Sensitivity calculation method, device, electronic device and computer-readable storage medium

Abstract

The present application discloses a sensitivity calculation method, device, electronic device, and computer-readable storage medium for calculating the sensitivity of the battery performance of a fuel cell system to the operating conditions. Specifically, the method comprises the following steps: obtaining multiple operating conditions of the fuel cell system; dividing the value range of each operating condition into intervals to obtain multiple partitioned data; calculating the performance fluctuation index of the specified performance of the fuel cell system within each partitioned data of each operating condition to obtain the performance index fluctuation state of the current operating condition, wherein the performance index fluctuation state includes multiple performance fluctuation indexes corresponding to each partitioned data; and calculating the sensitivity of the battery performance to each operating condition based on the performance index fluctuation state. This solution can overcome the limitations of traditional simulation methods and can determine the sensitivity of a fuel cell system in operation to various operating conditions. Based on the sensitivity determination, real-time adaptation of the fuel cell operating conditions can also be achieved.

Description

Technical Field

[0001] The present application relates to the field of fuel cell technology, and more specifically, to a sensitivity calculation method, device, electronic device, and computer-readable storage medium. Background Art

[0002] Adapting fuel cell operating conditions is a key factor in determining system efficiency and durability. At the same design operating point, varying operating conditions will result in varying fuel cell output power. Therefore, by analyzing the correlation between operating conditions and fluctuations in fuel cell performance indicators, we can measure the extent to which these conditions influence fuel cell performance.

[0003] During actual operation, fuel cells in different hydrothermal states have varying sensitivities to various operating conditions, and this sensitivity can often be determined through simulation. However, simulation methods still have limitations for operating fuel cell systems. Specifically, they cannot determine the sensitivity of an operating fuel cell system to various operating conditions, making it impossible to adapt the fuel cell operating conditions in real time. Summary of the Invention

[0004] In view of this, the present application provides a sensitivity calculation method, device, electronic device and computer-readable storage medium for calculating the sensitivity of a fuel cell system in a working state to various working conditions, so that the working conditions of the fuel cell system can be adapted in real time according to the sensitivity.

[0005] In order to achieve the above objectives, the following solutions are proposed:

[0006] A sensitivity calculation method is applied to an electronic device for calculating the sensitivity of battery performance of a fuel cell system to operating conditions. The sensitivity calculation method comprises the following steps:

[0007] obtaining a plurality of operating conditions of the fuel cell system;

[0008] Dividing the value range of each working condition into intervals to obtain a plurality of partition data;

[0009] calculating a specified performance of the fuel cell system in each partition data of each operating condition to obtain a performance fluctuation index of the specified performance;

[0010] The sensitivity of the battery performance to each of the operating conditions is calculated according to the performance indicator fluctuation index.

[0011] Optionally, the multiple working conditions include air flow, hydrogen flow, coolant flow, air outlet pressure, air inlet pressure, hydrogen outlet pressure, hydrogen inlet pressure, coolant outlet pressure, coolant inlet pressure, air outlet temperature, air inlet temperature, air humidity, air dew point temperature, hydrogen outlet temperature, hydrogen inlet temperature, hydrogen humidity, hydrogen dew point temperature, coolant outlet temperature and coolant inlet temperature and some or all of the variables derived from the above working conditions.

[0012] Optionally, dividing the value range of each working condition into intervals to obtain a plurality of partition data includes the steps of:

[0013] Limiting the value range of the standardized working condition based on a preset confidence level to obtain a limited value range;

[0014] Dividing the limited value range into intervals based on a preset interval step size to obtain the multiple interval partitions;

[0015] Data is extracted from the value range based on each of the interval partitions to obtain a plurality of the interval data.

[0016] Optionally, dividing the value range of each working condition into intervals to obtain a plurality of partition data further includes the steps of:

[0017] Before data extraction, verification is performed according to a preset number of samples, and the interval partition is corrected according to the verification result.

