CN114784324B - Fuel cell system control method and device, electronic equipment and storage medium - Google Patents
Fuel cell system control method and device, electronic equipment and storage medium Download PDFInfo
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Classifications
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M8/00—Fuel cells; Manufacture thereof
- H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
- H01M8/04082—Arrangements for control of reactant parameters, e.g. pressure or concentration
- H01M8/04089—Arrangements for control of reactant parameters, e.g. pressure or concentration of gaseous reactants
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M8/00—Fuel cells; Manufacture thereof
- H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
- H01M8/04298—Processes for controlling fuel cells or fuel cell systems
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M8/00—Fuel cells; Manufacture thereof
- H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
- H01M8/04298—Processes for controlling fuel cells or fuel cell systems
- H01M8/04694—Processes for controlling fuel cells or fuel cell systems characterised by variables to be controlled
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- H—ELECTRICITY
- H01—ELECTRIC ELEMENTS
- H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
- H01M8/00—Fuel cells; Manufacture thereof
- H01M8/04—Auxiliary arrangements, e.g. for control of pressure or for circulation of fluids
- H01M8/04298—Processes for controlling fuel cells or fuel cell systems
- H01M8/04992—Processes for controlling fuel cells or fuel cell systems characterised by the implementation of mathematical or computational algorithms, e.g. feedback control loops, fuzzy logic, neural networks or artificial intelligence
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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
- Y02E60/50—Fuel cells
Abstract
The present disclosure relates to the field of fuel cell technologies, and in particular, to a method and apparatus for controlling a fuel cell system, an electronic device, and a storage medium. The method comprises the following steps: acquiring a current operation parameter set of a fuel cell system; invoking a target operation control model to conduct parameter prediction on the current operation parameter set to obtain a reference operation parameter set of the fuel cell system; the target operation control model is obtained by carrying out constraint training on parameter prediction and battery system life prediction on an initial control model by using a sample parameter set of the fuel cell system, a reference sample parameter set corresponding to the sample parameter set and life labels of the reference sample parameter set; optimizing the reference operation parameter set based on a genetic algorithm to obtain a target operation parameter set corresponding to the current operation parameter set; the fuel cell system is controlled to operate based on the target set of operating parameters. The service life of each part in the fuel cell system is prolonged, and the reliability, durability and comprehensive performance of the system are improved.
Description
Technical Field
The present disclosure relates to the field of fuel cell technologies, and in particular, to a method and apparatus for controlling a fuel cell system, an electronic device, and a storage medium.
Background
The fuel cell is a device for directly converting fuel chemical energy into electric energy, and can be widely applied to various fields such as mobile, fixed and portable auxiliary power systems, submarines, space planes and the like.
Compared with the traditional internal combustion engine, the fuel cell has the advantages of high power density, high efficiency, no pollution and the like.
At present, the target fuel cell system comprises a plurality of parts such as a galvanic pile, a hydrogen circulating pump, an ejector, a PTC (positive temperature coefficient), an air compressor, a membrane humidifier and the like, the power output and the input of each part are not matched, the situation that the power of a certain single part is too high and other parts cannot reach the input or output power possibly occurs, for example, the rotating speed of the hydrogen circulating pump used in the fuel cell is controlled according to the pressure difference of the fuel cell requirement, the rotating speed of the air compressor is controlled according to the excessive air coefficient, the comprehensive influence of the performance of each part and the remarkable influence of the power change of each part on the performance of the hydrogen fuel cell system are not considered by a single control method, and the service life of each part in the fuel cell system is reduced. Accordingly, there is a need to provide an improved fuel cell system control method that increases the life of various components in a fuel cell system.
The invention comprises the following steps:
in order to solve the above problems in the prior art, the present application provides a method, an apparatus, an electronic device, and a storage medium for controlling a fuel cell system, so as to solve the problem that the service life of each component in the fuel cell system in the prior art is relatively low.
In order to achieve the purpose, the technical scheme adopted by the application is as follows:
in one aspect, the present application provides a fuel cell system control method, the method including:
acquiring a current operation parameter set of a fuel cell system;
invoking a target operation control model to conduct parameter prediction on the current operation parameter set to obtain a reference operation parameter set of the fuel cell system; the target operation control model is obtained by performing constraint training of parameter prediction and battery system life prediction on an initial control model by using a sample parameter set of the fuel cell system, a reference sample parameter set corresponding to the sample parameter set and a life label of the reference sample parameter set;
optimizing the reference operation parameter set based on a genetic algorithm to obtain a target operation parameter set corresponding to the current operation parameter set;
controlling the fuel cell system to operate based on the target set of operating parameters.
