CN110371103A - The energy management method of platoon driving hybrid vehicle based on generalized predictive control - Google Patents
The energy management method of platoon driving hybrid vehicle based on generalized predictive control Download PDFInfo
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- CN110371103A CN110371103A CN201910652916.8A CN201910652916A CN110371103A CN 110371103 A CN110371103 A CN 110371103A CN 201910652916 A CN201910652916 A CN 201910652916A CN 110371103 A CN110371103 A CN 110371103A
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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
- B60W20/00—Control systems specially adapted for hybrid vehicles
- B60W20/10—Controlling the power contribution of each of the prime movers to meet required power demand
- B60W20/15—Control strategies specially adapted for achieving a particular effect
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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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
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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
- B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
- B60W2050/0001—Details of the control system
- B60W2050/0019—Control system elements or transfer functions
- B60W2050/0028—Mathematical models, e.g. for simulation
- B60W2050/0031—Mathematical model of the vehicle
Abstract
The invention discloses a kind of hybrid vehicle energy conservation PREDICTIVE CONTROL Intelligent Optimization based on platoon driving by establishing the reduced-order model of hybrid vehicle system, and uses generalized forecast control method solving model PREDICTIVE CONTROL problem.Specific steps include establishing simplified system structure mould CARIMA type, PREDICTIVE CONTROL is carried out using (controlled autoregressive integral sliding average) prediction model, control strategy is formulated, acquires information in real time, solves optimal control problem with the mathematical model established.The present invention on the basis of broad sense minimal error controls, introduces the thought of multi-step prediction, the abilities such as anti-disturbance, random noise, Delay Variation significantly improve, and have stronger robustness in generalized predictive control in optimization.Its number of parameters is less, is easy to On-line Estimation parameter, and prediction model output error caused by parameter slow time-varying can be corrected timely, realizes self adaptive control.
Description
Technical field
The present invention relates to new-energy automobile field of energy management, specially the platoon driving state based on generalized predictive control
Under hybrid vehicle energy management method.
Background technique
Hybrid vehicle is one of the effective way that China handles energy crisis and environmental pollution at present.Highway
The speed of constantly construction, vehicle is continuously improved, however square directly proportional, the vehicle run at high speed of the air drag of vehicle and speed
It is all air drag that resistance is most of.Conventional hybrid automobile control strategy does not account for new-energy automobile platoon driving
Situation, and the utilization of inter-vehicle communication technology is not accounted for, whole work informations are made full use of, design hybrid vehicle is pre-
Control algolithm is surveyed, the fuel economy of vehicle can be effectively improved.
Summary of the invention
1, the purpose of the present invention
The present invention proposes a kind of platoon driving shape based on generalized predictive control to improve VE Vehicle Economy
The energy management method of hybrid vehicle under state
2, the technical solution adopted in the present invention
The energy management method of platoon driving hybrid vehicle based on generalized predictive control, it is characterised in that:
Step 1, the reduced-order model of hybrid vehicle system is established；
Step 2, system model is established, i.e., establishes controlled autoregressive integral moving average forecasting model with identification tool；
Step 3, formulate control strategy, i.e., velocity mode is optimized, vehicle headway use minimum value floating with
Upper strategy；
Step 4, online optimum control, and rolling optimization adjust weight parameter.
Further, the system model in the step 1 be based on two hybrid vehicles, according to vehicle mechanical coupling and
Electronics coupled relationship, column write system dynamics equation, decouple to kinetics equation, the final state-space model for obtaining system.
Further, step 2 specifically:
A(q-1) y (k)=B (q-1)u(k)+C(q-1)ξ(k)/δ
Wherein: δ is difference operator, δ=1-q-1；K indicates sampling instant sequence；Y (k), u (k) are the output, defeated of system
Enter；ξ (k) is the white noise sequence that mean value is zero；A(q-1) and B (q-1) it is q-1Multinomial, be system parameter.
