The front axle manufactured through forging has large dimension，and the stress on it is complex. It is difficult to lightweight due to the limitation of the I-beam structure. In this paper，the design method of hollow front axle is given，and the hollow rectangular structure with variable cross-section and wall thickness and a combination of leaf spring seats are used to achieve lightweight and improve bending and torsion resistance. The influence of wall thickness at the kingpin hole on the strength of the fist is revealed，and the appropriate wall thickness range of the fist and the height and width coefficient of the plugging hole is given，by the vertical and longitudinal working conditions of the finite element mode. The 1：1 hollow front axle sample with an axle load of 5 t is produced by using seamless steel pipes，which is 10.75% lighter than the forged front axle. The variation law of vertical displacement and axial stress changes on the lower surface of the shaft and the stress distribution at the edge of the sealing hole on the outer end face of the fist are revealed through stiffness tests under vertical and longitudinal working conditions，as well as static strength and fatigue life tests under vertical working condition. The results show that the stiffness and static strength of the variable cross-section hollow rectangular front shaft meet industry standards，and the fatigue life under vertical working condition is much higher than industry standards. It is indicated that the variable cross-section hollow front axle can improve performance and achieve lightweight.

To improve the lightweight level of electric cargo vehicles，a Cell To Frame （CTF） structure that integrates the frame and battery compartment is proposed in this paper. Firstly，a finite element model of the benchmark vehicle frame is established，and its static performance and free mode are calculated. The accuracy of the finite element model is verified through free mode experiments. Then，the fatigue life analysis of the frame is carried out using the nominal stress method in the time domain using the multi working condition combination fatigue load spectrum obtained from road sampling. Next，experimental design is conducted on the initial design of the CTF structure，which has been validated by finite element analysis，and a surrogate model is established. Finally，the global response search method is used for optimization design to obtain the optimal lightweight solution. The results show that after optimized design，the weight of the CTF structure is reduced by 139.95 kg compared to the traditional separation design of the frame and battery compartment，with a lightweight rate of 14.09%. At the same time，the mechanical properties and fatigue life of the CTF structure both meet the design requirements.

For the complex thin-walled structure design of the integrated front engine compartment of electric vehicles，a structure and performance collaborative design method based on topology optimization is proposed in this paper. The optimal load path under multiple load conditions is determined through topology optimization and feasibility design of the front engine compartment integrated die-casting component process is conducted. Material testing is conducted on the die-cast materials，and failure surfaces are developed based on stress triaxiality and Lode angle coefficient stress states. A simplified frontal collision model is constructed to assess the stiffness compatibility between the extruded longitudinal beams and the die-cast components within the integrated front compartment structure. The results show that the proposed design method can effectively obtain topological paths under multiple performance constraints，fully consider the feasibility of the front cabin structure process and structural performance matching，and reduce research and development cost.

Crashworthiness optimization is an effective way to achieve better passive safety protection performance of vehicles，but current optimization focuses on improving numerical response，while neglecting the control of a category response，namely，deformation modes. The deformation mode of key components is related to the effectiveness of vehicle force transmission path design. If an unsatisfactory deformation mode occurs in the optimization solution，the effectiveness of the optimization result cannot be guaranteed. Therefore，in this study a machine learning based deformation mode control optimization method is proposed to improve the crashworthiness index while ensuring that all samples in the optimization solution deform in ideal modes. Structural deformation is represented in the form of images，and deep learning auto encoder is used to extract deformation features and cluster them to identify different deformation modes. Then，machine learning prediction models based on Light Gradient Boosting Machine （LightGBM） are established for the identified deformation modes and numerical responses. Finally，the optimization is solved based on the machine learning prediction models. The proposed machine learning optimization method is validated using a full vehicle frontal collision case，and the results show that while improving the numerical crashworthiness responses，the deformation mode of the longitudinal beam is ensured to deform in an ideal mode. This study demonstrates the prospects of machine learning in improving the effectiveness of structural optimization.

In order to improve the traffic efficiency at the signalized intersections and the fuel economy of vehicles，a cooperative optimization method of traffic signals and speed of connected vehicles considering the human driver error is proposed in this paper. In the traffic layer，by transforming the traffic signal optimization problem into a sequencing problem to find the optimal sequence of vehicles passing through the intersections，the optimal control model for traffic signal optimization is constructed and a traffic signal optimization algorithm based on dynamic planning is proposed. In the vehicle layer，the optimal control model for vehicle speed optimization is constructed by considering the influence of driver error，and a speed optimization algorithm based on fast stochastic model predictive control for connected vehicles is proposed. The simulation and intelligent connected micro-vehicle test results show that the co-optimization strategy proposed in this paper can effectively alleviate the deceleration and stopping of vehicles at intersections due to driver errors，and further reduce the travel time，idling time and fuel consumption of vehicles.