Further, the current operation parameter set comprises one or more of current air compressor rotation speed information, hydrogen circulating pump rotation speed information, ejector injection ratio information, back pressure valve opening information, bypass valve opening information, stack anode metering ratio information, stack cathode metering ratio information, humidifier gas-liquid ratio information, heat conversion efficiency information and water pump power information of the fuel cell system.
Further, before the target operation control model is called to conduct parameter prediction on the current operation parameter set to obtain the reference operation parameter set of the fuel cell system, the method further includes:
acquiring a training data set, wherein the training data set comprises sample parameter sets of a plurality of fuel cell systems and corresponding life labels; wherein the sample parameter set and the corresponding life label are obtained based on a uniform experiment method;
constructing an initial control model;
taking a sample parameter set of the fuel cell system as an initial control model input, taking a reference sample parameter set corresponding to the sample parameter set and a service life label of the reference sample parameter set as output, and carrying out constraint training on parameter prediction and battery system service life prediction on the initial control model to obtain a target operation control model;
the life tag characterizes a battery life indicator that the fuel cell system is operating to based on the reference sample parameter set.
Further, the constructing the initial control model includes:
acquiring an initial prediction model;
invoking a quadratic model, and determining the parameter weight of each parameter in the sample parameter set based on the sample parameter set and the corresponding life label; the quadratic model takes a sample operation parameter in the sample parameter set as an independent variable and takes a life label corresponding to the sample parameter set as a dependent variable;
and taking the parameter weight as an initial model parameter of an initial prediction model to obtain the initial control model.
Further, the initial control model comprises an input layer, an implicit layer and an output layer, wherein the implicit layer is provided with a preset number of network elements, and the network elements of the implicit layer are in one-to-one correspondence with the parameter types corresponding to the sample parameter set.
Further, the optimizing the reference operation parameter set based on the genetic algorithm, and obtaining the target operation parameter set corresponding to the current operation parameter set includes:
taking the reference operation parameter set as an initial population, and calling the preset genetic algorithm to carry out reproduction treatment on the initial population to obtain a first generation sub population;
carrying out reproduction treatment on the first generation sub population to obtain a second generation sub population;
and under the condition that the second generation sub-population meets the preset convergence condition, determining the second generation sub-population as the target operation parameter set.
Further, under the condition that the updated child population does not meet the preset convergence condition, carrying out reproduction processing on the updated child population until the obtained updated second-generation child population meets the preset convergence condition, and determining the updated second-generation child population meeting the preset convergence condition as the target operation parameter information.
In another aspect, the present application also provides a control device of a fuel cell system, the device including:
parameter set acquisition module: for acquiring a current set of operating parameters of the fuel cell system;
a reference operation parameter set acquisition module: the method comprises the steps of calling a target operation control model to conduct parameter prediction on the current operation parameter set to obtain a reference operation parameter set of the fuel cell system; the target operation control model is obtained by performing constraint training of parameter prediction and battery system life prediction on an initial control model by using a sample parameter set of the fuel cell system, a reference sample parameter set corresponding to the sample parameter set and a life label of the reference sample parameter set;
the target operation parameter information acquisition module: the method comprises the steps of carrying out optimization processing on the reference operation parameter set based on a genetic algorithm to obtain a target operation parameter set corresponding to the current operation parameter set;
parameter information control module: for controlling the fuel cell system to operate based on the target set of operating parameters.
In another aspect, the present application also provides an electronic device, the device including a processor and a memory, the memory storing at least one instruction and at least one program, the at least one instruction and the at least one program being loaded and executed by the processor to implement the fuel cell system control method as described above.
In another aspect, the present application also provides a computer storage medium having at least one instruction and at least one program stored therein, the at least one instruction and the at least one program loaded and executed by a processor to implement the fuel cell system control method as described above.