Further, the formulation strategy of step 3:
Step 3.1 combines auto navigation, digital map, inter-vehicle communication technology and intelligent transportation system, is handed over using road
Logical situation, optimizes hybrid vehicle velocity mode；
Step 3.2 vehicle headway formulates control strategy form using the control strategy more than minimum value to float are as follows:
Wherein j=N1... N, N1=1 is minimum prediction time domain, and N is maximum predicted time domain, and P is control time domain, q (j), λ
(j) it is respectively softening coefficient matrix, controls weighting matrix, L is prediction and evaluation function；Control strategy is that minimum evaluation function is same
When reduce control amount fluctuation.
Further, step 4 specifically: in each sampling period, online acquisition relevant information and with the mathematics established
Model and formulation control strategy solve optimum control, finally defeated using first control amount of obtained optimal control sequence
Enter system；In next sampling instant, forecast interval is pushed forward, constantly repeats the process.
Further, according to predictive control theory, it is available to introduce Diophantine equation:
Wherein:To export predicted value；G in matrix G0,g1,...,gn-1For n item number before the step response of controlled device
Value；Δ U is controlling increment matrix；F is open-loop prediction vector；
F=[f (k+1), f (k+2) ..., f (k+n)]T
Wherein:For the estimated value of output valve L；Δ U is controlling increment；Objective function J is writeable
At: J=LTL+λΔUTΔU
Wherein: L is output matrix；
It enables:
Δ U=- (GTG+λI)-1GTf
If asking Δ U that must first know G and f；
L (k+n)=gn-1Δu(k)+...g0Δu(k+n-1)+f(k+n)+Enξ(k+n)
X (k)=[Δ u (k), Δ u (k+1) ..., Δ u (k+n-1), 1] is enabled,
θ (k)=[gn-1,gn-2,...,g0,f(k+n)]T, exporting predicted value can be write as:
Y (k+n/k)=X (k) θ (k)
In formula: X (k) is information vector；θ (k) is parameter vector to be estimated；θ (k) can be obtained with Least Square Method:
Wherein: λ1For forgetting factor, 0 < λ1< 1；It is the estimated value of parameter vector θ (k)；K is gain matrix；P is
Covariance matrix；It is obtained according to above-mentioned least-squares algorithmIt can be obtained each element g of matrix G0, g1..., gnAnd f
(k+n)；Predicted vector f can be obtained by following formula:
Wherein: f=[f (k+1), f (k+2) ..., f (k+n)]T；
a0=[1,1 ..., 1]T；
a0For error correction vectors；E is prediction error；After acquiring G and f, the value of control amount Δ U can be calculated, to count
Calculate prediction output valve.
3, beneficial effect of the present invention
(1) present invention combines auto navigation, digital map, inter-vehicle communication technology and intelligent transportation system, utilizes road
Traffic condition optimizes hybrid vehicle velocity mode；
(2) in the case that there is vehicle in front, the present still mainstream of the control algolithm of traditional fixation following distance, vehicle headway
The control strategy to float more than minimum value improves the freedom degree of changes in vehicle speed, makes hybrid vehicle fuel-economy
Property raising have may.
Detailed description of the invention
Fig. 1 is power partition type hybrid vehicle structure；
Fig. 2 is Model Predictive Control strategic process figure.
Specific embodiment
Below with reference to the attached drawing in present example, the technical solution in present example is clearly and completely retouched
It states, it is clear that described embodiments are only a part of the embodiments of the present invention, instead of all the embodiments.Based on the present invention
Embodiment, those skilled in the art's every other embodiment obtained under the premise of not doing creative work belongs to
Protection scope of the present invention.
Present example is described in further detail below in conjunction with attached drawing.
