In order to investigate the effects of Ionic Liquid (IL) additives on the tribological properties of the key pairs of internal combustion engine, nitriding cylinder liner and molybdenum spray piston ring under high-reinforcement conditions, the lubricating oil with 2% ionic liquid mass fraction is used to lubricate the pairs. By analyzing the surface morphology, composition and tribological chemical reaction products of the worn pairs, the paper investigates wear behavior and relates mechanism of pair under ionic liquid lubrication, and evaluates its influence on friction, wear and wear characteristics of pairs. The experimental results show that the ionic liquid additive can improve the tribological properties of nitriding cylinder liner and molybdenum injection piston ring pair. The ionic liquid has the best tribological properties at 180 ℃, which is related to the tribological chemical reaction of ionic liquid on the surface of cylinder liner and its products.

In order to meet the needs of charging safety, service experience of high-power DC charging piles and improve their power utilization, this paper proposes a flexible power allocation control strategy. Based on the topology of circular power allocation, the power allocation control timing and algorithm for charging start, charging in progress and release at the end are designed. The utilization rate of power nodes is improved by static and dynamic polling switching. To ensure stable operation of the system, the definition of minimum remaining required power is introduced, and the difference in remaining required power, the number of switching times in a single insertion gun, and the filtering time are comprehensively judged to avoid frequent switching. Verification result shows that this strategy can improve average power utilization rate from 1.76% to 2.24%, demonstrating significant optimization effect.

This paper systematically sorts out the generation methods of simulation test scenarios for autonomous vehicle, summarizes the latest research progress in the fields of autonomous vehicle simulation test scenario definition, scenario deconstruction, scenario generation based on data driven, and scenario generation based on mechanism modeling, and summarizes the relevant evaluation and application of test scenarios. Finally, the paper proposes that future research should focus on integrating the characteristics of Chinese driving scenarios, deepening the research on edge scenario generation strategies, and accelerating the construction of the standard system of scenario construction.

To achieve more efficient detection of small traffic sign targets under complex urban street background conditions, this paper proposes an improved YOLOv5s algorithm. This enhancement is achieved by incorporating a Convolution Block Attention Module (CBAM) Spatial Channel Attention Mechanism, an Adaptive Spatial Feature Fusion (ASFF) module, and an improved loss function for detection boxes. The validation results on the TT100K traffic sign dataset demonstrate that the proposed algorithm achieves a mean Average Precision (mAP) of 84.5% in traffic sign recognition.

To address the scarcity of multi-source heterogeneous data and insufficient scenario adaptability in current perception algorithm training and testing of autonomous driving, a typical scenario-based multimodal perception dataset is constructed. It contains 10 specific typical scenario segments, covering multimodal sensor data from LiDAR, cameras, and 4D millimeter-wave radar. The dateset provides annotation information for six categories of targets and offers detailed descriptions of data acquisition device configurations, including sensor parameters, calibration data, and a time synchronization processing scheme. By delivering scenario-specific driving context, the constructed dataset enhances perception accuracy in complex environments, thereby improving the safety and reliability of autonomous driving systems.

In order to study the seat safety of passenger cars in China, this paper analyzed the casualties of each seat in three collision modes based on China In-Depth Accident Study (CIDAS) (2011~2022) statistical data, calculated the fatal risk of passengers in the front and rear seats using the risk model, and used risk weighting model and geometric average model of the risk to calculate risk of each seat. The results show that the fatal risk coefficient of the front row is 1.18 compared with the back row. Taking the risk of 100% of the driver’s seat position as the reference standard, the risk of the passenger seat, the left rear seat, the right rear seat and the middle rear seat was 79.57%, 105.23%, 93.28% and 191.69%, respectively.

In the process of driving a vehicle, the complex and changing environment inside the vehicle, the change of lighting conditions and the diversity of drivers’ behavioral postures affect the detection and recognition of abnormal driver behavior. To address this issue, this paper proposes a driver abnormal driving behavior detection algorithm based on contrast learning. The paper firstly considers driver’s driving behavior detection as a binary classification task, and utilizes a contrast learning approach to compare driver’s normal driving with abnormal driving samples and to improve the performance of the model by contrasting loss functions. Secondly, the depth images right ahead and above the driver serves as inputs to solve the problems of complex in-vehicle environment to change the light intensity and blind spots in viewpoint by providing the depth information of the driver. Finally, 3D convolution is introduced in the lightweight network MobileNetV2, and the operation of channel blending is added to the convolution layer of each bottleneck structure to improve the accuracy of recognition. Test results show that accuracy of the proposed algorithm reaches 94.18% in the Driver’s Abnormality Detection (DAD) dataset and ROC AUC reaches 0.962, which shows the effectiveness of the algorithm in driver’s abnormal behavior detection.

In order to find the cause of sunroof water leakage, this paper systematically studies three types of sunroof water leakage modes based on the bottom-mounted sunroof structure, including water leakage between the body and the roof seal strip, water leakage between the roof seal strip and the sunroof glass, water leakage over the guiding gutter. The result show that the sunroof water leakage are related to factors like the sunroof seal strip, the sunroof guiding gutter, the sunroof drain pipe, the body and the assembly. The sunroof water leakage can be effectively solved by improving the form of the roof seal strip, compression load performance, coordination with environmental components and joint quality, improving the layout, structure and water conductivity of the guiding gutter, improving the form, layout and displacement of the drainage pipe, improving the flanging size and surface quality of the body, optimizing the assembly environment, techniques and tooling positioning.

The noise source and the noise transmission of the compressor NVH problem of the electric vehicle heat pump system are studied, and the improvement is made by optimizing the internal structure of the electric compressor, increasing the acoustic package and optimizing the air conditioning pipeline. The NVH test results show that the internal structure optimization can reduce the vibration excitation noise of the electric compressor while the addition of acoustic package and air conditioning pipeline optimization can reduce the compressor noise transmission. These enhancements contribute to a reduction in the noise level of the heat pump system during operation and the NVH performance of the vehicle.

To establish an effective electromagnetic radiation simulation prediction and design capability during the vehicle design phase, this paper investigates the generation mechanism of electromagnetic radiation from power cables in the motor drive system based on electromagnetic wave radiation theory, and proposes a vehicle finite element model for electromagnetic compatibility simulation. An electromagnetic interference model is built to simulate and predict the electromagnetic radiation emitted by the power cables. The accuracy of the model is validated by comparing the simulation results with actual measurement data. Utilizing this simulation model, the cable layout is optimized, thereby reducing the intensity of interior electromagnetic radiation. The findings indicate that this approach can identify the electromagnetic radiation risks associated with high-voltage cable routing during the design phase, thus avoiding costly modifications in the prototype testing stage. Furthermore, this method contributes to shortening the vehicle design and development cycle while reducing testing costs.