Hydrogen fuel cell process optimization method and system

Abstract

The invention relates to the technical field of hydrogen fuel cells, and discloses a hydrogen fuel cell process optimization method and system, and the method comprises the steps: obtaining a structure parameter set of a hydrogen fuel cell; inputting the structure parameter set into a pre-constructed humidity parameter matching model to obtain an internal humidity control parameter and an external humidity control parameter; when the hydrogen fuel cell is produced, respectively controlling an inner humidity control system and an outer humidity control system to carry out humidity control on the hydrogen fuel cell production according to the inner humidity control parameter and the outer humidity control parameter; and adjusting the internal humidity control parameter and the external humidity control parameter according to the monitored internal resistance of the hydrogen fuel cell. According to the hydrogen fuel cell process optimization method and system, internal humidity control and external humidity control are linked, the internal humidity control parameters and the external humidity control parameters are dynamically adjusted by combining the feedback of the internal resistance of the cell, the internal resistance consistency and reliability of the cell produced by the hydrogen fuel cell are enhanced, and the cell performance is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of hydrogen fuel cells, and in particular to a hydrogen fuel cell process optimization method and system. Background Art

[0002] As a clean and efficient energy conversion device, hydrogen fuel cells are of great significance in promoting the application of renewable energy and achieving sustainable development. The core principle is to release energy through the electrochemical reaction of hydrogen and oxygen to generate electricity and water. In order to achieve efficient operation of hydrogen fuel cells, the performance of the battery must be optimized under specific environmental conditions, among which humidity control is a crucial factor.

[0003] Studies have shown that humidity has a significant impact on multiple performance indicators of hydrogen fuel cells. For example, appropriate humidity can improve the ionic conductivity of hydrogen fuel cells and reduce the internal resistance of the battery, thereby improving the power density and operating efficiency of the battery. On the contrary, too low humidity will cause the membrane to dry out and reduce conductivity; too high humidity may cause water management problems, resulting in reduced efficiency and shortened life of the fuel cell. Therefore, accurately controlling the humidity level of hydrogen fuel cells during the production process is the key to ensuring their performance and stability.

[0004] The humidity control of hydrogen fuel cells includes internal humidity control and external humidity control. Internal humidity control means that no additional humidification system is introduced, and moisture is stored in the battery's own structure or by changing the intake flow and pressure to balance the generation and consumption of moisture. External humidity control is to use an additional humidifier in the anode and cathode intake system to humidify the gas introduced into the electrodes.

[0005] The existing technology lacks a systematic analysis of the impact of different humidity controls on battery performance, especially the analysis of the impact of dynamic regulation of internal and external humidity controls on battery internal resistance performance. It is unable to respond quickly to changes in battery internal resistance, affecting the consistency and reliability of hydrogen fuel cells. Summary of the invention

[0006] To this end, the technical problem to be solved by the present invention is to overcome the lack of systematic analysis of the impact of different humidity controls on the internal resistance of the battery in the prior art, and to provide a hydrogen fuel cell process optimization method and system, which links internal and external humidity control, and dynamically adjusts the internal and external humidity control parameters in combination with the feedback of the battery internal resistance, so as to enhance the consistency and reliability of the internal resistance of the battery produced by hydrogen fuel cells and improve the battery performance.

[0007] In the first aspect, in order to solve the above technical problems, the present invention provides a hydrogen fuel cell process optimization method, comprising: Obtaining a structural parameter set of a hydrogen fuel cell; Inputting the structural parameter set into a pre-built humidity parameter matching model to obtain internal humidity control parameters and external humidity control parameters; During the production of the hydrogen fuel cell, the internal humidity control system and the external humidity control system are respectively controlled according to the internal humidity control parameter and the external humidity control parameter to perform humidity control on the production of the hydrogen fuel cell; The internal humidity control parameter and the external humidity control parameter are adjusted according to the monitored internal resistance of the hydrogen fuel cell.

