CN107168104A - Pure electric intelligent automobile longitudinal method for controlling driving speed based on observer - Google Patents

Pure electric intelligent automobile longitudinal method for controlling driving speed based on observer Download PDF

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CN107168104A
CN107168104A CN201710483937.2A CN201710483937A CN107168104A CN 107168104 A CN107168104 A CN 107168104A CN 201710483937 A CN201710483937 A CN 201710483937A CN 107168104 A CN107168104 A CN 107168104A
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speed
torque
formula
moment
drive shaft
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CN107168104B (en
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胡云峰
韩振宇
朱大吉
陈虹
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Jilin University
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Jilin University
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    • GPHYSICS
    • G05CONTROLLING; REGULATING
    • G05BCONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B17/00Systems involving the use of models or simulators of said systems
    • G05B17/02Systems involving the use of models or simulators of said systems electric

Abstract

A kind of pure electric intelligent automobile longitudinal method for controlling driving speed based on observer, belongs to technical field of automobile control.The purpose of the present invention is to design controller using the rolling time horizon system optimizing control based on observer, go out the torque of operator demand by controller optimization, then it is driven and braking torque distribution, so as to realize the pure electric intelligent automobile longitudinal method for controlling driving speed based on observer that longitudinal speed is effectively controlled.The present invention realizes Matlab/Simulink and AMESim associative simulation, the interface module communicated with simulink is added in AMESim interfaces, after System build, model information in AMESim is retained in Simulink in the form of S function, so as to realize both associative simulation and communication.Present invention is generally directed to pure electric intelligent automobile longitudinal speed control problem, observer is designed for the important parameter of system, rolling time horizon system optimizing control can be good at completing on-line optimization solution, while processing constraint that can be dominant.

Description

Pure electric intelligent automobile longitudinal method for controlling driving speed based on observer
Technical field
The invention belongs to technical field of automobile control.
Background technology
In order to reduce the generation of traffic accident, reduction internal-combustion engines vehicle is adjoint to energy resource consumption and the influence of environmental pollution The development of internet, information, electronics and intellectual technology, the intellectuality of automobile and motorized technology, which have become, solves above-mentioned ask The effective way of topic.In recent years, the famous internet such as the well-known automobile manufacturing enterprise such as masses, BMW, Audi and Baidu, Google Enterprise, all in continuous increase to the manpower of intelligent driving automotive field, the input of financial resources, to seize the forward position skill of intelligent driving Art.The development of intelligent driving technology will necessarily lead the major transformation of an automobile industry new round.Longitudinal speed control is used as pure electricity The bottom control algorithm of dynamic intelligent automobile, its control effect directly affects the property such as security and ride comfort of intelligent automobile Energy.For pure electric intelligent automobile, due to the fast response time of motor, the moment of torsion and rotating speed of motor are easily obtained, and this is pure electricity Longitudinal speed control of dynamic intelligent automobile provides good basic condition.It is directed to centralized pure electric intelligent automobile longitudinal car Speed control, mainly there is problems with:
1. the control of intelligent automobile longitudinal direction speed, i.e., reasonably produce drive demand torque by designing controller(Including driving Torque and braking moment), so as to realize the tracing control of longitudinal speed.
2. pure electric intelligent automobile longitudinal vehicle speed control system has non-linear.Meanwhile, the output of controller, which will be met, holds The actual maximum output torque of the firm constraints of row device motor and brake, i.e. driving and braking moment signal no more than motor With the maximum braking moment of brake.
3. electric automobile needs electrical source of power, existing frequently-used is lithium battery group, to be powered to motor, the power supply electricity of motor Pressure also contributes to the maximum output torque of motor, therefore also must be considered that battery pack is defeated when considering the maximum output torque of motor It is a change constraint to go out the actual maximum output torque of the influence of voltage, i.e. motor.
4. systematic parameter can not be surveyed, complete vehicle quality is as the important parameter of influence system model, and it is with rider's How many and body weight and change, and it can not simultaneously be surveyed.
The content of the invention
The purpose of the present invention is to design controller using the rolling time horizon system optimizing control based on observer, passes through control The torque of device optimization operator demand processed, is then driven and braking torque distribution, so as to realize that longitudinal speed is effectively controlled The pure electric intelligent automobile longitudinal method for controlling driving speed based on observer of system.
The present invention realizes Matlab/Simulink and AMESim associative simulation,
1. the environmental variance of PC computers is must be provided with, makes both interrelated;
2. the interface module communicated with simulink is added in AMESim interfaces, by between Matlab/Simulink and AMESim The variable of communication is needed to be connected to this module;
3. pass through System build after, the model information in AMESim to be retained in the form of S-function in Simulink, So as to realize both associative simulation and communication.
