System and method for testing multi-impurity composite effect of fuel cell

Abstract

The invention relates to the technical field of cell testing, and discloses a fuel cell multi-impurity composite effect testing system and method. Comprising an impurity generation module, a particle capture module, a mixing and conveying module, a fuel cell test module, a data acquisition module and a data processing and analysis module, breaks through the limitation of pollutant interaction deficiency in a single factor test, and effectively solves the technical problem that the pollutant synergistic effect is difficult to simulate under a real working condition. And a dual correction mechanism is constructed based on the synergistic effect coefficient and the information entropy weight, and the impurity synergistic effect coefficient is introduced when the initial attenuation rate is determined, so that the simplified hypothesis of the traditional linear superposition model is broken through, and the nonlinear attenuation characteristics under the multi-impurity composite action are accurately captured. The initial attenuation rate is secondarily corrected by calculating the information entropy weight of each impurity, the contribution difference of the influence of different impurity concentration distributions on the overall performance is effectively quantified, and the accuracy and engineering applicability of fuel cell performance attenuation prediction are remarkably improved.

Description

Technical Field

[0001] The present invention relates to the technical field of battery testing, and in particular to a system and method for testing the combined effects of multiple impurities in a fuel cell. Background Art

[0002] Fuel cells, as a core component of clean energy technology, offer significant potential for applications in transportation, distributed power generation, and other fields with their efficient energy conversion and zero-emissions advantages. However, during actual fuel cell operation, ambient air quality poses a significant challenge to their performance stability and service life. Current research focuses on the mechanisms by which single pollutants affect fuel cells. However, these simplified models differ significantly from real-world application scenarios, making them difficult to validate under complex operating conditions.

[0003] However, pollutants in the atmospheric environment usually exist in complex forms. For example, nitrogen oxides, sulfur oxides, ammonia, volatile organic compounds and particulate matter often exist in synergistic forms, and their synergistic mechanism far exceeds the simple superposition of the effects of a single substance. For example, the coexistence of nitrogen oxides and sulfur oxides may form acidic aerosols, which induce synergistic corrosion on the surface of the catalytic layer; volatile organic compounds generate high-molecular deposits under the catalytic action of transition metals, forming a physical covering layer together with particulate matter; ammonia molecules may participate in the chemical degradation process of the proton exchange membrane under specific potential conditions. These multiple pollutants, through multiple pathways such as physical adsorption, electrochemical corrosion, and catalytic poisoning, trigger chain failure effects such as shielding of electrode active sites, destruction of the three-phase interface, and blockage of ion conduction channels, causing damage to key components such as electrodes and electrolytes that is different from the damage caused by a single pollutant, thereby affecting the output power, stability, and life of the fuel cell.

[0004] Existing techniques for testing the impact of impurities on battery performance in laboratory environments often employ single-factor experimental designs to simulate the concentration gradient of a single pollutant. While this approach can capture the linear effect of a specific pollutant on battery stack performance, it ignores the nonlinear interactions between multiple pollutant components under actual operating conditions. The significant differences between laboratory environments and real-world scenarios, coupled with the lack of synergistic interactions between pollutants and impurities, can lead to inaccurate battery performance evaluation results.

[0005] Therefore, there is an urgent need for a fuel cell multi-impurity composite effect testing system and method that can accurately simulate a multi-pollutant composite environment and accurately evaluate the impact of each impurity on battery performance. Summary of the Invention

[0006] In order to solve the above technical problems, the present invention provides a fuel cell multi-impurity composite effect test system, which includes an impurity generation module, a particle capture module, a mixing and transport module, a fuel cell test module, a data acquisition module, and a data processing and analysis module: The impurity generation module is used to generate impurity gas required for testing; The particle capture module is used to generate particulate matter required for testing; The mixing and delivery module is connected to the impurity generation module and the particle capture module, and is used to control the concentration of the impurity gas and the particulate matter according to a control algorithm; mix them to obtain a multi-impurity mixture, and then deliver the multi-impurity mixture to the fuel cell to participate in the test reaction; The fuel cell testing module is connected to the mixing and delivery module and is used to receive the multi-impurity mixture, act on the fuel cell, and monitor the working status of the fuel cell; The data acquisition module is connected to the fuel cell test module and is used to collect operating data of the fuel cell; The data processing and analysis module is connected to the data acquisition module and is used to determine the influence of multiple impurities on the performance of the fuel cell based on the operating data of the fuel cell.

