CN105539449B - A kind of coefficient of road adhesion real-time estimating method under damped condition - Google Patents

A kind of coefficient of road adhesion real-time estimating method under damped condition Download PDF

Info

Publication number
CN105539449B
CN105539449B CN201510897667.0A CN201510897667A CN105539449B CN 105539449 B CN105539449 B CN 105539449B CN 201510897667 A CN201510897667 A CN 201510897667A CN 105539449 B CN105539449 B CN 105539449B
Authority
CN
China
Prior art keywords
msub
mrow
coefficient
mfrac
mover
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201510897667.0A
Other languages
Chinese (zh)
Other versions
CN105539449A (en
Inventor
王健
张竹林
杨君
邱绪云
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shandong Jiaotong University
Original Assignee
Shandong Jiaotong University
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shandong Jiaotong University filed Critical Shandong Jiaotong University
Priority to CN201510897667.0A priority Critical patent/CN105539449B/en
Publication of CN105539449A publication Critical patent/CN105539449A/en
Application granted granted Critical
Publication of CN105539449B publication Critical patent/CN105539449B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • 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

Abstract

A kind of coefficient of road adhesion real-time estimating method under damped condition, including two-wheel vehicles Brake Dynamics model, ideal brake force square controller and coefficient of road adhesion observer.Wherein, two-wheel vehicles Brake Dynamics model is made of whole vehicle model and tire model.Based on two-wheel vehicles Brake Dynamics model, sliding mode controller is redesigned using saturation function and integration diverter surface, jitter problem is eliminated, establishes ideal brake force square controller.On the basis of two-wheel vehicles Brake Dynamics model and ideal brake force square controller, using second-order linearity extended state observer, coefficient of road adhesion observer, observation and the relevant expansion state amount of attachment coefficient are designed, and then completes the real-time estimation of road pavement attachment coefficient.Design parameter of the present invention is few, and computational efficiency is high, and eliminating Sliding mode variable structure control using saturation function and integration diverter surface mode buffets problem, and coefficient of road adhesion observer uses second-order linearity extended state observer, strong robustness.

