Article(id=1153780690140258543, tenantId=1146029695717560320, journalId=1146120084050784272, issueId=1153780685195170113, articleNumber=null, orderNo=null, doi=10.19562/j.chinasae.qcgc.2024.02.017, pmid=null, cstr=null, oa=null, hot=null, price=null, onlineType=0, articleFormat=0, articleType=null, articleTypeStr=null, receivedDate=1691683200000, receivedDateStr=2023-08-11, revisedDate=1692892800000, revisedDateStr=2023-08-25, acceptedDate=null, acceptedDateStr=null, onlineDate=1753012352737, onlineDateStr=2025-07-20, pubDate=1708790400000, pubDateStr=2024-02-25, doiRegisterDate=null, doiRegisterDateStr=null, onlineIssueDate=1753012352737, onlineIssueDateStr=2025-07-20, onlineJustAcceptDate=null, onlineJustAcceptDateStr=null, onlineFirstDate=null, onlineFirstDateStr=null, sourceXml=null, magXml=null, createTime=1753012352737, creator=13701087609, updateTime=1753012352737, updator=13701087609, issue=Issue{id=1153780685195170113, tenantId=1146029695717560320, journalId=1146120084050784272, year='2024', volume='46', issue='2', pageStart='187', pageEnd='374', issueExtLink='null', onlineDate='null', pubDate='null', beforeIssueId=null, nextIssueId=null, price=null, status=1, issueComplete=0, articleOrder=1, issueType=-1, specialIssue=null, createTime=1753012351559, creator=13701087609, updateTime=1753058330907, updator=13701087609, preIssue=null, nextIssue=null, ext={EN=IssueExt(id=1153973536604541183, tenantId=1146029695717560320, journalId=1146120084050784272, issueId=1153780685195170113, language=EN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=), CN=IssueExt(id=1153973536604541184, tenantId=1146029695717560320, journalId=1146120084050784272, issueId=1153780685195170113, language=CN, specialIssueTitle=, coverIllustrator=null, specialIssueEditor=, specialIssueAbout=)}, issueFiles=null}, startPage=346, endPage=355, ext={EN=ArticleExt(id=1153780690563883248, articleId=1153780690140258543, tenantId=1146029695717560320, journalId=1146120084050784272, language=EN, title=High-Performance Acquisition for Vehicle Sideslip Angle Based on Switch Strategy, columnId=1149809889280750125, journalTitle=Automotive Engineering, columnName=Selected Papers, runingTitle=null, highlight=null, articleAbstract=

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.

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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。

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赵景波,教授,博士,E-mail:
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参数 数值
整车质量m/kg 1 412
车辆绕z轴的转动惯量Iz /(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Reff/m 0.325
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车辆结构参数

, figureFileSmall=null, figureFileBig=null, tableContent=
参数 数值
整车质量m/kg 1 412
车辆绕z轴的转动惯量Iz /(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Reff/m 0.325
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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
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惯性器件性能指标

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采用的方法 β|max β|mean β|RMSE
本文方法 0.274° 0.059° 0.086°
对比方法 0.462° 0.076° 0.124°
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本文方法 0.274° 0.059° 0.086°
对比方法 0.462° 0.076° 0.124°
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采用的

方法

TRFC = 0.35 TRFC = 0.85
β|max β|mean β|RMSE β|max β|mean β|RMSE
本文方法 0.416° 0.073° 0.100° 1.074° 0.240° 0.338°
对比方法 0.567° 0.109° 0.146° 1.192° 0.286° 0.383°
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硬件在环试验的误差值统计结果