The present invention step be:
First, centralized electric vehicle simulation model buildings:
Electric vehicle simulation model includes Electric drive module, transmission module, tire module and longitudinal direction of car dynamics, vehicle mould Shape parameter such as table one
The electric automobile parameter list of table one
2nd, the rolling time horizon optimal controller based on observer:
2.1 Controller-orienteds, which design a model, to be built
2.1.1 longitudinal vehicle dynamic model
Travel longitudinal stress in the case where not considering cross force, on vehicle sloping route has according to Newton's second law:
(1)
Wherein:For driving quality,For driving force,For running resistance;Including air drag, pavement friction resistance Power, grade resistanceAnd mechanical braking force;
Car weightWith driving qualityRelation is represented with formula following formula:
(2)
WhereinFor the inertia of a wheel,For radius of wheel;
The automobile travelled on the road of slope is by grade resistanceFor:
(3)
WhereinFor acceleration of gravity;
The air drag that the automobile travelled on road surface is subject toFor:
(4)
WhereinFor air stickiness density,For air resistance coefficient,For the front face area of vehicle,For wind speed,For car Speed;
Ignore automobile air speed influence, therefore air drag is expressed as:
(5)
Frictional resistanceIt is the frictional force between road and tire, passes through following formula:
(6)
WhereinFor surface friction coefficient,For viscous friction coefficient;
Mechanical braking force,For braking moment;
Obtaining the running resistance that vehicle is subject to is:
(7);
2.1.2 power train is modeled
2.1.2.1 clutch
By rigidity it is assumed that the torque of its transmission is:
(8)
WhereinFor motor output torque,For clutch output torque,For motor output speeds,Exported for clutch Rotating speed;
2.1.2.2 speed changer
Gear-box output torqueModeling such as following formula:
(9)
WhereinTo reverse damped coefficient,For output speed,For gearratio,For gear gearratio,For Base ratio;
2.1.2.3 drive shaft
(10)
WhereinExported for drive shaft,For drive shaft output speed;
Formula 8 and formula 9 are substituted into formula 10 to arrange:
(11)
WithDriving force, is usedRadius of wheel is represented, then the relation between power and torque, while speed, therefore convolution 11:
(12)
Wherein radius of wheelTried to achieve by following formula, in formulaFor hub radius,For tire aspect ratio,For tyre width;
(13);
2.2 joint observation devices:
2.2.1 recurrent least square method quality is recognized
Convolution 1 and formula 7, obtain following equation:
(14)
In combination with formula(2)、(3)、(4)、(5)、(6)It is organized into least square form, drive shaft torque estimate, conversion For driving force, obtain
(15)
WhereinLongitudinal direction of car acceleration is represented,Equivalent rotary quality, its value, whereinFor a wheel Inertia,For radius of wheel;Represent to include drive shaft estimator system output quantity,Retrievable data vector is represented,For amount to be identified,For the process white noise of system;
According to principle of least square method, the complete vehicle quality that K-1, K moment System Discrimination obtains is defined respectively is, then quality identification model is obtained:
(16)
In formula,For the forgetting factor at K moment;
Forgetting factorRule is:
(17);
2.2.2 drive shaft torque observer
If drive shaft two ends rotating speed can be surveyed, then drive shaft torque generation model is built:
(18)
WhereinDrive shaft torque is calculated for open loop,For drive shaft two ends rotating speed,For gearbox output speed, For vehicle wheel rotational speed,For drive shaft equivalent stiffness coefficients,For drive shaft Equivalent damping coefficient;
Obtain equivalent wheel power model:
(19)
WhereinFor vehicle wheel rotational speed estimate,For driving moment,For the moment of resistance,For the rotation of jack shaft end Inertia,
(20)
Driving moment passes through resistanceMathematical modeling is tried to achieve, i.e.,, convolution 7
(21)
WhereinGrade resistance square is represented,Air drag square is represented,Represent frictional resistance moment,For mechanical braking torque,Represent car resistance square;Used during drive shaft torque estimatorInstead of
Define deviation, i.e. vehicle wheel rotational speed estimate subtracts actual value, to deviationDerivation, in combination with formula 19 and formula 21:
(22)
Take control inputFor following form:
(23)
Wherein,Inputted for virtual controlling, utilize feedback linearization method, the linearised form of formula 22
(24)
By virtual controlling In-put design into PI forms, i.e.,:
(25)
Wherein,For proportionality coefficient,For integral coefficient;
Association type 23 and 25, obtains controller:
(26)
Define Lyapunov functions such as formula(27):
(27)
Both sides derivation is obtained:
(28)
By formula(25)Bring above formula into and arrange:
(29)
Therefore haveWhen, so
Therefore drive shaft torque is:
(30).
Longitudinal speed controller of the invention based on observer:
Numerically converted between motor torque and mechanical braking torque by gearratio, i.e.,:
(31)
That is braking moment and driving moment is collectively expressed as,
Arranged by formula (1), (7) and (12) and obtain following relational expression:
(32)
It is quantity of state wherein to choose longitudinal speed, i.e.,, it is control variable to choose drive demand, i.e.,, same choosing Longitudinal speed is taken as output quantity, i.e.,;Discretization is carried out to state space equation by Euler's method, usedExpression is adopted Sample step-length, then existAt the moment, it is as follows that discretization obtains system discrete model:
(33)
For output factor matrix, the prediction time domain for defining system is, control the time domain to be, it is necessary to meet, So existThe prediction output sequence at moment is expressed as:
(34)
Meanwhile, the optimal control list entries at k momentIt is expressed as:
(35)
In sampling instant, the value of quantity of state is, derive quantity of state and the prediction process such as formula (19) of output quantity (20) shown in:
(36)
(37)
Meanwhile, reference input is desired speed, therefore obtain reference input sequence:
(38)
Need to consider following constraint in controller design:
(39)
Simultaneously in order to ensure the tracking of longitudinal speed, while the comfortableness taken is improved, therefore controller chooses performance indications mesh It is designated as:
(40)
Wherein
And then it is described as the optimization problem of (41) formula, even if object functionValue is minimum:
(41)
In formula (41),Reality output speed and the deviation of desired speed are reflected,The power of drive demand is reflected,WithThe respectively power of output signal sequence and control signal sequence Repeated factor;The optimization problem of 41 formulas, the control input sequence of optimization system, then by first in sequence are solved using NAG ElementAct on system;Subsequent time, repeats above-mentioned Optimization Solution process, the i.e. closed loop to the longitudinal speed of autonomous driving Optimal control,
(42);
Need that the part for being more than 0 in driver's torque demand is turned into driving moment to the torque progress Torque distribution of optimization, Part less than or equal to 0 turns to braking moment, i.e.,
(43)
WhereinFor the driving torque demand of optimization,Expect torque for motor,For braking moment, by brake force Square is converted into mechanical braking signal by following formula
(44).