[0007] Furthermore, the control algorithm is expressed as: ; in, represents the control output signal at time t, e(t) represents the deviation between the preset concentration and the actual concentration at time t, and K p Indicates the proportional coefficient of the corresponding speed adjustment, K i Indicates the integral coefficient for eliminating steady-state error, K d Represents the differential coefficient for suppressing overshoot.

[0008] Furthermore, the operating data of the fuel cell includes the voltage, current and power of the fuel cell.

[0009] Furthermore, the impurity generating module includes a nitrogen oxide generating device, a sulfur oxide generating device, an ammonia generating device and a volatile organic compound generating device.

[0010] Another aspect of the present invention provides a fuel cell multi-impurity composite effect testing method, which is performed by any of the fuel cell multi-impurity composite effect testing systems described above, and the method comprises the following steps: Determine the type and concentration of impurities required for testing based on test requirements; Determining the synergistic effect coefficients among various impurities based on the types and concentrations of the impurities; Determining the initial attenuation rate of the multi-impurity combination on the fuel cell based on the synergistic effect coefficient; Determine the impact weight of each impurity based on the type and concentration of the impurities; The initial attenuation rate is corrected by the influence weight to obtain the final attenuation rate of the multi-impurity composite effect on the fuel cell, so as to complete the fuel cell multi-impurity composite effect test.

[0011] Furthermore, the synergistic effect coefficients among various impurities are determined based on the types and concentrations of the impurities, including: Obtaining the type and concentration of the impurities; Determining the concentration combination of each impurity according to the type and concentration of the impurities; The decay rate of the fuel cell was tested under various concentration combinations; Based on the decay rates under the various concentration combinations, the synergistic effect coefficients among the various impurities were obtained by fitting using the least squares method.

[0012] Furthermore, the initial attenuation rate of the multi-impurity combination on the fuel cell is determined based on the synergistic effect coefficient, and the initial attenuation rate is obtained by the following formula: ; Among them, η 初始 represents the initial decay rate, β0 represents the constant term, β i represents the effect coefficient of the i-th impurity on the fuel cell alone, β ii represents the square term coefficient of the i-th impurity on the fuel cell, α ij represents the synergistic effect coefficient between the i-th impurity and the j-th impurity, n represents the number of impurity types, C i represents the concentration of the i-th impurity, C j represents the concentration of the jth impurity.

[0013] Furthermore, the influence weight of each impurity is determined based on the type and concentration of the impurities, wherein the calculation formula of the influence weight is: ; Among them, W k represents the influence weight of the kth impurity, n represents the number of impurity types, e k Represents the information entropy value of the kth impurity.

[0014] Furthermore, the information entropy value e of the k-th impurity k The calculation formula is: ; Among them, x mk represents the normalized concentration of the kth impurity in the mth sample, and M represents the number of samples.

[0015] Furthermore, the initial attenuation rate is corrected by the influence weight to obtain the final attenuation rate of the fuel cell due to the combination of multiple impurities. The calculation formula is: ; Among them, η 初始 represents the initial decay rate, η 最终 represents the final attenuation rate, n represents the number of impurity types, W k represents the influence weight of the kth impurity, C k represents the concentration of the kth impurity.

[0016] The present invention has the following technical effects: The present invention effectively solves the technical problem that the synergistic effect of pollutants under real working conditions is difficult to simulate by constructing a multi-impurity composite effect test system. The system realizes the synchronous generation and precise proportioning of multi-phase pollutants such as nitrogen oxides, sulfur oxides, ammonia, volatile organic compounds and particulate matter by integrating the impurity generation module, the particle capture module and the mixed delivery module, breaking through the limitation of the lack of pollutant interaction in single-factor testing. The mixed delivery module dynamically adjusts the concentration gradient of each impurity based on the PID control algorithm, and combines the multi-dimensional state monitoring of the fuel cell test module to construct a mapping relationship between the composite effect of multiple impurities and the attenuation of fuel cell performance. The steady-state generation and dynamic control of composite pollutants are achieved through the collaboration of multiple modules, providing a reliable experimental platform for revealing the mechanism of synergistic effect of multiple impurities, and significantly improving the consistency of test results with actual working conditions.

