CN108347072A - Method for running mixed tensor storage system - Google Patents

Method for running mixed tensor storage system Download PDF

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Publication number
CN108347072A
CN108347072A CN201810070394.6A CN201810070394A CN108347072A CN 108347072 A CN108347072 A CN 108347072A CN 201810070394 A CN201810070394 A CN 201810070394A CN 108347072 A CN108347072 A CN 108347072A
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energy accumulator
value
energy
amount
parameter
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CN108347072B (en
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R.韦格曼
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Robert Bosch GmbH
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Robert Bosch GmbH
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    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/0013—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries acting upon several batteries simultaneously or sequentially
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/34—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering
    • H02J7/345—Parallel operation in networks using both storage and other dc sources, e.g. providing buffering using capacitors as storage or buffering devices
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01G—CAPACITORS; CAPACITORS, RECTIFIERS, DETECTORS, SWITCHING DEVICES OR LIGHT-SENSITIVE DEVICES, OF THE ELECTROLYTIC TYPE
    • H01G11/00—Hybrid capacitors, i.e. capacitors having different positive and negative electrodes; Electric double-layer [EDL] capacitors; Processes for the manufacture thereof or of parts thereof
    • H01G11/10—Multiple hybrid or EDL capacitors, e.g. arrays or modules
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01G—CAPACITORS; CAPACITORS, RECTIFIERS, DETECTORS, SWITCHING DEVICES OR LIGHT-SENSITIVE DEVICES, OF THE ELECTROLYTIC TYPE
    • H01G11/00—Hybrid capacitors, i.e. capacitors having different positive and negative electrodes; Electric double-layer [EDL] capacitors; Processes for the manufacture thereof or of parts thereof
    • H01G11/14—Arrangements or processes for adjusting or protecting hybrid or EDL capacitors
    • H—ELECTRICITY
    • H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
    • H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
    • H02J7/00—Circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
    • H02J7/007—Regulation of charging or discharging current or voltage
    • H02J7/00712—Regulation of charging or discharging current or voltage the cycle being controlled or terminated in response to electric parameters
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T10/00—Road transport of goods or passengers
    • Y02T10/60—Other road transportation technologies with climate change mitigation effect
    • Y02T10/70—Energy storage systems for electromobility, e.g. batteries

Abstract

The present invention relates to a kind of for by determining best adjoint amount（λ*）To determine at least one first energy accumulator（2）With the second energy accumulator（3）Mixed tensor storage system（1）Best system performance method and it is attached for controlling include at least one first energy accumulator（2）With the second energy accumulator（3）Mixed tensor storage system（1）Method.Here, based on energy accumulator system is belonged to（1）Hamiltonian function（H）, best adjoint amount is calculated by solving optimization problem（λ*）.Best adjoint amount is in order to control mixed tensor storage system（1）And be provided, it can abandon solving optimization problem as a result, in the mixed tensor storage system.

Description

Method for running mixed tensor storage system
Technical field
The present invention relates to a kind of methods for running mixed tensor storage system.
Background technology
Energy requirement proposes the different power requirements to energy accumulator by the technological system that energy storage is supplied And energy requirement.Energy accumulator may, for example, be one-shot battery, secondary cell, fuel cell or double layer capacitor.
In fuel-cell vehicle, for the fuel cell operation in effectively acting range and in order to realize back It receives and in order to realize higher effective power, other electrical storage is commonly used.Therefore, in order to improve Effective power The passive connection of the battery of rate, different energy and power characteristic is, for example, well known.In addition following system is disclosed, at this In a little systems, two electrical storages are coupled to each other by DC/DC converters, and therefore can realize different voltage levels with And more independent operation.
Here, when these systems are for example coupled to each other fuel cell and battery or battery and double layer capacitor, these System is referred to as mixed tensor storage system.Make the special circumstances that two secondary cells are coupled to each other for system, the system Also referred to as hybrid battery storage system.
For current system with mixed tensor storage system mostly using simple control, executing in real time can profit It is realized with the expense of very little.However, the control causes about the possible target at runtime, such as about time of efficiency Excellent result.
Therefore, following system is, for example, well known, and in such systems, two memories are connect with DC/DC converters, and And simple current regulator is distributed for loading.For applying the hybrid battery in plug-in hybrid vehicle to control system System is also well known, is controlled using rule-based control piece or filter.
Following means are had been described above for hybrid vehicle, in order to minimize using optimization Fuel consumption during traveling in vehicle.These means are based on Pang Te lia king principle of minimums, and using in the car In the case of now frequently by simplifying and hypothesis derives that so-called equivalent consumption is minimum tactful（ECMS）.
In addition following means are disclosed, the mixed tensor for controlling vehicle is caused also based on Pang Te lia king principle of minimums Storage system is made of fuel cell and double layer capacitor group.The strategy minimizes hydrogen consumption and double layer capacitor fills Electricity condition is kept in the range of restriction.
The control for hybrid vehicle is disclosed in DE 102013014667A1.Describe internal combustion engine and motor Connection, wherein motor is battery powered.It is related with Pang Te lia king principle of minimums herein.
Invention content
It is according to the present invention to be used to determine at least one first energy accumulator by determining best adjoint amount With the method for the best system performance of the mixed tensor storage system of the second energy accumulator, the method includes：More Implement the first iterative cycles during a, wherein possible with amount（Adjungierte）It is belonging respectively to continuous process, wherein Implement secondary iteration cycle during each of first iterative cycles.Secondary iteration recycles：It is determined respectively for predetermined Period continuous time point Optimal Control parameter, wherein Optimal Control parameter is deposited by the second energy by its description Reservoir accommodate or output energy parameter and based on the minimum value of Hamiltonian function determine, wherein Hamiltonian function belongs to Energy accumulator system and the memory shape for depending on the systematic parameter of modeling, possible adjoint amount and the second energy accumulator State, wherein the systematic parameter modeled is the parameter to be optimized of mixed tensor storage system and according to scheduled power curve It determines, which is executed by energy accumulator system in predetermined time period；And hold when by the second energy accumulator Energy receive or output is deposited according to controling parameter best respectively to calculate the second energy when manipulating in predetermined time period The memory end-state of reservoir, the second energy accumulator have the memory end-state after a predetermined time period.The party Method includes in addition：Detection：Whether the second energy accumulator is located at by the memory end-state of secondary iteration cycle calculations In scheduled section, and best adjoint amount is provided, possibility of the best adjoint amount corresponding to the process of the first iterative cycles Adjoint amount, detected memory end-state for the process and be located in scheduled section.
It is according to the present invention for control include at least one first energy accumulator and the second energy accumulator mixing The method of energy accumulator system includes：The first input value and the second input value are detected, wherein the first input value describes most preferably Adjoint amount the value precalculated, the second input value describes the memory state of the second energy accumulator and memory benchmark The deviation of state；The best of the current memory state of the second energy accumulator is directed to based on the adjustment of the first and second input values Adjoint amount, so as to determine adjustment adjoint amount；The energy target values of description energy are provided, energy by the first energy accumulator and Second energy accumulator provides jointly；And Optimal Control parameter is determined based on the adjoint amount and energy target values of adjustment, wherein Optimal Control parameter is the parameter and Optimal Control that the energy by the receiving of the second energy accumulator or output is described by it Parameter determines that wherein Hamiltonian function is dependent on the system ginseng calculated according to the Hamiltonian function for belonging to energy accumulator system The current memory state of number, the adjoint amount and the second energy accumulator that adjust.
