基于NACKF的分布式驱动电动汽车轮胎侧向力与质心侧偏角估计

doi:10.3901/JME.2019.22.103

机械工程学报 ›› 2019, Vol. 55 ›› Issue (22): 103-112.doi: 10.3901/JME.2019.22.103

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基于NACKF的分布式驱动电动汽车轮胎侧向力与质心侧偏角估计

汪¹, 殷国栋¹, 耿可可¹, 董昊轩¹, 卢彦博¹, 张凤娇^1,2

1. 东南大学机械工程学院南京 211189;
2. 常州机电职业技术学院车辆工程学院常州 213164

收稿日期:2019-05-28 修回日期:2019-09-20 出版日期:2019-11-20 发布日期:2020-02-29
通讯作者: 殷国栋(通信作者),男,1976年出生,博士,教授,博士研究生导师。主要研究方向为先进电动汽车、车辆动力学与控制、智能无人汽车和车辆主动安全控制。E-mail:ygd@seu.edu.cn
作者简介:汪,男,1992年出生,博士研究生。主要研究方向为汽车主动安全控制。E-mail:yanwangiv@outlook.com
基金资助:
国家重点研发计划（2016YFD0700905）、国家自然科学基金（U1664258，51975118，51905095）、江苏省地联合招标成果转化（BA2018023）、常州市科技计划应用基础研究（CJ20190009）和东南大学优秀博士学位论文培育基金（YBPY1947）资助项目。

Tire Tateral Forces and Sideslip Angle Estimation for Distributed Drive Electric Vehicle Using Noise Adaptive Cubature Kalman Filter

WANG Yan¹, YIN Guodong¹, GENG Keke¹, DONG Haoxuan¹, LU Yanbo¹, ZHANG Fengjiao^1,2

1. School of Mechanical Engineering, Southeast University, Nanjing 211189;
2. School of Vehicle Engineering, Changzhou Vocational Institute of Mechatronic Technology, Changzhou 213164

Received:2019-05-28 Revised:2019-09-20 Online:2019-11-20 Published:2020-02-29

摘要/Abstract

摘要： 针对传统容积卡尔曼滤波算法在进行车辆关键状态估计时要求噪声统计特性已知的问题，提出一种噪声自适应容积卡尔曼滤波（Noise adaptive cubature Kalman filter， NACKF）算法来进行车辆关键状态的估计。基于次优无偏极大后验估计器对量测噪声协方差进行实时更新并将其嵌入到标准容积卡尔曼算法中实现自适应容积卡尔曼滤波。针对车辆不同子系统间耦合特性对滤波精度的影响，构建双重自适应容积卡尔曼滤波器分别进行侧向力与质心侧偏角的估计，两者在估计过程中互为输入构成闭环反馈，利用分布式模块化结构弱化系统耦合特性对估计精度的影响，实现轮胎侧向力与质心侧偏角的实时准确估计。利用Simulink-Carsim联合仿真平台进行仿真验证和实车试验验证。结果表明，基于双重自适应容积卡尔曼滤波的估计算法相对标准容积卡尔曼滤波估计精度更高，较好地改善了传统容积卡尔曼滤波器在噪声先验统计特性未知条件下非线性滤波精度下降的问题。

关键词: 噪声自适应容积卡尔曼滤波, 轮胎侧向力, 质心侧偏角, 分布式驱动电动汽车

Abstract: The traditional cubature Kalman filter algorithm requires that the statistical characteristics of noise are known, but the statistical characteristics of noise are often difficult to obtain. Therefore, a noise adaptive cubature Kalman filter (NACKF) algorithm is proposed to estimate the critical states of vehicles. Based on suboptimal and unbiased maximum posterior estimator, the covariance of measured noise is updated in real time and embeds it into the standard Cubature Kalman algorithm to realize the adaptive cubature Kalman filter. Aiming at the influence of coupling characteristics among different subsystems of vehicles on filtering accuracy, a dual adaptive cubature Kalman filter is constructed to estimate the lateral tire forces and the sideslip angle respectively. The distributed modular structure weakens the influence of the coupling characteristics of the system on the estimation accuracy and realizes the real-time accurate estimation of the tire lateral forces and the sideslip angle. Finally, to verify the effectiveness of the proposed algorithm, the simulation test and the real vehicle test are implemented. The results show that the estimation algorithm based on dual adaptive cubature Kalman filter has higher estimation accuracy than that based on standard cubature Kalman filter, and it better improves the nonlinear filtering accuracy reduction problem of traditional cubature Kalman filter when the prior statistical characteristics of noise are unknown.

Key words: noise adaptive cubature Kalman filter, tire lateral forces, sideslip angle, distributed drive electric vehicle

中图分类号:

U461

汪, 殷国栋, 耿可可, 董昊轩, 卢彦博, 张凤娇. 基于NACKF的分布式驱动电动汽车轮胎侧向力与质心侧偏角估计[J]. 机械工程学报, 2019, 55(22): 103-112.

WANG Yan, YIN Guodong, GENG Keke, DONG Haoxuan, LU Yanbo, ZHANG Fengjiao. Tire Tateral Forces and Sideslip Angle Estimation for Distributed Drive Electric Vehicle Using Noise Adaptive Cubature Kalman Filter[J]. Journal of Mechanical Engineering, 2019, 55(22): 103-112.

