The pre-chamber ignition system can enhance the lean mixture combustion， which will then improve the thermal efficiency significantly. Based on a self-developed active pre-chamber and its fuel supply system， the effect of premixed gas injection pressure and fuel ratio on combustion characteristics at the lean boundary of an engine with a compression ratio of 16 is respectively investigated. The experimental results show that among the three kinds of premixed gas injection pressure of 0.19，0.14 and 0.09 MPa， the indicated mean effective pressure is the highest， and the ignition delay time and combustion duration are the lowest at the injection pressure of 0.09 MPa， with the most stable combustion. When the proportion of premixed gas fuel increases from 0.54% to 2.69% gradually， the engine's indicated mean effective pressure （IMEP） increases first， and then decreases， reaching its maximum at a ratio of 1.61% when the combustion is most stable and both the ignition delay period and combustion duration are the shortest. The active pre-chamber can realize the stable combustion with a λ of 1.8. Indicated thermal efficiency increases from 32.9% to 39.4%， a relatively increase of 19.8% compared with that of spark plug ignition.

As global emission regulations and energy-saving policies become increasingly stringent， gasoline engines are facing significant challenges. The urgent technical challenge is to achieve high efficiency and ultra-low emission of gasoline engines. The pre-chamber turbulent jet ignition is one of the most promising technologies for improving the thermal efficiency of gasoline engines and reducing pollutant emission. In this paper， the influence of lean combustion limit expansion and ignition timing on the optimization of thermal efficiency is investigated systematically through three-dimensional flow simulation analysis coupled with a detailed chemical reaction mechanism. The results show that the passive pre-chamber can effectively expand the lean combustion limit， improve the thermal efficiency and reduce the pollutant emission of the engine in comparison with the spark ignition. At an excess air factor of 1.5， the maximum indicated thermal efficiency is 47.24%， which is 11.89% higher than that of the original engine， with the NO _x and Soot reduced by 29.27% and 98.76%， respectively.

With the increase of new energy vehicles in the market and battery energy density， thermal runaway events gradually increase. Battery safety issues become particularly important， whereas leakage is one of the key factors inducing battery thermal runaway. In this paper， the influence of leakage on electrical performance and safety is studied by simulating leakage at the cells and modules. At the same time， based on experimental data and remote vehicle data， the characteristics of the leaked battery are extracted and the warning logic is established to achieve online monitoring of the leakage warning. For cells test， a comparative analysis is conducted on the test data of leaking and normal battery cells under cyclic and static states. It is found that compared with normal cells， leaking cells show mass reduction， thickness increase， capacity fade， DC internal resistance increase and dismantling characterization abnormity， which proves that leakage has certain impact on the electrical performance and safety. For module test， characteristics of the thickness and DC internal resistance changes from the parallel units with different leakage degrees are studied. It proves that the thickness and DC internal resistance increase with the increase of leakage degrees， which also augments the potential safety risk of the battery. For vehicle level big data， the pressure difference characteristics of the leaked battery during the starting and ending stages of charge are identified to establish warning and identification logic and conduct online monitoring.

For the stability control problem of distributed four-wheel-drive electric vehicles under extreme conditions， considering the influence of sensor noise of yaw rate， lateral and longitudinal acceleration， as well as the estimation error of slip angle， a phase plane stability domain division method based on extension theory and an adaptive Tube-based Model Predictive Control algorithm （ATMPC） are proposed to quickly quantify the stability level of the vehicle and ensure the vehicle driving stability while maintaining tracking accuracy. The designed vehicle yaw stability control system utilizes hierarchical design architecture. The upper layer employs the extension theory to associate the vehicle slip angle-yaw rate phase plane with extension control domain and determines the control domain based on the actual vehicle state and calculates the dependent function to realize the decision-making of the control target weights and modes of the lower layer's Tube-MPC. The lower layer utilizes Tube-MPC to track the desired vehicle slip angle and yaw rate， enabling precise decision-making regarding the yaw moment， and adopts the tire loading ratios optimization method for the allocation of the yaw moment. The control strategy is validated by Carsim/Simulink co-simulation. The results show that the proposed control framework and ATMPC strategy can significantly enhance the driving stability of vehicles in extreme conditions and improve robustness in noisy environments， outperforming traditional MPC.

For the uncertainty of the shift control model and parameters and the existing unknown disturbances of the two-speed dual clutch transmission （2DCT） of pure electric vehicles， a linear active disturbance rejection controller （LADRC） method is proposed. Firstly， the dynamic model of the 2DCT shifting process is established， and the shifting process is analyzed. Then， considering the uncertainty and unknown disturbance of the control model， the LADRC controller is applied to the shifting process to track the desired rotational speed， and the Extended State Observer （ESO） is used to estimate the disturbance in real time and compensate for it. Finally， it is compared with the PID control. Simulation and experimental results show that the controller has smaller tracking error and strong robustness， which ensures good gear shifting quality.

Based on the current assessment status of the differential system of new energy vehicles， combined with the invalidation problem of the differential system installed on the cross-country type/sport sedan type， in the process of cross-country scene/racing track scene driving test， the application scenes， working principles， damage mechanisms and invalidation forms of the differential system installed on the cross-country type are analyzed in this paper. The Archard damage model is used for damage verification. Based on clustering analysis， the massive real automobile testing data collected from road test are screened and extracted， and then transformed into powertrain bench verification test conditions. The fault of the differential system of the road test automobile is reproduced， forming the reliability assessment ability of the differential system in the sand off-road scenario of new energy vehicles， which provides support for the selection and optimization of differential systems and the development of new automobile models. Meanwhile， this article also has certain reference value on how to improve the performance and reliability of differential systems.

Current crash protection research for pedestrians has been conducted primarily on adults， with no classification of adults and children. In this paper， a numerical simulation model of human-vehicle collision is established according to the relevant crash provisions of Euro-NCAP， and the simulation analysis shows that universal airbags cannot effectively reduce the head injuries of adults and children at the same time. Therefore， an airbag energy absorption device for adult and child classification protection is studied according to the characteristics of airbags and the physiological differences between adults and children， and an improved YOLOv5 pedestrian target detection model is proposed to realize the classified recognition of adults and children. According to the classification results， the vehicle control module dynamically adjusts the parameters of the airbag energy absorption device， so that the device can be deployed to different states for adults and children respectively， realizing the classification protection of pedestrians. The results show that the designed target detection model is able to achieve the classification and recognition of pedestrians， with an increase of 3.11% and 4.32% in terms of adult and child category detection accuracy， respectively， compared with the initial model. After installing the airbag energy absorption device， the HIC value of the adult head can be reduced by a maximum of 63.4% and the peak acceleration can be reduced by a maximum of 61.7%， while the HIC value of the child head can be reduced by 31.4% and the peak acceleration can be reduced by 53.2%. The thesis research results can provide scientific theoretical support for the design of pedestrian active and passive safety protection devices.

For the problem of difficulty in balancing accuracy and real-time performance of deep learning models for intelligent cockpit driver expression recognition， an expression recognition model called EmotionNet based on attention fusion and feature enhancement network is proposed. Based on GhostNet， the model utilizes two detection branches within the feature extraction module to fuse coordinate attention and channel attention mechanisms to realize complementary attention mechanisms and all-round attention to important features. A feature enhanced neck network is established to fuse feature information of different scales. Finally， decision level fusion of feature information at different scales is achieved through the head network. In training， transfer learning and central loss function are introduced to improve the recognition accuracy of the model. In the embedded device testing experiments on the RAF-DB and KMU-FED datasets， the model achieves the recognition accuracy of 85.23% and 99.95%， respectively， with a recognition speed of 59.89 FPS. EmotionNet balances recognition accuracy and real-time performance， achieving a relatively advanced level and possessing certain applicability for intelligent cockpit expression recognition tasks.

Air conditioning system as a key subsystem of environmental regulation within the entire vehicle system， the carbon emission of air conditioning system throughout its entire lifecycle is crucial for meeting the environmental protection and emission requirement of electric vehicle. Combining the life cycle climate performance （LCCP） model of electric vehicle air conditioning system with relevant data， the LCCP values in different provinces of China are analyzed in this paper. Besides， the LCCP values under two different heating schemes and different carbon intensities of electricity are compared. The results show that low-GWP mixed refrigerant RE170/R134a （RE170 to R134a mass fraction ratio 90∶10） can lead to a decrease in LCCP values for electric vehicle air conditioning system by 11.2% to 28.1% in China. Replacing the PTC heater with the heat pump results in a decrease in LCCP values by 0 to 33.1%. In addition， considering the future changes in China's carbon intensity of electricity and the proliferation of electric vehicles， it is anticipated that the LCCP values for an individual vehicle by 2035 will decrease by 31.7% to 39.3%， while the gross electric vehicle LCCP values in China will increase significantly.

The road noise problem in the frequency range of 35-40 Hz generated by a certain vehicle model on rough asphalt pavement at a speed of 30 km/h is analyzed by simulation and testing methods. Based on structural noise transfer path analysis （TPA） and node contribution analysis， it is determined that the problem frequency noise is mainly caused by low-frequency vibration of the vehicle body sheet metal parts. Therefore， based on the partial resonance principle， a resonance structure that can be industrialized according to the shape and local space of the sheet metal is designed， which is composed of a metal bracket and a metal block. By attaching this resonance structure to the sheet metal area with a significant contribution， simulation results show that the structure reduces the vibration of the sheet metal， thereby optimizing the interior noise. The peak noise levels in the front and rear seats are reduced by 8.6 and 6.4 dB， respectively. Meanwhile， the vehicle test results indicate that the interior noise has been improved by 7.0 and 5.8 dB. This provides a new research method and optimization scheme for controlling low-frequency noise caused by vibration of sheet metal.