Present invention is generally directed to pure electric intelligent automobile longitudinal speed control problem, for the important parameter design view of system Device is surveyed, rolling time horizon system optimizing control can be good at completing on-line optimization solution, while processing constraint that can be dominant.Tool The mechanism model said by setting up longitudinal vehicle speed control system of body, obtains driving the predictive equation of torque demand, then constructs Cost function, while taking into full account constraints, Optimization Solution obtains optimal driver's torque demand.
The beneficial effects of the invention are as follows:
1. in longitudinal speed control aspect, traditional control algolithm majority is all not based on model, and up in real road The vehicle working condition sailed is complicated and changeable, it is difficult to which finding a group controller parameter meets all operating modes.Estimator is added to control simultaneously In device design process processed, it is suppressed that the influence that this parameter of vehicle mass is controlled automobile longitudinal speed, the rolling based on observer Dynamic Optimization of Time Domain control algolithm mechanism model based on system in controller design, by some vehicles such as vehicle mass of observation Operating condition information is introduced directly into mechanism model, so that the control of longitudinal direction of car speed is more accurate.
2. the longitudinal vehicle speed control system designed in the present invention is a nonlinear system, and it is mechanical, electrical to take into account electricity The actuator firm constraints of pond group and brake, traditional control algolithm can not effective processing system constraint, and roll Optimization of Time Domain control algolithm can effectively handle the control problem of belt restraining, directly be compiled into constraints when solving Line solver in S_function in simulink.
Brief description of the drawings
Fig. 1 is to implement of the present invention based on the longitudinal speed control block diagram of rolling time horizon optimization;
Fig. 2 is to implement centralized electric automobile AMESim whole vehicle models of the present invention;
Fig. 3 is traveling longitudinal stress schematic diagram on vehicle sloping route of the present invention;
Fig. 4 is drive shaft torque estimation scheme of the present invention;
Fig. 5 is motor maximum output torque MAP of the present invention;
Fig. 6 is longitudinal speed controller design flow diagram of the design torsion based on rolling optimization algorithm in the present invention;
Fig. 7 is the desired speed that the present invention is controlled the complicated city operating mode used during device compliance test result, and abscissa is the time, Unit s, ordinate is longitudinal speed, and unit is m/s;
Fig. 8 is the simulation result of the present invention under level road operating mode, and quality identification correlation curve is followed successively by from top to bottom, is driven Moving axis torque estimates correlation curve and speed tracking curve.Wherein solid line is actual vehicle matter in quality identification correlation curve Amount, dotted line is identification quality.Dotted line is estimation torque in torque correlation curve, and solid line is actual torque.Actual vehicle speed and expectation In speed comparison diagram, dotted line is actual vehicle speed, and solid line is desired speed;
Fig. 9 is the simulation result of the present invention under constant heavy grade operating mode, and quality identification contrast is followed successively by from top to bottom Curve, drive shaft torque estimation correlation curve and speed tracking curve.Wherein solid line is actual in quality identification correlation curve Vehicle mass, dotted line is identification quality.Dotted line is estimation torque in torque correlation curve, and solid line is actual torque.Actual vehicle speed In desired speed comparison diagram, dotted line is actual vehicle speed, and solid line is desired speed;
Change in road slope situation when Figure 10 is change gradient emulation of the present invention;
Figure 11 is of the present invention in the simulation result become close to true road surface under gradient operating mode, and quality is followed successively by from top to bottom Recognize correlation curve, drive shaft torque estimation correlation curve and speed tracking curve.It is wherein real in quality identification correlation curve Line is actual vehicle quality, and dotted line is identification quality.Dotted line is estimation torque in torque correlation curve, and solid line is actual torque. In actual vehicle speed and desired speed comparison diagram, dotted line is actual vehicle speed, and solid line is desired speed.
