CN115221816B - Method and apparatus for determining a combination of fuel cell water management state operating conditions - Google Patents

Method and apparatus for determining a combination of fuel cell water management state operating conditions Download PDF

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CN115221816B
CN115221816B CN202211135066.2A CN202211135066A CN115221816B CN 115221816 B CN115221816 B CN 115221816B CN 202211135066 A CN202211135066 A CN 202211135066A CN 115221816 B CN115221816 B CN 115221816B
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叶夏明
马丽军
秦如意
赵波
章雷其
杨跃平
张雪松
俞佳捷
徐科兵
应芳义
何挺
张冲
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Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
Ningbo Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Ningbo Power Supply Co of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention relates to the technical field of battery control, and provides a method and a device for determining a water management state and operation condition combination of a fuel battery, wherein the method comprises the following steps: constructing a fuel cell internal resistance-operating condition model; determining the internal resistance-single operating condition simulation data of the fuel cell according to the internal resistance-operating condition model of the fuel cell; determining experimental data of the internal resistance-single operating condition of the fuel cell according to the simulation condition of the internal resistance-operating condition model of the fuel cell; transversely comparing and processing the experimental data by adopting an orthogonal experimental principle to determine the influence weight of each operating condition of the fuel cell on the water management state of the fuel cell; an optimal combination of operating conditions for the water management state of the fuel cell is determined based on the impact weights. The invention can quantitatively evaluate the influence degree of each operation condition on the water management state of the fuel cell, optimize the combination of the operation conditions of the fuel cell to obtain the optimal water management state, and effectively improve the performance of the fuel cell.

Description

Method and apparatus for determining a combination of fuel cell water management state operating conditions
Technical Field
The present invention relates to the field of battery control technology, and in particular to a method of determining a combination of water management state operating conditions for a fuel cell and an apparatus for determining a combination of water management state operating conditions for a fuel cell.
Background
In the modern society, the demand of people for clean and environment-friendly energy is continuously increasing, and proton exchange membrane fuel cells are gradually coming into the field of view of the public as an efficient alternative energy. The fuel cell has the advantages of being clean, pollution-free, high in energy conversion efficiency and the like, and has the characteristics of being close to normal temperature in working temperature, fast in starting time, free of electrolyte leakage corrosion and the like. Based on these advantages, proton exchange membrane fuel cells are considered as an ideal solution for future portable mobile power sources. Therefore, more and more researchers are focusing on the field of proton exchange membrane fuel cells and researching on aspects of hydrothermal management, model control and the like.
As is known, a proton exchange membrane fuel cell system is a complex system with nonlinearity, multivariable and strong coupling, most modeling and simulation strategies of proton exchange membrane fuel cells have their respective limitations, and the nonlinear and dynamic characteristics of the cells make the modeling process still have a great space for improvement. In addition, the output performance of the pem fuel cell is influenced by a plurality of operating conditions, and the change of the operating conditions may cause the dry and flooded states of the membrane generated by the fuel cell, thus seriously influencing the stability of the working state of the fuel cell. Therefore, in different current density regions, the comprehensive influence of different operating conditions on the output of the battery needs to be considered transversely on the basis of longitudinally analyzing the influence of a single factor on the output performance.
Disclosure of Invention
The present invention has been made to solve the above-mentioned problems, and an object of the present invention is to provide a method for determining a combination of operation conditions for a water management state of a fuel cell, which can quantitatively evaluate the influence of each operation condition on the water management state of the fuel cell by performing a lateral analysis of multiple influence factors through orthogonal experiments, and can optimize the combination of operation conditions of the fuel cell to obtain an optimal water management state.
The technical scheme adopted by the invention is as follows:
a method of determining a combination of fuel cell water management state operating conditions, comprising the steps of: constructing a fuel cell internal resistance-operating condition model; determining internal resistance-single operating condition simulation data of the fuel cell according to the internal resistance-operating condition model of the fuel cell; determining experimental data of the internal resistance-single operating condition of the fuel cell according to the simulation condition of the internal resistance-operating condition model of the fuel cell; processing the experimental data by transverse comparison using an orthogonal experimental principle to determine the weight of the influence of each operating condition of the fuel cell on the water management state of the fuel cell; determining an optimal operating condition combination for the fuel cell water management state based on the impact weight.
Further, the method of determining a combination of fuel cell water management state operating conditions further comprises the steps of: and verifying the fuel cell internal resistance-operating condition model according to the simulation data and the experimental data.
According to one embodiment of the invention, the constructing of the internal resistance-operating condition model of the fuel cell specifically comprises the following steps: constructing a fuel cell humidity-operating condition model based on the temperature and humidity coupling relation of the fuel cell; constructing a fuel cell internal resistance model based on a Randles equivalent circuit of the fuel cell; and determining the fuel cell internal resistance-operating condition model according to the fuel cell humidity-operating condition model and the fuel cell internal resistance model.
According to an embodiment of the present invention, wherein,
the fuel cell humidity-operating condition model is:
Figure 100002_DEST_PATH_IMAGE002
wherein, the first and the second end of the pipe are connected with each other,
Figure 100002_DEST_PATH_IMAGE004
indicates the humidity of the fuel cell stack,
Figure 100002_DEST_PATH_IMAGE006
representing the temperature of the fuel cell stack, i representing the current density of the fuel cell, n representing the number of fuel cell plates, a representing the electrochemical reaction area of the fuel cell, F representing the faradaic constant of the fuel cell,
Figure 100002_DEST_PATH_IMAGE008
represents the mass of fuel cell anode inlet hydrogen gas,
Figure 100002_DEST_PATH_IMAGE010
which represents the relative humidity of the fuel cell,
Figure 100002_DEST_PATH_IMAGE012
represents the molar mass of the hydrogen gas of the fuel cell,
Figure 100002_DEST_PATH_IMAGE014
which represents the anode inlet pressure of the fuel cell,
Figure 100002_DEST_PATH_IMAGE016
represents the molar mass of the fuel cell water,
Figure 100002_DEST_PATH_IMAGE018
representing the fuel cell cathode intake air mass,
Figure 100002_DEST_PATH_IMAGE020
representing the fuel cell air molar mass,
Figure 100002_DEST_PATH_IMAGE022
which represents the fuel cell cathode inlet air pressure,
Figure 100002_DEST_PATH_IMAGE024
which represents the volume of the fuel cell stack,
Figure 100002_DEST_PATH_IMAGE026
indicating the fuel cell anode inlet air flow rate,
Figure 100002_DEST_PATH_IMAGE028
indicating the fuel cell cathode inlet air flow rate,
Figure 100002_DEST_PATH_IMAGE030
representing the time of tail gas emission of the fuel cell, R representing the ideal gas constant of the fuel cell,
Figure 100002_DEST_PATH_IMAGE032
represents the standard gas molar volume of the fuel cell,
Figure 100002_DEST_PATH_IMAGE034
-
Figure 100002_DEST_PATH_IMAGE036
respectively representing the amount of said combustionVariation of fuel cell stack humidity.
