CN108313057B - Pure electric automobile self-adapting cruise control method based on MPC and convex optimized algorithm - Google Patents
Pure electric automobile self-adapting cruise control method based on MPC and convex optimized algorithm Download PDFInfo
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- CN108313057B CN108313057B CN201810313067.9A CN201810313067A CN108313057B CN 108313057 B CN108313057 B CN 108313057B CN 201810313067 A CN201810313067 A CN 201810313067A CN 108313057 B CN108313057 B CN 108313057B
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- 238000005265 energy consumption Methods 0.000 claims abstract description 8
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- 238000005096 rolling process Methods 0.000 claims description 9
- 230000001133 acceleration Effects 0.000 claims description 5
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- 238000004364 calculation method Methods 0.000 claims description 3
- 230000004048 modification Effects 0.000 claims description 2
- 238000012986 modification Methods 0.000 claims description 2
- 238000005457 optimization Methods 0.000 description 9
- 238000007726 management method Methods 0.000 description 5
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
- B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
- B60W30/14—Adaptive cruise control
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Abstract
The present invention relates to a kind of pure electric automobile self-adapting cruise control method based on MPC and convex optimized algorithm, belongs to technical field of new energy.This method specifically includes: S1: being required according to follow the bus control performance, establishes prediction model；S2: the model established according to S1 predicts the speed output of vehicle future time instance by MPC algorithm；S3: according to the speed output found out in MPC algorithm in S2 and the model established in S1, the power demand of vehicle future time instance is found out；S4: the power demand obtained according to S3 solves optimal torque with convex optimized algorithm and distributes, so that two motor work are in high efficiency region, it is minimum that battery exports electricity.The present invention by follow the bus control with it is energy-optimised, while two vehicle of front and back is maintained at the distance range of a safety, obtain optimal energy management strategies without influence Model Predictive Control real-time utilization, not only alleviate traffic pressure, moreover it is possible to reduce energy consumption.
Description
Technical field
The invention belongs to technical field of new energy, are related to a kind of pure electric vehicle based on model prediction and convex optimized algorithm
Follow the bus control and the vehicle energy management method of vehicle.
Background technique
From between the centuries of development of automobile, automotive engineering brings huge change to daily life.However, with
The increase of auto output in recent years, exhaust emissions amount reached the range that environment can bear, environment is caused very big
Pollution, therefore national governments greatly develop automobile energy-saving technology, to reduce pollution of the automobile to environment.
With the increase year by year of China's automobile quantity, requirement of the traveling of Quick safe automotive to traffic system is also increasingly
Height, various countries have started fleet queue control of the primary study under intelligent transportation, can be with by controlling multiple vehicles collaboration traveling
Effectively avoid traffic congestion, and can the less time for waiting red light, achieve the purpose that save fuel consumption.If two electricity
Motor-car does not pass through queue coordinated control, then its electric quantity consumption is larger in road driving.Queue control for fleet, mesh
Preceding control algolithm is the principle based on track following mostly, so-called track following, be exactly according to reference path curve and time,
Space is related, and vehicle is required to reach a certain scheduled reference path point before the deadline.Since automobile is being run over
The dynamic characteristic of speed changes fast feature in journey, and real-time control is still a problem to be solved, with vehicle-mounted inductor
The maturation of technology, by distance-sensor on vehicle, desired value in reference model PREDICTIVE CONTROL, Model Predictive Control Algorithm passes through
In conjunction with the historical information of vehicle, the output at lower a moment can be predicted, calculating speed is related to prediction duration and prediction step number.When
When the travel speed of the future time instance of automobile is predicted by Model Predictive Control Algorithm, power demand can pass through automobile
Longitudinal dynamics formula calculates.