[0018] Optionally, the following steps may also be included:

[0019] Selecting the working condition with the greatest sensitivity from the plurality of working conditions as the target working condition;

[0020] Calculating based on the multiple performance fluctuation indicators of the target working condition and the performance average of the specified performance to obtain a performance fluctuation fusion indicator for each interval partition of the target working condition;

[0021] Calculate the performance fluctuation fusion index to obtain a threshold judgment index;

[0022] Calculating the interval width of each of the interval partitions according to the threshold judgment index;

[0023] Select the widest interval width from all the interval widths as the target interval width;

[0024] An interval partition corresponding to the target interval width is selected as an optimal control parameter interval for the fuel cell system.

[0025] A sensitivity calculation device, applied to an electronic device, is used to calculate the sensitivity of battery performance of a fuel cell system to operating conditions. The sensitivity calculation device comprises:

[0026] a data acquisition module, configured to acquire a plurality of operating conditions of the fuel cell system;

[0027] An interval division module is configured to divide the value range of each working condition into intervals to obtain a plurality of partition data;

[0028] a first calculation module configured to calculate a specified performance of the fuel cell system in each partition data of each operating condition to obtain a performance fluctuation index of the specified performance;

[0029] The second calculation module is configured to calculate the sensitivity of the battery performance to each of the working conditions according to the fluctuation state of the performance indicator.

[0030] Optionally, the multiple working conditions include air flow, hydrogen flow, coolant flow, air outlet pressure, air inlet pressure, hydrogen outlet pressure, hydrogen inlet pressure, coolant outlet pressure, coolant inlet pressure, air outlet temperature, air inlet temperature, air humidity, air dew point temperature, hydrogen outlet temperature, hydrogen inlet temperature, hydrogen humidity, hydrogen dew point temperature, coolant outlet temperature and coolant inlet temperature and some or all of the variables derived from the above working conditions.

[0031] Optionally, the interval division module includes:

[0032] an interval limiting unit configured to limit the value range of the standardized working condition based on a preset confidence level to obtain a limited value range;

[0033] a partitioning execution unit, configured to perform interval partitioning on the limited value range based on a preset interval step size to obtain the plurality of interval partitions;

[0034] The data extraction unit is configured to extract data from a value range based on each of the interval partitions to obtain a plurality of the interval data.

[0035] Optionally, the interval division module further includes:

[0036] The interval correction unit is configured to perform verification according to a preset sample quantity before the data extraction unit performs data extraction, and perform correction processing on the interval partition according to the verification result.

[0037] Optionally, also include:

[0038] a target selection module configured to select the working condition with the greatest sensitivity from the plurality of working conditions as a target working condition;

[0039] a third calculation module configured to calculate, based on the plurality of performance fluctuation indicators of the target working condition and the performance average of the specified performance, to obtain a performance fluctuation fusion indicator for each interval partition of the target working condition;

[0040] a fourth calculation module, configured to perform calculations based on the performance fluctuation fusion index to obtain a threshold judgment index;

[0041] a fifth calculation module, configured to calculate the interval width of each of the interval partitions according to the threshold judgment indicator;

[0042] an interval selection module configured to select the widest interval width from all the interval widths as the target interval width;

[0043] The data selection module is configured to select an interval partition corresponding to the target interval width as an optimal control parameter interval for the fuel cell system.

[0044] An electronic device, applied to a fuel cell system, comprising at least one processor and a memory connected to the processor, wherein:

[0045] The memory is used to store computer programs or instructions;

[0046] The processor is configured to execute the computer program or instruction so as to enable the electronic device to implement the sensitivity calculation method as described above.

[0047] A computer-readable storage medium is applied to an electronic device, wherein the computer-readable storage medium carries one or more computer programs. When the electronic device executes the one or more computer programs, the electronic device can implement the sensitivity calculation method as described above.

