CN110435623A - A kind of grading automatical emergency braking control system of the electric vehicle of adjust automatically - Google Patents
A kind of grading automatical emergency braking control system of the electric vehicle of adjust automatically Download PDFInfo
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
- B60L7/00—Electrodynamic brake systems for vehicles in general
- B60L7/10—Dynamic electric regenerative braking
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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
- B60T13/00—Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; Brake systems incorporating such transmitting means, e.g. air-pressure brake systems
- B60T13/74—Transmitting braking action from initiating means to ultimate brake actuator with power assistance or drive; Brake systems incorporating such transmitting means, e.g. air-pressure brake systems with electrical assistance or drive
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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
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/02—Control of vehicle driving stability
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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
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
- B60W30/09—Taking automatic action to avoid collision, e.g. braking and steering
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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
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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
- B60W40/064—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
- 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
- B60W40/076—Slope angle of the road
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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
- B60W50/08—Interaction between the driver and the control system
- B60W50/14—Means for informing the driver, warning the driver or prompting a driver intervention
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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
- B60W50/08—Interaction between the driver and the control system
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Abstract
The present invention relates to a kind of grading automatical emergency braking control systems of the electric vehicle of adjust automatically.It include: vehicle-mounted ranging and range rate sensing equipment, grading forewarning system control system, Calculation of Safety Distance model, vehicle is against longitudinal dynamics computation model, hydraulic braking force and regenerative braking force distribute computing module, brake fluid system inversion model, ESC and Booster active boost hydraulic coupling distribution module and information of road surface estimate model, comfort when improving AEB system trigger reduces this vehicle simultaneously, and substantially deceleration causes the security risks such as car rear-end suddenly, the gradient of current driving road and road ahead to vehicle, the information such as attachment coefficient are estimated, the on-line tuning of control parameter, enhance AEB system to the adaptedness of different road surface, sufficiently recycling braking energy, improve course continuation mileage, electronic stability program ESC and electric mechanical braking booster Booster are given full play in master Advantage in dynamic pressurization.
Description
Technical field
The present invention relates to a kind of electric vehicle automatic emergency brake control system, in particular to a kind of electric vehicle of adjust automatically
Grading automatical emergency braking control system.
Background technique
In recent years, with the continuous development of Global Auto technology, car ownership is continuously increased, vehicle traffic accident also with
Increase.In order to reduce road traffic accident quantity, various automobile active safety technologies and passive security technology have been obtained rapidly
Development.The automobile active safety technology important as one, automatic emergency brake system (Automatic Emergency
Brake System, AEB, hereinafter referred to as AEB) it can be effectively prevented from the generation of a large amount of traffic accident, it has been increasingly becoming
Vehicle standard configuration.
AEB system is mainly made of information collection, control system and executing agency's three parts.Information collection mainly passes through
The sensors such as radar to ambient enviroment into real-time monitoring, object information conveyance to control system.Control system receives mesh
After marking object information, in conjunction with this vehicle motion state, go out it is expected the information such as deceleration and send it to hold by control strategy decision
Row mechanism.Executing agency carries out respective operations to the instruction of control system by electronic throttle and brake, thus reaches and keeps away
Exempt from the effect of collision or reducing collisions.
But current AEB system still has some problems, such as:
1, AEB systematic control algorithm vehicle-mounted at present is usually chosen in for security consideration and needs AEB when intervening with regard to straight
It connects and is stopped vehicle brake with maximum braking deceleration, member's comfort can be caused to seriously affect, and the emergency braking under high speed is also
It is easy to happen the danger such as car rear-end.
2, AEB systematic control algorithm does not include the function of information of road surface identification, and algorithm only considered vehicle in design
The kinematics parameters of structural parameters and itself, and it is the influence for considering different road surfaces to AEB algorithm implementation result.
3, AEB systematic control algorithm does not have the adaptivity of road pavement changed condition, such as vehicle in low attachment coefficient
On road surface when driving, due to the decline of attachment coefficient, the maximum braking deceleration of vehicle may be unable to reach the default of AEB system
Value, so as to cause the generation of collision；When vehicle driving is on the road with certain slope, slope roadlock power will lead to vehicle
Actual braking force changes, if AEB algorithm is adjusted not according to road gradient, may result in upward slope vehicle and slows down too early
Distrust or descending vehicle deceleration deficiency is brought to collide to driver.
4, for carrying the electric vehicle of partly decoupled or non-decoupling formula electric booster braking system, when AEB system
When triggering and starting to apply brake force to vehicle, due to there is mechanical connection between operator brake pedal and master cylinder, because
This brake pedal will automatically move under the drive of assist motor, and it is flurried on the one hand to may cause not accommodating for driver, another
Aspect has not been driven in initial position when driver wants independently to apply bigger brake force to vehicle due to brake pedal
Member foot is moved to time of brake pedal will be longer than usual, and difference may cause driver and hesitate or mistake behaviour this period
Make, causes brake force to apply insufficient to cause danger.
Summary of the invention
In order to solve the above technical problems, the present invention is the electric booster braking system for carrying non-decoupling formula or partly decoupled formula
Electric vehicle to provide a kind of electric vehicle based on information of road surface automatic identification and control parameter on-line tuning grading automatical tight
Anxious braking control system.
A kind of grading automatical emergency braking control system of the electric vehicle of adjust automatically of the present invention, comprising: vehicle-mounted ranging
Sensing equipment, grading forewarning system control system, Calculation of Safety Distance model, vehicle test the speed against longitudinal dynamics computation model, hydraulic
Brake force and regenerative braking force distribute computing module, brake fluid system inversion model, ESC and Booster active boost hydraulic coupling
Distribution module, information of road surface estimate model, and the control method of system is as described below:
(1) in the process of moving, vehicle-mounted ranging and range rate sensing equipment transmits Ben Che and risk object real-time range S to vehicle
Grading forewarning system control system is given, the movement state informations such as the speed of risk object vehicle, acceleration are passed into Calculation of Safety Distance
Model；Information of road surface estimates that the minimum coefficient of road adhesion μ in four wheels is passed to Calculation of Safety Distance model by model, will
Locating road gradient passes to vehicle against longitudinal dynamics computation model to vehicle at this time；
(2) severity of braking of AEB system is divided into light brake and with all strength two kinds of braking by the Calculation of Safety Distance model,
The target braking deceleration of the two staged braking intensity of AEB system is determined according to the minimum coefficient of road adhesion μ in four wheels
aexp, and this vehicle measured according to risk object movement state information and this vehicle vehicle speed sensor longitudinal speed v in real time0, determine
Three control signal activation threshold values of AEB system: sense organ early warning security distance threshold Sw, light brake safety distance threshold Sd、
The safety distance threshold S of braking with all strengthb；
(3) grading forewarning system control system is by Ben Che and risk object real-time range S and threshold value of warning S at different levelsw、Sd、SbIt is big
It is small to be compared, whether there is the deceleration-operation of active in conjunction with driver, analysis generates AEB early warning and controls signal, that is, vehicle target
Longitudinal deceleration aexpIf: S > Sw, then vehicle and leading vehicle distance are in safe range at this time for system judgement, without actuation；If Sw
< S < Sd, then system can carry out visual tactile early warning to driver, and driver is reminded to carry out deceleration-operation；If Sb< S < Sd, and
According to the judgement of brake-pedal-travel sensor signal, driver does not carry out deceleration-operation at this time, then system will control vehicle and carry out gently
Degree braking makes to carry out sense organ early warning to driver while vehicle deceleration, at this time target longitudinal deceleration aexpSubtract for light brake
Speed a1max, to make vehicle effectively be slowed down and providing tactile early warning for driver, while guaranteeing again uncomfortable to occupant
Property generate excessive influence, therefore light brake deceleration a1max0.25 μ g be can be taken as to 0.35 μ g；If driver has deceleration at this time
Operation, then AEB system can carry out sense organ early warning to driver until driver makes spacing be maintained at safe distance；If S < Sb, then
System can control vehicle and carry out all one's effort braking, make vehicle with most fast speed in preset minimum safe spacing range S0Interior stagnation of movement
Or reach speed identical with risk object, minimum safe spacing range S0For 2~3m, target longitudinal deceleration a at this timeexpFor
All one's effort braking deceleration a2max, when triggering is braked with all strength, in order to prevent wheel armful to enable the vehicle to slow down as early as possible simultaneously
Extremely, then all one's effort braking deceleration a2max0.75 μ g be can be taken as to 0.85 μ g；
(4) by target longitudinal deceleration aexpVehicle is passed to against longitudinal dynamics computation model, while information of road surface is estimated
Model calculates vehicle and the gradient i on locating road surface and passes it to vehicle against longitudinal dynamics computation model at this time, works as vehicle
It is calculated when upward slope by formula (1), is calculated when vehicle descending by formula (2), finally obtain and mentioned needed for motor vehicle braking system at this time
The target braking force F of confessionb:
Fb=maexp-Ff-Fw-Gi (1)
Fb=maexp+Gi-Ff-Fw (2)
Wherein m is vehicle mass, and G is vehicle weight, FfFor rolling resistance, FwFor air drag；
(5) target braking force FbIt passes to hydraulic braking force and regenerative braking force distributes computing module, while regenerative braking
System judges the regeneration system that can be provided at this time according to the working condition of the systems such as speed, vehicle power motor, battery at this time
Power Fbr, the hydraulic braking force and regenerative braking force distribution computing module calculate target hydraulic braking force F at this timebh=Fb-
Fbr；
(6) target hydraulic braking force FbhIt passes to brake fluid system inversion model to be calculated, each system is obtained by formula (3)
The target hydraulic power P of dynamic device wheel cylinderexp:
Wherein rr0For vehicle wheel roll radius, [BEF]fFor front wheel brake braking effectiveness factor, [BEF]rFor rear service brake
Device braking effectiveness factor is the conventional structure parameter of vehicle；
(7) the target hydraulic power P of each wheel-braking cylinderexpIt passes to ESC and Booster active boost hydraulic coupling distributes mould
Block judges the active boost mode of brake fluid system at this time: if PexpIn the active boost limit range of ESC system, then by
ESC system builds pressure in wheel-braking cylinder and carries out active boost control；If PexpBe more than the active boost limit of ESC system, then by
Booster builds pressure in wheel cylinder and carries out active boost control, makes vehicle deceleration to target vehicle speed；
The above process constantly carries out during entire AEB system trigger, with risk object real-time range, early warning threshold at different levels
The information such as value, Ben Che and risk object motion state constantly update adjustment, until vehicle deceleration to target vehicle speed or parking and with
Risk object is maintained at preset safe distance.
