CN105946858B - Four-drive electric car state observer parameter optimization method based on genetic algorithm - Google Patents
Four-drive electric car state observer parameter optimization method based on genetic algorithm Download PDFInfo
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- 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
- B60W40/10—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 vehicle motion
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- B60W2720/10—Longitudinal speed
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- B—PERFORMING OPERATIONS; TRANSPORTING
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Abstract
The invention discloses a kind of four-drive electric car state observer parameter optimization method based on genetic algorithm, it is intended to solve electronic vehicle attitude observer parameter regulation difficult problem.The following steps are included: establishing the Three Degree Of Freedom auto model of vehicle single-wheel roll modeling and simplification；Using vehicle sensors metrical information vehicle wheel rotation angular speed and driving moment as input, longitudinal tire force observer is designed using sliding mode observer method；Again using longitudinal tire force estimated value, front wheel angle, side acceleration and yaw velocity as input, the lateral tire force sliding mode observer of axle is separately designed；Finally using longitudinal and lateral tire force estimated value, longitudinal direction and side acceleration, yaw acceleration and vehicle front wheel angle as input, design vehicle speed omnidirectional vision；Modular vehicle state observer based on design is observed device parameter optimization to each estimation module respectively using genetic algorithm.
Description
Technical field
The modularization four-wheel driving electric vehicle state observer parameter optimization based on genetic algorithm that the present invention relates to a kind of
Method, belong to vehicle state estimation technical field.
Background technique
As the representative of new-energy automobile, electric car for gasoline combustion is as the orthodox car of power,
Cleaning, environmental protection, energy conservation etc. occupy apparent advantage.Therefore, the ownership of electric car is in increased trend year by year, and its
Control stability and active safety problem have also obtained extensive concern.
The active safety control system of electric car can effectively improve vehicle handling stability, to reduce traffic
The generation of accident.And the premise that its various control logic is able to effectively implement is the accurate running condition information for obtaining vehicle.So
And due to the restriction of the factors such as production cost and measurement error, in volume production vehicle, Some vehicles running condition information can not be direct
It is obtained by onboard sensor measurement.Therefore, using measurable car status information design observer to the vehicle being unable to measure
Status information carries out estimation and has been increasingly becoming research hotspot.
In vehicle state estimation problem, vehicle-state observer parameter is to influence an important factor for it estimates accuracy,
Observer parameter regulation problem is also its technological difficulties.What traditional observer adjusting generallyd use is the hand based on many experiments
Dynamic adjusting method, not only workload is very big for this adjusting method, and cannot be guaranteed parameter adjusted be current working most
Good parameter.Observer parameter is optimized therefore, it is necessary to design a kind of intelligent optimization algorithm.
Summary of the invention
To solve electronic vehicle attitude observer parameter regulation difficult problem, the present invention provides a kind of based on genetic algorithm
Four-drive electric car state observer parameter optimization method, by taking modular four-wheel driving electric vehicle state observer as an example,
The optimization of device parameter is observed using genetic algorithm.Wherein, modular vehicle-state observer is by longitudinal tire force sliding formwork
Observer, lateral tire force sliding mode observer and car speed omnidirectional vision are constituted.
The present invention is achieved by the following technical solutions:
A kind of four-drive electric car state observer parameter adjusting method based on genetic algorithm, comprising the following steps:
Step 1: establishing the Three Degree Of Freedom auto model of vehicle single-wheel roll modeling and simplification；
Step 2: modular vehicle state Observer Design: with vehicle sensors metrical information vehicle wheel rotation angular speed and
Driving moment is as input, using sliding mode observer method to four longitudinal tire force taken turns design longitudinal tire force sliding formwork observations
Device；Again using longitudinal tire force estimated value, front wheel angle, side acceleration and yaw velocity as input, separately design forward and backward
The lateral tire force sliding mode observer of axis；Finally accelerated with longitudinal and lateral tire force estimated value, longitudinal direction and side acceleration, sideway
As input, the car speed for designing longitudinal speed, lateral speed and yaw velocity ties up state entirely for degree and vehicle front wheel angle
Observer；
Step 3: the modular vehicle state observer based on step 2 design, is estimated to each respectively using genetic algorithm
Meter module is observed device parameter optimization.
