A working condition adjustment method, device, electronic device and storage medium

Abstract

The present application discloses a working condition adjustment method, device, electronic device and storage medium. The method and device are applied to electronic devices and are used to adjust the working conditions of fuel cells during endurance operation. Specifically, multiple working parameters of the fuel cell are collected and battery indicators are selected therefrom; based on the correlation indicators between the remaining working parameters and the battery indicators, at least one working parameter is selected as the working condition of the fuel cell to be adjusted; all data are divided into multiple data portions according to the working condition of the fuel cell, and a data curve of the variation of the derived statistics over time is drawn based on each data portion; the optimal data curve is found from multiple data curves according to the variation trend of the data curve, and the fuel cell is controlled based on the curve parameters of the optimal data curve. Through this solution, the working conditions of the fuel cell can be adapted in real time, so that the real-time optimization of the operating state of the fuel cell can be achieved.

Description

Technical Field

[0001] The present application relates to the field of fuel cell technology, and more specifically, to a method, device, electronic device and storage medium for adjusting working conditions. Background Art

[0002] After long-term operation, fuel cells will experience material attenuation, which will cause the electrochemical state, hydrothermal state and other battery states to decay relative to the battery states before the long-term operation. At this time, if the previous system control strategy is maintained, this attenuation trend will not be improved, and the health of the fuel cell may even deteriorate rapidly. Summary of the invention

[0003] In view of this, the present application provides a working condition adjustment method, device, electronic device and storage medium for real-time adaptation of the working conditions of the fuel cell to achieve real-time optimization of the operating state of the fuel cell.

[0004] In order to achieve the above objectives, the proposed solution is as follows:

[0005] A working condition adjustment method is applied to electronic equipment and is used to adjust the working conditions of a fuel cell during endurance operation. The working condition adjustment method comprises the following steps:

[0006] Collecting a plurality of operating parameters of the fuel cell, each of the operating parameters comprising a plurality of data in time sequence, and selecting at least one operating parameter from the plurality of operating parameters as a battery indicator of the fuel cell;

[0007] Based on the correlation indexes between the remaining operating parameters and the battery indexes, selecting at least one of the operating parameters as the fuel cell operating condition to be adjusted;

[0008] Divide the working conditions into M equal parts of data, and draw a data curve of the variation of the derivative statistic over time based on each part of the data, where M is an integer greater than 1;

[0009] An optimal data curve is searched from a plurality of data curves according to a change trend of the data curve, and the fuel cell is controlled based on curve parameters of the optimal data curve.

[0010] Optionally, the collecting of multiple operating parameters of the fuel cell and selecting at least one operating parameter from the multiple operating parameters as a battery indicator of the fuel cell comprises the steps of:

[0011] Collecting a plurality of operating parameters of the fuel cell based on a test bench, wherein the plurality of operating parameters at least include an operating voltage of the fuel cell;

[0012] Cleaning each of the working parameters to remove invalid data therein;

[0013] By limiting the stack current range to screen data, multiple fuel cell performance indicators are obtained;

[0014] The fuel cell performance index is selected as the battery index.

[0015] Optionally, based on the correlation between the other working parameters and the battery indicators, selecting at least one working parameter as the fuel cell working condition to be adjusted comprises the steps of:

[0016] Calculating a correlation index between each of the operating parameters and the battery index;

[0017] The battery indicator with the highest correlation index is selected as the fuel cell operating condition.

[0018] Optionally, dividing the working conditions into M equal parts of data, and drawing a data curve showing the variation of the derived statistics over time based on each part of the data, comprises the steps of:

[0019] Draw a distribution histogram of the working conditions;

[0020] The working conditions are divided into M data blocks by a data block method based on a cumulative distribution function, and a moving average filtering process is performed;

[0021] The data curve is drawn according to the data.

