A method and device for predicting the life of a fuel cell gas diffusion layer

Abstract

The embodiment of the application discloses a kind of based on fuel cell gas diffusion layer's life prediction method.The method includes: based on accelerated aging test scheme, gas diffusion layer is carried out accelerated aging test, and current key material parameter set is collected at multiple preset aging time nodes;Based on the evolution data of each parameter with aging time, fitting processing is carried out to establish attenuation kinetics model;Based on initial key material parameter set and corresponding time instant fuel cell polarization loss test data, performance mapping model is established;Attenuation kinetics model is substituted into performance mapping model to construct fuel cell performance attenuation prediction model;The voltage attenuation threshold corresponding to the end condition of fuel cell life is set;Voltage attenuation threshold is substituted into performance attenuation prediction model to determine the prediction result of the service life of fuel cell.Through the technical scheme of the embodiment of the application, time cost and economic cost are greatly reduced, and prediction accuracy and engineering applicability are significantly improved.

Description

Technical Field

[0001] The present invention relates to the field of fuel cell technology, and in particular to a method and apparatus for predicting the lifetime of a fuel cell gas diffusion layer. Background Technology

[0002] Proton exchange membrane fuel cells (PEMFCs) have broad application prospects in transportation, power generation, and other fields due to their high efficiency and zero emissions. The gas diffusion layer (GDL), as one of the core components of the membrane electrode assembly, performs multiple functions including gas transport, drainage, conductivity, and support. GDL performance degradation is a key factor leading to shortened fuel cell lifespan. However, fuel cell lifespan remains a major obstacle to its large-scale commercialization.

[0003] Currently, lifetime prediction for GDLs mainly relies on two methods. One is to conduct long-term durability tests of the entire stack or individual cells for thousands of hours, and then extrapolate empirically based on voltage decay trends. The other is to conduct accelerated aging tests on GDLs, and assess the degree of degradation by testing residual performance indicators such as permeability and resistivity before and after aging.

[0004] However, all of the aforementioned existing methods have significant drawbacks. Whole-stack testing is time-consuming and costly, and can only obtain the overall voltage decay trend, failing to establish a quantitative correlation between GDL material parameter evolution and battery performance loss. While accelerated aging comparison methods can quickly obtain degradation data at the material level, they lack an effective means to map the decay of material parameters (such as porosity, permeability, contact angle, and conductivity) to the actual performance loss of the fuel cell, thus failing to quantitatively answer the specific impact of material degradation on battery life. Furthermore, most existing methods focus only on a single performance indicator, failing to comprehensively consider the combined effects of GDL multi-parameter coupled decay, resulting in low prediction accuracy. Summary of the Invention

[0005] This invention provides a method and apparatus for predicting the lifespan of a fuel cell based on a gas diffusion layer. It can predict the lifespan of a fuel cell without conducting thousands of hours of stack durability testing, which significantly reduces time and economic costs, while also significantly improving prediction accuracy and engineering applicability.

[0006] In a first aspect, embodiments of the present invention provide a method for predicting the lifetime of a fuel cell gas diffusion layer, comprising: Obtain the initial set of key material parameters for the gas diffusion layer and determine the accelerated aging test scheme corresponding to the gas diffusion layer; wherein, the initial set of key material parameters includes at least: porosity, effective permeability, effective electronic conductivity, contact angle, interfacial contact resistance, and thickness; Accelerated aging test was conducted on the gas diffusion layer based on the accelerated aging test scheme, and the current key material parameter set was collected at multiple preset aging time nodes. The evolution data of each parameter with aging time was fitted and processed to establish the decay dynamic model of each key material parameter with time. Based on the initial set of key material parameters and the corresponding fuel cell polarization loss test data, a quantitative mapping relationship between the key material parameters and the concentration polarization loss and ohmic polarization loss of the fuel cell is established to obtain a performance mapping model. Substituting the degradation kinetics model into the performance mapping model, a fuel cell performance degradation prediction model is constructed; the fuel cell performance degradation prediction model includes a gas diffusion layer associated voltage loss function with time as the independent variable. Set a voltage decay threshold corresponding to the end-of-life condition of the fuel cell; substitute the voltage decay threshold into the performance decay prediction model to determine the time required for the voltage loss function to reach the voltage decay threshold; use the determined time as the prediction result of the fuel cell life determined by the gas diffusion layer.

[0007] Optionally, the concentration polarization loss and the ohmic polarization loss The quantitative mapping relationship with the key material parameters is expressed as follows: In the formula, concentration polarization loss Ohmic polarization loss ; For current density, The thickness of the gas diffusion layer. The effective permeability of the gas diffusion layer, The partial pressure of oxygen. The first empirical coefficient; For interface contact resistance, The effective electronic conductivity of the gas diffusion layer, This is the second empirical coefficient.

[0008] Optionally, the attenuation kinetic model includes at least the following two parameter evolution laws: porosity decreases exponentially with time; contact angle decreases exponentially with time; gas diffusion layer thickness decreases exponentially with time; effective electronic conductivity changes with porosity; and effective permeability is determined by porosity.

[0009] Optionally, the associated voltage loss function of the gas diffusion layer is expressed as follows: In the formula, It is a function of thickness over time. The effective penetration rate is a function of time. It is a function of effective electronic conductivity as a function of time.

[0010] Optionally, the accelerated aging test is selected from at least one of electrochemical oxidation accelerated aging, damp heat cycling aging, mechanical aging, or chemical corrosion immersion; the fitting process uses the least squares method for curve fitting.

[0011] Optionally, the fuel cell lifespan termination condition is: at the rated operating current density, the battery voltage decays to a predetermined proportion of the initial voltage; the voltage decay threshold is determined based on the product of the predetermined proportion and the initial voltage.

[0012] Secondly, embodiments of the present invention also provide a lifetime prediction device based on a fuel cell gas diffusion layer, the device comprising: The parameter acquisition module is used to acquire the initial key material parameter set of the gas diffusion layer and determine the accelerated aging test scheme corresponding to the gas diffusion layer; wherein, the initial key material parameter set includes at least: porosity, effective permeability, effective electronic conductivity, contact angle, interfacial contact resistance and thickness. The decay kinetics model establishment module is used to conduct accelerated aging tests on the gas diffusion layer based on the accelerated aging test scheme, and collect the current key material parameter set at multiple preset aging time nodes; and to establish a decay kinetics model of the evolution of each key material parameter over time by fitting the evolution data of each parameter with aging time. The performance mapping model establishment module is used to establish a quantitative mapping relationship between the key material parameters and the concentration polarization loss and ohmic polarization loss of the fuel cell based on the initial key material parameter set and the fuel cell polarization loss test data at the corresponding time, so as to obtain the performance mapping model. A fuel cell performance degradation prediction model construction module is used to substitute the degradation kinetic model into the performance mapping model to construct a fuel cell performance degradation prediction model; the fuel cell performance degradation prediction model includes a gas diffusion layer associated voltage loss function with time as the independent variable; The prediction result determination module is used to set the voltage decay threshold corresponding to the fuel cell life termination condition; substitute the voltage decay threshold into the performance decay prediction model to determine the time required for the voltage loss function to reach the voltage decay threshold; and use the determined time as the prediction result of the fuel cell life determined by the gas diffusion layer.

