CN112993344A - Neural network-based fuel cell system output performance prediction method and device - Google Patents

Neural network-based fuel cell system output performance prediction method and device Download PDF

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Publication number
CN112993344A
CN112993344A CN201911276297.3A CN201911276297A CN112993344A CN 112993344 A CN112993344 A CN 112993344A CN 201911276297 A CN201911276297 A CN 201911276297A CN 112993344 A CN112993344 A CN 112993344A
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output
fuel cell
output performance
performance prediction
cell system
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蒋尚峰
李维国
张龙海
赵琨
孟德水
余阳阳
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Zhengzhou Yutong Bus Co Ltd
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Zhengzhou Yutong Bus Co Ltd
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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
    • 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
    • H01M8/04305—Modeling, demonstration models of fuel cells, e.g. for training purposes
    • 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
    • H01M8/04313—Processes for controlling fuel cells or fuel cell systems characterised by the detection or assessment of variables; characterised by the detection or assessment of failure or abnormal function
    • H01M8/04537—Electric variables
    • H01M8/04574—Current
    • 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
    • H01M8/04313—Processes for controlling fuel cells or fuel cell systems characterised by the detection or assessment of variables; characterised by the detection or assessment of failure or abnormal function
    • H01M8/04537—Electric variables
    • H01M8/04604—Power, energy, capacity or load
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01M—PROCESSES OR MEANS, e.g. BATTERIES, FOR THE DIRECT CONVERSION OF CHEMICAL ENERGY INTO ELECTRICAL ENERGY
    • H01M2250/00—Fuel cells for particular applications; Specific features of fuel cell system
    • H01M2250/20—Fuel cells in motive systems, e.g. vehicle, ship, plane
    • 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

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  • Life Sciences & Earth Sciences (AREA)
  • General Chemical & Material Sciences (AREA)
  • Electrochemistry (AREA)
  • Chemical Kinetics & Catalysis (AREA)
  • Chemical & Material Sciences (AREA)
  • Sustainable Energy (AREA)
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  • Automation & Control Theory (AREA)
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  • Fuel Cell (AREA)

Abstract

The invention relates to a method and a device for predicting the output performance of a fuel cell system based on a neural network, belonging to the field of new energy vehicles and fuel cell passenger vehicles, and selecting characteristic parameters influencing the output performance of a fuel cell; testing by changing the value corresponding to the characteristic parameter to obtain the power/current output of the fuel cell system, and selecting the maximum power/current output from the output result to obtain a training sample; taking a value corresponding to the characteristic parameter as the input of a neural network, taking the maximum power output as the output of the neural network, constructing an output performance prediction model, and training the output performance prediction model by using a training sample; substituting the characteristic parameter values of the fuel cell system obtained currently into the trained output performance prediction model to obtain the prediction of the current maximum power output, and solving the problems of low system operation efficiency and poor output performance caused by the fact that the maximum allowable output power of the fuel cell system cannot be judged in real time.

