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
|
摘要
纵向车速和质心侧偏角是车辆主动安全控制系统的关键参考状态信号,通常采用卡尔曼滤波算法估计。当系统噪声和测量噪声的统计特性存在不确定性时,不仅估计精度会降低,甚至导致估计器发散。结合分布式驱动电动汽车4个车轮转矩和转速可直接测量的特点,提出一种车辆状态自适应扩展卡尔曼滤波估计方法。基于量纲一化新息平方实现车辆状态估计有效性检测,提出滑动窗口长度自适应调整规则;根据新息统计特性提出卡尔曼滤波增益和状态估计误差协方差矩阵的自适应调整策略,及基于车辆状态估计稳态误差和动态响应速度的自适应参数确定原则。数值仿真和试验证明,所提出的车辆状态估计方法,不仅估计精度较高,而且实时性和易用性较强。
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.
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.
引文
[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 4WDhybrid 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-GPSloosely 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:IEEEComputer 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.
[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 4WDhybrid 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-GPSloosely 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:IEEEComputer 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.