A temperature-adaptive shutdown purging method for low-damage cold start

Abstract

This invention discloses a temperature-adaptive shutdown purging method for low-damage cold start, relating to the field of fuel cell technology. This strategy establishes an impedance-moisture content mapping relationship across all temperature conditions through constant-frequency impedance testing, balanced purging experiments, and Gaussian process regression. It conducts cold start tests at multiple temperatures and different loading rates to determine the low-temperature start-up success boundary, and performs dry / wet and freeze / thaw cycle tests to clarify the damage boundary. Based on these three types of boundaries, a dynamic mapping mechanism of "moisture content-cold start success threshold-damage threshold" is constructed. The battery moisture content is monitored in real time and compared with the target moisture content threshold at the purging endpoint, automatically adjusting the purging parameters to form a temperature-adaptive purging strategy. This invention effectively solves the structural damage and durability reduction problems caused by freeze / thaw and dry / wet cycles during low-temperature cold start of fuel cells, achieving synergistic optimization of start-up success rate and long-term durability.

Description

Technical Field

[0001] This invention relates to the field of fuel cell technology, and in particular to a temperature-adaptive shutdown purging method for low-damage cold start. Background Technology

[0002] Proton exchange membrane fuel cells (PEMFCs), as the core carrier of hydrogen energy technology, have shown broad application prospects in new energy vehicles, distributed power generation, and other fields. However, insufficient cold-start durability at low temperatures has become a global technical bottleneck restricting their industrialization. When the ambient temperature is below freezing, the freezing of water inside the fuel cell leads to the propagation of cracks in the catalyst layer, deformation of the gas diffusion layer pores, and puncture of the proton exchange membrane, significantly reducing battery performance and lifespan. Although existing technologies have extended the lower limit of the cold-start temperature to -30°C through material modification and structural optimization, the battery still needs to withstand the cyclic stress of freezing expansion at -30°C and purging at temperatures above 60°C during frequent start-stop cycles in extremely cold regions, accelerating the structural degradation of the membrane electrode assembly.

[0003] Currently, cold start durability research mainly focuses on single damage mechanisms, lacking systematic analysis of multi-field coupled damage evolution. This limitation means that purging strategies remain focused on reducing pre-cold start moisture content to achieve a smooth cold start, making it difficult to address the complex damage challenges of "freeze / thaw-dry / wet" cycles. There is an urgent need to construct multi-field coupled damage evolution models and establish temperature-adaptive shutdown purging methods for low-damage cold starts. Summary of the Invention

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

[0005] To solve the above-mentioned technical problems, the present invention provides the following technical solution:

[0006] A temperature-adaptive shutdown purging method for low-damage cold start includes the following steps:

[0007] Step 1: Confirmation of water content under full temperature conditions: Construct a "temperature-impedance" dataset through fixed-frequency impedance testing, and establish a full-temperature "impedance-water content" mapping relationship by combining balanced purging test and Gaussian process regression fitting. Determine the internal water content of the battery based on the temperature after shutdown purging and high-frequency impedance testing.

[0008] Step 2: Determine the boundary for successful low-temperature start-up: Conduct cold start tests at multiple temperatures and with different loading rates to establish a three-dimensional quantitative relationship of "cold start conditions - loading rate - initial moisture content" and clarify the maximum moisture content threshold for successful start-up.

[0009] Step 3: Determine the damage boundary of dry / wet cycling: Conduct cycling tests with different moisture content variations, and combine microscopic morphology characterization and macroscopic performance degradation analysis to determine the moisture content variation threshold that causes interfacial damage.

[0010] Step 4: Determining the damage boundary of freeze / thaw cycles: Conduct cyclic tests with different initial water contents and temperature boundaries, analyze the characteristics of GDL pore deformation and CL crack propagation, and determine the critical water content value for phase transformation stress cumulative damage.

[0011] Step 5: Establishing the purging strategy: Based on three types of boundaries, a dynamic mapping mechanism of "temperature-moisture content-damage threshold" is constructed. The successful start-up boundary is used as the benchmark, the dry / wet cycle boundary limits the fluctuation range, and the freeze / thaw cycle boundary corrects the low temperature target. The moisture content of the low-damage purging target is determined, and a temperature-adaptive shutdown purging strategy is formed.

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

[0013] 1. Multi-dimensional damage analysis and systematic strategy construction: Existing technologies mostly focus on the study of single damage mechanisms, and the purging strategies have a single objective. However, this invention establishes a multi-dimensional analysis system to confirm the water content under all temperature conditions, determine the boundary for successful low-temperature start-up, and determine the damage boundaries of dry / wet and freeze / thaw cycles. It systematically studies the composite damage mechanism of the "freeze / thaw-dry / wet" cycle, and constructs a purging strategy based on multi-boundary coupling to more comprehensively address the damage problem of fuel cells under complex operating conditions.

