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Adaptive cruise control is one of the key technologies in advanced driver assistance systems. However, improving the performance of autonomous driving systems requires addressing various challenges, such as maintaining the dynamic stability of the vehicle during the cruise process, accurately controlling the distance between the ego vehicle and the preceding vehicle, resisting the effects of nonlinear changes in longitudinal speed on system performance. To overcome these challenges, an adaptive cruise control strategy based on the Takagi-Sugeno fuzzy model with a focus on ensuring vehicle lateral stability is proposed. Firstly, a collaborative control model of adaptive cruise and lateral stability is established with desired acceleration and additional yaw moment as control inputs. Then, considering the effect of the nonlinear change of the longitudinal speed on the performance of the vehicle system.

With the capability of avoiding failure in advance, failure prediction model is important not only to end users, but also to the service engineers in vehicle industry. This paper proposes an approach based on anomaly detection algorithms and telematic data to predict the failure of the engine air system with Turbo charger. Firstly, the relationship between air system and all obtained features are analyzed by both physical mechanism and data-wise. Then, the features including altitude, air temperature, engine output power, and charger pressure are selected as the input of the model, with the sampling interval of 1 minute. Based on the selected features, the healthy state for each vehicle is defined by the model as benchmark. Finally, the ‘Medium surface’ is determined for specific vehicle, which is a hyperplane with the medium points of the healthy state located at, to detect the minor weakness symptom (sub-health state).

The collision accidents of electric vehicles are gradually increasing, and the response of battery cell under mechanical abuse conditions has attracted more and more attention. In the real collision, the mechanical load on battery generally has the following characteristics, including multiple loading directions, dynamic impact and blunt intrusion. Therefore, it is necessary to study the mechanical response and deformation of battery under complex loading, especially in-plane dynamic loading condition. According to the actual accident, we designed the constrained blunt compression test of the battery in different speeds and directions. For out-of-plane loading, the structural stiffness of battery increases obviously and the fracture is advanced compared with the corresponding quasi-static tests. For in-plane constrained loading, the force response can be approximately divided into two linear segments, in which the structural stiffness increases abruptly after the inflection point.

Most of the skin injuries caused by traffic accidents, sports, falls, etc. are in the intermediate strain rate range (1-100s-1), and the injuries may occur at different sites, impact velocities, and orientations. To investigate the multifactorial mechanical properties of rat skin at intermediate strain rates, a three-factor, three-level experimental protocol was established using the standard orthogonal table L9(34), which includes site (upper dorsal, lower dorsal, and ventral side), strain rate (1s-1, 10s-1, and 100 s-1), and sampling orientation (0°, 45°, and 90° relative to the spine). Uniaxial tensile tests were performed on rat skin samples according to the protocol to obtain stress-stretch ratio curves. Failure strain energy was selected as the index, and the influence of each factor on these indexes, the differences between levels of each factor, and the influence of errors on the results were quantified by analysis of variance (ANOVA).

With the rapid development of intelligent driving technology, there has been a growing interest in the driving comfort of automated vehicles. As vehicles become more automated, the role of the driver shifts from actively engaging in driving tasks to that of a passenger. Consequently, the study of the passenger experience in automated driving vehicles has emerged as a significant research area. In order to examine the impact of automatic driving on passengers' riding experience in vehicle platooning scenarios, this study conducted real vehicle experiments involving six participants. The study assessed the subjective perception scores, eye movement, and electrocardiogram (ECG) signals of passengers seated in the front passenger seat under various vehicle speeds, distances, and driving modes. The results of the statistical analysis indicate that vehicle speed has the most substantial influence on passenger perception.

Ammonia and methanol are both future fuels with carbon-neutral potential. Ammonia has a high octane number, a slow flame speed, and a narrow ignition limit, while methanol has a fast flame speed with complementary combustion characteristics but is more likely to lead to pre-ignition and knock. In this paper, the combustion and emission characteristics of ammonia-methanol solution in a high compression ratio spark ignition engine are investigated. The experimental results show that the peak in-cylinder pressure and peak heat release rate of the engine when using ammonia-methanol solution are lower and the combustion phase is retarded compared with using methanol at the same spark timing conditions. Using ammonia-methanol solution in the engine resulted in a more ideal combustion phase than that of gasoline, leading to an increase in indicated thermal efficiency of more than 0.6% and a wider range of efficient operating conditions.