[0017] The present invention also constructs a dual correction mechanism based on the synergistic effect coefficient and information entropy weight. This mechanism incorporates the impurity synergistic effect coefficient when determining the initial decay rate, breaking through the simplified assumptions of the traditional linear superposition model and accurately capturing the nonlinear decay characteristics of multiple impurities. By calculating the information entropy weight of each impurity and performing a secondary correction to the initial decay rate, the contribution of different impurity concentration distributions to overall performance is effectively quantified, significantly improving the accuracy and engineering applicability of fuel cell performance decay predictions. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0019] Figure 1 This is a structural diagram of a fuel cell multi-impurity composite effect testing system provided by an embodiment of the present invention; Figure 2 This is a flowchart of the steps of a fuel cell multi-impurity composite effect testing method provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0020] To make the objectives, technical solutions, and advantages of the present invention more clear, the technical solutions of the present invention are described clearly and completely below. Obviously, the embodiments described are only some of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without inventive effort are also within the scope of protection of the present invention.

[0021] Based on a test system that accurately simulates a multi-pollutant composite environment and has dynamic response capabilities, a scientific and reasonable multi-factor coupling effect evaluation method is established to meet the urgent needs of fuel cell performance evaluation. The present invention provides a fuel cell multi-impurity composite effect test system, such as Figure 1 As shown, it includes impurity generation module, particle capture module, mixing and transportation module, fuel cell test module, data acquisition module and data processing and analysis module: Impurity generation module, used to generate impurity gas required for testing; In some embodiments, the impurity generating module includes a nitrogen oxide generating device, a sulfur oxide generating device, an ammonia generating device, and a volatile organic compound generating device.

[0022] Exemplarily, each generating device is equipped with a high-precision mass flow controller (MFC) and a laser spectral concentration sensor to achieve precise control of the impurity gas concentration (0-1000ppm) and the particle size (0.1-10μm).

[0023] The nitrogen oxide generator utilizes catalytic oxidation principles, precisely controlling reaction temperature and gas-phase residence time to achieve the selective production of nitrogen oxides of varying valences. Sulfur oxide production utilizes a combustion method, using sulfur-containing fuels to completely combust in oxygen-rich conditions to produce the target product, which is then diluted and mixed to achieve the desired concentration. The ammonia generator utilizes urea pyrolysis technology, achieving stable ammonia release through temperature gradient control. The volatile organic compound generator utilizes a multi-channel evaporation system, storing organic compounds of varying boiling points in independent temperature-controlled tanks and conveying them through a carrier gas to form a precisely proportioned vapor mixture.

[0024] Each generator is equipped with an independent exhaust gas purification unit to ensure that residual pollutants that have not participated in the reaction do not leak into the environment. The concentration calibration system, including a standard gas generator and a laser spectrometer, regularly calibrates the output of each branch online. The modular design allows for flexible configuration of pollutant types based on testing requirements, supporting rapid switching from single pollutants to multi-component composite pollutants.

[0025] A particle capture module is used to generate the particles required for testing; The particle mass concentration can be dynamically adjusted within a range of 5-1000 μg / m³, with an error of ≤±2%. For example, closed-loop concentration control is achieved by adjusting the carrier gas flow rate through a mass flow controller (MFC) combined with real-time feedback from a laser spectroscopy sensor (such as FTIR).

[0026] Particle size control range: supports a wide particle size range of 0.1-10μm, including: fine particulate matter (PM2.5): particle size ≤2.5μm, geometric standard deviation σg≤1.2; inhalable particulate matter (PM10): particle size ≤10μm, σg≤1.5.

[0027] The mixing and delivery module is connected to the impurity generation module and the particle capture module, and is used to control the concentration of impurity gas and particulate matter according to the control algorithm; and to obtain a multi-impurity mixture after mixing, and then deliver the multi-impurity mixture to the fuel cell to participate in the test reaction; For example, a multi-stage Venturi mixer combined with a static mixing tube ensures uniform mixing of impurity gases and particulate matter (mixing uniformity >95%). An integrated online gas composition monitoring system (including an FTIR spectrometer and a particle counter) provides real-time feedback on mixed gas parameters, combined with a PID control algorithm for dynamic regulation.

[0028] In some implementations, the control algorithm is expressed as: ; in, represents the control output signal at time t, e(t) represents the deviation between the preset concentration and the actual concentration at time t, and K p Indicates the proportional coefficient of the corresponding speed adjustment, K i Indicates the integral coefficient for eliminating steady-state error, K d Represents the differential coefficient for suppressing overshoot.

[0029] First adjust K p , first K i and K d Set to 0 to adjust only K p Increase K p Until the system responds fast enough but does not overshoot or oscillate. After the proportional coefficient is determined, gradually increase K i , so that the system can eliminate long-term errors. Fine-tune K d , until the system response becomes smoother.

