The design of the floating valve plate effectively addresses the problem of cylinder block tilting at high speed in axial piston pumps/motors，aligning with the trend towards high-speed development of axial piston pumps/motors and gaining attention in recent years. However，there is still a lack of systematic dynamic modeling and dynamic characteristic research for the piston pump/motor system designed with floating valve plate，which limits the design of floating valve plate piston pump/motor products. A comprehensive parameterized dynamic model of the system that considers its detailed structural features is established for the piston pump motor system designed with floating valve plate. The study focuses on the laws of dynamic changes of the pressure in the high-pressure oil circuit and the auxiliary hydraulic chambers，and the model's correctness is verified through bench tests. The results show that the high-pressure oil circuit exhibits a "sharp drop and slow rise" characteristic of "sawtooth" pressure pulsations，which are intense. At a pump speed of 1 000 revolutions per minute （r/min），the average pressure in the high-pressure oil circuit at 20 and 40 MPa pressure levels results in pulsation amplitudes as high as ±1.5 and ±3 MPa，respectively. The fluid pressure in the auxiliary hydraulic chamber exhibits a dynamic change pattern of "rapid follow-up and slow decline，" meaning that once the auxiliary hydraulic chamber is connected with the high-speed rotating piston chamber，its fluid pressure almost immediately follows the piston chamber pressure changes without attenuation or lag. After disconnection from the piston chamber，it can still maintain the chamber pressure effectively.

The microstructure evolution and deformation behavior of resistance spot-welded joints of the two layer plates of 1500HS hot-formed steel sheets are studied in this paper. Through metallographic analysis，heat input distribution map，and alloy material property map，the microstructural changes at various positions relative to the weld nugget are analyzed. As the distance from the center area of the weld nugget increases，the microstructure of the welded joint can be divided into columnar crystal martensite，coarse-grained martensite，fine-grained martensite，ferrite-martensite dual phase microstructure and tempered martensite microstructure. Combined with Vickers hardness analysis，the differences in hardness under different organizational characteristics are clarified. The results show that the hardness decreases significantly in the ferrite martensite dual phase structure and tempered martensite region，which are the weak areas of the welding joints. Based on the experimental results of fusion size，maximum failure load，fracture surface macro-morphology，initial fracture location，and Vickers hardness of different plate thickness combinations，the influence of plate thickness and plate strength on the fracture mode，initial fracture location，and maximum failure load of the spot welded joints is explained.

The axle and suspension are critical components of vehicles. To achieve real-time simulation of the solid axle and various suspension structures of a commercial vehicle，the property-based modeling technical route is adopted in this paper. The axle's movement is decoupled into motion kinematics and ride dynamics，while the suspension characteristics are divided into coupled carrying characteristics，RC/PC guiding characteristics，and coupled K&C kinematic characteristics. Innovatively considering the pitch dynamic effect of the axle，the nonlinear dynamic coupling relationship of suspension between axles，and the K&C coupling relationship of suspension between axles，a dynamic model for commercial vehicles is developed to trigger the negative phenomenon of brake vibration. Additionally，a K&C testing method for the coupled suspension and a method for model parameter identification are proposed. Finally，the accuracy of the model is validated at the system level by comparing K&C test data with TruckSim model results. Inputting vehicle parameters into the UniTruck software for simulation and comparing the results with TruckSim simulation，the model’s effectiveness is verified at the vehicle level.

To improve the lateral stability of four-in-wheel-motor-drive vehicles，in this paper a chassis coordinated control strategy that integrates torque coordination and active rear steering is proposed. The strategy aims to track the ideal yaw rate and sideslip angle while effectively reduce the body roll motion. Based on the characteristics of the vertical reaction force generated by the in-wheel motor，the decoupled control for the longitudinal，yaw and roll motions of the vehicles is designed based on torque coordination. To decrease the effect of ignored nonlinearity and uncertainty in modeling lateral dynamics on the control performance，a disturbance observer-based model predictive control for chassis cooperative control is designed to estimate and compensate the nonlinearity and uncertainty. To verify the effectiveness of the proposed method，a hardware-in-the-loop test is conducted for the double lane change maneuver. The results show that the proposed control strategy can improve the lateral stability and reduce the roll motion of the vehicle body. Furthermore，compared to the control without disturbance compensation，the disturbance observer-based control reduces the tracking errors of the desired yaw rate and sideslip angle by 56.9% and 27.3%，and the body roll angle and roll rate by 8.9% and 12.5%，respectively.

Suspension control requires good balance between ride comfort and driving stability，while considering system uncertainties，which is a complex task. In this paper，a disturbance observer-based suboptimal-nonsingular terminal sliding mode switching control algorithm （DOB-SNTSM） is proposed，with considerations of suspension dynamic performance indicators，algorithm robustness，and cost factors. Firstly，using spring mass acceleration information as input and by Kalman filter design，effective estimation of suspension deflection and spring mass velocity is achieved. Subsequently，a disturbance observer is devised to estimate uncertainties within the suspension system，with the disturbance estimation serving as feedforward compensation. Next，based on the sliding mode surface function，a suboptimal-nonsingular terminal sliding mode switching control algorithm is proposed，integrating with the feedforward compensation from the disturbance observer to formulate a novel active suspension control strategy. Finally，simulation and bench tests are conducted on both convex road surfaces and smooth random road surfaces. The results show that the introduction of disturbance observers can significantly improve the ride comfort index of the suspension. Compared to the SNTSM algorithm with the classical sky-hook control，the ideal state LQR method and without disturbance observer，the new algorithm not only effectively balances various suspension performance indicators but also achieves control effect close to the ideal state LQR using solely spring mass acceleration information. Additionally，the controller switching scheme significantly enhances algorithm robustness.

