Nonlinear Robust Fusion Estimation of Centroid Sideslip Angle and Tire Lateral Forces for Four-wheel-drive Electric Vehicles

doi:10.3969/j.issn.1004-132X.2022.22.004

Abstract

Abstract: It was rather difficult to measure the centroid side slip angle and the tire lateral forces of the four-wheel-drive electric vehicles directly. Considering the unmodeled dynamic characteristics of the systems, model parameter perturbations, system process noises and measurement noises, a joint estimation method was proposed based on FFRLS and RCKF. Based on applying FFRLS to estimate the mass of the vehicle in real time and the estimation error minimization in the background of the maximum value embedded into the standard cubature Kalman filter to realize RCKF. The improved strategy of the joint estimation algorithm was proposed, which effectively improved the anti-interference ability of the filter to the model parameter perturbations and the unmodeled noises under composite conditions. It may realize the accurate estimation of the centroid sideslip angle and tire lateral forces. By CarSim/Simulink, the accuracy, robustness and anti-interference of the algorithm were verified in different conditions. Through the actual vehicle platform of four-wheel-drive electric vehicle, the validity of the algorithm was verified. Research show that the results of the proposed method are more accurate than that of RCKF and the standard Cubature Kalman filter. The problems of joint estimation of centroid side slip angle and tire lateral forces of four-wheel-drive electric vehicle are solved under composite conditions.

Key words: four-wheel-drive electric vehicle, centroid sideslip angle, tire lateral force, robust cubature Kalman filter(RCKF), forgetting factor recursive least square（FFRLS）

摘要： 针对四轮驱动电动汽车质心侧偏角和轮胎侧向力难以直接测量的问题，考虑系统未建模的动态特性、模型参数摄动、系统过程噪声及测量噪声等因素，提出了一种基于遗忘因子递归最小二乘法(FFRLS)与鲁棒容积卡尔曼滤波(RCKF)的联合估计方法。基于FFRLS法对整车质量进行实时估计，并将极大值背景下的估计误差最小化嵌入标准容积卡尔曼滤波（CKF）以实现RCKF，提出了联合估计算法的改进策略，有效提高了复杂工况下滤波对模型参数摄动以及未建模噪声的抗干扰能力，可以实现质心侧偏角与轮胎侧向力的精准估计。在CarSim/Simulink联合仿真环境下，采用不同工况验证了算法的准确性、鲁棒性和抗干扰性。在四轮驱动电动汽车实车平台上分析了算法的有效性。研究结果表明，所提方法比RCKF和CKF精度更高，解决了复合工况下四驱电动汽车质心侧偏角和轮胎侧向力的联合估计问题。

关键词: 四轮驱动电动汽车, 质心侧偏角, 轮胎侧向力, 鲁棒容积卡尔曼滤波, 遗忘因子递归最小二乘法

CLC Number:

U461
TP274

WANG Fanxun, YIN Guodong, SHEN Tong, REN Yanjun, WANG Yan, FENG Bin. Nonlinear Robust Fusion Estimation of Centroid Sideslip Angle and Tire Lateral Forces for Four-wheel-drive Electric Vehicles[J]. China Mechanical Engineering, 2022, 33(22): 2673-2683.

王凡勋, 殷国栋, 沈童, 任彦君, 汪䶮, 冯斌. 四轮驱动电动汽车质心侧偏角与轮胎侧向力非线性鲁棒融合估计[J]. 中国机械工程, 2022, 33(22): 2673-2683.

