Intelligent detection method and device for gas diffusion performance of fuel cell

Abstract

The invention discloses an intelligent detection method and device for gas diffusion performance of a fuel cell, and relates to the technical field of intelligent detection of the fuel cell, and the method comprises the following steps: monitoring and obtaining a gas flow rate information sequence through a sensor arranged on a gas diffusion layer in the fuel cell; restoring, generating and obtaining a gas flow velocity distribution sequence, and performing gas diffusion flow velocity performance analysis to obtain gas diffusion flow velocity performance parameters; performing gas flow conversion to obtain gas flow; and calculating to obtain a gas diffusion performance parameter of the fuel cell as a detection result. According to the invention, the technical problem that the gas diffusion performance of the fuel cell is difficult to accurately, comprehensively and intelligently detect in the prior art is solved, and the technical effects of accurately calculating the gas diffusion velocity and the flow performance parameters and improving the accuracy and the intelligent degree of the detection of the gas diffusion performance of the fuel cell are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent detection of fuel cells, and in particular to an intelligent detection method and device for gas diffusion performance of fuel cells. Background Art

[0002] As an efficient and clean energy conversion device, fuel cells have broad application prospects in many fields. However, the performance of fuel cells is affected by many factors, among which gas diffusion performance is one of the key factors. The gas diffusion layer plays a vital role in fuel cells. It is responsible for evenly diffusing the reaction gas to the catalyst layer and discharging the water produced by the reaction. If the gas diffusion performance is poor, it will lead to problems such as uneven gas distribution and concentration polarization, which will seriously affect the power generation efficiency and stability of the fuel cell. At present, there are many limitations in the traditional fuel cell gas diffusion performance detection methods. For example, some methods can only perform single-point or local measurements, and cannot fully reflect the distribution of gas flow rate and flow in the gas diffusion layer, resulting in inaccurate evaluation of the overall performance. Some detection methods rely on complex experimental equipment and cumbersome operating procedures, which are not only costly, but also have a long test cycle, and it is difficult to meet the needs of rapid detection and real-time monitoring. In addition, the existing methods are relatively simple in data processing and analysis, lack the effective use of a large amount of monitoring data, and it is difficult to deeply explore the intrinsic relationship between gas diffusion performance and the overall performance of fuel cells, so as to provide a comprehensive and accurate basis for the optimization design and performance improvement of fuel cells.

[0003] The existing technology has the technical problem of being difficult to accurately, comprehensively and intelligently detect the gas diffusion performance of fuel cells. Summary of the invention

[0004] The present application provides a method and device for intelligent detection of fuel cell gas diffusion performance, which is used to solve the technical problem in the prior art that it is difficult to accurately, comprehensively and intelligently detect the gas diffusion performance of fuel cells.

[0005] In view of the above problems, the present application provides an intelligent detection method and device for the gas diffusion performance of a fuel cell.

[0006] In a first aspect of the present application, a method for intelligently detecting gas diffusion performance of a fuel cell is provided, the method comprising: A first gas flow rate information sequence and a second gas flow rate information sequence are monitored and acquired by a first sensor and a second sensor arranged in a gas diffusion layer in a fuel cell, wherein the first sensor and the second sensor are arranged at two ends of the gas diffusion layer close to an anode and a cathode, respectively; a first gas flow rate distribution sequence and a second gas flow rate distribution sequence are restored and generated according to the first gas flow rate information sequence and the second gas flow rate information sequence, and gas diffusion flow rate performance analysis is performed respectively to obtain first gas diffusion flow rate performance parameters and second gas diffusion flow rate performance parameters; gas flow conversion is performed respectively according to the first gas flow rate distribution sequence and the second gas flow rate distribution sequence to obtain first gas flow rate and second gas flow rate, and gas diffusion flow rate performance analysis is performed to obtain first gas diffusion flow rate performance parameters and second gas diffusion flow rate performance parameters; gas diffusion performance parameters of the fuel cell are calculated according to the first gas diffusion flow rate performance parameters, the second gas diffusion flow rate performance parameters, the first gas diffusion flow rate performance parameters and the second gas diffusion flow rate performance parameters as detection results.

[0007] The second aspect of the present application provides an intelligent detection device for gas diffusion performance of a fuel cell, the device comprising: A gas flow rate information sequence acquisition module, the gas flow rate information sequence acquisition module is used to monitor and acquire a first gas flow rate information sequence and a second gas flow rate information sequence through a first sensor and a second sensor arranged in a gas diffusion layer in a fuel cell, wherein the first sensor and the second sensor are arranged at two ends of the gas diffusion layer close to the anode and the cathode, respectively; a gas diffusion flow rate performance parameter acquisition module, the gas diffusion flow rate performance parameter acquisition module is used to restore and generate a first gas flow rate distribution sequence and a second gas flow rate distribution sequence according to the first gas flow rate information sequence and the second gas flow rate information sequence, and respectively perform gas diffusion flow rate performance analysis to obtain the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter. Flow rate performance parameters; a gas diffusion flow performance parameter acquisition module, the gas diffusion flow performance parameter acquisition module is used to perform gas flow conversion according to the first gas flow rate distribution sequence and the second gas flow rate distribution sequence, respectively, to obtain the first gas flow rate and the second gas flow rate, and perform gas diffusion flow performance analysis to obtain the first gas diffusion flow performance parameters and the second gas diffusion flow performance parameters; a gas diffusion performance parameter calculation module, the gas diffusion performance parameter calculation module is used to calculate the gas diffusion performance parameters of the fuel cell as the detection result according to the first gas diffusion flow rate performance parameter, the second gas diffusion flow rate performance parameter, the first gas diffusion flow performance parameter and the second gas diffusion flow performance parameter.

[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages: The first sensor and the second sensor arranged in the gas diffusion layer in the fuel cell are used to monitor and obtain the first gas flow rate information sequence and the second gas flow rate information sequence; the first gas flow rate distribution sequence and the second gas flow rate distribution sequence are restored and generated, and the gas diffusion flow rate performance analysis is performed respectively to obtain the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter; the gas flow rate is converted to obtain the first gas flow rate and the second gas flow rate, and the gas diffusion flow rate performance analysis is performed to obtain the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter; according to the first gas diffusion flow rate performance parameter, the second gas diffusion flow rate performance parameter, the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter, the gas diffusion performance parameter of the fuel cell is calculated as the detection result. The accurate calculation of the gas diffusion flow rate and flow rate performance parameters is achieved, and the accuracy and intelligence of the fuel cell gas diffusion performance detection are improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0010] Figure 1 A schematic flow chart of an intelligent detection method for gas diffusion performance of a fuel cell provided in an embodiment of the present application; Figure 2 A schematic diagram of the structure of an intelligent detection device for fuel cell gas diffusion performance provided in an embodiment of the present application.

[0011] Explanation of reference numerals: gas flow rate information sequence acquisition module 10, gas diffusion flow rate performance parameter acquisition module 20, gas diffusion flow rate performance parameter acquisition module 30, gas diffusion performance parameter calculation module 40. DETAILED DESCRIPTION

[0012] The present application provides a method and device for intelligently detecting the gas diffusion performance of a fuel cell, so as to solve the technical problem in the prior art that it is difficult to accurately, comprehensively and intelligently detect the gas diffusion performance of a fuel cell.