[0030] The control algorithm utilizes the classic proportional-integral-derivative regulation mechanism, forming the core strategy for dynamic concentration control. The proportional component generates a rapid response signal based on the instantaneous deviation between the preset and actual concentrations, ensuring the system can promptly correct the main error components. The integral component gradually eliminates long-term steady-state errors by accumulating historical deviations, making it particularly suitable for continuous testing scenarios where precise maintenance of target concentrations is required. The differential component monitors concentration trends and initiates proactive regulation at the initial stages of concentration fluctuations, effectively suppressing concentration overshoot. The three control components are weighted and superimposed to form the final control signal, which acts on the flow control valves of each pollutant branch.

[0031] During parameter tuning, a basic proportional coefficient was first determined through step-response experiments, ensuring rapid system response without excessive oscillation. Furthermore, an integral action was gradually introduced to fine-tune small deviations under low-concentration conditions, ensuring concentration stability over long-term testing. Finally, the differential coefficient was optimized, with a focus on suppressing transient fluctuations during high-concentration gradient switching. This control strategy achieves state-of-the-art mixed gas concentration control accuracy and maintains excellent dynamic characteristics even under complex multivariable coupling conditions.

[0032] A fuel cell test module is connected to the mixing and delivery module and is used to receive the multi-impurity mixture, act on the fuel cell, and monitor the working status of the fuel cell; The module includes a fuel cell stack, environmental parameter monitoring, and performance monitoring devices. It receives mixed impurities through the intake duct for reaction, and the monitoring device provides real-time monitoring of the fuel cell's operating status and environmental parameters. The module is also compatible with various proton exchange membrane fuel cell (PEMFC) and solid oxide fuel cell (SOFC) stacks. It also features a temperature and humidity control system (temperature range: 20-80°C, humidity range: 30-90% RH), a backpressure regulating valve (0-2 bar), and a stack voltage patrol device (accuracy: ±0.1mV).

[0033] A data acquisition module, connected to the fuel cell test module, for collecting operating data of the fuel cell; In some embodiments, fuel cell operating data includes fuel cell voltage, current, power, and the like. Exemplarily, a voltage sensor, current sensor, impedance spectrum analyzer, temperature sensor, and pressure sensor are provided in the data acquisition module to monitor and collect fuel cell operating parameters in real time. Voltage is measured using a voltmeter or sensor, current is measured using a current sensor, and power is calculated based on the voltage and current values. Alternatively, temperature is measured using a power analyzer or temperature sensor. Performance stability is analyzed through statistical analysis of voltage, current, and power variation data.

[0034] In electrochemical systems, voltage decay directly reflects the decline in catalyst activity, current changes reflect the obstruction of reaction kinetics, and power is a comprehensive performance indicator. By monitoring the curves of these parameters over time, the performance decay rate at different time points can be calculated. For example, in a constant current test, the voltage drop rate can be used to characterize the stability of battery performance; under dynamic conditions, power integration is required to evaluate the stability of battery performance. Battery performance stability can be represented by the drop in voltage or the change in power. For example, the decay rate of the battery can be calculated by monitoring the change in voltage over time.

[0035] The data processing and analysis module is connected to the data acquisition module and is used to determine the influence of multiple impurities on the performance of the fuel cell based on the working data of the fuel cell.

[0036] The present invention effectively solves the technical problem that the synergistic effect of pollutants under real working conditions is difficult to simulate by constructing a multi-impurity composite effect test system. The system realizes the synchronous generation and precise proportioning of multi-phase pollutants such as nitrogen oxides, sulfur oxides, ammonia, volatile organic compounds and particulate matter by integrating the impurity generation module, the particle capture module and the mixed delivery module, breaking through the limitation of the lack of pollutant interaction in single-factor testing. The mixed delivery module dynamically adjusts the concentration gradient of each impurity based on the PID control algorithm, and combines the multi-dimensional state monitoring of the fuel cell test module to construct a mapping relationship between the composite effect of multiple impurities and the attenuation of fuel cell performance. The steady-state generation and dynamic control of composite pollutants are achieved through the collaboration of multiple modules, providing a reliable experimental platform for revealing the mechanism of synergistic effect of multiple impurities, and significantly improving the consistency of test results with actual working conditions.