Embodiment
Control block diagram such as Fig. 1 that the electric automobile torque optimization method based on data-driven PREDICTIVE CONTROL is implemented in the present invention Shown, speed optimal controller is built in Simulink in figure, and the input of controller is desired speed, and actual vehicle speed is made For that can survey signal, quality and the gradient are as measurable disturbances Real-time Feedback to controller, and Tmax is motor maximum drive torque, and it is Together decided on by the mechanical property of motor and the output voltage of battery, both embodied the actuator firm constraints bar of motor in itself Part, has been embodied as battery discharge time increases influence of the voltage reduction to vehicle performance again.The drive that entire car controller is obtained Kinetic moment is necessarily less than equal to Tmax, therefore Tmax is to be given to controller as constraint.Centralized pure electric automobile mould in Fig. 2 Type is built in AMESim, for simulating the operation of real vehicle.Controller optimization drive demand torque is by Torque distribution Driving and braking moment signal are given to motor and brake module respectively, control the operation of vehicle, and the actual vehicle speed conduct of vehicle Feedback signal back is to controller.
The control targe of the present invention is the actual vehicle speed and desired speed that longitudinal speed controller is returned according to Real-time Feedback Signal contrast, under the premise of constraints is met, optimization obtains drive demand torque, then by Torque distribution, so as to obtain Driving moment and braking moment signal, and the motor in whole vehicle model and brake module are given to, the operation of vehicle is controlled, is finally allowed The upper desired speed of actual vehicle speed tracking.
The invention provides a set of based on the device for operating above principle and running.It is offline electronic i.e. based on PC Vehicle torsional moment optimization design test platform.Build and running is as follows:
Software is selected
The controlled device of the control system and the simulation model of controller pass through software Matlab/Simulink and AMESim respectively Built, software version is respectively Matlab R2009a and AMESim R10, solver is respectively chosen as ode3 and Euler. Simulation step length is fixed step size, and step-length selection is 0.01s.
The present invention will realize Matlab/Simulink and AMESim associative simulation.
1. the environmental variance of PC computers must be as requested set first, make both interrelated.
2. the interface module communicated with simulink is added and then in AMESim interfaces, by Matlab/Simulink with The variable of communication is needed to be connected to this module between AMESim;
3. eventually pass after System build, the model information in AMESim is retained in Simulink in the form of S-function In, so as to realize both associative simulation and communication.
When running Simulink simulation models, AMESim models are also calculated and solved at the same time.In simulation process The exchange of data is constantly carried out between the two.If be modified to the model structure or parameter setting in AMESim, Need to recompilate.It is worth noting that, both simulation step lengths must be consistent.
Step of the present invention is:
First, centralized electric vehicle simulation model buildings:
As shown in Fig. 2 whole electric vehicle simulation model includes Electric drive module, transmission module, tire module and vehicle and indulged To several parts such as dynamics, whole vehicle model parameter is as shown in Table 1.
The electric automobile parameter list of table one
Power drive system includes battery compartment and motor part, and the battery pack of pure electric automobile is lithium battery group, by multiple Cell connection in series-parallel is constituted.The terminal voltage that battery pack is externally exported is single battery output voltage and battery system output Terminal voltage is the voltage that battery pack is supplied to motor;Permagnetic synchronous motor is used in the present invention.
Transmission system includes speed changer, three parts of differential mechanism and drive shaft.The power of motor output is logical via speed changer Cross different tooth radius to produce not at the same speed than increasing square so as to slow down, the present invention have ignored the lateral dynamic of vehicle, then The output speed of differential mechanism both sides is identical, i.e., differential mechanism does not work, and differential mechanism output speed is the input speed of drive shaft, And the output speed of drive shaft is equal with vehicle wheel rotational speed, the torque transmitted on axle is calculated by drive shaft two ends speed discrepancy.Motor The torque of output is produced not at the same speed than increasing square so as to slow down via speed changer by different tooth radius, and this model master subtracts Speed is than being 2.2786, and gear speed reducing ratio is that 3.9431, i.e. gearratio are 8.6847.
Longitudinal direction of car kinetic part, power, brake force and traveling are driven it considers vehicle in the process of moving The effect of resistance, wherein running resistance include air drag, rolling resistance and frictional resistance.Car can be set in this module The parameter such as total quality, the gradient, wind speed.
2nd, the rolling time horizon optimal controller based on observer:
2.1 Controller-orienteds, which design a model, to be built
2.1.1 longitudinal vehicle dynamic model
In order to realize longitudinal speed control and the research of vehicle mass parameter Estimation, it is necessary to set up longitudinal vehicle dynamic model. Longitudinal stress schematic diagram is travelled in the case where not considering cross force, on vehicle sloping route as shown in Figure 3.In Fig. 3For road slope Degree,For driving quality, then gravity be,For driving force,For air drag with,For pavement friction resistance.According to Newton's second law has:
(1)
Wherein:For driving quality,For driving force,For running resistance;Including air drag, pavement friction resistance Power, grade resistanceAnd mechanical braking force.
It is to be noted that car weightWith driving qualityAnd it is unequal, it is used to when calculating driving quality by travel direction The influence of property effect, its relation can be represented approximately with formula following formula:
(2)
WhereinFor the inertia of a wheel,For radius of wheel;Driving force/brake forceIt is described in detail in next trifle, Running resistance to being subject in vehicle travel process belowCarry out analysis introduction.