[0048] As can be seen from the above technical solutions, this application discloses a sensitivity calculation method, device, electronic device, and computer-readable storage medium for calculating the sensitivity of battery performance to operating conditions of a fuel cell system. Specifically, the method comprises obtaining multiple operating conditions of the fuel cell system; dividing the value range of each operating condition into intervals to obtain multiple partitioned data; calculating the performance fluctuation index of the specified performance of the fuel cell system within each partitioned data of each operating condition to obtain the performance index fluctuation state of the current operating condition, wherein the performance index fluctuation state includes multiple performance fluctuation indexes corresponding to each partitioned data; and calculating the sensitivity of battery performance to each operating condition based on the performance index fluctuation state. This solution can overcome the limitations of traditional simulation methods and can determine the sensitivity of a fuel cell system in operation to various operating conditions.

[0049] In addition, based on the determined sensitivity, real-time adaptation of the fuel cell operating conditions can be achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. 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 any creative work.

[0051] Figure 1 A flowchart of a sensitivity calculation method according to an embodiment of the present application;

[0052] Figure 2 The performance fluctuation index of the specified working conditions in different value ranges in the embodiment of the present application;

[0053] Figure 3 A flowchart of another sensitivity calculation method according to an embodiment of the present application;

[0054] Figure 4 A schematic diagram of average voltages of various intervals under specified operating conditions of a fuel cell system according to an embodiment of the present application;

[0055] Figure 5 This is a block diagram of a sensitivity calculation device according to an embodiment of the present application;

[0056] Figure 6 A block diagram of another sensitivity calculation method according to an embodiment of the present application;

[0057] Figure 7 This is a block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0058] The following will be combined with the drawings in the embodiments of this application to clearly and completely describe the technical solutions in the embodiments of this application. Obviously, the embodiments described are only part of the embodiments of this application, not all of the embodiments. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of this application.

[0059] Example 1

[0060] Figure 1 This is a flowchart of a sensitivity calculation method according to an embodiment of the present application.

[0061] like Figure 1 As shown, the sensitivity calculation method provided in this embodiment is applied to an electronic device to calculate the sensitivity of battery performance to operating conditions of a fuel cell system. The fuel cell system in this embodiment operates dynamically between 30 and 330 A in a current control mode. The electronic device can be understood as a computer or server with information processing and data computing capabilities. The sensitivity calculation method includes the following steps:

[0062] S1. Acquire multiple operating conditions of the fuel cell system.

[0063] Generally, a fuel cell system can be tested on a test bench. Multiple operating conditions of the fuel cell system can be obtained through testing. These operating conditions include some or all of the following: air flow rate, hydrogen flow rate, coolant flow rate, air outlet pressure, air inlet pressure, hydrogen outlet pressure, hydrogen inlet pressure, coolant outlet pressure, coolant inlet pressure, air outlet temperature, air inlet temperature, air humidity, air dew point temperature, hydrogen outlet temperature, hydrogen inlet temperature, hydrogen humidity, hydrogen dew point temperature, coolant outlet temperature, and coolant inlet temperature. Derived variables may include: temperature difference = coolant outlet temperature - coolant inlet temperature, hydrogen chamber flow resistance = hydrogen outlet pressure - hydrogen inlet pressure, hydrogen stoichiometric ratio (the ratio of the actual hydrogen flow rate to the hydrogen flow rate required for the theoretical reaction current), etc.

[0064] In addition, the test bench can also obtain the performance indicators of the fuel cell system under different values ​​of the above operating conditions, such as operating voltage, operating current, and cell voltage. These performance indicators reflect the performance of the fuel cell system in the working state.

[0065] S2. Divide the value range of each working condition into intervals.

[0066] After determining the operating conditions, the range of values ​​for each operating condition is divided into intervals to obtain multiple interval partitions, and the partition data for each interval partition is obtained. In this embodiment, air flow is selected as the operating condition and operating current is selected as the performance indicator of the fuel cell system. Based on this, interval division is achieved according to the following steps.

[0067] First, the specified working conditions are standardized to obtain the standardized value range [F(-∈), F(∈)]. Subsequent interval division is only performed within [F(-∈), F(∈)]. The value of ∈ is related to the confidence level α, and the relationship between the two is as follows:

[0068]

[0069] Where f(x) is a Gaussian function And α∈[0,1].

