In order to improve the occupant protection performance of a vehicle in the frontal 25% offset crash test, the research on the test methods and optimization schemes of the evaluation items of the injury level of the dummy, the restraint system and the motion level of the dummy was carried out. First, an accurate model was established by simulation and benchmarking test. Then, side air curtain was added, the seat belt and ignition time and parameters of airbag were optimized, and the structure of steering column bracket and instrument panel bracket were also optimized. Finally, it was verified by CAE. The results show that the grades of the thighs and hips, legs and feet of the dummy have been improved from Marginal (M) and Poor (P) to Good (G) and Acceptable (A), and the evaluation items of the restraint system and the motion grade of the dummy have been improved from Poor (P) to Good (G). The overall evaluation of small offset crash was improved from Poor (P) to Good (G). The occupant Weighted Injury Criteria (WIC) decreased by 22.97%.

China’s characteristic side barrier was developed with the SUVs as the research object. Through extraction of characteristic parameters of typical SUV models, the development objectives of honeycomb aluminum barrier and trolley were defined, and the Tresca theoretical model was used to guide the selection of barrier materials and the determination of trolley component specifications. Based on the performance test of barrier trolley dynamic load-cell wall, the performance of honeycomb aluminum barrier was improved and verified. The results show that the improved barrier is stable in deformation mode, the mechanical properties meet the design specifications, and can characterize the appearance and stiffness of SUV.

The Taguchi method is adopted to optimize the structure of high-speed permanent magnet synchronous motor for an electric vehicle with rated speed of 5000 r/min and rated power of 60 kW, in an attempt to improve the electromagnetic performance and efficiency of high-speed permanent magnet synchronous motor for electric vehicles. By analyzing the effect of the optimization variables on the motor iron loss, the average electromagnetic torque and the torque ripple, this paper obtaines the optimal optimization solution. The iron loss model of motor is established, and the Sine-equivalence method, harmonic-component method and FEA method are used to calculate the iron loss of stator respectively. Finally, the rationality of iron loss analysis method of the motor is verified by comparing calculated results and measured results.

To solve the problem of low accuracy when classifying traffic signs using Convolutional Neural Networks(CNN), this article proposes a cascade super-resolution network structure by connecting an image super-resolution network to a classification network. A modified dual-attention mechanism super-resolution network is first used as a sub-network of the cascade network, and then the image classification network is trained for classifying the super-resolution processed images, and finally the classification accuracy is used to measure the effectiveness of super-resolution reconstruction for the image classification task. The validation results of both simulation and real traffic sign datasets show that the super-resolution processed images achieve higher classification accuracy in the classification model, which proves that the super-resolution technology has a facilitating effect on the improvement of the classification accuracy of traffic sign images.

In order to bring the energy saving potential of urban bus hybrid system to full play under variable passenger capacity and complex conditions during passenger transportation, the paper presents a powertrain parameter optimization method of hybrid electric bus based on operation data. Firstly, based on the Internet of Vechicle(IOV) data, K-Means clustering analysis using Kernel Principal Component Analysis (KPCA) and Particle Swarm Optimization (PSO) is used to construct representative driving conditions of an urban bus. In view of the characteristic of random variation of passenger number of urban bus in operation, a hybrid electric bus powertrain parameter bi-level optimization model is constructed based on the Optimal Latin Hypercube Design (Opt-LHD), Opt-LHD is used in the inner layer to generate number of passengers, the optimal control strategy of engine is used to transfer system response to outer layer optimization algorithm. Simulation results show that the optimized powertrain parameters have better adaptability to uncertain factors, and the fuel consumption is reduced by 9.97% compared with that before optimization.

In order to meet the real-time wear monitoring demand of smart tires, this paper presents a new tire wear detection method, which uses a three-axis acceleration sensor integrated device to collect the acceleration waveform of the tire, and uses the Caesars maximum normal variance method to perform principal component analysis on the acceleration waveform characteristics to make waveform feature value extraction and filtering based on the analysis results. The filtered feature value data is trained through the Error Back Propagation (BP) neural network, to achieve real-time detection of tire wear values. The test and comparison based on the real vehicle detection data show that the algorithm can reduce the average error of wear detection to 0.1 mm under low computational power demand.