Further, device parameter optimization is observed to each estimation module respectively using genetic algorithm in the step 3
The following steps are included:
3.1) it is observed using the longitudinal tire force sliding formwork that the standard genetic algorithm of bivariate first respectively takes turns four, vehicle
Device parameter optimizes, then using the front axle longitudinal tire force estimated value Jing Guo parameter optimization as input to the lateral tire force of front axle
Sliding mode observer parameter optimizes, and finally optimizes to the lateral tire force sliding mode observer parameter of rear axle；
3.2) using Jing Guo parameter optimization front axle longitudinal direction and the lateral tire force estimated value of axle as input, using list
The standard genetic algorithm of variable carries out parameter optimization to yaw rate state observer；
3.3) using Jing Guo parameter optimization longitudinal direction and lateral tire force estimated value as input, using multi-objective genetic algorithm
Longitudinal direction of car, lateral speed observer parameter are optimized, and obtain Pareto optimal solution set.
Due to the adoption of the above technical solution, the beneficial effects of the present invention are:
(1) difficult problem is manually adjusted for vehicle-state observer parameter, is proposed a kind of suitable for modularization four
The genetic algorithm parameter optimization method of wheel drive electronic vehicle attitude observer.
(2) validity has been carried out using observer parameter of the high-fidelity vehicle dynamics simulation software veDYNA to optimization to test
Card, the results showed that observer parameter optimization method proposed by the invention have the effect of it is certain, can guarantee observer estimation
As a result accuracy.
Detailed description of the invention
Fig. 1 vehicle single-wheel rolls kinetic model；
Fig. 2 vehicle overlooks stress diagram；
Fig. 3 modular vehicle state observer structure chart；
Fig. 4 genetic algorithm executes step；
Fig. 5 the near front wheel longitudinal tire force sliding mode observer parameter optimization result
The longitudinally, laterally forward position speed state observer parameter optimization Pareto Fig. 6
Fig. 7 longitudinal tire force simulation result
The lateral tire force simulation result of Fig. 8
Fig. 9 longitudinally, laterally speed and yaw velocity simulation result
1 vehicle-state observer parameter optimization result of table
2 four-wheel driving electric vehicle parameter of table
The longitudinally, laterally speed state observer parameter optimization Pareto optimal solution of table 3
Specific embodiment
With reference to the accompanying drawing, technical solution proposed by the invention is further elaborated and is illustrated.
The present invention provides a kind of, and the modularization four-wheel driving electric vehicle state observer parameter based on genetic algorithm is excellent
Change method, this method including the following steps:
Step 1: establishing the vehicle Three Degree Of Freedom model of vehicle single-wheel roll modeling and simplification
1. establishing vehicle single-wheel roll modeling
To design longitudinal tire force sliding mode observer, Vehicular system is reduced to vehicle single-wheel as shown in Figure 1 and rolls mould
Type.
It is rolled shown in kinetics equation such as formula (1) by the available single-wheel of Fig. 1:
Wherein, J is the rotary inertia of wheel, units/kg m2, ωiFor the rotational angular velocity of each wheel, unit rad/s,
ReffFor the effective radius of tire, unit m, TiFor the driving moment of each wheel, unit Nm.