The beneficial effects that this technical scheme of application brought are:
the method considers the comprehensive influence of the performance of each part and the significant influence of the power change of each part on the performance of the fuel cell system, and obtains the current operation parameter set of the fuel cell system; invoking a target operation control model to conduct parameter prediction on the current operation parameter set to obtain a reference operation parameter set of the fuel cell system; optimizing the reference operation parameter set based on a genetic algorithm to obtain a target operation parameter set corresponding to the current operation parameter set; the fuel cell system is controlled to operate based on the target set of operating parameters. The operation power balance of each part is ensured, and the service life of each part in the fuel cell system is prolonged, so that the fuel cell system is ensured to reach the optimal performance, the life cycle of the fuel cell system is prolonged, the reliability and the durability of the fuel cell system are improved, and the comprehensive performance of the fuel cell system is improved.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions and advantages of the prior art, the following description will briefly explain the drawings used in the embodiments or the description of the prior art, and it is obvious that the drawings in the following description are only some embodiments of the invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a control method of a fuel cell system according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a control method of a fuel cell system according to an embodiment of the present application;
fig. 3 is a schematic flow chart of a control method of a fuel cell system according to an embodiment of the present application;
fig. 4 is a schematic flow chart of a control method of a fuel cell system according to an embodiment of the present application;
fig. 5 is a schematic block diagram of a control device of a fuel cell system according to an embodiment of the present application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The embodiment of the application discloses a fuel cell system control method, a device, electronic equipment, a storage medium, a control method and a control device, wherein the fuel cell system control method based on a neural network and a genetic algorithm is simple in structure, reduces the consumption of electric energy and improves the heating efficiency.
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the foregoing figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that embodiments of the present application described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion.
Before further describing embodiments of the present application in detail, the terms and expressions that are referred to in the embodiments of the present application are described, and are suitable for the following explanation.
Genetic algorithm: in computer science and operation research, genetic algorithms are meta-heuristic algorithms inspired by the natural selection process, belonging to the general class of evolutionary algorithms. Genetic algorithms typically rely on biological heuristic operators, such as mutation, crossover, and selection, to produce high quality solutions to optimization and search problems.
Quadratic form: the quadratic polynomial of n variables is called a quadratic form, that is, a polynomial in which the number of unknowns is arbitrary plural but the degree of each term is 2.
Radial basis function neural network: in the field of mathematical modeling, a radial basis neural network is an artificial neural network that uses radial basis functions as activation functions. The output of the radial basis function network is a linear combination of the input radial basis function and the neuron parameters. Radial basis function networks have a variety of uses including function approximation, time series prediction, classification, and system control.
Referring to fig. 1, fig. 1 is a schematic flow chart of a fuel cell system control method according to an embodiment of the present application, where the method includes steps according to the embodiment or the flowchart, but may include more or less steps based on conventional or non-inventive labor. The order of steps recited in the embodiments is merely one way of performing the order of steps and does not represent a unique order of execution. When implemented in an actual apparatus, system, or device article, may be performed sequentially or in parallel (e.g., in a parallel processor or a multithreaded environment) in accordance with the methods shown in the embodiments or figures. As shown in fig. 1, the method may include:
s101: a current set of operating parameters of the fuel cell system is obtained.
In some embodiments, the current operating parameter set includes, but is not limited to, one or more of current air compressor speed information, hydrogen circulation pump speed information, ejector injection ratio information, back pressure valve opening information, bypass valve opening information, stack anode metering ratio information, stack cathode metering ratio information, humidifier gas-liquid ratio information, thermal conversion efficiency information, and water pump power information of the fuel cell system.
According to the method, the operation parameter set is arranged on the current air compressor rotating speed information, the hydrogen circulating pump rotating speed information, the ejector injection ratio information, the back pressure valve opening information, the bypass valve opening information, the galvanic pile anode metering ratio information, the galvanic pile cathode metering ratio information, the humidifier gas-liquid ratio information, the heat conversion efficiency information and the water pump power information of the fuel cell system, influences on the fuel system are comprehensively considered from 10 key performance index parameters, and parameter tuning is performed on the operation parameters so as to obtain an optimal operation state.
S102: and calling a target operation control model to conduct parameter prediction on the current operation parameter set to obtain a reference operation parameter set of the fuel cell system.
The target operation control model is obtained by carrying out constraint training on parameter prediction and battery system life prediction on an initial control model by using a sample parameter set of the fuel cell system, a reference sample parameter set corresponding to the sample parameter set and life labels of the reference sample parameter set.
Specifically, the reference operation parameter set is obtained by performing parameter optimization prediction on each operation parameter in the current operation parameter set based on the target operation control model.
In some embodiments, referring to fig. 2, before step S102, the method further includes:
s105: a training data set is obtained, the training data set comprising a plurality of sample parameter sets of the fuel cell system and corresponding life labels.
