To address the issue of large roll angle rates in the steadystate circular testing of a light commercial vehicle, its suspension system is optimized and improved. The multibody dynamics model of the vehicle is established using ADAMS/car. The accuracy of the suspension simulation model is verified by the antiphase parallel wheel travel test for the front suspension and theoretical calculations for the rear suspension. Through simulation analysis of the vehicle's steadystate circular test and oncenter steering test, it is concluded that the roll angle rate is higher than desired. To achieve the automated process of stability optimization analysis, a cosimulation method based on modeFRONTIER is proposed. Taking the suspension design parameters as optimization variables, and targeting the roll angle rate and yaw rate time delay as the optimization objectives, a hybrid agent model was fitted using the Latin hypercube experiment design method. This model was combined with the multiobjective particle swarm optimization algorithm (MOPSO) to carry out the multiobjective optimization of the suspension system, and the optimization scheme of the suspension system is obtained. The optimization results show that, while maintaining ride comfort, the roll angle rate is reduced by 13.93% and the yaw rate time delay is reduced by 2.75%, resulting in improved vehicle control and stability.

Simultaneous localization and mapping (SLAM) technology enables autonomous vehicles to estimate their own poses and establish the map of an unknown environment according to the data collected by onboard sensors. SLAM can provide localization information to the decisionmaking module for vehicle planning, and has become one of the research hotspots of autonomous driving technology in recent years. Based on the point cloud data collected by LiDAR, this paper focuses on the SLAM technology applied in autonomous driving. The related research at home and abroad has been reviewed including the frontend odometry, the backend optimization and loop closure detection. Due to the limitations of a single sensor, the opportunities and challenges of multisensor fusion SLAM technology for autonomous driving are discussed based on the research hotspots and difficulties in the field of multisensor fusion.

The hollow roof spoiler has become a new trend in automotive styling design, significantly impacting the vehicle's aerodynamic performance. To study the influence of the spoiler parameters on aerodynamic drag coefficient, CFD simulations were conducted to analyze cases with different spoiler angles, passage sizes, and Dpillar spoilers based on a BYD hatchback model with a hollow roof spoiler. The simulation results agree well with wind tunnel experiment results. It is found that the volume of the upper tail flow increases with a decrease in the spoiler angle, a reduction in the passage size or the addition of a Dpillar spoiler. A lower drag coefficient is obtained when the upper tail flow more effectively matches with the lower tail flow. The findings of this paper are valuable for guiding future designs of hollow roof spoilers.

To quantitatively evaluate the environmental benefits of the recycling process for new energy vehicle power batteries, and to provide support for the improvement of the management system and relevant standards for endoflife power batteries, thereby assisting in the implementation of the national “twocarbon" goal, the paper selects ternary lithium batteries as the research subject. The paper identifies typical scenarios for power battery recycling and utilization, divides the lifecycle of ternary lithium batteries into four stages: raw materials acquisition, manufacturing and assembly, usage, and scrap recovery and establishes the corresponding GaBi model. Based on different recycling methods, such as wet recycling A, wet recycling B, and firewet combined recovery, a life cycle evaluation model for ternary lithium batteries was built and the carbon reduction efficiency of the recycling and reuse process was calculated. The research results show that the recycling of ternary lithium batteries significantly reduces carbon reductions. Specifically, the carbon dioxide emission reductions for each recycling method are as follows: 60.71 kg CO2/kWh for wet recycling A, 150.00 kg CO2/kWh for wet recycling B, and 153.57 kg CO2/kWh for firewet combined recycling. The carbon dioxide emission reduction efficiency of the three different recovery processes is ranked from best to worst as follows: firewet combined recovery, wet recovery B and wet recovery A. Proper recycling of power batteries can significantly reduce carbon emissions during the recycling process, thus producing greater environmental benefits.

The forward development of vehicle integration indicates the R&D capabilities of the automobile industry. Therefore the paper studied the evaluation and comparison of the forward development of vehicle integration among different automobile companies. According to the work content of forward development, a Vshaped development process was adopted and revised. An model involving 9 firstlevel indicators and 33 secondlevel indicators was built to evaluate the forward development of vehicle integration of seven Chinesebranded vehicle companies. It is found that the maximum value of the weight coefficient of product planning leads to the largest value of the forward development capability. The Company J with the best comprehensive performance has clear advantages in product planning, project management, vehicle function definition and vehicle performance integration without any unfavorable indicators. The evaluation scores of other companies are affected by their unfavorable indicators.

