Fuel cell based test data processing method and computer program product

Abstract

The application discloses a kind of based on fuel cell's test data processing method and computer program product.Based on fuel cell field, the method includes: based on the simulation demand of fuel cell, determine the multiple operating parameter and current density point needed for simulation test to fuel cell;According to the operating parameter value range of the normal operation of the stack of fuel cell, determine the target value range corresponding to multiple operating parameters respectively;Based on the target value range corresponding to multiple operating parameters respectively, determine the numerical value of current density point in multiple operating parameters respectively corresponding;Based on the numerical value of current density point in multiple operating parameters respectively corresponding, determine the test data needed for simulation test to fuel cell.The application solves the technical problem that the applicability of selected test data is poor in related art when considering factors in the test data selection of fuel cell.

Description

Technical Field

[0001] This invention relates to the field of fuel cells, and more specifically, to a method for processing test data and a computer program product based on fuel cells. Background Technology

[0002] Testing fuel cells aims to determine their current performance, properties, and status, enabling design improvements or further testing. Simulation models built based on physical fuel cells can replace physical models for simulation testing, thereby reducing the number of experiments, shortening product testing and development cycles, and lowering development costs.

[0003] Before conducting simulation tests, test data must be input into the simulation software. Different simulation projects require different test plans, and the types and values ​​of the parameters used in the tests also vary. Currently, fuel cell test data comes from empirical data obtained from other previous test projects or reference data from publicly available literature. R&D testing personnel select or combine these raw data based on past work experience to obtain the test data used for simulation tests. However, this method of selecting and processing data is prone to incomplete consideration, resulting in poor applicability of the obtained test data, which leads to poor reliability and low accuracy of the simulation test results. Summary of the Invention

[0004] This invention provides a test data processing method and computer program product based on fuel cells, which at least solves the technical problem in the related art that the factors considered when selecting test data for fuel cells are not comprehensive, resulting in poor applicability of the selected test data.

[0005] According to one aspect of the present invention, a test data processing method based on a fuel cell is provided, comprising: determining multiple operating condition parameters and current density points required for simulating the fuel cell based on the simulation requirements of the fuel cell; determining target value ranges corresponding to the multiple operating condition parameters respectively, based on the value ranges of the operating condition parameters during normal operation of the fuel cell stack; determining the values ​​of the current density points corresponding to the multiple operating condition parameters respectively, based on the target value ranges corresponding to the multiple operating condition parameters; and determining the test data required for simulating the fuel cell based on the values ​​of the current density points corresponding to the multiple operating condition parameters.

[0006] According to another aspect of the present invention, a test data processing device based on a fuel cell is also provided, comprising: a first determining module, configured to determine multiple operating condition parameters and current density points required for simulating the fuel cell based on the simulation requirements of the fuel cell; a second determining module, configured to determine target value ranges corresponding to the multiple operating condition parameters respectively, based on the value ranges of the operating condition parameters during normal operation of the fuel cell stack; a third determining module, configured to determine the values ​​of the current density points corresponding to the multiple operating condition parameters respectively, based on the target value ranges corresponding to the multiple operating condition parameters; and a fourth determining module, configured to determine the test data required for simulating the fuel cell based on the values ​​of the current density points corresponding to the multiple operating condition parameters.

[0007] According to another aspect of the present invention, a computer program product is also provided, including a computer program that, when executed by a processor, implements the steps of any of the fuel cell-based test data processing methods described above.

[0008] In this embodiment of the invention, based on the simulation requirements of the fuel cell, multiple operating parameters and current density points required for simulating the fuel cell are determined; according to the value range of the operating parameters during normal operation of the fuel cell stack, target value ranges corresponding to the multiple operating parameters are determined; based on the target value ranges corresponding to the multiple operating parameters, the values ​​of the current density points corresponding to the multiple operating parameters are determined; based on the values ​​of the current density points corresponding to the multiple operating parameters, the test data required for simulating the fuel cell is determined. This achieves the goal of selecting test data in a targeted manner based on factors such as the simulation requirements of the fuel cell and the normal operating range of the fuel cell stack, thereby realizing the technical effect of targeted test data selection and improving the simulation applicability of the test data. This solves the technical problem in related technologies where insufficient consideration of factors leads to poor applicability of the selected test data when selecting fuel cell test data. Attached Figure Description

[0009] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0010] Figure 1 This is a flowchart of a test data processing method based on a fuel cell according to an embodiment of the present invention;

[0011] Figure 2 This is a flowchart of an optional fuel cell-based test data processing method according to an embodiment of the present invention;

[0012] Figure 3 This is a flowchart of an optional fuel cell-based test data processing method according to an embodiment of the present invention;

[0013] Figure 4 This is a flowchart of an optional fuel cell-based test data processing method according to an embodiment of the present invention;

[0014] Figure 5 This is a schematic diagram of a fuel cell-based test data processing device according to an embodiment of the present invention. Detailed Implementation

[0015] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0016] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0017] First, to facilitate understanding of the embodiments of the present invention, some terms or nouns involved in the present invention will be explained below:

[0018] Current density point: The current density point of a fuel cell stack refers to the current density per unit area, usually expressed in amperes per square centimeter (A / cm²). 2 The symbol () represents the relationship between the current intensity generated by the fuel cell stack and the surface area of ​​the stack, and is an important indicator for measuring the performance of the fuel cell stack.