[0022] A working condition adjustment device is applied to electronic equipment and is used to adjust the working condition of a fuel cell after endurance operation. The working condition adjustment device comprises:

[0023] A data acquisition module is configured to collect a plurality of operating parameters of the fuel cell, each of the operating parameters comprising a plurality of data in time sequence, and select at least one operating parameter from the plurality of operating parameters as a battery indicator of the fuel cell;

[0024] A condition selection module, configured to select at least one of the operating parameters as the fuel cell operating condition to be adjusted based on correlation indicators between the remaining operating parameters and the battery indicators;

[0025] A data partitioning module is configured to divide the working conditions into M equal parts of data, and draw a data curve of the variation of the derivative statistics over time based on each part of the data, where M is an integer greater than 1;

[0026] The control implementation module is configured to search for an optimal data curve from a plurality of the data curves according to a change trend of the data curve, and control the fuel cell based on curve parameters of the optimal data curve.

[0027] Optionally, the data acquisition module includes:

[0028] A data acquisition unit, configured to acquire a plurality of operating parameters of the fuel cell based on a test bench, wherein the plurality of operating parameters at least include an operating voltage of the fuel cell;

[0029] A data cleaning unit, configured to clean each of the working parameters to remove invalid data therein;

[0030] A data screening unit is configured to perform data screening by limiting the current range of the fuel cell stack to obtain multiple fuel cell performance indicators;

[0031] The indicator selection unit is configured to select the fuel cell performance indicator as the battery indicator.

[0032] Optionally, the condition selection module includes:

[0033] A correlation calculation unit, configured to calculate a correlation index between each of the battery indicators and the working condition;

[0034] The condition selection unit is configured to select the battery indicator with the highest correlation index as the fuel cell operating condition.

[0035] Optionally, the data partitioning module includes:

[0036] A first drawing unit, configured to draw a distribution histogram of the working conditions;

[0037] A data segmentation unit is configured to divide the working condition into M equal parts of data by a data segmentation method based on a cumulative distribution function, and perform a moving average filtering process;

[0038] The second drawing unit is configured to draw the data curve according to the data.

[0039] An electronic device for adjusting the working conditions of a fuel cell after endurance operation, the electronic device comprising at least one processor and a memory connected to the processor, wherein:

[0040] The memory is used to store computer programs or instructions;

[0041] The processor is used to execute the computer program or instruction so that the electronic device implements the working condition adjustment method as described above.

[0042] A storage medium is applied to an electronic device, wherein the storage medium carries one or more computer programs. When the one or more computer programs are executed by the electronic device, the electronic device can implement the working condition adjustment method as described above.

[0043] It can be seen from the above technical scheme that the present application discloses a working condition adjustment method, device, electronic device and storage medium. The method and device are applied to electronic devices and are used to adjust the working conditions of fuel cells during endurance operation. Specifically, multiple working parameters of the fuel cell are collected and battery indicators are selected therefrom; based on the correlation indicators between the remaining working parameters and the battery indicators, at least one working parameter is selected as the working condition of the fuel cell to be adjusted; all data are divided into multiple data according to the working condition of the fuel cell, and a data curve of the variation of the derived statistics over time is drawn based on each data; the optimal data curve is found from multiple data curves according to the variation trend of the data curve, and the fuel cell is controlled based on the curve parameters of the optimal data curve. Through this scheme, the working conditions of the fuel cell can be adapted in real time, so that the real-time optimization of the operating state of the fuel cell can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0045] Figure 1 A flowchart of a working condition adjustment method according to an embodiment of the present application;

[0046] Figure 2 This is the air stoichiometric ratio distribution histogram at 180A working current;

[0047] Figure 3 This is the working voltage change trend diagram corresponding to different air metering ratio intervals under 180A working current;

[0048] Figure 4 A block diagram of a working condition adjustment device according to an embodiment of the present application;

[0049] Figure 5 A block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0050] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions 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.

[0051] Embodiment 1

[0052] Figure 1 This is a flow chart of a working condition adjustment method according to an embodiment of the present application.

