In traffic accidents, the results of head injuries resulting from frontal and side impact of vehicles vary significantly, primarily due to the differing impact locations. To investigate the specific effect of impact locations on brain injuries with various impact strengths, experiments are conducted on male rats, focusing on cranial vertex and temporal lobe impact. An experimental protocol is established based on the L₄ (2³) orthogonal table, including impact strength and impact location factors. Rats are injured using the BIMIV rat head impact machine. The effect of impact factors and their levels on TBI is assessed systematically by behavioral performance and pathological findings of key brain regions in rats. The results show that impact strength is the primary factor influencing head injury, but the effect of impact location is not negligible. At the same impact strength, cranial vertex impact is more likely to cause coma, motor and memory deficits, and anxiety than temporal lobe impact. Furthermore, cranial vertex impact results in higher pathological injuries than the nonimpact side of temporal lobe impact, but lower than the impact side. The linear fitting between behavioral performance and pathological results reveals that postinjury behavioral performance in rats more closely aligns with the pathological outcomes on the less injured side of the brain. The findings of this study are crucial for understanding the mechanisms of head injury, proposing appropriate injury evaluation guidelines, and establishing effective protection strategies.

In a mixed traffic ecosystem, accurately predicting the trajectories of surrounding vehicles is crucial for the safety of autonomous vehicles. However, existing technologies still face issues of accuracy and computational complexity in longterm prediction. A spatiotemporal interactive sparse attention model combined with intention probability is proposed in this paper, which predicts trajectories through an efficient encoderdecoder structure. The position mask matrix is first constructed to extract positional information from historical trajectories, and key features are selected using the sparse attention mechanism. The intention behavior analysis module is utilized to improve the accuracy of intention recognition. Finally, spatiotemporal features, positional features, and intention features are fused and input into the decoder, and the model is trained using a multitask learning approach. The experimental results show that, compared to the optimal algorithm on the HighD and NGSIM datasets, the proposed model achieves a notable reduction in root mean square error (RMSE) in longterm prediction of 3 to 5 seconds, significantly enhancing prediction accuracy. In addition, the model's performance in realworld scenarios is validated through road tests, further demonstrating its application potential in complex traffic environment.

The development of hydrogen fuel cell vehicle is one of the important measures to realize the "Double carbon" strategic goal in our country. As the main power source of fuel cell vehicle, proton exchange membrane fuel cell (PEMFC) system has nonlinear, strong coupling and timedelay characteristics. Those characteristics make PEMFC system have many difficulties when it is faced with complex power demand under various conditions like vehicle acceleration and climbing, especially in terms of precise control of gas supply and dynamic regulation of system response. The flow rate and pressure of gas supply play a decisive role in the output performance of PEMFC. Improper gas supply can lead to low efficiency of the stack and even damage or failure of the stack, and then affect the overall performance and service life of the system. Therefore, accurate gas supply system by optimizing the gas supply system is the key to improve the performance and extend the service life of PEMFC. Based on the establishment of a gas supply system model for PEMFC, in this paper the influence of key operating parameters such as oxygen excess ratio, gas pressure and gas pressure difference on the output performance of the system is analyzed. The synergetic control of oxygen excess ratio, cathode pressure and bipolar gas pressure difference in PEMFC system using nonlinear active disturbance rejection control (ADRC) algorithm is researched, which is then compared with those under the proportional integral derivative (PID) controller. Under PID control, the maximum overshoot of the oxygen excess ratio can reach 1, while under ADRC control, the overshoot only around 0.2, and the time to reach steady state is approximately 0.1 seconds, compared to around 1 seconds under PID control. After a sudden change in load current, the overshoot of the cathode gas pressure under the PID control algorithm is around 0.08 with large fluctuations, reaching a stable value within 2 seconds. Under the ADRC control algorithm, the cathode gas pressure can reach stable value within 0.8 seconds, with an overshoot much smaller than the PID control algorithm. Under PID control, the overshoot of the twostage gas difference can reach up to 0.15 with large fluctuations and longer time to reach stability, but under the ADRC controller, it can quickly and stably reach the set value of 0.2 bar with smaller fluctuations. The results show that the ADRC controller has better decoupling, robustness and stability under the disturbance factors of load current and hydrogen displacement action.

An optimization method incorporating the Improved Harris Hawks Optimization (IHHO) algorithm is proposed for the lightweight research of a trusstype snowplow frame. Firstly, a finite element simulation model of the frame is constructed, and its strength, stiffness, and modal characteristics are quantitatively analyzed under various working conditions to determine its strength performance, stiffness performance, and natural frequencies. Subsequently, the Response Surface Methodology is employed, using maximum deformation and maximum stress as response variables, to optimize the crosssectional dimensions of the frame beams, yielding three sets of optimal solutions. On this foundation, the IHHO algorithm is proposed by improving the HHO algorithm, and the effectiveness of the optimal solutions is verified using the IHHO algorithm. The optimization results show that the overall mass of the frame is reduced by 33.6%, with the maximum deformation decreased by 6.33%, the maximum stress increased by 3.01%, and the firstorder modal frequency decreased by 19.48%, effectively avoiding the resonance range. This study provides an efficient and feasible optimization strategy for the lightweight design of trusstype frames. The method demonstrates significant advantages in model construction and obtaining accurate estimation results, offering theoretical references for engineering application in related fields.