To improve the lateral stability of vehicles on middle and low adhesion roads, a coordinated controller of Active Front Steering (AFS) and Direct Yaw-moment Control (DYC) based on Model Predictive Control (MPC) is constructed. The controller is composed of an upper decision-making layer and a lower executive layer. The upper layer is based on MPC to obtain the required additional yaw moment, and the lower one aims to correct the steering angles of front wheels or apply the wheel cylinder pressure by AFS and DYC. The effectiveness of the control strategies in Double Lane Change (DLC) scenarios is validated, which reveals that, with the road adhesion coefficient being 0.25, the vehicle slip angle and yaw rate are stabilized within -3.5°~3.5° and -16~16 (°)/s respectively, the longitudinal vehicle speed stabilized at roughly 88 km/h. With a road adhesion coefficient of 0.4, the relevant stability indices, including the longitudinal vehicle speed, vehicle slip angle and yaw rate, are significantly improved by the coordinative strategy. Analysis indicates that this AFS-DYC coordinated control strategy can significantly improve vehicle handling stability under medium and low adhesion coefficient condition.

In order to solve the problem that existing LiDAR attenuation models rely on statistics to generate point cloud and lack of noise interpretation, this paper presents a LiDAR attenuation model for raining environment. Firstly, the LiDAR emission-reception model is established, and the spatial distribution of raindrops is simulated according to the raindrop size distribution model. Secondly, the light intensity change of the laser propagation process is obtained by coupling scattering model and noise model, and simulation imaging of point clouds is obtained. Finally, the image of point clouds in normal weather and raining weather is collected, to simulate and generate the attenuated point clouds for different rainfalls. The point clouds obtained from the attenuation model is compared with the point clouds image corresponding to actual raining weather. The results show that the presented model is superior to the existing models in each evaluation index, and effectively explains the attenuation effect of the raining environment on LiDAR.

For the problem that current target detection methods generally require a high-power consumption GPU computing platform and are easily affected by lighting conditions, this paper proposed 2 infrared pedestrian detection methods in front of vehicles based on embedded platform: the trained YOLOv4-tiny model was optimized using NVIDIA’s open source inference acceleration library TensorRT and deployed on the embedded platform; the YOLOv4-tiny model was used as the basic architecture of the algorithm, which was combined with the visual attention mechanism and the spatial pyramid pooling idea, and a YOLO layer was added at the same time, a YOLOv4-tiny+3L+SPP+CBAM network model was proposed. The 2 methods were trained and tested on the FLIR dataset, and tested on the Jetson TX2 embedded platform. The test results show that: compared with the original network YOLOv4-tiny, the average accuracy of the first method is reduced by 0.54%, and the inference speed is increased by 86.43% (frame rate up to 26.1 frame/s), the average accuracy of the second method is improved by 16.21%, and the inference speed is reduced by 22.86% (frame rate up to 10.8 frame/s). Both methods can take into account the accuracy and real-time performance, and meet the needs of infrared pedestrian detection in front of vehicle.

The emission characteristics under different operating modes (engine mode and hybrid mode) and different test cycles (C-WTVC and CHTC) of a heavy-duty HEV was studies on the chassis dynamometer and Portable Emissions Measurement System (PEMS), emission performance of the HEV was analyzed in combination of the working condition characteristic parameters. The results show that NO_x emission of the test vehicle under hybrid mode is higher than that under engine mode, while the CO emission under hybrid mode is lower than engine mode. Under engine mode, the NO_x emission of CHTC is higher than that of C-WTVC. However, under hybrid mode, the NO_x emission of CHTC is lower than C-WTVC. Under engine mode, CO emission is concentrated at low speed and low load condition, while under hybrid mode, CO emission is concentrated at high speed and high load condition.