Embodiment 1
Step 1, the reduced-order model of hybrid vehicle system is established, it is assumed that there are two hybrid powers in platoon driving system
Automobile, they are respectively from vehicle and front truck.Each hybrid vehicle includes 5 Larger Dynamic components, as shown in Figure 1.They are
Engine 1, battery 2, the first electric generator all-in-one machine 3, the second electric generator all-in-one machine 4, planetary gear mechanism 5, inverter
6, main deceleration wheel 7 and wheel 8.According to vehicle mechanical coupling and electronics coupled relationship, it can arrange and write system dynamics equation.It is right
Kinetics equation decoupling, may finally obtain the state-space model of system.
Step 2, the system model established according to step 1, establishing CARIMA using identification tool, (controlled autoregressive integral is slided
It is dynamic average) prediction model, concrete form are as follows:
A(q-1) y (k)=B (q-1)u(k)+C(q-1)ξ(k)/δ
Wherein: δ is difference operator, δ=1-q-1；K indicates sampling instant sequence；Y (k), u (k) are the output, defeated of system
Enter；ξ (k) is the white noise sequence that mean value is zero；A(q-1) and B (q-1) it is q-1Multinomial, be system parameter.
Step 3, control strategy is formulated.It is characterized by: first, in conjunction with auto navigation, digital map, inter-vehicle communication
Technology and intelligent transportation system optimize hybrid vehicle velocity mode using road traffic condition；Second, it is preceding
In the case that there is vehicle in side, the present still mainstream of the control algolithm of traditional fixation following distance, vehicle headway is more than minimum value
The control strategy of floating improves the freedom degree of changes in vehicle speed, there is the raising of Fuel Economy for Hybrid Electric Vehicles
It may.Its concrete form are as follows:
Wherein j=N1... N, N1=1 is minimum prediction time domain, and N is maximum predicted time domain, and P is control time domain, q (j), λ
(j) it is respectively softening coefficient matrix, controls weighting matrix, L is prediction and evaluation function.
Step 4, online optimum control, and rolling optimization adjust weight parameter.According to predictive control theory, introduce
Diophantine equation is available:
Wherein:To export predicted value；G in matrix G0,g1,...,gn-1For n item number before the step response of controlled device
Value；Δ U is controlling increment matrix；F is open-loop prediction vector.
F=[f (k+1), f (k+2) ..., f (k+n)]T
Wherein:For the estimated value of output valve L；Δ U is controlling increment.
Objective function J can be write as: J=LTL+λΔUTΔU
Wherein: L is output matrix.
It enables:
Δ U=- (GTG+λI)-1GTf
If asking Δ U that must first know G and f.
L (k+n)=gn-1Δu(k)+...g0Δu(k+n-1)+f(k+n)+Enξ(k+n)
X (k)=[Δ u (k), Δ u (k+1) ..., Δ u (k+n-1), 1] is enabled,
θ (k)=[gn-1,gn-2,...,g0,f(k+n)]T, exporting predicted value can be write as:
Y (k+n/k)=X (k) θ (k)
In formula: X (k) is information vector；θ (k) is parameter vector to be estimated.θ (k) can be obtained with Least Square Method:
Wherein: λ1For forgetting factor, 0 < λ1< 1；It is the estimated value of parameter vector θ (k)；K is gain matrix；P is
Covariance matrix.It is obtained according to above-mentioned least-squares algorithmIt can be obtained each element g of matrix G0, g1..., gnAnd f
(k+n).Predicted vector f can be obtained by following formula:
Wherein: f=[f (k+1), f (k+2) ..., f (k+n)]T；
a0=[1,1 ..., 1]T。a0For error correction vectors；E is prediction error.
After acquiring G and f, the value of control amount Δ U can be calculated, to calculate prediction output valve.
