The current vehicle stability judgment methods in the stability control of distributed drive electric vehicles focuses on using the phase plane method, but the existing phase plane stability boundary delineation methods ignore the influence of the front wheel rotation angle. Therefore, a new phase plane partitioning method (NPPPM) is proposed in this paper, and a new phase plane stability domain library is established. Based on this, the direct yaw moment control of distributed drive electric vehicle through phase plane analysis is established. Firstly, the influence of the front wheel turning angle on the phase plane is considered in the design of the phase plane stability boundary, and a new stability boundary function is fitted by the least squares method. This approach avoids the lateral instability caused by too large a front-wheel turning angle and improves the accuracy of vehicle stability judgment. Next, a hierarchical direct transverse moment controller is constructed using Matlab/Simulink. Among them, the upper layer controller adopts sliding mode control with the sideslip angle error of the center of mass as the tracking target. The lower controller adopts the optimal configuration algorithm with the optimal road adhesion utilization as the objective function. Finally, the effectiveness and superiority of the proposed phase plane partitioning method (NPPPM) for distributed drive electric vehicles with sinusoidal and angular order inputs for the front wheel rotation angle are verified by simulation.

In vehicle lateral stability control, accurately estimating the vehicle’s lateral state is crucial. Vehicle state estimation is influenced by model accuracy, filter algorithms, and sensor accuracy, which are typically assumed to be affected by Gaussian noise. However, non-Gaussian noise often interferes with sensors in actual driving conditions, leading to decreased estimation accuracy and affecting subsequent stability analysis and control research. Traditional Kalman filters exhibit low accuracy and robustness under non-Gaussian noise conditions. This paper proposes the Maximum Correntropy Criterion Square Root Cubature Kalman Filter algorithm (MCCSCKF). By fusing signals from multiple vehicle sensors, the MCCSCKF processes non-Gaussian noise using the Maximum Correntropy Criterion and integrates it with the Square Root Cubature Kalman Filter to estimate the sideslip angle and yaw rate. The accuracy and robustness of the MCCSCKF algorithm are verified through comparisons with traditional Kalman filter variants under double-shift conditions at high-speed, low-adhesion, and fishhook conditions at medium-speed, low-adhesion in the MATLAB/Simulink co-simulation platform. Under high-speed conditions, the maximum estimation error of the sideslip angle using MCCSCKF is 0.003 rad, with a root mean square error (RMSE) reduction of 38.8%, significantly better than the UKF’s 90.5%. Under medium-speed conditions, the maximum sideslip angle error is 0.002 rad, with an RMSE of 12.2%. Finally, in real-vehicle tests using the VBOX-3iSL, the maximum sideslip angle error of the MCCSCKF is 0.0025 rad, with an RMSE of 9.7%, representing a 20.5% reduction compared to the SCKF, validating its superiority and robustness.

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.