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In this paper, a case study of Shanghai HEVs application and its effects on the social and environmental benefits are presented based on the multi views on the different aspects, such as, not only for the fuel consumption saving, but also emissions reduction and health effect, agriculture loss and cleaning cost. The results show that the potential benefits for the society from HEVs application are markedly with the increase of the ratio of HEV in the population of vehicle. Based on this, the policy to promote the HEV purchased by consumers is very important at the beginning of HEV into market.

Nowadays researches of automotive electromagnetic field mainly focus on the component level and electromagnetic compatibility, while there is a lack of relevant studies on internal electromagnetic environment of the vehicles. With the increasingly complex internal electromagnetic environment of the vehicle, people are increasingly concerned about its potential impact of human health. This article researches on a type of electric vehicle and the occupants and analyses its electromagnetic radiation effects on human health. Firstly, considering the characters of Pro/E, Hypermesh and FEKO, the “Characteristics grouping subdivision” method is used to establish the entire vehicle body FE model. According to the requirement of MOM/FEM method, the entire vehicle model is optimized to be a high quality body model with simple construction and moderate grid size.

Because of the high frequency and serious consequences of traffic accidents in the intersection area, it is of great significance to study the vehicle conflict and collision scenarios of the intersection area. Due to few actual crash accidents occurring in naturalistic driving studies data or field operational tests data, the data of traffic accident database should be also used to analyze the intersection conflict and collision. According to the China Field Operation Test (China-FOT) database and the China in Depth Accident Study (CIDAS) database, the distribution feature of the respective intersection scenario type is obtained from the data analysis. Based on the intersection scenario type, two characters of intersection conflict and collision, the environmental character and the vehicle dynamic character, are used to analyze for the integration process of intersection conflict and collision.

The performance of the rear axle plays an important role in the performance of vehicle, and its fatigue durability is an integral part in the vehicle development. Taking a SUV model as the research subject, a new methodology of multi-channel spindle coupled road simulator and fatigue simulation analysis for rear axle assembly was introduced in the paper, aiming to address the fatigue design and its verification for the rear axle in the development phase. Firstly, road loads in the proving ground was collected by arranging proper sensors. Secondly, physical iteration was performed on the multichannel spindle coupled road simulator by taking six component forces at the wheel hub as the target signals. Then, after the time waveform replication of the loads the durability test was conducted. Finally, the validated simulation model was successfully implemented to improve the fatigue life of the axle.

Automotive window seal has great influence on NVH (Noise-Vibration-Harshness) performance. The aerodynamic effect on ride comfort has attracted increasing research interest recently. A new method for quantifying and transferring aerodynamics-induced load on window seal re-design is proposed. Firstly, by SST (Shear Stress Transport) turbulence model, external turbulent flow field of full scale automotive is established by solving three-dimensional, steady and uncompressible Navier-Stokes equation. With re-exploited mapping algorithm, the aerodynamics pressure on overall auto-body is retrieved and transferred to local glass area to be external loads for seals, thus taking into account the aerodynamics effect of high speed fluid-structure interaction. This method is successfully applied on automotive front window seal design. The re-design header seal decreases the maximum displacements of leeward and windward glass with 9.3% and 34.21%, respectively.

Fuel cell vehicle commercialization and mass production are challenged by the durability of fuel cells. In order to research the durability of fuel cell stack, it is necessary to carry out the related durability test. The performance prediction of fuel cell stack can be based on a short time durability test result to accurately predict the performance of the fuel cell stack, so it can ensure the timeliness of the test results and reduce the cost of test. In this paper, genetic algorithm-BP neural network (GA-BPNN) is proposed to modeling automotive fuel cell stack to predict the performance of it. Based on the strong global searching ability of genetic algorithm, the initial weights and threshold selection of neural networks are optimized to solve the shortcoming that the random selection of the initial weights and thresholds of BP neural network which can easily lead to the local optimal value.

Visual sensors are widely used in autonomous vehicles (AVs) for object detection due to the advantages of abundant information and low-cost. But the performance of visual sensors is highly affected by low light conditions when AVs driving at nighttime and in the tunnel. The low light conditions decrease the image quality and the performance of object detection, and may cause safety of the intended functionality (SOTIF) problems. Therefore, to analyze the performance limitations of visual sensors in low light conditions, a controlled light experiment on a proving ground is designed. The influences of low light conditions on the two-stage algorithm and the single-stage algorithm are compared and analyzed quantificationally by constructing an evaluation index set from three aspects of missing detection, classification, and positioning accuracy.

