The torque distribution strategy plays a crucial role in improving the safety and energy efficiency of distributed drive electric vehicles. In order to reduce the energy consumption of electric vehicles with dualmotor drive on the front and rear axles, a multiobjective torque distribution method based on a hierarchical control architecture is proposed in this paper, that comprehensively considers vehicle safety, handling stability, and energy efficiency. The upper layer is the active safety layer, which uses nonlinear model predictive control (NMPC) to achieve vehicle safety and stability control. The lower layer is the torque distribution layer, which considers the torque control of the front and rear axle motors under noload loss of the motor. The simulation results show that compared with the average distribution method, the proposed multiobjective torque distribution method can improve the vehicle's stability while ensuring safe driving, with the total energy consumption reduced by 6.6% and 3.5% under the NEDC and WLTC driving cycles, respectively.

With the rapid development of the electronic and electrical architecture of intelligent and connected vehicles, the demand for realtime reliability in invehicle communication networks has significantly increased. In this context, TimeSensitive Networking (TSN) has become a critical technology to meet the demand. In this paper, the implementation of the IEEE 802.1CB protocol in vehicular networks is realized, filling the gap in current research regarding the combined use of link redundancy transmission and routing planning. An innovative multipath routing strategy is proposed which balances network efficiency and reliability through dualpath transmission involving both primary and redundant paths. The core contribution of this study includes: (1) a novel NSGA2based primary path routing algorithm, which achieves the dual objectives of load balancing and low latency through intelligent path planning, and (2) an improved Dijkstrabased redundant path routing algorithm, which ensures highreliability transmission for information flows with varying priority levels. Finally, a hardwaresoftware integrated experimental framework is proposed, demonstrating that the proposed algorithms outperform existing comparison algorithms by 18.19% to 62.29% in terms of load balancing and endtoend latency, while also enhancing network reliability by 19.18% to 42.87%.

With the rapid development of automotive active safety technology, the chassis electronic control unit of modern electric vehicles has seen explosive growth. In order to improve the realtime performance and accuracy of chassis active safety control, for the rapid growth of chassis electronic control units and the coupling conflict problems of low integration degree of control system and multiobjective cooptimization, in this paper firstly a chassis system integration control architecture based on multiagent is established, and a hierarchical control system integrating the front and rear wheels' active steering system and the differential braking control system is proposed. Secondly, based on this, the state equations of each agent and its contribution to the vehicle's center of mass model are established and combined with the model predictive control to consider the characteristics of constraints. The cost function containing global state tracking error and local control effort is designed considering both the actuator constraints and the ground friction ellipse constraints. Finally, each agent realizes its collaborative control through the interaction of dynamic information of its respective contribution. The results show that the vehicle stability control method based on multiagent model prediction proposed in this paper has obvious improvement in terms of traverse stability compared with independent control of each active safety unit under the driving conditions of high and low road attachment and large curvature curves, which has certain value for engineering application.

For the problem of tire force estimation deviation caused by the change of mechanical properties due to temperature rise during the rolling process, the vertical force estimation correction algorithm of heavyduty tires based on thermalmechanical coupling is studied in this paper. A variable temperature mechanical tensile test is carried out to obtain the mechanical parameters of the tire shoulder rubber with temperature change, and a heavyduty tire thermalmechanical coupling model is established. The ground loading test and modal test are carried out to verify the accuracy of the model. The grounding characteristics and mechanical characteristics of heavyduty tires under the action of variable temperature vertical force are discussed, and the sensitive characteristics of the grounding parameters of the vertical force are analyzed, with the sensitive signal offset caused by the temperature rise during rolling corrected. A heavyduty tire vertical force estimation model based on the Gaussian regression process is established and the vertical force estimation accuracy before and after temperature correction is compared. The results show that when the sensitive characteristic value after temperature correction is used as input, the maximum error of the model under vertical force loading of 10~80 kN is 3.45%, with good vertical force estimation effect, and an improvement of the estimation accuracy by 9.17% compared with that before temperature correction.

In order to achieve customizable design and highfidelity intelligent driving simulation test perception data generation, an intelligent driving test scenario simulation architecture that integrates virtual and real perception data is established in this paper. By fusing simulated traffic subject perception data with real environment scene data, perception simulation data can be continuously generated with dangerous test scenarios as the target. On this basis, the RANSAC method is used to extract the position of obstacles in the real point cloud and determine the operating space constraints of simulated traffic subjects in the real environment scene at each moment. Then, in order to realize the interactive relationship between the behavior and position of the main vehicle and other traffic subjects in the test scenario, in the simulation software, simulation modeling and behavior design of the main vehicle and traffic subjects are conducted based on the real main vehicle sensor parameters and motion trajectories for output of continuous simulated traffic participant perception data. Finally, the mask replacement method and ray replacement strategy are used to perform virtual and real fusion on the image and point cloud data respectively, and the virtual and real fusion perception data of dangerous driving test scenes in different real environment scenarios are obtained. The simulation data is tested and verified. The results show that most scenarios in the real road collection data set have the ability to support simulation data injection. The injected simulated traffic subject behaviors can match the test scene requirements and have high authenticity. At the perceptual level, the injected simulated traffic subject and the real traffic subject have a similarity of 86.5% in the target detection algorithm confidence level. The proposed method can controllably inject simulated traffic subjects that meet test requirements into real environment scene data, and quickly and synchronously obtain virtualreal fusion images and point cloud data with high realism.

