CN102009654B - Longitudinal speed evaluation method of full-wheel electrically-driven vehicle - Google Patents
Longitudinal speed evaluation method of full-wheel electrically-driven vehicle Download PDFInfo
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Abstract
The present invention relates to a kind of longitudinal vehicle speed estimation methods of electronic all wheel drive comprising: 1) vehicle speed measurement system is set; 2) in real time acquisition and
,
With
; 3) Kalman filtering mode is taken to be filtered collected signal; 4) speed estimation of the building based on Kalman filter space equation structure and the speed estimation based on integrated acceleration; 5) using speed algorithm for estimating switching differentiate: set/slip rate the absolute value that trackslips | λ | threshold values as ε, when | λ | when ＜ ε, using the speed estimation formulas based on Kalman filtering, when | λ | when ≥ε, using the speed estimation formulas based on integrated acceleration. The present invention be suitable for electronic all wheel drive online speed estimate, be included in wheel appearance excessively trackslip/slide, even locking when also longitudinal speed can accurately be observed.
Description
Technical field
The present invention relates to a kind of automobile speed method of estimation, especially relate to the method that a kind of real-time online is estimated the vertical speed of a motor vehicle of full wheel electro-motive vehicle.
Background technology
In the Study on Vehicle Dynamic Control process, the monitoring of vehicle real-time status is occupied very consequence.The accuracy of observation of some crucial vehicle-states has directly determined the effect of Study on Vehicle Dynamic Control.Vehicle state observer system carries out online in real time estimation according to onboard sensor to vehicle key state parameter, and effective information is passed to control system, thereby realizes the effective control to vehicle-state.The speed of a motor vehicle is the important reference of design vehicle stability controller parameter, utilizes vehicle-mounted common sensor that the speed of a motor vehicle is carried out real-time online and estimates it is that vehicle stability is controlled one of needed gordian technique.
Existing carry out the method that the speed of a motor vehicle estimates based on vehicle-mounted common sensor and mainly contain two kinds.Method one: be by wheel speed signal reduction, i.e. v
x=ω r ± 0.5w γ, wherein, v
xBe vertical speed of a motor vehicle estimated valve, r is tire radius, and w is wheelbase, and γ is yaw velocity.Under the free rotary state of non-driving wheel, can directly obtain vertical speed of a motor vehicle according to this formula.There is not non-driving wheel but entirely take turns the independent electric drive vehicle, in the driving/braking process, has all the time to trackslip/existence of slippage.If directly utilize non-driving wheel wheel speed signal reduction, trackslip/slip rate will make speed of a motor vehicle evaluated error larger.Therefore, the method based on the wheel speed signal reduction speed of a motor vehicle can't directly apply to full wheel independent electric drive vehicle.Method two: carry out vertical speed of a motor vehicle reduction, i.e. v based on longitudinal acceleration signal
x=v
0+ ∫ a
xDt, wherein, v
0The integration rate of onset, a
xBe longitudinal acceleration signal, t is length integration time.The advantage of the method is not to be subjected to brake or drive the impact of operating mode, but because longitudinal acceleration signal is with noise, integration can cause as a result substantial deviation actual value for a long time, therefore the method can only be used at short notice, is not suitable for the long-time speed of a motor vehicle of full wheel independent electric drive vehicle and estimates.Simultaneously, in this method, how to confirm integration initial value also is a major issue.
Some scholars have also studied based on the observation of the speed of a motor vehicle of sliding Mode Algorithm and based on the speed of a motor vehicle observation algorithm of Nonlinear Observer.These algorithms adopt complicated non-linear vehicle, tire model, have considered the impact of nonlinear characteristic on speed of a motor vehicle accuracy of observation in speed of a motor vehicle observation.When adopting these algorithms to carry out speed of a motor vehicle estimation, precision is generally higher, but owing to involve more nonlinear iteration calculating, real-time has been subject to considerable influence.
Summary of the invention
For the problems referred to above, the purpose of this invention is to provide a kind of efficient, accurately and the method for estimation of the vertical speed of a motor vehicle of full wheel electro-motive vehicle can real-time online be provided.