According to an embodiment of the present invention, wherein,
the fuel cell internal resistance model is as follows:
Figure 100002_DEST_PATH_IMAGE038
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE040
wherein the content of the first and second substances,
Figure 100002_DEST_PATH_IMAGE042
indicates the internal activation resistance of the fuel cell,
Figure 100002_DEST_PATH_IMAGE044
which represents the ohmic internal resistance of the fuel cell,
Figure 100002_DEST_PATH_IMAGE046
indicating the concentration difference internal resistance of the fuel cell, R the ideal gas constant of the fuel cell,
Figure 100002_DEST_PATH_IMAGE048
represents the constant of the electrochemical reaction of the fuel cell,
Figure 100002_DEST_PATH_IMAGE050
represents the thickness of the proton exchange membrane of the fuel cell,
Figure 100002_DEST_PATH_IMAGE052
which represents the water activity of the fuel cell membrane,
Figure 100002_DEST_PATH_IMAGE054
indicates the thickness of the diffusion layer of the fuel cell,
Figure 100002_DEST_PATH_IMAGE056
indicating fuel cell electrochemical reactionThe area of the film should be determined,
Figure DEST_PATH_IMAGE058
represents the total concentration of the fuel cell reactants,
Figure DEST_PATH_IMAGE060
represents the water migration coefficient of the fuel cell,
Figure DEST_PATH_IMAGE062
indicating the internal resistance of the fuel cell.
According to an embodiment of the invention, the determining of the internal resistance-single operating condition simulation data of the fuel cell according to the internal resistance-operating condition model of the fuel cell specifically comprises the following steps: and inputting the operating conditions of the fuel cell into the internal resistance-operating condition model of the fuel cell one by adopting a control variable principle so as to obtain the internal resistance-single operating condition simulation data of the fuel cell.
According to an embodiment of the present invention, the processing the experimental data using the orthogonal experimental principle to determine the influence weight of each operating condition of the fuel cell on the water management state of the fuel cell comprises the following steps: partitioning the experimental data based on current density to obtain a plurality of current density regions; determining input parameter factors for the fuel cell; determining an orthogonal experiment model of the input parameter factors in different current density areas by adopting an orthogonal experiment principle; determining a weight of an effect of each operating condition of the fuel cell on a water management state of the fuel cell based on the orthogonal experimental model.
An apparatus for determining a combination of fuel cell water management state operating conditions, comprising: a modeling module for constructing a fuel cell internal resistance-operating condition model; the simulation module is used for determining the simulation data of the internal resistance-single operating condition of the fuel cell according to the internal resistance-operating condition model of the fuel cell; the experiment module is used for determining experimental data of the internal resistance-single operating condition of the fuel cell according to the simulation condition of the internal resistance-operating condition model of the fuel cell; a processing module for transverse comparison processing of the experimental data using orthogonal experimental principles to determine a weight of the impact of each operating condition of the fuel cell on the water management status of the fuel cell; a combination module for determining an optimal combination of operating conditions for the water management status of the fuel cell based on the impact weights.
A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, when executing the computer program, implementing the method of determining a combination of operating conditions for a water management state of a fuel cell according to the embodiments described above.
A non-transitory computer-readable storage medium having stored thereon a computer program that, when executed by a processor, implements the method of determining a combination of water management state operating conditions for a fuel cell as described in the above embodiments.
The invention has the following beneficial effects:
1) According to the internal working mechanism and the output characteristic rule of the fuel cell, an internal resistance-operating condition model of the combustible fuel cell is constructed, the operating conditions influencing the water management state of the fuel cell are verified, the multiple influence factors are transversely analyzed by using an orthogonal experiment method, the optimal water management state of the fuel cell is taken as an optimization condition, and the influence weight and the optimal operating condition combination of each operating condition are obtained, so that the influence degree of each operating condition on the water management state of the fuel cell can be quantitatively evaluated, and the operating condition combination of the fuel cell can be optimized to obtain the optimal water management state;
2) The invention provides a more efficient water management control strategy for the fuel cell by determining the optimal operation condition combination of the water management state of the fuel cell, thereby effectively improving the performance of the fuel cell and effectively ensuring the service life and the operation stability of the fuel cell.
Drawings
FIG. 1 is a flow chart of a method of determining a combination of fuel cell water management state operating conditions in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of an embodiment of the present invention for cross-wise comparative processing of the experimental data using orthogonal experimental principles to determine weighting of the impact of each operating condition of the fuel cell on the water management status of the fuel cell;
fig. 3 is a block diagram of an apparatus for determining a combination of fuel cell water management state operating conditions in accordance with an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
The method and apparatus for determining the combination of operating conditions for the water management state of a fuel cell according to the present invention will be specifically described below by taking the fuel cell shown in table 1 as an example.