2. establishing simplified Three Degree Of Freedom auto model
Fig. 2 is that vehicle overlooks stress diagram, is studied for convenience, the present invention in view of vehicle longitudinally, laterally and
Whole vehicle model is reduced to Three Degree Of Freedom model by the stress condition of yaw direction.Coordinate system is established on vehicle, origin is located at vapour
The mass center of vehicle, the direction that vehicle advances are positive direction of the x-axis, and horizontal be positive direction of the y-axis to the left, and z-axis positive direction is determined by right-handed helix
It then determines, as shown in Figure 2.Simplified Three Degree Of Freedom auto model kinetics equation such as formula can be obtained using Newton's second law
(2) shown in
Wherein, m is automobile gross mass, and units/kg, r is the yaw velocity of automobile, unit rad/s, VxAnd VyIt is that vehicle exists
Longitudinal direction and side velocity under bodywork reference frame, unit m/s, IzIt is vehicle around the rotary inertia of vehicle axis system z-axis, unit
kg·m2, FxAnd FyRespectively indicate longitudinal direction of car and lateral tire force, unit N, MzIt is vehicle around z-axis rotating torque, unit Nm.
According to power and torque equilibrium equation, longitudinal direction of car and lateral tire force Fx、FyWith vehicle around z-axis rotating torque MzIt can
To indicate are as follows:
Wherein, Fxi/Fyi(i=1 ..., 4) is respectively four longitudinal directions taken turns and lateral tire force, unit N, δfBefore being vehicle
Take turns corner, unit rad, lFAnd lRIt is distance of the vehicle centroid away from front/rear axis, unit m respectively.
Step 2: modular vehicle state Observer Design: with vehicle sensors metrical information vehicle wheel rotation angular speed and
Driving moment is as input, using sliding mode observer method to four longitudinal tire force taken turns design longitudinal tire force sliding formwork observations
Device；Again using longitudinal tire force estimated value, front wheel angle, side acceleration and yaw velocity as input, separately design forward and backward
The lateral tire force sliding mode observer of axis；Finally with longitudinal and lateral tire force estimated value and longitudinal direction and side acceleration, sideway
Acceleration and vehicle front wheel angle are as input, and longitudinally, laterally the car speed of speed and yaw velocity ties up state entirely for design
Observer.Each observer module of above-mentioned design is carried out to integrate available modular vehicle state observer, structure
Figure is as shown in Figure 3.Estimation problem of the invention is introduced for convenience, will directly be surveyed by vehicle sensors first
The parameter of amount is explained as follows:
The driving moment T of (1) four wheeli(i=1,2,3,4) although not directly measuring, can by vehicle its
He can measure information (engine moment Te, engine speed ωe, pressure of wheel cylinder pt) be calculated, therefore can be regarded
Work can direct metrical information；(2) the angular signal δ of steering wheel for vehicle can be obtained by photoelectric encoder measurement, and then can be led to
Cross relational expression δf=δ/IswThe front wheel angle δ of vehicle is calculatedf, IswFor steering gear ratio；The angle of rotation speed of (3) four wheels
Spend ωi(i=1,2,3,4) it can be obtained by wheel speed sensors measurement；(4) longitudinal direction of car and side acceleration ax、ayIt can be by adding
Velocity sensor measurement obtains；(5) yaw rate r can be obtained by gyroscope measurement.
Modular vehicle state Observer Design specifically includes the following steps:
1, longitudinal tire force Design of Sliding Mode Observer
Kinetics equation is rolled according to single-wheel, it is as follows to provide first-order system:
Wherein, TiIt is system input, ωiMeasurement output quantity as system is also simultaneously system mode, when system mode changes
When change, Unknown worm amount FxiAlso it changes correspondingly.Herein, FxiIt is exactly our states to be estimated, then the estimation problem can be retouched
State into the process for going out system Unknown worm by measurement output estimation.
According to sliding mode observer theory, defining systematic error herein isIt is to be that the present invention, which chooses sliding-mode surface,
System error, i.e.,And choose liapunov function:
V=S2/2 (5)
To formula (4) derivation, can obtain:
According to State Observer Theory, formula (3) is configured as form, wherein LxiIt is observer gain.
