WO2021103797A1 - 考虑复杂激励条件的车辆路面附着系数自适应估计方法 - Google Patents
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- adhesion coefficient
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
- B60W40/068—Road friction coefficient
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T8/00—Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force
- B60T8/17—Using electrical or electronic regulation means to control braking
- B60T8/172—Determining control parameters used in the regulation, e.g. by calculations involving measured or detected parameters
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T8/00—Arrangements for adjusting wheel-braking force to meet varying vehicular or ground-surface conditions, e.g. limiting or varying distribution of braking force
- B60T8/17—Using electrical or electronic regulation means to control braking
- B60T8/174—Using electrical or electronic regulation means to control braking characterised by using special control logic, e.g. fuzzy logic, neural computing
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
- B60W40/02—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to ambient conditions
- B60W40/06—Road conditions
- B60W40/064—Degree of grip
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/25—Fusion techniques
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60T—VEHICLE BRAKE CONTROL SYSTEMS OR PARTS THEREOF; BRAKE CONTROL SYSTEMS OR PARTS THEREOF, IN GENERAL; ARRANGEMENT OF BRAKING ELEMENTS ON VEHICLES IN GENERAL; PORTABLE DEVICES FOR PREVENTING UNWANTED MOVEMENT OF VEHICLES; VEHICLE MODIFICATIONS TO FACILITATE COOLING OF BRAKES
- B60T2210/00—Detection or estimation of road or environment conditions; Detection or estimation of road shapes
- B60T2210/10—Detection or estimation of road conditions
- B60T2210/12—Friction
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0037—Mathematical models of vehicle sub-units
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/26—Wheel slip
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/28—Wheel speed
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2520/00—Input parameters relating to overall vehicle dynamics
- B60W2520/30—Wheel torque
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W2552/00—Input parameters relating to infrastructure
- B60W2552/40—Coefficient of friction
Definitions
- the invention relates to the field of automobile control, in particular to an adaptive estimation method of vehicle road adhesion coefficient considering complex excitation conditions.
- the peak adhesion coefficient of the vehicle road surface is a key parameter to realize the high-quality motion control of the vehicle.
- the existing method is based on the tire force excitation in a single direction to construct a state observer. This method cannot be accurately estimated when the excitation is not satisfied, and When the tire force produces longitudinal-side coupling, the tire model will be distorted, and the estimator has the characteristics of slow convergence and low robustness. Therefore, how to comprehensively utilize the road surface recognition method of the longitudinal and lateral tire excitation force will be the difficulty and focus of future research.
- the purpose of the present invention is to provide an adaptive estimation method of vehicle road adhesion coefficient considering complex excitation conditions in order to overcome the defects of the above-mentioned prior art.
- An adaptive estimation method of vehicle road adhesion coefficient considering complex excitation conditions including the following steps:
- the single-wheel dynamics model of the whole vehicle is specifically:
- ⁇ is the wheel angular velocity
- Is the wheel angular acceleration
- R is the wheel radius
- T m the driving/braking torque acting on the wheel
- F z is the vertical load on the wheel
- I w is the moment of inertia of the wheel
- ⁇ is the wheel slip rate
- v x is the longitudinal speed at the center of the wheel
- ⁇ x ( ⁇ x , ⁇ ) is the adhesion coefficient of the current tire to the ground obtained based on the tire model.
- the expression of the tire model is:
- ⁇ is the peak adhesion coefficient of the road surface, that is, the peak adhesion coefficient of the corresponding road surface at the highest point of the ⁇ - ⁇ curve
- c 1 is the longitudinal sliding stiffness of the tire, that is, the slope of the ⁇ - ⁇ curve at the origin
- c 2 , c 3 , C 4 are the control parameters of the descending curve of the road surface peak adhesion coefficient and slip rate respectively.
- step 1) the expression of estimating the longitudinal tire force and the peak adhesion coefficient of the road surface under longitudinal excitation is:
- K is the gain of the longitudinal force estimator
- ⁇ is the gain of the pavement adhesion coefficient estimator
- y is the intermediate variable
- the two-degree-of-freedom kinematics model of the vehicle is specifically:
- ⁇ is the front wheel turning angle
- l f and l r are the distance from the center of the front and rear wheels to the center of mass
- v 0 is the longitudinal speed of the vehicle
- ⁇ is the side slip angle of the center of mass of the vehicle
- ⁇ f and ⁇ r are the distances of the front and rear wheels, respectively.
