To improve the thermal management efficiency of vehicular lithium-ion batteries, Nano-Graphite (NG) and Nano-Aluminum (NA) were added to Paraffin (PA) to prepare Nano-Composite Phase Change Materials (NCPCM). Based on combinatorial mathematics modeling, the influence of different mass ratios of NCPCM’s physical parameters such as thermal conductivity and latent heat of phase change on battery thermal management efficiency was studied. Experimental analysis of thermal management of car lithium-ion batteries was conducted using RGO/BN/PA CPCM, RT44HC/CF CPCM and NCPCM3 under slow charge to fast charge conditions. Results show that at a 3.5 C discharge rate, NCPCM3 with a thermal conductivity of 2.1 W/(m·K) and a latent heat of 206.18 J/g could reduce the maximum temperature and maximum temperature difference of the car lithium-ion battery pack to 44.2 ℃ and 4.4 ℃ respectively. Further improvement of the thermal performance parameters of NCPCM did not significantly improve its thermal management performance. NCPCM3 shows significantly better battery thermal management efficiency than RGO/BN/PA CPCM and RT44HC/CF CPCM, ensuring normal operating temperature for the car lithium-ion battery pack.

To restrain the fluctuation of high frequency noise in the vehicle caused by The uncertainty of the parameters of the acoustic package, this paper proposed an uncertainty analysis and optimization method based on sub-interval modified perturbation method for high frequency noise of automobiles. Firstly, the basic equation of statistical energy for the analysis of high frequency noise was established. Secondly, the uncertainty parameters were divided into several sub-intervals, and the perturbation radius of each sub-interval was calculated by using the modified interval perturbation method to obtain the center value and the perturbation radius of the high-frequency noise performance. Finally, the high frequency noise performance and parts quality of the vehicle were optimized by the cross-interval uncertainty optimization method. This method was applied to analyze and optimize the interior front circumference and carpet of a vehicle. After optimization, the weight of acoustic package parts decreases by 3%, the noise fluctuation range decreases by more than 35%, and the robustness of the system is significantly improved.

To study the sound quality of vehicle in rainy day driving, objective parameter calculation and subjective evaluation were conducted for the sound quality of the vehicle cockpit aerodynamic noise signals in the wind-rain field, with a correlation analysis between the two parameters. Based on the Improved Whale Optimization Algorithm-Back Propagation (IWOA-BP) algorithm, six objective parameters including loudness, roughness, jitter, speech intelligibility, speech interference and sound pressure level as input, and subjective scoring as output were used to establish a prediction model, which was compared with the traditional BP neural network prediction model and the WOA-BP prediction model. The results indicate that the mean absolute percentage error of BP, WOA-BP and IWOA-BP algorithms are 28.33%, 6.35% and 2.82% respectively, proving that the sound quality prediction model of automobile cockpit aerodynamic noise in wind-rain field established based on IWOA-BP algorithm has a higher accuracy and a better effect.

To address the issue that eliminating a peak value of sound pressure level in the noise transfer function during the optimization of vehicle body noise transfer function for vehicle preparation can easily lead to new peak values of noise sound pressure level, an optimization method for vehicle body noise transfer function with the introduction of control variables is proposed. Taking the cab of a certain model as the research object, an optimization model is constructed, with the thickness of the structural panels in the cab as the variable. The proposed algorithm is used to iteratively optimize the relevant parameters. The results show that this method effectively reduces the peak value of sound pressure level in the noise transfer function within the target frequency band.

In order to improve the ability of the collision warning system to perceive the surrounding environment, this paper proposed a collision warning system based on YOLOv5 and hazardous area judgment. Firstly, the discriminative ability and accuracy of the model were improved by the channel attention module, then, the extraction ability of the model for multi-size features was improved by using path aggregation network and spatial pyramid pooling, and finally, the warning accuracy of the warning system was improved by filtering relatively safe targets through the introduction of warning activation regions. The results show that the introduction of warning activation regions improves the accuracy, precision and recall of the warning system by 20%, 50% and 26.7%, respectively, the running speed is increased by 49.1%, which further proves the effectiveness of the method.

To assess the safety risks of autonomous vehicles during cut-in scenarios on congested Chinese highways, 64 cut-in samples were extracted from a natural driving dataset. Employing a six-level model and correlation analysis, the static and dynamic factors of the scenarios were defined. Subsequently, 1 000 test cases were randomly generated through sampling, and a safety assessment index system was established to analyze the safety of vehicle operations. Lastly, the random forest algorithm was applied to identify the key factors triggering risks. Results indicate that risk scenarios account for 5.3% of the total, with longitudinal relative velocity identified as the crucial factor. Under congested conditions, a high-risk cut-in scenario is formed when the speed of surrounding vehicles is 23% lower than that of autonomous vehicles, this indicator serves as a crucial predictive measure for identifying collision risks in congested cut-in scenarios for autonomous vehicles and may be applied in determining liability of accident in such scenarios.

Based on THUMS digital human model, this paper studied the occupant injury characteristics of rear attitude frontal collision in a large tilt seat and discussed the mechanism of occupant injury. As the result shows, compared with regular sitting posture, on the one hand, the traditional restraint system cannot realize the effective restraint on the occupant under the reclining posture, which easily causes injury of occupant’s abdomen, thorax and neck due to descending of occupant. On the other hand, the occupant’s spine is subjected to a large axial acceleration load, and there is a risk of injury due to the seat cushion. Finally, based on the characteristics and mechanism of occupant injury, the paper also discussed the strategies on occupant protection in a large seat.

In order to improve the safety score of the driver’s side airbag under the side pole collision condition, the model of occupant’s restraint system in the vehicle side pole collision was built according to the 2021 version of C-NCAP, and the vehicle collision and simulation results show that the safety scores of the head, abdomen and pelvis all reach the standard, and the chest injury is more serious and the score is lower. By optimizing the pouch shape and stomatal diameter of the side airbags, the chest rib safety score is improved. The optimization results show that the compression of the upper rib, middle rib and lower rib of the chest decreases by 30.97%, 8.26% and 13.71%, respectively, which verifies the effectiveness of the optimization scheme.

In order to investigate the effects of diverse occupant out-of-position postures on occupant crash injuries, a variety of typical occupant out-of-position postures were considered, and 1 800 frontal crash simulations were conducted with the help of a mature occupant compartment model and dummy model. The results show that the occupant will suffer more injury in typical out-of-position posture than the standard posture; the increase of seatback angle leads to the increase of occupant head and neck injury level, while the decrease of thorax injury level; the increase of braking intensity of Automatic Emergency Braking (AEB) system increases the occupant thorax injury level, while decreasing the head and neck injury level; the occupants’ head and thorax injury level is greater in the rightward offset condition than in the leftward offset condition.

In order to explore the working principle of legform impactor calibration test, this paper compared in detail the structural differences between advanced Pedestrian Legform Impactor (aPLI) and Flexible Pedestrian Legform Impactor (FlexPLI), summarized the methods for determining of dynamic calibration limits and static calibration limits of legform impactor as the mixed group mean estimation method and the three-point bending deflection test method respectively. The paper also summarized and compared the significant differences between dynamic calibration limits and static calibration limits of the two types of legform impactor, and analyzed the immanent cause for these differences. The main cause for the difference in dynamic calibration limits was the addition of mass module to simulate human upper limbs in aPLI, while the main cause for the difference in static calibration limit was the structural changes in the leg structure and knee structure of aPLI.