vehicle are both uncertain and correlated, this paper investigated the inherent characteristics of electric vehicle PMS considering the correlation of probabilistic parameters. Firstly, the correlation matrix and probabilistic parameters were employed to describe the correlation and uncertainty of PMS parameters. Then, the Monte-Carlo method (MCM) was developed to calculate the PMS inherent characteristics with correlated probabilistic parametersbased on Monte-Carlo sampling. Then, an efficient method for calculating the statistical moments of PMS inherent characteristics was put forward based on sparse grid numerical integration (SGNI). Finally, the effectiveness of the proposed approach was demonstrated by the numerical example of an electric vehicle PMS. The analysis results show that, the SGNI method has good accuracy and efficiency in solving statistical moments and bounds of PMS natural frequencies and decoupling ratios, compared with MCM. The correlation of probabilistic parameters has a great influence on the upper and lower bounds of decoupling ratios. More reasonable analysis results can be obtained by taking the correlation of the probabilistic parameters into consideration.