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%.

For the problem that the spatiotemporal separation trajectory planning method used in autonomous vehicles is prone to insufficient vehicle flexibility, and even cannot generate feasible trajectories under complex working conditions, while the existing spatiotemporal unified trajectory planning method is difficult to meet the requirements of structured road application, a spatiotemporal unified planning method based on dynamic programming and numerical optimization algorithm is proposed. Firstly, the spatiotemporal unified coarse trajectory is generated by dynamic programming algorithm in Frenet coordinate system. In the process, deterministic sampling method is used to expand the child nodes. Then, taking the coarse trajectory as reference, the feasible spatiotemporal corridor is constructed in Cartesian coordinate system, and the NMPC optimization model is established to generate the final trajectory. Finally, the algorithm is verified by simulation. The results show that the proposed algorism has good adaptability to structured road, and can better balance the requirements of traffic efficiency, trajectory comfort and time consumption than other spatiotemporal unified algorithms.

In this paper a simulation testing method for intelligent vehicle based on a large language model is proposed to address the issues of heavy reliance on human resources and prominent efficiency bottlenecks in existing scenario based testing methods. Firstly, a simulation testing architecture for intelligent vehicle based on a large language model is designed, and corresponding data and simulation layers are established. On this basis, an intelligent car simulation testing process based on a large language model is constructed. Knowledge mining, model finetuning, and knowledge base enhancement retrieval application processes are designed for knowledge question answering tasks. Application paths for scenario type analysis, scenario element generation, and scenario toolchain invocation are designed for scenario generation tasks. For testing and evaluation tasks, a comprehensive application framework for testing scenario analysis, evaluation system construction, and simulation testing execution is designed. Finally, each task is tested. The results show that the testing method proposed in this paper can effectively solve different types of testing tasks and improve testing efficiency.

In this paper, the characteristics of driver out of position and active and passive fusion damage caused by AES are studied by using finite element method for several typical collision conditions caused by automatic emergency steering (AES) intervention. The results show that AES can cause significant lateral displacement of the driver, and the out of position degree increases slightly with the increase of initial speed. High HIC15 and BrIC values are easily generated in oblique angle and side-to-side collision conditions due to high speed and hard contact. The risk of craniocerebral injury in side impact is greater, and the strain of liver and lung is greater than that of other internal organs. Overall, AES intervention results in more significant head, neck, and chest injuries in oblique and lateral near-end collision.

With the increasing power levels and integration of electric vehicles, the thermal load of power modules is rising rapidly, which puts higher demand on the thermal management technology of power modules. The topology optimization design of power module liquid cooled plates is becoming a key technology for achieving high heat flux density heat dissipation due to its high heat transfer and low-pressure drop loss characteristics. In this paper, based on the density topology method, a topology optimization design model is constructed for the flow channel structure of the power module liquid cooling plate. Through the coupling of multiple physical fields of flow and heat transfer; multi-objective topology optimization design for the flow channel of the liquid cooling plate is carried out. The results show that the topology-optimized liquid cooling plate design presents a multi-level biomimetic flow channel structure, which significantly reduces pressure drop loss and improves heat dissipation capacity. Compared to the traditional finned liquid cooling plate structure of the benchmark, the pressure drop loss of the flow channel structure after topology optimization is reduced by 72.8%, with a maximum temperature reduction of 33.28 K, which provides a new design idea for high-performance liquid cooling plates of automotive electronic control power modules.

The power system and energy consumption characteristics of electric buses significantly differ from those of traditional buses with internal combustion engines, and conventional ecodriving strategies cannot fully adapt to electric buses. An energy consumption predictionbased deep reinforcement learning model is proposed for ecodriving of connected electric buses, taking into account of signal timing, information from preceding vehicles, energy consumption characteristics and comfort of passengers. Firstly, natural driving data from battery electric buses is collected, and a basic energy consumption model is established using vehicle dynamics, considering the regenerative braking characteristics of electric buses. A system identification model is then constructed to identify and estimate the unknown parameters in the basic energy consumption model. Next, the impact of different signal phases on speed patterns when entering and exiting signalized intersections is analyzed, and state variables that accurately describe traffic environment information are determined. Based on the constructed energy consumption model, a reward function is developed, considering safety, efficiency, energy conservation, and comfort. An optimization model for ecodriving strategies at signalized intersections for electric buses is established using the SAC (soft actor critic) algorithm. Finally, the proposed strategy is compared with the classic intersection passage strategy GLOSA. The results show that the proposed ecodriving strategy ensures vehicle safety across the four defined traffic scenarios. Despite an average increase in travel time of only 7.29%, the strategy enhances comfort by an average of 21.96% and reduces energy consumption by an average of 24.47%.

Based on the research on the combination of vehicle application scenarios and electric drive torque near zero anti-jerk control requirements, in this paper four vehicle anti-jerk control requirements and their software architectures from the perspective of vehicle anti-jerk, including transmission clearance, torque limitation, electric drive torque near zero, and high-frequency de-noising of wheel speed fluctuation. Based on the requirements of anti-jerk control, vehicle anti-jerk algorithm architecture as well as the control strategy with electric motor torque near zero is designed, and the corresponding control process and calculation analysis are provided. Through simulation and real vehicle testing of the designed anti-jerk control strategy, it is proved that the control algorithm architecture and strategy can effectively achieve the anti-jerk function of the vehicle. According to the longitudinal acceleration curve of the real vehicle test and the fluctuation of the electric drive speed, it can be seen that the designed software architecture and control strategy have achieved good results in vehicle drivability.

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.

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.

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.