Aging and decay prediction method for gas diffusion layers based on a combination of off-line and on-line methods

Abstract

A method for predicting the aging decay of a gas diffusion layer based on a combination of offline and online methods is provided. [Solution] The gas diffusion layer and initial performance to be predicted are obtained, and offline and online cycle stand tests of durability aging decay are conducted respectively, and the test times are recorded. After completion, the deterioration performance of the gas diffusion layer to be predicted is collected, and offline performance tests are conducted one by one on samples after the offline aging test and on samples obtained by disassembling the test stack, and the average performance of the gas diffusion layer to be predicted at that time is obtained. A set of aging decay acceleration coefficients from the offline durability test and the online cycle stand durability test are calculated and obtained, and the coefficients are added to calculate the average to obtain a final combination of acceleration coefficients, and then added and averaged to obtain the final acceleration coefficient.

Description

[Technical Field]

[0001] The present application relates to the technical field of fuel cells, and in particular to a method for predicting the aging decay of gas diffusion layers based on a combination of offline and online methods. [Background technology]

[0002] Durability is one of the key factors affecting the commercialization of fuel cells. The ability of fuel cells to support stable operation for long periods of time is a prerequisite for increasing consumer confidence and realizing large-scale load operation.

[0003] Fuel cells are currently in a stage of technological maturity that is constantly improving, and their durability is directly affected by the material performance of critical assemblies. Product durability can be determined through in-vehicle or bench cycle testing, but both in-vehicle and bench cycle testing incur high time and financial costs for completing selective conformance testing of critical components. Offline single-material testing is less costly and more efficient, but it is less likely to reflect the characteristics of in-vehicle or bench cycle operating conditions, resulting in a disconnect between offline and online performance. Summary of the Invention

[0004] To achieve the above object, the present application provides the following solutions.

[0005] According to a first aspect of the present invention, there is provided a method for predicting aging decay of a gas diffusion layer based on a combination of offline and online methods, comprising: Obtaining a gas diffusion layer to be predicted and an initial performance of the gas diffusion layer to be predicted; Mounting the gas diffusion layer to be predicted on a plurality of test stacks of equivalent power and size; performing a cycle bench test of durability aging decay on each test stack according to the set driving mode of the actual vehicle, and recording the cycle test time for each test stack; After the cycle test is completed, disassemble the test stacks one by one after the multiple aging decays of different cycle test times, take out the gas diffusion layers to be predicted for each test, and perform a performance test to obtain the first degradation average performance of the gas diffusion layers to be predicted at that time. performing an offline durability test on the gas diffusion layer to be predicted, recording the time of the offline durability test, and after completion of the test, collecting a second degradation average performance of the gas diffusion layer to be predicted after the offline durability test; calculating and obtaining an aging decay width of the gas diffusion layer to be predicted after cycle stand durability of each of the test stacks based on the second degradation average performance and the initial performance of the gas diffusion layer to be predicted; calculating and obtaining a set of aging decay acceleration coefficients for the offline durability test and the online cycle stand durability test based on the aging decay width, the first degradation average performance, the second degradation average performance, and the initial performance of the gas diffusion layer to be predicted; The indexes of each term in the set of aging decay acceleration coefficients are correspondingly added to calculate the average value to obtain a final set of acceleration coefficients, and then the final acceleration coefficients are obtained by adding and averaging them.

[0006] Furthermore, acquiring the gas diffusion layer to be predicted and the initial performance of the gas diffusion layer to be predicted includes: The measurement indexes of the gas diffusion layer to be predicted include thickness H, tensile strength T, roughness R, contact angle C, in-plane resistivity E, and air permeability P, If the initial performance of the gas diffusion layer to be predicted is X, then: X=[H,T,R,C,E,P] (1) If the performance of the gas diffusion layer to be predicted after aging and decay is set to X', then: X'=[H',T',R',C',E',P'] (2) At this time, the GDL aging decay width λ must satisfy the following:

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[0007] Furthermore, mounting the gas diffusion layer to be predicted on a plurality of test stacks of equivalent output and size, Let N be the number of the stack, N=[N1,N2,N3] (4) Here, 1, 2, and 3 represent the numbers of the first, second, and third test stacks.

[0008] Furthermore, performing a cycle test of durability aging decay on each test stack according to a set driving mode of an actual vehicle and recording the cycle test time for each test stack is The time for each stack cycle test is T. T = [T1, T2, T3 ] (5) Includes.

[0009] Furthermore, after the cycle test is completed, the test stacks after a plurality of aging attenuations of different cycle test times are disassembled one by one, and the gas diffusion layers to be predicted are taken out and subjected to a performance test, and at this time, a first degradation average performance of the gas diffusion layers to be predicted is obtained. The first degradation average performance X of the gas diffusion layer to be predicted after cycle stand durability of the plurality of test stacks N are respectively as follows:

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[0010] Furthermore, calculating and obtaining an aging decay width of the gas diffusion layer to be predicted after cycle stand durability of each of the test stacks based on the second degradation average performance and the initial performance of the gas diffusion layer to be predicted, The aging decay width of the GDL after cycle stand endurance of multiple test stacks is as follows:

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[0011] Furthermore, the method comprises: Predicted second average degradation performance of gas diffusion layers after offline durability testing X L is as follows:

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[0012] Furthermore, calculating and obtaining a set of aging decay acceleration coefficients for an offline durability test and an online cycle stand durability test based on the aging decay width, the first degradation average performance, the second degradation average performance, and the initial performance of the gas diffusion layer to be predicted, The difference between each variable in equation (8) and the initial performance is calculated using equation ( 6 ) by the difference between the initial performance and each variable, and the set of aging decay acceleration coefficients θ for the offline durability test and the online cycle stand durability test is calculated as follows:

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[0013] Furthermore, the indexes of each term in the set of aging decay acceleration coefficients are added correspondingly to calculate the average value to obtain a final combination of acceleration coefficients, and then the sum is averaged to obtain the final acceleration coefficients; The reserved indicators are added to the corresponding ones to calculate the average value to obtain the final combination of acceleration coefficients, and then the average is added to obtain the final acceleration coefficient δ;

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[0014] This application relates to the fuel cell technology field, and particularly to a method for predicting the aging degradation of a gas diffusion layer based on a combination of offline and online testing, by obtaining the gas diffusion layer and initial performance to be predicted, conducting offline and online cycle stand tests for durability aging degradation and recording the test times, and collecting the degradation performance of the gas diffusion layer to be predicted after the tests are completed. Then, performing offline performance tests on the samples after the offline aging test and on the samples obtained by disassembling the test stack, respectively, to obtain the average performance of the gas diffusion layer to be predicted at that time, calculating a set of aging degradation acceleration factors for the offline durability test and the online cycle stand durability test, adding and averaging the results to obtain a final set of acceleration factors, and then averaging them to obtain a final acceleration factor. The present invention combines offline aging durability testing and online cycle stand testing of gas diffusion layers, establishes a correlation between offline and online testing, realizes online cycle stand life prediction, and saves research and development costs. [Brief explanation of the drawings]

[0015] [Figure 1] FIG. 1 is an operational flow diagram of a method for predicting the aging decay of a gas diffusion layer based on a combination of offline and online methods, which is claimed as an embodiment of the present application. [Figure 2] FIG. 2 is a schematic diagram of the division of a gas diffusion layer to be predicted in the method for predicting the aging decay of a gas diffusion layer based on a combination of offline and online methods, which is claimed as an example of the present application. [Figure 3] FIG. 3 shows thickness test results after an online cycle rack durability test and an offline durability test of a method for predicting the aging degradation of a gas diffusion layer based on a combination of offline and online testing, which is claimed as an example of the present application. [Figure 4] FIG. 4 is a schematic diagram showing the average thickness results of three samples after a cycle stand durability test and an offline durability test of the method for predicting the aging decay of a gas diffusion layer based on a combination of offline and online methods claimed in the examples of the present application. DETAILED DESCRIPTION OF THE INVENTION

[0016] The technical solutions of the embodiments of the present application will be clearly and completely described below in conjunction with the drawings of the embodiments of the present application, but it is clear that the described embodiments are only some of the embodiments of the present application, and are not all of the embodiments. Based on the embodiments of the present invention, all other embodiments that can be obtained by those skilled in the art without inventive work fall within the scope of protection of the present invention.

[0017] The term "embodiment" in this specification means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the present application. The appearance of the phrase in various places in this specification does not necessarily mean that all of the phrases refer to the same embodiment, nor does it mean that the phrases are separate or alternative to other embodiments that are exclusive. Those skilled in the art will understand, both explicitly and implicitly, that the embodiments described herein may be combined with other embodiments.

[0018] The gas diffusion layer (GDL) of a fuel cell is one of the core components of the membrane electrode, and is responsible for key functions such as ventilation, drainage, electrical conductivity, and heat conduction. This application proposes a multi-zone discrete prediction method for the acceleration factor of the aging decay of a gas diffusion layer that combines offline and online testing. This method combines offline aging durability testing of the gas diffusion layer with online cycle rack testing, and establishes a relationship between offline and online testing by determining the acceleration factor. By using offline material-level testing to predict online cycle rack life, research and development costs are reduced.

[0019] According to a first embodiment of the present invention, the present invention claims protection for a method for predicting the aging decay of a gas diffusion layer based on a combination of offline and online, with reference to FIG. 1 , Obtaining a gas diffusion layer to be predicted and an initial performance of the gas diffusion layer to be predicted; Mounting the gas diffusion layer to be predicted on a plurality of test stacks of equivalent power and size; performing a cycle bench test of durability aging decay on each test stack according to the set driving mode of the actual vehicle, and recording the cycle test time for each test stack; After the cycle test is completed, disassemble the test stacks one by one after the multiple aging decays of different cycle test times, take out the gas diffusion layers to be predicted for each test, and perform a performance test to obtain the first degradation average performance of the gas diffusion layers to be predicted at that time. performing an offline durability test on the gas diffusion layer to be predicted, recording the time of the offline durability test, and after completion of the test, collecting a second degradation average performance of the gas diffusion layer to be predicted after the offline durability test; calculating and obtaining an aging decay width of the gas diffusion layer to be predicted after cycle stand durability of each of the test stacks based on the second degradation average performance and the initial performance of the gas diffusion layer to be predicted; calculating and obtaining a set of aging decay acceleration coefficients for the offline durability test and the online cycle stand durability test based on the aging decay width, the first degradation average performance, the second degradation average performance, and the initial performance of the gas diffusion layer to be predicted; The indexes of each term in the set of aging decay acceleration coefficients are correspondingly added to calculate the average value to obtain a final set of acceleration coefficients, and then the final acceleration coefficients are obtained by adding and averaging them.

[0020] In this example, a self-developed or selected GDL sample is selected to carry out offline performance testing. Due to the different active areas of the stack designs, the sizes of the installed GDLs are also different. The GDL of this type is S, for example, S = 110 cm. 2Because the reaction uniformity at the inlet and outlet of the GDL after the stack is loaded is different, the aging decay width in the later stage is also different, and the aging decay in a local area cannot represent the overall aging decay. Therefore, the GDL, which has a size of S, was divided into lattice areas of approximately the same size, and divided into 25 lattices of the same size as shown in Figure 2. Three test pieces were taken and the numerical average was calculated to determine the initial performance of the GDL. To avoid test errors due to edge effects, partial areas near the edges were truncated, for example, as shown in Figure 2, so that the intermediate size after discarding the edges was 100 cm. 2 is.

[0021]

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[0022] Furthermore, mounting the gas diffusion layer to be predicted on a plurality of test stacks of equivalent output and size, Let N be the number of the stack, N=[N1,N2,N3] (4) Here, 1, 2, and 3 represent the numbers of the first, second, and third test stacks.

[0023] Furthermore, performing a cycle test of durability aging decay on each test stack according to a set driving mode of an actual vehicle and recording the cycle test time for each test stack is The time for each stack cycle test is T. T = [T1, T2, T3 ] (5) Includes.

[0024] In this embodiment, T1=1000h, T2=1500h, T3=2000h, and so on. The set operation mode of the actual vehicle corresponds to the cyclic operation mode of the national standard GB / Z44116-2024.

[0025] Furthermore, after the cycle test is completed, the test stacks after a plurality of aging attenuations of different cycle test times are disassembled one by one, and the gas diffusion layers to be predicted are taken out and subjected to a performance test, and at this time, a first degradation average performance of the gas diffusion layers to be predicted is obtained. The first degradation average performance X of the gas diffusion layer to be predicted after cycle stand durability of the plurality of test stacks N are respectively as follows:

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[0026] Furthermore, calculating and obtaining an aging decay width of the gas diffusion layer to be predicted after cycle stand durability of each of the test stacks based on the second degradation average performance and the initial performance of the gas diffusion layer to be predicted, The aging decay width of the GDL after cycle stand endurance of multiple test stacks is as follows:

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[0027] Furthermore, the method comprises: Predicted second average degradation performance of gas diffusion layers after offline durability testing X L is as follows:

number

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[0028] Therefore, in this example, offline durability tests were performed using offline accelerated aging methods such as electrochemical oxidation, freeze-thaw cycles, thermal shock, and cyclic compression. For example, in the high-temperature acidic mixed accelerated aging method, the sample was placed in a 1 mol / L H2SO4 and 15% H2O2 mixed solution in ultrapure water at a constant temperature of 80°C. After sealing, the sample was subjected to an aging test for a time t, such as t=500 h, and then removed. Then, the sample was immersed in deionized water for rinsing and dried before being subjected to another test analysis.

[0029] calculating and obtaining a set of aging decay acceleration coefficients for an offline durability test and an online cycle stand durability test based on the aging decay width, first deterioration average performance, second deterioration average performance, and initial performance of the gas diffusion layer to be predicted; The difference between each variable in equation (8) and the initial performance is calculated using equation ( 6 ) by the difference between the initial performance and each variable, and the set of aging decay acceleration coefficients θ for the offline durability test and the online cycle stand durability test is calculated as follows:

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[0030] As can be seen from equation (10), each acceleration coefficient is a combination of multiple indices, including thickness H, tensile strength T, roughness R, contact angle C, in-plane resistivity E, air permeability P, and the ratio of change rate and test time, to reduce the prediction error of a single index. Comparing the calculation results for the same indices N1, N2, and N3 in equation (10), there is a large difference in the endurance operating time of N1, N2, and N3. Therefore, if there is little change in an index, it indicates that the index is not sensitive to durability aging decay, and that index will not be used in subsequent calculations.

[0031] Furthermore, the indexes of each term in the set of aging decay acceleration coefficients are added correspondingly to calculate the average value to obtain a final combination of acceleration coefficients, and then the sum is averaged to obtain the final acceleration coefficients; The reserved indicators are added to the corresponding ones to calculate the average value to obtain the final combination of acceleration coefficients, and then the average is added to obtain the final acceleration coefficient δ;

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[0032] In this embodiment, GDL samples are randomly sampled and then subjected to offline acid corrosion or other durability tests (time t) and cycle stand durability tests (time T) after installation. In the former case, performance tests are sequentially performed on the samples after the offline tests to obtain offline performance data set 1. In the latter case, the stack is sequentially disassembled, and the GDL samples are removed and subjected to offline tests to obtain offline performance data set 2. A relationship is established based on data sets 1 and 2 to predict the aging degradation of the gas diffusion layer.

[0033] In this example, a sample approximately 110 mm x 110 mm in size was tested by dividing the central 100 mm x 100 mm area into 25 equal grids to avoid edge effects. Each 100 mm x 100 mm area was then equally divided into 25 grids for testing. As shown in Figure 3, the test area was 100 mm x 100 mm, with the scale on the horizontal ordinate axis representing 20 mm, 40 mm, 60 mm, and 80 mm. Figure 3 shows the multi-zone discrete method used to represent different positional areas within the stack, i.e., 25 grids of discrete values. The average values ​​for the 25 grids were then calculated. Next, multiple samples were measured using the same method, and the average values ​​for multiple samples (e.g., three samples) were calculated. The resulting average values ​​are shown in Figure 4. Similarly, the average values ​​for tensile strength, roughness, contact angle, in-plane resistivity, and air permeability were calculated for multiple samples after online cycle rack testing and offline durability testing. The results are shown in Table 1.

[0034] [Table 1]

[0035] In equation (1), if the average initial performance of the GDL is X, then X=[242.1,18.8,6.5,148,17.1,2246.5].

[0036] Of these, the GDL aging performance XN after cycle stand durability (1000h, 1500h, 2000h) of three stacks is as follows: XN1=[238.6,16.8,6.6,146,16.2,2278.9] XN2=[236.2,15.9,6.6,145,15.4,2297.8] XN3=[234.8,14.2,6.6,143,14.2,2312.5].

[0037] In Equation (10), the set of aging acceleration coefficients θ for offline durability testing and online cycle stand durability is given in Table 2.

[0038] [Table 2]

[0039] In the table above, the indices corresponding to those marked in gray have not changed and are all 1.00, which indicates that these indices are not sensitive to durability aging decay, and therefore are not used in calculating the acceleration factor.

[0040] Therefore, the final combination of acceleration factors δ obtained by equation (11) is

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[0041] Therefore, the acceleration factor of this method for the offline aging durability test and the online cycle rack test is 6.19, i.e., one hour of offline testing is equivalent to the effect of 6.19 hours of online cycle rack durability testing.

[0042] Although the specific embodiments of the present invention have been described in detail above, these are merely examples, and the present application is not limited to the above specific embodiments. Any equivalent modifications or replacements of the present invention that a person skilled in the art would find fall within the scope of the present application, and therefore, equivalent changes, modifications, improvements, etc. made without departing from the spirit and scope of the present application shall be included within the scope of the present application.