- Side slip angle R is the radius of the wheel.
- the expression for estimating the peak adhesion coefficient of the road surface under the excitation of the tire return torque and the lateral force is:
- F z is the vertical load received by the wheel
- a y is the actual value of the lateral acceleration of the vehicle
- k 1 and k 2 are the estimator gains
- Is the estimated value of the pavement peak adhesion coefficient under lateral force excitation for The derivative with respect to time.
- Said step 3 specifically includes the following steps:
- the step 31) is specifically:
- the input membership function takes the slip rate reference ⁇ /C ⁇ and the cornering angle reference ⁇ /C ⁇ as input quantities, where C ⁇ and C ⁇ are the mutation points of the tire characteristics entering the non-linear region, which are taken as the peak adhesion coefficient Corresponding slip rate and slip angle, and use different estimators
- As the output set the domain of input and output to be [0,1], and divide the domain into corresponding intervals according to small, medium, and large fuzzy membership.
- step 32 the expression of adaptive estimation of the road surface peak adhesion coefficient under complex excitation is:
- Is the representative value of the longitudinal slip degree of the wheel Is the representative value of the degree of wheel side slip
- the present invention has the following advantages:
- the road adhesion coefficient estimation algorithm designed in the present invention can judge the longitudinal sliding and side slip state of the tire in real time under the action of complex excitation force, and make adaptive adjustments to the tire model, thereby ensuring stable convergence and non-divergence of the estimation.
- the road adhesion coefficient estimation algorithm designed in the present invention can simultaneously observe the longitudinal sliding and side slip conditions of the tires, make confidence judgments based on this, and fuse the estimation results, so it has better real-time performance, while the existing estimation
- the algorithm can only use one of these incentives.
- the road surface adhesion coefficient estimation algorithm designed by the present invention can realize rapid and accurate road surface estimation based on the return torque at the initial stage of steering.
- Fig. 1 is a flow chart of the method of the present invention.
- Figure 2 is a schematic diagram of a single-wheel kinetic model in the embodiment.
- Fig. 3 is a schematic diagram of a two-degree-of-freedom kinematics model of the entire vehicle in the embodiment.
- Fig. 4 is a schematic diagram of the estimation of the returning torque in the embodiment.
- the present invention provides an adaptive estimation method of vehicle road adhesion coefficient considering complex excitation conditions, which includes the following steps:
- Step 1 Design an estimator based on the single-wheel dynamics model to estimate the longitudinal tire force and the road peak adhesion coefficient under longitudinal excitation.
- the specific process includes:
- ⁇ is the wheel angular velocity
- R is the wheel radius
- T m is the driving/braking torque acting on the wheel
- F z is the vertical load on the wheel
- I ⁇ is the moment of inertia of the wheel
- ⁇ is the wheel slip rate
- V x is the longitudinal speed at the center of the wheel
- ⁇ x ( ⁇ x , ⁇ ) is the current tire adhesion coefficient to the ground based on the tire model
- ⁇ is the peak adhesion coefficient of the road surface, that is, the peak adhesion coefficient of the corresponding road surface at the highest point of the ⁇ - ⁇ curve
- ⁇ is the wheel slip rate
- c 1 is the longitudinal sliding stiffness of the tire, that is, the slope of the ⁇ - ⁇ curve at the origin
- C 2 , c 3 , and c 4 are the control parameters of the descending section of the curve of the road surface peak adhesion coefficient and slip rate, respectively.
- K is the gain of the longitudinal force estimator
- ⁇ is the gain of the pavement adhesion coefficient estimator
- Step 2 Design an estimator based on the two-degree-of-freedom kinematics model of the vehicle to estimate the peak adhesion coefficient of the road under the excitation of the tire return torque and lateral force.
- the specific process includes:
- ⁇ is the front wheel turning angle
- l f and l r are the distance from the center of the front and rear wheels to the center of mass
- v 0 is the longitudinal speed of the vehicle
- ⁇ is the side slip angle of the center of mass of the vehicle
- ⁇ f and ⁇ r are the distances of the front and rear wheels, respectively. Slip angle.
