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CN109552110B - Electric vehicle composite energy management method based on rule and nonlinear predictive control - Google Patents

Electric vehicle composite energy management method based on rule and nonlinear predictive control Download PDF

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CN109552110B
CN109552110B CN201811614337.6A CN201811614337A CN109552110B CN 109552110 B CN109552110 B CN 109552110B CN 201811614337 A CN201811614337 A CN 201811614337A CN 109552110 B CN109552110 B CN 109552110B
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power
lithium battery
usoc
bsoc
super capacitor
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CN109552110A (en
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陶吉利
韩凯
马龙华
蔡卫明
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Ningbo Institute of Technology of ZJU
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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L2240/00—Control parameters of input or output; Target parameters
    • B60L2240/40—Drive Train control parameters
    • B60L2240/54—Drive Train control parameters related to batteries
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

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  • Charge And Discharge Circuits For Batteries Or The Like (AREA)

Abstract

The invention discloses a rule and non-linear prediction control-based composite energy management method for an electric vehicle. In the nonlinear predictive control strategy, a controller predicts the speed of a certain period in the future, converts the speed into power through a vehicle running speed power model, optimizes the output current of the lithium battery by taking the minimum power loss as an index through a quadratic programming active set method, and completes the power distribution of the lithium battery and the super capacitor. On the basis of meeting the required power, the method can reduce the energy loss of the system, reduce the use of the lithium battery, prolong the service life of the lithium battery and improve the efficiency of the hybrid power system.

Description

Electric vehicle composite energy management method based on rule and nonlinear predictive control
Technical Field
The invention relates to an electric vehicle energy management method based on rule and nonlinear predictive control.
Background
The current automobile is highly dependent on non-renewable fuel, which is not consistent with the subject of global environmental sustainable development. In order to solve the problems of air pollution and resource exhaustion caused by the conventional automobile, the research on the electric automobile is highly valued by people. For an energy storage system of an electric automobile, a lithium battery is widely applied due to the characteristics of light weight, large energy storage, large power, no pollution and the like, but the single use of the lithium battery can cause overheating of a battery pack and shorten the service life of the battery pack. The super capacitor has the advantages of long service life, high instantaneous power and the like, and has a good auxiliary effect on a battery power system. In addition, the super capacitor has a wide working temperature range and can completely absorb the braking energy of the automobile. Therefore, the hybrid power system combining the lithium battery and the super capacitor can meet the requirements of the electric automobile. Therefore, how to efficiently exert the characteristics and advantages of the lithium battery and the super capacitor is the core and key of power system energy management in optimizing and distributing the energy of the lithium battery and the super capacitor.
Disclosure of Invention
The invention aims to solve the problem of energy distribution of a hybrid power system of an electric automobile, and provides a composite energy management method based on rule and non-linear predictive control. In the nonlinear predictive control strategy, the controller predicts the speed of a certain period in the future, converts the speed into power through a vehicle running speed power model, optimizes the output current of the lithium battery by taking the minimum power loss as an index through a quadratic programming algorithm, and completes the power distribution of the lithium battery and the super capacitor. Experimental results show that the method can avoid frequent charging and discharging of the battery pack, prolong the service life of the lithium battery, reduce the energy loss of the system and improve the efficiency of the hybrid power system.
The technical scheme adopted by the invention is as follows:
in the composite energy management method, an energy management strategy based on nonlinear predictive control is combined with an energy management strategy based on rules to complete energy distribution to a hybrid power system; when the required power of the automobile is higher than a power threshold value, an energy management strategy based on nonlinear predictive control is adopted, and output currents of a lithium battery and a super capacitor are obtained through a nonlinear predictive controller, so that energy distribution is completed; and when the required power of the automobile is lower than a power threshold value, directly obtaining the output power of the lithium battery and the super capacitor by adopting a rule-based energy management strategy.
Based on the above technical solutions, the present invention can also provide the following preferred embodiments.
Preferably, the hybrid power system of the electric automobile consists of a lithium battery and a super capacitor.
Preferably, the method is carried out by means of the power P required for the operation of the vehicle at each timenLithium battery power P is carried out to SOC of lithium battery and SOC of super capacitorbAnd super capacitor power PucThe specific allocation strategy is as follows:
if Pn<0 and USOC>USOCHThen make Pb＝PnAnd P isuc＝0；
If Pn<0 and USOC is less than or equal to USOCHThen make P b0 and Puc＝Pn；
If 0 is less than or equal to Pn≤PLAnd USOC>USOCLAnd BSOC>BSOCLThen make Pb＝PnAnd P isuc＝0；
If Pn>PLAnd USOC>USOCLAnd BSOC>BSOCLThen, an energy management strategy based on nonlinear predictive control is adopted for power distribution;
if Pn>0 and USOC is less than or equal to USOCLAnd BSOC>BSOCLThen make Pb＝PnAnd P isuc＝0；
If Pn>0 and USOC>USOCLAnd BSOC is less than or equal to BSOCLThen make P b0 and Puc＝Pn；
If Pn>0 and USOC is less than or equal to USOCLAnd BSOC is less than or equal to BSOCLThen make P b0 and Puc＝0；
Wherein, PnThe power required by the running of the automobile at each moment is represented, BSOC represents the SOC of the lithium battery, and USOC represents the SOC of the super capacitor; USOCH、USOCLRespectively representing the upper limit value and the lower limit value of the super capacitor SOC, BSOCLRepresents the lower limit value of the lithium battery SOC, PLRepresenting power thresholds for both strategies of the hybrid powertrain system.