[0013] The technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0014] Embodiment 1, as Figure 1 As shown, the present application provides an intelligent detection method for fuel cell gas diffusion performance, the method comprising: Step S100: Monitor and obtain a first gas flow rate information sequence and a second gas flow rate information sequence by means of a first sensor and a second sensor arranged in a gas diffusion layer in a fuel cell, wherein the first sensor and the second sensor are arranged at two ends of the gas diffusion layer close to the anode and the cathode, respectively.

[0015] Specifically, in order to accurately monitor the gas diffusion performance of the fuel cell, the first sensor and the second sensor are arranged in the key part of the gas diffusion layer in the fuel cell. Among them, the first sensor is placed near the anode in the gas diffusion layer, and the second sensor is located at one end near the cathode. Both sensors have high-precision gas flow rate monitoring functions and sense the flow of gas in the diffusion layer in real time. During the operation of the fuel cell, the gas continuously reacts and flows between the anode and the cathode. The first sensor and the second sensor continue to work, collecting gas flow rate data at a very high frequency. Starting from the initial moment of the fuel cell startup, the gas flow rate value at each tiny time interval is recorded. As time goes by, a large amount of gas flow rate information is accumulated. Then, this information is arranged in order in chronological order to form the first gas flow rate information sequence and the second gas flow rate information sequence. These information sequences are like a detailed record of gas flow rate changes, which fully presents the dynamic change process of the gas flow rate in the diffusion layer near the anode and cathode ends, laying a solid data foundation for the subsequent in-depth analysis of the gas diffusion performance of the fuel cell.

[0016] Step S200: Restore and generate a first gas flow rate distribution sequence and a second gas flow rate distribution sequence according to the first gas flow rate information sequence and the second gas flow rate information sequence, and perform gas diffusion flow rate performance analysis respectively to obtain first gas diffusion flow rate performance parameters and second gas diffusion flow rate performance parameters.

[0017] Specifically, with the help of advanced generative adversarial network technology, the first gas flow rate information sequence and the second gas flow rate information sequence obtained are used as key inputs. For the first gas flow rate information sequence, the generative adversarial network gradually restores and generates the first gas flow rate distribution sequence based on the gas flow rate information monitored at a single position contained therein, by deeply mining the inherent associations and laws in the data. In this process, each first gas flow rate distribution covers the gas flow rate at multiple positions near the anode. The flow rate information at these positions is interrelated and mutually influential, and together constitutes a complete flow rate distribution state description. Similarly, for the second gas flow rate information sequence, similar processing is also performed through the generative adversarial network to obtain the second gas flow rate distribution sequence, each of which reflects the gas flow rate at multiple positions near the cathode. After successfully generating the first gas flow rate distribution sequence and the second gas flow rate distribution sequence, the gas diffusion flow rate performance analysis is immediately carried out. First, the standard first gas flow rate distribution and the standard second gas flow rate distribution of fuel cell gas diffusion are obtained. These standard distributions are based on the flow rate distribution patterns under ideal conditions obtained from a large amount of experimental data and theoretical research. Then, taking the standard first gas velocity distribution as a reference, the velocity deviation amplitude distribution between each first gas velocity distribution and the standard distribution in the first gas velocity distribution sequence is calculated one by one, and the mean of these deviation amplitude distributions is further calculated to obtain the first average velocity deviation amplitude. Finally, the first average velocity deviation amplitude is subtracted from 1 to obtain the first gas diffusion velocity performance parameter, which can accurately reflect the degree of closeness between the velocity performance of the gas diffusion layer near the anode of the current fuel cell and the ideal state. Similarly, taking the standard second gas velocity distribution as a reference, a similar operation is performed on the second gas velocity distribution sequence to calculate the second average velocity deviation amplitude, and the second gas diffusion velocity performance parameter is obtained by subtracting the value from 1, thereby accurately evaluating the velocity performance of the gas diffusion layer near the cathode of the fuel cell. In this way, the gas diffusion velocity performance of the fuel cell at both the anode and cathode ends is comprehensively and meticulously quantified and analyzed, providing a key basis for the subsequent comprehensive evaluation of the gas diffusion performance of the fuel cell.

[0018] Step S300: According to the first gas flow rate distribution sequence and the second gas flow rate distribution sequence, respectively, gas flow conversion is performed to obtain a first gas flow rate and a second gas flow rate, and gas diffusion flow performance analysis is performed to obtain first gas diffusion flow performance parameters and second gas diffusion flow performance parameters.

[0019] Specifically, the gas flow conversion work is carried out according to the first gas flow rate distribution sequence and the second gas flow rate distribution sequence that have been obtained. For the first gas flow rate distribution sequence, the gas flow rate information of multiple positions covered by each distribution is carefully considered, and the flow rate is combined with the duration of the corresponding monitoring moment. The flow rate is calculated by the flow calculation principle, and the flow rate is comprehensively calculated with factors such as time and area to obtain multiple first interval gas flows. Then, these first interval gas flows are averaged to accurately obtain the first gas flow. Similarly, for the second gas flow rate distribution sequence, the above operation process is repeated to obtain multiple second interval gas flows and average them to determine the second gas flow. After obtaining the first gas flow and the second gas flow, the gas diffusion flow performance analysis is immediately carried out, and the standard first gas flow and standard second gas flow of the fuel cell gas diffusion are first obtained. These standard flows are reference values ​​based on the ideal working state of the fuel cell or after a large number of experimental verifications. Then, the deviation amplitude of the first gas flow and the standard first gas flow, as well as the deviation amplitude of the second gas flow and the standard second gas flow are calculated respectively. Finally, the two deviation amplitudes are subtracted from 1 to obtain the first gas diffusion flow performance parameters and the second gas diffusion flow performance parameters. These parameters can effectively characterize the degree of deviation of the current fuel cell from the ideal state in terms of gas diffusion flow, and thus comprehensively reflect the pros and cons of the fuel cell gas diffusion flow performance, providing important data support for the overall evaluation of the fuel cell gas diffusion performance.

[0020] Step S400: Calculate and obtain the gas diffusion performance parameter of the fuel cell as a detection result based on the first gas diffusion flow rate performance parameter, the second gas diffusion flow rate performance parameter, the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter.

[0021] Specifically, the first gas diffusion flow rate performance parameter, the second gas diffusion flow rate performance parameter, the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter carefully calculated in the previous step are used as key data input. First, the mean of the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter are calculated respectively. This mean can comprehensively reflect the overall performance of the fuel cell in terms of gas diffusion flow rate. It takes into account the flow rate characteristics at both ends of the anode and the cathode, and eliminates the influence of local differences by averaging to obtain a more representative gas diffusion flow rate performance parameter. Similarly, the mean of the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter is calculated to obtain a comprehensive parameter that can fully reflect the gas diffusion flow rate performance. Then, these two comprehensive parameters, namely the gas diffusion flow rate performance parameter and the gas diffusion flow rate performance parameter, are weighted. In the weighted calculation process, according to the importance of the flow rate and flow rate on the overall performance in the fuel cell performance evaluation system, they are given corresponding weights. For example, if the flow rate has a more critical impact on the performance of the fuel cell, then the gas diffusion flow rate performance parameter may be given a larger weight; conversely, if the flow rate has a greater impact, the weight of the gas diffusion flow rate performance parameter will be increased accordingly. Through such weighted calculations, the flow rate and flow performance are organically combined to ultimately obtain a comprehensive, accurate, and gas diffusion performance parameter that can comprehensively reflect the performance of the fuel cell gas diffusion layer. This parameter, as the final result of the entire detection process, can provide a valuable quantitative basis for fuel cell performance evaluation, fault diagnosis, optimal design, and operation control, so that targeted measures can be taken to improve the overall performance and reliability of the fuel cell.