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采用的

方法

TRFC = 0.35 TRFC = 0.85
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本文方法 0.416° 0.073° 0.100° 1.074° 0.240° 0.338°
对比方法 0.567° 0.109° 0.146° 1.192° 0.286° 0.383°
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基于切换策略的车辆质心侧偏角高性能获取*
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陈建锋 1, 2 , 吴强 2 , 葛新元 2 , 赵景波 1
汽车工程 | 精选论文 2024,46(2): 346-355
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汽车工程 | 精选论文 2024, 46(2): 346-355
基于切换策略的车辆质心侧偏角高性能获取*
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陈建锋1, 2, 吴强2, 葛新元2, 赵景波1
作者信息
  • 1. 常州工学院电气信息工程学院,常州 213032
  • 2. 江苏大学汽车工程研究院,镇江 212013

通讯作者:

赵景波,教授,博士,E-mail:
High-Performance Acquisition for Vehicle Sideslip Angle Based on Switch Strategy
Jianfeng Chen1, 2, Qiang Wu2, Xinyuan Ge2, Jingbo Zhao1
Affiliations
  • 1. School of Electrical and Information Engineering,Changzhou Institute of Technology,Changzhou 213032
  • 2. Automotive Engineering Research Institute,Jiangsu University,Zhenjiang 212013
出版时间: 2024-02-25 doi: 10.19562/j.chinasae.qcgc.2024.02.017
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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。

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

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.