Beneficial effects of the present invention:
1, by using Calculation of Safety Distance model and grading forewarning system control system, according to the movement shape of Ben Che and front truck
State can carry out the early warning of the sense organs such as sound sensation or vision to driver, apply light brake to vehicle or brake with all strength, improve AEB system
Comfort when system triggering reduces this vehicle simultaneously, and substantially deceleration causes the security risks such as car rear-end suddenly.
2, the gradient of current driving road and road ahead to vehicle, attached by design information of road surface algorithm for estimating
The information such as coefficient estimated, and pre-estimation is carried out to next stage information of road surface using sensors such as cameras, improves road surface
Information algorithm for estimating practicability.
3, the related information of road surface that can be obtained according to information of road surface algorithm for estimating carries out the on-line tuning of control parameter, increases
Strong AEB system provide AEB system can for vehicle maximumlly on any road surface the adaptedness of different road surface
Security performance, while avoiding AEB system from triggering too early and bringing distrust to driver.
4, for the electric car with regenerative braking capability, regeneration brake system and brake fluid system is made to coordinate work
Make, when AEB system requirements severity of braking is lower, carries out the reasonable distribution of regenerative braking force and hydraulic braking force, to greatest extent
The regenerative braking force of performance vehicle improve course continuation mileage sufficiently to recycle braking energy.
5, make electronics electronic stability program ESC and electric mechanical braking booster Booster cooperation, sufficiently
Play the advantage in each comfortable active boost, it is lower in AEB system requirements severity of braking, brake force can by regeneration brake system and
When ESC system provides, electric mechanical braking booster Booster is not involved in braking, makes brake pedal that autonomous not occur, mentions
High drive safety and comfort；And when AEB system requirements severity of braking increase above ESC system the active boost limit and
When the sum of the maximum braking force that regenerative braking system for vehicle is capable of providing at this time, by the stronger electric mechanical system of active boost ability
Dynamic booster Booster intervention makes vehicle stop or decelerate to as early as possible ideal velocity.
Detailed description of the invention
Fig. 1 is overall architecture schematic diagram of the present invention；
Fig. 2 is that information of road surface of the present invention estimates model algorithm schematic diagram；
Fig. 3 is grading forewarning system control system logic chart of the present invention；
Fig. 4 is ESC of the present invention and Booster active boost hydraulic coupling distribution principle figure；
Fig. 5 is the Three Degree Of Freedom four-wheel car model schematic in coefficient of road adhesion algorithm for estimating of the present invention；
Fig. 6 is the flow diagram that estimation is filtered in coefficient of road adhesion algorithm for estimating of the present invention；
Fig. 7 is coefficient of road adhesion algorithm for estimating verification result figure of the present invention；
Fig. 8 is this vehicle braking by grades process schematic in Calculation of Safety Distance model of the present invention；
Fig. 9 is this vehicle all one's effort braking process schematic diagram in Calculation of Safety Distance model of the present invention；
Figure 10 is that the road gradient in information of road surface algorithm for estimating of the present invention based on closed loop omnidirectional vision is estimated to calculate
Method verification result figure.
Specific embodiment
Please refer to attached drawing 1-10, a kind of electric vehicle of adjust automatically grading automatical emergency braking control system of the present invention
System includes: vehicle-mounted ranging and range rate sensing equipment, grading forewarning system control system, Calculation of Safety Distance model, vehicle against Longitudinal
Learn computation model, hydraulic braking force and regenerative braking force distribution computing module, brake fluid system inversion model, ESC and Booster
Active boost hydraulic coupling distribution module, information of road surface estimate model, and the control method of system is as described below:
(1) refering to attached drawing 2, in the process of moving, vehicle-mounted ranging and range rate sensing equipment is real by Ben Che and risk object for vehicle
When distance S pass to grading forewarning system control system, the movement state informations such as the speed of risk object vehicle, acceleration are passed into peace
Full distance computation model；Information of road surface estimates that the minimum coefficient of road adhesion μ in four wheels is passed to safe distance by model
Computation model, by vehicle, locating road gradient passes to vehicle against longitudinal dynamics computation model at this time；
(2) severity of braking of AEB system is divided into light brake and with all strength two kinds of braking by the Calculation of Safety Distance model,
The target braking deceleration of the two staged braking intensity of AEB system is determined according to the minimum coefficient of road adhesion μ in four wheels
aexp, and this vehicle measured according to risk object movement state information and this vehicle vehicle speed sensor longitudinal speed v in real time0, determine
Three control signal activation threshold values of AEB system: sense organ early warning security distance threshold Sw, light brake safety distance threshold Sd、
The safety distance threshold S of braking with all strengthb；
(3) refering to attached drawing 3, grading forewarning system control system is by Ben Che and risk object real-time range S and threshold value of warning at different levels
Sw、Sd、SbSize be compared, whether have the deceleration-operation of active in conjunction with driver, analysis generates AEB early warning and controls signal
That is the target longitudinal deceleration a of vehicleexpIf: S > Sw, then vehicle and leading vehicle distance are in safe range at this time for system judgement, no
Carry out actuation；If Sw< S < Sd, then system can carry out sense organ early warning to driver, and driver is reminded to carry out deceleration-operation；If Sb
< S < Sd, and driver does not carry out deceleration-operation at this time according to the judgement of brake-pedal-travel sensor signal, then system will control
Vehicle carries out light brake, makes to carry out sense organ early warning to driver while vehicle deceleration, at this time target longitudinal deceleration aexpFor
Light brake deceleration a1max；If driver has a deceleration-operation at this time, AEB system can carry out sense organ early warning to driver until
Driver makes spacing be maintained at safe distance；If S < Sb, then system can control vehicle and carry out all one's effort braking, make vehicle with most fast
Speed stagnation of movement or reach speed identical with risk object within the scope of preset minimum safe spacing, minimum safe spacing model
Enclose S0Value is 2~3m, at this time target longitudinal deceleration aexpFor all one's effort braking deceleration a2max；
(4) grading forewarning system control system is by target longitudinal deceleration aexpVehicle is passed to against longitudinal dynamics computation model,
The basis of vehicle against longitudinal dynamics computation model is vehicle travel resistance equation:
F=Ff+Fw+Fi+Fj+Fb
Wherein F is longitudinal resistance suffered by vehicle, FfFor rolling resistance, FwFor air drag, FiFor slope roadlock power, FjFor
Acceleration resistance, FbThe brake force generated for motor vehicle braking system.
The vehicle receives the AEB pre-warning signal of the grading forewarning system control system transmitting against longitudinal dynamics computation model
That is target longitudinal deceleration, the traveling when the grading forewarning system control system requires to carry out deceleration-operation to vehicle, in above formula
Resistance F is answered are as follows:
F=maexp
Wherein, m is vehicle mass.