Description

A kind of coefficient of road adhesion real-time estimating method under damped condition
Technical field
The present invention relates to the coefficient of road adhesion under automobile active safety control field, more particularly to a kind of damped condition is real When evaluation method.
Background technology
At present, advanced driver assistance system is housed mostly, such as Emergency avoidance system (ECA), adaptively on existing automobile Cruise control system (ACC), anti-blocking brake system (ABS), Traction control system (TCS) and electronic stability program (ESP) The safety and stability of vehicle traveling can be greatly improved Deng, these DAS (Driver Assistant System)s.Advanced driver assistance system energy It is enough that control logic is adjusted automatically according to coefficient of road adhesion change, so as to play the property of control system to greatest extent Can, and it is the prerequisite for realizing active safety control to obtain coefficient of road adhesion in real time, exactly.
For the acquisition methods of coefficient of road adhesion, mainly there are two kinds of direct detecting method and evaluation method both at home and abroad.Its In, direct detecting method mainly using optical sensor absorption of the measurement road surface to light and scattering situation, according to road surface form with Physical characteristic carries out coefficient of road adhesion identification, although this method application is simple direct, sensor is expensive, is unfavorable for The promotion and application on volume production car.And evaluation method then passes through measurement and the relevant vehicle of coefficient of road adhesion or tire dynamics Respond to estimate the size of coefficient of road adhesion, this method can make full use of onboard sensor, reduce cost.At present, mainly Evaluation method has three kinds of Kalman filtering algorithm, double expanded Kalman filtration algorithms and extended state observer method.Compared to Kalman filtering algorithm and double expanded Kalman filtration algorithms, extended state observer method can ensure higher calculating essence Avoid solving cumbersome Jacobian matrix on the premise of degree, but it does not account for the transfer of load under damped condition, and Design parameter is more, and computational efficiency is not high.
The content of the invention
For current techniques means there are the problem of and defect, propose under a kind of damped condition consider before and after axle load shift Coefficient of road adhesion real-time estimating method.
The present invention uses following technical scheme to solve above-mentioned technical problem：
A kind of coefficient of road adhesion real-time estimating method under damped condition, including two-wheel vehicles Brake Dynamics model, Ideal brake force square controller and coefficient of road adhesion observer.Wherein, two-wheel vehicles Brake Dynamics model is by whole vehicle model Formed with tire model.Based on two-wheel vehicles Brake Dynamics model, former rear wheel slip rate tracking desired slip rate is in order to control Target, establishes sliding mode controller, and redesigns sliding mode controller using saturation function and integration diverter surface, eliminates shake and asks Topic, establishes ideal brake force square controller.Finally, in two-wheel vehicles Brake Dynamics model and ideal brake force square controller On the basis of, using second-order linearity extended state observer, design and inputted using wheel speed signal and braking moment signal as observer, wheel Attachment coefficient is the coefficient of road adhesion observer of observer output between tire and road surface, will using coefficient of road adhesion observer Come out with the relevant item of attachment coefficient as expansion state discharge observation, and then complete the real-time estimation of road pavement attachment coefficient.
The present invention compared with prior art, has following technique effect using above technical scheme：
1. the evaluation method considers the axle load transfer under damped condition, design parameter is few, and computational efficiency is high；
2. eliminating Sliding mode variable structure control using saturation function and integration diverter surface mode buffets problem, and road surface is adhered to Coefficient observer uses second-order linearity extended state observer, strong robustness.
Brief description of the drawings
Fig. 1 is the procedure chart of the coefficient of road adhesion evaluation method.