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*. 汽车工程, 2024 , 46 (2) : 346 -355 . DOI: 10.19562/j.chinasae.qcgc.2024.02.017
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 . DOI: 10.19562/j.chinasae.qcgc.2024.02.017
车辆质心侧偏角(vehicle sideslip angle,VSA)对车辆的稳定性控制具有重要意义,VSA过大,轮胎侧向力趋近饱和,车辆极易失稳。在电子稳定程序(electronic stability program,ESP)等主动安全技术中,VSA是主要的控制变量,且须能够准确测量。近年来,随着主动安全技术的广泛应用,车辆的经济性、安全性与驾驶性都得到显著提升[1]。但是,由于成本原因,量产车上的VSA信息难以利用传感器直接测得[2]。相比较而言,基于软测量原理的VSA获取方法具有成本低、可移植性好等显著优点[3-4],已成为学术界研究的重点方向之一。
根据所采用的模型不同,基于软测量原理的VSA获取方案主要可分为运动学和动力学两类[5]。前者的实现依赖于对惯性器件(包括陀螺仪和加速度计)的量测信号进行积分,其性能不受车辆运动的影响、计算量小且对道路条件的适应性较好[6-7]。但是,惯性器件的量测信号中存在偏置。这些偏置难以准确估计且会随时间累积,最终严重影响VSA获取的长期性能[8]。后者的性能主要取决于车辆动力学建模的准确性,特别是轮胎的动力学建模。目前,轮胎力的计算主要依赖观测器或轮胎模型。利用滑模观测器[9]或未知输入观测器[10],可以实现轮胎力的有效观测,但存在观测结构复杂以及需要进行稳定性设计等问题。轮胎模型可分为非线性和线性轮胎模型两类[11]:非线性轮胎模型精度高,但含有较多难以准确获取的时变参数;线性轮胎模型结构简单,但其性能会在非线性区呈现显著下降。Li等[12]和Liao等[13]在线性轮胎模型的基础上,分别提出一种基于反馈信息的轮胎侧偏刚度修正方法,以实现对轮胎侧偏刚度的自适应。但是,此类做法对系统设计的复杂度要求较高,且需要保证修正信号在任意情况下均可靠。
考虑到运动学和动力学方案各自的优点,有学者提出将两种方案得到的结果进行融合的观点,相关的研究可大致分为两类。第一类需要根据量测信号判定车辆的运动状态,进而确定融合系数。Cheng等[14]和Jiang等[15]分别利用侧向车速和侧向加速度量测信号设计融合策略,在部分运动场景下可获得较好的融合效果。但是,上述做法未涉及量测信号(如侧向加速度和横摆角速度等)数值小但变化快的场景。Cheli等[16]和Villano等[17]提出基于量测信号均方误差的车辆运动状态判定方法,以尽可能应对所有场景。但是,计算均方误差会使车辆运动状态的判定结果产生滞后。Piyabongkarn等[18]和Li等[12]利用低通和高通滤波原理,从频域角度设计融合策略。类似地,滤波器的引入会使信号的相位发生改变。第二类研究则着眼于利用运动学或动力学方案对另一种方案中的关键参数进行修正。例如,Liao等[13]提出利用运动学方案求解时变的轮胎侧偏刚度,以实现轮胎侧向力的准确获取。但是,轮胎侧偏刚度的非线性特征仅出现在非线性区,上述做法会产生不必要的运算负担。Hong等[19]考虑车辆结构参数的时变特性,提出基于双滤波器结构的融合策略,通过迭代修正提升VSA获取的鲁棒性。需要注意的是,该方法仅涉及某些特殊场景下的参数时变问题,其普适性有待进一步验证。此外,Song等[20]和Liu等[21]使用卫星信号对车载传感器量测信号进行修正,通过改善动力学方案的信号输入质量来提升动力学方案的VSA获取效果。但是,卫星信号易受环境影响且信号频率低。
总体而言,现有的研究主要集中在车辆运动状态的判定以及融合策略的设计等方面,较少涉及对运动学和动力学方案优点的直接利用。此外,现有的做法需要针对不同的场景、结构或信号进行优化和改进,这些都会增加计算复杂度和系统成本。事实上,对于运动学和动力学方案,合理利用切换系统理论进行设计,可以在显著降低计算负担的同时获得较好的动态性能和抗干扰能力。本文提出一种基于切换策略的VSA高性能获取方法,利用贝塞尔滤波器和可靠性检验模块对传感器量测信号进行预处理,以实现相应的相位调节、噪声滤除以及可靠性判别;在此基础上,利用运动学和动力学方案的优势设计切换策略,根据侧向加速度信号的幅值确定切换时机,通过两种方案的切换来缩短运动学方案的连续工作区间,进而改善VSA获取的效果。