[0053] like Figure 1 As shown, the working condition adjustment method provided in this embodiment is applied to an electronic device, and is used to adjust the working condition of a fuel cell after endurance operation. The electronic device can be understood as a computer or server with data computing and information processing capabilities, or can be understood as an embedded device applied to the fuel cell. The working condition adjustment method includes the following steps:

[0054] S1. Collect multiple operating parameters of the fuel cell.

[0055] First, the fuel cell-based test bench collects multiple operating parameters during operation, including but not limited to operating voltage, operating current, air flow, hydrogen flow, air inlet / inlet pressure, hydrogen inlet / inlet pressure, coolant inlet / inlet pressure, air inlet / inlet temperature, hydrogen inlet / inlet temperature, coolant inlet / inlet temperature, single cell voltage and other bench sensor settings and feedback values. During the collection process, it must be ensured that the collected data files comply with a unified format to facilitate subsequent data integration and processing. TXT or CSV format can be selected. After undergoing a 4800h endurance test, the required large-capacity data to be analyzed can be obtained (specifically 1601 TXT files with a total capacity of 12GB).

[0056] Then, set the sensor value within a reasonable range for data cleaning, extract key variables such as working voltage and working current, and write them into a temporary cache file. Traverse the data folder and perform the above data cleaning and file writing operations until all large-capacity segmented data are integrated into a reduced-capacity intermediate data file (4.18GB).

[0057] Finally, one or more parameters are selected as the battery index of the fuel cell. In this application, the operating voltage of the fuel cell is selected as the battery index. Battery indexes include but are not limited to external characteristic indexes such as operating voltage, battery power, and single cell consistency, or internal characteristic indexes such as high-frequency impedance and low-frequency impedance.

[0058] S2. Selecting operating parameters as the fuel cell operating conditions to be adjusted.

[0059] Based on the correlation index between other working parameters other than battery index and battery index, one or more working parameters are selected as the working condition of the fuel cell. In the selection process, through correlation analysis and sorting, it can be known that the air flow has a major determining effect on the working voltage, so the air flow is selected as the working condition of the fuel cell to be adjusted. The specific process includes:

[0060] First, calculate the correlation of each operating parameter with respect to the battery indicator. The definition of correlation can be Pearson correlation coefficient, Spearman correlation coefficient or Kendall correlation coefficient, etc. The following takes the definition of Pearson correlation coefficient as an example:

[0061]

[0062] Among them, cov(X,V) is the covariance of the X and V sequences, σ(X) and σ(V) are the standard deviations of X and V. The higher the correlation between the X and V sequences, the more important the working condition X is.

[0063] Then, based on the above analysis, the operating parameter with the highest correlation is selected as the operating condition of the fuel cell to be adjusted.

[0064] S3. Divide the fuel cell operating conditions into equal parts and draw a data curve.

[0065] According to the data classification algorithm, the fuel cell working conditions are equally divided into M data. For different data blocks, the changing trend of the curve of the derivative statistics changing over time is calculated respectively, which can be quantified by methods including but not limited to linear regression or nonlinear regression; the derived statistics include but are not limited to the working voltage of the fuel cell, the single cell voltage and their derived variables.

[0066] Based on the distribution function of the working condition X to be analyzed within the time range to be analyzed, the corresponding cumulative distribution function (CDF) can be obtained by the following formula:

[0067]

[0068] The cumulative distribution function has the property:

[0069]

[0070] Where N is the number of all sampling points within the time range to be analyzed.

[0071] Consider dividing N data into M parts, each of which contains N / M data. The threshold array {x i}, which has the following relationship with the sample data N and the number of blocks M:

[0072]

[0073] Combine equations (2)-(4) to calculate the threshold array {x i}, the threshold array is used to classify the N data points according to the distribution of the working condition X.

[0074] The specific process is:

[0075] First, the working current is fixed at 180A, and the air metering ratio is calculated for the air flow rate to obtain its distribution histogram, as shown in Figure 2 .