Description

Neural network-based fuel cell system output performance prediction method and device
Technical Field
The invention relates to a method and a device for predicting the output performance of a fuel cell system based on a neural network, belonging to the field of new energy vehicles and fuel cell buses.
Background
The power demand of the whole fuel cell vehicle is influenced by actual road working conditions, loads, driver driving habits, environment and the like, the power demand sends a power demand command to the fuel cell DCDC through the whole vehicle VCU, and when the DCDC sends the power demand command to the fuel cell system FCU, because of the power increase process of the fuel cell, air is not supplied by the air compressor in time, simultaneously, water generated inside can not be discharged in time, the mass transfer problem causes that the system can not follow along with the power demand of the whole vehicle, and the increase of the power output of the fuel cell has hysteresis. Therefore, the output power allowed by the current fuel cell system is limited, and if the fuel cell system is forced to output in excess, the system can be under-inflated, reversed in polarity, and even cause safety accidents.
The allowable output power of the current fuel cell system is controlled and realized by the system temperature, the single-chip minimum voltage, the hydrogen-air pressure difference and the like, and the maximum allowable output power of the system can not be judged according to the internal condition of the system in real time, so that the system can not run in a high-efficiency area, and the efficiency of the system and the hydrogen utilization rate are not favorably improved.
Therefore, in order to improve the service life of the fuel cell system and the safety of the whole vehicle, it is important to research the maximum power/current output allowed by the fuel cell system in a certain state.
Disclosure of Invention
The invention aims to provide a method and a device for predicting the output performance of a fuel cell system based on a neural network, which aim to solve the problems of low system operation efficiency and poor output performance caused by the fact that the maximum allowable output power of the system cannot be judged in real time according to the internal condition of the system in the prior art.
The invention adopts the following technical scheme: the invention provides a fuel cell system output performance prediction method based on a neural network, which comprises the following steps:
1) selecting characteristic parameters influencing the output performance of the fuel cell, wherein the characteristic parameters at least comprise: the operating pressure, flow rate, reaction temperature and reaction humidity of the reaction gas;
2) testing by changing the value corresponding to the characteristic parameter to obtain the power/current output of the fuel cell system, and selecting the maximum power/current output from the output result to obtain a training sample;
3) taking the value corresponding to the characteristic parameter as the input of a neural network, taking the maximum power output as the output of the neural network, constructing an output performance prediction model, and training the output performance prediction model by using the training sample;
4) and substituting the characteristic parameter values of the fuel cell system obtained currently into the trained output performance prediction model to obtain the prediction of the current maximum power output.
The method comprises the steps of selecting characteristic parameters influencing the performance of the fuel cell, obtaining a training sample through a test, and obtaining a prediction model of the allowable output performance of the fuel cell through neural network training based on the training sample. By utilizing the model, the allowable output performance of the fuel cell under the current condition can be predicted on line by acquiring the operation parameters of the fuel cell in real time. Through the steps, the maximum allowable output power/current value of the system can be analyzed and judged according to the real-time state of the system, so that the system can be protected, the service life of the system can be prolonged, and the system efficiency and the hydrogen utilization rate can be effectively improved.
Further, the operating pressure includes at least: hydrogen pressure, air pressure; the reaction temperature includes the temperature of the coolant.
Further, in order to improve the prediction accuracy, the method further comprises the step of verifying the prediction accuracy of the output performance prediction model: and if the difference between the output prediction result of the output performance prediction model and the test result is larger than a set threshold value, continuing to train the output performance prediction model.
The invention also provides a fuel cell system output performance prediction device based on the neural network, which comprises a processor and a memory, wherein the memory is stored with a computer program, and the processor executes the computer program to realize the following steps:
1) selecting characteristic parameters influencing the output performance of the fuel cell, wherein the characteristic parameters at least comprise: the operating pressure, flow rate, reaction temperature and reaction humidity of the reaction gas;
2) testing by changing the value corresponding to the characteristic parameter to obtain the power/current output of the fuel cell system, and selecting the maximum power/current output from the output result to obtain a training sample;
3) taking the value corresponding to the characteristic parameter as the input of a neural network, taking the maximum power output as the output of the neural network, constructing an output performance prediction model, and training the output performance prediction model by using the training sample;
4) and substituting the characteristic parameter values of the fuel cell system obtained currently into the trained output performance prediction model to obtain the prediction of the current maximum power output.
Further, the operating pressure includes at least: hydrogen pressure, air pressure; the reaction temperature includes the temperature of the coolant.
Further, in order to improve the prediction accuracy, the method further comprises the step of verifying the prediction accuracy of the output performance prediction model: and if the difference between the output prediction result of the output performance prediction model and the test result is larger than a set threshold value, continuing to train the output performance prediction model.
Drawings
Fig. 1 is a flow chart of a method in an embodiment of the fuel cell performance prediction method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention. The components of embodiments of the present invention generally described and illustrated in the figures herein may be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present invention, presented in the figures, is not intended to limit the scope of the invention, as claimed, but is merely representative of selected embodiments of the invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