[0019] Polarization curve: The polarization curve of a fuel cell stack refers to the relationship between the output voltage and current density of the fuel cell stack under different loads.

[0020] Taguchi algorithm (Orthogonal experimental design): This is a design method for studying multiple factors and levels. It selects representative points from a comprehensive experiment based on orthogonality, ensuring that the experimental points are evenly distributed across the entire range and can reflect the overall situation. These representative points are characterized by "uniform dispersion and neat comparability".

[0021] According to an embodiment of the present invention, a method embodiment for processing test data based on fuel cells is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0022] Figure 1 This is a flowchart of a fuel cell-based test data processing method according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0023] Step S102: Based on the simulation requirements of the fuel cell, determine the multiple operating parameters and current density points required for the simulation test of the fuel cell.

[0024] Optionally, the fuel cell can be, but is not limited to, a fuel cell prototype, a fuel cell stack, or a fuel cell system. Simulation requirements can include, but are not limited to, simulation software, self-programmed models, and simulation projects. Different simulation software, self-programmed models, and simulation projects have different requirements for the amount of current density data points. For example, under certain access requirements, when the number of required current density points is small, only a few current density points that characterize the polarization curve can be selected; when the number of required current density points is large, current density points across the entire current density range can be selected with a smaller step size; this entire current density range represents the range of all current densities. For example, electrochemical stack models do not have high requirements for the amount of current density data points.

[0025] Optionally, different simulation software, self-programmed models, and simulation projects can calibrate different operating parameters. It is necessary to determine all calibrable operating parameters based on the specific simulation conditions, i.e., the simulation requirements.

[0026] By taking into account the simulation requirements of fuel cells, the above methods select multiple operating parameters and current density points needed for fuel cell simulation testing. These methods consider a wide range of factors, making the subsequent test data more targeted.

[0027] Step S104: Based on the range of operating parameters for normal operation of the fuel cell stack, determine the target range of values ​​for each of the multiple operating parameters.

[0028] Optionally, the range of operating parameters is determined based on the operating performance of the fuel cell, and is used to indicate the range of operating parameters supported during normal stack operation, i.e., the upper and lower limits of the operating parameters that enable the fuel cell to operate normally; within this range of operating parameters, the fuel cell can operate normally. Correspondingly, the target ranges of multiple operating parameters determined based on this range of operating parameters are the ranges of operating parameters that can ensure the normal operation of the fuel cell.

[0029] In one optional embodiment, based on the range of operating condition parameters for normal operation of the fuel cell stack, a target range of values ​​corresponding to multiple operating condition parameters is determined, including: determining a first range of values ​​corresponding to multiple operating condition parameters that match the range of operating condition parameter values; obtaining a preset range of values ​​corresponding to multiple operating condition parameters, wherein the preset range of values ​​is determined based on the actual application goals of the fuel cell; and determining the target range of values ​​corresponding to multiple operating condition parameters based on the first range of values ​​and the preset range of values ​​corresponding to multiple operating condition parameters.

[0030] Optionally, this preset value range is determined based on the actual application goals of the fuel cell. These actual application goals may include, but are not limited to, factors such as fuel cell efficiency targets and output power targets. In other words, this preset value range can be understood as follows: to achieve the value ranges of each operating condition parameter set for the actual application goals, when determining the target value ranges of the operating condition parameters, not only the value ranges of the operating condition parameters must be considered, but also the actual application goals must be taken into account. This approach not only ensures the normal operation of the fuel cell during simulation testing but also guarantees that the actual application goals of the fuel cell are achieved during the simulation testing process.

[0031] In one optional embodiment, determining the target value range for each of the multiple operating condition parameters based on their respective first value ranges and preset value ranges includes: detecting whether there is a target operating condition parameter among the multiple operating condition parameters whose preset value range is greater than the corresponding first value range; if a target operating condition parameter exists among the multiple operating condition parameters, determining that the preset value range of the target operating condition parameter exceeds the corresponding first value range by an excess range; detecting whether the fuel cell stack can operate normally within the excess range; if the fuel cell stack can operate normally within the excess range, using the preset value range of the target operating condition parameter as the target value range of the target operating condition parameter; if the fuel cell stack cannot operate normally within the excess range, using the first value range of the target operating condition parameter as the target value range of the target operating condition parameter; and using the first value range of other operating condition parameters as the target value range of other operating condition parameters, wherein the other operating condition parameters are operating condition parameters other than the target operating condition parameter among the multiple operating condition parameters.