The sample parameter set and the corresponding life label are obtained based on a uniform experiment method. The training data set is a data set subjected to normalization processing.
In some embodiments, the sample parameter set includes, but is not limited to, air compressor speed, hydrogen circulation pump speed, ejector injection ratio, back pressure valve opening, bypass valve opening, stack anode metering ratio, stack cathode metering ratio, humidifier gas-liquid ratio, heat conversion efficiency, and water pump power. The uniformity experiment method refers to that the measuring range of each parameter mentioned above can be the normal use range of rated use power of the component, the component range of the measuring range of each parameter is determined, each parameter is divided into a plurality of parameter values according to the component range in the measuring range, the measuring range of the anode metering ratio is 0.1-0.3, the sub-range is 0.02, and the anode metering ratio parameter is divided into 0.10, 0.12 and 0.14 … … 0.30, and the total of a plurality of values is 11 parameter values. And carrying out random combination matching on the obtained multiple parameter values of each parameter for experiment to obtain the life index of the fuel cell system matched with the random combination of the parameters. It should be noted that the number of experiments is performed according to actual requirements, and in particular, multiple groups of experiments can be performed depending on the performance parameter index change of each part.
S106: constructing an initial control model;
s107: taking a sample parameter set of the fuel cell system as an initial control model input, taking a reference sample parameter set corresponding to the sample parameter set and a life label of the reference sample parameter set as output, and carrying out parameter prediction and constraint training of life prediction of the battery system on the initial control model to obtain a target operation control model; it should be noted that the lifetime label characterizes the battery lifetime index achieved by the fuel cell system operating based on the reference sample parameter set.
Specifically, whether the initial control model is trained or not can be judged through a loss function, and the loss function can be expressed as follows:
where S is a loss function, L' is a lifetime label corresponding to the sample parameter set, and L is a lifetime label corresponding to the reference sample parameter set.
In some embodiments, when the loss S obtained by the current iteration in the training process is greater than the target value, it indicates that the preset iteration training suspension condition is not satisfied, if the iteration training of the initial control model is continued, when the loss function S in the training is less than or equal to the target value, it indicates that the preset iteration training suspension condition is satisfied, then the current obtained initial control model is determined as the target operation control model, and the training of the initial control model is completed. By way of example, the target value may be 5%.
In some embodiments, the initial control model includes an input layer, an implicit layer, and an output layer, the implicit layer is provided with a preset number of network elements, and the network elements of the implicit layer are in one-to-one correspondence with the parameter types corresponding to the sample parameter sets.
In a specific embodiment, the model framework of the initial control model is a radial basis function neural network and comprises an input layer, an hidden layer and an output layer, and the sample parameter set corresponds to 10 parameter types and comprises an air compressor rotating speed, a hydrogen circulating pump rotating speed, an ejector injection ratio, a back pressure valve opening, a bypass valve opening, a galvanic pile anode metering ratio, a galvanic pile cathode metering ratio, a humidifier gas-liquid ratio, heat conversion efficiency and water pump power. Correspondingly, the input layer of the initial control model is provided with 10 input units, 10 network units are arranged in the hidden layer, the output layer is provided with at least one output unit, the network units in the hidden layer correspond to the 10 parameter types in a one-to-one correspondence manner with the sample parameter set, the output layer is used for outputting a life prediction result, the input layer and a single network unit in the hidden layer form neurons, and the hidden layer and the output layer form neurons.
Wherein a single neuron corresponds to a local linear equation, each local linear equation is provided with 10 variables, and the single variable characterizes one sample operation parameter in a sample parameter set; based on the input of the sample parameter set, 11 neurons in the initial control model generate 11 corresponding local linear equations, and then the 11 local linear equations are subjected to linear processing to obtain a normalized linear equation. The coefficient of each variable in the normalized linear equation is the model parameter of the initial control model.
And predicting and optimizing the current operation parameter set based on the radial basis function neural network, and locally approximating and optimizing the current operation parameter set through the radial basis function neural network, so that the optimizing calculation efficiency of the current operation parameter set is improved, and the model convergence speed is improved. The local approximation means that the distance between an input variable and the center of a basis function (such as Euclidean distance) is used as an independent variable of an activation function, the radial basis function is used as the activation function, the independent variable of the activation function and the kernel regression concept are similar, wherein the influence on model parameter adjustment is smaller when the data are far from each center, and the influence on model parameter adjustment is larger when the sample data are close to the center variable.