[0014] 2. Precise Moisture Content Control and Damage Prevention: This invention utilizes the "impedance-moisture content" mapping relationship under all temperature conditions, combined with real-time temperature and high-frequency impedance testing, to achieve precise monitoring of the internal moisture content of the battery. By determining the target moisture content threshold at the purging endpoint through a dynamic mapping mechanism, compared with the existing technology that only reduces the moisture content before cold start, it can more accurately control the moisture content under all temperature conditions, effectively limiting interface damage caused by moisture content fluctuations in dry / wet cycles, correcting the moisture content target for freeze / thaw cycles at low temperatures, reducing phase change stress accumulation damage, and significantly improving the durability of fuel cells.

[0015] 3. Temperature Adaptability and Performance Co-optimization: Existing purging strategies lack the ability to adapt to temperature changes, making it difficult to balance successful start-up and long-term durability. This invention, based on ambient temperature and battery operating status, uses a dynamic mapping model optimized by machine learning algorithms to automatically adjust parameters such as purging gas flow rate, humidity, temperature, and time, forming a temperature-adaptive purging strategy. This achieves synergistic optimization of low-damage cold start-up and long-term performance stability, breaking through the performance bottleneck of existing technologies under frequent start-stop conditions in extremely cold regions and broadening the application scope of fuel cells. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of an impedance testing system;

[0017] Figure 2 This is a flowchart for establishing a "resistance-moisture content" dataset under all temperature conditions;

[0018] Figure 3 This is a flowchart of the water content damage boundary. Detailed Implementation

[0019] The technical solutions in the embodiments of the present invention will be clearly and completely described below. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1, such as Figure 1 As shown, a temperature-adaptive shutdown purging method for low-damage cold start includes the following steps:

[0021] S1 moisture content confirmation under all temperature conditions

[0022] Constructing a "temperature-impedance" dataset for fixed-frequency impedance testing: Using an electrochemical workstation 5, impedance tests were conducted under full temperature conditions. Keeping other conditions constant, fixed-frequency impedance tests were performed over the entire temperature range to establish a "temperature-impedance" dataset.

[0023] A temperature-moisture content dataset was constructed through equilibration purging tests: The dew point temperature corresponding to different temperatures was controlled by the gas temperature regulating device 3, and equilibration purging tests with different humidity levels were carried out within the full temperature range. After each purging until the impedance stabilized, the internal moisture content of the battery under each temperature condition was calibrated using empirical formulas for the relative humidity and moisture content of the purging gas, thus establishing a temperature-moisture content dataset.

[0024] A Gaussian process regression-based dataset for impedance-water content under all temperature conditions: By merging the temperature-impedance and temperature-water content datasets, training and test sets were constructed with temperature as the independent variable and impedance and water content as dependent variables. A Gaussian process regression model was used to fit the nonlinear relationship between temperature and impedance-water content, and the fit was corrected through hyperparameter optimization and cross-validation. High-resolution full-temperature condition data were extrapolated using the well-fitted model to establish the impedance-water content dataset for all temperature conditions.

[0025] Determination of water content by temperature and high-frequency impedance test after shutdown and purging: When the fuel cell is shut down, the temperature sensor 6 collects the fuel cell temperature data in real time, and the electrochemical workstation 5 collects high-frequency impedance data at a fixed frequency. The "impedance-water content" dataset under all temperature conditions is called up, and the central control system 8 is used to quickly calculate the water content inside the battery corresponding to the current temperature and impedance.

[0026] S2 cryogenic start-up success boundary determined

[0027] Analysis of Cold Start Characteristics under Single Temperature Conditions: Utilizing the "S1 Full Temperature Condition Moisture Content Determination Method," cold start tests were conducted under different loading rates for a single temperature condition. By scientifically setting multiple representative loading rate gradients and combining them with various initial moisture content conditions, a rich combination of test conditions was constructed. During the tests, key parameters such as start-up time, current fluctuations, and voltage changes were recorded systematically to deeply explore the cold start characteristics under different loading rate and initial moisture content combinations at this fixed temperature.

[0028] Confirmation of the maximum moisture content threshold under all cold start conditions: Based on the "load-maximum moisture content for successful start" relationship established in the first step of the single-temperature test, the experimental dimensions were further expanded. By changing the start temperature, cold start tests with different combinations of load rates and initial moisture contents were repeatedly conducted at multiple temperature points. After collecting the test data under each temperature condition, the evolution of the "moisture content-start success boundary" with temperature was analyzed in depth. The interaction between temperature and the maximum moisture content for successful start and load rate was analyzed. Finally, all data were integrated to construct a three-dimensional quantitative relationship model of "cold start condition-load rate-initial moisture content", clarifying the maximum moisture content threshold for successful start under each cold start condition.

