CN108238025B - Distributed driving electric automobile road surface adhesion coefficient estimation system - Google Patents

Distributed driving electric automobile road surface adhesion coefficient estimation system Download PDF

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CN108238025B
CN108238025B CN201810011576.6A CN201810011576A CN108238025B CN 108238025 B CN108238025 B CN 108238025B CN 201810011576 A CN201810011576 A CN 201810011576A CN 108238025 B CN108238025 B CN 108238025B
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adhesion coefficient
longitudinal
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CN108238025A (en
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熊璐
林雪峰
夏新
刘伟
余卓平
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Tongji University
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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
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Abstract

The invention relates to a distributed driving electric automobile road adhesion coefficient estimation system which comprises a longitudinal vehicle speed acquisition module, a slip rate estimation module, a longitudinal force estimation module, a road adhesion coefficient rough estimation module and a road adhesion coefficient fine estimation module, wherein the longitudinal vehicle speed acquisition module is connected with the slip rate estimation module, the slip rate estimation module is respectively connected with the longitudinal force estimation module and the road adhesion coefficient rough estimation module, the longitudinal force estimation module is connected with the road adhesion coefficient rough estimation module, the road adhesion coefficient rough estimation module is connected with the road adhesion coefficient fine estimation module, and the road adhesion coefficient fine estimation module is connected with the longitudinal force estimation module in a feedback mode. Compared with the prior art, the method is simple and convenient, and the estimation result is accurate.