S103: and optimizing the reference operation parameter set based on the genetic algorithm to obtain a target operation parameter set corresponding to the current operation parameter set.
Specifically, the target operation parameter set is obtained by carrying out parameter optimization prediction on each operation parameter in the reference operation parameter set based on a genetic algorithm.
In some embodiments, referring to fig. 3, step S103 includes:
s1031: and taking the reference operation parameter set as an initial population, and calling a preset genetic algorithm to carry out reproduction treatment on the initial population to obtain a first generation sub population.
It should be noted that the reproduction process may include, but is not limited to, three operations of selection, crossover and mutation. And calling a preset genetic algorithm to carry out reproduction treatment on the initial population to obtain a first generation sub population, and specifically, carrying out three operations of selection, intersection and mutation after non-dominant sorting on the initial population to obtain the first generation sub population.
In some embodiments, the algorithm parameters are set to 20000 iterative genetic variations.
S1032: and carrying out reproduction treatment on the first generation sub population to obtain a second generation sub population.
S1033: and under the condition that the second generation sub-population meets the preset convergence condition, determining the second generation sub-population as a target operation parameter set.
In some embodiments, the convergence condition may be that a difference between the child population obtained in the previous iteration and the child population obtained in the current iteration is less than or equal to a preset threshold, so as to make a difference between the child population obtained in the previous iteration and the child population obtained in the current iteration approach 0 indefinitely.
In other embodiments, the convergence condition may be that the number of current iterations is greater than or equal to a preset number of iterations.
S1034: and under the condition that the updated child population does not meet the preset convergence condition, carrying out reproduction processing on the updated child population until the obtained updated second generation child population meets the preset convergence condition, and determining the updated second generation child population meeting the preset convergence condition as target operation parameter information.
In the parameter operation control of the fuel cell system, the fusion genetic algorithm optimizes the reference operation parameter set, and further performs parameter optimization on each operation parameter in the reference operation parameter set, so that the optimizing value is more accurate, the operation states of all parts in the fuel cell system are further balanced, the fuel cell system outputs optimal performance in a feasible operation range, the working efficiency of the fuel cell system is further improved, and the service life of the fuel cell system is prolonged.
S104: the fuel cell system is controlled to operate based on the target set of operating parameters.
The method considers the comprehensive influence of the performance of each part and the significant influence of the power change of each part on the performance of the fuel cell system, and obtains the current operation parameter set of the fuel cell system; invoking a target operation control model to conduct parameter prediction on the current operation parameter set to obtain a reference operation parameter set of the fuel cell system; optimizing the reference operation parameter set based on a genetic algorithm to obtain a target operation parameter set corresponding to the current operation parameter set; the fuel cell system is controlled to operate based on the target set of operating parameters. The operation power balance of each part is ensured, and the service life of each part in the fuel cell system is prolonged, so that the fuel cell system is ensured to reach the optimal performance, the life cycle of the fuel cell system is prolonged, the reliability and the durability of the fuel cell system are improved, and the comprehensive performance of the fuel cell system is improved.
In some embodiments, referring to FIG. 4, constructing the initial control model includes:
s108: an initial predictive model is obtained.
S109: invoking a quadratic model, and determining the parameter weight of each parameter in the sample parameter set based on the sample parameter set and the corresponding life label; the quadratic model takes a sample operation parameter in a sample parameter set as an independent variable and takes a life label corresponding to the sample parameter set as an independent variable.
S110: and taking the parameter weight as an initial model parameter of the initial prediction model to obtain an initial control model. The quadratic model may beThe parameter weight is used as an initial weight of the initial control model, and the initial weight refers to the weight from an input layer to an implicit layer.
According to the method, the quadratic model is called through the sample parameter set and the corresponding life label obtained based on the uniform experiment method, the sample operation parameters in the sample parameter set are used as independent variables, the life label corresponding to the sample parameter set is used as a dependent variable, the parameter weight of each parameter is obtained, the maximum influence factor of the current fuel cell system on the system performance for prolonging the service life, namely the significance influence of each parameter in the operation process of the fuel cell system, is evaluated, and data support is provided for the follow-up construction of the target operation control model.