- K is the gain of the longitudinal force estimator
- ⁇ is the gain of the pavement adhesion coefficient estimator
- Step 3 Judging the excitation conditions that the vehicle satisfies through the vehicle state parameters, fuzzy inference to the limit that the current longitudinal and lateral tire force can reach, and designing a fusion observer based on this to fuse the estimated results.
- the specific process includes:
- the input membership function takes the slip rate reference ⁇ /C ⁇ and the cornering angle reference ⁇ /C ⁇ as input variables, where C ⁇ and C ⁇ are the mutation points of the tire characteristics entering the nonlinear region, which can be considered to reach the peak adhesion coefficient corresponding Slip rate and slip angle, these two are based on Calculated in real time through numerical calculations; for different estimators As the output.
- the hardware device of the present invention requires sensors to include GPS, inertial elements, steering wheel angle and torque sensors, and uses mass-produced electric vehicle controllers for data sampling to realize online estimation of the algorithms designed in steps one and two.
- the fuzzy logic designed in step 3 is burned in the controller in the form of a lookup table to obtain the final fusion estimation result.
- the superscript ⁇ indicates the estimated value
- the superscript ⁇ indicates the first derivative
- the subscript x indicates the longitudinal direction
- the subscript y indicates the lateral direction.
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Abstract
一种考虑复杂激励条件的车辆路面附着系数自适应估计方法,包括下列步骤:1)根据整车的单轮动力学模型设计估计器,并估计纵向轮胎力和纵向激励下路面峰值附着系数;2)基于整车二自由度运动学模型设计估计器,并估计轮胎回正力矩和侧向力激励下路面峰值附着系数;3)通过车辆状态参数判断车辆满足的激励条件,模糊推理出当前纵侧向轮胎力所能达到的极限,并据此设计融合观测器进行估计结果融合。本方法鲁棒性好、实时性高、快速准确。
Description
本发明涉及汽车控制领域,尤其是涉及一种考虑复杂激励条件的车辆路面附着系数自适应估计方法。
