To improve the fuel economy of vehicles，simulation and experiments are combined to improve the aerodynamic drag coefficient during driving，taking a certain SUV model as the research object. Firstly，wind tunnel tests are used to determine the areas or components that have significant impact on the overall aerodynamic drag of the vehicle. Secondly，optimizations are made to the components or areas with high contribution values to the air resistance coefficient. The results show that the front wheel deflectors，taillights and spoilers contribute greatly to the overall air resistance coefficient of the vehicle. The restyling of the front wheel deflectors effectively reduces the frontal pressure area and interference drag from the wheels. Optimizations on taillights and spoilers improve the rear negative pressure zone and shorten the reattachment distance of separated flows on the upper part of the rear window. Based on the intrinsic orthogonal decomposition method for extracting and analyzing local flow field information，it can be concluded that the first and second order modals mainly constitute the key flow states in the wake. Compared to the initial scheme，a drag reduction rate of 7.5% can be achieved by the optimized combination design，which is verified by tests and simulations. Theoretical basis and technical support are provided in this paper for restyling and model change of the next generation of SUV.

The promotion of intelligent cockpit and virtual testing protocols bring new challenge to assess the occupant injury，with the injury mechanism and injury risk assessment parameters more diversified. Based on the TUST IBMs 6YO-O and the BP neural network algorithm，a predictive model for the correlation between occupant sitting angle and head injury indicators in frontal 100% overlapping rigid barrier condition is constructed in this paper，and the correlation and difference between evaluation indicators with the different seating postures are explored. The results show that the constructed correlation injury prediction model has high reliabilities （R ² > 0.90），which can be used for injury prediction and analysis. Existing head injury evaluation indicators have good consistency in the small angle range （95°~108°），but for the occupants with larger seating postures，there are significant differences to assess the head injury risks using different injury evaluation indicators. Therefore，there is certain limitation of the head injury assessment parameters implemented currently. In the future virtual testing，the kinematic and biomechanical parameters should be integrated to assess more comprehensively for the head injury risks. The research results can provide data and theoretical support for the improvement of child restraint systems，virtual testing，and selection of head injury evaluation parameters for occupants with larger seating postures.

Collaborative lane change technology for intelligent connected vehicles has been widely studied，but existing strategies can hardly solve the problem of vehicle collaboration in mandatory lane change scenarios or may cause notable impact on upstream traffic. For mandatory lane change scenarios demand，a two-stage cooperative lane change strategy considering theoretical minimal safety space is proposed in this paper. Firstly，the control architecture for a two-vehicle cooperative lane change system is proposed and a collaborative lane change scheme is developed for mandatory lane change scenarios. Then，a two-stage receding-horizon trajectory planning strategy of spacing adjustment and collaborative lane change is designed，where the theoretical minimum safe distance is embedded as a constraint of spacing adjustment stage，to solve the problem of conservative spacing strategies in existing research. Finally，numerical simulation and hardware in-loop experiments are performed to verify the effectiveness，advantages and computational real-time performance of the proposed strategy. The results show that the proposed strategy can effectively improve the success rate of lane change，reduce the negative traffic impact while ensuring lane change safety，and is also applicable in real time computing and communication environment of actual edge cloud platform.

In the process of electrification and intellectualization of the automobile industry，the automotive safety testing and evaluation technology has also been extended and expanded from simple passive safety to active and passive safety integration. In this paper，the differences between the world's mainstream automotive safety assessment procedures are compared and analyzed from three aspects： occupant protection in the vehicle，vulnerable road user protection outside the vehicle，and active safety. The key technical points of vehicle safety development for each evaluation condition are summarized and the development trend of safety evaluation procedures for new energy and intelligent networked vehicles is discussed. The research concludes that the mainstream automotive safety evaluation procedures are becoming more and more stringent in passive safety evaluation，with the proportion of active safety evaluation conditions gradually increasing，and the development focus of the future evaluation procedures will focus on the integration of active and passive safety and virtual evaluation for complex working conditions. In addition，the battery safety test for new energy vehicles has been relatively perfect，and the future research focus can be expanded to the direction of electronic control system testing，chassis stability testing，and unified standardization certification of charging and swapping facilities and supporting equipment. In the medium and long term，the construction of reasonable and reliable evaluation methods such as OTA （over the air） testing of intelligent networked vehicles and HMI （human machine interface） safety and comfort will become a major difficulty concerned by the industry，and a composite evaluation system combining the virtual and reality can be built with the help of tools such as autonomous driving simulators.

For the difficulties in feature extraction and low recognition rate in defect types and grades of rivet on aluminum alloy plates for car body，the diagnosis model and detection method for rivet failure defects are proposed based on the Gaussian convolutional deep belief network and long short-term memory network. Firstly，the specimens are designed for five types of fracture defects and an automatic detection system is constructed. The planned path and pose of the probe are set to lower lift-off effect on signals. Secondly，the dual network fusion diagnostic model is designed to extract and learn the multi-dimensional defect feature information，solving the problem of extracting defect information represented by temporal variation characteristics and spatial distribution state in detection curves. The experiments results show that the optimized model has an average recognition rate of 99.85%，with an increase of 14.54% compared with that of the traditional convolutional network and single deep belief network. The model has better compatibility and robustness，which can realize online diagnosis of internal defects of rivets.