Furthermore, in the energy management strategy based on the nonlinear predictive control, the speed of a certain period in the future is predicted, the required power in the certain period in the future is calculated through a vehicle speed power model, the output current of the lithium battery is optimized by taking the minimum power loss as an index through a secondary planning active set method, and the power distribution of the lithium battery and the super capacitor is completed; the energy management strategy based on the nonlinear predictive control comprises the following steps:
step 1) predicting the speed in a certain period by the torque at the current moment and the vehicle speed;
step 2) calculating the power required by each moment in the prediction period;
step 3), calculating the optimal solution of the target function at each moment in the prediction period by using a quadratic programming active set method, and outputting the solution at the first moment as an optimal control command;
step 4) calculating the power P of the lithium battery and the super capacitorb＝iL·Ub,Puc＝ic·UcRepeating the steps at the next moment; wherein iLFor the output current of lithium batteries, icFor super-capacitor output current, UbIs the voltage of a lithium battery, UcIs the supercapacitor voltage.
Further, in the steps 1) to 4), the specific calculation process is as follows:
assuming that the drive torque decreases exponentially during the prediction period, and is expressed as:
Figure BDA0001925493350000031
wherein, TW(k) Represents the driving torque acting on the wheel at the moment k, and the acceleration is positive and the deceleration is negative; Δ t denotes the sampling time, τdRepresents the attenuation coefficient, HFRepresents a prediction period step size;
the predicted vehicle speed is:
V(k)＝ua；
Figure BDA0001925493350000032
meanwhile, the vehicle speed power model is:
Figure BDA0001925493350000033
wherein V (k) represents the vehicle speed at time k, PnRepresenting the power required for the operation of the vehicle, M representing the mass of the vehicle, f representing the rolling resistance coefficient, g representing the gravitational acceleration, α representing the road gradient, CarRepresenting the coefficient of air resistance, A representing the frontal wind of the automobileArea, representing the rotating mass correction factor, ηTIndicating the efficiency of the transmission system, RWRepresenting the wheel radius, uaRepresenting the current speed of the vehicle;
based on the predicted speed, the power required for prediction is obtained through a vehicle speed power model, and then the output current i of the lithium battery is optimized through secondary programmingLAnd output current i of super capacitorc；
Setting the output current i of the lithium batteryLFor the control quantity, the objective function J is defined as follows:
Figure BDA0001925493350000034
wherein iL(k) And ic(k) The output current of the lithium battery and the output current of the super capacitor at the moment k are respectively; riIs the internal resistance of lithium battery, RcThe internal resistance of the super capacitor is shown, and N is an optimized step length; meanwhile, the constraint conditions are as follows:
Pn＝Puc+Pb；
Pb＝iL·Ub；
Puc＝ic·Uc；
0.2≤BSOC(k)≤1；
0.56≤USOC(k)≤0.92；
0≤iL(k)≤100；
40≤ic(k)≤200；
wherein BSOC (k) is the lithium battery SOC at the moment k, and USOC (k) is the super capacitor SOC at the moment k;
obtaining a group of lithium battery output currents through optimization: i.e. iL(0),iL(1),…,iL(N-1), selecting the first element iL(0) And the current is used as an optimal control command, namely the output current of the lithium battery at the current moment.