[0022] In a possible implementation, step S100 further includes: Step S110: By disposing a first sensor and a second sensor in the gas diffusion layer in the fuel cell, the gas flow rate information at multiple monitoring times is monitored and acquired, and multiple first gas flow rate information and multiple second gas flow rate information are obtained, wherein the first sensor and the second sensor are respectively disposed at both ends of the gas diffusion layer near the anode and the cathode.

[0023] Step S120: Arrange the plurality of first gas flow rate information and the plurality of second gas flow rate information according to the plurality of monitoring moments to obtain a first gas flow rate information sequence and a second gas flow rate information sequence.

[0024] Specifically, in order to accurately grasp the gas diffusion in the fuel cell, the first sensor and the second sensor are arranged at key positions in the gas diffusion layer, close to the anode and cathode respectively. During the operation of the fuel cell, these sensors monitor the gas flow rate at a very high frequency and record the gas flow rate information at multiple precise monitoring times. From the start of the fuel cell, the sensor continues to work, collecting data at specific time intervals, thereby accumulating rich gas flow rate information. The first sensor close to the anode obtains multiple first gas flow rate information, and the second sensor close to the cathode obtains multiple second gas flow rate information.

[0025] Subsequently, based on the multiple monitoring moments obtained previously, these moments are used as the time axis to arrange the corresponding multiple first gas flow rate information and multiple second gas flow rate information in order. For the first gas flow rate information, the flow rate information corresponding to each moment is arranged in sequence according to the order of the monitoring moments to form a complete time series, namely the first gas flow rate information sequence. Similarly, a similar operation is performed on the second gas flow rate information to obtain the second gas flow rate information sequence. These information sequences are like a detailed gas flow rate dynamic record manual, which clearly shows the flow rate of the gas in the diffusion layer near the anode and cathode ends that changes with time, providing a solid data foundation for the subsequent in-depth analysis of the gas diffusion performance of the fuel cell, so that the changing law of the gas flow rate can be fully grasped from the time dimension, and the gas diffusion characteristics of the fuel cell can be more accurately evaluated.

[0026] In a possible implementation, step S200 further includes: Step S210: Based on a generative adversarial network, a first gas flow rate information sequence and a second gas flow rate distribution sequence are restored and generated according to the first gas flow rate information sequence and the second gas flow rate information sequence, wherein each first gas flow rate distribution includes gas flow rate information at multiple positions near the anode.

[0027] Step S220: obtaining a standard first gas flow velocity distribution and a standard second gas flow velocity distribution for fuel cell gas diffusion.

[0028] Step S230: Based on the standard first gas flow velocity distribution and the standard second gas flow velocity distribution, a gas diffusion flow velocity performance analysis is performed on the first gas flow velocity distribution sequence and the second gas flow velocity distribution sequence to obtain first gas diffusion flow velocity performance parameters and second gas diffusion flow velocity performance parameters.

[0029] Specifically, advanced generative adversarial network technology is used to perform in-depth processing on the first gas flow rate information sequence and the second gas flow rate information sequence obtained as the basic data. With its powerful data processing capabilities and model architecture, the generative adversarial network mines the spatial and temporal correlation information hidden in the information sequence, and gradually restores and generates the first gas flow rate distribution sequence and the second gas flow rate distribution sequence. In this process, each first gas flow rate distribution contains the gas flow rate information of multiple positions near the anode. The flow rate data of these positions are interrelated and influence each other, and together constitute a whole that reflects the gas flow rate distribution state in the anode area. Similarly, each distribution in the second gas flow rate distribution sequence also covers the gas flow rate conditions at multiple positions near the cathode, showing the gas flow state in the cathode area from different angles.

[0030] Through a large number of experimental studies, theoretical analysis and simulation of the ideal working state of fuel cells, the standard first gas flow velocity distribution and standard second gas flow velocity distribution of fuel cell gas diffusion are obtained. These standard distributions are obtained under ideal conditions or in a fully verified reference model, representing the flow velocity distribution pattern of the gas near the anode and cathode of the fuel cell under the best performance state, providing a key benchmark for subsequent performance comparison analysis.

[0031] Based on the obtained standard first gas velocity distribution and standard second gas velocity distribution, a comprehensive gas diffusion velocity performance analysis is carried out on the restored first gas velocity distribution sequence and the second gas velocity distribution sequence. For the first gas velocity distribution sequence, the velocity deviation amplitude distribution between each first gas velocity distribution and the standard first gas velocity distribution in the sequence is calculated one by one, and the first average velocity deviation amplitude is obtained by statistically analyzing these deviation amplitude distributions and calculating their mean. Then, the first gas diffusion velocity performance parameter is obtained by subtracting the first average velocity deviation amplitude from 1. This parameter can accurately reflect the degree of deviation of the velocity performance of the gas diffusion layer near the anode of the current fuel cell from the ideal state, thereby quantitatively evaluating the quality of its velocity performance. Similarly, for the second gas velocity distribution sequence, the above calculation process is repeated to obtain the second average velocity deviation amplitude, and the second gas diffusion velocity performance parameter is further calculated to comprehensively evaluate the velocity performance of the gas diffusion layer near the cathode of the fuel cell. Through such comparative analysis, the actual performance of the fuel cell in terms of gas diffusion velocity can be accurately grasped, providing an important decision-making basis for subsequent optimization and improvement and performance improvement.

[0032] In a possible implementation, step S210 further includes: Step S211: according to the gas flow rate test data records at both ends of the anode and cathode, collect a sample first gas flow rate information set and a sample second gas flow rate information set, and collect a sample first gas flow rate distribution set and a sample second gas flow rate distribution set.

[0033] Step S212: constructing a first gas flow rate reduction channel and a second gas flow rate reduction channel based on a generative adversarial network.

[0034] Step S213: Using the sample first gas flow rate information set and the sample first gas flow rate distribution set, supervise the first gas flow rate reduction channel until convergence, and using the sample second gas flow rate information set and the sample second gas flow rate distribution set, supervise the second gas flow rate reduction channel until convergence.

[0035] Step S214: input the multiple first gas flow rate information and the multiple second gas flow rate information in the first gas flow rate information sequence and the second gas flow rate information sequence into the first gas flow rate reduction channel and the second gas flow rate reduction channel respectively, and restore and generate output to obtain the first gas flow rate distribution sequence and the second gas flow rate distribution sequence.

[0036] Specifically, the gas flow rate test data records accumulated in the past at both ends of the anode and cathode are fully and deeply mined and utilized. From these rich data resources, representative sample data are carefully selected and collected to construct the sample first gas flow rate information set and the sample second gas flow rate information set respectively. Each element in these sets is the gas flow rate information measured under different working conditions and at different time points, which contain various dynamic characteristics of the gas diffusion process in the fuel cell. At the same time, according to the corresponding experimental conditions and measurement position information, the corresponding sample first gas flow rate distribution set and sample second gas flow rate distribution set are sorted out. These distribution sets record the gas flow rate combinations at multiple positions in detail in the form of matrices or vectors, providing a comprehensive target output reference for subsequent model training.

