Search Results

Good heat dissipation of Lithium battery can prevent the battery from shortening its life due to rapid aging or thermal runaway. In this paper, an air-cooled structure of 5 series and 3 parallel battery packs is designed, which combines the advantages of series and parallel air ducts and optimizes the heat dissipation effect and the space ratio of air ducts. First, the heat generation model of NCR18650PF lithium battery is established, and the heat generation rate and time under different discharge rates are calculated. Combined with the working conditions of the battery itself, the necessity of battery pack heat dissipation was found.

This paper combines fluid software STAR-CCM+ and finite element software ABAQUS to solve the temperature field of this Gasoline engine exhaust manifold based on loose coupling method. Through the simulation of car parking cooling - full load condition at full speed, we estimate thermal fatigue life of the exhaust manifold with the plastic strain increment as the evaluation parameters. It can guide the direction of optimal design of the exhaust manifold. Here we also revealed how the bolt force affects the manifold elastic and plastic behavior.

A flow channel design of the battery liquid cooling plate is carried out through the variable density topology optimization method according to the heat dissipation requirements of lithium-ion power batteries under actual working conditions. Firstly, given the non-uniform heat generation of lithium battery cells, the heat generation mechanism is studied so that the battery electro-thermal model is established, then the distribution regularity of heat generation rate in the cell at different discharge rates is obtained. Subsequently, through COMSOL Multiphysics simulation software, the multi-objective topology optimization of the primary configuration radiator is conducted. The weights of the optimization objectives minimum temperature and minimum flow resistance are determined by practical engineering application. Finally, an optimized model with a volume fraction of 50% was obtained.

Lane changing is an essential action in commercial vehicles to prevent collisions. However, steering system malfunctions significantly escalate the risk of head-on collisions. With the advancement of intelligent chassis control technologies, some autonomous commercial vehicles are now equipped with a four-wheel independent braking system. This article develops a lane-changing control strategy during steering failures using torque vectoring through brake allocation. The boundaries of lane-changing capabilities under different speeds via brake allocation are also investigated, offering valuable insights for driving safety during emergency evasions when the steering system fails. Firstly, a dual-track vehicle dynamics model is established, considering the non-linearity of the tires. A quintic polynomial approach is employed for lane-changing trajectory planning. Secondly, a hierarchical controller is designed.

It is very significant for autonomous vehicles to have the ability to operate beyond the stable handling limits, which plays a vital role in vehicles’ active safety and enhances riding and driving pleasure. For traditional vehicles, it is rather difficult to control the longitudinal speed, sideslip angle and yaw rate simultaneously when drifting along a given trajectory because they are under-actuated. Nevertheless, for a 4-wheel-independent-drive (4WID) vehicle, it is possible and controllable thanks to its over-actuated characteristics. This article designs a trajectory following control strategy for automated drifting of 4WID vehicles. First, a double-track 7 degree of freedom (7DOF) vehicle dynamic model is established, which incorporates longitudinal and lateral load transfer and considers nonlinear tire models. The controller which proposes a hierarchical architecture is then designed.

This paper proposes a dynamic obstacle avoidance system to help autonomous vehicles drive on high-speed structured roads. The system is mainly composed of trajectory planning and tracking controllers. The potential field (PF) model is introduced to establish a three-dimensional potential field for structured roads and obstacle vehicles. The trajectory planning problem that considers the vehicle’s and tires’ dynamics constraints is transformed into an optimization problem with muti-constraints by combining the model predictive control (MPC) algorithms. The trajectory tracking controller used in this paper is based on the 7 degrees of freedom (DOF) vehicle model and the UniTire tire model, which was discussed in detail in previous work [25, 26]. The controller maintains good trajectory tracking performance even under extreme driving conditions, such as roads with poor adhesion conditions, where the car’s tires enter the nonlinear region easily.

Lane-changing is a typical traffic scene effecting on road traffic with high request for reliability, robustness and driving comfort to improve the road safety and transportation efficiency. The development of connected autonomous vehicles with V2V communication provide more advanced control strategies to research of lane-changing. Meanwhile, four-wheel steering is an effective way to improve flexibility of vehicle. The front and rear wheels rotate in opposite direction to reduce the turning radius to improve the servo agility operation at the low speed while those rotate in same direction to reduce the probability of the slip accident to improve the stability at the high speed. Hence, this paper established Four-Wheel-Steering(4WS) vehicle dynamic model and quasi real lane-changing scenes to analyze the motion constraints of the vehicles.