[0032] Optionally, the target value range for each operating parameter can be determined by comparing the first value range of each operating parameter, determined based on the range of operating parameter values, with the preset value range of each operating parameter, determined based on the actual application target. This allows for more accurate calibration of the value range of each operating parameter. Specifically, if a preset value range exceeds the first value range of an operating parameter (e.g., excessive air intake pressure exceeding the capacity of the air compressor; excessive cooling water temperature exceeding the capacity of the cooling system), meaning that among multiple operating parameters there is a target operating parameter with a preset value range greater than the corresponding first value range, and the stack can operate normally within the portion exceeding the range, then the operating parameter is adjusted, and the preset value range of the operating parameter is used as the target value range to cover the actual application target (such as fuel cell efficiency target, output power target, etc.). If there is no exceedance, or if the exceedance would cause abnormal operation of the stack, then no adjustment is made, and the first value range of the operating parameter is directly used as the target value range. The above methods can not only ensure the normal operation of the fuel cell during the simulation test, but also ensure that the actual application goals of the fuel cell can be achieved during the simulation test.

[0033] Step S106: Based on the target value range corresponding to the multiple operating parameters, determine the current density point corresponding to the multiple operating parameters.

[0034] Optionally, the values ​​of each operating condition parameter can be selected within the target value range, so that the selected values ​​can meet the operating requirements of the fuel cell, thereby ensuring the normal operation of the fuel cell during the simulation test.

[0035] In one optional embodiment, determining the value of the current density point corresponding to each of the multiple operating parameters based on the target value ranges corresponding to the multiple operating parameters includes: determining the number of values ​​corresponding to each of the multiple operating parameters based on the number of current density points; and determining the value of the current density point corresponding to each of the multiple operating parameters from the target value ranges corresponding to the multiple operating parameters according to the number of values ​​corresponding to each of the multiple operating parameters.

[0036] Optionally, the level value (or operating condition value) of each operating condition parameter can be determined for each current density point. The number of values ​​corresponding to each operating condition parameter can be determined based on the number of current density points. Based on the determined number of values ​​for each operating condition parameter, values ​​can be selected from the corresponding target value range to ensure the total number of tests is within a reasonable range (e.g., meeting model data requirements, testing time, and cost). Specifically, if there are many operating condition parameters, such as a number exceeding a predetermined number, fewer levels should be selected (the upper and lower boundaries of the operating condition parameter value range must be selected); if there are fewer factors, more levels should be selected. Optionally, for simulation projects requiring polarization curve test data, the number of values ​​for each operating condition parameter must be the same; for simulation projects requiring test data at each current density point, the number of values ​​for each operating condition parameter can be different.

[0037] Optionally, but not limited to, the number of values ​​corresponding to multiple operating condition parameters may be determined based on the number of current density points in the following ways: determining the range to which the number of multiple current density points belongs; determining the number of multiple values ​​based on the range to which the number of multiple current density points belongs.

[0038] Step S108: Based on the values ​​of the current density points at various operating conditions, determine the test data required for simulating the fuel cell.

[0039] Optionally, the test data required for fuel cell simulation testing can be obtained by calculating the values ​​corresponding to various current density points under multiple operating conditions. The test data obtained in this way comprehensively considers factors such as the simulation requirements of the fuel cell and the normal operating range of the stack, making the test data better meet the testing needs and more targeted.

[0040] In one optional embodiment, there are multiple current density points. Based on the values ​​corresponding to the current density points in multiple operating parameters, the test data required for simulating the fuel cell is determined, including: for each of the multiple current density points, determining the numerical arrangement of the operating parameters for each current density point; combining the values ​​corresponding to the multiple operating parameters for each current density point according to the numerical arrangement of the operating parameters for each current density point to obtain multiple operating parameter combinations corresponding to each current density point; and determining the test data based on the multiple operating parameter combinations corresponding to each current density point.

[0041] Optionally, the numerical arrangement of the operating parameters for each current density point can be determined, whereby the arrangement indicates the combination of values ​​for multiple operating parameters at each current density point. Based on this arrangement, the values ​​corresponding to multiple operating parameters for each current density point are combined to obtain multiple combinations of operating parameters for each current density point. The test data obtained in this way considers the combination of different operating parameter levels for each current density point, making it more comprehensive and targeted.

[0042] In one optional embodiment, determining the numerical arrangement of the operating parameters for each current density point includes: using the Taguchi algorithm to determine the numerical arrangement of the operating parameters for each current density point.

[0043] Optionally, the Taguchi algorithm is an algorithm used to sort and classify operating conditions. Based on this Taguchi algorithm, operating condition parameters can be arranged according to different values, thereby providing a clearer understanding of the priority and importance among different operating condition parameters.

[0044] In one optional embodiment, determining test data based on multiple combinations of operating parameters corresponding to each current density point includes: detecting whether there are duplicate operating parameter combinations among the multiple combinations of operating parameters corresponding to each current density point; if duplicate operating parameter combinations are detected, deleting the duplicate operating parameter combinations to obtain processed operating parameter combinations for each current density point; and determining test data based on the processed operating parameter combinations for each current density point.

