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Polyvinyl Butyral (PVB) laminated glass has been widely used in automotive industry as windshield material. Cracks on the PVB laminated glass contain large amount of impact information, which can contribute to accident reconstruction investigation. In this study, the impact-induced in-plane dynamic cracking of the PVB laminated glass is investigated. Firstly, a drop-weight combined with high-speed photography experiment device is set up to investigate the radial cracks propagation on the PVB laminated glass sheet. Both the morphology and the velocity time history curve of the radial cracks are recorded and analyzed to investigate the basic mechanism of the crack propagation process. Afterwards, a three-dimensional laminated plate finite element (FE) model is set up and dynamic cracking process is simulated based on the extended finite element method (XFEM).

Detailed chemical kinetics is essential for accurate prediction of combustion performance as well as emissions in practical combustion engines. However, implementation of that is challenging. In this work, dynamic adaptive chemistry (DAC) is integrated into large eddy simulations (LES) of an n-heptane spray flame in a constant volume chamber (CVC) with realistic application conditions. DAC accelerates the time integration of the governing ordinary differential equations (ODEs) for chemical kinetics through the use of locally (spatially and temporally) valid skeletal mechanisms. Instantaneous flame structures and global combustion characteristics such as ignition delay time, flame lift-off length (LOL) and emissions are investigated to assess the effect of DAC on LES-DAC results. The study reveals that in LES-DAC simulations, the auto-ignition time and LOL obtain a well agreement with experiment data under different oxygen concentrations.

Modelling of disc in brake squeal analysis is complicated because of the rotation of disc and the sliding contact between disc and pads. Many analytical or analytical numerical combined modeling methods have been developed considering the disc brake vibration and squeal as a moving load problem. Yet in the most common used complex eigenvalue analysis method, the moving load nature normally has been ignored. In this paper, a new modelling method for rotating disc from the point of view of modal is presented. First finite element model of stationary disc is built and modal parameters are calculated. Then the dynamic response of rotating disc which is excited and observed at spatial fixed positions is studied. The frequency response function is derived through space and time transformations. The equivalent modal parameter is extracted and expressed as the function of rotation speed and original stationary status modal parameters.

The study and prevention of unstable vibration is a challenging task for vehicle industry. Improving predicting accuracy of braking squeal model is of great concern. Closed-loop coupling disc brake model is widely used in complex eigenvalue analysis and further analysis. The coupling stiffness of disc rotor and pads is one of the most important parameters in the model. But in most studies the stiffness is calculated by simple static force-deformation simulation. In this paper, a closed-loop coupling disc brake model is built. Initial values of coupling stiffness are estimated from static calculation. Experiment modal analysis of stationary disc brake system with brake line pressure and brake torques applied is conducted. Then an optimization process is initiated to minimize the differences between modal frequencies predicted by the stationary model and those from test. Thus model parameters more close to reality are found.

Fuel quality has a significant influence on the combustion engine operation. In recent years the increasing concerns about environmental protection, energy saving, energy security and the requirements of protecting fuel injection and aftertreatment systems have been major driving forces for the Chinese fuel specification evolution. The major property changes in the evolution of Chinese national gasoline and diesel standards are introduced and the reasons behind these changes are analyzed in this paper. The gasoline fuel development from State I to State VI-B involved a decrease of sulfur, manganese, olefins, aromatics and benzene content. The diesel fuel quality improvement from State I to State VI included achieving low sulfur fuels and a cetane number (CN) increase. Provincial fuel standards, stricter than corresponding national standards, were implemented in economically developed areas in the past.

A computational fluid dynamics (CFD) guided design optimization was conducted for the piston bowl geometry for a heavy-duty diesel engine. The optimization goal was to minimize engine-out NOx emissions without sacrificing engine peak power and thermal efficiency. The CFD model was validated with experiments and the combustion system optimization was conducted under three selected operating conditions representing low speed, maximum torque, and rated power. A hundred piston bowl shapes were generated, of which 32 shapes with 3 spray angles for each shape were numerically analyzed and one optimized design of piston bowl geometry with spray angle was selected. On average, the optimized combustion system decreased nitrogen oxide (NOx) emissions by 17% and soot emissions by 41% without compromising maximum engine power and fuel economy.