Description

Distributed driving electric automobile road surface adhesion coefficient estimation system
Technical Field
The invention relates to the field of distributed driving electric automobiles, in particular to a distributed driving electric automobile pavement adhesion coefficient estimation system.
Background
The main index for measuring whether the vehicle is in a stable state is the road surface adhesion utilization rate, and the road surface adhesion coefficient is known first to know the road surface adhesion coefficient utilization rate. The road adhesion coefficient is input information required by active safety control such as anti-lock braking, anti-skid driving, control stability control and the like of a vehicle, and the estimation of the road adhesion coefficient in real time is the basis of the active safety control of the vehicle.
The current estimation method of the road surface adhesion coefficient at home and abroad mainly comprises the following steps: 1. the road surface is directly identified, complex vehicle dynamics can be avoided, but the road surface adhesion coefficient is the combined action result of the vehicle tire and the road surface, and the identification processing difficulty is large, so that the method is not practical. 2. The method is an estimation method based on kinematics, fully considers the interaction between a vehicle and a road surface, but has higher requirement on the accuracy of a vehicle model.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a distributed driving electric automobile road adhesion coefficient estimation system.
The purpose of the invention can be realized by the following technical scheme:
a distributed driving electric automobile road adhesion coefficient estimation system comprises a longitudinal vehicle speed acquisition module, a slip rate estimation module, a longitudinal force estimation module, a road adhesion coefficient rough estimation module and a road adhesion coefficient fine estimation module, wherein the longitudinal vehicle speed acquisition module is connected with the slip rate estimation module, the slip rate estimation module is respectively connected with the longitudinal force estimation module and the road adhesion coefficient rough estimation module, the longitudinal force estimation module is connected with the road adhesion coefficient rough estimation module, the road adhesion coefficient rough estimation module is connected with the road adhesion coefficient fine estimation module, and the road adhesion coefficient fine estimation module is connected with the longitudinal force estimation module in a feedback manner;
the longitudinal vehicle speed obtaining module obtains a longitudinal vehicle speed, the slip ratio estimating module obtains a longitudinal slip ratio according to the longitudinal vehicle speed, the longitudinal force estimating module obtains a longitudinal force estimated value according to the longitudinal slip ratio and a pavement adhesion coefficient precise estimated value estimated by the pavement adhesion coefficient precise estimating module, the pavement adhesion coefficient rough estimating module obtains a pavement adhesion coefficient rough estimated value according to the longitudinal force estimated value and the longitudinal slip ratio, and the pavement adhesion coefficient precise estimating module estimates the pavement adhesion coefficient precise estimated value according to the pavement adhesion coefficient rough estimated value.
The longitudinal vehicle speed acquisition module comprises a GPS for acquiring longitudinal vehicle speed.
The estimation function of the slip rate estimation module is:
Figure GDA0002425516150000021
wherein, lambda is longitudinal slip rate, omega is wheel rotating speed, r is dynamic radius of wheel, vxIs the longitudinal vehicle speed.
The estimation function of the longitudinal force estimation module is:
Figure GDA0002425516150000022
Figure GDA0002425516150000023
wherein,
Figure GDA0002425516150000024
for longitudinal force estimation, r is the dynamic radius of the wheel, ImIs the moment of inertia of the wheel, FzIs the vertical load of the wheel, lambda is the longitudinal slip ratio, omega is the wheel speed, TmMu (theta, lambda) is a tire power model, theta is a road surface adhesion coefficient,
Figure GDA0002425516150000025
and the road adhesion coefficient fine estimation value is estimated by the road adhesion coefficient fine estimation module.
The estimation function of the rough estimation module for the road adhesion coefficient is as follows:
Figure GDA0002425516150000026
wherein,
Figure GDA0002425516150000027
for longitudinal force estimation, r is the dynamic radius of the wheel, ImIs the moment of inertia of the wheel, FzThe vertical load of the wheel is shown, mu (theta, lambda) is a tire power model, lambda is a longitudinal slip rate, and theta is a road adhesion coefficient;
estimating to obtain a rough estimated value theta of the road adhesion coefficient according to the estimation function*。
The tire dynamic model comprises the following concrete steps:
Figure GDA0002425516150000028
wherein, c1、c2、c3And c4And e is a natural constant.
The estimation function of the road adhesion coefficient fine estimation module is as follows:
Figure GDA0002425516150000029
wherein,
Figure GDA00024255161500000210
for fine estimation of the road adhesion coefficient, theta*The rough estimated value of the road adhesion coefficient is gamma, a set constant is gamma, and the Proj is a projection function.
The projection function is specifically:
Figure GDA0002425516150000031
wherein,
Figure GDA0002425516150000032
for fine estimation of the road adhesion coefficient, theta*Is a rough estimation value of the road adhesion coefficient, gamma is a set constant, and lambdarefIs a design parameter, and is a reference slip ratio.
Compared with the prior art, the invention has the following advantages:
(1) the method avoids a vehicle dynamic model, only relates to a dynamic model of a wheel part, and has high model accuracy, so that the estimation result of the road adhesion coefficient is accurate, the calculated amount is small, and the method can be widely applied;
(2) the invention only needs to acquire the longitudinal speed on line through the GPS and simultaneously acquires the relevant parameters of the wheels, including the wheel rotating speed and the wheel driving torque, and the acquisition is convenient and reliable.
Drawings
Fig. 1 is a block diagram of a road adhesion coefficient estimation system of a distributed drive electric vehicle according to the present invention.
In the figure, 1 is a longitudinal vehicle speed acquisition module, 2 is a slip ratio estimation module, 3 is a longitudinal force estimation module, 4 is a road adhesion coefficient rough estimation module, and 5 is a road adhesion coefficient fine estimation module.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments.
Examples
As shown in fig. 1, a distributed driving electric vehicle road adhesion coefficient estimation system includes a longitudinal vehicle speed acquisition module 1, a slip ratio estimation module 2, a longitudinal force estimation module 3, a road adhesion coefficient rough estimation module 4 and a road adhesion coefficient fine estimation module 5, where the longitudinal vehicle speed acquisition module 1 is connected to the slip ratio estimation module 2, the slip ratio estimation module 2 is respectively connected to the longitudinal force estimation module 3 and the road adhesion coefficient rough estimation module 4, the longitudinal force estimation module 3 is connected to the road adhesion coefficient rough estimation module 4, the road adhesion coefficient rough estimation module 4 is connected to the road adhesion coefficient fine estimation module 5, and the road adhesion coefficient fine estimation module 5 is feedback-connected to the longitudinal force estimation module 3;