[0076] Then, the data was divided into 7 parts by a data block method based on the cumulative distribution function. After interpolation processing, moving average filtering with a sliding window of 60 was performed on each of them.

[0077] Finally, for each data set, a data curve of the working voltage changing with the operation time of the fuel cell is obtained. Among them, different air stoichiometric ratio ranges correspond to different working voltage change curves. Among them, 3 groups of typical curves have been Figure 3 Drawn in.

[0078] S4. Control the fuel cell according to the optimal data curve.

[0079] According to the change trend of each data curve, the optimal data curve is found from multiple data curves, and the fuel cell is controlled based on the curve parameters of the optimal data curve. Specifically in this embodiment, the 7 sets of data can be linearly regressed based on the least squares method to obtain 7 sets of slopes and intercepts. The working voltage decay of the fuel cell within a certain time range is regarded as linear decay, and the slope of the regression line can be regarded as the working voltage decay rate at this time.

[0080] Based on the sorting of the working voltage decay rate, the air stoichiometric ratio range (1.9004-1.9022) that minimizes the average working voltage decay rate of the fuel cell (0.2103 mV / h) under a current of 180 A can be selected. Thus, the fuel cell can be controlled based on the air stoichiometric ratio range as the control target of the current fuel cell and its reference accuracy range, and the working condition of the fuel cell can be adjusted.

[0081] It can be seen from the above technical scheme that this embodiment provides a working condition adjustment method, which is applied to electronic equipment and is used to adjust the working conditions of a fuel cell after long-term operation. Specifically, multiple working parameters of the fuel cell are collected and battery indicators are selected therefrom; based on the correlation indicators between the other working participating battery indicators, at least one working parameter is selected as the fuel cell working condition to be adjusted; all data are divided into multiple data according to the fuel cell working condition, and a data curve of the variation of the derived statistics over time is drawn based on each data; the optimal data curve is found from multiple data curves according to the variation trend of the data curve, and the fuel cell is controlled based on the curve parameters of the optimal data curve. Through this scheme, the working conditions of the fuel cell can be adapted in real time, so that the real-time optimization of the operating state of the fuel cell can be achieved.

[0082] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0083] Although operations are depicted in a particular order, this should not be understood as requiring that the operations be performed in the particular order shown or in a sequential order.Multitasking and parallel processing may be advantageous under certain circumstances.

[0084] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0085] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages ​​such as Python, Julia, Java, Smalltalk, C++, and conventional procedural programming languages ​​such as C or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0086] Embodiment 2

[0087] Figure 4 This is a block diagram of a working condition adjustment device according to an embodiment of the present application.

[0088] like Figure 4 As shown, the working condition adjustment device provided in this embodiment is applied to an electronic device, and is used to adjust the working condition of a fuel cell after endurance operation. The electronic device can be understood as a computer or server with data computing and information processing capabilities, or can be understood as an embedded device applied to the fuel cell. The working condition adjustment device includes a data acquisition module 10, a condition selection module 20, a data partitioning module 30, and a control implementation module 40.

[0089] The data acquisition module is used to collect multiple working parameters of the fuel cell. The module includes a data acquisition unit, a data cleaning unit and an indicator selection unit.

[0090] The data acquisition unit is used to collect multiple operating parameters of the fuel cell test bench during operation. These operating parameters include but are not limited to operating voltage, operating current, air flow, hydrogen flow, air inlet / inlet pressure, hydrogen inlet / inlet pressure, coolant inlet / inlet pressure, air inlet / inlet temperature, hydrogen inlet / inlet temperature, coolant inlet / inlet temperature, single cell voltage and other bench sensor setting values ​​and feedback values. During the collection process, it must be ensured that the collected data files comply with a unified format to facilitate subsequent data integration and processing. TXT or CSV format can be selected. After undergoing a 4800h endurance test, the required large-capacity data to be analyzed can be obtained (specifically 1601 TXT files with a total capacity of 12GB).