The vehicle dynamics during turning-braking maneuver are more complex than those on the straight lanes due to tire sideslip，load transfer and other factors. In depth investigation of the braking allocation strategy for enhancing the vehicle tracking performance in this maneuver is of great significance for driving safety. In this regard，a dynamic braking allocation strategy within electro-mechanical brake （EMB） is further investigated in this study. Firstly，the 2-DOF-vehicle dynamics model is taken as a reference，and the minimum lateral force requirements for stable driving of the front and rear axles are solved based on the model predictive control （MPC） algorithm. Then，the maximum longitudinal force available for braking each wheel is obtained by solving the friction circle online. Moreover，the braking allocation ratios are calculated according to the obtained maximum longitudinal force to realize the optimal braking allocation，The simulation and test results show that the proposed strategy enhances the vehicle tracking performance in turning-braking by dynamically adjust the braking force allocation ratios according to the driving conditions，load status and road adhesion conditions of the vehicle.

In order to improve the path tracking ability and handling stability of in wheel motor driven electric vehicles，a novel coordination control strategy for active four-wheel steering （4WS） and direct yaw moment control （DYC） is proposed. Firstly，considering the path tracking performance and handling stability of vehicles，a shared steering model is established and on this basis，the 4WS control strategy based on non-cooperative Nash game theory is proposed. Secondly，in order to improve the lateral stability of the vehicle under extreme conditions，the vehicle state is divided into stable，transitional，and unstable regions based on the phase plane of the center of mass sideslip angle，and the DYC controller is established in each region. Then，in order to achieve coordinated control of rear wheel steering and direct yaw moment，the coordination controller based on fuzzy neural network is established between ARS and DYC. Finally，the CarSim/Simulink co-simulation platform and Hardware-in-the Loop （HIL） platform are used to conduct experimental verification under dual line shifting conditions. The research results show that the proposed control strategy can effectively improve the path tracking precision and handling stability of the vehicle under extreme driving conditions.

To consider both stability and trajectory tracking performance of autonomous vehicles operating in extreme conditions，a trajectory planning and control method based on autonomous drift is proposed. A neural network tire dynamics model is designed based on neural network to improve the accuracy of the traditional magic tire formulation. In order to further expand the stability boundaries under the extreme working conditions of autonomous vehicles，the drift stability boundaries are designed based on the tire saturation and maximum sideslip characteristics combined with the center-of-mass lateral deflection angle-transverse swing angular velocity phase plane constraints during drift，and the nonlinear model predictive control （NMPC） is used to plan a safe drift trajectory within a wider stability range，and the drift tracking control is carried out for the planned trajectory. The results of the joint simulation of Simulink/CarSim show that the method can fully utilize the advantages of drift motion to ensure that the vehicle does not go out of control under extreme working conditions，while accurately tracking the desired trajectory.

Due to the limitation of the computing power of the vehicle platform，there is an irreconcilable contradiction between the long-term control/prediction time domain and the short-term control step. In this paper，a necessary condition is derived to decouple the translational motion from yawing motion based on the time-scale separation. Consequently，the translational motion is regulated over an extended control horizon to generate a human-like tracking trajectory. The yawing motion is regulated based on a more accurate dynamic model and a shorter control cycle. In addition，model predictive path integral （MPPI） strategy is used to mitigate the computational burden of nonlinear motion planning through sampling-based optimization. Finally，a model predictive output regulator is proposed to solve the underactuated control problem in vehicle lateral dynamics and reduce steady-state errors in yaw angel. Theoretical analysis and simulation results show that the proposed method enhances computing efficiency，improves the parameters adaptability and steering smoothness and reduces the lateral jerk by an average of 50% in all driving scenarios.

Path tracking control is a key technology for intelligent vehicles. However，the existing vehicle tracking control methods mostly rely on more accurate vehicle control models，while actual vehicle control systems mostly have modeling errors，parameter perturbations and external disturbances，which significantly affect path tracking control accuracy. In this paper，a learning path tracking control method for intelligent vehicles considering unmodeled dynamics of vehicles is proposed. Firstly，a nominal model of the vehicle is established and a linear prediction model is used to approximate the compensation for the unmodeled dynamics of the vehicle to improve the accuracy of the vehicle model. Then，learning and updating of the parameters of the unmodeled dynamics are realized based on the principle of Extended Kalman Filtering. Next，learning Model Predictive Controller （LMPC） considering the unmodeled dynamics of the system is established. Finally，the effectiveness of the proposed method in improving the path tracking accuracy is verified by designing a joint simulation test with Carsim and Matlab/Simulink for multiple operating conditions and multiple groups.