[0008] In one embodiment of the present invention, the internal humidity control parameter and the external humidity control parameter are adjusted according to the monitored internal resistance of the hydrogen fuel cell, including: Divide the process of the hydrogen fuel cell into stages according to the process, and test the local internal resistance of the stage product at each stage of the process; Analyze the local internal resistance to obtain an identified internal resistance stage; wherein the identified internal resistance stage is a process stage in which the local internal resistance is greater than or equal to a local internal resistance threshold; Determining a local internal resistance index of the internal resistance identification stage; The internal humidity control parameter and the external humidity control parameter are adjusted according to the local internal resistance index.

[0009] In one embodiment of the present invention, determining the local internal resistance index of the internal resistance identification stage includes: Performing regional division according to the structure of the product at the stage to obtain a plurality of divided regions; The local regional resistance of the plurality of divided regions is tested, and the local regional resistance gradient of the adjacent divided regions is calculated; wherein the local regional resistance gradient is calculated as follows: ; in, , is the number of the adjacent partition area, is the local intra-regional resistance gradient of the adjacent divided regions; For the The local area resistance of the divided area; For the The local area resistance of the divided area; For the The division area and The distance between the divided areas; The local internal resistance index of the internal resistance identification stage is determined according to the local regional internal resistance gradient.

[0010] In one embodiment of the present invention, it also includes: Performing stability analysis on the monitored battery internal resistance to obtain a battery internal resistance stability coefficient of a batch of hydrogen fuel cells; Taking minimizing the difference between the battery internal resistance stability coefficient and the preset stability coefficient as a feedback target, optimizing the internal humidity control parameter and the external humidity control parameter to obtain an optimized humidity control parameter; The internal humidity control system and the external humidity control system are humidified according to the optimized humidity control parameters.

[0011] In one embodiment of the present invention, the set of structural parameters includes catalyst layer thickness, electrode structure, electrolyte membrane thickness and gas diffusion layer thickness.

[0012] In one embodiment of the present invention, it also includes: Divide the structure parameter set according to process to obtain a multi-process structure parameter set; Inputting the multi-process structure parameter set into the humidity parameter matching model to obtain multi-process internal humidity control parameters and multi-process external humidity control parameters; During the production of the hydrogen fuel cell, the multi-process internal humidity control parameters are started in stages to control the internal humidity control system, and the multi-process external humidity control parameters are started in stages to control the external humidity control system to control the humidity of the hydrogen fuel cell production.

[0013] In one embodiment of the present invention, a pre-configured synchronous starter is also included, and when the hydrogen fuel cell is produced, the internal humidity control system and the external humidity control system are synchronously started by the synchronous starter to perform synchronous humidity control on the production of the hydrogen fuel cell.

[0014] In one embodiment of the present invention, constructing the humidity parameter matching model includes: Extracting a sample catalyst layer thickness set, a sample electrode structure set, a sample gas diffusion layer thickness set, a sample electrolyte membrane thickness set, a sample internal humidity control parameter set, and a sample external humidity control parameter set from the historical data of the hydrogen fuel cell production; A convolutional neural network model is trained with a sample catalyst layer thickness set, a sample electrode structure set, a sample gas diffusion layer thickness set, and a sample electrolyte membrane thickness set as input data, and with the sample in-sample humidity control parameter set and the sample out-of-sample humidity control parameter set as output data to obtain a humidity parameter matching model.

[0015] In one embodiment of the present invention, the humidity parameter matching model includes a plurality of humidity parameter matching branches, and the number of the humidity parameter matching branches is consistent with the number of divisions of the process stages; The multiple in-process humidity control parameters and the multiple out-process humidity control parameters are respectively input into the multiple humidity parameter matching branches to obtain the multiple in-process humidity control parameters and the multiple out-process humidity control parameters.

[0016] In the second aspect, based on the same inventive concept, the present invention provides a hydrogen fuel cell process optimization system, comprising: A structural parameter acquisition module, which is used to obtain a structural parameter set of a hydrogen fuel cell; A humidity parameter matching module, which is used to obtain internal humidity control parameters and external humidity control parameters according to the structural parameter set; A humidity control module, which is used to control the humidity of the hydrogen fuel cell production by controlling the internal humidity control system and the external humidity control system according to the internal humidity control parameter and the external humidity control parameter respectively during the production of the hydrogen fuel cell; A humidity control optimization module is used to adjust the internal humidity control parameter and the external humidity control parameter according to the monitored battery internal resistance of the hydrogen fuel cell.