[0070] Then, the standardized value range [F(-∈), F(∈)] and the set interval step size δ are combined to preliminarily divide the interval into multiple partitions.

[0071] According to the above definition, the initial range of each interval partition can be calculated as [F(-∈), F(-∈)+δ), [F(-∈)+δ, F(-∈)+2δ)......[F(-∈)+(n-1)δ, F(-∈)+nδ), where F(-∈)+nδ<F(∈), and n is a positive integer.

[0072] Then, to eliminate the influence of the number of samples on the variance, the range of the divided interval partition is corrected through the sample number verification. The total data volume is N, i is a positive integer, and the interval partition [F(-∈)+(i-1)δ, F(-∈)+iδ) is re-interval-delineated according to the following steps:

[0073] (1) Data extraction is performed based on the interval partitioning of [F(-∈)+(i-1)δ, F(-∈)+(i-1)δ+w) (w>0), and the extracted data set is S;

[0074] (2) When the number of data elements in set S is less than When , increase w by a step of δ / 100 and repeat step (1);

[0075] (3) When the number of data elements in set S is greater than or equal to When , we fix the value of w at this time and set [F(-∈)+(i-1)δ, F(-∈)+(i-1)δ+w) as the range of the interval partition for the final specified working condition. We then re-filter the data to obtain the set S as the partition data within the specified working condition and the specified interval partition. It is worth noting that revising the interval partition is an optional operation.

[0076] Finally, the partition data of the interval partitions delineated above is obtained to obtain the partition data within the range of each interval partition under the specified working conditions.

[0077] Taking the air stoichiometric ratio as an example, we first perform Z-score normalization on it. Setting the confidence level α to 0.7 and the specified step size to 0.02, we obtain the initial ranges of each interval partition as [0.15, 0.17), [0.17, 0.19), and [0.83, 0.85]. The range of the interval partition [0.15, 0.17) is redefined: when the amount of filtered data is greater than or equal to 5812, the corresponding interval partition range is updated to [0.150, 0.328), and the corresponding air stoichiometric ratio range is [2.424, 2.673).

[0078] S3. Calculate the fluctuation state of each performance indicator of the fuel cell system.

[0079] That is, the specified performance of the fuel cell system in each partition data of each working condition is calculated, thereby obtaining the performance fluctuation index of each specified performance.

[0080] Generally, the variance of the operating voltage V of the fuel cell system can be used as the fuel cell performance volatility index W:

[0081]

[0082] Where {V i} is the set of operating voltages of the fuel cell system within the specified working conditions and interval partitions, is the average value of the above set, N is the set {V i}. It should be noted that the above performance fluctuation indicators include but are not limited to fuel cell operating voltage, output power, and patrol voltage variance; and the quantification methods for the fluctuation bandwidth include but are not limited to performance indicator variance and performance indicator range.

[0083] The width of the performance index fluctuation band corresponding to the [0.150, 0.328) interval partition can be quantified by the variance of the operating voltage, specifically 166.513. Similarly, the fluctuation performance index of the specified working conditions in different value ranges can be obtained. The calculation results are as follows Figure 2 shown.

[0084] S4. Calculate the influence of each operating condition on the specified performance of the fuel cell.

[0085] The impact factor (IF) is defined as the magnitude of the change in the performance volatility index in the interval partition to be analyzed, that is, the range of the performance volatility index W:

[0086]

[0087] It should be noted that the range of change of the performance volatility indicators mentioned above includes but is not limited to the range and variance of the performance volatility indicators.

[0088] Subtracting the minimum operating voltage variance (81.732 @ [2.978, 3.000]) from the maximum operating voltage variance (184.446 @ [3.022, 3.677]) yields 102.714, representing the impact of the air stoichiometric ratio on the fuel cell system's performance under this operating condition. Following these steps, the impact of various operating conditions on the fuel cell system's performance can be determined.

