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

doi:10.3901/JME.2020.16.204

Abstract

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

CLC Number:

U461

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.

References

[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.