[0045] Optionally, since the multiple operating condition parameter combinations corresponding to each current density point are obtained by arranging and combining the values ​​of multiple operating condition parameters, there may be duplicates in the multiple operating condition parameter combinations obtained. Based on this, duplicate operating condition combinations in the multiple operating condition parameter combinations corresponding to each current density point can be deduplicated to avoid the same operating condition parameter combinations being executed repeatedly during the simulation test. The above methods help to improve the simulation test efficiency of fuel cells.

[0046] In an optional embodiment, the method further includes: detecting whether there is a first operating condition parameter combination among multiple operating condition parameter combinations that causes stack abnormality; and adjusting the first operating condition parameter combination if the first operating condition parameter combination is detected among multiple operating condition parameter combinations.

[0047] Optionally, the stack anomaly may include, but is not limited to, temperature or pressure anomalies. For each of the multiple operating parameter combinations, the system detects whether there are temperature or pressure anomalies in the stack operation under those operating parameters, such as excessively high pressure or temperature. If so, the system adaptively adjusts the operating parameter combination to ensure that the fuel cell can operate normally during the simulation test.

[0048] In an optional embodiment, the method further includes: detecting whether there is a second operating condition parameter combination among multiple operating condition parameter combinations that causes the actual operating condition of the fuel cell to exceed a preset value, wherein the preset value is the upper limit of the value supported by the test bench for simulating the fuel cell; and adjusting the second operating condition parameter combination if the existence of the second operating condition parameter combination is detected among multiple operating condition parameter combinations.

[0049] Optionally, this preset value is the maximum value supported by the test bench required for fuel cell simulation testing, used to indicate the test bench's capacity. After obtaining multiple combinations of operating parameters, it is also necessary to check whether each combination of operating parameters will exceed the test bench's capacity during the simulation test. If it does, the operating parameter combinations that cause the test bench to exceed its capacity need to be adjusted to ensure that the fuel cell can operate normally during the simulation test.

[0050] Through the above steps S102 to S108, the purpose of selecting test data in a targeted manner can be achieved based on factors such as the simulation requirements of the fuel cell and the normal operating range of the fuel cell stack. This achieves the technical effect of targeted test data selection, improves the simulation applicability of test data, and solves the technical problem in related technologies where the factors considered when selecting test data for fuel cells are not comprehensive, resulting in poor applicability of the selected test data.

[0051] Based on the above embodiments and optional embodiments, the present invention proposes an optional implementation method. Figure 2 This is a flowchart of an optional fuel cell-based test data processing method according to an embodiment of the present invention, such as... Figure 2 As shown, the method includes:

[0052] Step S21: Different simulation software, self-programmed models, and simulation projects have different requirements for the amount of current density data points. Determine the multiple current density points required for the simulation project, i.e., multiple current density point level values, based on the testing requirements of the simulation project.

[0053] In step S22, different simulation software, self-programmed models, and simulation projects can calibrate different operating parameters. It is necessary to determine the multiple operating parameters required by the simulation project based on the specific simulation situation (i.e., simulation requirements).

[0054] Step S23: Based on the range of operating parameters during normal operation of the fuel cell stack, determine the range of values ​​for each operating parameter required for simulation testing, i.e., the upper and lower limits, and obtain the first range of values ​​for each of the multiple operating parameters.

[0055] Step S24: Compare the first value range corresponding to each operating condition parameter determined in step S23 with the preset value range. If any preset value range exceeds the first value range of the operating condition parameter (e.g., excessive air intake pressure exceeding the capacity range of the air compressor; excessive cooling water temperature exceeding the capacity range of the cooling system), and the fuel cell stack can operate normally in the portion exceeding the range, then the value range of the operating condition parameter is adjusted, and the preset value range of the operating condition parameter is used as the target value range to cover the actual application target (such as the fuel cell utilization efficiency target, output power target, etc.). If there is no exceedance, or if the exceedance would cause abnormal operation of the fuel cell stack, then no adjustment is made, and the first value range of the operating condition parameter is used as the target value range.

[0056] Step S25: Determine the values ​​of each operating condition parameter at each current density point. Considering that the total number of tests needs to be within a reasonable range (e.g., meeting model data requirements, testing time, and cost), if there are many operating condition parameters, select fewer values ​​(upper and lower boundaries must be selected); if there are few factors, select more values. For simulation projects requiring polarization curve test data, the number of values ​​for each operating condition parameter needs to be the same; for simulation projects requiring test data at each current density point, the number of values ​​for each operating condition parameter can be different.

[0057] Step S26: Determine the numerical arrangement of the operating parameters for each current density point according to the Taguchi algorithm.

[0058] Step S27: Combine the values ​​of the operating parameters determined in step S25 according to the arrangement in step S6 to obtain multiple combinations of operating parameters for each current density point, and delete duplicate test schemes.

[0059] Step S28: Confirm one by one whether each combination of operating parameters in the test plan will cause abnormalities in the fuel cell stack, such as the fuel cell stack operating under excessively high pressure or temperature. If so, adjust the values ​​of the operating parameters related to temperature and pressure.