图1为VSA获取方法的总体架构,主要包括传感器量测信号预处理、系统的运动学和动力学建模以及切换策略设计等部分。其中,传感器量测信号预处理部分由贝塞尔滤波器和可靠性检验模块组成。侧向加速度量测信号aym经贝塞尔滤波器处理后分为两路:一路经可靠性检验后作为运动学方案的输入ay,另一路ayb直接送至切换策略设计部分。此外,可靠性检验模块还负责对横摆角速度量测信号rm进行可靠性判别,其结果r也作为运动学方案的输入。在系统的运动学和动力学建模部分,分别根据相应的信号实现基于运动学和动力学方案的VSA获取。最后,在切换策略设计部分对ayb进行中值滤波,并根据滤波后的结果ayf设计具体的切换模块,实现车辆质心侧偏角β在运动学方案输出结果βk和动力学方案输出结果βd之间的高效切换。
本文中,侧向加速度是运动学和动力学方案之间进行切换的重要依据。考虑到侧向加速度量测信号aym与VSA信号之间存在一定的相位差[12],可以通过减小此类相位差来提升VSA获取的精度。贝塞尔滤波器具有向其截止频率以下的所有频率提供等量延时的特性,其带通相位响应几乎呈线性,可以保证滤波后信号波形的完整性,相应的传递函数[22]可表示为
H ( s ) = θ ( 0 ) θ ( s / ω 0 )
式中:θ(0)是比例系数;θs/ω0)是贝塞尔多项式,ω0是选定的期望截止频率。2阶贝塞尔滤波器结构相对简单,且具有较高的阻带衰减率,可以较快地消除通频带外的高频噪声。本文选用2阶贝塞尔滤波器对侧向加速度量测信号aym进行处理(传递函数为 H ( s ) = 10000 s 2 + 200 s + 10000,期望截止频率为ω0 = 100 Hz),在减小aym与VSA信号之间相位差的同时实现对相应高频噪声的滤除,以保证信号处理的准确性。
实际车辆的运行环境较为复杂,温差、冲击等都会影响车载传感器量测信号(如横摆角速度信号rm等)的可靠性[23]。因此,有必要在使用之前对此类信号进行可靠性检验[24]
作为基础信号,对横摆角速度进行可靠性检验的参考信息较少,故采用图2所示的可靠性判别流程。
其中,将参考横摆角速度rref沿垂直方向进行平移(具体的平移幅值取决于传感器量测信号的噪声幅值,可以根据其噪声统计特性确定,是一个经验值),得到某一可信区间rrange。在此基础上,将k时刻处于可信区间rrange内的横摆角速度量测值rm, k 视为可靠值r并存入缓存器,对可信区间rrange之外的横摆角速度量测值则用缓存器中最新的数据进行替代。此外,可靠性判别过程中的参考横摆角速度rref按下式计算:
r r e f = δ f v x ( 1 + K v x 2 ) l
式中:δf为前轮转向角;l为车辆轴距; v x为车辆质心处的纵向车速, v x = 1 2 ( ω 3 + ω 4 ) R e f fω3ω4分别为后轴左、右从动轮的转速; K为稳定性因数, K = m l 2 ( a C y r - b C y f )m为车辆整备质量,ab分别为车辆质心到前、后轴的距离,CyfCyr分别为前、后轴总的轮胎侧偏刚度;Reff为车轮有效半径。
对于经时延滤波处理的侧向加速度信号ayb,也可以采用类似的流程进行可靠性检验,获得相应的可靠值ay
考虑车辆的平面运动,有如下的侧向运动学关系:
v ˙ y = a y - r v x
式中vyayr分别为车辆质心处的侧向车速、侧向加速度和横摆角速度。对上式进行积分运算,得到运动学方案输出的车辆质心侧偏角 β k 1 v x ( a y - r v x ) d t
需要注意的是,测量横摆角速度和侧向加速度的车载惯性器件一般为低成本器件,其信号中含有较大的常值偏置。相应地,对此类信号进行积分操作会使误差随时间快速累积。考虑常值偏置的特点,采用合理的建模补偿方法对车载惯性传感器信号进行处理,可以有效减少短时间内积分误差的累积[8],进而可准确地描述短时间内VSA的变化趋势。其中,常值偏置的数值可根据传感器手册或静态测试得到。本文中涉及的rmaym信号均默认为已经过合理的传感器误差建模补偿。
前驱车辆的2自由度模型如图3所示,相应的侧向和横摆运动可表示为
m a y = F y f c o s δ f + F y r + F x f s i n δ f I z r ˙ = ( F y f c o s δ f + F x f s i n δ f ) a - F y r b
式中:FyfFyr分别表示前、后轴的轮胎侧向合力;Fxf为前轴的轮胎纵向合力;Iz 为车辆绕z轴的转动惯量。
对于较为平缓的工况,前、后轴的轮胎侧向合力可根据如下的线性轮胎模型得到[25]