Since vehicle need to carry out deceleration-operation, so acceleration resistance:
Fj=0
Vehicle can receive the road gradient that information of road surface estimation model is calculated against longitudinal dynamics computation model simultaneously
Influence of the gradient to braking process must be taken into consideration if vehicle needs to carry out emergency braking on the slope in i:
1. if slope roadlock power can provide a part of deceleration for vehicle, slope roadlock power at this time to go up a slope:
Fi=Gi
Wherein, G is vehicle weight.
Synthesis can obtain, in order to obtain the desired longitudinal deceleration a of vehicleexp, braking system need brake force to be offered are as follows:
Fb=maexp-Ff-Fw-Gi (1)
2. component of the vehicle weight along slope road can provide an acceleration under the road direction of slope to vehicle if being at this time descending
Degree must be taken into consideration offset this part acceleration by increasing brake force at this time, then braking system needs brake force to be offered are as follows:
Fb=maexp+Gi-Ff-Fw (2)
Therefore the vehicle that the vehicle can be transmitted against longitudinal dynamics computation model according to the grading forewarning system control system
Target longitudinal deceleration aexp, in conjunction with the road gradient i that information of road surface estimation model is estimated, braking system is answered at this time for judgement
The brake force F of the offerb, avoid vehicle from triggering AEB system too early on upward slope road surface or vehicle made to stop too early stopping, give driver
Bring distrust；Avoiding vehicle AEB system intervention on descending road surface causes to brake deficiency too late and collides danger.
(5) target braking force FbIt passes to hydraulic braking force and regenerative braking force distributes computing module, while regenerative braking
System judges the regeneration system that can be provided at this time according to the working condition of the systems such as speed, vehicle power motor, battery at this time
Power Fbr, the hydraulic braking force and regenerative braking force distribution computing module calculate target hydraulic braking force F at this timebh=Fb-
Fbr；Hydraulic braking force and regenerative braking force distribution computing module can give full play to the regeneration function stopping power of electric vehicle to return
Braking energy is received, while meeting the braking deceleration demand of vehicle.
(6) target hydraulic braking force FbhBrake fluid system inversion model is passed to be calculated, by formula:
Obtain the target hydraulic power P of each wheel-braking cylinderexp:
Wherein rr0For vehicle wheel roll radius, [BEF]fFor front wheel brake braking effectiveness factor, [BEF]rFor rear service brake
Device braking effectiveness factor is the conventional structure parameter of vehicle；
(7) refering to attached drawing 4, the target hydraulic power P of each wheel-braking cylinderexpPass to ESC and Booster active boost liquid
Pressure distribution module judges the active boost mode of brake fluid system at this time: if PexpIn the active boost limit of ESC system
In range, then pressure is built in wheel-braking cylinder by ESC system and carry out active boost control；If PexpIt has been more than the active increasing of ESC system
The limit is pressed, then builds pressure in wheel cylinder by Booster and carries out active boost control, make vehicle deceleration to target vehicle speed；
The ESC and Booster active boost hydraulic coupling distribution module can make full use of ESC system and Booster master
The advantage of dynamic pressurization aspect: when brake pressure demand is not high, response time and the relatively low ESC system of precision be can use
System, for carrying the vehicle of non-decoupling formula braking system, can be avoided while meeting pressure demand and utilize Booster master
Dynamic pressurization causes brake pedal to move down automatically to give driver's bring sense of discomfort, and reduces security risk；When brake pressure demand
It is high using fast response time, pressure controling precision when higher, it builds and the Booster of upper limit for height is pressed to carry out active brake, make vehicle with all strength
Reach desired braking deceleration.
The above process constantly carries out during entire AEB system trigger, with risk object real-time range, early warning threshold at different levels
The information such as value, Ben Che and risk object motion state constantly update adjustment, until vehicle deceleration to target vehicle speed or parking and with
Risk object is maintained at preset safe distance.
In step (1), information of road surface estimation model is calculated by the coefficient of road adhesion algorithm for estimating based on filtering
The method of minimum coefficient of road adhesion μ is as follows:
Dugoff tire model is initially set up to obtain normalization tire force, Dugoff tire model is by the longitudinal direction on tire
Power and lateral force indicate are as follows:
F in formulaxFor longitudinal force of tire, FyFor side force of tire, FzFor tire normal force, λ is straight skidding rate, CyFor wheel
Tire cornering stiffness, CxFor longitudinal tire stiffness, α is slip angle of tire, and μ is coefficient of road adhesion, and ε is tire effect coefficient, is
One parameter related with tire structure and material itself, to correct influence of the vehicle slip speed to tire force, L is boundary
Value, to describe the nonlinear characteristic of wheelslip bring tire force；In order to facilitate design coefficient of road adhesion algorithm for estimating,
Dugoff tire model is reduced to following normalizing forms:
In formulaWithNormalization tire force respectively longitudinally and laterally, it is unrelated with attachment coefficient μ, it is convenient for the base
The coefficient matrix of system state space expression formula is determined in the coefficient of road adhesion algorithm for estimating of filtering；
It needs to calculate the normalization tire force in model after establishing normalization Dugoff tire model, i.e., counts respectively
Calculate the vertical load F of each wheelZfl、FZfr、FZrl、FZrr, straight skidding rate λfl、λfr、λrl、λrr, slip angle of tire αfl, αfr, αrl,
αrr, footmark fl, fr, rl, rr respectively represent the near front wheel, off-front wheel, left rear wheel, the off hind wheel of vehicle；
Each wheel vertical load calculation formula:
Each tyre slip angle calculation formula:
The calculation formula of each wheel slip:
The speed v of each ground contact point in above formulafl、vfr、vrl、vrrCalculation formula are as follows:
Each meaning of parameters in above-mentioned each formula are as follows: β is vehicle centroid side drift angle,vcogFor vehicle centroid
Speed,M is complete vehicle quality, and a is the horizontal distance at front-wheel center and vehicle centroid, and b is rear-wheel center and vehicle
The horizontal distance of mass center, l is vehicle wheelbase, hgFor vehicle centroid height, axFor longitudinal acceleration of the vehicle, ayIt is lateral for vehicle
Acceleration, TfFor two front wheels wheelspan, TrFor two rear tracks, vxFor vehicle longitudinal velocity, vyVehicle side velocity, RωFor wheel
Rolling speed, ωfl、ωfr、ωrl、ωrrThe respectively rate of roll of the near front wheel, off-front wheel, left rear wheel, off hind wheel；
Therefore longitudinal normalization tire force of four wheels may be expressed as:
The lateral normalization tire force of four wheels may be expressed as:
The parameter needed in calculating process in conjunction with the modified normalization tire force of Dugoff tire model includes vehicle knot
Two class of structure parameter and kinematics parameters, structural parameters can directly measure to obtain, and kinematics parameters can pass through related onboard sensor
Measurement；
In order to arrange the system state equation and observational equation write in the algorithm for estimating of the coefficient of road adhesion based on filtering, need
The Three Degree Of Freedom four-wheel car model of vehicle is established to describe longitudinal direction of car, lateral and weaving；
Refering to attached drawing 5, longitudinal direction of car, lateral and three directions of sideway the equation of motion are as follows:
In formula, axFor longitudinal acceleration of the vehicle, ayFor vehicle lateral acceleration,For the sideway angular acceleration of vehicle, δ is
Two front wheel angles, m are complete vehicle quality, μfl、μfr、μrl、μrrRoad respectively where the near front wheel, off-front wheel, left rear wheel, off hind wheel
Face attachment coefficient, a are the horizontal distance at front-wheel center and vehicle centroid, and b is the horizontal distance at rear-wheel center and vehicle centroid, Tf
For two front wheels wheelspan, TrFor two rear tracks, IzThe yaw rotation inertia of vehicle；
Vehicle kinematics parameter needed for related sensor measurement on vehicle obtains algorithm simultaneously passes it to Dugoff
Tire model, Dugoff tire model, which is calculated, longitudinally and laterally to be normalized tire force and passes it to vehicle Three Degree Of Freedom
Four-wheel model can be with based on these three kinetics equations to obtain longitudinally, laterally three kinetics equations with yaw direction
The system state equation and observational equation of coefficient of road adhesion filter estimator are obtained, to carry out subsequent attachment coefficient estimation；
