Temperature self-adaptive shutdown purging method for low-damage cold start

Abstract

The invention discloses a temperature self-adaptive shutdown purging method for low-damage cold start, and relates to the technical field of fuel cells. According to the strategy, a full-temperature working condition'impedance-water content 'mapping relation is established through a fixed-frequency impedance test, a balance purging test and Gaussian process regression, a low-temperature starting success boundary is determined by carrying out multi-temperature and different-loading-rate cold starting tests, and a damage boundary is determined by carrying out dry / wet and freezing / thawing cycle tests. Based on the three types of boundaries, a water content-cold start success threshold-damage threshold dynamic mapping mechanism is constructed, the water content of the battery is monitored in real time and compared with a target water content threshold at a purging end point, purging parameters are automatically adjusted, and a temperature self-adaptive purging strategy is formed. The problems of structural damage and durability reduction caused by freezing / thawing and dry / wet circulation in low-temperature cold start of the fuel cell can be effectively solved, and collaborative optimization of start success rate and long-term durability is realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of fuel cell technology, in particular to a temperature adaptive shutdown purge method for low-damage cold start. BACKGROUND

[0002] Proton exchange membrane fuel cells, as the core carrier of hydrogen energy technology, have shown broad application prospects in new energy vehicles, distributed power generation and other fields. However, the lack of low-temperature cold start durability has become a global technical bottleneck restricting its industrialization. When the ambient temperature is below freezing point, the ice formation of water in the fuel cell leads to the expansion of catalyst layer cracks, the deformation of gas diffusion layer pores and the puncture of proton exchange membrane, which significantly reduces the performance and life of the cell. Although the existing technology has extended the lower limit of cold start temperature to-30℃ through material modification and structure optimization, in the process of frequent start-stop in extremely cold regions, the cell needs to withstand the cyclic stress of-30℃ ice swelling and above 60℃ high temperature purge, which accelerates the structural degradation of the membrane electrode assembly.

[0003] Currently, the research on cold start durability mainly focuses on a single damage mechanism, and lacks systematic analysis of multi-field coupled damage evolution. This research limitation results in the fact that the purge strategy still stays at the single goal of reducing the water content before cold start to achieve smooth cold start, which is difficult to cope with the composite damage challenge of "freeze / thaw-dry / wet" cycle. Therefore, it is urgent to build a multi-field coupled damage evolution model and establish a temperature adaptive shutdown purge method for low-damage cold start. SUMMARY

[0004] In order to make up for the shortcomings of the prior art, the present application proposes a temperature adaptive shutdown purge method for low-damage cold start to solve the problems existing in the prior art.

[0005] In order to solve the above technical problems, the present application provides the following technical scheme:

[0006] A temperature adaptive shutdown purge method for low-damage cold start, comprising the following steps:

[0007] Step 1, water content confirmation under full temperature conditions: a "temperature-impedance" dataset is constructed through a constant frequency impedance test, and a full temperature "impedance-water content" mapping relationship is established by combining a balance purge test and Gaussian process regression fitting. The water content inside the cell is determined based on the temperature and high-frequency impedance test after shutdown purge.

[0008] Step 2, determination of low-temperature start success boundary: cold start tests at different temperatures and different load rates are carried out to establish a three-dimensional quantitative relationship of "cold start condition-load rate-initial water content", and the maximum water content threshold for successful start is determined.

[0009] Step 3, dry / wet cycle damage boundary determination: conduct cycle tests with different water content variation ranges, combine micro-morphology characterization with macro-performance degradation analysis, and determine the water content variation threshold that triggers interface damage.

[0010] Step 4, freeze / thaw cycle damage boundary determination: conduct cycle tests with different initial water content and temperature boundaries, analyze GDL pore deformation, CL crack propagation, and other characteristics, and determine the water content critical value of phase change stress cumulative damage.

[0011] Step 5, purge strategy establishment: based on the three types of boundaries, a dynamic mapping mechanism of "temperature-water content-damage threshold" is constructed, the low-damage purge target water content is determined based on the successful start boundary, the dry / wet cycle boundary is limited to fluctuate within a certain range, and the freeze / thaw cycle boundary is corrected to a low temperature target, forming a temperature-adaptive shutdown purge strategy.

[0012] The one or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0013] 1. Multi-dimensional damage analysis and systematic strategy construction: existing technologies mainly focus on single damage mechanism research, and the purge strategy target is single. However, the present application establishes a multi-dimensional analysis system of water content confirmation under full temperature conditions, low-temperature start success boundary determination, dry / wet and freeze / thaw cycle damage boundary determination, systematically studies the combined damage mechanism of "freeze / thaw-dry / wet" cycle, and constructs the purge strategy based on multi-boundary coupling, which more comprehensively deals with the damage problem of fuel cells under complex working conditions.