the longitudinal vehicle speed obtaining module 1 obtains a longitudinal vehicle speed, the slip ratio estimating module 2 obtains a longitudinal slip ratio according to the longitudinal vehicle speed, the longitudinal force estimating module 3 obtains a longitudinal force estimated value according to the longitudinal slip ratio and a pavement adhesion coefficient fine estimated value estimated by the pavement adhesion coefficient fine estimating module 5, the pavement adhesion coefficient rough estimating module 4 obtains a pavement adhesion coefficient rough estimated value according to the longitudinal force estimated value and the longitudinal slip ratio, and the pavement adhesion coefficient fine estimating module 5 estimates and obtains a pavement adhesion coefficient fine estimated value according to the pavement adhesion coefficient rough estimated value.
The longitudinal vehicle speed acquisition module 1 includes a GPS for acquiring a longitudinal vehicle speed.
The estimation function of the slip rate estimation module 2 is:
Figure GDA0002425516150000041
wherein,lambda is longitudinal slip ratio, omega is wheel speed, r is dynamic radius of wheel, vxIs the longitudinal vehicle speed.
The estimation function of the longitudinal force estimation module 3 is:
Figure GDA0002425516150000042
Figure GDA0002425516150000043
wherein,
Figure GDA0002425516150000044
for longitudinal force estimation, r is the dynamic radius of the wheel, ImIs the moment of inertia of the wheel, FzIs the vertical load of the wheel, lambda is the longitudinal slip ratio, omega is the wheel speed, TmMu (theta, lambda) is a tire power model, theta is a road surface adhesion coefficient,
Figure GDA0002425516150000045
and the road adhesion coefficient fine estimation value is estimated by the road adhesion coefficient fine estimation module 5.
The rough road adhesion coefficient estimation module 4 has an estimation function of:
Figure GDA0002425516150000046
wherein,
Figure GDA0002425516150000047
for longitudinal force estimation, r is the dynamic radius of the wheel, ImIs the moment of inertia of the wheel, FzThe vertical load of the wheel is shown, mu (theta, lambda) is a tire power model, lambda is a longitudinal slip rate, and theta is a road adhesion coefficient;
estimating to obtain a rough estimated value theta of the road adhesion coefficient according to the estimation function*。
The tire dynamic model comprises the following concrete steps:
Figure GDA0002425516150000048
wherein, c1、c2、c3And c4And e is a natural constant.
The estimation function of the road adhesion coefficient fine estimation module 5 is as follows:
Figure GDA0002425516150000049
wherein,
Figure GDA00024255161500000410
for fine estimation of the road adhesion coefficient, theta*Is a rough estimated value of the road adhesion coefficient, lambda is the longitudinal slip ratio,
Figure GDA00024255161500000411
for longitudinal force estimation, γ is a set constant and Proj is a projection function.
The projection function is specifically:
Figure GDA00024255161500000412
wherein,
Figure GDA0002425516150000051
for fine estimation of the road adhesion coefficient, theta*Is a rough estimation value of the road adhesion coefficient, gamma is a set constant, and lambdarefIs a design parameter, and is a reference slip ratio.
The estimation is carried out by adopting the system, and the method specifically comprises the following steps:
the longitudinal vehicle speed acquisition module 1 acquires the longitudinal vehicle speed vxSending the longitudinal slip ratio lambda to a slip ratio estimation module 2, sending the longitudinal slip ratio lambda obtained by estimation to a longitudinal force estimation module 3 and a road adhesion coefficient rough estimation module 4 by the slip ratio estimation module 2, and sending the longitudinal force estimation module 3 to a road adhesion coefficient fine estimation value fed back by a longitudinal slip ratio lambda and a road adhesion coefficient fine estimation module 5 by the longitudinal force estimation module 3
Figure GDA0002425516150000052
Estimating to obtain an estimated value of longitudinal force
Figure GDA0002425516150000053
The longitudinal force estimation module 3 then estimates the longitudinal force
Figure GDA0002425516150000054
Sending the data to a rough estimation module 4 of the road adhesion coefficient, and the rough estimation module 4 of the road adhesion coefficient according to the longitudinal slip ratio lambda and the longitudinal force estimation value
Figure GDA0002425516150000055
Estimating to obtain a rough estimated value theta of the road adhesion coefficient*And the road adhesion coefficient fine estimation module 5 further estimates the rough estimation value theta according to the road adhesion coefficient*Estimating to obtain a precise estimation value of the road adhesion coefficient
Figure GDA0002425516150000056
In the process, the pavement adhesion coefficient precise estimation value is obtained
Figure GDA0002425516150000057
The data are fed back to the longitudinal force estimation module 3, so that joint estimation is realized, and an accurate pavement adhesion coefficient precise estimation value is obtained
Figure GDA0002425516150000058
The principle of the invention is as follows:
road surface adhesion coefficient estimation error
Figure GDA0002425516150000059
And estimation error of longitudinal force
Figure GDA00024255161500000510
Comprises the following steps:
Figure GDA00024255161500000511
where θ is the actual road surface adhesion coefficient value and η is the actual longitudinal force value.
Taking the Lyapunov function as:
Figure GDA00024255161500000512
the Lyapunov function satisfies:
Figure GDA00024255161500000513
taking a derivative of the Lyapunov function to obtain:
Figure GDA00024255161500000514
the formula of the estimation system:
Figure GDA00024255161500000515
due to rough estimation value theta of road surface adhesion coefficient*Is a function of the slip ratio lambda and the longitudinal force η, i.e. can be written as
θ*＝θ*(λ,η)
The road adhesion coefficient is a fixed value:
Figure GDA0002425516150000061
obtaining:
Figure GDA0002425516150000062
the method comprises the following steps:
Figure GDA0002425516150000063
wherein a is a positive number.
While the derivative of the actual longitudinal force is:
Figure GDA0002425516150000064
the simultaneous subtraction of the longitudinal force estimates on both sides of the equation yields:
Figure GDA0002425516150000065
wherein K is a positive number.
Among them are:
Figure GDA0002425516150000066
the method comprises the following steps:
Figure GDA0002425516150000067
the derivative of the lyapunov function is obtained as:
Figure GDA0002425516150000068
the two-order major-minor of the intermediate matrix has:
Figure GDA0002425516150000069
the above formula holds when the following formula is satisfied.
Figure GDA00024255161500000610
When the proper a and K are selected, the convergence of the algorithm can be ensured, and the accurate pavement adhesion coefficient is ensured to be obtained.