In figure, 1- whole vehicle models, 2- tire models, 3- two-wheel vehicles Brake Dynamics models, 4- sliding mode controllers, 5- reasons Think braking moment controller, 6- coefficient of road adhesion observers.
Embodiment
Technical scheme is described in further detail below in conjunction with the accompanying drawings：
As shown in Figure 1, the invention discloses the coefficient of road adhesion real-time estimating method under a kind of damped condition, including it is double Wheeled vehicle Brake Dynamics model 3, ideal brake force square controller 5 and coefficient of road adhesion observer 6.Wherein, two-wheel vehicles Brake Dynamics model 3 is made of whole vehicle model 1 and tire model 2.
Establish whole vehicle model 1：
Assuming that x is displacement in vehicle travel process, mf、mrRespectively nonspring carried mass before and after vehicle, hf、hrRespectively car Front and rear nonspring carried mass height, msFor sprung mass, hsFor sprung mass height, Fzf、FzrRespectively front and back wheel is subject to Ground normal reaction, lf、lrRespectively barycenter to wheel base from dynamic equation group：
Wherein,
In formula：μ(λf) and μ (λr) it is respectively attachment coefficient between front and back wheel and road surface, m is complete vehicle quality, and V is vehicle Barycenter longitudinal velocity, Tbf、TbrRespectively front and back wheel brake force square, Jf、JrRespectively front and back wheel rotary inertia, ωf、ωrRespectively Front and back wheel angular speed, RωFor radius of wheel.
Establish tire model 2：
Based on Magic Formula models, Burckhardt models are can obtain by theory deformation, simulation analysis, its road Face attachment coefficient is related with tyre skidding rate λ and vehicle velocity V：
In formula：C1、C2、C3For tire enclosing characteristic parameter, C4Affecting parameters for automobile driving speed to attachment characteristic.
Based on two-wheel vehicles Brake Dynamics model 3, former rear wheel slip rate tracks desired slip rate target in order to control, builds Vertical sliding mode controller 4：
In formula：λf、λrRespectively front and back wheel actual slip rate；λfd、λrdRespectively front and back wheel target slip ratio.
Equivalent control torque can be obtained to its derivation：
Then front and back wheel ideal brake force square is：
Wherein：
Ff(λf, λr)=F2(1-λf)+RωF3
Fr(λf, λr)=F2(1-λr)+RωF4
In formula：η1And η2All it is positive number.
In order to eliminate buffeting problem, by saturation function sat () be applied in sliding formwork control and using integration diverter surface Sliding mode controller is redesigned, establishes ideal brake force square controller 5：
S1=λf-λfd+ξ1∫(λf-λfd)dt
S2=λr-λrd+ξ2∫(λr-λrd)dt
In formula：ξ1And ξ2For constant.
Then front and back wheel ideal brake force square is respectively：
In formula：WithFor constant.
Finally, using second-order linearity extended state observer, establish using wheel speed signal and braking moment signal as observer Input, attachment coefficient is the coefficient of road adhesion observer 6 of observer output between tire and road surface：
It can be obtained by two-wheel vehicles Brake Dynamics model 3,
Above formula is contained into the disturbance that coefficient of road adhesion item regards system as, and as the expansion state variable of system, order：
ωf=x1；ωr=x3； Tbf=u1；Tbr=u2。
Then obtaining two integrator tandem type systems is respectively：
By taking first integrator tandem type system as an example, it can be observed using following second-order linearity extended state observer State x1With expansion state x2：
Wherein：ω0Bandwidth for the linear extended state observer obtained by POLE PLACEMENT USING；u1And y1Respectively observer Input signal；z1And z2The respectively output signal of linear extended state observer, is respectively state x1With expansion state x2's Observation；b0Gain b in order to control1Estimate.Second integrator train state x can similarly be obtained3And expansion state x4Observation.
Then have for above two integrator tandem type systems：
Wherein：z1And z2For state x1(front-wheel wheel speed) and expansion state x2Observation, z3And z4For state x3(rear wheel rotation Speed) and expansion state x4Observation.
Coefficient of road adhesion observer 6 based on foundation, will be with the relevant item of coefficient of road adhesion as expansion state amount Observe and, using above-mentioned expression formula can conveniently, real-time estimation go out attachment coefficient between front and back wheel and road surface.