TABLE 1
Parameters of battery Numerical value
Number of tablets 24
Active area 180cm2
Thickness of the exchange membrane 51μm
Current density 860mA /cm2
Temperature of fuel cell 300-360K
Anode inlet pressure 0.1-0.3MPa
Cathode inlet pressure 0.1-0.3MPa
Relative humidity of steam in anode 1
Relative humidity of steam in cathode 1
Anode inlet gas flow 0-30L/min
Cathode inlet flow 0-120L/min
As shown in fig. 1, a method of determining a combination of operating conditions for a water management state of a fuel cell in accordance with an embodiment of the present invention includes the steps of:
s1, constructing a fuel cell internal resistance-operation condition model.
Specifically, the internal resistance-operating condition model of the fuel cell may be constructed in accordance with the fuel cell operation performance parameters shown in table 1. More specifically, a fuel cell humidity-operating condition model can be constructed based on the temperature-humidity coupling relationship of the fuel cell, a fuel cell internal resistance model can be constructed based on the Randles equivalent circuit of the fuel cell, and then the fuel cell internal resistance-operating condition model can be determined according to the fuel cell humidity-operating condition model and the fuel cell internal resistance model.
In one embodiment of the invention, the fuel cell humidity-operating condition model may be:
Figure DEST_PATH_IMAGE063
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE064
indicates the humidity of the fuel cell stack,
Figure 975152DEST_PATH_IMAGE006
representing the temperature of the fuel cell stack, i representing the current density of the fuel cell, n representing the number of fuel cell plates, a representing the electrochemical reaction area of the fuel cell, F representing the faradaic constant of the fuel cell,
Figure DEST_PATH_IMAGE065
represents the mass of fuel cell anode inlet hydrogen gas,
Figure 464296DEST_PATH_IMAGE010
which represents the relative humidity of the fuel cell,
Figure DEST_PATH_IMAGE066
represents the molar mass of the hydrogen gas of the fuel cell,
Figure DEST_PATH_IMAGE067
which represents the anode inlet pressure of the fuel cell,
Figure 2724DEST_PATH_IMAGE016
represents the molar mass of the fuel cell water,
Figure DEST_PATH_IMAGE068
representing the fuel cell cathode intake air mass,
Figure 325121DEST_PATH_IMAGE020
representing the fuel cell air molar mass,
Figure DEST_PATH_IMAGE069
which represents the fuel cell cathode inlet air pressure,
Figure DEST_PATH_IMAGE070
the volume of the fuel cell stack is shown,
Figure DEST_PATH_IMAGE071
indicating the fuel cell anode inlet air flow rate,
Figure 216985DEST_PATH_IMAGE028
indicating the fuel cell cathode inlet air flow rate,
Figure 449383DEST_PATH_IMAGE030
representing the time of tail gas emission of the fuel cell, R representing the ideal gas constant of the fuel cell,
Figure 221030DEST_PATH_IMAGE032
represents the standard gas molar volume of the fuel cell,
Figure 765144DEST_PATH_IMAGE034
-
Figure DEST_PATH_IMAGE072
respectively, representing variables that measure the humidity of the fuel cell stack.
Further, with respect to the expression formula of the above fuel cell humidity-operating condition model, the following supplementary explanation can be made:
Figure DEST_PATH_IMAGE074
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE076
indicating the saturation vapor pressure of the fuel cell and the variable only corresponding to the operating conditions
Figure DEST_PATH_IMAGE078
Is instantly firedThe fuel cell stack temperature is relevant.
In one embodiment of the invention, the fuel cell internal resistance model may be:
Figure 428075DEST_PATH_IMAGE038
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE079
wherein the content of the first and second substances,
Figure 374035DEST_PATH_IMAGE042
indicates the internal activation resistance of the fuel cell,
Figure 785424DEST_PATH_IMAGE044
which represents the ohmic internal resistance of the fuel cell,
Figure 957780DEST_PATH_IMAGE046
indicating the concentration difference internal resistance of the fuel cell, R indicating the ideal gas constant of the fuel cell,
Figure 378397DEST_PATH_IMAGE048
represents the constant of the electrochemical reaction of the fuel cell,
Figure 460753DEST_PATH_IMAGE050
represents the thickness of the proton exchange membrane of the fuel cell,
Figure 308624DEST_PATH_IMAGE052
which represents the water activity of the fuel cell membrane,
Figure 968275DEST_PATH_IMAGE054
which represents the thickness of the diffusion layer of the fuel cell,
Figure 927004DEST_PATH_IMAGE056
which represents the electrochemical reaction area of the fuel cell,
Figure 847555DEST_PATH_IMAGE058
represents the total concentration of the fuel cell reactants,
Figure 866327DEST_PATH_IMAGE060
represents the water migration coefficient of the fuel cell,
Figure 13274DEST_PATH_IMAGE062
indicating the internal resistance of the fuel cell.
Further, for the expression formula of the fuel cell internal resistance model, the following supplementary explanation can be made:
Figure DEST_PATH_IMAGE081
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE083
indicates the humidity of the fuel cell stack,
Figure DEST_PATH_IMAGE085
which represents the conductivity coefficient of the fuel cell,
Figure DEST_PATH_IMAGE087
represents the number of moles of transferred ions of the fuel cell, and T represents the temperature of the fuel cell.
In summary, the fuel cell humidity-operating condition model and the fuel cell internal resistance model can be integrated and combined based on the fuel cell stack humidity, so that a fuel cell internal resistance-operating condition model can be obtained, wherein the fuel cell internal resistance-operating condition model takes a plurality of operating conditions influencing the output of the fuel cell, such as the fuel cell temperature, the cathode and anode inlet gas flow rate, the cathode and anode gas pressure, and the like as inputs, and the total internal resistance of the fuel cell stack as an output.
And S2, determining the internal resistance-single operating condition simulation data of the fuel cell according to the internal resistance-operating condition model of the fuel cell.
Specifically, the operating conditions of the fuel cell can be input into the internal resistance-operating condition model of the fuel cell one by adopting the control variable principle so as to obtain the internal resistance-single operating condition simulation data of the fuel cell.