Verifying
Step 1, the reduced-order model of hybrid vehicle system is established, it is assumed that there are two hybrid powers in platoon driving system
Automobile, they are respectively from vehicle and front truck.Each hybrid vehicle includes 5 Larger Dynamic components, as shown in Figure 1.They are
Engine, battery, 2 motors and wheel.Effect of the planetary gear as the existing speed coupler of distributing means for power supply, and have
The effect of electronics buncher.According to vehicle mechanical coupling and electronics coupled relationship, it can arrange and write system dynamics equation.According to
Vehicle mechanical coupling and electronics coupled relationship can arrange and write system dynamics equation.Kinetics equation is decoupled, may finally be obtained
The state-space model for the system of obtaining:
X=[p1v1w1SOC1p2v2w2SOC2]
U=[u1u2Pbatt1Pbatt2]
In formula, x is quantity of state, and u is control amount.Parameter p1, v1, w1And SOC1For car's location, speed considers delay
Driving acceleration and storage battery charge state.Parameter p2, v2, w2And SOC2For the position of front truck, speed considers the drive of delay
Dynamic acceleration and storage battery charge state.Parameter u1,u2,Pbatt1And Pbatt2For from the driving acceleration of vehicle, the driving of front truck adds
Speed, from the charge-discharge electric power of vehicle battery and the charge-discharge electric power of front truck battery.Parameter ρ, CD1,CD2,A1,A2,m1,m2, g,
μ, θ1, and θ2, it is atmospheric density, from vehicle coefficient of air resistance, front truck coefficient of air resistance, from vehicle front face area, front truck windward side
Product, from vehicle quality, front truck quality, acceleration of gravity, coefficient of rolling resistance, from vehicle road grade and front truck road grade.Voc, Ra
And Q, it is battery open-circuit voltage, internal resistance and capacity.
Step 2, the system model established according to step 1, establishing CARIMA using identification tool, (controlled autoregressive integral is slided
It is dynamic average) prediction model, concrete form is
A(q-1) L (k)=B (q-1)u(k)+C(q-1)ξ(k)/δ
Wherein: δ is difference operator, δ=1-q-1；K indicates sampling instant sequence；L (k), u (k) are the output, defeated of system
Enter；ξ (k) is the white noise sequence that mean value is zero.
Step 3, control strategy is formulated.It is characterized by: first, in conjunction with auto navigation, digital map, inter-vehicle communication
Technology and intelligent transportation system optimize hybrid vehicle velocity mode using road traffic condition；Second, it is preceding
In the case that there is vehicle in side, the present still mainstream of the control algolithm of traditional fixation following distance, vehicle headway is more than minimum value
The control strategy of floating improves the freedom degree of changes in vehicle speed, there is the raising of Fuel Economy for Hybrid Electric Vehicles
It may.Its concrete form are as follows:
Wherein j=N1... N, N1=1 is minimum prediction time domain, and N is maximum predicted time domain, and P is control time domain, q (j), λ
(j) it is respectively softening coefficient matrix, controls weighting matrix, L is prediction and evaluation function:
L=wxLx+wyLy+wzLz+wdLd+weLe+wfLf+wrLr
Ly=(v1-vd)2-(v2-vd)2
Ld=(SOC1-SOCd)2+(SOC2-SOCd)2
Le=(m1w1v1/1000-Pbatt1)2+(m2w2v2/1000-Pbatt2)2
Lf=(- ln [SOC1-0.6]-ln[0.8-SOC1])+
(-ln[SOC2-0.6]-ln[0.8-SOC2])
Lr=-ln (d-dd)
SOC in formuladIt is target storage battery charge state.vdIt is vehicle target speed, its value is the optimal constant speed fuel oil of vehicle
Economy speed.wx, wy, wz, wd, we, wf, wrIt is weight coefficient.ddFor minimum vehicle spacing, evaluation function setting makes it most
It more than low vehicle spacing floats, to increase control freedom degree, improves VE Vehicle Economy.
Step 4, online optimum control, and rolling optimization adjust weight parameter.According to predictive control theory, introduce
Diophantine equation is available:
Wherein:To export predicted value；G in matrix G0,g1,...,gn-1For n item number before the step response of controlled device
Value；Δ U is controlling increment matrix；F is open-loop prediction vector.
