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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

李韶华, 王桂洋, 杨泽坤, 王雪玮. 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. 力学学报, 2022, 54(7): 1853-1865 doi: 10.6052/0459-1879-21-551
引用本文: 李韶华, 王桂洋, 杨泽坤, 王雪玮. 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. 力学学报, 2022, 54(7): 1853-1865 doi: 10.6052/0459-1879-21-551
Li Shaohua, Wang Guiyang, Yang Zekun, Wang Xuewei. Dynamic joint estimation of vehicle sideslip angle and road adhesion coefficient based on DRBF-EKF algorithm. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(7): 1853-1865 doi: 10.6052/0459-1879-21-551
Citation: Li Shaohua, Wang Guiyang, Yang Zekun, Wang Xuewei. Dynamic joint estimation of vehicle sideslip angle and road adhesion coefficient based on DRBF-EKF algorithm. Chinese Journal of Theoretical and Applied Mechanics, 2022, 54(7): 1853-1865 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
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

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

  • 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.

     

  • 图  1  四轮独立驱动车辆动力学模型

    Figure  1.  Dynamic model for four-wheel independentdrive vehicle

    图  2  PCA相关性分析

    Figure  2.  PCA correlation analysis

    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

    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

    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

    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

    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

    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

    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

    图  10  实验车辆和传感器设备

    Figure  10.  Vehicle and sensors equipment for the experiment

    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

    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)

    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

    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

    表  1  车辆主要参数

    Table  1.   Main parameters of the vehicle

    ParameterValue
    mass/kg880
    external dimensions long × high × width/mm32870 × 1510 × 1830
    rotational inertia/(kg·m2)832.3
    wheelbase/mm2040
    front wheel wheelbase/mm1300
    rear wheel wheelbase/mm1285
    distance from center of mass to front axle/mm1415
    tire specifications225/65-14
    tire cornering stiffness/(kN·rad−1)52
    wheel mass/kg100
    front in-wheel motor rated power/kW8
    rear in-wheel motor rated power/ kW12
    下载: 导出CSV

    表  2  采集不同工况数据

    Table  2.   Collect different working conditions data

    μVelocity/(km·h−1)Condition
    0.8530, 60, 90serpentine
    0.630, 60, 90serpentine
    0.230, 60serpentine
    下载: 导出CSV

    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/hRMSEMAE
    β0.02120.0293°
    μ0.03650.0102
    下载: 导出CSV

    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/hRMSEMAE
    β0.01080.0048°
    μ0.07480.0162
    下载: 导出CSV

    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/hMAE (β)MAE (μ)
    μ = 0.2~0.850.07350.0277
    μ = 0.85~0.20.06280.0208
    下载: 导出CSV

    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/hMAE(β)MAE(μ)
    μ=0.2 ~ 0.850.03080.0195
    μ=0.85 ~ 0.20.02430.0204
    下载: 导出CSV

    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

    βRMSEMAE/(°)
    DRBF-EKF0.07370.0367
    DRBF0.16070.1162
    下载: 导出CSV
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  • 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

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