CN116428513A - Hydrogen optimization filling method and system for hydrogen filling station based on model predictive control - Google Patents
Hydrogen optimization filling method and system for hydrogen filling station based on model predictive control Download PDFInfo
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- UFHFLCQGNIYNRP-UHFFFAOYSA-N Hydrogen Chemical compound [H][H] UFHFLCQGNIYNRP-UHFFFAOYSA-N 0.000 title claims abstract description 329
- 239000001257 hydrogen Substances 0.000 title claims abstract description 329
- 229910052739 hydrogen Inorganic materials 0.000 title claims abstract description 329
- 238000011049 filling Methods 0.000 title claims abstract description 99
- 238000000034 method Methods 0.000 title claims abstract description 40
- 238000005457 optimization Methods 0.000 title claims description 22
- 238000003860 storage Methods 0.000 claims abstract description 215
- 238000005984 hydrogenation reaction Methods 0.000 claims abstract description 84
- 238000011217 control strategy Methods 0.000 claims abstract description 38
- 239000007789 gas Substances 0.000 claims abstract description 32
- 230000007246 mechanism Effects 0.000 claims abstract description 18
- 238000005429 filling process Methods 0.000 claims abstract description 12
- 230000008859 change Effects 0.000 claims abstract description 11
- 210000000349 chromosome Anatomy 0.000 claims description 52
- 239000004973 liquid crystal related substance Substances 0.000 claims description 42
- 230000006870 function Effects 0.000 claims description 28
- 239000011159 matrix material Substances 0.000 claims description 27
- 238000004364 calculation method Methods 0.000 claims description 11
- 230000035772 mutation Effects 0.000 claims description 7
- 238000004458 analytical method Methods 0.000 claims description 4
- 150000002431 hydrogen Chemical class 0.000 claims description 3
- 238000005259 measurement Methods 0.000 claims description 3
- 238000005070 sampling Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 8
- 239000007788 liquid Substances 0.000 description 6
- 238000004422 calculation algorithm Methods 0.000 description 4
- 230000008569 process Effects 0.000 description 4
- 239000000446 fuel Substances 0.000 description 3
- 230000002068 genetic effect Effects 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 238000012545 processing Methods 0.000 description 3
- VNWKTOKETHGBQD-UHFFFAOYSA-N methane Chemical compound C VNWKTOKETHGBQD-UHFFFAOYSA-N 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 239000004065 semiconductor Substances 0.000 description 2
- 229920002430 Fibre-reinforced plastic Polymers 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000004590 computer program Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 239000011151 fibre-reinforced plastic Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000003345 natural gas Substances 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17C—VESSELS FOR CONTAINING OR STORING COMPRESSED, LIQUEFIED OR SOLIDIFIED GASES; FIXED-CAPACITY GAS-HOLDERS; FILLING VESSELS WITH, OR DISCHARGING FROM VESSELS, COMPRESSED, LIQUEFIED, OR SOLIDIFIED GASES
- F17C5/00—Methods or apparatus for filling containers with liquefied, solidified, or compressed gases under pressures
- F17C5/06—Methods or apparatus for filling containers with liquefied, solidified, or compressed gases under pressures for filling with compressed gases
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17C—VESSELS FOR CONTAINING OR STORING COMPRESSED, LIQUEFIED OR SOLIDIFIED GASES; FIXED-CAPACITY GAS-HOLDERS; FILLING VESSELS WITH, OR DISCHARGING FROM VESSELS, COMPRESSED, LIQUEFIED, OR SOLIDIFIED GASES
- F17C13/00—Details of vessels or of the filling or discharging of vessels
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17C—VESSELS FOR CONTAINING OR STORING COMPRESSED, LIQUEFIED OR SOLIDIFIED GASES; FIXED-CAPACITY GAS-HOLDERS; FILLING VESSELS WITH, OR DISCHARGING FROM VESSELS, COMPRESSED, LIQUEFIED, OR SOLIDIFIED GASES
- F17C13/00—Details of vessels or of the filling or discharging of vessels
- F17C13/04—Arrangement or mounting of valves
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17C—VESSELS FOR CONTAINING OR STORING COMPRESSED, LIQUEFIED OR SOLIDIFIED GASES; FIXED-CAPACITY GAS-HOLDERS; FILLING VESSELS WITH, OR DISCHARGING FROM VESSELS, COMPRESSED, LIQUEFIED, OR SOLIDIFIED GASES
- F17C2250/00—Accessories; Control means; Indicating, measuring or monitoring of parameters
- F17C2250/03—Control means
- F17C2250/032—Control means using computers
-
- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17C—VESSELS FOR CONTAINING OR STORING COMPRESSED, LIQUEFIED OR SOLIDIFIED GASES; FIXED-CAPACITY GAS-HOLDERS; FILLING VESSELS WITH, OR DISCHARGING FROM VESSELS, COMPRESSED, LIQUEFIED, OR SOLIDIFIED GASES
- F17C2250/00—Accessories; Control means; Indicating, measuring or monitoring of parameters
- F17C2250/04—Indicating or measuring of parameters as input values
- F17C2250/0404—Parameters indicated or measured
- F17C2250/043—Pressure
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- 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
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
- Y02E60/30—Hydrogen technology
- Y02E60/32—Hydrogen storage
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- Mechanical Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Filling Or Discharging Of Gas Storage Vessels (AREA)
Abstract
The invention relates to a hydrogen optimizing and filling method for a hydrogen adding station, in particular to a hydrogen optimizing and filling method and a hydrogen adding station hydrogen optimizing and filling system based on model predictive control. Comprising the following steps: setting parameter information; establishing a discrete time model to obtain the gas flow characteristics in the hydrogenation station; according to the gas flow characteristics, a mixed integer nonlinear programming model is established by taking the start-stop state of a compressor as a known parameter and the connection state of a hydrogenation machine as a control variable; the discrete time model is adopted as a prediction model of model prediction control, the pressure change curve of the vehicle-mounted hydrogen storage device in the ideal filling process is adopted as a target curve of model prediction control, and the prediction time domain is N p The control time domain is determined according to an event trigger mechanism; and calculating a control strategy according to the mixed integer nonlinear programming model. The inventionThe method can realize the cross-correlation between complex working conditions and equipment in the hydrogenation station, and establish a discrete time model which can be matched with a large-scale and high-configuration hydrogenation station.
Description
Technical Field
The invention relates to a hydrogen optimizing and filling method for a hydrogen adding station, in particular to a hydrogen optimizing and filling method and a hydrogen adding station hydrogen optimizing and filling system based on model predictive control.
Background
Currently, the hydrogenation stations are mainly divided into two forms, namely gaseous storage and liquid storage, wherein the former is usually stored in a high-pressure storage tank in a grading manner after pressurizing low-pressure hydrogen by adopting a compressor, and the latter is used for pressurizing low-pressure liquid hydrogen by a liquid hydrogen pump, gasifying the low-pressure liquid hydrogen into high-pressure liquid hydrogen and then storing the high-pressure liquid hydrogen in the high-pressure storage tank in a grading manner or directly filling hydrogen fuel automobiles by a hydrogenation machine.
