Search Results

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.

Heavy commercial vehicles have large variations in load and high centroid positions, so it is particularly important to obtain timely and accurate load information during driving. If the load information can be accurately obtained and the braking force of each axle can be distributed on this basis, the braking performance and safety of the entire vehicle can be improved. Heavy commercial vehicle load information is different from passenger vehicles, so it is particularly important to study commercial vehicles engaged in freight and passenger transportation. Presently, numerous research endeavors focus on evaluating the quality of passenger vehicles. However, heavy commercial vehicles exhibit notable distinctions compared to their passenger counterparts. Due to substantial variations in vehicle mass pre and post-loading, coupled with notable suspension deformations, significant changes are observed.

In order to improve the trajectory tracking accuracy and yaw stability of vehicles under extreme conditions such as high speed and low adhesion, a coordinated control method of trajectory tracking and yaw stability is proposed based on four-wheel-independent-driving vehicles with four-wheel-steering. The hierarchical structure includes the trajectory tracking control layer, the lateral stability control decision layer, and the four-wheel angle and torque distribution layer. Firstly, the upper layer establishes a three-degree-of-freedom vehicle dynamics model as the controller prediction model, the front wheel steering controller is designed to realize the lateral path tracking based on adaptive model predictive control algorithm and the longitudinal speed controller is designed to realize the longitudinal speed tracking based on PID control algorithm.

Steering movement is the most basic movement of the vehicle, in the car driving process, the driver through the steering wheel has always been to control the direction of the car, in order to achieve their own driving intention. Four Wheel Steering (4WS) is an advanced vehicle control technique which can markedly improve vehicle steering characteristics. Compared with traditional front wheel steering vehicles, 4WS vehicles can steer the front wheels and the rear wheels individually for cornering, according to the vehicle motion states such as the information of vehicle speed, yaw velocity and lateral acceleration. Therefore, 4WS can enhance the handling stability and improve the active safety for vehicles.

This paper presents a fault-tolerant control (FTC) method for four-wheel independently driven and steered (4WIS/4WID) electric vehicles based on a reconfigurable control allocation to increase the flexibility for vehicle control and improve the safety of vehicle after the steering actuator fails. The proposed fault tolerant control method consists of the following three parts: 1) a fault detection and diagnosis (FDD) module that monitors vehicle steering condition, detects and diagnoses actuator failures; 2) an upper controller that computes the generalized forces/moments to track the desired vehicle motion and trajectory; 3) a reconfigurable control allocator that optimally distributes the generalized forces/moments to four wheels. The FTC approach based on the reconfigurable control allocation reallocates the generalized forces/moments among healthy steering actuators and driving motors once the actuator failures is detected.

It is important to verify the requirements of battery management strategy (BMS), which is directly related to the safety of the electric vehicles. A model-in-loop test framework was established to realize the automated test based on the application scenario of BMS, including battery simulation module, battery management strategy module, test sequence module and function assessment module. The inputs of the automated test were designed in the test sequence module. The function requirements of state estimation, balance control, security protection, thermal management and fault diagnosis were designed in the function assessment module. The test results show that the proposed model-in-loop test can reach 100% of function requirements, and 87% of execution coverage. Additional automated test cases can be added to the proposed model-in-loop test, and it will be an effective method to the requirements verification of battery management strategy.

As one of the key technologies for EVs and HEVs, the design of their motors has been researched extensively, and some novel rotors of permanent magnet motor were proposed in order to improve torque capability, including average torque and torque ripple. Rotor structure selection of drive motor for various electric vehicles has been one of the key issues in matching of electric vehicle power system. Three motors are analyzed for providing visible insights to the contribution of different rotor structures to the torque characteristics, efficiency and extended speed range. First, an iterative comparative analysis of torque-speed characteristics with different flux linkage, d-axis inductance and rotor saliency ratio is performed for demonstrating the design principle. Then, the three motors are optimized by a genetic algorithm (GA) for further improving the torque characteristics.

A low-speed and heavy-duty electric powertrain was selected as the research object, the influence of electric powertrain characteristics on whole vehicle noise under accelerating condition (Speed range 800 r∙min−1~2200 r∙min−1; Torque range 500 N∙m~960 N∙m) was studied. Considering the traveling characteristics around the city, the interior and powertrain noise were recorded as the accelerating working range (10 km/h~50 km/h).

