A fuel cell performance evaluation method and device, electronic equipment and storage medium

Abstract

The present application relates to fuel cell performance evaluation method, device, electronic equipment and storage medium, the method obtains membrane electrode selection scheme and diffusion layer selection scheme, carries out performance simulation test to membrane electrode selection scheme and diffusion layer selection scheme through simulation model, obtains corresponding simulation test result, carries out verification test to simulation test result, obtains verification test result, adjusts the parameter of simulation model based on verification test result, until the simulation test result obtained by simulation model after parameter adjustment is consistent with verification test result, the simulation model after parameter adjustment is used as the best simulation model, and the performance of fuel cell is evaluated through the best simulation model; the present application can effectively guarantee the accuracy of simulation test model, and reduce the gap between simulation test result and test result.

Description

Technical Field

[0001] This invention relates to the field of fuel cell performance evaluation technology, specifically to a fuel cell performance evaluation method, apparatus, electronic device, and storage medium. Background Technology

[0002] As the power source and crucial power generation device for fuel cell vehicles, the performance of fuel cells directly impacts the vehicle's power and fuel economy. For example, the performance of core components such as the fuel cell membrane electrode assembly (MEA) and diffusion layer directly affects the performance of the fuel cell stack in the vehicle.

[0003] For performance evaluation of fuel cell membrane electrode assemblies (MEAs) and diffusion layers, relevant technologies typically employ two approaches: on-site testing at testing facilities and performance prediction using simulation software. However, on-site testing suffers from drawbacks such as long testing cycles, high costs, and limited testing space resources, and is highly dependent on the consistency of test equipment installation and operator operation. Simulation calculations also suffer from significant discrepancies between simulation and experimental processes, leading to misjudgments of performance.

[0004] For example, Chinese invention patent CN114171760A discloses a testing method for fuel cells. Although the accuracy of battery test results is improved by combining simulation models, this testing method still cannot reduce the gap between simulation test results and experimental results. Therefore, there is an urgent need for a testing method that can effectively ensure the accuracy of simulation test models and reduce the gap between simulation test results and experimental results. Summary of the Invention

[0005] In view of the shortcomings of the prior art described above, this application provides a fuel cell performance evaluation method, apparatus, electronic device and storage medium to solve the technical problem of reducing the gap between simulation test results and experimental results of fuel cells.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] According to one aspect of the embodiments of this application, a fuel cell performance evaluation method is provided. The fuel cell performance evaluation method includes: obtaining a membrane electrode selection scheme and a diffusion layer selection scheme; performing performance simulation tests on the membrane electrode selection scheme and the diffusion layer selection scheme using a simulation model to obtain corresponding simulation test results; performing verification tests on the simulation test results to obtain verification test results; adjusting the parameters of the simulation model based on the verification test results until the simulation test results obtained by the parameter-adjusted simulation model are consistent with the verification test results, and taking the parameter-adjusted simulation model as the optimal simulation model; and evaluating the performance of the fuel cell using the optimal simulation model.

[0008] In one embodiment of this application, the performance simulation test of the membrane electrode selection scheme is performed using a simulation model to obtain corresponding simulation test results, including: acquiring the simulation model, which includes a one-dimensional simulation model and a three-dimensional simulation model; performing a performance simulation test on the membrane electrode selection scheme using the one-dimensional simulation model to obtain corresponding first simulation test results; sorting the first simulation test results in descending order to select a first predetermined number of membrane electrode selection schemes with the highest ranking as a primary output scheme; performing a performance simulation test on the primary output scheme using the three-dimensional simulation model to obtain corresponding second simulation test results; sorting the second simulation test results in descending order; and calculating the relationship between the second simulation test results and the first predetermined number of membrane electrode selection schemes. The first error between the simulation test results; if the first error is not within a first preset error range, adjust the parameters of the one-dimensional simulation model until the first error is within the first preset error range; if the first error is within the first preset error range, compare the order of the second simulation test results with the order of the first simulation test results to obtain a comparison result; if the comparison result is consistent, the membrane electrode selection scheme ranked first in the second simulation test results, the three-dimensional simulation model, and the one-dimensional simulation model with adjusted parameters are respectively taken as the optimal membrane electrode selection scheme, the first optimal three-dimensional simulation model, and the first optimal one-dimensional simulation model; if the comparison result is inconsistent, perform a verification test on the simulation test results.

[0009] In one embodiment of this application, the verification test of the simulation test results includes: selecting the second predetermined number of membrane electrode selection schemes with the highest ranking as secondary output schemes; selecting membrane electrode products according to the secondary output schemes, and conducting test tests on the membrane electrode products through test equipment to obtain the verification test results corresponding to the membrane electrode products, and using the verification test results corresponding to the membrane electrode products as the first test results.

[0010] In one embodiment of this application, based on the verification test results, the parameters of the simulation model are adjusted until the simulation test results obtained through the adjusted simulation model are consistent with the verification test results, and the simulation model with adjusted parameters is taken as the optimal simulation model. This includes: sorting the first experimental test results in descending order; adjusting the parameters of the one-dimensional simulation model according to the first experimental test results until the error between the first simulation test results and the first experimental test results is within a second preset error range, and taking the one-dimensional simulation model with adjusted parameters as the first optimal one-dimensional simulation model; adjusting the parameters of the three-dimensional simulation model according to the first experimental test results until the error between the second simulation test results and the first experimental test results is within the second preset error range, and taking the three-dimensional simulation model with adjusted parameters as the first optimal three-dimensional simulation model; and taking the membrane electrode selection scheme ranked first in the first experimental test results as the optimal membrane electrode selection scheme.

[0011] In one embodiment of this application, the performance simulation test of the diffusion layer selection scheme is performed using a simulation model to obtain corresponding simulation test results, including: performing a performance simulation test of the diffusion layer selection scheme using a first optimal one-dimensional simulation model to obtain corresponding third simulation test results; sorting the third simulation test results in descending order to select the top-ranked third predetermined number of diffusion layer selection schemes as the third output schemes; performing a performance simulation test of the three output schemes using the first optimal three-dimensional simulation model to obtain corresponding fourth simulation test results; sorting the fourth simulation test results in descending order; calculating a second error between the third simulation test results and the fourth simulation test results; if the... If the second error is not within the third preset error range, adjust the parameters of the first optimal one-dimensional simulation model until the second error is within the third preset error range. If the second error is within the third preset error range, compare the order of the fourth simulation test results with the order of the third simulation test results to obtain a comparison result. If the comparison result is consistent, the diffusion layer selection scheme ranked first in the fourth simulation test results, the first optimal three-dimensional simulation model, and the first optimal one-dimensional simulation model after parameter adjustment are respectively taken as the optimal diffusion layer selection scheme, the second optimal three-dimensional simulation model, and the second optimal one-dimensional simulation model. If the comparison result is inconsistent, verify the simulation test results.

[0012] In one embodiment of this application, the verification test of the simulation test results includes: selecting the top-ranked fourth predetermined number of diffusion layer selection schemes as four output schemes; selecting diffusion layer products according to the four output schemes, and conducting test tests on the diffusion layer products using test equipment to obtain the verification test results corresponding to the diffusion layer products, and using the verification test results corresponding to the diffusion layer products as the second test results.

[0013] In one embodiment of this application, based on the verification test results, the parameters of the simulation model are adjusted until the simulation test results are consistent with the verification test results, and the parameter-adjusted simulation model is taken as the optimal simulation model. This includes: sorting the second experimental test results in descending order; adjusting the parameters of the first optimal one-dimensional simulation model according to the second experimental test results until the error between the third simulation test result and the second experimental test result is within a fourth preset error range, and taking the parameter-adjusted first optimal one-dimensional simulation model as the second optimal one-dimensional simulation model; adjusting the parameters of the first optimal three-dimensional simulation model according to the second experimental test results until the error between the fourth simulation test result and the second experimental test result is within the fourth preset error range, and taking the parameter-adjusted first optimal three-dimensional simulation model as the second optimal three-dimensional simulation model; and taking the diffusion layer selection scheme ranked first in the second experimental test results as the optimal diffusion layer selection scheme.