[0029] S3 Dry / Wet, Freeze / Thaw Cycle Damage Boundary Determination

[0030] Cyclic test implementation phase: Design freeze / thaw cycle test with water content and temperature range as variables, and design dry / wet cycle test with water content variation range and initial water content as variables. Set cycle step size and cycle period. Use electrochemical workstation 5 to collect the current-voltage characteristic curve, AC impedance spectrum and electrochemical active area and other electrical parameters of fuel cell after each cycle, and complete the performance calibration.

[0031] Microscopic Morphology and Performance Degradation Analysis: After cyclic testing, non-destructive disassembly techniques were employed, and high-resolution imaging of the gas diffusion layer and catalyst layer was performed using a high-magnification optical electron microscope and an optical electron microscope, respectively. This allowed for observation of fiber fracture and pore collapse in the gas diffusion layer, and catalyst agglomeration and interfacial delamination in the catalyst layer. Quantitative parameters such as porosity and crack length were extracted using image processing software. Based on the macroscopic electrical performance data obtained from the experiment, a performance degradation kinetic model was established. Multiple linear regression analysis was used to determine the influence weights of various factors on the degradation rate. Combined with the microstructure characterization results, a correlation model of "microstructure evolution - macroscopic performance degradation" was constructed to reveal the failure modes and performance degradation mechanisms during cyclic testing. The threshold for water content change in interface damage induced by swelling-shrinkage stress cycling, as well as the critical water content value for phase transformation stress cumulative damage, were determined.

[0032] S4 purge strategy establishment

[0033] Boundary data integration forms the foundation: The "impedance-moisture content" dataset under all temperature conditions in S1 is used as the basis for moisture content monitoring. This dataset is generated by constant-frequency impedance testing, balanced purging tests, and Gaussian process regression. The three-dimensional model of "cold start condition-load rate-initial moisture content" in S2 is extracted to clarify the moisture content threshold for successful cold start. The damage moisture content thresholds of dry / wet and freeze / thaw cycles in S3 are summarized. Through structured integration, a multi-dimensional database is constructed, providing a data foundation for the construction of purging strategies.

[0034] Dynamic mapping mechanism construction: Based on the integrated three types of boundary data, a multivariate nonlinear fitting algorithm is used to construct a dynamic mapping model of "moisture content - cold start success threshold - damage threshold" to accurately determine the target moisture content threshold at the purging endpoint under different operating conditions. During fuel cell operation and purging, the "temperature-impedance" dataset established by S1 is called in real time, and combined with the online collected battery temperature and high-frequency impedance data, the central control system 8 quickly calculates the current internal moisture content of the battery and compares it in real time with the fitted moisture content threshold at the purging endpoint.

[0035] Adaptive purging strategy generation: A nonlinear relationship between ambient temperature and purging parameters is established through a dynamic mapping mechanism. Machine learning algorithms such as support vector machines or artificial neural networks are used to train and optimize the mapping model. During fuel cell purging, the central control system, based on real-time collected parameters such as ambient temperature and battery impedance, calls the model to calculate the low-damage purging moisture content and automatically adjusts the purging gas flow rate, humidity, temperature, and time. This forms a temperature-adaptive purging strategy that can dynamically adjust according to the environment and operating conditions, improving the fuel cell start-up success rate and service life.

[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 implemented 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 exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, it is intended that all variations falling within the meaning and scope of equivalents of the claims be included within the present invention.

[0037] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This way of describing the specification is only for clarity. Those skilled in the art should regard the specification as a whole, and the technical solutions in each embodiment have been appropriately combined to form other embodiments that are easy for those skilled in the art to understand.