The system that the coefficient of road adhesion algorithm for estimating based on filtering is chosen is nonlinear system, state equation
It is respectively as follows: with observational equation
State equation:
Observational equation:
Y (t)=h (x (t), u (t), v (t))
Stochastic variable w (t), v (t) are respectively process noise and measurement noise, are adhered on the road surface based on filtering
Mutual independence is taken as in coefficient algorithm for estimating and white Gaussian noise that mean value is zero, probability distribution are as follows:
P (w)~N (0, Q) p (v)~N (0, R)
And the covariance matrix for setting them is respectively Q and R, it may be assumed that
Q=cov [w (t), w (τ)]
R=cov [v (t), v (τ)]
Refering to attached drawing 6, the coefficient of road adhesion algorithm for estimating based on filtering the specific implementation process is as follows:
(1) system state equation and observational equation are determined:
It is longitudinally, laterally determined with the equation of motion of yaw direction according to the Three Degree Of Freedom four-wheel car model:
State variable: x (t)=[μfl μfr μrl μrr]T,
Observational variable: y (t)=[ax ay r]T,
Control input: u (t)=[δ],
According to the Three Degree Of Freedom four-wheel car model longitudinally, laterally with the expression formula of the equation of motion of yaw direction and each
Variable is write as state equation and observational equation:
State equation:
Observational equation:
Wherein:
(2) estimator assigns initial value:
Initial value in recursive process, measurement noise covariance battle array R are 3 unit matrix for multiplying 3, and process noise covariance battle array Q is 4
Multiply 4 unit matrix；
(3) coefficient of road adhesion is estimated using filtering algorithm:
1. it is filtered initialization, iterative steps k=0, then the initial mean value of state variable xWith covariance P (0) point
Not are as follows:
2. 2n+1 point is calculated using Unscented transform, the mean value and covariance for making this 2n+1 point are equal to original state minute
The mean value and covariance of cloth, this 2n+1 point are referred to as Sigma point, and n takes n=4, then according to above-mentioned state equation for state dimension
It needs exist for obtaining 9 Sigma point sets, the state dimension of each Sigma point set is consistent with the dimension of state variable x, as 4 dimensions
Column vector, the then matrix for being 4*9 by the matrix χ that this 9 Sigma point sets form；If χiIt (0) is the matrix in iterative steps k=0
Each column vector, that is, each Sigma point set in χ, χ0(0) first row for being Sigma matrix χ, and so on, then it is initial
Sigma matrix are as follows:
Wherein λ is scale parameter, λ=(α2- 1) n=4 (α2- 1), α is used to determine Sigma point in state variable mean value
Neighbouring distribution is the positive number 10 of a very little-4≤ α≤1, usually takes α=10-3；
The calculating process of initial Sigma point set when above-mentioned iterative steps k=0, is equally applicable to other iterative steps
Sigma point set calculates, and being only used for state variable mean value and the state variable covariances value that Sigma point set calculates at this time is k
The value at moment, i.e. k momentWith P (kk), calculation formula are as follows:
3. entering next step iteration, iterative steps k increases by 1, carries out next step prediction to 9 Sigma point sets, 2. by step
The Sigma point set of obtained previous step substitutes into the state equation (a) of system, obtains transformed Sigma point set:
χ (kk-1)=f (χ (k-1), u (k-1)) k=1,2 ...
4. according to Unscented transform principle, the next step prediction mean value and prediction variance of computing system state variable, system
The prediction mean value of state variable is obtained by the predicted value weighted sum of Sigma point set:
W in formulai (m)For the mean value weight of each Sigma point, specific value are as follows:
W0 (m)=λ/(4+ λ) i=0
Wi (m)=1/ [2 (4+ λ)] i=1,2 ..., 8
The prediction variance of system state variables is summed to obtain by the prediction covariance-weighted of Sigma point set:
χ in above formulai(k | k-1) is the i-th column of matrix χ (k | k-1), i=0,1 ..., 8；Wi (c)For the association side of each Sigma point
Poor weight, specific value are as follows:
W0 (c)=λ/(n+ λ)+(1- α2+ β) i=0
Wi (c)=1/ [2 (n+ λ)] i=1,2 ..., 8
Wherein β is used to be associated with the prior state of off status variable x distribution, and the coefficient of road adhesion based on filtering is estimated
State variable x Gaussian distributed, usually takes β=2 in calculating method；
5. according to the next step prediction mean value and next step prediction variance of 4. system state variables that step obtains, again
Using Unscented transform, generate new Sigma point set, calculating process and step 2. in initial Sigma point set calculating process phase
It is same:
6. 5. new Sigma point set that step is obtained is brought observational equation (b) into and is obtained under the observed quantity of Sigma point set
One-step prediction value:
U (k-1) in formula is that the system that iterative steps are the k-1 moment inputs, i.e., front wheel angle δ at this time can be by existing
The vehicle-mounted rotary angle transmitter having measures；
By observational equation (b) it is found that the output matrix of system is 3 matrixes for multiplying 4, Sigma matrix χ (kk-1) multiplies 9 for 4
Matrix, therefore it is observed that the next step predicted value of the observed quantity of Sigma point set is calculated in equation (b)Multiply 9 for 3
Matrix；
7. by the observed quantity next step predicted value of 6. Sigma point set that step obtainsIt is obtained by weighted sum
To the prediction mean value of systematic observation variable:
In formulaIt is the next step prediction value matrix of the observed quantity of Sigma point setI-th column, i=
0,1,…,8；
8. by the observed quantity next step predicted value of 6. Sigma point set that step obtainsPass through weighted sum meter
Calculate updated observation covariance matrix and state variable and output variable cross-correlation matrix:
Observe covariance matrix are as follows:
State variable and output variable cross-correlation matrix are as follows:
9. the updated filtering feedback gain matrix of computing system:
The updated state variable Mean Matrix of computing system:
Matrix(1,1) element, (2,1) element, (3,1) element, (4,1) element be respectively iterative steps be k when
The near front wheel attachment coefficient, off-front wheel attachment coefficient, left rear wheel attachment coefficient, off hind wheel attachment coefficient filtering estimated value；
The longitudinal direction that actual observational variable value at the time of be k that y (k) in formula is iterative steps, i.e. iterative steps are the k moment
Acceleration ax, side acceleration ayAnd sideway angular accelerationThree above vehicle kinematics parameter can be by assembling on vehicle
Associated acceleration sensor carry out real-time measurement, then pass to the coefficient of road adhesion algorithm for estimating based on filtering；
The updated state variable covariances value of computing system:
P (k | k)=P (k | k-1)-K (k) PyyKT(k)
State variable mean value after system updateWith state variable covariances value P (k | k) 2. return step is generated
Next group of Sigma point set, starts the calculating of next iterative steps；The above process will be repeated constantly, until completing all iteration
Step number finally obtains the coefficient of road adhesion μ of four wheels；
The iterative steps k of the coefficient of road adhesion algorithm for estimating based on filtering depends on the sampling step of algorithm setting
Long, the selection of sampling step length directly affects road surface where the final degree of convergence of algorithm, convergence rate, that is, four wheels again
The estimating speed and precision of attachment coefficient, thus algorithm sampling step length need to specifically be adjusted according to the relevant parameter of different vehicle so that
Algorithm obtains optimum performance.
Refering to attached drawing 7, the coefficient of road adhesion algorithm for estimating based on filtering is verified, by attached drawing it is found that
Each wheel coefficient of road adhesion can complete convergence within 1s, and keep substantially with the practical reference value of coefficient of road adhesion
Unanimously, error is minimum, illustrates that the coefficient of road adhesion algorithm for estimating of the present invention based on filtering is functional, road surface attachment
Coefficient estimating speed and estimated accuracy are high, meet vehicle grade algorithm requirement.
The coefficient of road adhesion algorithm for estimating based on filtering considers vehicle in the wheel attachment of four, split road surface etc.
The case where being travelled on coefficient difference road surface, after obtaining each wheel coefficient of road adhesion estimated value, the road surface based on filtering
Attachment coefficient algorithm for estimating can be compared and then obtain the minimum value in four wheel attachment coefficients to four values, then by this
Minimum value μ feeds back to the Calculation of Safety Distance model, to prevent from not generating between low attachment coefficient tire and road surface enough
Brake force cause vehicle braking deceleration deficiency to collide danger.