Lamb waves，with the characteristics of long propagation distance，low cost，and good sensitivity to various damages，offer significant potential for studying the visually undetectable damage caused by low-velocity impact in carbon fiber reinforced polymer （CFRP） battery box. Although relative acoustic nonlinear parameters （RANP） have been shown to be effective in quantifying the degree of impact damage to composite materials，the mechanism by which damage affects them has not been explored. In this study，a combination of experimental and simulation method is used to study for the first time the effect of different impact damages on the propagation of Lamb waves in CFRP battery boxes. To this end，a geometric model of the battery box structure is first established. Then，impact tests are carried out on CFRP，and a simulation model for damage monitoring of CFRP battery boxes is built. Finally，the effect of delamination，matrix compression damage，and fiber tensile damage on the damage assessment parameters of CFRP battery boxes is studied. The results indicate that the established CFRP simulation model is reliable in calculation accuracy，with the RANP parameter being sensitive to the damage area of each mode，though not to the damage position in the thickness direction. Damage causes the Lamb wave to generate new frequency components during propagation. The calculation of the RANP parameter can thus analyze the degree of damage. When the degree of damage is low，the size of the RANP parameter depends more on the interlayer shedding damage，and once the damage exceeds a certain threshold，the size of the RANP parameter depends more on the intralayer damage such as the fiber damage of the CFRP. The research results have important guiding value for the structural-functional integrated design of automobile collision safety components.

Lattice mechanical metamaterials are widely applied in various protective structures due to their excellent mechanical properties and crashworthiness. Traditional lattice structures are often composed of periodically arranged regular porous materials. Inspired by the microcrystalline structures of metals，in this paper random grain boundary structures are incorporated into the design of lattice materials，then polycrystal lattice material specimens using 3D printing technology are prepared. Furthermore，crashworthiness studies are conducted based on the finite element models validated by experiments. The results show that compared to single crystal lattice materials，polycrystal lattice materials significantly improve specific energy absorption （SEA） at the same lattice angle，especially with 143% increase at the lattice angle of 30°. The crashworthiness of polycrystal lattice materials is influenced by grain size，intragranular lattice angle，and grain randomness. When grain size decreases，the energy absorption process becomes smoother，but excessively small grains may exacerbate fluctuations in the energy absorption process due to boundary effect. Polycrystal lattice materials with a 45° lattice angle and random lattice angles of 30°/60° exhibit stable energy absorption processes，and those with higher randomness in grain orientations show an even smoother energy absorption process. The novel polycrystal lattice mechanical metamaterials proposed in this paper can effectively enhance the crashworthiness of traditional lattice materials and provide guidance for the design and optimization of new lightweight lattice metamaterials.

In order to adapt to the development of urban electric buses and the needs of national energy conservation and environmental protection policies，a lightweight method for vehicle body structure is proposed in this paper based on sensitivity analysis of the static strength and rollover safety performance of electric buses. Firstly，a finite element model of a certain electric bus is established，and the static strength and rollover safety analysis of the body structure is conducted through the finite element method. Secondly，sensitivity analysis of the static strength and rollover safety of the vehicle body under bending and twisting conditions is conducted on the finite element model. Based on the sensitivity analysis results，the component plate thickness that is beneficial for lightweighting and has little impact on the static strength and rollover safety of the vehicle body is selected as the design variable. The size optimization is carried out with the goal of minimizing the mass of the vehicle body skeleton，and the constraint that the maximum von Mises stress of each material unit does not exceed its material yield strength. The optimization results indicate that the static strength and rollover safety performance of the vehicle body structure meet regulatory requirements，and the weight of the vehicle body frame is reduced by 3.8%.

In order to overcome the influence of network latency on the cooperative perception accuracy and simultaneously improve the point cloud feature expression capability，a cooperative perception method based on point cloud spatio-temporal feature compensation network for intelligent connected vehicles is proposed. Firstly，the point-to-pillar feature extraction method is used to process the raw point cloud data，and the local neighborhood features of the laser points are then spliced with pillar feature maps. Secondly，the temporal latency compensation module based on the PredRNN algorithm is designed to predict the point cloud features of historical frames received from the surrounding connected vehicles，so as to achieve the synchronization of point cloud features from two vehicles. Thirdly，the spatial feature fusion compensation module is utilized to aggregate the inter-vehicle point cloud features，and multi-resolution features are fused through the bidirectional multi-scale feature pyramid network. The output includes vehicle target geometry size，heading angle and other information. Finally，the test results on the V2V4real dataset and the self-collected dataset demonstrate that the detection accuracy of the proposed method is superior to classical cooperative perception algorithms. Furthermore，it exhibits good adaptability to various latency cases and the inference process meets the real-time requirements.

The goal of the structural design of a stamping die is to obtain the optimal structural configuration and the corresponding size parameters while considering both structural performance and die weight，which is difficult to achieve with a single topology optimization process. Therefore，a design method combining topology and size optimization for stamping die structure is proposed in this paper. The method avoids the complicated load mapping calculation step by adopting the node-to-node load mapping strategy，thus directly transferring the load distribution on the contact surface to the loading step in the static model. The relaxation coefficients of the structural performance in the topology optimization model are determined by the given performance evaluation index and the corresponding selection strategy so that the mechanical properties of the topology optimized dies are not weaker than those of the initial design while reducing the weight as much as possible. Finally，according to the optimal structural configuration obtained from the topology optimization，the corresponding structural parameters are determined by the multiple surrogate models-based size optimization method. The method is successfully applied to the optimal design of stamping dies for automotive structural components and its effectiveness is verified by comparing the results of the initial design，topology-optimized design，and topology size joint optimization design.