Claims

[Claim 1] A method for predicting the aging decay of a gas diffusion layer based on a combination of offline and online methods, comprising: The measurement indexes of the gas diffusion layer to be predicted include thickness H, tensile strength T, roughness R, contact angle C, in-plane resistivity E, and air permeability P. Obtaining the gas diffusion layer to be predicted and the initial performance of the measurement indicator of the gas diffusion layer to be predicted; Mounting the gas diffusion layer to be predicted on a plurality of test stacks of equivalent power and size; performing an online cycle stand durability test for durability aging decay on each test stack according to the set driving mode of the actual vehicle, and recording the online cycle stand durability test time for each test stack; After the online cycle stand durability test is completed, the test stacks after a plurality of aging attenuation tests for different online cycle stand durability test times are disassembled one by one, and the gas diffusion layers to be predicted are taken out and subjected to a performance test, and the values ​​of the measurement indexes of the gas diffusion layers to be predicted at this time are obtained as a first degradation average performance. performing an offline durability test on the gas diffusion layer to be predicted under an environment that physically deteriorates the gas diffusion layer, recording the time of the offline durability test, and after the test is completed, collecting the values ​​of the measurement indexes of the gas diffusion layer to be predicted after the offline durability test as a second degradation average performance; calculating and obtaining an aging decay width of the gas diffusion layer to be predicted after the online cycle stand durability test of each of the test stacks based on the second degradation average performance and the initial performance of the gas diffusion layer to be predicted; calculating and obtaining a set of aging decay acceleration coefficients for the offline durability test and the online cycle stand durability test based on the aging decay width, the first degradation average performance, the second degradation average performance, and the initial performance of the gas diffusion layer to be predicted; The indexes of each term in the set of aging decay acceleration coefficients are added correspondingly to calculate an average value to obtain a final set of acceleration coefficients, and then the final acceleration coefficients are obtained by averaging the values; Obtaining the gas diffusion layer to be predicted and the initial performance of the gas diffusion layer to be predicted includes: the measurement indexes of the gas diffusion layer to be predicted include thickness H, tensile strength T, roughness R, contact angle C, in-plane resistivity E, and air permeability P; If the initial performance of the gas diffusion layer to be predicted is X, then: X=[H,T,R,C,E,P] (1) the performance of the gas diffusion layer to be predicted after aging decay includes thickness H', tensile strength T', roughness R', contact angle C', in-plane resistivity E', and air permeability P'; X'=[H', T', R', C', E', P'] (2) At this time, the gas diffusion layer aging decay width λ is as follows: [0016] It further includes: Mounting the predicted gas diffusion layer on a plurality of test stacks of equivalent power and size, Let the stack number be N, N=[N １ ,N ２ ,N ３ ・・・・・・] (4) wherein 1, 2, and 3 represent the numbers of the first, second, and third test stacks; performing an online cycle stand durability test of durability aging decay on each test stack according to the set driving mode of the actual vehicle, and recording the online cycle stand durability test time for each test stack; The time for each stack online cycle stand durability test is T; T=[T １ ,T ２ ,T ３ ・・・・・・] (5) further comprising After the online cycle stand durability test is completed, the test stacks after a plurality of aging attenuation tests under different online cycle stand durability test times are disassembled one by one, and the gas diffusion layers to be predicted are taken out and subjected to performance tests, and the first degradation average performance of the gas diffusion layers to be predicted is obtained. a first average deterioration performance X of the gas diffusion layer to be predicted after an online cycle stand durability test of a plurality of the test stacks; Ｎ are respectively as follows, [Equation 17] wherein 1, 2, and 3 represent the numbers of the first, second, and third test stacks; calculating and obtaining an aging decay width of the gas diffusion layer to be predicted after the online cycle stand durability test of each of the test stacks based on the second degradation average performance and the initial performance of the gas diffusion layer to be predicted; The aging decay width of the gas diffusion layer after the online cycle stand durability test of the plurality of test stacks is as follows: [Equation 18] wherein 1, 2, and 3 represent the numbers of the first, second, and third test stacks; Predicted second average deterioration performance of gas diffusion layer after offline durability test X Ｌ is as follows: [Equation 19] At the same time, the aging decay width of the gas diffusion layer to be predicted obtained by an offline durability test is as follows: [Equation 20] It further includes: calculating and obtaining a set of aging decay acceleration coefficients for an offline durability test and an online cycle stand durability test based on the aging decay width, the first degradation average performance, the second degradation average performance, and the initial performance of the gas diffusion layer to be predicted; The set θ of aging decay acceleration coefficients for the offline durability test and the online cycle stand durability test is calculated by dividing the difference between each variable in Equation (8) and the initial performance by the difference between each variable in Equation (6) and the initial performance, and is as follows: [0000] It further includes: The indexes of each term in the set of aging decay acceleration coefficients are added correspondingly to calculate the average value to obtain a final set of acceleration coefficients, and then the sum is averaged to obtain the final acceleration coefficients; The indexes of each term in the set of aging decay acceleration coefficients are correspondingly added to calculate the average value to obtain a final combination of acceleration coefficients, and then the final acceleration coefficient δ is obtained by averaging the values; [Equation 22] Further, in equation (11), all indicators are set to have an effect on durability aging decay, and t represents the offline test time. A method for predicting aging decay of a gas diffusion layer based on a combination of offline and online methods, characterized by:

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