EN
    您的位置: 首页 > 研究成果 > 研究论文
 
按时间查看




基于最小二乘法的车辆瞬态燃油消耗估计
作者 张金辉,李克强,徐彪,李红
出版年份 2018 引用
摘要
精确的车辆瞬态燃油消耗估计是车辆节能控制研究的基础,稳态燃油消耗模型受燃油发动机的非线性工作特性、驾驶员的驾驶习惯、车辆行驶的环境、车辆行驶状态、车辆负载等多种因素影响,计算的瞬态燃油消耗与实际燃油消耗偏...展开全文
精确的车辆瞬态燃油消耗估计是车辆节能控制研究的基础,稳态燃油消耗模型受燃油发动机的非线性工作特性、驾驶员的驾驶习惯、车辆行驶的环境、车辆行驶状态、车辆负载等多种因素影响,计算的瞬态燃油消耗与实际燃油消耗偏差较大,现有瞬态油耗模型参数不易标定,因此本文中通过车辆速度与加速度构建了一种新的瞬态燃油消耗估计模型。采用最小二乘法对模型中的参数进行求解,为进一步降低瞬态燃油消耗率的估计偏差,引入指数速度衰减的加权因子,即采用带指数衰减因子的最小二乘法求解油耗模型中的参数,并通过实车试验对瞬态油耗估计方法进行验证。试验结果表明,基于最小二乘法的油耗模型可精确地估计车辆瞬态油耗,带指数速度衰减因子的最小二乘法可进一步降低油耗模型的油耗估计偏差,且估计精度受车辆行驶状态和道路环境等因素影响较小。
收起全文

Energy-Efficient Torque Allocations for a Two-Axle Four-Wheel-Drive Electric Bus
作者 Zeyu Yang, Zhanyi Hu, ManjiangHu, Zhihua, Zhong
出版年份 2017 引用
摘要
Optimal torque distribution approaches are introduced for a two-axle dual-motor four-wheel-drive (4WD) electric bus (E-Bus) developed by our team to improve vehicle economical performance. Real-time vehicle speed and demand ...展开全文
Optimal torque distribution approaches are introduced for a two-axle dual-motor four-wheel-drive (4WD) electric bus (E-Bus) developed by our team to improve vehicle economical performance. Real-time vehicle speed and demand torque are taken as the input of these strategies. The objective function is formulated based on the practical energy consumption in both driving and regenerative braking conditions according to the efficiency map of each motor. Two torque distribution strategies-off-line calculation with on-line look-up-table strategy and the fuzzy logic based torque distribution strategy are proposed. Co-simulations with Simulink and TruckSim demonstrate the effectiveness of the two distribution strategies. The comparison with original uniform distribution strategy is also carried out to illustrate the effectiveness on reducing vehicle energy consumption of the two torque distribution strategies. The mileage of E-bus can be improved without extra cost through optimal torque distribution strategies.收起全文

V2I based cooperation between traffic signal and approaching automated vehicles
作者 Biao Xu,Xuegang(Jeff)Ban,Yougang Bian,Jianqiang Wang,Keqiang Li
出版年份 2017 引用
摘要
Existing traffic signal optimization and vehicle speed optimization at signalized intersections cannot work together for the lack of proper cooperation methods. We propose the V2I (vehicle to infra...展开全文
Existing traffic signal optimization and vehicle speed optimization at signalized intersections cannot work together for the lack of proper cooperation methods. We propose the V2I (vehicle to infrastructure) based cooperation between traffic signal and approaching vehicles which optimizes the traffic signal and vehicles' speed trajectories simultaneously. The cooperation consists of roadside traffic signal optimization and onboard speed control, of which the former calculates the optimal traffic signal timing and vehicles' arriving time to minimize trip time and the latter optimizes the vehicle engine power and brake force to minimize the fuel consumption in the whole trip. A simulation study is conducted to compare the proposed cooperation method and the actuated signal control method. The simulation results show significant improvement of transportation efficiency and vehicle fuel economy by using the cooperation method.
收起全文

Coordinated collision avoidance for connected vehicles using relative kinetic energy density
作者 Manjiang Hu,Jian Wu,Hongmao Qin,Yougang Bian,Biao Xu,Qing Xu,Jugang He,Jianqiang Wang
出版年份 2017 引用
摘要
Vehicular collision often leads to serious casualties and traffic congestion, and the consequences are worse for multiple-vehicle collision. Many previous works on collision avoidance have only focus...展开全文
Vehicular collision often leads to serious casualties and traffic congestion, and the consequences are worse for multiple-vehicle collision. Many previous works on collision avoidance have only focused on the case for two consecutive vehicles using on-board sensors, which ignored the influence on upstream traffic flow. This paper proposes a novel coordinated collision avoidance (CCA) strategy for connected vehicles, which has potential to avoid collision and smooth the braking behaviors of multiple vehicles, leading to an improvement of traffic smoothness. Specifically, model predictive control (MPC) framework is used to formulate the CCA into an optimization problem, where the objective is to minimize the total relative kinetic energy density (RKED) among connected vehicles. Monte Carlo simulations are used to demonstrate the effectiveness of proposed CCA strategy by comparison with other two strategies. Among all the three control strategies, the RKED based control strategy shows the best performance of collision avoidance, including the best crash prevention rates (99.2 % on dry asphalt road and 90.5 % on wet asphalt road) and the best control of distance headways between vehicles.
收起全文

