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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.

An autonomous vehicle is a comprehensive intelligent system that includes environment sensing, vehicle localization, path planning and decision-making control, of which environment sensing technology is a prerequisite for realizing autonomous driving. In the early days, vehicles sensed the surrounding environment through sensors such as cameras, radar, and lidar. With the development of 5G technology and the Vehicle-to-everything (V2X), other information from the roadside can also be received by vehicles. Such as traffic jam ahead, construction road occupation, school area, current traffic density, crowd density, etc. Such information can help the autonomous driving system understand the current driving environment more clearly. Vehicles are no longer limited to areas that can be sensed by sensors. Vehicles with different autonomous driving levels have different adaptability to the environment.

In the field of autonomous driving trajectory planning, it’s virtual to ensure real-time planning while guaranteeing feasibility and robustness. Current widely adopted approaches include decoupling path planning and velocity planning based on optimization method, which can’t always yield optimal solutions, especially in complex dynamic scenarios. Furthermore, search-based and sampling-based solutions encounter limitations due to their low resolution and high computational costs. This paper presents a novel spatio-temporal trajectory planning approach that integrates both search-based planning and optimization-based planning method. This approach retains the advantages of search-based method, allowing for the identification of a global optimal solution through search. To address the challenge posed by the non-convex nature of the original solution space, we introduce a spatio-temporal semantic corridor structure, which constructs a convex feasible set for the problem.

Intake system is an important noise source for commercial vehicles, which has a significant impact on their NVH performance. To predict the intake noise more accurately, a new one-dimensional prediction model is proposed in this paper. An air compressor model is introduced into the traditional model, and the acoustic properties of the intake system are simulated by GT-power. The simulation data of the inlet noise is obtained to make a comparison with the inlet noise data acquired from a test. The result shows that the proposed model can make a more precise prediction of the inlet noise. Compared with the traditional model, the proposed model can identify the noise coming from the air compressor, and achieve a more accurate prediction of the total sound pressure level of the inlet noise.

Considering the change of vehicle future power demand in the process of energy distribution can improve the fuel saving effect of hybrid system. However, current studies are mostly based on historical information to predict the future power demand, where it is difficult to guarantee the accuracy of prediction. To tackle this problem, this paper combines hybrid energy management with predictive cruise control, proposing a hierarchical control strategy of predictive energy management (PEM) that includes two layers of algorithms for speed planning and energy distribution. In the interest of decreasing the energy consumed by power components and ensuring transportation timeliness, the upper-level introduces a predictive cruise control algorithm while considering vehicle weight and road slope, planning the future vehicle speed during long-distance driving.

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.

The hippocampus plays a crucial role in brain function and is one of the important areas of concern in closed head injury. Hippocampal injury is related to a variety of factors including the strength of mechanical load, animal age, and helmet material. To investigate the order of these factors on hippocampal injury, a three-factor, three-level experimental protocol was established using the L9(34) orthogonal table. A closed head injury experiment regarding impact strength (0.3MPa, 0.5MPa, 0.7MPa), rat age (eight- week-old, ten-week-old, twelve-week-old), and helmet material (steel, plastic, rubber) were achieved by striking the rat's head with a pneumatic-driven impactor. The number of hippocampal CA3 cells was used as an evaluation indicator. The contribution of factors to the indicators and the confidence level were obtained by analysis of variance.

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.

Autonomous driving technology, as the product of the fifth stage of the information technology revolution, is of great significance for improving urban traffic and environmentally friendly sustainable development. Autonomous driving can be divided into three main modules. The input of the decision module is the perception information from the perception module and the output of the control strategy to the control module. The deep reinforcement learning method proposes an end-to-end decision-making system design scheme. This paper adopts the Deep Deterministic Policy Gradient Algorithm (DDPG) that incorporates the Priority Experience Playback (PER) method. The framework of the algorithm is based on the actor-critic network structure model. The model takes the continuously acquired perception information as input and the continuous control of the vehicle as output.

Aiming at the low transmission efficiency and power density of the hydraulic automatic transmission (AT), and the increasingly complex structure of its planetary gear with the increase of transmission gears, this paper proposes a new type of binary logic transmission (BLT), which adopts the double internal meshing planetary row (DIMPR), based on a heavy-duty commercial vehicle. By introducing the concept of BLT and analyzing the transmission performance of the DIMPR, the process of scheme design of binary double internal meshing planetary gear transmission (BDIMPGT) is established. According to the structural characteristics of the DIMPG, the support structure of the planetary gear is designed based on CAD and CATIA. In the structural design of binary clutches, V-groove clutch parts are coupled to the transmission case, planetary carrier, and sun shaft, respectively, in each DIMPG.

In this paper, in order to avoid the frequent switching of engine operating points and improve the fuel economy during driving, this paper proposes a control strategy for the 4-wheel drive (4WD) hybrid vehicle based on wavelet transform. First of all, the system configuration and the original control strategy of the 4WD hybrid vehicle were introduced and analyzed, which summarized the shortcomings of this control strategy. Then, based on the analyze of the original control strategy, the wavelet transform was used to overcome its weaknesses. By taking advantage over the superiority of the wavelet transform method in multi signal disposition, the demand power of vehicle was decomposed into the stable drive power and the instantaneous response power, which were distributed to engine and electric motor respectively. This process was carried out under different driving modes.

In order to improve the mode switching performance of parallel hybrid electric vehicles (PHEV) and make better use of the dynamics of the vehicle, this paper proposes a three-stage control method for the start-up mode of start-up, speed synchronization, and clutch slip based on the response characteristics of actual vehicle components and the complex working conditions of the actual road. In the speed synchronization phase, a coordinated control method of “engine speed active following + continuously variable transmission (CVT) speed ratio motor speed limiting” is proposed. The real vehicle test results show that the engine starting-up coordinated control method can significantly accelerate the speed synchronization and shorten the starting-up mode duration during the rapid acceleration, so that the vehicle’s power performance can be well played and the ride comfort can be effectively guaranteed.