[0017] The above technical solution of the present invention has the following beneficial effects compared with the prior art: The hydrogen fuel cell process optimization method and system described in the present invention link internal and external humidity control to ensure that the electrolyte membrane operates in an optimal hydration state, thereby significantly reducing the internal resistance of the battery and directly improving the power density and overall energy conversion efficiency of the hydrogen fuel cell; according to the monitored internal resistance of the battery, the internal humidity control parameters and the external humidity control parameters are dynamically adjusted to ensure that under different production environments and conditions, humidity fluctuations in the production process are effectively reduced, thereby enhancing product consistency and quality reliability and improving battery performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to make the content of the present invention more clearly understood, the present invention is further described in detail below according to specific embodiments of the present invention in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart of a hydrogen fuel cell process optimization method in a preferred embodiment of the present invention; Figure 2 This is a flow chart of adjusting humidity control parameters according to battery internal resistance in a preferred embodiment of the present invention; Figure 3 It is a structural block diagram of a hydrogen fuel cell process optimization system in a preferred embodiment of the present invention. DETAILED DESCRIPTION

[0019] The present invention is further described below in conjunction with the accompanying drawings and specific embodiments so that those skilled in the art can better understand the present invention and implement it, but the embodiments are not intended to limit the present invention. Embodiment 1

[0020] Reference Figure 1As shown, the embodiment of the present invention discloses a hydrogen fuel cell process optimization method, which aims to optimize the performance and production efficiency of the fuel cell by accurately adjusting the humidity control parameters. The following is a further refinement of the method, including: S100, obtaining a structural parameter set of a hydrogen fuel cell; In specific application scenarios, at various stages of the hydrogen fuel cell production line, such as the electrode manufacturing stage, electrolyte membrane manufacturing stage, and membrane electrode assembly stage, high-precision measurement tools (such as laser scanners, X-ray CT scans, scanning electron microscopes SEM, etc.) are used to detect the various components that make up the hydrogen fuel cell, and obtain accurate data on the catalyst layer thickness, electrode structure, electrolyte membrane thickness, gas diffusion layer thickness, etc., to form the structural parameter set of the hydrogen fuel cell. These structural parameters will directly affect the humidity control requirements of the battery and have an impact on the final battery internal resistance. The structural parameter set provides an accurate basis for humidity control, so that the subsequent humidity adjustment can be accurately matched with the battery structure characteristics, thereby improving battery performance.

[0021] S200, inputting the structural parameter set into a pre-built humidity parameter matching model to obtain internal humidity control parameters and external humidity control parameters; In a specific application scenario, a humidity matching model is constructed through deep learning based on a large amount of experimental data and humidity control effects under different structural parameters in the actual production process. Specifically, constructing the humidity parameter matching model includes extracting a sample catalyst layer thickness set, a sample electrode structure set, a sample gas diffusion layer thickness set, a sample electrolyte membrane thickness set, a sample in-humidity control parameter set, and a sample out-of-sample humidity control parameter set from the historical data of the hydrogen fuel cell production; using the sample catalyst layer thickness set, the sample electrode structure set, the sample gas diffusion layer thickness set, and the sample electrolyte membrane thickness set as input data, and using the sample in-sample humidity control parameter set and the out-of-sample humidity control parameter set as output data to train a convolutional neural network model to obtain a humidity parameter matching model. The humidity matching model should be understood as a convolutional neural network model constructed using a convolutional neural network architecture, and the convolutional neural network model is trained using historical data from hydrogen fuel cell production. During training, the sample catalyst layer thickness set, sample electrode structure set, sample gas diffusion layer thickness set, and sample electrolyte membrane thickness set are used as input data, and the sample internal humidity control parameter set and sample external humidity control parameter set are used as output data. During the training process, the model parameters are continuously optimized until the convergence requirements are met. The neural network basic model that is qualified after the training is completed and verified is the humidity matching model. After the humidity matching model is obtained, the structural parameter set is input into the humidity matching model to obtain the internal humidity control parameters and the external humidity control parameters. The humidity parameter matching model predicts the internal humidity control parameters and the external humidity control parameters based on different structural parameters, which significantly improves the accuracy of humidity control. Accurate humidity control will reduce unnecessary production defects and product inconsistencies caused by improper humidity control.

