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Turbulent jet ignition (TJI) combustion using pre-chamber ignition can accelerate the combustion speed in the cylinder and has garnered growing interest in recent years. However, it is complicated for the optimization of the pre-chamber structure and combustion system. This study investigated the effects of the pre-chamber structure and the intake ports on the combustion characteristics of a gasoline engine through CFD simulation. Spark ignition (SI) combustion simulation was also conducted for comparison. The results showed that the design of the pre-chamber that causes the jet flame colliding with walls severely worsen the combustion, increasing the knocking intendency, and decrease the thermal efficiency. Compared with SI combustion mode, the TJI combustion mode has the higher heat transfer loss and lower unburned loss. The well-optimized pre-chamber can accelerate the flame propagation with knock suppression.

The driving risk field model offers a feasible approach for assessing driving risks and planning safe trajectory in complex traffic scenarios. However, the conventional risk field fails to account for the vehicle size and acceleration, results in the same trajectories are generated when facing different vehicle types and unable to make safe decisions in emergency situations. Therefore, this paper firstly introduces the acceleration and vehicle size of surrounding vehicles for improving the driving risk model. Then, an integrated decision-making and planning model is proposed based on the combination of the novelty risk field and model predictive control (MPC), in which driving risk and vehicle dynamics constraints are taken into consideration. Finally, the multiple driving scenarios are designed and analyzed for validate the proposed model.

Accurate identification of driver’s braking intention is essential in advanced driver assistance system and can make the driving process more comfortable and trustworthy. In this paper, a novel method for driver braking intention identification in cut-in scenarios was proposed by using driver’s gaze information and motion information of cut-in vehicles. Firstly, a "looking in and looking out" experimental platform including three eye-tracking cameras and one front-view camera was built to collect driver's gaze information and the vehicle motion information. Secondly, driver’s gaze features and motion features of cut-in vehicles were selected and the braking intention identification performance of several decision tree-based ensemble learning algorithms was compared. Thirdly, the feature importance was analyzed by using SHAP (SHapley Additive exPlanations) values. This novel method of braking intention identification makes full use of in-vehicle camera sensors.

According to the statistics of National Highway Traffic Safety Administration, driver’s cognitive distraction, which is usually caused by drivers using mobile phones, has become one of the main causes of traffic accidents. To solve this problem and guarantee the safety of man-vehicle-road system, the most critical work is to improve the accuracy of driver’s cognitive state detection. In this paper, a novel driver’s cognitive state detecting method based on LightGBM (Light Gradient Boosting Machine) is proposed. Firstly, cognitive distraction experiments of making calls are carried out on a driving simulator to collect vehicle states, eye tracking and EEG (electron encephalogram) data simultaneously and feature extraction is conducted. Then a classifier considering road and individual characteristics used for detecting cognitive states is trained based on LightGBM algorithm, with 3 predefined cognitive states including concentration, ordinary distraction and extreme distraction.

Smart vehicles have become an important development direction of the transformation and upgrading of the automotive industry. Highly intelligent smart vehicles can free human drivers from driving tasks, endowing cars with the mobility and instrument properties. Smart cockpits integrate the media for interactions between humans and environments inside and outside cars. This paper has explored the components of smart cockpits, sorted out three development stages of smart cockpits from such three dimensions as man, car and environment, analyzed the characteristics of the second development stage (Stage 2.0), and illustrated the necessity of the competition between smartphones and head units at the second stage. Based on the comparison of merits and demerits between smartphones and head units, this paper has proposed three principles for an ideal division of duties of smartphones and head units.

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.

In this paper, the differences between multi-hole and single-hole spray contour under the same conditions were compared by using high-speed photography. The difference between the contour area of multi-hole and that of single-hole spray was used as a parameter to describe the degree of spray collapse. Three dimensionless parameters (i.e. degree of superheat, degree of undercooling, and nozzle pressure ratio) were applied to characterize inside-nozzle thermodynamic, outside-nozzle thermodynamic and kinetic factors, respectively. In addition, the relationship between the three dimensionless parameters and the spray collapse was analyzed. A semi-empirical equation was proposed for evaluation of the degree of collapse based on dimensionless parameters of flash and non-flash boiling sprays respectively.