The performance of electric vehicles could be enhanced by more flexible drivetrain configurations combined with advanced control methods. Based on four wheel independent driving and front and rear axle modular steering configuration, which was proposed by our research group last year, the problem of slippery under close-to-limit conditions are further discussed and simulated. A new torque vectoring method based on obtainable parameters and variables in real driving situations is introduced to reduce the sideslip when turning on low friction surfaces or with high speed. This method is developed from a comprehensive index, which reflects the stability and maneuverability, by adding additional torques when stability could not be compensated enough by basic torque vectoring. Besides, an improvement of adding a simu-Torsen differential mechanism is made to the model of the vehicle, which enables another control method with the same purpose as before.

Transport vehicles consume a large amount of fuel with low efficiency, which is significantly affected by drivers' behaviors. An assessment system of eco-driving pattern for buses could identify the deficiencies of driver operation as well as assist transportation enterprises in driver management. This paper proposes an assessment method regarding drivers' economic efficiency, considering driving conditions. To this end, assessment indexes are extracted from driving economy theories and ranked according to their effect on fuel consumption, derived from a database of 135 buses using multiple regression. A layered structure of assessment indexes is developed with application of AHP, and the weight of each index is estimated. The driving pattern score could be calculated with these weights.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

This article is concerned with identification of true stress-strain curve under biaxial tension of thermoplastic polymers. A new type of biaxial tension attachment was embedded first in a universal material test machine, which is able to transform unidirectional loading of the test machine to biaxial loading on the specimen with constant velocity. Cruciform specimen geometry was optimized via FE modeling. Three methods of calculating true stress in biaxial tension tests were compared, based on incompressibility assumption, linear elastic theory and inverse engineering method, respectively. The inverse engineering method is more appropriate for thermoplastic polymers since it considers the practical volume change of the material during biaxial tension deformation. The strategy of data processing was established to obtain biaxial tension true stress-strain curves of different thermoplastic polymers.

Traffic congestions are increasingly severe in urban areas, especially at the merging areas of the ramps and the arterial roads. Because of the complex conflict relationship of the vehicles in ramps and arterial roads in terms of time-spatial constraints, it is challenging to coordinate the motion of these vehicles, which may easily cause congestions at the merging areas. The connected and automated vehicles (CAVs) provides potential opportunities to solve this problem. A centralized merging control method for CAVs is proposed in this paper, which can organize the traffic movements in merging areas efficiently and safely. In this method, the merging control model is built to formulate the vehicle coordination problem in merging areas, which is then transformed to the discrete nonlinear optimization form. A simulation model is built to verify the proposed method.

Threat assessment (TA) method is vital in the decision-making process of intelligent vehicles (IVs), especially for ADAS systems. In the research of TA, the probabilistic threat assessment (PTA) method is acting an increasing role, which can reduce the uncertainties of driver’s maneuvers. However, the driver behavior model (DBM) used in present PTA methods was mainly constructed by limited data or simple functions, which is not entirely reasonable and may affect the performance of the TA process. This work aims to utilize crash data extracted from Event Data Recorder (EDR) to establish more accurate DBM and improve the current PTA method in rear-end situations. EDR data with responsive maneuvers were firstly collected, which were then employed to construct the initial DBM (I-DBM) model by using the multivariate Gaussian distribution (MGD) framework. Besides, the model was further subdivided into six parts by two important risk indicators, Time-to-collision (TTC) and velocity.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

The combustion characteristics and soot emissions of three types of fuels were studied in a high pressure and temperature vessel. In order to achieve better volatility, proper cetane number and high oxygen content, the newly designed WDEP fuel was proposed and investigated. It is composed of wide distillation fuel (WD), PODE3-6 mixture (PODEn) and ethanol. For comparison, the test on WD and the mixture of PODEn-ethanol (EP) are also conducted. OH* chemiluminescence during the combustion was measured and instantaneous PLII was also applied to reveal the soot distribution. Abel transformation was adopted to calculate the total soot of axisymmetric flame. The results show that WDEP has similar ignition delays and flame lift-off lengths to those of WD at 870-920 K. But the initial ignition locations of WDEP flame in different cycles were more concentrated, particularly under the condition of low oxygen atmosphere.

