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浙江大学学报(工学版)
浙江大学学报(工学版)  2023, Vol. 57 Issue (12): 2391-2400    DOI: 10.3785/j.issn.1008-973X.2023.12.006
机械工程、能源工程     
多方法融合的汽车质心侧偏角估计
高自群1(),谢桂芝2,周兵1,许艳1,*(),吴晓建3,柴天1
1. 湖南大学 汽车车身先进设计制造国家重点实验室，湖南 长沙 410082
2. 湖南大学 国家高效磨削工程技术研究中心，湖南 长沙 410082
3. 南昌大学 先进制造学院，江西 南昌 330031
Estimation of vehicle sideslip angle based on multi-method fusion
Zi-qun GAO1(),Gui-zhi XIE2,Bing ZHOU1,Yan XU1,*(),Xiao-jian WU3,Tian CHAI1
1. State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, Hunan University, Changsha 410082, China
2. National Engineering Research Center for High Efficiency Grinding, Hunan University, Changsha 410082, China
3. School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China
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摘要：

汽车质心侧偏角融合估计过程中存在量测噪声不确定、依赖路面附着系数问题，影响估计精度，为此提出联合运动学方式与运动几何方式的质心侧偏角融合估计算法. 利用三自由度车辆运动学方程建立自适应扩展卡尔曼滤波器，用于车辆侧、纵向速度估计. 设计轮胎侧偏刚度模糊自适应的运动几何估计器，以实现车辆侧向速度估计. 根据2种估计方式适用性的差异，设计基于瞬态特性提取的侧向速度加权融合算法，利用侧向速度融合结果与运动学方式估计的纵向速度计算车辆质心侧偏角. Carsim-Simulink的仿真与驾驶员在环试验结果表明，所提算法具有高实时估计精度和路面附着系数变化的鲁棒性.
关键词： 质心侧偏角估计;  运动学估计方式;  运动几何估计方式;  加权融合估计;  模糊控制    
Abstract:

Due to the measurement noise uncertainty and the dependence on the road friction coefficient, the estimation accuracy of the vehicle sideslip angle fusion estimation was affected, and a fusion estimation algorithm of the vehicle sideslip angle based on the kinematic method and the kinematic-geometry method was proposed. An adaptive extended Kalman filter was established based on the three-degree-of-freedom vehicle kinematics equation to estimate the lateral and the longitudinal velocities of vehicles. A fuzzy adaptive kinematic geometry estimator for the tyre lateral stiffness was designed to estimate the vehicle lateral velocity. According to the differences in the applicability of two estimation methods, a lateral velocity weighted fusion algorithm based on the transient characteristics extraction was designed. Using the fusion result of the lateral velocity and the longitudinal velocity estimated by kinematic method, the vehicle sideslip angle was calculated. Results of Carsim-Simulink simulation and the driver in loop test show that the proposed algorithm has high real-time estimation accuracy and robustness to changes in the road friction coefficient.
Key words: sideslip angle estimation    kinematic estimation method    kinematic-geometric estimation method    weighting fusion estimation    fuzzy control
收稿日期: 2022-10-18 出版日期: 2023-12-27
CLC:  U 461.1  
基金资助: 国家自然科学基金资助项目（51875184，52002163，52262054）
通讯作者: 许艳     E-mail: gzq96@hnu.edu.cn;xuyanzb@163.com
作者简介: 高自群（1996—），男，硕士生，从事车辆动力学与控制研究. orcid.org/0000-0002-1869-291X. E-mail： gzq96@hnu.edu.cn
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高自群
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柴天

引用本文:

高自群,谢桂芝,周兵,许艳,吴晓建,柴天. 多方法融合的汽车质心侧偏角估计[J]. 浙江大学学报(工学版), 2023, 57(12): 2391-2400.

Zi-qun GAO,Gui-zhi XIE,Bing ZHOU,Yan XU,Xiao-jian WU,Tian CHAI. Estimation of vehicle sideslip angle based on multi-method fusion. Journal of ZheJiang University (Engineering Science), 2023, 57(12): 2391-2400.

链接本文:

https://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2023.12.006    或    https://www.zjujournals.com/eng/CN/Y2023/V57/I12/2391

图 1  三自由度车辆模型
图 2  质心侧偏角融合观测器结构图
图 3  扩展卡尔曼滤波算法流程图
图 4  运动几何观测器流程图
图 5  隶属度函数
er e
NB NM NS Z PS PM PB
NB Z NS NVS NM NVS NS Z
NM NS NVS NM NVM NM NVS NS
NS NVS NM NVM NB NVM NM NVS
Z NM NVM NB NVB NB NVM NM
PS NVS NM NVM NB NVM NM NVS
PM NS NVS NM NVM NM NVS NS
PB Z NS NVS NM NVS NS Z
表 1  后轮胎侧偏刚度的模糊规则
图 6  运动学与运动几何方式的估计结果对比
图 7  融合算法流程
图 8  融合权重分配图
参数 数值 参数 数值
整车质量m /kg 1230 质心高度 ${h_{\text{g}}}$/ m 0.54
整车绕z轴转动惯量 $ {I}_{\mathrm{z}} $/(kg·m2) 1480 前轮轮距 $ {b}_{\mathrm{f}} $/ m 1.485
质心到前轴距离 $ {l}_{\mathrm{f}} $/m 1.065 后轮轮距 $ {b}_{\mathrm{r}} $/ m 1.480
质心到后轴距离 $ {l}_{\mathrm{r}} $/m 1.535 车轮半径 ${r}$ / m 0.51
表 2  Carsim车辆参数
图 9  2种工况的方向盘转角
工况 eRMSE
运动学方式
(无自适应) 		运动学方式
(有自适应) 		运动几何方式 融合方式
双移线 0. 087 0. 054 0. 098 0. 041
角阶跃 0. 199 0. 098 0. 070 0. 043
表 3  2种工况估计结果的均方根误差
图 10  2种工况的质心侧偏角估计结果
图 11  变路面附着系数对接路面-双移线工况
图 12  驾驶员在环仿真平台
图 13  2种工况的驾驶员在环试验质心侧偏角估计结果
工况 eRMSE t/μs
运动学方式
(有自适应) 		运动几何方式 融合方式
双移线 0.064 0.095 0.042 350
角阶跃 0.184 0.133 0.071 321
表 4  驾驶员在环试验估计结果的均方根误差与观测器单步仿真平均用时
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