车辆路面峰值附着系数是实现汽车精确车辆高品质运动控制的关键参数,现有的方法是基于单一方向的轮胎力激励条件下构建状态观测器,这种方法在激励不满足时无法准确估计,而且当轮胎力产生纵侧耦合时,轮胎模型会产生失真,估计器具有估计收敛缓慢、鲁棒性不高的特点。因此,如何综合利用纵侧向轮胎激励力的路面识别方法将会是今后研究的难点与重点。
发明内容
本发明的目的就是为了克服上述现有技术存在的缺陷而提供一种考虑复杂激励条件的车辆路面附着系数自适应估计方法。
本发明的目的可以通过以下技术方案来实现:
一种考虑复杂激励条件的车辆路面附着系数自适应估计方法,包括下列步骤:
1)根据整车的单轮动力学模型设计估计器,并估计纵向轮胎力和纵向激励下路面峰值附着系数;
2)基于整车二自由度运动学模型设计估计器,并估计轮胎回正力矩和侧向力激励下路面峰值附着系数;
3)通过车辆状态参数判断车辆满足的激励条件,模糊推理出当前纵侧向轮胎力所能达到的极限,并据此设计融合观测器进行估计结果融合。
所述的步骤1)中,整车的单轮动力学模型具体为:
其中,ω为车轮角速度,
为车轮角加速度,R为车轮半径,T
m为作用在车轮上的驱/制动力矩,F
z为车轮受到的垂向载荷,I
w为车轮的转动惯量,λ为车轮滑移率,v
x为车轮中心处的纵向速度,μ
x(θ
x,λ)为基于轮胎模型获得当前轮胎对地面的利用附着系数。
所述的轮胎模型的表达式为:
其中,θ为路面峰值附着系数,即μ-λ曲线最高点的对应路面的峰值附着系数,,c
1为轮胎的纵滑刚度,即μ-λ曲线在原点处的斜率,c
2、c
3、c
4分别为路面峰值附着系数与滑移率的曲线下降段控制参数。
所述的步骤1)中,估计纵向轮胎力和纵向激励下路面峰值附着系数的表达式为:
其中:
为轮胎纵向力的估计值,
为基于路面附着系数估计值和滑移率计算得到的利用附着系数,K为纵向力估计器增益,
为根据当前的纵向力和滑动率在轮胎模型描述的曲线上计算得到的路面峰值附着系数,
为纵向激励下路面峰值附着系数的估计值,γ为路面附着系数估计器增益,y为中间变量,
为y对时间的导数,
为
对时间的导数。
所述的步骤2)中,整车二自由度运动学模型具体为:
其中,δ为前轮转角,l
f和l
r分别为前后车轮中心到质心的距离,v
0为车辆的纵向车速,β为车辆的质心侧偏角,α
f和α
r分别为前后车轮的侧偏角,R为车轮半径。
所述的步骤2)中,估计轮胎回正力矩和侧向力激励下路面峰值附着系数的表达式为:
其中,α为车轮侧偏角,δ
w为方向盘转角,i
s(δ
w)为助力电机到主销处的力矩转动比,i
m(δ
w)为方向盘到主销处的力矩转动比,M
m为施加在方向盘的力矩,M
s为助力电机力矩,A和B为拟合参数,M
k为拟合总回正力矩,
为根据车轮垂向载荷和侧偏角计算得到的回正力矩估计值,F
z为车轮受到的垂向载荷,
为车辆的侧向加速度估计值,a
y为车辆的侧向加速度实际值,k
1和k
2为估计器增益,
为侧向力激励下路面峰值附着系数估计值,
为
对时间的导数。
所述的步骤3)具体包括如下步骤:
31)车辆激励状态模糊推理;
32)进行复杂激励下的路面峰值附着系数自适应估计。
所述的步骤31)具体为:
输入隶属度函数以滑动率参考λ/C
λ和侧偏角参考α/C
α作为输入量,其中,C
λ和C
α为轮胎特性进入非线性区的突变点,将其作为达到峰值附着系数对应的滑动率和侧偏角,并以不同估计器的
作为输出量,设置输入量和输出量的论域均为[0,1],并对论域按照小、中、大进行模糊隶属度划分相应的区间。
所述的步骤32)中,进行复杂激励下的路面峰值附着系数自适应估计的表达式为:
与现有技术相比,本发明具有以下优点:
1、本发明设计的路面附着系数估计算法在复杂激励力作用下,能通过实时判断轮胎的纵向滑动和侧偏状态,对轮胎模型做自适应调整,从而保证估计稳定收敛不发散。
2、本发明设计的路面附着系数估计算法能在同时观测轮胎的纵向滑动和侧偏状态的基础上,据此做置信判别,融合估计结果,因此具有较好的实时性,而现有的估计算法只能利用其中某一种激励力。
3、本发明设计的路面附着系数估计算法在转向初始阶段就能依据回正力矩即可实现路面快速、准确估计。
图1为本发明的方法流程框图。
图2为实施例中单轮动力学模型示意图。
图3为实施例中整车二自由度运动学模型示意图。
图4为实施例中回正力矩估计示意图。
下面结合附图和具体实施例对本发明进行详细说明。
实施例
下面结合附图和具体实施例对本发明进行详细说明。显然,所描述的实施例是本发明的一部分实施例,而不是全部实施例。基于本发明中的实施例,本领域普通技术人员在没有做出创造性劳动的前提下所获得的所有其他实施例,都应属于本发明保护的范围。