With the continuous upgrading of the hydrogen energy industry and the substantial increase in hydrogen demand, optimization of hydrogen storage and supply systems for hydrogen stations has become a technical bottleneck limiting the size of hydrogen stations. Corresponding work has been carried out on the gas supply optimization aspect of CNG (Compressed Natural Gas ) gas stations, including thermodynamic model establishment of staged storage and filling, analysis of filling efficiency and system energy conservation, and the like. In the aspect of gas supply optimization of the hydrogenation station, a multi-objective optimization model and a control method are adopted, and the hydrogen supply quantity and the gas supply sequence of each grading storage tank in the filling process are researched. However, the method in the prior art is feasible for optimizing and scheduling the gas paths in the hydrogen stations with small scale and less configuration, but cannot meet the working conditions of multiple groups of pressurizing units, more than three-level gas storage devices and multiple hydrogen machines under the continuous filling requirement, so that a new optimizing control method is required to be provided for matching the hydrogen stations with large scale and high configuration.
Disclosure of Invention
In view of the shortcomings of the prior art, the invention aims to provide a hydrogen filling optimization method and a hydrogen filling optimization system for a hydrogen filling station based on model predictive control, which solve the problem that the prior art cannot meet the working conditions under the continuous filling demands of a plurality of groups of pressurizing units, more than three-stage gas storage devices and a plurality of hydrogen filling machines.
To achieve the above and other related objects, the present invention provides a hydrogen filling method for a hydrogen filling station based on model predictive control, which is implemented based on a hydrogen filling station system, wherein the hydrogen filling station system comprises a compressor, a hydrogen storage device and a hydrogen filling machine, and the hydrogen filling method for a hydrogen filling station based on model predictive control comprises:
s1, setting parameter information among the compressor, the hydrogen storage device and the hydrogenation machine;
s2, establishing a discrete time model to obtain gas flow characteristics in the hydrogenation station;
s3, according to the gas flow characteristics, establishing a mixed integer nonlinear programming model by taking the start-stop state of the compressor as a known parameter and the connection state of the hydrogenation machine as a control variable;
s4, using the discrete time model as a model prediction control prediction model, using a pressure change curve of the vehicle-mounted hydrogen storage device in an ideal filling process as a model prediction control target curve, and predicting the time domain as N p The control time domain is determined according to an event trigger mechanism;
s5, calculating a control strategy according to the mixed integer nonlinear programming model.
In an embodiment of the present invention, the number of the compressors includes three, the hydrogen storage device includes a high-pressure hydrogen storage device, a medium-pressure hydrogen storage device, and a low-pressure hydrogen storage device, the hydrogenation machine includes three compressors, the three compressors are respectively in one-to-one correspondence with the high-pressure hydrogen storage device, the medium-pressure hydrogen storage device, and the low-pressure hydrogen storage device, and the high-pressure hydrogen storage device, the medium-pressure hydrogen storage device, the low-pressure hydrogen storage device, and the three hydrogenation machines are in one-to-one correspondence with each other.
In an embodiment of the present invention, the setting parameter information between the compressor, the hydrogen storage device, and the hydrogenation machine in the step S1 includes:
by means of
Representing a valve between the compressor and the hydrogen storage device, wherein C is the compressor, C e {1, …, C }, using +.>
And a valve between the hydrogen storage device and the hydrogenation machine is shown, wherein D is the hydrogenation machine, and D is {1, …, D }.
In one embodiment of the present invention, the establishing a discrete time model in step S2, obtaining the gas flow characteristics in the hydrogen station includes:
S21, the discrete time sequence number and the sampling period of the discrete time model are respectively expressed as k and t s ；
S22, at the time k+1, the pressure formula of each stage of hydrogen storage device is expressed as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
k represents the flow rate from the compressor to the hydrogen storage device of each stage hp 、K mp 、K lp Respectively represent constants determined by volumes of the hydrogen storage devices of the respective stages, f hp 、f mp 、f lp Representing the flow rate of hydrogen from each stage of hydrogen storage device to the hydrotreater;
s23, at the time k, the formula of the hydrogen flow rate from each stage of hydrogen storage device to the vehicle-mounted hydrogen storage device is expressed as follows:
wherein, gamma is the specific heat ratio of hydrogen, C dis Is the flow coefficient of a valve of a hydrogenation machine, A orifice For the cross-sectional area of the valve of the hydrogenation machine, ρh p 、ρ mp 、ρ lp To the density of hydrogen in each stage of hydrogen storage device, p hp,veh 、p mp,veh 、p lp，veh The pressure of the vehicle-mounted hydrogen storage device connected with each level of hydrogen storage device;
s24, at the time k, the pressure formula in the vehicle-mounted hydrogen storage device connected with each stage of hydrogen storage device is expressed as follows:
wherein θ d,i In order to achieve the connection state of the hydrogenation machine and the vehicle,
i represents a vehicle, which is the pressure of the on-vehicle hydrogen storage device;
s25, at the time k+1, the pressure formula of the vehicle-mounted hydrogen storage device is expressed as follows:
wherein K is i As a constant determined by the volume of the in-vehicle hydrogen storage device, 
A flow rate for filling hydrogen gas into the vehicle;
s26, at the time k, filling the flow rate of hydrogen into the vehicle
And the flow rate f of hydrogen passing through the hydrogenation machine d (k) The formula is:
in an embodiment of the present invention, according to the gas flow characteristics in the step S3, with a start-stop state of the compressor as a known parameter and a connection state of the hydrogenation machine as a control variable, establishing the mixed integer nonlinear programming model includes:
s31, at any time k, a constraint formula of the connection state of the hydrogenation machine and the vehicle is expressed as follows:
valve v of hydrogenation machine connected with hydrogen storage devices at all levels d Is expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
the valve switch state from the high-pressure hydrogen storage device to the hydrogenation machine at the moment k is shown, a value of 1 indicates that the valve is opened, and a value of 0 indicates that the valve is closed, +.>
The valve switch state from the medium-pressure hydrogen storage device to the hydrogenation machine at the moment k is shown, a value of 1 indicates that the valve is opened, and a value of 0 indicates that the valve is closed, +.>
The valve switch state from the low-pressure hydrogen storage device to the hydrogenation machine at the moment k is shown, wherein a value of 1 indicates that the valve is opened, and a value of 0 indicates that the valve is closed;
s32, valve v of compressor connected with hydrogen storage devices at all levels e Is expressed as:
Wherein, the liquid crystal display device comprises a liquid crystal display device,
the valve switch state from the compressor to the high-pressure hydrogen storage device at the time k is shown: a value of 1 indicates valve opening and a value of 0 indicates valve closing, +.>
The valve switch state from the compressor to the medium-pressure hydrogen storage device at the time k is shown: a value of 1 indicates valve opening and a value of 0 indicates valve closing, +.>
Valve representing time k from compressor to low pressure hydrogen storage deviceA door open/close state; a value of 1 indicates valve opening, a value of 0 indicates valve closing, u c (k) The start-stop state of the compressor at the moment k is represented, a value of 1 represents that the compressor is running, and a value of 0 represents that the compressor stops running;
s33, valve v of compressor connected with each stage of hydrogen storage device C Valve v connected with hydrogen storage devices at all levels and hydrogenation machine d Is expressed as:
s34, constraint formulas for meeting the minimum pressure and the maximum pressure of each level of hydrogen storage device and the vehicle-mounted hydrogen storage device are expressed as follows:
p hp，min ≤p hp (k)≤p hp，max ，
p mp，mm ≤p mp (k)≤p mp，max ，
p lp，min ≤p lp (k)≤p lp，max ，
wherein p is hp，min Represents the minimum pressure allowed by the high-pressure hydrogen storage device, p mp，min Represents the minimum pressure allowed by the medium-pressure hydrogen storage device, p lp，min Indicating the minimum pressure allowed by the low pressure hydrogen storage device,
on-board hydrogen storage representing a vehicleStoring the minimum pressure allowed by the device, p hp，max Indicating the maximum pressure allowed by the high-pressure hydrogen storage device, p mp，max Indicating the maximum pressure allowed by the medium-pressure hydrogen storage device, p lp，max Indicating the maximum pressure allowed by the low pressure hydrogen storage device, < >>
Indicating the maximum pressure allowed by the on-board hydrogen storage device of the vehicle.