For achieving the above object, the present invention takes following technical scheme: a kind of vertical vehicle speed estimation method of full wheel electro-motive vehicle, and it may further comprise the steps:
1) vehicle speed measurement system is set, it comprises longitudinal acceleration sensor, steering wheel angle sensor and tire rotational speed sensor;
2) arrive the tire rotational speed signal by the vehicle speed measurement system Real-time Collection
, the steering wheel angle signal
With automobile barycenter longitudinal acceleration signal
, the wheel limit speed of tire is
3) taking the Kalman filtering mode that the signal that collects is carried out filtering processes;
4) signal after utilizing filtering to process, adopt two kinds of methods that the speed of a motor vehicle is carried out On-line Estimation:
I, structure are estimated based on the speed of a motor vehicle of Kalman filter space equation structure
1. obtain following relational expression according to vehicle kinematics:
a
x(t)＝v′
x-v
yγ (4)
v
w(t)＝v
x(t)+Δv (5)
2. the discretization processing being done in formula (4), (5) obtains:
v
x(k+1)＝v
x(k)+a
xΔT+v
y(k)γ(k)ΔT (6)
v
w(k)＝v
x(k)+Δv (7)
3. with w in the formula (6)
s=v
y(k) γ (k) Δ T is defined as the process noise of system, with w in the formula (7)
0=Δ v is defined as the observation noise of system, can obtain the space equation structure of Kalman filter:
Equation of state v
x(k+1)=v
x(k)+a
xΔ T+w
s(8)
Observational equation v
w(k)=v
x(k)+w
0(9)
Can estimate the online speed of a motor vehicle of vehicle by the space equation structure of Kalman filter;
II, structure are estimated based on the speed of a motor vehicle of integrated acceleration
Trackslip when wheel enters excessively/the slippage stage, at this moment, vertically first derivative and the longitudinal acceleration of the speed of a motor vehicle have following relation
v′
x＝a
x+v
yγ (10)
(10) integration is obtained
v
x＝v
0+∫(a
x+v
yγ)dt (11)
Wherein, v
0Be the initial speed of a motor vehicle, this moment is with the speed of a motor vehicle estimated valve v of a upper moment based on Kalman filtering
xAs the initial value of integrated acceleration, make up based on the speed of a motor vehicle of integrated acceleration and estimate;
5) utilizing speed of a motor vehicle algorithm for estimating to switch differentiates:
Setting is trackslipped/the slip rate absolute value | λ | threshold values be ε,
As | λ | during＜ε, think not occur excessively not trackslip/slippage that adopt 4 this moment) described in the speed of a motor vehicle estimation formulas based on Kalman filtering,
As | λ | during 〉=ε, think that wheel enters excessively to trackslip/the slippage stage that adopt 4 this moment) described in the speed of a motor vehicle estimation formulas based on integrated acceleration.
In step 4) in, suppose w
sBe Gaussian distribution stochastic signal independently, and suppose that its variance battle array is Q, be defined as process noise, then it is regulated as follows: according to the two degrees of freedom auto model, the yaw velocity γ of vehicle and side direction speed of a motor vehicle v
yAll are front wheel angle δ
fLinear function, w
sThe quadratic function that can regard steering wheel angle as, namely
, k wherein
QBy vehicle-state, the parameter that determines of the speed of a motor vehicle especially.
In step 4) in, suppose w
0Be Gaussian distribution stochastic signal independently, and suppose that its variance battle array is R, be defined as observation noise, then it is regulated as follows: when tire occur trackslipping/during slippage, the absolute value of the rate of trackslipping/move of tire | λ |, and vehicle wheel rotation angular acceleration a
wWith barycenter longitudinal acceleration a
xBetween absolute difference | a
x-a
w| all can change, therefore when formulating fuzzy rule, choose | λ | and Δ a=|a
x-a
w| as the input of fuzzy rule, set up following fuzzy reasoning and regulate rule relation:
S wherein, M, L, the VL representative is little, in, greatly and very large.
The present invention is owing to take above technical scheme, and it has the following advantages: 1, the method is applicable to entirely take turns the online speed of a motor vehicle estimation of electro-motive vehicle; 2, occur excessively trackslipping/slippage at wheel, even also can accurately observe vertical speed of a motor vehicle during locking fully, the method has higher precision; 3, invention has considered to turn to the impact on wheel speed, so that the estimation of the speed of a motor vehicle in steering procedure is also more accurate.
Description of drawings
Fig. 1 is general frame scheme drawing of the present invention;
Fig. 2 is that process noise Q of the present invention regulates MAP figure;
Fig. 3 is of the present invention | λ | and membership function figure;
Fig. 4 is of the present invention | the membership function figure of Δ a|;
Fig. 5 is the membership function figure of observation noise R of the present invention;
Fig. 6 is the adjustment mapping graph of observation noise R of the present invention.