实施例
如图1所示,本发明提供一种考虑复杂激励条件的车辆路面附着系数自适应估计方法,包括如下步骤:
步骤一、基于单轮动力学模型设计估计器,估计纵向轮胎力和纵向激励下路面峰值附着系数。具体过程包括:
1.1、建立整车的单轮动力学模型。
首先获取车轮角速度和车轮滑移率:
其中,ω为车轮角速度,R为车轮半径,T
m为作用在车轮上的驱/制动力矩,F
z为车轮受到的垂向载荷,I
ω为车轮的转动惯量,λ为车轮滑移率,v
x为车轮中心处的纵向速度,μ
x(θ
x,λ)为基于轮胎模型获得当前轮胎对地面的利用附着系数;
然后,轮胎模型的表达式为:
其中,θ为路面峰值附着系数,即μ-λ曲线最高点的对应路面的峰值附着系数,λ为车轮滑移率,c
1为轮胎的纵滑刚度,即μ-λ曲线在原点处的斜率,c
2、c
3、c
4分别为路面峰值附着系数与滑移率的曲线下降段控制参数。
1.2、纵向轮胎力和纵向激励下路面峰值附着系数估计算法的表达式为:
其中:
为轮胎纵向力的估计,
为基于路面附着系数估计值和滑移率计算的利用附着系数,K为纵向力估计器增益,
为根据当前的纵向力和滑动率通过数值计算的方法在轮胎模型描述的曲线上计算得到的路面峰值附着系数,
为纵向激励下路面峰值附着系数的估计值,γ为路面附着系数估计器增益。
步骤二、基于整车二自由度运动学模型设计估计器,估计轮胎回正力矩和侧向 力激励下路面峰值附着系数。具体过程包括:
2.1、建立整车二自由度运动学模型。
获取车轮侧偏角:
其中,δ为前轮转角,l
f和l
r分别为前后车轮中心到质心的距离,v
0为车辆的纵向车速,β为车辆的质心侧偏角,α
f和α
r分别为前后车轮的侧偏角。
2.2、纵向轮胎力和纵向激励下路面峰值附着系数估计算法。
表达式为:
其中:
为轮胎纵向力的估计,
为基于路面附着系数估计值和滑移率计算的利用附着系数,K为纵向力估计器增益,
为根据当前的纵向力和滑动率通过数值计算的方法在轮胎模型描述的曲线上计算得到的路面峰值附着系数,
为纵向激励下路面峰值附着系数的估计值,γ为路面附着系数估计器增益。
步骤三、通过车辆状态参数判断车辆满足的激励条件,模糊推理出当前纵侧向轮胎力所能达到的极限,并据此设计融合观测器进行估计结果融合。具体过程包括:
3.1、车辆激励状态模糊推理。
输入隶属度函数以滑动率参考λ/C
λ和侧偏角参考α/C
α作为输入量,其中C
λ和C
α为轮胎特性进入非线性区的突变点,可以认为是达到峰值附着系数对应的滑动率和侧偏角,这两项依据
通过数值计算实时得出;对不同估计器的
作为输出量。设置输入量和输出量的论域均为[0,1],并对论域按照S、M、B(对应小、中、大)进行模糊隶属度划分相应的区间。
3.2、复杂激励下的路面峰值附着系数自适应估计算法。
表达式为:
本发明的硬件设备要求传感器包括GPS、惯性元件、方向盘转角和转矩传感器,使用量产的电动汽车整车控制器进行数据采样,实现步骤一和步骤二设计的算法的在线估计。步骤三设计的模糊逻辑以查询表的方式烧录在控制器中,获取最终的融合估计结果。
本实施例中参数说明:
上标^表示估计值,上标·表示一阶导数,下标x表示纵向,下标y表示侧向。
以上所述,仅为本发明的具体实施方式,但本发明的保护范围并不局限于此,任何熟悉本技术领域的工作人员在本发明揭露的技术范围内,可轻易想到各种等效的修改或替换,这些修改或替换都应涵盖在本发明的保护范围之内。因此,本发明的保护范围应以权利要求的保护范围为准。
Claims (9)
- 一种考虑复杂激励条件的车辆路面附着系数自适应估计方法,其特征在于,包括下列步骤:1)根据整车的单轮动力学模型设计估计器,并估计纵向轮胎力和纵向激励下路面峰值附着系数;2)基于整车二自由度运动学模型设计估计器,并估计轮胎回正力矩和侧向力激励下路面峰值附着系数;3)通过车辆状态参数判断车辆满足的激励条件,模糊推理出当前纵侧向轮胎力所能达到的极限,并据此设计融合观测器进行估计结果融合。
- 根据权利要求5所述的一种考虑复杂激励条件的车辆路面附着系数自适应估计方法,其特征在于,所述的步骤2)中,估计轮胎回正力矩和侧向力激励下路面峰值附着系数的表达式为:
- 根据权利要求6所述的一种考虑复杂激励条件的车辆路面附着系数自适应估计方法,其特征在于,所述的步骤3)具体包括如下步骤:31)车辆激励状态模糊推理;32)进行复杂激励下的路面峰值附着系数自适应估计。
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