CN109835199A - Vehicle-mounted composite power source power distribution optimization method - Google Patents
Vehicle-mounted composite power source power distribution optimization method Download PDFInfo
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Abstract
The invention discloses a kind of vehicle-mounted composite power source power distribution optimization methods, comprising: the demand power that acquisition battery and the state-of-charge and electric car of supercapacitor load in the process of running in real time；The value range of battery power and supercapacitor power weight factor in power allocation procedure is determined according to multiple inference rule；Objective function is established, constraint condition is set；It is searched in the search space formed by value range using dual chaos algorithm, obtains the initial value of weighted factor；The optimal solution of weighted factor in value range is obtained using harmony optimization algorithm；Determine that battery and supercapacitor need power to be offered under current operating conditions；Judge to lack in battery or supercapacitor with the presence or absence of energy, if so, carrying out energy exchange according to energy shared mechanism, composite power source is enable to meet electric car simultaneously to the dual requirements of energy and power.
Description
Technical field
The present invention relates to battery technology field more particularly to a kind of vehicle-mounted composite power source power distribution optimization methods.
Background technique
In recent years, being constantly progressive with power electronic technique and microprocessor technology, the research and application of electric car
Increasing, the power supply formed by the single energy storage device such as battery, supercapacitor, flywheel and solar battery is not
Electric car is able to satisfy to the dual requirements of energy and power, it is a kind of feasible for combining multiple energy storage devices and constituting composite power source
And general solution.
Currently, favor of the battery-supercapacitor compound electric depth of origin by domestic and foreign scholars, wherein battery is used for
High-energy density needed for providing vehicle, supercapacitor is for high power density needed for providing vehicle.But battery and
The dual requirements for not ensuring that and capable of farthest meeting electric automobile energy and power are applied in combination in supercapacitor,
The design of power distribution control strategy optimization algorithm is the key difficulties problem that current scholars study between two kinds of energy storage devices
Place.Common optimization algorithm includes genetic algorithm, particle swarm algorithm, simulated annealing etc., but composite power source power distribution
Belong to multi-objective optimization question, these algorithms technical problem not high there are precision.
Summary of the invention
In view of the above shortcomings of the prior art, the present invention provides a kind of vehicle-mounted composite power source power distribution optimization method,
Efficiently solve the technical issues of power is unable to reasonable distribution between battery and supercapacitor in prior art composite power source.
To achieve the goals above, the invention is realized by the following technical scheme:
A kind of vehicle-mounted composite power source power distribution optimization method includes for providing the electric power storage of energy in the composite power source
Pond and for providing the supercapacitor of power includes: in the power distribution optimization method
S100 obtains the state-of-charge of battery and supercapacitor in real time and electric car loads in the process of running
Demand power；
S200 determines battery power and super capacitor in power allocation procedure according to preset multiple inference rule
The value range of device power weight factor；
S300 establishes the objective function for optimizing vehicle-mounted composite power source power distribution, and sets corresponding constraint condition；
S400 is existed according to the objective function, constraint condition and preset searching times using dual chaos algorithm
Searched in the search space formed by the value range, obtain battery power and supercapacitor power weight factor just
Initial value；
S500 is optimized according to the initial value, objective function, constraint condition and preset searching times using harmony
Algorithm obtains the optimal solution of weighted factor in the value range；
S600 determines that battery and supercapacitor are distinguished under current operating conditions according to the optimal solution of the weighted factor
Power to be offered is needed, the distribution of power in composite power source is completed；
S700 needs power to be offered according to battery and supercapacitor, judge in battery or supercapacitor whether
There are energy missings, if so, carrying out energy exchange according to energy shared mechanism between battery and supercapacitor.
In vehicle-mounted composite power source power distribution optimization method provided by the invention, based on preset multiple reasoning rule
It then determines in composite power source in battery and supercapacitor power allocation procedure after the value range of weighted factor, based on double
Weight chaos harmony chess game optimization algorithm realizes battery power weighted factor wbat(t) and supercapacitor power weight factor wuc
(t) biobjective scheduling obtains the optimal solution in value range, and energy is arranged between battery and supercapacitor and shares
Mechanism, when in battery or supercapacitor there are when energy missing, it is shared according to energy between battery and supercapacitor
Mechanism carry out energy exchange, with control battery undertake the consecutive mean power demand of electric car during the motion (can
Amount demand), supercapacitor undertakes the Instantaneous peak power demand of electric car, and composite power source is enable to meet electronic vapour simultaneously
Vehicle reduces the negative effect that load instantaneous power generates battery cycle life, simultaneously to the dual requirements of energy and power
Control supercapacitor recycles the regenerating braking energy of automobile to the maximum extent.