More specifically, the fuel cell stack temperature input to the fuel cell internal resistance-operating condition model may be sequentially changed according to the control variable principle while keeping other operating conditions unchanged
Figure DEST_PATH_IMAGE089
Anode and cathode inlet flow
Figure DEST_PATH_IMAGE091
Anode and cathode gas pressures
Figure DEST_PATH_IMAGE093
And obtaining simulation output data of the fuel cell internal resistance-operation condition model, namely total internal resistance output data of the fuel cell, so that the internal resistance-single operation condition simulation data of the fuel cell can be obtained, and the variation trend of the total internal resistance of the fuel cell along with the influence factors of the single operation conditions can be determined according to the simulation data.
And S3, determining experimental data of the internal resistance-single operating condition of the fuel cell according to the simulation condition of the internal resistance-operating condition model of the fuel cell.
Specifically, the fuel cell large cell parameter may be controlled in accordance with the simulation process based on the simulation condition of the fuel cell internal resistance-operating condition model, that is, the variation process of the operating condition in the simulation process, for example, the temperature of the fuel cell stack may be sequentially changed in accordance with the simulation process
Figure 418104DEST_PATH_IMAGE089
Anode and cathode inlet flow rates
Figure DEST_PATH_IMAGE095
Anode and cathode gas pressures
Figure DEST_PATH_IMAGE096
And obtaining the output data of the fuel cell stack, namely the internal resistance of the fuel cell-single operation condition experimental data.
And S4, transversely comparing and processing the experimental data by adopting an orthogonal experimental principle to determine the influence weight of each operating condition of the fuel cell on the water management state of the fuel cell.
Specifically, as shown in fig. 2, the step S4 may further include the following steps:
s401, partitioning the experimental data based on the current density to obtain a plurality of current density areas.
Specifically, the corresponding experimental data may be divided into a low current density region, a medium current density region, and a high current density region according to the magnitude of the current density of the fuel cell. The voltage division threshold between the different current density areas can be set and adjusted according to actual needs, and is not limited herein.
S402, input parameter factors of the fuel cell are determined.
Specifically, fuel cell stack temperature may be determined
Figure 678316DEST_PATH_IMAGE089
Anode and cathode inlet flow rates
Figure 602409DEST_PATH_IMAGE095
Anode and cathode gas pressures
Figure 236653DEST_PATH_IMAGE093
These 5 operating conditions are input parameter factors for the fuel cell.
And S403, determining an orthogonal experimental model of the input parameter factors in different current density areas by adopting an orthogonal experimental principle.
Specifically, orthogonal experimental models of input parameter factors can be designed in a low current density region, a medium current density region and a high current density region respectively by adopting an orthogonal experimental principle, for example, the factor number k =5, namely the temperature of the fuel cell stack, can be designed in different current density regions
Figure 661818DEST_PATH_IMAGE089
Anode and cathode inlet flow rates
Figure DEST_PATH_IMAGE097
Anode and cathode gas pressures
Figure DEST_PATH_IMAGE099
Orthogonal experimental model with 5 input parameter factors and m =4 levels, namely orthogonal table
Figure DEST_PATH_IMAGE101
. Wherein orthogonal experimental models, i.e. orthogonal tables
Figure 9492DEST_PATH_IMAGE101
Based on an orthogonal experiment principle formula
Figure DEST_PATH_IMAGE103
And (4) obtaining the product.
Further, the total internal resistance of the fuel cell under the corresponding operating conditions can be selected from the experimental data in different current density regions, i.e., three low, medium and high current density regions
Figure DEST_PATH_IMAGE105
Output values to complete an orthogonal experimental model, i.e. an orthogonal table
Figure 697962DEST_PATH_IMAGE101
。
And S404, determining the influence weight of each operating condition of the fuel cell on the water management state of the fuel cell according to the orthogonal experimental model.
In particular, the method can be based on a completed orthogonal experimental model, i.e. an orthogonal table
Figure 819502DEST_PATH_IMAGE101
Calculating the average value of the total internal resistance of the fuel cell obtained by 4 experiments of each input parameter factor, and respectively marking the average value as
Figure DEST_PATH_IMAGE107
Further, the range of the average value of the total internal resistance of the fuel cell corresponding to each input parameter factor can be calculated and can be marked as R. Wherein the range R can be expressed as each termThe operating condition, i.e., the weight of the influence of each input parameter factor on the water management state of the fuel cell, is specified, and the larger the range R, the greater the degree of influence of the change in the level of the input parameter factor on the total internal resistance value of the fuel cell, i.e., the greater the weight of the influence of the corresponding operating condition.
And S5, determining the optimal operation condition combination of the water management state of the fuel cell according to the influence weight.
Specifically, the corresponding operating conditions, i.e., the input parameter factors, may be sorted according to the influence weight, i.e., the range R is in the different current density regions, i.e., the low, medium, and high current density regions, and then the water management state of the fuel cell may be optimized, i.e., the total internal resistance is the minimum as the optimization condition, and the average value of the total internal resistance of the fuel cell obtained by 4 experiments according to each input parameter factor is obtained
Figure DEST_PATH_IMAGE108
Determines the optimum level value of each operating condition, i.e., input parameter factor, and its corresponding optimum operating condition combination.
It should be noted that, in the method for determining the combination of the water management state and the operating condition of the fuel cell according to the present invention, the internal resistance-operating condition model of the fuel cell may also be verified according to simulation data and experimental data, so as to ensure the validity and accuracy of the internal resistance-operating condition model of the fuel cell.
The invention has the following beneficial effects:
1) According to the internal working mechanism and the output characteristic rule of the fuel cell, an internal resistance-operating condition model of the combustible fuel cell is constructed, the operating conditions influencing the water management state of the fuel cell are verified, the multiple influence factors are transversely analyzed by using an orthogonal experiment method, the optimal water management state of the fuel cell is taken as an optimization condition, and the influence weight and the optimal operating condition combination of each operating condition are obtained, so that the influence degree of each operating condition on the water management state of the fuel cell can be quantitatively evaluated, and the operating condition combination of the fuel cell can be optimized to obtain the optimal water management state;
2) The invention provides a more efficient water management control strategy for the fuel cell by determining the optimal operation condition combination of the water management state of the fuel cell, thereby effectively improving the performance of the fuel cell and effectively ensuring the service life and the operation stability of the fuel cell.