[0014] In one embodiment of this application, the performance evaluation of a fuel cell using the optimal simulation model includes: selecting a first influencing factor in the membrane electrode selection scheme and changing the value of the first influencing factor multiple times; inputting the values ​​of multiple first influencing factors into the first optimal one-dimensional simulation model and the first optimal three-dimensional simulation model respectively to obtain multiple first performance evaluation results and multiple second performance evaluation results; determining the variation law of membrane electrode performance based on the multiple first performance evaluation results and the multiple second performance evaluation results; selecting a second influencing factor in the diffusion layer selection scheme and changing the value of the second influencing factor multiple times; inputting the values ​​of multiple second influencing factors into the second optimal one-dimensional simulation model and the second optimal three-dimensional simulation model respectively to obtain multiple third performance evaluation results and multiple fourth performance evaluation results; determining the variation law of diffusion layer performance based on the multiple third performance evaluation results and the multiple fourth performance evaluation results; and evaluating the performance of the fuel cell based on the variation law of membrane electrode performance and the variation law of diffusion layer performance.

[0015] According to one aspect of the embodiments of this application, a fuel cell performance evaluation device is provided, the fuel cell performance evaluation device comprising: an acquisition module for acquiring a membrane electrode selection scheme and a diffusion layer selection scheme; a simulation testing module for performing performance simulation tests on the membrane electrode selection scheme and the diffusion layer selection scheme using a simulation model to obtain corresponding simulation test results; a verification testing module for performing verification tests on the simulation test results to obtain verification test results; a parameter adjustment module for adjusting the parameters of the simulation model based on the verification test results until the simulation test results are consistent with the verification test results, and taking the parameter-adjusted simulation model as the optimal simulation model; and a performance evaluation module for evaluating the performance of the fuel cell using the optimal simulation model.

[0016] According to one aspect of the embodiments of this application, an electronic device is provided, including: one or more processors; and a storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the fuel cell performance evaluation method as described above.

[0017] According to one aspect of the embodiments of this application, a computer storage medium is provided, comprising: computer-readable instructions stored thereon, wherein when the computer-readable instructions are executed by a computer processor, the computer performs the fuel cell performance evaluation method described above.

[0018] The beneficial effects of this invention are as follows: By adjusting the parameters of the simulation model based on the verification test results until the simulation test results are consistent with the verification test results, the adjusted simulation model is taken as the optimal simulation model. The performance of the membrane electrode assembly (MEA) and diffusion layer of the fuel cell is then evaluated using this optimal simulation model. This effectively ensures the accuracy of the simulation test model and reduces the gap between simulation test results and experimental results. Furthermore, this invention has good engineering applicability, flexible operation, and high computational efficiency, which can reduce experimental costs and shorten project development cycles, significantly improving the efficiency of fuel cell MEA and diffusion layer performance evaluation. The simulation model's accuracy is not affected by changes in the test environment, demonstrating its versatility. Moreover, this invention improves the accuracy of the simulation calculation model through simulation-experiment benchmarking. Attached Figure Description

[0019] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application. It is obvious that the drawings described below are merely some embodiments of this application, and those skilled in the art can obtain other drawings based on these drawings without any inventive effort. In the drawings:

[0020] Figure 1 This is a schematic diagram illustrating an exemplary system architecture as shown in an exemplary embodiment of this application;

[0021] Figure 2 This is a flowchart illustrating a fuel cell performance evaluation method as shown in an exemplary embodiment of this application;

[0022] Figure 3 This is a flowchart illustrating a membrane electrode performance evaluation method according to an exemplary embodiment of this application;

[0023] Figure 4 This is a flowchart illustrating a diffusion layer performance evaluation method in an exemplary embodiment of this application;

[0024] Figure 5 This is a block diagram illustrating a fuel cell performance evaluation device according to an exemplary embodiment of this application;

[0025] Figure 6 A schematic diagram of the structure of a computer system suitable for implementing the embodiments of this application is shown;

[0026] Figure 7 This is a schematic diagram illustrating the comparison between simulation test values ​​and experimental test values, as shown in an exemplary embodiment of this application. Detailed Implementation

[0027] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0028] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0029] The flowcharts shown in the accompanying drawings are merely illustrative and do not necessarily include all content and operations / steps, nor do they necessarily have to be performed in the described order. For example, some operations / steps can be broken down, while others can be combined or partially combined; therefore, the actual execution order may change depending on the specific circumstances.

[0030] In this application, "multiple" refers to two or more. "And / or" describes the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A alone, A and B simultaneously, or B alone. The character " / " generally indicates that the preceding and following related objects have an "or" relationship.

[0031] The technical solutions of this application relate to technologies such as lithium-ion battery management, and are specifically illustrated through the following embodiments:

[0032] Figure 1 This is a schematic diagram illustrating an exemplary system architecture as shown in an exemplary embodiment of this application.

[0033] Reference Figure 1 As shown, the system architecture may include a solution storage device 101 and a computer device 102. The computer device 102 may be at least one of a desktop graphics processing unit (GPU) computer, a GPU computing cluster, or a neural network computer. Those skilled in the art can use the computer device 102 to adjust the parameters of the simulation model based on the verification test results until the simulation test results match the verification test results. The simulation model with adjusted parameters is then used as the optimal simulation model, and the performance of the membrane electrode assembly (MEA) and diffusion layer of the fuel cell is evaluated using this optimal simulation model. The solution storage device 101 is used to collect MEA selection schemes and diffusion layer selection schemes. In this embodiment, the solution storage device 101 uses a memory or similar device to store the MEA selection schemes and provides them to the computer device 102 for processing.

[0034] Indicatively, after acquiring the membrane electrode selection scheme and diffusion layer selection scheme from the scheme storage device 101, the computer device 102 adjusts the parameters of the simulation model based on the verification test results until the simulation test results are consistent with the verification test results. The simulation model with adjusted parameters is then used as the optimal simulation model, and the performance of the membrane electrode and diffusion layer of the fuel cell is evaluated using the optimal simulation model. This effectively ensures the accuracy of the simulation test model and reduces the gap between the simulation test results and the experimental results.

[0035] It should be noted that the fuel cell performance evaluation method provided in this application embodiment is generally executed by computer device 102, and correspondingly, the fuel cell performance evaluation device is generally installed in computer device 102.

[0036] The implementation details of the technical solutions in the embodiments of this application are described in detail below:

[0037] Figure 2This is a flowchart illustrating an exemplary embodiment of a fuel cell performance evaluation method, which can be executed by a computational processing device. The computational processing device may be... Figure 1 The computer device 102 shown is illustrated. (Refer to...) Figure 2 As shown, the fuel cell performance evaluation method includes at least steps S210 to S250, which are described in detail below:

[0038] In step S210, the membrane electrode selection scheme and the diffusion layer selection scheme are obtained.

[0039] In one embodiment of this application, the membrane electrode selection scheme includes a combination of multiple membrane electrode influencing factors, such as membrane thickness, porosity, permeability, contact angle, solid matrix conductivity, and contact resistance. Changing the values ​​of these influencing factors results in different membrane electrode selection schemes. Similarly, the diffusion layer selection scheme is a combination of multiple diffusion layer influencing factors, including membrane thickness, porosity, permeability, and contact angle. Changing the values ​​of these factors also results in different diffusion layer selection schemes.

[0040] In step S220, the performance of the membrane electrode selection scheme and the diffusion layer selection scheme is simulated and tested using a simulation model to obtain the corresponding simulation test results.

[0041] In this embodiment, the simulation model includes a one-dimensional simulation model and a three-dimensional simulation model. The one-dimensional simulation model is used to perform performance simulation tests on the membrane electrode selection schemes, obtaining corresponding first simulation test results. These first simulation test results are then sorted in descending order, and a first predetermined number of membrane electrode selection schemes with the highest rankings are selected as the primary output schemes. This achieves preliminary and rapid screening of membrane electrode selection schemes, improving the efficiency of preliminary simulation evaluation and saving computational resources. Then, the three-dimensional simulation model is used to perform performance simulation tests on the primary output schemes, obtaining corresponding second simulation test results. These second simulation test results are then used to verify the first simulation test results, improving the accuracy of the parameter settings in the one-dimensional simulation model, thereby improving the simulation test accuracy of both the one-dimensional and three-dimensional simulation models.

[0042] In this embodiment, the performance simulation test of the diffusion layer selection scheme is performed using the first optimal one-dimensional simulation model to obtain the corresponding third simulation test results. The third simulation test results are then sorted in descending order, and the top-ranked third predetermined number of diffusion layer selection schemes are selected as the third output schemes. This achieves preliminary and rapid screening of diffusion layer selection schemes, improves the efficiency of preliminary simulation evaluation, and saves computational resources. Furthermore, the accuracy of the third simulation test results is further improved because they are obtained through performance simulation testing using the first optimal one-dimensional simulation model. Then, the performance simulation test of the three output schemes is performed using the first optimal three-dimensional simulation model to obtain the corresponding fourth simulation test results. The accuracy of the fourth simulation test results is further improved because they are obtained through the first optimal three-dimensional simulation model. The fourth simulation test results are then used to verify the third simulation test results, further improving the parameter setting accuracy of the second optimal one-dimensional simulation model, thereby improving the simulation test accuracy of both the second optimal one-dimensional and the second optimal three-dimensional simulation models.

[0043] In step S230, the simulation test results are verified to obtain the verification test results. The parameters of the one-dimensional simulation model and the three-dimensional simulation model are further adjusted based on the verification test results to improve the parameter setting accuracy and simulation test accuracy of the one-dimensional simulation model, the three-dimensional simulation model, the first optimal one-dimensional simulation model, and the first optimal three-dimensional simulation model.

[0044] In step S240, based on the verification test results, the parameters of the simulation model are adjusted until the simulation test results obtained through the parameter-adjusted simulation model are consistent with the verification test results, and the parameter-adjusted simulation model is taken as the best simulation model.

[0045] In this embodiment, the verification test results include the first experimental test results and the second experimental test results. Based on the first experimental test results, the parameters of the one-dimensional simulation model and the three-dimensional simulation model are adjusted until the error between the first simulation test result and the first experimental test result is within a second preset error range, and the error between the second simulation test result and the first experimental test result is also within a second preset error range. Based on the second experimental results, the parameters of the first optimal one-dimensional simulation model and the first optimal three-dimensional simulation model are adjusted until the error between the third simulation test result and the second experimental test result is within a fourth preset error range, and the error between the fourth simulation test result and the second experimental test result is also within a fourth preset error range. By verifying the simulation test results through the verification test results, the parameter setting accuracy of the one-dimensional simulation model, the three-dimensional simulation model, the first optimal one-dimensional simulation model, and the first optimal three-dimensional simulation model is further improved, thereby improving the calculation accuracy and simulation result accuracy of the one-dimensional simulation model, the three-dimensional simulation model, the first optimal one-dimensional simulation model, and the first optimal three-dimensional simulation model, and reducing the gap between the simulation test results and the experimental results.

[0046] In step S250, the performance of the fuel cell is evaluated using the optimal simulation model.

[0047] In this embodiment, as the computational accuracy and simulation result accuracy of the first optimal one-dimensional simulation model, the first optimal three-dimensional simulation model, the second optimal one-dimensional simulation model, and the second optimal three-dimensional simulation model are improved, the accuracy of multiple first performance evaluation results, multiple second performance evaluation results, multiple third performance evaluation results, and multiple fourth performance evaluation results are also improved, thereby improving the accuracy of performance evaluation of fuel cells.

[0048] In this embodiment, by verifying the results of three-dimensional simulation tests with those of one-dimensional simulation tests, as well as by verifying the results of verification tests with those of simulation tests, the experimental costs are reduced and the project development cycle is shortened, which has good engineering practicality. It significantly improves the efficiency and accuracy of performance evaluation of fuel cell membrane electrodes and diffusion layers. Moreover, the simulation test accuracy of one-dimensional simulation models and three-dimensional simulation models does not change with the test environment, and the operation is flexible and versatile.

[0049] In one embodiment of this application, a performance simulation test is performed on the membrane electrode selection scheme using a simulation model to obtain the corresponding simulation test results, including:

[0050] Obtain the simulation model. The simulation model includes one-dimensional simulation models and three-dimensional simulation models.

[0051] In this embodiment, the initial structural parameters, initial physical performance parameters, and initial electrochemical parameters are set identically for the same membrane electrode selection scheme in both the one-dimensional and three-dimensional simulation models. The initial structural parameters, initial physical performance parameters, and initial electrochemical parameters are also set identically for the same diffusion layer selection scheme in both the one-dimensional and three-dimensional simulation models.

[0052] The performance of the membrane electrode selection scheme was simulated and tested using a one-dimensional simulation model, and the corresponding first simulation test results were obtained.

[0053] In this embodiment, the one-dimensional simulation model can be pre-built using software such as MATLAB, GT, or Aimsim. The method for pre-building the one-dimensional simulation model is not limited here. Because the one-dimensional simulation model has a fast computation speed, the first simulation test results can be obtained quickly, significantly improving simulation computation efficiency and saving computational resources.

[0054] The first simulation test results are sorted in descending order, and the first predetermined number of membrane electrode selection schemes with the highest ranking are selected as the first output scheme.

[0055] In this embodiment, by sorting the first simulation test results in descending order, the descending order of the membrane electrode selection schemes is obtained, thereby quickly selecting the first predetermined number of membrane electrode selection schemes with better performance, improving the efficiency of one-dimensional simulation test evaluation and saving computing resources.

[0056] In this embodiment, the first predetermined quantity is less than the number of membrane electrode selection schemes. For example, when the number of membrane electrode selection schemes is 10, the first predetermined quantity can be 6, or it can be other values.

[0057] The performance of the first output scheme was simulated and tested using a 3D simulation model, and the corresponding second simulation test results were obtained.

[0058] In this embodiment, the three-dimensional simulation model can be pre-built using software such as Fluent, Star-CCM+, or AVL-Fire. The method of pre-building the three-dimensional simulation model is not limited here.

[0059] The second simulation test results are sorted in descending order to obtain the descending order of the first predetermined number of membrane electrode selection schemes.

[0060] Calculate the first error between the second simulation test result and the first simulation test result.

[0061] In this embodiment, the first error can be the ratio of the difference between the second simulation test result and the first simulation test result to the second simulation test result.

[0062] If the first error is not within the first preset error range, adjust the parameters of the one-dimensional simulation model until the first error is within the first preset error range.

[0063] In this embodiment, the first preset error range can be (0-10)%, or it can be any other value range. The parameters of the one-dimensional simulation model can be adjusted according to the relationship between the first error and the first preset error range. Here, the parameter adjustment process of the one-dimensional simulation model is not specifically limited.

[0064] If the first error is within the first preset error range, the order of the second simulation test results is compared with the order of the first simulation test results to obtain the comparison result. If the comparison result is consistent, the membrane electrode selection scheme, the three-dimensional simulation model and the one-dimensional simulation model after parameter adjustment ranked first in the second simulation test results are respectively taken as the best membrane electrode selection scheme, the first best three-dimensional simulation model and the first best one-dimensional simulation model.

[0065] In this embodiment, when the first error is within a first preset error range, the arrangement order of the second simulation test results is compared with the arrangement order of the first simulation test results, and the first simulation test results are further verified by the second simulation test results, thereby improving the accuracy of the simulation verification.

[0066] If the comparison results are inconsistent, the simulation test results should be verified.

[0067] In this embodiment, the process of verifying the simulation test results of the membrane electrode selection scheme includes: (1) selecting the second predetermined number of membrane electrode selection schemes with the highest ranking as secondary output schemes; (2) selecting membrane electrode products according to the secondary output schemes, and conducting test tests on the membrane electrode products through test equipment to obtain the verification test results corresponding to the membrane electrode products, and taking the verification test results corresponding to the membrane electrode products as the first test test results.

[0068] In one embodiment of this application, the simulation test results are verified by:

[0069] The second-ranked membrane electrode selection scheme with the highest number of samples is selected as the secondary output scheme.

[0070] In this embodiment, the second predetermined quantity is less than the first predetermined quantity. For example, when the first predetermined quantity is 6, the second predetermined quantity can be 3, or it can be other values. By selecting the membrane electrode selection schemes with the highest-ranking second predetermined quantities, a secondary output scheme with better performance is further screened from the primary output schemes.

[0071] Select membrane electrode products according to the secondary output scheme, and test the membrane electrode products through test equipment to obtain the corresponding verification test results of the membrane electrode products. The verification test results of the membrane electrode products are used as the first test results.

[0072] In this embodiment, membrane electrode products corresponding to all or part of the membrane electrode selection schemes in the secondary output scheme can be selected for testing. The testing equipment is used to perform performance testing on the membrane electrode products; the type and model of the testing equipment are not specifically limited here.

[0073] In one embodiment of this application, based on the verification test results, the parameters of the simulation model are adjusted until the simulation test results obtained through the adjusted simulation model are consistent with the verification test results. The simulation model with adjusted parameters is then taken as the optimal simulation model. This includes:

[0074] The test results of the first experiment were sorted in descending order, and the performance ranking of the membrane electrode products was obtained based on the descending order.

[0075] The parameters of the one-dimensional simulation model are adjusted based on the results of the first test until the error between the first simulation test result and the first test result is within the second preset error range. The one-dimensional simulation model with adjusted parameters is then taken as the first optimal one-dimensional simulation model.

[0076] In this embodiment, the second preset error range can be (0-10)%, or it can be any other value range. The parameters of the one-dimensional simulation model can be adjusted according to the relationship between the error between the first simulation test result and the first experimental test result and the second preset error range. Here, the parameter adjustment process of the one-dimensional simulation model is not specifically limited.

[0077] In this embodiment, the error between the first simulation test result and the first experimental test result corresponding to the same membrane electrode selection scheme can be the ratio of the difference between the first simulation test result and the first experimental test result to the first simulation test result, or it can be the ratio of the difference between the first simulation test result and the first experimental test result to the first experimental test result.

[0078] In this embodiment, the first simulation test results are verified by the first experimental test results, which further improves the parameter setting accuracy of the one-dimensional simulation model, thereby improving the calculation accuracy and simulation results accuracy of the one-dimensional simulation model and reducing the gap between the first simulation test results and the first experimental test results.

[0079] The parameters of the three-dimensional simulation model are adjusted based on the results of the first test until the error between the second simulation test result and the first test result is within the second preset error range. The three-dimensional simulation model with adjusted parameters is then taken as the first optimal three-dimensional simulation model.

[0080] In this embodiment, the parameters of the three-dimensional simulation model can be adjusted according to the relationship between the error between the second simulation test result and the first experimental test result and the second preset error range. Here, the parameter adjustment process of the three-dimensional simulation model is not specifically limited.

[0081] In this embodiment, the error between the second simulation test result and the first experimental test result corresponding to the same membrane electrode selection scheme can be the ratio of the difference between the second simulation test result and the first experimental test result to the second simulation test result, or it can be the ratio of the difference between the second simulation test result and the first experimental test result to the first experimental test result.

[0082] In this embodiment, the accuracy of parameter settings of the three-dimensional simulation model is further improved by verifying the second simulation test results with the first experimental test results, thereby improving the calculation accuracy and simulation results of the three-dimensional simulation model and reducing the gap between the second simulation test results and the first experimental test results.

[0083] The membrane electrode selection scheme that ranks first in the first test results is taken as the optimal membrane electrode selection scheme.

[0084] In this embodiment, the selection of the optimal membrane electrode selection scheme not only ensures the reliability of the membrane electrode selection scheme, but also meets the design and development requirements.

[0085] In one embodiment of this application, a performance simulation test is performed on the diffusion layer selection scheme using a simulation model to obtain the corresponding simulation test results, including:

[0086] The performance of the diffusion layer selection scheme was tested using the first optimal one-dimensional simulation model, and the corresponding third simulation test results were obtained.

[0087] In this embodiment, the performance simulation test of the diffusion layer selection scheme is performed using a first optimal one-dimensional simulation model. Because the first optimal one-dimensional simulation model has a fast calculation speed, the third simulation test results can be obtained quickly, significantly improving simulation calculation efficiency and saving computing resources. The third simulation test results are obtained through performance simulation testing using the first optimal one-dimensional simulation model, thereby improving the accuracy of the third simulation test results.

[0088] The third simulation test results are sorted in descending order, and the top-ranked diffusion layer selection schemes of the third predetermined number are selected as the three output schemes.

[0089] In this embodiment, by sorting the third simulation test results in descending order, the descending order of the diffusion layer selection schemes is obtained, thereby quickly selecting a third predetermined number of diffusion layer selection schemes with better performance, improving the efficiency of one-dimensional simulation test evaluation and saving computing resources.

[0090] In this embodiment, the third predetermined quantity is less than the number of diffusion layer selection schemes. For example, when the number of diffusion layer selection schemes is 10, the third predetermined quantity can be 6, or it can be other values.

[0091] The performance of the three output schemes was tested using the first optimal three-dimensional simulation model, and the corresponding fourth simulation test results were obtained.

[0092] In this embodiment, the fourth simulation test result is obtained by performing performance simulation test using the first optimal three-dimensional simulation model, thereby improving the accuracy of the fourth simulation test result.

[0093] The fourth simulation test results are sorted in descending order to obtain the descending order of the third predetermined number of diffusion layer selection schemes.

[0094] Calculate the second error between the third simulation test result and the fourth simulation test result.

[0095] In this embodiment, the second error can be the ratio of the difference between the fourth simulation test result and the third simulation test result to the fourth simulation test result.

[0096] If the second error is not within the third preset error range, adjust the parameters of the first optimal one-dimensional simulation model until the second error is within the third preset error range.

[0097] In this embodiment, the third preset error range can be (0-10)%, or it can be any other value range. The parameters of the first optimal one-dimensional simulation model can be adjusted according to the relationship between the second error and the third preset error range. Here, the parameter adjustment process of the first optimal one-dimensional simulation model is not specifically limited.

[0098] If the second error is within the third preset error range, compare the order of the fourth simulation test results with the order of the third simulation test results to obtain the comparison results. If the comparison results are consistent, the diffusion layer selection scheme ranked first in the fourth simulation test results, the first best three-dimensional simulation model, and the first best one-dimensional simulation model after parameter adjustment will be taken as the best diffusion layer selection scheme, the second best three-dimensional simulation model, and the second best one-dimensional simulation model, respectively.

[0099] In this embodiment, when the second error is within the third preset error range, the order of the fourth simulation test results is compared with the order of the third simulation test results, and the third simulation test results are further verified by the fourth simulation test results, thereby improving the accuracy of the simulation verification.

[0100] If the comparison results are inconsistent, the simulation test results should be verified.

[0101] In this embodiment, the process of verifying the simulation test results of the diffusion layer selection scheme includes: (1) selecting the fourth predetermined number of diffusion layer selection schemes with the highest ranking as the fourth output scheme; (2) selecting diffusion layer products according to the four output schemes, and conducting test tests on the diffusion layer products through test equipment to obtain the verification test results corresponding to the diffusion layer products, and taking the verification test results corresponding to the diffusion layer products as the second test results.

[0102] In one embodiment of this application, the simulation test results are verified by:

[0103] The fourth predetermined number of diffusion layer selection schemes with the highest ranking are selected as the fourth output schemes.

[0104] In this embodiment, the fourth predetermined quantity is less than the third predetermined quantity. For example, when the third predetermined quantity is 6, the fourth predetermined quantity can be 3, or it can be other values. By selecting the diffusion layer selection scheme with the highest-ranking fourth predetermined quantity, a better-performing four-output scheme is further selected from the three-output schemes.

[0105] The diffusion layer product is selected based on the four output schemes, and the diffusion layer product is tested using experimental equipment to obtain the corresponding verification test results. The verification test results of the diffusion layer product are used as the second experimental test results.

[0106] In this embodiment, membrane electrode products corresponding to all or part of the four output schemes can be selected for testing. The testing equipment is for performance testing of diffusion layer products; the type and model of the testing equipment are not specifically limited here.

[0107] In one embodiment of this application, based on the verification test results, the parameters of the simulation model are adjusted until the simulation test results are consistent with the verification test results. The simulation model with adjusted parameters is then taken as the optimal simulation model. This includes:

[0108] The test results of the second experiment were sorted in descending order, and the performance ranking of the diffusion layer products was obtained based on the descending order ranking.

[0109] Based on the results of the second test, the parameters of the first optimal one-dimensional simulation model are adjusted until the error between the third simulation test result and the second test result is within the fourth preset error range. The first optimal one-dimensional simulation model after parameter adjustment is then used as the second optimal one-dimensional simulation model.

[0110] In this embodiment, the fourth preset error range can be (0-10)%, or it can be any other value range. The parameters of the first optimal one-dimensional simulation model can be adjusted according to the relationship between the error between the third simulation test result and the second experimental test result and the fourth preset error range. Here, the parameter adjustment process of the first optimal one-dimensional simulation model is not specifically limited.

[0111] In this embodiment, the error between the third simulation test result and the second experimental test result corresponding to the same diffusion layer selection scheme can be the ratio of the difference between the third simulation test result and the second experimental test result to the third simulation test result, or it can be the ratio of the difference between the third simulation test result and the second experimental test result to the second experimental test result.

[0112] In this embodiment, the parameter setting accuracy of the first optimal one-dimensional simulation model is further improved by verifying the third simulation test results with the second experimental test results. This improves the calculation accuracy and simulation result accuracy of the first optimal one-dimensional simulation model, and reduces the gap between the third simulation test results and the second experimental test results.

[0113] Based on the results of the second test, the parameters of the first optimal three-dimensional simulation model are adjusted until the error between the fourth simulation test result and the second test result is within the fourth preset error range. The first optimal three-dimensional simulation model after parameter adjustment is then used as the second optimal three-dimensional simulation model.

[0114] In this embodiment, the parameters of the first optimal three-dimensional simulation model can be adjusted according to the relationship between the error between the fourth simulation test result and the second experimental test result and the fourth preset error range. Here, the parameter adjustment process of the first optimal three-dimensional simulation model is not specifically limited.

[0115] In this embodiment, the error between the fourth simulation test result and the second experimental test result corresponding to the same diffusion layer selection scheme can be the ratio of the difference between the fourth simulation test result and the second experimental test result to the fourth simulation test result, or it can be the ratio of the difference between the fourth simulation test result and the second experimental test result to the second experimental test result.

[0116] In this embodiment, the verification of the fourth simulation test results by the second experimental test results further improves the parameter setting accuracy of the first optimal three-dimensional simulation model, thereby improving the calculation accuracy and simulation result accuracy of the first optimal three-dimensional simulation model and reducing the gap between the fourth simulation test results and the second experimental test results.

[0117] The diffusion layer selection scheme that ranks first in the second test results is taken as the optimal diffusion layer selection scheme.

[0118] In this embodiment, the selection of the optimal diffusion layer not only ensures the reliability of the diffusion layer selection scheme, but also meets the design and development requirements.

[0119] In one embodiment of this application, the performance evaluation of a fuel cell is performed using an optimal simulation model, including:

[0120] The first influencing factor in the membrane electrode selection scheme was selected, and the value of the first influencing factor was changed multiple times.

[0121] In this embodiment, the first influencing factor can be the membrane thickness, porosity, permeability, contact angle, solid matrix conductivity, or contact resistance of the membrane electrode, etc. Changing the value of the first influencing factor allows for the creation of different membrane electrode selection schemes from multiple influencing factors.

[0122] The values ​​of multiple first influencing factors are input into the first optimal one-dimensional simulation model and the first optimal three-dimensional simulation model, respectively, to obtain multiple first performance evaluation results and multiple second performance evaluation results.

[0123] In this embodiment, as the accuracy of the first optimal one-dimensional simulation model and the first optimal three-dimensional simulation model is improved, the accuracy of multiple first performance evaluation results and multiple second performance evaluation results is also improved.

[0124] Based on multiple first performance evaluation results and multiple second performance evaluation results, the variation law of membrane electrode performance was determined.

[0125] In this embodiment, a single variable is used to change the value of a certain first influencing factor in the membrane electrode selection scheme multiple times, and the degree and pattern of the influence of the certain first influencing factor on the membrane electrode performance are predicted by multiple first performance evaluation results and multiple second performance evaluation results.

[0126] In this embodiment, the changing trends of multiple first performance evaluation results and multiple second performance evaluation results can provide guidance for the optimal selection of membrane electrodes.

[0127] The second influencing factor in the diffusion layer selection scheme was selected, and the value of the second influencing factor was changed multiple times.

[0128] In this embodiment, the second influencing factor can be the film thickness, porosity, permeability, and contact angle of the diffusion layer. Changing the value of the second influencing factor results in different diffusion layer selection schemes.

[0129] The values ​​of multiple second influencing factors are input into the second optimal one-dimensional simulation model and the second optimal three-dimensional simulation model, respectively, to obtain multiple third performance evaluation results and multiple fourth performance evaluation results.

[0130] In this embodiment, as the accuracy of the second optimal one-dimensional simulation model and the second optimal three-dimensional simulation model is improved, the accuracy of multiple third performance evaluation results and multiple fourth performance evaluation results is also improved.

[0131] Based on multiple third-level and fourth-level performance evaluation results, the variation pattern of the diffusion layer performance was determined.

[0132] In this embodiment, a single variable is used to change the value of a certain second influencing factor in the diffusion layer selection scheme multiple times, and the degree and pattern of the influence of a certain second influencing factor on the diffusion layer performance are predicted through multiple third performance evaluation results and multiple fourth performance evaluation results.

[0133] In this embodiment, the changing trends of multiple third performance evaluation results and multiple fourth performance evaluation results can provide guidance for the optimization selection of the diffusion layer.

[0134] Based on the variation patterns of membrane electrode performance and diffusion layer performance, the performance of fuel cells is evaluated.

[0135] In this embodiment, when a certain first influencing factor or a certain second influencing factor is changed, the variation law of membrane electrode performance and the variation law of diffusion layer performance are comprehensively considered to provide guidance for the optimal selection of membrane electrode and diffusion layer, and at the same time improve the accuracy of fuel cell performance evaluation.

[0136] Figure 3 This is a flowchart illustrating a membrane electrode performance evaluation method according to an exemplary embodiment of this application, with reference to... Figure 3 As shown, the membrane electrode performance evaluation method includes: (1) the influencing factors of the membrane electrode include the membrane thickness, porosity, permeability, contact angle, solid matrix conductivity, and contact resistance of the membrane electrode; (2) measuring the influencing factors in the membrane electrode selection scheme (e.g., measuring the porosity of the membrane electrode); (3) conducting one-dimensional simulation tests: inputting multiple influencing factors from different membrane electrode selection schemes into a one-dimensional simulation model to quickly perform preliminary screening of membrane electrode selection schemes and sort them according to performance; (4) conducting three-dimensional simulation tests. And further simulate and verify the first 6 membrane electrode selection schemes obtained by the one-dimensional simulation test through the three-dimensional simulation model; (5) whether the error between the three-dimensional simulation test results and the one-dimensional simulation test results is <10%. If not, fine-tune some parameters of the one-dimensional simulation model until the error between the three-dimensional simulation test results and the one-dimensional simulation test results is <10%; (6) if it is satisfied, determine whether the ranking of the three-dimensional simulation test results is consistent with the ranking of the one-dimensional simulation test results. If not, further test and verify the first 3 membrane electrode selection schemes obtained by the three-dimensional simulation test to obtain the verification test results; if consistent, the membrane electrode selection scheme ranked first in the three-dimensional simulation test results is the best membrane electrode selection scheme, the three-dimensional simulation model is the first best three-dimensional simulation model, and the one-dimensional simulation model after parameter adjustment is the first best one-dimensional simulation model; (7) determine whether the one-dimensional simulation test results and the verification test results are within the error range and whether the three-dimensional simulation test results and the verification test results are within the error range. If not, further parameter adjustment of the one-dimensional simulation test model is required until the error meets the requirements, and further parameter adjustment of the three-dimensional simulation model is required until the error meets the requirements. If the requirements are met, the membrane electrode selection scheme ranked first in the verification test results shall be selected as the best membrane electrode selection scheme, the three-dimensional simulation model after parameter adjustment shall be selected as the first best three-dimensional simulation model, and the one-dimensional simulation model after parameter adjustment shall be selected as the first best one-dimensional simulation model; if not, the membrane electrode selection scheme ranked first in the verification test results shall be selected as the best membrane electrode selection scheme, the three-dimensional simulation model shall be selected as the first best three-dimensional simulation model, and the one-dimensional simulation model shall be selected as the first best one-dimensional simulation model; (8) Output the best membrane electrode selection scheme, the first best three-dimensional simulation model and the first best one-dimensional simulation model.

[0137] Figure 4This is a flowchart illustrating a diffusion layer performance evaluation method in an exemplary embodiment of this application, with reference to... Figure 4 As shown, the diffusion layer performance evaluation method includes: (1) the influencing factors of the diffusion layer include the film thickness, porosity, permeability, and contact angle of the diffusion layer; (2) measuring the influencing factors in the diffusion layer selection scheme (e.g., measuring the porosity of the diffusion layer); (3) conducting one-dimensional simulation tests: inputting multiple influencing factors from different diffusion layer selection schemes into the first optimal one-dimensional simulation model, quickly performing preliminary screening of diffusion layer selection schemes, and sorting them according to performance; (4) conducting three-dimensional simulation tests, and using the first optimal three-dimensional simulation model to evaluate the one-dimensional simulation test results. Further simulation verification of the first 6 diffusion layer selection schemes; (5) Whether the error between the 3D simulation test results and the 1D simulation test results is <10%. If not, fine-tune some parameters of the first best 1D simulation model until the error between the 3D simulation test results and the 1D simulation test results is <10%; (6) If satisfied, determine whether the sorting of the 3D simulation test results is consistent with the sorting of the 1D simulation test results. If not, further experimental verification tests are needed on the first 3 diffusion layer selection schemes obtained from the 3D simulation test to obtain verification test results; if consistent, the 3D simulation test results are then verified. The diffusion layer selection scheme ranked first in the test results is the best diffusion layer selection scheme, the first best three-dimensional simulation model is the second best three-dimensional simulation model, and the first best one-dimensional simulation model after parameter adjustment is the second best one-dimensional simulation model; (7) Determine whether the one-dimensional simulation test results and the verification test results are within the error range and whether the three-dimensional simulation test results and the verification test results are within the error range. If not, further parameter adjustment of the first best one-dimensional simulation test model is required until the error meets the requirements, and further parameter adjustment of the first best three-dimensional simulation model is required until the error meets the requirements. Then select the best diffusion layer selection scheme from the verification test results, the first best three-dimensional simulation model after parameter adjustment as the second best three-dimensional simulation model, and the first best one-dimensional simulation model after parameter adjustment as the second best one-dimensional simulation model; if it is, then select the best diffusion layer selection scheme from the verification test results, the three-dimensional simulation model as the second best three-dimensional simulation model, and the one-dimensional simulation model as the second best one-dimensional simulation model; (8) Output the best diffusion layer selection scheme, the second best three-dimensional simulation model, and the second best one-dimensional simulation model.

[0138] Figure 7This is a schematic diagram illustrating the comparison between simulated test values ​​and experimental test values, as shown in an exemplary embodiment of this application. The horizontal axis of the diagram represents the current density value, and the vertical axis represents the voltage value. The diagram includes three voltage value curves: the voltage test value obtained through verification testing, the one-dimensional simulated voltage value obtained through the second optimal one-dimensional simulation model, and the three-dimensional simulated voltage value obtained through the second optimal three-dimensional simulation model. A comparison of the three voltage value curves shows that when the current density is less than or equal to 2000 A / m... 2 At that time, both the one-dimensional and three-dimensional simulated voltage values ​​were equal to the measured voltage values, even when the current density was greater than 2000 A / m. 2 And less than 12000A / m 2 At that time, the errors between the one-dimensional simulated voltage value and the voltage test value, as well as the errors between the three-dimensional simulated voltage value and the voltage test value, were all within the error range. Therefore, it can be seen that the verification between the three-dimensional simulation test results and the one-dimensional simulation test results, as well as the verification between the verification test results and the simulation test results, greatly improved the accuracy of the second best one-dimensional simulation model and the second best three-dimensional simulation model, and reduced the gap between the simulation test results and the experimental results.

[0139] The beneficial effects of this application are as follows: (1) The parameters of the simulation model are adjusted by verifying the test results until the simulation test results are consistent with the verification test results. The simulation model after parameter adjustment is used as the best simulation model. The performance of the membrane electrode and diffusion layer of the fuel cell is evaluated by the best simulation model. This can effectively ensure the accuracy of the simulation test model and reduce the gap between the simulation test results and the test results. (2) By verifying the three-dimensional simulation test results with the one-dimensional simulation test results and the verification test results with the simulation test results, the test cost is reduced and the project development cycle is shortened. This has good engineering practicality. The efficiency and accuracy of the performance evaluation of the membrane electrode and diffusion layer of the fuel cell are greatly improved. Moreover, the simulation test accuracy of the one-dimensional simulation model and the three-dimensional simulation model does not change with the test environment. The operation is flexible and universal.

[0140] The following describes an embodiment of the apparatus described in this application, which can be used to execute the fuel cell performance evaluation method described in the above embodiments of this application. For details not disclosed in the apparatus embodiments of this application, please refer to the embodiments of the fuel cell performance evaluation method described above in this application.

[0141] Figure 5 This is a block diagram illustrating a fuel cell performance evaluation device according to an exemplary embodiment of this application. This device can be applied to… Figure 2 The implementation environment shown is specifically configured in computer device 102. This device can also be applied to other exemplary implementation environments and specifically configured in other devices. This embodiment does not limit the implementation environment to which the device is applicable.

[0142] like Figure 5 As shown, this exemplary fuel cell performance evaluation device includes:

[0143] The acquisition module 501 is used to acquire the membrane electrode selection scheme and the diffusion layer selection scheme.

[0144] The simulation test module 502 is used to perform performance simulation tests on the membrane electrode selection scheme and the diffusion layer selection scheme through the simulation model, and obtain the corresponding simulation test results.

[0145] The verification test module 503 is used to verify the simulation test results and obtain the verification test results.

[0146] The parameter adjustment module 504 is used to adjust the parameters of the simulation model based on the verification test results until the simulation test results are consistent with the verification test results, and the simulation model with adjusted parameters is taken as the best simulation model.

[0147] The performance evaluation module 505 is used to evaluate the performance of the fuel cell using the best simulation model.

[0148] In one embodiment of this application, the membrane electrode selection scheme includes a combination of multiple membrane electrode influencing factors, such as membrane thickness, porosity, permeability, contact angle, solid matrix conductivity, and contact resistance. Changing the values ​​of these influencing factors results in different membrane electrode selection schemes. Similarly, the diffusion layer selection scheme is a combination of multiple diffusion layer influencing factors, including membrane thickness, porosity, permeability, and contact angle. Changing the values ​​of these factors also results in different diffusion layer selection schemes.

[0149] In this embodiment, the simulation model includes a one-dimensional simulation model and a three-dimensional simulation model. The one-dimensional simulation model is used to perform performance simulation tests on the membrane electrode selection schemes, obtaining corresponding first simulation test results. These first simulation test results are then sorted in descending order, and a first predetermined number of membrane electrode selection schemes with the highest rankings are selected as the primary output schemes. This achieves preliminary and rapid screening of membrane electrode selection schemes, improving the efficiency of preliminary simulation evaluation and saving computational resources. Then, the three-dimensional simulation model is used to perform performance simulation tests on the primary output schemes, obtaining corresponding second simulation test results. These second simulation test results are then used to verify the first simulation test results, improving the accuracy of the parameter settings in the one-dimensional simulation model, thereby improving the simulation test accuracy of both the one-dimensional and three-dimensional simulation models.

[0150] In this embodiment, the performance simulation test of the diffusion layer selection scheme is performed using the first optimal one-dimensional simulation model to obtain the corresponding third simulation test results. The third simulation test results are then sorted in descending order, and the top-ranked third predetermined number of diffusion layer selection schemes are selected as the third output schemes. This achieves preliminary and rapid screening of diffusion layer selection schemes, improves the efficiency of preliminary simulation evaluation, and saves computational resources. Furthermore, the accuracy of the third simulation test results is further improved because they are obtained through performance simulation testing using the first optimal one-dimensional simulation model. Then, the performance simulation test of the three output schemes is performed using the first optimal three-dimensional simulation model to obtain the corresponding fourth simulation test results. The accuracy of the fourth simulation test results is further improved because they are obtained through the first optimal three-dimensional simulation model. The fourth simulation test results are then used to verify the third simulation test results, further improving the parameter setting accuracy of the second optimal one-dimensional simulation model, thereby improving the simulation test accuracy of both the second optimal one-dimensional and the second optimal three-dimensional simulation models.

[0151] In this embodiment, the parameters of the one-dimensional simulation model and the three-dimensional simulation model are further adjusted by verifying the test results, thereby improving the accuracy of parameter settings and simulation test accuracy of the one-dimensional simulation model and the three-dimensional simulation model.

[0152] In this embodiment, the verification test results include first experimental test results and second experimental test results. Based on the first experimental test results, the parameters of the one-dimensional simulation model and the three-dimensional simulation model are adjusted until the error between the first simulation test result and the first experimental test result is within a second preset error range, and the error between the second simulation test result and the first experimental test result is also within a second preset error range. Based on the second experimental results, the parameters of the first optimal one-dimensional simulation model and the first optimal three-dimensional simulation model are adjusted until the error between the third simulation test result and the second experimental test result is within a fourth preset error range, and the error between the fourth simulation test result and the second experimental test result is also within a fourth preset error range. By verifying the simulation test results through the verification test results, the parameter setting accuracy of the one-dimensional simulation model, the three-dimensional simulation model, the first optimal one-dimensional simulation model, and the first optimal three-dimensional simulation model is further improved, thereby improving the calculation accuracy and simulation result accuracy of the one-dimensional simulation model, the three-dimensional simulation model, the first optimal one-dimensional simulation model, and the first optimal three-dimensional simulation model, and reducing the gap between the simulation test results and the experimental results.

[0153] In this embodiment, as the computational accuracy and simulation result accuracy of the first optimal one-dimensional simulation model, the first optimal three-dimensional simulation model, the second optimal one-dimensional simulation model, and the second optimal three-dimensional simulation model are improved, the accuracy of multiple first performance evaluation results, multiple second performance evaluation results, multiple third performance evaluation results, and multiple fourth performance evaluation results are also improved, thereby improving the accuracy of performance evaluation of fuel cells.

[0154] In this embodiment, by verifying the results of three-dimensional simulation tests with those of one-dimensional simulation tests, as well as by verifying the results of verification tests with those of simulation tests, the experimental costs are reduced and the project development cycle is shortened, which has good engineering practicality. It significantly improves the efficiency and accuracy of performance evaluation of fuel cell membrane electrodes and diffusion layers. Moreover, the simulation test accuracy of one-dimensional simulation models and three-dimensional simulation models does not change with the test environment, and the operation is flexible and versatile.

[0155] It should be noted that the fuel cell performance evaluation device and the fuel cell performance evaluation method provided in the above embodiments belong to the same concept. The specific operation methods of each module and unit have been described in detail in the method embodiments and will not be repeated here. In practical applications, the fuel cell performance evaluation device provided in the above embodiments can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. This is not a limitation here.

[0156] Embodiments of this application also provide an electronic device, including: one or more processors; and a storage device for storing one or more programs, which, when executed by one or more processors, cause the electronic device to implement the fuel cell performance evaluation method provided in the above embodiments.

[0157] Figure 6 A schematic diagram of a computer system suitable for implementing the embodiments of this application is shown. It should be noted that... Figure 6 The computer system of the electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0158] like Figure 6As shown, the computer system 600 includes a Central Processing Unit (CPU) 601, which can perform various appropriate actions and processes, such as executing the methods described in the above embodiments, based on a program stored in Read-Only Memory (ROM) 602 or a program loaded from Storage Section 608 into Random Access Memory (RAM) 603. The RAM 603 also stores various programs and data required for system operation. The CPU 601, ROM 602, and RAM 603 are interconnected via a bus 604. An Input / Output (I / O) interface 605 is also connected to the bus 604.

[0159] The following components are connected to I / O interface 605: an input section 606 including a keyboard, mouse, etc.; an output section 607 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and speakers, etc.; a storage section 608 including a hard disk, etc.; and a communication section 609 including a network interface card such as a LAN (Local Area Network) card, modem, etc. The communication section 609 performs communication processing via a network such as the Internet. A drive 610 is also connected to I / O interface 605 as needed. A removable medium 611, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on drive 610 as needed so that computer programs read from it can be installed into storage section 608 as needed.

[0160] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program including a computer program for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication section 609, and / or installed from removable medium 611. When the computer program is executed by central processing unit (CPU) 601, it performs various functions defined in the system of this application.

[0161] It should be noted that the computer-readable medium shown in the embodiments of this application can be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium can be, for example, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM), flash memory, optical fiber, portable compact disc read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying a computer-readable computer program. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media can also be any computer-readable medium other than computer-readable storage media, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The computer program contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, etc., or any suitable combination thereof.

[0162] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. Each block in a flowchart or block diagram may represent a module, segment, or portion of code, which contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0163] The units described in the embodiments of this application can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0164] Another aspect of this application provides a computer-readable storage medium storing computer-readable instructions that, when executed by a computer's processor, cause the computer to perform the fuel cell performance evaluation method provided in the various embodiments described above. This computer-readable storage medium may be included in the electronic device described in the above embodiments, or it may exist independently and not incorporated into the electronic device.

[0165] It should be noted that although several modules or units for the device used to perform actions have been mentioned in the detailed description above, this division is not mandatory. In fact, according to the embodiments of this application, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided and embodied by multiple modules or units.

[0166] Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein can be implemented by software or by combining software with necessary hardware. Therefore, the technical solutions according to the embodiments of this application can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (such as a CD-ROM, USB flash drive, external hard drive, etc.) or on a network, including several instructions to cause a computing device (such as a personal computer, server, touch terminal, or network device, etc.) to execute the method according to the embodiments of this application.

[0167] Other embodiments of this application will readily occur to those skilled in the art upon consideration of the specification and practice of the embodiments disclosed herein. This application is intended to cover any variations, uses, or adaptations of this application that follow the general principles of this application and include common knowledge or customary techniques in the art not disclosed herein.

[0168] It should be understood that the above content is only a preferred exemplary embodiment of this application and is not intended to limit the implementation of this application. Those skilled in the art can easily make corresponding modifications or alterations based on the main concept and spirit of this application. Therefore, the scope of protection of this application should be determined by the scope of protection claimed in the claims.

Claims

1. A method for evaluating the performance of a fuel cell, characterized in that, The fuel cell performance evaluation method includes: Obtain membrane electrode selection schemes and diffusion layer selection schemes; The performance of the membrane electrode selection scheme and the diffusion layer selection scheme was tested using a simulation model, and the corresponding simulation test results were obtained. The simulation test results were verified to obtain the verification test results. Based on the verification test results, the parameters of the simulation model are adjusted until the simulation test results obtained by the parameter-adjusted simulation model are consistent with the verification test results. The parameter-adjusted simulation model is then taken as the optimal simulation model. The performance of the fuel cell was evaluated using the optimal simulation model. The performance simulation test of the membrane electrode selection scheme is carried out using a simulation model to obtain corresponding simulation test results. This includes: acquiring the simulation model, which includes a one-dimensional simulation model and a three-dimensional simulation model; performing performance simulation tests on the membrane electrode selection scheme using the one-dimensional simulation model to obtain corresponding first simulation test results; sorting the first simulation test results in descending order to select a first predetermined number of membrane electrode selection schemes with the highest ranking as primary output schemes; performing performance simulation tests on the primary output schemes using the three-dimensional simulation model to obtain corresponding second simulation test results; sorting the second simulation test results in descending order; and calculating the performance of the second simulation test results compared to the first simulation test results. The first error between the results; if the first error is not within the first preset error range, adjust the parameters of the one-dimensional simulation model until the first error is within the first preset error range; if the first error is within the first preset error range, compare the order of the second simulation test results with the order of the first simulation test results to obtain a comparison result; if the comparison result is consistent, the membrane electrode selection scheme ranked first in the second simulation test results, the three-dimensional simulation model, and the one-dimensional simulation model with adjusted parameters are respectively taken as the optimal membrane electrode selection scheme, the first optimal three-dimensional simulation model, and the first optimal one-dimensional simulation model; if the comparison result is inconsistent, perform a verification test on the simulation test results.

2. The fuel cell performance evaluation method according to claim 1, characterized in that, The simulation test results are verified, including: Select the second predetermined number of membrane electrode selection schemes that rank highest as the secondary output scheme; Select a membrane electrode product according to the secondary output scheme, and test the membrane electrode product through the test equipment to obtain the corresponding verification test result of the membrane electrode product. The verification test result of the membrane electrode product is used as the first test result.

3. The fuel cell performance evaluation method according to claim 2, characterized in that, Based on the verification test results, the parameters of the simulation model are adjusted until the simulation test results obtained through the adjusted simulation model are consistent with the verification test results. The simulation model with adjusted parameters is then taken as the optimal simulation model, including: Sort the test results of the first experiment in descending order; The parameters of the one-dimensional simulation model are adjusted according to the first experimental test results until the error between the first simulation test results and the first experimental test results is within the second preset error range, and the one-dimensional simulation model with adjusted parameters is taken as the first optimal one-dimensional simulation model. The parameters of the three-dimensional simulation model are adjusted according to the first test results until the error between the second simulation test results and the first test results is within the second preset error range, and the three-dimensional simulation model with adjusted parameters is taken as the first optimal three-dimensional simulation model. The membrane electrode selection scheme that ranks first in the first test results is taken as the optimal membrane electrode selection scheme.

4. The fuel cell performance evaluation method according to any one of claims 1-3, characterized in that, The performance of the diffusion layer selection scheme was tested using a simulation model, and the corresponding simulation test results were obtained, including: The performance simulation test of the diffusion layer selection scheme is carried out using the first optimal one-dimensional simulation model to obtain the corresponding third simulation test results. The third simulation test results are sorted in descending order, and the top-ranked third predetermined number of diffusion layer selection schemes are selected as the third output schemes. The performance of the three output schemes is simulated and tested using the first optimal three-dimensional simulation model to obtain the corresponding fourth simulation test results. Sort the fourth simulation test results in descending order; Calculate the second error between the third simulation test result and the fourth simulation test result; If the second error is not within the third preset error range, adjust the parameters of the first optimal one-dimensional simulation model until the second error is within the third preset error range; If the second error is within the third preset error range, the order of the fourth simulation test results is compared with the order of the third simulation test results to obtain a comparison result. If the comparison result is consistent, the diffusion layer selection scheme ranked first in the fourth simulation test results, the first optimal three-dimensional simulation model, and the first optimal one-dimensional simulation model after parameter adjustment are respectively taken as the optimal diffusion layer selection scheme, the second optimal three-dimensional simulation model, and the second optimal one-dimensional simulation model. If the comparison result is inconsistent, the simulation test results are verified.

5. The fuel cell performance evaluation method according to claim 4, characterized in that, The simulation test results are verified, including: The fourth predetermined number of diffusion layer selection schemes with the highest ranking are selected as the fourth output schemes; The diffusion layer product is selected according to the four output schemes, and the diffusion layer product is tested by the test equipment to obtain the corresponding verification test result of the diffusion layer product. The verification test result of the diffusion layer product is used as the second test result.

6. The fuel cell performance evaluation method according to claim 5, characterized in that, Based on the verification test results, the parameters of the simulation model are adjusted until the simulation test results are consistent with the verification test results. The simulation model with adjusted parameters is then taken as the optimal simulation model, including: Sort the results of the second test in descending order; The parameters of the first optimal one-dimensional simulation model are adjusted according to the second experimental test results until the error between the third simulation test results and the second experimental test results is within the fourth preset error range, and the first optimal one-dimensional simulation model after parameter adjustment is used as the second optimal one-dimensional simulation model. The parameters of the first optimal three-dimensional simulation model are adjusted according to the second test results until the error between the fourth simulation test results and the second test results is within the fourth preset error range, and the first optimal three-dimensional simulation model after parameter adjustment is used as the second optimal three-dimensional simulation model. The diffusion layer selection scheme that ranks first in the second test results is taken as the optimal diffusion layer selection scheme.

7. The fuel cell performance evaluation method according to claim 6, characterized in that, The performance of the fuel cell is evaluated using the optimal simulation model, including: Select the first influencing factor in the membrane electrode selection scheme, and change the value of the first influencing factor multiple times; The values ​​of multiple first influencing factors are respectively input into the first optimal one-dimensional simulation model and the first optimal three-dimensional simulation model to obtain multiple first performance evaluation results and multiple second performance evaluation results. Based on multiple first performance evaluation results and multiple second performance evaluation results, the variation law of membrane electrode performance is determined; Select the second influence factor in the diffusion layer selection scheme, and change the value of the second influence factor multiple times; The values ​​of multiple second influencing factors are input into the second optimal one-dimensional simulation model and the second optimal three-dimensional simulation model respectively, to obtain multiple third performance evaluation results and multiple fourth performance evaluation results respectively; Based on the multiple third performance evaluation results and the multiple fourth performance evaluation results, the variation law of the diffusion layer performance is determined; The performance of the fuel cell is evaluated based on the variation patterns of the membrane electrode performance and the diffusion layer performance.

8. A fuel cell performance evaluation device applied to the method described in any one of claims 1 to 7, characterized in that, The fuel cell performance evaluation device includes: The acquisition module is used to acquire membrane electrode selection schemes and diffusion layer selection schemes; The simulation test module is used to perform performance simulation tests on the membrane electrode selection scheme and the diffusion layer selection scheme through simulation models, and obtain the corresponding simulation test results. The verification test module is used to verify the simulation test results and obtain the verification test results. The parameter adjustment module is used to adjust the parameters of the simulation model based on the verification test results until the simulation test results are consistent with the verification test results, and the simulation model with adjusted parameters is taken as the best simulation model. The performance evaluation module is used to evaluate the performance of the fuel cell using the optimal simulation model.

9. An electronic device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by the one or more processors, cause the electronic device to implement the fuel cell performance evaluation method as described in any one of claims 1 to 7.

10. A computer storage medium, characterized in that, include: It stores computer-readable instructions that, when executed by a computer's processor, cause the computer to perform the fuel cell performance evaluation method according to any one of claims 1 to 7.

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