The specific embodiment
Below in conjunction with drawings and Examples the present invention is described in detail.
Vehicle speed measurement system of the present invention gathers vehicle-mounted common sensor signal (comprising longitudinal acceleration sensor signal, steering wheel angle sensor signal) and drive motor feedback signal (being the tire rotational speed signal), utilization is estimated respectively based on the vehicle speed estimation method of Kalman filtering with based on the vehicle speed estimation method of integrated acceleration, and take the mode of slippage rate threshold value to switch differentiation, and then obtained comparatively accurately vehicle speed value.
As shown in Figure 1, the inventive method may further comprise the steps:
1) acquisition of signal
The information such as the wheel speed of Real-time Collection vehicle, longitudinal acceleration and steering wheel angle.Collect the tire rotational speed signal
, the steering wheel angle signal
With automobile barycenter longitudinal acceleration signal
The wheel limit speed of tire is
2) signal that gathers being carried out filtering processes
Because be mingled with into environmental noise in acquisition of signal and the transmission course, the original signal that collects often is with jagged and error.This signal with high frequency noise not only itself can't satisfy operating needs, and can further amplify this error when extending derivative signal obtaining, and causes the result can't identification.Therefore must carry out filtering to these signals before using and process, filter should satisfy following requirement: order is low, signal smoothing.Can design accordingly any filter that meets the demands and carry out the filtering processing, the present invention takes the mode of Kalman filtering that the signal that collects is carried out the filtering processing.
Kalman filtering is a kind of optimal filtering method that is based upon on the Time-series Theory basis.Adopt kalman filter method to carry out online Real-Time Filtering to original signal, simultaneously, utilize the characteristics of Kalman filter state-based space equation, the extension derivative signal of reduction original signal from Kalman filter that can be spontaneous.
Filtering signal by this step is processed, and obtains comparatively level and smooth wheel limit speed v
w, wheel limit acceleration/accel a
w, longitudinal acceleration a
xWith steering wheel angle δ
wSignal.
3) structure is based on the vehicle speed estimation method of Kalman filtering
Setting is trackslipped/the slip rate absolute value | λ | threshold values be ε, as | λ | during＜ε, think not occur excessively not trackslip/slippage, at this moment, use the vehicle speed estimation method based on Kalman filtering can obtain reasonable result.
Obtain following relational expression according to vehicle kinematics:
a
x(t)＝v′
x-v
yγ (4)
v
w(t)＝v
x(t)+Δv (5)
The discretization processing is done in formula (4), (5) to be obtained:
v
x(k+1)＝v
x(k)+a
xΔT+v
y(k)γ(k)ΔT (6)
v
w(k)＝v
x(k)+Δv (7)
With w in the formula (6)
s=v
y(k) γ (k) Δ T is defined as the process noise of system, with w in the formula (7)
0=Δ v is defined as the observation noise of system, and formula (6), (7) can be rewritten as follows:
Equation of state v
x(k+1)=v
x(k)+a
xΔ T+w
s(8)
Observational equation v
w(k)=v
x(k)+w
0(9)
Suppose w
sAnd w
0Be independently Gaussian distribution stochastic signal, and suppose that its variance battle array is respectively Q and R.Through type (8), (9) namely can complete the space equation structure of Kalman filter.
Process noise in the Kalman filtering algorithm and observation noise are affected by vehicle-state, when estimating the speed of a motor vehicle, according to the vehicle current state process noise and observation noise are carried out on-line control, can obtain more accurately result.
The adjusting of process noise variance Q: process noise w
s=v
y(k) γ (k) Δ T depends on whether vehicle turns to.According to the two degrees of freedom auto model, the yaw velocity γ of vehicle and side direction speed of a motor vehicle v
yAll are front wheel angle δ
wLinear function.So process noise w
sThe quadratic function that can regard steering wheel angle as, namely
, k wherein
QBy vehicle-state, the parameter that determines of the speed of a motor vehicle especially.Fig. 2 is the adjustment curve that process noise variance Q changes with front wheel angle and the speed of a motor vehicle.