Active control of low-frequency engine order noise helps to improve the passenger’s sense of hearing, so it has become one of the hot topics in the automotive field. Depth improvement of active noise control (ANC) performance from the perspective of novel algorithms has attracted the attention of researchers. The conventional notch dual-channel filtered-x least mean square (NDFxLMS) algorithm shows acceptable noise reduction for the elimination of engine order noise. To further enhance the steady-state ANC effect, this paper proposed two new notch algorithms: the notch dual-channel filtered-x recursive least square (NDFxRLS) algorithm and the notch dual-channel affine projection (NDAP) algorithm. Vehicle simulation tests show that both the proposed algorithms, especially the NDFxRLS algorithm, have a satisfying performance for the cancellation of interior noise from the engine.

The purpose of this study is to evaluate the impact of two-stage pilot injection parameters on the combustion and emissions of pilot diesel compression ignition natural gas (CING) engine at low load. Experiments were performed using a diesel/natural gas dual-fuel engine, which was modified from a six-cylinder diesel engine. The effect of injection timing and injection pressure of two-stage pilot diesel were analyzed in order to reduce both the fuel consumption and total hydrocarbon (HC) and carbon monoxide (CO) emissions under low load conditions. The results indicate that, because injection timing can determine the degree of pilot diesel stratification, in-cylinder thermodynamic state, and the available mixing time prior to the combustion, the combustion process can be controlled and optimized through adjusting injection timing.

Based on traditional heavy commercial vehicles, a hydraulic hub-motor drive vehicle (HHMDV) is equipped with a set of hydraulic hub-motor auxiliary system (HHMAS) to improve the traction performance and adaptability under complex conditions. In the case of low-speed operation or mechanical transmission failure, the creep mode (CM) can be used to drive the vehicle. Aiming at a common hydraulic system problem that flow loss increases due to temperature variation, a temperature compensation control strategy of the CM is proposed in this paper. By analyzing the speed regulation characteristics of the closed loop of the system in the CM, combined with the efficiency of the hydraulic variable pump (HP) and the hydraulic quantitative motor (HM), and aiming at adjusting the engine work in the optimal curve of the engine, the temperature compensation factor is introduced to control the HP displacement with hydraulic stepless speed regulation.

Based on the traditional heavy commercial vehicle, hydraulic hub-motor drive vehicle (HHMDV) is equipped with a hydraulic hub-motor auxiliary drive system, which makes the vehicle change from the rear-wheel drive to the four-wheel drive to improve the traction performance on low-adhesion road. In the typical operating mode of the vehicle, the leakage of the hydraulic system increases because of the oil temperature rising, this makes the control precision of the hydraulic system drop. Therefore, a temperature compensation control strategy for the assist mode is proposed in this paper. According to the principle of flow continuity, considering the loss of the system and the expected wheel speed, the control strategy of multifactor target pump displacement based on temperature compensation is derived. The control strategy is verified by the co-simulation platform of MATLAB/Simulink and AMESim.

With more and more countries proposing timetables for stopping selling of fuel vehicles, China has also issued a “dual-slope” policy. As electric vehicles are the most promising new energy vehicle, which is worth researching. The integration and control of the motor and gearbox have gradually become a hot research topic due to low cost with better performance. This paper takes an electric vehicle equipped with permanent magnet synchronous motor and two-gear automatic transmission without synchronizer and clutch as the research object.

The requirement for low emissions and better vehicle performance has led to the demand for lightweight vehicle structures. Two new lightweight methods of design and optimization for the lower control arm were proposed in this research to improve the effectiveness of the traditional lightweight method. Prior to the two lightweight design and optimization methods, the static performance, including strength, stiffness and mode, and fatigue performance for the lower control arm were analyzed and they provided constraints for subsequent design and optimization. The first method of lightweight design and optimization was integrated application of topography optimization, size optimization, shape optimization and free shape optimization for the control arm. Topography optimization was first applied to find the optimal distribution form of reinforcement rib for the lower control arm. Size optimization was then applied in this study to optimize the plate thickness.

The Electronic Stability Program (ESP), as a key actuator of traditional automobile braking system, plays an important role in the development of intelligent vehicles by accurately controlling the pressure of wheels. However, the ESP is a highly nonlinear controlled object due to the changing of the working temperature, humidity, and hydraulic load. In this paper, an accurate pressure control strategy of single wheel during active braking of ESP is proposed, which doesn’t rely on the specific parameters of the hydraulic system and ESP. First, the structure and working principle of ESP have been introduced. Then, we discuss the possibility of Pulse Width Modulation (PWM) control based on the mathematical model of the high-speed switching valve. Subsequently, the pressure building characteristics of the inlet and outlet valves are analyzed by the hardware in the Loop (HiL) experimental platform.

As a critical part of auto-body, the design of tailgate not only affects the beauty, usability and safety of automobile, but also involves more and more issues about environmental protection and energy saving. Hence, it is of vital importance to investigate lightweight of tailgate. This paper mainly focuses on lightweight design of CFRP tailgate based on conventional SUV metal tailgate, which can be realized under the condition of meeting requirements of stiffness, modal and manufacturing with the adoption of multi-step optimization method. To start with, finite element (FE) model of metal tailgate is established. Meanwhile, the stiffness and modal analyses, including bending stiffness, torsional stiffness, lateral stiffness, vertical stiffness and free modal are set up. Then, the structural performances of metal tailgate are analyzed, and the topology optimization of CFRP tailgate is performed.