[0013] Thirdly, embodiments of the present invention also provide an electronic device, the electronic device comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the lifetime prediction method based on the fuel cell gas diffusion layer as provided in any embodiment of the present invention.

[0014] Fourthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the lifetime prediction method based on a fuel cell gas diffusion layer as provided in any embodiment of the present invention.

[0015] Fifthly, embodiments of the present invention provide a computer program product, including a computer program that, when executed by a processor, implements the lifetime prediction method based on a fuel cell gas diffusion layer as provided in any embodiment of the present invention.

[0016] The technical solution of this invention involves obtaining an initial set of key material parameters for a gas diffusion layer and determining an accelerated aging test scheme corresponding to the gas diffusion layer. The initial set of key material parameters includes at least: porosity, effective permeability, effective electronic conductivity, contact angle, interfacial contact resistance, and thickness. This provides an accurate data foundation and experimental basis for subsequently establishing the evolution law of material parameters and performance mapping relationships, ensuring that the entire lifetime prediction method is based on precise initial state calibration and thus guaranteeing the reliability of the prediction results. Based on the accelerated aging test scheme, an accelerated aging test is conducted on the gas diffusion layer, and the current set of key material parameters is collected at multiple preset aging time nodes. Based on the evolution data of each parameter over aging time, a fitting process is performed to establish a decay kinetic model for the evolution of each key material parameter over time. This allows the complete evolution law of material parameters over time to be obtained in a short time, without waiting for thousands of hours of actual operation time, significantly shortening the data acquisition cycle and reducing time costs. Based on the initial set of key material parameters and the corresponding fuel cell polarization loss test data, a quantitative mapping relationship is established between the key material parameters and the fuel cell concentration polarization loss and ohmic polarization loss, resulting in a performance mapping model. This establishes a physically interpretable quantitative correlation between the gas diffusion layer material parameters and fuel cell performance loss for the first time, separating the impact of gas diffusion layer degradation from the overall stack polarization loss, avoiding prediction bias caused by multi-factor coupling, and significantly improving prediction accuracy. Substituting the degradation kinetic model into the performance mapping model, a fuel cell performance degradation prediction model is constructed. This model includes a gas diffusion layer-related voltage loss function with time as the independent variable. This allows for the direct acquisition of a complete prediction curve of fuel cell performance degradation over time by testing only the gas diffusion layer at the material level, eliminating the need for long-term durability testing of the entire stack, and significantly reducing the time and economic costs of lifetime prediction. A voltage decay threshold corresponding to the end-of-life condition of a fuel cell is set; the voltage decay threshold is substituted into the performance decay prediction model to determine the time required for the voltage loss function to reach the voltage decay threshold; the determined time is used as the prediction result of the fuel cell life determined by the gas diffusion layer; the test data at the material level is converted into life values ​​that can be directly used in engineering, providing a quantitative decision basis for the maintenance, replacement and optimization design of fuel cell systems, and has good engineering applicability and portability.

[0017] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0019] Figure 1 This is a flowchart of a lifespan prediction method based on a fuel cell gas diffusion layer provided in Embodiment 1 of the present invention; Figure 2 This is a schematic diagram of a life prediction device based on a fuel cell gas diffusion layer provided in Embodiment 2 of the present invention; Figure 3 This is a schematic diagram of the structure of an electronic device that implements the lifetime prediction method based on the fuel cell gas diffusion layer according to an embodiment of the present invention. Detailed Implementation

[0020] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0021] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0022] Example 1 Figure 1This document provides a flowchart of a method for predicting the lifetime of a fuel cell gas diffusion layer, as described in Embodiment 1 of the present invention. This embodiment is applicable to the prediction of the lifetime of a gas diffusion layer in a proton exchange membrane fuel cell. This method can be executed by a lifetime prediction device based on the fuel cell gas diffusion layer. This device can be implemented in hardware and / or software and can be configured in an electronic device. Figure 1 As shown, the method includes: S110. Obtain the initial key material parameter set of the gas diffusion layer and determine the accelerated aging test scheme corresponding to the gas diffusion layer; wherein, the initial key material parameter set includes at least: porosity, effective permeability, effective electronic conductivity, contact angle, interfacial contact resistance and thickness.

[0023] In this embodiment, the gas diffusion layer can refer to a porous conductive material layer located between the catalyst layer and the flow field plate in a proton exchange membrane fuel cell. The gas diffusion layer performs multiple functions, including the transport of reactant gases from the flow field to the catalyst layer, the discharge of generated water from the catalyst layer to the flow field, electron conduction between the catalyst layer and the flow field plate, heat transfer, and providing mechanical support for the membrane electrode assembly. The initial set of key material parameters can refer to the set of parameters obtained by characterizing the physical structure and performance of the gas diffusion layer in its original state before aging or use. The accelerated aging test scheme can refer to a set of experimental conditions designed to simulate the performance degradation behavior of the gas diffusion layer during long-term actual operation in a relatively short time. The accelerated aging test scheme includes at least the aging method, aging stress level, total aging time, and sampling node distribution.

[0024] In this embodiment, porosity can refer to the proportion of pore volume to the total volume in the gas diffusion layer. Porosity reflects the degree of looseness of the material. Porosity directly affects the abundance of gas transport channels. Porosity can be measured using the mercury porosimetry method. Effective permeability refers to the ability of gas to flow in a porous medium. Effective permeability reflects the conductivity of the gas diffusion layer for reactant gases. Effective permeability can be measured using a gas permeability tester based on Darcy's law. Effective electronic conductivity refers to the ability of the gas diffusion layer to conduct electrons in the thickness direction. Effective electronic conductivity reflects its electrical conductivity. Effective electronic conductivity can be measured using the four-probe method.

[0025] In this embodiment, the contact angle can refer to the static contact angle formed by a droplet on the surface of the gas diffusion layer. The contact angle reflects the hydrophobic properties of the material. The contact angle can be measured using a contact angle measuring instrument. The interfacial contact resistance can refer to the additional resistance generated at the interface between the gas diffusion layer and an adjacent component (such as a bipolar plate). The interfacial contact resistance reflects the interfacial conductivity. The thickness can refer to the geometric dimension of the gas diffusion layer in the vertical direction. The thickness can be measured using a micrometer.