Detailed description of the invention
In conjunction with attached drawing, and by reference to following detailed description, it will more easily have more complete understanding to the present invention
And its adjoint advantage and feature is more easily to understand, in which:
Fig. 1 is composite power source Distributed Power Architecture schematic diagram in the present invention；
Fig. 2 is the flow chart that weighted factor value range is determined in power allocation procedure of the present invention；
Fig. 3 is the operation interval figure of each regular battery and supercapacitor state-of-charge in the present invention；
Fig. 4 is the search space figure of each regular battery power and supercapacitor power weight factor in the present invention.
Specific embodiment
To keep the contents of the present invention more clear and easy to understand, below in conjunction with Figure of description, the contents of the present invention are made into one
Walk explanation.Certainly the invention is not limited to the specific embodiment, general replacement known to those skilled in the art
It is included within the scope of protection of the present invention.
It include being used in the specific composite power source the present invention provides a kind of vehicle-mounted composite power source power distribution optimization method
The battery of energy is provided and for providing the supercapacitor of power, includes: in the power distribution method
S100 obtains the state-of-charge of battery and supercapacitor in real time and electric car loads in the process of running
Demand power；
S200 determines battery power and super capacitor in power allocation procedure according to preset multiple inference rule
The value range of device power weight factor；
S300 establishes the objective function for optimizing vehicle-mounted composite power source power distribution, and sets corresponding constraint condition；
S400 is according to objective function, constraint condition and preset searching times, using dual chaos algorithm by taking
It is worth in the search space that range is formed and searches for, obtains the initial value of battery power and supercapacitor power weight factor；
S500 is according to initial value, objective function, constraint condition and preset searching times, using harmony optimization algorithm
Obtain the optimal solution of weighted factor in value range；
S600 determines that battery and supercapacitor are respectively necessary under current operating conditions according to the optimal solution of weighted factor
The power of offer completes the distribution of power in composite power source；
S700 needs power to be offered according to battery and supercapacitor, judge in battery or supercapacitor whether
There are energy missings, if so, carrying out energy exchange according to energy shared mechanism between battery and supercapacitor.
Fig. 1 show composite power source Distributed Power Architecture schematic diagram in the present invention, and in the power distribution method, target is
Meet the demand power of electric car load always by the power that battery and supercapacitor provide:
Pde(t)=Pbat(t)+Puc(t)
Wherein, t indicates the time of running of electric car, PdeIt (t) is the demand power loaded in electric car operational process,
PbatIt (t) is battery power, PucIt (t) is supercapacitor power.
The demand power that electric car loads is divided into consecutive mean power Pave(t) and Instantaneous peak power Ppea(t), and
Active control battery undertakes consecutive mean power Pave(t), supercapacitor undertakes Instantaneous peak power Ppea(t), it may be assumed that
Pbat(t)=Pave(t)
Puc(t)=Ppea(t)
In practical applications, load demand power is different under electric car difference operating status, thus battery and super
Capacitor needs power to be offered also different, may be expressed as: with the demand power that this electric car loads in the process of running
Wherein, N indicates the total time of Electric Vehicles Driving Cycle, wbat(t) weighted factor of battery power, w are indicateduc
(t) weighted factor of supercapacitor power, and w are indicatedbat(t)∈[-1,1]、wuc(t)∈[-1,1]；Indicate battery
Maximum power,Indicate the maximum power of supercapacitor.
During power distribution, the state-of-charge of battery and supercapacitor is obtained, electric car is being run
After the demand power loaded in the process, determine battery and supercapacitor in function according to preset multiple inference rule
Weighted factor w in rate assigning processbat(t) and wuc(t) value range, and then use dual chaos harmony chess game optimization algorithm
Optimal solution is determined in value range.In addition, to prevent battery or supercapacitor from there is energy missing during the work time
The case where, design energy shared mechanism therebetween can be according to actual power demand that is, between battery and supercapacitor
It realizes energy exchange, has the function that energy balance between the two.
Specifically, step S200 determines battery in power allocation procedure according to preset multiple inference rule
In the value range of power and supercapacitor power weight factor, further comprise:
S210 judges whether the state-of-charge of battery and supercapacitor is located according to preset multiple inference rule
In minimum state；If so, determining battery power and super capacitor in power allocation procedure according to locating minimum state
The weighted factor value range of device power and the S300 that gos to step；Otherwise, go to step S220；
S220 judges whether the state-of-charge of battery and supercapacitor is located according to preset multiple inference rule
In maximum state of value；If so, determining battery power and super capacitor in power allocation procedure according to locating maximum state of value
The weighted factor value range of device power and the S230 that gos to step；Otherwise, go to step S230；
S230 judges the operating status of electric car according to the demand power of acquisition, and according to preset multiple reasoning
The state-of-charge of rule, the operating status of electric car and demand power, battery and supercapacitor determines battery and surpasses
Weighted factor value range and go to step S300 of the grade capacitor in power allocation procedure.