The features and properties of the present invention are described in further detail below with reference to examples.
Fuel cell system output performance prediction method embodiment:
in the embodiment, a method for predicting the output performance of a fuel cell system based on a neural network is provided, as shown in fig. 1, the specific implementation process of the prediction method is as follows:
1) selecting characteristic parameters influencing the output performance of the fuel cell, wherein the characteristic parameters at least comprise: the operating pressure, flow rate, reaction temperature and reaction humidity of the reaction gas.
The output performance during the operation of the fuel cell system is affected by the pressure, temperature and humidity of the reaction environment of the reaction gas, and the degree of influence of each condition, i.e., the sensitivity of the performance to each parameter, differs, for example, the influence of the pressure of the reaction gas may be greater than the influence of the humidity, and thus, it is necessary to quantify their degree of influence as a weight.
In this embodiment, the output performance is mainly studied by using the operating pressure, flow rate, reaction temperature, and reaction humidity of the reaction gas, which affect the output performance of the fuel cell, as characteristic parameters, wherein the operating pressure at least includes: hydrogen pressure, air pressure; the reaction temperature includes the temperature of the coolant.
For example, first, a characteristic parameter that affects the outputtable performance of the fuel cell is selectedNumbers, e.g. hydrogen pressure, air pressure, coolant temperature, humidity, etc., are recorded as set Xi[x1,x2……xn]。
2) And testing by changing the value corresponding to the characteristic parameter to obtain the power/current output of the fuel cell system, and selecting the maximum power/current output from the output result to obtain a training sample.
Forming multiple groups of test data by changing the values corresponding to the characteristic parameters in the sets, testing the fuel cell system under the set conditions of each set, recording the maximum output performance of the fuel cell, and recording as a set Yi[y1,y2,……yn]Wherein Y isiIs the maximum output performance, y1,y2,……ynFor each set corresponding test results.
3) And taking the value corresponding to the characteristic parameter as the input of a neural network, taking the maximum power output as the output of the neural network, constructing an output performance prediction model, and training the output performance prediction model by using the training sample.
In this embodiment, a logistic regression neural network algorithm is adopted, a value corresponding to a characteristic parameter is used as input of the neural network, the maximum power output is used as output of the neural network, and a neural network prediction model is established:
Yi＝aiXi+b
wherein a isiIs a characteristic parameter xiWeight of influence on exportable performance, b being bias information, e.g. characteristic parameter x1Represents the hydrogen pressure, and the weight of the influence of the hydrogen pressure on the outputtable performance of the fuel cell system is 3.
Then, the data in the obtained training sample is substituted into a neural network model for training, so as to obtain the corresponding weight aiAnd biasing b, thereby obtaining the neural network prediction model.
As an improvement to the above embodiment, in this embodiment, in order to improve the prediction accuracy, if the difference between the prediction result output by the output performance prediction model and the test result is greater than a set threshold, the output performance prediction model continues to be trained.
4) And substituting the characteristic parameter values of the fuel cell system obtained currently into the trained output performance prediction model to obtain the prediction of the current maximum power output.
When the fuel cell system runs, the current characteristic parameter value x is acquired in real time through the signal acquisition systemtX is to betAnd substituting the trained neural network prediction model to dynamically predict the maximum allowable output performance of the fuel cell system under the current condition.
Through the process, all the influence parameters are subjected to weighting processing to form a dimension which can represent the outputtable performance of the fuel cell. And meanwhile, setting a threshold range, and when the dimension is smaller than the threshold, the fuel cell cannot output power, namely the output power is 0.
Fuel cell system output performance prediction apparatus embodiment:
the present embodiment provides a fuel cell system output performance prediction apparatus based on a neural network, which includes a processor and a memory, where the memory stores a computer program for running on the processor, the processor may be implemented by a single chip microcomputer, an FPGA, a DSP, a PLC, or an MCU, and the memory may be implemented by a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art, and the storage medium may be coupled to the processor, so that the processor can read information from the storage medium, or the storage medium may be a component of the processor.
The processor executes the computer program stored in the memory to implement the method of:
1) selecting characteristic parameters influencing the output performance of the fuel cell, wherein the characteristic parameters at least comprise: the operating pressure, flow rate, reaction temperature and reaction humidity of the reaction gas;
2) testing by changing the value corresponding to the characteristic parameter to obtain the power/current output of the fuel cell system, and selecting the maximum power/current output from the output result to obtain a training sample;
3) taking the value corresponding to the characteristic parameter as the input of a neural network, taking the maximum power output as the output of the neural network, constructing an output performance prediction model, and training the output performance prediction model by using the training sample;
4) and substituting the characteristic parameter values of the fuel cell system obtained currently into the trained output performance prediction model to obtain the prediction of the current maximum power output.
The specific process of predicting the output performance of the fuel cell system by using the above device has been described in detail in the embodiment of the method for predicting the output performance of the fuel cell system, and will not be described herein again.
The above description is only a preferred embodiment of the present invention, and not intended to limit the present invention, the scope of the present invention is defined by the appended claims, and all structural changes that can be made by using the contents of the description and the drawings of the present invention are intended to be embraced therein.

Claims (6)

1. A fuel cell system output performance prediction method based on a neural network is characterized by comprising the following steps:
1) selecting characteristic parameters influencing the output performance of the fuel cell, wherein the characteristic parameters at least comprise: the operating pressure, flow rate, reaction temperature and reaction humidity of the reaction gas;
2) testing by changing the value corresponding to the characteristic parameter to obtain the power/current output of the fuel cell system, and selecting the maximum power/current output from the output result to obtain a training sample;
3) taking the value corresponding to the characteristic parameter as the input of a neural network, taking the maximum power output as the output of the neural network, constructing an output performance prediction model, and training the output performance prediction model by using the training sample;
4) and substituting the characteristic parameter values of the fuel cell system obtained currently into the trained output performance prediction model to obtain the prediction of the current maximum power output.
2. The neural network-based fuel cell system output performance prediction method of claim 1, wherein the operating pressure includes at least: hydrogen pressure, air pressure; the reaction temperature includes the temperature of the coolant.
3. The neural network-based fuel cell system output performance prediction method according to claim 1 or 2, characterized by further comprising the step of performing prediction accuracy verification on the output performance prediction model: and if the difference between the output prediction result of the output performance prediction model and the test result is larger than a set threshold value, continuing to train the output performance prediction model.
4. A neural network-based fuel cell system output performance prediction apparatus comprising a processor and a memory, the memory having stored therein a computer program, the processor executing the computer program to implement the steps of:
1) selecting characteristic parameters influencing the output performance of the fuel cell, wherein the characteristic parameters at least comprise: the operating pressure, flow rate, reaction temperature and reaction humidity of the reaction gas;
2) testing by changing the value corresponding to the characteristic parameter to obtain the power/current output of the fuel cell system, and selecting the maximum power/current output from the output result to obtain a training sample;
3) taking the value corresponding to the characteristic parameter as the input of a neural network, taking the maximum power output as the output of the neural network, constructing an output performance prediction model, and training the output performance prediction model by using the training sample;
4) and substituting the characteristic parameter values of the fuel cell system obtained currently into the trained output performance prediction model to obtain the prediction of the current maximum power output.
5. The neural network-based fuel cell system output performance prediction device of claim 4, wherein the operating pressure includes at least: hydrogen pressure, air pressure; the reaction temperature includes the temperature of the coolant.
6. The neural network-based fuel cell system output performance prediction apparatus according to claim 4 or 5, characterized by further comprising a step of verifying the prediction accuracy of the output performance prediction model: and if the difference between the output prediction result of the output performance prediction model and the test result is larger than a set threshold value, continuing to train the output performance prediction model.
CN201911276297.3A 2019-12-12 2019-12-12 Neural network-based fuel cell system output performance prediction method and device Pending CN112993344A (en)

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Cited By (2)

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CN113991151A (en) * 2021-10-12 2022-01-28 广东省武理工氢能产业技术研究院 Fuel cell self-adaptive control method and system based on power prediction
CN114937793A (en) * 2022-05-24 2022-08-23 苏州溯驭技术有限公司 Sensitivity analysis and adjustment method for environmental parameter change of fuel cell

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Cited By (3)

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Publication number Priority date Publication date Assignee Title
CN113991151A (en) * 2021-10-12 2022-01-28 广东省武理工氢能产业技术研究院 Fuel cell self-adaptive control method and system based on power prediction
CN113991151B (en) * 2021-10-12 2023-07-07 广东省武理工氢能产业技术研究院 Fuel cell self-adaptive control method and system based on power prediction
CN114937793A (en) * 2022-05-24 2022-08-23 苏州溯驭技术有限公司 Sensitivity analysis and adjustment method for environmental parameter change of fuel cell

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