This study investigated the effects of underhood structure parameters (two types of air ducts, two types of inlet grilles and the opening angle of inlet grilles) on the cooling characteristics of the rear-engine bus; then, the optimum design scheme of the underhood was determined. The air-side resistance load of the cooling system, which is based on fan performance, was selected as the optimization objective. Simulations were created based on a porous media model and standard a k-ε model. The next step was to build a 1D/3D coupling simulation to utilize the advantages of 1D simulation’s fast convergence speed and 3D simulation’s extensive research range. Besides, the use of 1D/3D coupling simulation can efficiently avoid the errors of simulation results which arise from the non-uniform airflow on the cooling module. Results show that the airflow rate of the rectangular air duct increased by 7 to 11percent.

Object detection is an important visual content of the autonomous vehicle, the traditional detecting methods usually cost a lot of computational memory and elapsed time. This paper proposes to use lightweight deep convolutional neural network (MobilenetV3-SSDLite) to carry out the object detection task of autonomous vehicles. Simulation analysis based on this method is implemented, the feature layer obtained after h-swish activation function in the first Conv of the 13th bottleneck module in MobilenetV3 is taken as the first effective feature layer, and the feature layer before pooling and convolution of the antepenultimate layer in MobilenetV3 is taken as the second effective feature layer, and these two feature layers are extracted from the MobilenetV3 network.

The effect of jet geometry on flow, heat transfer and defrosting characteristics was numerically investigated for elliptic and rectangular impinging jets on an automobile windshield. Initially, various turbulence models within the commercial computational fluid dynamics (CFD) package FLUENT were employed and validated for a single jet, and the results indicated that the impinging jet heat transfer was more accurately predicted by the SST k -ω turbulence model, which was then utilized for this study. The aspect ratios (AR) of elliptic and rectangular jets were respectively 0.5, 1.0, and 2.0, with jet-to-target spacing h/d=2, 4 and jet-to-jet spacing c/d=4, and all those situations were numerically analyzed with the same air mass flow and jet open area. It was observed that the heat transfer coefficient and defrosting performance of the inclined windshield were significantly affected by the shape of the jet, and the best results were obtained with the elliptic jet arrangements.

For the DCC (Dynamic Chassis Control) system, in addition to the requirement of ride and comfort, it is also necessary to consider the requirement of handling and stability, and these two requirements are often not met at the same time. This poses a great challenge to the design of the controller, especially in the face of complex working conditions. In order to solve this problem, this paper proposes a comprehensive DCC controller that considers road roughness class recognition. Firstly, a quarter vehicle model is established, the road surface roughness is calculated from the vertical acceleration of the wheels measured by the sensors. Then we calculate the autocorrelation function and the Fourier transform to estimate the PSD (Power Spectral Density) to get the road roughness class. Then control algorithms are designed for the vertical motion control, roll control and pitch control.

Aerodynamic noise transmits through automotive window, causing great adverse influence on comfortability and noise-vibration-harshness (NVH) performance. However, the complicated external turbulent air flow, as well as the internal metal-rubber nonlinear sealing constraint, makes the mechanism of aerodynamic noise generation and transmission very difficult. Regarding the complex exterior aerodynamics-induced load and nonlinear metal-rubber interaction and constraint, an efficient two-step numerical prediction method is presented in order to study the mechanism of its generation and transmission. The first step uses the commercial ANSYS-Fluent computational fluid dynamics (CFD) analysis based on the shear stress transport (SST) - turbulence kinetic energy (k) - the rate of dissipation of turbulence kinetic energy ε (epsilon) model and Lighthill’s noise source theory.

The accuracy of parking slot detection is a challenge for the safety of the Automated Valet Parking (AVP), while traditional methods of range sensor-based parking slot detection have mostly focused on the detection rate in a scenario, where the ego-vehicle must pass by the slot. This paper uses three-dimensional Light Detection And Ranging (3D LiDAR) to efficiently search parking slots around without passing by them and highlights the accuracy of detecting and tracking. For this purpose, a universal process of 3D LiDAR-based high-accuracy slot perception is proposed in this paper. First, the method Minimum Spanning Tree (MST) is applied to sort obstacles, and Separating Axis Theorem (SAT) are applied to the bounding boxes of obstacles in the bird’s-eye view, to find a free space between two adjacent obstacles. These bounding boxes are obtained by using common point cloud processing methods.