In step (2), the target braking deceleration a of the light brake of AEB system1max=0.25 μ g of μ g~0.35, with all strength
The target braking deceleration a of braking2max=0.75 μ of μ g~0.85 g；The three-level early warning security distance threshold calculating side of AEB system
Method is as follows:
Refering to attached drawing 8, from the angle of vehicle braking, this vehicle braking by grades process is divided into six stages:
First stage: since AEB system issues light brake signal, until active brake system starts to generate vehicle gently
Until spending braking deceleration, phase duration t11It is determined, can be led to by the response lag time of the active brake system of vehicle
Overtesting is tested to obtain.In this stage, since braking system does not set up brake pressure, vehicle deceleration 0, the position of this vehicle
It moves are as follows:
S11=v0t11
In formula: v0For this vehicle initial speed；
Second stage: the stage starts pressurization for starting point, until brake pressure reaches light brake with active brake system
Until target hydraulic power, pressurization time t12；During this, with the increase of brake pressure, vehicle braking deceleration a1, speed
v12And displacement S12Expression formula difference is as follows:
v12=v0-∫a1·dt
In formula: a1maxFor light brake phase targets braking deceleration；
Phase III: the stage is the AEB system stable light brake stage, and vehicle keeps a1maxSeverity of braking not
Become, the duration t of the process13, by AEB default, can be taken as 1~2s, in the process this vehicle speed v13And displacement S13
Change as follows:
v13=v0-0.5a1maxt12-a1maxt
Fourth stage: the stage since AEB system issues urgent all one's effort brake signal, until active brake system starts to increase
Until pressure, in this stage, this vehicle still keeps a1maxBraking deceleration it is constant, the stage response lag duration t21=
t11；This stage speed v21And displacement S21It is respectively as follows:
v21=v0-a1max(0.5t12+t13)-a1maxt
5th stage: the stage is the pressurization stages of brake fluid system, and active brake pressure is by light brake target value
Rise to all one's effort braking target value, pressurization time t22；At this stage, this vehicle deceleration a2, speed v22And displacement S22Expression formula difference
It is as follows:
v22=v0-a1max(0.5t12+t13+t21)-∫a2dt
In formula: a2maxFor all one's effort deboost phase target braking deceleration；
6th stage: stable all one's effort deboost phase, the stage since this vehicle brake pressure reaches target braking pressure,
0 is down to this vehicle speed or is down to risk object vehicle speed vtUntil；In this stage, this vehicle initial speed v2, the duration
t23, real-time speed v23And braking distance S23Expression formula difference is as follows:
v2=v0-a1max(0.5t12+t13+t21+0.5t22)-0.5a2maxt22
v23=v2-a2max·t
So far, this vehicle braking by grades calculating process terminates；
Since there are the possibility of emergency braking for danger ahead target vehicle, under this operating condition, this vehicle will be tight simultaneously with two vehicles
The spacing suddenly braked without collision is the safety distance threshold of light brake, therefore moves braking system system with all strength to this car owner
Dynamic process is calculated:
The process is divided into three phases:
First stage: for active brake system response lag phase, in this process, the distance S of this vehicle traveling1Are as follows:
S1=v0·t1
Second stage: for active brake pressure establishment stage, active brake pressure is linearly increasing in the stage, the braking of this vehicle
Deceleration a, this vehicle speed v1And S is moved in this parking stall2Expression formula be respectively as follows:
v1=v0-∫adt
In formula: t2For active brake system pressure settling time, a2maxFor the target braking deceleration of this vehicle；
Phase III: the stage even deceleration of this vehicle is up to parking, in this stage, this vehicle initial speed v30, real-time speed
v3, duration t3And braking distance S3It is respectively as follows:
v3=v30-a2max·t
Tri- warning grade safety distance threshold S of AEB are carried out beloww Sd SbCalculating:
1. all one's effort braking threshold Sb:
By this vehicle braking by grades process it is found that when risk object vehicle in front is with speed vtRemain a constant speed traveling, this Che Quan
When dynamic braking is decelerated to danger ahead target carriage speed, the braking distance S of this vehicleh1_mAre as follows:
Sh1_m=S21+S22+S23
During being somebody's turn to do, the distance S of danger ahead target carriage travelingt1Are as follows:
St1=vt(t21+t22+t23)
If the target range that can be kept with front truck is S after vehicle completes automatic emergency brake0, then AEB system trigger
The safe distance threshold value S of braking with all strengthbAre as follows:
Sb=Sh1_m-St1+S0
2. light brake safety distance threshold Sd:
When front, risk object vehicle is braked suddenly with maximum deceleration, braking distance St2Are as follows:
This vehicle also uses all one's effort brake model calculated braking distance, the braking distance S that this vehicle is braked with all strengthh2_mAre as follows:
Sh2_m=S1+S2+S3
Then AEB system light brake safe distance threshold value SdAre as follows:
Sd=Sh2_m-St2+S0
3. audiovisual early warning security distance threshold Sw:
Sw=Sd+tw·v0
T in formulawFor sound sensation, vision early warning duration, 1~1.5s can be taken as.
AEB system is light in the Calculation of Safety Distance model it can be seen from above-mentioned AEB warning grade secure threshold
Degree braking deceleration is 0.3 μ g, and it is that the information of road surface estimates that model calculates that the braking deceleration of braking, which is 0.8 μ g, μ, with all strength
Four wheel coefficient of road adhesion minimum values of the vehicle at this time arrived, therefore the present invention can be according to actual minimum road surface attachment system
Number μ, real-time online change light brake and with all strength braking deceleration value, prevent AEB system carry out automatic braking when, road surface without
Method provides preset braking deceleration and the danger that collides.Meanwhile the Calculation of Safety Distance model also can be according at this time
Coefficient of road adhesion adjusts threshold value of warning at different levels, prevents AEB system from causing danger too late.
In step (4), information of road surface estimation model is estimated based on the road gradient algorithm for estimating of closed loop omnidirectional vision
The method for calculating the vehicle gradient i on locating road surface at this time is as follows:
Vehicle parameter needed for road gradient estimation is passed to information of road surface estimation model, vehicle by the related sensor of vehicle
Equation formula is Ft=Ff+Fw+Fi+Fj；
Vehicle drive force FtAre as follows:
Wherein r is vehicle wheel roll radius, TeFor vehicle engine torque, igFor transmission for vehicles transmission ratio, i0For vehicle master
Retarder, ηtFor vehicle drive system gross efficiency；
Vehicle air resistance FwAre as follows:
Wherein CDFor coefficient of air resistance, A is front face area, and ρ is atmospheric density, vxFor vehicular longitudinal velocity；
Vehicle acceleration resistance FjAre as follows:
Fj=δ Max
Wherein δ is vehicle correction coefficient of rotating mass；M is complete vehicle quality, axFor longitudinal acceleration of the vehicle；
Vehicle rolling resistance FfAre as follows:
Ff=Mgfcos α ≈ Mgf
Wherein f is rolling resistance coefficient of vehicle；
The grade resistance F of vehicleiAre as follows:
Fi=Mgsin α ≈ Mgi
Then the longitudinal dynamics equation of vehicle on the slope can be written as:
The vehicle related parameters being related in above-mentioned calculating process are ginseng readily available in Car design manufacturing process
Number.For application state observer, linearization process need to be carried out to the longitudinal dynamics equation, by vehicle drive force Ft, air
Resistance FwWith rolling resistance FfRegard a resultant force F asinputLongitudinal dynamics equation is inputted as system, then longitudinal dynamics equation
Simplify are as follows:
δ Ma=Finput-Mgi
Above formula is system state equation, and the state-space expression of system can be written based on system state equation；With vehicle
Longitudinal velocity vxWith gradient i as system state variables, with resultant force FinputFor system input variable, then automobile longitudinal power is based on
Learn the state-space expression of equation are as follows:
Z=Cx
Wherein:C=[1 0]；
System ornamental, the observability matrix of the system are verified below are as follows:
Observability matrix QBFull rank illustrates the system Observable, therefore designs road gradient observer are as follows:
WhereinFor the observation vector of observer,H is the feedback gain matrix of observer；E be error to
Amount, i.e.,
It willWithSubtracting each other can obtain:
IfFor j, then above formula becomesIt is converted into the linear first-order differential equation about j, if initially
Moment is t0, Solutions of Ordinary Differential Equations are as follows:
In order to guarantee observer stability, should ensure that when the time tending to be infinite, observer error vector e is equal to 0, it may be assumed that
As long as the characteristic root λ of (A-HC) is made to have negative real part, error vector will decay to 0 with exponential law, and decay
Speed is determined by the characteristic root of (A-HC)；If the feedback gain matrix of observerThe then characteristic equation of (A-HC) are as follows:
The expectation pole of default full micr oprocessorism is to have (A-HC) characteristic root of negative real part for λ1And λ2, λ1And λ2It can be by
The user of this system voluntarily selects, as long as guaranteeing it with negative real part, it is expected that pole λ1And λ2Corresponding desired character side
Journey are as follows:
(λ-λ1)(λ-λ2)=λ2-(λ1+λ2)λ+λ1λ2 (5)
Enable the element of formula (4) and the equal feedback gain matrix H for obtaining observer of the corresponding term coefficient of formula (5) are as follows:
h1=-λ1-λ2
Feedback gain matrix H is calculated according to the method described above can guarantee eigenvalue λ1And λ2With negative real part, even if seeing
Device is surveyed to stablize；Omnidirectional vision observational variableIn the second row elementAs vehicle at this time estimate by locating road gradient
Evaluation i.