Formula (4) and formula (7) are substituted into (6), available:
Where it is assumed thatMeet with lower inequality:
In above-mentioned hypothesis, FxiMeet Bounded Conditions, as long as then ρxiTake sufficiently large value, it assumes that can set up.It will
Formula (9) is brought into formula (8), available:
At this point, if takingWherein sign (S) is sign function, and then formula (10) can be indicated
Are as follows:
By above-mentioned derivation process, the sliding mode observer form that the present invention designs is as follows:
Systematic error derivative is further expressed as by convolution (4) and formula (12):
It is t when the time1, when system reaches stable, can obtainTherefore:
Then according to formula (14), Unknown worm amount FxiEstimated value can be expressed as form:
Formula (15) is exactly the present invention for Unknown worm amountThe sliding mode observer of design, wherein LxiIt is feedback oscillator,
ρxiIt is sliding formwork gain.
Since time lag, Spatial lag and system inertia etc. influence, sliding mode system is easy to appear chattering phenomenon, this will
Increase evaluated error to influence estimated result.In order to weaken the influence of buffeting, the present invention replaces symbol using saturation function (16)
Number function sign (S).
Wherein, S indicates evaluated error, and φ > 0 is for reconciling function signeqThe slope of (S, φ).
Formula (16) is brought into formula (15), available longitudinal tire force sliding mode observer form is as follows:
2, lateral tire force Design of Sliding Mode Observer
According to simplified vehicle Three Degree Of Freedom kinetics equation, it is contemplated that vehicle is along the lateral motion equations of y-axis and around z-axis
Torque balance equation, available following vehicle two degrees of freedom kinetics equation:
Wherein, ayFor vehicle lateral acceleration, unit m/s, Fyf=Fy1+Fy2For the lateral tire force of front axle, unit N, Fyr=
Fy1+Fy2For the lateral tire force of rear axle, unit N, Fxf=Fx1+Fx2For front axle longitudinal tire force, unit N.
By the lateral tire force F of front axle in formula (18)yfWith the lateral tire force F of rear axleyrUncoupling obtains:
Tire force F lateral for front-wheelyf, formula (19) is turned into the first-order system shaped like formula (4):
Wherein, r is system mode, while being also systematic survey output, ayFor system input, FyfFor system Unknown worm
Amount, while being also intended to the state of estimation.
According to longitudinal tire force Design of Sliding Mode Observer process, the lateral tire force sliding mode observer of the front axle that the present invention designs
Form is as follows:
Wherein, LyfFor the feedback oscillator of the lateral tire force sliding mode observer of front axle, ρyfIt is seen for the lateral tire force sliding formwork of front axle
Survey the sliding formwork gain of device.Convolution (20) and formula (21) as can be seen that when estimating the lateral tire force of front axle, need with
The value of front axle longitudinal tire force is as input.
It is as follows that the lateral tire force sliding mode observer form of rear axle can similarly be designed:
Wherein, LyrFor the feedback oscillator of the lateral tire force sliding mode observer of rear axle, ρyrIt is seen for the lateral tire force sliding formwork of rear axle
Survey the sliding formwork gain of device.
3, car speed omnidirectional vision designs
According to the equilibrium equation of power, longitudinally, laterally the relationship between acceleration and vehicle tyre power be can be described as:
Wherein, ax、ayThe respectively longitudinal direction of vehicle and side acceleration, unit m/s2.According to formula (2) and formula (23), indulge
It can be further indicated that, lateral speed and yaw velocity are as follows:
It obtains, selects since longitudinally, laterally acceleration and yaw velocity can directly be measured by vehicle sensors
These three amounts are exported as systematic survey, and using the difference of they and its estimated value as the correction term of car speed observer, base
In non-linear full micr oprocessorism structure, it can be designed that the car speed of longitudinal direction of car, lateral speed and yaw velocity is tieed up entirely
Shown in observer expression formula such as formula (25):
Wherein, Ki(i=x, y, r) represents observer gain.Using tire force estimated value, will can longitudinally, laterally accelerate
Spend estimated valueAnd vehicle is around z-axis rotating torque estimated valueIt indicates are as follows:
Step 3: the modular vehicle state observer based on step 2 design, is estimated to each respectively using genetic algorithm
Meter module is observed device parameter optimization.The range and optimum results of Optimal Parameters are as shown in table 1:
1 vehicle-state observer parameter optimization result of table
The more conservative range that empirical value when wherein Optimal Parameters range is by manually adjusting provides, to guarantee it
Optimized parameter is contained, specifically includes the following steps:
1. the longitudinal tire force sliding mode observer taken turns respectively to four, vehicle first using the standard genetic algorithm of bivariate
Parameter ρxi/Lxi(i=1,2,3,4) is optimized, then using the front axle longitudinal tire force estimated value Jing Guo parameter optimization as input
To the lateral tire force sliding mode observer parameter ρ of front axleyf/LyrIt optimizes, finally the lateral tire force sliding mode observer of rear axle is joined
Number ρyr/LyrIt optimizes.