Hot forming process of ultrahigh strength boron steel 22MnB5 is widely applied in vehicle industry. It is one of the most effective approaches for vehicle light weighting. Dynamic recovery is the major softening mechanism of the boron steel under austenite state at elevated temperatures. Deformation mechanism of the boron steel can be revealed by investigation on the behavior of dynamic recovery, which could also improve the accuracy of forming simulations for hot stamping. Uniaxial tensile experiments of the boron steel are carried out on the thermo-mechanical simulator Gleeble3800 at elevated temperatures. The true stress-strain curves and the relations between the work hardening rate and flow stress are obtained in different deformation conditions. The work hardening rate decreases linearly with increasing the flow stress.

With the development of automotive HMI and mobile internet, many interactive modes are available for drivers to fulfill the in-vehicle secondary tasks, e.g. dialing, volume adjustment, music playing. For driving safety and drivers’ high expectation for HMI, it is urgent to effectively evaluate interactive mode with good efficiency, safety and good user experience for each secondary tasks, e.g. steering wheel buttons, voice control. This study uses a static driving simulation cockpit to provide driving environment, and sets up a high-fidelity driving cockpit based on OKTAL SacnerStudio and three-dimensional modeling technology. The secondary tasks supported by HMI platform are designed by customer demands research. The secondary task test is carried out based on usability test theory, and the influence on driving safety by different interactive modes is analyzed.

The roof sensor system is an indispensable part of intelligent vehicles to observe the environment, however, it deteriorates the aerodynamic and noise performance of the vehicle. In this paper, large eddy simulation and the acoustic perturbation equation are combined to simulate the flow and sound fields of the intelligent vehicle. Firstly, test and simulation differences of aerodynamic drag and pressure coefficients on the roof and rear of the intelligent vehicle without roof sensor system are discussed. It is found that the difference in aerodynamic drag coefficient is 5.5%, and the pressure coefficients’ differences at 21 out of 24 measurement points are less than 0.05. On this basis, under the influence of the sensor system, the aerodynamic drag coefficient of the intelligent vehicle is increased by 23.4%.

Driving posture study is essential for the evaluation of the occupant packaging. This paper presents a method of reconstructing driver’s postures in a real vehicle using a 3D laser scanner and Human Builder (HB), the digital human modeling tool under CATIA. The scanning data was at first converted into the format readable by CATIA, and then a personalized HB manikin was generated mainly using stature, sitting height and weight. Its pelvis position and joint angles were manually adjusted so as to match the manikin with the scan envelop. If needed, a fine adjustment of some anthropometric dimensions was also preceded. Finally the personalized manikin was put in the vehicle coordinate system, and joint angels and joint positions were extracted for further analysis.

The driving conditions of commercial logistics vehicles have the characteristics of combined urban and suburban roads with relatively fixed mileage and cargo load alteration, which affect the vehicular fuel economy. To this end, an adaptive equivalent consumption minimization strategy (A-ECMS) with vehicle speed and weight recognition is proposed to improve the fuel economy for a range-extender electric van for logistics in this work. The driving conditions are divided into nine representative groups with different vehicle speed and weight statuses, and the driving patterns are recognized with the use of the bagged trees algorithm through vehicle simulations. In order to generate the reference SOC near the optimal values, the optimal SOC trajectories under the typical driving cycles with different loads are solved by the shooting method and the optimal slopes for these nine patterns are obtained.

It is very important to investigate how road irregularity excitation will affect the durability, reliability, and performance degradation of fuel cell vehicle powertrain and its key components, including the electric motor, power control unit, power battery package and fuel cell engine system. There are very few published literatures in this research area. In this paper, an elementary but integrated experimental work is described, including the real road load sample on proving ground, road load reproduction on vibration test rig, total vehicle road simulation test and key components vibration tests. Remote parameter control technology is adopted to reproduce the real road load on road simulator and six-degree-of-freedom vibration table, which is used respectively for total vehicle and components vibration tests.

External rear view mirror is attached at the side of the vehicle which is to permit clear vision for the driver to the rear of the vehicle. When the vehicle is running, the flow field around external rear view mirror is highly three-dimensional, unsteady, separated and turbulent which is known to be a significant source of aerodynamic noise and a contributor to the total drag force on the vehicle. While among all the researches on the flow field around external rear view mirror, different installation environment were employed. The external rear view mirror is mounted on a production car in most researches which presents the real condition and it can also be mounted on the ground of a wind tunnel, a specially designed table, or a generic vehicle model based on the SAE model. While, the relationship between the flow field around external rear view mirror and the installation environment is not very clear.