[0026] Specifically, a commercial gas diffusion layer sample of the model to be tested, such as SGL 28BC, was selected. The initial porosity of the sample was measured using the mercury intrusion porosimetry method. The initial effective permeability was measured using a gas permeability testing platform based on Darcy's law. The initial effective electronic conductivity was measured using the four-probe method. The initial contact angle was measured using a contact angle meter. The interfacial contact resistance was measured using a dedicated testing device. The initial thickness was measured using a micrometer. After completing the above initial parameter measurements, an accelerated aging test scheme was determined based on the actual operating conditions to be predicted and the target aging mechanism.

[0027] For example, if the gas diffusion layer mainly bears electrochemical oxidation stress in actual operating conditions, then an electrochemical oxidation accelerated aging scheme is adopted, and specific conditions such as potential range, scan rate, and number of cycles are set. If the gas diffusion layer mainly bears damp heat cycling stress in actual operating conditions, then a damp heat cycling aging scheme is adopted, and specific conditions such as temperature range, humidity range, and number of cycles are set.

[0028] This step, by acquiring a comprehensive and accurate initial set of key material parameters, provides a reliable data foundation for establishing a quantitative mapping relationship between gas diffusion layer material parameters and fuel cell performance. Simultaneously, by determining the accelerated aging test scheme, it provides experimental evidence for obtaining data on the evolution of material parameters over time.

[0029] S120. Based on the accelerated aging test scheme, the gas diffusion layer is subjected to accelerated aging test, and the current key material parameter set is collected at multiple preset aging time nodes. Based on the evolution data of each parameter with aging time, the attenuation dynamic model of each key material parameter with time is established by fitting the data.

[0030] In this embodiment, accelerated aging testing refers to applying aging conditions higher than the actual operating stress level to the gas diffusion layer according to a determined accelerated aging test scheme to accelerate its performance degradation process, thereby obtaining the evolution law of material parameters with aging time in a shorter time. Preset aging time nodes refer to the time points pre-set according to the accelerated aging test scheme for removing gas diffusion layer samples from the aging test and measuring parameters. These time points are more densely distributed in the early stage of aging to capture the rapid degradation stage, and more sparsely distributed in the later stage of aging to capture the slow degradation stage. The current set of key material parameters refers to the current set of values ​​for parameters such as porosity, effective permeability, effective electronic conductivity, contact angle, interfacial contact resistance, and thickness measured at each preset aging time node after removing the gas diffusion layer sample from the accelerated aging test.

[0031] In this embodiment of the disclosure, evolution data can refer to the sequence of measured values ​​of each key material parameter at different preset aging time points. The evolution data reflects the changing trend of each parameter with aging time. Fitting processing can refer to using mathematical methods to perform curve fitting on the evolution data to determine the undetermined coefficients in the functional relationship describing the change of each parameter with aging time. The decay kinetic model can refer to the set of functional expressions obtained after fitting processing, used to describe the changing law of each key material parameter with time.

[0032] Specifically, the gas diffusion layer sample is placed in an accelerated aging test apparatus, and the aging test is initiated according to the determined accelerated aging test plan. When the aging reaches the first preset time node, the sample is removed, and the current porosity, current effective permeability, current effective electronic conductivity, current contact angle, current interfacial contact resistance, and current thickness are measured and recorded. The sample is returned to the aging apparatus to continue aging, and at the second preset time node, it is removed again to measure and record the current parameter set. The above process is repeated until sampling and measurement are completed at all preset time nodes, obtaining the evolution data of each parameter with aging time.

[0033] For example, for the electrochemical oxidation accelerated aging scheme, samples can be taken and measured at 0, 500, 1000, 2000, 3000 and 5000 cycles respectively to obtain the evolution data shown in Table 1.

[0034] Table 1 Key material parameters for measuring GDL Specifically, based on the aforementioned evolution data, a fitting method was used to establish a decay kinetic model for each parameter. Taking porosity as an example, porosity exhibits an exponential decay law with aging time, i.e. Where N is the number of iterations. The initial porosity, This represents the porosity attenuation coefficient. It can be determined by fitting the porosity evolution data using the least squares method. The specific values ​​are as follows. Similarly, the contact angle decreases exponentially over time, the thickness decreases exponentially over time, the effective electronic conductivity changes with porosity, and the effective permeability is determined by porosity.

[0035] This step uses accelerated aging tests to obtain the complete evolution of material parameters over time in a short period of time, eliminating the need to wait for thousands of hours of actual operation and significantly shortening the data acquisition cycle. Simultaneously, by establishing a decay kinetic model, it provides a basis for predicting the time-dependent evolution of material parameters to support subsequent performance degradation predictions.

[0036] As an optional implementation of this disclosure, the attenuation kinetic model includes at least the following two parameter evolution laws: porosity decreases exponentially with time; contact angle decreases exponentially with time; gas diffusion layer thickness decreases exponentially with time; effective electronic conductivity changes with porosity; and effective permeability is determined by porosity.

[0037] In this embodiment, the parameter evolution law can refer to the mathematical function form of how key material parameters change over time during accelerated aging. Exponential decay can mean that the parameter value decreases monotonically with time as a negative exponential function, i.e., the parameter value decreases rapidly in the initial stage, and then the rate of decrease gradually slows down and approaches zero. Exponential approach decay can mean that the parameter value gradually approaches a certain limit value from its initial value over time. Its rate of change is proportional to the difference between the current value and the limit value.

[0038] Specifically, when establishing the decay kinetic model, an appropriate function form is selected based on the actual evolution characteristics of each parameter. The porosity is represented by an exponential decay equation: In the formula The initial porosity, This represents the porosity decay coefficient. This equation reflects the gradual decrease in porosity over time and the gradual slowing of the decay rate. The contact angle uses an exponentially approaching decay equation: In the formula The initial contact angle, This represents the maximum attenuation of the contact angle. Let be the hydrophobic decay rate constant. This equation reflects the law that the contact angle gradually approaches a certain stable lower limit value from its initial value. The thickness is represented by an exponentially approaching decay equation: In the formula The asymptotic thickness after infinite aging. This is the maximum attenuation. This represents the thickness attenuation coefficient. This equation reflects the law that the thickness gradually approaches a certain stable lower limit value from its initial value. The effective electronic conductivity is calculated using an equation related to porosity: In the formula The initial effective electronic conductivity, The tortuosity index is used. This equation reflects the relationship between electrical conductivity and porosity. Effective permeability is calculated using an equation related to porosity: In the formula, C is the permeability coefficient. This equation reflects the relationship between permeability and porosity.

[0039] This optional implementation provides multiple parameter evolution laws, enabling the decay kinetic model to comprehensively reflect the actual changing trends of each material parameter with aging, and providing accurate time evolution inputs of material parameters for subsequent performance prediction.