[0022] S300, during the production of the hydrogen fuel cell, the internal humidity control system and the external humidity control system are respectively controlled according to the internal humidity control parameter and the external humidity control parameter to perform humidity control on the production of the hydrogen fuel cell; In specific application scenarios, the internal humidity control system and the external humidity control system are pre-configured systems for the hydrogen fuel cell production line. The internal humidity control system is a system that stores water through the battery's own structure or balances the generation and consumption of water by changing the intake flow and pressure. It is the humidity control system of the hydrogen fuel cell production line itself; the external humidity control system is an additional humidifier that humidifies the gas introduced into the electrode in the anode and cathode intake systems. The internal humidity control system controls humidity according to the internal humidity control parameters; the external humidity control system controls humidity according to the external humidity control parameters.

[0023] S400: adjusting the internal humidity control parameter and the external humidity control parameter according to the monitored internal resistance of the hydrogen fuel cell.

[0024] In specific application scenarios, the internal resistance changes of each hydrogen fuel cell in a batch are continuously monitored. The internal resistance test module is used to collect and analyze the battery internal resistance data in real time. When the battery internal resistance value is abnormal, the feedback adjustment mechanism is activated. If the battery internal resistance is large, it indicates that the humidity in certain areas is insufficient. Conversely, if the battery internal resistance is too low, it indicates that the humidity in certain areas is too high. By combining with the real-time feedback mechanism of the battery internal resistance, the humidity can be accurately adjusted to avoid unstable performance caused by uneven humidity and improve the consistency and reliability of the battery internal resistance.

[0025] The hydrogen fuel cell process optimization method described in the present invention links internal and external humidity control to ensure that the electrolyte membrane operates in an optimal hydration state, thereby significantly reducing the internal resistance of the battery and directly improving the power density and overall energy conversion efficiency of the hydrogen fuel cell; according to the monitored internal resistance of the battery, the internal humidity control parameters and the external humidity control parameters are dynamically adjusted to ensure that under different production environments and conditions, humidity fluctuations in the production process are effectively reduced, thereby enhancing product consistency and quality reliability and improving battery performance.

[0026] Specifically, refer to Figure 2 As shown, the internal humidity control parameter and the external humidity control parameter are adjusted according to the monitored battery internal resistance of the hydrogen fuel cell, including dividing the hydrogen fuel cell into stages according to the process of the hydrogen fuel cell, and testing the local internal resistance of the stage product in each process stage; analyzing the local internal resistance to obtain an identified internal resistance stage; wherein the identified internal resistance stage is a process stage in which the local internal resistance is greater than or equal to a local internal resistance threshold; determining a local internal resistance index of the identified internal resistance stage; and adjusting the internal humidity control parameter and the external humidity control parameter according to the local internal resistance index.

[0027] In specific application scenarios, according to the production process of hydrogen fuel cells, the production process is divided into multiple stages, such as the electrode manufacturing stage, the electrolyte membrane manufacturing stage, and the membrane electrode assembly stage. Each process stage sets process requirements and corresponding test nodes, and the local internal resistance of the stage product is tested using the electrochemical impedance spectroscopy method EIS or the constant current test method. The test results include ohmic internal resistance and diffusion internal resistance. Ohmic resistance is determined by membrane resistance and contact resistance, and diffusion internal resistance is related to humidity management. The humidity control of multiple processes involved in the entire process of hydrogen fuel cell production affects the internal resistance of the battery. By measuring the local internal resistance of the stage product at each process stage and analyzing the measured local internal resistance, it is possible to clearly locate whether there are humidity control problems in each process stage, avoiding the problem of difficulty in locating and optimizing when humidity control problems occur.

