For the problem of mapping failure in the high-speed tracking and figure-eight scenarios of the Formula Student Autonomous China (FSAC) due to the limited recognition and low accuracy of single-sensor cone detection, this paper proposes a cone mapping algorithm based on the loose coupling of LiDAR, industrial cameras, and a combined inertial navigation system. By projecting LiDAR data onto the camera coordinate system, the similarity between the target detection bounding boxes from the camera’s deep learning framework (YOLOv5) and the LiDAR cone bounding boxes is matched. The fused point cloud, containing RGB color information, is then transformed from the LiDAR coordinate system to the map coordinate system. The real-time vehicle pose calculated by the combined inertial navigation system is used to update the fused cone point cloud map. Real-vehicle comparative test results show that the algorithm achieves an average recall rate of 98.6% and an average precision of 99.1%, enabling the distinction between the inner and outer tracks of the cone map, thereby enhancing the vehicle’s perception, anticipation capabilities and path planning efficiency.

In view of the exponential increase in the number of key scene scenarios generated in high-dimensional space, and the difficulty of traditional artificial construction or random search methods to balance coverage and efficiency, this paper proposes a search method based on single-objective Tree structure Parzen Estimator (TPE) and Multi-ObjectiveTree structure Parzen Estimator (MOTPE). A software-in-the-loop automated simulation testing framework is built by using the CARLA simulator. Taking weather elements as an example, the critical scenario generation effects of the different search algorithms are compared. The experimental results indicate that the TPE-based search method and the MOTPE-based method increase the number of key scenarios generated by 3.11 times and 2.06 times, respectively, compared to the random search method. The MOTPE method is 1.53 times better than TPE in terms of scenario quality. When combined with scenario automaed generation and testing frameworks, these methods effectively address the issue of exploding scenario numbers, allowing for the discovery of scenarios with high testing value.

Aiming at the issue of lacking slope information in urban driving cycles used for vehicle performance evaluation, this paper proposes a method for Urban Ramp Driving Cycle (URDC) construction based on Self-Organizing Map (SOM) neural network. Typical road driving data with urban ramp characteristics is collected using the average traffic flow method. After pre-processing, the data is segmented into short trips, and 20 parameters representing road operation characteristics are selected as the feature parameters of the short trips. The dimensionality of these feature parameters is then reduced via principal component analysis, followed by clustering the short trips analysis using a SOM neural network. According to the principle of smooth ramp connection, short trips with high correlation are selected to construct an urban ramp driving cycle that includes both speed and slope information. The results of automatic transmission operated in slope performance test indicate that the constructed driving cycle can reflect the driving characteristics of vehicles on road with urban ramp features, which can be used as the benchmark driving cycle for performance test of vehicle driving on urban ramps.

This paper studies the noise of a Permanent Magnet Synchronous Motor (PMSM) for electric vehicles. Through the analysis of the contribution of different electromagnetic excitations to the noise, the paper identifies key factors such as motor torque ripple and spatial zero-order electromagnetic force, as well as their contributions to the order noise in different speed ranges. Moreover, through the simulation analysis of the noise under the conditions of eccentricity and current harmonics, the changes in the order noise after considering practical factors are obtained. The Kriging method is used to create an electromagnetic surrogate model, which can quickly calculate the electromagnetic force and torque ripple based on the design parameters of the stator and rotor. On the basis of the surrogate model, the genetic algorithm is used for multi-objective optimization. The order noise obtained from the simulation is significantly reduced compared with the original scheme. The good consistency between the simulation and the experiment is verified through the noise test of the optimized prototype on the test bench.

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.