Therefore pass through the method according to the present invention for determining the best system performance of mixed tensor storage system Determine the value best for specific energy accumulator system with amount.By according to the present invention for controlling mixing The method of energy accumulator system, by the value for controlling the specific energy accumulator system.As each side of these methods When method is advantageous respectively, best control is just realized by the collective effect of these methods.It is according to the present invention by combining Method provides Optimal Control.It is Lagrange's multiplier for the mathematical name with amount.Memory state is the state of memory And the characteristic of respective energy accumulator is therefore described, especially there is the influence to systematic parameter to be optimized, because This systematic parameter changes.
Optimal Control is for the energy in mixed tensor storage system, especially hybrid battery control system best Power curve.In addition following functionality is obtained：The control is to always further emptying through one of energy accumulator shape At high energy storage device part react, and effective power can be maintained in high power memory portion.
When Optimal Control is particularly advantageous energy according to the present invention for battery system in real time, the best control System can be used for the mixed tensor storage system of each type.Therefore non-mandatorily it is necessary that：One of them or two A energy accumulator is electricity or the energy accumulator of electrochemistry.Energy accumulator equally can be mechanical energy memory, such as Flywheel.The other energy accumulator being contemplated that of a part as mixed tensor storage system is fuel cell.
The operation of mixed tensor storage system needs the control piece preserved.Efficiency can be realized according to the method for the present invention Control, and herein not by simply filter or regulating measure based on.Controlling value, i.e. adjustment parameter are determined according to of the invention It is furthermore possible to realize that the wide in range power of mixed tensor storage system at runtime receives.
It can realize herein according to the method for the present invention and energy accumulator system is optimized according to arbitrary systematic parameter.Cause This, systematic parameter can for example describe system loss, i.e. energy loss, and energy accumulator system especially can transport to efficiency Row.In other examples, which can describe system aging, and energy accumulator system can especially for a long time Ground is run.
Method according to the present invention for controlling mixed tensor storage system is especially advantageous, this is because the party Method can in real time be implemented in the case of the calculating power of very little.It is according to the present invention to be used to determine mixed tensor memory system Therefore the method for the best system performance of system is especially advantageous, this is because this method can also be not physically exist phase Implement in the case of the energy accumulator system answered.
Dependent claims show the preferred improvement project of the present invention.
Advantageously：Optimal Control parameter describes the electric current exported by the second energy accumulator, and/or model Systematic parameter is the system loss of modeling and/or memory state is charged state, and wherein memory end-state is most Whole charged state and/or section are charging sections.This selection, which can be realized, is used in determining mixed tensor memory system The method of the best system performance of system is matched with battery system, and wherein this method, which is especially adapted for use in, minimizes battery system Loss power.Just for the battery unit with very high internal resistance, can ordinatedly it be minimized with system scheme The strategy of energy loss is advantageous.As seondary effect, this method can pass through high-energy in mixed tensor storage system Partial current rate-electric discharge leads to anti-aging benefit.Minimize electrical loss（The minimum can be by according to the method for the present invention It realizes）Also cause the temperature development of the reduction in energy accumulator, the temperature development of reduction to pass through ohmic loss shape in addition in addition At.
Advantageously：First iterative cycles implement dichotomy, possible with amount to determine.Therefore, the first iteration is followed A certain number of processes of ring can be minimized.Herein it is possible to adjoint amount be based especially on section bisection method or interval division Method determines.
Advantageously：In the case where determining the best system performance of mixed tensor storage system, in secondary iteration Optimal Control parameter is determined during each of cycle, method is to implement third iterative cycles, wherein possible controlling value point Do not belong to the continuous process in third iterative cycles, wherein third iterative cycles it is each during, Hamiltonian function Value is calculated according to the possible controling parameter for the respective process for belonging to third iterative cycles, and possible controling parameter is true It is set to Optimal Control parameter, for Optimal Control parameter, the value of Hamiltonian function has minimum value.In this manner, can be with Determination is limited to a certain number of possible controlling values, and possible controlling value can be existed by the adjuster of energy accumulator system It handles and applies in practice.This determination of Optimal Control parameter can be executed simply in addition, and need very little in force Calculating power.As an alternative advantageously：Determine that Optimal Control parameter passes through analysis during each of secondary iteration cycle It calculates and realizes.Being directed to the especially accurate value of Optimal Control parameter as a result, can determine.
It is also advantageous that：It is scheduled in the case where determining the best system performance of mixed tensor storage system Section is the section near the initial memory state of the second energy accumulator, and the second energy accumulator is in the scheduled time With the initial memory state when section starts.Initial charged state of the section especially with the second energy accumulator Percentile deviation.It is enough which accuracy can be limited in this way when determining with amount.
Also advantageously：In the case where determining the best system performance of mixed tensor storage system, modeling is D.C. resistance of the parameter of uniting based on the first energy accumulator, the D.C. resistance of the second energy accumulator, according to scheduled power song The efficiency for the power and DC/DC converters of line exported by energy accumulator system for respective time point determines, the first energy Amount memory and the second energy accumulator are connected in energy accumulator system by DC/DC converters.In this manner, modeling System loss can be close to the actual system loss of energy accumulator system.Therefore it can realize and especially critically determine most preferably Adjoint amount.Here, the system loss of systematic parameter especially energy accumulator system.
Also advantageously：In the case where determining the best system performance of mixed tensor storage system, calculating When the value of Hamiltonian function, the systematic parameter of modeling is added with the derivative of the memory state of the second energy accumulator, the derivative It is multiplied with amount with possible.The system performance of the Hamiltonian function stated in this way, mixed tensor storage system obtains Especially accurate description.
Advantageously：Memory state is charged state and/or energy target values are current target values, and/or Person's Optimal Control parameter describes the electric current exported by the second energy accumulator, and/or the systematic parameter calculated is mixing The system loss of energy accumulator system.This selection, which can be realized, is used in the best of determining mixed tensor storage system The method of system performance is matched with battery system, and wherein this method is especially adapted for use in the loss power for minimizing battery system.
Advantageously：In the method for controlling mixed tensor storage system, the systematic parameter of calculating is dependent on the The loss power of the loss power of one energy accumulator and the second energy accumulator.Herein especially advantageously：Calculate be Parameter of uniting depends on the loss power of energy converter in addition, and the first energy accumulator and the second energy accumulator are in energy stores It is connected by energy converter in device system.Energy converter is preferably DC/DC converters.Therefore the systematic parameter of calculating can be It particularly precisely calculates, this leads to the particularly effective control to mixed tensor storage system.This method therefore can be with needle The minimum of loss power is optimized.The systematic parameter especially system loss of energy accumulator system herein.
Also advantageously：In the method for controlling mixed tensor storage system, Optimal Control parameter is with the side of calculating Formula determines.In this manner, the value for Optimal Control parameter is calculated, and need not provide in advance.Therefore it is needed not be provided The value of preceding calculating.
Also advantageously：In the method for controlling mixed tensor storage system, Optimal Control parameter is with table Mode determines, wherein inquiring following table, in this table, Optimal Control parameter is associated with the value of the adjoint amount adjusted respectively The different combination with the value of current target value.The special rapid reaction of mixed tensor storage system may be implemented in this way Adjusting.