[0028] The measured local internal resistance is analyzed to determine whether it exceeds the set local internal resistance threshold. The local internal resistance threshold is determined based on experimental data. For example, the ohmic internal resistance is generally higher under relatively dry conditions and is usually controlled in the range of tens of milliohms; the diffusion internal resistance generally increases significantly when the humidity is insufficient or the gas transmission is limited; the local internal resistance threshold is dynamically adjusted according to the specific process conditions and production requirements. The process stage where the local internal resistance value is greater than or equal to the threshold is recorded as the identification internal resistance stage; for example: if the local internal resistance is high after the membrane electrode is pressed, it may be that the humidity is insufficient, resulting in poor bonding between the membrane and the catalyst layer; if the local internal resistance increases after the battery stack is assembled, it may be that the diffusion layer is not wet enough or the pressure drop in the stack is uneven. According to the local internal resistance index of the identification internal resistance stage, the internal and external humidity control parameters are optimized, wherein the humidity of the internal humidity control system is increased by increasing the gas flow in the stack, increasing the intake pressure, or optimizing the lifting circulation path; the humidity of the external humidity control system is increased by adjusting the humidifier parameters to increase the ambient humidity compensation.

[0029] The overall closed-loop control is formed. Through the real-time monitoring of local internal resistance and humidity data, the internal and external humidity control parameters are continuously optimized. The internal resistance identification stage is focused on regulation and control, and the humidity distribution in other process stages is gradually optimized. In addition, through the coordinated optimization of staged humidity control and internal and external parameters, unnecessary external humidification operations are reduced, and the energy consumption of the humidifier is reduced; the internal humidity is accurately controlled to reduce the power consumption of the gas circulation system.

[0030] Further, refer to Figure 3 As shown, determining the local internal resistance index of the internal resistance identification stage includes dividing the area according to the structure of the product in the stage to obtain multiple divided areas; testing the local regional internal resistance of the multiple divided areas, and calculating the local regional internal resistance gradient of adjacent divided areas; and determining the local internal resistance index of the internal resistance identification stage according to the local regional internal resistance gradient. The calculation method of the local regional internal resistance gradient is: ; in, , is the number of the adjacent partition area, is the local intra-regional resistance gradient of the adjacent divided regions; For the The local area resistance of the divided area; For the The local area resistance of the divided area; For the The division area and The distance between the divided areas.

[0031] In specific application scenarios, the regional division is carried out according to the structure of the product at the stage, such as the membrane electrode assembly, which is divided into the central area, the edge area, etc. according to the geometric distribution of the electrode, and further subdivided for the central area and the edge area to obtain multiple divided areas; by calculating the local regional resistance gradient of the adjacent divided areas, the change trend of the local regional resistance of the adjacent areas is calculated. If the gradient is large, it means that the internal resistance of the adjacent areas is large, which is usually caused by uneven distribution of humidity and temperature or uneven hydration of the electrolyte membrane, and uneven catalyst activity and obstruction of the gas diffusion layer may also be the cause of the increase in the local internal resistance gradient. If the gradient is small, it means that the hydration and catalyst activity distribution of the area are relatively uniform, which may indicate that the humidity control at this time is relatively balanced and the working state of the battery is relatively stable. In this case, there is no need to adjust the humidity, and the existing humidity control strategy can be maintained to ensure that the battery operates in the best state. The introduction of the local regional resistance gradient can dynamically adjust the humidity control parameters to ensure that the humidity distribution of the battery is uniform and the hydration of the electrolyte membrane is appropriate, thereby improving the overall performance and reliability of the hydrogen fuel cell.

[0032] In another embodiment of the scheme of the present invention, after monitoring the battery internal resistance, it also includes performing stability analysis on the monitored battery internal resistance to obtain the battery internal resistance stability coefficient of the batch hydrogen fuel cell; taking minimizing the difference between the battery internal resistance stability coefficient and the preset stability coefficient as the feedback target, optimizing the internal humidity control parameters and the external humidity control parameters to obtain the optimized humidity control parameters; and humidifying the internal humidity control system and the external humidity control system according to the optimized humidity control parameters.