[0037] Based on the advanced generative adversarial network technology framework, we set out to build the first gas flow rate reduction channel and the second gas flow rate reduction channel specifically for processing the anode and cathode gas flow rate information. During the construction process, the structural parameters of the network, including the number of network layers, the number of neurons in each layer, and the selection of activation functions, were carefully designed according to the characteristics and distribution law of the gas flow rate information. For example, for the first gas flow rate reduction channel, a multi-layer convolutional neural network structure is adopted, and the local perception and weight sharing characteristics of the convolutional layer are utilized to effectively extract the spatial features in the gas flow rate information; while the second gas flow rate reduction channel is optimized according to the flow rate characteristics of the cathode end to ensure that the gas flow rate distribution near the cathode can be accurately captured and restored.

[0038] At the critical stage of model training, the elements in the sample first gas flow rate information set are input into the first gas flow rate reduction channel in sequence, and the elements in the corresponding sample first gas flow rate distribution set are taken as the expected output. The difference between the two is calculated, and the network parameters in the channel are continuously adjusted through the back propagation algorithm to reduce this difference. This training process continues until the loss function converges to a stable minimum value, which indicates that the first gas flow rate reduction channel can accurately restore the corresponding flow rate distribution from the input flow rate information. Similarly, for the second gas flow rate reduction channel, similar supervised training is performed using the sample second gas flow rate information set and the sample second gas flow rate distribution set until it reaches a convergence state to ensure that it has reliable cathode gas flow rate distribution reduction capabilities.

[0039] The multiple first gas flow rate information and multiple second gas flow rate information in the first gas flow rate information sequence and the second gas flow rate information sequence acquired in real time are respectively input into the trained first gas flow rate reduction channel and the second gas flow rate reduction channel. After complex calculations and conversions inside the channel, the input flow rate information is restored to a complete first gas flow rate distribution sequence and a second gas flow rate distribution sequence according to the mapping relationship learned during the training process. These restored sequences not only contain gas flow rate information at more locations, but also can more realistically reflect the actual flow rate distribution state of the gas in the gas diffusion layer of the fuel cell, providing a solid data foundation for subsequent accurate gas diffusion performance analysis, thereby helping to gain a deeper understanding of the gas diffusion mechanism and performance inside the fuel cell.

[0040] In a possible implementation, step S230 further includes: Step S231: Taking the standard first gas flow rate distribution as a reference, calculate multiple first flow rate deviation amplitude distributions between multiple first gas flow rate distributions in the first gas flow rate distribution sequence and the standard first gas flow rate distribution, and calculate the mean to obtain a first average flow rate deviation amplitude.

[0041] Step S232: subtract the first average flow rate deviation amplitude from 1 to obtain a first gas diffusion flow rate performance parameter.

[0042] Step S233: Taking the standard second gas flow rate distribution as a reference, calculate multiple second flow rate deviation amplitude distributions of multiple second gas flow rate distributions in the second gas flow rate distribution sequence and the standard second gas flow rate distribution, and calculate the mean to obtain a second average flow rate deviation amplitude.

[0043] Step S234: subtract the second average flow rate deviation amplitude from 1 to obtain a second gas diffusion flow rate performance parameter.

[0044] Specifically, the obtained standard first gas flow rate distribution is set as the reference for evaluation. For each first gas flow rate distribution in the first gas flow rate distribution sequence, the flow rate difference between it and the standard distribution is compared point by point in space. For each spatial position, the absolute value of the flow rate difference between the two is calculated to form a deviation amplitude distribution reflecting the difference between the two. Since the first gas flow rate distribution sequence contains multiple such distributions, multiple first flow rate deviation amplitude distributions will be obtained. After that, by performing an average operation on these deviation amplitude distributions, the deviation amplitudes at each position are summed and averaged, and finally the first average flow rate deviation amplitude is obtained. This value comprehensively reflects the overall deviation degree of the first gas flow rate distribution sequence relative to the standard first gas flow rate distribution, and provides a key quantitative indicator for the subsequent evaluation of the gas diffusion flow rate performance near the anode of the fuel cell. Its size directly reflects the degree of closeness of the actual gas diffusion flow rate to the ideal state. The smaller the deviation, the closer the gas diffusion flow rate performance of the fuel cell at the anode end is to the ideal state. On the contrary, it indicates that there is a large performance difference, which requires further analysis and optimization.

[0045] Based on the calculated first average flow rate deviation amplitude, further calculations are performed to obtain the first gas diffusion flow rate performance parameter. First, it is clear that the first average flow rate deviation amplitude is a value between 0 and 1 (including 0 and 1), which quantifies the average deviation degree of the first gas flow rate distribution sequence relative to the standard first gas flow rate distribution, where 0 represents complete consistency, that is, the actual first gas flow rate distribution is no different from the ideal standard distribution, and 1 represents complete deviation, which means that the actual situation is far from the ideal state. Next, the calculation method of 1 minus the first average flow rate deviation amplitude is adopted. The principle of doing this is that we hope to obtain a parameter that can directly reflect the performance of the gas diffusion flow rate, and the numerical characteristics of the parameter should be positively correlated with the performance. Since the smaller the first average flow rate deviation amplitude represents the better performance, the larger the result will be after subtracting it from 1, thereby realizing the conversion of the reverse indicator of the degree of deviation into a positive indicator of the performance. For example, if the first average flow rate deviation amplitude is 0.2, then the first gas diffusion flow rate performance parameter is 0.8 calculated by 1-0.2=0.8. The value range of this parameter is also between 0 and 1. At this time, the value of 0.8 indicates that the gas diffusion flow rate performance of the fuel cell near the anode is at a good level, that is, the actual gas diffusion flow rate is close to the ideal state, and the gas diffusion process near the anode is relatively uniform and efficient, which can better meet the normal operation requirements of the fuel cell. On the contrary, if the calculated first gas diffusion flow rate performance parameter is close to 0, it means that the gas diffusion flow rate performance of the fuel cell near the anode is poor, and there may be problems such as gas diffusion obstruction and uneven flow rate. Further inspection and optimization of related components or operating conditions of the fuel cell are required. Through this calculation method, the first gas diffusion flow rate performance parameter provides an intuitive and effective quantitative basis for evaluating the anode gas diffusion flow rate performance of the fuel cell, which helps to more accurately grasp the working state and performance of the fuel cell.

[0046] The standard second gas velocity distribution of fuel cell gas diffusion is introduced. This distribution is a reference model that represents the ideal velocity state of gas near the cathode of the fuel cell, which is determined through a series of precise experiments and theoretical analysis. Subsequently, for each second gas velocity distribution in the second gas velocity distribution sequence, the absolute value of the velocity difference between it and the standard second gas velocity distribution is calculated one by one at each corresponding position in the space. For example, for a specific position, the velocity values ​​of the position in the standard distribution and the actual distribution are obtained respectively, and then the absolute value of the difference is obtained to obtain the velocity deviation amplitude of the position. By performing such an operation on the positions within the entire spatial range, a second velocity deviation amplitude distribution reflecting the difference between the second gas velocity distribution and the standard distribution is generated. Since the second gas velocity distribution sequence contains multiple such distributions, a series of second velocity deviation amplitude distributions will be obtained. Finally, all the velocity deviation amplitudes in these second velocity deviation amplitude distributions are summed and divided by the total number of distributions to calculate the mean, which is the second average velocity deviation amplitude. It quantifies the degree of deviation of the second gas flow velocity distribution sequence from the standard second gas flow velocity distribution as a whole, and provides key basic data for the subsequent evaluation of the gas diffusion flow velocity performance near the cathode of the fuel cell. Its numerical value directly reflects the degree of closeness between the actual state of the cathode gas diffusion flow velocity and the ideal state. The greater the deviation, the less ideal the cathode gas diffusion flow velocity performance is, and further in-depth analysis of the reasons and corresponding improvement measures are needed.