In response to the method for determining the combination of the operating conditions in the water management state of the fuel cell according to the above embodiment, the present invention also provides an apparatus for determining the combination of the operating conditions in the water management state of the fuel cell.
As shown in fig. 3, the apparatus for determining a combination of operating conditions for a water management state of a fuel cell according to an embodiment of the present invention includes a modeling module 10, a simulation module 20, an experiment module 30, a processing module 40, and a combination module 50. The modeling module 10 is used for constructing a fuel cell internal resistance-operating condition model; the simulation module 20 is used for determining the internal resistance-single operating condition simulation data of the fuel cell according to the internal resistance-operating condition model of the fuel cell; the experiment module 30 is used for determining experimental data of the internal resistance-single operating condition of the fuel cell according to the simulation condition of the internal resistance-operating condition model of the fuel cell; the processing module 40 is used for transversely contrastively processing the experimental data by adopting an orthogonal experimental principle to determine the influence weight of each operating condition of the fuel cell on the water management state of the fuel cell; the combination module 50 is used to determine the optimum combination of operating conditions for the water management state of the fuel cell based on the impact weights.
In one embodiment of the present invention, the modeling module 10 may be particularly useful for constructing an internal resistance-operating condition model of a fuel cell based on the operating performance parameters of the fuel cell as shown in Table 1. More specifically, a fuel cell humidity-operating condition model can be constructed according to the temperature and humidity coupling relation of the fuel cell, a fuel cell internal resistance model can be constructed according to the Randles equivalent circuit of the fuel cell, and then the fuel cell internal resistance-operating condition model can be determined according to the fuel cell humidity-operating condition model and the fuel cell internal resistance model.
In one embodiment of the invention, the fuel cell humidity-operating condition model may be:
Figure 267932DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure 627369DEST_PATH_IMAGE004
indicates the humidity of the fuel cell stack,
Figure 424424DEST_PATH_IMAGE006
representing the temperature of the fuel cell stack, i representing the current density of the fuel cell, n representing the number of fuel cell plates, a representing the electrochemical reaction area of the fuel cell, F representing the faradaic constant of the fuel cell,
Figure 361156DEST_PATH_IMAGE008
represents the mass of fuel cell anode inlet hydrogen gas,
Figure 269069DEST_PATH_IMAGE010
is indicative of the relative humidity of the fuel cell,
Figure 14171DEST_PATH_IMAGE012
represents the molar mass of the hydrogen gas of the fuel cell,
Figure 105097DEST_PATH_IMAGE014
which represents the anode inlet pressure of the fuel cell,
Figure 935650DEST_PATH_IMAGE016
represents the molar mass of the fuel cell water,
Figure 381675DEST_PATH_IMAGE018
representing the fuel cell cathode intake air mass,
Figure 981284DEST_PATH_IMAGE020
represents the air molar mass of the fuel cell,
Figure 979195DEST_PATH_IMAGE022
which represents the fuel cell cathode inlet air pressure,
Figure 297044DEST_PATH_IMAGE024
which represents the volume of the fuel cell stack,
Figure 281181DEST_PATH_IMAGE026
indicating the fuel cell anode inlet air flow rate,
Figure 266454DEST_PATH_IMAGE028
indicating the fuel cell cathode inlet air flow rate,
Figure 310634DEST_PATH_IMAGE030
represents the time of tail gas emission of the fuel cell, R represents the ideal gas constant of the fuel cell,
Figure 991145DEST_PATH_IMAGE032
represents the standard gas molar volume of the fuel cell,
Figure 778972DEST_PATH_IMAGE034
-
Figure 353173DEST_PATH_IMAGE036
respectively, representing variables that measure the humidity of the fuel cell stack.
Further, with respect to the expression formula of the above fuel cell humidity-operating condition model, the following supplementary explanation can be made:
Figure DEST_PATH_IMAGE110
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE112
indicating the saturation vapor pressure of the fuel cell and the variable only corresponding to the operating conditions
Figure DEST_PATH_IMAGE114
I.e. fuel cell stack temperature.
In one embodiment of the invention, the fuel cell internal resistance model may be:
Figure 896150DEST_PATH_IMAGE038
wherein the content of the first and second substances,
Figure 923012DEST_PATH_IMAGE040
wherein the content of the first and second substances,
Figure 888432DEST_PATH_IMAGE042
indicates the internal activation resistance of the fuel cell,
Figure 317139DEST_PATH_IMAGE044
which represents the ohmic internal resistance of the fuel cell,
Figure 437542DEST_PATH_IMAGE046
indicating the concentration difference internal resistance of the fuel cell, R indicating the ideal gas constant of the fuel cell,
Figure 341913DEST_PATH_IMAGE048
represents the constant of the electrochemical reaction of the fuel cell,
Figure 471543DEST_PATH_IMAGE050
the thickness of the proton exchange membrane of the fuel cell is shown,
Figure 754756DEST_PATH_IMAGE052
which represents the water activity of the fuel cell membrane,
Figure 577219DEST_PATH_IMAGE054
which represents the thickness of the diffusion layer of the fuel cell,
Figure 454039DEST_PATH_IMAGE056
indicates the electrochemical reaction area of the fuel cell,
Figure 387360DEST_PATH_IMAGE058
represents the total concentration of the fuel cell reactants,
Figure 525080DEST_PATH_IMAGE060
represents the water migration coefficient of the fuel cell,
Figure 518444DEST_PATH_IMAGE062
indicating the internal resistance of the fuel cell.
Further, for the expression formula of the fuel cell internal resistance model, the following supplementary explanation can be made:
Figure DEST_PATH_IMAGE115
wherein the content of the first and second substances,
Figure 866249DEST_PATH_IMAGE083
indicates the humidity of the fuel cell stack,
Figure 337682DEST_PATH_IMAGE085
which represents the conductivity coefficient of the fuel cell,
Figure 595488DEST_PATH_IMAGE087
represents the number of moles of transferred ions of the fuel cell, and T represents the temperature of the fuel cell.