[0060] Step S29: Confirm one by one whether each combination of operating parameters in the test plan will cause the actual operating conditions to exceed the test bench's capabilities. If so, adjust the values ​​of the relevant operating parameters. At this point, the test plan is complete.

[0061] Based on the above embodiments and optional embodiments, the present invention proposes another optional implementation method. Figure 3This is a flowchart of another optional fuel cell-based test data processing method according to an embodiment of the present invention. This method can be applied to the acquisition of polarization curve test data for the calibration of electrochemical stack models (i.e., EC models), such as... Figure 3 As shown, the method includes:

[0062] Step S31: Determine the current density points for calibrating the electrochemical pile model. Setting up the electrochemical pile model is relatively simple, requiring a small amount of current density data. Select several current density points and connect them with appropriate software to form the electrochemical pile model. The following current density points can be selected: 0.1 A / cm², 0.2 A / cm², 0.3 A / cm², 0.5 A / cm², and 1.2 A / cm².

[0063] Step S32: Determine the operating parameters required for calibrating the electrochemical stack model. Based on the simulation tool, determine the calibrable operating parameters: anode / cathode metering ratio, anode / cathode inlet pressure, and temperature.

[0064] Step S33: Based on the range of operating parameters under normal operation of the fuel cell stack, determine the first range of values ​​(i.e., upper and lower limits) of each current density point required for simulation testing for each operating parameter. Assume the following as shown in Table 1.

[0065] Table 1

[0066]

[0067] In step S34, the system air compressor outlet is the cathode inlet, and the lower limit of the air compressor's supply pressure is 100 kPa, which is lower than the lower limit of the cathode inlet pressure of 0.1 A / cm² or 0.2 A / cm². Since the fuel cell stack cannot operate normally when the cathode inlet pressure is 100 kPa, the lower limit of the cathode inlet pressure of 0.1 A / cm² or 0.2 A / cm² is adjusted to 105 kPa. The new value ranges of the operating conditions (i.e., the target value ranges of each operating parameter) are shown in Table 2 below.

[0068] Table 2

[0069]

[0070] Step S35: Determine the values ​​of each operating condition parameter at each current density point. Considering that the total number of tests needs to be within a reasonable range (meeting the model data requirements, testing time, and cost), approximately 15 polarization curves are required. Since the number of operating condition parameters is relatively small, two additional values ​​are interpolated beyond the upper and lower boundaries, for a total of four values. Electrochemical stack simulation is a simulation project that requires polarization curve test data; therefore, the number of values ​​for each operating condition parameter must be the same, four values ​​each. The values ​​for each current density point in the example electrochemical stack model are shown in Tables 3a and 3b below.

[0071] Table 3a

[0072]

[0073] Table 3b

[0074]

[0075] Step S36: Determine the numerical arrangement of the operating parameters at each current density point according to the Taguchi algorithm. For example, the numerical arrangement of the operating parameters at each current density point in the electrochemical stack model is shown in Table 4 below.

[0076] Table 4

[0077]

[0078]

[0079] Step S37: Combine the values ​​of the working condition factors determined in step S35 according to the arrangement in step S36 to obtain multiple combinations of working condition parameters for each density point, and delete duplicate test schemes.

[0080] Step S38: Confirm one by one whether each combination of operating parameters (i.e., combination of factor levels) in the test plan will cause abnormalities in the fuel cell stack, such as the fuel cell stack operating under excessively high pressure or temperature. If so, adjust the level values ​​related to temperature and pressure.

[0081] Step S39: Confirm one by one whether the combination of operating parameters in the test plan will cause the actual value to exceed the test bench's capacity. If so, adjust the values ​​of the relevant operating parameters. At this point, the test plan is complete. For example, a polarization curve test plan composed of parameters arranged in sequence 1 is shown in Table 5 below.

[0082] Table 5

[0083]

[0084]

[0085] Based on the above embodiments and optional embodiments, the present invention proposes another optional implementation method. Figure 4 This is a flowchart of another optional fuel cell-based test data processing method according to an embodiment of the present invention. This method can be applied to acquiring test data at various current density points used for training an artificial neural network fuel cell stack model (i.e., an ANN model). Figure 4 As shown, the method includes:

[0086] Step S41: Determine the values ​​of the current density points on the polarization curve for training the artificial neural network fuel cell model. Since the artificial neural network fuel cell model can be tested for all parameter types and can combine more parameter values, it requires a large amount of data on current density points, directly affecting prediction accuracy. Assume the selected current density points are: 0.1 A / cm², 0.2 A / cm², 0.3 A / cm², 0.5 A / cm², and 1.2 A / cm².

[0087] Step S42: Determine the operating parameters required for training the artificial neural network fuel cell stack model. Based on the predicted requirements, the operating parameters to be calibrated are: anode / cathode metering ratio, anode / cathode inlet pressure, and temperature.

[0088] Step S43: Based on the value range of operating factors during normal operation of the fuel cell stack, determine the value range of each operating parameter at each current density point required for simulation testing, i.e., the upper and lower limits of each operating parameter value, which serve as the first value range for each operating parameter. Examples of the upper and lower limits of operating factors at each current density point in the artificial neural network fuel cell stack model are shown in Table 6 below.