In an embodiment of the present invention, in the step S4, the discrete time model is used as a prediction model for model prediction control, and a pressure change curve of the on-vehicle hydrogen storage device in an ideal filling process is used as a target curve for model prediction control, where a prediction time domain is N p The control time domain determining according to the event trigger mechanism comprises:
at time k, the control input u (k) for the valve combination is formulated as:
at time k, the pressure state x (k) of the hydrogen storage device is formulated as:
x(k)＝[p hp (k) p mp (k) p lp (k) (p veh (k)) T ] T ，
wherein, the liquid crystal display device comprises a liquid crystal display device,
objective function J dev (k) Embody N p The deviation between the predicted value and the target value of the pressure of the vehicle-mounted hydrogen storage device in each time interval is calculated by the formula J dev (k) The representation is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
target value representing the pressure of vehicle i at time k + j +.>
A predicted value of the pressure of the vehicle-mounted hydrogen storage device of the vehicle i at the moment k+j by the current control strategy at the moment k;
objective function J uti (k) Embody N p The influence of the state of the sequential valve group on the hydrogen utilization efficiency in a certain time interval is represented by a formula J uti (k) The representation is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
predictive value representing the state of the valve between the compressor and the hydrogen storage device, +.>
A predicted value representing a valve state between the hydrogen storage device and the hydrogenation machine;
the objective function J (k) is J dev (k) And J uti (k) Is expressed as:
wherein w is 1 Is J dev (k) Weight, w 2 Is J uti (k) The weight of the weight is taken up by the weight,
for controlling the predicted value of the input to the k+j moment by the control strategy at the k moment，/>For the prediction value of the pressure state at time k+j+1 by the control strategy at time k, +.>
For a feasible set of control inputs, +.>
Is a viable set of pressure conditions.
In an embodiment of the present invention, the calculating the control strategy in step S5 according to the mixed integer nonlinear programming model includes:
s51, determining a mixed integer linear programming related formula and parameters, wherein an objective function of the mixed integer nonlinear programming model comprises minimizing a matrix adjustment amplitude and minimizing a valve switching frequency, wherein,
weights representing the minimum matrix adjustment amplitude, +.>
Representing the weight to minimize the valve switching frequency, is represented by the following equation:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the generation of chromosomes meeting integer constraints using mixed integer linear programming, the objective function that needs to be minimized,/- >
Representing 6 XN p The matrix M of (2) represents a chromosome, matrix +.>
A chromosome representing a chromosome to be adjusted to satisfy the whole-value constraint state,/->
Values representing elements of row 1, column j of the matrix, ">
Representing 6 XN p The matrix M of (2) represents a chromosome, matrix,/-or->
Representing chromosomes which have been adapted to fulfil the state of the whole-value constraint +.>
Values representing elements of row 1, column j of the matrix;
determining a fitness function related formula and parameters, wherein the formula of the fitness function is expressed as follows:
s52, coding: the predictive value of the control input in the predictive time domain is 6 XN p A matrix M, wherein,
j∈{1，2，…，N p }；
s53, initializing: randomly generating chromosome population meeting integer constraint, wherein the population quantity is S pop By said mixingAnd (3) adjusting chromosomes in the population by integer linear programming: randomly generated chromosomes
Obtaining a chromosome ++meeting integer constraint through the mixed integer nonlinear programming model of S51>
S54, selecting: reserving chromosomes with lower fitness function values in the population by calculating the fitness function of each chromosome as described in S51;
s55, crossing: random selection of parent chromosome M P1 、M P2 Randomly crossing to obtain offspring chromosome M C1 、M C2 Obtaining offspring chromosomes meeting integer constraint through the mixed integer nonlinear programming model, and randomly selecting j when cross operation is carried out each time c ∈{1，…，N p Single point crossover, represented by the formula:
M C1 (l，1：N p )＝[M P1 (l，1：j c )M P2 (j c ，1：N p )]，
M C2 (l，1：N p )＝[M P2 (l，1：j c )M P1 (j c ，1：N p )]；
s56, mutation: checking a control strategy represented by each chromosome in the current population, and if the pressure state brought by the control strategy does not meet the real-value constraint, adjusting the control strategy according to the type of the violated real-value constraint to perform variation operation;
s57, setting an event trigger mechanism: threshold epsilon of error veh The formula of (2) is:
the hydrogenation machine connection state comprises two conditions of connection establishment when a vehicle arrives and disconnection when the vehicle leaves, and the formula is as follows:
if the start-stop state of the compressor changes, the control input is updated, and the compressor switch state is represented by the formula:
if the control strategy in the prediction time domain is implemented, updating the control input, wherein k represents the current calculated time, and k e Representing the time of last update, represented by equation ζ tim (k) The representation is:
wherein, xi veh (k)、ξ arr (k)、ξ lea (k)、ξ cmp (k)、ξ tim (k) Representing the trigger rule, analysis result ζ (k) =ζ of k-moment event trigger mechanism veh (k)∨ξ arr (k)∨ξ lea (k)∨ξ cmp (k)∨ξ tim (k)。
The invention also provides a hydrogen filling system for hydrogen filling station based on model predictive control, which comprises:
the parameter setting module is used for setting parameter information among the compressor, the hydrogen storage device and the hydrogenation machine;
the discrete time model building module is used for building a discrete time model to obtain the gas flow characteristics in the hydrogen adding station;
the mixed integer nonlinear programming model building module is used for building a mixed integer nonlinear programming model by taking the start-stop state of the compressor as a known parameter and the connection state of the hydrogenation machine as a control variable according to the gas flow characteristics;
the control module is used for adopting the discrete time model as a prediction model of model prediction control, adopting a pressure change curve of the vehicle-mounted hydrogen storage device in an ideal filling process as a target curve of model prediction control, and predicting the time domain as N p The control time domain is determined according to an event trigger mechanism;
and the calculation module is used for calculating a control strategy according to the mixed integer nonlinear programming model.
The invention also provides electronic equipment, which comprises a processor and a memory, wherein the memory stores program instructions, and the processor runs the program instructions to realize the hydrogen optimizing and filling method of the hydrogen adding station based on model prediction control.
