To address the rapid particle convergence and the decrease of particle diversity during map construction, as well as the tendency of the traditional DWA to become trapped in local optima during the path planning, the paper proposes two improvements for intelligent vehicles. The first improvement is an enhanced Gmapping algorithm based on K-Means hierarchical re-sampling. The particle set is clustered into high-, medium- and low- weight groups by using K-Means algorithm, and the weights are adjusted to slow down the decline in particle diversity, thereby improving mapping accuracy. The second improvement is an enhanced DWA path planning algorithm that fuses A* global guidance with turn-stability awareness. The adaptive velocity evaluation function considering the angular velocity magnitude, and a separate angular velocity evaluation function are added. The A* global path turning points serve as the key points to integrate the A* and DWA algorithms. Together, these two efforts improve the global optimization ability of the DWA algorithm. The simulation and real vehicle testing results show that the improved Gmapping algorithm increases the average number of effective particles by 4.6% during grid-map construction. The improved DWA algorithm reduces the number of global path turns by 67% and the search nodes by 37.5% under the set scenario, effectively improving the turning stability of intelligent vehicles.

In order to ensure the safety, comfort and fuel economy of hybrid electric vehicle platoon, a hierarchical control strategy based on intelligent transportation systems is proposed. The upper controller used vehicle-vehicle(V2V) communication technology and used Nonlinear Distributed Model Predictive Control (NDMPC) to optimize the speed control and calculate the optimal speed. The lower controller obtained the upper vehicle speed, used the Deep Deterministic Policy Gradient (DDPG) reinforcement learning algorithm for energy management, and embedded the engine best economic curve and battery characteristic curve as expert experience to improve the convergence and stability of the algorithm. The results show that under this strategy, the maximum relative error of vehicle distance is 4.83%, and the average acceleration of the platoon is 0.331 m/s². Compared with the Deep Q-Network (DQN) algorithm used in the lower layer, the fuel consumption is reduced by 14.25% on average, and the maximum is reduced by 15.30%. Compared with the DP algorithm, the average increase is 8.31%. It not only ensures the safety and comfort, but also effectively improves the economy of the platoon.

To address the issues of high pressure drop and poor temperature uniformity in traditional channel-type battery liquid cooling plates, a multi-objective topology optimization method was employed for the design optimization of the liquid cooling plate. An experimental model of battery heat generation was established, and a topology optimization model of the liquid cooling plate was constructed based on the variable density method. The impact of different inlet and outlet arrangements on the performance of the optimized liquid cooling plate was investigated, and the best-performing liquid cooling plate was selected and compared with the traditional straight-channel liquid cooling plate. The results indicate that the topology channels obtained under different inlet and outlet arrangements exhibit significant differences in temperature and pressure drop performance. When the inlet and outlet are arranged along the central symmetry line of the long edge of the liquid cooling plate, the topology-optimized liquid cooling plate demonstrates the best overall performance. Compared to the straight-channel liquid cooling plate, it exhibits stronger flow and heat transfer performance, with the maximum temperature, temperature standard deviation, and pressure drop reduced by 1.38%, 22.35%, and 28.36%, respectively, at an inlet flow rate of 5 g/s. This novel liquid cooling plate can provide new insights for the thermal design of future battery thermal management system.

Aerodynamic design is one of the key elements in Formula SAE (FSAE) car design, which not only has a decisive impact on the car's handling performance but also affects the cooling performance of the radiator. In this paper, the method of Computation Fluid Dynamics (CFD) is adopted to carry out the analysis and optimization of aerodynamic performance and heat dissipation performance of FSAE racing car considering the characteristics of the rear compartment structure. By analyzing the trend of changes in heat dissipation performance and aerodynamic performance, four representative schemes were selected for comparison. The results indicate a strong positive correlation between the optimized local aerodynamic performance and heat dissipation performance. After optimization, the average volume temperature of the radiator decreased by 29.82%, and the negative lift of the wing increased by 26.92%. This study provides new ideas for the CFD simulation of the full-car model of FSAE race cars, and also provides guidance for the layout design of the cooling device for open-wheel race cars with the radiator placed at the rear.

To resolve the difficulty of identifying representative data and the poor fatigue-damage consistency in low-sampling-rate online data when constructing electric vehicle load spectra on big data platforms, the paper proposes a method with strong user association for compiling the load spectrum of an electric vehicle drive system. First, on the big data platform, user characteristics are described from five dimensions: road type, driving style, load capacity, vehicle speed, and torque. Based on these user profiles, the paper proposes a global-optimal-pairing filter that selects a representative online user dataset, and applies a constraint-based fragment stitching method to join the data segments in order, establishing a multi-feature association between the load spectra and users. To improve damage consistency in low sampling rate online data, high-sampling-rate offline data collected from real vehicles are incorporated to enhance damage equivalence between the load spectra and users. The feature matching results show that the filtered data set deviates from the target user by only about 0.05 for each feature parameter, with no deviation exceeding 0.15. Fatigue-damage calculations confirm that the fusion of low-rate online data with high-rate offline data effectively enhances the damage equivalence between the load spectrum and the users.

