Estimation of vehicle velocities and side-slip angles based on the unscented Kalman filter

doi:10.3969/j.issn.1674-8484.2023.01.004

Abstract

Abstract:

A modular state observer structure of vehicle lateral and longitudinal velocity based on the Unscented Kalman Filter (UKF) was proposed to meet the requirements of vehicle active safety control function and to improve the accuracy of vehicle state observation under strong nonlinear characteristics. The structure used the information of vehicle sensors combined with UKF to observe the longitudinal and lateral velocity, and calculated the vehicle side-slip angle according to the definition of side-slip angle. Numerical simulations and real vehicle experiments were carried out on dry road surfaces. The results show that in the strong nonlinear state, the root mean square error (RMSE) of the simulation results of the UKF-based vehicle side-slip angle estimation is 0.425°, the RMSE of the real vehicle experiment is 0.001°, while the RMSE of the simulation results using the Extended Kalman Filter (EKF) estimation is 0.968°, and the RMSE of the real vehicle experiment is 0.009°. Therefore, the UKF can suppress the influence of vehicle driving interference on observation, so that the observer structure has high observation accuracy and can meet the needs of engineering.

Key words: vehicle active safety control, vehicle velocity, modular state observer structure, side-slip angle, unscented Kalman filter (UKF)

CLC Number:

U461.6

LIU Zhaoyong, LIU Wudong, SHAO Weishu, TAN Xiaoqiang, WU Guangqiang. Estimation of vehicle velocities and side-slip angles based on the unscented Kalman filter[J]. Journal of Automotive Safety and Energy, 2023, 14(1): 31-37.

