To improve the control accuracy of intelligent vehicle tracking controllers in variable operating conditions, controllers generally use multidimensional control parameter tables based on operating condition characteristics. When engineers manually adjust multidimensional control parameter tables, the workload is large and the tuning effect is not satisfactory. In order to enable the tracking controller of dynamic parameter adjustment capability, in this paper a vehicle speed and curvature adaptive parameter tuner is proposed based on radial basis function (RBF) neural network. Besides, a training set construction method based on Monte Carlo Probabilistic Inference for Learning Control (MC-PILCO) algorithm is proposed to address the problems of excessive real vehicle testing interactions and heavy tuning workload encountered during the training of tuner. By grouping typical operating conditions based on vehicle speed in the construction process of the training set, all different curvature working conditions within each vehicle speed working condition group are trained using the dynamic model trained on the data collected from tracking the straight-line scene at that vehicle speed for parameter tuning. By sharing the model, the number of real vehicle interactions is reduced. Real vehicle experiments show that the parameter adaptive tracking controller proposed in this paper has better lateral trajectory-tracking performance compared to controllers with fixed parameters under medium and low speed conditions.

In this paper, the characteristics of driver out of position and active and passive fusion damage caused by AES are studied by using finite element method for several typical collision conditions caused by automatic emergency steering (AES) intervention. The results show that AES can cause significant lateral displacement of the driver, and the out of position degree increases slightly with the increase of initial speed. High HIC15 and BrIC values are easily generated in oblique angle and side-to-side collision conditions due to high speed and hard contact. The risk of craniocerebral injury in side impact is greater, and the strain of liver and lung is greater than that of other internal organs. Overall, AES intervention results in more significant head, neck, and chest injuries in oblique and lateral near-end collision.

Transformer-based models have made significant progress in Remaining Useful Life (RUL) prediction. However, existing Transformer models have the following limitation of difficulty in local feature extraction and failure to consider the importance of varying temporal and spatial input features. To solve the problems, in this paper, an enhanced two-stream Transformer model is proposed, which is reinforced by the local feature extraction module and the interaction fusion module. Firstly, the local feature extraction module captures local features from both the temporal and spatial streams to compensate for the Transformer's deficiency in local feature extraction. Then, the two-stream Transformer is used to extract long-term dependencies in the temporal and spatial dimensions, enhancing complementary learning between the two streams. Finally, the interaction fusion module is constructed to capture stream-level interaction using bilinear fusion, further improving prediction performance. Experiments using multiple models on two real-world datasets from a diesel engine manufacturer demonstrate that the evaluation metrics RMSE and Score are reduced by at least 3.23% and 5.89%, respectively.

Al-Si coated press hardening steels (PHS) with coating thicknesses ranging from 8 to 18 µm demonstrate enhanced toughness, drawing significant attention from the industry. However, there is limited evalua-tion of the resistance spot welding performance of Al-Si coated PHS with reduced coating thickness. This research compares the weldability of PHS with thin Al-Si coatings at strengths of 1 000, 1500, and 2 000 MPa. The results show that the weldability current range and mechanical properties of welds for all three grades of PHS meet industri-al production requirements. Further analysis reveals that the mechanical properties of the welds are closely linked to the strength and toughness of the martensite in the nugget. As the matrix strength increases, the strength (hardness) of the martensite in the nugget also rises, while toughness decreases. Consequently, the tensile-shear ultimate load increases with rising weld strength, whereas the cross-tensile ultimate load decreases as weld toughness diminishes.

Constructing accurate surrogate models is an effective solution to addressing the problem of multi-dimensional design variables and implicit nonlinear responses in the reliability design of complex structures. However, using experiment design based on a predetermined sample size to construct surrogate models may face challenges of inefficiency or insufficient accuracy. Therefore, an active learning PC-Kriging model for reliability analysis is proposed, which combines the advantages of Polynomial Chaos Expansion for enhancing global approximation accuracy and Kriging for capturing local features. The active learning strategy is utilized to adaptively select the optimal sample points to minimize the training sample size, reducing computational cost of structural performance analysis, and improving analysis efficiency. Further, an active learning PC-Kriging model-driven multi-software co-design framework is constructed. Secondary development of pre-processing and post-processing software is conducted to enable seamless integration of parametric modeling, performance analysis, and post-processing, forming a comprehensive automated analysis workflow. Finally, reliability analysis is performed using a battery pack structure as a case study to verify the efficiency and accuracy of the proposed method.