[0091] The data cleaning unit is used to set a reasonable range of sensor values ​​for data cleaning, extract key variables such as working voltage and working current, and write them into a temporary cache file. Traverse the data folder and perform the above data cleaning and file writing operations until all large-capacity segmented data are integrated into a reduced-capacity intermediate data file (4.18GB).

[0092] The index selection unit is used to select one or more parameters as the battery index of the fuel cell. In this application, the operating voltage of the fuel cell is selected as the battery index. The battery index includes but is not limited to external characteristic indexes such as operating voltage, battery power, and single cell consistency, or internal characteristic indexes such as high-frequency impedance and low-frequency impedance.

[0093] The condition selection module is used to select one or more operating parameters as the fuel cell operating conditions to be adjusted.

[0094] Based on the correlation index between other working parameters other than the battery index and the battery index, one or more working parameters are selected as the working condition of the fuel cell to be adjusted. In the selection process, through correlation analysis and sorting, it can be known that the air flow has a major determining effect on the working voltage, so the air flow is selected as the working condition here. This module includes a correlation calculation unit and a condition selection unit.

[0095] The correlation calculation unit is used to calculate the correlation of each working parameter with respect to the battery indicator. The definition of correlation can be Pearson correlation coefficient, Spearman correlation coefficient or Kendall correlation coefficient, etc. The following takes the definition of Pearson correlation coefficient as an example:

[0096]

[0097] Among them, cov(X,V) is the covariance of the X and V sequences, σ(X) and σ(V) are the standard deviations of X and V. The higher the correlation between the X and V sequences, the more important the working condition X is.

[0098] The condition selection unit is used to select the operating parameter with the highest correlation as the operating condition of the fuel cell based on the above analysis.

[0099] The data partition module is used to divide the fuel cell working conditions into equal parts and then draw the data curve.

[0100] According to the data classification algorithm, the fuel cell working conditions are equally divided into M data. For different data blocks, the changing trend of the curve of the derivative statistics changing over time is calculated respectively, which can be quantified by methods including but not limited to linear regression or nonlinear regression; the derived statistics include but are not limited to the working voltage of the fuel cell, the single cell voltage and their derived variables.

[0101] Based on the distribution function of the working condition X to be analyzed within the time range to be analyzed, the corresponding cumulative distribution function (CDF) can be obtained by the following formula:

[0102]

[0103] The cumulative distribution function has the property:

[0104]

[0105] Where N is the number of all sampling points within the time range to be analyzed.

[0106] Consider dividing N data into M parts, each of which contains N / M data. The threshold array {x i}, and the sample data N and the number of blocks M have the following relationship:

[0107]

[0108] Combine equations (2)-(4) to calculate the threshold array {x i}, classify the N data points according to the distribution of the working condition X through the threshold array. The module specifically includes a first drawing unit, a data segmentation unit and a second drawing unit.

[0109] The first drawing unit is used to fix the working current to 180A, calculate the air metering ratio for the air flow rate, and obtain its distribution histogram, such as Figure 2 .

[0110] The data segmentation unit is used to divide the data into 7 parts by using a data block method based on the cumulative distribution function, and perform moving average filtering on each of them after interpolation processing.

[0111] The second drawing unit is used to obtain a data curve of the working voltage changing with the operation time of the fuel cell for each data. Among them, different air stoichiometric ratio ranges correspond to different working voltage change curves. Among them, 3 groups of typical curves have been Figure 3 Drawn in.

[0112] The control implementation module is used to control the fuel cell according to the optimal data curve.

[0113] According to the change trend of each data curve, the optimal data curve is found from multiple data curves, and the fuel cell is controlled based on the curve parameters of the optimal data curve. Specifically in this embodiment, the 7 sets of data can be linearly regressed based on the least squares method to obtain 7 sets of slopes and intercepts. The working voltage decay of the fuel cell within a certain time range is regarded as linear decay, and the slope of the regression line can be regarded as the working voltage decay rate at this time.