In the past two decades, rapidly expanding economy in China led to burst in travel demand and pursuit of quality of life. It further promoted the rapid growth of China's passenger car market. China had already become the largest vehicle sales country, exceeding the U.S. in 2010. By the end of 2018, there had been over 240 million cars in China, with over 200 million passenger cars. The surge of car ownership has also brought a series of problems, like traffic congestion, long commuting time, insufficient parking space, etc. Therefore, some local governments in China introduced vehicle purchase restriction policies to control the growth and gross of vehicle stock. Different cities issued different rules. Lottery and auction mechanisms both exist. There are also differences in classification and licensing of electric vehicles. However, with the recent slowdown of economic development, China's car sales began to decline in 2018, and the trend of 2019 is also not optimistic.

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.

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.

The use of deposit control additives in European market gasoline is well documented for maintaining high levels of engine cleanliness and subsequent sustained fuel and emissions performance. Co-ordinating European Council (CEC) industry fuels tests have played a crucial role in helping to drive market relevant, effective and low-cost deposit control additives into European market fuels. Until now, there hasn’t been a Gasoline Direct Injection engine test available to fuel marketers in any market globally. However, a new CEC engine test is currently being developed to address that gap. Based on an in-house VW injector coking test, it shows promise for becoming a useful tool with which to develop and measure the performance of deposit control additives. A key requirement of industry tests should be to replicate issues seen in consumer vehicles, thereby providing a platform for relevant solutions.

Electric vehicles are capable of more flexible drivetrain configurations, such that driving dynamics of each wheel could be controlled independently to increase its stability and maneuverability bounds. We hereby propose a configuration consisting of four wheel independent driving and front and rear axle modular steering. The vehicle implements drive-by-wire technology, which means the control program running on vehicle control computer will have direct control authority of the vehicle under normal driving conditions, based on inputs of higher level systems such as human drivers and autonomous driving programs. Both the torque allocation on four wheels and the steering allocation on axles are completely independent on the mechanical hardware level, thus the vehicle is able to harness adverse contact conditions with confidence.

Compression ratio and specific heat ratio are two dominant factors influencing engine thermal efficiency. Therefore, ultra-lean burn may be one method to deal with increasingly stringent fuel consumption and emission regulations in the approaching future. To achieve high efficiency and clean combustion, innovative combustion modes have been applied on research engines including homogeneous charge compression ignition (HCCI), spark-assisted compression ignition (SACI), and gasoline direct-injection compression ignition (GDCI), etc. Compared to HCCI, SACI can extend the load range and more easily control combustion phase while it is constrained by the limit of flame propagation. For SACI with ultra-lean burn in engines, equivalence ratio (φ), rich-fuel mixture around spark plug, and supercharging are three essentials for combustion stability.

Water management, particularly in the gas diffusion layers (GDL), plays an important role in the performance and reliability of the proton exchange membrane fuel cells (PEMFCs). In this study, a two-phase multiple-relaxation-time (MRT) lattice Boltzmann method (LBM) is employed to simulate water transport in a reconstructed GDL and the effect of perforation shapes is investigated. The revised pseudopotential multiphase model is adopted to realize high-density ratio, good thermodynamic consistency, adjustable surface tension and high contact angle. The transport characteristics are analyzed in both vertical and horizontal transport directions. The LBM simulation provides detailed results in mesoscale and indicates that the surface tension dominates the process of water transport in the perforated GDL, which exhibits unexpectedly similarities in the vertical and horizontal transport.