Preset multiple inference rule includes:
First reasoning again: automobile is in halted state；
Rule 1: if Pde(t)=0, then wbat(t)∈[0,0]、wuc(t)∈[0,0]；
Rule 2: if Pde(t)=0,Then wbat(t)∈[0,1]、 wuc(t)
∈[-1,0]；
Rule 3: if Pde(t)=0,Then wbat(t)∈[-1,0]、wuc(t)∈[0,1]。
Second reasoning again: automobile is in light acceleration or cruising condition；
Rule 4: if Pde(t)>0、Then wbat(t)∈[0,1]、wuc(t)∈[0,0]；
Rule 5: if Pde(t)>0、Then wbat(t)∈
[0,1]、wuc(t)∈[-1,0]；
Rule 6: if Pde(t)>0、Then wbat(t)∈[-1,0.5]、 wuc(t)
∈[0,1]。
Third reasoning again: automobile is in high acceleration mode；
Rule 7: ifThen wbat(t)∈[0,1]、wuc(t)∈[0,1]。
Quadruple reasoning: automobile is in braking or deceleration regime；
Rule 8: if Pde(t) < 0, then wbat(t)∈[0,0]、wuc(t)∈[-1,0]；
Rule 9: if Pde(t)<0、Then wbat(t)∈[0,1]、wuc(t)∈[-1,0]。
5th reasoning again: SOCbat(t) or SOCuc(t) it is in minimum state；
Rule 10: ifThen wbat(t)∈[0,0]、wuc(t)∈[0,
0]；
Rule 11: ifThen wbat(t)∈[-1,0]、wuc(t)∈[-1,
1]。
Sixfold reasoning: SOCbat(t) or SOCuc(t) in maximum state of value；
Rule 12: ifThen wbat(t)∈[0,1]、wuc(t)∈[-1,0]；
Rule 13: ifThen wbat(t)∈[-1,1]、wuc(t)∈[0,1]。
Wherein, SOCbat(t) state-of-charge of battery, and SOC are indicatedbat(t) [0,1] ∈, storage battery charge state
Minimum valueSOCuc(t) state-of-charge of supercapacitor, and SOC are indicateduc(t) [0,1] ∈, supercapacitor lotus
The minimum value of electricity condition Indicate the predetermined minimum of battery charge state,Indicate battery charge
The preset maximum value of state,Indicate the predetermined minimum of supercapacitor charge state,Indicate supercapacitor charge
The preset maximum value of state is set according to the actual situation in practical applications, e.g., in one example,
Determine that the process of weighted factor value range is as shown in Figure 2 based on this, in power allocation procedure:
Firstly, judging whether the state-of-charge of battery and supercapacitor is in minimum state according to step S210
In, comprising:
S211 judgementIt is whether true, if so, executing rule 10, determines wbat
(t)∈[0,0]、wuc(t) ∈ [0,0] and the S300 that gos to step, end judge process；Otherwise, go to step S212；
S212 judgementIt is whether true, if so, executing rule 11, determines wbat
(t)∈[-1,0]、wuc(t) ∈ [- 1,1] and the S300 that gos to step, end judge process；Otherwise, go to step S220.
Later, according to step S220, judge whether the state-of-charge of battery and supercapacitor is in maximum state of value
In, comprising:
S221 judgementIt is whether true, if so, executing rule 12, determines wbat(t)∈[0,1]、 wuc
(t) ∈ [- 1,0] and the S230 that gos to step, whereinIndicate the preset maximum value of battery charge state；Otherwise, it jumps
To step S222；
S222 judgementIt is whether true, if so, executing rule 13, determines wbat(t)∈[-1,1]、wuc(t)
∈ [0,1] and the S230 that gos to step；Otherwise, go to step S230.
Finally, judging the operating status of electric car according to the demand power of acquisition, and according to electronic according to step S230
The state-of-charge of the operating status and demand power of automobile, battery and supercapacitor, determines battery and supercapacitor
Weighted factor value range in power allocation procedure, comprising:
S231 judges Pde(t)=0 whether true, if so, judging that electric car is in halted state and gos to step
S232；
S232 judgementIt is whether true, if so, executing rule 2, determines wbat(t)
∈[0,1]、wuc(t) ∈ [- 1,0] and the S300 that gos to step, end judge process；Otherwise, go to step S233；
S233 judgementIt is whether true, if so, executing rule 3, determines wbat(t)∈[-1,0]、 wuc(t)
∈ [0,1] and the S300 that gos to step, end judge process；Otherwise, executing rule 1 determines wbat(t)∈[0,0]、 wuc(t)
∈ [0,0] and the S300 that gos to step, end judge process.
In step S231, if judging Pde(t)=0 invalid, go to step S234；
S234 judges Pde(t)>0、It is whether true, if so, judging that electric car is in light acceleration mode or patrols
Boat state and the S235 that gos to step；
S235 judgementIt is whether true, if so, executing rule 5, determines wbat(t)
∈[0,1]、wuc(t) ∈ [- 1,0] and the S300 that gos to step, end judge process；Otherwise, go to step S236；
S236 judgementIt is whether true, if so, executing rule 6, determines wbat(t)∈[-1,0.5]、 wuc
(t) ∈ [0,1] and the S300 that gos to step, end judge process；Otherwise, executing rule 4 determine wbat(t)∈[0,1]、 wuc
(t) ∈ [0,0] and the S300 that gos to step, end judge process.
In step S234, if judging Pde(t)>0、Invalid, go to step S237；
S237 judges Pde(t) < 0 whether true, if so, judging that electric car is in braking or deceleration regime, executing rule
8, determine wbat(t)∈[0,0]、wuc(t) ∈ [- 1,0] and the S300 that gos to step, end judge process；Otherwise, step is jumped to
Rapid S238；
S238 judgementIt is whether true, if so, executing rule 9, determines wbat(t)∈[0,1]、 wuc(t)
∈ [- 1,0] and the S300 that gos to step, end judge process；Otherwise, go to step S239；
S239 judgementIt is whether true, if so, judge that electric car is in high acceleration mode, executing rule 7,
Determine wbat(t)∈[0,1]、wuc(t) ∈ [0,1] and the S300 that gos to step, end judge process.
Battery and supercapacitor the weighted factor w in power allocation procedure has been determinedbat(t) and wuc(t) value model
After enclosing, using mode appropriate by weighted factor wbat(t) and wuc(t) it is searched for simultaneously in the search space that value range is formed
Value realizes the power distribution between battery and supercapacitor.According to multiple inference rule, battery and super in each rule
The operation interval figure of grade capacitor state-of-charge is as shown in Figure 3, wherein Fig. 3 (a) indicates to store in rule 1, rule 2 and rule 3
The operation interval figure of battery and supercapacitor state-of-charge, Fig. 3 (b) indicate rule 4, rule 5 and rule 6 in battery and
The operation interval figure of supercapacitor state-of-charge, Fig. 3 (c) indicate battery and supercapacitor state-of-charge in rule 7
Operation interval figure, Fig. 3 (d) indicate the operation interval figure of battery and supercapacitor state-of-charge in rule 8 and rule 9, Fig. 3
(e) indicate that the operation interval figure of battery and supercapacitor state-of-charge in rule 10 and rule 11, Fig. 3 (f) indicate rule
12 and rule 13 in battery and supercapacitor state-of-charge operation interval figure, the number 1~13 in each figure, which represents, advises
Then 1~13.The search space figure of battery power and supercapacitor power weight factor is as shown in Figure 4 in each rule, wherein
Fig. 4 (a) indicates the search space figure of battery power and supercapacitor power weight factor in rule 1, regular 2 and rule 3,
Fig. 4 (b) indicates the search space figure of battery power and supercapacitor power weight factor in rule 4, regular 5 and rule 6,
Fig. 4 (c) indicates that the search space figure of battery power and supercapacitor power weight factor in rule 7, Fig. 4 (d) indicate rule
Then 8 and rule 9 in battery power and supercapacitor power weight factor search space figure, Fig. 4 (e) indicates regular 10 Hes
The search space figure of battery power and supercapacitor power weight factor in rule 11, Fig. 4 (f) indicate rule 12 and rule
The search space figure of battery power and supercapacitor power weight factor in 13,1~13 delegate rules of number in each figure
1~13.
For optimization process, the objective function J of foundation:
The constraint condition of setting:
Wherein, at the time of t indicates electric car operation, N indicates the total time of Electric Vehicles Driving Cycle, Pde(t) it indicates
The demand power of capacitor vehicle load,For battery power,Indicate the minimum power of battery,Indicate the maximum power of battery,Indicate supercapacitor power,Indicate supercapacitor
Minimum power,Indicate the maximum power of supercapacitor, wbat(t) weighted factor of battery power, w are indicateduc(t) table
Show the weighted factor of supercapacitor power, SOCbat(t) state-of-charge of battery is indicated,Indicate battery charge shape
The minimum value of state,Indicate the maximum value of battery charge state, SOCuc(t) state-of-charge of supercapacitor is indicated,Indicate the minimum value of supercapacitor charge state,Indicate the maximum value of supercapacitor charge state.
According to the objective function of foundation it is found that there are two optimization aims: battery power weighted factor wbat(t) and it is super
Grade capacitor power weighted factor wuc(t), belong to biobjective scheduling problem.Harmony optimization algorithm is a kind of heuristic global optimization
Algorithm, it is more sensitive to initial value, it is easy to generate locally optimal solution, convergence is to be improved.Chaos algorithm has stronger time
The property gone through, randomness and regularity are searched using chaotic motion what its value range was formed for the initial value of harmony optimization algorithm
Rope is traversed in space, undoubtedly can be improved the performance of harmony optimization algorithm.Therefore, dual chaos harmony is introduced in the present invention
Chess game optimization algorithm, solves the problems, such as biobjective scheduling.
During optimization, firstly, according to objective function, constraint condition and preset searching times, using double
Weight chaos algorithm is searched in the search space formed by value range, obtains battery power and supercapacitor power weightings
The initial value of the factor.In search process, Chaos Variable is generated based on Logistic mapping, then become chaos in a manner of carrier wave
The traversal range of amount expands to optimization aim battery power weighted factor wbat(t) and supercapacitor power weight factor wuc
(t) value range, i.e., the target of dual chaos algorithm are traversal battery power weighted factor wbat(t) and supercapacitor
Power weight factor wuc(t) search space.The method taken are as follows: first establish a heavy Chaos Search, battery power is weighted
Factor wbat(t) chaos optimization is carried out, when the number of iterations for reaching a weight Chaos Search or obtains meeting objective function and constraint item
The optimal solution of part resettles double Chaos Search, to supercapacitor power weight factor wuc(t) chaos optimization is carried out.It says
Bright, the output of the dual chaos algorithm is not based on the output of double Chaos Search, but by the knot of double Chaos Search
Fruit obtains dual solution space after merging with the result of a weight Chaos Search, judges whether to reach double further according to the result after merging
The number of iterations of Chaos Search or the step of obtaining the optimal solution for meeting objective function and constraint condition, specifically including, have:
S410 initializes chaotic optimization algorithm parameter, including a heavy Chaos Search number S1With double Chaos Search number S2；
S420 establishes dual chaotic Logistic map, and expands to battery power weighted factor wbat(t) and super electricity
Container power weighted factor wuc(t) value range；
S430 is directed to battery power weighted factor wbat(t) weight is carried out in the search space formed by value range
Chaos Search；
S440 judges whether the number of iterations of a weight Chaos Search reaches preset one heavy Chaos Search number S1And
Whether the result for judging a weight Chaos Search is the optimal solution for meeting objective function and constraint condition, if meeting one of item
Part, go to step S450, into double Chaos Search；If two conditions are not satisfied, go to step S430, again to storage
Power of battery weighted factor wbat(t) a heavy Chaos Search is carried out；
S450 is directed to supercapacitor power weight factor wuc(t) two are carried out in the search space formed by value range
Weight Chaos Search；
S460 carries out the result of double Chaos Search in the result of a weight Chaos Search in step S430 and step S450
Merge；
S470 judges whether the number of double Chaos Search reaches preset double Chaos Search number S2And judgement
Whether the search result merged in step S460 is the optimal solution for meeting objective function and constraint condition, if meeting one of item
Part, using search result as the initial value of battery power and supercapacitor power weight factor and the S500 that gos to step；
If two conditions are not satisfied, go to step S450, again to supercapacitor power weight factor wuc(t) it carries out double mixed
Ignorant search.
Using dual chaos algorithm search for obtain battery power and supercapacitor power weight factor initial value it
Afterwards, weighting in battery power and supercapacitor power weight factor value range is further obtained using harmony optimization algorithm
The optimal solution of the factor.
For harmony optimization algorithm, firstly, definition includes n decision variable x1,x2...,xnSolution space, n indicate decision
The number of variable, it will be appreciated that being includes n musical instrument, i-th of decision variableIndicate that each musical instrument has high pitchWith it is low
SoundLater, from by decision variable xiM harmony is generated in the solution space of formation at random, is put into harmony data base；Secondly, fixed
Adopted harmony data base retains probability HMCR, musical instrument syllable regulation PAR and disturbance bandwidth BW:
Wherein, HMCRmaxAnd HMCRminThe upper lower limit value that harmony data base retains probability is respectively indicated, NI indicates total iteration
Number, g indicate current iteration number, PARmaxAnd PARminIndicate the upper lower limit value of musical instrument syllable regulation, BWmaxAnd BWminFor
Disturb the upper lower limit value of bandwidth；Finally, generating random number R between [0,1], if R > HMCR, randomly selected out of solution space
Decision variable, and update harmony data base；If R < HMCR, decision variable is randomly selected from harmony data base, and with
The probability of PAR carries out the disturbance that bandwidth is BW, same to update harmony data base.
Based on this, in the present invention, according to the initial value, objective function, constraint condition and preset searching times,
The step of obtaining the optimal solution of weighted factor in the value range using harmony optimization algorithm include:
S510 is according to battery power weighted factor wbat(t) value range is defined by n decision variable x1,x2...,
xnThe solution space of composition, and define the domain of each decision variable, wherein [ai,bi] indicate i-th of decision variable xiDetermine
Adopted domain；According to supercapacitor power weight factor wuc(t) value range defines decision variable x in harmony data baseiAnd
Row variable xij, j=1,2 ..., n；
S520 is based on by decision variable xiWith parallel variable xijThe solution space of formation generates M harmony at random, is put into harmony
Data base, wherein M < n includes a decision variable and its corresponding parallel variable in each harmony；
S530 initializes harmony optimization algorithm parameter, including decision variable xi, parallel variable xij, data base retain probability
HMCR, musical instrument syllable regulation PAR, disturbance bandwidth BW and total the number of iterations NI；
S540 generates random number R between [0,1]；
S550 judges whether random number R is greater than data base and retains probability HMCR, if so, selecting decision at random out of solution space
Variable；If it is not, randomly selecting decision variable from harmony data base, and band is carried out with the probability of musical instrument syllable regulation PAR
Width is the disturbance of BW；
S560 enters the decision variable and its corresponding parallel variable update according to the decision variable selected in step S550
Harmony data base；
S570 judges whether the number that harmony data base updates reaches preset total the number of iterations NI and judgement updates
Whether the result of harmony data base afterwards is the optimal solution for meeting objective function and constraint condition, if meeting one of condition,
Result in harmony data base as the optimal solution of battery power and supercapacitor power weight factor and is jumped into step
Rapid S600；If two conditions are not satisfied, go to step S540, is updated again to harmony data base.
It obtains after the optimal solution of battery power and supercapacitor power weight factor to get having arrived battery and super
The power of capacitor, if the case where there are energy missings in battery or supercapacitor at this time, battery and supercapacitor
Between according to energy shared mechanism carry out energy exchange, complete composite power source in power distribution.
Claims (11)
1. a kind of vehicle-mounted composite power source power distribution optimization method, which is characterized in that include for providing in the composite power source
The battery of energy and for providing the supercapacitor of power includes: in the power distribution optimization method
The demand that S100 obtains the state-of-charge of battery and supercapacitor in real time and electric car loads in the process of running
Power；
S200 determines battery power and supercapacitor function in power allocation procedure according to preset multiple inference rule
The value range of rate weighted factor；
S300 establishes the objective function for optimizing vehicle-mounted composite power source power distribution, and sets corresponding constraint condition；
S400 is according to the objective function, constraint condition and preset searching times, using dual chaos algorithm by institute
Search in the search space of value range formation is stated, the initial of battery power and supercapacitor power weight factor is obtained
Value；
S500 is according to the initial value, objective function, constraint condition and preset searching times, using harmony optimization algorithm
Obtain the optimal solution of weighted factor in the value range；
S600 determines that battery and supercapacitor are respectively necessary under current operating conditions according to the optimal solution of the weighted factor
The power of offer completes the distribution of power in composite power source；
S700 needs power to be offered according to battery and supercapacitor, judges to whether there is in battery or supercapacitor
Energy missing, if so, carrying out energy exchange according to energy shared mechanism between battery and supercapacitor.
2. vehicle-mounted composite power source power distribution optimization method as described in claim 1, which is characterized in that in step S300,
The objective function J of foundation are as follows:
The constraint condition of setting are as follows:
Wherein, at the time of t indicates electric car operation, N indicates the total time of Electric Vehicles Driving Cycle, Pde(t) capacitor is indicated
The demand power of vehicle load,For battery power,Indicate the minimum power of battery,Table
Show the maximum power of battery,Indicate supercapacitor power,Indicate the minimum of supercapacitor
Power,Indicate the maximum power of supercapacitor, wbat(t) weighted factor of battery power, w are indicateduc(t) indicate super
The weighted factor of grade capacitor power, SOCbat(t) state-of-charge of battery is indicated,Indicate battery charge state
Minimum value,Indicate the maximum value of battery charge state, SOCuc(t) state-of-charge of supercapacitor is indicated,
Indicate the minimum value of supercapacitor charge state,Indicate the maximum value of supercapacitor charge state.
3. vehicle-mounted composite power source power distribution optimization method as claimed in claim 1 or 2, which is characterized in that in step S400
In, further comprise:
S410 initializes chaotic optimization algorithm parameter, including a heavy Chaos Search number S1With double Chaos Search number S2；
S420 establishes dual chaotic Logistic map, and expands to battery power weighted factor wbat(t) and supercapacitor
Power weight factor wuc(t) value range；
S430 is directed to battery power weighted factor wbat(t) weight is carried out in the search space formed by the value range
Chaos Search；
S440 judges whether the number of iterations of a weight Chaos Search reaches preset one heavy Chaos Search number S1And judge one
Whether the result of weight Chaos Search is that the optimal solution for meeting objective function and constraint condition jumps if meeting one of condition
To step S450, into double Chaos Search；If two conditions are not satisfied, go to step S430, again to battery function
Rate weighted factor wbat(t) a heavy Chaos Search is carried out；
S450 is directed to supercapacitor power weight factor wuc(t) two are carried out in the search space formed by the value range
Weight Chaos Search；
S460 merges the result of double Chaos Search in the result of a weight Chaos Search in step S430 and step S450；
S470 judges whether the number of double Chaos Search reaches preset double Chaos Search number S2And judgment step
Whether the search result merged in S460 is the optimal solution for meeting objective function and constraint condition, if meeting one of condition,
Using search result as the initial value of battery power and supercapacitor power weight factor and the S500 that gos to step；If two
A condition is not satisfied, and go to step S450, again to supercapacitor power weight factor wuc(t) double chaos is carried out to search
Rope.
4. vehicle-mounted composite power source power distribution optimization method as claimed in claim 1 or 2, which is characterized in that in step S500
In, further comprise:
S510 is according to battery power weighted factor wbat(t) value range is defined by n decision variable x1,x2...,xnIt constitutes
Solution space, and define the domain of each decision variable, wherein [ai,bi] indicate i-th of decision variable xiDomain；Root
According to supercapacitor power weight factor wuc(t) value range defines decision variable x in harmony data baseiParallel variable
xij, j=1,2 ..., n；
S520 is based on by decision variable xiWith parallel variable xijThe solution space of formation generates M harmony at random, is put into and sound memory
Library, wherein M < n includes a decision variable and its corresponding parallel variable in each harmony；
S530 initializes harmony optimization algorithm parameter, including decision variable xi, parallel variable xij, data base retain probability HMCR,
Musical instrument syllable regulation PAR, disturbance bandwidth BW and total the number of iterations NI；
S540 generates random number R between [0,1]；
S550 judges whether random number R is greater than data base and retains probability HMCR, becomes if so, selecting decision at random out of solution space
Amount；If it is not, randomly selecting decision variable from harmony data base, and bandwidth is carried out with the probability of musical instrument syllable regulation PAR
For the disturbance of BW；
The decision variable and its corresponding parallel variable update are entered harmony according to the decision variable selected in step S550 by S560
Data base；
Whether the number that S570 judges that harmony data base updates reaches preset total the number of iterations NI and judges updated
Whether the result of harmony data base is the optimal solution for meeting objective function and constraint condition, will be with if meeting one of condition
Result in sound memory library as the optimal solution of battery power and supercapacitor power weight factor and gos to step
S600；If two conditions are not satisfied, go to step S540, is updated again to harmony data base.
5. vehicle-mounted composite power source power distribution optimization method as described in claim 1, which is characterized in that wrap in step s 200
It includes:
S210 judges whether the state-of-charge of battery and supercapacitor is in most according to preset multiple inference rule
Small state of value；If so, determining battery power and supercapacitor function in power allocation procedure according to locating minimum state
The weighted factor value range of rate and the S300 that gos to step；Otherwise, go to step S220；
S220 judges whether the state-of-charge of battery and supercapacitor is in most according to preset multiple inference rule
Big state of value；If so, determining battery power and supercapacitor function in power allocation procedure according to locating maximum state of value
The weighted factor value range of rate and the S230 that gos to step；Otherwise, go to step S230；
S230 judges the operating status of electric car according to the demand power of acquisition, and is advised according to preset multiple reasoning
Then, the operating status and demand power of electric car, battery and supercapacitor state-of-charge, determine battery and super
Weighted factor value range and go to step S300 of the capacitor in power allocation procedure.
6. vehicle-mounted composite power source power distribution optimization method as claimed in claim 5, which is characterized in that the multiple reasoning rule
Then include:
First reasoning again, automobile are in halted state；
Rule 1: if Pde(t)=0, then wbat(t)∈[0,0]、wuc(t)∈[0,0]；
Rule 2: if Pde(t)=0,Then wbat(t)∈[0,1]、wuc(t)∈[-
1,0]；
Rule 3: if Pde(t)=0,Then wbat(t)∈[-1,0]、wuc(t)∈[0,1]；
Second reasoning again: automobile is in light acceleration or cruising condition；
Rule 4: if Pde(t) > 0,Then wbat(t)∈[0,1]、wuc(t)∈[0,0]；
Rule 5: if Pde(t) > 0,Then wbat(t)∈[0,
1]、wuc(t)∈[-1,0]；
Rule 6: if Pde(t) > 0,Then wbat(t)∈[-1,0.5]、wuc(t)∈[0,
1]；
Third reasoning again: automobile is in high acceleration mode；
Rule 7: ifThen wbat(t)∈[0,1]、wuc(t)∈[0,1]；
Quadruple reasoning: automobile is in braking or deceleration regime；
Rule 8: if Pde(t) < 0, then wbat(t)∈[0,0]、wuc(t)∈[-1,0]；
Rule 9: if Pde(t)<0、Then wbat(t)∈[0,1]、wuc(t)∈[-1,0]；
5th reasoning again: SOCbat(t) or SOCuc(t) it is in minimum state；
Rule 10: ifThen wbat(t)∈[0,0]、wuc(t)∈[0,0]；
Rule 11: ifThen wbat(t)∈[-1,0]、wuc(t)∈[-1,1]；
Sixfold reasoning: SOCbat(t) or SOCuc(t) in maximum state of value；
Rule 12: ifThen wbat(t)∈[0,1]、wuc(t)∈[-1,0]；
Rule 13: ifThen wbat(t)∈[-1,1]、wuc(t)∈[0,1]。
7. vehicle-mounted composite power source power distribution optimization method as claimed in claim 6, which is characterized in that in step S210, root
Judge whether the state-of-charge of battery and supercapacitor is in minimum state according to preset multiple inference rule,
Include:
S211 judgementIt is whether true, if so, executing rule 10, determines wbat(t)∈
[0,0]、wuc(t) ∈ [0,0] and the S300 that gos to step, otherwise, go to step S212；
S212 judgementIt is whether true, if so, executing rule 11, determines wbat(t)∈
[-1,0]、wuc(t) ∈ [- 1,1] and the S300 that gos to step, whereinIndicate the predetermined minimum of battery charge state；
Otherwise, go to step S220.
8. vehicle-mounted composite power source power distribution optimization method as claimed in claim 6, which is characterized in that in step S220, root
Judge whether the state-of-charge of battery and supercapacitor is in maximum state of value according to preset multiple inference rule,
Include:
S221 judgementIt is whether true, if so, executing rule 12, determines wbat(t)∈[0,1]、wuc(t)∈[-
1,0] and the S230 that gos to step, whereinIndicate the preset maximum value of battery charge state；Otherwise, it gos to step
S222；
S222 judgementIt is whether true, if so, executing rule 13, determines wbat(t)∈[-1,1]、wuc(t)∈[0,
1] and the S230 that gos to step, whereinIndicate the preset maximum value of battery charge state；Otherwise, it gos to step
S230。
9. vehicle-mounted composite power source power distribution optimization method as claimed in claim 6, which is characterized in that in step S230,
Include:
S231 judges Pde(t)=0 whether true, if so, judging that electric car is in halted state and the S232 that gos to step；
S232 judgementIt is whether true, if so, executing rule 2, determines wbat(t)∈[0,
1]、wuc(t) ∈ [- 1,0] and the S300 that gos to step, whereinIndicate the predetermined minimum of supercapacitor charge state；
Otherwise, go to step S233；
S233 judgementIt is whether true, if so, executing rule 3, determines wbat(t)∈[-1,0]、wuc(t)∈[0,
1] and the S300 that gos to step；Otherwise, executing rule 1 determines wbat(t)∈[0,0]、wuc(t) it ∈ [0,0] and gos to step
S300。
10. vehicle-mounted composite power source power distribution optimization method as claimed in claim 9, which is characterized in that in step S231,
If judging Pde(t)=0 invalid, go to step S234；
S234 judges Pde(t) > 0,It is whether true, if so, judging that electric car is in light acceleration mode or cruise
State and the S235 that gos to step；
S235 judgementIt is whether true, if so, executing rule 5, determines wbat(t)∈[0,
1]、wuc(t) ∈ [- 1,0] and the S300 that gos to step；Otherwise, go to step S236；
S236 judgementIt is whether true, if so, executing rule 6, determines wbat(t)∈[-1,0.5]、wuc(t)∈
[0,1] and the S300 that gos to step；Otherwise, executing rule 4 determine wbat(t)∈[0,1]、wuc(t) ∈ [0,0] and step is jumped to
Rapid S300.
11. power distribution method as claimed in claim 10, which is characterized in that in step S234, if judging Pde(t) > 0,Invalid, go to step S237；
S237 judges Pde(t) whether < 0 is true, if so, judging that electric car is in braking or deceleration regime, executing rule 8 are sentenced
Determine wbat(t)∈[0,0]、wuc(t) ∈ [- 1,0] and the S300 that gos to step；Otherwise, go to step S238；
S238 judgementIt is whether true, if so, executing rule 9, determines wbat(t)∈[0,1]、wuc(t)∈[-1,
0] and the S300 that gos to step；Otherwise, go to step S239；
S239 judgementWhether true, if so, judging that electric car is in high acceleration mode, executing rule 7 determines
wbat(t)∈[0,1]、wuc(t) ∈ [0,1] and the S300 that gos to step.
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| CN110481325A (en) * | 2019-08-08 | 2019-11-22 | 江苏理工学院 | Electric car composite power source parameter optimization method |
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