To consider both stability and trajectory tracking performance of autonomous vehicles operating in extreme conditions，a trajectory planning and control method based on autonomous drift is proposed. A neural network tire dynamics model is designed based on neural network to improve the accuracy of the traditional magic tire formulation. In order to further expand the stability boundaries under the extreme working conditions of autonomous vehicles，the drift stability boundaries are designed based on the tire saturation and maximum sideslip characteristics combined with the center-of-mass lateral deflection angle-transverse swing angular velocity phase plane constraints during drift，and the nonlinear model predictive control （NMPC） is used to plan a safe drift trajectory within a wider stability range，and the drift tracking control is carried out for the planned trajectory. The results of the joint simulation of Simulink/CarSim show that the method can fully utilize the advantages of drift motion to ensure that the vehicle does not go out of control under extreme working conditions，while accurately tracking the desired trajectory.

Accurately predicting the remaining useful life （RUL） of lithium-ion batteries is important for the efficient and safe operation of energy storage systems. For the deficiencies of existing data-driven methods for RUL estimation，which extract aging features non-comprehensively enough and need to predict health state changes before estimating RUL，a RUL estimation method using multi-dimensional and multi-scale features is proposed in this paper to directly estimate the RUL of a battery using constant-current charging voltage segment data. The model maps RUL by extracting aging features of voltage segments using different scale convolution operation after dimensionally transforming the data. The model is validated using publicly available datasets from the University of Oxford，NASA，and the University of Maryland. The validation results show that the model can directly estimate the RUL of the batteries using the voltage segment data without the need of the current historical SOH of the batteries as the training data，which has higher accuracy and universality compared to fixed scale features based on a single dimension.

In order to extend the application scenario of model predictive control and improve the trajectory tracking accuracy of intelligent vehicles in extreme wind environment，an adaptive horizon control method considering crosswind stability is proposed. Firstly，taking the process of car overtaking on the sea-crossing bridge as the research object，the crosswind stability analysis model of car overtaking is established by using the coupling method of vehicle aerodynamics and system dynamics. Then，the safety risk model of vehicle lateral motion is established，and the adaptive horizon regulator is designed taking into consideration of lateral motion risk level，vehicle speed and lateral error，so as to realize the dynamic adjustment of prediction horizon and control horizon. Finally，CarSim and Simulink are used to build a joint simulation scenario，and the overtaking trajectory is planned by quintic polynomial to verify the tracking accuracy and robustness of the controller. The results show that compared with the fixed horizon and variable weight model predictive controller，the improved controller can better resist the aerodynamic interference of ' wind-vehicle-bridge' and improve the vehicle trajectory tracking accuracy at a lower real-time cost，with significant improvement in vehicle crosswind stability.

The module arrangement of passenger car power battery packs usually has three different forms： single-layer arrangement，duplex arrangement and local duplex arrangement. The local duplex arrangement，which combines the characteristics of the other two methods，is more widely used. However，the study of electric vehicle fire accidents shows that battery packs with local duplex arrangement account for a higher proportion of spontaneous combustion accidents，which indicates that this arrangement may adversely affect the thermal uniformity of the battery module. In view of this，in this paper taking an electric vehicle battery pack with local duplex arrangement as the research object，a three-dimensional numerical model of the battery pack is established and the accuracy of the model is verified by comparing the experimental data with the simulation results. Using the validated model，the module temperature distribution characteristics of the battery pack under fast charging and three discharge rates are analyzed by numerical calculation methods，revealing the thermal uniformity of the local duplex arrangement of the modules under these conditions，especially the with the temperature difference of the double-layer module in the local duplex module larger than that of the single-layer module. In addition，the effect of coolant inlet temperature and flow rate on the thermal uniformity of the modules is explored respectively. It is found that attempting to reduce the coolant inlet temperature to improve the thermal uniformity of the module has limited effect，while increasing the coolant inlet flow rate can only reduce the temperature difference of the module under high discharge rate conditions，without obvious effect under low discharge rate conditions. This study provides a meaningful reference for the development and design of the battery thermal management system with local duplex modules.

The biomechanical human body model with detailed anatomical characteristics is urgently needed computational tool for the intelligent and digital development in the field of automotive safety. In this study，based on the latest Chinese adult body size standards，a biomechanical model of the 50^th percentile Chinese male anthropometry car passenger （TUST IBMs M50-O） is developed with independent intellectual property rights. By reconstructing 3 sets of frontal blunt impacts，5 sets of side blunt impacts，and 3 sets of sled cadaver or volunteer tests，the C-NCAP deformable moving barrier side impact test is simulated to verify the effectiveness and application value of the developed model from multiple angles and all directions. The results show that all the 11 sets of reconstructed experimental data are within the corresponding cadaveric and volunteer corridors，with an average difference of approximately 10%，verifying the effectiveness of the model. The TUST IBMs M50-O model and WorldSID 50^th model exhibit the same kinematic responses in the side impact simulation. However，due to the compression from the upper extremity and elbow joint of the TUST IBMs M50-O model，the peak resultant accelerations of T4 and T12 thoracic spine reach 43.5g and 47.3g，higher than the values of 38.5g and 41.2g of the WorldSID 50^th model. The TUST IBMs M50-O model is also used to analyze the human tissue level injury risks based on stress and strain. In conclusion，the TUST IBMs M50-O model exhibits high biofedility and can be used to investigate the impact of injury mechanisms and virtual testing. As a reliable computational tool，this model can offer technological support to the research and development of safety protection devices in intelligent vehicles and intelligent high-end equipment.