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.

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.

The development of intelligent transportation improves road efficiency, reduces automobile energy consumption, and improves driving safety. The core of intelligent transportation is the two-way information interaction between vehicles and the road environment. At present, road environmental information can flow to the vehicle, while the vehicle’s information rarely flows to the outside world. The electronic throttle and electronic braking systems of some vehicles use sensors to get the state of the accelerator and brake pedal, which can be transmitted to the outside environment through technologies such as the Internet of Vehicles. But the Internet of Vehicles technology has not been widely used, and it relies on signal sources, which is a passive way of information acquisition. In this paper, an active identification method is proposed to get the vehicle pedal on-off state as well as the driver’s operation behavior through existing traffic facilities.

In the process of truck overload and over-limit detection, it is necessary to detect the characteristics of the vehicle's size, type, and wheel number. In addition, in some vehicle vision-based load recognition systems, the vehicle load can be calculated by detecting the vibration frequency of specific parts of the vehicle or the change in the length of the suspension during the vehicle's forward process. Therefore, it is essential to quickly and accurately identify vehicle features through the camera. This paper proposes a vehicle feature recognition method based on image semantic segmentation and Python, which can identify the length, height, number of wheels and vibration frequency at specific parts of the vehicle based on the vehicle driving video captured by the roadside camera.

Vehicle Longitudinal Control (VLC) algorithm is the basis function of automotive Cruise Control system. The main task of VLC is to achieve a longitudinal acceleration tracking controller, performance requirements of which include fast response and high tracking accuracy. At present, many control methods are used to implement vehicle longitudinal control. However, the existing methods are need to be improved because these methods need a high accurate vehicle dynamic model or a number of experiments to calibrate the parameters of controller, which are time consuming and costly. To overcome the difficulties of controller parameters calibration and accurate vehicle dynamic modeling, a vehicle longitudinal control algorithm based on iterative learning control (ILC) is proposed in this paper. The algorithm works based on the information of input and output of the system, so the method does not require a vehicle dynamics model.

Autonomous driving technology represents a significant direction for future transportation, encompassing four key aspects: perception, planning, decision-making, and control. Among these aspects, vehicle trajectory planning and control are crucial for achieving safe and efficient autonomous driving. This paper introduces a Combined Model Predictive Control algorithm aimed at ensuring collision-free and comfortable driving while adhering to appropriate lane trajectories. Due to the algorithm is divided into two layers, it is also called the Bi-Level Model Predictive Control algorithm (BLMPC). The BLMPC algorithm comprises two layers. The upper-level trajectory planner, to reduce planning time, employs a point mass model that neglects the vehicle's physical dimensions as the planning model. Additionally, obstacle avoidance cost functions are integrated into the planning process.

Accurately predicting the future trajectories of surrounding traffic agents is important for ensuring the safety of autonomous vehicles. To address the scenario of frequent interactions among traffic agents in the highway merging area, this paper proposes a trajectory prediction method based on interactive graph attention mechanism. Our approach integrates an interactive graph model to capture the complex interactions among traffic agents as well as the interactions between these agents and the contextual map of the highway merging area. By leveraging this interactive graph model, we establish an agent-agent interactive graph and an agent-map interactive graph. Moreover, we employ Graph Attention Network (GAT) to extract spatial interactions among trajectories, enhancing our predictions. To capture temporal dependencies within trajectories, we employ a Transformer-based multi-head self-attention mechanism.

Combustion visualizations were carried out in a constant volume vessel to study the partially premixed combustion of a gasoline-like fuel using high speed imaging. The test fuel (G80H20) is composed by volume 80% commercial gasoline and 20% n-heptane. The effects of ambient gas composition, ambient temperature and injection pressure on G80H20 combustion characteristics were analyzed. Meanwhile, a comparison of the EGR effect on combustion process between G80H20 and diesel was made. Four ambient gas conditions that represent the in-cylinder gas compositions of a heavy-duty diesel engine with EGR ratios of 0%, 20%, 40% and 60% were used to simulate EGR conditions. Variables also include two ambient temperature (910K and 870K) and two injection pressure (20 MPa and 50 MPa) conditions.