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 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.

Developing a highprecision Statistical Energy Analysis (SEA) model to predict vehicle wind noise response requires a significant amount of time and cost. In this paper, a method is proposed for rapidly constructing an equivalent SEA model for vehicle wind noise based on parameter identification, which simplifies the modeling process while ensuring prediction accuracy. An initial SEA model of the compartment is established according to the vehicle's body structure and dimensions, with the pressure fluctuation excitation on the side window surface and the actual wind tunnel response serving as the model's input and output, respectively. The Grey Wolf Optimizer (GWO) algorithm is employed to identify the acoustic cavity parameters of the model, resulting in an equivalent model that approximates the true wind noise response characteristics. Taking a prototype vehicle as an example, the equivalent wind noise SEA model is used to predict the wind noise response in the compartment under different design schemes. The average prediction error for the total sound pressure level is 1.47%, and the root mean square error of the spectrum is 1.23 dB. The results show that the equivalent model can accurately predict the invehicle wind noise response under different design schemes, thereby reducing the number of wind tunnel tests and having high engineering application value.

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.

A speed loop optimization strategy based on cascaded extended state observer (ESO) is proposed to address the insufficient transient response of permanent magnet synchronous motor (PMSM) for steering power oil pump application in pure electric commercial vehicles. An extended Kalman filter (EKF) is designed as the basis of position sensorless control, with the adaptive design to avoid the problems of complicated parameter tuning and slow convergence. The antidisturbance and tracking ability of the speed loop is improved by the cascaded observation of internal and external disturbances, and the use of the linear state error feedback control rate (LSEFC) for replacement of the traditional PI controller. The bench tests show that the sensorless control scheme proposed in this paper significantly reduces the position estimation error under dynamic and steadystate conditions, with a steadystate error of only 1.4°. The optimized speed loop control effectively improves the system's performance of disturbance rejection and transient response. The reliability test shows that the steering power motor controller operates stably without performance failure.

The total vehicle mass is an important parameter for both power and safety control of the vehicle, especially for heavyduty trucks. Based on the theory of vehicle longitudinal dynamics, a method for estimating the total vehicle mass according to the vehicle operating conditions is proposed in this paper. Firstly, under acceleration conditions, engine torque and longitudinal acceleration are obtained through CAN bus, using the Kalman filter algorithm (KF) to estimate the total vehicle mass. Then the estimated mass is used to identify the unknown parameters. At constant speed, the mass is estimated based on the identified unknown parameters and a simplified vehicle longitudinal dynamics model. The effectiveness of the method is verified through joint simulation suing the estimator constructed by TruckSim/Simulink. By the vehicle road test, the results show that the method is able to estimate the total vehicle mass more accurately under different operating conditions.

For safe and feasible pathplanning in real time of autonomous parking system, a parking path planning algorithm based on constrained reinforcement learning with a hybrid action space is proposed in this paper. Specifically, the proposed algorithm employs a hybrid action space reinforcement learning framework that integrates discrete actions with continuous parameters to achieve parameterized trajectory planning, thereby enhancing the executability of planned paths. On this basis, a constrained reinforcement learning algorithm within the hybrid action space is designed to optimize safe policy execution, ensuring the safety of parking paths. Moreover, a curriculum learning mechanism is introduced during model training to guide exploration progressively, improving training stability and convergence speed. Finally, extensive comparative and ablation experiments are conducted on both perpendicular and parallel parking scenarios. The experimental results show that the proposed parking path planning algorithm outperforms existing stateoftheart methods in terms of success rate, safety, and realtime performance, exhibiting superior overall effectiveness.

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.

Accurate and reliable vehicle pose estimation is a critical input for intelligent vehicle decision, planning and motion control modules. In this paper, a positioning algorithm that integrates realtime slip ratio estimation and compensation for intelligent vehicles is proposed, which significantly enhances the fusion positioning accuracy of the Inertial Navigation System (INS) and Wheel Speed Sensor (WSS) during Global Navigation Satellite System (GNSS) interruption. Firstly, a realtime slip ratio estimation algorithm is proposed to correct the wheel speed information for different driving conditions, which uses vehicle acceleration and wheel speed data. Then, based on errorstate Kalman filter (ESKF), the corrected wheel speed data is fused with GNSS and Inertial Measurement Unit (IMU) information to achieve accurate and reliable vehicle pose estimation. The results of the realvehicle experiments show that during GNSS interruption, the Root Mean Square Error (RMSE) of velocity improves by up to 30% and the average horizontal position error mileage ratio reaches 1.68%.