吉林大学学报(工学版) ›› 2023, Vol. 53 ›› Issue (1): 41-49.doi: 10.13229/j.cnki.jdxbgxb20210528

• 车辆工程·机械工程 • 上一篇    下一篇

轮毂电机电动汽车主动悬架约束状态反馈H控制

李杰(),贾长旺,成林海,赵旗   

  1. 吉林大学 汽车仿真与控制国家重点实验室,长春 130022
  • 收稿日期:2022-05-28 出版日期:2023-01-01 发布日期:2023-07-23
  • 作者简介:李杰(1964-),男,教授,博士生导师. 研究方向:汽车仿真与控制. E-mail: lj@jlu.edu.cn
  • 基金资助:
    国家自然科学基金国际(地区)合作与交流重点项目(61520106008);汽车仿真与控制国家重点实验室自由探索项目(Ascl-zytsxm-202001);吉林省省校共建项目(SXGJSF2017-2-1-1)

Constrained state feedback H control for active suspension of in⁃wheel motor electric vehicle

Jie LI(),Chang-wang JIA,Lin-hai CHENG,Qi ZHAO   

  1. State Key Laboratory of Automotive Simulation and Control,Jilin University,Changchun 130022,China
  • Received:2022-05-28 Online:2023-01-01 Published:2023-07-23

摘要:

为了控制轮毂电机偏心和其他不可预见因素对电动汽车平顺性的影响,研究了主动悬架约束状态H控制对轮毂电机电动汽车随机路面平顺性改善问题。基于标准状态反馈H控制,建立了约束状态反馈H控制的线性矩阵不等式表示。考虑路面和电机偏心共同作用建立了包含主动悬架的轮毂电机电动汽车四自由度平面模型,实现了轮毂电机电动汽车主动悬架约束状态反馈H控制设计。应用Matlab/Simulink开发了轮毂电机电动汽车主动悬架约束状态反馈H控制仿真模型,通过其实现了主动悬架和被动悬架的随机路面平顺性仿真与比较。研究结果表明,轮毂电机偏心会影响主动悬架的改善能力,主动悬架约束状态反馈H控制改善了轮毂电机电动汽车随机路面平顺性。

关键词: 车辆工程, 电动汽车, 轮毂电机, 主动悬架, 约束状态反馈, H控制

Abstract:

To control the influence of in-wheel motor eccentricity and other unforeseen factors on the ride comfort of electric vehicles, the problem of improving ride comfort of in-wheel motor electric vehicle by H control of active suspension constraint state is studied on random road. Based on the standard state feedback H control, the linear matrix inequality representation of constrained state feedback H control is established. Considering the interaction of road and motor eccentricity, a four degree of freedom plane model of in-wheel motor electric vehicle with active suspension is built, and the constrained state feedback H control design of in-wheel motor electric vehicle active suspension is realized. The H control simulation model of active suspension of in-wheel motor electric vehicle with constraint state feedback is developed by using Matlab/Simulink. The ride comfort simulation and comparison of active suspension and passive suspension are completed through the model on random road. The results show that the eccentricity of in-wheel motor will affect the improvement ability of the active suspension, and the constraint state feedback H control of the active suspension improves ride comfort of in-wheel motor electric vehicle on random road.

Key words: vehicle engineering, electric vehicle, in-wheel motor, active suspension, constrained state feedback, H control

中图分类号: 

  • U469.72

图1

轮毂电机电动汽车四自由度平面模型"

表1

模型参数"

符号参数符号参数
ms车身质量IsL车身俯仰转动惯量
muf前轴非簧载质量mur后轴非簧载质量
csf前轴悬架阻尼csr后轴悬架阻尼
ksf前轴悬架刚度ksr后轴悬架刚度
ktf前轴轮胎刚度ktr后轴轮胎刚度
Lf前轴至车身质心距离Lr后轴至车身质心距离
Fvf前轴电机偏心激励力Fvr后轴电机偏心激励力
Faf前轴悬架控制力Far后轴悬架控制力
zs车身位移?s车身俯仰角位移
zsf前悬架车身点位移zsr后悬架车身点位移
zuf前轴非簧载质量位移zur后轴非簧载质量位移
qf前轮路面激励qr后轮路面激励

图2

轮毂电机电动汽车主动悬架约束状态反馈H∞控制仿真模型"

图3

前后电机偏心时两种悬架的振动响应量"

图4

级路面4种情况的性能指标"

