State Estimation of Distributed Drive Electric Vehicle Based on Adaptive Extended Kalman Filter

doi:10.3901/JME.2019.06.156

Abstract

Abstract: Longitudinal velocity and sideslip angle are the key referent state signals for vehicle active safety control system, and are usually estimated by Kalman filtering algorithm. The uncertainties of the statistical characteristics of system noise and measurement noise may cause filter to deviate or even diverge. Using the characteristics of the torques and speeds of four wheels can measurement directly in a distributed drive electric vehicle, an adaptive extended Kalman filtering method for vehicle state estimation is proposed. With normalized innovation square, the validity of vehicle state estimation is detected, and an adaptive adjustment rule of sliding window length is designed. An adaptive adjustment strategy of the gain of Kalman filter and the covariance matrix of state estimation error are proposed based on the statistical characteristics of innovation. The determination principle of adaptive parameters based on the steady-state error of vehicle state estimation and the dynamic response speed is determined. The numerical simulation and experiment can prove that the proposed algorithm of vehicle state estimation not only can improve estimation accuracy, but also has advantages of high real-time and easy to implement.

Key words: adaptive control, distributed drive, electric vehicle, extended Kalman filter, states estimation

CLC Number:

U461

ZHANG Zhiyong, ZHANG Shuzhi, HUANG Caixia, ZHANG Liuzhu, LI Bohao. State Estimation of Distributed Drive Electric Vehicle Based on Adaptive Extended Kalman Filter[J]. Journal of Mechanical Engineering, 2019, 55(6): 156-165.

References

[1] 章仁燮,熊璐,余卓平. 智能汽车转向轮转角主动控制[J]. 机械工程学报,2017,53(14):106-113. ZHANG Renxie,XIONG Lu,YU Zhuoping. Active steering angle control for intelligent vehicle[J]. Journal of Mechanical Engineering,2017,53(14):106-113.
[2] MOUSAVINEJAD E,HAN Q L,YANG F,et al. Integrated control of ground vehicles dynamics via advanced terminal sliding mode control[J]. Vehicle System Dynamics,2017,55(2):268-294.
[3] DAHMANI H,PAGÈS O,HAJJAJI A E. Observer-based state feedback control for vehicle chassis stability in critical situations[J]. IEEE Transactions on Control Systems Technology,2016,24(2):636-643.
[4] 李刚,赵德阳,解瑞春,等. 基于改进的Sage-Husa自适应扩展卡尔曼滤波的车辆状态估计[J]. 汽车工程,2015,37(12):1426-1432. LI Gang,ZHAO Deyang,XIE Ruichun,et al. Vehicle state estimation based on improved Sage-Husa adaptive extended Kalman filtering[J]. Automotive Engineering,2015,37(12):1426-1432.
[5] 沈法鹏,赵又群,孙秋云,等. 基于IEKF-APF算法的汽车状态估计[J]. 机械工程学报,2014,50(22):136-141. SHEN Fapeng,ZHAO Youqun,SUN Qiuyun,et al. Ehicle state estimation based on IEKF-APF[J]. Journal of Mechanical Engineering,2014,50(22):136-141.
[6] BOADA B L,BOADA M J L,DIAZ V. Vehicle sideslip angle measurement based on sensor data fusion using an integrated ANFIS and an unscented Kalman filter algorithm[J]. Mechanical Systems and Signal Processing,2016,72:832-845.
[7] 赵治国,朱强,周良杰,等. 分布式驱动HEV自适应无迹卡尔曼车速估计[J]. 中国科学:技术科学,2016,46(5):481-492. ZHAO Zhiguo,ZHU Qiang,ZHOU Liangjie,et al. Vehicle speed estimation in driving case based on distributed self-adaptive unscented Kalman filter for 4WD hybrid electric car[J]. Scientia Sinica Technologica,2016,46(5):481-492.
[8] 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.
[9] ZHOU H,LIU Z,YANG X. Motor torque fault diagnosis for four wheel independent motor-drive vehicle based on unscented Kalman filter[J]. IEEE Transactions on Vehicular Technology,2018,67(3):1969-1976.
[10] 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.
[11] KIM K H,LEE J G,PARK C G. Adaptive two-stage extended Kalman filter for a fault-tolerant INS-GPS loosely coupled system[J]. IEEE Transactions on Aerospace and Electronic Systems,2009,45(1):125-137.
[12] MOHAMED A H,SCHWARZ K P. Adaptive Kalman filtering for INS/GPS[J]. Journal of Geodesy,1999,73(4):193-203.
[13] XIONG R,GONG X,MI C C,et al. A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter[J]. Journal of Power Sources,2013,243:805-816.
[14] WANG X,YOU Z,ZHAO K. Inertial/celestial-based fuzzy adaptive unscented Kalman filter with Covariance Intersection algorithm for satellite attitude determination[J]. Aerospace Science and Technology,2016,48:214-222.
[15] JIANG J,ZHANG Y. A novel variable-length sliding window blockwise least-squares algorithm for on-line estimation of time-varying parameters[J]. International Journal of Adaptive Control and Signal Processing,2004,18(6):505-521.
[16] HAO Y L,GUO Z,SUN F,et al. Adaptive extended Kalman filtering for SINS/GPS integrated navigation systems[C]//International Joint Conference on Computational Sciences and Optimization. Sanya:IEEE Computer Society,2009:192-194.
[17] MAKSAROV D,DURRANT-WHYTE H. Mobile vehicle navigation in unknown environments:A multiple hypothesis approach[J]. IEE Proceedings-Control Theory and Applications,1995,142(4):385-400.
[18] CALISKAN F,AYKAN R,HAJIYEV C. Aircraft icing detection,identification,and reconfigurable control based on Kalman filtering and neural networks[J]. Journal of Aerospace Engineering,2008,21(2):51-60.