The automobile travelled on the road of slope is by grade resistanceFor:
(3)
WhereinFor acceleration of gravity;For car weight.In view of the gradient in AMESim modelsFor the road percentagewised The gradient, the unification of gradient form when being tested for subsequent simulation, we are unified here calculates the gradient by hundred-mark system, then need to turn Change
The air drag that the automobile travelled on road surface is subject toFor:
(4)
WhereinFor air stickiness density,For air resistance coefficient,For the front face area of vehicle,For wind speed,For car Speed;Here because air drag is smaller relative to grade resistance and frictional resistance numerical value, the automobile wind then travelled in city Speed is again smaller relative to speed, and the influence of wind speed is have ignored in controller design, therefore air drag is expressed as:
(5)
Frictional resistanceIt is the frictional force between road and tire, passes through following formula:
(6)
WhereinFor surface friction coefficient,For viscous friction coefficient;
Mechanical braking force,For braking moment;
Obtaining the running resistance that vehicle is subject to is:
(7);
2.1.2 power train is modeled
Rigidity is carried out to clutch, power transmission shaft, drive shaft during modeling it is assumed that have ignored simultaneously main reducing gear and speed transmission it Between transmit torque loss.
2.1.2.1 clutch
By rigidity it is assumed that the torque of its transmission is:
(8)
WhereinFor motor output torque,For clutch output torque,For motor output speeds,Turn for clutch output Speed.
2.1.2.2 speed changer
Here because we have ignored the torque transmitted between main reducing gear and speed transmission loss, therefore we are right in modeling It is unified, gear-box output torqueModeling such as following formula:
(9)
WhereinTo reverse damped coefficient,For output speed, reversing the product of damped coefficient and output speed is used to approximately rub Torque loss is wiped,For gearratio,For gear gearratio,For base ratio.
2.1.2.3 drive shaft
(10)
WhereinExported for drive shaft,For drive shaft output speed;As vehicle wheel rotational speed.
Formula 8 and formula 9 are substituted into formula 10 to arrange:
(11)
WithDriving force, is usedRadius of wheel is represented, then the relation between power and torque, while speed, Therefore convolution 11:
(12)
Wherein radius of wheelTried to achieve by following formula, in formulaFor hub radius,For tire aspect ratio,For tyre width;
(13);
2.2 joint observation devices:
In order to accurately estimate vehicle mass, our designing qualities and drive shaft torque joint observation device.It is contemplated that to quality and There is coupled relation between drive shaft torque, that is, it is to need to use drive shaft moment information to estimate quality, and estimation torque is desirable Use vehicle mass information.Thus it is determined that during estimation scheme, analyze two respective characteristics of amount to be observed, the quality of automobile Change mainly in number of passengers, fuel tank it is oily number and loading goods number cause, quality is in vehicle travel process In stablize relatively, thus vehicle mass is slow variable, can be recognized with the method for identification, while considering this characteristic with ought The quality identification result at preceding moment is estimated the torque of subsequent time to produce torque estimated result influence very little, so may be used To improve the efficiency of algorithm, while also solving coupled relation therebetween.
2.2.1 recurrent least square method quality is recognized
The kinetic model that analysis vehicle is travelled on the road of slope, convolution 1 and formula 7, obtain following equation:
(14)
In combination with formula(2)、(3)、(4)、(5)、(6)Least square form is organized into, in combination with analysis, is used during estimation quality To drive shaft torque estimate, it is converted into driving force, obtain
(15)
WhereinLongitudinal direction of car acceleration is represented,Equivalent rotary quality, its value, whereinFor wheel Inertia,For radius of wheel;Represent to include drive shaft estimator system output quantity,Retrievable data vector is represented, For amount to be identified,For the process white noise of system;It is to be noted that we are by drive shaft during identification vehicle mass Torque is as measurable, then according to the relation between power and torque, driving force is also measurable, while thinking longitudinal acceleration It is that can survey parameter in real time with mechanical braking torque.
According to previously described principle of least square method, the complete vehicle quality that K-1, K moment System Discrimination are obtained is defined respectively For, then quality identification model is obtained:
(16)
In formula,For the forgetting factor at K moment.
Vehicle mass is slow variable, because the quality initial value and actual mass of setting there may be larger deviation, therefore Identification is just when starting, it is necessary to set a larger degree of belief decay, i.e., the forgetting factor value now chosen is smaller, with distinguishing The continuous progress known, identification result can be converged near actual vehicle quality, then be accomplished by a larger forgetting factor, with Less degree of belief is obtained to decay, therefore the forgetting factor chosen hereinRule is:
(17);
2.2.2 drive shaft torque observer
This technology devises close-loop driven axle torque observer, by torque generation model open loop result of calculation, then passes through car Speed deviation divided ring result of calculation is corrected, so that drive shaft torque observation problem is converted into rotating-speed tracking problem.Estimation Scheme is as shown in Figure 4.In view of the nonlinear feature of loading moment, with reference to the advantage of feedback linearization method, therefore use anti- Linearization method carries out torque observer design.
If drive shaft two ends rotating speed can be surveyed, then drive shaft torque generation model is built:
(18)
WhereinDrive shaft torque is calculated for open loop,For drive shaft two ends rotating speed,For gearbox output speed, For vehicle wheel rotational speed,For drive shaft equivalent stiffness coefficients,For drive shaft Equivalent damping coefficient.
