CN108238025B - Distributed driving electric automobile road surface adhesion coefficient estimation system - Google Patents
Distributed driving electric automobile road surface adhesion coefficient estimation system Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
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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
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:
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:
wherein,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,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:
wherein,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:
wherein, c1、c2、c3And c4And e is a natural constant.
The estimation function of the road adhesion coefficient fine estimation module is as follows:
wherein,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:
wherein,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:
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:
wherein,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,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:
wherein,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:
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:
wherein,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,for longitudinal force estimation, γ is a set constant and Proj is a projection function.
The projection function is specifically:
wherein,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 3Estimating to obtain an estimated value of longitudinal forceThe longitudinal force estimation module 3 then estimates the longitudinal forceSending 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 valueEstimating 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 coefficientIn the process, the pavement adhesion coefficient precise estimation value is obtainedThe 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
The principle of the invention is as follows:
road surface adhesion coefficient estimation errorAnd estimation error of longitudinal forceComprises the following steps:
where θ is the actual road surface adhesion coefficient value and η is the actual longitudinal force value.
Taking the Lyapunov function as:
the Lyapunov function satisfies:
taking a derivative of the Lyapunov function to obtain:
the formula of the estimation system:
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:
obtaining:
the method comprises the following steps:
wherein a is a positive number.
While the derivative of the actual longitudinal force is:
the simultaneous subtraction of the longitudinal force estimates on both sides of the equation yields:
wherein K is a positive number.
Among them are:
the method comprises the following steps:
the derivative of the lyapunov function is obtained as:
the two-order major-minor of the intermediate matrix has:
the above formula holds when the following formula is satisfied.
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:
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:
wherein,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,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:
wherein,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*。
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:
wherein,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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Inventor after: Yu Zhuoping Inventor after: Xiong Lu Inventor after: Lin Xuefeng Inventor after: Xia Xin Inventor after: Liu Wei Inventor before: Xiong Lu Inventor before: Lin Xuefeng Inventor before: Xia Xin Inventor before: Liu Wei Inventor before: Yu Zhuoping |