F=[f (k+1), f (k+2) ..., f (k+n)]T
Wherein:For the estimated value of output valve L；Δ U is controlling increment.
Objective function J can be write as: J=LTL+λΔUTΔU
Wherein: L is output matrix.
It enables:
Δ U=- (GTG+λI)-1GTf
As shown in above formula, if asking Δ U that must first know G and f.
L (k+n)=gn-1Δu(k)+...g0Δu(k+n-1)+f(k+n)+Enξ(k+n)
X (k)=[Δ u (k), Δ u (k+1) ..., Δ u (k+n-1), 1] is enabled,
θ (k)=[gn-1,gn-2,...,g0,f(k+n)]T, exporting predicted value can be write as:
Y (k+n/k)=X (k) θ (k)
Wherein: X (k) is information vector；θ (k) is parameter vector to be estimated.θ (k) can be obtained with Least Square Method:
Wherein: λ1For forgetting factor, 0 < λ1< 1；It is the estimated value of parameter vector θ (k)；K is gain matrix；P is
Covariance matrix.It is obtained according to above-mentioned least-squares algorithmIt can be obtained each element g of matrix G0, g1..., gnAnd f
(k+n).Predicted vector f can be obtained by following formula:
Wherein: f=[f (k+1), f (k+2) ..., f (k+n)]T；
a0=[1,1 ..., 1]T。a0For error correction vectors；E is prediction error.
After acquiring G and f, the value of control amount Δ U can be calculated, to calculate prediction output valve.
Control strategy flow chart is as shown in Figure 2, at every sampling moment, firstly, front vehicle position is measured, and it is preceding from truck position
Vehicle speed, from vehicle speed, front truck acceleration, from vehicle acceleration, front truck storage battery charge state and from vehicle storage battery charge state
Etc. real-time status signal, secondly, utilizing global positioning system and the following certain section vehicle of intelligent transportation system prediction and surrounding
The state of environment according to the auto model and optimal control problem of foundation, is solved using above-mentioned numerical value fast resolution and is predicted again
Optimal control sequence in section.First control amount of the optimal control sequence in applied forecasting section is in vehicle.Exist later
Next sampling instant, forecast interval is pushed forward, and is looped back and forth like this, and realizes online optimum control.
The foregoing is only a preferred embodiment of the present invention, but scope of protection of the present invention is not limited thereto,
Within the technical scope of the present disclosure, any changes or substitutions that can be easily thought of by anyone skilled in the art,
It should be covered by the protection scope of the present invention.Therefore, protection scope of the present invention should be with the protection model of claims
Subject to enclosing.
Claims (6)
1. the energy management method of the platoon driving hybrid vehicle based on generalized predictive control, it is characterised in that:
Step 1, the reduced-order model of hybrid vehicle system is established；
Step 2, system model is established, i.e., establishes controlled autoregressive integral moving average forecasting model with identification tool；
Step 3, control strategy is formulated, i.e., velocity mode is optimized, vehicle headway uses minimum value to float with very wise move
Slightly；
Step 4, online optimum control, and rolling optimization adjust weight parameter.
2. the energy management side of the platoon driving hybrid vehicle according to claim 1 based on generalized predictive control
Method, which is characterized in that the system model in the step 1 is based on two hybrid vehicles, according to vehicle mechanical coupling and electricity
Sub- coupled relation, column write system dynamics equation, decouple to kinetics equation, the final state-space model for obtaining system.
3. the energy management side of the platoon driving hybrid vehicle according to claim 1 based on generalized predictive control
Method, which is characterized in that step 2 specifically:
A(q-1) y (k)=B (q-1)u(k)+C(q-1)ξ(k)/δ
Wherein: δ is difference operator, δ=1-q-1；K indicates sampling instant sequence；Y (k), u (k) are the output of system, input；ξ
(k) it is white noise sequence that mean value is zero；A(q-1) and B (q-1) it is q-1Multinomial, be system parameter.
