分布式电动汽车状态与参数无迹卡尔曼滤波估计

doi:10.3901/JME.2020.16.204

机械工程学报 ›› 2020, Vol. 56 ›› Issue (16): 204-213.doi: 10.3901/JME.2020.16.204

扫码分享

分布式电动汽车状态与参数无迹卡尔曼滤波估计

宋义彤^1,2, 舒红宇^1,2, 陈仙宝^1,2, 靖长青^1,2, 郭成^1,2

1. 重庆大学机械传动国家重点实验室重庆 400044;
2. 重庆大学汽车工程学院重庆 400044

收稿日期:2019-08-29 修回日期:2020-02-26 出版日期:2020-08-20 发布日期:2020-10-19
通讯作者: 舒红宇(通信作者),男,1963年出生,博士,教授,博士研究生导师。主要研究方向为新能源汽车及电动轮设计与控制,车辆系统动力学与控制,汽车传动核心基础部件。E-mail:shycqu616@163.com
作者简介:宋义彤,男,1989年出生,博士研究生。主要研究方向为车辆系统动力学及底盘集成控制。E-mail:yitongsong@163.com
基金资助:
国家自然科学基金（51975069）、重庆市自然科学基金（cstc2018jcyjAX0077）和重庆市研究生科研创新（CYB18059）资助项目。

State and Parameters Estimation for Distributed Drive Electric Vehicle Based on Unscented Kalman Filter

SONG Yitong^1,2, SHU Hongyu^1,2, CHEN Xianbao^1,2, JING Changqing^1,2, GUO Cheng^1,2

1. State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044;
2. School of Automotive Engineering, Chongqing University, Chongqing 400044

Received:2019-08-29 Revised:2020-02-26 Online:2020-08-20 Published:2020-10-19

摘要/Abstract

摘要： 为提高分布式电动汽车的控制性能，提出一种更加综合、全面的车辆状态及参数估计方法。针对车辆在行驶过程中某些动力学状态及参数难以实时量测的问题，以分布式电驱动汽车为研究对象，探讨基于无迹卡尔曼滤波的车辆状态估计及参数识别方法。建立7自由度时变参数车辆模型；以车辆易于测量的纵向加速度、侧向加速度、横摆角速度和轮速为观测变量，通过状态扩维，将车辆相关参数引入到车辆状态矢量中，采用无迹卡尔曼滤波算法设计一种车辆状态和参数的联合观测器，以便同时估计和辨识车辆纵向速度、侧向速度、轮胎侧向力、车辆质量、转动惯量、质心位置及其高度；在Simulink/Carsim平台上进行鱼钩角输入、滑行和加速工况的仿真验证，结果表明，该联合观测器能够有效的估计和辨识出上述相关车辆状态和参数，收敛效果较好。

关键词: 无迹卡尔曼滤波, 观测器, 估计, Simulink/Carsim

Abstract: In order to improve the control performance of distributed drive electric vehicle (EV) a more comprehensive vehicle state and parameter estimation method is proposed. For the problem that it is difficult to measure the vehicle states and parameters in real time, the distributed EV is considered as the object, and a method of vehicle states and parameters estimation based on unscented Kalman filter (UKF) is discussed. Firstly, the 7-DOF time-varying parameter vehicle model is established; secondly, taking the longitudinal acceleration, lateral acceleration, yaw angular velocity and wheel speed which are easy to be measured as the measured variables, a combined observer of vehicle states and system parameters is designed using the UKF algorithm to estimate longitudinal velocity, lateral velocity, lateral tire forces, vehicle mass, center position and inertia moment by extending the dimension of state variables. Finally, the combined observer is simulated and analyzed on Simulink/Carsim platform. The results show that the observer can estimate vehicle state and system parameters effectively, and has a good convergence effect.

Key words: unscented Kalman filter, observer, estimation, Simulink/Carsim

中图分类号:

U461

宋义彤, 舒红宇, 陈仙宝, 靖长青, 郭成. 分布式电动汽车状态与参数无迹卡尔曼滤波估计[J]. 机械工程学报, 2020, 56(16): 204-213.