The tire mechanics characteristics are essential for analysis and control of vehicle dynamics. Basically, the effects of sideslip, longitudinal slip, camber angle and vertical load are able to be represented accurately by current existing tire models. However, the research of velocity effects for tire forces and moments are still insufficient. Some experiments have demonstrated that the tire properties actually vary with the traveling velocity especially when the force and moment are nearly saturated. This paper develops an enhanced brush tire model and the UniTire semi-physical model for tire forces and moments under different traveling velocities for raising need of advanced tire model. The primary effects of velocity on tire performances are the rubber friction distribution characteristics at the tire-road interface.

LiDAR sensors have played more and more important role on Intelligent and Connected Vehicles (ICV) and Advanced Driver Assistance Systems (ADAS) .However, the development and testing of LiDAR sensors under real driving environment for ADAS applications are greatly limited by various factors, and often are impossible due to safety concerns. This paper proposed a novel functional LiDAR model under virtual driving environment to support development of LiDAR-based ADAS applications under early stage. Unlike traditional approaches on LiDAR sensor modeling, the proposed method includes both geometrical modeling approach and physical modeling approach. While geometric model mainly produces ideal scanning results based on computer graphics, the physical model further brings physical influences on top of the geometric model. The range detection is derived and optimized based on its physical detection and measurement mechanism.

With the development of the advanced driver assistance system and autonomous vehicle techniques, a precise description of the driver’s steering behavior with mathematical models has attracted a great attention. However, the driver’s steering maneuver demonstrates the stochastic characteristic due to a series of complex and uncertain factors, such as the weather, road, and driver’s physiological and psychological limits, generating negative effects on the performance of the vehicle or the driver assistance system. Hence, this paper explores the stochastic characteristic of driver’s steering behavior and a novel steering controller considering this stochastic characteristic is proposed based on stochastic model predictive control (SMPC). Firstly, a search algorithm is derived to describe the driver’s road preview behavior.

The driving range of the electric vehicle (EV) greatly restricts the development of EVs. The vehicles waste plenty of energy on account of automobiles frequently braking under the city cycle. The regenerative braking system can convert the braking kinetic energy into the electrical energy and then returns to the battery, so the energy regeneration could prolong theregenerative braking system. According to the characteristics of robustness in regenerative braking, both regenerative braking and friction braking based on fuzzy logic are assigned after the front-rear axle’s braking force is distributed to meet the requirement of braking security and high-efficient braking energy regeneration. Among the model, the vehicle model and the mechanical braking system is built by the CRUISE software. The paper applies the MATLAB/SIMULINK to establish a regenerative braking model, and then selects the UEDC city cycle for model co-simulation analysis.

This paper presents a fault-tolerant control (FTC) algorithm for four-wheel independently driven and steered (4WID/4WIS) electric vehicle. The Extended Kalman Filter (EKF) algorithm is utilized in the fault detection (FD) module so as to estimate the in-wheel motor parameters, which could detect parameter variations caused by in-wheel motor fault. A motion controller based on sliding mode control (SMC) is able to compute the generalized forces/moments to follow the desired vehicle motion. By considering the tire adhesive limits, a reconfigurable control allocator optimally distributes the generalized forces/moments among healthy actuators so as to minimize the tire workloads once the actuator fault is detected. An actuator controller calculates the driving torques of the in-wheel motors and steering angles of the wheels in order to finally achieve the distributed tire forces. If one or more in-wheel motors lose efficacy, the FD module diagnoses the actuator failures first.

This paper establishes a brake pedal model for braking intention identification, using the structural features of electronic braking system and selecting the proper parameters. A three-dimensional model is built that the input parameters are pedal displacement and pedal displacement change rate, and the output parameter is braking intensity. The relationship between the driver braking operation and braking intention are designed. A hardware-in-the-loop test bench experiment has been taken under several skilled drivers to practice the established the brake pedal model with the operation data during the braking. Thus, it results a model indicating the braking intention by braking operation that means effectively improve the braking comfort and applies to the research of electronic braking system of commercial vehicle.

