Search Results

Due to the high center of gravity of medium-duty vehicles, rollover accidents can easily occur during high-speed cornering and lane changes. In order to prevent the deformation of the body structure, which would restrict the survival space and cause compression injuries to occupants, it is necessary to investigate methods for mitigating these incidents. This paper establishes a numerical model of right-side rollover for a commercial medium-duty vehicle in accordance with ECE R66 regulations, and the accuracy of the model is verified by experiment. According to the results, the material and size parameters of the key components of the right side pillar are selected as design variables. The response result matrix was constructed using the orthogonal design method for total mass, energy absorption, maximum collision acceleration, and minimum distance from the survival space.

Distributed drive electric vehicles (DDEVs), characterized by independently controllable torque at each wheel, redundant actuators, and highly integrated drive systems, are considered as the optimal platform for achieving intelligent driving with high safety and efficiency. This paper focuses on the trajectory tracking and lateral stability coordination control problems in high-speed emergency collision avoidance and autonomous lane change scenarios for DDEVs. A trajectory tracking control algorithm is proposed based on model predictive control (MPC) and coordinated optimization of distributed drive torques. The method adopts a hierarchical control architecture. Firstly, the upper-level trajectory planning layer calculates the lane change trajectory data. Based on the trajectory planning results, the middle-level controller designs a time-varying linear model predictive control method to solve the desired front wheel steering angle and additional yaw moment.

Traditional vector control needs the installation of mechanical sensors to gather rotor position and speed information in order to enhance the control performance and dynamic quality of electric vehicle wheel motors, which increases system cost and reduces system reliability and stability. On the basis of Popov's super-stability theory, an appropriate adjustable model and reference model are constructed, and the system's reference adaptive law is determined. Furthermore, to solve the problem of the standard PI regulator's poor anti-interference capabilities in speed controllers, the approach of utilizing a sliding-mode speed controller in the speed loop is presented. Finally, a MATLAB/SIMULINK simulation model is created to simulate the motor in three scenarios: no-load start, abrupt speed change, and sudden load change, and a permanent magnet synchronous motor experimental platform is created to validate the control approach.

Vehicle failure prediction technology is an important part of PHM (Prognostic and Health Management) technology, which is of great significance to the safety of vehicles and to improve driving safety. Based on the vehicle operating data collected by the on-board terminal (T-box) of the telematics system, the research on the state of vehicle failure is conducted. First, this paper conducts statistical analysis on vehicle historical fault data. Preprocessing procedures such as cleaning, integration, and protocol are performed to group the data set. Then, three indexes including recency (R) frequency (F), and days (D) are selected to construct a vehicle security status subdivision system, and K -Means algorithm is utilized to divide different vehicle categories from the perspective of vehicle value. Labeled information of vehicles in different security status are further established.

In this paper, for the front wall of a certain automobile, the defects of drawing splits, excessive thinning and excessive springback in the sheet metal forming process are analyzed and predicted. The stamping process has been simulated. The influence of different technical parameters (blank holder force, stamping speed, die gap and friction coefficient) on the forming results was further investigated using the center composite experiment. Through preliminary finite element simulation, the main drawing defects and trimming springback were analyzed. The second-order response surface model was established to perform the multi-objective optimization design of the stamping process with a NGSA-II genetic algorithm. Based on the relevant simulation data, multiple springback compensations are performed on the die surface to reduce the final springback of the part to meet the requirements.

Thin-walled tubes have been mostly used in passive vehicle safety systems due to high crash energy absorption. The structures with negative Poisson’s ratio (NPR) property will contract to increase its stiffness. In this paper, a double-arrowed NPR structure is designed as a new energy-absorption filler for thin-walled tubes to apply as a novel crash energy absorber. Different beam thicknesses, angles and half cellular width are taken into account in the double-arrowed NPR filling tubes (DAFT) designing and the crashworthiness of the structures are analyzed by using validated nonlinear finite element method. The crashworthiness performances of DAFT are also compared with the singular NPR and hollow tube with the same outer dimension to show the efficiency of DAFT.

Lane keeping assist (LKA) is an autonomous driving technique that enables vehicles to travel along a desired line of lanes by adjusting the front steering angle. Reinforcement learning (RL) is one kind of machine learning. Agents or machines are not told how to act but instead learn from interaction with the environment. It also frees us from coding complex policies manually. But it has not yet been successfully applied to autonomous driving. Two control strategies using different deep reinforcement learning (DRL) algorithms have been proposed and used in the lane keeping assist scenario in this paper. Deep Q-network (DQN) algorithm with discrete action space and deep deterministic policy gradient (DDPG) algorithm with continuous action space have been implemented, respectively. Based on MATLAB/Simulink, deep neural networks representing the control policy are designed. The environment as well as the vehicle dynamics are also modelled in Simulink.

The safety and comfort of the truck cab, as driver’s location, are significant to the driver, and the white body of the truck cab is an important part of the truck, whose modal and stiffness are directly impact its safety and comfort. Moreover, its modal and stiffness are the key factors that affect the performance of the car, which are also the important indicators that evaluate the ability of preventing fatigue failure and resisting deformation. In order to study whether the modal and stiffness of a truck cab can meet the working requirements, the white body of a truck cab was taken as the research object, and the finite element method (FEM) was chosen to study the modal and stiffness in this paper. The finite element model of the white body of this truck cab was established. The natural frequency and vibration model of white body were obtained by modal analysis. And the lowest frequency of the white body was 22.8Hz, obtained by modal analysis.

In order to reflect the actual power consumption of logistics electric vehicles in a city, sample real vehicle road data. After preprocessing, the short-stroke analysis method is used to divide it into working blocks of no less than 20 seconds. Based on principal component analysis, three of the 12 characteristic parameters were selected as the most expressive. K-means clustering algorithm is adopted to obtain the proportions of various short strokes, according to the proportion, select the short stroke with small deviation degree to combine, and construct the driving cycle, it has the characteristics of low average speed, high idle speed ratio and short driving distance. AVL-cruise software builds the vehicle model and runs the driving cycle of urban logistic EV. Compared with WLTC, the difference in power consumption is 34.3%, which is closer to the actual power consumption, the areas with the highest motor speed utilization are concentrated only in the idle area.

In this advanced technological era, lightweight design for fuel efficiency and environmental friendliness is essential for both conventional and hybrid electric vehicles (HEVs), without sacrificing the durability which is an important design factor for vehicle safety. To achieve these objectives, reduction of the structural mass of the full vehicle plays a vital role. The scope of this paper is to describe design methodologies for the vehicle differential case applied to achieve light weight and to ensure product life. The focus of this paper includes two tasks. The topology optimization and fatigue analysis of a vehicle differential case are conducted. Finite element analysis (FEA) is used to simulate the stress with constraint. After that, optimization parameters (design variables, responses, objective functions and constraints) of a vehicle differential case are selected for lightweight design by solid isotropic microstructures with penalization (SIMP) method.

As is known to all, the modeling of vehicles and durability simulation is becoming more accurate and more compatible with physical testing, resulting in shortening of the analysis process, and a lower cost. It would be more advantageous in the future to simulate the full vehicle system before the physical testing. Thus, in the analysis of vehicle durability performance, the need for more precise rigid and flexible vehicle modeling and more precise external loadings acquisition method is increasing. In view of the typical difficulties faced in the vehicle multi-body dynamics (MBD) simulation and in the associated loading extraction, this paper proposes a method to achieve accurate external vehicle loadings by virtual simulation. This method is performed based on the physical testing and compensates for the imperfections in the MBD modeling, thus being able to improve the quality of fatigue life prediction (FLP).