The coordinated control of stability and steering systems in collision avoidance steering evasion has been widely studied in vehicle active safety area, but the studies are mainly aimed at autonomous vehicle without driver or conventional combustion engine vehicle. This paper focuses on the control of hybrid vehicle integrated with rear hub in emergency steering evasion situation, and considering the driver’s characteristics. First, the mathematics model of vehicle dynamics and driver has been given. Second, based on the planned steering evasion path, the model predictive control method is presented for achieving higher evasion path tracking accuracy under driver’s steering input. The prediction model includes an adaptive preview distance driver model and a vehicle dynamics model to predict the driver input and the vehicle trajectory.

An autonomous emergency braking (AEB) system can detect emergency conditions using sensors (e.g., radar and camera) to automatically activate the braking actuator without driver input. However, during the hard braking phase, crash conditions for the restraint system can easily change (e.g., vehicle velocity and occupant position), causing an out-of-position (OOP) phenomenon, especially for unbelted occupants entering the airbag deployment range, which may lead to more severe injuries than in a normal position. A critical step in reducing the injury of unbelted occupants would be to design an AEB system while considering the effect of deployed airbags on the occupants. Thus far, few studies have paid attention to the compatibility between AEB and airbag systems for unbelted occupants. This study aims to provide a method that combines AEB and airbag systems to explore the potential injury reduction capabilities for unbelted occupants.

Diesel particulate filter (DPF) is indispensable for diesel engines to meet the increasingly stringent emission regulations. Both the peak temperature and the maximum temperature gradient of the DPF during active regeneration should be well controlled in order to enhance the reliability and durability of the filter. In this paper, the temperature field of the DPF during active regeneration with hydrocarbon (HC) injection was investigated with engine bench tests and numerical simulation. For the experimental study, 24 thermocouples were inserted into the DPF channels to measure the inner temperature of the filter to capture its temperature field, and the circumferential, axial and radial distribution of the filter temperature was analyzed to understand the DPF temperature field behavior during active regeneration.

This study compared the combustion and emission characteristics of Homogeneous Charge Compression Ignition (HCCI) and Direct Injection Compression Ignition (DICI) modes in a boosted and high compression ratio (17) engine fueled with gasoline and gasoline/diesel blend (80% gasoline by volume, denoted as G80). The injection strategy was adjusted to achieve the highest thermal efficiency at different intake pressures. The results showed that Low Temperature Heat Release (LTHR) was not observed in gasoline HCCI. However, 20% additional diesel could lower down the octane number and improve the autoignition reactivity of G80, which contributed to a weak LTHR, accounting for approximately 5% of total released heat. The combustion efficiency in gasoline DICI was higher than those in gasoline HCCI and G80 HCCI, while the exhaust loss and heat transfer loss in DICI mode were higher than those in HCCI mode.

Highway traffic safety has been the most serious problem in current society, statistics show that about 70% to 90% of accidents are caused by driver operational errors. The autonomous emergency braking (AEB) is one of important vehicle intelligent safety technologies to avoid or mitigate collision. The AEB system applies the vehicle brakes when a collision is eminent in spite of any reaction by the driver. In some technologies, the system forewarns the driver with an acoustic signal when a collision is still avoidable, but subsequently applies the brakes automatically if the driver fails to respond. This paper presents the development and implementation of a rear-end collision avoidance system based on hierarchical control framework which consists of threat assessment layer, wheel slip ratio control layer and integrated-electro-hydraulic brake (IEHB) actuator control layer.

Vehicle automation is a fundamental approach to reduce traffic accidents and driver workload. However, there is a notable risk of pushing human drivers out of the control loop before automation technology fully matures. Cooperative driving (or vehicle co-piloting) is a novel paradigm which is defined as the vehicle being jointly navigated by a human driver and an automatic controller through shared control technology. Indirect shared control is an emerging shared control method, which is able to realize cooperative driving through input complementation instead of haptic guidance. In this paper we first establish an indirect shared control method, in which the driver’s commanded input and the controller’s desired input are balanced with a weighted summation. Thereafter, we propose a predictive model to capture driver adaptation and trust in indirect shared control.