According to Lagrange's dynamical equations, and assume that wheel does pure rolling positive motion in running car, therefore obtain To equivalent wheel power model:
(19)
WhereinFor vehicle wheel rotational speed estimate,For driving moment,For the moment of resistance,It is used to for the rotation of jack shaft end Amount, is approximately calculated with formula 20 and obtained
(20)
The relation between power and torque is considered simultaneously, and driving moment passes through resistanceMathematical modeling is tried to achieve, i.e.,, Convolution 7
(21)
WhereinGrade resistance square is represented,Air drag square is represented,Represent frictional resistance moment,For mechanical braking torque,Represent car resistance square;While the expression in view of using last moment quality when being driven axle power moments estimation As a result, that is, calculatingWhen use, therefore used in design drive torque estimatorInstead of
Define deviation, i.e. vehicle wheel rotational speed estimate subtracts actual value, to deviationDerivation, in combination with formula 19 and formula 21:
(22)
Take control inputFor following form:
(23)
Wherein,Inputted for virtual controlling, utilize feedback linearization method, the linearised form of formula 22
(24)
By virtual controlling In-put design into PI forms, i.e.,:
(25)
Wherein,For proportionality coefficient,For integral coefficient;
Association type 23 and 25, obtains controller:
(26)
Define Lyapunov functions such as formula(27):
(27)
Both sides derivation is obtained:
(28)
By formula(25)Bring above formula into and arrange:
(29)
Therefore haveWhen, so
In summary, estimate that obtained drive shaft torque is:
(30).
Longitudinal speed controller of the invention based on observer:
This paper control targe is to realize the tracing control of longitudinal speed during autonomous driving, under different operating modes, passes through control The desired drive demand of device optimization processed, then realizes that actual vehicle speed tracking is last by executing agencies such as motor and mechanical brakings Hope speed.
Controller design optimizes for convenience, and we uniformly optimize mechanical braking torque and motor braking torque, I.e. in the case where considering that motor is desired motor, optimization reaches operator demand during desired speed, then passing through Certain control strategy is classified as motor torque demand and mechanical force moment demand.
Closed to realize unified optimization driver's torque demand, it is necessary to set up between mechanical braking torque and motor torque It is that we ignore the inertia loss of power train here, then numerically pass through between motor torque and mechanical braking torque and pass Move than being converted, i.e.,:
(31)
That is braking moment and driving moment is collectively expressed as, the system model that to sum up analysis set is above set up, while in order to Speed control is more accurately realized, we use the quality that joint estimator is estimated by the complete vehicle quality when designing controller.
Arranged by formula (1), (7) and (12) and obtain following relational expression:
(32)
It is quantity of state wherein to choose longitudinal speed, i.e.,, it is control variable to choose drive demand, i.e.,, equally Longitudinal speed is chosen as output quantity, i.e.,;Discretization is carried out to state space equation by Euler's method, usedTable Show sampling step length, then existAt the moment, it is as follows that discretization obtains system discrete model:
(33)
For output factor matrix, according to Model Predictive Control Theory, the prediction time domain for defining system is, control the time domain to be, it is necessary to meet, thenThe prediction output sequence at moment is expressed as:
(34)
Meanwhile, the optimal control list entries at k momentIt is expressed as:
(35).
In sampling instant, the value of quantity of state is, according to the basic principle and correlation theory of Model Predictive Control, Derive shown in quantity of state and the prediction process such as formula (19) of output quantity and (20):
(36)
(37).
The state variable value and last moment system input value at current time are calculated by analyzing, Optimization Solution goes out controlled quentity controlled variable Sequence, and first amount of the controlled quentity controlled variable sequence for only going out Optimization Solution acts on system.And in next sampling instant, electronic vapour Vehicle model can feed back to new input variable and quantity of state, and controller re-optimization solves control problem.
Meanwhile, reference input is desired speed, therefore obtain reference input sequence:
(38)
During autonomous driving, in order to ensure driving safety, it is necessary to enter row constraint to quantity of state, while in view of motor in itself Characteristic, its output torque has constraint, while in view of the limitation of mechanical structure, having the constraint of maximum braking moment again, to sum up It is described, need to consider following constraint in controller design:
(39)
The maximum moment that wherein motor is provided is tabled look-up by the MAP in Fig. 5 to be obtained.
Simultaneously in order to ensure the tracking of longitudinal speed, while improving the comfortableness taken(Accelerate to control with braking procedure Braking is made as small as possible), therefore controller choose performance indications target be:
(40)
Wherein
And then it is described as the optimization problem of (41) formula, even if object functionValue is minimum:
(41)
In formula (41),Reality output speed and the deviation of desired speed are reflected,The power of drive demand is reflected,WithRespectively output signal sequence and control signal sequence Weight factor.Size reflect speed tracing precision requirement,Bigger, the deviation of speed tracing is closer to zero.Then Reflect the requirement to control action,Bigger, control action is smaller, and ride comfort is more preferable.Use a kind of NAG (rolling time horizons Optimized algorithm MATLAB solve tool box) solve 41 formulas optimization problem, the control input sequence of optimization system, then by sequence First element in rowAct on system;Subsequent time, repeats above-mentioned Optimization Solution process, i.e., to autonomous driving longitudinal direction The closed optimized control of speed,
(42);
The flow of rolling time horizon Optimal Controller Design is as shown in Figure 6:Rolling time horizon optimal controller optimization driver's torque Demand, but the control signal needed in control is motor torque demand and mechanical braking signal, therefore need to optimization Torque carry out Torque distribution by driver's torque demand be more than 0 part turn to driving moment, the part less than or equal to 0 Braking moment is turned to, i.e.,
(43)
WhereinFor the driving torque demand of optimization,Expect torque for motor,For braking moment, by brake force Square is converted into mechanical braking signal by following formula
(44).
