A method and system for controlling humidity in variable working conditions of fuel cells based on fuzzy theory

Abstract

The present invention belongs to the field of intelligent humidity control for fuel cells and provides a method and system for variable humidity control of fuel cells based on fuzzy theory. The method includes determining the current proton exchange membrane water content and the optimal membrane water content of the fuel cell for the next target operating condition, calculating the difference between the two and determining the amount of humidity increase / decrease; forming a fuzzy membership function based on the driver's urgency to change the operating condition and the amount of humidity increase / decrease, combined with the load of the current and next operating conditions, and constructing a fuzzy control rule library based on the driver's urgency to change the operating condition, load changes, and humidity changes; collecting real-time operating data and determining the optimal amount of humidity increase / decrease using the fuzzy control rule library; and converting the optimal amount of humidity increase / decrease into an output PWM wave to control the humidifier motor voltage, thereby controlling the motor speed to achieve the desired amount of humidity increase / decrease.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent humidity control of fuel cells, and in particular relates to a variable operating condition humidity control method and system for fuel cells based on fuzzy theory. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] For vehicles currently being widely researched that use proton exchange membrane fuel cells (PEMFCs) as power sources, the humidity control system significantly impacts their overall performance. The moisture content of the PEM, which is controlled by the humidity control system, is a key parameter affecting performance. Excessively dry reactant gases result in too few water molecules in the PEM, reducing fuel cell efficiency and potentially damaging the membrane. Excessively humid reactant gases can cause flooding and other degradation of the battery system's performance. Experimental data from both domestic and international sources indicates that fuel cell reaction efficiency is highest when the intake air humidity of the fuel cell maintains the PEM's humidity between 80% and 100%. Therefore, intake air humidity control technology in fuel cell engine systems is crucial for maintaining an appropriate PEM moisture content and improving and protecting fuel cell performance.

[0004] According to the inventors, in the past, during the operation of a vehicle's fuel cell, the humidity control of the fuel cell was often completed through conventional control, with a specific motor speed given within a certain range. This resulted in poor real-time tracking of the humidity increase / decrease control. Summary of the Invention

[0005] In order to solve the technical problems existing in the above-mentioned background technology, the present invention provides a fuel cell variable operating condition humidity control method and system based on fuzzy theory, which adopts fuzzy functions under multiple fuzzy membership degrees to realize the control of increase / decrease of humidity, and has the characteristics of clear structure, strong system robustness, and easy implementation.

[0006] In order to achieve the above object, the present invention adopts the following technical solutions:

[0007] A first aspect of the present invention provides a method for controlling humidity of a fuel cell under variable operating conditions based on fuzzy theory.

[0008] A method for controlling humidity in a fuel cell under variable operating conditions based on fuzzy theory, comprising:

[0009] Determine the current proton exchange membrane water content and the optimal membrane water content of the fuel cell under the next target operating condition, and calculate the difference between the two to determine the amount of humidity increase / decrease;

[0010] According to the urgency of the driver changing the working condition and the amount of humidity increase / decrease, a fuzzy membership function is formed by combining the load of the current working condition and the next working condition, and a fuzzy control rule base is constructed based on the urgency of the driver changing the working condition, load change and humidity change.

[0011] Collect real-time operation data and obtain the optimal humidity increase / decrease through the fuzzy control rule base;

[0012] The optimal increase / decrease in humidity is converted into an output PWM wave to control the voltage of the humidifier motor, thereby controlling the motor speed to achieve the purpose of controlling the increase / decrease in humidity.

[0013] The second aspect of the present invention provides a fuel cell variable operating condition humidity control system based on fuzzy theory.