[0047] The second average flow rate deviation amplitude is a value that characterizes the deviation of the second gas flow rate distribution sequence from the standard second gas flow rate distribution. Its value range is between 0 and 1 (including 0 and 1), where 0 means that the actual second gas flow rate distribution is completely consistent with the standard distribution, that is, the gas diffusion velocity near the cathode of the fuel cell is in an ideal state; and 1 means that the two are completely deviated, indicating that the cathode gas diffusion velocity condition is very different from the ideal state. Next, the calculation method of 1 minus the second average flow rate deviation amplitude is adopted. The purpose of this is to convert the original indicator representing the degree of deviation into a parameter that can directly reflect the performance of the gas diffusion velocity, and to make the numerical value of the parameter positively correlated with the performance. Since the smaller the second average flow rate deviation amplitude, the closer the cathode gas diffusion velocity is to the ideal state and the better the performance, then the result obtained by subtracting it from 1 will be larger, so that this result can more intuitively reflect the performance. For example, if the second average flow rate deviation amplitude is 0.3, the second gas diffusion velocity performance parameter is calculated to be 0.7 by 1-0.3= 0.7. This parameter also takes values ​​between 0 and 1. At this time, the value of 0.7 indicates that the gas diffusion velocity performance of the fuel cell near the cathode is at a relatively good level, which means that the diffusion process of the gas near the cathode is relatively smooth and uniform, which can better support the normal operation of the fuel cell and is close to the ideal state. On the contrary, if the calculated second gas diffusion velocity performance parameter is close to 0, it means that the cathode gas diffusion velocity performance is poor, and there may be problems such as obstructed gas flow and uneven distribution. The cathode structure, gas supply system or operating parameters of the fuel cell need to be carefully checked and optimized to improve the overall performance of the fuel cell. In this way, the second gas diffusion velocity performance parameter provides a clear and effective quantitative indicator for accurately evaluating the cathode gas diffusion velocity performance of the fuel cell, which helps to gain a deeper understanding of the working state and performance characteristics of the cathode region of the fuel cell, and thus provides an important basis for optimizing the design and operation of the fuel cell.

[0048] In a possible implementation, step S300 further includes: Step S310: Calculate a plurality of first interval gas flow rates and a plurality of second interval gas flow rates according to the duration of the plurality of monitoring moments in combination with the first gas flow rate distribution sequence and the second gas flow rate distribution sequence, and calculate the average to obtain the first gas flow rate and the second gas flow rate.

[0049] Step S320: Obtaining a standard first gas flow rate and a standard second gas flow rate for fuel cell gas diffusion.

[0050] Step S330: respectively calculating the deviation amplitudes of the first gas flow rate and the second gas flow rate from the standard first gas flow rate and the standard second gas flow rate to obtain the first gas flow rate deviation amplitude and the second gas flow rate deviation amplitude.

[0051] Step S340: Subtract the first gas flow deviation amplitude and the second gas flow deviation amplitude from 1 to obtain a first gas diffusion flow performance parameter and a second gas diffusion flow performance parameter.

[0052] Specifically, first, it is clear that there is a series of accurately recorded monitoring time durations, and these time duration data mark the time interval of each gas flow rate measurement. At the same time, the first gas flow rate distribution sequence and the second gas flow rate distribution sequence have been obtained, which record in detail the flow rate of the gas near the anode and cathode of the fuel cell at different positions. For each distribution in the first gas flow rate distribution sequence, it is assumed that one of the distributions contains flow rate values ​​at several positions. For the flow rate at each position, according to the basic calculation formula of flow rate (flow rate equals flow rate multiplied by cross-sectional area multiplied by time), the flow rate at the position is multiplied by the predetermined gas flow cross-sectional area (this is an inherent property of the gas channel inside the fuel cell), and then multiplied by the corresponding monitoring time duration. Then, the flow values ​​calculated at all positions in this distribution are summed up to obtain the first interval gas flow corresponding to the moment. By performing such calculations for each distribution in the first gas flow rate distribution sequence, multiple first interval gas flows are obtained. The multiple calculated first interval gas flows are summarized, all first interval gas flows are added up, and then divided by the total number of interval flows, and the result is the first gas flow. This first gas flow rate is an average flow rate value that integrates flow rate information at multiple monitoring moments and multiple locations. It reflects the average flow level of the gas near the anode of the fuel cell over a period of time. The same method is applied to the second gas flow rate distribution sequence. For each distribution in the second gas flow rate distribution sequence, the above flow calculation and summation steps are followed to obtain multiple second interval gas flow rates, and then the average is calculated to finally obtain the second gas flow rate. This second gas flow rate represents the average flow rate of the gas near the cathode of the fuel cell in the corresponding time period, and provides a key data basis for the subsequent evaluation of the fuel cell cathode gas diffusion flow performance.

[0053] In order to accurately evaluate the flow performance of fuel cell gas diffusion, it is necessary to obtain the standard first gas flow and standard second gas flow of fuel cell gas diffusion. These two standard flows are based on a large number of precise experimental measurements and in-depth theoretical analysis. During the experiment, the operating parameters of the fuel cell, such as temperature, pressure, gas composition, etc., are strictly controlled by carefully designing the experimental conditions to make it in an ideal working state. Then, the gas flow near the anode and cathode is measured using high-precision flow measurement equipment (such as a mass flow meter). For the gas near the anode, the gas flow is measured multiple times under different stable operating conditions, and these measured values ​​are statistically analyzed. After removing the outliers, the average value is calculated to obtain the standard first gas flow. For the gas near the cathode, the same experimental method and analysis process are used to determine the standard second gas flow. These standard flow values ​​represent the ideal flow level that the anode and cathode gas diffusion of the fuel cell should reach under the best performance state, and provide a key benchmark for the subsequent comparison of the first gas flow and the second gas flow actually measured, so that the degree of deviation of the fuel cell gas diffusion flow performance from the ideal state can be accurately judged, and important reference data can be provided for optimizing the design and operation of fuel cells.

[0054] Based on the first gas flow rate and the second gas flow rate obtained in the previous steps, as well as the pre-acquired standard first gas flow rate and standard second gas flow rate, the deviation amplitude is calculated. First, for the first gas flow rate, it is compared with the standard first gas flow rate. Subtract the standard first gas flow rate from the first gas flow rate to obtain the difference between the two, and then take the absolute value of the difference. This absolute value is the first gas flow deviation amplitude, which reflects the degree of deviation between the actual measured first gas flow rate and the standard first gas flow rate under the ideal state. The larger the deviation amplitude, the greater the difference between the actual flow rate and the ideal flow rate, which may imply that there are problems such as uneven gas supply and obstructed diffusion channels in the gas diffusion process near the anode of the fuel cell. Similarly, for the second gas flow rate, the deviation amplitude from the standard second gas flow rate is calculated in the same way, and the standard second gas flow rate is subtracted from the second gas flow rate, and then the absolute value is taken to obtain the second gas flow deviation amplitude. This value reflects the degree of deviation between the actual gas flow rate near the cathode of the fuel cell and the ideal state, and its size is of great significance for evaluating the quality of the cathode gas diffusion performance. By accurately calculating these two deviation amplitudes, it is possible to provide a key data basis for further analysis of the gas diffusion flow performance of the fuel cell, and to gain a deeper understanding of the difference between the actual working state and the ideal state of the fuel cell in terms of gas diffusion flow, thereby optimizing the operation and performance of the fuel cell.