The embodiment of the application also provides a control device of a fuel cell system, referring to fig. 5, the device includes:
parameter set acquisition module 11: for acquiring a current set of operating parameters of the fuel cell system;
reference operating parameter set acquisition module 12: the method comprises the steps of calling a target operation control model to conduct parameter prediction on a current operation parameter set to obtain a reference operation parameter set of the fuel cell system; the target operation control model is obtained by carrying out constraint training on parameter prediction and battery system life prediction on an initial control model by using a sample parameter set of the fuel cell system, a reference sample parameter set corresponding to the sample parameter set and life labels of the reference sample parameter set;
the target operation parameter set acquisition module 13: the method comprises the steps of carrying out optimization processing on a reference operation parameter set based on a genetic algorithm to obtain a target operation parameter set corresponding to a current operation parameter set;
parameter control module 14: for controlling the operation of the fuel cell system based on the target set of operating parameters.
In some embodiments, the apparatus further comprises:
training set acquisition module: for obtaining a training data set comprising a plurality of sample parameter sets of the fuel cell system and corresponding life labels; the sample parameter set and the corresponding life label are obtained based on a uniform experiment method. The training data set is a data set subjected to normalization processing.
The initial control model building module: for constructing an initial control model;
the target operation control model determining module: the method comprises the steps of inputting a sample parameter set of a fuel cell system as an initial control model, outputting a reference sample parameter set corresponding to the sample parameter set and a life label of the reference sample parameter set, and carrying out constraint training on parameter prediction and life prediction of the fuel cell system on the initial control model to obtain a target operation control model; it should be noted that the lifetime label characterizes the battery lifetime index achieved by the fuel cell system operating based on the reference sample parameter set.
In some embodiments, the target operating parameter information acquisition module 13 further includes:
a first sub-module: and the method is used for taking the reference operation parameter set as an initial population, calling a preset genetic algorithm to carry out reproduction treatment on the initial population, and obtaining a first generation sub population. The reproduction process includes three operations of selection, crossover and mutation. And calling a preset genetic algorithm to carry out reproduction treatment on the initial population to obtain a first generation sub population, and specifically, carrying out three basic operations of selection, intersection and mutation after non-dominant sorting on the initial population to obtain the first generation sub population.
A second sub-module: and the method is used for carrying out the reproduction treatment on the first generation sub population to obtain the second generation sub population.
And a third sub-module: and the second generation sub-population is used for determining the second generation sub-population as a target operation parameter set under the condition that the second generation sub-population meets the preset convergence condition.
A fourth sub-module: and the method is used for carrying out reproduction processing on the updated child population until the obtained updated second generation child population meets the preset convergence condition under the condition that the updated child population does not meet the preset convergence condition, and determining the updated second generation child population meeting the preset convergence condition as target operation parameter information.
In some embodiments, the apparatus further comprises:
an initial prediction model acquisition module: for obtaining an initial predictive model.
Parameter weight acquisition module: the method comprises the steps of invoking a quadratic model, and determining the parameter weight of each parameter in a sample parameter set based on the sample parameter set and a corresponding life label; the quadratic model takes a sample operation parameter in a sample parameter set as an independent variable and takes a life label corresponding to the sample parameter set as an independent variable.
An initial control model obtaining module: and the initial model parameters are used for taking the parameter weights as the initial model parameters of the initial prediction model to obtain an initial control model.
It should be noted that, in the parameter weights as initial weights of the initial control model, the initial weights refer to weights from the input layer to the hidden layer.
The specific manner in which the respective modules perform the operations in the control device in the above-described embodiment has been described in detail in the embodiment concerning the method, and will not be described in detail here.
Embodiments of the present application also provide an electronic device including a processor and a memory having at least one instruction and at least one program stored therein, the at least one instruction and the at least one program loaded and executed by the processor to implement a fuel cell system control method as described above.
Further, fig. 6 shows a schematic hardware structure of an electronic device for implementing the method for controlling a fuel cell system provided in the embodiments of the present application, where the electronic device may participate in forming or including the apparatus provided in the embodiments of the present application. As shown in fig. 6, the electronic device 1 may include one or more (shown in the figures as 902a, 902b, … …,902 n) processors 902 (the processors 902 may include, but are not limited to, processing means such as a microprocessor MCU or a programmable logic device FPGA), a memory 904 for storing data, and a transmission means 906 for communication functions. In addition, the method may further include: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power supply, and/or a camera. It will be appreciated by those of ordinary skill in the art that the configuration shown in fig. 6 is merely illustrative and is not intended to limit the configuration of the electronic device described above. For example, the electronic device 1 may also include more or fewer components than shown in fig. 6, or have a different configuration than shown in fig. 6.