[0014] A fuel cell variable operating condition humidity control system based on fuzzy theory, comprising:

[0015] A humidity increase / decrease amount determination module is configured to: determine a current proton exchange membrane water content and an optimal membrane water content of a fuel cell under a next target operating condition, and calculate a difference between the two to determine a humidity increase / decrease amount;

[0016] a fuzzy control rule base construction module configured to: form a fuzzy membership function based on the urgency of the driver changing the working condition and the amount of humidity increase / decrease, combined with the load of the current working condition and the next working condition, and construct a fuzzy control rule base based on the urgency of the driver changing the working condition, the load change, and the humidity change;

[0017] An optimal humidity increase / decrease amount determination module is configured to: collect real-time operation data and obtain an optimal humidity increase / decrease amount through a fuzzy control rule base;

[0018] The control module is configured to convert the optimal humidity increase / decrease amount into an output PWM wave to control the humidifier motor voltage, thereby controlling the motor speed to achieve the purpose of controlling the humidity increase / decrease amount.

[0019] A third aspect of the present invention provides a computer-readable storage medium.

[0020] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of the fuel cell variable operating condition humidity control method based on fuzzy theory as described in the first aspect above.

[0021] A fourth aspect of the present invention provides a computer device.

[0022] A computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the fuel cell variable operating condition humidity control method based on fuzzy theory as described in the first aspect above are implemented.

[0023] Compared with the prior art, the present invention has the following beneficial effects:

[0024] The present invention obtains the real-time membrane water content through the internal resistance of the current proton exchange membrane, obtains the optimal membrane water content of the fuel cell in the next working condition according to the table lookup method, and calculates the difference between the two to determine the humidification amount or dehumidification amount; forms a fuzzy membership function according to the urgency of the driver to change the working condition and combines the load, that is, the change amplitude of the current density, of the current working condition and the next working condition, and establishes a rule base for fuzzy control of humidity changes; collects real-time operating data and obtains the optimal humidification / dehumidification amount through the fuzzy control rule base; converts the optimal humidification / dehumidification amount into an output PWM wave through calculation to control the humidifier motor voltage, thereby controlling the motor speed to achieve the purpose of controlling the humidification / dehumidification amount, thereby improving the real-time followability of the humidification / dehumidification amount control, and has the characteristics of clear structure, strong system robustness, and easy implementation.

[0025] Advantages of additional aspects of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0027] Figure 1 This is a flow chart of a variable operating condition humidity control method for a fuel cell based on fuzzy theory, shown in the first embodiment of the present invention. DETAILED DESCRIPTION

[0028] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0029] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0030] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present invention. As used herein, unless the context clearly indicates otherwise, the singular form is intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, they indicate the presence of features, steps, operations, devices, components and / or combinations thereof.

[0031] It should be noted that the flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the methods and systems according to the various embodiments of the present disclosure. It should be noted that each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code can include one or more executable instructions for implementing the logical functions specified in the various embodiments. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in an order different from that marked in the accompanying drawings. For example, two boxes represented in succession can actually be executed substantially in parallel, or they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the flowchart and / or block diagram, and the combination of the boxes in the flowchart and / or block diagram, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or can be implemented using a combination of dedicated hardware and computer instructions.

[0032] Example 1

[0033] like Figure 1 As shown, this embodiment provides a method for controlling humidity in variable working conditions of a fuel cell based on fuzzy theory. This embodiment uses the method applied to a server as an example for illustration. It is understandable that the method can also be applied to a terminal, and can also be applied to a system including a terminal, a server, and a server, and is implemented through the interaction between the terminal and the server. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network servers, cloud communications, middleware services, domain name services, security services CDN, and big data and artificial intelligence platforms. The terminal can be a smart phone, tablet computer, laptop computer, desktop computer, etc., but is not limited thereto. The terminal and the server can be directly or indirectly connected via wired or wireless communication, which is not limited in this application. In this embodiment, the method includes the following steps:

[0034] Determine the current proton exchange membrane water content and the optimal membrane water content of the fuel cell under the next target operating condition, and calculate the difference between the two to determine the amount of humidity increase / decrease;

[0035] According to the urgency of the driver changing the working condition and the amount of humidity increase / decrease, a fuzzy membership function is formed by combining the load of the current working condition and the next working condition, and a fuzzy control rule base is constructed based on the urgency of the driver changing the working condition, load change and humidity change.

[0036] Collect real-time operation data and obtain the optimal humidity increase / decrease through the fuzzy control rule base;

[0037] The optimal increase / decrease in humidity is converted into an output PWM wave to control the voltage of the humidifier motor, thereby controlling the motor speed to achieve the purpose of controlling the increase / decrease in humidity.

[0038] The specific solution of this embodiment can be implemented according to the following contents:

[0039] The internal resistance of the fuel cell proton exchange membrane is measured in real time, and the current proton exchange membrane water content is determined by the relationship between the proton exchange membrane ohmic resistance and the membrane electrode water content:

[0040]

[0041] Among them, R m Proton exchange membrane ohmic resistance, λ m is the water content of the membrane electrode, A1, A2, A3, and A4 are constants determined by the real-time operating conditions of the fuel cell.

[0042] Use the table lookup method to obtain the theoretical optimal membrane water content λ of the fuel cell under the next target operating condition a , according to the difference between the two, the value of the next step of increasing / decreasing humidity λn is: λ n =λ a -λ m .

[0043] The driver's operating data (the rate of change of the accelerator pedal's depression and release) is normalized to i, where 0≤i≤1. This is used to determine the driver's urgency for changing the operating conditions, i.e., the degree of urgency in increasing or decreasing humidity. A larger i indicates a higher urgency in the driver's changing the operating conditions. Based on the size of i, the conditions are categorized as urgent, normal, or slow.

[0044] Combine the load of the current working condition and the load of the next working condition to determine the impact of the change in the load, that is, the current density, on the humidity. For example, when the load changes from small to large, the cathode water production increases, so the dehumidification demand of the membrane is greater. When the load changes from large to small, the water production decreases, the membrane will dry out quickly, and the humidification demand is greater. The theoretical current density j of the next working condition is obtained by the table lookup method. a , and the current density j measured by the sensor m The difference is: j = j a -j m Combining the urgency of the variable working condition i, six different fuzzy membership functions of humidity increase / decrease are obtained:

[0045] The rapid humidification when the load changes from large to small conforms to the normal distribution of the current density change:

[0046]

[0047] Normal humidification with a load that decreases from large to small follows a gamma distribution with respect to the change in current density:

[0048]

[0049] The slow humidification from large to small loads conforms to the Weibull distribution of the current density change:

[0050]

[0051] The urgent dehumidification when the load changes from small to large conforms to the normal distribution of the current density change:

[0052]

[0053] Normal dehumidification with a load change from small to large follows the gamma distribution of current density variation:

[0054]

[0055] The slow dehumidification from a small load to a large load conforms to the Weibull distribution of the current density change:

[0056]

[0057] Where f(i,j) represents the calculated optimal amount of humidity increase / decrease, i represents the urgency of the real-time driver operation, and j represents the real-time current density change amplitude.

[0058] Based on the above six membership functions, a fuzzy control rule library is constructed for the relationship between the urgency of the variable working condition i, the load change j and the humidity change.

[0059] An enthalpy wheel humidifier is used for humidity control. The exhaust gas from the fuel cell is recycled and passed into the enthalpy wheel humidifier to absorb the heat and moisture in the exhaust gas and humidify the fresh air entering the enthalpy wheel. Adjusting the motor speed of the enthalpy wheel humidifier can directly affect its output of humidity increase / decrease.

[0060] The amplitude and urgency of the fuel cell's operating condition changes are collected in real time and placed into a fuzzy control rule library for humidity changes to obtain the optimal amount of humidity increase / dehumidification at that time. Based on the amount of humidity increase / dehumidification, a PWM wave is determined, i.e., a series of pulses with equal amplitudes and different widths. The width of each pulse is modulated according to certain rules, thereby changing the motor circuit of the enthalpy wheel humidifier, controlling the motor speed, and adjusting the amount of humidity increase / dehumidification output by the humidifier.

[0061] Example 2

[0062] This embodiment provides a fuel cell variable operating condition humidity control system based on fuzzy theory.

[0063] A fuel cell variable operating condition humidity control system based on fuzzy theory, comprising:

[0064] A humidity increase / decrease amount determination module is configured to: determine a current proton exchange membrane water content and an optimal membrane water content of a fuel cell under a next target operating condition, and calculate a difference between the two to determine a humidity increase / decrease amount;

[0065] a fuzzy control rule base construction module configured to: form a fuzzy membership function based on the urgency of the driver changing the working condition and the amount of humidity increase / decrease, combined with the load of the current working condition and the next working condition, and construct a fuzzy control rule base based on the urgency of the driver changing the working condition, the load change, and the humidity change;

[0066] An optimal humidity increase / decrease amount determination module is configured to: collect real-time operation data and obtain an optimal humidity increase / decrease amount through a fuzzy control rule base;

[0067] The control module is configured to convert the optimal humidity increase / decrease amount into an output PWM wave to control the humidifier motor voltage, thereby controlling the motor speed to achieve the purpose of controlling the humidity increase / decrease amount.

[0068] It should be noted that the above-mentioned humidity increase / decrease amount determination module, fuzzy control rule base construction module, optimal humidity increase / decrease amount determination module, and control module are implemented in the same examples and application scenarios as those in the steps of Example 1, but are not limited to the contents disclosed in the above-mentioned Example 1. It should be noted that the above-mentioned modules, as part of the system, can be executed in a computer system, such as a set of computer-executable instructions.

[0069] Example 3

[0070] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the steps of the fuel cell variable operating condition humidity control method based on fuzzy theory as described in the first embodiment above are implemented.

[0071] Example 4

[0072] This embodiment provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the steps of the fuel cell variable operating condition humidity control method based on fuzzy theory as described in the first embodiment above are implemented.

[0073] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of hardware embodiments, software embodiments, or embodiments combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage and optical storage, etc.) containing computer-usable program code.

[0074] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0075] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0076] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0077] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing related hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0078] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Those skilled in the art will readily appreciate that various modifications and variations of the present invention are possible. Any modifications, equivalent substitutions, or improvements made within the spirit and principles of the present invention are intended to be within the scope of protection of the present invention.

Claims

1. A method for controlling humidity in a fuel cell under variable operating conditions based on fuzzy theory, characterized in that: include: Determine the current proton exchange membrane water content and the optimal membrane water content of the fuel cell under the next target operating condition, and calculate the difference between the two to determine the amount of humidity increase / decrease; According to the urgency of the driver changing the working condition and the amount of humidity increase / decrease, a fuzzy membership function is formed by combining the load of the current working condition and the next working condition, and a fuzzy control rule base is constructed based on the urgency of the driver changing the working condition, load change and humidity change. The fuzzy membership function includes: The rapid humidification when the load changes from large to small conforms to the normal distribution of the current density change: Normal humidification with a load that decreases from large to small follows a gamma distribution with respect to the change in current density: The slow humidification from large to small loads conforms to the Weibull distribution of the current density change: The urgent dehumidification when the load changes from small to large conforms to the normal distribution of the current density change: Normal dehumidification with a load change from small to large follows the gamma distribution of current density variation: The slow dehumidification from a small load to a large load conforms to the Weibull distribution of the current density change: Where, f ( i , j ) represents the calculated optimal amount of humidity increase / decrease, i Indicates the urgency of the real-time driver operation collected, j Represents the real-time current density change amplitude collected, Indicates the amount of humidity increase / decrease in the next step; Collect real-time operation data and obtain the optimal humidity increase / decrease through the fuzzy control rule base; The optimal increase / decrease in humidity is converted into an output PWM wave to control the voltage of the humidifier motor, thereby controlling the motor speed to achieve the purpose of controlling the increase / decrease in humidity.