[0040] As an optional implementation of this disclosure, the accelerated aging test is selected from at least one of electrochemical oxidation accelerated aging, damp heat cycling aging, mechanical aging, or chemical corrosion immersion; the fitting process uses the least squares method for curve fitting.

[0041] In this embodiment, electrochemical oxidation accelerated aging refers to an aging method that accelerates the oxidation of carbon materials and degradation of hydrophobic agents in the gas diffusion layer under electrochemical action by applying a constant potential or cyclic potential. Electrochemical oxidation accelerated aging is suitable for simulating the degradation mechanism under high potential conditions on the cathode side of a fuel cell. Damp-heat cycling aging refers to an aging method that accelerates structural fatigue and performance degradation of the gas diffusion layer due to thermal expansion and contraction and moisture adsorption-desorption cycles by alternating temperature and humidity conditions. Damp-heat cycling aging is suitable for simulating the degradation mechanism of fuel cells under start-up, shutdown, and variable load conditions. Mechanical aging refers to an aging method in which irreversible structural damage or performance degradation occurs in the target component due to physical effects such as cyclic mechanical stress, vibration, impact, or long-term compression experienced by the fuel cell during assembly, operation, and start-up / shutdown. Mechanical aging is suitable for simulating the degradation mechanism of irreversible structural damage and performance degradation of components caused by the accumulation of physical and mechanical stresses such as long-term compression, wet-dry cycling, thermal cycling, and vibration impact during actual assembly, operation, start-up / shutdown, and transportation of fuel cells. Chemical corrosion immersion refers to an aging process in which a gas diffusion layer is immersed in a specific chemical solution (such as acidic or oxidizing solutions) to accelerate material corrosion. Least squares method refers to a curve fitting method. It determines the undetermined coefficients in the fitting equation by minimizing the sum of squares of the deviations between the measured data points and the fitted curve, thus making the fitted curve optimally approximate the measured data.

[0042] Specifically, the accelerated aging test method is determined based on the main attenuation mechanism of the gas diffusion layer under actual operating conditions. If frequent start-stop cycles in actual operating conditions lead to large potential fluctuations, then electrochemical oxidation accelerated aging is selected. Specific conditions can be set as follows: a three-electrode system is used, with the gas diffusion layer as the working electrode, a platinum sheet as the counter electrode, a saturated calomel electrode as the reference electrode, and the electrolyte as... The solution was subjected to triangular wave cyclic scanning at a scan rate of 50 mV / s within a potential range of 1.0–1.5 V (relative to the reversible hydrogen electrode). If drastic temperature and humidity changes occur in actual operating conditions, a damp-heat cyclic aging method was selected. After obtaining parameter measurement data at each preset time point, the evolution data of each parameter were fitted using the least squares method. Taking porosity as an example again, the following steps were performed... Taking the logarithm of both sides to transform into linear form Then, it is determined by linear regression using the least squares method. The value of .

[0043] This optional implementation provides multiple accelerated aging methods and specific fitting methods, making the method applicable to gas diffusion layer lifetime prediction under different actual operating conditions, exhibiting good flexibility and wide applicability. Meanwhile, the least squares method ensures the objectivity and accuracy of the fitting results.

[0044] S130. Based on the initial set of key material parameters and the corresponding fuel cell polarization loss test data, establish a quantitative mapping relationship between key material parameters and fuel cell concentration polarization loss and ohmic polarization loss to obtain a performance mapping model.

[0045] In this embodiment, the corresponding time can refer to the moment when the gas diffusion layer sample is assembled into a fuel cell and polarization loss is tested. This moment corresponds to the moment when the initial set of key material parameters is obtained, i.e., it corresponds to the initial unaged state of the gas diffusion layer. Fuel cell polarization loss test data can refer to various voltage loss data obtained through polarization curve testing and electrochemical impedance spectroscopy testing at different current densities after assembling the unaged gas diffusion layer sample into a single cell. Fuel cell concentration polarization loss can refer to the voltage loss caused by the concentration of reactants on the catalyst layer surface being lower than the concentration in the flow field due to obstructed transport of reactant gases in the gas diffusion layer. The magnitude of concentration polarization loss reflects the gas transport capability of the gas diffusion layer.

[0046] In this embodiment, ohmic polarization loss can refer to the voltage loss caused by the resistance of proton and electron conduction. The portion of ohmic polarization loss related to the gas diffusion layer includes losses due to the bulk resistance of the gas diffusion layer and the interfacial contact resistance. The magnitude of the ohmic polarization loss reflects the conductivity of the gas diffusion layer. The quantitative mapping relationship can refer to a functional relationship that mathematically correlates the specific values ​​of key material parameters with the specific values ​​of polarization losses. The performance mapping model can refer to a set of mathematical expressions that include the above-mentioned quantitative mapping relationship. The performance mapping model can calculate the corresponding fuel cell polarization loss given the known material parameters of the gas diffusion layer.

[0047] Specifically, polarization loss test data for fuel cells is obtained. For example, an unaged gas diffusion layer sample is assembled into a single cell, operated and tested under set conditions. Exemplary test conditions include: temperature 80°C, anode pressure 80 kPa, cathode pressure 100 kPa, relative humidity of 50% for both anode and cathode, and hydrogen and air stoichiometry ratios of 1.5 and 2.5, respectively. The actual output voltage of the cell is recorded at different current densities. And calculate the reversible potential based on the Nernst equation. Total voltage drop The total ohmic impedance was obtained by electrochemical impedance spectroscopy. and activation resistance Calculate Ohmic loss Activation loss After deducting activation loss and ohmic loss from the total voltage loss, the remainder is the concentration polarization loss. .

[0048] Specifically, a quantitative mapping relationship is established. The quantitative relationship between concentration polarization loss and gas diffusion layer parameters is as follows: Where i is the current density and d is the thickness of the gas diffusion layer. For effective penetration, The partial pressure of oxygen. This is the first empirical coefficient. This relationship indicates that the higher the current density, the thicker the gas diffusion layer, the lower the effective permeability, and the lower the oxygen partial pressure, the greater the concentration polarization loss. The quantitative relationship between ohmic polarization loss and gas diffusion layer parameters is: .in For interface contact resistance, For effective electronic conductivity, This is the second empirical coefficient. This relationship shows that the greater the current density, the greater the interfacial contact resistance, the greater the thickness, and the lower the effective conductivity, the greater the ohmic polarization loss. The total voltage loss associated with the gas diffusion layer is the sum of the above two. Combining the above relationships yields the performance mapping model.

[0049] This step establishes a physical quantitative mapping relationship between material parameters and polarization loss, enabling lifetime prediction to be based on an interpretable material degradation mechanism rather than purely empirical fitting. It separates the impact of gas diffusion layer degradation on battery performance from the overall stack polarization loss, providing a theoretical basis for directly mapping material parameter evolution to performance degradation in the future.