It is also advantageous that：In the method for controlling mixed tensor storage system, the adjustment of best adjoint amount It is realized by pi regulator.This structural detail can be used inexpensively, and allow the Rapid Implementation of this method in real time.
Particularly advantageously：Best adjoint amount is by according to the present invention for determining mixed tensor storage system The method of system performance determines that the best adjoint amount is used as first in the method for controlling mixed tensor storage system Input value receives.Therefore, best adjoint amount can determine for example in terms of factory, and be transmitted in adjustment equipment, pass through Adjustment equipment implements the method for controlling mixed tensor storage system.Adjustment equipment can for example be built in the car.
The equipment for generating best adjoint amount including computing unit has the advantages that all of this method, the calculating Unit setting is for implementing the method according to the present invention for determining the best system performance of mixed tensor storage system.
The device for controlling mixed tensor storage system including adjusting unit has the advantages that all of this method, institute It states and adjusts unit setting for implementing the method according to the present invention for controlling mixed tensor storage system.
Description of the drawings
The embodiment of the present invention is specifically described referring to the attached drawing below.Wherein：
Fig. 1 shows the circuit diagram of illustrative mixed tensor storage system,
Fig. 2 shows the best systems for determining mixed tensor storage system according to the preferred embodiment of the present invention The flow chart according to the method for the present invention of characteristic,
Fig. 3 show for control mixed tensor storage system according to the preferred embodiment of the present invention according to the present invention Equipment block diagram, and
Fig. 4 shows the equipment according to the present invention for generating best adjoint amount.
Specific implementation mode
Fig. 1 shows the circuit diagram of illustrative mixed tensor storage system.Energy accumulator system is battery storage Device system 1.Battery memory system 1 includes the first battery 2 as the first energy accumulator.Battery memory system 1 includes The second battery 3 as the second energy accumulator.
The anode of first battery 2 couples with the input terminal of DC/DC converters 4.The anode of second battery 3 is converted with DC/DC The output end of device 4 couples.The cathode of first battery 2 is coupled by circuit ground with the cathode of the second battery 3.DC/DC is converted Device 4 is energy converter.
First and second batteries 2,3 are different types of structure.For example, the first battery 2 is to be suitble to short-term offer high power Battery.For example, the second battery 3 is, for example, to be suitble to be provided for a long term the battery of firm power.In alternative mixed tensor memory In system, 2, at least one of 3 battery of battery is replaced by another energy accumulator, such as capacitor.
Contact connector is there also is provided in the output of DC/DC converters 4, can contact battery by the contact connector deposits Reservoir system 1, to take total current I from battery memory systemges.Total current IgesTurned by what is exported from DC/DC converters 4 Parallel operation electric current I1dWith the second electric current I provided from the second battery 32Composition.When the first battery 3 discharges, DC/DC converters 4 by First battery 2 is with the first electric current I1Power supply.When charging to the first battery 3, the first battery 3 is by DC/DC converters 4 with the first electricity Flow I1Power supply.Here, the first electric current I1Flow direction change.
In order to realize the efficient and economic electric discharge of battery memory system 1, battery memory system is by means of according to this The method of invention is run.Here, according to Optimal Control parameter I2 *To adjust the second electric current I2, it is electric which defines second Flow I2Magnitude.Second electric current I2Such as it is adjusted by DC/DC converters 4.First battery 2 has the first cell voltage U1。 Second battery 3 has the second cell voltage U2。
The present invention and basic mathematical principle is eplained in more detail below.
The energy efficient of mixed tensor storage system, such as battery memory system 1 is realized according to the method for the present invention Control, and respectively define a step in two steps.In the first step offline, namely priori is being calculated Under the case where knowing example, finds and best adjoint amount λ is provided*Or Lagrange's multiplier.This can be selectively for difference The case where example be performed.In the second step, then a kind of self adaptive control of subordinate is disclosed, in fact existing real time execution energy Storage system, such as battery memory system 1 and the first electric current I of adjusting1With the second electric current I2Magnitude.
By the method for the best system performance for determining mixed tensor storage system, realizes and solve for mixing The Optimal Control problem of battery memory system.
First, the general optimization problem of Optimal Control problem is described.Next it introduces especially for hybrid battery memory The Optimal Control problem that system 1 occurs and its solution by means of Pang Te lia king principle of minimums.There is described herein as What is by means of according to the method for the present invention, best adjoint amount λ iteratively being determined by so-called dichotomy*。
In Optimal Control problem, it should minimize cost function J under given constraints.It is given below and asks in this way One example of topic.Lagrange quality standard L provides depending on state parameter each time point hereinx(t) it and controls Parameteru(t) cost.
Optimal Control problem can be expressed as follows：
。
Optimal Control parameter u*Then meet following equation：
。
Optimization problem and its by means of Pang Te lia king principle of minimums solution referring now to mixing shown in FIG. 1 Battery memory system 1.The target of this method is in predetermined time period [ta,te] on make in hybrid battery storage system 1 Loss minimizes.Utilize the electrical loss P in the first batteryv,1(t), the electrical loss P in the second batteryv,2(t) and DC/DC converters In electrical loss Pv,dcdc, the cost function J for the hybrid battery storage system 1 in Fig. 1 can be established.In the period [ta,te] on minimize cost function J when, minimize the battery memory system 1 shown in electrical loss：
。
Total current IgesAccording to following modal equations by the side of the second battery of direction 3 in DC/DC converters 4 Converter current I1dWith the second electric current I of the second battery 32Summation composition：
。
On the side of the first battery 2, the case where being discharged for the first battery 2（This battery memory system shown in System 1 is the situation, because of the first electric current I1More than 0 (I1>0)）, it is suitable for DC/DC converter efficiencies ηdcdc：
。
In the case where the first battery 2 is electrically charged, it is applicable in accordingly following：
。
The charged state SOC of first battery1With the charged state SOC of the second battery2Can by means of electric current it is corresponding when Between integral, nominal charge volume（Qnom,1Or Qnom,2）And initial state of charge (SoCinit,1,SoCinit,2) calculate, the electricity Stream flows through corresponding battery 2,3.Calculate such as equation（12）With（13）It is shown.Here, the convention of selection, which is positive current, corresponds to phase Answer the discharge scenario of battery 2,3.
Referring now to the above-mentioned general type of the Optimal Control problem of hybrid battery storage system 1.Select the second battery SoC2Charged state as state parameter x (x=SoC2)。Such as equation（14）Shown, the battery current of the second battery 3 indicates control Parameter.
For optimizing boundary condition mentioned by parameter and constraints for the use in hybrid battery storage system 1 In equation（15）With（16）In show.The initial state of charge SoC of second battery 32(ta) it is fixed value.Second battery 3 it is final Charged state SoC2(te) should be close to the initial value, i.e. initial state of charge SoC2(ta).Range is located at for charged state Battery in 0% to 100%, value in the range of 50% or so are meaningful.Initial state of charge SoC2 (ta's) determines Justice can also depend on other factors herein, such as system structure, voltage request, the selection of the inverter connected or the energy The characteristic of memory itself.
It is described below through Pang Te lia king principle of minimum solving-optimizing problems.Herein, Lagrangian quality standard （Referring to equation（2））Corresponding to the electrical loss component in battery 2,3 and DC/DC converters 4 in equation（8）The sum of middle integral, such as Pass through equation（17）It is described such.
In equation（18）Show the Hamiltonian function H being defined.Other than Lagrangian quality standard L, this includes another One, that is, time-varying is come from amount λ (t)TWith the product of state differential equation f (x (t), u (t), t).