The lateral, longitudinal, and yaw motions of corner module vehicles can be planned and controlled relatively independently. However, the impact of the trajectory on the vehicles' yaw motion is not adequately considered by traditional trajectory planning methods. A polynomial-based pose trajectory planning method for corner module vehicles is proposed in this paper. Firstly, a quintic polynomial-based pose trajectory parameter model is established to generate pose trajectory clusters, Then, considering the road adhesion state constraint, kinematic model constraint, and sideslip angle constraint, the evaluation functions including lane-changing efficiency, lateral performance, yaw angle deviation, and yaw performance are established to generate the optimal polynomial pose trajectory as well as the optimal classical position trajectory. Finally, the two optimal trajectories are compared in high-way lane-changing scenarios, and the traceability of the polynomial pose trajectory is verified using MATLAB/Simulink and CarSim co-simulation. The simulation results show that the efficiency of lane-changing can be increased by the polynomial pose trajectory, and the vehicle's yaw comfort and stability can be substantially improved.

To study the impact of undulating road on driving stability of hazardous chemical liquid tank truck，the medium and long wave road models are constructed based on road roughness data. Taking a multi-axles liquid tank truck as the object，a vehicle dynamics model considering the tire deformation and suspension nonlinear characteristics is established. Equivalent mechanical models of the longitudinal and lateral sloshing of the liquid are established，which are coupled with the vehicle dynamics model. Kinetic parameters of the tank on different roads are obtained to excite the forced liquid sloshing model. The results show that the increase in the phase difference between the two sides of the road has an adverse effect on the lateral stability of the truck. The frequency of liquid lateral sloshing decreases with the increase of road wavelength and increases with the rise of liquid filling ratio. When the distance between the tractor saddle and the axle of the semi-trailer is an integer multiple of the road wavelength，there is significant longitudinal liquid sloshing. The oblique wave deflector can suppress lateral liquid sloshing，but the suppression effect is poor when the liquid filling ratio is low under small sloshing conditions. When the truck passes through uneven roads with a low filling ratio，increasing the vehicle speed appropriately can reduce the liquid sloshing. While the filling ratio is high，excessive vehicle speed will lead to a significant increase in liquid sloshing amplitude and wall load，reducing the driving stability of the vehicle.

In order to conduct more comprehensive safety analysis of electric cars in side pole collision scenarios，in the paper a locally refining scheme is used to build the finite element model of battery pack，which is applied to simulation at both the whole car and the battery pack levels. Based on accident statistics，the side pole collision responses of the car are examined by changing the impact positions，angles and collision speed，including the conditions defined in the national standards. Considering the high cost of the whole car simulation，a parameterized model of battery pack level is established by adjusting collision speed and mass compensation for large-scale side pole collision simulation. A fast prediction model based on the energy method is proposed for battery pack level collision safety，which can predict the deformation and mechanical failure risk in real time under different side pole impact conditions. The model is validated with an average prediction error of 3.22%.

In addition to the design and optimization of the catalyst layer （CL），the interface between CL and the microporous layer （MPL） also needs to be considered for the research of membrane electrode assembly （MEA）. In this paper，three different CL/MPL interface structures are fabricated to verify their effect on PEMFC performance and durability under simulated vehicle operating conditions. The performance test results show that the performance of the MEA sample obtained by introducing Nafion ionomers into the CL/MPL interface （MEA-Nafion） decreases slightly compared with the pristine sample （MEA-0） at high current density，whereas the performance of the MEA sample obtained by introducing Nafion ionomers into the CL/MPL interface followed by hot pressing （MEA-Nafion-HP） is basically the same as that of MEA-0. Specially，the durability test results under simulated vehicle conditions show that the voltage decay rates of MEA-0，MEA-Nafion，and MEA-Nafion-HP samples are 42.3，29.9 and 15.2 μV/h，respectively. In conclusion，the MEA durability can be greatly improved without affecting performance by optimizing the CL/PEM interface structure design.

In order to investigate the electrical response of proton exchange membrane fuel cell （PEMFC） stack under impact load，and to reveal the mechanical-electrical coupling mechanism of PEMFC stack，the mechanical-electrical coupling modeling method of the PEMFC stack under impact load is studied. A systematic investigation is undertaken to investigate the effect of impact velocity and direction on the electrical response of the PEMFC stack，based on the established mechanical-electrical coupling model of the PEMFC stack. The results show that the proposed method for modeling the mechanical-electrical coupling of the PEMFC stack can accurately simulate the inherent mechanical-electrical coupling characteristics within the PEMFC stack. The ohmic loss of the single cell inside the PEMFC stack increases as the shock load increases. Meanwhile，the impact load results in the formation of additional electrical contact between the gas diffusion layer （GDL） and the ribs of the bipolar plate，which causes a reduction in the average value of the current density on the surface of the GDL and deterioration in the distribution uniformity. This study has certain guiding significance for the modeling of PEMFC mechanical-electrical coupling and the study of electrical response under impact load.