As described above, the hydrogen filling optimization method and system for the hydrogen filling station based on model predictive control have the following beneficial effects:
according to the hydrogen optimizing and filling method for the hydrogen filling station based on model predictive control, provided by the invention, under the condition that the continuous filling requirements of a plurality of groups of pressurizing units, a plurality of gas storage devices and a plurality of hydrogen filling devices are considered, the complex working conditions in the hydrogen filling station and the cross correlation between the devices are used for establishing a discrete time model which can be matched with a large-scale high-configuration hydrogen filling station. Aiming at the complex gas flow characteristics among a compressor, a hydrogenation machine, a classifying storage tank and a vehicle storage tank, the invention establishes a mixed integer nonlinear programming model by taking the start-stop state of the compressor as a known parameter and the connection state of the hydrogenation machine as a control variable, and defines an optimization target.
According to the hydrogen optimization filling method for the hydrogen filling station based on model predictive control, in order to realize optimal control, a solving framework combining mixed integer linear programming and genetic algorithm is introduced, so that the problem of mixed integer nonlinear programming is solved. The invention can ensure that the generated chromosome is feasible in whole value and can ensure that the selected chromosome has better performance.
In order to avoid redundant calculation, the hydrogen optimizing and filling method for the hydrogen adding station based on model predictive control introduces an event triggering mechanism, and the control strategy is updated and calculated only when the hydrogen adding station is detected to meet specific conditions. The calculation amount is greatly reduced by adjusting the control time domain of the model predictive control through event triggering.
Drawings
Fig. 1 is a block diagram of a hydrogen station system according to an embodiment of the present application.
Fig. 2 is a flowchart of a hydrogen filling method for hydrogen optimization of a hydrogen filling station based on model predictive control according to an embodiment of the present application.
Fig. 3 is a block diagram of a hydrogen optimization filling system of a hydrogen filling station based on model predictive control according to an embodiment of the present application.
Fig. 4 is a schematic block diagram of an electronic device according to an embodiment of the present application.
Fig. 5 is a schematic block diagram of a computer readable storage medium according to an embodiment of the present application.
Description of element reference numerals
1. Parameter setting module
2. Discrete time model building module
3. Mixed integer nonlinear programming model building module
4. Control module
5. Calculation module
6. Processor and method for controlling the same
7. Memory device
8. Computer instructions
9. Computer readable storage medium
10. Compressor with a compressor body having a rotor with a rotor shaft
20. Valve of compressor is connected to storage tank at every stage
30. Hydrogen storage device
40. Valve of hydrogenation machine is connected to storage tank at every stage
50. Hydrogenation machine
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
It should be noted that the illustrations provided in the following embodiments merely illustrate the basic concept of the present invention by way of illustration, and only the components related to the present invention are shown in the illustrations, not according to the number, shape and size of the components in actual implementation, and the form, number and proportion of each component in actual implementation may be arbitrarily changed, and the layout of the components may be more complex.
Referring to fig. 1, fig. 1 is a block diagram of a hydrogen adding station system according to an embodiment of the present application. The hydrogen filling optimization method of the hydrogen filling station based on model predictive control is realized based on a hydrogen filling station system, the hydrogen filling station system comprises three compressors 10, three hydrogen storage devices 30 and three hydrogen filling machines 50, specifically, the number of the compressors 1O is three, the number of the hydrogen storage devices 30 is three, namely a high-pressure hydrogen storage device, a medium-pressure hydrogen storage device and a low-pressure hydrogen storage device, valves 20, of which the storage tanks are connected with the compressors, are arranged between the compressors 10 and the hydrogen storage devices 30, valves 40, of which the storage tanks are connected with the hydrogen filling machines, are arranged between the hydrogen storage devices 30 and the hydrogen filling machines 50, and hydrogen sources are hydrogen tube bundle vehicles of 5-20 MPa. The valves between the compressor 10 and the storage tank, namely the hydrogen storage device 30, the storage tank and the hydrogenation machine 50, belong to the sequence control valve group, and the pressure levels of the high, medium and low three high-pressure hydrogen storage devices are respectively 8 5MPa, 45MPa and 25MPa. Wherein valves between the compressor 10 and the high, medium and low three high pressure hydrogen storage devices are the valves 20 of each stage of storage tank connected with the compressor
C e {1, …, C }, c=3; valves between the high, medium and low three high pressure hydrogen storage devices and the hydrogenation machine 50 are the valves 40 of each level of storage tank connected with the hydrogenation machine>
Representing D e {1, …, D }, d=3. The flow of gas within the hydrogen station is described using a discrete time model.
Referring to fig. 2, fig. 2 is a flowchart of a hydrogen filling method for hydrogen optimization in a hydrogen filling station based on model predictive control according to an embodiment of the present application. The hydrogen optimization filling method of the hydrogen filling station based on model predictive control comprises the following steps:
and S1, setting parameter information among the compressor, the hydrogen storage device and the hydrogenation machine.
And S2, establishing a discrete time model to obtain the gas flow characteristics in the hydrogen adding station.
And step S3, according to the gas flow characteristics, establishing a mixed integer nonlinear programming model by taking the start-stop state of the compressor as a known parameter and the connection state of the hydrogenation machine as a control variable.
S4, using the discrete time model as a model predictive control prediction model, using a pressure change curve of the vehicle-mounted hydrogen storage device in an ideal filling process as a model predictive control target curve, and predicting the time domain as N p The control time domain is determined according to an event trigger mechanism.
And S5, calculating a control strategy according to the mixed integer nonlinear programming model.
The step S2 of establishing a discrete time model, the obtaining of the gas flow characteristics in the hydrogen adding station comprises the following steps:
s21, discrete time sequence number and sampling of the discrete time modelThe periods are denoted as k and t, respectively s ；
S22, at the time k+ 1, the pressure formula of each stage of hydrogen storage device is expressed as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
k represents the flow rate from the compressor to the hydrogen storage device of each stage hp 、K mp 、K lp Respectively represent constants determined by volumes of the hydrogen storage devices of the respective stages, f hp 、f mp 、f lp Representing the flow rate of hydrogen from each stage of hydrogen storage device to the hydrotreater;
s23, at the time k, the formula of the hydrogen flow rate from each stage of hydrogen storage device to the vehicle-mounted hydrogen storage device is expressed as follows:
wherein, gamma is the specific heat ratio of hydrogen, C dis Is the flow coefficient of a valve of a hydrogenation machine, A orifice Is a hydrogenation machine valveCross-sectional area of door ρ hp 、ρ mp 、ρ lp To the density of hydrogen in each stage of hydrogen storage device, p hp,veh 、p mp,veh 、p lp，veh The pressure of the vehicle-mounted hydrogen storage device connected with each level of hydrogen storage device;
s24, at the time k, the pressure formula in the vehicle-mounted hydrogen storage device connected with each stage of hydrogen storage device is expressed as follows:
Wherein θ d，i In order to achieve the connection state of the hydrogenation machine and the vehicle,
i represents a vehicle, which is the pressure of the on-vehicle hydrogen storage device;
s25, at the time k+ 1, the pressure formula of the vehicle-mounted hydrogen storage device is expressed as follows:
wherein K is i As a constant determined by the volume of the in-vehicle hydrogen storage device,
the flow rate to the vehicle for hydrogen gas.