The adjusting of observation noise variance R: observation noise w
0=Δ v characterizes the difference between wheel speed and the speed of a motor vehicle.When the rate of trackslipping/move was low, difference was less between wheel speed and the speed of a motor vehicle, thereby observation noise variance R also should be corresponding less.Slippage/when trackslipping increasing, the difference between wheel speed and the speed of a motor vehicle increases gradually when tire begins to occur, and this moment, observation noise variance R also should increase accordingly.In order to regulate noise variance R, adopt fuzzy mode of regulating herein.When tire occur trackslipping/during slippage, the absolute value of the rate of trackslipping/move of tire | λ |, and the absolute difference between vehicle wheel rotation angular acceleration (conversion is tangential acceleration) and the barycenter longitudinal acceleration | a
x-a
w| all can change.Therefore when formulating fuzzy rule, choose | λ | and Δ a=|a
x-a
w| as the input of fuzzy rule.Following table is depicted as the fuzzy reasoning of observation noise R and regulates rule.
The fuzzy rule of form 1 observation noise R is regulated
Wherein respectively with S, M, L, the VL representative is little, in, greatly and very large, design the membership function (such as Fig. 3, Fig. 4 and shown in Figure 5) of each input and output amount.Figure 6 shows that the adjustment curved surface of observation noise R.
4) structure is based on the vehicle speed estimation method of integrated acceleration
As | λ | during 〉=ε, think that wheel enters excessively to trackslip/the slippage stage that at this moment, the speed of a motor vehicle algorithm for estimating that switches to based on integrated acceleration can obtain reasonable effect.
Shown in (10), vertically first derivative and the longitudinal acceleration of the speed of a motor vehicle have following relation
v′
x＝a
x+v
yγ (10)
(10) integration is obtained
v
x＝v
0+∫(a
x+v
yγ)dt (11)
Wherein, v
0The initial speed of a motor vehicle, when the algorithm for estimating that switches to based on integrated acceleration, with upper one constantly based on the speed of a motor vehicle estimated valve v of Kalman filtering
xInitial value as integrated acceleration.General, when algorithm for estimating switches to algorithm based on integrated acceleration, must be at emergency braking, the situation when acceleration/accel is larger.In this case, v
yThe γ item is with respect to a
xBe event.Simultaneously, on the vehicle that has been equipped with ABS, wheel seized time of Somatic Embryogenesis is generally very short, and in this case, formula (11) can be approximated to be
v
x＝v
0+∫a
xdt (12)
And within this short integration time, can ignore because the error that the acceleration noise integration causes.
5) speed of a motor vehicle algorithm for estimating switches differentiation
Setting is trackslipped/the slip rate absolute value | λ | threshold values be ε, as | λ | during＜ε, think not occur excessively not trackslip/slippage, as | λ | during 〉=ε, think that wheel enters excessively to trackslip/the slippage stage.
As | λ | during＜ε, think not occur excessively not trackslip/slippage that adopt 3 this moment) described in the vehicle speed estimation method based on Kalman filtering, by the Kalman filtering iteration, can estimate each constantly speed of vehicle.
As | λ | during 〉=ε, think that wheel enters excessively to trackslip/the slippage stage that switch to 4 this moment) described in the vehicle speed estimation method based on integrated acceleration.This is because be with wheel speed signal v based on the Kalman filter of formula (9)
wAs feedback compensation mechanism, when wheel occur excessively to trackslip/during slippage, wheel speed and speed of a motor vehicle relevance are very little; In wheel generation locking or when trackslipping fully, no longer related between wheel speed and the speed of a motor vehicle.At this moment, if still adopt Kalman filter algorithm, larger deviation will appear in the result who obtains.
When the slippage rate absolute value | λ | again satisfy | λ | during＜ε, thinking that wheel has withdrawed from excessively trackslips/the slippage stage, can switch back 3) described in the vehicle speed estimation method based on Kalman filtering, the initial value of Kalman filtering is upper one velocity amplitude that constantly obtains based on integrated acceleration.