参考文献

[1] HORI Y. Future vehicle driven by electricity and control research on four-wheel-motored[J]. IEEE Transactions on Industrial Electronics,2004,51(5):954-962.
[2] JUNGSIK K,SEONGHO K. Estimation of lateral tire force from objective measurement data for handling analysis[J]. SAE International Journal of Passenger Cars-Mechanical Systems,2013,6(2):495-505.
[3] DOUMIATI M,VICTORINO A,LECHNER D,et al. Observers for vehicle tyre/road forces estimation:experimental validation[J]. Vehicle System Dynamics,2010,48(11):1345-1378.
[4] EUNJAE L,HOJIN J,SEIBUM C. Tire lateral force estimation using Kalman filter[J]. International Journal of Automotive Technology,2018,19(4):669-676.
[5] CHENG Qi,CORREAVICTORINO A,CHARARA A. A new nonlinear observer of sideslip angle with unknown vehicle parameter using the dual unscented Kalman filter[C]//International IEEE Conference on Intelligent Transportation Systems,2012:1716-1721.
[6] DOUMIATI M,VICTORINO A C,CHARARA A,et al. Onboard real-time estimation of vehicle lateral tire-road forces and sideslip angle[J]. IEEE/ASME Transactions on Mechatronics,2011,16(4):601-614.
[7] M"SIRDI N K,RABHI A,ZBIRI N,et al. Vehicle-road interaction modelling for estimation of contact forces[J]. Vehicle System Dynamics,2005,43(Suppl.):403-411.
[8] BAFFET G,CHARARA A,DHERBOMEZ G. An observer of tire-road forces and friction for active security vehicle systems[J]. IEEE/ASME Transactions on Mechatronics,2007,12(6):651-661.
[9] BAFFET G,CHARARA A,STEPHANT J. Vehicle-dynamic observers:Simulations and experiments[J]. International Journal of Vehicle Autonomous Systems,2006,5(3/4):184-203.
[10] BAFFET G,CHARARA A,LECHNER D,et al. Experimental evaluation of observers for tire-road forces,sideslip angle and wheel cornering stiffness[J]. Vehicle System Dynamics,2008,46(6):501-520.
[11] JIN X J,YIN G. Estimation of lateral tire-road forces and sideslip angle for electric vehicles using interacting multiple model filter approach[J]. Journal of the Franklin Institute,2015,352(2):686-707.
[12] 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.
[13] ACOSTA M,KANARACHOS S. Tire lateral force estimation and grip potential identification using neural networks,extended Kalman filter,and recursive least squares[J]. Neural Computing and Applications,2017,30(11):3445-3465.
[14] CHUNG S,LEE H. Vehicle sideslip estimation and compensation for banked road[J]. International Journal of Automotive Technology,2016,17(1):63-69.
[15] DAKHLALLAH J,AUBERT D,SA?D M. Vehicle sideslip angle estimation with stiffness adaptation[J]. International Journal of Vehicle Autonomous Systems,2010,8(1):56-79.
[16] NAETS F,VAN AALST S,BOULKROUNE B,et al. Design and experimental validation of a stable two-stage estimator for automotive sideslip angle and tire parameters[J]. IEEE Transactions on Vehicular Technology,2017,66(11):9727-9742.
[17] DU H,LAM J,CHEUNG K C,et al. Side-slip angle estimation and stability control for a vehicle with a non-linear tyre model and a varying speed[J]. Proceedings of the Institution of Mechanical Engineers,Part D:Journal of Automobile Engineering,2015,229(4):486-505.
[18] JING L,JIAXU Z. Vehicle sideslip angle estimation based on hybrid Kalman filter[J/]. Mathematical Problems in Engineering,2016(3):1-10.
[19] WANG W,BEI S,ZHANG L,et al. Vehicle sideslip angle estimation based on general regression neural network[J]. Mathematical Problems in Engineering,2016(4):1-7.
[20] YU R,XIA X. Vehicle handling evaluation models using artificial neural networks[J]. International Journal of Control&Automation,2015,8(5):249-258.
[21] ZAREI J,SHOKRI E. Convergence analysis of non-linear filtering based on cubature Kalman filter[J]. IET Science Measurement&Technology,2015,9(3):294-305.
[22] XIN X,CHEN J,ZOU J. Vehicle state estimation using cubature Kalman filter//[C]//In Proceedings of the 17th IEEE International Conference on Computational Science and Engineering,2014:44-48.
[23] 金贤建,殷国栋,陈南,等.分布式驱动电动汽车的平方根容积卡尔曼滤波状态观测[J].东南大学学报,2016,46(5):992-996. JIN Xianjian,YIN Guodong,CHEN Nan,et al. State observation of distributed drive electric vehicle using square root cubature Kalman filter[J]. Journal of Southeast Univesity,2016,46(5):992-996.
[24] LI Jing,ZHANG Jiaxu. Vehicle sideslip angle estimation based on hybrid Kalman filter[J]. Mathematical Problems in Engineering,2016:3269142.
[25] GUO H,CAO D,CHEN H,et al. Vehicle dynamic state estimation:State of the art schemes and perspectives[J]. IEEE/CAA Journal of Automatica Sinica,2018,5(2):418-431.
[26] PACEJKA H B. Tyre and vehicle dynamics[M]. Oxford:Elsevier,2012.
[27] RAJAMANI R. Vehicle dynamics and control[M]. Berlin:Springer,2005.
[28] SAGE A P,HUSA G W. Adaptive filtering with unknown prior statistics[C]//Joint Automatic Control Conference. 1969:760-769.

基于NACKF的分布式驱动电动汽车轮胎侧向力与质心侧偏角估计

Tire Tateral Forces and Sideslip Angle Estimation for Distributed Drive Electric Vehicle Using Noise Adaptive Cubature Kalman Filter

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