S26, at the time k, filling the flow rate of hydrogen into the vehicle
And the flow rate f of hydrogen passing through the hydrogenation machine d (k) The formula is:
at any time k, the constraint formula of the connection condition of the hydrotreater d and the vehicle i is expressed as:
one hydrogenation machine is connected with one hydrogen fuel automobile at most, and one hydrogen fuel automobile is connected with one hydrogenation machine at most. At any time k, only if the hydrogenation machine d is connected with the vehicle, the valve v between each level of storage tank and the hydrogenation machine can be opened d 。
According to the gas flow characteristics in the step S3, the step of establishing a mixed integer nonlinear programming model with the start-stop state of the compressor as a known parameter and the connection state of the hydrogenation machine as a control variable includes:
s31, at any time k, a constraint formula of the connection state of the hydrogenation machine and the vehicle is expressed as follows:
valve v of hydrogenation machine connected with hydrogen storage devices at all levels d Is expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
the valve switch state from the high-pressure hydrogen storage device to the hydrogenation machine at the moment k is shown, a value of 1 indicates that the valve is opened, and a value of 0 indicates that the valve is closed, +.>
The valve switch state from the medium-pressure hydrogen storage device to the hydrogenation machine at the moment k is shown, a value of 1 indicates that the valve is opened, and a value of 0 indicates that the valve is closed, +.>
The valve switch state from the low-pressure hydrogen storage device to the hydrogenation machine at the moment k is shown, wherein a value of 1 indicates that the valve is opened, and a value of 0 indicates that the valve is closed;
s32, valve v of compressor connected with hydrogen storage devices at all levels c Is expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
the valve switch state from the compressor to the high-pressure hydrogen storage device at the time k is shown: a value of 1 indicates valve opening and a value of 0 indicates valve closing, +.>
The valve switch state from the compressor to the medium-pressure hydrogen storage device at the time k is shown: a value of 1 indicates valve opening and a value of 0 indicates valve closing, +.>
The valve switch state from the compressor to the low pressure hydrogen storage device at time k is shown: a value of 1 indicates valve opening, a value of 0 indicates valve closing, u C (k) The start-stop state of the compressor at the moment k is represented, a value of 1 represents that the compressor is running, and a value of 0 represents that the compressor stops running;
S33, valve v of compressor connected with each stage of hydrogen storage device C Valve v connected with hydrogen storage devices at all levels and hydrogenation machine d Is expressed as:
s34, constraint formulas for meeting the minimum pressure and the maximum pressure of each level of hydrogen storage device and the vehicle-mounted hydrogen storage device are expressed as follows:
p hp，min ≤p hp (k)≤p hp，max ，
p mp，mm ≤p mp (k)≤p mp，max ，
p lp，min ≤p lp (k)≤p lp，max ，
wherein p is hp，min Represents the minimum pressure allowed by the high-pressure hydrogen storage device, p mp，min Represents the minimum pressure allowed by the medium-pressure hydrogen storage device, p lp，min Indicating the minimum pressure allowed by the low pressure hydrogen storage device,
representing the minimum pressure allowed by the on-board hydrogen storage device of the vehicle, p hp，max Indicating the maximum pressure allowed by the high-pressure hydrogen storage device, p mp，max Indicating the maximum pressure allowed by the medium-pressure hydrogen storage device, p lp，max Indicating the maximum pressure allowed by the low pressure hydrogen storage device, < >>
Indicating the maximum pressure allowed by the on-board hydrogen storage device of the vehicle.
The invention can know the optimization problem of nonlinear system, multi-objective optimization and complex constraint in the filling process through the related calculation formula and constraint formula, and the invention selects Model Predictive Control (MPC) to solve the problem. In the model prediction control, the discrete time model is adopted as a prediction model, a target curve adopts a change curve of the pressure of the vehicle-mounted hydrogen storage device comprehensively considering the filling time, the filling energy consumption, the hydrogen utilization efficiency and the like, and a prediction time domain is fixed to be N p The control time domain depends on the time at which various types of events occur.
In the step S4, the discrete time model is used as a prediction model for model prediction control, and the pressure change curve of the vehicle-mounted hydrogen storage device in the ideal filling process is used as a target curve for model prediction control, where the prediction time domain is N p The control time domain determining according to the event trigger mechanism comprises:
at time k, the control input u (k) for the valve combination is formulated as:
at time k, the pressure state x (k) of the hydrogen storage device is formulated as:
x(k)＝[p hp (k) p mp (k) pl p (k) (p veh (k)) T ] T ，
wherein, the liquid crystal display device comprises a liquid crystal display device,
objective function J dev (k) Embody N p The deviation between the predicted value and the target value of the pressure of the vehicle-mounted hydrogen storage device in each time interval is calculated by the formula J dev (k) The representation is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
target value representing the pressure of vehicle i at time k + j +.>
A predicted value of the pressure of the vehicle-mounted hydrogen storage device of the vehicle i at the moment k+j by the current control strategy at the moment k;
objective function J uti (k) Embody N p The influence of the state of the sequential valve group on the hydrogen utilization efficiency in a certain time interval is represented by a formula J uti (k) The representation is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
predictive value representing the state of the valve between the compressor and the hydrogen storage device, +.>
A predicted value representing a valve state between the hydrogen storage device and the hydrogenation machine;
The objective function J (k) is J dev (k) And J uti (k) Is expressed as:
wherein W is 1 Is J dev (k) Weight, w 2 Is J uti (k) The weight of the weight is taken up by the weight,
for controlling the predicted value of the input at time k+j by the control strategy at time k,/for the time k+j>
For the prediction value of the pressure state at time k+j+1 by the control strategy at time k, +.>
For a feasible set of control inputs, +.>Is a viable set of pressure conditions.
The solving framework of the model prediction problem based on the mixed integer nonlinear programming comprises the processes of chromosome coding, initializing, selecting, crossing, mutation and the like, and finally an ideal control strategy is obtained. The fitness function of the genetic algorithm can ensure that the selected chromosome meets real-value constraint, and the mixed integer linear programming can ensure that the randomly generated chromosome meets integer constraint.
