Most of the current vehicle route planning is based on the grid map planning method， which will greatly increase the amount of calculation when the search area is large. In contrast， the method based on visibility graph can reduce the amount of calculation during path search， but is greatly affected by the complexity of obstacles. For this problem， combining the SLAM and visibility graph methods， a simplified visibility graph construction and planning method is proposed in this paper. Firstly， the improved SLAM algorithm is used to generate point cloud maps， and dynamic obstacles are removed. Then a visibility graph is generated， and the complex edges of polygons in the visibility graph are simplified based on the size of the obstacle and the size of the concave angle at the vertex to eliminate redundant vertices. Finally， through simulation experiments and real vehicle experiments， it is proved that compared with the original algorithm， this method can reduce the number of polygon vertices in the visibility graph by 20%-30% while ensuring the accuracy of mapping. The map update time and the running time of the overall algorithm are also reduced by more than 30%. It shows that the method in this paper can effectively reduce the amount of calculation and the running time of the algorithm in the mapping and planning process.

It is crucial to develop a lightweight real-time online temperature prediction model for electric drive assembly （EDA） to effectively monitor its future abnormal temperature state in advance and ensure vehicle safety. Based on multi-physics coupling and data-driven fusion modeling， this paper proposes an online prediction method for the transient temperature field of EDA. Firstly， a multi-physical coupling finite element model of EDA electric-magnetic-thermal-flow multi-physics coupling is established， and the accuracy of the model is verified by bench test. Secondly， several transient temperature field datasets under normal working conditions are generated via multi-physical field coupling model for subsequent proxy model verification. Then， combined with the finite element model to obtain the simplified thermal network topology and the graph convolutional neural network， a relational spatial-temporal graph convolutional neural network prediction model driven by model and data is proposed. Finally， the effectiveness and real-time performance of the proposed temperature prediction model are verified by offline simulation and online test under different ambient temperatures and working conditions. Analysis results on the measured offline dataset show that the global prediction error and average absolute error are 4.4 and 1.25 ℃， reduced by 17.3%， 28.1%， 5.3% and 29.3%， respectively， compared with the conventional temporal graph convolutional neural network and gated recurrent unit. Meanwhile， the online prediction results of the bench are also very close to the real measured values， with the global prediction error and average absolute error of 3.99 and 0.66 ℃. In conclusion， the proposed real-time on-line temperature prediction method can accurately predict the real temperature change of EDA.

As an important stage of the automotive design process， conceptual design requires rapid conceptual design and evaluation. The current methods generally use a combination of parametric design and CAE to achieve analysis based conceptual design of car body structures. With the development and maturity of machine learning and deep learning algorithms， intelligent design methods will become the main innovative technology for body structure design. In this article， a combination of data-driven and optimization design method is used to independently develop the vehicle structure intelligent design software tool （S-iVCD）. Firstly， based on residual networks and thermal map regression algorithms， feature points of the vehicle body structure are extracted to achieve automated modeling of the conceptual model of the vehicle body structure. Secondly， based on Gaussian process sampling， a body structure dataset is collected and a fully connected neural network model is used to construct the body structure network model. The parameters of various components of the vehicle body can be input into the trained network model to obtain the overall performance results of the vehicle body. Finally， by combining data-driven computing with the moving asymptote algorithm， a multi-objective optimization design of the vehicle body structure that considers mass， bending stiffness， and torsional stiffness is quickly achieved. By comparing with finite element examples， the error of the calculation results is within the allowable range， with the optimization calculation time greatly shortened， and the lightweight rate reaching 7.4%. This indicates that the data-driven body structure optimization design method is effective in improving efficiency in the conceptual design stage of automobiles.

Electrically controlled air suspension （ECAS） has the function of adjusting suspension stiffness and body height， which can effectively improve vehicle ride comfort and handling stability. Taking a passenger car ECAS as an example， the viscoelastic damping characteristics of rubber airbag are described by fractional theory， and the thermodynamic model is optimized considering the equivalent damping and hysteretic characteristics， which is in good agreement with the experimental data， and the precision of the optimized air spring model is verified. On this basis， considering the longitudinal and lateral dynamic characteristics of the vehicle and the Dugoff tire model， a 14-degree-of-freedom vehicle ECAS dynamic model is established， and a Model Predictive Control （MPC） active suspension control method is proposed， with measurable variables as the input of the controller， to realize the active control under straight and turning driving conditions. Simulation and vehicle bench test show that the fractional correction model can well reflect the variable stiffness characteristics of ECAS， and the active suspension control strategy based on MPC can adjust the air spring stiffness in real time， control the body posture， and effectively improve the ride comfort and stability of the electric vehicle. The research method in this paper provides a new idea for vehicle suspension system modeling and active control.

The heat exchange effect of internal compressed air leads to strong thermal hysteresis and frequency correlation of air springs’ mechanical properties. Therefore， a thermal hysteresis equivalent mechanical model is constructed to describe the energy exchange process of compressed air inside air springs in this paper. Based on the rubber airbag modal， an air spring hysteresis mechanical characteristic model covering both rubber airbag hysteresis and compressed air thermal hysteresis is constructed， and an identification method for the key parameters of the model is provided. The experiments show that the maximum errors of the hysteresis loop and dynamic stiffness are less than 3.3% and 6.7%， respectively， verifying the accuracy of the hysteresis mechanical characteristic model. Finally， the inherent law of the thermal hysteresis of compressed air with frequency varying is revealed. The research results provide theoretical support for identifying the hysteresis nonlinear mechanism of air springs and its effective utilization.