Pang Te lia king principle of minimums define a series of conditions that must satisfy, so that optimality is applicable in.This is in side Journey（19）Extremely（23）Middle expression.Best track and parameter is here by label ()*To identify.
Pass through the differential equation of specification（19）With（20）Describe state x and Lagrange's multiplier（Also referred to as with amount λ） Time change.Pass through Optimal Control parameter u*Selection, Hamiltonian function H's has value to be minimized according to equation（23）Always It is smaller than such value of Hamiltonian function H：The value by with Optimal Control parameter u*Different controling parameter and occur.
Now, in t ∈ [ta,te] period on, from the controlling value u of permission（t）In the range of can find optimal trajectory u*(t), in accordance with the boundary condition and constraints of controling parameter and state parameter, the optimal trajectory is by Hami The function H that pauses is minimized.Need not have extreme value by dH/du=0 in the form of always herein because may happens is that, Hamilton The minimum value of function H is exactly at the fringe region of the controling parameter range of permission, and wherein the derivative of Hamiltonian function H is not also It is 0.Accordingly, usually applicable requirement is u*=arg minH, wherein consider the controling parameter u within the scope of the controlling value of permission Value.
It solves by state differential equation（19）With the adjoint amount differential equation（20）The two-point boundary value problem of composition can be used down The hypothesis in face solves.
The loss of DC/DC converters 4 is substantially dependent on input voltage and output voltage, i.e. the first and second cell voltages U1, U2And the power P of transmissionv,dcdc.Assuming that the cell voltage U1 of the second battery 3 is during operation within the scope of portion voltage With therefore in small range of charge states keep x2(t)=SoC2(t) ≈ constants, then can be in equation（24）It is simple below middle progress Change.
For the loss power P of the second batteryv,2Partial derivative, equation（25）Assume made by being suitable for.
The loss power P of first batteryv,1With the second battery SoC2Charged state it is unrelated, and therefore equation（26）It is suitable Partial derivative for the loss power.
In addition, equation（27）Suitable for having SoC2(t) state differential equation of ≈ constants.
Utilize done hypothesis and resulting partial differential equation（24）Extremely（27）, equation can be solved（20）In must Want condition.Necessary condition obtains 0.Such as by equation（28）Find out, be similarly obtained, is constant with amount λ.
It can be according to equation（29）With（30）The damage in battery portion is determined by the D.C. resistance of respective battery part Consumption, i.e. loss in the first battery 2 and the second battery 3.
Utilize equation（29）With（30）, modal equation（9）, relationship u=I2With the current efficiency equation at DC/DC converters 4 （10）With（11）, the electrical loss P of the first battery can be adjustedv,1Calculating and according to equation（31）It indicates.Here, for The discharge scenario of one battery 3 is applicable in z=- 1, and is applicable in z=+ 1 for charging situation.Herein according to equation（10）With（11）Show First battery I1Electric current.
The output power P of DC/DC converters 4out,dcdcCoefficient k related with current direction can be utilized according in electric discharge The discharge power P of first battery1dIt indicates, and also by equation（32）It indicates.
Now according to equation（33）, according to controling parameter u, can indicate the loss power of DC/DC converters 4.Here, Discharge power P under the discharge scenario of first battery 31dIt can be represented as：P1d=U2·(Iges−u).To the first battery 3 into It is applicable in the case of row charging：P1d=U2·(Iges−u)·1/ηdcdc.Using these relational expressions, for the damage of DC/DC converters Consume power Pv,dcdcFollowing equation is obtained（33）.
Such as equation（34）As middle progress, equation is utilized（17）、（19）、（28）、（32）With（33）It can be according to control Parameter u=I2Indicate Hamiltonian function H：
。
Pang Te lia kings principle of minimum it is meant that Hamiltonian function H predetermined amount of time each time point t ∈ [ta, te] there is global minimum.It finds minimum value and corresponds to solution extreme-value problem.Controling parameter u is asked by Hamiltonian function H It leads and is zeroed, can determine extreme value first.This is equivalent to the necessary condition for extreme value validity.It has to check for：Whether when Between point t in control area u ∈ U（t）In controling parameter u be related to global extremum.Equation（35）It shows to controling parameter u derivations Hamiltonian function H, be equal to zero（∂H/∂u=0）And according to controling parameter U=I2To solve.The parameter of battery 2,3（Voltage, Resistance in direct current）It can at any point in time be calculated according to state parameter and electric current or they can be as nominally charging Amount is fixed storage like that.Likewise it is possible to determine the efficiency of DC/DC converters 4 for corresponding operating point.It only determines best Adjoint amount λ∗.This is by the best system performance for determining mixed tensor storage system with corresponding manner according to this The method of invention is realized.
Therefore, the method for determining the best system performance of mixed tensor storage system describe for by means of Dichotomy determines constant best adjoint amount λ*Method.
According to given equation（35）, can be according to the electric current I needed for battery memory system 1gesTo calculate Optimal Control Parameter u*Or optimum current I2 *.However, for calculating, still lack best adjoint amount λ*, for this is with amount, Follow constraints and Boundary Conditions.
Method for determining the best system performance of mixed tensor storage system is a kind of iterative cycles method, Flow chart is as shown in Figure 2.It, can be for by the method for the best system performance for determining mixed tensor storage system [t at a given time perioda, te] on the power curve P that a priori providesges(t) it determines（It is time-invariant）Best is adjoint Measure λ*.Assuming that in the second battery 3, the charged state SoC of the second battery2Influence to Hamiltonian function H is small, the hypothesis by up to At precondition under occur hereafter the case where.
In the starting point of this method, subscribed by initialization 40：For possible with amount λmInitial value and mixing electricity The model parameter of pond storage system 1, especially when predetermined time period starts the first battery initial state of charge SoC1 (ta), the initial state of charge SoC of the second battery when predetermined time period starts2(ta) and when predetermined time period starts Zero current.
After initialization, start the first iterative cycles 10.First iterative cycles have multiple processes, wherein possible companion Whatever you like λmIt is belonging respectively to continuous process.Therefore, when implementing the first iterative cycles, implement a certain number of processes, in the mistake Cheng Zhong completes corresponding identical method and step.Here, for possible with amount λmChanging value, implement corresponding identical side Method step.Herein from multiple possible with amount λmDetermine best adjoint amount λ*.This is completed by means of so-called dichotomy 's.This is also referred to as section nesting.In described embodiment, such as use section dichotomy.
For this purpose, defining the value range of description value, which can have possible with amount λm.The value range is from initial value Start λuAnd extend to end value λo.The value range initially has minimum initial value λu,0And highest end value λo,0。
During the first of the first iterative cycles 10, in selecting step 14 will likely adjoint amount λmValue be set as Positioned at initial value λu,0With highest end value λo,0Between value.The possible adjoint amount λ of first iterative cyclesmValue be referred to as First is possible with amount λm,1.Therefore, first is possible with amount λm,1By minimum initial value λu,0With highest end value λo,0It obtains：λm,1 = (λu,0 +λo,0)/2。
The first iterative cycles 10 it is further during, it is possible with amount λmValue be newly determining.It determines thus： Best adjoint amount λ*Value be above or below the first iterative cycles 10 active procedure it is possible with amount λmValue. This is after the first process of the first iterative cycles 10 for example by checking whether that meeting following standard carries out：
[SoC2(te,λm,1) – SoC2, Ref] * [SoC2(te,λ0,1) – SoC2, Ref] < 0。
In this variable SoC2(te,λm,1) charged state is described, using the possible adjoint amount λ for belonging to the processmWhen, During the first of the first iterative cycles 10, namely possible with amount λ using firstm,1When, obtain the charging shape State.This charged state is to belong to corresponding possible with amount λmFinal charged state SoC2(te), for last time step teObtain the final charged state.Correspondingly, variable SoC2(te,λ0,1) charged state is described, belong to the process using End value λo,0When, when the value of end value is selected for use in possible adjoint amount λmValue when, the of the first iterative cycles 10 The charged state is obtained during one.This charged state is to belong to corresponding end value λo,0Final charged state SoC2 (te), for last time step teObtain the final charged state.