S51, determining a mixed integer linear programming related formula and parameters, wherein an objective function of the mixed integer nonlinear programming model comprises minimizing a matrix adjustment amplitude and minimizing a valve switching frequency, wherein, 
Weights representing the minimum matrix adjustment amplitude, +.>
Representing the weight to minimize the valve switching frequency, is represented by the following equation:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the generation of chromosomes meeting integer constraints using mixed integer linear programming, the objective function that needs to be minimized,/->
Representing 6 XN p The matrix M of (2) represents a chromosome, matrix +.>
A chromosome representing a chromosome to be adjusted to satisfy the whole-value constraint state,/->
Values representing elements of row 1, column j of the matrix, ">
Representing 6 XN p The matrix M of (2) represents a chromosome, matrix,/-or->
Representing chromosomes which have been adapted to fulfil the state of the whole-value constraint +.>
Values representing elements of row 1, column j of the matrix;
determining a fitness function related formula and parameters, wherein the formula of the fitness function is expressed as follows:
s52, coding: the predictive value of the control input in the predictive time domain is 6 XN p A matrix M, wherein,
s53, initializing: randomly generating chromosome population meeting integer constraint, wherein the population quantity is S pop Adjusting chromosomes in a population by the mixed integer linear programming: randomly generated chromosomes
Obtaining a chromosome ++meeting integer constraint through the mixed integer nonlinear programming model of S51 >
S54, selecting: reserving chromosomes with lower fitness function values in the population by calculating the fitness function of each chromosome as described in S51;
s55, crossing: random selection of parent chromosome M P1 、M P2 Randomly crossing to obtain offspring chromosome M C1 、M C2 Obtaining offspring chromosomes meeting integer constraint through the mixed integer nonlinear programming model, and randomly selecting j when cross operation is carried out each time c ∈{1，…，N p Single point crossover, represented by the formula:
M C1 (l，1：N p )＝[M P1 (l，1：j c )M P2 (j c ，1：N p )]，
M C2 (l，1：N p )＝[M P2 (l，1：j c )M P1 (j c ，1：N p )]。
in the mutation operation of the invention, firstly, the control strategy represented by each chromosome in the current population is checked, and if the pressure state brought by the control strategy does not meet the real-value constraint, the control strategy is adjusted according to the violated real-value constraint type, and the mutation operation is carried out. This process is not random.
In the solving process, on one hand, the population can be updated through the selecting, crossing and mutation processes, and on the other hand, some random chromosomes can be introduced. The design of the algorithm termination condition needs to balance the performance and the calculation efficiency of the solution, and the calculation time can be shortened through parallel calculation.
In addition, the invention provides an event triggering mechanism. By determining whether a specific event has occurred, it is determined whether to update the control input, avoiding redundant computation.
S56, mutation: checking a control strategy represented by each chromosome in the current population, and if the pressure state brought by the control strategy does not meet the real-value constraint, adjusting the control strategy according to the type of the violated real-value constraint to perform variation operation;
s57, setting an event trigger mechanism: threshold epsilon of error veh The formula of (2) is:
the hydrogenation machine connection state comprises two conditions of connection establishment when a vehicle arrives and disconnection when the vehicle leaves, and the formula is as follows:
if the start-stop state of the compressor changes, the control input is updated, and the compressor switch state is represented by the formula:
if the control strategy in the prediction time domain is implemented, updating the control input, wherein k represents the current calculated time, and k e Representing the time of last update, represented by equation ζ tim (k) The representation is:
wherein, xi veh (k)、ξ arr (k)、ξ lea (k)、ξ cmp (k)、ξ tim (k) Representing the trigger rule, analysis result ζ (k) =ζ of k-moment event trigger mechanism veh (k)Vξ arr (k)Vξ lea (k)vξ cmp (k)Vξ tim (k) A. The invention relates to a method for producing a fibre-reinforced plastic composite A value of ζ (k) of 1 indicates that at least one particular event has occurred, and a value of 0 indicates that no particular event has occurred. In the same way, xi veh (k)、ξ arr (k)、ξ lea (k)、ξ cmp (k)、ξ tim (k) Respectively indicates whether the following events occur at the time k: the pressure of the vehicle-mounted hydrogen storage device exceeds a threshold value, the connection state of the hydrogenation machine changes (the connection is established when the vehicle arrives), the connection state of the hydrogenation machine changes (the connection is disconnected when the vehicle leaves), the start-stop state of the compressor changes, and the control strategy in the prediction time domain is finished.
Referring to fig. 3, fig. 3 is a block diagram of a hydrogen optimizing and filling system of a hydrogen adding station based on model prediction control according to an embodiment of the present application. The invention also provides a hydrogen filling system for hydrogen filling station based on model predictive control, which comprises: the parameter setting module 1 is used for setting parameter information among the compressor, the hydrogen storage device and the hydrogenation machine; the discrete time model building module 2 is used for building a discrete time model to obtain the gas flow characteristics in the hydrogenation station; the mixed integer nonlinear programming model building module 3 is used for building a mixed integer nonlinear programming model by taking the start-stop state of the compressor as a known parameter and the connection state of the hydrogenation machine as a control variable according to the gas flow characteristics; the control module 4 is configured to use the discrete time model as a prediction model for model prediction control, and use a pressure change curve of the vehicle-mounted hydrogen storage device in an ideal filling process as a target curve for model prediction control, where a prediction time domain is N p The control time domain is determined according to an event trigger mechanism; the calculation module 5 is configured to calculate a control strategy according to the mixed integer nonlinear programming model.
Referring to fig. 4, fig. 4 is a schematic block diagram of an electronic device according to an embodiment of the present application. The invention also provides electronic equipment, which comprises a processor 6 and a memory 7, wherein the memory 7 stores program instructions, and the processor 6 runs the program instructions to realize the hydrogen optimizing and filling method of the hydrogen adding station based on model prediction control. The processor 6 may be a general-purpose processor, including a central processing unit (Central Processing Unit, CPU for short), a network processor (Network Processor, NP for short), etc.; but also digital signal processors (Digital Signal Processing, abbreviated as DSP), application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components; the Memory 7 may comprise a random access Memory (Random Access Memory, RAM for short) and may also comprise a Non-Volatile Memory (Non-Volatile Memory), such as at least one magnetic disk Memory. The memory 7 may also be an internal memory of the random access memory (Random Access Memory, RAM) type, and the processor 6, the memory 7 may be integrated into one or more separate circuits or hardware, such as: an application specific integrated circuit (Application SpecificIntegrated Circuit, ASIC). The computer program in the memory 7 may be implemented in the form of a software functional unit and may be stored in a computer readable storage medium when sold or used as a separate product. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, an electronic device, or a network device, etc.) to perform all or part of the steps of the method of the various embodiments of the present invention.
Referring to fig. 5, fig. 5 is a schematic block diagram illustrating a structure of a computer readable storage medium according to an embodiment of the present application. The invention also provides a computer readable storage medium 9, wherein the computer readable storage medium 9 stores computer instructions 8, and the computer instructions 8 are used for enabling the computer to execute the hydrogen optimizing and filling method of the hydrogen filling station based on the model prediction control. The computer readable storage medium 9 may be an electronic medium, a magnetic medium, an optical medium, an electromagnetic medium, an infrared medium, or a semiconductor system or propagation medium. The computer-readable storage medium 9 may also include semiconductor or solid state memory, magnetic tape, removable computer diskette, random Access Memory (RAM), read-only memory (ROM), rigid magnetic disk and optical disk. Optical discs may include compact disc-read only memory (CD-ROM), compact disc-read/write (CD-RW), and DVD.
In summary, the hydrogen optimizing and filling method for the hydrogen filling station based on model predictive control disclosed by the invention establishes a discrete time model capable of matching a large-scale and high-configuration hydrogen filling station by considering the cross correlation between complex working conditions and equipment in the hydrogen filling station under the requirement of continuously filling a plurality of groups of pressurizing units, a plurality of gas storage devices and a plurality of hydrogen filling equipment. According to the invention, aiming at the complex gas flow characteristics among the compressor, the hydrogenation machine, the classifying storage tank and the vehicle storage tank, the mixed integer nonlinear programming model is established by taking the start-stop state of the compressor as a known parameter and the connection state of the hydrogenation machine as a control variable, so that the calculation efficiency is improved.