[0037] Another aspect of the present invention provides a fuel cell multi-impurity composite effect test method, which is performed by any one of the fuel cell multi-impurity composite effect test systems described above. Figure 2 This is a flow chart of the steps of a fuel cell multi-impurity composite effect testing method provided by an embodiment of the present invention. Figure 2 It can be seen that the method includes the following steps: S1: Determine the type and concentration of impurities required for testing based on test requirements; For example, based on research into the impact of air quality on fuel cell performance, the impurity gases present in the air and the limits of their impact on fuel cell performance are determined. Based on this, the types and concentrations of impurities required for testing are determined, and environmental parameters are determined based on the climate characteristics of different regions. Based on these preset parameters, the impurity generation module and particle capture module generate impurities of corresponding concentrations, which are then transported to the mixing and delivery module for thorough mixing. The mixed impurities are then transported to the fuel cell under test in the fuel cell test module for testing.

[0038] S2: Determine the synergistic effect coefficient among various impurities based on their types and concentrations; In some embodiments, the synergistic effect coefficients among various impurities are determined based on the types and concentrations of the impurities, including: S21: Obtain the type and concentration of impurities; S22: Determine the concentration combination of each impurity based on the type and concentration of the impurities; The method for determining the concentration combinations of each impurity must ensure that all possible interaction scenarios are covered. Box-Behnken designs or central composite designs (CCD) can be used. The Box-Behnken design is suitable for intermediate-level studies that do not contain extreme values, thus preventing damage to the fuel cell stack caused by high-concentration combinations. Central composite designs (CCDs) add star points (extreme points) to the three-level design to fit a quadratic model.

[0039] S23: testing the decay rate of the fuel cell under each concentration combination; S24: Based on the decay rates at various concentration combinations, the synergistic effect coefficients among various impurities are obtained by fitting using the least squares method.

[0040] S3: Determine the initial attenuation rate of the fuel cell due to the combination of multiple impurities based on the synergistic effect coefficient; In some embodiments, the initial attenuation rate of the multi-impurity combination on the fuel cell is determined based on the synergistic effect coefficient, and the initial attenuation rate is obtained by the following formula: ; Among them, η 初始 represents the initial decay rate, β0 represents the constant term, β i It represents the effect coefficient of the i-th impurity on the fuel cell alone, characterizing the linear influence, β ii represents the square term coefficient of the i-th impurity on the fuel cell, α ij It represents the synergistic effect coefficient between the i-th impurity and the j-th impurity, characterizing the interaction effect, n represents the number of impurity types, C i represents the concentration of the i-th impurity, C j represents the concentration of the jth impurity.

[0041] The constant term β0 reflects the background attenuation characteristics of the fuel cell in the absence of impurities, such as the aging of the material itself; the linear term is the individual effect coefficient of the i-th impurity β i , characterizes the linear change in attenuation rate when the impurity concentration increases by a unit value (reflecting the direct impact of a single impurity). Square term coefficient β ii Describe the concentration saturation effect, reveal the self-inhibition characteristics of the pollutant action mechanism, and describe the saturation effect or nonlinear changes of impurities at high concentrations (such as the slowdown of the attenuation rate when the concentration is too high). Synergistic effect coefficient αij The synergistic or antagonistic effects of two pollutants acting together are quantitatively expressed, with the sign of the coefficient determining whether the effect is enhanced or weakened. Parameter estimation utilizes regularized least squares to balance model complexity and goodness of fit. >0 indicates that impurities i and j have a synergistic effect, which synergistically promotes the attenuation of the battery performance corresponding to the two impurities, making the attenuation rate greater than the linear sum of the effects of each impurity alone; <0 indicates that there is an antagonistic effect between impurities i and j, which jointly suppress the attenuation effect of the two impurities on the battery performance, making the attenuation rate lower than the sum of the linear superposition of the effects of each impurity alone. =0 means that impurities i and j have no interaction, and the decay rate is the linear superposition of the battery performance decay rates corresponding to the two impurities.

[0042] S4: Determine the impact weight of each impurity based on its type and concentration; In some embodiments, the impact weight of each impurity is determined based on the type and concentration of the impurity, wherein the calculation formula for the impact weight is: ; Among them, W k represents the influence weight of the kth impurity, n represents the number of impurity types, e k Represents the information entropy value of the kth impurity. The greater the concentration fluctuation, the higher the weight.