Focusing on a hybrid electric vehicle equipped with a lithium battery and supercapacitor, the paper establishes a realtime wavelet transformfuzzy control energy management control strategy. The objective is to enable the supercapacitor to handle high frequency signals at a reasonable state of charge (SOC) and the lithium battery to manage low frequency signals, all under the premise of satisfying dynamic performance requirements. Secondly, the vehicle model was developed based on Matlab/Simulink and ADVISOR software, and simulation tests were carried out under NEDC cycle conditions. The simulation results show that, compared with the single lithium battery, the peak driving current of the lithium battery in the composite power supply is reduced by 20.68% and its lifespan is increased by 16.75% under the wavelet transformfuzzy control strategy. Finally, a scaleddown experimental platform of the composite power system was built and tested under NEDC conditions. The experimental results show that the proposed control strategy effectively manages the energy of the hybrid electric vehicle.

According to the definition rule of A2L file in automobile calibration system, the regular expression is designed to match the internal key information of A2L file, and the information of A2L file is represented by class structure and multitree model. In addition, the corresponding general analysis flow is proposed for different data modules in the file. Aiming at the problems of measurement value visualization and calibration value download, the conversion formula of raw quantity to physical quantity is designed according to various predefined types and conversion methods, which can provide conversion tools for upper computer software. The A2L data analysis management software is developed by using C# and window applications to realize the visual management and modification of the analysis results. The software design pattern of "high cohesion and low coupling" and dynamic link library are used to further develop the measurement calibration software. Finally, the effectiveness of the proposed A2L file parsing method is verified by experiments.

As the first pure electric sedan of BYD TOYOTA EV Technology Co., Ltd, the bZ3 has extremely high energy consumption requirements, which brings great challenges to its aerodynamic development. To achieve this target, CFD simulations and wind tunnel tests were conducted by the aerodynamics team. Optimizations were primarily focused on areas including styling, undercover design, cooling air inlet management, wheels and sealing. The final production car was tested in the wind tunnel, achieving a drag coefficient of 0.218, which is at a leading level among vehicles in its class. Different CFD methods were compared with test results, showing that the LBM has better accuracy, although its precision in simulating underbody airflow still needs to be improved.

Due to factors that the wiper swing frequency does not match the structure of the waterguiding channel and the air conditioning (AC) external circulation inlet, some vehicle models under rainy conditions experience water droplets or even water flow entering the AC system through the inlet, leading to the unpleasant odors from the air conditioning, and even water leakage into the passenger compartment, causing serious perceived quality issues. By employing the SPH method considering wiper movement, body posture, and the effects of airflow conditions, the paper uses PreonLab software to simulate a vehicle in rainy scenarios to address the water management issues. By adding a baffle at the position of the AC external circulation inlet, the entry of the rainwater into the AC system is significantly reduced, and this solution is verified through the real vehicle tests under rainy conditions. The proposed method can find and rectify the unreasonable flow path designs for vehicles in the rainy scenarios during the development stage without relying on real vehicle tests. The method can also be applied to other performance quality control of vehicle water management, such as wading and rain pollution.

Road surface unevenness significantly affects both the driving safety of road vehicles and their dynamic responses. However, the existing datadriven methods for road surface classification struggle to efficiently handle timevarying parameters and vehicle speeds. Meanwhile, the existing modelbased road surface recognition algorithms require known and accurate vehicle models, facing the challenge of acquiring vehicle physical parameters in realworld applications. This paper proposes a novel pavement classification algorithm that begins by backcalculating the equivalent pavement profile, followed by data preprocessing. Subsequently, it computes time and frequency domain features for the equivalent pavement profile and response information, and key features are extracted using the ReliefF algorithm. A radial basis function neural network is used to construct a classifier for pavement grading and recognition. Finally, the robustness of the proposed algorithm is verified through simulation tests and realvehicle tests with different vehicle parameters and speeds.