[0114] Based on the sorting of the working voltage decay rate, the air stoichiometric ratio range (1.9004-1.9022) that minimizes the average working voltage decay rate of the fuel cell (0.2103 mV / h) under a current of 180 A can be selected. The fuel cell can be controlled based on the air stoichiometric ratio range as the control target of the current fuel cell and its reference accuracy range, thereby completing the adjustment of the working conditions of the fuel cell.

[0115] It can be seen from the above technical scheme that this embodiment provides a working condition adjustment device, which is applied to electronic equipment and is used to adjust the working conditions of a fuel cell after long-term operation, specifically collecting multiple working parameters of the fuel cell and selecting battery indicators therefrom; based on the correlation indicators between the remaining working parameters and the battery indicators, selecting at least one working parameter as the working condition of the fuel cell to be adjusted; dividing all data into multiple data portions according to the working condition of the fuel cell, and drawing a data curve of the variation of the derived statistics over time based on each data portion; finding the optimal data curve from multiple data curves according to the variation trend of the data curve, and controlling the fuel cell based on the curve parameters of the optimal data curve. This scheme can improve the working conditions of the fuel cell, thereby enabling real-time optimization of the health status of the fuel cell.

[0116] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware. The name of a unit does not limit the unit itself in some cases. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses".

[0117] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0118] Embodiment 3

[0119] This embodiment provides an electronic device. Figure 5As shown, it shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The terminal device in the embodiments of the present disclosure may include but is not limited to mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. The electronic device is only an example and should not bring any limitations to the functions and scope of use of the embodiments of the present disclosure.

[0120] The electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 401, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 402 or a program loaded from a storage device 406 into a random access memory (RAM) 403. In the RAM 403, various programs and data required for the operation of the electronic device are also stored. The processing device 601, the ROM 602, and the RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to the bus 404.

[0121] Typically, the following devices may be connected to the I / O interface 405: input devices 406 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 407 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 406 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 409. The communication device 409 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various devices, it should be understood that it is not required to implement or have all the devices shown. More or fewer devices may be implemented or have alternatively.

[0122] Embodiment 4

[0123] This embodiment provides a computer-readable storage medium. The storage medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device can collect multiple operating parameters of the fuel cell and select battery indicators from them; based on the correlation indicators between the remaining operating parameters and the battery indicators, select at least one operating parameter as the fuel cell operating condition to be adjusted; divide all data into multiple data according to the fuel cell operating condition, and draw a data curve of the variation of the derived statistics over time based on each data; find the optimal data curve from multiple data curves according to the variation trend of the data curve, and control the fuel cell based on the curve parameters of the optimal data curve. Through this solution, the working conditions of the fuel cell can be adapted in real time, so that the real-time optimization of the operating state of the fuel cell can be achieved.

[0124] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0125] The various embodiments in this specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referenced to each other.

[0126] Although the preferred embodiments of the present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the embodiments of the present invention.

[0127] Finally, it should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or terminal device including the elements.

[0128] The technical solution provided by the present invention is introduced in detail above. Specific examples are used in this article to illustrate the principle and implementation mode of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. At the same time, for those skilled in the art, according to the idea of ​​the present invention, there will be changes in the specific implementation mode and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A method for adjusting working conditions, applied to electronic equipment, for adjusting working conditions of a fuel cell during endurance operation, characterized in that: The working condition adjustment method comprises the steps of: Collecting a plurality of operating parameters of the fuel cell, each of the operating parameters comprising a plurality of data in time sequence, and selecting at least one operating parameter from the plurality of operating parameters as a battery indicator of the fuel cell; Based on the correlation indexes between the remaining operating parameters and the battery indexes, selecting at least one of the operating parameters as the fuel cell operating condition to be adjusted; Divide the working conditions into M equal parts of data, and draw a data curve of the variation of the derivative statistic over time based on each part of the data, where M is an integer greater than 1; An optimal data curve is searched from a plurality of data curves according to a variation trend of the data curve, and the fuel cell is controlled based on curve parameters of the optimal data curve to complete the adjustment of the working conditions of the fuel cell.