[0089] As can be seen from the above technical solution, this embodiment provides a sensitivity calculation method for calculating the sensitivity of battery performance to operating conditions of a fuel cell system. Specifically, the method comprises the following steps: obtaining multiple operating conditions of the fuel cell system; dividing the value range of each operating condition into intervals to obtain multiple partitioned data; calculating a performance fluctuation index of a specified performance of the fuel cell system within each partitioned data of each operating condition to obtain a performance index fluctuation state for the current operating condition, wherein the performance index fluctuation state includes multiple performance fluctuation indexes corresponding to each partitioned data; and calculating the sensitivity of battery performance to each operating condition based on the performance index fluctuation state. This solution can overcome the limitations of traditional simulation methods and can determine the sensitivity of an operating fuel cell system to various operating conditions.

[0090] In addition, in a specific implementation of this embodiment, the following steps are also included: Figure 3 As shown:

[0091] S5. Select the working condition with the greatest sensitivity as the target working condition.

[0092] After obtaining the sensitivity of each operating condition to the fuel cell system's operating performance through the above calculations, that is, after obtaining multiple sensitivities, all sensitivities are sorted. Based on the sorting results, the operating condition corresponding to the greatest sensitivity is selected as the target operating condition for subsequent optimization. At the same time, the partition data for this target operating condition is obtained.

[0093] S6. Calculate the fusion index by considering the performance volatility and the performance average.

[0094] That is, based on multiple performance fluctuation indicators of the target working conditions and the performance average of the specified performance, a performance fluctuation fusion indicator of each interval partition of the target working conditions is obtained. Specifically, the calculation is achieved through the following scheme:

[0095] Calculate the average value of the working voltage As the performance average, denoted as U j . And calculate the performance volatility index W of each interval partition according to the above scheme j Then calculate the performance fluctuation fusion index considering the performance fluctuation and average level. The specific method is as follows:

[0096]

[0097] in, is the set of performance fluctuation indicators of each partition {W j}, is the average value of the working voltage of each partition {U j}, such as Figure 4 As shown, the number of partitions is M, j is a positive integer and j∈[0,M]. Through this scheme, the pool performance fluctuation fusion index of each interval partition is obtained. Figure 4 The horizontal axis is the range of working conditions (air stoichiometric ratio), and the vertical axis is the average voltage.

[0098] For example, for the partitioned data in the air ratio range [2.424, 2.673), the calculated volatility index (standard deviation) is 12.904 (mV), and the average voltage is 741.623 (mV). The calculated fusion index is 0.000378, as shown in the table below.

[0099]

[0100]

[0101] S7. Calculate a threshold judgment indicator based on the performance fluctuation fusion indicator.

[0102] Fusion index {WF j Then calculate the threshold judgment index.

[0103] Select the optimization degree α, N is the set {V i}, calculate the threshold αN; calculate the threshold judgment index based on the threshold as follows: when the partition fusion index ranking value is less than the threshold αN, the threshold judgment index is 1; otherwise, it is 0. The specific calculation method is as follows:

[0104]

[0105] For example, if the optimization degree is selected as 40%, that is, the threshold judgment index of the interval range of the top ten fusion indicators is set to 1, the threshold judgment index is (1,1,0,0,0,0,1,1,1,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0).

[0106] S8. Calculate the interval width of each interval partition based on the threshold judgment indicator.

[0107] The specific steps are as follows:

[0108] First, the corresponding interval partition [a j ,b j ] threshold judgment index B j :If B j =1, then record the lower limit a of the interval partition j , denoted as a lower,k If B j =0, then record the upper limit b of the previous interval partition j-1 , denoted as b upper,k .

[0109] Then, calculate the width of the working condition interval according to the following formula:

[0110] C k =b upper,k -a lower,k

[0111] Where k is a positive integer.

[0112] S9. Select the widest interval width from all interval widths as the target interval width.

[0113] For example, for the interval [2.602, 2.732), the threshold judgment index is continuously 1, and the control interval length is 0.13, which is the widest range compared to other operating condition intervals. Taking into account multiple factors such as average performance level, volatility, and robustness, the air metering ratio operating condition control interval of 2.667 ± 0.065 was selected as the target control interval.

