[1]袁策,柳江,赵健,等.货运车队悬架超轴距车联预瞄控制系统[J].华侨大学学报(自然科学版),2021,42(5):580-589.[doi:10.11830/ISSN.1000-5013.202010031]
　YUAN Ce,LIU Jiang,ZHAO Jian,et al.Vehicular Preview Control System of Suspension Super Wheelbase for Freight Transport Fleet[J].Journal of Huaqiao University(Natural Science),2021,42(5):580-589.[doi:10.11830/ISSN.1000-5013.202010031]
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货运车队悬架超轴距车联预瞄控制系统()
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《华侨大学学报（自然科学版）》[ISSN:1000-5013/CN:35-1079/N]

卷:
第42卷
期数:
2021年第5期
页码:
580-589
栏目:
出版日期:
2021-09-20

文章信息/Info

Title:
Vehicular Preview Control System of Suspension Super Wheelbase for Freight Transport Fleet
文章编号:
1000-5013(2021)05-0580-10
作者:
袁策;  柳江;  赵健;  李明星
青岛理工大学 机械与汽车工程学院, 山东 青岛 266520
Author(s):
YUAN Ce;  LIU Jiang;  ZHAO Jian;  LI Mingxing
School of Mechanical and Automotive Engineering, Qingdao University of Technology, Qingdao 266520, China
关键词:
悬架;  车联网;  超轴距预瞄;  粒子群优化算法;  寻优算法
Keywords:
suspension;  internet of vehicles(IoV);  super wheelbase preview;  particle swarm optimization algorithm;  optimum algorithm
分类号:
U463
DOI:
10.11830/ISSN.1000-5013.202010031
文献标志码:
A
摘要:
根据车队货车运输典型的重复性特征,将车联网理论引入主动悬架控制.首先,提出一种结对车联加地理信息检测的通讯网络构架和方案,降低悬架控制数据通讯的总体需求.然后,采用超轴距预瞄控制方法,基于粒子群优化算法计算最优车距,利用当量参数改进轴距预瞄算法,有效地改善悬架综合性能.最后,通过Matlab/Simulink平台分别对车身加速度、轮胎动位移和悬架动行程3个参数影响悬架性能进行仿真分析,并借助悬架控制车联网,使超轴距预瞄的寻优算法具备更高效率的迭代过程.结果表明:超轴距预瞄具有与轴距预瞄相似的响应特性,在B级路面上的寻优算法具有更高效率的迭代过程.
Abstract:
According to the typical repeatability characteristics of fleet truck transportation, the theory of internet of vehicles(IoV)was introduced into active suspension control. First,the communication network framework and scheme of vehicle coupling with geographic information detection were proposed for the reduction of the overall demand of the suspension control data communication. Then,a super wheelbase preview control method was proposed to calculate the optimal vehicle distance based on particle swarm optimization algorithm,and the wheelbase preview algorithm was improved by using equivalent parameters to effectively improve the comprehensive performance of the suspension. Finally,the performance of the suspension was simulated and analyzed using three parameters: car body acceleration, tire dynamic displacement and suspension dynamic travel by Matlab/Simulink platform. With the help of the suspension control IoV, the super wheelbase preview optimization algorithm has more efficient iterative process. The results show that the super wheelbase preview has the similar response characteristics to the wheelbase preview, and the optimization algorithm on B-class road has more efficient iterative process.

参考文献/References:

[1] KUMAR N,KAUR K,JINDAL A,et al.Providing healthcare services on-the-fly using multi-player cooperation game theory in internet of vehicles(IoV)environment[J].Digital Communications and Networks,2015,1(3):191-203.DOI:10.1016/j.dcan.2015.05.001.
[2] LI C S,FRANKE H,PARRIS C,et al.Composable architecture for rack scale big data computing[J].Future Generation Computer Systems,2017,67:180-193.DOI:10.1016/j.future.2016.07.014.
[3] SHARMA S,KAUSHIK B.A survey on internet of vehicles: Applications, security issues and solutions[J].Vehicular Communications,2019,20:100289.DOI:100289.10.1016/j.vehcom.2019.100182.
[4] PAPADOGIANNIS A S,FARMAKOPOULOS A G,CHONDROS T G.Road tankers axles load share design[J].Int Journal of Heavy Vehicle Systems,2010,17(3/4):256-275.DOI:10.1504/ijhvs.2010.035990.
[5] RAKSHITHA K S,RADHIKA K R.Car stream an industrial system of big data processing for internet of vehicles[J].Journal of Trend in Scientific Research and Development,2018,2(4):1766-1777.DOI:10.31142/ijtsrd14408.
[6] CHEN M S,HWANG C P,YEE T H,et al.Driving behaviors analysis based on feature selection and statistical approach: A preliminary study[J].The Journal of Super Computing,2019,75(4):2007-2026.DOI:10.1007/s11227-018-2618-9.
[7] SHARP R S,EVANGELOU S,THOMMYPPILLAI M.Rear-heavy car control by adaptive linear optimal preview[J].Vehicle System Dynamics,2010,48(5):645-658.DOI:10.1080/00423110903023337.
[8] KRTOLICA R,HROVAT D.Optimal active suspension control based on a half-car model[C]//Proceedings of the 29th IEEE Conference on Decision and Control.Honolulu:IEEE Press,1990:2238-2243.DOI:10.1109/CDC.1990.204023.
[9] 王琦,何仁.轴距预瞄的车辆主动悬架多目标控制与仿真[J].中国工程机械学报,2017,15(3):227-231.DOI:10.15999/j.cnki.311926.2017.03.008.
[10] 李忠立,闫祥海,周志立.无线通信技术在拖拉机牵引性能测试中的应用[J].河南科技大学学报(自然科学版),2019,40(1):78-83.DOI:10.15926/j.cnki.issn1672-6871.2019.01.014.
[11] 夏立荣,李润学,刘启玉,等.基于动态层次分析的自适应多目标粒子群优化算法及其应用[J].控制与决策,2015,30(2):215-221.DOI:10.13195/j.kzyjc.2013.1389.
[12] WIPKE K B,CUDDY M R,BURCH S D.ADVISO 2.1: A user-friendly advanced power train simulation using a combined backward/forward approach[J].IEEE Transactions on Vehicular Technology,1999,48(6):1751-1761.DOI:10.1109/25.806767.
[13] 余铖铨.考虑驾驶风格的混合动力汽车队列速度优化研究[D].镇江:江苏大学,2019.
[14] ZHOU Aimin,QU Boyang,LI Hui,et al.Multiobjective evolutionary algorithms: A survey of the state of the art[J].Swarm and Evolutionary Computation,2011,1(1).DOI:10.1016/j.swevo.2011.03.001.
[15] LIU Jiang,LI Xinjun,ZHANG Xilong,et al.Modeling and simulation of energy-regenerative active suspension based on BP neural network PID control[J].Shock and Vibration,2019(3):1-8.DOI:10.1155/2019/4609754.
[16] 黄宇,王佳荣.水轮机调速系统的线性自抗扰优化控制[J].系统仿真学报,2016,28(12):3033-3040.DOI:10.16182/j.issn1004731x.joss.201612022.

备注/Memo

备注/Memo:
收稿日期: 2020-10-22
通信作者: 柳江(1976-),男,副教授,博士,主要从事汽车系统动力学的研究.E-mail:zeh@163.com.
基金项目: 国家自然科学基金资助项目(51575288)
更新日期/Last Update: 2021-09-20