In order to study the optimal posture angles and body-pressure distribution for passengers in an automotive zero-gravity seat, 30 subjects were recruited for subjective comfort assessments and static body pressure distribution tests. They adjusted the seat to their most comfortable position based on personal preference. Cameras recorded the resulting posture angles of each subject in the zero-gravity posture. Meanwhile, the pressure-sensing equipment captured the interface pressures between the human body and the seat. Non-parametric statistics was used to examine the influence of gender, stature percentile, and body mass index (BMI) on those posture angles and pressure distributions. The results show that gender significantly influences only the hip angle. Variations in stature percentile significantly affect the hip angle, the knee angle, and the mean pressure at the left shoulder. Changes in BMI significantly alter the mean pressure at the left shoulder region of the backrest, the lower back, and the entire backrest.

The strong uncertainty of acoustic material parameters leads to the poor stability of its sound absorption and insulation performance. In this paper, a method based on evidence theory is proposed to analyze the stability of acoustic materials. The uncertainty of acoustic material parameters is described by evidence theory, and the focal element interval and the basic confidence assignment of each parameter are determined. Using the interval perturbation method, the upper and lower bounds of sound absorption and insulation performance corresponding to all focal element interval combinations are calculated. The reliability and plausibility of uncertainty problems are calculated according to the identification framework. Reliability and plausibility are taken as optimization objectives, and particle swarm optimization is used to improve the stability of acoustic material's sound absorption and insulation performance. Taking an inner front wall as an example, the stability analysis and optimization of the acoustic insulation performance of the acoustic material were carried out. After optimization, the quality of the part was reduced by 18%, and the stability of the acoustic insulation performance was greatly improved in the whole frequency band, especially in the middle and low frequency band.

Biodiesel is a good substitute for petroleum diesel, because of its good environmental performance, good engine starting performance, good fuel performance, wide source of raw materials, renewable and so on, it has a wide application prospect. However, the fat composition and content of different raw materials are different, and the performance and emission characteristics of the prepared biodiesel also show different trends during combustion. Based on the types of raw materials for biodiesel production, this paper classifies biodiesel and summarizes the characteristics of different kinds of biodiesel. Firstly, the physical and chemical properties and the preparation process of biodiesel was introduced in detail. Secondly, this paper is different from the classification of biodiesel based on the technological iteration of biodiesel production, but classifies biodiesel according to the source of raw materials, which provides a basis for the summary of the table and the horizontal comparison of biodiesel produced from various raw materials. Then, the use characteristics and changing trends of biodiesel prepared from different kinds of raw materials are summarized and sorted out in the table, and the summarized experimental results are visually presented, the characteristics of biodiesel produced from different raw materials were evaluated, in all the studies on biodiesel, it has been shown that in terms of pollution emissions, the engine found that the emission of CO and carbon oxides was reduced when using most biodiesel, and most studies showed that the emission of hydrocarbons was also reduced, but the emission of NO_x was increased, which is almost a common feature of all different types of biodiesel. Finally, according to the limitations and shortcomings of different types of biodiesel in the use process, the future direction of biodiesel use characteristics optimization was proposed.

Traffic accident mortality, as one of the most important safety indicators, is rarely considered in current AEB performance evaluation studies. Given that the pedestrians have the highest fatality rate in traffic accidents, the paper proposes an evaluation method for pedestrian AEB performance field testing. The pedestrian AEB test scenarios are constructed according to the C-NCAP and I-VISTA protocols and a performance evaluation model is established by using the analytic hierarchy process. The proposed test and evaluation model is verified through real vehicle testing. The results show that the pedestrian AEB performance score of the test vehicle is 7.23, which is much lower than the benchmark score of 34.05. This indicates that the AEB performance of the vehicle is only average.

At present, the vehicle side impact safety evaluations rarely consider the effect of pre-crash braking on occupant posture. In order to study the occupant displacement induced by braking during the pre-crash phase and its impact on occupant kinematics and injury outcomes under side impact conditions, the paper combines volunteer experiments with CAE simulations. Three side impact models, including a standard posture model, a muscle-tensed model, and a muscle-relaxed model, were developed to compare differences in occupant kinematics and key injury indicators across the models. The results show that, during braking, the volunteers experienced greater displacement under the relaxed muscle state, with the maximum displacements of the head and first thoracic vertebra (T1) reaching 225 mm and 145 mm, respectively. This displacement significantly changed the contact between the upper body of the dummy and the side restraint system during the side impact, creating a risk that the dummy's chest could move outside the effective protection zone of side airbag. The peak Y-acceleration of the head was increased by 163.98 m/s²(79.4%), and the maximum abdominal compression was increased by 13.53 mm (64.0%) during the side impact. These results provide valuable insights for the development of advanced restraint systems and integrated safety testing methods.