A deep-learning LSTM-based POD model （LSTM-POD） based on long short-term memory （LSTM） and proper orthogonal decomposition （POD） is developed for the turbulent wake of the square-back Ahmed automotive general model. A high time-resolution reconstruction is achieved by establishing the mapping relationship between the POD modal coefficients of the non-time-resolved planar velocity field and the time-resolved velocity signals at a number of discrete points， and the effect of different time-step configurations， i.e.， the single time step （LSTM-Sin） and multiple time steps （LSTM-Mul） on the reconstruction results is compared. The results show that the LSTM-POD model has strong learning and generalization ability in time series reconstruction， In addition， LSTM-Mul considers temporal continuity and correlation， the reconstructed mode coefficients （lower order） and velocity fields of which are more consistent with the POD reconstructed results compared with that of LSTM-Sin. The deep learning model proposed in this study can alleviate the problems of high resource consumption and low computational efficiency in obtaining high time resolution flow field data through experiments and high-precision numerical simulation.

The requirement of low carbon and lightweight in the auto industry is growing now. The new mega-casting technology applied on vehicle body can better achieve weight， cost and emission reduction， and has become spotlight to automobile manufacturers. In this paper， the traditional steel front compartment of passenger car body is replaced by integrated die casting part， and lightweight design on the aluminum alloy integrated front engine compartment is conducted. The optimal load path for stiffness is obtained through topology optimization of the front cabin by SIMP method. Considering the castability of the front cabin， the draft direction， thickness size and position distribution of the ribs are designed. Frontal impact simulation is conducted according to C-NCAP2021 and the impact resistance of the integrated die cast body is improved through Taguchi experimental design method and response surface optimization. Simulation analysis is conducted on the optimized performance of the white body. Compared with the traditional scheme， the weight of the optimal design is reduced by 13.9%， with the bending stiffness of the BIW increased by 9.7%， and the first modal meets the requirements. The research in this paper is meaningful for the platform design and industrialized application of integrated die casting car body structure in the future.

Aluminum alloy multi-cell thin-walled tubes have better mechanical properties in energy absorption than ordinary square tubes in axial compression conditions， with a wide range of application prospects in automotive， aviation， military equipment， and other industries. To study the anisotropic characteristics of extruded 6061-T6 aluminum alloy material， uniaxial tensile mechanical properties tests are conducted on the sheet along the extrusion direction of 0°， 45°， and 90°. The corresponding stress-strain curves and anisotropic characteristic parameters are obtained， and the material constitutive model is established based on the yield criterion of anisotropic hardening behavior. Tubes with different cross-sectional configurations shaped as the Chinese characters of mouth， day and eye are designed and quasi-static crushing tests are conducted. By analyzing the deformation crushing force curve， it is shown that the thin-walled structure of the triple-cell alloy has superior crash resistance performance. In order to further obtain the optimal design parameters of the triple-shaped tube， considering the uncertain effect of material parameter fluctuations such as Poisson's ratio and elastic modulus on the structural impact resistance， the multi-cell aluminum alloy thin-walled tube impact resistance interval uncertainty optimization model is established. The interval possibility degree method is used to transform it into a deterministic problem. By combining the Artificial Neural Networks （ANNs） model with the Intergeneration Projection Genetic Algorithm （IP-GA） method， a double-layer nested optimization is performed on this problem to analyze the impact of different likelihood levels on uncertainty optimization results， providing guidance for the selection of different reliability optimization design.

In the process of electrification and intellectualization of the automobile industry，the automotive safety testing and evaluation technology has also been extended and expanded from simple passive safety to active and passive safety integration. In this paper，the differences between the world's mainstream automotive safety assessment procedures are compared and analyzed from three aspects： occupant protection in the vehicle，vulnerable road user protection outside the vehicle，and active safety. The key technical points of vehicle safety development for each evaluation condition are summarized and the development trend of safety evaluation procedures for new energy and intelligent networked vehicles is discussed. The research concludes that the mainstream automotive safety evaluation procedures are becoming more and more stringent in passive safety evaluation，with the proportion of active safety evaluation conditions gradually increasing，and the development focus of the future evaluation procedures will focus on the integration of active and passive safety and virtual evaluation for complex working conditions. In addition，the battery safety test for new energy vehicles has been relatively perfect，and the future research focus can be expanded to the direction of electronic control system testing，chassis stability testing，and unified standardization certification of charging and swapping facilities and supporting equipment. In the medium and long term，the construction of reasonable and reliable evaluation methods such as OTA （over the air） testing of intelligent networked vehicles and HMI （human machine interface） safety and comfort will become a major difficulty concerned by the industry，and a composite evaluation system combining the virtual and reality can be built with the help of tools such as autonomous driving simulators.

Collaborative lane change technology for intelligent connected vehicles has been widely studied，but existing strategies can hardly solve the problem of vehicle collaboration in mandatory lane change scenarios or may cause notable impact on upstream traffic. For mandatory lane change scenarios demand，a two-stage cooperative lane change strategy considering theoretical minimal safety space is proposed in this paper. Firstly，the control architecture for a two-vehicle cooperative lane change system is proposed and a collaborative lane change scheme is developed for mandatory lane change scenarios. Then，a two-stage receding-horizon trajectory planning strategy of spacing adjustment and collaborative lane change is designed，where the theoretical minimum safe distance is embedded as a constraint of spacing adjustment stage，to solve the problem of conservative spacing strategies in existing research. Finally，numerical simulation and hardware in-loop experiments are performed to verify the effectiveness，advantages and computational real-time performance of the proposed strategy. The results show that the proposed strategy can effectively improve the success rate of lane change，reduce the negative traffic impact while ensuring lane change safety，and is also applicable in real time computing and communication environment of actual edge cloud platform.