It should be noted that the one or more processors 902 and/or other data processing circuitry described above may be referred to herein generally as "data processing circuitry. The data processing circuit may be embodied in whole or in part in software, hardware, firmware, or any other combination. Furthermore, the data processing circuitry may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the electronic device 1 (or mobile device). As referred to in the embodiments of the present application, the data processing circuit acts as a processor control (e.g., selection of the path of the variable resistor termination to interface).
The memory 904 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the methods in the embodiments of the present application, and the processor 902 executes the software programs and modules stored in the memory 904 to perform various functional applications and data processing, i.e., implement the above-described fuel cell system control methods. The memory 904 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 904 may further include memory remotely located relative to the processor 902, which may be connected to the electronic device 1 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission means 906 is used for receiving or transmitting data via a network. The specific examples of the network described above may include a wireless network provided by a communication provider of the electronic device 1. In one example, the transmission means 906 comprises a network adapter (Network Interface Controller, NIC) that can be connected to other network devices via a base station to communicate with the internet. In one example, the transmission device 906 may be a Radio Frequency (RF) module for communicating with the internet wirelessly.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the electronic device 1 (or mobile device).
In the embodiment of the application, the memory may be used for storing software programs and modules, and the processor executes the software programs and modules stored in the memory to perform various functional applications and data processing. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, application programs required for functions, and the like; the storage data area may store data created according to the use of the device, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other volatile solid-state storage device. Accordingly, the memory may also include a memory controller to provide access to the memory by the processor.
Embodiments of the present application also provide a computer storage medium having at least one instruction and at least one program stored therein, the at least one instruction and the at least one program loaded and executed by a processor to implement the fuel cell system control method as described above.
Alternatively, in this embodiment, the storage medium may be located in at least one network server among a plurality of network servers of the computer network. Alternatively, in the present embodiment, the storage medium may include, but is not limited to: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
In conclusion, the beneficial effects brought by the technical scheme of the application are as follows:
the method considers the comprehensive influence of the performance of each part and the significant influence of the power change of each part on the performance of the fuel cell system, and obtains the current operation parameter set of the fuel cell system; invoking a target operation control model to conduct parameter prediction on the current operation parameter set to obtain a reference operation parameter set of the fuel cell system; optimizing the reference operation parameter set based on a genetic algorithm to obtain a target operation parameter set corresponding to the current operation parameter set; the fuel cell system is controlled to operate based on the target set of operating parameters. The operation power balance of each part is ensured, and the service life of each part in the fuel cell system is prolonged, so that the fuel cell system is ensured to reach the optimal performance, the life cycle of the fuel cell system is prolonged, the reliability and the durability of the fuel cell system are improved, and the comprehensive performance of the fuel cell system is improved.
It should be noted that: the foregoing sequence of the embodiments of the present application is only for describing, and does not represent the advantages and disadvantages of the embodiments. And the foregoing description has been directed to specific embodiments of this application. Other embodiments are within the scope of the following claims. In some cases, the actions or steps recited in the claims can be performed in a different order than in the embodiments and still achieve desirable results. In addition, the processes depicted in the accompanying figures do not necessarily require the particular order shown, or sequential order, to achieve desirable results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.
All embodiments in the application are described in a progressive manner, and identical and similar parts of all embodiments are mutually referred, so that each embodiment mainly describes differences from other embodiments. In particular, for the apparatus and device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, with reference to the description of the method embodiments in part.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program for instructing relevant hardware, where the program may be stored in a computer readable storage medium, and the storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The foregoing description of the preferred embodiments of the present application is not intended to be limiting, but rather is intended to cover any and all modifications, equivalents, alternatives, and improvements within the spirit and principles of the present application.
Claims (10)
1. A fuel cell system control method, characterized by comprising:
acquiring a current operation parameter set of a fuel cell system;
constructing an initial control model, and calling a target operation control model to conduct parameter prediction on each operation parameter in the current operation parameter set to obtain a reference operation parameter set of the fuel cell system; the target operation control model is obtained by performing constraint training of parameter prediction and battery system life prediction on an initial control model by using a sample parameter set of the fuel cell system, a reference sample parameter set corresponding to the sample parameter set and a life label of the reference sample parameter set; taking a sample parameter set of the fuel cell system as an initial control model input, and taking a reference sample parameter set corresponding to the sample parameter set and a life label of the reference sample parameter set as output, wherein the life label characterizes a battery life index reached by the operation of the fuel cell system based on the reference sample parameter set;
optimizing the reference operation parameter set based on a genetic algorithm to obtain a target operation parameter set corresponding to the current operation parameter set;
controlling the fuel cell system to operate based on the target set of operating parameters.