Furthermore, solving the objective function adopts a quadratic programming active set method, and the specific calculation process is as follows:
at each moment, the objective function can be transformed into the following quadratic programming problem:
Figure BDA0001925493350000041
Figure BDA0001925493350000042
Figure BDA0001925493350000043
wherein,
Figure BDA0001925493350000044
Figure BDA0001925493350000045
Figure BDA0001925493350000046
Figure BDA0001925493350000047
the solving process by using the quadratic programming active set method is as follows:
1) given an initial feasible point x(0)Let A0＝A(x(0)) The parameter p is 0, and a represents the active set of the quadratic programming problem at that point;
2) further converting the quadratic programming problem into an equality constraint quadratic programming problem:
Figure BDA0001925493350000048
Figure BDA0001925493350000049
wherein A isp＝A(x(p))，x(p)For feasible point of p-th wheel, solve d ═[d1,d2]T，d1,d2Elements in the solution of the transformed quadratic programming problem; solving the quadratic programming problem in the p round(p)And corresponding Lagrange multiplier
Figure BDA0001925493350000051
q∈Ap；
3) If d is(p)Not equal to 0, then
Figure BDA0001925493350000052
Figure BDA0001925493350000053
x(p+1)＝x(p)+αpd(p),Ap+1＝Ap∪{q0}
Wherein, αpFeasible factor of p round, q0Is αpCorresponding sequence value when taking value;
making p equal to p +1, and turning to the step 2);
4) if d is(p)When the value is equal to 0, then
Figure BDA0001925493350000054
Then, the optimal solution x can be obtained(p)(ii) a Otherwise
Figure BDA0001925493350000055
x(p+1)＝x(p),Ap+1＝Ap\{qn}
Wherein q isnTo make it possible to
Figure BDA0001925493350000056
Taking a corresponding sequence value when the minimum value is obtained;
let p be p +1, go to step 2).
The method provided by the invention carries out energy management according to the power requirement of the vehicle at each moment and the SOC conditions of the lithium battery and the super capacitor. In the nonlinear predictive control strategy, a controller predicts the speed of a certain period in the future, converts the speed into power through a vehicle running speed power model, optimizes the output current of the lithium battery by taking the minimum power loss as an index through a quadratic programming active set method, and completes the power distribution of the lithium battery and the super capacitor. On the basis of meeting the required power, the method can reduce the energy loss of the system, reduce the use of the lithium battery, prolong the service life of the lithium battery and improve the efficiency of the hybrid power system.
Drawings
FIG. 1 is a structural diagram of an electric vehicle and a bidirectional electric energy conversion research experiment platform;
FIG. 2 is an ECE driving condition speed map;
FIG. 3 shows a comparison of data for two strategies under ECE driving conditions: (a) the method comprises the following steps of (a) outputting current by a lithium battery, (b) outputting current by a super capacitor, (c) outputting power by the lithium battery, and (d) outputting power by the super capacitor.
Detailed Description
The invention will be further elucidated and described with reference to the drawings and the detailed description.
The invention relates to an electric automobile composite energy management method based on rule and nonlinear predictive control, which is mainly used for energy distribution management of a hybrid electric automobile. By the method, the power of the lithium battery and the super capacitor during hybrid power output of the electric automobile can be reasonably distributed, and the loss of system energy can be reduced on the basis of meeting the required power.
In the combined energy management method, an energy management strategy based on nonlinear predictive control is combined with an energy management strategy based on rules to complete energy distribution to the hybrid power system; when the required power of the automobile is higher than a power threshold value, an energy management strategy based on nonlinear predictive control is adopted, and output currents of a lithium battery and a super capacitor are obtained through a nonlinear predictive controller, so that energy distribution is completed; and when the required power of the automobile is lower than a power threshold value, directly obtaining the output power of the lithium battery and the super capacitor by adopting a rule-based energy management strategy.
In particular, the method is implemented by the power P required by the operation of the vehicle at each momentnLithium battery power P is carried out to SOC of lithium battery and SOC of super capacitorbAnd super capacitor power PucThe specific allocation strategy is as follows:
if Pn<0 and USOC>USOCHThen make Pb＝PnAnd P isuc＝0；
If Pn<0 and USOC is less than or equal to USOCHThen make P b0 and Puc＝Pn；
If 0 is less than or equal to Pn≤PLAnd USOC>USOCLAnd BSOC>BSOCLThen make Pb＝PnAnd P isuc＝0；
If Pn>PLAnd USOC>USOCLAnd BSOC>BSOCLThen, an energy management strategy based on nonlinear predictive control is adopted for power distribution;
if Pn>0 and USOC is less than or equal to USOCLAnd BSOC>BSOCLThen make Pb＝PnAnd P isuc＝0；
If Pn>0 and USOC>USOCLAnd BSOC is less than or equal to BSOCLThen make P b0 and Puc＝Pn；
If Pn>0 and USOC is less than or equal to USOCLAnd BSOC is less than or equal to BSOCLThen make P b0 and Puc＝0；
Wherein, PnThe power required by the running of the automobile at each moment is represented, BSOC represents the SOC of the lithium battery, and USOC represents the SOC of the super capacitor; USOCH、USOCLRespectively representing the upper limit value and the lower limit value of the super capacitor SOC, BSOCLRepresents the lower limit value of the lithium battery SOC, PLRepresenting power thresholds for both strategies of the hybrid powertrain system.
In the above strategy, except for Pn>PLAnd USOC>USOCLAnd BSOC>BSOCLIn the state ofThe output power of the lithium battery and the output power of the super capacitor are directly obtained by adopting the energy management strategy based on the rules except the energy management strategy based on the nonlinear predictive control.