Due to the limitation of the computing power of the vehicle platform，there is an irreconcilable contradiction between the long-term control/prediction time domain and the short-term control step. In this paper，a necessary condition is derived to decouple the translational motion from yawing motion based on the time-scale separation. Consequently，the translational motion is regulated over an extended control horizon to generate a human-like tracking trajectory. The yawing motion is regulated based on a more accurate dynamic model and a shorter control cycle. In addition，model predictive path integral （MPPI） strategy is used to mitigate the computational burden of nonlinear motion planning through sampling-based optimization. Finally，a model predictive output regulator is proposed to solve the underactuated control problem in vehicle lateral dynamics and reduce steady-state errors in yaw angel. Theoretical analysis and simulation results show that the proposed method enhances computing efficiency，improves the parameters adaptability and steering smoothness and reduces the lateral jerk by an average of 50% in all driving scenarios.

The precision of speed tracking in the longitudinal motion control of intelligent vehicles is affected by multiple sources of disturbances，such as model mismatch and changes in external environments. In this paper，a longitudinal motion anti-disturbance control method that combines disturbance observation and Model Predictive Control （MPC） algorithm is accordingly proposed. Firstly，the relationship between the longitudinal acceleration of the vehicle and various forces is analyzed according to the longitudinal dynamics model of the vehicle，and then it is simplified into a particle motion model with multiple sources of disturbance and a model predictive controller is designed as the upper controller. Secondly，for the internal unmodeled dynamic disturbances and external random disturbances，a linear extended state observer （LESO） is designed to perform real-time estimation and compensation is implemented through a feedforward loop. The closed-loop stability of MPC and the convergence of LESO are analyzed，and finally a model predictive optimal regulation control law of disturbance compensation and state feedback is formed. Furthermore，in order to ensure efficient execution of the control strategy，a first-order anti-disturbance controller is proposed as the lower controller to convert the desired acceleration into engine torque，thereby realizing closed-loop control of the vehicle speed. Finally，the algorithm is deployed on a in-vehicle Microcontroller Unit （MCU） and tested on a real vehicle under multi-speeds and road conditions. The results show that the proposed strategy can quickly and accurately track the target vehicle speed，with excellent anti-disturbance ability.

In order to improve the path tracking ability and handling stability of in wheel motor driven electric vehicles，a novel coordination control strategy for active four-wheel steering （4WS） and direct yaw moment control （DYC） is proposed. Firstly，considering the path tracking performance and handling stability of vehicles，a shared steering model is established and on this basis，the 4WS control strategy based on non-cooperative Nash game theory is proposed. Secondly，in order to improve the lateral stability of the vehicle under extreme conditions，the vehicle state is divided into stable，transitional，and unstable regions based on the phase plane of the center of mass sideslip angle，and the DYC controller is established in each region. Then，in order to achieve coordinated control of rear wheel steering and direct yaw moment，the coordination controller based on fuzzy neural network is established between ARS and DYC. Finally，the CarSim/Simulink co-simulation platform and Hardware-in-the Loop （HIL） platform are used to conduct experimental verification under dual line shifting conditions. The research results show that the proposed control strategy can effectively improve the path tracking precision and handling stability of the vehicle under extreme driving conditions.

Accurate prediction of the future trajectory of surrounding vehicles is crucial to the decision-making and motion planning of autonomous vehicle. Existing research tends to use Recurrent Neural Networks （RNN） to model the time interaction of vehicles，but its interpretability of vehicle interaction modeling is poor，ignoring the actual lane structure，and there are deficiencies in capturing the interaction between vehicles and the environment. To address this problem，in this paper，a vehicle trajectory prediction model based on graph convolutional interactive networks that considers lane topology constraints is proposed. The vehicle interaction relationship extraction module adds edge weights when constructing the spatial relationship of vehicles to consider their neighboring interaction，making the interaction more interpretable. The driving scene representation module aims to improve the accuracy of vehicle trajectory prediction by extracting lane topology from high-precision maps. The trajectory prediction module integrates the output of the above two modules and outputs the predicted future trajectory. This integration allows for more precise modeling of the interaction between road structures and vehicle driving trajectories. The experimental results show that compared with mainstream methods，this model has achieved good performance on the Argoverse dataset，improving the accuracy and rationality of vehicle trajectory planning under complex road structures.

To solve the problem of significant degradation in braking efficiency of medium and heavy-duty vehicles during prolonged downhill descents due to frequent engagement of the main brake system，a four salient poles liquid-cooled electromagnetic retarder structure is proposed in this paper. A vehicle downhill dynamics model is established to analyze the braking demand，using the magnetic equivalent circuit method to calculate its braking torque，and using the finite element method to numerically analyze its braking characteristics. The hierarchical variable domain fuzzy control strategy combined with a retarder is used for vehicle downhill braking control，and the controller and overall vehicle downhill braking model are established using MATLAB/Simulink for joint simulation. A 2 100 N·m prototype is designed. The braking characteristics of the prototype are tested through bench test and on-road vehicle tests. The results show that the actual measured value and the calculated value is basically consistent，with an average error within 5%，with the braking torque reaching 2 200 N·m when the speed is 1 250 r/min，which can meet the needs of medium and heavy duty vehicle braking.