Figures/Tables 7

References 24

[1]	Eder P, Amhofer T, Gerstorfer T, Lex C. Tire deformation modelling for high-speed open-wheel aerodynamic investigations[J]. SAE Int’l J Vehi Dyna, Stab NVH, 2021, 5(3): 233-248.
[2]	XU Nan, ZHOU Jianfeng, LI Xiaoyu, LI Fei. Analysis of the effect of inflation pressure on vehicle handling and stability under combined slip conditions based on the UniTire Model[J]. SAE Int’l J Vehi Dyn, Stab NVH, 2021, 5(3): 259-277.
[3]	张亮修, 杨家颖, 吴光强. 考虑侧倾的半主动悬架与电子稳定控制系统集成控[J]. 同济大学学报(自然科学版), 2016, 44(3): 402-410.
	ZHANG Liangxiu, YANG Jiaying, WU Guangqiang. Integrated control based on semi-active suspension and electronic stability control system considering active roll control[J]. J Tongji Univ (Nat Sci), 2016, 44(3): 402-410. (in Chinese)
[4]	ZHANG Jie, WANG Hai, MA Mingyao, et al. Active front steering-based electronic stability control for steer-by-wire vehicles via terminal sliding mode and extreme learning machine[J]. *IEEE T Vehi Technol*, 2020, 69(12): 14713-14726.
[5]	吴光强, 张亮修, 刘兆勇, 等. 汽车自适应巡航控制系统研究现状与发展趋势[J]. 同济大学学报(自然科学版), 2017, 45(04): 544-553.
	WU Guangqiang, ZHANG Liangxiu, LIU Zhaoyong, et al. Research status and development trend of vehicle adaptive cruise control systems[J]. J Tongji Univ (Nat Sci), 2017, 45(4): 544-553. (in Chinese)
[6]	胡远志, 丁晓木, 刘西, 等. 全速域自适应巡航控制方法研究[J]. 汽车安全与节能学报, 2019, 10(3): 357-365.
	HU Yuanzhi, DING Xiaomu, LIU Xi, et al. Research on full speed range adaptive cruise control[J]. J Automo Safe Energ, 2019, 10(3): 357-365. (in Chinese)
[7]	Alquran M, Mayyas A. Design of a nonlinear stability controller for ground vehicles subjected to a tire blowout using double-integral sliding-mode controller[J]. SAE Int’l J Vehi Dyna, Stab NVH, 2021, 5(3): 291-305.
[8]	YUE Lishengsa, Abdel-Aty M, WU Yina, et al. Effects of forward collision warning technology in different pre-crash scenarios[J]. Transp Res Part F, 2021, 76: 336-352. doi: 10.1016/j.trf.2020.12.004 URL
[9]	HE Ren, FENG Haipeng. Research and development of autonomous emergency brake(AEB) technology[J]. J Automo Safe Energ, 2019, 10(1): 1-15.
[10]	LIU Wei, XIA Xin, XIONG Lu, et al. Automated vehicle sideslip angle estimation considering signal measurement characteristic[J]. IEEE Sens J, 2021, 21(19): 21675-21687. doi: 10.1109/JSEN.2021.3059050 URL
[11]	LI Cong, XIE Yunfeng, WANG Gang, et al. Experimental study of electric vehicle yaw rate tracking control based on differential steering[J]. J Adv Transp, 2021, 10: 1-8.
[12]	LIU Jizheng, WANG Zhenpo, ZHANG Lei, et al. Sideslip angle estimation of ground vehicles: a comparative study[J]. IET Control Theory A, 2020, 14(20): 3490-3505. doi: 10.1049/cth2.v14.20 URL
[13]	吴光强. 汽车理论(第三版)[M]. 北京: 人民交通出版社股份有限公司, 2021: 184-243.
	WU Guangqiang. Automotive Theory 3^rd Ed[M]. Beijing: China Communications Press, 2021: 184-243. (in Chinese)
[14]	Bersani M, Vignati M, Mentasti S, et al. Vehicle state estimation based on Kalman filters[C]. 2019 AEIT International Conference of Electrical and Electronic Technologies for Automotive (AEIT AUTOMOTIVE). Politecnico di Milano, Milano, Italy; 2019.
[15]	LIU Wei, XIONG Lu, XIA Xin et al., Intelligent vehicle sideslip angle estimation considering measurement signals delay[C]. 2018 IEEE Intelligent Vehicles Symposium (IV), Changshu, China, 2018.
[16]	DING Xiaolin, WANG Zhenpo, ZHANG Lei, et al. Longitudinal vehicle speed estimation for four-wheel-independently-actuated electric vehicles based on multi-sensor fusion[J]. IEEE T Vehi Technol, 2020, 69(11): 12797-12806.
[17]	高博麟, 谢书港, 龚进峰. 基于运动学-动力学方法融合的汽车质心侧偏角估计[J]. 汽车安全与节能学报, 2015, 6(1): 72-78.
	GAO Bolin, XIE Shugang, GONG Jingfeng. Vehicle sideslip angle estimation based on fusion of kinematics-dynamics methods[J]. J Automo Safe Energ, 2015, 6(1): 72-78. (in Chinese)
[18]	DING Xiaolin, WANG Zhenpo, ZHANG Lei. Event-Triggered vehicle sideslip angle estimation based on low-cost sensors[J]. IEEE T Indu Info, 2022, 18(7): 4466-4476.
[19]	XIONG Lu, XIA Xin, LU Yishi, et al. IMU-based automated vehicle body sideslip angle and attitude estimation aided by GNSS using parallel adaptive Kalman filters[J]. IEEE T Vehi Technol, 2020, 69(10): 10668-10680.
[20]	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 Syst Dyn, 2019, 4: 1-22. doi: 10.1080/00423117508968458 URL
[21]	LIAO Yiwen, Borrelli F. An adaptive approach to real-time estimation of vehicle sideslip, road bank angles and sensor bias[J]. IEEE T Vehi Technol, 2019, 68(8): 7443-7454.
[22]	JIN Xianjian, YANG Junpeng, LI Yanjun, et al. Online estimation of inertial parameter for lightweight electric vehicle using dual unscented Kalman filter approach[J]. IET Intell Transp Sy, 2020, 14(5): 412-422.
[23]	Imsland L, Johansen T A, Fossen T I, et al. Vehicle velocity estimation using nonlinear observers[J]. Automatica, 2006, 42(12): 2091-2103. doi: 10.1016/j.automatica.2006.06.025 URL
[24]	CHEN Jie, SONG Jian, LI Liang, et al. UKF‐based adaptive variable structure observer for vehicle sideslip with dynamic correction[J]. IET Control Theory Appl, 2016, 10(14): 1641-1652. doi: 10.1049/cth2.v10.14 URL

车辆重心到前轴的距离, l_r	1.895 m
车辆重心到后轴的距离, l_f	1.015 m
转动惯量, I_z	1.537 kNm
整车质量, m	1.270 t
左右轮的中心距离, b	1.675 m

车辆重心到前轴的距离, l_r	1.895 m
车辆重心到后轴的距离, l_f	1.015 m
转动惯量, I_z	1.537 kNm
整车质量, m	1.270 t
左右轮的中心距离, b	1.675 m

估计方法	RMSE [v_x / (m·s^-1)]		RMSE [β / (°)]
估计方法	仿真	试验	仿真	试验
UKF	0.002	0.035	0.425	0.001
EKF	0.430	0.923	0.968	0.009

估计方法	RMSE [v_x / (m·s^-1)]		RMSE [β / (°)]
估计方法	仿真	试验	仿真	试验
UKF	0.002	0.035	0.425	0.001
EKF	0.430	0.923	0.968	0.009