[0089] Table 6

[0090]

[0091] Step S44: Determine the values ​​of each operating condition parameter at each current density point. Considering that the total number of tests needs to be within a reasonable range (meeting the model data requirements, testing time, and cost), approximately 20 operating condition factors need to be measured at a single current density point. Due to the relatively small number of operating condition parameters, interpolation is performed after considering the upper and lower boundaries, resulting in different values ​​for different current density points under different operating condition parameters. Artificial neural network fuel cell stack simulation is a simulation project requiring test data at different current density points; the number of values ​​for different operating condition factors at different current densities can vary. Examples of the numerical values ​​of operating condition factors at each current density point in the artificial neural network fuel cell stack model are shown in Table 7 below.

[0092] Table 7

[0093]

[0094] Step S45: Determine the numerical arrangement of operating parameters for each current density point according to the Taguchi algorithm. The numerical arrangement of operating parameters using the Taguchi algorithm covers the values ​​of all operating factors. For cases where the current density points have fewer levels, redundant levels can be arbitrarily replaced with other levels. An example of the operating parameter arrangement for the artificial neural network fuel cell model at 0.5 A / cm² is shown in Table 8 below.

[0095] Table 8

[0096]

[0097]

[0098] Step S46: Combine the factor level values ​​determined in step S44 according to the arrangement in step S45 to obtain multiple combinations of operating condition parameters for each current density point, and delete duplicate test schemes.

[0099] Step S47: Confirm one by one whether each combination of operating parameters (i.e., factor level combination) in the test plan will cause the fuel cell stack to malfunction, such as the fuel cell stack operating under excessively high pressure or temperature. If so, adjust the values ​​of the operating factors related to temperature and pressure.

[0100] Step S48: Confirm one by one whether the combination of operating parameters in the test plan will cause the actual operating conditions to exceed the capacity of the test bench. If so, adjust the values ​​of the relevant operating factors. At this point, the test plan is complete. For example, the 0.5A / cm² test plan arranged according to step e is shown in Table 9 below.

[0101] Table 9

[0102]

[0103]

[0104] It should be noted that the embodiments of this invention are applicable not only to the development of test schemes for fuel cell stack-level simulations, but also to the fuel cell system level; the invention is applicable not only to the development of test schemes for fuel cell system-level / stack-level simulations of fuel cell vehicles, but also to the power sources of pure electric / hybrid / internal engine vehicles, such as engines, power batteries, and motors. If the embodiments of this invention are applied to other power sources, the relevant operating parameters will also change accordingly, such as current density points, metering ratios, and pressures. In an internal combustion engine, these can be replaced with temperature, intake pressure, and air-fuel ratio. Through the embodiments of this invention, the different test data requirements of different simulation projects can be met; while simultaneously considering test costs, test bench capabilities, and the actual application of the product.

[0105] The embodiments of the present invention can achieve at least one of the following effects: 1) Balancing simulation requirements and testing capabilities: First, the operating parameters and their value ranges are selected according to simulation requirements, and then the number of operating parameter values ​​and the number of tests are determined based on testing resources. 2) Fewer tests but sufficient reflection of fuel cell stack performance characteristics: The Taguchi algorithm is used to sort the values ​​of the operating parameters. This reduces the number of tests while ensuring orthogonality. 3) High reliability and accuracy of the simulation model: The test results obtained based on the test plan formulated using the Taguchi algorithm can reflect the performance characteristics of the fuel cell stack; the fuel cell stack model calibrated using this data can guarantee the reliability and accuracy of the fuel cell stack model.

[0106] This embodiment also provides a fuel cell-based test data processing device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the terms "module" and "device" can refer to a combination of software and / or hardware that performs a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0107] According to an embodiment of the present invention, an apparatus embodiment for implementing the above-described fuel cell-based test data processing method is also provided. Figure 5 This is a schematic diagram of the structure of a fuel cell-based test data processing device according to an embodiment of the present invention, as shown below. Figure 5 As shown, the above-mentioned fuel cell-based test data processing device includes: a first determining module 500, a second determining module 502, a third determining module 504, and a fourth determining module 506, wherein:

[0108] The first determination module 500 is used to determine multiple operating parameters and current density points required for simulating and testing the fuel cell based on the simulation requirements of the fuel cell.

[0109] The second determining module 502 is connected to the first determining module 500 and is used to determine the target value ranges corresponding to multiple operating parameters based on the value ranges of the operating parameters during normal operation of the fuel cell stack.

[0110] The third determining module 504 is connected to the second determining module 502 and is used to determine the value of the current density point corresponding to the multiple operating parameters based on the target value range corresponding to the multiple operating parameters.

[0111] The fourth determining module 506, connected to the third determining module 504, is used to determine the test data required for simulating the fuel cell based on the values ​​corresponding to the current density points at multiple operating conditions.