Experimental verification
Control parameter is adjusted repeatedly, and output signal sequence and the weight factor Γ of control signal sequence are chosen respectivelyy =100, Γu =2, the sampling time is 0.01s, we have selected acceleration and deceleration frequently city operating mode, desired speed such as Fig. 7.Set vehicle matter Measure as 1500kg, controller is carried out under level road operating mode, constant heavy grade and the change gradient operating mode for pressing close to true road surface respectively Checking.
1)Level road operating mode simulating, verifying
We select level road to verify first, set road grade as 0, and in emulation, wind speed setting is 0, and vehicle mass is 1500 Kg, i.e. vehicle no-load running, simulation result are followed successively by quality identification contrast song from top to bottom as shown in figure 8, being provided in figure Line, drive shaft torque estimation correlation curve and speed tracking curve, as can be seen from the figure estimator and controller all have good Good effect.
2)Constant heavy grade operating mode simulating, verifying
Under simulated environment, we set road grade as 10%, i.e., on constant larger slope road, verify long lasting for larger Whether the control effect of slope road travel controller is stablized, and simulation result is as shown in Figure 9.Provided in figure and be followed successively by quality from top to bottom Recognize correlation curve, drive shaft torque estimation correlation curve and speed tracking curve.Simulation result shows, up on larger slope road Sail, very well, actual vehicle speed can track desired speed in the most of the time to estimator estimation effect, but left in 200 ~ 300s The right side, actual vehicle speed does not track desired speed, but speed maintains 20 m/s, this be due to motor expect torque by The effect of our motor maximum motor torque constraints during controller design, by Fig. 5 motor maximum output torques map can Know, motor now expects that torque is just equal to motor maximum output torque.Illustrate that controller's effect is good, simultaneity factor constraint Play a good role.
3)Become gradient operating mode simulating, verifying
In actual vehicle running environment, road grade can't keep constant, thus we set closer actual motion work The change gradient of condition(Road grade such as Figure 10)Verified.Simulation result can be seen that as shown in Figure 10 mono- from simulation result Joint estimator estimation effect is good, while longitudinal direction of car speed tracks desired speed well in the case where becoming gradient operating mode.
The present invention is directed to longitudinal speed controller of the pure electric intelligent Automobile Design based on rolling time horizon optimization method, this The method of kind realizes on-line optimization well, while dominant processing constraint.In order to verify having for longitudinal speed optimal controller Effect property, has built centralized electric automobile model, and in complicated urban road, exist respectively in the senior simulation softwares of AMESim The performance of controller is demonstrated under the change gradient operating mode of level road operating mode, constant heavy grade operating mode and closing to reality road surface.Emulation knot Fruit shows that the longitudinal speed controller of rolling time horizon optimization is under different driving cycles, with good control performance.

Claims (3)

1. a kind of pure electric intelligent automobile longitudinal method for controlling driving speed based on observer, it is characterised in that:Realize Matlab/ Simulink and AMESim associative simulation,
1. the environmental variance of PC computers is must be provided with, makes both interrelated;
2. the interface module communicated with simulink is added in AMESim interfaces, by between Matlab/Simulink and AMESim The variable of communication is needed to be connected to this module;
3. pass through System build after, the model information in AMESim to be retained in the form of S-function in Simulink, So as to realize both associative simulation and communication.
2. the pure electric intelligent automobile longitudinal method for controlling driving speed based on observer described in claim 1, it is characterised in that:
First, centralized electric vehicle simulation model buildings:
Electric vehicle simulation model includes Electric drive module, transmission module, tire module and longitudinal direction of car dynamics, vehicle mould Shape parameter such as table one
The electric automobile parameter list of table one
2nd, the rolling time horizon optimal controller based on observer:
2.1 Controller-orienteds, which design a model, to be built
2.1.1 longitudinal vehicle dynamic model
Travel longitudinal stress in the case where not considering cross force, on vehicle sloping route has according to Newton's second law:
(1)
Wherein:For driving quality,For driving force,For running resistance;Including air drag, pavement friction resistance Power, grade resistanceAnd mechanical braking force;
Car weightWith driving qualityRelation is represented with formula following formula:
(2)
WhereinFor the inertia of a wheel,For radius of wheel;
The automobile travelled on the road of slope is by grade resistanceFor:
(3)
WhereinFor acceleration of gravity;
The air drag that the automobile travelled on road surface is subject toFor:
(4)
WhereinFor air stickiness density,For air resistance coefficient,For the front face area of vehicle,For wind speed,For car Speed;
Ignore automobile air speed influence, therefore air drag is expressed as:
(5)
Frictional resistanceIt is the frictional force between road and tire, passes through following formula:
(6)
WhereinFor surface friction coefficient,For viscous friction coefficient;
Mechanical braking force,For braking moment;
Obtaining the running resistance that vehicle is subject to is:
(7);
2.1.2 power train is modeled
2.1.2.1 clutch
By rigidity it is assumed that the torque of its transmission is:
(8)