In the energy management strategy based on the nonlinear predictive control, the speed of a certain period in the future needs to be predicted, the required power in the certain period in the future is calculated through a vehicle speed power model, the output current of the lithium battery is optimized by taking the minimum power loss as an index through a quadratic programming active set method, and the power distribution of the lithium battery and the super capacitor is completed. The main steps of the non-linear predictive control based energy management strategy are detailed below:
step 1) predicting the speed in a certain period by the torque at the current moment and the vehicle speed;
step 2) calculating the power required by each moment in the prediction period;
step 3), calculating the optimal solution of the target function at each moment in the prediction period by using a quadratic programming active set method, and outputting the solution at the first moment as an optimal control command;
step 4) calculating the power P of the lithium battery and the super capacitorb＝iL·Ub,Puc＝ic·UcRepeating the steps at the next moment; wherein iLFor the output current of lithium batteries, icFor super-capacitor output current, UbIs the voltage of a lithium battery, UcIs the supercapacitor voltage.
In the above steps, the specific calculation process of the torque and the vehicle speed is as follows:
assuming that the drive torque decreases exponentially during the prediction period, and is expressed as:
Figure BDA0001925493350000071
wherein, TW(k) Represents the driving torque acting on the wheel at the moment k, and the acceleration is positive and the deceleration is negative; Δ t denotes the sampling time, τdRepresents the attenuation coefficient, HFRepresents a prediction period step size;
according to the vehicle driving torque T at the current momentW(k) Calculating to obtain future HFDriving torque T in one momentW(k+1),TW(k+2),…,TW(k+HF)。
The predicted vehicle speed is:
V(k)＝ua；
Figure BDA0001925493350000072
according to the current vehicle running speed uaCalculating to obtain the current time and the future HFPredicted vehicle speeds V (k), V (k +1), V (k +2), …, V (k + H) at respective timesF)。
Meanwhile, the vehicle speed power model is:
Figure BDA0001925493350000073
wherein V (k) represents the vehicle speed at time k, PnRepresenting the power required for the operation of the vehicle, M representing the mass of the vehicle, f representing the rolling resistance coefficient, g representing the gravitational acceleration, α representing the road gradient, CarRepresenting the coefficient of air resistance, a the frontal area of the vehicle, representing the rotor mass correction coefficient, ηTIndicating the efficiency of the transmission system, RWRepresenting the wheel radius, uaIndicating the current speed of the vehicle.
Calculating the obtained speeds V (k), V (k +1), V (k +2), … and V (k + H)F) The current time and the future H are obtained by substituting the formulaFPredicted required power for each time instant: pn(k),Pn(k+1),Pn(k+2),…,Pn(k+HF)。
Based on the predicted speed, the power required for prediction is obtained through a vehicle speed power model, and then the output current i of the lithium battery is optimized through secondary programmingLAnd output current i of super capacitorc；
Setting the output current i of the lithium batteryLFor controlling the quantity, on the premise of ensuring the success rate requirement, for reducing the loss of the super capacitor and the lithium batteryThe power consumption is minimal and the objective function J is defined as follows:
Figure BDA0001925493350000081
wherein iL(k) And ic(k) The output current of the lithium battery and the output current of the super capacitor at the moment k are respectively; riIs the internal resistance of lithium battery, RcThe internal resistance of the super capacitor is shown, and N is an optimized step length; meanwhile, in order to ensure that the energy supply and the system safety of the system can be completed, the constraint conditions are as follows:
Pn＝Puc+Pb；
Pb＝iL·Ub；
Puc＝ic·Uc；
0.2≤BSOC(k)≤1；
0.56≤USOC(k)≤0.92；
0≤iL(k)≤100；
40≤ic(k)≤200；
BSOC (k) is the lithium battery SOC at the moment k, and USOC (k) is the super capacitor SOC at the moment k.
At each moment, the objective function can be transformed into the following quadratic programming problem:
Figure BDA0001925493350000082
Figure BDA0001925493350000083
Figure BDA0001925493350000084
wherein,
Figure BDA0001925493350000085
Figure BDA0001925493350000086
Figure BDA0001925493350000087
Figure BDA0001925493350000088
the solving process by using the quadratic programming active set method is as follows:
1) given an initial feasible point x(0)Let A0＝A(x(0)) The parameter p is 0, and a represents the active set of the quadratic programming problem at that point;
2) further converting the quadratic programming problem into an equality constraint quadratic programming problem:
Figure BDA0001925493350000091
Figure BDA0001925493350000092
wherein A isp＝A(x(p))，x(p)For the feasible point of the p-th wheel, solve d ═ d1,d2]T，d1,d2Elements in the solution of the transformed quadratic programming problem; solving the quadratic programming problem in the p round(p)And corresponding Lagrange multiplier
Figure BDA0001925493350000093
q∈Ap；
3) If d is(p)Not equal to 0, then
Figure BDA0001925493350000094
Figure BDA0001925493350000095
x(p+1)＝x(p)+αpd(p),Ap+1＝Ap∪{q0}
Wherein, αpFeasible factor of p round, q0Is αpCorresponding sequence value when taking value;
making p equal to p +1, and turning to the step 2);
4) if d is(p)When the value is equal to 0, then
Figure BDA0001925493350000096
Then, the optimal solution x can be obtained(p)(ii) a Otherwise
Figure BDA0001925493350000097
x(p+1)＝x(p),Ap+1＝Ap\{qn}
Wherein q isnTo make it possible to
Figure BDA0001925493350000098
Taking a corresponding sequence value when the minimum value is obtained;
let p be p +1, go to step 2).
