Lateral stability control of distributed drive electric vehicle based on accurate driving state estimation using MCSCKF algorithm

Accurate estimation of lateral dynamic states is crucial for the stability control of distributed drive electric vehicles (DDEVs), particularly under non-Gaussian noise conditions. This paper presents an integrated framework for sensor signal processing and vehicle stability control. A novel maximum correntropy square-root cubature Kalman filter (MCSCKF) is proposed to achieve robust estimation of key lateral dynamic variables, including sideslip angle and yaw rate, from noisy sensor measurements. By incorporating higher-order statistical characteristics into the filtering process, the proposed estimator enhances robustness against impulsive disturbances and modeling uncertainties. Based on the improved state estimation performance, a hierarchical direct yaw moment control (DYC) scheme is designed. The upper control layer employs a non-singular terminal sliding mode (NTSM) controller to generate yaw moment commands, while the lower layer allocates driving torques to individual in-wheel motors. The effectiveness of the proposed stability control strategy is validated through CarSim/Simulink co-simulations. In addition, real-vehicle experiments are conducted to evaluate the robustness and noise suppression capability of the proposed MCSCKF under practical driving conditions using a VBOX3i measurement system. The results demonstrate that the proposed estimator provides more stable and robust state estimation under non-Gaussian disturbances, which is beneficial for subsequent control applications.