The above embodiments are merely illustrative of the principles of the present invention and its effectiveness, and are not intended to limit the invention. Modifications and variations may be made to the above-described embodiments by those skilled in the art without departing from the spirit and scope of the invention. Accordingly, it is intended that all equivalent modifications and variations of the invention be covered by the claims, which are within the ordinary skill of the art, be within the spirit and scope of the present disclosure.
Claims (9)
1. The hydrogen filling optimization method for the hydrogen filling station based on the model predictive control is characterized by being realized based on a hydrogen filling station system, wherein the hydrogen filling station system comprises a compressor, a hydrogen storage device and a hydrogen filling machine, and the hydrogen filling station hydrogen filling optimization method based on the model predictive control comprises the following steps:
s1, setting parameter information among the compressor, the hydrogen storage device and the hydrogenation machine;
s2, establishing a discrete time model to obtain gas flow characteristics in the hydrogenation station;
s3, according to the gas flow characteristics, establishing a mixed integer nonlinear programming model by taking the start-stop state of the compressor as a known parameter and the connection state of the hydrogenation machine as a control variable;
S4, using the discrete time model as a model predictive control prediction model, and using a pressure change curve of the vehicle-mounted hydrogen storage device in an ideal filling process as a model predictive controlA target curve is made, and the prediction time domain is N p The control time domain is determined according to an event trigger mechanism;
s5, calculating a control strategy according to the mixed integer nonlinear programming model.
2. The model predictive control-based hydrogen optimization filling method for a hydrogen filling station, as set forth in claim 1, is characterized in that: the number of the compressors comprises three, the hydrogen storage device comprises a high-pressure hydrogen storage device, a medium-pressure hydrogen storage device and a low-pressure hydrogen storage device, the hydrogenation machine comprises three compressors, the three compressors are respectively in one-to-one correspondence with the high-pressure hydrogen storage device, the medium-pressure hydrogen storage device and the low-pressure hydrogen storage device, and the high-pressure hydrogen storage device, the medium-pressure hydrogen storage device, the low-pressure hydrogen storage device and the three hydrogenation machines are in one-to-one correspondence with each other.
3. The method for optimizing hydrogen filling in a hydrogen filling station based on model predictive control according to claim 2, wherein the setting of parameter information among the compressor, the hydrogen storage device and the hydrogen filling machine in step S1 comprises:
By means of
Representing a valve between the compressor and the hydrogen storage device, wherein C is the compressor, C e {1, …, C }, using +.>
And a valve between the hydrogen storage device and the hydrogenation machine is shown, wherein D is the hydrogenation machine, and D is {1, …, D }.
4. A method for optimizing hydrogen filling in a hydrogen filling station based on model predictive control as set forth in claim 3, wherein said establishing a discrete time model in step S2 to obtain the gas flow characteristics in the hydrogen filling station comprises:
s21, the discrete time sequence number and the sampling period of the discrete time model are respectively expressed as k and t s ；
S22, at the time k+1, the pressure formula of each stage of hydrogen storage device is expressed as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,
k represents the flow rate from the compressor to the hydrogen storage device of each stage hp 、K mp 、K lp Respectively represent constants determined by volumes of the hydrogen storage devices of the respective stages, f hp 、f mp 、f lp Representing the flow rate of hydrogen from each stage of hydrogen storage device to the hydrotreater;
s23, at the time k, the formula of the hydrogen flow rate from each stage of hydrogen storage device to the vehicle-mounted hydrogen storage device is expressed as follows:
wherein, gamma is the specific heat ratio of hydrogen, C dis Is the flow coefficient of a valve of a hydrogenation machine, A orifice Is the cross-sectional area of the valve of the hydrogenation machine, ρ hp 、ρ mp 、ρ lp To the density of hydrogen in each stage of hydrogen storage device, p hp，veh 、p mp，veh 、p lp，veh The pressure of the vehicle-mounted hydrogen storage device connected with each level of hydrogen storage device;
s24, at the time k, the pressure formula in the vehicle-mounted hydrogen storage device connected with each stage of hydrogen storage device is expressed as follows:
wherein θ d，i In order to achieve the connection state of the hydrogenation machine and the vehicle,
i represents a vehicle, which is the pressure of the on-vehicle hydrogen storage device;
s25, at the time k+1, the pressure formula of the vehicle-mounted hydrogen storage device is expressed as follows:
wherein K is i Is a constant determined by the volume of the on-vehicle hydrogen storage device, < > for the hydrogen storage device>
A flow rate for filling hydrogen gas into the vehicle;
s26, at the time k, filling the flow rate of hydrogen into the vehicle
And the flow rate f of hydrogen passing through the hydrogenation machine d (k) The formula is:
5. the method for optimizing hydrogen filling in a hydrogen filling station based on model predictive control as set forth in claim 4, wherein the step S3 of establishing a mixed integer nonlinear programming model with the start-stop state of the compressor as a known parameter and the connection state of the hydrogen filling machine as a control variable according to the gas flow characteristics includes:
s31, at any time k, a constraint formula of the connection state of the hydrogenation machine and the vehicle is expressed as follows:
valve v of hydrogenation machine connected with hydrogen storage devices at all levels d Is expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
the valve switch state from the high-pressure hydrogen storage device to the hydrogenation machine at the moment k is shown, a value of 1 indicates that the valve is opened, and a value of 0 indicates that the valve is closed, +.>
The valve switch state from the medium-pressure hydrogen storage device to the hydrogenation machine at the moment k is shown, a value of 1 indicates that the valve is opened, and a value of 0 indicates that the valve is closed, +.>
The valve switch state from the low-pressure hydrogen storage device to the hydrogenation machine at the moment k is shown, wherein a value of 1 indicates that the valve is opened, and a value of 0 indicates that the valve is closed;
s32, valve v of compressor connected with hydrogen storage devices at all levels c Is expressed as:
wherein, the liquid crystal display device comprises a liquid crystal display device,
the valve switch state from the compressor to the high-pressure hydrogen storage device at the time k is shown: a value of 1 indicates valve opening and a value of 0 indicates valve closing, +.>
The valve switch state from the compressor to the medium-pressure hydrogen storage device at the time k is shown: a value of 1 indicates a valveDoor open, a value of 0 indicates valve closed, +.>
The valve switch state from the compressor to the low pressure hydrogen storage device at time k is shown: a value of 1 indicates valve opening, a value of 0 indicates valve closing, u c (k) The start-stop state of the compressor at the moment k is represented, a value of 1 represents that the compressor is running, and a value of 0 represents that the compressor stops running;
S33, valve v of compressor connected with each stage of hydrogen storage device c Valve v connected with hydrogen storage devices at all levels and hydrogenation machine d Is expressed as:
s34, constraint formulas for meeting the minimum pressure and the maximum pressure of each level of hydrogen storage device and the vehicle-mounted hydrogen storage device are expressed as follows:
p hp，min ≤p hp (k)≤p hp，max ，
p mp，min ≤p mp (k)≤p mp，max ，
p lp，min ≤p lp (k)≤p lp，max ，
wherein p is hp，min Represents the minimum pressure allowed by the high-pressure hydrogen storage device, p mp，min Represents the minimum pressure allowed by the medium-pressure hydrogen storage device, p lp，min Indicating the minimum pressure allowed by the low pressure hydrogen storage device,
representing the minimum pressure allowed by the on-board hydrogen storage device of the vehicle, p hp，max Indicating the maximum pressure allowed by the high-pressure hydrogen storage device, p mp，max Indicating the maximum pressure allowed by the medium-pressure hydrogen storage device, p lp，max Indicating the maximum pressure allowed by the low pressure hydrogen storage device, < >>
Indicating the maximum pressure allowed by the on-board hydrogen storage device of the vehicle.