According to whether the condition is met, for the next process of the first iterative cycles 10, the initial value λ is determinedu With the end value λo.If meeting the condition, after the first process of the first iterative cycles 10, the first iteration will be used for The initial value λ of second process of cycle 10uIt is determined as λu,1=λm,1.For the second process of the first iterative cycles 10, end value λo,0It remains unchanged, and therefore obtains：λo,1=λo,0.If it is does not satisfy the condition, being then used for the second of the first iterative cycles 10 The end value λ of processo,0It is determined as λo,1=λm,1.In this case, initial value λu,0In the second process of the first iterative cycles 10 In remain unchanged, and therefore obtain：λu,1=λu,0。
Each of first iterative cycles 10 then during, it would be possible to adjoint amount λmIt is set as new value.Here, By initial value λuWith end value λoBe selected as they iterative cycles 10 it is previous during determine value.Correspondingly, by subordinate Initial value λuWith the end value λ of subordinateo, the respective process of the first iterative cycles 10 is determined possible with amount λm.For Thus second process of one iterative cycles 10 has obtained possible with amount λ as followsm：λm,2=(λu,1+λo,1)/2。
Correspondingly, it during the second of the first iterative cycles 10, checks whether and meets following standard：
[SoC2(te,λm,2) – SoC2, Ref] * [SoC2(te,λ0,2) – SoC2, Ref] < 0。
Therefore, for each process of the first iterative cycles 10, initial value λ is determinedu, end value λoIt is possible with subordinate With amount λm。
The first iterative cycles 10 are carried out so frequent, and until meeting interrupt criteria, the interrupt criteria is in testing procedure 13 In be detected, which is also performed during each of first iterative cycles.
In the first iterative cycles（10）Each of during, execute secondary iteration cycle（20）.It obtains, repeatedly implements Secondary iteration recycles（20）, wherein it is recycled in secondary iteration（20）The method and step of middle execution is based on for possible with amount λmDifferent value.
Secondary iteration cycle 20 has multiple processes, wherein comes from predetermined time period [ta, te] time value t difference Belong to continuous process.Therefore, when executing secondary iteration cycle, a certain number of processes are executed, in this process, complete phase Answer identical method and step.Here, for the changing value of time value t, corresponding identical method and step is executed.
For this purpose, predetermined time period [ta, te] pass through initial time point taWith end time point teIt limits.In secondary iteration During the first of cycle 20, the value of time value t is set equal to initial time point ta.In each of secondary iteration cycle 20 When further process starts, the value is improved in time value incremental steps 21.Secondary iteration cycle 20 is carried out so frequent, Until the value of time value t is equal to end time point te.20 are recycled by secondary iteration, in predetermined time period [ta, te] upper mold The characteristic of quasi- battery memory system 1.Time value t there is described herein from predetermined time period [ta, te] in change second Currently considered time point in the respective process of generation cycle 20.
Secondary iteration cycle 20 it is each during, for the continuous time point of predetermined time period, realize respectively Determine Optimal Control parameter u*.Therefore, for each process of secondary iteration cycle 20, the Optimal Control parameter u of subordinate is determined*。 From multiple possible controling parameter umIn for secondary iteration cycle 20 each process determine the Optimal Control parameter.
Here, Optimal Control parameter u*Describing is had electric current I to be output by the second battery 32.Therefore Optimal Control parameter Describe the energy for being accommodated by the second battery 3 or being exported.For example, Optimal Control parameter U*Value be equal to such value：For Secondary iteration cycle 20 respective process in considered in predetermined time period [ta, te] in time point t, by the second electric current I2It adjusts in described value, to utilize the operation of the low-energy-efficiency as far as possible of DC/DC converters realization battery memory system 1.
It is determined during each of secondary iteration cycle 20 and stores Optimal Control parameter u*.It is based on Hamilton letter herein The minimum value of H is counted to determine Optimal Control parameter u*.Optimal Control parameter u*Determination at this in secondary iteration cycle each of 20 It carries out in the process, method is：Execute third iterative cycles 30.
Third iterative cycles 30 have multiple processes, wherein possible controlling value umIt is belonging respectively to continuous process.Cause This executes a certain number of processes when executing third iterative cycles 30, completes corresponding identical method step in this process Suddenly.Here, for possible controlling value umChanging value, implement corresponding identical method and step.
For this purpose, the controlling value range of description value is defined, possible controlling value umThere can be described value.The controlling value model It encloses with minimum u- initial values and highest u- end values.Secondary iteration cycle 20 it is each during, in third iteration Cycle 30 first during, it would be possible to controlling value umValue be set as u- initial values.In each of third iterative cycles 10 When further process starts, the value is improved in incremental steps 31.Third iterative cycles 30 are carried out so frequent, until possible Controlling value umEqual to u- end values.Therefore for a certain number of possible controlling value umCarry out third iterative cycles 30, from Possible controlling value u in u incremental steps 31mValue raising in obtain the controlling value.
Third iterative cycles 30 it is each during, in calculating step 11, according to possible controling parameter umCategory In the value of the respective process of third iterative cycles 30, calculate such as in formula（34）Shown in Hamiltonian function H value.It is calculating When the value of Hamiltonian function H, therefore by the system loss L of modelingmWith the charged state SoC of the second battery 32Derivative be added, should Derivative is with possible with amount λmIt is multiplied.The system loss L of modelingmIt is parameter, which describes energy accumulator system, i.e. electricity The characteristic of pond storage system 1, and be therefore the systematic parameter of modeling.In being formed by Hamiltonian function H, thus model System loss LmIt is the parameter to be optimized of hybrid battery storage system 1, which should be minimized.Meanwhile second is electric The charged state SoC in pond 32It is the state of the second energy accumulator and is therefore memory state.
Herein by means of possible with amount λmThe selected value of active procedure for the first iterative cycles 10, may Controling parameter umThe selected value of the active procedure for third iterative cycles 30 and the second battery 3 nominal charging Measure Qnom,2, for currently considered time point, calculate the charged state SOC of the second battery 32Derivative.
The system loss L of modeling is determined for currently considered time point in modeling procedure 32mWith the second battery 3 Nominal charge volume Qnom,2, modeling procedure is executed before the calculating step 11 of third iterative cycles 30.