Claims

1. A temperature-adaptive shutdown purging method for low-damage cold start, characterized in that, Includes the following steps: Step 1: Confirmation of water content under full temperature conditions: A temperature-impedance dataset is constructed by constant frequency impedance testing. Combined with equilibrium purging test and Gaussian process regression fitting, a full temperature impedance-water content mapping relationship is established. The water content inside the battery is determined based on the temperature after shutdown purging and high frequency impedance testing. Step 2: Determining the successful low-temperature start-up boundary: Conduct cold start-up tests at multiple temperatures and different loading rates to establish a three-dimensional quantitative relationship between cold start-up conditions, loading rate, and initial moisture content, and clarify the maximum moisture content threshold for successful start-up. Step 3, Determining the damage boundary of dry / wet cycling: Conduct cycling tests with different moisture content variations, and combine microscopic morphology characterization and macroscopic performance degradation analysis to determine the moisture content variation threshold that causes interfacial damage. Step 4: Determining the damage boundary of freeze / thaw cycles: Conduct cyclic tests with different initial water contents and temperature boundaries, analyze the pore deformation of GDL and the crack propagation characteristics of CL, and determine the critical water content value for cumulative damage from phase transformation stress. Step 5: Establishing the purging strategy: Based on three types of boundaries, a dynamic mapping mechanism of temperature-moisture content-damage threshold is constructed. The successful start-up boundary is used as the benchmark, the dry / wet cycle boundary limits the fluctuation range, and the freeze / thaw cycle boundary corrects the low temperature target. The moisture content of the low-damage purging target is determined, and a temperature-adaptive shutdown purging strategy is formed. Step 1 requires the use of a fuel cell testing system. The fuel cell testing system includes a gas temperature regulation device, an electrochemical workstation, and a central control system. The gas temperature regulation device includes a hydrogen supply device and an air supply device. The central control system includes a fuel cell, a temperature sensor, and a battery temperature regulation device. The fuel cell is connected to the hydrogen supply device, the air supply device, the electrochemical workstation, the temperature sensor, and the battery temperature regulation device, respectively. Step 1 specifically includes: Step 1.1, Full-temperature impedance testing and dataset construction: Full-temperature impedance testing was carried out using the fixed-frequency impedance test of the electrochemical workstation. The sampling frequency was adjusted by judging the relaxation change stage to establish a "temperature-impedance" dataset. Step 1.2, Moisture content calibration under all temperature conditions: Adjust the temperature of the purge gas through the gas temperature regulating device and the temperature of the fuel cell through the battery temperature regulating device to conduct a balance purging test under all temperature conditions. Combine the empirical formulas for relative humidity and moisture content of the purge gas to calibrate the moisture content under each temperature condition and establish a temperature-moisture content dataset. Step 1.3: Fitting and Validation of Full Temperature Data Based on Gaussian Process Regression: The established dataset is fitted and predicted using the Gaussian process regression model to determine the boundary and accuracy of independent variable values. The fit is corrected through various methods and verified by multiple sets of working condition data experiments to obtain the full temperature working condition impedance-water content mapping dataset. Step 1.4 Determination of water content under full temperature conditions: Determine the water content inside the battery based on the temperature during the shutdown purging process and the results of high-frequency impedance testing.

2. The temperature-adaptive shutdown purging method for low-damage cold start according to claim 1, characterized in that, Step 2 specifically involves conducting equivalent single-cell cold start tests at different loading rates under multiple temperatures to establish a quantitative relationship between cold start conditions, loading rate, and initial water content, and to determine the maximum water content required for successful start-up under different temperatures and loading rates.

3. The temperature-adaptive shutdown purging method for low-damage cold start according to claim 2, characterized in that, Step 3 specifically involves conducting freeze / thaw cycle tests with different initial moisture contents and different temperature boundaries, analyzing the pore deformation of GDL and the crack propagation damage characteristics of CL, and determining the critical moisture content value that triggers phase transformation stress accumulation damage.

4. The temperature-adaptive shutdown purging method for low-damage cold start according to claim 3, characterized in that, Step 4 specifically involves conducting freeze / thaw cycle tests with different initial moisture contents and different temperature boundaries, analyzing the pore deformation of GDL and the crack propagation damage characteristics of CL, and determining the critical moisture content value that triggers phase transformation stress accumulation damage.

5. A temperature-adaptive shutdown purging method for low-damage cold start according to claim 4, characterized in that, Step 5 includes: Step 5.1: Determine the boundary for successful low-temperature start-up: Through cold start-up tests at different loading rates at different temperatures, and based on the initial moisture content determined according to claim 3, establish the correspondence between "loading rate - maximum moisture content for successful start-up" and the three-dimensional quantitative relationship between "cold start-up condition - loading rate - initial moisture content", and clarify the maximum moisture content for successful start-up. Step 5.2: Determine the dry / wet cycle damage boundary: Conduct dry / wet cycle tests with different moisture content boundary changes, and combine microscopic morphology characterization and macroscopic performance degradation analysis to determine the moisture content change threshold that causes interfacial damage. Step 5.3: Determine the damage boundary of freeze / thaw cycle: Conduct freeze / thaw cycle tests with different initial water contents, analyze the damage characteristics, and determine the critical water content value for cumulative damage from phase transformation stress. Step 5.4, Establishment of Temperature Adaptive Shutdown and Purging Strategy: Based on the three types of boundaries determined in Steps 5.1-5.3, a dynamic mapping mechanism of moisture content, cold start success threshold, and damage threshold is constructed. The maximum moisture content benchmark for successful start-up under different operating conditions is determined through three-dimensional quantitative relationship. The moisture content fluctuation range is limited by the dry / wet cycle damage threshold. The purging target is then corrected based on the freeze / thaw cycle critical value. Finally, the moisture content of the low-damage shutdown and purging target is determined. According to the method in Step 1, a temperature adaptive shutdown and purging strategy is established.

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