And (3) optimizing by a secondary programming active set method to obtain the output current of the lithium battery at each moment: i.e. iL(0),iL(1),…, iL(N-1), selecting the first element iL(0) And the current is used as an optimal control command, namely the optimal output current of the lithium battery at the current moment.
Therefore, the power of the lithium battery and the super capacitor can be obtained according to the step 4), and the power distribution at the current moment is completed. The next time these processes can continue to be repeated, power allocation can resume.
Based on the above method, the technical effects of the method are further shown in combination with the specific embodiments, and the definitions of some parameters are as described above and are not described again.
Examples
The method is adopted on an electric vehicle and a bidirectional electric energy conversion research experiment platform to carry out experiments by utilizing the ECE (EconomicCommission of Europe) driving condition. The structure diagram of the experimental platform is shown in fig. 1, the whole research experimental platform is uniformly managed by an industrial personal computer 1, the industrial personal computer 1 controls a charger, an inverter, a battery management system and a bidirectional DC/DC converter through a CAN network, and the industrial personal computer 1 communicates with an industrial personal computer 2 of an electric power measuring system through an Ethernet, so that a motor and a frequency converter are realized. ECE driving conditions are shown in FIG. 2.
In the hybrid energy management method, a nonlinear predictive control-based energy management strategy is combined with a rule-based energy management strategy to accomplish energy distribution to the hybrid powertrain system. When the power required by the automobile is high, a nonlinear prediction control strategy is adopted, and the output currents of the lithium battery and the super capacitor are obtained through a nonlinear prediction controller, so that energy distribution is completed; when the power required by the automobile is low, the output power of the lithium battery and the super capacitor is directly obtained by adopting a rule-based energy management strategy. And the power required by the automobile can be set with a certain threshold value as the basis for strategy selection adjustment. In the present embodiment, the method is implemented by the power (P) required by the operation of the vehicle at each momentn) SOC (BSOC) of lithium battery and SOC (USOC) of super capacitor for performing lithium battery power (P)b) And super capacitor power (P)uc) The specific strategy is as follows:
if Pn<0 and USOC>USOCHThen make Pb＝PnAnd P isuc＝0；
If Pn<0 and USOC is less than or equal to USOCHThen make P b0 and Puc＝Pn；
If 0 is less than or equal to Pn≤PLAnd USOC>USOCLAnd BSOC>BSOCLThen make Pb＝PnAnd P isuc＝0；
If Pn>PLAnd USOC>USOCLAnd BSOC>BSOCLThen, an energy management strategy based on nonlinear predictive control is adopted for power distribution;
if Pn>0 and USOC is less than or equal to USOCLAnd BSOC>BSOCLThen make Pb＝PnAnd P isuc＝0；
If Pn>0 and USOC>USOCLAnd BSOC is less than or equal to BSOCLThen make P b0 and Puc＝Pn；
If Pn>0 and USOC is less than or equal to USOCLAnd BSOC is less than or equal to BSOCLThen make P b0 and Puc＝0；
Wherein, the USOCH，USOCLRespectively represent the upper and lower limit values of the supercapacitor SOC, which are 0.92 and 0.56, respectively. BSOCLRepresents the lower limit value of the lithium battery SOC, and the value is 0.2. PLThe power threshold values for both strategies of the hybrid system are shown, which in this embodiment is 1500W.
In an energy management strategy based on nonlinear predictive control, the speed of a certain period in the future needs to be predicted, the required power in the certain period in the future is calculated through a vehicle speed power model, the output current of a lithium battery is optimized by taking the minimum power loss as an index through a quadratic programming active set method, and the power distribution of the lithium battery and a super capacitor is completed.
Assuming that the drive torque decreases exponentially during the prediction period, and is expressed as:
Figure BDA0001925493350000111
wherein, TWRepresenting the driving torque acting on the wheel (positive acceleration, negative deceleration), Δ t representing the sampling time, τdRepresents the attenuation coefficient, HFRepresenting the prediction period step size.