Steer-By-Wire (SBW) system directly transmits the driver's steering input to the wheels through electrical signals. However, the reliability of electronic equipment is significantly lower than that of mechanical structures, and the risk of failure increases, so it is important to conduct functional safety studies on SBW systems. This paper develops the functional safety of the SBW system according to the requirements of the international standard ISO26262, and first defines the relevant items and application scope of SBW system. Secondly, the Hazard and Operability (HAZOP) method was used to combine scenarios and possible dangerous events to carry out Hazard Analysis and Risk Assessment (HARA), and the Automotive Safety Integrity Level (ASIL) was obtained according to the three evaluation indicators of Exposure, Severity and Controlabillity, and then the corresponding safety objectives were established and Fault Tolerant Time Interval (FTTI) was set.

Ammonia (NH3), a zero-carbon fuel, has great potential for internal combustion engine development. However, its high ignition energy, low laminar burning velocity, narrow range of flammability limits, and high latent heat of vaporization are not conducive for engine application. This paper numerically investigates the feasibility of utilizing ammonia in a heavy-duty diesel engine, specifically through low-pressure direct injection (LP-DI) of hydrogen to ignite ammonia combustion. Due to the lack of a well-corresponding mechanism for the operating conditions of ammonia-hydrogen engines, this study serves only as a trend-oriented prediction. The paper compares the engine's combustion and emission performance by optimizing four critical parameters: excess air ratio, hydrogen energy ratio, ignition timing, and hydrogen injection timing. The results reveal that excessively high hydrogen energy ratios lead to an advanced combustion phase, reducing indicated thermal efficiency.

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.

Lean combustion is an approach to achieving higher thermal efficiency for spark ignition engines. However, it faces low burning velocity and unstable combustion problems near the lean flammability limits region. The current work is attempting to investigate the combustion characteristics of iso-octane flame with 0% and 30% H2 up to near lean limits (λ = 1.7) at 100-300 kPa and 393-453 K. The flame appeared spherically by 37 mJ spark energy at λ = 0.8-1.2, whereas the ultra-lean mixtures, λ ≥ 1.3, ignited at 3000 mJ under wrinkles and buoyancy effects. The impact of initial pressure and temperature on the lean mixture was stronger than the stoichiometric mixture regarding flame radius and diffusional-thermal instability. The buoyancy appeared at the highest burning velocity of 27.41 cm/s.

In this paper, tensile experiments were performed on the dorsal and ventral skin of rats, and the mechanical properties of the skin in these two sites were compared and analyzed. A three-factor experimental protocol of site (dorsal and ventral), strain rate (0.71s-1, 7.1×10-3s-1), and sampling orientation (0°, 45° and 90° relative to the spine) was established for tensile test using the L6(31×22) orthogonal table modified from the standard orthogonal table L4 (23). Uniaxial tensile experiments were performed on rat skin samples to calculate the stress-strain curve. The failure strain energy was selected as the index, and the sum of squared deviations of the factors to the index was calculated by analysis of variance (ANOVA), and the contributions of the factors to the failure strain energy were evaluated. The results showed that the site factor has the largest effect on the tensile strain energy with a contribution of 88.9% and a confidence level of 95%.

Accurate sensing of road conditions is one of the necessary technologies for safe driving of intelligent vehicles. Compared with the structured road, the unstructured road has complex road conditions, and the response characteristics of vehicles under different road conditions are also different. Therefore, accurately identifying the road categories in front of the vehicle in advance can effectively help the intelligent vehicle timely adjust relevant control strategies for different road conditions and improve the driving comfort and safety of the vehicle. However, traditional road identification methods based on vehicle kinematics or dynamics are difficult to accurately identify the road conditions ahead of the vehicle in advance. Therefore, this paper proposes an unstructured road region detection and road classification algorithm based on machine vision to obtain the road conditions ahead.

It is generally considered that the material properties of foam are the most important factors in vehicle seat, which affect the human-seat interface pressure. Therefore, only the role of foam is usually considered when the finite element method is used to simulate the human-seat interface pressure. In this paper, the mechanical properties and the modeling method of commonly used seat cover material were studied. The models of the seat with and without cover were established respectively according to the real-vehicle seat geometric data, and the human-seat interface pressure was simulated after the seat and human model consisting of bones, soft tissue and skin were assembled. The simulation result was compared with the actual measurement results from test, which verified the accuracy of the simulation and the role of seat cover in the human-seat interface pressure simulation.