References

［1］殷国栋, 李广民, 朱侗, 等. 四轮驱动电动汽车多Agent能量管理体系研究［J］. 中国机械工程, 2018, 29(15):1765-1771.
YIN Guodong, LI Guangmin,ZHU Tong, et al. Study on Multi-agent System of Energy Managements for 4WD Electric Vehicles［J］. China Mechanical Engineering, 2018, 29(15):1765-1771.
［2］殷国栋, 朱侗, 任祖平, 等. 基于多Agent的电动汽车底盘智能控制系统框架［J］. 中国机械工程, 2018, 29(15):1796-1801.
YIN Guodong, ZHU Tong, REN Zuping, et al. Intelligent Control System Framework for Multi-agent Based Electric Vehicle Chassises［J］. China Mechanical Engineering, 2018, 29(15):1796-1801.
［3］余卓平, 高晓杰. 车辆行驶过程中的状态估计问题综述［J］. 机械工程学报, 2009, 45(5):20-33.
YU Zhuoping, GAO Xiaojie. Review of Vehicle State Estimation Problem under Driving Situation［J］. Journal of Mechanical Engineering, 2009, 45(5):20-33.
［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, 2018, 13(2):303-312.
［5］SINGH K B, ARAT M A, TAHERI S. Literature Review and Fundamental Approaches for Vehicle and Tire State Estimation［J］. Vehicle System Dynamics, 2018, 57(11):1643-1665.
［6］汪, 魏民祥, 赵万忠, 等. 基于递推最小二乘法与模糊自适应扩展卡尔曼滤波相结合的车辆状态估计［J］. 中国机械工程, 2017, 28(6):750-755.
WANG Yan, WEI Minxiang, ZHAO Wanzhong, et al. Vehicle State Estimation Based on Combined RLS and FAEKF［J］. China Mechanical Engineering, 2017, 28(6):750-755.
［7］ZHAO Z G, ZHOU L J, ZHANG J T, et al. Distributed and Self-adaptive Vehicle Speed Estimation in the Composite Braking Case for Four-wheel Drive Hybrid Electric Car［J］. Vehicle System Dynamics, 2017, 55(5):750-773.
［8］BONFITTO A, FERACO S, TONOLI A, et al. Combined Regression and Classification Artificial Neural Networks for Sideslip Angle Estimation and Road Condition Identification［J］. Vehicle System Dynamics, 2020, 58(11):1766-1787.
［9］NOVI T, CAPITANI R, ANNICCHIARICO C. An Integrated Artificial Neural Network-unscented Kalman Filter Vehicle Sideslip Angle Estimation Based on Inertial Measurement Unit Measurements［J］. Proceedings of the Institution of Mechanical Engineers, Part D:Journal of Automobile Engineering, 2019, 233(7):1864-1878.
［10］CHEN T, CHEN L, CAI Y, et al. Robust Sideslip Angle Observer with Regional Stability Constraint for an Uncertain Singular Intelligent Vehicle System［J］. IET Control Theory & Applications, 2018, 12(13):1802-1811.
［11］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, 2018, 30(11):3445-3465.
［12］REGOLIN E, ALATORRE A, ZAMBELLI M, et al. A Sliding Mode Virtual Sensor for Wheel Forces Estimation with Accuracy Enhancement via EKF［J］. IEEE Transactions on Vehicular Technology, 2019, 68(4):3457-3471.
［13］JUNG H, CHOI S B. Real-time Individual Tire Force Estimation for an All-wheel Drive Vehicle［J］. IEEE Transactions on Vehicular Technology, 2017, 67(4):2934-2944.
［14］WILKIN M A, MANNING W J, CROLLA D A, et al. Use of an Extended Kalman Filter as a Robust Tyre Force Estimator［J］. Vehicle System Dynamics, 2006, 44(S1):50-59.
［15］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, 2010, 16(4):601-614.
［16］JIN X, 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.
［17］刘刚, 靳立强. 基于多模型交互的复杂工况下车辆状态估计［J］. 汽车工程, 2018, 40(5):584-589.
LIU Gang, JIN Liqiang. Vehicle State Parameter Estimation in Complicated Based on Interacting Multiple Model Algorithm［J］. Automotive Engineering, 2018, 40(5):584-589.
［18］余志生. 汽车理论［M］. 5版.北京:机械工业出版社, 2009:2-53.
YU Zhisheng. Vehicle Fundamentals［M］.5nd ed.　Beijing:China Machine Press, 2009:2-53.
［19］金贤建. 分布式驱动电动汽车状态参数估计与侧向稳定性鲁棒控制研究［D］. 南京：东南大学, 2017.
JIN Xianjian. Parameter Estimation and Robust Control of Lateral Stability for Distributed Drive Electric Vehicles［D］. Nanjing:Southeast University, 2017.
［20］NAM K, OH S, FUJIMOTO H, et al. Estimation of Sideslip and Roll Angles of Electric Vehicles Using Lateral Tire Force Sensors through RLS and Kalman Filter Approaches［J］. IEEE Transactions on Industrial Electronics, 2012, 60(3):988-1000.
［21］LIU J, CAI B, WANG J. Cooperative Localization of Connected Vehicles:Integrating GNSS with DSRC Using a Robust Cubature Kalman Filter［J］. IEEE Transactions on Intelligent Transportation Systems, 2016, 18(8):2111-2125.
［22］付梦印, 邓志红, 闫丽萍. Kalman滤波理论及其在导航系统中的应用［M］. 2版.北京:科学出版社, 2010.
FU Mengyin, DENG Zhihong, YAN Liping. Kalman Filtering Theory and Its Application in Navigation System［M］. 2nd ed.　Beijing:Science Press, 2010.
［23］WANG Y, GENG K K, XU L, et al. Estimation of Sideslip Angle and Tire Cornering Stiffness Using Fuzzy Adaptive Robust Cubature Kalman Filter［J］. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2022， 52（3）:1451-1462.

[1]	ZHAO Linfeng1;YANG Jun1;ZHANG Rongyun2;CHEN Wuwei1. Estimation Algorithm Tire Lateral Forces by Yaw Moments [J]. China Mechanical Engineering, 2018, 29(19): 2284-2289,2297.
[2]	CHAI Tian, LI Fan, LEI Fei, LIU Jie, ZENG Xia , TANG Yingshi, . Study on Steering Geometry of Racing Cars with Consideration of Tire Cornering Characteristics [J]. China Mechanical Engineering, 2017, 28(09): 1106-1111.
[3]	Shen Fapeng, Zhao Youqun, Zhao Hongguang, Liu Yingjie. Effects of Nonlinear Tire Lateral Force on Vehicle Steering Stability [J]. China Mechanical Engineering, 2015, 26(1): 135-139.