Claims (2)

  1. A kind of 1. coefficient of road adhesion real-time estimating method under damped condition, it is characterised in that：It is dynamic including two-wheel vehicles braking Mechanical model (3), ideal brake force square controller (5) and coefficient of road adhesion observer (6)；The two-wheel vehicles braking is dynamic Mechanical model (3), including whole vehicle model (1) and tire model (2)；Whole vehicle model (1) meets following relational expression：
    <mrow> <mover> <mi>x</mi> <mo>&amp;CenterDot;</mo> </mover> <mo>=</mo> <mi>V</mi> </mrow>
    <mrow> <mover> <mi>V</mi> <mo>&amp;CenterDot;</mo> </mover> <mo>=</mo> <mo>-</mo> <mi>g</mi> <mfrac> <mrow> <mi>&amp;mu;</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;lambda;</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>m</mi> <mn>1</mn> </msub> <mo>+</mo> <mi>&amp;mu;</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;lambda;</mi> <mi>r</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>m</mi> <mn>2</mn> </msub> </mrow> <mrow> <mi>m</mi> <mo>-</mo> <mi>&amp;mu;</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;lambda;</mi> <mi>f</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>m</mi> <mn>3</mn> </msub> <mo>+</mo> <mi>&amp;mu;</mi> <mrow> <mo>(</mo> <msub> <mi>&amp;lambda;</mi> <mi>r</mi> </msub> <mo>)</mo> </mrow> <msub> <mi>m</mi> <mn>3</mn> </msub> </mrow> </mfrac> </mrow>
    <mrow> <msub> <mover> <mi>&amp;omega;</mi> <mo>&amp;CenterDot;</mo> </mover> <mi>f</mi> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mn>2</mn> <msub> <mi>J</mi> <mi>f</mi> </msub> </mrow> </mfrac> <mrow> <mo>(</mo> <mo>-</mo> <msub> <mi>T</mi> <mrow> <mi>b</mi> <mi>f</mi> </mrow> </msub> <mo>+</mo> <mi>&amp;mu;</mi> <mo>(</mo> <msub> <mi>&amp;lambda;</mi> <mi>f</mi> </msub> <mo>)</mo> <msub> <mi>m</mi> <mn>1</mn> </msub> <msub> <mi>R</mi> <mi>&amp;omega;</mi> </msub> <mi>g</mi> <mo>-</mo> <mi>&amp;mu;</mi> <mo>(</mo> <msub> <mi>&amp;lambda;</mi> <mi>f</mi> </msub> <mo>)</mo> <msub> <mi>m</mi> <mn>3</mn> </msub> <msub> <mi>R</mi> <mi>&amp;omega;</mi> </msub> <mover> <mi>x</mi> <mo>&amp;CenterDot;&amp;CenterDot;</mo> </mover> <mo>)</mo> </mrow> </mrow>
    <mrow> <msub> <mover> <mi>&amp;omega;</mi> <mo>&amp;CenterDot;</mo> </mover> <mi>r</mi> </msub> <mo>=</mo> <mfrac> <mn>1</mn> <mrow> <mn>2</mn> <msub> <mi>J</mi> <mi>r</mi> </msub> </mrow> </mfrac> <mrow> <mo>(</mo> <mo>-</mo> <msub> <mi>T</mi> <mrow> <mi>b</mi> <mi>r</mi> </mrow> </msub> <mo>+</mo> <mi>&amp;mu;</mi> <mo>(</mo> <msub> <mi>&amp;lambda;</mi> <mi>r</mi> </msub> <mo>)</mo> <msub> <mi>m</mi> <mn>2</mn> </msub> <msub> <mi>R</mi> <mi>&amp;omega;</mi> </msub> <mi>g</mi> <mo>+</mo> <mi>&amp;mu;</mi> <mo>(</mo> <msub> <mi>&amp;lambda;</mi> <mi>r</mi> </msub> <mo>)</mo> <msub> <mi>m</mi> <mn>3</mn> </msub> <msub> <mi>R</mi> <mi>&amp;omega;</mi> </msub> <mover> <mi>x</mi> <mo>&amp;CenterDot;&amp;CenterDot;</mo> </mover> <mo>)</mo> </mrow> </mrow>
    Wherein,
    <mrow> <msub> <mi>m</mi> <mn>1</mn> </msub> <mo>=</mo> <mfrac> <msub> <mi>l</mi> <mi>r</mi> </msub> <mrow> <msub> <mi>l</mi> <mi>f</mi> </msub> <mo>+</mo> <msub> <mi>l</mi> <mi>r</mi> </msub> </mrow> </mfrac> <mi>m</mi> </mrow>