F y f = C y f α f F y r = C y r α r
式中:前轮侧偏角 α f = v y + r a v x - δ f;后轮侧偏角 α r = v y - r b v x
当前轮转向角δf很小时,有sin δf ≈ 0、cos δf ≈ 1,且车辆的侧向运动满足 a y = v ˙ y + r v x。将上述关系及式(5)代入式(4),并考虑系统的过程噪声,可以得到如下的状态方程:
x ˙ = A ( t ) x + B ( t ) u + w
式中:状态向量 x = [vy r]T;控制量u = δf w 为过程噪声向量; A ( t ) B ( t )式(7)
A ( t ) = C y f + C y r m v x a C y f - b C y r m v x - v x a C y f - b C y r I z v x a 2 C y f + b 2 C y r I z v x B ( t ) = - C y f m - a C y f I z
进一步,在前轮转向角δf很小时,将sin δf ≈ 0、cos δf ≈ 1和式(5)代入式(4)的第一部分,同时考虑实际侧向加速度信号和横摆角速度信号的构成,有如下的量测关系式:
z = C ( t ) x + D ( t ) u + v
式中:量测向量 z = [aym rm]T v 为量测噪声向量; C ( t ) D ( t )式(9)
C ( t ) = C y f + C y r m v x a C y f - b C y r m v x 0 1 D ( t ) = - C y f m 0
式(6)式(8)进行离散化,并利用卡尔曼滤波器(Kalman filter,KF)进行迭代计算,可以得到侧向车速的估计值 v ^ y。最后,确定动力学方案输出的车辆质心侧偏角 β d = a r c t a n   v ^ y / v x
需要注意的是,经时延滤波处理的侧向加速度信号ayb中仍存在噪声尖峰。这会导致切换策略的误触发,进而影响VSA获取的效果。中值滤波器可以有效克服偶然因素引起的噪声波动,能在抑制噪声尖峰的同时保护信号的边缘不被模糊[18]。本文对经时延滤波处理的侧向加速度信号ayb再进行中值滤波,不仅可以通过相位调节使侧向加速度信号与VSA信号达到近乎同步,而且可以利用非线性的方法对侧向加速度信号中的噪声尖峰部分进行有效平滑。
此外,对于不同的轮胎-路面附着系数(tire-road friction coefficient,TRFC),轮胎侧向力Fy 与轮胎侧偏角α之间存在如下关系(见图4):当α较小时,Fyα基本呈线性关系,可以认为轮胎侧偏刚度Cy 基本为常值,且TRFC对Fy 的影响较小;随着α的逐渐增大(即车辆从较为平缓的工况逐渐进入剧烈工况),上述关系进入非线性区和饱和区,Cy 发生显著变化。此时,式(5)所示的线性轮胎模型已不再适用。相应地,动力学方案的VSA获取精度会出现明显的下降。相比较而言,运动学方案适用于各种工况。但是,即使经过合理的传感器误差建模补偿,长时间采用3.1节中描述的运动学方案,仍会使VSA的获取结果中存在较大的累积误差。
综合考虑以上因素,本文提出如下的切换策略设计思路:在如图4所示的非线性区和饱和区采用运动学方案,在车辆运动较为平缓的线性区则采用动力学方案;同时,利用经中值滤波之后的侧向加速度信号的幅值|ayf|表征车辆侧向机动的剧烈程度,通过设置相应的阈值ayT实现VSA获取在运动学和动力学方案之间的高效切换。
车辆进行侧向移动时,侧向加速度信号与VSA的变化趋势相同。因此,利用易于直接测量的侧向加速度信号的幅值|ayf|来表征车辆侧向移动的剧烈程度,是一种较为合理的近似。
当|ayf|不超过设定的阈值ayT时,可以近似认为轮胎侧向力Fy 与轮胎侧偏角α之间的关系处于图4中的线性区,轮胎侧偏刚度Cy 基本为常值。此时,采用动力学方案获取VSA,其精度主要取决于惯性器件的噪声统计特性,VSA的误差值|Δβ|较小(例如图5中的[t1t2]区间)。当|ayf| > ayT时,Fyα之间的关系进入非线性区和饱和区,Cy 不再是常值。此时,采用基于线性轮胎模型的动力学方案的VSA获取精度出现显著下降,故须及时切换到对车辆运行工况不敏感的运动学方案。进行此类切换时(例如图5中的t2时刻),可以将动力学方案中对状态量vy 的估计结果直接作为运动学方案的积分初值。在图5中的[t2t3]区间,VSA由运动学方案确定,其误差随时间累积,故应尽量缩短运动学方案的工作时间。当再次出现|ayf| ≤ ayT时(例如图5中的t3时刻),Fyα之间的关系重新进入线性区,Cy 可视为常值。此时,运动学方案的误差值即为一个切换过程中VSA的最大误差。对于图5中的[t3t5]区间,VSA由动力学方案确定,且t3时刻运动学方案中对vy 的积分结果也可作为动力学方案中KF的初值。其原因在于KF算法本身对系统状态的初值并不敏感,在模型建立准确的前提下,KF的估计误差能够快速收敛(例如图5中的[t3t4]区间)。总体而言,t3时刻的切换能够起到类似误差零点重置的作用。当再次出现|ayf| > ayT时,按上述过程依次循环切换即可。