2. The working condition adjustment method according to claim 1, characterized in that: The collecting of multiple operating parameters of the fuel cell and selecting at least one operating parameter from the multiple operating parameters as a battery indicator of the fuel cell comprises the steps of: Collecting a plurality of operating parameters of the fuel cell based on a test bench, wherein the plurality of operating parameters at least include an operating voltage of the fuel cell; Cleaning each of the working parameters to remove invalid data therein; By limiting the stack current range to screen data, multiple fuel cell performance indicators are obtained; The fuel cell performance index is selected as the battery index.

3. The working condition adjustment method according to claim 1, characterized in that: The step of selecting at least one of the operating parameters as the fuel cell operating condition to be adjusted based on the correlation between the remaining operating parameters and the battery index comprises the following steps: Calculating a correlation index between each of the battery indicators and the working condition; The battery indicator with the highest correlation index is selected as the fuel cell operating condition.

4. The working condition adjustment method according to claim 1, characterized in that: The method of dividing the working conditions into M data portions, and drawing a data curve of the variation of the derived statistics over time based on each data portion, comprises the steps of: Draw a distribution histogram of the working conditions; The working conditions are divided into M data blocks by a data block method based on a cumulative distribution function, and moving average filtering is performed on each of the data blocks; The data curve is drawn according to the data.

5. A working condition adjustment device, applied to electronic equipment, for adjusting the working conditions of a fuel cell after long-term operation, characterized in that: The working condition adjustment device comprises: A data acquisition module is configured to collect a plurality of operating parameters of the fuel cell, each of the operating parameters comprising a plurality of data in time sequence, and select at least one operating parameter from the plurality of operating parameters as a battery indicator of the fuel cell; A condition selection module, configured to select at least one of the operating parameters as a fuel cell operating condition to be adjusted based on correlation indicators between the remaining operating parameters and the battery indicators; A data partitioning module is configured to divide the working conditions into M equal parts of data, and draw a data curve of the variation of the derivative statistics over time based on each part of the data, where M is an integer greater than 1; The control implementation module is configured to search for an optimal data curve from a plurality of the data curves according to a change trend of the data curve, and control the fuel cell based on curve parameters of the optimal data curve to complete the adjustment of the working conditions of the fuel cell.

6. The working condition adjustment device according to claim 5, characterized in that: The data acquisition module comprises: A data acquisition unit, configured to acquire a plurality of operating parameters of the fuel cell based on a test bench, wherein the plurality of operating parameters at least include an operating voltage of the fuel cell; A data cleaning unit, configured to clean each of the working parameters to remove invalid data therein; A data screening unit is configured to perform data screening by limiting the current range of the fuel cell stack to obtain multiple fuel cell performance indicators; The indicator selection unit is configured to select the fuel cell performance indicator as the battery indicator.

7. The working condition adjustment device according to claim 5, characterized in that: The condition selection module comprises: A correlation calculation unit, configured to calculate a correlation index between each of the battery indicators and the working condition; The condition selection unit is configured to select the operating parameter with the highest correlation index as the fuel cell operating condition.

8. The working condition adjustment device according to claim 5, characterized in that: The data partition module comprises: A first drawing unit, configured to draw a distribution histogram of the working conditions; A data segmentation unit is configured to divide the working condition into M equal parts of data by a data segmentation method based on a cumulative distribution function, and perform a moving average filtering process; The second drawing unit is configured to draw the data curve according to the data.

9. An electronic device for adjusting the working conditions of a fuel cell after long-term operation, characterized in that: The electronic device comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs or instructions; The processor is used to execute the computer program or instruction so that the electronic device implements the working condition adjustment method as described in any one of claims 1 to 4.

10. A storage medium, applied to an electronic device, characterized in that: The storage medium carries one or more computer programs, and when the one or more computer programs are executed by the electronic device, the electronic device can implement the working condition adjustment method as described in any one of claims 1 to 4.

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