WhereinFor motor output torque,For clutch output torque,For motor output speeds,Turn for clutch output Speed;
2.1.2.2 speed changer
Gear-box output torqueModeling such as following formula:
(9)
WhereinTo reverse damped coefficient,For output speed,For gearratio,For gear gearratio,For Base ratio;
2.1.2.3 drive shaft
(10)
WhereinExported for drive shaft,For drive shaft output speed;
Formula 8 and formula 9 are substituted into formula 10 to arrange:
(11)
WithDriving force, is usedRadius of wheel is represented, then the relation between power and torque, while speed, therefore convolution 11:
(12)
Wherein radius of wheelTried to achieve by following formula, in formulaFor hub radius,For tire aspect ratio,For tyre width;
(13);
2.2 joint observation devices:
2.2.1 recurrent least square method quality is recognized
Convolution 1 and formula 7, obtain following equation:
(14)
In combination with formula(2)、(3)、(4)、(5)、(6)It is organized into least square form, drive shaft torque estimate, it is converted into Driving force, obtain
(15)
WhereinLongitudinal direction of car acceleration is represented,Equivalent rotary quality, its value, whereinFor a wheel Inertia,For radius of wheel;Represent to include drive shaft estimator system output quantity,Retrievable data vector is represented,For amount to be identified,For the process white noise of system;
According to principle of least square method, the complete vehicle quality that K-1, K moment System Discrimination obtains is defined respectively is, then quality identification model is obtained:
(16)
In formula,For the forgetting factor at K moment;
Forgetting factorRule is:
(17);
2.2.2 drive shaft torque observer
If drive shaft two ends rotating speed can be surveyed, then drive shaft torque generation model is built:
(18)
WhereinDrive shaft torque is calculated for open loop,For drive shaft two ends rotating speed,For gearbox output speed, For vehicle wheel rotational speed,For drive shaft equivalent stiffness coefficients,For drive shaft Equivalent damping coefficient;
Obtain equivalent wheel power model:
(19)
WhereinFor vehicle wheel rotational speed estimate,For driving moment,For the moment of resistance,It is used to for the rotation of jack shaft end Amount,
(20)
Driving moment passes through resistanceMathematical modeling is tried to achieve, i.e.,, convolution 7
(21)
WhereinGrade resistance square is represented,Air drag square is represented,Represent frictional resistance moment,For mechanical braking torque,Represent car resistance square;Used during drive shaft torque estimatorInstead of
Define deviation, i.e. vehicle wheel rotational speed estimate subtracts actual value, to deviationDerivation, in combination with the He of formula 19 Formula 21:
(22)
Take control inputFor following form:
(23)
Wherein,Inputted for virtual controlling, utilize feedback linearization method, the linearised form of formula 22
(24)
By virtual controlling In-put design into PI forms, i.e.,:
(25)
Wherein,For proportionality coefficient,For integral coefficient;
Association type 23 and 25, obtains controller:
(26)
Define Lyapunov functions such as formula(27):
(27)
Both sides derivation is obtained:
(28)
By formula(25)Bring above formula into and arrange:
(29)
Therefore haveWhen, so
Therefore drive shaft torque is:
(30).
3. the pure electric intelligent automobile longitudinal method for controlling driving speed according to claim 1 or 2 based on observer, its feature It is:Longitudinal speed controller based on observer:
Numerically converted between motor torque and mechanical braking torque by gearratio, i.e.,:
(31)
That is braking moment and driving moment is collectively expressed as,
Arranged by formula (1), (7) and (12) and obtain following relational expression:
(32)
It is quantity of state wherein to choose longitudinal speed, i.e.,, it is control variable to choose drive demand, i.e.,, equally Longitudinal speed is chosen as output quantity, i.e.,;Discretization is carried out to state space equation by Euler's method, usedTable Show sampling step length, then existAt the moment, it is as follows that discretization obtains system discrete model:
(33)
For output factor matrix, the prediction time domain for defining system is, control the time domain to be, it is necessary to meet, So existThe prediction output sequence at moment is expressed as:
(34)
Meanwhile, the optimal control list entries at k momentIt is expressed as:
(35)
In sampling instant, the value of quantity of state is, derive quantity of state and output quantity prediction process such as formula (19) and (20) shown in:
(36)
(37)
Meanwhile, reference input is desired speed, therefore obtain reference input sequence:
(38)
Need to consider following constraint in controller design:
(39)
Simultaneously in order to ensure the tracking of longitudinal speed, while the comfortableness taken is improved, therefore controller chooses performance indications mesh It is designated as:
(40)
Wherein
And then it is described as the optimization problem of (41) formula, even if object functionValue is minimum:
(41)
In formula (41),Reflect the inclined of reality output speed and desired speed Difference,The power of drive demand is reflected,WithRespectively output signal sequence and control signal sequence The weight factor of row;The optimization problem of 41 formulas, the control input sequence of optimization system, then by sequence are solved using NAG First elementAct on system;Subsequent time, repeats above-mentioned Optimization Solution process, i.e., to the longitudinal speed of autonomous driving Closed optimized control,
(42);
Need that the part for being more than 0 in driver's torque demand is turned into driving moment to the torque progress Torque distribution of optimization, Part less than or equal to 0 turns to braking moment, i.e.,
(43)
WhereinFor the driving torque demand of optimization,Expect torque for motor,For braking moment, by brake force Square is converted into mechanical braking signal by following formula
(44).
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