The predicted vehicle speed may be expressed as:
V(k)＝ua；
Figure BDA0001925493350000112
meanwhile, the vehicle speed power model may be expressed as:
Figure BDA0001925493350000113
wherein, PnRepresenting the power required for the operation of the vehicle, M representing the mass of the vehicle, f representing the rolling resistance coefficient, g representing the gravitational acceleration, α representing the road gradient, CarRepresenting the coefficient of air resistance, a the frontal area of the vehicle, representing the rotor mass correction coefficient, ηTIndicating the efficiency of the transmission system, RWRepresenting the wheel radius, uaIndicating the current speed of the vehicle. The values and units of the respective parameters are shown in table 1.
TABLE 1 Power speed model parameters
Δt 0.1s α 0
τd 7 Car 0.3
H F 3 Α 1.51m2
M 400kg δ 1.1
f 0.009 ηT 0.95
g 9.81m/s2 RW 0.282m
Based on the predicted speed, the power required for prediction can be obtained through a vehicle speed power model, and then the output current i of the lithium battery is optimized through secondary programmingLAnd output current i of super capacitorc。
Setting the output current i of the lithium batteryLIs a control quantity. On the premise of ensuring the success rate requirement, in order to minimize the power loss of the super capacitor and the lithium battery, the objective function is defined as follows:
Figure BDA0001925493350000114
wherein R isiIs the internal resistance of lithium battery, RCAnd N is the internal resistance of the super capacitor, and the value of N is 4. Meanwhile, in order to ensure that the energy supply and the system safety of the system can be completed, the constraint conditions are as follows:
Pn＝Puc+Pb；
Pb＝iL·Ub；
Puc＝ic·Uc；
0.2≤BSOC(k)≤1；
0.56≤USOC(k)≤0.92；
0≤iL(k)≤100；
40≤ic(k)≤200；
the voltage of the lithium battery and the voltage of the super capacitor can be obtained through sampling by equipment.
At each moment, the objective function can be transformed into the following quadratic programming problem:
Figure BDA0001925493350000121
Figure BDA0001925493350000122
Figure BDA0001925493350000123
wherein,
Figure BDA0001925493350000124
Figure BDA0001925493350000125
Figure BDA0001925493350000126
Figure BDA0001925493350000127
the solving process by using the quadratic programming active set method is as follows:
1) given an initial feasible point x(0)Let A0＝A(x(0)) The parameter p is 0, and a represents the active set of the quadratic programming problem at that point;
2) further converting the quadratic programming problem into an equality constraint quadratic programming problem:
Figure BDA0001925493350000128
Figure BDA0001925493350000129
wherein A isp＝A(x(p))，x(p)For the feasible point of the p-th wheel, solve d ═ d1,d2]T，d1,d2Elements in the solution of the transformed quadratic programming problem; solving the quadratic programming problem in the p round(p)And corresponding Lagrange multiplier
Figure BDA00019254933500001210
q∈Ap；
3) If d is(p)Not equal to 0, then
Figure BDA0001925493350000131
Figure BDA0001925493350000132
x(p+1)＝x(p)+αpd(p),Ap+1＝Ap∪{q0}
Wherein, αpFeasible factor of p round, q0Is αpCorresponding sequence value when taking value;
making p equal to p +1, and turning to the step 2);
4) if d is(p)When the value is equal to 0, then
Figure BDA0001925493350000133
Then, the optimal solution x can be obtained(p)(ii) a Otherwise
Figure BDA0001925493350000134
x(p+1)＝x(p),Ap+1＝Ap\{qn}
Wherein q isnTo make it possible to
Figure BDA0001925493350000135
Taking a corresponding sequence value when the minimum value is obtained;
let p be p +1, go to step 2).
By two timesAnd (3) optimizing by a planning active set method to obtain the output current of the lithium battery at each moment: i.e. iL(0),iL(1),…, iL(N-1), selecting the first element iL(0) And the current is used as an optimal control command, namely the output current of the lithium battery at the current moment.
