CN102009654A - 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 invention relates to a longitudinal speed evaluation method of a full-wheel electrically-driven vehicle. The method comprises the following steps: 1) configuring a speed measuring system; 2) collecting signals shown in the specification in real time; 3) carrying out filtering treatment on the collected signals in a Kalman filtering mode; 4) establishing a speed evaluation formula based on a Kalman filter space equation structure and a speed evaluation formula based on integrated acceleration; and 5) switching discretion by using a speed evaluation algorithm: setting a threshold value for the absolute value of the trackslip/slip rate lambda as epsilon, when the absolute value of lambda is less than the epsilon, adopting the speed evaluation formula based on the Kalman filtering, and when the absolute value of lambda is more than or equal to the epsilon, adopting the speed evaluation formula based on the integrated acceleration. The method provided by the invention is suitable for online speed evaluation of the full-wheel electrically-driven vehicle, comprising exact observation for the longitudinal speed when the wheel is subject to excessive trackslip/slippage and even locking.
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 crucial status.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 according to onboard sensor to vehicle key state parameter to be estimated, effective information is passed to control system, thereby realize the effective control to vehicle-state.The speed of a motor vehicle is the important reference of design vehicle Stability Control device 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 speed of a motor vehicle estimation approach 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 a tire radius, and w is a wheelbase, and γ is a 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 take turns independent electro-motive vehicle entirely, in the driving 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 bigger.Therefore, the method based on the wheel speed signal reduction speed of a motor vehicle can't directly apply to the independent electro-motive vehicle of full wheel.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
0Be the integration rate of onset, a
xBe longitudinal acceleration signal, t is length integration time.The advantage of this method is not to be subjected to brake or drive the influence of operating mode, but because longitudinal acceleration signal has noise, integration can cause substantial deviation actual value as a result for a long time, therefore this method can only be used at short notice, is not suitable for the long-time speed of a motor vehicle of the independent electro-motive vehicle of full wheel and estimates.Simultaneously, in this method, how to determine that the integration initial value also is a major issue.
Some scholars have also studied based on the speed of a motor vehicle observation of sliding formwork algorithm and based on the speed of a motor vehicle of nonlinear state observer and have observed algorithm.These algorithms adopt complicated non-linear vehicle, tire model, have considered the influence of nonlinear characteristic to 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 subjected to considerable influence.
Summary of the invention
At 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) collect the tire rotational speed signal in real time by vehicle speed measurement system
The steering wheel angle signal
With automobile barycenter longitudinal acceleration signal
The wheel limit speed of tire is
3) take the Kalman filtering mode that the signal that collects is carried out Filtering Processing;
4) utilize signal after the Filtering Processing, 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. according to the vehicle movement following relational expression that learns:
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 the 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, will go up the speed of a motor vehicle estimated valve v of a moment this 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 and do not take place 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), suppose w
sBe independent Gaussian distribution stochastic signal, 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, promptly
K wherein
QBe by vehicle-state, the parameter of speed of a motor vehicle decision especially.
In step 4), suppose w
0Be independent Gaussian distribution stochastic signal, 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 wheel 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 big.
The present invention is owing to take above technical scheme, and it has the following advantages: 1, this method is applicable to the online speed of a motor vehicle estimation of full wheel electro-motive vehicle; 2, occur excessively trackslipping/slippage at wheel, even also can accurately observe vertical speed of a motor vehicle during locking fully, this method has higher precision; 3, invention has considered to turn to the influence to wheel speed, makes that the speed of a motor vehicle estimation in steering procedure is also more accurate.
Description of drawings
Fig. 1 is a 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 is gathered 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 vehicle speed value comparatively accurately.
As shown in Figure 1, the inventive method may further comprise the steps:
1) acquisition of signal
The information such as wheel speed, longitudinal acceleration and steering wheel angle of real-time collection vehicle.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 of gathering is carried out Filtering Processing
Because be mingled with into environmental noise in acquisition of signal and the transmission course, the original signal that collects often has burr and error.This signal that has 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 Processing to these signals before using, filter should satisfy following requirement: order is low, signal smoothing.Can design any filter that meets the demands in view of the above and carry out Filtering Processing, the present invention takes the mode of Kalman filtering that the signal that collects is carried out 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 in real time filtering, simultaneously, utilize the characteristics of Kalman filter based on state space equation to original signal, can be self-generating from Kalman filter the extension derivative signal of reduction original signal.
Filtering signal by this step is handled, 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 and do not take place excessively not trackslip/slippage that at this moment, utilization can obtain reasonable result based on the vehicle speed estimation method of Kalman filtering.
According to the vehicle movement following relational expression that learns:
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 independent Gaussian distribution stochastic signal, and suppose that its variance battle array is respectively Q and R.Through type (8), (9) promptly can constitute the space equation structure of complete Kalman filter.
Process noise in the Kalman filtering algorithm and observation noise are influenced 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 online adjusting, can obtain result more accurately.
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, promptly
K wherein
QBe by vehicle-state, the parameter of speed of a motor vehicle decision especially.Fig. 2 is the adjustment curve that process noise variance Q changes with the 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 the wheel speed and the speed of a motor vehicle.When the rate of trackslipping/move was low, difference was less between the 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 the 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, adopt fuzzy mode of regulating herein to noise variance R.When tire occur trackslipping/during slippage, the absolute value of the rate of trackslipping/move of tire | λ |, and the absolute difference between wheel 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 big, design the membership function (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.
As the formula (10), vertically the 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, when the algorithm for estimating that switches to based on integrated acceleration, with last 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 big.In this case, v
yThe γ item is with respect to a
xBe event.Simultaneously, on the vehicle that has been equipped with ABS, it is generally very short that tire takes place by the seized time, and in this case, formula (11) can be approximated to be
v
x＝v
0+∫a
xdt (12)
And in 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 and do not take place excessively not trackslip/slippage, as | λ | during 〉=ε, think that wheel enters excessively to trackslip/the slippage stage.
As | λ | during＜ε, think and do not take place 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 speed of vehicle constantly.
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 take place 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 the wheel speed and the speed of a motor vehicle.At this moment, if still adopt Kalman filter algorithm, bigger deviation will appear in the result who obtains.
When the slippage rate absolute value | λ | satisfy once more | 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 last 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) collect the tire rotational speed signal in real time by vehicle speed measurement system
The steering wheel angle signal
With automobile barycenter longitudinal acceleration signal
The wheel limit speed of tire is
3) take the Kalman filtering mode that the signal that collects is carried out Filtering Processing;
4) utilize signal after the Filtering Processing, 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. according to the vehicle movement following relational expression that learns:
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 the 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, will go up the speed of a motor vehicle estimated valve v of a moment this 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 and do not take place 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 (8) of step 4) formula, suppose w
sBe independent Gaussian distribution stochastic signal, 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, promptly
K wherein
QBe by vehicle-state, the parameter of speed of a motor vehicle decision especially.
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 (9) of step 4) formula, suppose w
0Be independent Gaussian distribution stochastic signal, 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 the absolute difference between wheel angular 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, set up following fuzzy reasoning and regulate rule relation:
S wherein, M, L, the VL representative is little, in, greatly and very big.
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