Refering to attached drawing 10, the road gradient algorithm for estimating based on closed loop omnidirectional vision is verified,
By attached drawing it is found that road gradient estimated value can be completed to restrain in a very short period of time, and substantially with the practical reference of road gradient
Value is consistent, and error is minimum, illustrates the road gradient algorithm for estimating of the present invention based on closed loop omnidirectional vision
Functional, road gradient estimating speed and estimated accuracy are high, meet vehicle grade algorithm requirement.
Claims (5)
1. a kind of grading automatical emergency braking control system of the electric vehicle of adjust automatically, it is characterised in that: the control system
It include: vehicle-mounted ranging and range rate sensing equipment, grading forewarning system control system, Calculation of Safety Distance model, vehicle against longitudinal dynamics
Computation model, hydraulic braking force and regenerative braking force distribute computing module, brake fluid system inversion model, ESC and Booster master
Dynamic boost fluid pressure distribution module and information of road surface estimate model, and the control method of system is as described below:
(1) in the process of moving, vehicle-mounted ranging and range rate sensing equipment passes to Ben Che and risk object real-time range S point to vehicle
The movement state informations such as the speed of risk object vehicle, acceleration are passed to Calculation of Safety Distance model by grade pre-alarming control system；
Information of road surface estimates that the minimum coefficient of road adhesion μ in four wheels is passed to Calculation of Safety Distance model by model, by vehicle
Locating road gradient passes to vehicle against longitudinal dynamics computation model at this time；
(2) severity of braking of AEB system is divided into light brake and with all strength two kinds of braking by the Calculation of Safety Distance model, according to
Minimum coefficient of road adhesion μ in four wheels determines the target braking deceleration a of the two staged braking intensity of AEB systemexp, and
This vehicle measured according to risk object movement state information and this vehicle vehicle speed sensor longitudinal speed v in real time0, determine AEB system
Three control signal activation threshold values of system: sense organ early warning security distance threshold Sw, light brake safety distance threshold Sd, with all strength
The safety distance threshold S of brakingb；
(3) grading forewarning system control system is by Ben Che and risk object real-time range S and threshold value of warning S at different levelsw、Sd、SbSize into
Row compares, and whether has the deceleration-operation of active in conjunction with driver, it is longitudinal that analysis generates AEB early warning control signal, that is, vehicle target
Deceleration aexpIf: S > Sw, then vehicle and leading vehicle distance are in safe range at this time for system judgement, without actuation；If Sw< S
< Sd, then system can carry out visual tactile early warning to driver, and driver is reminded to carry out deceleration-operation；If Sb< S < Sd, and root
According to the judgement of brake-pedal-travel sensor signal, driver does not carry out deceleration-operation at this time, then system will control vehicle and carry out slightly
It brakes, at this time target longitudinal deceleration aexpFor light brake deceleration a1max；If driver has deceleration-operation, AEB system at this time
System can carry out sense organ early warning to driver until driver makes spacing be maintained at safe distance；If S < Sb, then system can control vehicle
Carry out all one's effort braking, make vehicle with most fast speed in preset minimum safe spacing range S0Interior stagnation of movement reaches and danger
The identical speed of target, at this time target longitudinal deceleration aexpFor all one's effort braking deceleration a2max；
(4) grading forewarning system control system is by target longitudinal deceleration aexpPass to vehicle against longitudinal dynamics computation model, simultaneously
Information of road surface estimation model calculates vehicle and the gradient i on locating road surface and passes it to vehicle at this time and calculate against longitudinal dynamics
Model is calculated when vehicle driving up by formula (1), is calculated when vehicle descending by formula (2), is finally obtained vehicle braking at this time
The target braking force F provided needed for systemb:
Fb=maexp-Ff-Fw-Gi (1)
Fb=maexp+Gi-Ff-Fw (2)
Wherein m is vehicle mass, and G is vehicle weight, FfFor rolling resistance, FwFor air drag；
(5) target braking force FbIt passes to hydraulic braking force and regenerative braking force distributes computing module, while regeneration brake system root
The working condition of the systems such as speed, vehicle power motor, battery when accordingly judges the regenerative braking force F that can be provided at this timebr,
The hydraulic braking force and regenerative braking force distribution computing module calculate target hydraulic braking force F at this timebh=Fb-Fbr；
(6) target hydraulic braking force FbhIt passes to brake fluid system inversion model to be calculated, each brake is obtained by formula (3)
The target hydraulic power P of wheel cylinderexp:
Wherein rr0For vehicle wheel roll radius, [BEF]fFor front wheel brake braking effectiveness factor, [BEF]rFor rear wheel brake braking
Efficiency factor is the conventional structure parameter of vehicle；
(7) the target hydraulic power P of each wheel-braking cylinderexpESC and Booster active boost hydraulic coupling distribution module are passed to, is sentenced
The active boost mode of disconnected brake fluid system at this time: if PexpIn the active boost limit range of ESC system, then by ESC system
System builds pressure in wheel-braking cylinder and carries out active boost control；If PexpBe more than the active boost limit of ESC system, then by
Booster builds pressure in wheel cylinder and carries out active boost control, makes vehicle deceleration to target vehicle speed；
The above process constantly carries out during entire AEB system trigger, with risk object real-time range, threshold value of warning at different levels,
The information such as this vehicle and risk object motion state constantly update adjustment, until vehicle deceleration to target vehicle speed or parking and with danger
Target is maintained at preset safe distance.
2. a kind of grading automatical emergency braking control system of the electric vehicle of adjust automatically according to claim 1, feature
Be: in step (1), information of road surface estimates that minimum is calculated by the coefficient of road adhesion algorithm for estimating based on filtering in model
The method of coefficient of road adhesion μ is as follows:
Dugoff tire model is initially set up to obtain normalization tire force, Dugoff tire model by tire longitudinal force and
Lateral force indicates are as follows:
F in formulaxFor longitudinal force of tire, FyFor side force of tire, FzFor tire normal force, λ is straight skidding rate, CyFor Wheel slip
Rigidity, CxFor longitudinal tire stiffness, α is slip angle of tire, and μ is coefficient of road adhesion, and ε is tire effect coefficient, be one with
The related parameter of tire structure and material itself, to correct influence of the vehicle slip speed to tire force, L is boundary value, is used
To describe the nonlinear characteristic of wheelslip bring tire force；It, will in order to facilitate design coefficient of road adhesion algorithm for estimating
Dugoff tire model is reduced to following normalizing forms:
In formulaWithNormalization tire force respectively longitudinally and laterally, it is unrelated with attachment coefficient μ, convenient for described based on filter
The coefficient matrix of system state space expression formula is determined in the coefficient of road adhesion algorithm for estimating of wave；
It needs to calculate the normalization tire force in model after establishing normalization Dugoff tire model, that is, calculates separately each
The vertical load F of wheelZfl、FZfr、FZrl、FZrr, straight skidding rate λfl、λfr、λrl、λrr, slip angle of tire αfl, αfr, αrl, αrr,
Footmark fl, fr, rl, rr respectively represent the near front wheel, off-front wheel, left rear wheel, the off hind wheel of vehicle；
Each wheel vertical load calculation formula:
Each tyre slip angle calculation formula:
The calculation formula of each wheel slip:
The speed v of each ground contact point in above formulafl、vfr、vrl、vrrCalculation formula are as follows:
Each meaning of parameters in above-mentioned each formula are as follows: β is vehicle centroid side drift angle,vcogFor vehicle centroid speed,M is complete vehicle quality, and a is the horizontal distance at front-wheel center and vehicle centroid, and b is rear-wheel center and vehicle matter
The horizontal distance of the heart, l are vehicle wheelbase, hgFor vehicle centroid height, axFor longitudinal acceleration of the vehicle, ayLaterally accelerate for vehicle
Degree, TfFor two front wheels wheelspan, TrFor two rear tracks, vxFor vehicle longitudinal velocity, vyVehicle side velocity, RωFor wheel rolling
Speed, ωfl、ωfr、ωrl、ωrrThe respectively rate of roll of the near front wheel, off-front wheel, left rear wheel, off hind wheel；
Therefore longitudinal normalization tire force of four wheels may be expressed as:
The lateral normalization tire force of four wheels may be expressed as:
The parameter needed in calculating process in conjunction with the modified normalization tire force of Dugoff tire model includes vehicle structure ginseng
Several and two class of kinematics parameters, structural parameters can directly measure to obtain, and kinematics parameters can be measured by related onboard sensor；
In order to arrange the system state equation and observational equation write in the algorithm for estimating of the coefficient of road adhesion based on filtering, need to establish
The Three Degree Of Freedom four-wheel car model of vehicle describes longitudinal direction of car, lateral and weaving；
Longitudinal direction of car, lateral and three directions of sideway the equation of motion are as follows:
In formula, axFor longitudinal acceleration of the vehicle, ayFor vehicle lateral acceleration,For the sideway angular acceleration of vehicle, before δ is two
Corner is taken turns, m is complete vehicle quality, μfl、μfr、μrl、μrrRoad surface attachment respectively where the near front wheel, off-front wheel, left rear wheel, off hind wheel
Coefficient, a are the horizontal distance at front-wheel center and vehicle centroid, and b is the horizontal distance at rear-wheel center and vehicle centroid, TfBefore two
Take turns wheelspan, TrFor two rear tracks, IzThe yaw rotation inertia of vehicle；
Vehicle kinematics parameter needed for related sensor measurement on vehicle obtains algorithm simultaneously passes it to Dugoff tire
Model, Dugoff tire model, which is calculated, longitudinally and laterally to be normalized tire force and passes it to vehicle Three Degree Of Freedom four-wheel
Model, so that longitudinally, laterally three kinetics equations with yaw direction are obtained, it is available based on these three kinetics equations
The system state equation and observational equation of coefficient of road adhesion filter estimator, to carry out subsequent attachment coefficient estimation；
The system that the coefficient of road adhesion algorithm for estimating based on filtering is chosen is nonlinear system, state equation and sight
Equation is surveyed to be respectively as follows:
State equation:
Observational equation:
Y (t)=h (x (t), u (t), v (t))
Stochastic variable w (t), v (t) are respectively process noise and measurement noise, in the coefficient of road adhesion based on filtering
Mutual independence is taken as in algorithm for estimating and white Gaussian noise that mean value is zero, probability distribution are as follows:
P (w)~N (0, Q) p (v)~N (0, R)
And the covariance matrix for setting them is respectively Q and R, it may be assumed that
Q=cov [w (t), w (τ)]
R=cov [v (t), v (τ)]
The coefficient of road adhesion algorithm for estimating based on filtering the specific implementation process is as follows:
(1) system state equation and observational equation are determined:
It is longitudinally, laterally determined with the equation of motion of yaw direction according to the Three Degree Of Freedom four-wheel car model:
State variable: x (t)=[μfl μfr μrl μrr]T,
Observational variable: y (t)=[ax ay r]T,
Control input: u (t)=[δ],
According to the Three Degree Of Freedom four-wheel car model longitudinally, laterally with the expression formula of the equation of motion of yaw direction and each change
Amount, state equation and observational equation are write as:
State equation:
Observational equation:
Wherein:
(2) estimator assigns initial value:
Initial value in recursive process, measurement noise covariance battle array R are 3 unit matrix for multiplying 3, and process noise covariance battle array Q multiplies 4 for 4
Unit matrix；
(3) coefficient of road adhesion is estimated using filtering algorithm:
1. it is filtered initialization, iterative steps k=0, then the initial mean value of state variable xIt is respectively as follows: with covariance P (0)
2. 2n+1 point is calculated using Unscented transform, the mean value of this 2n+1 point and covariance is made to be equal to original state distribution
Mean value and covariance, this 2n+1 point are referred to as Sigma point, and n takes n=4, then here according to above-mentioned state equation for state dimension
Need to obtain 9 Sigma point sets, the state dimension of each Sigma point set is consistent with the dimension of state variable x, as 4 dimension column to
Amount, the then matrix for being 4*9 by the matrix χ that this 9 Sigma point sets form；If χi(0) in iterative steps k=0 in matrix χ
Each column vector, that is, each Sigma point set, χ0(0) first row for being Sigma matrix χ, and so on, then initial Sigma
Matrix are as follows:
Wherein λ is scale parameter, λ=(α2- 1) n=4 (α2- 1), α is used to determine Sigma point in state variable mean valueNear
Distribution, be the positive number 10 of a very little-4≤ α≤1, usually takes α=10-3；
The calculating process of initial Sigma point set when above-mentioned iterative steps k=0, is equally applicable to the Sigma of other iterative steps
Point set calculates, and is used for state variable mean value and the state variable covariances value that Sigma point set calculates at this time only as the k moment
Value, i.e. k momentWith P (k | k), calculation formula are as follows:
3. entering next step iteration, iterative steps k increases by 1, carries out next step prediction to 9 Sigma point sets, 2. step is obtained
Previous step Sigma point set substitute into system state equation (a) in, obtain transformed Sigma point set:
χ (k | k-1)=f (χ (k-1), u (k-1)) k=1,2 ...
4. according to Unscented transform principle, the next step prediction mean value and prediction variance of computing system state variable, system mode
The prediction mean value of variable is obtained by the predicted value weighted sum of Sigma point set:
W in formulai (m)For the mean value weight of each Sigma point, specific value are as follows:
Wi (m)=1/ [2 (4+ λ)] i=1,2 ..., 8
The prediction variance of system state variables is summed to obtain by the prediction covariance-weighted of Sigma point set:
χ in above formulai(k | k-1) is the i-th column of matrix χ (k | k-1), i=0,1 ..., 8；Wi (c)It is weighed for the covariance of each Sigma point
Weight, specific value are as follows:
Wi (c)=1/ [2 (n+ λ)] i=1,2 ..., 8
Wherein β is used to be associated with the prior state of off status variable x distribution, and the coefficient of road adhesion based on filtering is estimated to calculate
State variable x Gaussian distributed, usually takes β=2 in method；
5. being utilized again according to the next step prediction mean value and next step prediction variance of 4. system state variables that step obtains
Unscented transform, generates new Sigma point set, calculating process and step 2. in the initial calculating process of Sigma point set it is identical:
6. 5. new Sigma point set that step is obtained brings the next step that observational equation (b) obtains the observed quantity of Sigma point set into
Predicted value:
U (k-1) in formula is that the system that iterative steps are the k-1 moment inputs, i.e., front wheel angle δ at this time can be by existing
Vehicle-mounted rotary angle transmitter measures；
By observational equation (b) it is found that the output matrix of system is 3 matrixes for multiplying 4, Sigma matrix χ (k | k-1) is 4 squares for multiplying 9
Battle array, therefore it is observed that the next step predicted value of the observed quantity of Sigma point set is calculated in equation (b)The square for multiplying 9 for 3
Battle array；
7. by the observed quantity next step predicted value of 6. Sigma point set that step obtainsSystem is obtained by weighted sum
The prediction mean value of observational variable:
In formulaIt is the next step prediction value matrix of the observed quantity of Sigma point setI-th column, i=0,
1,…,8；
8. by the observed quantity next step predicted value of 6. Sigma point set that step obtainsAnd systematic observation variable is pre-
Survey mean valueUpdated observation covariance matrix and state variable are calculated by weighted sum and output variable is mutual
Correlation matrix:
Observe covariance matrix are as follows:
State variable and output variable cross-correlation matrix are as follows:
9. the updated filtering feedback gain matrix of computing system:
The updated state variable Mean Matrix of computing system:
Matrix(1,1) element, (2,1) element, (3,1) element, (4,1) element be respectively iterative steps be k when a left side
The filtering estimated value of front-wheel attachment coefficient, off-front wheel attachment coefficient, left rear wheel attachment coefficient, off hind wheel attachment coefficient；
Actual observational variable value at the time of be k that y (k) in formula is iterative steps, i.e. iterative steps are that the longitudinal of k moment accelerates
Spend ax, side acceleration ayAnd sideway angular accelerationThree above vehicle kinematics parameter can be by the phase assembled on vehicle
It closes acceleration transducer and carries out real-time measurement, then pass to the coefficient of road adhesion algorithm for estimating based on filtering；
The updated state variable covariances value of computing system:
P (k | k)=P (k | k-1)-K (k) PyyKT(k)
State variable mean value after system updateWith state variable covariances value P (k | k) 2. return step generated next
Group Sigma point set, starts the calculating of next iterative steps；The above process will be repeated constantly, until completing all iterative steps,
Finally obtain the coefficient of road adhesion μ of four wheels；Four wheel attachment coefficient values are compared and are minimized, then by this
Minimum value μ passes to the Calculation of Safety Distance model.