[0043] Information entropy calculations are based on the probability distribution of concentration data, with the entropy value reflecting the degree of fluctuation in pollutant concentrations. A weighting mechanism assigns greater influence to pollutants with high dispersion, consistent with the impact of sudden pollution events on system stability in real-world environments. Normalization ensures that the sum of the weights of each pollutant remains constant, maintaining model calculation stability. A dynamic weight adjustment function allows entropy values ​​to be updated based on real-time monitoring data, adapting to the time-varying nature of pollution characteristics.

[0044] In some embodiments, the information entropy value e of the kth impurity is k The calculation formula is: ; Among them, x mk represents the normalized concentration of the kth impurity in the mth sample, and M represents the number of samples.

[0045] Normalized concentration processing utilizes extreme value normalization to eliminate the impact of dimensional differences on entropy calculations. Probability density estimation utilizes kernel density smoothing techniques to accurately reflect the true characteristics of concentration distributions. The entropy calculation module integrates fast algorithms to support real-time processing of large datasets. Multidimensional entropy analysis extends to the joint concentration-time distribution to capture the temporal and spatial variations of pollutants.

[0046] S5: By influencing the weights, the initial attenuation rate is corrected to obtain the final attenuation rate of the multi-impurity composite effect on the fuel cell, thereby completing the fuel cell multi-impurity composite effect test.

[0047] In some embodiments, the initial attenuation rate is modified by the influence weight to obtain the final attenuation rate of the fuel cell due to the combination of multiple impurities. The calculation formula is: ; Among them, η 初始 represents the initial decay rate, η 最终 represents the final attenuation rate, n represents the number of impurity types, W k represents the influence weight of the kth impurity, C k represents the concentration of the kth impurity. This can be interpreted as impurities with higher weights (such as NO2 with large concentration fluctuations) accounting for a higher proportion of the total attenuation rate, thus strengthening their influence on the results.

[0048] Final decay rate η 最终 The computational integration of physical models and statistical weighting creates a prediction method that combines mechanistic explanatory power with data adaptability. Concentration weighting reflects actual exposure dose variations, while weighting coefficients reflect regional characteristics of pollution signatures. Model validation utilizes cross-validation, evaluating the distribution of prediction errors using multiple independent experimental data sets. Uncertainty analysis quantifies the sensitivity of each input parameter, guiding the optimization of test accuracy.

[0049] The same-effect coefficient determination utilizes a combination of experimental design and numerical fitting to construct a multivariate response surface model including quadratic and interaction terms. The initial decay rate calculation breaks through the traditional linear superposition assumption by introducing synergistic effects between pollutants to accurately describe the nonlinear decay behavior under combined pollution. Information entropy weight analysis, based on the distribution characteristics of historical concentration data, quantifies the contribution of individual pollutant concentration fluctuations to the overall impact.

[0050] During the test requirements analysis phase, the atmospheric pollution characteristics of the actual application scenario were comprehensively considered. Typical pollutant combinations and their concentration ranges were determined through statistical analysis of environmental monitoring data. The synergistic effect coefficient was determined by combining experimental design with numerical fitting, constructing a multivariate response surface model with quadratic and interaction terms. The initial decay rate calculation broke through the traditional linear superposition assumption by introducing synergistic effects between pollutants to accurately describe the nonlinear decay behavior under complex pollution. Information entropy weight analysis, based on the distribution characteristics of historical concentration data, quantified the contribution of individual pollutant concentration fluctuations to the overall impact.

[0051] The present invention also constructs a dual correction mechanism based on the synergistic effect coefficient and information entropy weight. This introduces the impurity synergistic effect coefficient when determining the initial decay rate, breaking through the simplified assumptions of the traditional linear superposition model and accurately capturing the nonlinear decay characteristics under the combined effects of multiple impurities. By calculating the information entropy weight of each impurity and performing a secondary correction on the initial decay rate, the contribution of different impurity concentration distributions to overall performance is effectively quantified, avoiding deviations caused by experimental design limitations (such as not covering extreme concentrations), and improving the accuracy and engineering applicability of fuel cell performance decay predictions.

[0052] Exemplarily, the specific application process is described by taking the synergistic effect of two impurities, NO2 and SO2, on the voltage decay rate of a fuel cell as an example.

[0053] 1. Experimental Background and Objectives Impurity type: Gaseous pollutants NO2 and SO2 (two factors).

[0054] Response variable: fuel cell voltage decay rate ƞ.

[0055] Objective: To fit the NO2 concentration C by response surface methodology NO2 and SO2 concentration C SO2 and the initial decay rate η 初始 The relationship between NO2 and SO2 is obtained .

