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.

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.

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.

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.

To enhance the passenger space and safety performance of electric vehicles, Cell to Body (CTB) technology is used to integrate batteries as structural components into the bottom of the vehicle body, which not only reduces the number of body components and connectors, but also helps to achieve the lightweight and range requirements of electric vehicles. Addressing potential collision safety issues and the risk of interrupted force transfer paths associated with the CTB structure, in this paper a frontal collision safety design process for vehicle bodies based on the CTB structure is proposed. The design method of "force decomposition-simulation analysis-test benchmarking" is adopted. Firstly, safety of the battery under frontal collision is ensured. Then, multi-level force transmission path is designed for optimization of vehicle body and the front structure of the vehicle is planned and designed based on the collision force value. The feasibility of the research method in this paper is verified through finite element simulation analysis and experiments, providing an effective design method for future vehicle body design and application.

Rain and snow often lead to slippery and low adhesion road surface, and the bottleneck of vehicle braking technology caused by it needs to be broken through. Among them, due to the differences in road adhesion in complex open-side road conditions, higher stability requirements are put forward for emergency braking control of multi-axle commercial vehicles. In order to improve the braking efficiency, the model free adaptive control (MFAC) algorithm is used to control the slip rate of each tire near the ideal value, and the electric and hydraulic coupling braking torque distribution strategy is set for distributed drive technology. In order to reduce the lateral errors in the braking process, PID-sliding mode observer (PID-SMO) is used to accurately observe the longitudinal force of each wheel, and the additional yaw torque caused by the longitudinal force difference is compensated by the middle and rear axle assisted steering. Through the joint simulation analysis, the emergency braking control strategy based on MFAC reduces the braking distance and avoids the slip rate fluctuation at the end of the braking period, ensuring the consistency of the wheel speed. The intervention of middle and rear axle steering greatly improves the lateral stability of the vehicle during braking.

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.

In order to realize the estimation of tire mechanical characteristics and the identification of tire models that do not rely on the physical sample of tires, and to accelerate satisfying the technical and accuracy requirements of tire virtual delivery, in this paper, based on the finite element software ABAQUS, a method for simulating the tire camber-turn-slip combined condition is proposed, and the influence of camber on the turn-slip is analyzed. Firstly, the tire finite element model is constructed, and the simulation accuracy of the model is verified by the bench test data, and simulation methods of tire camber-turn-slip combined using implicit solver is proposed. Secondly, According to the spin turn condition under the special case of turn-slip, the influence of the inclination angle on the spin turn aligning moment is analyzed. Finally, the camber-turn-slip conditions of tires with different load are simulated, and the influence of camber on the lateral force, aligning moment stiffness region and the whole area of the turn-slip mechanical characteristics are analyzed. It is concluded that the camber has a significant nonlinearity on the stiffness area of the lateral force and aligning moment, and affect the curve features of the decay rate of the lateral force, the curvature of the transition zone of the aligning moment and so on.

Most of the existing domain adaptive visual object detection algorithms are based on two-stage detector design and fail to exploit the semantic topological relationship between different elements in the image space, resulting in suboptimal cross-domain adaptation performance. Therefore, in this paper a domain adaptive visual object detection algorithm based on multi-granularity relationship reasoning is proposed. Firstly, a coarse-grained patch relationship reasoning module is proposed, which uses the coarse-grained patch graph structure to capture the topological relationship between the foreground and background and perform cross-domain adaptation on the foreground area. Then, a fine-grained semantic relationship reasoning module is designed to reason about the fine-grained semantic graph structure to enhance cross-domain multi-category semantic dependencies. Finally, a granularity-induced feature alignment module is proposed to adjust the weight of feature alignment according to the affinity of the nodes, thereby improving the adaptability of the detection model when facing overall scene changes. The experimental results on multiple cross-domain scenarios of autonomous driving verify the robustness and real-time performance of the proposed algorithm.

Torsional vibration of power system is a hot and difficult problem in NVH field of extended range electric vehicles. In order to investigate the torsional vibration characteristics of the range extender under electromechanical coupling, taking a diesel engine range extender as the research object, systematic quantification of the shaft system is carried out and a torsional vibration mechanical model of the eight-degree-of-freedom shafting system is established. A non-contact measurement method is used to conduct torsional vibration tests on the range extender platform to verify the accuracy of the model. The method of obtaining shafting structure parameters, electromagnetic parameters and excitation torque is discussed. The coupling calculation of the range-extender shafting is carried out and the comparison analysis with the original machine is made. It is concluded that the addition of the motor rotor system will reduce the natural frequency of shafting by 27.6Hz and the maximum amplitude by about 21%. The resonant speed is shifted forward by about 200 r/min on the basis of the original machine, and a natural frequency is increased in the first 12 steps. The influence of electromagnetic parameters on torsional vibration characteristics of shafting is analyzed according to the particularity of range extender working condition. The results show that the electromagnetic damping is linearly and negatively correlated with torsional vibration amplitude, but it does not change the natural frequency and resonant speed of shafting. The electromagnetic stiffness has no obvious effect on the amplitude of torsional vibration, and mainly affects the size of zero frequency, which will lead to low frequen-