In summary, the fuel cell humidity-operating condition model and the fuel cell internal resistance model can be integrated and combined based on the fuel cell stack humidity, so that a fuel cell internal resistance-operating condition model can be obtained, wherein the fuel cell internal resistance-operating condition model takes a plurality of operating conditions influencing the output of the fuel cell, such as the fuel cell temperature, the cathode and anode inlet gas flow rate, the cathode and anode gas pressure, and the like as inputs, and the total internal resistance of the fuel cell stack as an output.
In one embodiment of the present invention, the simulation module 20 may be specifically configured to input the operating conditions of the fuel cell into the internal resistance-operating condition model of the fuel cell one by using the control variable principle, so as to obtain the internal resistance-individual operating condition simulation data of the fuel cell.
More specifically, the input to the internal resistance-operating condition model of the fuel cell may be sequentially changed according to the control variable principle while keeping other operating conditions unchangedFuel cell stack temperature
Figure 136584DEST_PATH_IMAGE089
Anode and cathode inlet flow rates
Figure DEST_PATH_IMAGE116
Anode and cathode gas pressures
Figure 581472DEST_PATH_IMAGE093
And obtaining simulation output data of the fuel cell internal resistance-operation condition model, namely total internal resistance output data of the fuel cell, so that the internal resistance-single operation condition simulation data of the fuel cell can be obtained, and the variation trend of the total internal resistance of the fuel cell along with the influence factors of the single operation conditions can be determined according to the simulation data.
In one embodiment of the present invention, the experiment module 30 may be specifically configured to control the fuel cell large cell parameters according to the simulation conditions of the fuel cell internal resistance-operation condition model, i.e. the variation process of the operation conditions in the simulation process, and the fuel cell stack temperature may be sequentially changed according to the simulation process
Figure 856595DEST_PATH_IMAGE089
Anode and cathode inlet flow rates
Figure DEST_PATH_IMAGE117
Anode and cathode gas pressures
Figure 93541DEST_PATH_IMAGE093
And obtaining the output data of the fuel cell stack, namely the internal resistance of the fuel cell-single operation condition experimental data.
In an embodiment of the present invention, as shown in fig. 2, the processing procedure of the processing module 40 may specifically include the following steps:
s401, partitioning the experimental data based on the current density to obtain a plurality of current density areas.
Specifically, the corresponding experimental data may be divided into a low current density region, a medium current density region, and a high current density region according to the magnitude of the current density of the fuel cell. The voltage division threshold between the different current density areas can be set and adjusted according to actual needs, and is not limited herein.
S402, input parameter factors of the fuel cell are determined.
Specifically, fuel cell stack temperature may be determined
Figure 163128DEST_PATH_IMAGE089
Anode and cathode inlet flow rates
Figure DEST_PATH_IMAGE118
Anode and cathode gas pressures
Figure DEST_PATH_IMAGE119
These 5 operating conditions are input parameter factors for the fuel cell.
And S403, determining an orthogonal experimental model of the input parameter factors in different current density areas by adopting an orthogonal experimental principle.
Specifically, orthogonal experimental models of input parameter factors can be designed in a low current density area, a medium current density area and a high current density area respectively by adopting an orthogonal experimental principle, for example, the factor number k =5, namely the temperature of the fuel cell stack, can be designed in different current density areas
Figure 970679DEST_PATH_IMAGE089
Anode and cathode inlet flow rates
Figure 783914DEST_PATH_IMAGE097
Anode and cathode gas pressures
Figure 281891DEST_PATH_IMAGE099
Orthogonal experimental model with 5 input parameter factors and m =4 levels, namely orthogonal table
Figure 381434DEST_PATH_IMAGE101
. In which orthogonal experimental models, i.e. orthogonal tables
Figure 332073DEST_PATH_IMAGE101
Is based on an orthogonal experimental principle formula
Figure 948999DEST_PATH_IMAGE103
And (4) obtaining the product.
Further, the total internal resistance of the fuel cell under the corresponding operating conditions can be selected from the experimental data in different current density regions, i.e., three low, medium and high current density regions
Figure 301483DEST_PATH_IMAGE105
Output values to complete an orthogonal experimental model, i.e. an orthogonal table
Figure 978452DEST_PATH_IMAGE101
。
And S404, determining the influence weight of each operating condition of the fuel cell on the water management state of the fuel cell according to the orthogonal experimental model.
In particular, the method can be based on a completed orthogonal experimental model, i.e. an orthogonal table
Figure 524709DEST_PATH_IMAGE101
Calculating the average value of the total internal resistance of the fuel cell obtained by 4 experiments of each input parameter factor, and respectively marking the average value as the average value
Figure DEST_PATH_IMAGE120
Further, the range of the average value of the total internal resistance of the fuel cell corresponding to each input parameter factor can be calculated and can be marked as R. Wherein the range R may be expressed as a weight of influence of each operation condition, i.e., each input parameter factor, on the water management state of the fuel cell, and the greater the range R, the greater the degree of influence of the change in the level of the input parameter factor on the total internal resistance value of the fuel cell, i.e., the greater the weight of influence on the corresponding operation condition.
In one embodiment of the invention, the combination module 50 may be specifically adapted to operate in different current density regions, i.e. three currents low, medium and high, depending on the influence weight, i.e. the range RIn the density area, sorting the corresponding operation conditions, i.e. input parameter factors, optimizing the water management state of the fuel cell, i.e. minimizing the total internal resistance as an optimizing condition, and obtaining the average value of the total internal resistance of the fuel cell according to 4 experiments of each input parameter factor
Figure 414167DEST_PATH_IMAGE120
Determines each operating condition, i.e., the optimum level value of the input parameter factor, and its corresponding optimum operating condition combination.