The lateral tire force sliding mode observer of longitudinal tire force sliding mode observer and axle according to designed by step 2 can
Know, L, ρ are the observer parameters for needing to optimize.For tire force sliding mode observer Parametric optimization problem, due to longitudinally and laterally
Tire force sliding mode observer has similar structure, therefore the present invention only provides and is with the near front wheel longitudinal tire force sliding mode observer
Example, the process that observer parameter is optimized using genetic algorithm.
When optimizing to observer parameter, using high-fidelity dynamics simulation software veDYNA, four-wheel drive is selected
For electric car as emulation vehicle, vehicle parameter is as shown in table 2:
2 four-wheel driving electric vehicle parameter of table
Allow vehicle driving under conventional high attachment two-track line operating condition, specific operating condition setting are as follows: surface friction coefficient μ=
On 0.8 road, vehicle accelerates by static, when car speed accelerates to 80km/h, carries out the operation of two-track line, Zhi Houbao
Hold linear uniform motion.Wherein, it is contemplated that onboard sensor measurement error in practice turns to sensor measurement information wheel respectively
Dynamic angular velocity omegai, driving moment Ti, longitudinal acceleration ax, side acceleration ay, yaw acceleration r and vehicle front wheel angle δf
In addition the zero-mean white noise that amplitude is 0.0001.
Genetic algorithm (Genetic Algorithm, abbreviation GA) is a kind of reference living nature natural selection and heredity naturally
The random search algorithm of mechanism.Breeding, intersection and the gene occurred in genetic algorithm simulation natural selection and natural genetic process
Jumping phenomenon all retains one group of candidate solution in each iteration, and chooses preferably individual, benefit from Xie Qunzhong by fitness function
These individuals are combined with genetic operator (selection, intersection and variation), the candidate solution group of a new generation is generated, repeats this mistake
Journey, until meeting certain convergence index, specific execution step is as shown in Figure 4.Genetic algorithm program is carried out first initial
Change, design parameter setting are as follows: population scale 10, evolutionary generation are that 20 elite numbers are 2, crossover probability 0.8, mutation probability
It is 0.2.
Fitness function is the only criterion for instructing the direction of search, and how to select it is critical issue in GA.Carry out
When the near front wheel longitudinal tire force sliding mode observer parameter optimization, the present invention chooses evaluated errorMean value and
Evaluation index of the variance as parameter optimization.In order to make optimum results more it is accurate rationally, first according to formula (27) by the two into
Row normalized,
Wherein, G (xi) ∈ [0 1], xminAnd xmaxMinimum value and maximum value in respectively one group of data, present invention selection
Fitness function is as follows:
Wherein, Γmx1And Γex1It is the mean value and mean square deviation weight factor of the near front wheel longitudinal tire force error, M () respectively
It is respectively the function for seeking mean value and mean square deviation with D (),
Wherein, N is the total number of single variable, and N=t/s, t are simulation time, and s is simulation step length.
