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.

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.

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.

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.

To address limitation in prediction accuracy and data utilization efficiency of supervised learning-based trajectory prediction models， a trajectory prediction model and a general self-supervised pretraining strategy are proposed. Firstly， a lightweight trajectory prediction model based on Transformer is established to extract temporal-spatial features while modeling interaction relationship. Secondly， three types of masks， namely motion information temporal mask， road information spatial mask， and interaction relationship mask， are designed for self-supervised pre-training tasks on the model to enhance the model's ability to extract general scene features. Finally， pretraining weights are used as initialization parameters for supervised learning fine-tuning in downstream tasks. Experimental results on the Argoverse2 Motion Forecasting dataset show that the model can effectively reconstruct traffic scenes in pretraining tasks. The introduction of self-supervised pretraining improves prediction accuracy and data utilization efficiency. Moreover， it exhibits universality for different prediction tasks， achieving a 3.3% and 3.7% improvement in the minFDE₆ for single-agent and multi-agent trajectory prediction tasks， respectively.

In recent years， with the continuous enhancement of fuel cell power， hydrogen supply systems have been evolving towards blind-end anode topologies with hydrogen circulation. However， research on testing systems for hydrogen supply and circulation lags noticeably， particularly in the performance testing of core components such as hydrogen circulation pumps and injectors. Therefore， a multifunctional testing platform for fuel cell hydrogen supply systems is developed in this paper， enabling component testing， characteristic data acquisition， offline calibration， and other functionalities for hydrogen circulation systems with different configurations. The platform， by simulating the pressure drop， hydrogen consumption， and the production of water and heat in real fuel cells， mitigates the additional cost incurred by testing on the performance and lifespan of actual fuel cells. Ultimately， based on this platform， an anode pressure control and anode purge control test are conducted on the hydrogen supply system of a 150 kW fuel cell， verifying the capability of the developed testing platform to meet specific testing requirements for different loads.

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.

Accurate performance evaluation of power battery cells is of great significance to ensuring the safety of power batteries. For the existing data-driven battery fault diagnosis algorithms， mostly individual cells are compared with each other and the outlier cells are identified as faulty cells by classification， based on differences in characteristic parameters such as single cell voltage. However， if there are multiple cells of similar abnormally performance in the power battery pack， or all individual batteries show an overall performance deterioration， it is difficult to distinguish individual cells or even there is no significant outliers， and the application of the mutual comparison strategy is limited. A power battery fault diagnosis method is proposed based on 1dCNN-LSTM to quantify the abnormality of a single cell in this paper. Combining the three types of characteristics of vehicle motion status， drive system status and power battery electrical signal， the 1dCNN-LSTM fusion model is established to estimate the individual cell voltage under ideal conditions as reference. The difference between the real-time voltage reference value and the measured voltage value is used to quantify the abnormality of each cell. Combined with actual cases， it is shown that for thermal runaway case due to single cell failure， the abnormal performance of the faulty cell compared to others can be identified 7 days before accident， and potential risk can be recognized in discharge processes from a year of more before the accident. For overall deterioration cases without obvious individual cells inconsistency， the deterioration evolution within the last 7 days can be tracked.

To ensure the reliability of aluminum-plastic film encapsulation for lithium-ion batteries， strict control of the aluminum layer thickness after forming is necessary. However， obtaining the thickness relies heavily on physical experiments， resulting in high cost for both early design optimization and later production process quality monitoring. In this paper， a combination of physical experiments and simulation modeling is adopted to establish a constitutive equation that can well characterize the mechanical properties of the pouch during forming. Additionally， a prediction method for the aluminum layer thickness based on the overall aluminum-plastic film thickness is proposed， enabling precise prediction of the aluminum-plastic film and aluminum layer thickness after forming. Furthermore， based on simulation Design of Experiments （DOE）， key influencing factors are screened to construct a response surface model， facilitating rapid prediction and optimal parameter matching design for different products， which also provides a solution for online monitoring of forming quality during production. The results show that the multi-layer composite aluminum-plastic film exhibits obvious anisotropy during the plastic stage. The 3-parameters Barlat-Lian constitutive model effectively represents the anisotropic properties of the film， and outperforms the single-directional elastic-plastic model， achieving accurate prediction of the aluminum-plastic film performance after forming. The constructed response surface model can replace the refined finite element model， and have excellent prediction accuracy for the thickness of the composite aluminum-plastic film and the aluminum layer， with an error less than 5%. By optimizing the process parameters， the formed thickness of the aluminum layer can be increased by 10%~20%. The integrated development application APP can meet the requirements for quick design evaluation， parameters optimization， and online monitoring of the forming quality.

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.