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.

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.

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.

In response to the difficulty of silicon-based Insulated Gate Bipolar Transistor (IGBT) in meeting the high power density, low conduction loss and high heat dissipation requirements of electric vehicles, this paper reviews the latest research progress on Silicon Carbide-Metal Oxide Semiconductor Field-Effect Transistor (SiC-MOSFET) for automotive applications. By summarizing the characteristics of SiC-MOSFET in the application scenarios of electric vehicle traction inverters, DC/DC power converters and On-Board Chargers (OBC), this paper analyzes the current technical challenges of SiC-MOSFET in terms of cost, reliability as well as heat dissipation, and explores their future development trends in miniaturization, advanced packaging, multi-chip integration and cost.

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 evaluate and predict the Electromagnetic Compatibility (EMC) performance of DC/DC converter in the early stage of design, the mainstream electromagnetic compatibility “three elements” method is first used to analyze the main interference source and propagation path of DC/DC converter. Secondly, based on the high-frequency parameter theory of transformer, the parasitic parameter theory of Printed-Circuit Board (PCB) and the parameter extraction method of common mode chokes, the common mode interference of transformer, PCB wiring and common mode chokes are analyzed separately. The high-frequency equivalent model of transformer and PCB, experimental environment test benches and high-voltage filtering modules are established using Maxwell, HFSS, SIwave, and Q3D software in the ANSYS simulation platform. Finally, the integration of each module of DC/DC converter and the simulation analysis of conduction and radiation emission are completed in Simplorer software. The results indicate that the conducted and radiated interference exceeds the standard more severely in the Metal-Oxide-Semiconductor Field Effect Transistor (MOSFET) switching frequency band and its harmonics of the main interference source. The model simulation results are basically consistent with theoretical analysis and actual experimental results, and the simulation model has high accuracy.

With the introduction of deep learning technology in recent years, target detection algorithms for autonomous vehicle have made significant progress. This paper analyzes and organizes the traditional object detection algorithms and deep learning object detection algorithms currently applied in autonomous driving from the perspective of the development of object detection technology, analyzes milestone detectors, network structures and the latest detection methods, and explores the development direction of target detection technology.

Three types of IoV communication network test platforms of the 5th Generation mobile communication technology (5G), Enhanced Ultra High Throughput (EUHT) and Long-Term Evolution-Vehicle to Everything (LTE-V2X) are constructed based on the closed test field. And four test scenarios are designed, including the full-field performance test, dynamic and static performance test, Vehicle-to-Infrastructure (V2I) communication performance test and Vehicle-to-Vehicle (V2V) communication performance test. Communication delay, data packet delivery rate and throughput are used as evaluation metrics to verify and comparatively analyze the performance of the above three communication networks in typical application scenarios, such as varying communication distances, different vehicle speeds, V2I communication uplink, downlink and hybrid transmission, and end-to-end V2V communication in real-vehicle tests. Test results indicate that the three above networks generally satisfy the requirements of IoV applications in dynamic traffic environment, among which 5G demonstrates the superior performance, followed by EUHT, but in terms of deployment cost and complexity, LTE-V2X has a significant advantage.

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.