It is as shown in Figure 4 that genetic algorithm specifically executes step.When emulation, Γ is chosenmx1=0.5, Γex1=0.5, simulation time
For 23s, simulation step length 0.01.The near front wheel longitudinal tire force sliding mode observer parameter optimization result based on genetic algorithm is as schemed
Shown in 5, in optimization process, with the increase of population algebra, fitness function converges on a minimum value, is obtained by optimizing us
It is L to sliding formwork gain at this timex1=25.3026, feedback oscillator ρx1=682.3490, corresponding fitness function value is
0.13376。
It is excellent that the longitudinal tire force and the lateral tire force observer parameter of axle of its excess-three wheel press above process progress
Change, the results are shown in Table 1 for parameter optimization.
2. using Jing Guo parameter optimization front axle longitudinal direction and the lateral tire force estimated value of axle as input, use is monotropic
The standard genetic algorithm of amount carries out parameter optimization to vehicle yaw acceleration state observer.
For yaw rate observer Parametric optimization problem, the parameter for needing to optimize is Kr, program parameter is initial
When change, each parameter is identical as above-mentioned setting.The fitness function of selection such as formula (30) is shown, wherein evaluated error?
Under conventional high attachment two-track line operating condition, the results are shown in Table 1 for parameter optimization.
3. using Jing Guo parameter optimization longitudinal direction and lateral tire force estimated value as input, using multi-objective genetic algorithm pair
Longitudinal direction of car, lateral speed observer parameter optimize, and obtain Pareto optimal solution set:
As shown in formula (25) longitudinally, laterally speed observer form it is found that longitudinally and laterally both speed is mutually coupled,
When estimating longitudinal speed will using the estimated value of lateral speed as input, meanwhile, when estimating lateral speed
It also will be using the estimated value of longitudinal speed as input.Therefore, single when to longitudinally and laterally speed observer parameter optimizes
The standard genetic algorithm of target is no longer applicable in.In view of the relationship that influences each other between the two estimated value, the present invention is utilized
Gamultiobj function in the tool box Matlab solves multi-objective optimization question.Gamultiobj function uses controlled
Elite genetic algorithm, which is nondominated sorting genetic algorithm II (nondominated
Sorting genetic algorithm II, NSGA-II) variant.The basic principle is that: it finds in feasible zone by optimizing
The vector of variable composition so that one group of conflicting objective function reaches minimum simultaneously as far as possible, and by be arranged it is optimal before
The number for holding individual (elite individual) on coefficient (Pareto Fraction) limitation forward position Pareto (Pareto), to make institute
Solution converges on Pareto leading surface.Formula (31) is the objective function of longitudinally and laterally speed observer parameter optimization
Multi-objective genetic algorithm parameter setting are as follows: optimal front end coefficient is 0.3, population scale 50, evolutionary generation 50,
Crossover probability is 0.8, mutation probability 0.2.Under conventional high attachment two-track line operating condition, before the Pareto obtained by parameter optimization
Along as shown in fig. 6, institute's optimized variable value and its corresponding target function value are as shown in table 3:
The longitudinally/laterally speed state observer parameter optimization Pareto optimal solution of table 3
| Serial number | Kx | Ky | f1 | f2 |
| 1 | 0.0018 | 0.0099 | 0.363410 | 0.376690 |
| 2 | 0.0041 | 0.0096 | 0.368920 | 0.376650 |
| 3 | 0.0043 | 0.0098 | 0.373136 | 0.376647 |
| 4 | 0.0044 | 0.0098 | 0.376021 | 0.376645 |
| 5 | 0.0046 | 0.0094 | 0.379915 | 0.376643 |
| 6 | 0.0050 | 0.0094 | 0.388216 | 0.376638 |
| 7 | 0.0053 | 0.0098 | 0.393134 | 0.376632 |
| 8 | 0.0057 | 0.0087 | 0.398318 | 0.376631 |
| 9 | 0.0058 | 0.0095 | 0.400976 | 0.376628 |
| 10 | 0.0061 | 0.0092 | 0.404255 | 0.376626 |
| 11 | 0.0064 | 0.0093 | 0.408570 | 0.376622 |
| 12 | 0.0070 | 0.0097 | 0.416050 | 0.376618 |
| 13 | 0.0076 | 0.0091 | 0.422540 | 0.376616 |
| 14 | 0.0096 | 0.0098 | 0.435631 | 0.376608 |
As seen from Figure 6, two objective functions are conflicting, and the reduction of one of target function value can then draw
Therefore the increase for playing another target function value then needs to weigh two objective functions in the forward position Pareto, selects one group
Suitable solution.In table 3 as can be seen that in the 14 groups of optimal solutions enumerated, objective function f1Value it is opposite with objective function f2's
Value changes greatly.Gap between its minimum value and maximum value is also bigger, thus the present invention emphatically consider variation range compared with
Big objective function f1Value, select one group of objective function f1The lesser solution of value.The solution of final choice is such as 1 institute of serial number in table 3
Show, i.e. Kx=0.0018, Ky=0.0099.
