Thin-walled tubes have gained wide applications in aerospace, automobile and other engineering fields due to their excellent energy absorption and lightweight properties. In this study, a novel method of entropy-weighted TOPSIS was adopted to study the energy absorption characteristics of a thin-walled circular tube under axial crushing. Three types of thin-walled circular tubes, namely, aluminum (Al) tubes, carbon-fiber-reinforced plastics (CFRP) tubes and CFRP-Al hybrid thin-walled tubes, were fabricated. Quasi-static axial crushing tests were then carried out for these specimens, and their failure modes and energy absorption performance were analyzed. The CFRP material parameters were obtained through tensile, compression and in-plane shear tests of CFRP laminates. The finite element models for the quasi-static axial crushing of these three types of circular tubes were established. The accuracy of the finite element models was verified by comparing the simulation results with the test results. On this basis, the effects of the geometric dimension and ply parameters of a CFRP-Al hybrid thin-walled circular tube on the axial crushing energy absorption characteristics were studied based on an orthogonal design and entropy-weighted TOPSIS method. The results showed that Al tube thickness, CFRP ply thickness and orientation have great effect on the energy absorption performance of a CFRP-Al hybrid thin-walled circular tube, whereas the tube diameter and length have little effect. The energy absorption capability of a CFRP-Al hybrid tube can be improved by increasing the thickness of the Al tube and the CFRP tube as well as the number of ±45° plies.

Ride comfort and handling performances are known conflicts for off-road vehicles. Recent publications focus on passenger vehicles on class B and class C roads, while, for off-road vehicles, they should be able to run on rougher roads: class D, class E, or class F roads. In this paper, a quarter vehicle model with nonlinear damping is established to analyze the suspension performance of a medium off-road vehicle on the class F road. The ride comfort, road holding, and handling performance of the vehicle are indicated by the weighted root mean square (RMS) value of the vertical acceleration of the sprung mass, suspension travel, and tire deflection. To optimize these objectives, the genetic algorithm (GA), particle swarm optimization (PSO), and a genetic algorithm based on the particle swarm optimization (GA-PSO) are initiated. The efficiency and accuracy of these algorithms are compared to find the best suspension parameters. The effect of the optimized method is validated by the field test result. The ride comfort, road holding, and handling performance are improved by approximately 20%.

Lateral stability is vital to vehicle handling stability and traffic safety. It is also a crucial factor for the path-tracking ability of the vehicle in the intelligent transportation system (ITS). Most of the research focuses on vehicles with an Ackerman steering system. The ASV (articulated steering vehicle) has a lower steering radius. Thus, it is widely used in some special vehicles, such as mining and construction vehicles. The ASV has weaker lateral stability than the vehicle with an Ackerman steering system. To improve the stability of an ASV, the nonlinear lateral dynamic model is established and validated by field test. With the lateral dynamic model, the steering characteristic of the ASV is analyzed. Based on the stability criteria analysis, the vehicle sideslip angle and angular velocity phase portrait are chosen as the stability indicator. An integrated AASS (active articulated steering system) DYC (direct yaw control) controller based on the adaptive MPC (model predictive control) method is designed according to the track on the phase plane. The double lane-change and 0.7 Hz sine with dwell maneuver are initiated based on the integrated vehicle dynamic model. The results suggest that the provided controller has a better stability performance than the current antiwindup PID control algorithm. It lays a good foundation for the vehicle safety and path tracking of ASV in the ITS.

A vehicle equipped with an articulated steering system is often used in a particular area, such as a mine or the construction field. These sealing or semi-sealing fields make autonomous driving by an ASV (Articulated Steering Vehicle) easier than in passenger cars. Path-tracking is essential for an autonomous vehicle. To improve the path-tracking ability of an ASV, a path-tracking model is established based on dynamic theory. Then, the slide-mode predictive-control (SMPC) strategy is initiated to design the path-tracking controller. The improvement in the path-tracking ability on roads with different adhesion coefficients is validated via the HIL (Hardware in Loop) platform. According to the simulation in HIL with the provided strategy, the path-tracking error during the typical condition decreased by 21.73~93.84%, demonstrating the SMC controller can be used to improve the path-tracking performance of an ASV.

With the development of automated vehicles, researches related to automated vehicle platoon (AVP) have received more and more attention. AVP is considered one of the effective means to alleviate traffic congestion and reduce vehicle energy consumption. This paper studies a three‐layer method of avoiding obstacle vehicles in traffic by switching the formation for the automated heavy vehicle platoon. In the decision‐making layer, a decision‐making system based on the finite‐state machine is established for formation switching. In the second layer, the lane‐changing trajectory is optimized based on the quantic polynomial curve fitting for vehicles that need to change lanes. In terms of vehicle control layer, each vehicle has a longitudinal controller based on sliding mode control and a lateral controller based on model predictive control to track the planned trajectory to complete the target formation. Finally, the proposed method is simulated in MATLAB/TruckSim. The simulation results show that the proposed method could effectively avoid the obstacle vehicles by switching the formation and has a small average value of errors in speed tracking and trajectory tracking.