[0114] S10. Select partition data corresponding to the target interval width as the optimal control parameter.

[0115] By selecting the optimal control parameter and adapting the optimal control parameter to the fuel cell system in real time, it is possible to ensure that the fuel cell system achieves optimal operation.

[0116] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operations of the systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than that marked in the accompanying drawings. For example, two 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 box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented with a dedicated hardware-based system that performs the specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0117] Although the 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.

[0118] 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.

[0119] 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, C++, Java, Smalltalk, 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 stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer.

[0120] Example 2

[0121] Figure 5 This is a block diagram of a sensitivity calculation device according to an embodiment of the present application.

[0122] like Figure 5As shown, the sensitivity calculation device provided in this embodiment is applied to an electronic device for calculating the sensitivity of a fuel cell system's battery performance to operating conditions. The fuel cell system in this embodiment operates dynamically between 30 and 330 A in a current control mode. The electronic device can be understood as a computer or server with information processing and data computing capabilities. The sensitivity calculation device includes a data acquisition module 10, an interval division module 20, a first calculation module 30, and a second calculation module 40.

[0123] The data acquisition module is used to obtain multiple operating conditions of the fuel cell system.

[0124] Fuel cell systems can generally be tested on a test bench, and multiple operating conditions of the fuel cell system can be obtained through testing. These multiple operating conditions include some or all of the following: air flow, hydrogen flow, coolant flow, air outlet pressure, air inlet pressure, hydrogen outlet pressure, hydrogen inlet pressure, coolant outlet pressure, coolant inlet pressure, air outlet temperature, air inlet temperature, air humidity, air dew point temperature, hydrogen outlet temperature, hydrogen inlet temperature, hydrogen humidity, hydrogen dew point temperature, coolant outlet temperature, and coolant inlet temperature. Derived variables may include: temperature difference = coolant outlet temperature - coolant inlet temperature, hydrogen chamber flow resistance = hydrogen outlet pressure - hydrogen inlet pressure, hydrogen stoichiometric ratio (the ratio of the actual hydrogen flow to the hydrogen flow required for the theoretical reaction current), etc.

[0125] In addition, the test bench can also obtain the performance indicators of the fuel cell system under different values ​​of the above operating conditions, such as operating voltage, operating current, and cell voltage. These performance indicators reflect the performance of the fuel cell system in the working state.

[0126] The interval division module is used to divide the value range of each working condition into intervals.

[0127] After determining the operating conditions, the range of values ​​for each operating condition is divided into intervals, resulting in multiple interval partitions, and partition data for each interval partition is obtained. In this embodiment, air flow is selected as the operating condition, and operating current is selected as the performance indicator of the fuel cell system. Based on this, the module specifically includes an interval definition unit, a partition execution unit, an interval correction unit, and a data extraction unit.

[0128] The interval limiting unit is used to standardize the specified working conditions to obtain the standardized value range [F(-∈), F(∈)]. Subsequent interval division is only performed within [F(-∈), F(∈)]. The value of ∈ is related to the confidence level α, and the relationship between the two is as follows:

[0129]

[0130] Where f(x) is a Gaussian function And α∈[0,1].

[0131] The partition execution unit is used to preliminarily divide the interval into multiple partitions based on the standardized value range [F(-∈), F(∈)] and the set interval step size δ.

[0132] According to the above definition, the initial range of each interval partition can be calculated as [F(-∈), F(-∈)+δ), [F(-∈)+δ, F(-∈)+2δ)......[F(-∈)+(n-1)δ, F(-∈)+nδ), where F(-∈)+nδ<F(∈), and n is a positive integer.