SONG Yitong, SHU Hongyu, CHEN Xianbao, JING Changqing, GUO Cheng. State and Parameters Estimation for Distributed Drive Electric Vehicle Based on Unscented Kalman Filter[J]. Journal of Mechanical Engineering, 2020, 56(16): 204-213.

参考文献

[1] HUI Z,WANG J. Adaptive sliding-mode observer design for a selective catalytic reduction system of ground-vehicle diesel engines[J]. IEEE/ASME Transactions on Mechatronics,2016,21(4):2027-2038.
[2] 王震坡,薛雪,王亚超. 基于自适应无迹卡尔曼滤波的分布式驱动电动汽车车辆状态参数估计[J]. 北京理工大学学报,2018,38(7):42-46. WANG Zhenpo,XUE Xue,WANG Yachao. State parameter estimation of distributed drive electric vehicle based on adaptive unscented kalman filter[J]. Transactions of Beijing Institute of Technology,2018,38(7):42-46.
[3] JUNG H,CHOI S B. Real-time individual tire force estimation for an all-wheel drive vehicle[J]. IEEE Transactions on Vehicular Technology,2018,67(4):2934-2944.
[4] SONG Y,SHU H,CHEN X,et al. Direct-yaw-moment control of four-wheel-drive electrical vehicle based on lateral tyre-road forces and sideslip angle observer[J]. IET Intelligent Transport Systems,2019,13(2):303-312.
[5] CHEN T,XU X,CHEN L,et al. Estimation of longitudinal force,lateral vehicle speed and yaw rate for four-wheel independent driven electric vehicles[J]. Mechanical Systems and Signal Processing,2018,101:377-388.
[6] BOADA B L,GARCIA-POZUELO D,BOADA M J L,et al. A constrained dual Kalman filter based on pdf truncation for estimation of vehicle parameters and road bank angle:Analysis and experimental validation[J]. IEEE Transactions on Intelligent Transportation Systems,2017,18(4):1006-1016.
[7] WIELITZKA M,DAGEN M,ORTMAIER T. Joint unscented Kalman filter for state and parameter estimation in vehicle dynamics[C/CD]//2015 IEEE Conference on Control Applications (CCA). IEEE,2015.
[8] KANGHYUN N. Application of novel lateral tire force sensors to vehicle parameter estimation of electric vehicles[J]. Sensors,2015,15(11):28385-28401.
[9] JIANG K,VICTORINO A C,CHARARA A. Adaptive estimation of vehicle dynamics through RLS and Kalman filter approaches[C/CD]//2015 IEEE 18th International Conference on Intelligent Transportation Systems-(ITSC 2015). IEEE,2015.
[10] HONG S,LEE C,BORRELLI F,et al. A novel approach for vehicle inertial parameter identification using a dual Kalman filter[J]. IEEE Transactions on Intelligent Transportation Systems,2015,16(1):151-161.
[11] 白艳,管欣. 车辆横摆转动惯量及轮胎侧偏刚度识别方法的研究[J]. 科学技术与工程,2015,15(9):125-128. BAI Yan,GUAN Xin. Research on system identification of vehicle moment of inertia abont the Z axis and tire cornering stiffness[J]. Science Technology and Engineering,2015,15(9):125-128.
[12] LIN F,WANG H,WANG W,et al. Vehicle state and parameter estimation based on dual unscented particle filter algorithm[J]. Transactions of Nanjing University of Aeronautics and Astronautics,2014,31(5):568-575.
[13] 解少博,林程. 基于无迹卡尔曼滤波的车辆状态与参数估计[J]. 农业机械学报,2011,42(12):6-12. XIE Shaobo,LIN Cheng. State and parameters estimation of vehicle based on UKF[J]. Transactions of the Chinese Society for Agricultural Machinery,2011,42(12):6-12.
[14] PACEJKA H B. Tyre and vehicle dynamics[M]. Helmond:Elsevier,2006.
[15] SIMON D. Optimal state estimation:Kalman,H_∞,and nonlinear approaches[M]. New Jersey:Wiley-Interscience,2006.
[16] NIJMEIJER H. Observability of autonomous discrete time non-linear systems:A geometric approach[J]. International Journal of Control,1982,36(5):867-874.
[17] DOUMIATI M,VICTORINO A,CHARARA A,et al. A method to estimate the lateral tire force and the sideslip angle of a vehicle:Experimental validation[C]//Proceedings of the 2010 American Control Conference. IEEE,2010:6936-6942.