[0050] As an optional implementation of this disclosure, concentration polarization loss and Ohmic polarization loss The quantitative mapping relationship with key material parameters is expressed as follows: In the formula, concentration polarization loss Ohmic polarization loss ; For current density, The thickness of the gas diffusion layer. The effective permeability of the gas diffusion layer, The partial pressure of oxygen. The first empirical coefficient; For interface contact resistance, The effective electronic conductivity of the gas diffusion layer, This is the second empirical coefficient.

[0051] In this embodiment, current density refers to the amount of current passing through a unit area. Current density is one of the core parameters of fuel cell operation. The magnitude of current density directly affects the amplitude of various polarization losses. Oxygen partial pressure refers to the partial pressure of oxygen in the gas diffusion layer. Oxygen partial pressure can be determined by the inlet pressure, gas composition, and pressure drop during gas transport. The magnitude of oxygen partial pressure directly affects concentration polarization loss. The first empirical coefficient can refer to the mass transfer-related empirical parameter determined by fitting experimental data. The first empirical coefficient reflects the proportional relationship between the physical quantities in concentration polarization loss. The second empirical coefficient can refer to the ohmic-related empirical parameter determined by fitting experimental data. The second empirical coefficient reflects the proportional relationship between the physical quantities in ohmic polarization loss.

[0052] Specifically, the first empirical coefficient Second empirical coefficient It needs to be determined based on experimental data through multiple linear regression fitting. The concentration polarization loss measured at multiple current densities... And Ohm loss And the thickness d and effective permeability under the corresponding working conditions. Effective conductivity partial pressure of oxygen and interface contact resistance Substitute this into the mapping model above. As the dependent variable, as independent variable Linear regression fitting was used to obtain .by As the dependent variable, as independent variable Linear regression fitting was used to obtain For example, for the SGL 28BC gas diffusion layer sample, the fitting yielded... , .

[0053] This optional implementation clarifies the mathematical relationships between parameters by providing specific quantitative mapping expressions, making the performance mapping model computable and operable. By calibrating the first and second empirical coefficients, the model can accurately reflect the actual performance characteristics of a specific type of gas diffusion layer.

[0054] S140. Substitute the degradation kinetics model into the performance mapping model to construct a fuel cell performance degradation prediction model; the fuel cell performance degradation prediction model includes a gas diffusion layer associated voltage loss function with time as the independent variable.

[0055] In this embodiment, the fuel cell performance degradation prediction model can be a mathematical model that directly calculates the time-dependent performance degradation of the fuel cell by coupling the evolution of the gas diffusion layer material parameters with the material parameter-performance mapping law. The gas diffusion layer-related voltage loss function can be a functional expression in this prediction model used to calculate the time-dependent voltage loss caused by the gas diffusion layer. Time is the independent variable, and the output is the voltage loss value contributed by the gas diffusion layer.

[0056] Specifically, the attenuation kinetics model of each key material parameter in the established gas diffusion layer is substituted into the established performance mapping model. That is, the function of each material parameter with time as the independent variable. , , , Substitute the corresponding positions in the performance mapping model and replace the original constant parameters. , , , The gas diffusion layer associated voltage loss function with time as the only independent variable is obtained: .

[0057] In the above function, other parameters besides time t (such as current density i, oxygen partial pressure) ) are operating condition parameters, which are set to constant values ​​based on actual operating conditions during specific predictions. and These are the calibrated empirical coefficients.

[0058] This step couples the degradation kinetics model with the performance mapping model to construct a complete link for directly predicting battery performance degradation from material parameters. This allows for the acquisition of complete prediction results of fuel cell performance degradation over time by only testing the gas diffusion layer at the material level, eliminating the need for long-term durability testing of the entire stack and significantly reducing the time and economic costs of lifetime prediction.

[0059] As an optional embodiment of this disclosure, the gas diffusion layer associated voltage loss function is expressed as follows: In the formula, It is a function of thickness over time. The effective penetration rate is a function of time. It is a function of effective electronic conductivity as a function of time.

[0060] Specifically, each term in this voltage loss function has a clear physical meaning. The first term... This represents the change in concentration polarization loss over time, and its increasing trend is caused by both increased thickness and decreased effective permeability. (Second term) Representing the change of ohmic polarization loss over time, its increasing trend is jointly influenced by the increase in interfacial contact resistance, the relative change in bulk resistance due to thickness reduction, and the decrease in effective conductivity. Total voltage drop. This is the sum of the two terms. When t=0, the function returns the voltage loss associated with the gas diffusion layer in the initial state. As t increases, the function value gradually increases, reflecting the battery performance degradation caused by the aging of the gas diffusion layer.

[0061] This optional implementation provides a specific voltage loss function expression, clarifying a calculable model for performance prediction, thus making lifetime prediction quantitative and operable, facilitating its implementation and application in practical engineering.

[0062] S150. Set the voltage decay threshold corresponding to the end-of-life condition of the fuel cell; substitute the voltage decay threshold into the performance decay prediction model to determine the time required for the voltage loss function to reach the voltage decay threshold; use the determined time as the prediction result of the fuel cell life determined by the gas diffusion layer.

[0063] In this embodiment of the disclosure, the fuel cell lifespan termination condition can refer to a pre-set criterion used to determine that the fuel cell has reached the end of its service life. The fuel cell lifespan termination condition can be marked by a performance parameter decreasing to a certain threshold. The voltage decay threshold can refer to a specific numerical value of voltage decay corresponding to the lifespan termination condition. When the voltage loss associated with the gas diffusion layer reaches this threshold, the fuel cell is considered to have reached the end of its service life. The fuel cell service life can refer to the time elapsed from the start of use until the lifespan termination condition is reached. The prediction result can refer to the service life value calculated using this method.

[0064] Specifically, the lifespan termination conditions for fuel cells are set. For example, in on-board fuel cell applications, the lifespan is terminated when the battery voltage drops to a predetermined percentage (e.g., 10% or 20%) of the initial voltage at the rated operating current density. If the initial voltage... =0.7V, the predetermined ratio is 20%, then the voltage attenuation threshold is Substituting the voltage attenuation threshold into the established performance degradation prediction model, i.e., solving the equation... .in Let be the voltage loss function associated with the gas diffusion layer. Solving this equation yields the time... This refers to the fuel cell lifespan determined by the gas diffusion layer. For example, for the SGL 28BC gas diffusion layer sample, at the rated current density... Solve below The critical number of cycles is obtained. Furthermore, by combining an acceleration factor (such as 5), the actual operating life is calculated to be approximately 25,000 hours.