[0133] The interval correction unit is used to correct the range of the divided interval partitions by checking the number of samples to eliminate the influence of the number of samples on the variance. The total data volume is N, i is a positive integer, and the interval partition [F(-∈)+(i-1)δ, F(-∈)+iδ) is re-interval-delineated according to the following steps:

[0134] (1) Data extraction is performed based on the interval partitioning of [F(-∈)+(i-1)δ, F(-∈)+(i-1)δ+w) (w>0), and the extracted data set is S;

[0135] (2) When the number of data elements in set S is less than When , increase w by a step of δ / 100 and repeat step (1);

[0136] (3) When the number of data elements in set S is greater than or equal to When w is fixed at this time, [F(-∈)+(i-1)δ, F(-∈)+(i-1)δ+w) is used as the range of the interval partition for the final specified working condition. The data is re-screened to obtain the set S as the partition data within the specified working condition and the specified interval partition. It is worth noting that when an interval does not need to be corrected, the interval correction unit can be eliminated according to the specific situation.

[0137] The data extraction unit is used to acquire partition data for the interval partitions defined above, so as to obtain partition data within the range of each interval partition under specified working conditions.

[0138] Taking the air stoichiometric ratio as an example, we first perform Z-score normalization on it. Setting the confidence level α to 0.7 and the specified step size to 0.02, we obtain the initial ranges of each interval partition as [0.15, 0.17), [0.17, 0.19), and [0.83, 0.85]. The range of the interval partition [0.15, 0.17) is redefined: when the amount of filtered data is greater than or equal to 5812, the corresponding interval partition range is updated to [0.150, 0.328), and the corresponding air stoichiometric ratio range is [2.424, 2.673).

[0139] The first calculation module is used to calculate the performance indicator fluctuation state of each performance of the fuel cell system.

[0140] That is, the specified performance of the fuel cell system in each partition data of each working condition is calculated, thereby obtaining the performance fluctuation index of each specified performance.

[0141] Generally, the variance of the operating voltage V of the fuel cell system can be used as the fuel cell performance volatility index W:

[0142]

[0143] Where {V i} is the set of operating voltages of the fuel cell system within the specified working conditions and interval partitions, is the average value of the above set, NN is the set {V i}. It should be noted that the above performance fluctuation indicators include but are not limited to fuel cell operating voltage, output power, and patrol voltage variance; and the quantification methods for the fluctuation bandwidth include but are not limited to performance indicator variance and performance indicator range.

[0144] The width of the performance index fluctuation band corresponding to the [0.150, 0.328) interval partition can be quantified by the variance of the operating voltage, specifically 166.513. Similarly, the fluctuation performance index of the specified working conditions in different value ranges can be obtained. The calculation results are as follows Figure 2 shown.

[0145] The second calculation module is used to calculate the influence of each operating condition on the specified performance of the fuel cell.

[0146] The impact factor (IF) is defined as the magnitude of the change in the performance volatility index in the interval partition to be analyzed, that is, the range of the performance volatility index W:

[0147]

[0148] It should be noted that the range of change of the performance volatility indicators mentioned above includes but is not limited to the range and variance of the performance volatility indicators.

[0149] Subtracting the minimum operating voltage variance (81.732 @ [2.978, 3.000]) from the maximum operating voltage variance (184.446 @ [3.022, 3.677]) yields 102.714, representing the impact of the air stoichiometric ratio on the fuel cell system's performance under this operating condition. Following these steps, the impact of various operating conditions on the fuel cell system's performance can be determined.

[0150] As can be seen from the above technical solution, this embodiment provides a sensitivity calculation device for calculating the sensitivity of a fuel cell system's battery performance to operating conditions. Specifically, the device obtains multiple operating conditions for the fuel cell system; divides the value range of each operating condition into intervals to obtain multiple partitioned data; calculates a performance fluctuation index for a specified performance of the fuel cell system within each partitioned data for each operating condition to obtain a performance index fluctuation state for the current operating condition. The performance index fluctuation state includes multiple performance fluctuation indexes corresponding to each partitioned data; and calculates the sensitivity of the battery performance to each operating condition based on the performance index fluctuation state. This solution overcomes the limitations of the reverse method and can determine the sensitivity of an operating fuel cell system to various operating conditions.