The development of lightweight car body technology has led to widespread usage of highstrength steel in automotive industry, which brings new challenges to the resistance spot welding (RSW) process in car body welding and manufacturing. The occurrence of common abnormal working conditions, such as electrode axis off normal (ON) can negatively impact the consistency of RSW process. In this paper, the influence mechanism of ON condition on spot welding process is revealed by comparing multisensor process signals, weld surface morphology, nugget size and joint formation process under standard and ON conditions. The results indicate that compared with the standard condition, the ON condition increases the initial contact area of the sheetsheet interface, which leads to a slower temperature rise of sheets, a later peak in resistance signal and a delayed nucleation time. In the welding process, the contact area of sheetsheet and electrodesheet interface increases, which leads to the decrease of dynamic resistance signal and heat generation, so the nugget size and electrode displacement signal are smaller than the standard condition. Furthermore, the larger contact area along the length direction leads to more heat generation, ultimately resulting in a larger nugget dimension and indentation size in this particular direction. This study can provide theoretical support for the optimization of highstrength steel resistance spot welding process in actual production environment and online quality monitoring of spot welding under complex working conditions.

In this paper, an adaptive wheelbase preview robust H control method is proposed based on vibration based road roughness recognition to address the impact of unknown road surface input on the control effect of active suspension. By collecting the vibration acceleration response of the wheels through real vehicle experiments, the longitudinal road surface roughness information is identified based on the vibration based road surface roughness detection method of the front wheels. A speed adaptive wheelbase preview method is designed to obtain the delay relationship of the road surface excitation received by the front and rear wheels of the vehicle, providing real vehicle data for the wheelbase preview control of the rear wheel suspension. On this basis, a multiobjective speed adaptive wheelbase preview robust H. control method considering motion constraints is designed, and the optimal solution of parameters in linear matrix inequality (LMI) is achieved through multiobjective genetic algorithm (MOGA) to improve control accuracy. The experimental and simulation results show that the method proposed in this paper can accurately identify road roughness information and effectively improve suspension performance indicators and vehicle vibration frequency, effectively suppress vibration within the frequency range sensitive to motion sickness, and balance passenger driving experience while meeting driving smoothness requirements. Meanwhile, this method also provides a new approach for vertical vibration control of multi axle vehicles.

To improve the handling stability of distributed drive electric vehicles (EVs) at high speeds on different road surfaces, in this paper an integrated control strategy for AFS/DYC based on hybrid model predictive control is proposed. Firstly, a piece affine tire model is constructed based on system identification methods. In conjunction with the vehicle dynamics model and the conversion relationship between propositional logic and linear inequalities, the vehicle system's mixed logical dynamic model is constructed. Then, an integrated control strategy for AFS/DYC based on hybrid model predictive control is designed. The strategy uses mixed integer quadratic programming to track target reference values for decisionmaking on additional yaw moment and additional steering angle, and constructs an optimized wheel driving torque distribution control strategy with the goal of minimizing tire load rate. Finally, a driverinloop handling stability test experiment is conducted on the CarSimSimulink cosimulation platform. The test results show that compared to the traditional model predictive control, the designed hybrid model predictive control strategy reduces the root mean square error of yaw rate and side slip angle by 31.61% and 19.51% respectively under highspeed double lane change conditions and the peak average torque amplitude of the four wheels is reduced by 24.27%.

Given the high exposure and risk of rainfall as a trigger condition for visual perception systems, various rainfall simulation tests are the main research methods. However, the realism of rain simulation of different testing methods impacts the confidence in test conclusions. In this study indicators are selected to quantify the impact of rainfall on machine vision from the aspects of image quality and object detection. Using the numerical range and trend of index changes under real rainfall as a benchmark, the comparative study of the realism of different rainfall simulation methods in the dimension of machine vision is carried out. Additionally, in this study 1 950 images of no rain and various levels of real rainfall are collected to construct a dataset, so as to obtain statistical patterns of the impact of real rainfall on machine vision. Two simulated rainfall test sites, three simulation software, and one generative model are selected for rainfall simulation tests to compare and analyze the realism of different types of rainfall simulation methods horizontally. The results show that, in terms of image quality, simulation software and rainfall simulation equipment can better simulate the real rain in terms of DR value range and trend. Regarding target detection, simulation software and generative model are closer to real rainfall in terms of CC change values. Overall, in terms of realism, digital simulation of rainfall performs best, followed by physical rainfall simulation on site and generative model, providing a reference for testing the SOTIF of the visual perception system of intelligent and connected vehicles.

In order to meet the requirements of large output force value, high working frequency and good linearity of forcedisplacement of electromagnetic actuator for active mounting, a multiobjective parameter hierarchical optimization method is proposed to solve the problems of different influence of different structural parameters on optimization objectives, difficulty of expression of dynamic electromagnetic force by analytical formula, and difficulty of realization of optimal characteristics at the same time of the output force value, working frequency and forcedisplacement. In the upper layer, Taguchi algorithm is used to preliminarily optimize parameters, screen sensitive parameters and update the optimization range of high sensitivity parameters. In the lower layer, the backpropagation (BP) neural network prediction model is used to characterize the dynamic electromagnetic force, and the multiobjective genetic algorithm (NSGAII) is used to search and optimize the dynamic electromagnetic force. Through simulation and experiments, the results show that the parameters of electromagnetic actuator obtained by the optimization method in this paper have better comprehensive performance, which verifies the effectiveness of this method.