此外,对于同样的转向盘输入,工况条件的差异(包括TRFC和纵向车速等因素)可能显著影响车辆的侧向加速度响应曲线,而且侧向加速度峰值的不准确会使两种方案的切换出现少许的超前或滞后。在实际车辆运行工况下,此类超前或滞后不可避免,但量值较小,不会对VSA获取的整体效果产生显著影响。对于本文设计的切换策略而言,在|ayf|与阈值ayT之间的关系再次出现反转时,可以利用两种方案之间的切换实现前述的“误差零点重置”,由此及时消除此类影响。这也是切换策略的重要优势之一。
为验证所提出的VSA获取方法的效果,在多个工况下进行仿真和硬件在环试验。试验车辆选择CarSim中的C级车,相关的车辆结构参数和惯性器件性能指标分别见表1表2
验证工况为双移线(double lane change,DLC)工况,纵向车速为120 km/h,相应的侧向加速度信号的时延滤波结果和横摆角速度信号的可靠性判别结果如图6所示。可以发现,经贝塞尔滤波处理,ayb信号较参考值有一定的时延,且ayb信号较其量测信号aym在噪声滤除方面有明显的提升(见图6(a))。此外,在图6(b)中,除一些异常量测点外(如第2和第7 s附近,幅值分别约为0.47和0.84 rad/s),横摆角速度量测信号rm均落在可信区间rrange内;而在经可靠性检验后得到的横摆角速度信号r中,这些异常量测点处的突变均已被有效识别并替换。
选取较为典型的连续DLC工况进行验证(见图7)。其中,3个顺序相连的DLC工况的TRFC分别设为0.85、0.5和0.35,以模拟包含积水及冰冻等路况的真实路面,纵向车速为120 km/h。此工况下的VSA获取结果(包括β及其误差值|Δβ|)如图8所示,对于本文提出的基于切换策略的VSA获取方法而言,其中的橙色背景区间为动力学方案工作区域,其余为运动学方案工作区域,切换的阈值ayT取1.5 m/s2(下同)。需要注意的是,切换阈值ayT是一个经验值,可以通过多次仿真试验确定。为方便比较,同时给出基于典型融合策略的VSA获取方法[14]的结果(简称对比方法,下同)。
显然,采用本文提出的方法,在橙色背景区间获得的β曲线与参考值吻合得较好;在其他区间存在一定的误差,但总体较为平滑。对误差值曲线而言,采用本文提出的方法,其|Δβ|若在之前的运动学方案工作区域快速累积,则会在接下来的橙色背景区间出现快速下降。该现象与5.2节中对切换过程的描述相符。此外,在路面附着条件发生突变时(如第9和第18 s),VSA的获取结果不存在明显的变化,这表明本文提出的方法和对比方法对路况的变化均具有较好的鲁棒性。
为进一步比较两种方法的精度,表3中给出了相应的误差值统计结果。可以发现,相较于对比方法,本文提出的方法在最大绝对误差|Δβ|max、平均绝对误差|Δβ|mean以及均方根误差|Δβ|RMSE等方面均具有明显的提升,提升幅度分别为40.7%、22.4%和30.6%。此外,同样执行图7所示的仿真任务(纵向车速120 km/h,持续时间30 s),采用本文提出的方法和对比方法所需的运行时间分别为15.14和20.02 s,节省幅度约为24.4%。其原因在于对比方法在任意时刻都需要同时使用动力学和运动学方案进行计算,而本文提出的方法仅需使用其中的一种。
硬件在环试验平台如图9所示,VSA的获取由Simulink实现,其与受驾驶员操控的CarSim中的车辆模型一起被送入dSPACE中。选择具有代表性的低、高附着路面条件进行验证,加速踏板保持固定开度,纵向车速从12.5 m/s逐渐提高至中、高速区间,转向盘转角输入为类正弦信号。
图10为低附着路面条件(TRFC = 0.35)下的VSA获取结果,同时给出相应的转向盘转角输入和纵向车速曲线。可以发现,采用本文提出的方法仅在初始阶段存在较差的VSA获取效果,具体见[7,18]s区间;在第18 s之后,采用对比方法的VSA误差则明显大于本文提出的方法。
高附着路面条件(TRFC = 0.85)下的相关曲线和结果如图11所示。在[0,30]s区间,采用本文提出的方法可以得到与参考值一致性较高的VSA获取结果,且该结果明显优于对比方法;在此之后,该优势则出现相对下降。