Driving assistance system is regarded as an effective method to improve driving safety and comfort and is widely used in automobiles. However, due to the different driving styles of different drivers, their acceptance and comfort of driving assistance systems are also different, which greatly affects the driving experience. The key to solving the problem is to let the system understand the driving style and achieve humanization or personalization. This paper focuses on clustering and identification of different driving styles. In this paper, based on the driver's real vehicle experiment, a driving data acquisition platform was built, meanwhile driving conditions were set and drivers were recruited to collect driving information. In order to facilitate the identification of driving style, the correlation analysis of driving features is conducted and the principal component analysis method is used to reduce the dimension of driving features.

In order to improve the ignition capacity and burning rate for spark-ignited engines, pre-chamber jet ignition is a promising technique to achieve fast premixed combustion and low pollutant emissions. However, few studies focus on the interaction between multiple reacting (i.e. flamelet) or reacted (i.e. radical) jets, its effect on ignition, exotherm and flow behaviors also remain to be revealed. This paper investigated two types of jet interaction under different pre-chamber structures, including the jet-crossing and unequal nozzle designs. Optical experiments under different conditions were conducted in a constant volume combustion chamber with CH4 as fuel, using simultaneous high speed schlieren and OH* chemiluminescence method. Meanwhile, computational fluid dynamics (CFD) simulations with CH4 and NH3/CH4 blend fuels were carried out using Converge software to provide further insights of turbulent flow and ignition process.

In order to obtain a good understanding of mechanical behaviors of lithium-ion battery modules in electric vehicles, comprehensive experimental and numerical investigations were performed in the study. Mechanical indentation tests with different indentation heads, different loading directions and different impact speeds were performed on battery modules with prismatic cells. To mitigate thermal runaway, only fully discharged battery modules were used. The force-displacement responses and open circuit voltage were recorded and compared. It was found that the battery modules experienced different failure modes when subjected to mechanical abuse. Besides internal short circuit of cells, external short circuit from bus bar and vapor leakage of electrolyte were also found to deteriorate the mechanical and electrical integrity of the tested modules. Mechanical anisotropy and dynamic effect were found on the battery module.

In this decade, the detailed multi-layer FE model is always applied for investigating the mechanical behavior of Li-ion batteries under mechanical abuse. However, establishing a detailed model of different types of batteries requires a series of material characterization of components. To improve the efficiency of the procedure of component calibration, we introduce a procedure of automatic coating material characterization as an example to represent the strategy. The proposed method is constructing a response solver through MATLAB to predict the mechanical behavior of the coating specimen's representative volume element (RVE) under designated test conditions. The coating material is represented through Drucker-Prager-Cap (DPC) model. All parameters, including boundary conditions and material parameters, are included in this solver.

With the aim of addressing the problem of inconsistency of the traditional proportion integration (PI) driver model with the actual driving behavior, a longitudinal driver model based on the elastic net regression (ENR) and adaptive network fuzzy inference system (ANFIS) method is proposed. First, longitudinal driving behavior data are collected through bench tests to extract the characteristic parameters that affect driving behavior. A quadratic regression model is established after considering the nonlinear characteristics of the driver behavior. The multi-collinear problem of high-dimensional variables in the regression model is solved by the ENR method, and the parameters with significant influence on driving behavior selected. A longitudinal driver model of ANFIS was established with the selected characteristic parameters as input. Finally, the validity of the model is verified by comparing it with the PI and ENR driver models.

The RON(Research Octane Number) is the most important indicator of motor petrol, and the petrol refining process is one of the important links in petrol production. However, RON is often lost during petrol refining and RON Loss means the value of RON lost during petrol refining. The prediction of the RON loss of petrol during the refining process is helpful to the improvement of petrol refining process and the processing of petrol. The traditional RON prediction method relied on physical and chemical properties, and did not fully consider the high nonlinearity and strong coupling relationship of the petrol refining process. There is a lack of data-driven RON loss models. This paper studies the construction of the RON loss model in the petrol refining process.

A feasible method was developed to reconstruct the three-dimensional flame surface of SI combustion based on 2D images. A double-window constant volume vessel was designed to simultaneously obtain the side and bottom images of the flame. The flame front was reconstructed based on 2D images with a slicing model, in which the flame characteristics were derived by slicing flame contour modeling and flame-piston collision area analysis. The flame irregularity and anisotropy were also analyzed. Two different principles were used to build the slicing model, the ellipse hypothesis modeling and deep learning modeling, in which the ellipse hypothesis modeling was applied to reconstruct the flame in the optical SI engine. And the reconstruction results were analyzed and discussed. The reconstruction results show that part of the wrinkled and folded structure of the flame front in SI engines can be revealed based on the bottom view image.