In summary, the nonlinear predictive control strategy comprises the following steps:
and 1) predicting the speed in a certain period by the torque at the current moment and the vehicle speed. The vehicle driving torque T at the current moment is obtained through samplingW(k) And let T (0) be TW(k) The driving torques T (1), T (2) and T (3) in the future 3 moments are calculated according to the formula. Simultaneously sampling to obtain the vehicle running speed u at the current momenta(k) Calculating the predicted vehicle speeds V (0), V (1), V (2) and V (3) at the current moment and in 3 future moments according to the formula;
and 2) calculating the power required by each moment in the prediction period according to the vehicle speed power model. Obtaining the predicted required power P of the current moment and the future 3 moments in the speed vehicle speed power model obtained in the step 1n(0),Pn(1),Pn(2),Pn(3)。
Step 3), calculating the optimal solution of the objective function at each moment in the prediction period by utilizing a quadratic programming active set method, outputting the solution at the first moment as an optimal control command, and sampling to obtain the voltage U of the lithium battery at the current momentb(k) And super capacitor voltage Uc(k) And respectively obtaining the output current of the lithium battery at each moment in the prediction period by utilizing a quadratic programming active set method according to a formula objective function and constraint conditions: i.e. iL(0),iL(1),iL(2),iL(3) And selecting the first element iL(0) As an optimal control command at the present moment, i.e. command iL(k)＝iL(0) And simultaneously obtaining the output current i of the super capacitorc(k)。
Step 4), calculating the power of the lithium battery and the super capacitor at the current moment:
Pb(k)＝iL(k)·Ub(k),Puc(k)＝ic(k)·Uc(k)
thereby completing the energy distribution of the lithium battery and the super capacitor.
At the next moment, let T (0) become TW(k +1), and continuing to repeat the steps 1) to 4) and performing allocation again.
The output of the lithium battery and the output of the super capacitor after the system performs energy management by adopting the method (NPC-EMS) are the same as the output of the rule-based energy management method (R-EMS), and the output pair is shown in FIG. 3. As can be seen from the figure, under the ECE working condition, the output current and the power of the lithium battery in energy management by adopting the method are lower as a whole, which shows that the method reduces the use of the lithium battery and is beneficial to prolonging the service life of the lithium battery. Meanwhile, the total required energy of the system is 139830J by adopting the method, and the required energy of the system is 144010J by adopting the rule-based energy management method, so that the method can reduce the energy loss of the system.
The above-described embodiments are merely preferred embodiments of the present invention, which should not be construed as limiting the invention. Various changes and modifications may be made by one of ordinary skill in the pertinent art without departing from the spirit and scope of the present invention. Therefore, the technical scheme obtained by adopting the mode of equivalent replacement or equivalent transformation is within the protection scope of the invention.

Claims (4)

1. A composite energy management method for an electric vehicle based on rule and nonlinear predictive control is characterized by comprising the following steps: in the composite energy management method, an energy management strategy based on nonlinear predictive control is combined with an energy management strategy based on rules to complete energy distribution to the hybrid power system; when the required power of the automobile is higher than a power threshold value, an energy management strategy based on nonlinear predictive control is adopted, and output currents of a lithium battery and a super capacitor are obtained through a nonlinear predictive controller, so that energy distribution is completed; when the required power of the automobile is lower than a power threshold value, directly obtaining the output power of the lithium battery and the super capacitor by adopting a rule-based energy management strategy;
the method is implemented by the power P required by the running of the automobile at each momentnSOC of lithium batteryAnd performing lithium battery power P by SOC of the super capacitorbAnd super capacitor power PucThe specific allocation strategy is as follows:
if Pn<0 and USOC>USOCHThen make Pb＝PnAnd P isuc＝0；
If Pn<0 and USOC is less than or equal to USOCHThen make Pb0 and Puc＝Pn；
If 0 is less than or equal to Pn≤PLAnd USOC>USOCLAnd BSOC>BSOCLThen make Pb＝PnAnd P isuc＝0；
If Pn>PLAnd USOC>USOCLAnd BSOC>BSOCLThen, an energy management strategy based on nonlinear predictive control is adopted for power distribution;
if Pn>0 and USOC is less than or equal to USOCLAnd BSOC>BSOCLThen make Pb＝PnAnd P isuc＝0；
If Pn>0 and USOC>USOCLAnd BSOC is less than or equal to BSOCLThen make Pb0 and Puc＝Pn；
If Pn>0 and USOC is less than or equal to USOCLAnd BSOC is less than or equal to BSOCLThen make Pb0 and Puc＝0；
Wherein, PnThe power required by the running of the automobile at each moment is represented, BSOC represents the SOC of the lithium battery, and USOC represents the SOC of the super capacitor; USOCH、USOCLRespectively representing the upper limit value and the lower limit value of the super capacitor SOC, BSOCLRepresents the lower limit value of the lithium battery SOC, PLPower thresholds representing two strategies of the hybrid system;
in the energy management strategy based on the nonlinear predictive control, the speed of a certain period in the future is predicted, the required power in the certain period in the future is calculated through a vehicle speed power model, the output current of the lithium battery is optimized by taking the minimum power loss as an index through a secondary planning active set method, and the power distribution of the lithium battery and the super capacitor is completed; the energy management strategy based on the nonlinear predictive control comprises the following steps:
step 1) predicting the speed in a certain period by the torque at the current moment and the vehicle speed;
step 2) calculating the power required by each moment in the prediction period;
step 3), calculating the optimal solution of the target function at each moment in the prediction period by using a quadratic programming active set method, and outputting the solution at the first moment as an optimal control command;
step 4) calculating the power P of the lithium battery and the super capacitorb＝iL·Ub,Puc＝ic·UcRepeating the steps at the next moment; wherein iLFor the output current of lithium batteries, icFor super-capacitor output current, UbIs the voltage of a lithium battery, UcIs the supercapacitor voltage.