In modeling procedure 32, the system loss L of computation modelingm.This is referred to as " modeling " in the method, this is because The described method for determining the best system performance of mixed tensor storage system can be executed by software, and Battery memory system 1 is physically unavailable.In formula（34）In reflected by system loss L.From the electricity damage in the first battery Consume Pv,1(t), the electrical loss P in the second batteryv,2(t) the electrical loss P and in DC/DC convertersv,dcdc(t) it is modeled in System loss Lm.In order to calculate these electrical losses, it must be understood that by the total current I required by battery memory system 1ges, right The total current is required in currently considered time point.Therefore, in modeling procedure 32, according to scheduled power curve Pges(t) Determine the total current I required by battery memory system 1ges.In power curve Pges(t) in, for predetermined time period [ta, te] each time point, store required power, which passes through battery memory system 1 and provide.Work(required by this Rate passes through inquiry 22 --- inquiry carries out in the frame of secondary iteration cycle 20 --- in secondary iteration cycle 20 The time point currently considered in respective process is read and is supplied to the calculating step 11.Based on required power, Required total current I is calculated for currently considered time pointges.Initialize 40 when store remaining thus needed for Value.Therefore according to scheduled power curve Pges(t), the system loss L of modeling is determinedm, the power curve is by battery memory system System 1 is in predetermined time period [ta, te] in execute.Here, the D.C. resistance R based on the first battery 2dc,1, the second battery 3 it is straight Leakage resistance Rdc,2, by battery memory system 1 for corresponding time point according to scheduled power curve Pges(t) have to be output Power P and DC/DC converters 4 efficiency etadcdc, determine the system loss L of modelingm。
In addition, in modeling procedure 32, for currently considered time point, the nominal charge volume of the second battery 3 is calculated Qnom,2.For this purpose, access secondary iteration cycle 20 it is previous during the charged state SoC of the second battery 3 that is stored2.The This charged state SoC of two batteries2According to formula（13）It updates and is provided for calculating step 11.In addition, the second electricity This charged state SoC in pond2The charging shape can be accessed in the subsequent process that secondary iteration recycles 20 by being stored such that State.During the first of secondary iteration cycle 20, if there is not yet the of the previous process from secondary iteration cycle 20 The charged state SoC of two batteries2, then the initial state of charge SoC provided in initialization 40 of the second battery is accessed2(ta)。
In order to calculate Hamiltonian function H, such as formula in calculating step 11（34）It is shown, it is also necessary to amount λ.As companion Whatever you like λ belongs to the possible with amount λ of the corresponding existing process of the first iterative cycles 10mIt is used to calculate.From formula（34） It can obtain, Hamiltonian function H depends on the system loss L of modelingm, it is possible with amount λmWith the charged state of the second battery SoC2.Hamiltonian function H belongs to battery memory system 1, because the function is made of the value of description battery memory system 1.
For each process of third iterative cycles 30, the value of storage Hamiltonian function H is calculating in calculating step 11 Described value is obtained when Hamiltonian function H.In calculating step 11, the minimum value in stored value is determined.It is followed in third iteration Minimum value in the last value stored accordingly obtained in the process of ring 30 is the minimum value of Hamiltonian function H.Possible control Parameter u processedm--- minimum value in stored value is generated when third iterative cycles 30 execute the controling parameter --- is true It is set to Optimal Control parameter u*.Accordingly, it is possible to controling parameter umIt is confirmed as Optimal Control parameter u*, for the Optimal Control For parameter, the value of Hamiltonian function H has minimum value.
When executing secondary iteration cycle 20 every time, the final charged state SoC of the second battery is calculated2E（Also referred to as SoC2 (te)）, the second battery 3 has final charged state after by predetermined time period, when second exported from the second battery 3 Electric current I2According to corresponding Optimal Control parameter u*In predetermined time period [ta, te] in when being controlled.For this purpose, accessing the The last stored charged state SoC in modeling procedure 32 in the process in secondary iteration cycle 20 of two batteries2, when The possible controling parameter u in third iterative cycles 30mEqual to Optimal Control parameter u*When, obtain the charged state.
If secondary iteration cycle 20 completes operation, so all processes of secondary iteration cycle 20 are performed completely, because This is detected in the first iterative cycles 10：By means of the final charged state for the second battery that secondary iteration cycle 20 calculates SOC2EWhether it is located in scheduled charging section and best adjoint amount λ is provided*, the best adjoint amount is corresponding to first The possible adjoint amount λ of the process of iterative cycles 10, final charged state SoC is had been detected by for the process2EPositioned at predetermined Charging section in.Scheduled charging section is the initial state of charge SOC of the second battery2ANeighbouring section, i.e. the second battery Initial state of charge SoC2(ta) neighbouring section.So examining in this embodiment：The final charging shape of second battery 3 State SoC2EWhether when the initialize 40 selected initial state of charge SoC of second battery is located at2(ta) in neighbouring section. Therefore it checks：The final charged state SoC of second battery 3 of the respective process for the first iterative cycles 102EWhether to be less than 1% amplitude deviates from the initial state of charge SoC of the second battery2(ta).If it is the case, then will likely adjoint amount λm The value of the respective process for belonging to the first iterative cycles 10 be determined as best adjoint amount λ*Value.
In the method for best system performance for determining mixed tensor storage system, in order to which solving-optimizing is asked Topic, discretization is kinetically carried out by means of alternative manner with model in time.After first time step initialization, for Follow-up time walks, model parameter（Charged state in battery）Development can more new state.Then it is directed in " u=I2Cycle " Namely controlling value u=I in third iterative cycles 302Need scheduled amount, for it is scheduled with amount λ by means of equation （34）Come calculate Hamiltonian function H each subordinate value.The amount of controlling value corresponds to the possible current value of the first battery 3.
Then, in " min（H）" in block, determine the second electric current I2Current value, and thereby determine that Optimal Control parameter u*, In the case where following constraints, the current value minimizes Hamiltonian function H.
Then, in testing procedure 12, entire power curve L is checked（t）Whether final time step t has been gone toe.Such as Fruit is not in this way, then scheduled possible with the meter for measuring λ progress optimum current regulated values to entire problem and in this step It calculates.
If in last time step teIn realize calculating, then check whether for given possible with amount λm, abide by Boundary condition is followed, that is, the final charged state SoC of the second battery 32(te) correspond to initial state of charge SoC2(ta).Due to Model is discretization, therefore can select such as SoC2(t2The boundary of)+/ 1%.If it is not the case, please according to following It is described to continue like that.
The method in another cyclic process of the first iterative cycles 10, is used for most preferably at present by iterative approximation With amount λ*Optimum value, select other possible with amount λm, the adjoint amount is according to the scheme in selecting step 14 It is provided in calculating process.
As can be seen in Figure 2, the process is so repeated long, until meeting standard SoC in testing procedure 132 (ta)≈SoC2(t2).Then best adjoint amount λ is determined*.This standard is interrupt criteria.Best adjoint amount λ*It is possible With amount λm, this is possible with the basis for measuring the process for meeting interrupt criteria for being the first iterative cycles 10.
For determining mixed tensor storage system（1）Best system performance the method alternative implementation In mode, recycled in secondary iteration（20）Each of during be determined Optimal Control parameter（u*）It is calculated in fact by analyzing It is existing.Here, realizing in equation（34）In give Hamiltonian function computational minimization.This can be by means of for example can also be The algorithm that is run in controller is completed.In this case, it is applicable in general optimization task u*=arg min H.Therefore, real Now from dH/du=0 to more generally requiring u*The transformation of=arg min H.Therefore this is favourable, because can occur It is that, in the edge of this value range, the minimum value of the Hamiltonian function in the limited valid value range of controling parameter u has Minimum value.In this case, condition is not dH/du=0.Therefore, more generally form is advantageous.