    <mrow> <msub> <mi>m</mi> <mn>2</mn> </msub> <mo>=</mo> <mfrac> <msub> <mi>l</mi> <mi>f</mi> </msub> <mrow> <msub> <mi>l</mi> <mi>f</mi> </msub> <mo>+</mo> <msub> <mi>l</mi> <mi>r</mi> </msub> </mrow> </mfrac> <mi>m</mi> </mrow>
    <mrow> <msub> <mi>m</mi> <mn>3</mn> </msub> <mo>=</mo> <mfrac> <mrow> <msub> <mi>m</mi> <mi>f</mi> </msub> <msub> <mi>l</mi> <mi>f</mi> </msub> <mo>+</mo> <msub> <mi>m</mi> <mi>s</mi> </msub> <msub> <mi>l</mi> <mi>s</mi> </msub> <mo>+</mo> <msub> <mi>m</mi> <mi>r</mi> </msub> <msub> <mi>l</mi> <mi>r</mi> </msub> </mrow> <mrow> <msub> <mi>l</mi> <mi>f</mi> </msub> <mo>+</mo> <msub> <mi>l</mi> <mi>r</mi> </msub> </mrow> </mfrac> </mrow>
    In formula：X be vehicle travel process in displacement, mf、mrRespectively nonspring carried mass before and after vehicle, hf、hrRespectively before vehicle Nonspring carried mass height afterwards, msFor sprung mass, hsFor sprung mass height, Fzf、FzrThe ground that respectively front and back wheel is subject to Normal reaction, lf、lrRespectively barycenter to wheel base from μ (λf) and μ (λr) it is respectively between front and back wheel and road surface Attachment coefficient, m are complete vehicle quality, and V is vehicle centroid longitudinal velocity, Tbf、TbrRespectively front and back wheel brake force square, Jf、JrRespectively For front and back wheel rotary inertia, ωf、ωrRespectively front and back wheel angular speed, RωFor radius of wheel；
    Tire model (2) meets following relational expression：
    <mrow> <mi>&amp;mu;</mi> <mrow> <mo>(</mo> <mi>&amp;lambda;</mi> <mo>,</mo> <mi>V</mi> <mo>)</mo> </mrow> <mo>=</mo> <mrow> <mo>(</mo> <msub> <mi>C</mi> <mn>1</mn> </msub> <mo>(</mo> <mrow> <mn>1</mn> <mo>-</mo> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msub> <mi>C</mi> <mn>2</mn> </msub> <mi>&amp;lambda;</mi> </mrow> </msup> </mrow> <mo>)</mo> <mo>-</mo> <msub> <mi>C</mi> <mn>3</mn> </msub> <mi>&amp;lambda;</mi> <mo>)</mo> </mrow> <msup> <mi>e</mi> <mrow> <mo>-</mo> <msub> <mi>C</mi> <mn>4</mn> </msub> <mi>&amp;lambda;</mi> <mi>V</mi> </mrow> </msup> </mrow>
    In formula：C1、C2、C3For tire enclosing characteristic parameter, C4Affecting parameters for automobile driving speed to attachment characteristic, λ are wheel Tire slip rate, V are speed.
  2. 2. the coefficient of road adhesion real-time estimating method under a kind of damped condition as claimed in claim 1, it is characterised in that：Institute The ideal brake force square controller (5) stated, is redesigned by sliding mode controller (4) using saturation function and integration diverter surface Arrive；
    Ideal brake force square controller (5) meets following relation：
    S1=λf-λfd+ξ1∫(λf-λfd)dt
    S2=λr-λrd+ξ2∫(λr-λrd)dt
    In formula：λf、λrRespectively front and back wheel actual slip rate, λfd、λrdRespectively front and back wheel target slip ratio, ξ1And ξ2For constant.
CN201510897667.0A 2015-12-07 2015-12-07 A kind of coefficient of road adhesion real-time estimating method under damped condition Active CN105539449B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201510897667.0A CN105539449B (en) 2015-12-07 2015-12-07 A kind of coefficient of road adhesion real-time estimating method under damped condition

