基于DRBF-EKF算法的车辆质心侧偏角与路面附着系数动态联合估计

李韶华; 王桂洋; 杨泽坤; 王雪玮

doi:10.6052/0459-1879-21-551

基于DRBF-EKF算法的车辆质心侧偏角与路面附着系数动态联合估计

doi: 10.6052/0459-1879-21-551

李韶华^{*, †},
王桂洋^{*, †, **,},
杨泽坤^{*, †, **},
王雪玮^*

*.
石家庄铁道大学省部共建交通工程结构力学行为与系统安全国家重点实验室, 石家庄 050043
†.
石家庄铁道大学河北省交通工程结构力学行为演变与控制重点实验室, 石家庄 050043
**.
石家庄铁道大学机械工程学院, 石家庄 050043

基金项目: 国家自然科学基金(11972238), 河北省重点研发计划(21342202D)和河北省博士研究生创新项目(CXZZBS2021119)资助项目

详细信息

作者简介:
王桂洋, 博士, 主要研究方向: 车辆动力学与控制. E-mail: wanggy518@stdu.edu.cn

中图分类号: U461.6
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- 被引次数: 0
出版历程
- 收稿日期: 2021-10-27
- 录用日期: 2022-04-01
- 网络出版日期: 2022-04-02
- 刊出日期: 2022-07-15

DYNAMIC JOINT ESTIMATION OF VEHICLE SIDESLIP ANGLE AND ROAD ADHESION COEFFICIENT BASED ON DRBF-EKF ALGORITHM

Li Shaohua^{*, †},
Wang Guiyang^{*, †, **
,},
Yang Zekun^{*, †, **},
Wang Xuewei^*

*.
State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
†.
Key Laboratory of Mechanical Behavior Evolution and Control of Traffic Engineering Structures in Hebei, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
**.
School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China

摘要

摘要: 车辆质心侧偏角和路面附着系数是实现车辆底盘智能化所需要的关键参数. 车辆质心侧偏角对于提高车辆安全性和操控性至关重要, 轮胎-路面附着系数决定轮胎力的峰值, 进而确定汽车的动力学稳定性边界. 本文针对四轮独立驱动电动汽车提出了一种基于惯性测量单元、轮毂电机内置转速/转角传感器的车辆质心侧偏角和路面附着系数动态联合估计方法. 对四轮独立驱动电动汽车进行车辆动力学分析, 结合Dugoff轮胎计算模型得到车辆质心侧偏角估计器; 利用机器学习中高维数据降维PCA多元分析方法, 提取主元特征参数, 建立路面附着系数估计器. 采用可自适应调节网络结构的双径向基神经网络和扩展卡尔曼滤波DRBF-EKF方法, 通过K-means算法改进RBF神经网络结构, 扩展卡尔曼滤波进行噪声滤波提高估计精度, 实现车辆质心侧偏角和路面附着系数的动态联合估计. 通过仿真和实车实验表明, 所设计的DRBF-EKF动态联合估计器实时性和估计精度均优于扩展卡尔曼滤波算法, 可以适应车辆行驶过程中路面附着特性与车速的变化, 表现出较强的鲁棒性; 与DRBF方法相比, 显著提高了估计精度; 并且分析了可以同时满足估计精度和实时性要求的最佳隐含层神经元个数.
- 四轮独立驱动 /
- 参数估计 /
- 质心侧偏角 /
- 路面附着系数 /
- 神经网络
Abstract: The vehicle center of mass slip angle and the road adhesion coefficient are the key parameters required to realize the intelligence of the vehicle chassis. The vehicle's center of mass slip angle is crucial for improving vehicle safety and handling, and the tire-road adhesion coefficient determines the peak tire force, which in turn determines the vehicle's dynamic stability boundary. In this paper, a dynamic joint estimation method of vehicle mass side slip angle and road adhesion coefficient based on inertial measurement unit (IMU) and built-in speed/angle sensor (WSS) of in-wheel motor is proposed for four-wheel independent drive electric vehicles. The vehicle dynamics analysis of the four-wheel independent drive electric vehicle is carried out, and the vehicle center of mass side slip angle estimator is obtained by combining the Dugoff tire calculation model; the PCA multivariate analysis method is used to reduce the dimension of the high-dimensional data in machine learning, and the principal component characteristic parameters are extracted to establish the road adhesion coefficient estimator. The double radial basis neural network and the extended Kalman filter (double radial basis function and extended Kalman filter, DRBF-EKF) method with an adaptively adjustable network structure are used, the RBF neural network structure is improved by the K-means algorithm, and the extended Kalman filter is used for noise filtering to improve estimation accuracy. Therefore, dynamic joint estimation of vehicle center of mass slip angle and road adhesion coefficient is realized. Simulation and real-vehicle experiments show that the designed DRBF-EKF dynamic joint estimator has better real-time performance and estimation accuracy than the extended Kalman filter algorithm, which can adapt to the changes in road adhesion characteristics and vehicle speed during vehicle driving. Compared with the DRBF method, the estimation accuracy is significantly improved; and the optimal number of neurons in the hidden layer that can meet the estimation accuracy and real-time requirements at the same time is analyzed.
- four-wheelindependentdrive /
- parameter estimation /
- sideslip angle /
- road adhesion coefficient /
- neural network

HTML全文

图 1 四轮独立驱动车辆动力学模型

Figure 1. Dynamic model for four-wheel independentdrive vehicle

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图 2 PCA相关性分析

Figure 2. PCA correlation analysis

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图 3 质心侧偏角与路面附着系数动态联合估计结构框图

Figure 3. Structural diagram of dynamic joint estimation of sideslip angle and road adhesion coefficient

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4 冰路面质心侧偏角和路面附着系数联合估计

4. Joint estimation of side slip angle and road adhesion coefficient on ice road

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5 干燥柏油路质心侧偏角和路面附着系数联合估计

5. Joint estimation of side slip angle and road adhesion coefficient on dry asphalt road

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图 6 由冰路面至干燥柏油路质心侧偏角和路面附着系数联合估计

Figure 6. Joint estimation of sideslip angle and road adhesion coefficient from ice road to dry asphalt road

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图 7 由干燥柏油路至冰路面质心侧偏角和路面附着系数联合估计

Figure 7. Joint estimation of sideslip angle and road adhesion coefficient from dry asphalt road to ice road

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图 8 由冰路面至干燥柏油路质心侧偏角和路面附着系数联合估计

Figure 8. Joint estimation of sideslip angle and road adhesion coefficient from ice road to dry asphalt road

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图 9 由干燥柏油路至冰路面质心侧偏角和路面附着系数联合估计

Figure 9. Joint estimation of sideslip angle and road adhesion coefficient from dry asphalt road to ice road

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图 10 实验车辆和传感器设备

Figure 10. Vehicle and sensors equipment for the experiment

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11 轮毂电机内置传感器和惯导测得数据

11. In-wheel motor sensors and IMU measurement data

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11 轮毂电机内置传感器和惯导测得数据 (续)

11. In-wheel motor sensors and IMU measurement data (continued)

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12 DRBF-EKF质心侧偏角和路面附着系数联合估计

12. DRBF-EKF sideslip angle and road adhesion coefficient joint estimation

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图 13 DRBF-EKF估计精度与隐含层神经元个数关系

Figure 13. The relationship between DRBF-EKF estimation accuracy with the number of hidden layer neurons

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表 1 车辆主要参数

Table 1. Main parameters of the vehicle

Parameter	Value
mass/kg	880
external dimensions long × high × width/mm³	2870 × 1510 × 1830
rotational inertia/(kg·m²)	832.3
wheelbase/mm	2040
front wheel wheelbase/mm	1300
rear wheel wheelbase/mm	1285
distance from center of mass to front axle/mm	1415
tire specifications	225/65-14
tire cornering stiffness/(kN·rad⁻¹)	52
wheel mass/kg	100
front in-wheel motor rated power/kW	8
rear in-wheel motor rated power/ kW	12

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表 2 采集不同工况数据

Table 2. Collect different working conditions data

μ	Velocity/(km·h⁻¹)	Condition
0.85	30, 60, 90	serpentine
0.6	30, 60, 90	serpentine
0.2	30, 60	serpentine

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表 3 车速60 km/h冰路面估计误差

Table 3. Error of vehicle speed 60 km/h on ice road

60 km/h	RMSE	MAE
β	0.0212	0.0293°
μ	0.0365	0.0102

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表 4 车速90 km/h干燥柏油路估计误差

Table 4. Error of vehicle speed 90 km/h on dry asphalt

90 km/h	RMSE	MAE
β	0.0108	0.0048°
μ	0.0748	0.0162

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表 5 车速60 km/h对接路面估计误差

Table 5. Error of vehicle speed 60 km/h on joint road

60 km/h	MAE (β)	MAE (μ)
μ = 0.2~0.85	0.0735	0.0277
μ = 0.85~0.2	0.0628	0.0208

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表 6 车速90 km/h对接路面估计误差

Table 6. Error of vehicle speed 90 km/h on joint road

90 km/h	MAE(β)	MAE(μ)
μ=0.2 ~ 0.85	0.0308	0.0195
μ=0.85 ~ 0.2	0.0243	0.0204

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表 7 DRBF-EKF和DRBF质心侧偏角估计误差结果对比

Table 7. The error between DRBF-EKF and DRBF sideslip angle estimation

β	RMSE	MAE/(°)
DRBF-EKF	0.0737	0.0367
DRBF	0.1607	0.1162

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