[0014] 2. Precise water content control and damage prevention: the present application uses the "impedance-water content" mapping relationship under full temperature conditions, combines real-time temperature and high-frequency impedance testing, and realizes precise monitoring of the internal water content of the battery; through the dynamic mapping mechanism, the target water content threshold at the end of the purge is determined, which can more accurately control the water content under full temperature conditions compared to the existing technology of only reducing the water content before cold start, effectively limits the interface damage caused by water content fluctuation in dry / wet cycle, corrects the water content target in freeze / thaw cycle at low temperature, reduces the cumulative damage of phase change stress, and significantly improves the durability of the fuel cell.

[0015] 3. Temperature adaptation and performance co-optimization: existing purge strategies lack the ability to adapt to temperature changes, making it difficult to balance start success and long-term durability. The present application automatically adjusts the parameters such as purge gas flow, humidity, temperature, and time based on the environmental temperature and battery operating state through a dynamic mapping model optimized by machine learning algorithm, forms a temperature-adaptive purge strategy, realizes the co-optimization of low-damage cold start and long-term performance stability, breaks through the performance bottleneck of existing technologies under frequent start-stop working conditions in extremely cold regions, and widens the application range of fuel cells. BRIEF DESCRIPTION OF DRAWINGS

[0016] Figure 1 is an impedance test system schematic diagram;

[0017] Figure 2 is a full-temperature operating condition "impedance-moisture content" data set establishment flowchart;

[0018] Figure 3 is a moisture content damage boundary flowchart. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be described clearly and completely below. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0020] Embodiment 1, as shown in a temperature self-adaptive shutdown purge method for low-damage cold start, comprising the following steps: Figure 1

[0021] S1 full-temperature operating condition moisture content confirmation

[0022] Fixed-frequency impedance test to build "temperature-impedance" data set: use an electrochemical workstation 5 to carry out full-temperature operating condition impedance test, keep other conditions unchanged, carry out fixed-frequency impedance test in the full-temperature operating condition range, and build "temperature-impedance" data set.

[0023] Balanced purge test to build "temperature-moisture content" data set: control the dew point temperature corresponding to different temperatures through a gas temperature adjusting device 3, carry out balanced purge test of different humidity in the full-temperature operating condition range. After each purge to impedance stability, combine the relative humidity of the purge gas with the empirical formula of the moisture content, calibrate the internal moisture content of the battery under each temperature condition, and build "temperature-moisture content" data set.

[0024] Full-temperature operating condition "impedance-moisture content" data set based on Gaussian process regression: merge the "temperature-impedance" and "temperature-moisture content" data sets, take temperature as independent variable, impedance and moisture content as dependent variable to build training set and test set. Adopt Gaussian process regression model to fit the nonlinear relationship between temperature and "impedance-moisture content", correct the fitting degree through hyperparameter optimization and cross-validation. Extrapolate high-resolution full-temperature operating condition data by using high fitting degree model, and build full-temperature operating condition "impedance-moisture content" data set.

[0025] ​Temperature and high-frequency impedance test after shutdown purge: After the fuel cell is shut down, the temperature data of the fuel cell are collected in real time by the temperature sensor 6, and the high-frequency impedance data are collected by the electrochemical workstation 5 at a fixed frequency, the “impedance-humidity” data set under the full-temperature condition is called, and the internal humidity of the fuel cell corresponding to the current temperature and impedance is quickly calculated by the central control system 8.

[0026] S2 low-temperature start success boundary determination

[0027] Single-temperature-condition cold start characteristic analysis: By using the “S1 full-temperature-condition humidity determination method”, cold start tests at different load rates are carried out for a single temperature condition. By scientifically setting multiple representative load rate gradients and combining multiple initial humidity conditions, a rich combination of test conditions is constructed. During the test, the system records key parameters such as start time, current fluctuation, voltage change, and in-depth explores the cold start characteristics under different load rates and initial humidity conditions at a fixed temperature.

[0028] Maximum humidity threshold determination under cold start full-temperature condition: Based on the “load rate-start success maximum humidity” corresponding relationship established in the first single-temperature test, the test dimension is further expanded. By changing the start temperature, cold start tests at different load rates and initial humidity combinations are repeated at multiple temperature points. After collecting the test data at each temperature condition, the evolution law of the “humidity-start success boundary” with temperature change is analyzed in-depth, the interaction of temperature on the maximum humidity for successful start and the load rate is analyzed, and finally all the data are integrated to construct a “cold start condition-load rate-initial humidity” three-dimensional quantitative relationship model, and the maximum humidity threshold for successful start under each cold start condition is determined.