[0055] In order to obtain parameters that can intuitively reflect the performance of the fuel cell gas diffusion flow, it is necessary to further process the first gas flow deviation amplitude and the second gas flow deviation amplitude calculated above. Since the smaller the deviation amplitude, the closer the actual gas flow is to the ideal standard flow, and the better the gas diffusion flow performance, the two deviation amplitudes are subtracted from 1. For the first gas flow deviation amplitude, the value obtained by subtracting it from 1 is the first gas diffusion flow performance parameter. The value range of this parameter is between 0 and 1. When the first gas flow deviation amplitude is 0, that is, the actual first gas flow is completely equal to the standard first gas flow, the first gas diffusion flow performance parameter is 1, indicating that the gas diffusion flow performance near the anode of the fuel cell reaches the best state; and when the first gas flow deviation amplitude is close to 1, it means that the actual flow is greatly different from the standard flow, and the first gas diffusion flow performance parameter is close to 0, which means that the anode gas diffusion flow performance is poor, and there may be problems such as gas supply system failure and diffusion layer blockage, which need further inspection and optimization. Similarly, for the second gas flow deviation amplitude, the second gas diffusion flow performance parameter is obtained by subtracting it from 1. This parameter also takes values ​​between 0 and 1, and its numerical value directly reflects the gas diffusion flow performance near the cathode of the fuel cell. When the second gas diffusion flow performance parameter is close to 1, it means that the cathode gas diffusion flow performance is good and the gas diffusion process near the cathode is relatively ideal; when the parameter is close to 0, it means that the cathode gas diffusion flow performance is poor, and the cathode-related components or operating conditions need to be analyzed and improved. Through this calculation method, the first gas diffusion flow performance parameter and the second gas diffusion flow performance parameter provide an important quantitative basis for the comprehensive and accurate evaluation of the fuel cell gas diffusion flow performance, which helps to gain a deeper understanding of the working state of the fuel cell in terms of gas diffusion flow, thereby providing strong support for optimizing the design and operation of fuel cells.

[0056] In a possible implementation, step S400 further includes: Step S410: Calculate the mean of the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter, and the mean of the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter to obtain gas diffusion flow rate performance parameters and gas diffusion flow rate performance parameters.

[0057] Step S420: performing weighted calculation on the gas diffusion velocity performance parameter and the gas diffusion flow rate performance parameter to obtain the gas diffusion performance parameter of the fuel cell.

[0058] Specifically, first clarify the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter, which reflect the performance of the gas diffusion flow rate near the anode and cathode of the fuel cell from different angles. Add these two parameters and divide them by 2 to get the gas diffusion flow rate performance parameter. This mean combines the flow rate performance information at both ends of the anode and cathode, and can more comprehensively reflect the overall performance of the fuel cell in terms of gas diffusion flow rate. For example, if the first gas diffusion flow rate performance parameter is 0.8 and the second gas diffusion flow rate performance parameter is 0.7, then the gas diffusion flow rate performance parameter is (0.8+0.7) / 2=0.75. Similarly, for the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter, they characterize the performance of the anode and cathode of the fuel cell in terms of gas diffusion flow. Sum and average these two parameters to get the gas diffusion flow rate performance parameter. This parameter comprehensively considers the performance of the gas diffusion layer of the fuel cell from the dimension of flow rate. Assuming that the first gas diffusion flow performance parameter is 0.6 and the second gas diffusion flow performance parameter is 0.7, the gas diffusion flow performance parameter is (0.6+0.7) / 2=0.65. Through such calculation, the gas diffusion flow rate performance parameter and the gas diffusion flow performance parameter are obtained respectively, which lays the foundation for the subsequent comprehensive evaluation of the gas diffusion performance of the fuel cell.

[0059] It is clear that the gas diffusion velocity performance parameter reflects the flow velocity characteristics of the gas when it diffuses in the fuel cell. It reflects the flow velocity-related performance such as the smoothness and uniformity of the gas during the diffusion process; while the gas diffusion flow performance parameter focuses on the gas flow situation, including the performance characteristics of the flow such as the degree of proximity between the actual flow and the ideal flow. Then, according to the specific application scenarios, design requirements and key factors in actual operation of the fuel cell, determine the weights of the flow velocity performance and flow performance in the overall performance evaluation. For example, in some applications with high reaction rate requirements, a higher weight may be given to the flow velocity performance; and in some cases that are more sensitive to the total gas supply, the weight of the flow performance may be greater. Let the gas diffusion flow velocity performance parameter be P V , whose weight is W V ; The gas diffusion flow performance parameter is Pq, its weight is Wq, and W V +Wq=1. The gas diffusion performance parameter P of the fuel cell is calculated by weighted summation, and the calculation formula is P=W V ×P V +Wq×Pq. For example, if the analysis determines that flow rate performance is more critical in the current evaluation, set W V =0.6, then Wq=1-0.6=0.4. V=0.7 (indicating that the flow rate performance is at a good level), Pq=0.6 (the flow performance is also relatively good), it can be calculated that P=0.6×0.7+0.4×0.6=0.66. The gas diffusion performance parameter P obtained in this way can more comprehensively and accurately reflect the comprehensive performance of the gas diffusion layer of the fuel cell in actual operation, and provide a key quantitative basis for evaluating the working state and performance of the fuel cell, which helps to deeply understand the overall performance of the gas diffusion process inside the fuel cell, so as to carry out targeted optimization design, fault diagnosis and performance improvement of the fuel cell.

[0060] Embodiment 2 is based on the same inventive concept as the intelligent detection method for gas diffusion performance of a fuel cell in the above embodiment. Figure 2 As shown, the present application provides an intelligent detection device for the gas diffusion performance of a fuel cell, and the device and method embodiments in the present application are based on the same inventive concept. The device includes: A gas flow rate information sequence acquisition module 10, the gas flow rate information sequence acquisition module 10 is used to monitor and acquire a first gas flow rate information sequence and a second gas flow rate information sequence through a first sensor and a second sensor arranged in a gas diffusion layer in a fuel cell, wherein the first sensor and the second sensor are arranged at both ends of the gas diffusion layer close to the anode and the cathode, respectively.

[0061] A gas diffusion flow rate performance parameter acquisition module 20 is used to restore and generate a first gas flow rate distribution sequence and a second gas flow rate distribution sequence according to the first gas flow rate information sequence and the second gas flow rate information sequence, and perform gas diffusion flow rate performance analysis respectively to obtain first gas diffusion flow rate performance parameters and second gas diffusion flow rate performance parameters.

[0062] A gas diffusion flow performance parameter acquisition module 30 is used to perform gas flow conversion according to the first gas flow velocity distribution sequence and the second gas flow velocity distribution sequence, respectively, to obtain the first gas flow and the second gas flow, and to perform gas diffusion flow performance analysis to obtain the first gas diffusion flow performance parameter and the second gas diffusion flow performance parameter.

[0063] A gas diffusion performance parameter calculation module 40 is used to calculate the gas diffusion performance parameters of the fuel cell as a detection result based on the first gas diffusion flow rate performance parameter, the second gas diffusion flow rate performance parameter, the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter.

[0064] Furthermore, the gas flow rate information sequence acquisition module 10 further includes: A gas flow rate information acquisition unit, the gas flow rate information acquisition unit is used to monitor and acquire gas flow rate information at multiple monitoring times through a first sensor and a second sensor arranged in a gas diffusion layer in a fuel cell, and obtain multiple first gas flow rate information and multiple second gas flow rate information, wherein the first sensor and the second sensor are respectively arranged at both ends of the gas diffusion layer close to the anode and the cathode.