Lane-changing systems have been developed and applied to improve environmental adaptability of advanced driver assistant system (ADAS) and driver comfort. Lane-changing control consists of three steps: decision making, trajectory planning and trajectory tracking. Current methods are not perfect due to weaknesses such as high computation cost, low robustness to uncertainties, etc. In this paper, a novel lane changing control method is proposed, where lane-changing behavior is analogized to parallel parking behavior. In the perspective of host vehicle with lane-changing intention, the space between vehicles in the target adjacent lane can be regarded as dynamic parking space. A decision making and path planning algorithm of parallel parking is adapted to deal with lane change condition. The adopted algorithm based on rules checks lane-changing feasibility and generates desired path in the moving reference system at the same speed of vehicles in target lane.

Studying drifting dynamics and control could extend the usable state-space beyond handling limits and maximize the potential safety benefits of autonomous vehicles. Distributed electric vehicles provide more possibilities for drifting control with better grip and larger maximum drift angle. Under the state of drifting, the distributed electric vehicle is a typical nonlinear over-actuated system with actuator redundancy, and the coupling of input vectors impedes the direct use of control algorithm of upper. This paper proposes a novel automated drifting controller for the distributed electric vehicle. First, the nonlinear over-actuated system, comprised of driving system, braking system and steering system, is formulated and transformed to a square system through proposed integrative recombination method of control channel, making general nonlinear control algorithms suitable for this system.

A novel fault tolerant brake strategy for In-wheel motor driven electric vehicles based on integral sliding mode control and optimal online allocation is proposed in this paper. The braking force distribution and redistribution, which is achieved in online control allocation segment, aim at maximizing energy efficiency of the vehicle and isolating faulty actuators simultaneously. The In-wheel motor can generate both driving torque and braking torque according to different vehicle dynamic demands. In braking procedure, In-wheel motors generate electric braking torque to achieve energy regeneration. The strategy is designed to make sure that the stability of vehicle can be guaranteed which means vehicle can follow desired trajectory even if one of the driven motor has functional failure.

In low-temperature environment, heat supply requires considerable energy, which significantly increases energy consumption and shortens the mileage of electric vehicle. In the fuel cell vehicles, waste heat generated by the fuel cell system can supply heat for vehicle. In this paper, a thermal management system is designed for a the fuel cell interurban bus. Thermal management strategy aiming at temperature regulation for the fuel cell stack and the passenger compartment and minimal energy consumption is proposed. System model is developed and simulated based on AMESim and Matlab/Simulink co-simulation. Simulation results show that the fuel cell system can provide about 78 % energy of maximum heat requirement in -20 °C ambient temperature environment.

Pedestrian upper leg impact protection is a challenging requirement in the Euro NCAP assessment. In upper legform to bonnet leading edge tests, the legform impact force, the legform intrusion and the injury parameters (impact force and bending moment measured on the upper legform) are highly related to design of vehicle front-end styling and structure, as well as clearance underneath bonnet leading edge. In the course of impact, the contact area variation has significant influence on the stress distribution and consequently the force and the bending moment on the upper legform. Using finite element simulations of upper legform impact with a typical sedan, the deformation of the legform and the vehicle structure, and the variation of the contact force distribution are characterized and analyzed.

Automotive safety is always the focus of consumers, the selling point of products, the focus of technology. In order to achieve automatic driving, interconnection with the outside world, human-automatic system interaction, the security connotation of intelligent and connected vehicles (ICV) changes: information security is the basis of its security. Functional safety ensures that the system is operating properly. Behavioral safety guarantees a secure interaction between people and vehicles. Passive security should not be weakened, but should be strengthened based on new constraints. In terms of information safety, the threshold for attacking cloud, pipe, and vehicle information should be raised to ensure that ICV system does not fail due to malicious attacks. The cloud is divided into three cloud platforms according to functions: ICVs private cloud, TSP cloud, public cloud.