6. The hydrogen optimizing and filling method for hydrogen filling station based on model predictive control as set forth in claim 5, wherein in the step S4, the discrete time model is adopted as a predictive model for model predictive control, a pressure change curve of the vehicle-mounted hydrogen storage device in an ideal filling process is adopted as a target curve for model predictive control, and a prediction time domain is N p The control time domain determining according to the event trigger mechanism comprises:
at time k, the control input u (k) for the valve combination is formulated as:
at time k, the pressure state x (k) of the hydrogen storage device is formulated as:
x(k)＝[p hp (k)p mp (k)p lp (k)(p veh (k) T ] T ，
wherein, the liquid crystal display device comprises a liquid crystal display device,
objective function J dev (k) Embody N p The deviation between the predicted value and the target value of the pressure of the vehicle-mounted hydrogen storage device in each time interval is calculated by the formula J dev (k) The representation is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
target value representing the pressure of vehicle i at time k + j +.>
A predicted value of the pressure of the vehicle-mounted hydrogen storage device of the vehicle i at the moment k+j by the current control strategy at the moment k;
objective function J uti (k) Embody N p The influence of the state of the sequential valve group on the hydrogen utilization efficiency in a certain time interval is represented by a formula J uti (k) The representation is:
wherein, the liquid crystal display device comprises a liquid crystal display device,
predictive value representing the state of the valve between the compressor and the hydrogen storage device, +.>
A predicted value representing a valve state between the hydrogen storage device and the hydrogenation machine;
the objective function J (k) is J dev (k) And J uti (k) Is expressed as:
wherein w is 1 Is J dev (k) Weight, w 2 Is J uti (k) The weight of the weight is taken up by the weight,
for controlling the predicted value of the input at time k+j by the control strategy at time k,/for the time k+j>
For the prediction value of the pressure state at time k+j+1 by the control strategy at time k, +. >
For a feasible set of control inputs, +.>
Is a viable set of pressure conditions.
7. The method for optimizing hydrogen filling in a hydrogen filling station based on model predictive control of claim 6, wherein the calculating a control strategy in step S5 according to the mixed integer nonlinear programming model comprises:
s51, determining a mixed integer linear programming related formula and parameters, wherein an objective function of the mixed integer nonlinear programming model comprises minimizing a matrix adjustment amplitude and minimizing a valve switching frequency, wherein,
weights representing the minimum matrix adjustment amplitude, +.>
Representing the weight to minimize the valve switching frequency, is represented by the following equation:
wherein, the liquid crystal display device comprises a liquid crystal display device,
representing the generation of chromosomes meeting integer constraints using mixed integer linear programming, the objective function that needs to be minimized,/->
Representing 6 XN p The matrix M of (2) represents a chromosome, matrix +.>
A chromosome representing a chromosome to be adjusted to satisfy the whole-value constraint state,/->
Values representing elements of the first row, the j-th column of the matrix, ">
Representing 6 XN p The matrix M of (2) represents a chromosome, matrix,/-or->
Representing chromosomes which have been adapted to fulfil the state of the whole-value constraint +.>
Values representing elements of a first row, a j-th column of the matrix;
Determining a fitness function related formula and parameters, wherein the formula of the fitness function is expressed as follows:
s52, coding: the predictive value of the control input in the predictive time domain is 6 XN p A matrix M, wherein,
s53, initializing: randomly generating chromosome population meeting integer constraint, wherein the population quantity is S pop Adjusting chromosomes in a population by the mixed integer linear programming: randomly generated chromosomes
Obtaining a chromosome ++meeting integer constraint through the mixed integer nonlinear programming model of S51>
S54, selecting: reserving chromosomes with lower fitness function values in the population by calculating the fitness function of each chromosome as described in S51;
s55, crossing: random selection of parent chromosome M P1 、M P2 Randomly crossing to obtain offspring chromosome M C1 、M C2 Obtaining offspring chromosomes meeting integer constraint through the mixed integer nonlinear programming model, and randomly selecting j when cross operation is carried out each time c ∈{1，…，N p Single point crossover, represented by the formula:
M C1 (l，1：N p )＝{M P1 (l，1：j c )M P2 (j c ，1：N p )]，
M C2 (l，1：N p )＝{M P2 (l，1：jc ) M P1 (j c ，1：N p )]；
s56, mutation: checking a control strategy represented by each chromosome in the current population, and if the pressure state brought by the control strategy does not meet the real-value constraint, adjusting the control strategy according to the type of the violated real-value constraint to perform variation operation;
S57, setting an event trigger mechanism: threshold epsilon of error veh The formula of (2) is:
the hydrogenation machine connection state comprises two conditions of connection establishment when a vehicle arrives and disconnection when the vehicle leaves, and the formula is as follows:
if the start-stop state of the compressor changes, the control input is updated, and the compressor switch state is represented by the formula:
if the control strategy in the prediction time domain is implemented, updating the control input, wherein k represents the current calculated time, and k e Representing the time of last update, represented by equation ζ tim (k) The representation is:
wherein, xi veh (k)、ξ arr (k)、ξ lea (k)、ξ cmp (k)、ξ tim (k) Representing the trigger rule, analysis result ζ (k) =ζ of k-moment event trigger mechanism veh (k)∨ξ arr (k)∨ξ lea (k)∨ξ cmp (k)∨ξ tim (k)。
8. A hydrogen optimization filling system for a hydrogen filling station based on model predictive control, which is characterized by comprising:
the parameter setting module is used for setting parameter information among the compressor, the hydrogen storage device and the hydrogenation machine;
the discrete time model building module is used for building a discrete time model to obtain the gas flow characteristics in the hydrogen adding station;
the mixed integer nonlinear programming model building module is used for building a mixed integer nonlinear programming model by taking the start-stop state of the compressor as a known parameter and the connection state of the hydrogenation machine as a control variable according to the gas flow characteristics;
The control module is used for adopting the discrete time model as a prediction model of model prediction control, adopting a pressure change curve of the vehicle-mounted hydrogen storage device in an ideal filling process as a target curve of model prediction control, and predicting the time domain as N p The control time domain is determined according to an event trigger mechanism;
and the calculation module is used for calculating a control strategy according to the mixed integer nonlinear programming model.
9. An electronic device comprising a processor and a memory, the memory storing program instructions, characterized in that: the processor runs program instructions to realize the hydrogen optimizing filling method of the hydrogen adding station based on model prediction control as claimed in any one of claims 1 to 7.
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