[0065] This step transforms material-level test results into engineering-usable life prediction results by setting clear life-end conditions and substituting them into the prediction model, providing a quantitative basis for the maintenance, replacement, and optimization design of fuel cell systems.

[0066] As an optional embodiment of this disclosure, the fuel cell lifespan termination condition is: at the rated operating current density, the battery voltage decays to a predetermined proportion of the initial voltage; the voltage decay threshold is determined based on the product of the predetermined proportion and the initial voltage.

[0067] In this embodiment, the rated operating current density can refer to the design current density of the fuel cell under normal operating conditions. The rated operating current density is one of the rated operating parameters of the fuel cell. The performance degradation at this current density best reflects the lifespan in actual use. The battery voltage can refer to the actual output voltage of the fuel cell at the rated operating current density. The initial voltage can refer to the output voltage of the fuel cell at the rated operating current density when the gas diffusion layer is in an unaged state. The predetermined percentage can refer to a pre-set voltage degradation percentage based on the fuel cell application scenario and lifespan definition. The predetermined percentage can be used to quantitatively define the end of the lifespan.

[0068] Specifically, the rated operating current density can be determined according to the product specifications of the target fuel cell. For example, for automotive fuel cells, the rated current density is typically 1.0 to 1.5. The initial voltage is obtained by assembling an unaged gas diffusion layer into a single cell and testing it at the rated operating current density. The predetermined percentage can be set according to specific application requirements; for example, it is typically set to 20% for the transportation sector and 10% for stationary power generation. The voltage decay threshold is equal to the initial voltage multiplied by the predetermined percentage.

[0069] This optional implementation method clarifies the specific definition of the lifetime termination condition and the calculation method of the voltage decay threshold, making the termination criteria of lifetime prediction clear and operable, avoiding the uncertainty caused by subjective judgment, and ensuring the consistency and comparability of prediction results.

[0070] The technical solution of this invention obtains an initial set of key material parameters for the gas diffusion layer and determines the corresponding accelerated aging test scheme. The initial set of key material parameters includes at least: porosity, effective permeability, effective electronic conductivity, contact angle, interfacial contact resistance, and thickness. This provides an accurate data foundation and experimental basis for subsequently establishing the evolution law of material parameters and performance mapping relationships, ensuring that the entire lifetime prediction method is based on precise initial state calibration and thus guaranteeing the reliability of the prediction results. Accelerated aging tests are conducted on the gas diffusion layer based on the accelerated aging test scheme, and the current set of key material parameters is collected at multiple preset aging time nodes. Based on the evolution data of each parameter over aging time, fitting processing is performed to establish a decay kinetic model for the evolution of each key material parameter over time. This allows the complete evolution law of material parameters over time to be obtained in a short time, without waiting for thousands of hours of actual operation time, significantly shortening the data acquisition cycle and reducing time costs. Based on the initial set of key material parameters and corresponding fuel cell polarization loss test data, a quantitative mapping relationship between key material parameters and fuel cell concentration polarization loss and ohmic polarization loss is established, resulting in a performance mapping model. This establishes a physically interpretable quantitative correlation between gas diffusion layer material parameters and fuel cell performance loss for the first time, separating the impact of gas diffusion layer degradation from the overall stack polarization loss, avoiding prediction bias caused by multi-factor coupling, and significantly improving prediction accuracy. Substituting the degradation kinetics model into the performance mapping model, a fuel cell performance degradation prediction model is constructed. This model includes a gas diffusion layer-related voltage loss function with time as the independent variable. This allows for the direct acquisition of a complete prediction curve of fuel cell performance degradation over time by testing only the gas diffusion layer at the material level, eliminating the need for long-term durability testing of the entire stack, and significantly reducing the time and economic costs of lifetime prediction. The system sets a voltage decay threshold corresponding to the end-of-life condition of the fuel cell; substitutes the voltage decay threshold into the performance decay prediction model to determine the time required for the voltage loss function to reach the voltage decay threshold; uses the determined time as the prediction result of the fuel cell life determined by the gas diffusion layer; and transforms the test data at the material level into life values ​​that can be directly used in engineering, providing a quantitative decision basis for the maintenance, replacement and optimization design of the fuel cell system, with good engineering applicability and portability.

[0071] For example, a step of extrapolating the complete lifetime prediction curve using short-term accelerated aging test data of 200 to 500 hours can be used to achieve rapid lifetime prediction based on a small amount of test data.

[0072] In this embodiment of the disclosure, the complete lifetime prediction curve can refer to the complete curve of the change of the gas diffusion layer associated voltage loss over time from the initial state of the gas diffusion layer to the attainment of the lifetime termination condition. The complete lifetime prediction curve can reflect the overall picture of fuel cell performance degradation. The limited test data can refer to the material parameter measurement data collected at a limited time point in the early stage of accelerated aging test (e.g., within 200 to 500 hours).

[0073] Specifically, in practice, when time or resource constraints prevent the completion of all preset aging time points, a short-term accelerated aging test of 200–500 hours can be conducted first to obtain material parameter evolution data within this time period. Based on the limited data obtained, the established attenuation kinetic model is used for parameter fitting and extrapolation. Taking the extrapolation of porosity as an example, the attenuation coefficient in the fitting is obtained using the porosity data from the first few time points. The porosity value at any subsequent time point is extrapolated using the fitted model. Substituting the extrapolated data of each material parameter changing over time into the performance mapping model yields the complete voltage drop curve over time, which can then be used to solve for the end-of-life time.

[0074] This exemplary step extrapolates a complete lifetime prediction curve using short-time accelerated aging data, enabling rapid lifetime prediction based on a small amount of test data even when testing time is limited in actual engineering. This further reduces testing and time costs and improves the engineering practicality of the method.

[0075] It should be noted that the process framework of the technical solution of the present invention can also be applied to proton exchange membranes (MEM), catalysts, catalyst layers, membrane electrode assemblies (MEA), plates, stacks, systems, and vehicles. Specifically, the proton exchange membrane-based prediction determines the fuel cell lifespan determined by membrane chemical degradation / mechanical fatigue; the catalyst-based prediction determines the fuel cell lifespan determined by catalyst activity decay / particle growth; the catalyst layer-based prediction determines the fuel cell lifespan determined by catalyst layer structural degradation; the MEA-based prediction determines the fuel cell lifespan determined by the overall performance degradation of the MEA; the plate-based prediction determines the fuel cell lifespan determined by plate corrosion / coating degradation; the stack-based prediction determines the fuel cell lifespan determined by the overall performance degradation of the stack; the system-based prediction determines the fuel cell lifespan determined by system-level performance degradation (including the influence of auxiliary components); and the vehicle-based prediction determines the fuel cell system lifespan under vehicle operating conditions.