分布式电动汽车状态与参数无迹卡尔曼滤波估计

State and Parameters Estimation for Distributed Drive Electric Vehicle Based on Unscented Kalman Filter

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	李国涛, 李兵, 郭宏伟, 黄海林. 桁架式可展开抓取机构展开动力学建模及其自适应鲁棒控制[J]. 机械工程学报, 2020, 56(5): 39-46.
[2]	李小雨, 许男, 郭孔辉. 基于运动学方法和运动几何方法融合的质心侧偏角估计[J]. 机械工程学报, 2020, 56(2): 121-129.
[3]	熊璐, 金达, 冷搏, 余卓平, 杨兴. 考虑复杂激励条件的分布式驱动电动汽车路面附着系数自适应估计方法[J]. 机械工程学报, 2020, 56(18): 123-133.
[4]	向兆军, 胡凤玲, 罗明华, 方崇全, 胡晓松. 基于电池组模型和聚类算法的锂离子电池组SOC不一致估计[J]. 机械工程学报, 2020, 56(18): 154-163.
[5]	罗阳, 李战东, 陶建国, 邓立平, 邓宗全. 类空化效应下的水下螺旋桨推进器推力预测[J]. 机械工程学报, 2020, 56(17): 1-11.
[6]	熊瑞, 李幸港. 基于双卡尔曼滤波算法的动力电池内部温度估计[J]. 机械工程学报, 2020, 56(14): 146-151.
[7]	薛培林, 吴愿, 殷国栋, 刘帅鹏, 林乙蘅, 黄文涵, 张云. 基于信息融合的城市自主车辆实时目标识别[J]. 机械工程学报, 2020, 56(12): 165-173.
[8]	郭俊康, 李宝童, 洪军, 李鑫波. 基于误差状态最优估计的精密机床装配调整工艺决策[J]. 机械工程学报, 2020, 56(11): 172-180.
[9]	张志勇, 张淑芝, 黄彩霞, 张刘铸, 李博浩. 基于自适应扩展卡尔曼滤波的分布式驱动电动汽车状态估计[J]. 机械工程学报, 2019, 55(6): 156-165.
[10]	何凯, 林成涛, 李亮, 王翔宇. 电控机械式变速箱换档过程迭代学习控制[J]. 机械工程学报, 2019, 55(4): 84-90.
[11]	金坤善, 宋建丽, 李永堂, 仉志强. 四辊卷板机侧辊位移线性自抗扰控制[J]. 机械工程学报, 2019, 55(24): 72-82.
[12]	汪步云, 王月朋, 梁艺, 汪志红, 季景, 许德章. 下肢外骨骼助力机器人关节驱动设计及试验分析[J]. 机械工程学报, 2019, 55(23): 55-66.
[13]	平先尧, 李亮, 程硕, 王恒阳. 四轮独立驱动汽车多工况路面附着系数识别研究[J]. 机械工程学报, 2019, 55(22): 80-92.
[14]	金贤建, 杨俊朋, 殷国栋, 王金湘, 陈南, 卢彦博. 分布式驱动电动汽车双无迹卡尔曼滤波状态参数联合观测[J]. 机械工程学报, 2019, 55(22): 93-102.
[15]	周洪亮, 贾凤娇, 刘志远, 刘海峰. 四轮独立驱动汽车驱动系统故障诊断与容错控制[J]. 机械工程学报, 2019, 55(22): 174-182.