A new electromagnetic friction braking system was designed in an attempt to improve vehicle braking safety. Structure of the traditional braking system was optimized, and the auxiliary control algorithm of the brake was added to the Electronic Control Unit (ECU), and the working principle of the new braking system under different working conditions was analyzed, the relationship between braking torque at different speeds, the eddy current of the brake disc and the vehicle speed and the applied magnetic field was derived, and the vehicle dynamics model, electromagnetic brake model and vehicle power supply model were established. The joint simulation was carried out based on the Simulink and Maxwell platforms. The results show that on the basis of the traditional braking system, the braking distance can be significantly shortened and the braking performance can be improved by optimizing the electromagnetic brake. And the energy recovery device has successfully realized the recovery and reuse of the vehicle’s kinetic energy.

In order to realize the design of pressure-casting parts for passenger car body, this paper, starting from the type and characteristics of pressure-casting process, analyzed the application of high pressure vacuum casting in large pressure-casting parts of passenger car body, then it analyzed the design principles and requirements of pressure-casting in terms of wall thickness, stiffener and pin layout for the application location of the common use parts of pressure-casting parts of body, including key joints, important beam systems, main reinforcement parts, shock absorber tower, A-pillar module, rear floor assembly, front cabin assembly, etc..

This paper proposed a trajectory tracking control strategy that combined Model Predictive Control (MPC), Radial Basis Function (RBF) neural network, and Sliding Mode Control (SMC) to address the low accuracy of vehicle trajectory tracking caused by model mismatch and external environmental interference during the driving process of autonomous vehicles. By establishing a vehicle kinematic model predictive control, the expected yaw rate of the vehicle in the current state was calculated, and the deviation value from the actual yaw rate was input to the RBF-SMC controller. By utilizing RBF’s ability to quickly approach nonlinear models, combined with sliding mode control to output front wheel angles, the lateral trajectory tracking control of the vehicle was achieved. The simulation experimental results show that this method significantly improves trajectory tracking accuracy compared with traditional controllers, and exhibits good robustness under different driving conditions.

Nonlinear Model Predictive Control (NMPC) method-based motion control has attracted considerable attention in the field of autonomous driving. However, the steady-state error problem has not been comprehensively investigated, especially for nonlinear MPC. This paper seeks to solve the steady-state error problem based on Offset-Free NMPC (OF-NMPC) with a lateral-longitudinal coupling structure. The proposed OF-NMPC uses an Unscented Kalman Filter (UKF) to observe the states and disturbances and incorporates them into the prediction model and reference calculation to eliminate the steady-state error. One of the challenges of OF-NMPC is the need to use optimization methods to obtain reference values, which will obviously increase the considerable computational burden. Based on the appropriate simplification, we get the reference analytical solution without solving nonlinear optimization problems online in real-time. Simulation and real vehicle experiments show that the proposed OF-NMPC can effectively eliminate the steady-state error and improve the system’s dynamic performance.

In order to achieve accurate and stable lateral control, and improve the safety of vehicle autonomous driving and ensure the comfort of passengers’ experience, this paper reviewed the latest progress of lateral control methods for autonomous vehicle in recent years, including classical control methods and deep learning based methods, discussed the performance characteristics of these methods and their advantages and disadvantages in application, and prospected the development trend of lateral control methods for autonomous vehicle.

In order to eliminate fault of gearbox gear switch in use, this paper, by correlating with the faulty parts in the use of gear switch, analyzed oil intake problem caused by poor sealing of gear switch, switch on-and-off failure under actuating pressure as well as contact ablation, to further improve the performance of the gear switch. The verification results show than the proposed scheme can effectively solve the problems existing in the use of the gear switch.

To improve calculation efficiency of lane-keep control algorithm, a Model Predictive Control (MPC) algorithm for lane-keeping was constructed. A single-rail vehicle model was derived based on rigid body dynamics. The standard vehicle model was based on rigid body dynamics and considered lateral and longitudinal tire force characteristic. Based on that, a simplified vehicle model was derived by assuming zero slip angle and slip ratio. The simplified model formulated yaw rate using linear equations and eliminated the tire model, thereby reducing the complexity of the constraint equations in the MPC. Considering the tracking error, control input, and the cost item of its change rate as the objective function, the control effect of standard vehicle model and simplified vehicle model were compared and analyzed. The results show that the simplified vehicle model achieves similar control performances to the standard model in lane-keeping MPC and avoids the problem of being unable to solve the tire model when the vehicle speed is close to zero. Additionally, the simplified model significantly reduces the computational time required for MPC optimization.

In order to reduce the weight and cost, the topology optimization method based on variable density method was used to carry out multi-objective topology optimization of the battery bracket. The optimization results show that the mass of the optimized battery bracket has been reduced by 16.7%, and the structural performance meets the design requirements, which has been verified through vehicle road tests. The optimization process shows that topology optimization provides a fast and effective solution for lightweight design of structures.

To address the shortcomings of the traditional method of only considering dimensional chain tolerance calculation in the appearance matching of closure, this paper introduced the deformation of decklid caused by boundary condition changes into the tolerance calculation process based on the comparison of finite element analysis and bench measurement analysis. Based on the analysis of the influencing parameters of the appearance deformation of decklid and the corresponding tolerance calculation results, a combination of CAE analysis and tolerance calculation was used to improve the appearance quality matching evaluation method of decklid. This method supports for improving the appearance matching performance of vehicle closures during the development stage and online appearance matching adjustment.

To predict injury of the occupant in vehicle collision more rapidly and accurately, a training database for deep learning models was established based on frontal 100% overlap rigid barrier real-world collision data, and data preprocessing and features extraction were conducted. Deep learning models were constructed separately based on Long Short-Term Memory (LSTM), Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) neural network, and Temporal Convolutional Networks (TCN) for injury prediction training. The validation results show that the model prediction accuracy reaches 0.8579, 0.8209 and 0.9674, respectively, demonstrating feasibility of the proposed method.