Autonomous parking control for intelligent vehicles based on a novel algorithm
作者 Hongbo Gao, Guotao Xie,Xinyu Zhang,Bo Cheng
出版年份 2017 引用
摘要
Along with the increasing number of vehicles, parking space becomes narrow gradually, safety parking puts forward higher requirements on the driver's driving technology. How to safely, quickly ...展开全文
Along with the increasing number of vehicles, parking space becomes narrow gradually, safety parking puts forward higher requirements on the driver's driving technology. How to safely, quickly and accurately park the vehiclo to parking space right? This paper presents an automatic parking scheme based on trajectory planning, which analyzing the mechanical model of the vehicle, establishing vehicle steering model and parking model, coming to the conclusion that it is the turning radius is independent of the vehicle speed at low speed. The Matlab simulation environment verifies the correctness and effectiveness of the proposed algorithm for parking. A class of the automatic parking problem of intelligent vehicles is solved.
收起全文

Lateral control of autonomous vehicles based on learning driver behavior via cloud model
作者 Hongbo Gao,Guotao Xie,Hongzhe Liu,Xinyu Zhang,Deyi Li
出版年份 2017 引用
摘要
In order to achieve the lateral control of the intelligent vehicle, use the bi-cognitive model based on cloud model and cloud reasoning, solve the decision problem of the qualitative and quantitative o...展开全文
In order to achieve the lateral control of the intelligent vehicle, use the bi-cognitive model based on cloud model and cloud reasoning, solve the decision problem of the qualitative and quantitative of the lateral control of the intelligent vehicle. Obtaining a number of experiment data by driving a vehicle, classify the data according to the concept of data and fix the input and output variables of the cloud controller, design the control rules of the cloud controller of intelligent vehicle, and clouded and fix the parameter of cloud controller: expectation, entropy and hyper entropy. In order to verify the effectiveness of the cloud controller, joint simulation platform based on Matlab/Simulink/CarSim is established. Experimental analysis shows that: driver's lateral controller based on cloud model is able to achieve tracking of the desired angle, and achieve good control effect, it also verifies that a series of mental activities such as feeling, cognition, calculation, decision and so on are fuzzy and uncertain.
收起全文

Modeling Discretionary Cut-in Risks Using Naturalistic Driving Data
作者 Guotao Xie, Hongmao Qin,Manjiang Hu,Daiheng Ni,Jianqiang Wang
出版年份 2017 引用
摘要
One of the operational issues that intelligent vehicles have to deal with is cut-into and by other vehicles. A vehicle cut-in risk model helps determine how an intelligent vehicle should react to th...展开全文
One of the operational issues that intelligent vehicles have to deal with is cut-into and by other vehicles. A vehicle cut-in risk model helps determine how an intelligent vehicle should react to the other vehicle’s cut-in behavior. On the other hand, such a model could also help intelligent vehicles carry out cut-in maneuver in a considerate manner to minimize the impact on following vehicles in the target lane. In this study, a discretionary cut-in risk model for vehicles is developed on the basis of field driving data and machine learning methods, namely, decision trees and Support Vector Machine (SVM). A united algorithm is developed to combine the two machine learning models for achieving enhanced conservativeness to the traffic states with high misclassification costs. To build the naturalistic driving database, the wavelet method is employed for filtering; the K-means approach, an unsupervised data learning method, is used to categorize the cut-in impact on the following vehicles in the target lane into three groups. The impact is indicated by the following vehicle’s average and maximum deceleration. Using this model, intelligent vehicles can assess the risk level during other vehicles’ cut-in process as well as their own impact on the following vehicle in the target lane when carrying out cut-in maneuver.
收起全文

Situational Assessments Based on Uncertainty-Risk Awareness in Complex Traffic Scenarios
作者 Guotao Xie, Xinyu Zhang,Hongbo Gao,Lijun Qian,Jianqiang Wang,Umit Ozguner
出版年份 2017 引用
摘要
Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehen...展开全文
Situational assessment (SA) is one of the key parts for the application of intelligent alternative-energy vehicles (IAVs) in the sustainable transportation. It helps IAVs understand and comprehend traffic environments better. In SA, it is crucial to be aware of uncertainty-risks, such as sensor failure or communication loss. The objective of this study is to assess traffic situations considering uncertainty-risks, including environment predicting uncertainty. According to the stochastic environment model, collision probabilities between multiple vehicles are estimated based on integrated trajectory prediction under uncertainty, which combines the physics- and maneuver-based trajectory prediction models for accurate prediction results in the long term. The SA method considers the probabilities of collision at different predicting points, the masses, and relative speeds between the possible colliding objects. In addition, risks beyond the prediction horizon are considered with the proposition of infinite risk assessments (IRAs). This method is applied and proved to assess risks regarding unexpected obstacles in traffic, sensor failure or communication loss, and imperfect detections with different sensing accuracies of the environment. The results indicate that the SA method could evaluate traffic risks under uncertainty in the dynamic traffic environment. This could help IAVs’ plan motion trajectories and make high-level decisions in uncertain environments.
收起全文



  Copyright © 2019 www.hive-hnu.org All rights reseved. 版权所有: 湖南大学智能车辆课题组
通讯地址: 湖南省长沙市岳麓区 湖南大学 现代工程训练中心A栋 邮编:410082