[0076] The following are embodiments of the life prediction device based on the fuel cell gas diffusion layer provided by the present invention. This device and the life prediction method based on the fuel cell gas diffusion layer in the above embodiments belong to the same inventive concept. For details not described in detail in the embodiments of the life prediction device based on the fuel cell gas diffusion layer, please refer to the embodiments of the life prediction method based on the fuel cell gas diffusion layer described above.

[0077] Example 2 Figure 2 This is a schematic diagram of a lifetime prediction device based on a fuel cell gas diffusion layer provided in Embodiment 2 of the present invention. Figure 2 As shown, the device includes: a parameter acquisition module 210, a decay kinetics model establishment module 220, a performance mapping model establishment module 230, a fuel cell performance decay prediction model construction module 240, and a prediction result determination module 250.

[0078] The parameter acquisition module 210 is used to acquire the initial key material parameter set of the gas diffusion layer and determine the accelerated aging test scheme corresponding to the gas diffusion layer. The initial key material parameter set includes at least: porosity, effective permeability, effective electronic conductivity, contact angle, interfacial contact resistance, and thickness. The decay kinetic model establishment module 220 is used to conduct accelerated aging tests on the gas diffusion layer based on the accelerated aging test scheme, and collect the current key material parameter set at multiple preset aging time nodes. Based on the evolution data of each parameter over aging time, a decay kinetic model of each key material parameter over time is established through fitting processing. The performance mapping model establishment module 230 is used to establish a performance mapping model based on the initial key material parameter set and the corresponding fuel cell electrode... The system uses chemical loss test data to establish a quantitative mapping relationship between key material parameters and the concentration polarization loss and ohmic polarization loss of the fuel cell, thus obtaining a performance mapping model. A fuel cell performance degradation prediction model construction module 240 is used to substitute the degradation kinetic model into the performance mapping model to construct a fuel cell performance degradation prediction model. The fuel cell performance degradation prediction model includes a gas diffusion layer-related voltage loss function with time as the independent variable. A prediction result determination module 250 is used to set the voltage degradation threshold corresponding to the fuel cell lifespan termination condition. The voltage degradation threshold is substituted into the performance degradation prediction model to determine the time required for the voltage loss function to reach the voltage degradation threshold. The determined time is used as the prediction result for the fuel cell lifespan determined by the gas diffusion layer.

[0079] The technical solution of this invention obtains an initial set of key material parameters for the gas diffusion layer and determines the corresponding accelerated aging test scheme. The initial set of key material parameters includes at least: porosity, effective permeability, effective electronic conductivity, contact angle, interfacial contact resistance, and thickness. This provides an accurate data foundation and experimental basis for subsequently establishing the evolution law of material parameters and performance mapping relationships, ensuring that the entire lifetime prediction method is based on precise initial state calibration and thus guaranteeing the reliability of the prediction results. Accelerated aging tests are conducted on the gas diffusion layer based on the accelerated aging test scheme, and the current set of key material parameters is collected at multiple preset aging time nodes. Based on the evolution data of each parameter over aging time, fitting processing is performed to establish a decay kinetic model for the evolution of each key material parameter over time. This allows the complete evolution law of material parameters over time to be obtained in a short time, without waiting for thousands of hours of actual operation time, significantly shortening the data acquisition cycle and reducing time costs. Based on the initial set of key material parameters and corresponding fuel cell polarization loss test data, a quantitative mapping relationship between key material parameters and fuel cell concentration polarization loss and ohmic polarization loss is established, resulting in a performance mapping model. This establishes a physically interpretable quantitative correlation between gas diffusion layer material parameters and fuel cell performance loss for the first time, separating the impact of gas diffusion layer degradation from the overall stack polarization loss, avoiding prediction bias caused by multi-factor coupling, and significantly improving prediction accuracy. Substituting the degradation kinetics model into the performance mapping model, a fuel cell performance degradation prediction model is constructed. This model includes a gas diffusion layer-related voltage loss function with time as the independent variable. This allows for the direct acquisition of a complete prediction curve of fuel cell performance degradation over time by testing only the gas diffusion layer at the material level, eliminating the need for long-term durability testing of the entire stack, and significantly reducing the time and economic costs of lifetime prediction. The system sets a voltage decay threshold corresponding to the end-of-life condition of the fuel cell; substitutes the voltage decay threshold into the performance decay prediction model to determine the time required for the voltage loss function to reach the voltage decay threshold; uses the determined time as the prediction result of the fuel cell life determined by the gas diffusion layer; and transforms the test data at the material level into life values ​​that can be directly used in engineering, providing a quantitative decision basis for the maintenance, replacement and optimization design of the fuel cell system, with good engineering applicability and portability.

[0080] Optionally, concentration polarization loss and Ohmic polarization loss The quantitative mapping relationship with key material parameters is expressed as follows: In the formula, concentration polarization loss Ohmic polarization loss ; For current density, The thickness of the gas diffusion layer. The effective permeability of the gas diffusion layer, The partial pressure of oxygen. The first empirical coefficient; For interface contact resistance, The effective electronic conductivity of the gas diffusion layer, This is the second empirical coefficient.

[0081] Optionally, the decay kinetic model includes at least the following two parameter evolution laws: porosity decays exponentially with time; contact angle decays exponentially with time; gas diffusion layer thickness decays exponentially with time; effective electronic conductivity varies with porosity; and effective permeability is determined by porosity.

[0082] Alternatively, the voltage loss function associated with the gas diffusion layer is expressed as follows: In the formula, It is a function of thickness over time. The effective penetration rate is a function of time. It is a function of effective electronic conductivity as a function of time.

[0083] Optionally, the accelerated aging test is selected from at least one of electrochemical oxidation accelerated aging, damp heat cycling aging, mechanical aging, or chemical corrosion immersion; the fitting process uses the least squares method for curve fitting.

[0084] Optionally, the fuel cell lifespan termination condition is: at the rated operating current density, the cell voltage decays to a predetermined proportion of the initial voltage; the voltage decay threshold is determined based on the product of the predetermined proportion and the initial voltage.

[0085] The lifetime prediction device based on the fuel cell gas diffusion layer provided in the embodiments of the present invention can execute the lifetime prediction method based on the fuel cell gas diffusion layer provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects for executing the lifetime prediction method based on the fuel cell gas diffusion layer.

[0086] It is worth noting that in the above embodiments of lifetime prediction based on fuel cell gas diffusion layer, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.

[0087] Example 3 Figure 3A schematic diagram of an electronic device 24, which can be used to implement embodiments of the present invention, is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (e.g., helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0088] like Figure 3 As shown, the electronic device 24 includes at least one processor 25 and a memory, such as a read-only memory (ROM) 26 and a random access memory (RAM) 27, communicatively connected to the at least one processor 25. The memory stores computer programs executable by the at least one processor. The processor 25 can perform various appropriate actions and processes based on the computer program stored in the ROM 26 or loaded from storage unit 32 into the RAM 27. The RAM 27 can also store various programs and data required for the operation of the electronic device 24. The processor 25, ROM 26, and RAM 27 are interconnected via a bus 28. An input / output (I / O) interface 29 is also connected to the bus 28.

