State estimation of a vehicle is an important direction under the research branch of automotive dynamics, with the aim of determining state variables that reflect vehicle handling stability and other characteristics. In order to solve the problem of poor estimation accuracy caused by heavy tailed non Gaussian noise in traditional state estimation methods, a new filtering algorithm based on the Maximum Correlation Entropy criterion (MCC) and the Square-root Cubature Kalman Filter (MCSCKF) is proposed. On the basis of establishing a nonlinear 3-DOF vehicle model, the yaw rate and the side slip angle as well as the longitudinal velocity of the vehicle were estimated. And the effectiveness of the algorithm was verified through joint simulation with Carsim and Matlab/Simulink. The results show that the MCSCKF algorithm can adapt to complex working conditions and has better accuracy in vehicle state estimation than traditional state estimation algorithms. Meanwhile, the MCSCKF algorithm can effectively reduce the impact of heavy tail non Gaussian noise and improve the accuracy of vehicle state estimation.