[0056] 2. Experimental Design (Box-Behnken Design) 1. Variable level settings, as shown in Table 1.

[0057] Table 1 Variable level settings

[0058] 2. Experimental Configuration and Data Collection A total of 9 groups of experiments (including center point repetitions) were designed. The test system generated mixed gases of corresponding concentrations and measured the attenuation rate of the fuel cell under various concentration combinations, as shown in Table 2.

[0059] Table 2 Test results

[0060] 3. Response surface model establishment and fitting 1. Assume the model form A quadratic polynomial model is used to describe the relationship between the decay rate and C NO2 , C SO2 Relationship: ; 2. Data standardization (coding processing) Convert the concentration to the coded value X1 of the dimensionless concentration of NO2 and the coded value X2 of the dimensionless concentration of SO2 (centered at 0 and ranging from ±1):

[0061] For example: NO2 concentration C NO2 =150ppm =+1, SO2 concentration C SO2 =25ppm =-1.

[0062] 3. Least squares fitting parameters Substitute the experimental data into the model and solve the synergistic effect coefficient and other coefficients through matrix operations or software. The fitting results are as follows:

[0063] The expression corresponding to the original variable is:

[0064] When acting alone, the initial decay rate of NO2 = 1.9×1 = 1.9% / kh, and the initial decay rate of SO2 = 0.8×1 = 0.8% / kh. The linear superposition is 2.7% / kh. According to the above formula 2, the actual combined initial decay rate of NO2 and SO2 is 9.2% / kh, indicating that NO2 and SO2 have a synergistic effect on each other, that is, the decay rate when the two act together is higher than the sum of their individual effects.

[0065] In another embodiment of the present application, the weight is used as a correction factor to optimize the initial attenuation rate:

[0066] Physical significance: Impurities with high weights (such as NO2 with large concentration fluctuations) account for a higher proportion in the total attenuation rate, strengthening their impact on the results.

[0067] Take the concentration in Table 3 as an example: Table 3 Concentration and its weight

[0068] Initial decay rate (assumed): =7.5% / kh; Final decay rate: =7.5% / kh×0.355=2.66% / kh.

[0069] Comparative experiments show that the double-correction model has significantly improved prediction accuracy in complex pollution scenarios compared with traditional linear models, especially in concentration ranges where pollutants interact significantly.

[0070] The response surface methodology employed in this invention captures the physicochemical interactions between impurities (e.g., synergistic poisoning and catalytic layer degradation), addressing the mechanistic question of "how these interactions occur." The entropy weighting method quantifies the statistical characteristics (discreteness) of impurity concentration fluctuations, addressing the data distribution question of "which impurities are more sensitive." Complementary integration of dual information avoids the one-sidedness of a single method. For example, if an impurity has a weak synergistic effect but large concentration fluctuations (e.g., NH3 in industrial scenarios), the weighting method can highlight its actual impact. If an impurity has a strong synergistic effect but stable concentration (e.g., low-concentration VOCs), the response surface methodology can prevent its underestimation.

[0071] The weights are dynamically adjusted with the concentration distribution, and the revised model can more realistically reflect the actual working conditions. When the NO2 concentration fluctuates greatly, the weighting method gives it a higher proportion, making the calculated results closer to the measured attenuation rate; when the synergy of multiple pollutants is serious, the synergistic terms of the response surface method and the high-weight impurities of the weighting method form a "mechanism + data" double verification.

[0072] Initial models may be biased due to experimental design limitations (e.g., not covering extreme concentrations). Weighted methods provide independent correction paths based on the statistical characteristics of the data. For example, if the experimental NO2 concentration range is 50-150ppm, and an extreme value of 200ppm occurs in actual application, the weighted method can automatically assign a high weight to this value based on the high dispersion (low entropy) of the historical data, thus avoiding model extrapolation errors.

[0073] By combining the response surface methodology simulation mechanism with the entropy weighting method to optimize weights, the final decay rate calculation integrates physical laws and statistical data characteristics. This not only explains how impurity interactions lead to performance degradation, but also quantifies the sensitivity of each impurity in the actual environment. This dual constraint makes the model more realistic and is particularly suitable for fuel cell life prediction and anti-pollution design optimization in complex pollution environments.