4. the energy management side of the platoon driving hybrid vehicle according to claim 1 based on generalized predictive control
Method, which is characterized in that the formulation strategy of step 3:
Step 3.1 combines auto navigation, digital map, inter-vehicle communication technology and intelligent transportation system, utilizes road traffic shape
Condition optimizes hybrid vehicle velocity mode；
Step 3.2 vehicle headway formulates control strategy form using the control strategy more than minimum value to float are as follows:
Wherein j=N1... N, N1=1 is minimum prediction time domain, and N is maximum predicted time domain, and P is control time domain, and q (j), λ (j) divide
Not Wei softening coefficient matrix, control weighting matrix, L be prediction and evaluation function；Control strategy is to minimize evaluation function to subtract simultaneously
Small control amount fluctuation.
5. the energy management side of the platoon driving hybrid vehicle according to claim 1 based on generalized predictive control
Method, which is characterized in that step 4 specifically: in each sampling period, online acquisition relevant information and with the mathematical model established
Optimum control is solved with formulation control strategy, finally first control amount of the obtained optimal control sequence of application inputs system
System；In next sampling instant, forecast interval is pushed forward, constantly repeats the process.
6. the energy management side of the platoon driving hybrid vehicle according to claim 5 based on generalized predictive control
Method, it is characterised in that specifically, it is available to introduce Diophantine equation according to predictive control theory:
Wherein:To export predicted value；G in matrix G0,g1,...,gn-1For n numerical value before the step response of controlled device；ΔU
For controlling increment matrix；F is open-loop prediction vector；
F=[f (k+1), f (k+2) ..., f (k+n)]T
Wherein:For the estimated value of output valve L；ΔUFor controlling increment；Objective functionJIt can be write as:
J=LTL+λΔUTΔU
Wherein: L is output matrix；
It enables:
Δ U=- (GTG+λI)-1GTf
If asking Δ U that must first know G and f；
L (k+n)=gn-1Δu(k)+...g0Δu(k+n-1)+f(k+n)+Enξ(k+n)
X (k)=[Δ u (k), Δ u (k+1) ..., Δ u (k+n-1), 1] is enabled,
θ (k)=[gn-1,gn-2,...,g0,f(k+n)]T, exporting predicted value can be write as:
Y (k+n/k)=X (k) θ (k)
In formula: X (k) is information vector；θ (k) is parameter vector to be estimated；θ (k) can be obtained with Least Square Method:
Wherein: λ1For forgetting factor, 0 < λ1< 1；It is the estimated value of parameter vector θ (k)；K is gain matrix；P is association side
Poor matrix；It is obtained according to above-mentioned least-squares algorithmIt can be obtained each element g of matrix G0, g1..., gnAnd f (k+
n)；Predicted vector f can be obtained by following formula:
Wherein: f=[f (k+1), f (k+2) ..., f (k+n)]T；
a0=[1,1 ..., 1]T；
a0For error correction vectors；E is prediction error；After acquiring G and f, the value of control amount Δ U can be calculated, to calculate prediction
Output valve.
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| CN112255918A (en) * | 2020-10-21 | 2021-01-22 | 东南大学 | Method and system for optimizing control of automobile queue |
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| CN113759847A (en) * | 2021-09-08 | 2021-12-07 | 重庆交通职业学院 | Cooperative distributed heat management method and system for high-power hybrid power system |
| CN115891947A (en) * | 2023-02-22 | 2023-04-04 | 北京全路通信信号研究设计院集团有限公司 | Medium-low speed maglev train electro-hydraulic hybrid braking cooperative control method |
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| CN112255918B (en) * | 2020-10-21 | 2022-04-08 | 东南大学 | Method and system for optimizing control of automobile queue |
| CN112327628A (en) * | 2020-11-16 | 2021-02-05 | 江康(上海)科技有限公司 | Improved self-adaptive generalized predictive control method for data-driven time-lag system |
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