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%.

In the process of electrification and intellectualization of the automobile industry，the automotive safety testing and evaluation technology has also been extended and expanded from simple passive safety to active and passive safety integration. In this paper，the differences between the world's mainstream automotive safety assessment procedures are compared and analyzed from three aspects： occupant protection in the vehicle，vulnerable road user protection outside the vehicle，and active safety. The key technical points of vehicle safety development for each evaluation condition are summarized and the development trend of safety evaluation procedures for new energy and intelligent networked vehicles is discussed. The research concludes that the mainstream automotive safety evaluation procedures are becoming more and more stringent in passive safety evaluation，with the proportion of active safety evaluation conditions gradually increasing，and the development focus of the future evaluation procedures will focus on the integration of active and passive safety and virtual evaluation for complex working conditions. In addition，the battery safety test for new energy vehicles has been relatively perfect，and the future research focus can be expanded to the direction of electronic control system testing，chassis stability testing，and unified standardization certification of charging and swapping facilities and supporting equipment. In the medium and long term，the construction of reasonable and reliable evaluation methods such as OTA （over the air） testing of intelligent networked vehicles and HMI （human machine interface） safety and comfort will become a major difficulty concerned by the industry，and a composite evaluation system combining the virtual and reality can be built with the help of tools such as autonomous driving simulators.

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 recent years, the thermal runaway problem of lithiumion battery has become the main bottleneck restraining the development of power battery of new energy vehicles. In this paper, a comprehensive review of the research on the thermal runaway problem of the power battery of new energy vehicles is carried out, with the inducment of the thermal runaway of lithiumion battery expounded and the thermal runaway process of lithiumion battery and the characteristics of the thermal runaway of lithiumion battery under different variable conditions introduced. Based on the characteristic parameters of thermal runaway of lithiumion battery, the early warning methods and fire suppression methods applicable to lithiumion battery fire are reviewed, and the shortcomings and development trend of the current research on thermal runaway of power battery of new energy vehicles are summarized, providing certain reference for the development of power battery of new energy vehicles.

With the development of intelligent vehicles and autonomous driving technologies，zero-gravity seat，with occupant comfort as core function，has been equipped in some vehicles. Compared to upright seating，reclined occupants face a higher risk of injury in collision，making the development of crash safety solutions imminent. In this paper，a review of the current research status and development trends regarding the crash safety of reclined occupants is conducted，focusing on injury mechanism，restraint systems，and research tools. The findings are summarized as follows： （1） Injury patterns for reclined occupants differ from those for upright occupants，and the injury mechanism at typical sites such as the lumbar spine and the iliac crest have not been fully clarified. （2） Traditional restraint systems with three-point seat belts as the core，even after improvement and optimization，is still difficult to provide effective overall protection for reclined occupants. Development of new protective means that can reasonably balance submarine and spinal injuries under the integrated active-passive safety system is a key issue in crash protection research for reclined occupants. （3） Crash dummies and human body models （HBMs），as the primary research and evaluation tools，need to improve their usability and bio-fidelity for reclined conditions.

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.

Cell to body （CTB） is a key technology to improve the endurance mileage of electric vehicles. The VRB/OW-GFRP hybrid structure formed by the variable-thickness rolled blanks （VRB） structure and the orthotropic woven GFRP （OW-GFRP） through the bonding process is an innovative structure，which can reduce the weight of the CTB battery pack，thus improve the endurance mileage of electric vehicles. Taking an electric vehicle as the research object，a CTB battery body integrated structure is designed to realize the integration of the upper cover of the battery pack and the floor of car body. Furthermore，the cover assembly of the uniform thickness （UT） CTB battery pack are replaced by the VRB structure，UT/OW-GFRP and VRB/OW-GFRP hybrid structure. The lightweight design of the upper cover assembly of the three types of CTB battery pack are carried out based on the multi-stage optimization method. The results show that the weight of VRB structure is reduced by 6.4% compared with that of UT structure when the stiffness performance of the CTB battery pack is met. The lightweight level of the upper cover assembly of the CTB battery pack based on the VRB/OW-GFRP hybrid structure is about three times that of the metal structure，with the weight of the VRB/OW-GFRP reduced further by 4.2% compared with that of the UT/OW-GFRP hybrid battery pack upper cover assembly. Thus，the VRB/OW-GFRP hybrid structure is the inevitable trend of the development of automotive lightweight technology in the future，showing a great application prospect in CTB battery pack cover assembly.

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%.

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.