Aiming at the stability issues of disributed drive electric vehicles attributed to the response discrepancies of in-wheel motors, such as loss of control, understeer and oversteer, the paper proposes a hierarchical vehicle stability control framework. The upper layer incorporates an additional yaw moment controller designed using Model Predictive Control (MPC), which computes the required corrective yaw moment online to track both the desired yaw rate and the sideslip angle at the vehicle's center of mass. The lower layer performs optimal four-wheel torque allocation while coordinating vehicle stability and energy efficiency. A co-simulation platform combining Simulink and CarSim is developed, and straight-line driving and double-lane-change maneuvers are selected for validation. To assess the effectiveness of the MPC-based controller, comparative simulations are conducted against a reference upper-layer controller based on Sliding Mode Control (SMC). The results indicate that the proposed hierarchical strategy effectively compensates for motor response discrepancies, enhances vehicle stability, and improves overall driving economy. The simulation results demonstrate that under the straight-line driving scenario, the proposed MPC strategy reduces the lateral deviation from 13.01 m to 1.71 m, achieving a control efficiency of 86.9%, while simultaneously decreasing motor energy consumption by approximately 6.8%. In the double lane change maneuver, the strategy effectively suppresses the oscillations of both yaw rate and sideslip angle. These results validate that the proposed hierarchical control strategy can effectively compensate for the impact of hub motor response discrepancies, thereby significantly enhancing driving stability and improving overall vehicle energy efficiency.

To address the issue of unstable motor output torque resulting from torque response discrepancies in Four-Wheel Independent Drive (FWID) electric vehicles, the paper proposes a drive antislip control strategy that accounts for motor response errors. On the basis of comprehensively analyzing the impact of the dynamic and steady-state responses of the motors on drive slip control, a target slip ratio tracking algorithm based on sliding mode control is designed, and the motor torque response errors are compensated at the control level. Through co-simulation of CarSim and MATLAB/Simulink, the proposed drive slip control strategy is verified under low-adhesion coefficient road and medium-adhesion coefficient road conditions. The results show that, compared with the drive slip control strategy that does not consider motor response errors, the proposed method increases the vehicle speed by 55.6% and 31.8% respectively to achieve a rapid start effect and effectively suppress the overshoot of the slip ratio in drive slip control, thus ensuring the safety and stability of vehicle driving.

To address the issue of severe drive wheel slippage in pure electric light-duty truck under operating conditions such as low-adhesion coefficient roads and full-throttle acceleration, a feedforward-feedback fusion anti-slip control strategy based on road adhesion coefficient is proposed. First, the road adhesion coefficient is calculated in real time based on the friction ellipse principle, and the basic driving torque that can be provided is pre-calculated in combination with wheel load. Simultaneously, the driving torque is computed based on the target angular velocity and actual angular velocity, and the feedforward and feedback components are superimposed for fusion control. A real-vehicle test is conducted on the pure electric light-duty truck under the full-throttle acceleration uphill condition. The results indicate that when the anti-slip function is disabled, the wheels experience severe slippage at the 4th, 11th, and 18th seconds, with the slip rate of the drive wheels exceeding 80%. When the proposed method is applied, the wheels slip at the 11th and 14th seconds, and the slip rate of the drive wheels is less than 60%, which significantly reduces the degree of drive wheel slippage.

Traditional Linear Quadratic Regulator (LQR) control in vehicle active suspension systems faces challenges in precisely directing optimization due to fixed weight coefficients, which fail to meet the demands of complex operating conditions. To address this issue, a permanent magnet linear motor actuator model, a seven-degree-of-freedom active suspension model and state-space equations are established. Multiple operating modes are defined according to different road profiles and vehicle speeds. The Analytic Hierarchy Process (AHP) is employed to determine weighted coefficients for the fitness function, and the Red-billed Blue Magpie Optimization (RBMO) algorithm is utilized to optimize the LQR weight coefficient matrices. Simulations under various operating conditions are conducted using MATLAB/Simulink. The results indicate that, compared with a passive suspension system, the proposed strategy improves vertical body acceleration, pitch angular acceleration and the dynamic deflection of the right front, left front, left rear, and right rear suspensions in comfort mode by 27%, 30%, 17%, 14%, 8%, and 15%, respectively. In safety mode, roll angular acceleration and the dynamic loads of the right front, left front, left rear, and right rear tires are improved by 41%, 15%, 18%, 15%, and 17%, respectively. In integrated mode, body acceleration pitch angular acceleration and roll angular acceleration are improved by 35%, 32%, and 48%, respectively. Further validation through CarSim/Simulink co-simulation demonstrates that the proposed control strategy achieves a notable level of performance improvement across different modes.

In order to improve smoothness and ride comfort of the vehicle, this paper proposes a Genetic Algorithm (GA) optimization method to address the subjectivity and limitations of performance optimization of Linear Quadratic Regulators (LQR) in the vertical dynamic system of vehicles. Firstly, a vertical dynamic model of the vehicle is established, an LQR controller is designed, and genetic algorithm is introduced for parameter optimization. The simulation results show that compared with passive suspension, the controller optimized by genetic algorithm demonstrates better control effect in performance index including body acceleration, suspension dynamic travel and tire dynamic displacement.

For the response delay and insufficient robustness of the anti-lock braking control in commercial vehicle Electromechanical Braking (EMB) systems, an anti-lock braking sliding mode control algorithm based on a triple closed-loop structure of braking motor clamping force, rotational speed, and current is proposed. A single-wheel vehicle dynamics model and an EMB actuator model are established, and a sliding mode controller is designed to track the target slip ratio in real time. In TruckSim/MATLAB co-simulation, the slip ratio is stably regulated around the target value, with small wheel-speed fluctuations and good braking consistency. Furthermore, a Hardware-in-the-Loop (HIL) test bench is developed, in which the ABS sliding mode controller and the EMB braking motor controller are implemented in a rapid prototyping unit and a motor control board, respectively. The experimental results show that the slip ratio can effectively track the target value and meet the ABS control performance requirements, thereby validating the effectiveness and engineering feasibility of the proposed control strategy in both simulation and HIL testing.

To investigate the jerk issue caused by mode transitions in hybrid electric vehicles under different clutch control and energy management strategies, a typical P2+Dual-Clutch Transmission (DCT) architecture is selected as the research object. A clutch fuzzy control strategy and an active mode transition optimization control strategy are designed based on the MATLAB/Simulink and AVL Cruise M co-simulation platform. The results indicate that the vehicle model utilizing the clutch fuzzy control strategy achieves superior driving smoothness while maintaining excellent dynamic performance. Furthermore, the active mode transition optimization strategy reduces the mode switching frequency by 25.66%, with only a marginal 0.97% increase in equivalent fuel consumption compared to the conventional rule-based energy management strategy. This approach successfully balances driving smoothness and fuel economy while meeting the dynamic performance requirements.