2. The method according to claim 1, wherein the current operation parameter set includes one or more of current air compressor rotation speed information, hydrogen circulation pump rotation speed information, ejector injection ratio information, back pressure valve opening information, bypass valve opening information, stack anode metering ratio information, stack cathode metering ratio information, humidifier gas-liquid ratio information, heat conversion efficiency information, and water pump power information of the fuel cell system.
3. The method according to claim 1, wherein before the invoking the target operation control model to perform parameter prediction on the current operation parameter set to obtain the reference operation parameter set of the fuel cell system, the method further comprises:
acquiring a training data set, wherein the training data set comprises sample parameter sets of a plurality of fuel cell systems and corresponding life labels; wherein the sample parameter set and the corresponding lifetime signature are obtained based on a homogeneous assay.
4. The fuel cell system control method according to claim 3, wherein the constructing an initial control model includes:
acquiring an initial prediction model;
invoking a quadratic model, and determining the parameter weight of each parameter in the sample parameter set based on the sample parameter set and the corresponding life label; the quadratic model takes a sample operation parameter in the sample parameter set as an independent variable and takes a life label corresponding to the sample parameter set as a dependent variable; and taking the parameter weight as an initial model parameter of an initial prediction model to obtain the initial control model.
5. The method for controlling a fuel cell system according to claim 3, wherein,
the initial control model comprises an input layer, an implicit layer and an output layer, wherein the implicit layer is provided with a preset number of network elements, and the network elements of the implicit layer are in one-to-one correspondence with the parameter types corresponding to the sample parameter sets.
6. The method according to claim 1, wherein the optimizing the reference operation parameter set based on the genetic algorithm to obtain the target operation parameter set corresponding to the current operation parameter set includes:
taking the reference operation parameter set as an initial population, and calling a preset genetic algorithm to carry out reproduction treatment on the initial population to obtain a first generation sub population;
carrying out reproduction treatment on the first generation sub population to obtain a second generation sub population;
and under the condition that the second generation sub-population meets the preset convergence condition, determining the second generation sub-population as the target operation parameter set.
7. The method for controlling a fuel cell system according to claim 6, wherein,
and under the condition that the updated child population does not meet the preset convergence condition, carrying out reproduction processing on the updated child population until the obtained updated second generation child population meets the preset convergence condition, and determining the updated second generation child population meeting the preset convergence condition as the target operation parameter information.
8. A control device of a fuel cell system, characterized by comprising:
parameter set acquisition module: for acquiring a current set of operating parameters of the fuel cell system;
a reference operation parameter set acquisition module: the method comprises the steps of constructing an initial control model, and calling a target operation control model to conduct parameter prediction on each operation parameter in the current operation parameter set to obtain a reference operation parameter set of the fuel cell system; the target operation control model is obtained by performing constraint training of parameter prediction and battery system life prediction on an initial control model by using a sample parameter set of the fuel cell system, a reference sample parameter set corresponding to the sample parameter set and a life label of the reference sample parameter set; taking a sample parameter set of the fuel cell system as an initial control model input, and taking a reference sample parameter set corresponding to the sample parameter set and a life label of the reference sample parameter set as output, wherein the life label characterizes a battery life index reached by the operation of the fuel cell system based on the reference sample parameter set;
the target operation parameter information acquisition module: the method comprises the steps of carrying out optimization processing on the reference operation parameter set based on a genetic algorithm to obtain a target operation parameter set corresponding to the current operation parameter set;
parameter information control module: for controlling the fuel cell system to operate based on the target set of operating parameters.
9. An electronic device, characterized in that the device comprises a processor and a memory, in which at least one instruction and at least one program are stored, which at least one instruction and at least one program are loaded and executed by the processor to implement the fuel cell system control method as claimed in claims 1-7.
10. A computer storage medium having stored therein at least one instruction and at least one program, the at least one instruction and the at least one program loaded and executed by a processor to implement the fuel cell system control method as recited in claims 1-7.
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