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201510897667.0A CN105539449B (en) 2015-12-07 2015-12-07 A kind of coefficient of road adhesion real-time estimating method under damped condition

Publications (2)

Publication Number Publication Date
CN105539449A CN105539449A (en) 2016-05-04
CN105539449B true CN105539449B (en) 2018-05-01

Family

ID=55819123

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201510897667.0A Active CN105539449B (en) 2015-12-07 2015-12-07 A kind of coefficient of road adhesion real-time estimating method under damped condition

Country Status (1)

Country Link
CN (1) CN105539449B (en)

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109131336B (en) * 2017-06-15 2020-07-28 华为技术有限公司 Method and system for acquiring road adhesion coefficient
CN108454597B (en) * 2018-01-03 2020-01-24 江苏大学 Vehicle anti-lock control system based on LQG controller and slip rate jitter suppression method
CN108528419B (en) * 2018-01-31 2019-12-03 江苏大学 A kind of bicyclic forecast Control Algorithm of the vehicle line control brake system towards full application of brake operating condition
CN109131306B (en) * 2018-08-31 2020-10-30 北京新能源汽车股份有限公司 Brake control method and brake control system of electric automobile and automobile
CN109733410A (en) * 2018-12-21 2019-05-10 浙江万安科技股份有限公司 A kind of real-time pavement identification method of ABS and system
CN110597064B (en) * 2019-09-24 2021-04-16 燕山大学 Active suspension output feedback control method based on nonlinear and uncertain models
CN110884496A (en) * 2019-10-30 2020-03-17 北京理工大学 Method and device for identifying road adhesion coefficient suitable for tracked vehicle

Family Cites Families (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN100437074C (en) * 2006-06-20 2008-11-26 吉林大学 Real-time measuring method for longitudinal attachment characteristic of tyre and pavement and measuring vehicle therefor
CN101419456A (en) * 2008-11-25 2009-04-29 长安大学 Pavement adhesion coefficient simulator control system based on rack
CN101480946B (en) * 2009-02-16 2010-06-02 华南理工大学 Wheel load-based type intelligent sensing wheel brake performance monitoring methods
CN101581659B (en) * 2009-06-05 2011-06-29 清华大学 Tyre-pavement maximum attachment coefficient test method
CN102768177B (en) * 2012-07-12 2014-10-22 吉林大学 Real-time road adhesion coefficient detection method and detection system

Also Published As

Publication number Publication date
CN105539449A (en) 2016-05-04

Similar Documents

Publication Publication Date Title
CN105539449B (en) A kind of coefficient of road adhesion real-time estimating method under damped condition
Cheng et al. Multiple-objective adaptive cruise control system integrated with DYC
Li et al. Comprehensive tire–road friction coefficient estimation based on signal fusion method under complex maneuvering operations
Cho et al. Estimation of tire forces for application to vehicle stability control
CN105253141B (en) A kind of vehicle handling stability control method adjusted based on wheel longitudinal force
Alipour et al. Lateral stabilization of a four wheel independent drive electric vehicle on slippery roads
CN105691403B (en) The full drive electric automobile coefficient of road adhesion method of estimation of four-wheel
CN105936273B (en) Between automobile-used active torque wheel, between centers distribution method
CN105667520B (en) A kind of front-wheel side force method of estimation of distributed driving electric car
CN103909933B (en) A kind of front wheel side of distributed electro-motive vehicle is to force evaluating method
CN102165300B (en) Method and device for determining center of gravity of motor vehicle
CN107685733B (en) The estimation method of four motorized wheels electric car coefficient of road adhesion
CN104015711B (en) A kind of bi-fuzzy control method of automobile ABS
CN105270397B (en) The formulating method of vehicle electric stabilitrak stability control criterion
CN1329722C (en) Cargo vehicle ABS road identification method
CN103754218B (en) Coefficient of road adhesion method of estimation under a kind of motor tire lateral deviation operating mode
EP1544760A2 (en) Three dimensional road-vehicle modeling system
CN103886190A (en) Drive skid prevention control algorithm for four-wheel independent drive electric automobile
CN109263483A (en) Consider the distributed-driving electric automobile antiskid control system and method for body roll
CN108791276B (en) Method for rapidly judging linear/nonlinear working state of tire lateral force
CN104553992A (en) Vehicle rollover warning method
Dabladji et al. Unknown-input observer design for motorcycle lateral dynamics: Ts approach
CN108241773A (en) A kind of improved vehicle running state method of estimation
CN105774458A (en) Method For Controlling Suspension System
CN105291885A (en) Pure electric bus drive control method

Legal Events

Date Code Title Description
C06 Publication
PB01 Publication
C10 Entry into substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant