CN109031946A - A kind of control method and device of mixed energy storage system - Google Patents
A kind of control method and device of mixed energy storage system Download PDFInfo
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Abstract
The present invention discloses a kind of control method and device of mixed energy storage system.Wherein, which comprises establish the power battery power loss model and super capacitor power loss model of mixed energy storage system；The power loss Optimized model of the mixed energy storage system is established with the minimum target of power loss of mixed energy storage system under the conditions of full working scope based on the power battery power loss model and the super capacitor power loss model；The power loss Optimized model is solved based on dynamic programming algorithm, obtains the Optimal Control Strategy of the mixed energy storage system.Described device is for executing the above method.The control method and device of mixed energy storage system provided by the invention reduce the energy consumption of mixed energy storage system.
Description
Technical field
The present invention relates to rail traffic energy technology fields, and in particular to a kind of control method and dress of mixed energy storage system
It sets.
Background technique
Due to the features such as tramcar passenger capacity is big, speed is high, frequently starting is braked in operational process, lead to it not only
Having higher power demand, there are also biggish energy requirements.The energy-storage system mixed using power battery with super capacitor, not only
It can satisfy the power energy demand of train, moreover it is possible to improve train operation efficiency and power performance, reduce cost.
For the energy-storage system that power battery is mixed with super capacitor, it is to improve to have that real vehicle operation control how is carried out to it
The key point of rail electric car operational efficiency.The fuzzy logic power distribution plan based on stochastic prediction that prior art discloses a kind of
Slightly, this method is focused on solving to power demand in future in real vehicle operational process based on the Markov stochastic prediction principle
Forecasting problem, and using prediction power as one of input quantity of fuzzy control, to realize the power distribution of hybrid energy-storing and mention
High train operation efficiency, but there is no optimize FUZZY ALGORITHMS FOR CONTROL；The prior art also discloses a kind of using population
Algorithm is solved with the fuzzy controller relevant parameter of the minimum target of energy consumption of vehicles, and then completes the design of fuzzy controller, real
Existing vehicle On-line Control, but since this method is to be realized based on certain fuzzy rule, thus acquired results are not optimal solution；
The vehicle-mounted mixed energy storage system efficiency-optimization control of tramcar carries out the vehicle air-conditioning of train, root based on SUMT interior point method
Train optimizing decision at this time is solved according to train current state, completes energy distribution, but according to principle of optimality it is found that train is instantaneous
The optimal result of decision may not be exactly the result of decision of global optimum.
Therefore, the control method for how proposing a kind of mixed energy storage system can be realized mixed energy storage system entirety energy consumption
Minimum becomes industry important topic urgently to be resolved to reduce the energy consumption of mixed energy storage system.
Summary of the invention
For the defects in the prior art, the present invention provides a kind of control method and device of mixed energy storage system.
On the one hand, the present invention proposes a kind of control method of mixed energy storage system, comprising:
Establish the power battery power loss model and super capacitor power loss model of mixed energy storage system；
Based on the power battery power loss model and the super capacitor power loss model, with the hybrid energy-storing
The minimum target of power loss of system under the conditions of full working scope establishes the power loss optimization mould of the mixed energy storage system
Type；
The power loss Optimized model is solved based on dynamic programming algorithm, obtains the mixed energy storage system
Optimal Control Strategy.
On the other hand, the present invention provides a kind of control device of mixed energy storage system, comprising:
First establishing unit, for establishing the power battery power loss model and super capacitor power of mixed energy storage system
Loss model；
Second establishes unit, for being based on the power battery power loss model and the super capacitor power loss mould
Type establishes the mixed energy storage system with the minimum target of power loss of mixed energy storage system under the conditions of full working scope
Power loss Optimized model；
Unit is solved, for solving based on dynamic programming algorithm to the power loss Optimized model, described in acquisition
The Optimal Control Strategy of mixed energy storage system.
In another aspect, the present invention provides a kind of electronic equipment, comprising: processor, memory and communication bus, in which:
The processor and the memory complete mutual communication by the communication bus；
The memory is stored with the program instruction that can be executed by the processor, and the processor calls described program to refer to
Enable the control method that the mixed energy storage system provided such as the various embodiments described above is provided.
Another aspect, the present invention provide a kind of non-transient computer readable storage medium, and the non-transient computer is readable
Storage medium stores computer instruction, and the computer instruction makes the computer execute the mixing provided such as the various embodiments described above
The control method of energy-storage system.
The control method and device of mixed energy storage system provided by the invention, due to by establishing the dynamic of mixed energy storage system
Power battery power consumption model and super capacitor power loss model, and it is based on power battery power loss model and super capacitor
Power loss model establishes hybrid energy-storing system with the minimum target of power loss of mixed energy storage system under the conditions of full working scope
The power loss Optimized model of system is then based on dynamic programming algorithm and solves to power loss Optimized model, mixed
The Optimal Control Strategy of energy-storage system reduces the energy consumption of mixed energy storage system.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is this hair
Bright some embodiments for those of ordinary skill in the art without creative efforts, can be with root
Other attached drawings are obtained according to these attached drawings.
Fig. 1 is the structural schematic diagram of the mixed energy storage system of tramcar of the embodiment of the present invention；
Fig. 2 is the flow diagram of the control method of one embodiment of the invention mixed energy storage system；
Fig. 3 is solution flow chart of the one embodiment of the invention based on dynamic programming algorithm；
Fig. 4 is the structural schematic diagram of the control device of one embodiment of the invention mixed energy storage system；
Fig. 5 is the entity structure schematic diagram of one embodiment of the invention electronic equipment.
Specific embodiment
To make the object, technical solutions and advantages of the present invention clearer, below in conjunction with attached in the embodiment of the present invention
Figure, technical solution in the embodiment of the present invention are explicitly described, it is clear that described embodiment is a part of the invention
Embodiment, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not making wound
Every other embodiment obtained under the premise of the property made labour, shall fall within the protection scope of the present invention.
Fig. 1 is the structural schematic diagram of the mixed energy storage system of tramcar of the embodiment of the present invention, as shown in Figure 1, described mixed
Closing energy-storage system includes super capacitor, power battery and DC converter, the power battery by the DC converter with
The super capacitor is connected in parallel on the DC bus of tramcar, is the vehicle traction system and auxiliary power supply of the tramcar
System provides energy.Meanwhile the mixed energy storage system can also inhale the receipts feedback braking energy of the tramcar, complete institute
State the interaction of power and energy of the tramcar under different operating conditions.The control method of mixed energy storage system provided by the invention,
For above-mentioned mixed energy storage system, by the power battery power loss model and super capacitor power of establishing mixed energy storage system
Loss model, and it is based on power battery power loss model and super capacitor power loss model, with mixed energy storage system complete
The minimum target of power loss under working condition, establishes the power loss Optimized model of mixed energy storage system, is then based on dynamic
State planning algorithm solves power loss Optimized model, carries out so as to the mixed energy storage system to the tramcar
Efficient and accurate energy distribution, so that the power of the tramcar mixed energy storage system under the conditions of full working scope
Loss is minimum, and can guarantee that each element works in safe and reliable range in the mixed energy storage system, is guaranteeing rail
On the basis of the safe operation of electric car, the energy loss of the mixed energy storage system is efficiently and securely reduced.It is intelligible
It is that the mixed energy storage system cannot only be applied on the tramcar, can also be used in automobile etc. certainly can use institute
State industry or the field of mixed energy storage system.
Fig. 2 is the flow diagram of the control method of one embodiment of the invention mixed energy storage system, as shown in Fig. 2, this hair
The control method of the mixed energy storage system of bright offer, comprising:
S201, the power battery power loss model for establishing mixed energy storage system and super capacitor power loss model；
Specifically, energy, the loss of energy are provided by power battery and super capacitor due to the mixed energy storage system
The power battery and the super capacitor are mostly come from, therefore establishes the power battery power loss mould of mixed energy storage system
Type calculates the power loss of the power battery, and establishes super capacitor power loss model to calculate the super capacitor
Power loss.
S202, it is based on the power battery power loss model and the super capacitor power loss model, with described mixed
Power loss minimum target of energy-storage system under the conditions of full working scope is closed, the power loss for establishing the mixed energy storage system is excellent
Change model；
Specifically, after obtaining the power battery power loss model and the super capacitor power loss model,
With the minimum target of power loss of mixed energy storage system under the conditions of full working scope, according to the power battery power loss
Model and the super capacitor power loss model can establish the power loss Optimized model of the mixed energy storage system.It is described
Power loss Optimized model may include optimization object function and constraint condition, the constraint condition include equality constraint and
Inequality constraints condition.Wherein, the full working scope condition refers to one that the mixed energy storage system is run in practical applications
Complete cycle, for example, the tramcar mixed energy storage system full working scope condition refer to the tramcar from starting point run
To terminal.
S203, the power loss Optimized model is solved based on dynamic programming algorithm, obtains the hybrid energy-storing
The Optimal Control Strategy of system.
It specifically, can be by dynamic programming algorithm to the power after obtaining the power loss Optimized model
Loss optimizing model is solved, i.e., runing time of the mixed energy storage system under the conditions of full working scope is discrete for N
A stage, with the state-of-charge (State of Charge, hereinafter referred to as SOC) of the power battery for state variable, with described
The current-order of power battery passes through the power loss using ampere-hour integral formula as state transition equation for decision variable
The Dynamic Programming Equation of Optimized model can obtain the mixed energy storage system in the case where meeting the constraint condition
Optimal Control Strategy controls the mixed energy storage system according to the Optimal Control Strategy, can be realized described complete
The minimum power loss of the mixed energy storage system under working condition.Wherein, the current-order is for being arranged the power electric
The size of current in pond.
The control method of mixed energy storage system provided by the invention, due to the power battery by establishing mixed energy storage system
Power loss model and super capacitor power loss model, and damaged based on power battery power loss model and super capacitor power
It consumes model and the function of mixed energy storage system is established with the minimum target of power loss of mixed energy storage system under the conditions of full working scope
Rate loss optimizing model is then based on dynamic programming algorithm and solves to power loss Optimized model, obtains hybrid energy-storing system
The Optimal Control Strategy of system reduces the energy consumption of mixed energy storage system.
On the basis of the various embodiments described above, further, the power battery power loss model are as follows:
Wherein, Vp(k) the polarizing voltage value of the power battery described in the kth moment, V are indicatedp(k-1) it indicates at -1 moment of kth
The polarizing voltage value of the power battery, k are positive integer, TsIndicate time step, e indicates natural constant, Ib(k) it indicates in kth
The current value of power battery described in moment, Rp[SOC(k)] indicate the power battery described in the kth moment in current SOC(k) pole under
Change internal resistance value, SOC(k) state-of-charge of the power battery described in the kth moment, V are indicatedo(k) power described in the kth moment is indicated
The terminal voltage value of battery, Vocv[SOC(k)] indicate the power battery described in the kth moment in current SOC(k) open-circuit voltage values under,
Ro[SOC(k)] indicate the power battery described in the kth moment in current SOC(k) the ohmic internal resistance value under, Cp[SOC(k)] it indicates the
Power battery described in the k moment is in current SOC(k) the polarization capacity value under, Pbat_loss(k) power electric described in the kth moment is indicated
The loss power in pond；Vp(0)=0；
The super capacitor damage power consumes model are as follows:
Puc_loss(k)=Iuc(k)2Ruc
Wherein, Vuc(k) open-circuit voltage values in kth moment super capacitor, V are indicateduc(k-1) it indicates in -1 moment of kth institute
State the open-circuit voltage values of super capacitor, Iuc(k) current value of the super capacitor described in the kth moment, C are indicateducIt indicates in kth
Carve the capacitance of the super capacitor, Vc(k) terminal voltage value of the super capacitor described in the kth moment, R are indicateducIndicate described super
The internal resistance value of grade capacitor, Puc_loss(k) loss power of the super capacitor described in the kth moment is indicated；Vuc(0)=0.
Specifically, the power battery is in the polarizing voltage value at the 0th moment, i.e. the polarizing voltage value V of initial timep(0)
=0, the polarizing voltage value V at kth momentp(k), recurrence formula can be passed throughIt obtains
?.The time step Ts, can be set to 1s.State-of-charge S of the power battery at the 0th momentOC(0) be it is given,
Pass through ampere-hour integral formulaThe power battery can be obtained at the kth moment
State-of-charge SOC(k)。Ib(k) it is arranged to the decision variable.Rp[SOC(k)] the polarization resistance value measured using laboratory with
The relationship of the state-of-charge is found out, Vocv[SOC(k)] pass of the open-circuit voltage values and the state-of-charge measured using laboratory
System finds out, Cp[SOC(k)] relationship of the polarization capacity value and the state-of-charge that are measured using laboratory is found out, Ro[SOC(k)]
The relationship of the ohmic internal resistance value and the state-of-charge that are measured using laboratory is found out.Wherein, the mixed energy storage system is in institute
Runing time under the conditions of full working scope is stated by discrete as N number of stage, N number of stage correspond to N+1 a time points.
The power battery is in the open-circuit voltage values at the 0th moment, i.e. the open-circuit voltage values V of initial timeuc(0)=0, kth
The polarizing voltage value V at momentuc(k), recurrence formula can be passed throughIt obtains.Wherein,
What the loss power of the super capacitor considered is loss caused by ohmic internal resistance.
On the basis of the various embodiments described above, further, the power loss Optimized model includes optimization object function,
The optimization object function are as follows:
Wherein, Ploss(i)=Pbat_loss(i)+Puc_loss(i)+Pdd_loss(i), Ploss(i) it indicates to mix described in the i-th moment
Close the loss power of energy-storage system, Pbat_loss(i) loss power of the power battery described in the i moment, P are indicateduc_loss(i) it indicates
The loss power of the super capacitor described in the i-th moment, Pdd_loss(i) loss power in the i-th moment DC converter, i are indicated
More than or equal to 0 and i is less than or equal to N-1, and N is the mixed energy storage system discrete rank of runing time under the conditions of the full working scope
Number of segment.
Specifically, when establishing the power loss Optimized model, in order to more accurately calculate the mixed energy storage system
Loss power, introduce the loss power of DC converter, the loss power of the mixed energy storage system is equal to the power electric
The sum of the loss power in pond, the loss power of the super capacitor and loss power of the DC converter.Described in foundation
Power loss Optimized model is the minimum value in order to obtain the mixed energy storage system loss power under the conditions of full working scope.
Loss power P of the DC converter at the i-th momentdd_loss(i)=(1- η) Pbat(i), wherein η is the DC converting
The energy conversion efficiency of device, PbatIt (i) is the output power of the power battery described in the i-th moment.
On the basis of the various embodiments described above, further, the power loss Optimized model includes equality constraint
With inequality constraints condition, in which:
The equality constraint are as follows:
Wherein, Puc(i) output power of the super capacitor described in the i-th moment, P are indicatedbd(i) it indicates in the i-th moment direct current
The on high-tension side output power of converter, Preq(i) it indicates in the i-th moment load demand power, Pbat(i) it indicates in the i-th moment institute
State the output power of power battery, PSIV(i) power in the i-th moment auxiliary power supply system, P are indicateddd_loss(i) it indicates i-th
The loss power of DC converter described in moment, i is integer and i is more than or equal to 0.
The inequality constraints condition are as follows:
Wherein, Ib_minIndicate the minimum value of the charging and discharging currents of the power battery, Ib(i) it indicates described in the i-th moment
The current value of power battery, Ib_maxIndicate the maximum value of the charging and discharging currents of the power battery, Iuc_minIndicate the super electricity
The minimum value of the charging and discharging currents of appearance, Iuc(i) current value of the super capacitor described in the i-th moment, I are indicateduc_maxIndicate described super
The maximum value of the charging and discharging currents of grade capacitor, SOC_minIndicate the minimum value of the state-of-charge of power battery, SOC(i) it indicates i-th
The state-of-charge of power battery described in moment, SOC_maxIndicate the maximum value of the state-of-charge of power battery, Vuc_minDescribed in expression
The minimum value of the open-circuit voltage of super capacitor, Vuc(i) open-circuit voltage values in the i-th moment super capacitor, V are indicateduc_maxIndicate institute
The maximum value of the open-circuit voltage of super capacitor is stated, i is integer and i is more than or equal to 0.
It is further, described excellent to the power loss based on dynamic programming algorithm on the basis of the various embodiments described above
Change model to be solved, the Optimal Control Strategy for obtaining the mixed energy storage system includes:
The state-of-charge that the power battery is arranged is state variable, and the current-order that the power battery is arranged is certainly
Plan variable establishes following Dynamic Programming Equation:
Wherein, xi(k), i-th of state-of-charge in kth stage, u are indicatedj(k) j-th of current-order in kth stage is indicated,
V(xi(k), k) it is multistep objective function from the 0th stage to the kth stage, V (xj(k-1), k-1) it is from the 0th stage to kth -1
The multistep objective function in stage, J (xi(k),uj(k)) indicate the state variable from xj(k-1) x is changed toi(k) single step when
Objective function, M be the power battery state-of-charge from state-of-charge minimum value to state-of-charge maximum value it is discrete described in
The quantity of state-of-charge；N is the discrete number of stages of runing time under the conditions of the full working scope of the mixed energy storage system.
Specifically, when solving using dynamic programming algorithm to the power loss Optimized model, need to be arranged institute
The state-of-charge for stating power battery is state variable, and the state-of-charge of the power battery is from state-of-charge minimum value to charged
Discrete state maximum value is M state-of-charge, such as with the 20% of the rated capacity of the power battery for the state-of-charge
Minimum value, with the 80% of the rated capacity of the power battery for the state-of-charge maximum value, by the lotus of the power battery
Electricity condition is divided into 12001 parts.Meanwhile the current-order that the power battery is arranged is decision variable, the current-order is used
In the electric current that the power battery is arranged, the state-of-charge of the power battery is influenced.It can establish following Dynamic Programming Equation:
Wherein, the multistep objective function V (x in the 0th stage to kth stagei(k), k) i.e. described mixed energy storage system is from the 0th
The sum of the loss power in stage to kth stage, the state variable is from xj(k-1) x is changed toi(k) single step objective function J when
(xi(k),uj(k)) the i.e. described mixed energy storage system is x in state-of-chargei(k), decision uj(k) under, from -1 stage of kth to kth
The loss power in stage.
Fig. 3 is solution flow chart of the one embodiment of the invention based on dynamic programming algorithm, as shown in figure 3, being advised based on dynamic
The step of cost-effective method solves the power loss Optimized model is as follows:
(1) runing time under the conditions of full working scope of the mixed energy storage system is discrete for N number of stage, setting institute
State power battery state-of-charge be state variable x, and by the state-of-charge of the power battery from state-of-charge minimum value to
Discrete state-of-charge maximum value is the M state-of-charges, and the current-order that the power battery is arranged is decision variable u, with
Ampere-hour integral formula is as state transition equation, using the optimization object function as single step objective function；
(2) initialize, and input solve the power loss Optimized model needed for primary data；
(3) the kth stage；
(4) kth stage, state x are calculatedi(k) decision region；
(5) kth stage, state x are calculatedi(k), decision uj(k) the state x in corresponding -1 stage of kth whenj(k-1)；
(6) judge xj(k-1) whether meet constraint condition, if meeting constraint condition, carry out in next step；Otherwise j=j+
1, return step (5)；
(7) kth stage, state x are calculatedi(k), decision uj(k) single step objective function J (x wheni(k),uj(k))；
(8) the multistep objective function V (x from the 0th stage to the kth stage is calculatedi(k), k)=J (xi(k),uj(k))+V(xj
(k-1), k-1), and judge V (xi(k), k) it whether is minimum value, if it is minimum value, carry out in next step；Otherwise j=j+1 is returned
It returns step (5)；
(9) judge whether i is greater than M, if i is greater than M, illustrate to have traversed kth stage all state-of-charge, entrance is next
A step；Otherwise i=i+1, return step (4)；
(10) judge whether k is equal to N, if k is equal to N, calculating is finished, and exports calculated result；Otherwise k=k+1 returns to step
Suddenly (3).
Fig. 4 is the structural schematic diagram of the control device of one embodiment of the invention mixed energy storage system, as shown in figure 4, this hair
The control device of the mixed energy storage system of bright offer include first establishing unit 401, second establish unit 402 and solve unit
403, in which:
First establishing unit 401 is used to establish the power battery power loss model and super capacitor function of mixed energy storage system
Rate loss model；Second establishes unit 402 for based on the power battery power loss model and the super capacitor power
Loss model establishes the mixing storage with the minimum target of power loss of mixed energy storage system under the conditions of full working scope
The power loss Optimized model of energy system；Unit 403 is solved to be used to optimize mould to the power loss based on dynamic programming algorithm
Type is solved, and the Optimal Control Strategy of the mixed energy storage system is obtained.
Specifically, energy, the loss of energy are provided by power battery and super capacitor due to the mixed energy storage system
The power battery and the super capacitor are mostly come from, therefore first establishing unit 401 establishes the dynamic of mixed energy storage system
Power battery power consumption model calculates the power loss of the power battery, and establishes super capacitor power loss model to count
Calculate the power loss of the super capacitor.
After obtaining the power battery power loss model and the super capacitor power loss model, second is established
Unit 402 is with the minimum target of power loss of mixed energy storage system under the conditions of full working scope, according to the power battery
Power loss model and the super capacitor power loss model can establish the power loss optimization of the mixed energy storage system
Model.The power loss Optimized model may include optimization object function and constraint condition, and the constraint condition includes equation
Constraint condition and inequality constraints condition.Wherein, the full working scope condition refers to the mixed energy storage system in practical applications
One complete cycle of operation, such as the full working scope condition of mixed energy storage system of the tramcar refer to the tramcar
Terminal is run to from starting point.
After obtaining the power loss Optimized model, solving unit 403 can be by dynamic programming algorithm to described
Power loss Optimized model is solved, i.e., runing time of the mixed energy storage system under the conditions of full working scope is discrete
Become using the state-of-charge of the power battery as state variable by decision of the current-order of the power battery for N number of stage
Amount, using ampere-hour integral formula as state transition equation, by the Dynamic Programming Equation of the power loss Optimized model, full
In the case where the foot constraint condition, the Optimal Control Strategy of the mixed energy storage system can be obtained, is controlled according to the optimization
System strategy controls the mixed energy storage system, can be realized the mixed energy storage system under the conditions of full working scope
Minimum power loss.Wherein, the current-order is used to be arranged the size of current of the power battery.
The control device of mixed energy storage system provided by the invention, due to the power battery by establishing mixed energy storage system
Power loss model and super capacitor power loss model, and damaged based on power battery power loss model and super capacitor power
It consumes model and the function of mixed energy storage system is established with the minimum target of power loss of mixed energy storage system under the conditions of full working scope
Rate loss optimizing model is then based on dynamic programming algorithm and solves to power loss Optimized model, obtains hybrid energy-storing system
The Optimal Control Strategy of system reduces the energy consumption of mixed energy storage system.
On the basis of the above embodiments, further, the power battery power loss model are as follows:
Wherein, Vp(k) the polarizing voltage value of the power battery described in the kth moment, V are indicatedp(k-1) it indicates at -1 moment of kth
The polarizing voltage value of the power battery, k are positive integer, TsIndicate time step, e indicates natural constant, Ib(k) it indicates in kth
The current value of power battery described in moment, Rp[SOC(k)] indicate the power battery described in the kth moment in current SOC(k) pole under
Change internal resistance value, SOC(k) state-of-charge of the power battery described in the kth moment, V are indicatedo(k) power described in the kth moment is indicated
The terminal voltage value of battery, Vocv[SOC(k)] indicate the power battery described in the kth moment in current SOC(k) open-circuit voltage values under,
Ro[SOC(k)] indicate the power battery described in the kth moment in current SOC(k) the ohmic internal resistance value under, Cp[SOC(k)] it indicates the
Power battery described in the k moment is in current SOC(k) the polarization capacity value under, Pbat_loss(k) power electric described in the kth moment is indicated
The loss power in pond；Vp(0)=0；
The super capacitor power loss model are as follows:
Puc_loss(k)=Iuc(k)2Ruc
Wherein, Vuc(k) open-circuit voltage values in kth moment super capacitor, V are indicateduc(k-1) it indicates in -1 moment of kth institute
State the open-circuit voltage values of super capacitor, Iuc(k) current value of the super capacitor described in the kth moment, C are indicateducIt indicates in kth
Carve the capacitance of the super capacitor, Vc(k) terminal voltage value of the super capacitor described in the kth moment, R are indicateducIndicate described super
The internal resistance value of grade capacitor, Puc_loss(k) loss power of the super capacitor described in the kth moment is indicated；Vuc(0)=0.
Specifically, the power battery is in the polarizing voltage value at the 0th moment, i.e. the polarizing voltage value V of initial timep(0)
=0, the polarizing voltage value V at kth momentp(k), recurrence formula can be passed throughIt obtains
?.The time step Ts, can be set to 1s.State-of-charge S of the power battery at the 0th momentOC(0) be it is given,
Pass through ampere-hour integral formulaThe power battery can be obtained at the kth moment
State-of-charge SOC(k)。Ib(k) it is arranged to the decision variable.Rp[SOC(k)] the polarization resistance value measured using laboratory with
The relationship of the state-of-charge is found out, Vocv[SOC(k)] pass of the open-circuit voltage values and the state-of-charge measured using laboratory
System finds out, Cp[SOC(k)] relationship of the polarization capacity value and the state-of-charge that are measured using laboratory is found out, Ro[SOC(k)]
The relationship of the ohmic internal resistance value and the state-of-charge that are measured using laboratory is found out.Wherein, the mixed energy storage system is in institute
Runing time under the conditions of full working scope is stated by discrete as N number of stage, N number of stage correspond to N+1 a time points.
The power battery is in the open-circuit voltage values at the 0th moment, i.e. the open-circuit voltage values V of initial timeuc(0)=0, kth
The polarizing voltage value V at momentuc(k), recurrence formula can be passed throughIt obtains.Wherein,
What the loss power of the super capacitor considered is loss caused by ohmic internal resistance.
On the basis of the above embodiments, further, the power loss Optimized model includes optimization object function, institute
State optimization object function are as follows:
Wherein, Ploss(i)=Pbat_loss(i)+Puc_loss(i)+Pdd_loss(i), Ploss(i) it indicates to mix described in the i-th moment
Close the loss power of energy-storage system, Pbat_loss(i) loss power of the power battery described in the i moment, P are indicateduc_loss(i) it indicates
The loss power of the super capacitor described in the i-th moment, Pdd_loss(i) loss power in the i-th moment DC converter, i are indicated
More than or equal to 0 and i is less than or equal to N-1, and N is the mixed energy storage system discrete rank of runing time under the conditions of the full working scope
Number of segment.
Specifically, when establishing the power loss Optimized model, in order to more accurately calculate the mixed energy storage system
Loss power, introduce the loss power of DC converter, the loss power of the mixed energy storage system is equal to the power electric
The sum of the loss power in pond, the loss power of the super capacitor and loss power of the DC converter.Described in foundation
Power loss Optimized model is the minimum value in order to obtain the mixed energy storage system loss power under the conditions of full working scope.
Loss power P of the DC converter at the i-th momentdd_loss(i)=(1- η) Pbat(i), wherein η is the DC converting
The energy conversion efficiency of device, PbatIt (i) is the output power of the power battery described in the i-th moment.
The embodiment of device provided by the invention specifically can be used for executing the process flow of above-mentioned each method embodiment,
Details are not described herein for function, is referred to the detailed description of above method embodiment.
Fig. 5 is the entity structure schematic diagram of one embodiment of the invention electronic equipment, as shown in figure 5, the electronic equipment packet
Include processor (processor) 501, memory (memory) 502 and communication bus 503；
Wherein, processor 501, memory 502 complete mutual communication by communication bus 503；
Processor 501 is used to call the program instruction in memory 502, to execute provided by above-mentioned each method embodiment
Method, for example, establish the power battery power loss model and super capacitor power loss model of mixed energy storage system；Base
In the power battery power loss model and the super capacitor power loss model, with the mixed energy storage system in full work
The minimum target of power loss under the conditions of condition, establishes the power loss Optimized model of the mixed energy storage system；Based on dynamic
Planning algorithm solves the power loss Optimized model, obtains the Optimal Control Strategy of the mixed energy storage system.
The present embodiment discloses a kind of computer program product, and the computer program product includes being stored in non-transient calculating
Computer program on machine readable storage medium storing program for executing, the computer program include program instruction, when described program instruction is calculated
When machine executes, computer is able to carry out method provided by above-mentioned each method embodiment, for example, establishes mixed energy storage system
Power battery power loss model and super capacitor power loss model；Based on the power battery power loss model and institute
Super capacitor power loss model is stated, with the minimum target of power loss of mixed energy storage system under the conditions of full working scope,
Establish the power loss Optimized model of the mixed energy storage system；Based on dynamic programming algorithm to the power loss Optimized model
It is solved, obtains the Optimal Control Strategy of the mixed energy storage system.
The present embodiment provides a kind of non-transient computer readable storage medium, the non-transient computer readable storage medium
Computer instruction is stored, the computer instruction makes the computer execute method provided by above-mentioned each method embodiment, example
It such as include: the power battery power loss model and super capacitor power loss model for establishing mixed energy storage system；Based on described
Power battery power loss model and the super capacitor power loss model, with the mixed energy storage system in full working scope condition
Under the minimum target of power loss, establish the power loss Optimized model of the mixed energy storage system；It is calculated based on Dynamic Programming
Method solves the power loss Optimized model, obtains the Optimal Control Strategy of the mixed energy storage system.
In addition, the logical order in above-mentioned memory can be realized and as independence by way of SFU software functional unit
Product when selling or using, can store in a computer readable storage medium.Based on this understanding, of the invention
Technical solution substantially the part of the part that contributes to existing technology or the technical solution can be with software in other words
The form of product embodies, which is stored in a storage medium, including some instructions use so that
One computer equipment (can be personal computer, device or the network equipment etc.) executes described in each embodiment of the present invention
The all or part of the steps of method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-
Only Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. are various can be with
Store the medium of program code.
The apparatus embodiments described above are merely exemplary, wherein described, unit can as illustrated by the separation member
It is physically separated with being or may not be, component shown as a unit may or may not be physics list
Member, it can it is in one place, or may be distributed over multiple network units.It can be selected according to the actual needs
In some or all of the modules achieve the purpose of the solution of this embodiment.Those of ordinary skill in the art are not paying creativeness
Labour in the case where, it can understand and implement.
Through the above description of the embodiments, those skilled in the art can be understood that each embodiment can
It realizes by means of software and necessary general hardware platform, naturally it is also possible to pass through hardware.Based on this understanding, on
Stating technical solution, substantially the part that contributes to existing technology can be embodied in the form of software products in other words, should
Computer software product may be stored in a computer readable storage medium, such as ROM/RAM, magnetic disk, CD, including several fingers
It enables and using so that a computer equipment (can be personal computer, server or the network equipment etc.) executes each implementation
Method described in certain parts of example or embodiment.
Finally, it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although
Present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that: it still may be used
To modify the technical solutions described in the foregoing embodiments or equivalent replacement of some of the technical features；
And these are modified or replaceed, technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution spirit and
Range.
Claims (10)
1. a kind of control method of mixed energy storage system characterized by comprising
Establish the power battery power loss model and super capacitor power loss model of mixed energy storage system；
Based on the power battery power loss model and the super capacitor power loss model, with the mixed energy storage system
The minimum target of power loss under the conditions of full working scope, establishes the power loss Optimized model of the mixed energy storage system；
The power loss Optimized model is solved based on dynamic programming algorithm, obtains the optimization of the mixed energy storage system
Control strategy.
2. the method according to claim 1, wherein the power battery power loss model are as follows:
Wherein, Vp(k) the polarizing voltage value of the power battery described in the kth moment, V are indicatedp(k-1) it indicates described in -1 moment of kth
The polarizing voltage value of power battery, k are positive integer, TsIndicate time step, e indicates natural constant, Ib(k) it indicates at the kth moment
The current value of the power battery, Rp[SOC(k)] indicate the power battery described in the kth moment in current SOC(k) in the polarization under
Resistance value, SOC(k) state-of-charge of the power battery described in the kth moment, V are indicatedo(k) power battery described in the kth moment is indicated
Terminal voltage value, Vocv[SOC(k)] indicate the power battery described in the kth moment in current SOC(k) open-circuit voltage values under, Ro
[SOC(k)] indicate the power battery described in the kth moment in current SOC(k) the ohmic internal resistance value under, Cp[SOC(k)] it indicates in kth
Power battery described in moment is in current SOC(k) the polarization capacity value under, Pbat_loss(k) power battery described in the kth moment is indicated
Loss power；Vp(0)=0；
The super capacitor power loss model are as follows:
Puc_loss(k)=Iuc(k)2Ruc
Wherein, Vuc(k) open-circuit voltage values in kth moment super capacitor, V are indicateduc(k-1) it indicates to surpass described in -1 moment of kth
The open-circuit voltage values of grade capacitor, Iuc(k) current value of the super capacitor described in the kth moment, C are indicateducIt indicates in kth moment institute
State the capacitance of super capacitor, Vc(k) terminal voltage value of the super capacitor described in the kth moment, R are indicateducIndicate the super electricity
The internal resistance value of appearance, Puc_loss(k) loss power of the super capacitor described in the kth moment is indicated；Vuc(0)=0.
3. the method according to claim 1, wherein the power loss Optimized model includes optimization aim letter
Number, the optimization object function are as follows:
Wherein, Ploss(i)=Pbat_loss(i)+Puc_loss(i)+Pdd_loss(i), Ploss(i) it indicates to mix storage described in the i-th moment
The loss power of energy system, Pbat_loss(i) loss power of the power battery described in the i moment, P are indicateduc_loss(i) it indicates the
The loss power of super capacitor described in the i moment, Pdd_loss(i) indicate that the loss power in the i-th moment DC converter, i are greater than
Equal to 0 and i is less than or equal to N-1, and N is mixed energy storage system runing time discrete stage under the conditions of the full working scope
Number.
4. the method according to claim 1, wherein the power loss Optimized model includes equality constraint
With inequality constraints condition, in which:
The equality constraint are as follows:
Wherein, Puc(i) output power of the super capacitor described in the i-th moment, P are indicatedbd(i) it indicates in the i-th moment DC converting
The on high-tension side output power of device, Preq(i) it indicates in the i-th moment load demand power, Pbat(i) it indicates to move described in the i-th moment
The output power of power battery, PSIV(i) power in the i-th moment auxiliary power supply system, P are indicateddd_loss(i) it indicates at the i-th moment
The loss power of the DC converter, i is integer and i is more than or equal to 0.
The inequality constraints condition are as follows:
Wherein, Ib_minIndicate the minimum value of the charging and discharging currents of the power battery, Ib(i) power described in the i-th moment is indicated
The current value of battery, Ib_maxIndicate the maximum value of the charging and discharging currents of the power battery, Iuc_minIndicate the super capacitor
The minimum value of charging and discharging currents, Iuc(i) current value of the super capacitor described in the i-th moment, I are indicateduc_maxIndicate the super electricity
The maximum value of the charging and discharging currents of appearance, SOC_minIndicate the minimum value of the state-of-charge of power battery, SOC(i) it indicates at the i-th moment
The state-of-charge of the power battery, SOC_maxIndicate the maximum value of the state-of-charge of power battery, Vuc_minIndicate described super
The minimum value of the open-circuit voltage of capacitor, Vuc(i) open-circuit voltage values in the i-th moment super capacitor, V are indicateduc_maxIndicate described super
The maximum value of the open-circuit voltage of grade capacitor, i is integer and i is more than or equal to 0.
5. the method according to claim 1, wherein described excellent to the power loss based on dynamic programming algorithm
Change model to be solved, the Optimal Control Strategy for obtaining the mixed energy storage system includes:
The state-of-charge that the power battery is arranged is state variable, and the current-order of the power battery is arranged as decision change
Amount, establishes following Dynamic Programming Equation:
Wherein, xi(k), i-th of state-of-charge in kth stage, u are indicatedj(k) j-th of current-order in kth stage, V (x are indicatedi
(k), k) it is multistep objective function from the 0th stage to the kth stage, V (xj(k-1), k-1) it is from the 0th stage to -1 stage of kth
Multistep objective function, J (xi(k), uj(k)) indicate the state variable from xj(k-1) x is changed toi(k) single step target when
Function, M are the state-of-charge of the power battery from state-of-charge minimum value to discrete described charged of state-of-charge maximum value
The quantity of state；N is the discrete number of stages of runing time under the conditions of the full working scope of the mixed energy storage system.
6. a kind of control device of mixed energy storage system characterized by comprising
First establishing unit, for establishing the power battery power loss model and super capacitor power loss of mixed energy storage system
Model；
Second establishes unit, for being based on the power battery power loss model and the super capacitor power loss model,
With the minimum target of power loss of mixed energy storage system under the conditions of full working scope, the function of the mixed energy storage system is established
Rate loss optimizing model；
Unit is solved, for solving based on dynamic programming algorithm to the power loss Optimized model, obtains the mixing
The Optimal Control Strategy of energy-storage system.
7. device according to claim 6, which is characterized in that the power battery power loss model are as follows:
Wherein, Vp(k) the polarizing voltage value of the power battery described in the kth moment, V are indicatedp(k-1) it indicates described in -1 moment of kth
The polarizing voltage value of power battery, k are positive integer, TsIndicate time step, e indicates natural constant, Ib(k) it indicates at the kth moment
The current value of the power battery, Rp[SOC(k)] indicate the power battery described in the kth moment in current SOC(k) in the polarization under
Resistance value, SOC(k) state-of-charge of the power battery described in the kth moment, V are indicatedo(k) power battery described in the kth moment is indicated
Terminal voltage value, Vocv[SOC(k)] indicate the power battery described in the kth moment in current SOC(k) open-circuit voltage values under, Ro
[SOC(k)] indicate the power battery described in the kth moment in current SOC(k) the ohmic internal resistance value under, Cp[SOC(k)] it indicates in kth
Power battery described in moment is in current SOC(k) the polarization capacity value under, Pbat_loss(k) power battery described in the kth moment is indicated
Loss power；Vp(0)=0；
The super capacitor power loss model are as follows:
Puc_loss(R)=Iuc(R)2Ruc
Wherein, Vuc(k) open-circuit voltage values in kth moment super capacitor, V are indicateduc(k-1) it indicates to surpass described in -1 moment of kth
The open-circuit voltage values of grade capacitor, Iuc(k) current value of the super capacitor described in the kth moment, C are indicateducIt indicates in kth moment institute
State the capacitance of super capacitor, Vc(k) terminal voltage value of the super capacitor described in the kth moment, R are indicateducIndicate the super electricity
The internal resistance value of appearance, Puc_loss(k) loss power of the super capacitor described in the kth moment is indicated；Vuc(0)=0.
8. device according to claim 6, which is characterized in that the power loss Optimized model includes optimization aim letter
Number, the optimization object function are as follows:
Wherein, Ploss(i)=Pbat_loss(i)+Puc_loss(i)+Pdd_loss(i), Ploss(i) it indicates to mix storage described in the i-th moment
The loss power of energy system, Pbat_loss(i) loss power of the power battery described in the i moment, P are indicateduc_loss(f) it indicates the
The loss power of super capacitor described in the i moment, Pdd_loss(i) indicate that the loss power in the i-th moment DC converter, i are greater than
Equal to 0 and i is less than or equal to N-1, and N is mixed energy storage system runing time discrete stage under the conditions of the full working scope
Number.
9. a kind of electronic equipment characterized by comprising processor, memory and communication bus, in which:
The processor and the memory complete mutual communication by the communication bus；
The memory is stored with the program instruction that can be executed by the processor, and the processor calls described program to instruct energy
It is enough to execute such as method described in any one of claim 1 to 5.
10. a kind of non-transient computer readable storage medium, which is characterized in that the non-transient computer readable storage medium is deposited
Computer instruction is stored up, the computer instruction makes the computer execute such as method described in any one of claim 1 to 5.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110435631A (en) * | 2019-07-23 | 2019-11-12 | 同济大学 | Energize control method and system |
| CN111725797A (en) * | 2020-06-18 | 2020-09-29 | 安徽能汇轨道交通技术有限公司 | Power distribution method for ground type hybrid energy storage system for rail transit |
| CN112117906A (en) * | 2019-06-21 | 2020-12-22 | 天津工业大学 | Optimization method of double-active full-bridge converter under triple phase-shifting control |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106998072A (en) * | 2017-05-15 | 2017-08-01 | 国网江苏省电力公司电力科学研究院 | A kind of mixed energy storage system capacity configuration optimizing method for optimizing operation towards power distribution network |
| CN107042762A (en) * | 2016-11-29 | 2017-08-15 | 北京交通大学 | A kind of vehicle-mounted mixed energy storage system of rail vehicle and its application |
-
2018
- 2018-05-07 CN CN201810426334.3A patent/CN109031946A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107042762A (en) * | 2016-11-29 | 2017-08-15 | 北京交通大学 | A kind of vehicle-mounted mixed energy storage system of rail vehicle and its application |
| CN106998072A (en) * | 2017-05-15 | 2017-08-01 | 国网江苏省电力公司电力科学研究院 | A kind of mixed energy storage system capacity configuration optimizing method for optimizing operation towards power distribution network |
Non-Patent Citations (3)
| Title |
|---|
| LONG CHENG等: "An Improved Energy Management Strategy for", 《ENERGIES》 * |
| 申彩英等: "基于改进型动态规划算法的串联混合动力汽车控制策略", 《控制理论与应用》 * |
| 韦绍远等: "有轨电车车载混合储能系统效率优化控制", 《电源学报》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112117906A (en) * | 2019-06-21 | 2020-12-22 | 天津工业大学 | Optimization method of double-active full-bridge converter under triple phase-shifting control |
| CN112117906B (en) * | 2019-06-21 | 2024-01-30 | 天津工业大学 | Double-active full-bridge converter optimization method under triple phase shift control |
| CN110435631A (en) * | 2019-07-23 | 2019-11-12 | 同济大学 | Energize control method and system |
| CN110435631B (en) * | 2019-07-23 | 2021-04-06 | 同济大学 | Energy supply control method and system |
| CN111725797A (en) * | 2020-06-18 | 2020-09-29 | 安徽能汇轨道交通技术有限公司 | Power distribution method for ground type hybrid energy storage system for rail transit |
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