[0074] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. A fuel cell multi-impurity composite effect test system, characterized in that: It includes impurity generation module, particle capture module, mixing and transportation module, fuel cell test module, data acquisition module and data processing and analysis module: The impurity generation module is used to generate impurity gas required for testing; The particle capture module is used to generate particulate matter required for testing; The mixing and delivery module is connected to the impurity generation module and the particle capture module, and is used to control the concentration of the impurity gas and the particulate matter according to a control algorithm; mix them to obtain a multi-impurity mixture, and then deliver the multi-impurity mixture to the fuel cell to participate in the test reaction; The fuel cell testing module is connected to the mixing and delivery module and is used to receive the multi-impurity mixture, act on the fuel cell, and monitor the working status of the fuel cell; The data acquisition module is connected to the fuel cell test module and is used to collect operating data of the fuel cell; The data processing and analysis module is connected to the data acquisition module and is used to determine the influence of multiple impurities on the performance of the fuel cell based on the operating data of the fuel cell.

2. A fuel cell multi-impurity composite effect testing system according to claim 1, characterized in that: The expression of the control algorithm is: ; in, represents the control output signal at time t, e(t) represents the deviation between the preset concentration and the actual concentration at time t, and K p Indicates the proportional coefficient of the corresponding speed adjustment, K i Indicates the integral coefficient for eliminating steady-state error, K d Represents the differential coefficient for suppressing overshoot.

3. A fuel cell multi-impurity composite effect testing system according to claim 1, characterized in that: The operating data of the fuel cell includes the voltage, current and power of the fuel cell.

4. A fuel cell multi-impurity composite effect testing system according to claim 1, characterized in that: The impurity generating module includes a nitrogen oxide generating device, a sulfur oxide generating device, an ammonia generating device and a volatile organic compound generating device.

5. A fuel cell multi-impurity composite effect testing method, performed by a fuel cell multi-impurity composite effect testing system according to any one of claims 1 to 4, characterized in that: The method comprises the following steps: Determine the type and concentration of impurities required for testing based on test requirements; Determining the synergistic effect coefficients among various impurities based on the types and concentrations of the impurities; Determining the initial attenuation rate of the multi-impurity combination on the fuel cell based on the synergistic effect coefficient; Determine the impact weight of each impurity based on the type and concentration of the impurities; The initial attenuation rate is corrected by the influence weight to obtain the final attenuation rate of the multi-impurity composite effect on the fuel cell, so as to complete the fuel cell multi-impurity composite effect test.

6. A fuel cell multi-impurity composite effect testing method according to claim 5, characterized in that: Determining the synergistic effect coefficients among various impurities based on the types and concentrations of the impurities includes: Obtaining the type and concentration of the impurities; Determining the concentration combination of each impurity according to the type and concentration of the impurities; The decay rate of the fuel cell was tested under various concentration combinations; Based on the decay rates under the various concentration combinations, the synergistic effect coefficients among the various impurities were obtained by fitting using the least squares method.

7. A fuel cell multi-impurity composite effect testing method according to claim 5, characterized in that: The initial attenuation rate of the multi-impurity combination on the fuel cell is determined based on the synergistic effect coefficient, and the initial attenuation rate is obtained by the following formula: ； Among them, η 初始 represents the initial decay rate, β0 represents the constant term, β i represents the effect coefficient of the i-th impurity on the fuel cell alone, β ii represents the square term coefficient of the i-th impurity on the fuel cell, α ij represents the synergistic effect coefficient between the i-th impurity and the j-th impurity, n represents the number of impurity types, C i represents the concentration of the i-th impurity, C j represents the concentration of the jth impurity.

8. A fuel cell multi-impurity composite effect testing method according to claim 5, characterized in that: The influence weight of each impurity is determined based on the type and concentration of the impurities, wherein the calculation formula of the influence weight is: ； Among them, W k represents the influence weight of the kth impurity, n represents the number of impurity types, e k Represents the information entropy value of the kth impurity.

9. A fuel cell multi-impurity composite effect testing method according to claim 8, characterized in that: The information entropy value e of the kth impurity k The calculation formula is: ; Among them, x mk represents the normalized concentration of the kth impurity in the mth sample, and M represents the number of samples.

10. A fuel cell multi-impurity composite effect testing method according to claim 5, characterized in that: The initial attenuation rate is corrected by the influence weight to obtain the final attenuation rate of the fuel cell due to the combination of multiple impurities. The calculation formula is: ； Among them, η 初始 represents the initial decay rate, η 最终 represents the final attenuation rate, n represents the number of impurity types, W k represents the influence weight of the kth impurity, C k represents the concentration of the kth impurity.

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