基于切换策略的车辆质心侧偏角高性能获取

doi:10.19562/j.chinasae.qcgc.2024.02.017

摘要/Abstract

摘要：

现有的基于融合策略的车辆质心侧偏角（vehicle sideslip angle， VSA）获取方法需要针对不同的场景、结构或信号进行优化和改进，计算复杂度和系统成本也随之增加。本文提出一种基于切换策略的VSA软测量方法。在传感器量测信号预处理部分，利用贝塞尔滤波器对侧向加速度信号进行时延处理和噪声滤除，设计可靠性检验模块对横摆角速度等信号中的突变实现有效清除。在此基础上，根据现有运动学和动力学方案的优势确定切换策略，尽量缩短运动学方案的连续工作区间以抑制其误差累积，同时将动力学方案限制在线性区工作以避免其性能下降，并在多个工况下进行仿真和硬件在环试验，以验证所提出的VSA获取方法的效果。试验结果表明，相较于采用典型的融合策略的对比方法，本文提出的方法在精度和运行时间方面都具有明显的优势，且二者对路况的变化均具有较强的鲁棒性。

关键词: 车辆质心侧偏角, 切换策略, 运动学方案, 动力学方案, 高性能获取

Abstract:

For the existing acquisition method of vehicle sideslip angle （VSA） based on fusion strategy， optimization and improvement are required for different scenarios， structures or signals. As a result， the calculation complexity and system cost rise up. A soft-sensing method for the VSA based on switch strategy is proposed in this paper. In the pretreatment part for sensor measurement signals， the Bessel filter is employed to realize the delay processing and noise filtering of lateral acceleration signal， and a reliability test module is designed to effectively eliminate the mutations in the signals of yaw rate， et al. On this basis， the switch strategy is determined based on the advantages of the existing kinematic and dynamic schemes. For the presented strategy， the continuous operating intervals of the kinematic scheme are shortened in a great deal of effort to restrain the error accumulation， and the dynamic one is restricted in linear region to avoid performance degradation. Simulations and hardware-in-loop experiments are implemented in multiple conditions to verify the effect of the proposed method. The experimental results show that the proposed method in this paper has obvious advantages in accuracy and execution time compared to the one utilizing the classical fusion strategy. Moreover， they are both robust to the change of road conditions.

Key words: vehicle sideslip angle, switch strategy, kinematic scheme, dynamic scheme, high-performance acquisition

陈建锋,吴强,葛新元,赵景波. 基于切换策略的车辆质心侧偏角高性能获取[J]. 汽车工程, 2024, 46(2): 346-355.

Jianfeng Chen,Qiang Wu,Xinyuan Ge,Jingbo Zhao. High-Performance Acquisition for Vehicle Sideslip Angle Based on Switch Strategy[J]. Automotive Engineering, 2024, 46(2): 346-355.

图/表 16

图1

图2

图3

图4

图5

表1

表2

图6

图7

图8

表3

图9

图10

图11

表4

图12

参考文献 25

1	HAJILOO R， ABROSHAN M， KHAJEPOUR A， et al. Integrated steering and differential braking for emergency collision avoidance in autonomous vehicles［J］. IEEE Transactions on Intelligent Transportation Systems， 2021， 22（5）： 3167-3178.
2	SINGH K B， ARAT M A， TAHERI S. Literature review and fundamental approaches for vehicle and tire state estimation［J］. Vehicle System Dynamics， 2019， 57（11）： 1643-1665.
3	周卫琪，齐翔，陈龙，等. 基于无迹卡尔曼滤波与遗传算法相结合的车辆状态估计［J］. 汽车工程， 2019， 41（2）： 198-205.
	ZHOU W Q， QI X， CHEN L， et al. Vehicle state estimation based on the combination of unscented Kalman filtering and genetic algorithm［J］. Automotive Engineering， 2019， 41（2）： 198-205.
4	高振海，温文昊，唐明弘，等. 基于混合神经网络的汽车运动状态估计［J］. 汽车工程， 2022， 44（10）： 1527-1536.
	GAO Z H， WEN W H， TANG M H， et al. Estimation of vehicle motion state based on hybrid neural network［J］. Automotive Engineering， 2022， 44（10）： 1527-1536.
5	XIA X， HANG P， XU N， et al. Advancing estimation accuracy of sideslip angle by fusing vehicle kinematics and dynamics information with fuzzy logic［J］. IEEE Transactions on Vehicular Technology， 2021， 70（7）： 6577-6590.
6	SELMANAJ D， CORNO M， PANZANI G， et al. Vehicle sideslip estimation： a kinematic based approach［J］. Control Engineering Practice， 2017， 67： 1-12.
7	BASCETTA L， BAUR M， FERRETTI G. A simple and reliable technique to design kinematic-based sideslip estimators［J］. Control Engineering Practice， 2020， 96： 104317.
8	BEVLY D M. Global positioning system （GPS）： a low-cost velocity sensor for correcting inertial sensor errors on ground vehicles［J］. Journal of Dynamic Systems， Measurement， and Control， 2004， 126（2）： 255-264.
9	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.
10	NGUYEN A， GUERRA T， SENTOUH C， et al. Unknown input observers for simultaneous estimation of vehicle dynamics and driver torque： theoretical design and hardware experiments［J］. IEEE/ASME Transactions on Mechatronics， 2019， 24（6）： 2508-2518.
11	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， 2011， 16（4）： 601-614.
12	LI X， XU N， LI Q， et al. A fusion methodology for sideslip angle estimation on the basis of kinematics-based and model-based approaches［J］. Proceedings of the Institution of Mechanical Engineers， Part D： Journal of Automobile Engineering， 2019， 234（7）： 1930-1943.
13	LIAO Y， BORRELLI F. An adaptive approach to real-time estimation of vehicle sideslip， road bank angles， and sensor bias［J］. IEEE Transactions on Vehicular Technology， 2019， 68（8）： 7443-7454.
14	CHENG S， LI L， CHEN J. Fusion algorithm design based on adaptive SCKF and integral correction for side-slip angle observation［J］. IEEE Transactions on Industrial Electronics， 2018， 65（7）： 5754-5763.
15	JIANG G， LIU L， GUO C， et al. A novel fusion algorithm for estimation of the side-slip angle and the roll angle of a vehicle with optimized key parameters［J］. Proceedings of the Institution of Mechanical Engineers， Part D： Journal of Automobile Engineering， 2016， 231（2）： 161-174.
16	CHELI F， SABBIONI E， PESCE M， et al. A methodology for vehicle sideslip angle identification： comparison with experimental data［J］. Vehicle System Dynamics， 2007， 45（6）： 549-563.
17	VILLANO E， LENZO B， SAKHNEVYCH A. Cross-combined UKF for vehicle sideslip angle estimation with a modified dugoff tire model： design and experimental results［J］. Meccanica， 2021， 56（11）： 2653-2668.
18	PIYABONGKARN D N， RAJAMANI R， GROGG J A， et al. Development and experimental evaluation of a slip angle estimator for vehicle stability control［J］. IEEE Transactions on Control Systems Technology， 2009， 17（1）： 78-88.
19	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.
20	SONG R， FANG Y. Vehicle state estimation for INS/GPS aided by sensors fusion and SCKF-based algorithm［J］. Mechanical Systems and Signal Processing， 2021， 150： 107315.
21	LIU W， XIA X， XIONG L， et al. Automated vehicle sideslip angle estimation considering signal measurement characteristic［J］. IEEE Sensors Journal， 2021， 21（19）： 21675-21687.
22	JOHNSON J， JOHNSON D， BOUDRA P， et al. Filters using Bessel-type polynomials［J］. IEEE Transactions on Circuits and Systems， 1976， 23（2）： 96-99.
23	FANG H， HAILE M A， WANG Y. Robust extended Kalman filtering for systems with measurement outliers［J］. IEEE Transactions on Control Systems Technology， 2022， 30（2）： 795-802.
24	CHEN J， HU S， YE Y， et al. A cascaded scheme for high-performance estimation of vehicle states［J］. Proceedings of the Institution of Mechanical Engineers， Part D： Journal of Automobile Engineering， 2021， 235（8）： 2101-2113.
25	BERNTORP K， CAIRANO S D. Tire-stiffness and vehicle-state estimation based on noise-adaptive particle filtering［J］. IEEE Transactions on Control Systems Technology， 2019， 27（3）： 1100-1114.

参数	数值
整车质量m/kg	1 412
车辆绕z轴的转动惯量I_z /（kg?m²）	1 964.4
车辆前/后轴到质心的距离a/b /m	1.015/1.895
车辆轴距l/m	2.91
车轮有效半径R_eff/m	0.325

惯性器件	偏置	噪声（1σ）
陀螺仪	0.1（°）/s	1（°）/s
加速度计	0.5 m/s²	0.3 m/s²

采用的方法	\|Δβ\|_max	\|Δβ\|_mean	\|Δβ\|_RMSE
本文方法	0.274°	0.059°	0.086°
对比方法	0.462°	0.076°	0.124°