Abstract: The accurate estimation of vehicle state is crucial for the control of its lateral and longitudinal stability. In vehicle state estimation, the Cubature Kalman Filter (CKF) and Square-root Cubature Kalman Filter (SCKF) are susceptible to heavy-tailed non-Gaussian noise, leading to decreased estimation accuracy. To address the problem, this paper proposes a novel filtering algorithm based on the Maximum Correntropy Square-root Cubature Kalman Filter (MCSCKF) that utilizes the maximum correntropy criterion. The algorithm reconstructs the measurement noise covariance matrix by approximating the state prediction and measurement values. Nonlinear 7-degree-of-freedom (DOF) vehicle model, Dugoff tire model, and Carsim distributed electric drive vehicle model are built to estimate three state variables of the vehicle, namely longitudinal velocity, lateral velocity, and yaw angular velocity, under sinusoidal and double lane-change conditions. The algorithm is verified by the joint simulation of Carsim and Matlab/Simulink. The results show the MCSCKF algorithm adapts to complex working conditions and improves the the accuracy of vehicle state estimation compared with CKF and SCKF algorithms.