2. The electric vehicle composite energy management method based on the regular and nonlinear predictive control as claimed in claim 1, characterized in that: the hybrid power system of the electric automobile consists of a lithium battery and a super capacitor.
3. The electric vehicle composite energy management method based on the rule and the nonlinear predictive control as claimed in claim 1, wherein in the steps 1) to 4), the specific calculation process is as follows:
assuming that the drive torque decreases exponentially during the prediction period, and is expressed as:
Figure FDA0002376838370000021
wherein, TW(k) Represents the driving torque acting on the wheel at the moment k, and the acceleration is positive and the deceleration is negative; Δ t denotes the sampling time, τdRepresents the attenuation coefficient, HFRepresents a prediction period step size;
the predicted vehicle speed is:
V(k)＝ua；
Figure FDA0002376838370000022
meanwhile, the vehicle speed power model is:
Figure FDA0002376838370000023
wherein V (k) represents the vehicle speed at time k, PnRepresenting the power required for the operation of the vehicle, M representing the mass of the vehicle, f representing the rolling resistance coefficient, g representing the gravitational acceleration, α representing the road gradient, CarRepresenting the coefficient of air resistance, a the frontal area of the vehicle, representing the rotor mass correction coefficient, ηTIndicating the efficiency of the transmission system, RWRepresenting the wheel radius, uaRepresenting the current speed of the vehicle;
based on the predicted speed, the power required for prediction is obtained through a vehicle speed power model, and then the output current i of the lithium battery is optimized through secondary programmingLAnd output current i of super capacitorc；
Setting the output current i of the lithium batteryLFor the control quantity, the objective function J is defined as follows:
Figure FDA0002376838370000024
wherein iL(k) And ic(k) The output current of the lithium battery and the output current of the super capacitor at the moment k are respectively; riIs the internal resistance of lithium battery, RcThe internal resistance of the super capacitor is shown, and N is an optimized step length; meanwhile, the constraint conditions are as follows:
Pn＝Puc+Pb；
Pb＝iL·Ub；
Puc＝ic·Uc；
0.2≤BSOC(k)≤1；
0.56≤USOC(k)≤0.92；
0≤iL(k)≤100；
40≤ic(k)≤200；
wherein BSOC (k) is the lithium battery SOC at the moment k, and USOC (k) is the super capacitor SOC at the moment k;
obtaining a group of lithium battery output currents through optimization: i.e. iL(0),iL(1),…,iL(N-1), selecting the first element iL(0) And the current is used as an optimal control command, namely the output current of the lithium battery at the current moment.
4. The electric vehicle composite energy management method based on the rule and the nonlinear predictive control as claimed in claim 3, characterized in that a quadratic programming active set method is adopted for solving the objective function, and the specific calculation process is as follows:
at each moment, the objective function can be transformed into the following quadratic programming problem:
Figure FDA0002376838370000031
Figure FDA0002376838370000032
Figure FDA0002376838370000033
wherein,
Figure FDA0002376838370000034
Figure FDA0002376838370000035
Figure FDA0002376838370000036
Figure FDA0002376838370000037
the solving process by using the quadratic programming active set method is as follows:
6.1) give initial feasible Point x(0)Let A0＝A(x(0)) The parameter p is 0, and a represents the active set of the quadratic programming problem at that point;
6.2) further converting the quadratic programming problem into an equality constraint quadratic programming problem:
Figure FDA0002376838370000041
Figure FDA0002376838370000042
wherein A isp＝A(x(p))，x(p)For the feasible point of the p-th wheel, solve d ═ d1,d2]T，d1,d2Elements in the solution of the transformed quadratic programming problem; solving the quadratic programming problem in the p round(p)And corresponding Lagrange multiplier
Figure FDA0002376838370000043
6.3) if d(p)Not equal to 0, then
Figure FDA0002376838370000044
Figure FDA0002376838370000045
x(p+1)＝x(p)+αpd(p),Ap+1＝Ap∪{q0}
Wherein, αpFeasible factor of p round, q0Is αpCorresponding sequence value when taking value;
let p be p +1, go to step 6.2);
6.4) ifd(p)When the value is equal to 0, then
Figure FDA0002376838370000046
Then, the optimal solution x can be obtained(p)(ii) a Otherwise
Figure FDA0002376838370000047
x(p+1)＝x(p),Ap+1＝Ap\{qn}
Wherein q isnTo make it possible to
Figure FDA0002376838370000048
Taking a corresponding sequence value when the minimum value is obtained;
let p be p +1, go to step 6.2).
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