It should be noted that, in the apparatus for determining the combination of operating conditions of the water management state of the fuel cell according to the present invention, the internal resistance-operating condition model of the fuel cell may also be verified based on simulation data and experimental data to ensure the validity and accuracy of the internal resistance-operating condition model of the fuel cell.
The invention has the following beneficial effects:
1) The invention constructs an internal resistance-operation condition model of the combustible fuel cell according to the internal working mechanism and the output characteristic rule of the fuel cell, verifies the operation condition influencing the water management state of the fuel cell, carries out transverse analysis of multiple influence factors by using an orthogonal experiment method, obtains the influence weight and the optimal operation condition combination of each operation condition by taking the optimal water management state of the fuel cell as an optimization condition, thereby being capable of quantitatively evaluating the influence degree of each operation condition on the water management state of the fuel cell and optimizing the operation condition combination of the fuel cell to obtain the optimal water management state;
2) The invention provides a more efficient water management control strategy for the fuel cell by determining the optimal operation condition combination of the water management state of the fuel cell, thereby effectively improving the performance of the fuel cell and effectively ensuring the service life and the operation stability of the fuel cell.
Corresponding to the above embodiment, the present invention further provides a computer device.
The computer apparatus of an embodiment of the present invention includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the computer program is executed by the processor, the method for determining the combination of the water management status and the operating conditions of the fuel cell according to the above embodiment may be implemented.
According to the computer device of the embodiment of the invention, when the processor executes the computer program stored on the memory, the internal resistance-operation condition model of the fuel cell is constructed according to the internal work mechanism and the output characteristic rule of the fuel cell, the operation condition influencing the water management state of the fuel cell is verified, the multiple influence factors are transversely analyzed by using the orthogonal experiment method, the influence weight of each operation condition and the optimal operation condition combination are obtained by taking the optimal water management state of the fuel cell as the optimization condition, therefore, the influence degree of each operation condition on the water management state of the fuel cell can be quantitatively evaluated, the operation condition combination of the fuel cell can be optimized to obtain the optimal water management state, in addition, the performance of the fuel cell can be effectively improved, and the service life and the operation stability of the fuel cell can be effectively ensured.
The invention also provides a non-transitory computer readable storage medium corresponding to the above embodiment.
A non-transitory computer-readable storage medium of an embodiment of the present invention has stored thereon a computer program that, when executed by a processor, implements the method of determining a combination of operating conditions for a water management state of a fuel cell of the above-described embodiment.
According to the non-transitory computer-readable storage medium of the embodiment of the present invention, when the processor executes the computer program stored thereon, the internal resistance-operating condition model of the fuel cell is constructed according to the internal operation mechanism and the output characteristic rule of the fuel cell, the operating conditions affecting the water management state of the fuel cell are verified, the multiple influence factors are transversely analyzed by using the orthogonal experiment method, the optimal water management state of the fuel cell is used as the optimization condition, and the influence weight and the optimal operating condition combination of each operating condition are obtained, so that the influence degree of each operating condition on the water management state of the fuel cell can be quantitatively evaluated, the operating condition combination of the fuel cell can be optimized to obtain the optimal water management state, the performance of the fuel cell can be effectively improved, and the service life and the operation stability of the fuel cell can be effectively ensured.
In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The meaning of "plurality" is two or more unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless otherwise expressly stated or limited, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through an intermediate. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, e.g., an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, the various steps or methods may be implemented in software or firmware stored in memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following technologies, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a stand-alone product, may also be stored in a computer readable storage medium.
The storage medium mentioned above may be a read-only memory, a magnetic or optical disk, etc. Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (8)

1. A method of determining a combination of fuel cell water management state operating conditions, comprising the steps of:
constructing a fuel cell internal resistance-operating condition model;
determining internal resistance-single operation condition simulation data of the fuel cell according to the internal resistance-operation condition model of the fuel cell, specifically according to a control variable principle, and under the premise of keeping other operation conditions unchanged, sequentially changing the stack temperature of the fuel cell, the inlet flow of the anode and the cathode, and the gas pressure of the anode and the cathode which are input into the internal resistance-operation condition model of the fuel cell to obtain the internal resistance-single operation condition simulation data of the fuel cell;
determining experimental data of the internal resistance-single operating condition of the fuel cell according to the simulation condition of the internal resistance-operating condition model of the fuel cell, and specifically controlling the cell parameters of the fuel cell to be consistent with the simulation process according to the change process of the operating condition in the simulation process of the internal resistance-operating condition model of the fuel cell to obtain the experimental data of the internal resistance-single operating condition of the fuel cell;
processing the experimental data by transverse comparison using an orthogonal experimental principle to determine the weight of the influence of each operating condition of the fuel cell on the water management state of the fuel cell;
determining an optimal combination of operating conditions for the water management state of the fuel cell based on the impact weights,
the processing of the experimental data using the orthogonal experimental principle to determine the weight of the influence of each operating condition of the fuel cell on the water management status of the fuel cell specifically comprises the steps of:
partitioning the experimental data based on current density to obtain a plurality of current density regions;
determining an input parameter factor for a fuel cell, the input parameter factor for the fuel cell being an operating condition of the fuel cell;
determining an orthogonal experiment model of the input parameter factors in different current density areas by adopting an orthogonal experiment principle;
determining the influence weight of each operation condition of the fuel cell on the water management state of the fuel cell according to the orthogonal experimental model, specifically calculating the total internal resistance mean value of the fuel cell of each input parameter factor according to the completed orthogonal experimental model, and calculating the range of the total internal resistance mean value of the fuel cell corresponding to each input parameter factor, wherein the range is positively correlated with the influence weight of the corresponding operation condition.
2. The method of determining a combination of fuel cell water management status and operating conditions according to claim 1, further comprising the steps of:
and verifying the fuel cell internal resistance-operating condition model according to the simulation data and the experimental data.