Auto model of the present invention is pure electric automobile two of bi-motor arrangement, and power train includes two
The components such as motor, dynamic lithium battery, the clutch of size difference (peak power output is different from torque), the power train of two cars
It is identical.Wherein clutch is arranged between big motor (postposition) and rear drive shaft, and when demand torque is smaller, clutch is disconnected,
Big motor does not work, (preposition) the offer driving torque of small machine；Running car is driven, when demand torque is larger, is calculated with optimization
Demand torque is pressed the smallest electric quantity consumption distribution of torque by method.Selection for optimization method, if only wanting to guarantee the overall situation most
Excellent requirement, then Dynamic Programming (DP, Dynamic Programming) algorithm can satisfy requirement, but Dynamic Programming is sought
Excellent process time is presented exponential form and increases with the increase of control variable, with guarantee real-time control phase when Model Predictive Control
Conflict, is unable to satisfy the validity of track following.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of, the intelligent follow the bus based on pure electric vehicle controls unified algorithm,
Under conditions of guaranteeing that follow the bus driving safety, stationarity and dynamic property meet, while optimizing the energy consumption of electric vehicle.
In order to achieve the above objectives, the invention provides the following technical scheme:
A kind of pure electric automobile self-adapting cruise control method based on MPC and convex optimized algorithm, specifically includes following step
It is rapid:
S1: it is required according to follow the bus control performance, establishes prediction model；
S2: the prediction model established according to S1, by Model Predictive Control (Model Predictive Control,
MPC the speed of) algorithm, prediction vehicle future time instance exports vc(k)；
S3: v is exported according to the speed found out in MPC algorithm in S2c(k) and in S1 the vehicle overall design mould established
Type finds out the power demand P of vehicle future time instancedem(k)；
S4: the power demand obtained according to S3 solves optimal torque with convex optimized algorithm and distributes, so that two motor works
Make in high efficiency region, it is minimum that battery exports electricity.
Further, the step S2 specifically includes the following steps:
S21: using vehicle safety and stationarity as target, to meet vehicle-following behavior, Model Predictive Control calculation is established
The cost function J of methodmpc,cost:
Jmpc,cost=min (α1ds+α2v′c)
Wherein α1,α2For weight factor, value is bigger, illustrate it is higher to performance requirement corresponding to it, such as α1Value increases, then
It is stringenter to the limitation of two vehicle distances；Ds is the distance of two vehicles in the process of moving, v 'cFor the speed difference of two vehicles when driving；
S22: according to the constraint in step S1, v is found outc(k)。
Further, Model Predictive Control Algorithm described in step S21 specifically includes the following steps:
1): by rolling optimization, obtaining optimal rear car traveling acceleration and velocity amplitude ac(k), vc(k), rolling optimization
When, suitable forecast interval length is selected, length is controlled, predicts step number；The present invention selects forecast interval for 1；
2): the state value gone out according to rolling optimization in step 1), compared with desired output, by error feedback modifiers,
Revised output ac(k), vc(k)；Its formula is as follows:
Wherein,To predict output quantity, x0(k+1) it indicates to control the constant output quantity of variable, a at the k moment1Δu
(k) output quantity after controlling the increase of variable, e (k+1) are the margin of error, and x (k+1) is reality output amount.
Further, the power demand P of vehicle future time instance described in step S3dem(k) calculation formula are as follows:
Ft(k)=Fair(k)+Froll(k)+Fslope(k)+Fa(k)
Pdem(k)=Ft(k)*v(k)
Wherein, FtIt (k) is the tractive force of k moment automobile, Fair(k) be running car when air drag, FrollIt (k) is vapour
The rolling resistance of vehicle when driving, Fa(k) be running car when acceleration resistance, v (k) be k moment automobile speed.
Further, the step S4 is specifically included: cost function is (for seeking the minimum energy of rear car in energy management strategies
Consumption) are as follows:
Jopt,cost=min ∫ Pbat dt
Using the torque distribution that Numerical Methods Solve is optimal, by continuous variable PbatIt is converted into discrete variable, above-mentioned cost letter
Number modification are as follows:
Wherein, N is sampling number, and Δ t is sampling interval, PbatIndicate that rear car travels energy consumption.
