In order to address the issue of human-machine conflict caused by the neglect of human-machine interaction in traditional lane keeping assistance systems, this paper proposes a lane keeping human-machine co-driving strategy. A lane departure decision model considering the drivers’ lateral driving habits is designed by characterizing the drivers’ lateral driving habits based on their historical lateral positions and dynamically dividing the road boundary according to 3σ principles. At the same time, the control of the assisted driving system is allocated based on the risk assessment value and driver fatigue factor. The experimental results show that the proposed human-machine co-driving strategy can effectively avoid lane departure risks caused by fatigue driving and driving errors. The lane keeping assistance systems, which considers the lateral driving habits of drivers can provide drivers with sufficient freedom while applying appropriate constraints to suppress lane departure, effectively reducing human-machine conflicts and ensuring safety.

To address the problem that distributed-drive electric vehicles cannot accurately track the desired trajectory after steering system failure, this paper proposes a fault-tolerant control strategy that considers vehicle stability and trajectory tracking after steering failure. Firstly, the desired front wheel angle and additional transverse moment are calculated based on model prediction control and sliding mode control, respectively. Then, for the steering system failure fault, a front wheel angle tracking controller is designed based on non-singular terminal sliding mode control to solve the differential steering moment required to achieve the desired angle. Secondly, with the optimization objectives of minimizing the tire loading rate and the control volume error, the tire force distribution is realized based on the quadratic programming algorithm. Finally, simulation tests are carried out under two working conditions of medium-speed high adhesion coefficient and high-speed low adhesion coefficient respectively, and the results show that the proposed fault-tolerant control strategy can still make the vehicle track the desired trajectory stably after the steering system fails, and it has good control effect.

In order to accurately estimate the battery parameters, state of charge and power state at different temperatures, a recursive least squares method combined with adaptive extended Kalman filter algorithm based on adaptive forgetting factor is proposed. By correcting and updating parameters in real time, the accuracy of battery parameter identification and state-of-charge estimation is improved. Based on the constraints of the model terminal voltage identification results, the state-of-charge estimation results and the maximum discharge current of the battery, the joint estimation of the battery power state is realized. The test results show that the maximum absolute error of the identification voltage and the maximum absolute error of the state of charge are 62.699 mV and 1.894%, respectively under the dynamic stress test condition. When the continuous discharge time is 5 s, 30 s and 120 s, the average error of battery power is 5.6×10^-3 W, 6.5×10^-3 W and 8.0×10^-3 W, respectively. The proposed adaptive joint estimation algorithm can improve the accuracy of parameter identification and state estimation effectively.

This paper proposes a fuzzy PID control strategy with start-stop function for the direct battery cooling systems of electric vehicle. A whole vehicle model is developed using AMESim and Simulink co-simulation, to simulate the battery heat dissipation process under different control strategies. The results indicate that compared with traditional PID control, the start-stop fuzzy PID control has advantages including faster response time, shorter overshoot duration and lower system power consumption. Furthermore, the effect of shortened overshoot duration and power consumption savings is more pronounced in scenarios with higher ambient temperatures or lower driving speeds.

To address the issue of exterior rear view mirror whistle of a home-made passenger car, vehicle subjective evaluation is conducted to confirm and analyze the issue, then schemes are proposed using Computational Fluid Dynamics (CFD) to eliminate the unreasonable flush and cavity of exterior rear view mirror shell and camera, and improve stream field pressure of the monitoring point, vehicle test and subjective evaluation prove effectiveness of this scheme. The results show that exterior rear view mirror whistle problem can be solved by reducing Error State Index (ESI) which comprehensively considers factors such as road type, speed range and severity of NVH from 1.12 to 0.

To address the issues of low efficiency and low qualification rates in mesh generation for plate and shell structures, this paper proposes an intelligent finite element meshing technology based on deep learning. First, typical features of plate and shell structures are classified, and meshing strategies are developed for each feature type. Second, a feature recognition model is trained using convolutional neural networks to automatically invoke the corresponding strategies for meshing in feature regions. Finally, geometry cleanup and mesh optimization are performed for non-feature regions. Verified by the white body of a passenger car, this method increases the automatic meshing qualification rate from 82.1% to 92.6% and reduces total working hours by 66.7% compared with the mainstream batchmesh approach, significantly improving both mesh quality and efficiency. By combining AI models with predefined strategies, this technology minimizes manual intervention and provides an intelligent solution for meshing in plate and shell structures.

To solve the problem of unevenness and large difference in temperature at the air outlet of car air conditioners, temperature control curve of a car air conditioning outlet is simulated and tested. The correlation analysis of the outer length, inner length ard inclination angle of the deflector structure with the awerage temperature and temperature difference is performed by using response surface optimization to derive the regression polynomials, response surface graphs, and the optimal parameter design of the deflector structure. The simulation results show that this method improves the temperature control curve performance of the car air conditioner