表4为低、高附着路面条件下的硬件在环试验误差值统计结果。其中,高附着路面条件下的各项指标提升较为有限,分别为9.9%、16.1%和11.7%。总体而言,本文提出的方法在各项误差值统计指标方面均优于对比方法。
仿真及硬件在环试验结果表明:(1)本文提出的传感器量测信号预处理方法,可有效实现对侧向加速度信号的时延处理和噪声滤除,对横摆角速度等信号中异常量测点处的突变也可有效清除;(2)利用本文设计的切换策略,可以较短的运行时间实现对VSA的高精度获取,且该方法对路况的变化具有较强的鲁棒性。
需要注意的是,对本文设计的切换策略而言,运动学方案不宜长时间连续工作。其原因在于运动学方案所依赖的车载惯性器件中存在无法避免的误差分量,对其进行长时间积分会导致较大的累积误差。此外,本文中将切换阈值ayT设为定值。对图4中的高、低TRFC情况而言,该设置导致Fy -α关系在虚线所示的临界点附近与其真实关系之间存在较大偏差,即高、低TRFC对应的Cy 不应简单视为相等。这也是图11中本文提出的方法在第30 s后的精度优势出现相对下降的根本原因,即第30 s后,车辆侧向运动加剧(转向盘转角输入的幅度增加),运动学方案持续工作,相应的累积误差无法及时通过与动力学方案的切换而得到消除,具体可见图12所示的误差值|Δβ|曲线。后续可针对不同的附着路面条件研究切换阈值ayT的确定问题,以进一步缩短运动学方案的连续工作区间。
本文提出了一种基于切换策略的VSA高性能获取方法。在传感器量测信号预处理阶段,利用贝塞尔滤波器的近似线性时延特性,调节侧向加速度量测信号的相位并滤除其中的高频噪声;采用可靠性检验模块清除横摆角速度等信号中的异常突变点。在切换策略设计部分,利用平滑后的侧向加速度信号幅值表征车辆侧向移动的剧烈程度,通过设置恒定的阈值实现VSA获取在运动学和动力学方案之间的高效切换。
试验结果表明,相较于采用典型融合策略的对比方法,本文提出的方法在精度方面至少有约10%的提升,同时可以节省约24.4%的运行时间。此外,二者对路况的变化均具有较强的鲁棒性。后续将搭建实车试验平台,进一步验证本文提出的方法的有效性;也可考虑针对切换阈值的设置进行优化,例如研究其对不同附着路面条件的自适应等,以充分发挥切换策略的优势。
  • *国家自然科学基金面上项目(52072159)
  • 常州市科技支撑计划(社会发展)项目(CE20225072)
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2024年第46卷第2期
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doi: 10.19562/j.chinasae.qcgc.2024.02.017
  • 接收时间:2023-08-11
  • 首发时间:2025-07-20
  • 出版时间:2024-02-25
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  • 收稿日期:2023-08-11
  • 修回日期:2023-08-25
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*国家自然科学基金面上项目(52072159)
常州市科技支撑计划(社会发展)项目(CE20225072)
作者信息
    1. 常州工学院电气信息工程学院,常州 213032
    2. 江苏大学汽车工程研究院,镇江 212013

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赵景波,教授,博士,E-mail:
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2种不同金属材料的力学参数

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genus
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species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
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Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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