The beneficial effects of the present invention are: the present invention by follow the bus control with it is energy-optimised, two vehicle of front and back is maintained at one
While a safe distance range, real-time fortune of the optimal energy management strategies without influencing Model Predictive Control is obtained
With can not only alleviate traffic pressure, moreover it is possible to reduce energy consumption.It is specific as follows:
1) it is controlled by follow the bus, traffic congestion can be effectively reduced；
2) vehicle in the process of moving, is travelled in a manner of queue, can largely improve travel safety；
3) combination of Model Predictive Control and convex optimized algorithm can provide possibility to use in real time:
4) convex optimized algorithm can reduce vehicle energy consumption with rapid Optimum power distribution；
5) control of unified algorithm can save the energy consumption of fleet in the process of moving.
Detailed description of the invention
In order to keep the purpose of the present invention, technical scheme and beneficial effects clearer, the present invention provides following attached drawing and carries out
Illustrate:
Fig. 1 is follow the bus control schematic diagram；
Fig. 2 is the follow the bus coordinated control schematic diagram of Liang Che fleet；
Fig. 3 is unified algorithm control figure；
Fig. 4 is electric vehicle configuration used in the present invention.
Specific embodiment
Below in conjunction with attached drawing, a preferred embodiment of the present invention will be described in detail.
Implementation of the invention can realize (as shown in Figure 4) by pure electric sedan model, wherein follow the bus control structure
For figure as shown in Figure 1, under the conditions of guaranteeing that the safety of follow the bus process meets, rear car passes through distance, velocity sensor, obtains front truck
Driving information obtains desired output, then by Model Predictive Control, under conditions of meeting dynamic property, stationarity, calculates
Corresponding output valve out, the output of Model Predictive Control are calculated as the input of energy management control by convex optimized algorithm
The smallest torque apportionment ratio of energy consumption, specific algorithm process are as shown in Figure 2；Fig. 3 is the optimization figure of Fig. 2, is whole shown in Fig. 3
The drive line efficiency of vehicle powertrain arrangement figure.
The specific steps of the present invention are as follows:
S1: according to and then performance requirement, prediction model is established.In the follow the bus control at k moment, first have to meet two spacings
In safe range from holding, the driving process of rear car is steady:
xk+1=f (xk,uk)
ds∈[dsmax,dsmin]；
vc∈[vcmin,vcmax]；
Wherein xk+1State for rear car at the k+1 moment, xk,ukRespectively state and control variable value of the rear car at the k moment,
I.e. the state of future time instance vehicle is the function of current time state, and ds is the distance of two vehicles in the process of moving, vcFor rear car
Speed in the process of moving.Speed, position, output torque when the state of automobile of the present invention only includes running car, are not wrapped
The content of dynamics containing lateral direction of car.
S2: the prediction model established according to S1 predicts the speed of vehicle future time instance by Model Predictive Control Algorithm
Export vc(k):
Initially set up the cost function J of Model Predictive Control Algorithmmpc,cost, the cost function of control algolithm will be with garage
Sailing safety and stationarity is target:
Jmpc,cost=min (α1ds+α2v′c)
Wherein α1,α2For weight factor, value is bigger, illustrate it is higher to performance requirement corresponding to it, such as α1Value increases, then
It is stringenter to the limitation of two vehicle distances.Then according to the constraint in step S1, v is found outc(k)。
S3: according to the output v found out in Model Predictive Control Algorithm in S2c(k) and in S1 the automobile longitudinal power established
Model is learned, the power demand P of vehicle future time instance is found outdem(k):
Ft(k)=Fair(k)+Froll(k)+Fslope(k)+Fa(k)
Pdem(k)=Ft(k)*v(k)
Wherein, FtIt (k) is the tractive force of k moment automobile, Fair(k) be running car when air drag, FrollIt (k) is vapour
The rolling resistance of vehicle when driving, Fa(k) be running car when acceleration resistance, v (k) be k moment automobile speed.