Illustrate for controlling mixed tensor storage system 1 according to the method for the present invention below with reference to Fig. 3.At this In, it realizes most preferably with λ*Adaptation, for can real-time self adaptive control.
It is offline described in method using the above-mentioned best system performance for being used to determine mixed tensor storage system Confirmable solution is switched to now to be controlled in real time.Accordingly, target be on the period it is arbitrary with Time, related electric current inquired Iges(t), Optimal Control is realized.To produce cause and effect energy management, when not from prediction Between range information in the case of carry out the cause and effect energy management.Here, it is thus achieved that a kind of adaptive approach, this is adaptive Method is based on by for determining that the predetermined offline solution of method institute of best system performance works.
The best adjoint amount λ calculated for known problem*Can be only applied to, with equation（35）The relationship of middle restriction Cooperation in realize control, or realize have the electric current I to be determined in the first battery in each time step1d(t) and second is electric The electric current I in pond2(t) current distribution between.In each time step, the electric current I of the second battery is in addition calculated2(t), the electric current pair It should be in controling parameter u（t）.Thus it is applicable in：I2(t)=u(t)。
It now, will not if solved similar to the still inaccurate control problem for corresponding to offline solution procedure The end value standard of fixed charged state is followed in any situation.
Equally, end time point teIt is unknown.In order to appropriately handle this problem, self adaptive control, anti-work are introduced For the baseline charge state SoC with the second battery 32,SollDeviation.This is realized using pi regulator 51, the PI tune Section device reacts to the difference between the specified charged state and practical charged state of the second battery 3.Baseline charge state SoC2,SollHerein in particular corresponding to baseline charge state SoC2,Ref, in determination best first with amount λ*When select the benchmark to fill Electricity condition.
Formula（36）In show the equation of pi regulator 51.Thus according to the specified charged state and reality of the second battery 3 The adjusting difference of border charged state avoids too fast electric discharge or charging.Charged state is joined according to the adjuster for being provided to adjuster Amount, i.e. ratio value Kp and integrated value Ki are in baseline charge state SoC2,SollRange in performance characteristic.Ratio value Kp and integrated value The size of Ki can be found by parameter study.
Fig. 3 shows the controller structure of hybrid battery storage system 1 being embedded in energy management.This is from second Adjusting difference between the specified charged state and practical charged state of battery 3 is together with scheduled best adjoint amount λ*And adjuster Parameter starts.
Therefore, Fig. 3 shows that the equipment 50 for controlling hybrid battery storage system 1, the equipment include adjusting unit 54, which is arranged for executing the method for controlling hybrid battery storage system 1.
Equipment 50 includes that there are two the pi regulators 51 of input terminal for tool.The first input of detection is realized by the two input terminals Value and the second input value, wherein the best adjoint amount λ of the first input value description*The value precalculated, and the second input value The charged state SoC of second battery 3 is described2, i.e. the second battery 3 memory state and baseline charge state SOC2,SollIt is inclined Difference.Best adjoint amount λ*Value for example provided by memory cell, stored in the memory cell according to being used for Determine adjoint amount λ best determined by the method for the best system performance of mixed tensor storage system*.Benchmark charging shape State SOC2,SollIt also is stored in the memory cell, and is provided for examining by pi regulator 51 as the second input value It surveys.Baseline charge state SOC2,SollSuch as equal in the best system performance for determining mixed tensor storage system In predetermined time period [t in methoda, te] the initial state of charge SoC of selected first battery when starting1(ta) value.
Based on the first and second input values, that is to say, that based on best adjoint amount λ*With baseline charge state SOC2,Soll, By the adjustment of pi regulator 51 for the present charge state SoC of the second battery2Best adjoint amount λ*, adjusted to determine Adjoint amount λa.Thus, it is first determined the present charge state SOC of the second battery2.This at the second battery 2 by means of detecting At least one measured value detect.The adjoint amount λ of adjustmentaAccording to formula（36）It is determined and in the output end of pi regulator 51 Place is provided.
For the adjoint amount λ of adjustmentaIdentified value is transmitted to control unit 52 from pi regulator 51.Control unit 52 is also With input terminal, current target value is provided in the input end, current target value description is total to by the first battery 2 and the second battery 3 With the electric current provided.Therefore, current target value is energy target values, because current target value is described by the first battery and second The energy that battery 3 provides.The current target value is for example by determining that the external electronic device of electric current provides, and the electric current is by being connected to Needed for the load of battery memory system 1.The required total current I of current target value descriptionges。
By control unit 52, according to formula（35）, determine the Optimal Control ginseng for the electric current that description is exported from the second battery 3 Measure I2 *Value (u=I2 *).Therefore Optimal Control parameter I2 *Describe the energy for being accommodated by the second battery 3 or being exported.Here, adjustment Adjoint amount λ a be used as according to formula（35）Calculate Optimal Control parameter I2 *Adjoint amount λ.By in control unit 52 Middle storage formula（35）And solution is calculated to determine Optimal Control parameter I2 *.This can come for example, by digital computing unit At.Alternatively advantageously, Optimal Control parameter I2 *It is determined with table by control unit 52, wherein inquire following tables Lattice, in this table, Optimal Control parameter I2 *It is associated with the adjoint amount λ of adjustment respectivelyaValue and current target value IgesValue Different combinations.It means that for the accompaniment λ of adjustmentaWith the combination of all possible value of current target value, count The Optimal Control parameter I of subordinate is calculated2 *, and these controling parameters are already stored in control unit 52.
Adjoint value λ based on adjustment in both casesaWith current target value IgesIt realizes and determines Optimal Control parameter I2 *.No matter Optimal Control parameter I2 *Value whether be pre-calculated and be selected now or these Optimal Control parameters are It is no to be calculated in real time, Optimal Control parameter I2 *It is determined according to the Hamiltonian function H for belonging to battery memory system 1, because of base In by formula（34）Described in Hamiltonian function H solution formula（35）The Optimal Control parameter is obtained.Such as preceding institute It states, Hamiltonian function H depends on calculated system loss Lb, the adjoint amount λ of adjustmentaIt is current with the second energy accumulator Charged state SoC2.Here, formula（35）L is lost in middle institute's computing systembIt is understood to system loss L.Therefore, system loss L exists This is referred to as the system loss L calculatedb, because the system loss of the calculating is the battery memory system 1 for physical presence And calculate.
As also from formula（34）It can be seen that like that, the system loss L of calculatingbLoss power dependent on the first battery Pv,1, the loss power P of the second batteryv,2, the loss power P of DC/DC convertersv,dcdc。
The Optimal Control parameter I determined by control unit 52 is provided2 *, to adjust accordingly the second electric current I2, namely The electric current provided by the second battery 3.In order to control the first electric current I1Or in Fig. 1 known battery memory system 1 the case where The electric current of output of the lower control in DC/DC converters 4, i.e. converter current I1d, determine Optimal Control parameter I2 *With electric current Difference between desired value.This is for example completed by means of difference engine 53.
Fig. 4 is shown for generating best adjoint amount λ*Equipment according to the present invention 60.For generating best companion Whatever you like λ*Equipment 60 include computing unit 61, such as processor, the computing unit be adapted for carrying out for determining hybrid battery The preceding method of the best system performance of storage system 1.Equipment 60 has input terminal 62, can be provided by the input terminal Define the value needed for battery memory system 1.Equipment 60 has output end 63, and best adjoint amount is provided by the output end λ*。
It further explicitly points out, can carry out in the corresponding way according to the method for the present invention, if mixed for determining Close the Optimal Control parameter u in the method for the best system performance of energy accumulator system 1*With for controlling mixed tensor Optimal Control parameter I in the method for storage system 12 *Describe the electric current exported by the first battery memory.