1 Kulkarni A, Ranjha S A, Kapoor A. A quarter-car suspension model for dynamic evaluations of an in-wheel electric vehicle[J]. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 2017, 232(9): 1139-1148.
2 李杰, 高雄, 王培德. 开关磁阻电机和路面对电动汽车振动影响的分析[J]. 汽车工程, 2018, 40(4): 411-416.
Li Jie, Gao Xiong, Wang Pei-de. An analysis on the influence of switched reluctance motor and road on the vibration of electric vehicle[J]. Automotive Engineering, 2018, 40(4): 411-416.
3 Bicek M, Kunc R, Zupan S. Mechanical impact on in-wheel motor's performance[J]. Journal of Mechanics, 2017, 33(5): 607-618.
4 Oksuztepe E. In-wheel switched reluctance motor design for electric vehicles by using a pareto-based multiobjective differential evolution algorithm[J]. IEEE Transaction on Vehicular Technology, 2017, 66(6): 4707-4715.
5 陈龙, 董红亮, 李利明. 适合轮毂电机驱动的新型悬架系统设计[J]. 振动与冲击, 2015, 34(8): 174-180.
Cheng Long, Dong Hong-liang, Li Li-ming. A new type suspension design suitable for an in-wheel motor driving system[J]. Journal of Vibration and Shock, 2015, 34(8): 174-180.
6 Ma F, Wang J, Wang Y, et al. Optimization design of semi-active controller for in-wheel motors suspension[J]. Journal of Vibroengineering, 2018, 20(8): 2908-2924.
7 Li Z, Zheng L, Ren Y, et al. Multi-objective optimization of active suspension system in electric vehicle with in-wheel motor against the negative electromechanical coupling effects[J]. Mechanical Systems and Signal Processing, 2019,116: 545-565.
8 姜长生, 孙隆和, 吴庆宪, 等. 系统理论与鲁棒控制[M]. 北京: 航空工业出版社, 1998.
9 林逸, 俞凡. 汽车系统动力学[M]. 北京: 机械工业出版社, 2016.
10 余志生.汽车理论[M].北京:机械工业出版社,2009.
11 陈少帅. 基于磁流变减振器逆模型的轮毂电机式电动汽车悬架控制研究[D]. 长春: 吉林大学汽车工程学院, 2019.
Chen Shao-shuai. Research on suspension control of electric vehicle with in-wheel motor based on magnetorheological damper inverse model[D]. Changchun: College of Automotive Engineering, Jilin University, 2019.
12 Shao X, Naghdy F, Du H. Reliable fuzzy H control for active suspension of in-wheel motor driven electric vehicles with dynamic damping[J]. Mechanical Systems and Signal Processing, 2017, 87: 365-383.
[1] 陈磊,王杨,董志圣,宋亚奇. 一种基于转向意图的车辆敏捷性控制策略[J]. 吉林大学学报(工学版), 2023, 53(5): 1257-1263.
[2] 陈鑫,张冠宸,赵康明,王佳宁,杨立飞,司徒德蓉. 搭接焊缝对铝合金焊接结构轻量化设计的影响[J]. 吉林大学学报(工学版), 2023, 53(5): 1282-1288.
[3] 张勇,毛凤朝,刘水长,王青妤,潘神功,曾广胜. 基于Laplacian算法的汽车外流场畸变网格优化[J]. 吉林大学学报(工学版), 2023, 53(5): 1289-1296.
[4] 李艳波,柳柏松,姚博彬,陈俊硕,渠开发,武奇生,曹洁宁. 考虑路网随机特性的高速公路换电站选址[J]. 吉林大学学报(工学版), 2023, 53(5): 1364-1371.
[5] 汪少华,储堃,施德华,殷春芳,李春. 基于有限时间扩张状态观测的HEV鲁棒复合协调控制[J]. 吉林大学学报(工学版), 2023, 53(5): 1272-1281.
[6] 尹燕莉,黄学江,潘小亮,王利团,詹森,张鑫新. 基于PID与Q⁃Learning的混合动力汽车队列分层控制[J]. 吉林大学学报(工学版), 2023, 53(5): 1481-1489.
[7] 于贵申,陈鑫,武子涛,陈轶雄,张冠宸. AA6061⁃T6铝薄板无针搅拌摩擦点焊接头结构及性能分析[J]. 吉林大学学报(工学版), 2023, 53(5): 1338-1344.
[8] 田彦涛,黄兴,卢辉遒,王凯歌,许富强. 基于注意力与深度交互的周车多模态行为轨迹预测[J]. 吉林大学学报(工学版), 2023, 53(5): 1474-1480.
[9] 杨红波,史文库,陈志勇,郭年程,赵燕燕. 基于NSGA⁃II的斜齿轮宏观参数多目标优化[J]. 吉林大学学报(工学版), 2023, 53(4): 1007-1018.
[10] 赵睿,李云,胡宏宇,高镇海. 基于V2I通信的交叉口车辆碰撞预警方法[J]. 吉林大学学报(工学版), 2023, 53(4): 1019-1029.
[11] 陈小波,陈玲. 定位噪声统计特性未知的变分贝叶斯协同目标跟踪[J]. 吉林大学学报(工学版), 2023, 53(4): 1030-1039.
[12] 田彦涛,季言实,唱寰,谢波. 深度强化学习智能驾驶汽车增广决策模型[J]. 吉林大学学报(工学版), 2023, 53(3): 682-692.
[13] 张建,刘金波,高原,刘梦可,高振海,杨彬. 基于多模交互的车载传感器定位算法[J]. 吉林大学学报(工学版), 2023, 53(3): 772-780.
[14] 何科,丁海涛,赖宣淇,许男,郭孔辉. 基于Transformer的轮式里程计误差预测模型[J]. 吉林大学学报(工学版), 2023, 53(3): 653-662.
[15] 刘嫣然,孟庆瑜,郭洪艳,李嘉霖. 图注意力模式下融合高精地图的周车轨迹预测[J]. 吉林大学学报(工学版), 2023, 53(3): 792-801.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
No Suggested Reading articles found!