S4: the power demand obtained according to S3 solves optimal torque with convex optimized algorithm and distributes, so that two motor works
Make in high efficiency region, battery output electricity is minimum, the foundation of cost function in energy management strategies are as follows:
Jopt,cost=min ∫ Pbatdt
The present invention solves the optimal solution in S4 using numerical method, it is therefore desirable to by continuous variable PbatIt is converted into discrete
Variable, so cost function is modified are as follows:
In follow the bus control of the invention, the follow the bus control based on model prediction algorithm, as shown in Fig. 2, specifically comprising as follows
Step:
S21: by rolling optimization, Optimal Control variable a is obtainedc(k) ', vc(k) ', when rolling optimization, it is suitable to select
Forecast interval length controls length, predicts step number, and it is 1 that the present invention, which selects forecast interval,
S22: the state value gone out according to rolling optimization in S21 step is repaired compared with desired output by error feedback
Just, revised output ac(k), vc(k).Its formula is as follows:
WhereinTo predict output quantity, x0(k+1) it indicates to control the constant output quantity of variable, a at the k moment1Δu
(k) output quantity after controlling the increase of variable, e (k+1) are the margin of error, and x (k+1) is reality output amount.
Finally, it is stated that preferred embodiment above is only used to illustrate the technical scheme of the present invention and not to limit it, although logical
It crosses above preferred embodiment the present invention is described in detail, however, those skilled in the art should understand that, can be
Various changes are made to it in form and in details, without departing from claims of the present invention limited range.
Claims (3)
1. a kind of pure electric automobile self-adapting cruise control method based on MPC and convex optimized algorithm, which is characterized in that this method
Specifically includes the following steps:
S1: it is required according to follow the bus control performance, establishes prediction model；
S2: the prediction model established according to S1 passes through Model Predictive Control (Model Predictive Control, MPC)
The speed of algorithm, prediction vehicle future time instance exports vc(k) and acceleration value ac(k)；
S3: v is exported according to the speed found out in MPC algorithm in S2c(k) and ac(k) and in S1 the vehicle overall design established
Model finds out the power demand P of vehicle future time instancedem(k)；
S4: the power demand obtained according to S3 solves optimal torque with convex optimized algorithm and distributes, so that two motor work exist
High efficiency region, it is minimum that battery exports electricity；
The step S2 specifically includes the following steps:
S21: using vehicle safety and stationarity as target, the cost function J of Model Predictive Control Algorithm is establishedmpc,cost:
Jmpc,cost=min (α1ds+α2v′c)
Wherein α1,α2For weight factor, ds is the distance of two vehicles in the process of moving, v 'cFor the speed difference of two vehicles；
S22: according to the constraint in step S1, v is found outc(k) and ac(k)。
2. the pure electric automobile self-adapting cruise control method according to claim 1 based on MPC and convex optimized algorithm,
It is characterized in that, the power demand P of vehicle future time instance described in step S3dem(k) calculation formula are as follows:
Ft(k)=Fair(k)+Froll(k)+Fslope(k)+Fa(k)
Pdem(k)=Ft(k)*v(k)
Wherein, FtIt (k) is the tractive force of k moment automobile, Fair(k) be running car when air drag, FrollIt (k) is garage
Rolling resistance when sailing, Fa(k) be running car when acceleration resistance, v (k) be k moment automobile speed.
3. the pure electric automobile self-adapting cruise control method according to claim 1 based on MPC and convex optimized algorithm,
It is characterized in that, the step S4 is specifically included: cost function in energy management strategies are as follows:
Jopt,cost=min ∫ Pbatdt
Optimal torque distribution is solved using numerical discretization schemes, by continuous variable PbatIt is converted into discrete variable, above-mentioned cost letter
Number modification are as follows:
Wherein, N is sampling number, and Δ t is sampling interval, PbatIndicate that rear car travels energy consumption.
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| CN111231930B (en) * | 2020-01-09 | 2021-06-29 | 重庆大学 | Multi-target energy management method in HEV adaptive cruise based on MPC |
| CN112435504B (en) * | 2020-11-11 | 2022-07-08 | 清华大学 | Centralized collaborative track planning method and device under vehicle-road collaborative environment |
| CN112685960B (en) * | 2021-01-04 | 2022-08-19 | 北京理工大学 | Energy management method of pure electric sweeping vehicle |
| CN113085860B (en) * | 2021-05-07 | 2022-05-17 | 河南科技大学 | Energy management method of fuel cell hybrid electric vehicle in following environment |
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