The four-drive electric car state observer parameter optimization method of the present invention based on genetic algorithm is given below
Off-line simulation verifying.
In order to verify the validity of observer parameter optimization method, the parameter of institute's optimization in table 1 is input to mould first
In the vehicle-state observer of block, and using the four-wheel driving electric vehicle in veDYNA as emulation vehicle.And with conventional height
Adhere to two-track line operating condition as emulation operating condition, to verify and the vehicle-state observer under operating condition identical when observer parameter optimization
Estimation effect.Specific experimental result and analysis is given below.
In view of the symmetry of Vehicular system, for longitudinal tire force, the simulation result of left side longitudinal tire force is only provided.
Fig. 7-9, for the simulation result diagram under the operating condition.Fig. 7 is respectively the near front wheel and left rear wheel longitudinal tire force observer estimated result
True value correlation curve and its evaluated error are exported with veDYNA.Fig. 8 is respectively front-wheel and the lateral tire force observer of rear-wheel
Estimated result and veDYNA output true value correlation curve and its evaluated error.Fig. 9 distinguishes longitudinally/laterally speed and yaw angle
Speed observer estimated result and veDYNA output true value correlation curve and its evaluated error.It can be seen by simulation result diagram
Out, for estimated vehicle-state, the observer estimated value by parameter optimization can preferably track straight by veDYNA
The true value of output is connect, and has lesser evaluated error, this illustrates the observer proposed by the invention based on genetic algorithm
Parameter optimization method has certain validity.
Claims (3)
1. a kind of four-drive electric car state observer parameter adjusting method based on genetic algorithm, which is characterized in that including with
Lower step:
Step 1: establishing the Three Degree Of Freedom auto model of vehicle single-wheel roll modeling and simplification；
Step 2: modular vehicle state Observer Design: with vehicle sensors metrical information vehicle wheel rotation angular speed and driving
Torque designs longitudinal tire force observer as input, using the longitudinal tire force that sliding mode observer method takes turns four；Again with
It is lateral to separately design axle as input for longitudinal tire force estimated value, front wheel angle, side acceleration and yaw velocity
Tire force sliding mode observer；Finally with longitudinal and lateral tire force estimated value, longitudinal direction and side acceleration, yaw acceleration and vehicle
Front wheel angle designs the car speed omnidirectional vision of longitudinal speed, lateral speed and yaw velocity as input；
Step 3: the modular vehicle state observer based on step 2 design, using genetic algorithm respectively to each estimation mould
Block is observed device parameter optimization, specifically includes the following steps:
3.1) joined using the longitudinal tire force sliding mode observer that the standard genetic algorithm of bivariate first respectively takes turns four, vehicle
Number optimizes, then using the front axle longitudinal tire force estimated value Jing Guo parameter optimization as input to the lateral tire force sliding formwork of front axle
Observer parameter optimizes, and finally optimizes to the lateral tire force sliding mode observer parameter of rear axle
3.2) using Jing Guo parameter optimization front axle longitudinal direction and the lateral tire force estimated value of axle as input, using single argument
Standard genetic algorithm parameter optimization is carried out to yaw rate state observer, and it is the observer parameter of optimization is defeated
Enter to yaw rate observer；
3.3) using Jing Guo parameter optimization longitudinal direction and lateral tire force estimated value as input, using multi-objective genetic algorithm to vehicle
Longitudinally, laterally speed observer parameter optimizes, and obtains Pareto optimal solution set.