3. a kind of grading automatical emergency braking control system of the electric vehicle of adjust automatically according to claim 1, feature
It is: in step (2), the target braking deceleration a of the light brake of AEB system1max=0.25 μ g of μ g~0.35 is braked with all strength
Target braking deceleration a2max=0.75 μ of μ g~0.85 g；The three-level early warning security distance threshold calculation method of AEB system is such as
Under:
From the angle of vehicle braking, this vehicle braking by grades process is divided into six stages:
First stage: since AEB system issues light brake signal, until active brake system starts that vehicle is made to generate slight system
Until dynamic deceleration, phase duration t11For the response lag time of the active brake system of vehicle, in this stage, due to
Braking system does not set up brake pressure, vehicle deceleration 0, the displacement of this vehicle are as follows:
S11=v0t11
In formula: v0For this vehicle initial speed；
Second stage: the stage starts pressurization for starting point, until brake pressure reaches the target of light brake with active brake system
Until hydraulic coupling, pressurization time t12；During this, with the increase of brake pressure, vehicle braking deceleration a1, speed v12And
It is displaced S12Expression formula difference is as follows:
v12=v0-∫a1·dt
In formula: a1maxFor light brake phase targets braking deceleration；
Phase III: the stage is the AEB system stable light brake stage, and vehicle keeps a1maxSeverity of braking it is constant, the mistake
The duration t of journey13, by AEB default, can be taken as 1~2s, in the process this vehicle speed v13And displacement S13Variation is such as
Under:
v13=v0-0.5a1maxt12-a1maxt
Fourth stage: the stage is since AEB system issues urgent all one's effort brake signal, until active brake system starts pressurization is
Only, in this stage, this vehicle still keeps a1maxBraking deceleration it is constant, the stage response lag duration t21=t11；This
Stage speed v21And displacement S21It is respectively as follows:
v21=v0-a1max(0.5t12+t13)-a1maxt
5th stage: the stage is the pressurization stages of brake fluid system, and active brake pressure is risen to by light brake target value
All one's effort braking target value, pressurization time t22；At this stage, this vehicle deceleration a2, speed v22And displacement S22Expression formula is respectively such as
Under:
v22=v0-a1max(0.5t12+t13+t21)-∫a2dt
In formula: a2maxFor all one's effort deboost phase target braking deceleration；
6th stage: stable all one's effort deboost phase, the stage is since this vehicle brake pressure reaches target braking pressure, until sheet
Vehicle speed is down to 0 or is down to risk object vehicle speed vtUntil；In this stage, this vehicle initial speed v2, duration t23, it is real
When speed v23And braking distance S23Expression formula difference is as follows:
v2=v0-a1max(0.5t12+t13+t21+0.5t22)-0.5a2maxt22
v23=v2-a2max·t
So far, this vehicle braking by grades calculating process terminates；
Since there are the possibility of emergency braking for danger ahead target vehicle, under this operating condition, this vehicle will be made so that two vehicles are urgent simultaneously
The dynamic spacing without collision is the safety distance threshold of light brake, therefore moves braking system to this car owner and braked with all strength
Cheng Jinhang is calculated:
The process is divided into three phases:
First stage: for active brake system response lag phase, in this process, the distance S of this vehicle traveling1Are as follows:
S1=v0·t1
Second stage: for active brake pressure establishment stage, active brake pressure is linearly increasing in the stage, this vehicle braking deceleration
Spend a, this vehicle speed v1And S is moved in this parking stall2Expression formula be respectively as follows:
v1=v0-∫adt
In formula: t2For active brake system pressure settling time, a2maxFor the target braking deceleration of this vehicle；
Phase III: the stage even deceleration of this vehicle is up to parking, in this stage, this vehicle initial speed v30, real-time speed v3, hold
Continuous time t3And braking distance S3It is respectively as follows:
v3=v30-a2max·t
Tri- warning grade safety distance threshold S of AEB are carried out beloww Sd SbCalculating:
1. all one's effort braking threshold Sb:
By this vehicle braking by grades process it is found that when risk object vehicle in front is with speed vtRemain a constant speed traveling, this vehicle is braked with all strength
When being decelerated to danger ahead target carriage speed, the braking distance S of this vehicleh1_mAre as follows:
Sh1_m=S21+S22+S23
During being somebody's turn to do, the distance S of danger ahead target carriage travelingt1Are as follows:
St1=vt(t21+t22+t23)
If, can be with target range, that is, minimum safe spacing range S of front truck holding after vehicle completes automatic emergency brake0, S0
For 2~3m, then the safe distance threshold value S that AEB system trigger is braked with all strengthbAre as follows:
Sb=Sh1_m-St1+S0
2. light brake safety distance threshold Sd:
When front, risk object vehicle is braked suddenly with maximum deceleration, braking distance St2Are as follows:
This vehicle also uses all one's effort brake model calculated braking distance, the braking distance S that this vehicle is braked with all strengthh2_mAre as follows:
Sh2_m=S1+S2+S3
Then AEB system light brake safe distance threshold value SdAre as follows:
Sd=Sh2_m-St2+S0
3. audiovisual early warning security distance threshold Sw:
Sw=Sd+tw·v0
T in formulawFor sound sensation, vision early warning duration, 1~1.5s can be taken as.
4. a kind of grading automatical emergency braking control system of the electric vehicle of adjust automatically according to claim 1, feature
It is: in step (3), minimum safe spacing range S0For 2~3m.
5. a kind of grading automatical emergency braking control system of the electric vehicle of adjust automatically according to claim 1, feature
Be: in step (4), information of road surface estimates the road gradient algorithm for estimating estimation in model based on closed loop omnidirectional vision
The method of the vehicle gradient i on locating road surface at this time is as follows out:
Vehicle parameter needed for road gradient estimation is passed to information of road surface estimation model, vehicle row by the related sensor of vehicle
Sailing equation is Ft=Ff+Fw+Fi+Fj；
Vehicle drive force FtAre as follows:
Wherein r is vehicle wheel roll radius, TeFor vehicle engine torque, igFor transmission for vehicles transmission ratio, i0For the main deceleration of vehicle
Device, ηtFor vehicle drive system gross efficiency；
Vehicle air resistance FwAre as follows:
Wherein CDFor coefficient of air resistance, A is front face area, and ρ is atmospheric density, vxFor vehicular longitudinal velocity；
Vehicle acceleration resistance FjAre as follows:
Fj=δ Max
Wherein δ is vehicle correction coefficient of rotating mass；M is complete vehicle quality, axFor longitudinal acceleration of the vehicle；
Vehicle rolling resistance FfAre as follows:
Ff=Mgfcos α ≈ Mgf
Wherein f is rolling resistance coefficient of vehicle；
The grade resistance F of vehicleiAre as follows:
Fi=Mgsin α ≈ Mgi
Then the longitudinal dynamics equation of vehicle on the slope can be written as:
By vehicle drive force Ft, air drag FwWith rolling resistance FfRegard a resultant force F asinput, as longitudinal direction of car kinetic simulation
The system of type inputs, then longitudinal dynamics equation simplification are as follows:
δ Ma=Finput-Mgi
Above formula is system state equation, and the state-space expression of system can be written based on system state equation；With longitudinal direction of car
Speed vxWith gradient i as system state variables, with resultant force FinputFor system input variable, then vehicle overall design side is based on
The state-space expression of journey are as follows:
Z=Cx
Wherein:C=[1 0]；
The observability matrix of the system are as follows:
Observability matrix QBFull rank illustrates the system Observable, therefore designs road gradient observer are as follows:
WhereinFor the observation vector of observer,H is the feedback gain matrix of observer；E is error vector, i.e.,
It willWithSubtracting each other can obtain:
IfFor j, then above formula becomesIt is converted into the linear first-order differential equation about j, if initial time
For t0, then Solutions of Ordinary Differential Equations are as follows:
In order to guarantee observer stability, should ensure that when the time tending to be infinite, observer error vector e is equal to 0, it may be assumed that
As long as the characteristic root λ of (A-HC) is made to have negative real part, error vector will decay to 0, and the rate of decay with exponential law
It is determined by the characteristic root of (A-HC)；If the feedback gain matrix of observerThe then characteristic equation of (A-HC) are as follows:
The expectation pole of default full micr oprocessorism is to have (A-HC) characteristic root of negative real part for λ1And λ2, it is expected that pole λ1And λ2It is right
The desired character equation answered are as follows:
(λ-λ1)(λ-λ2)=λ2-(λ1+λ2)λ+λ1λ2 (5)
Enable the element of formula (4) and the equal feedback gain matrix H for obtaining observer of the corresponding term coefficient of formula (5) are as follows:
h1=-λ1-λ2
Feedback gain matrix H is calculated according to the method described above can guarantee eigenvalue λ1And λ2With negative real part, can make to observe
Device is stablized；Omnidirectional vision observational variableIn the second row elementThe as estimation of vehicle locating road gradient at this time
Value i.
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