Claims (7)

1. The system is characterized by comprising a longitudinal vehicle speed acquisition module, a slip rate estimation module, a longitudinal force estimation module, a road adhesion coefficient rough estimation module and a road adhesion coefficient fine estimation module, wherein the longitudinal vehicle speed acquisition module is connected with the slip rate estimation module, the slip rate estimation module is respectively connected with the longitudinal force estimation module and the road adhesion coefficient rough estimation module, the longitudinal force estimation module is connected with the road adhesion coefficient rough estimation module, the road adhesion coefficient rough estimation module is connected with the road adhesion coefficient fine estimation module, and the road adhesion coefficient fine estimation module is connected with the longitudinal force estimation module in a feedback manner;
the longitudinal vehicle speed obtaining module obtains a longitudinal vehicle speed, the slip ratio estimating module obtains a longitudinal slip ratio according to the longitudinal vehicle speed, the longitudinal force estimating module obtains a longitudinal force estimated value according to the longitudinal slip ratio and a pavement adhesion coefficient precise estimated value estimated by the pavement adhesion coefficient precise estimating module, the pavement adhesion coefficient rough estimating module obtains a pavement adhesion coefficient rough estimated value according to the longitudinal force estimated value and the longitudinal slip ratio, and the pavement adhesion coefficient precise estimating module estimates the pavement adhesion coefficient precise estimated value according to the pavement adhesion coefficient rough estimated value.
2. The system for estimating road adhesion coefficient of a distributed-drive electric vehicle according to claim 1, wherein the longitudinal vehicle speed acquisition module comprises a GPS for acquiring longitudinal vehicle speed.
3. The system for estimating the road adhesion coefficient of the distributed-drive electric vehicle according to claim 1, wherein the estimation function of the slip ratio estimation module is as follows:
Figure FDA0002425516140000011
wherein, lambda is longitudinal slip rate, omega is wheel rotating speed, r is dynamic radius of wheel, vxIs the longitudinal vehicle speed.
4. The system for estimating road adhesion coefficient of distributed-drive electric vehicle according to claim 1, wherein the estimation function of the longitudinal force estimation module is:
Figure FDA0002425516140000012
Figure FDA0002425516140000013
wherein,
Figure FDA0002425516140000014
for longitudinal force estimation, r is the dynamic radius of the wheel, ImIs the moment of inertia of the wheel, FzIs the vertical load of the wheel, lambda is the longitudinal slip ratio, omega is the wheel speed, TmMu (theta, lambda) is a tire power model, theta is a road surface adhesion coefficient,
Figure FDA0002425516140000015
and the road adhesion coefficient fine estimation value is estimated by the road adhesion coefficient fine estimation module.
5. The system for estimating the road adhesion coefficient of the distributed-drive electric vehicle according to claim 1, wherein the estimation function of the rough road adhesion coefficient estimation module is as follows:
Figure FDA0002425516140000021
wherein,
Figure FDA0002425516140000022
for longitudinal force estimation, r is the dynamic radius of the wheel, ImIs the moment of inertia of the wheel, FzThe vertical load of the wheel is shown, mu (theta, lambda) is a tire power model, lambda is a longitudinal slip rate, and theta is a road adhesion coefficient;
estimating to obtain a rough estimated value theta of the road adhesion coefficient according to the estimation function*。
6. The system for estimating road adhesion coefficient of a distributed-drive electric vehicle according to claim 4 or 5, wherein the tire dynamics model is specifically:
Figure FDA0002425516140000023
wherein, c1、c2、c3And c4And e is a natural constant.
7. The system for estimating the road adhesion coefficient of the distributed-type-driven electric automobile according to claim 1, wherein the estimation function of the road adhesion coefficient fine estimation module is as follows:
Figure FDA0002425516140000024
wherein,
Figure FDA0002425516140000025
for fine estimation of the road adhesion coefficient, theta*Is a rough estimated value of the road adhesion coefficient, gamma is a set constant, Proj is a projection function, lambda is a longitudinal slip ratio, and lambdarefIs a longitudinal slip rate reference value and is a set constant.
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