Other than disclosure above, explicitly with reference to the disclosure of Fig. 1 to 4.

Claims (15)

1. for by determining best adjoint amount（λ*）To determine at least one first energy accumulator（2）With the second energy Measure memory（3）Mixed tensor storage system（1）Best system performance method, the method includes：
Implement the first iterative cycles during multiple（10）, wherein possible with amount（λ）It is belonging respectively to continuous process,
Wherein in first iterative cycles（10）Each of during implement secondary iteration cycle（20）, the secondary iteration Cycle includes the following steps：
The Optimal Control parameter at the continuous time point for predetermined time period is determined respectively（u*）,
Wherein Optimal Control parameter（u*）It is the parameter that the energy by the receiving of the second energy accumulator or output is described by it And it is based on Hamiltonian function（H）Minimum value determine,
Wherein Hamiltonian function（H）Belong to energy accumulator system（1）And dependent on the systematic parameter of modeling（Lm）, it is possible With amount（λ）With the second energy accumulator（3）Memory state（SoC2）,
The systematic parameter wherein modeled（Lm）It is mixed tensor storage system（1）Parameter to be optimized and according to scheduled Power curve determines that the power curve is by energy accumulator system（1）It is executed in predetermined time period, and
When by the second energy accumulator（3）Energy accommodate or output is in predetermined time period according to control best respectively Parameter processed（u*）When manipulating, to calculate the second energy accumulator（3）Memory end-state（SoC2E）, second energy deposits Reservoir（3）There is the memory end-state after a predetermined time period,
Detection：Second energy accumulator（3）Recycled by secondary iteration（20）The memory end-state of calculating（SoC2E）It is It is no to be located in scheduled section, and best adjoint amount is provided（λ*）, best adjoint amount is corresponding to the first iterative cycles （10）Process it is possible with amount（λ）, memory end-state has been detected for the process（SoC2E）Positioned at pre- In fixed section.
2. according to the method described in claim 1, it is characterized in that, the Optimal Control parameter（u*）It describes by the second energy Memory（3）The electric current of output, and/or modeling systematic parameter（Lm）It is the system loss of modeling, and/or storage Device state is charged state, wherein memory end-state（SoC2E）It is final charged state and/or the section is to fill Electric section.
3. according to the method described in claim 1, it is characterized in that, first iterative cycles（10）Implement dichotomy, so as to It determines possible with amount（λ）.
4. according to the method described in claim 1, it is characterized in that,
It is recycled in secondary iteration（20）Each of during determine Optimal Control parameter（u*）, method is to implement third iteration Cycle（30）, wherein possible controlling value（u）It is belonging respectively to third iterative cycles（30）In continuous process, wherein in third Iterative cycles（30）Each of during, Hamiltonian function（H）Value according to belonging to third iterative cycles（30）Respective mistake The possible controling parameter of journey（u）It calculates, and possible controling parameter（u）It is determined as Optimal Control parameter（u*）, for For Optimal Control parameter, Hamiltonian function（H）Value have minimum value, or
It is recycled in secondary iteration（20）Each of during determine Optimal Control parameter（u*）It is realized by analyzing to calculate.
5. according to any method of the preceding claims, which is characterized in that scheduled section is deposited in the second energy Reservoir（3）Initial memory state（SoC2A）Neighbouring section, second energy accumulator（3）In predetermined time period With the initial memory state when beginning.
6. according to any method of the preceding claims, which is characterized in that the systematic parameter of modeling（Lm）Based on first Energy accumulator（2）D.C. resistance, the second energy accumulator（3）D.C. resistance, being directed to according to scheduled power curve Respective time point is by energy accumulator system（1）The power of output（P）With DC/DC converters（4）Efficiency determine, described the One energy accumulator（2）With second energy accumulator（3）In energy accumulator system（1）In connected by DC/DC converters It connects.
7. according to any method of the preceding claims, which is characterized in that calculating Hamiltonian function（H）Value When, the systematic parameter of modeling（Lm）With the second energy accumulator（3）Memory state（SoC2）Derivative be added, the derivative With possible with amount（λ）It is multiplied.
8. including at least one first energy accumulator for controlling（2）With the second energy accumulator（3）Mixed tensor storage Device system（1）Method, the method includes：
The first input value and the second input value are detected, wherein the first input value describes best adjoint amount（λ*）Advance meter The value of calculation, the second input value describe the second energy accumulator（3）Memory state（SoC2）With memory normal condition （SOC2,soll）Deviation；
It is directed to the second energy accumulator based on the adjustment of the first and second input values（3）Current memory state（SoC2）Most Good adjoint amount（λ*）, to determine the adjoint amount of adjustment（λa）；
The energy target values of description energy are provided（Iges）, the energy is by the first energy accumulator（2）With the second energy stores Device（3）It is common to provide；
Adjoint amount based on adjustment（λa）And energy target values（Iges）Determine Optimal Control parameter（I2 *）, wherein Optimal Control join Amount（I2 *）It is the parameter and Optimal Control parameter that the energy by the receiving of the second energy accumulator or output is described by it （I2 *）According to belonging to energy accumulator system（1）Hamiltonian function（H）It determines, wherein Hamiltonian function（H）Dependent on mixing Energy accumulator system（1）Calculating systematic parameter（Lb）, adjustment adjoint amount（λa）It is current with the second energy accumulator Memory state（SoC2）.
9. according to the method described in claim 8, it is characterized in that, the memory state is charged state and/or institute State energy target values（Iges）It is current target value and/or Optimal Control parameter（I2 *）It describes by the second energy accumulator （3）The electric current of output, and/or calculate systematic parameter（Lb）It is mixed tensor storage system（1）System loss.
10. method according to claim 8 or claim 9, which is characterized in that the systematic parameter of calculating（Lb）Dependent on the first energy Memory（2）Loss power and the second energy accumulator（3）Loss power, and especially in addition depend on energy convert Device（4）Loss power, first energy accumulator（2）With second energy accumulator（3）In energy accumulator system （1）In connected by the energy converter.
11. the method according to any one of claim 8 to 10, which is characterized in that the Optimal Control parameter（I2 *）With Calculation determines or the Optimal Control parameter（I2 *）It determines in tabular form, wherein in feelings determining in tabular form Following table is inquired under condition, in the table, the Optimal Control parameter（I2 *）It is associated with the adjoint amount adjusted respectively （λa）Value and current target value（Iges）Value different combinations.
12. the method according to any one of claim 8 to 11, which is characterized in that the best adjoint amount（λ*）'s Adjustment is by pi regulator（7）It realizes.
13. the method according to any one of claim 8 to 12, which is characterized in that the best adjoint amount（λ*）It is logical Method according to any one of claim 1 to 6 is crossed to generate.
14. being used to generate best adjoint amount including computing unit（λ*）Equipment, computing unit setting is for implementing It is according to any one of claim 1 to 7 to be used to determine mixed tensor storage system（1）Best system performance Method.
15. including adjusting unit（54）The equipment for controlling mixed tensor storage system（50）, the adjusting unit sets The fixed method for controlling mixed tensor storage system for implementing according to any one of claim 8 to 13.
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