A dual-motor non-synchronizer multi-gear mechanical transmission system can effectively improve the energy economy and power performance of heavy-duty commercial vehicles. In order to analyze the optimal performance of the system under the optimal parameters，and to consider the effect of vehicle weight and differences between no load and full load，this study optimizes both the drive motor parameters and mechanical transmission speed ratio parameters，and compares the energy economy and power performance of the single/dual-motor multi-gear mechanical transmission system under the optimal parameters. The results show that the dual-motor drive system reduces the sensitivity of the vehicle energy economy to the difference in vehicle weight and no/full load，with the maximum speed of the dual-motor drive system increased by about 8%，and the acceleration time saved by about 28%.

The wet clutch is the core component of the vehicle transmission system. It is prone to rub-impact between the friction plate and steel plate during high-speed separation，resulting in a sharp increase in drag torque，and affecting its transmission efficiency and reliability. Therefore，in this paper，to reduce the rub-impact drag torque in high-speed wet clutch，the micro-texture on the surface of the friction plate is optimally designed. Firstly，a parameterized modeling method of arbitrary micro-texture shape lines on the surface of friction plate is proposed. Then the number，depth，circumferential proportion，radial proportion and shape line parameters of the micro-texture are selected to construct the design variables，constraint conditions and optimal objective function for micro-texture optimization. By combining the experiment design method，approximation modeling simulation and global search optimization method，an optimal design model of micro-texture on the surface of friction plate is established. Finally，a comparison test of drag torque before and after micro-texture optimization is carried out. The results show that the optimized micro-texture can significantly reduce the rub-impact drag torque at high circumferential speed，and greatly delay the critical speed at which the rub-impact phenomenon of friction pair occurs.

Cell to body （CTB） is a key technology to improve the endurance mileage of electric vehicles. The VRB/OW-GFRP hybrid structure formed by the variable-thickness rolled blanks （VRB） structure and the orthotropic woven GFRP （OW-GFRP） through the bonding process is an innovative structure，which can reduce the weight of the CTB battery pack，thus improve the endurance mileage of electric vehicles. Taking an electric vehicle as the research object，a CTB battery body integrated structure is designed to realize the integration of the upper cover of the battery pack and the floor of car body. Furthermore，the cover assembly of the uniform thickness （UT） CTB battery pack are replaced by the VRB structure，UT/OW-GFRP and VRB/OW-GFRP hybrid structure. The lightweight design of the upper cover assembly of the three types of CTB battery pack are carried out based on the multi-stage optimization method. The results show that the weight of VRB structure is reduced by 6.4% compared with that of UT structure when the stiffness performance of the CTB battery pack is met. The lightweight level of the upper cover assembly of the CTB battery pack based on the VRB/OW-GFRP hybrid structure is about three times that of the metal structure，with the weight of the VRB/OW-GFRP reduced further by 4.2% compared with that of the UT/OW-GFRP hybrid battery pack upper cover assembly. Thus，the VRB/OW-GFRP hybrid structure is the inevitable trend of the development of automotive lightweight technology in the future，showing a great application prospect in CTB battery pack cover assembly.

In the early design stage of the vehicle body, in order to assess the impact of the extreme pothole road conditions on the vehicle body structure, according to the related universal global pothole road test standard, for the extreme impact of the pothole#3, the body structure failure of a vehicle in the road test of pothole#3 is taken as the research object in this paper, to find out the shortcomings of the traditional sheet metal failure criteria CAE method, which can't reproduce the problem of test failure. The Fracture Forming Limit Diagram (FFLD) is established through a large number of sheet metal coupon tests as a new CAE method for sheet metal failure criteria. Then, based on this new CAE method, the Virtual road load data of pothole#3 calculated by the vehicle dynamics discipline is taken as load input to carry out finite element simulation analysis on the body structure, and the test failure is successfully reproduced. According to the analysis results of the new CAE method, the body structure is improved, and the pothole#3 road test certificate is finally passed. The test and the finite element analysis have high correlation. It is proved that the method can accurately predict the real damage condition of sheet metal under complex deformation conditions in the early stage of body development, thus reduce the risk of body structure failure in the later test.

For the problem of high temperature and uneven distribution affecting the power and safety of the electric vehicle during the battery management systems slave control board's service, a commercial BMS slave control board thermal analysis model is built and verified using the CFD theory and Icepak software. For the first time under vehicle service conditions, temperature field analysis and thermal uniformity optimization research are carried out based on the thermal analysis model. The BMS slave control board thermal simulation analysis shows that the balancing and power supply modules exceed the BMS's design temperature limit of 60 °C due to local heat accumulation, with the maximum temperature difference of the entire BMS slave control board being 21.0 °C. A heat dissipation path analysis of the BMS slave control board is further carried out, and heat dissipation optimization design is realized by altering the distance, layout of the balancing resistor, PCB substrate and adding thermal pads. By increasing the heat dissipation capacity of the BMS slave control board, the highest temperature of the BMS slave control board can be controlled below the design limit of 60 °C, and the temperature difference of the entire circuit board can be reduced to 6.9 °C, which enhances the safety and reliability of the BMS slave control board under actual vehicle service conditions, providing theoretical methods for the thermal design and optimization of the BMS slave control board.