[0065] A gas flow rate information sequence acquisition unit is used to arrange the multiple first gas flow rate information and the multiple second gas flow rate information according to the multiple monitoring moments to obtain a first gas flow rate information sequence and a second gas flow rate information sequence.

[0066] Furthermore, the gas diffusion velocity performance parameter acquisition module 20 also includes: A gas flow rate distribution sequence acquisition unit, wherein the gas flow rate distribution sequence acquisition unit is based on a generative adversarial network, and restores and generates a first gas flow rate distribution sequence and a second gas flow rate distribution sequence according to the first gas flow rate information sequence and the second gas flow rate information sequence, wherein each first gas flow rate distribution includes gas flow rate information at multiple positions near the anode.

[0067] A gas flow velocity distribution acquisition unit is used to acquire a standard first gas flow velocity distribution and a standard second gas flow velocity distribution for fuel cell gas diffusion.

[0068] A gas diffusion flow rate performance analysis unit, wherein the gas diffusion flow rate performance analysis unit is used to perform gas diffusion flow rate performance analysis on the first gas flow rate distribution sequence and the second gas flow rate distribution sequence based on the standard first gas flow rate distribution and the standard second gas flow rate distribution, so as to obtain first gas diffusion flow rate performance parameters and second gas diffusion flow rate performance parameters.

[0069] Furthermore, the gas flow velocity distribution sequence acquisition unit further includes: A gas flow velocity distribution set acquisition unit, the gas flow velocity distribution set acquisition unit is used to collect a sample first gas flow velocity information set and a sample second gas flow velocity information set according to the gas flow velocity test data records at both ends of the anode and the cathode, and collect a sample first gas flow velocity distribution set and a sample second gas flow velocity distribution set.

[0070] A gas flow rate reduction channel construction unit is provided, wherein the gas flow rate reduction channel construction unit constructs a first gas flow rate reduction channel and a second gas flow rate reduction channel based on a generative adversarial network.

[0071] A supervised training unit, wherein the supervised training unit is used to use the sample first gas flow rate information set and the sample first gas flow rate distribution set to supervise the first gas flow rate reduction channel until convergence, and use the sample second gas flow rate information set and the sample second gas flow rate distribution set to supervise the second gas flow rate reduction channel until convergence.

[0072] A reduction generation unit, wherein the reduction generation unit is used to input multiple first gas flow rate information and multiple second gas flow rate information in the first gas flow rate information sequence and the second gas flow rate information sequence into the first gas flow rate reduction channel and the second gas flow rate reduction channel respectively, and reduce and generate output to obtain a first gas flow rate distribution sequence and a second gas flow rate distribution sequence.

[0073] Furthermore, the gas diffusion flow rate performance analysis unit also includes: The first average flow rate deviation amplitude acquisition unit is used to calculate the first flow rate deviation amplitude distributions of multiple first gas flow rate distributions in the first gas flow rate distribution sequence and the multiple first flow rate deviation amplitude distributions of the standard first gas flow rate distribution based on the standard first gas flow rate distribution, and calculate the mean to obtain the first average flow rate deviation amplitude.

[0074] A gas diffusion flow rate performance parameter acquisition unit is used to obtain a first gas diffusion flow rate performance parameter by subtracting the first average flow rate deviation amplitude from 1.

[0075] The second average flow rate deviation amplitude acquisition unit is used to calculate the second flow rate deviation amplitude distributions of multiple second gas flow rate distributions in the second gas flow rate distribution sequence and the multiple second flow rate deviation amplitude distributions of the standard second gas flow rate distribution based on the standard second gas flow rate distribution, and calculate the mean to obtain the second average flow rate deviation amplitude.

[0076] The second gas diffusion flow rate performance parameter acquisition unit is used to obtain the second gas diffusion flow rate performance parameter by subtracting the second average flow rate deviation amplitude from 1.

[0077] Furthermore, the gas diffusion flow performance parameter acquisition module 30 also includes: A gas flow acquisition unit, the gas flow acquisition unit is used to calculate a plurality of first interval gas flow rates and a plurality of second interval gas flow rates according to the duration of the plurality of monitoring moments in combination with the first gas flow rate distribution sequence and the second gas flow rate distribution sequence, and calculate the average to obtain the first gas flow rate and the second gas flow rate.

[0078] A standard gas flow acquisition unit is used to acquire a standard first gas flow and a standard second gas flow for fuel cell gas diffusion.

[0079] A gas flow deviation amplitude calculation unit is used to calculate the deviation amplitudes of the first gas flow and the second gas flow from the standard first gas flow and the standard second gas flow, respectively, to obtain the first gas flow deviation amplitude and the second gas flow deviation amplitude.

[0080] A gas diffusion flow performance parameter acquisition unit is used to obtain a first gas diffusion flow performance parameter and a second gas diffusion flow performance parameter by subtracting the first gas flow deviation amplitude and the second gas flow deviation amplitude from 1.

[0081] Furthermore, the gas diffusion performance parameter calculation module 40 also includes: A mean calculation unit, the mean calculation unit is used to calculate the mean of the first gas diffusion flow rate performance parameter, the second gas diffusion flow rate performance parameter, and the mean of the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter to obtain gas diffusion flow rate performance parameters and gas diffusion flow rate performance parameters.

[0082] A weighted calculation unit is used to perform weighted calculation on the gas diffusion velocity performance parameter and the gas diffusion flow rate performance parameter to obtain the gas diffusion performance parameter of the fuel cell.

[0083] It should be noted that the above-mentioned sequence of the embodiments of the present application is only for description and does not represent the advantages and disadvantages of the embodiments. And the above-mentioned specific embodiments of this specification are described. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0084] The above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

[0085] This specification and drawings are merely exemplary illustrations of the present application and are deemed to cover any and all modifications, variations, combinations or equivalents within the scope of the present application. Obviously, a person skilled in the art may make various modifications and variations to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalents, the present application intends to include these modifications and variations.

Claims

1. An intelligent detection method for fuel cell gas diffusion performance, characterized in that: The method comprises: A first gas flow rate information sequence and a second gas flow rate information sequence are monitored and acquired by a first sensor and a second sensor arranged in a gas diffusion layer in a fuel cell, wherein the first sensor and the second sensor are arranged at two ends of the gas diffusion layer close to an anode and a cathode, respectively; According to the first gas flow rate information sequence and the second gas flow rate information sequence, a first gas flow rate distribution sequence and a second gas flow rate distribution sequence are restored and generated, and gas diffusion flow rate performance analysis is performed respectively to obtain first gas diffusion flow rate performance parameters and second gas diffusion flow rate performance parameters; According to the first gas flow rate distribution sequence and the second gas flow rate distribution sequence, respectively, gas flow conversion is performed to obtain a first gas flow rate and a second gas flow rate, and gas diffusion flow performance analysis is performed to obtain a first gas diffusion flow performance parameter and a second gas diffusion flow performance parameter; The gas diffusion performance parameter of the fuel cell is calculated and obtained as a detection result according to the first gas diffusion flow rate performance parameter, the second gas diffusion flow rate performance parameter, the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter.