[0033] When the specific application is implemented, the battery internal resistance is constantly monitored in continuous production, and the battery internal resistance of the obtained batch of hydrogen fuel cells is subjected to stability analysis to evaluate the stability of the humidity control process. The goal of the stability analysis is to determine whether the battery internal resistance can quickly reach the set value and remain stable after humidity adjustment, or whether there will be excessive fluctuations when adjusting the humidity. The battery internal resistance value is analyzed, and the time and fluctuation amplitude required for the battery internal resistance to reach the set value are calculated. These data are converted into a battery internal resistance stability coefficient. The battery internal resistance stability coefficient is used to quantify the stability parameters of the humidity control process. It reflects the stability of the system when the set battery internal resistance is reached. The smaller the battery internal resistance stability coefficient, the smoother the humidity control process; conversely, the larger the battery internal resistance stability coefficient, the greater the fluctuation or instability of the humidity control process.

[0034] The preset stability coefficient is a pre-set target stability value, which reflects the stable state that humidity control should achieve under ideal working conditions. The difference between the battery internal resistance stability coefficient and the preset stability coefficient is used to measure the deviation between the actual humidity control process and the ideal working conditions. The larger the difference, the worse the stability; the smaller the difference, the closer the humidity control is to the ideal state. With the goal of minimizing the difference, the humidity control data is adjusted to make the battery internal resistance stability coefficient as close to the preset stability coefficient as possible. Through continuous iteration and adjustment, the optimized internal and external humidity control parameters are output to ensure that the humidity control adjustment process is smoother, the fluctuation is reduced, and it is close to the ideal stable state.

[0035] In another embodiment of the scheme of the present invention, it also includes dividing the structural parameter set according to the process to obtain a multi-process structural parameter set; inputting the multi-process structural parameter set into the humidity parameter matching model to obtain multi-process internal humidity control parameters and multi-process external humidity control parameters; when the hydrogen fuel cell is produced, the multi-process internal humidity control parameters are started in stages to control the internal humidity control system, and the multi-process external humidity control parameters are started in stages to control the external humidity control system to control the humidity of the hydrogen fuel cell production.

[0036] In specific application scenarios, according to the process flow of hydrogen fuel cells, the humidity control process is divided into different process stages. Each process stage requires different humidity control requirements. According to the requirements of each process stage, the structural parameter set is divided into multiple process structural parameter sets. The multiple process structural parameter sets are input into the humidity parameter matching model. The humidity parameter matching model predicts the internal humidity control parameters and external humidity control parameters of each process stage according to the humidity control requirements of each process stage. At the beginning of each process stage, the corresponding start is used to control the humidity of the internal humidity control system and the external humidity control system according to the internal humidity control parameters and the external humidity control parameters. This process is staged, that is, the humidity control parameters of different process stages are different. The humidity control parameters are set adaptively according to different process stages to achieve fast and effective humidity adjustment and control, and reduce the battery internal resistance stability coefficient.

[0037] Furthermore, it also includes a pre-configured synchronous starter, through which the internal humidity control system and the external humidity control system are synchronously started to perform synchronous humidity control on the production of the hydrogen fuel cell when the hydrogen fuel cell is produced. The synchronous starter is responsible for connecting the humidity control system with the internal humidity control system and the external humidity control system, which can be achieved through an industrial communication protocol so that the three can communicate in real time. When the hydrogen fuel cell is produced, the internal humidity control system and the external humidity control system are synchronously started and run, and the internal and external humidity control systems are coordinated with the production pace to ensure that the humidity change matches the battery internal resistance change.

[0038] Furthermore, the humidity parameter matching model includes multiple humidity parameter matching branches, and the number of the humidity parameter matching branches is consistent with the number of process stage divisions; the multiple in-process humidity control parameters and the multiple out-process humidity control parameters are respectively input into the multiple humidity parameter matching branches to obtain the multiple in-process humidity control parameters and the multiple out-process humidity control parameters.