2. a kind of four-drive electric car state observer parameter adjusting method based on genetic algorithm as described in claim 1,
It is characterized in that, the Three Degree Of Freedom auto model of vehicle single-wheel roll modeling and simplification that the step 1 is established are as follows:
1.1) vehicle single-wheel roll modeling:
Wherein, J is the rotary inertia of wheel, units/kg m2, ωi(i=1,2,3,4) be respectively four wheels angle of rotation speed
Degree, unit rad/s, ReffFor the effective radius of tire, unit m, T are the driving moment of each wheel, unit N/m；
1.2) the Three Degree Of Freedom auto model simplified
Longitudinal direction of car and lateral tire force Fx、FyWith vehicle around z-axis rotating torque MzIt can indicate are as follows:
Fx=(Fx1+Fx2)cosδf-(Fy1+Fy2)sinδf+Fx3+Fx4
Fy=(Fx1+Fx2)sinδf+(Fy1+Fy2)cosδf+Fy3+Fy4
Mz=lF(Fx1+Fx2)sinδf+lF(Fy1+Fy2)cosδf-lR(Fy3+Fy4)
Wherein, Fxi/Fyi(i=1 ..., 4) is respectively four longitudinal directions taken turns and lateral tire force, unit N, δfIt is to rotate before vehicle
Angle, unit rad, lFAnd lRIt is distance of the vehicle centroid away from front axle and rear axle, unit m respectively.
3. a kind of four-drive electric car state observer parameter adjusting method based on genetic algorithm as described in claim 1,
It is characterized in that, the modular vehicle state observer of the step 2 design specifically includes:
2.1) longitudinal tire force sliding mode observer, form are as follows:
Wherein, Lxi(i=1 ..., 4) is respectively the feedback oscillator of tire force sliding mode observer longitudinally in each, ρxi(i=1 ...,
4) the respectively sliding formwork gain of tire force sliding mode observer longitudinally in each, φ are a constant greater than 0；
2.2) lateral tire force sliding mode observer, comprising:
The lateral tire force synovial membrane observer of front axle are as follows:
Wherein, LyfFor the feedback oscillator of the lateral tire force sliding mode observer of front axle, ρyfFor the lateral tire force sliding mode observer of front axle
Sliding formwork gain, IzIt is vehicle around the rotary inertia of z-axis, units/kg m2, r is automobile yaw velocity, unit rad/s；
The lateral tire force synovial membrane observer of rear axle:
Wherein, LyrFor the feedback oscillator of the lateral tire force sliding mode observer of rear axle, ρyrFor the lateral tire force sliding mode observer of rear axle
Sliding formwork gain；
2.3) car speed omnidirectional vision designs:
Longitudinal acceleration, side acceleration and yaw velocity is selected to export as systematic survey, and by they and its estimated value
Correction term of the difference as car speed observer, be based on non-linear full micr oprocessorism structure, design longitudinal direction of car, lateral vehicle
The car speed full micr oprocessorism of speed and yaw velocity, expression formula are as follows:
Wherein, Ki(i=x, y, r) represents observer gain, can will longitudinally, laterally acceleration estimation using tire force estimated value
ValueAnd vehicle is around z-axis rotating torque estimated valueIt indicates are as follows:
Wherein, ax、ayThe respectively longitudinal direction of vehicle and side acceleration, unit m/s2, m is the gross mass of vehicle, units/kg.
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| CN112659921B (en) * | 2020-12-14 | 2022-11-22 | 北京航空航天大学 | Design method of independent drive electric vehicle optimization controller based on genetic algorithm |
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| WO2023173280A1 (en) * | 2022-03-15 | 2023-09-21 | Huawei Technologies Co.,Ltd. | System and method for autonomous vehicle motion planner optimisation |
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