[0112] In this embodiment of the invention, a first determining module 500 is set up to determine multiple operating condition parameters and current density points required for simulating fuel cells based on the simulation requirements of the fuel cell. A second determining module 502, connected to the first determining module 500, is used to determine the target value range corresponding to each of the multiple operating condition parameters according to the value range of the operating condition parameters during normal operation of the fuel cell stack. A third determining module 504, connected to the second determining module 502, is used to determine the value of the current density point corresponding to each of the multiple operating condition parameters based on the target value range corresponding to each of the multiple operating condition parameters. A fourth determining module 506, connected to the third determining module 504, is used to determine the test data required for simulating fuel cells based on the value of the current density point corresponding to each of the multiple operating condition parameters. This achieves the purpose of selecting test data in a targeted manner based on factors such as the simulation requirements of the fuel cell and the normal operating range of the fuel cell stack, thereby realizing the technical effect of targeted test data selection and improving the simulation applicability of the test data. This solves the technical problem in related technologies where the factors considered when selecting test data for fuel cells are not comprehensive, resulting in poor applicability of the selected test data.

[0113] It should be noted that the above modules can be implemented by software or hardware. For example, for the latter, it can be implemented in the following ways: the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0114] It should be noted that the first determining module 500, the second determining module 502, the third determining module 504, and the fourth determining module 506 mentioned above correspond to steps S102 to S108 in the embodiments. The instances and application scenarios implemented by the above modules and their corresponding steps are the same, but are not limited to the content disclosed in the above embodiments. It should be noted that the above modules, as part of the device, can run in a computer terminal.

[0115] It should be noted that the optional or preferred implementation methods of this embodiment can be found in the relevant descriptions in the embodiments, and will not be repeated here.

[0116] The aforementioned fuel cell-based test data processing device may further include a processor and a memory. The first determining module 500, the second determining module 502, the third determining module 504, and the fourth determining module 506 are all stored in the memory as program modules, and the processor executes the aforementioned program modules stored in the memory to realize the corresponding functions.

[0117] The processor contains a core that retrieves the corresponding program modules from memory. One or more cores may be configured. Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory includes at least one memory chip.

[0118] According to an embodiment of this application, an embodiment of a non-volatile storage medium is also provided. Optionally, in this embodiment, the non-volatile storage medium includes a stored program, wherein, when the program runs, it controls the device containing the non-volatile storage medium to execute any of the aforementioned fuel cell-based test data processing methods.

[0119] Optionally, in this embodiment, the non-volatile storage medium may be located in any computer terminal in a group of computer terminals in a computer network, or in any mobile terminal in a group of mobile terminals, and the non-volatile storage medium includes stored programs.

[0120] Optionally, during program execution, the device containing the non-volatile storage medium may be controlled to perform the following functions: based on the simulation requirements of the fuel cell, determine multiple operating condition parameters and current density points required for the simulation test of the fuel cell; based on the value range of the operating condition parameters during normal operation of the fuel cell stack, determine the target value range corresponding to each of the multiple operating condition parameters; based on the target value range corresponding to each of the multiple operating condition parameters, determine the value of the current density point corresponding to each of the multiple operating condition parameters; based on the value of the current density point corresponding to each of the multiple operating condition parameters, determine the test data required for the simulation test of the fuel cell.

[0121] According to an embodiment of this application, an embodiment of a processor is also provided. Optionally, in this embodiment, the processor is used to run a program, wherein the program executes any of the aforementioned fuel cell-based test data processing methods.

[0122] According to an embodiment of this application, an embodiment of a computer program product is also provided, which, when executed on a data processing device, is adapted to execute a program that initializes the fuel cell-based test data processing method steps described above.

[0123] Optionally, when the aforementioned computer program product is executed on a data processing device, it is suitable to execute an initialization program with the following method steps: based on the simulation requirements of the fuel cell, determine multiple operating condition parameters and current density points required for simulating and testing the fuel cell; based on the value range of the operating condition parameters during normal operation of the fuel cell stack, determine the target value range corresponding to each of the multiple operating condition parameters; based on the target value range corresponding to each of the multiple operating condition parameters, determine the value of the current density point corresponding to each of the multiple operating condition parameters; based on the value of the current density point corresponding to each of the multiple operating condition parameters, determine the test data required for simulating and testing the fuel cell.

[0124] This invention provides an electronic device, which includes a processor, a memory, and a program stored in the memory and executable on the processor. When the processor executes the program, it performs the following steps: based on the simulation requirements of a fuel cell, it determines multiple operating condition parameters and current density points required for simulating and testing the fuel cell; based on the range of operating condition parameters during normal operation of the fuel cell stack, it determines the target value ranges corresponding to each of the multiple operating condition parameters; based on the target value ranges corresponding to each of the multiple operating condition parameters, it determines the values ​​of the current density points corresponding to each of the multiple operating condition parameters; and based on the values ​​of the current density points corresponding to each of the multiple operating condition parameters, it determines the test data required for simulating and testing the fuel cell.

[0125] The order of the above embodiments of the present invention is merely for description and does not represent the superiority or inferiority of the embodiments.

[0126] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0127] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of modules described above can be a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces, or indirect coupling or communication connection between modules, and may be electrical or other forms.

[0128] The modules described above as separate components may or may not be physically separate. Similarly, the components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple modules. Some or all of the modules can be selected to achieve the purpose of this embodiment, depending on actual needs.

[0129] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module. The integrated modules described above can be implemented in hardware or as software functional modules.

[0130] If the aforementioned integrated modules are implemented as software functional modules and sold or used as independent products, they can be stored in a computer-readable non-volatile storage medium. Based on this understanding, the technical solution of this invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a non-volatile storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this invention. The aforementioned non-volatile storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0131] The above are merely preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for processing test data based on fuel cells, characterized in that, include: Based on the simulation requirements of fuel cells, multiple operating parameters and current density points required for simulating and testing the fuel cells are determined. Based on the range of operating condition parameters for normal operation of the fuel cell stack, the target value ranges corresponding to the plurality of operating condition parameters are determined, including: determining a first value range corresponding to each of the plurality of operating condition parameters that matches the range of operating condition parameter values; obtaining a preset value range corresponding to each of the plurality of operating condition parameters, wherein the preset value range is determined based on the actual application goals of the fuel cell; and determining the target value range corresponding to each of the plurality of operating condition parameters based on the first value range and the preset value range corresponding to each of the plurality of operating condition parameters. Based on the target value ranges corresponding to the multiple operating parameters, the values ​​of the current density points corresponding to the multiple operating parameters are determined. Based on the values ​​of the current density points corresponding to the various operating parameters, the test data required for the simulation test of the fuel cell is determined.

2. The method according to claim 1, characterized in that, The step of determining the target value range corresponding to each of the plurality of operating condition parameters based on the first value range and the preset value range corresponding to the plurality of operating condition parameters includes: Detect whether there is a target operating condition parameter among the plurality of operating condition parameters whose preset value range is greater than the corresponding first value range; If the target operating condition parameter exists among the plurality of operating condition parameters, it is determined that the preset value range of the target operating condition parameter exceeds the corresponding first value range. The test is conducted to determine whether the fuel cell stack is able to operate normally within the range beyond which it is being tested. If the fuel cell stack can operate normally within the range, the preset value range of the target operating condition parameter shall be taken as the target value range of the target operating condition parameter. If the fuel cell stack cannot operate normally within the range, the first value range of the target operating condition parameter shall be taken as the target value range of the target operating condition parameter. The first value range of other operating condition parameters is taken as the target value range of the other operating condition parameters, wherein the other operating condition parameters are the operating condition parameters other than the target operating condition parameters among the plurality of operating condition parameters.

3. The method according to claim 1, characterized in that, The step of determining the current density point's value corresponding to each of the multiple operating parameters based on the target value ranges corresponding to those parameters includes: Based on the number of current density points, determine the number of values ​​corresponding to each of the multiple operating condition parameters; Based on the number of values ​​corresponding to the plurality of operating parameters, the current density point is determined from the target value range corresponding to the plurality of operating parameters.

4. The method according to claim 1, characterized in that, The current density points are multiple, and the test data required for the simulation test of the fuel cell is determined based on the values ​​of the current density points corresponding to the multiple operating condition parameters, including: For each of the multiple current density points, determine the numerical arrangement of the operating parameters for each current density point; According to the numerical arrangement of the operating parameters of each current density point, the values ​​corresponding to the multiple operating parameters of each current density point are combined to obtain the multiple operating parameter combinations corresponding to each current density point. The test data is determined based on the combination of multiple operating parameters corresponding to each current density point.

5. The method according to claim 4, characterized in that, The method for determining the numerical arrangement of the operating parameters for each current density point includes: The Taguchi algorithm is used to determine the numerical arrangement of the operating parameters for each current density point.

6. The method according to claim 4, characterized in that, The step of determining the test data based on multiple combinations of operating parameters corresponding to each current density point includes: Detect whether there are duplicate operating parameter combinations among the multiple operating parameter combinations corresponding to each current density point; If a duplicate combination of operating parameters is detected, the duplicate combination of operating parameters is deleted to obtain the processed combination of operating parameters for each current density point. The test data is determined based on the processed combination of operating parameters for each current density point.

7. The method according to claim 4, characterized in that, The method further includes: Detect whether there is a first combination of operating parameters among the multiple combinations of operating parameters that causes the stack to malfunction; If the first operating condition parameter combination is detected among the plurality of operating condition parameter combinations, the first operating condition parameter combination is adjusted.

8. The method according to claim 4, characterized in that, The method further includes: The system detects whether there is a second combination of operating parameters among the multiple combinations of operating parameters that causes the actual operating condition of the fuel cell to exceed a preset value, wherein the preset value is the upper limit of the value supported by the test bench for simulating the fuel cell. If the second operating condition parameter combination is detected among the plurality of operating condition parameter combinations, the second operating condition parameter combination is adjusted.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the steps of the fuel cell-based test data processing method according to any one of claims 1 to 8.

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