3. The method of determining a combination of fuel cell water management states and operating conditions of claim 1 wherein said constructing a fuel cell internal resistance-operating condition model comprises the steps of:
constructing a fuel cell humidity-operating condition model based on the temperature and humidity coupling relation of the fuel cell;
constructing a fuel cell internal resistance model based on a Randles equivalent circuit of the fuel cell;
and determining the fuel cell internal resistance-operating condition model according to the fuel cell humidity-operating condition model and the fuel cell internal resistance model.
4. The method of determining a combination of fuel cell water management status and operating conditions according to claim 3, wherein,
the fuel cell humidity-operating condition model is:
Figure DEST_PATH_IMAGE002
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE004
indicates the humidity of the fuel cell stack,p sat which represents the saturated vapor pressure of the fuel cell,
Figure DEST_PATH_IMAGE006
representing the temperature of the fuel cell stack, i representing the current density of the fuel cell, n representing the number of fuel cell plates, a representing the electrochemical reaction area of the fuel cell, F representing the faradaic constant of the fuel cell,
Figure DEST_PATH_IMAGE008
represents the mass of fuel cell anode inlet hydrogen gas,
Figure DEST_PATH_IMAGE010
which represents the relative humidity of the fuel cell,
Figure DEST_PATH_IMAGE012
represents the molar mass of the hydrogen gas of the fuel cell,
Figure DEST_PATH_IMAGE014
which represents the anode inlet pressure of the fuel cell,
Figure DEST_PATH_IMAGE016
represents the molar mass of the fuel cell water,
Figure DEST_PATH_IMAGE018
representing the fuel cell cathode intake air mass,
Figure DEST_PATH_IMAGE020
representing the fuel cell air molar mass,
Figure DEST_PATH_IMAGE022
which represents the fuel cell cathode inlet air pressure,
Figure DEST_PATH_IMAGE024
which represents the volume of the fuel cell stack,
Figure DEST_PATH_IMAGE026
indicating the fuel cell anode inlet air flow rate,
Figure DEST_PATH_IMAGE028
indicating the fuel cell cathode inlet air flow rate,
Figure DEST_PATH_IMAGE030
representing the time of tail gas emission of the fuel cell, R representing the ideal gas constant of the fuel cell,
Figure DEST_PATH_IMAGE032
represents the standard gas molar volume of the fuel cell.
5. The method of determining a combination of fuel cell water management status and operating conditions according to claim 3, wherein,
the fuel cell internal resistance model is as follows:
Figure DEST_PATH_IMAGE034
wherein, the first and the second end of the pipe are connected with each other,
Figure DEST_PATH_IMAGE036
wherein the content of the first and second substances,
Figure DEST_PATH_IMAGE038
indicates the internal activation resistance of the fuel cell,
Figure DEST_PATH_IMAGE040
which represents the ohmic internal resistance of the fuel cell,
Figure DEST_PATH_IMAGE042
indicating the concentration difference internal resistance of the fuel cell, R the ideal gas constant of the fuel cell,
Figure DEST_PATH_IMAGE044
represents the constant of the electrochemical reaction of the fuel cell,
Figure 995449DEST_PATH_IMAGE006
representing the temperature of the fuel cell stack, i representing the current density of the fuel cell, n representing the number of fuel cell plates, F representing the faradaic constant of the fuel cell,
Figure DEST_PATH_IMAGE046
represents the thickness of the proton exchange membrane of the fuel cell,
Figure DEST_PATH_IMAGE048
which is indicative of the water activity of the fuel cell membrane,
Figure DEST_PATH_IMAGE050
which represents the thickness of the diffusion layer of the fuel cell,
Figure DEST_PATH_IMAGE052
which represents the electrochemical reaction area of the fuel cell,
Figure DEST_PATH_IMAGE054
represents the total concentration of the fuel cell reactants,
Figure DEST_PATH_IMAGE056
representing the fuel cell water migration coefficient.
6. An apparatus for determining a combination of fuel cell water management state operating conditions, comprising:
a modeling module for constructing a fuel cell internal resistance-operating condition model;
the simulation module is used for determining the internal resistance-single operation condition simulation data of the fuel cell according to the internal resistance-operation condition model of the fuel cell, and specifically, according to a control variable principle, on the premise of keeping other operation conditions unchanged, sequentially changing the temperature of a fuel cell stack, the inlet flow of the anode and the cathode and the gas pressure of the anode and the cathode which are input into the internal resistance-operation condition model of the fuel cell to obtain the internal resistance-single operation condition simulation data of the fuel cell;
the experimental module is used for determining experimental data of the internal resistance-single operating condition of the fuel cell according to the simulation condition of the internal resistance-operating condition model of the fuel cell, and specifically controlling the cell parameters of the fuel cell to be consistent with the simulation process according to the change process of the operating condition in the simulation process of the internal resistance-operating condition model of the fuel cell so as to obtain the experimental data of the internal resistance-single operating condition of the fuel cell;
a processing module for transverse comparison processing of the experimental data using orthogonal experimental principles to determine a weight of the impact of each operating condition of the fuel cell on the water management status of the fuel cell;
a combination module for determining an optimal combination of operating conditions for the water management status of the fuel cell based on the impact weights,
the processing module is specifically configured to:
partitioning the experimental data based on current density to obtain a plurality of current density regions;
determining an input parameter factor for a fuel cell, the input parameter factor for the fuel cell being an operating condition of the fuel cell;
determining an orthogonal experiment model of the input parameter factors in different current density areas by adopting an orthogonal experiment principle;
determining the influence weight of each operation condition of the fuel cell on the water management state of the fuel cell according to the orthogonal experimental model, specifically calculating the total internal resistance mean value of the fuel cell of each input parameter factor according to the completed orthogonal experimental model, and calculating the range of the total internal resistance mean value of the fuel cell corresponding to each input parameter factor, wherein the range is positively correlated with the influence weight of the corresponding operation condition.
7. A computer arrangement comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor, when executing the computer program, carries out the method of determining a combination of operating conditions for a water management state of a fuel cell according to any one of claims 1-5.
8. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program, when executed by a processor, implements a method of determining a combination of fuel cell water management state operating conditions according to any one of claims 1-5.
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