Heavy vehicles have the characteristics of with high center of gravity position, large weight and volume, wheelbase is too narrow relative to the body height and so on, so that they always prone to rollover. In response to the above heavy security problems of heavy vehicle in running process, this paper mainly analyzes roll stability and yaw stability mechanism of heavy vehicles and studies the influence of vehicle parameters on stability by establishing the vehicle dynamics model. At the same time, this paper focuses on heavy vehicles stability control methods based on simulation and differential braking technology. At last, verify the effect of heavy vehicle stability control by computer simulation. The results shows that self-developed stability control algorithm can control vehicle stability effectively, so that the heavy vehicles instability can be avoided, the vehicle driving safety and braking stability are improved.

Aiming at estimating the vehicle mass and the position of center of gravity, an on-line two-stage estimator, based on the recursive least square method, is proposed for buses in this paper. Accurate information of the center of gravity position is crucial to vehicle control, especially for buses whose center of gravity position can be varied substantially because of the payload onboard. Considering that the buses start and stop frequently, the first stage of the estimator determines the bus total mass during acceleration, and the second stage utilizes the recursive least-square methods to estimate the position of the center of gravity during braking. The proposed estimator can be validated by the co-simulation with MATLAB/Simulink and TruckSim software, simulation results exhibit good convergence and stability, so the center of gravity position can be estimated through the proposed method in a certain accuracy range.

This paper proposes a novel allocation-based control method for four-wheel independently actuated electric vehicles. In the proposed method, both actuator dynamics and input/output constraints are fully taken into consideration in the control design. First, the actuators are modeled as first-order dynamic systems with delay. Then, the control allocation is formulated as an optimization problem, with the primary objective of minimizing errors between the actual and desired control outputs. Other objectives include minimizing the power consumption and the slew rate of the actuator outputs. As a result, this leads to frequency-dependent allocation that reflects the bandwidth of each actuator. To solve the optimization problem, an efficient numerical algorithm is employed. Finally the proposed control allocation method is implemented to control a four-wheel independently actuated electric vehicle.

Four-wheel independent drive EV is driven by four brushless DC motors which are embedded in the wheel hubs. It enables each wheel's driving torque to be controlled independently. Due to the motors' torque and rotational speed easily measured, as well as the features of fast response and precise control, the EV enjoys obvious advantages over traditional vehicles in acceleration slip regulation. In this paper a novel acceleration slip regulation strategy for four-wheel independent drive EV is studied. The strategy includes a road identification module for the peak value of road adhesion coefficient and a slip regulation logic based on PID algorithm. Through comparing the current wheel slip ratio and the utilized adhesion coefficient with the typical roads' value, the identification module adopts the fuzzy control algorithm to recognize the similarity between the current road and the typical roads. Utilizing the similarity we can calculate the optimal slip ratio of the current road.

It is known that the automobile cabin thermal comfort, could keep the driver and passengers feel better which has a great effect on traffic safety. In this paper, to the FAW truck cab, we did some researches about automobile cabin thermal comfort. Our plan is to calculate the air flow distribution and the temperature in steady and transient state when there is warm or cool air flow. The heating and cooling experiment methods standard of cabin are based on the national standard and the automobile industry standard of China. Then the numerical simulation process becomes very important. So we used the commercial CFD code- STAR-CCM+ for study in this paper. Firstly, Geometry Clean up. Secondly, Wrap and Remesh, we chose the internal surface at the wrap surface of cabin and air conditioning pipes, then we remesh the surface. Thirdly, generate the volume mesh which is polyhedral mesh, and the number of the volume mesh is 9.4 millions.

The tire lateral force is essential to the vehicle handling and stability under cornering. However, it is difficult for engineers to get the tire lateral force under high loading condition due to the limitation of loading ability for most tire test machine in the world. The widely used semi-empirical tire lateral force models are obtained by curve-fitting experiments data and thus unable to predict the load dependent lateral force. The objective of this paper is to predict the tire lateral force under high-load condition based on the low-load tire data. The nonlinear characteristics of the tire cornering stiffness with the load are greatly affected by the tire carcass compliance. In this paper, a theoretical tire lateral model was built by considering carcass complex deformation. Combined with the relationship between the half-length of the tire contact patch and the load, the non-linear characteristics of the tire cornering stiffness with load were obtained.