By above vehicle speed estimation method, can get speed of a motor vehicle estimated valve at each sampling instant k
Claims (3)
1. vertical vehicle speed estimation method of a full wheel electro-motive vehicle, it may further comprise the steps:
1) vehicle speed measurement system is set, it comprises longitudinal acceleration sensor, steering wheel angle sensor and tire rotational speed sensor;
2) arrive the tire rotational speed signal by the vehicle speed measurement system Real-time Collection
, the steering wheel angle signal
With automobile barycenter longitudinal acceleration signal
, the wheel limit speed of tire is
, wherein r is tire radius;
3) taking the Kalman filtering mode that the signal that collects is carried out filtering processes;
4) signal after utilizing filtering to process, adopt two kinds of methods that the speed of a motor vehicle is carried out On-line Estimation:
I, structure are estimated based on the speed of a motor vehicle of Kalman filter space equation structure
1. obtain following relational expression according to vehicle kinematics:
a
x(t)＝v′
x-v
yγ (4)
v
w(t)＝v
x(t)+Δv (5)
Wherein, a
x(t) be t longitudinal acceleration constantly, v '
xBe vertical speed of a motor vehicle derivative, v
yBe the side direction speed of a motor vehicle, γ is yaw velocity, v
w(t) be t wheel limit speed constantly, v
x(t) be t vertical speed of a motor vehicle constantly, Δ v is the observation noise of system;
2. the discretization processing being done in formula (4), (5) obtains:
v
x(k+1)＝v
x(k)+a
xΔT+v
y(k)γ(k)ΔT (6)
v
w(k)＝v
x(k)+Δv (7)
Wherein, v
x(k+1) be k+1 vertical speed of a motor vehicle constantly, v
x(k) be k vertical speed of a motor vehicle constantly, a
xBe longitudinal acceleration, v
y(k) be the k side direction speed of a motor vehicle constantly, γ (k) is k yaw velocity constantly, v
w(k) be k wheel limit speed constantly;
3. with w in the formula (6)
s=v
y(k) γ (k) Δ T is defined as the process noise of system, with w in the formula (7)
0=Δ v is defined as the observation noise of system, can obtain the space equation structure of Kalman filter:
Equation of state v
x(k+1)=v
x(k)+a
xΔ T+w
s(8)
Observational equation v
w(k)=v
x(k)+w
0(9)
Can estimate the online speed of a motor vehicle of vehicle by the space equation structure of Kalman filter;
II, structure are estimated based on the speed of a motor vehicle of integrated acceleration
Trackslip when wheel enters excessively/the slippage stage, at this moment, vertically first derivative and the longitudinal acceleration of the speed of a motor vehicle have following relation
v′
x＝a
x+v
yγ (10)
(10) integration is obtained
v
x＝v
0+∫(a
x+v
yγ)dt (11)
Wherein, v
0Be the initial speed of a motor vehicle, this moment is with the speed of a motor vehicle estimated valve v of a upper moment based on Kalman filtering space equation structure
xAs the initial value of integrated acceleration, make up based on the speed of a motor vehicle of integrated acceleration and estimate;
5) utilizing speed of a motor vehicle algorithm for estimating to switch differentiates:
Setting is trackslipped/the slip rate absolute value | λ | threshold values be ε,
As | λ | during＜ε, think not occur excessively not trackslip/slippage that adopt 4 this moment) described in the speed of a motor vehicle estimation formulas based on Kalman filtering,
As | λ | during 〉=ε, think that wheel enters excessively to trackslip/the slippage stage that adopt 4 this moment) described in the speed of a motor vehicle estimation formulas based on integrated acceleration.
2. vertical vehicle speed estimation method of a kind of full wheel electro-motive vehicle as claimed in claim 1 is characterized in that: in step 4) (8) formula in, suppose w
sBe Gaussian distribution stochastic signal independently, and suppose that its variance battle array is Q, be defined as process noise, then it is regulated as follows:
According to the two degrees of freedom auto model, the yaw velocity γ of vehicle and side direction speed of a motor vehicle v
yAll are front wheel angle δ
fLinear function, w
sThe quadratic function that can regard steering wheel angle as, namely
, k wherein
QIt is the parameter that is determined by the speed of a motor vehicle.
3. vertical vehicle speed estimation method of a kind of full wheel electro-motive vehicle as claimed in claim 1 is characterized in that: in step 4) (9) formula in, suppose w
0Be Gaussian distribution stochastic signal independently, and suppose that its variance battle array is R, be defined as observation noise, then it is regulated as follows:
When tire occur trackslipping/during slippage, the absolute value of the trackslipping of tire/slip rate | λ |, and the absolute difference between longitudinal acceleration and the vehicle wheel rotation angular acceleration | a
x-a
w| all can change, therefore when formulating fuzzy rule, choose | λ | and Δ a=|a
x-a
w| as the input of fuzzy rule, set up following fuzzy reasoning and regulate rule relation:
S wherein, M, L, the VL representative is little, in, greatly and very large.
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