[0029] S3 dry / wet and freeze / thaw cycle damage boundary determination

[0030] Cycle test implementation phase: The freeze / thaw cycle test is designed with humidity and temperature range as variables, the dry / wet cycle test is designed with humidity change range and initial humidity as variables, the cycle step and cycle period are set, the volt-ampere characteristic curve, impedance spectrum and electrochemical active area of the fuel cell after each cycle period are collected by the electrochemical workstation 5, and the performance calibration is completed.

[0031] Microstructure and performance degradation analysis: After the cycle test, the non-destructive disassembly technique is used to observe the fiber fracture, pore collapse of the gas diffusion layer, and the catalyst agglomeration and interface peeling of the catalyst layer by high-resolution imaging of the gas diffusion layer and the catalyst layer with high-power optical electron microscope and optical electron microscope. Quantitative parameters such as porosity and crack length are extracted by image processing software. Based on the macroscopic electrical performance data obtained by the test, a performance degradation kinetics model is established. The influence weight of each factor on the degradation rate is analyzed by multivariate linear regression analysis. Combined with the microstructure characterization results, a “microstructure evolution-macroscopic performance degradation” correlation model is constructed to reveal the failure mode and performance degradation mechanism in the cycle process, determine the water content change threshold that causes interface damage under swelling-shrinking stress cycle, and the critical value of water content that causes phase change stress cumulative damage.

[0032] S4 Purge strategy establishment

[0033] Boundary data integration and foundation building: The “impedance-moisture content” data set under the full temperature condition of S1 is used as the basis for moisture content monitoring. This data set is generated by constant frequency impedance test, equilibrium purge test and Gaussian process regression; the “cold start condition-load rate-initial moisture content” three-dimensional model in S2 is extracted to determine the moisture content threshold for successful cold start; the damage moisture content threshold of dry / wet and freeze / thaw cycles in S3 is summarized. Through structured integration, a multi-dimensional database is established to provide a data cornerstone for the construction of the purge strategy.

[0034] Dynamic mapping mechanism construction: Based on the integrated three types of boundary data, a multivariate nonlinear fitting algorithm is used to construct a “moisture content-cold start success threshold-damage threshold” dynamic mapping model to accurately determine the target moisture content threshold at the end of the purge under different conditions. During the operation and purge of the fuel cell, the “temperature-impedance” data set established in S1 is called in real time, combined with the online collected cell temperature and high-frequency impedance data, and the current internal moisture content of the cell is quickly calculated through the central control system 8, and compared with the purge end moisture content threshold obtained by fitting in real time.

[0035] Adaptive purge strategy generation: Through the dynamic mapping mechanism, a nonlinear relationship between environmental temperature and purge parameters is established. Machine learning algorithms such as support vector machines or artificial neural networks are used to train and optimize the mapping model. During the purge of the fuel cell, the central control system calculates the low-damage purge moisture content based on the real-time collected environmental temperature, cell impedance and other parameters, automatically adjusts the purge gas flow, humidity, temperature and time, forms a temperature adaptive purge strategy that can be dynamically adjusted according to the environment and operating state, and improves the start-up success rate and service life of the fuel cell.

[0036] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations that come within the meaning and range of equivalents of the claims be embraced therein.

[0037] In addition, it should be understood that although this specification is described in terms of implementation methods, not every implementation method contains only one independent technical solution. This narrative method of the specification is only for the sake of clarity. Those skilled in the art should regard the specification as a whole. The technical solutions in each embodiment have also been appropriately combined to form other implementation methods that are easy for those skilled in the art to understand.

Claims

1. A temperature-adaptive shutdown purge method for low-damage cold start, characterized in that: The following steps are involved: Step 1: Confirm the water content under all temperature conditions: Build a temperature-impedance data set through a fixed-frequency impedance test. Combined with a balanced purge test and Gaussian process regression fitting, establish a mapping relationship between full-temperature impedance and water content. Determine the water content inside the battery based on the temperature after shutdown and purge and the high-frequency impedance test. Step 2: Determine the successful low-temperature start boundary: Conduct cold start tests at multiple temperatures and different loading rates to establish a three-dimensional quantitative relationship between cold start conditions, loading rate, and initial moisture content, and clarify the maximum moisture content threshold for a successful start. Step 3: Determine the dry / wet cycle damage boundary: Conduct cyclic tests with different moisture content ranges, combine microscopic morphology characterization with macroscopic performance degradation analysis, and determine the moisture content change threshold that triggers interface damage; Step 4: Determine the freeze / thaw cycle damage boundary: Conduct cyclic tests with different initial moisture contents and temperature boundaries, analyze the GDL pore deformation and CL crack growth characteristics, and determine the critical moisture content for phase transformation stress accumulation damage; Step 5. Establish a purge strategy: Based on the three types of boundaries, a temperature-moisture content-damage threshold dynamic mapping mechanism is constructed. The startup success boundary is used as the benchmark, the dry / wet cycle boundary limits the fluctuation amplitude, and the freeze / thaw cycle boundary corrects the low temperature target. The low-damage purge target moisture content is determined to form a temperature-adaptive shutdown purge strategy.