[0039] In specific application scenarios, the number of humidity parameter matching branches included in the humidity parameter matching model is consistent with the number of process stages. For example, the process flow is divided into three process stages according to the humidity control requirements. The humidity parameter matching model includes three humidity parameter matching branches, each of which corresponds to a process stage one by one, and matches the internal control humidity parameters and external control humidity parameters for the corresponding process stage. Each humidity parameter matching branch corresponds one by one to a specific process stage to ensure that the humidity control parameters (internal control humidity and external control humidity) can be accurately matched according to the requirements of each process stage. For example, higher humidity may be required in some stages to ensure the hydration of the membrane electrode, while lower humidity may be required in other stages to maintain the performance of the gas diffusion layer; this targeted humidity matching can optimize the humidity control of each stage and avoid performance fluctuations caused by excessively high or low humidity. Embodiment 2

[0040] Based on the same inventive concept, the present invention discloses a hydrogen fuel cell process optimization system, referring to Figure 3 As shown, including, A structural parameter acquisition module, which is used to obtain a structural parameter set of a hydrogen fuel cell; A humidity parameter matching module, which is used to obtain internal humidity control parameters and external humidity control parameters according to the structural parameter set; A humidity control module, which is used to control the humidity of the hydrogen fuel cell production by controlling the internal humidity control system and the external humidity control system according to the internal humidity control parameter and the external humidity control parameter respectively during the production of the hydrogen fuel cell; A humidity control optimization module is used to adjust the internal humidity control parameter and the external humidity control parameter according to the monitored battery internal resistance of the hydrogen fuel cell.

[0041] The hydrogen fuel cell process optimization system disclosed in the embodiment of the present invention is used to execute the hydrogen fuel cell process optimization method in the above embodiment, is based on the same inventive concept, has the same technical effect, and will not be repeated here.

[0042] In summary, the hydrogen fuel cell process optimization method and system described in the present invention link internal and external humidity control to ensure that the electrolyte membrane operates in an optimal hydration state, thereby significantly reducing the internal resistance of the battery and directly improving the power density and overall energy conversion efficiency of the hydrogen fuel cell; according to the monitored internal resistance of the battery, the internal humidity control parameters and the external humidity control parameters are dynamically adjusted to ensure that under different production environments and conditions, humidity fluctuations in the production process are effectively reduced, thereby enhancing product consistency and quality reliability and improving battery performance.

[0043] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0044] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, 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 generate 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 flowchart and / or block diagram. 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.

[0045] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate 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 A function specified in one or more boxes.

[0046] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0047] Obviously, the above embodiments are merely examples for clear explanation and are not intended to limit the implementation methods. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to list all the implementation methods here. The obvious changes or modifications derived from these are still within the protection scope of the invention.

Claims

1. A hydrogen fuel cell process optimization method, characterized in that: include, Obtaining a structural parameter set of a hydrogen fuel cell; Inputting the structural parameter set into a pre-built humidity parameter matching model to obtain internal humidity control parameters and external humidity control parameters; During the production of the hydrogen fuel cell, the internal humidity control system and the external humidity control system are respectively controlled according to the internal humidity control parameter and the external humidity control parameter to perform humidity control on the production of the hydrogen fuel cell; The internal humidity control parameter and the external humidity control parameter are adjusted according to the monitored internal resistance of the hydrogen fuel cell.

2. The hydrogen fuel cell process optimization method according to claim 1, characterized in that: The internal humidity control parameter and the external humidity control parameter are adjusted according to the monitored internal resistance of the hydrogen fuel cell, including: Divide the process of the hydrogen fuel cell into stages according to the process, and test the local internal resistance of the stage product at each stage of the process; Analyze the local internal resistance to obtain an identified internal resistance stage; wherein the identified internal resistance stage is a process stage in which the local internal resistance is greater than or equal to a local internal resistance threshold; Determining a local internal resistance index of the internal resistance identification stage; The internal humidity control parameter and the external humidity control parameter are adjusted according to the local internal resistance index.

3. The hydrogen fuel cell process optimization method according to claim 2, characterized in that: Determining the local internal resistance index of the internal resistance identification stage includes: Performing regional division according to the structure of the product at the stage to obtain a plurality of divided regions; The local regional resistance of the plurality of divided regions is tested, and the local regional resistance gradient of the adjacent divided regions is calculated; wherein the local regional resistance gradient is calculated as follows: ； in, , is the number of the adjacent partition area, is the local intra-regional resistance gradient of the adjacent divided regions; For the The local area resistance of the divided area; For the The local area resistance of the divided area; For the The division area and The distance between the divided areas; The local internal resistance index of the internal resistance identification stage is determined according to the local regional internal resistance gradient.

4. The hydrogen fuel cell process optimization method according to claim 1, 2 or 3, characterized in that: Also includes, Performing stability analysis on the monitored battery internal resistance to obtain a battery internal resistance stability coefficient of a batch of hydrogen fuel cells; Taking minimizing the difference between the battery internal resistance stability coefficient and the preset stability coefficient as a feedback target, optimizing the internal humidity control parameter and the external humidity control parameter to obtain an optimized humidity control parameter; The internal humidity control system and the external humidity control system are humidified according to the optimized humidity control parameters.

5. The hydrogen fuel cell process optimization method according to claim 1, characterized in that: The structural parameter set includes catalyst layer thickness, electrode structure, electrolyte membrane thickness and gas diffusion layer thickness.

6. The hydrogen fuel cell process optimization method according to claim 1, characterized in that: Also includes, Divide the structure parameter set according to process to obtain a multi-process structure parameter set; Inputting the multi-process structure parameter set into the humidity parameter matching model to obtain multi-process internal humidity control parameters and multi-process external humidity control parameters; During the production of the hydrogen fuel cell, the multi-process internal humidity control parameters are started in stages to control the internal humidity control system, and the multi-process external humidity control parameters are started in stages to control the external humidity control system to control the humidity of the hydrogen fuel cell production.

7. The hydrogen fuel cell process optimization method according to claim 6, characterized in that: It also includes a pre-configured synchronous starter, through which the internal humidity control system and the external humidity control system are synchronously started during the production of the hydrogen fuel cell to perform synchronous humidity control on the production of the hydrogen fuel cell.

8. The hydrogen fuel cell process optimization method according to claim 6, characterized in that: Constructing the humidity parameter matching model, including: Extracting a sample catalyst layer thickness set, a sample electrode structure set, a sample gas diffusion layer thickness set, a sample electrolyte membrane thickness set, a sample internal humidity control parameter set, and a sample external humidity control parameter set from the historical data of the hydrogen fuel cell production; A convolutional neural network model is trained with a sample catalyst layer thickness set, a sample electrode structure set, a sample gas diffusion layer thickness set, and a sample electrolyte membrane thickness set as input data, and with the sample in-sample humidity control parameter set and the sample out-of-sample humidity control parameter set as output data to obtain a humidity parameter matching model.

9. The hydrogen fuel cell process optimization method according to claim 8, characterized in that: The humidity parameter matching model includes a plurality of humidity parameter matching branches, and the number of the humidity parameter matching branches is consistent with the number of divisions of the process stages; The multiple in-process humidity control parameters and the multiple out-process humidity control parameters are respectively input into the multiple humidity parameter matching branches to obtain the multiple in-process humidity control parameters and the multiple out-process humidity control parameters.

10. A hydrogen fuel cell process optimization system, characterized in that: include, A structural parameter acquisition module, which is used to obtain a structural parameter set of a hydrogen fuel cell; A humidity parameter matching module, which is used to obtain internal humidity control parameters and external humidity control parameters according to the structural parameter set; A humidity control module, which is used to control the humidity of the hydrogen fuel cell production by controlling the internal humidity control system and the external humidity control system according to the internal humidity control parameter and the external humidity control parameter respectively during the production of the hydrogen fuel cell; A humidity control optimization module is used to adjust the internal humidity control parameter and the external humidity control parameter according to the monitored battery internal resistance of the hydrogen fuel cell.

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