[0089] Multiple components in electronic device 24 are connected to I / O interface 29, including: input unit 30, such as keyboard, mouse, etc.; output unit 31, such as various types of monitors, speakers, etc.; storage unit 32, such as disk, optical disk, etc.; and communication unit 33, such as network card, modem, wireless transceiver, etc. Communication unit 33 allows electronic device 24 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0090] Processor 25 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 25 include, but are not limited to, central processing unit (CPU), graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any suitable processor, controller, microcontroller, etc. Processor 25 performs the various methods and processes described above, such as lifetime prediction methods based on fuel cell gas diffusion layers.

[0091] In some embodiments, the fuel cell gas diffusion layer-based lifetime prediction method can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 32. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 24 via ROM 26 and / or communication unit 33. When the computer program is loaded into RAM 27 and executed by processor 25, one or more steps of the fuel cell gas diffusion layer-based lifetime prediction method described above can be performed. Alternatively, in other embodiments, processor 25 can be configured to perform the fuel cell gas diffusion layer-based lifetime prediction method by any other suitable means (e.g., by means of firmware).

[0092] Various implementations of the systems and techniques described above herein can be implemented in digital circuit systems, integrated circuits, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-chips (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0093] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0094] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0095] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0096] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or middleware components (e.g., application servers), or frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0097] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0098] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the lifetime prediction method based on a fuel cell gas diffusion layer as provided in any embodiment of this application.

[0099] In implementing the computer program product, computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof. Programming languages ​​include object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as C or similar languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider). This program product belongs to the same inventive concept as the fuel cell gas diffusion layer-based lifetime prediction method disclosed in the embodiments of this application, and therefore will not be described further here.

[0100] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0101] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for predicting the lifetime of a fuel cell based on a gas diffusion layer, characterized in that, The method includes: Obtain the initial set of key material parameters for the gas diffusion layer and determine the accelerated aging test scheme corresponding to the gas diffusion layer; wherein, the initial set of key material parameters includes at least: porosity, effective permeability, effective electronic conductivity, contact angle, interfacial contact resistance, and thickness; Accelerated aging test was conducted on the gas diffusion layer based on the accelerated aging test scheme, and the current key material parameter set was collected at multiple preset aging time nodes. The evolution data of each parameter with aging time was fitted and processed to establish the decay dynamic model of each key material parameter with time. Based on the initial set of key material parameters and the corresponding fuel cell polarization loss test data, a quantitative mapping relationship between the key material parameters and the concentration polarization loss and ohmic polarization loss of the fuel cell is established to obtain a performance mapping model. Substituting the degradation kinetics model into the performance mapping model, a fuel cell performance degradation prediction model is constructed; the fuel cell performance degradation prediction model includes a gas diffusion layer associated voltage loss function with time as the independent variable. Set a voltage decay threshold corresponding to the end-of-life condition of the fuel cell; substitute the voltage decay threshold into the performance decay prediction model to determine the time required for the voltage loss function to reach the voltage decay threshold; use the determined time as the prediction result of the fuel cell life determined by the gas diffusion layer.

2. The method according to claim 1, characterized in that, The concentration polarization loss and the ohmic polarization loss The quantitative mapping relationship with the key material parameters is expressed as follows: ； In the formula, concentration polarization loss Ohmic polarization loss ; For current density, The thickness of the gas diffusion layer. The effective permeability of the gas diffusion layer, The partial pressure of oxygen. The first empirical coefficient; For interface contact resistance, The effective electronic conductivity of the gas diffusion layer, This is the second empirical coefficient.

3. The method according to claim 1, characterized in that, The decay dynamics model includes at least the following two parameter evolution laws: Porosity decreases exponentially over time; The contact angle decreases exponentially over time. The thickness of the gas diffusion layer decreases exponentially over time. Effective electronic conductivity varies with porosity; Effective permeability is determined by porosity.

4. The method according to claim 1, characterized in that, The associated voltage loss function of the gas diffusion layer is expressed as follows: ； In the formula, It is a function of thickness over time. The effective penetration rate is a function of time. It is a function of effective electronic conductivity as a function of time.

5. The method according to claim 1, characterized in that, The accelerated aging test is selected from at least one of electrochemical oxidation accelerated aging, damp heat cycling aging, mechanical aging, or chemical corrosion immersion; the fitting process uses the least squares method for curve fitting.

6. The method according to claim 1, characterized in that, The fuel cell lifespan termination condition is: at the rated operating current density, the battery voltage decays to a predetermined proportion of the initial voltage; the voltage decay threshold is determined based on the product of the predetermined proportion and the initial voltage.

7. A lifetime prediction device based on a fuel cell gas diffusion layer, characterized in that, The device includes: The parameter acquisition module is used to acquire the initial key material parameter set of the gas diffusion layer and determine the accelerated aging test scheme corresponding to the gas diffusion layer; wherein, the initial key material parameter set includes at least: porosity, effective permeability, effective electronic conductivity, contact angle, interfacial contact resistance and thickness. The decay kinetics model establishment module is used to conduct accelerated aging tests on the gas diffusion layer based on the accelerated aging test scheme, and collect the current key material parameter set at multiple preset aging time nodes; and to establish a decay kinetics model of the evolution of each key material parameter over time by fitting the evolution data of each parameter with aging time. The performance mapping model establishment module is used to establish a quantitative mapping relationship between the key material parameters and the concentration polarization loss and ohmic polarization loss of the fuel cell based on the initial key material parameter set and the fuel cell polarization loss test data at the corresponding time, so as to obtain the performance mapping model. A fuel cell performance degradation prediction model construction module is used to substitute the degradation kinetic model into the performance mapping model to construct a fuel cell performance degradation prediction model; the fuel cell performance degradation prediction model includes a gas diffusion layer associated voltage loss function with time as the independent variable; The prediction result determination module is used to set the voltage decay threshold corresponding to the fuel cell life termination condition; substitute the voltage decay threshold into the performance decay prediction model to determine the time required for the voltage loss function to reach the voltage decay threshold; and use the determined time as the prediction result of the fuel cell life determined by the gas diffusion layer.

8. An electronic device, characterized in that, The electronic device includes: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the lifetime prediction method based on the fuel cell gas diffusion layer as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the lifetime prediction method based on the gas diffusion layer of a fuel cell as described in any one of claims 1-6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the lifetime prediction method based on the gas diffusion layer of a fuel cell as described in any one of claims 1-6.

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