2. The temperature-adaptive shutdown purge method for low-damage cold start according to claim 1, characterized in that: The step 1 needs to be implemented using a fuel cell test system, which includes a gas temperature regulating device (3), an electrochemical workstation (5) and a central control system (8). The gas temperature regulating device (3) includes a hydrogen supply device (1) and an air supply device (2). The central control system (8) includes a fuel cell, a temperature sensor (6), and a battery temperature regulating device (7). The fuel cell (4) is respectively connected to the hydrogen supply device (1), the air supply device (2), the electrochemical workstation (5), the temperature sensor (6) and the battery temperature regulating device (7).

3. The temperature-adaptive shutdown and purging method for low-damage cold start according to claim 1, characterized in that: The step 1 specifically includes: Step 1.

1. Full-temperature impedance testing and data set construction: Conduct full-temperature impedance testing using the fixed-frequency impedance tester on the electrochemical workstation 5. Adjust the sampling frequency by determining the relaxation change stage to create a "temperature-impedance" data set. Step 1.2, Calibrate the water content under all temperature conditions: Regulate the purge gas temperature using the gas temperature regulator 3 and the fuel cell 4 temperature using the battery temperature regulator 7 to perform a balanced purge test under all temperature conditions. Using the empirical formula for purge gas relative humidity and water content, calibrate the water content under each temperature condition to establish a temperature-water content dataset. Step 1.3: Fitting and Verification of Full-Temperature Data Based on Gaussian Process Regression: A Gaussian process regression model was used to fit and predict the established data set, determining the boundaries and precision of the independent variable values. The fit was corrected using various methods and verified through multiple sets of operating condition data tests to obtain a full-temperature operating condition impedance-water content mapping data set. Step 1.4: Determine the water content under all temperature conditions: Determine the water content inside the battery based on the temperature during the shutdown purge process and the high-frequency impedance test results.

4. The temperature-adaptive shutdown purge method for low-damage cold start according to claim 3, characterized in that: The step 2 specifically includes: conducting an equivalent single-cell cold start test at multiple temperatures and different loading rates, establishing a quantitative relationship between cold start conditions, loading rate, and initial water content, and determining the maximum water content for successful start at different temperatures and loading rates.

5. The temperature-adaptive shutdown purge method for low-damage cold start according to claim 4, characterized in that: The step 3 specifically includes: conducting freeze / thaw cycle tests with different initial moisture contents and different temperature boundaries, analyzing the GDL pore deformation and CL crack extension damage characteristics, and determining the critical moisture content that triggers phase transition stress accumulation damage.

6. The temperature-adaptive shutdown purge method for low-damage cold start according to claim 5, characterized in that: The step 4 specifically includes: conducting freeze / thaw cycle tests with different initial moisture contents and different temperature boundaries, analyzing the GDL pore deformation and CL crack extension damage characteristics, and determining the critical moisture content that triggers phase transition stress accumulation damage.

7. The temperature-adaptive shutdown and purging method for low-damage cold start according to claim 6, characterized in that: The step 5 comprises: Step 5.

1. Determine the low-temperature startup success boundary: Through cold start tests at different temperatures and loading rates, based on the initial moisture content determined in claim 3, establish the corresponding relationship between "loading rate-maximum moisture content for successful startup" and the three-dimensional quantitative relationship between "cold start operating conditions-loading rate-initial moisture content" to clearly define the maximum moisture content for successful startup; Step 5.2: Determine the dry / wet cycle damage boundary: Conduct dry / wet cycle tests with different moisture content boundary changes. Combine microscopic morphology characterization with macroscopic performance degradation analysis to determine the moisture content change threshold that triggers interface damage. Step 5.3: Determine the freeze / thaw cycle damage boundary: Conduct freeze / thaw cycle tests with different initial moisture contents, analyze the damage characteristics, and determine the critical moisture content for phase transition stress cumulative damage. Step 5.4: Establish a temperature-adaptive shutdown and purge strategy: Based on the three boundaries determined in Steps 5.1-5.3, a dynamic mapping mechanism is constructed: moisture content, cold start success threshold, and damage threshold. A three-dimensional quantitative relationship is used to determine the maximum moisture content benchmark for a successful startup under different operating conditions. The moisture content fluctuation range is limited by the dry / wet cycle damage threshold. The purge target is then adjusted based on the freeze / thaw cycle critical value. Ultimately, a low-damage shutdown and purge target moisture content is determined. Following the method in Step 1, a temperature-adaptive shutdown and purge strategy is established.

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