In this paper, an electric vehicle's aerodynamic drag and wake are numerically studied. The results show that the flow separates from the rear of the car may roll up into a largescale vortex at Re_L=1.1 × 10⁷. The ratio of the RMS drag and the mean drag reaches to 3.27%, making an unneglectable effect on ride comfort and mileage prediction. The pressure at the back, the underbody, the middle and lower parts of the near wake, the aerodynamic resistance of the entire vehicle, the pressure near the wall of the rear guard plate in the bottom, and the separation flow at the bottom all have a characteristic frequency of 12 Hz. However, the flow separation at the top and Cpillar of the car does not have this characteristic frequency. It proves that the underbody flow separation at the rear is the main cause of dynamic changes of the aerodynamic drag.

To meet the stringent thermal management requirements of electric vehicles and address global climate change issues, in this paper an integrated thermal management system is developed for electric vehicles based on R290 refrigerant. The system's performance is analyzed and validated through simulation and experiments. The results show that the cooling and heating capacities of the R290 dualside indirect thermal management system increase with the increase of the compressor speed, while the coefficient of performance (COP) decreases with the increase of the compressor speed. The heating capacity from the compressor's hot gas bypass increases with higher system pressures. Under hightemperature cooling conditions at 40 °C, the system's maximum cooling capacity is 9.25 kW. Under lowtemperature heating conditions at 18 °C, the maximum heating capacity is 7.24 kW. At extremely low temperatures of 20 °C, the maximum heating capacity from the compressor's hot gas bypass is 4.3 kW.

To meet the demand of efficient and accurate perception in autonomous driving systems, relying solely on cameras makes it challenging to achieve highprecision and robust 3D object detection. An effective solution to address this issue is to combine cameras with costeffective millimeterwave radar sensors, enabling more reliable multimodal 3D object detection. An effective approach to address this problem is to combine cameras with costeffective millimeterwave radar sensors, enabling more reliable multimodal 3D object detection, which not only improves the accuracy of environmental perception but also enhances the system's robustness and safety. In this paper, an autonomous driving perception algorithm based on the fusion of millimeterwave radar and cameras, named HPRDet (historical pillar of ray cameraradar fusion bird's eye view for 3D object detection) is proposed. Specifically, a radar BEV (bird's eye view) feature extraction module called RadarPRANet (radar point RCS attention net) is designed firstly. It comprises a dualstream radar backbone that extracts radar features with two representations, and an RCSaware BEV encoder that distributes radar features into the BEV space based on radarspecific RCS characteristics. Secondly, Historical radar of Object Prediction paradigm is adopted, designing both longterm and shortterm decoders that operate only during training, thus avoiding additional inference overhead. Due to the sparsity of the input data in this network, multimodal historical multiframe input is introduced to facilitate more accurate BEV feature learning. Lastly, the millimeterwaveoptimized ray denoising method is proposed, which utilizes the information from the current frame's millimeterwave radar point cloud as prior knowledge to assist in proposal generation, thereby enhancing the query feature representation for the camera. The proposed algorithm is trained and validated on the largescale public dataset nuScenes, with the NDS reaching 56.7% on the backbone of Resnet50.

In order to solve the problem of roaring sound inside the vehicle caused by intermittent engine intervention during charging and discharging of dieselelectric hybrid vehicles, in this paper a semicoupled cluster control strategy with better comprehensive performance is proposed based on the traditional multichannel active noise control (ANC) system by combining the advantages of the centralized control strategy and decentralized control strategy. Compared with the centralized control strategy, the computational cost of the cluster control strategy is reduced by about 50%, and the noise attenuation performance is comparable to that of the centralized control strategy. Compared with the decentralized control strategy, the stability is obviously better, and the noise reduction effect is outstanding. Based on the MATLAB simulation platform, a variety of cluster control strategies and traditional control strategies in the vehicle are compared and analyzed, and the road test experiments of a rangeextended electric vehicle are carried out under its common working conditions. The results show that the cluster control strategy can be well applied to the multichannel active noise control system in the vehicle, and the average noise reduction amount of the second, fourth, and sixthorder range extender noise at the four seat headrest positions can reach 15.9, 10.6 and 5.7 dB(A), respectively, showing good noise reduction effect and stability. The research results can be applied to the noise control of manned cabins, such as aircraft, submarines and other fields, which has important scientific significance and engineering value.

An adaptive antidisturbance angle control strategy is proposed to address the nonlinear disturbances such as system parameter uncertainty, tire return torque obstruction, and coupling of electromagnetic characteristics of steering motors, which are faced by the active steering of SteerbyWire (SBW) system. A radial basis function neural network and robust sliding mode theory are used to design the outerloop cornering controller to adaptively compensate for the SBW system parameter uncertainty and tire return torque obstruction. Linear selfimmunity control is introduced into the innerloop current controller to cope with the coupling problem of electromagnetic characteristics of the steering actuator motor so as to improve the dynamic response performance of the SBW system. The simulation and hardwareintheloop test results show that the designed control strategy can help the SBW maintain the cornering steady state following error within 1.5° under various operating conditions.

To investigate the impact of variation in tire tread depth on vehicle performance, performance tests are conducted using tires with differing tread depth as our subjects on dry, wet, and lowadhesion road surface at the proving ground. The results indicate that the braking distance decreases as tread depth diminishes on dry surface while the trend reverses compared to dry surface on wet surface, and tread depth variation has a minor impact on braking distance on lowadhesion surface. The maximum lateral acceleration and yaw rate of the vehicle initially increase and then decrease with reducing tread depth on dry surface, peaking at 4.0 mm. The maximum yaw rate follows a similar pattern to dry surface on wet surface, but there is no clear trend in maximum lateral acceleration.