2. The intelligent detection method of fuel cell gas diffusion performance according to claim 1, characterized in that: The first gas flow rate information sequence and the second gas flow rate information sequence are monitored and acquired by a first sensor and a second sensor arranged in a gas diffusion layer in a fuel cell, including: By means of a first sensor and a second sensor arranged in a gas diffusion layer in a fuel cell, gas flow rate information at multiple monitoring moments is monitored and acquired, and multiple first gas flow rate information and multiple second gas flow rate information are obtained, wherein the first sensor and the second sensor are arranged in the gas diffusion layer at two ends close to the anode and the cathode, respectively; The plurality of first gas flow rate information and the plurality of second gas flow rate information are arranged according to the plurality of monitoring moments to obtain a first gas flow rate information sequence and a second gas flow rate information sequence.

3. The intelligent detection method of fuel cell gas diffusion performance according to claim 1, characterized in that: According to the first gas flow rate information sequence and the second gas flow rate information sequence, a first gas flow rate distribution sequence and a second gas flow rate distribution sequence are restored and generated, and gas diffusion flow rate performance analysis is performed respectively to obtain first gas diffusion flow rate performance parameters and second gas diffusion flow rate performance parameters, including: Based on a generative adversarial network, a first gas flow rate distribution sequence and a second gas flow rate distribution sequence are restored and generated according to the first gas flow rate information sequence and the second gas flow rate information sequence, wherein each first gas flow rate distribution includes gas flow rate information at multiple positions near the anode; Obtaining a standard first gas flow velocity distribution and a standard second gas flow velocity distribution for fuel cell gas diffusion; Based on the standard first gas flow velocity distribution and the standard second gas flow velocity distribution, a gas diffusion flow velocity performance analysis is performed on the first gas flow velocity distribution sequence and the second gas flow velocity distribution sequence to obtain first gas diffusion flow velocity performance parameters and second gas diffusion flow velocity performance parameters.

4. The intelligent detection method of fuel cell gas diffusion performance according to claim 3, characterized in that: Based on the generative adversarial network, the first gas flow rate information sequence and the second gas flow rate information sequence are restored and generated to obtain the first gas flow rate distribution sequence and the second gas flow rate distribution sequence, including: According to the gas flow rate test data records at both ends of the anode and the cathode, a sample first gas flow rate information set and a sample second gas flow rate information set are collected, and a sample first gas flow rate distribution set and a sample second gas flow rate distribution set are collected; Based on the generative adversarial network, a first gas flow rate reduction channel and a second gas flow rate reduction channel are constructed; Using the sample first gas flow rate information set and the sample first gas flow rate distribution set, supervised training is performed on the first gas flow rate restoration channel until convergence, and using the sample second gas flow rate information set and the sample second gas flow rate distribution set, supervised training is performed on the second gas flow rate restoration channel until convergence; The multiple first gas flow rate information and the multiple second gas flow rate information in the first gas flow rate information sequence and the second gas flow rate information sequence are respectively input into the first gas flow rate reduction channel and the second gas flow rate reduction channel, and the reduction generates output to obtain the first gas flow rate distribution sequence and the second gas flow rate distribution sequence.

5. The intelligent detection method of fuel cell gas diffusion performance according to claim 3, characterized in that: Based on the standard first gas flow velocity distribution and the standard second gas flow velocity distribution, a gas diffusion flow velocity performance analysis is performed on the first gas flow velocity distribution sequence and the second gas flow velocity distribution sequence to obtain first gas diffusion flow velocity performance parameters and second gas diffusion flow velocity performance parameters, including: Taking the standard first gas flow velocity distribution as a reference, calculating a plurality of first flow velocity deviation amplitude distributions between a plurality of first gas flow velocity distributions in the first gas flow velocity distribution sequence and the standard first gas flow velocity distribution, and calculating the mean, to obtain a first average flow velocity deviation amplitude; Subtract the first average velocity deviation amplitude from 1 to obtain a first gas diffusion velocity performance parameter; Taking the standard second gas flow rate distribution as a reference, calculating a plurality of second flow rate deviation amplitude distributions between a plurality of second gas flow rate distributions in the second gas flow rate distribution sequence and the standard second gas flow rate distribution, and calculating the mean, to obtain a second average flow rate deviation amplitude; The second gas diffusion flow rate performance parameter is obtained by subtracting the second average flow rate deviation amplitude from 1.

6. The intelligent detection method of fuel cell gas diffusion performance according to claim 2, characterized in that: According to the first gas flow rate distribution sequence and the second gas flow rate distribution sequence, gas flow conversion is performed respectively to obtain the first gas flow rate and the second gas flow rate, and gas diffusion flow performance analysis is performed to obtain the first gas diffusion flow performance parameter and the second gas diffusion flow performance parameter, including: According to the duration of the plurality of monitoring moments, in combination with the first gas flow rate distribution sequence and the second gas flow rate distribution sequence, a plurality of first interval gas flow rates and a plurality of second interval gas flow rates are calculated, and the average is calculated to obtain the first gas flow rate and the second gas flow rate; Obtaining a standard first gas flow rate and a standard second gas flow rate for fuel cell gas diffusion; Calculating deviation amplitudes of the first gas flow rate and the second gas flow rate from the standard first gas flow rate and the standard second gas flow rate respectively, to obtain a first gas flow rate deviation amplitude and a second gas flow rate deviation amplitude; The first gas flow rate deviation amplitude and the second gas flow rate deviation amplitude are respectively subtracted from 1 to obtain the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter.

7. The intelligent detection method of fuel cell gas diffusion performance according to claim 1, characterized in that: The gas diffusion performance parameter of the fuel cell is calculated based on the first gas diffusion velocity performance parameter, the second gas diffusion velocity performance parameter, the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter, including: Calculating the average of the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter, and the average of the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter to obtain a gas diffusion flow rate performance parameter and a gas diffusion flow rate performance parameter; The gas diffusion velocity performance parameter and the gas diffusion flow rate performance parameter are weightedly calculated to obtain the gas diffusion performance parameter of the fuel cell.

8. An intelligent detection device for fuel cell gas diffusion performance, characterized in that: The device is used to execute the intelligent detection method of fuel cell gas diffusion performance according to any one of claims 1 to 7, and the device comprises: A gas flow rate information sequence acquisition module, the gas flow rate information sequence acquisition module is used to monitor and acquire a first gas flow rate information sequence and a second gas flow rate information sequence through a first sensor and a second sensor arranged in a gas diffusion layer in a fuel cell, wherein the first sensor and the second sensor are arranged in the gas diffusion layer at two ends close to an anode and a cathode, respectively; A gas diffusion flow rate performance parameter acquisition module, the gas diffusion flow rate performance parameter acquisition module is used to restore and generate a first gas flow rate distribution sequence and a second gas flow rate distribution sequence according to the first gas flow rate information sequence and the second gas flow rate information sequence, and perform gas diffusion flow rate performance analysis respectively to obtain a first gas diffusion flow rate performance parameter and a second gas diffusion flow rate performance parameter; A gas diffusion flow performance parameter acquisition module, the gas diffusion flow performance parameter acquisition module is used to perform gas flow conversion according to the first gas flow velocity distribution sequence and the second gas flow velocity distribution sequence, respectively, to obtain the first gas flow rate and the second gas flow rate, and to perform gas diffusion flow performance analysis to obtain the first gas diffusion flow performance parameter and the second gas diffusion flow performance parameter; A gas diffusion performance parameter calculation module is used to calculate the gas diffusion performance parameters of the fuel cell as a detection result based on the first gas diffusion flow rate performance parameter, the second gas diffusion flow rate performance parameter, the first gas diffusion flow rate performance parameter and the second gas diffusion flow rate performance parameter.

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