2. The method for controlling humidity in a fuel cell under variable operating conditions based on fuzzy theory according to claim 1, characterized in that: The current proton exchange membrane water content is determined according to the relationship between the proton exchange membrane ohmic resistance and the membrane electrode water content; The relationship between the ohmic resistance of the proton exchange membrane and the water content of the membrane electrode is: Among them, R m Proton exchange membrane ohmic resistance, λ m is the water content of the membrane electrode, A1, A2, A3, and A4 are constants determined by the real-time operating conditions of the fuel cell.

3. The method for controlling humidity in a fuel cell under variable operating conditions based on fuzzy theory according to claim 1, characterized in that: The optimal membrane water content of the fuel cell under the next target operating condition is obtained by a table lookup method.

4. The method for controlling humidity in a fuel cell under variable operating conditions based on fuzzy theory according to claim 1, characterized in that: The urgency of the driver changing the operating conditions is determined by the rate of change of the driver's accelerator pedal's depression and release strokes.

5. The method for controlling humidity in a fuel cell under variable operating conditions based on fuzzy theory according to claim 1, characterized in that: The loads of the current working condition and the next working condition include: the change amplitude of the current density of the current working condition and the next working condition.

6. The method for controlling humidity in a fuel cell under variable operating conditions based on fuzzy theory according to claim 1, characterized in that: The humidifier adopts an enthalpy wheel humidifier, which recovers the exhaust gas of the fuel cell and passes it into the enthalpy wheel humidifier to absorb the heat and moisture in the exhaust gas and humidify the fresh air entering the enthalpy wheel humidifier. The output of the increase / decrease in humidity is directly related to the motor speed of the enthalpy wheel humidifier.

7. A fuel cell variable operating condition humidity control system based on fuzzy theory, characterized in that: include: A humidity increase / decrease amount determination module is configured to: determine a current proton exchange membrane water content and an optimal membrane water content of a fuel cell under a next target operating condition, and calculate a difference between the two to determine a humidity increase / decrease amount; a fuzzy control rule base construction module configured to: form a fuzzy membership function based on the urgency of the driver changing the working condition and the amount of humidity increase / decrease, combined with the load of the current working condition and the next working condition, and construct a fuzzy control rule base based on the urgency of the driver changing the working condition, the load change, and the humidity change; The fuzzy membership function includes: The rapid humidification when the load changes from large to small conforms to the normal distribution of the current density change: Normal humidification with a load that decreases from large to small follows a gamma distribution with respect to the change in current density: The slow humidification from large to small loads conforms to the Weibull distribution of the current density change: The urgent dehumidification when the load changes from small to large conforms to the normal distribution of the current density change: Normal dehumidification with a load change from small to large follows the gamma distribution of current density variation: The slow dehumidification from a small load to a large load conforms to the Weibull distribution of the current density change: Where, f ( i , j ) represents the calculated optimal amount of humidity increase / decrease, i Indicates the urgency of the real-time driver operation collected, j Represents the real-time current density change amplitude collected, Indicates the amount of humidity increase / decrease in the next step; An optimal humidity increase / decrease amount determination module is configured to: collect real-time operation data and obtain an optimal humidity increase / decrease amount through a fuzzy control rule base; The control module is configured to convert the optimal humidity increase / decrease amount into an output PWM wave to control the humidifier motor voltage, thereby controlling the motor speed to achieve the purpose of controlling the humidity increase / decrease amount.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the fuel cell variable operating condition humidity control method based on fuzzy theory are implemented as described in any one of claims 1 to 6.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps of the fuel cell variable operating condition humidity control method based on fuzzy theory are implemented as described in any one of claims 1 to 6.

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