[0151] In addition, if Figure 6 As shown, in a specific implementation of this embodiment, it also includes a target selection module 50, a third calculation module 60, a fourth calculation module 70, a fifth calculation module 80, an interval selection module 90 and a data selection module 100.

[0152] The target selection module is used to select the working condition with the greatest sensitivity as the target working condition.

[0153] After obtaining the sensitivity of each operating condition to the fuel cell system's operating performance through the above calculations, that is, after obtaining multiple sensitivities, all sensitivities are sorted. Based on the sorting results, the operating condition corresponding to the greatest sensitivity is selected as the target operating condition for subsequent optimization. At the same time, the partition data for this target operating condition is obtained.

[0154] The third calculation module is used to calculate the fusion index by considering the performance volatility and the performance average value.

[0155] That is, based on multiple performance fluctuation indicators of the target working conditions and the performance average of the specified performance, a performance fluctuation fusion indicator of each interval partition of the target working conditions is obtained. Specifically, the calculation is achieved through the following scheme:

[0156] Calculate the average value of the working voltage As the performance average, denoted as U j . And calculate the performance volatility index W of each interval partition according to the above scheme j Then calculate the performance fluctuation fusion index considering the performance fluctuation and average level. The specific method is as follows:

[0157]

[0158] in, is the set of performance fluctuation indicators of each partition {W j}, is the average value of the working voltage of each partition {U j}, such as Figure 4 As shown, the number of partitions is M, j is a positive integer and j∈[0,M]. Through this scheme, the pool performance fluctuation fusion index of each interval partition is obtained. Figure 4 The horizontal axis is the range of working conditions (air stoichiometric ratio), and the vertical axis is the average voltage.

[0159] For example, for the partitioned data in the air ratio range [2.424, 2.673), the calculated volatility index (standard deviation) is 12.904 (mV), and the average voltage is 741.623 (mV). The calculated fusion index is 0.000378, as shown in the table below.

[0160]

[0161]

[0162] The fourth calculation module is used to calculate the threshold judgment index based on the performance fluctuation fusion index. The calculation results are shown in the above table.

[0163] Fusion index {WF j Then calculate the threshold judgment index.

[0164] Select the optimization degree α, N is the set {V i}, calculate the threshold αN; calculate the threshold judgment index based on the threshold as follows: when the partition fusion index ranking value is less than the threshold αN, the threshold judgment index is 1; otherwise, it is 0. The specific calculation method is as follows:

[0165]

[0166] For example, if the optimization degree is selected as 40%, that is, the threshold judgment index of the interval range of the top ten fusion indicators is set to 1, the threshold judgment index is (1,1,0,0,0,0,1,1,1,1,0,0,1,1,0,0,0,1,0,0,0,0,0,0,0,0).

[0167] The fifth calculation module is used to calculate the interval width of each interval partition according to the threshold judgment index.

[0168] The specific plan is as follows:

[0169] First, the corresponding interval partition [a j ,b j ] threshold judgment index B j :If B j =1, then record the lower limit a of the interval partition j , denoted as a lower,k If B j =0, then record the upper limit b of the previous interval partition j-1 , denoted as b upper,k .

[0170] Then, calculate the width of the working condition interval according to the following formula:

[0171] C k =b upper,k -a lower,k

[0172] Where k is a positive integer.

[0173] The interval selection module is used to select the widest interval width from all interval widths as the target interval width.

[0174] For example, for the interval [2.602, 2.732), the threshold judgment index is continuously 1, and the control interval length is 0.13, which is the widest range compared to other operating condition intervals. Taking into account multiple factors such as average performance level, volatility, and robustness, the air metering ratio operating condition control interval of 2.667 ± 0.065 was selected as the target control interval.

[0175] The data selection module is used to select the partition data corresponding to the target interval width as the optimal control parameter interval.

[0176] By selecting the optimal control parameter and adapting the optimal control parameter to the fuel cell system in real time, it is possible to ensure that the fuel cell system operates in an optimal state.

[0177] The units involved in the embodiments described in this disclosure may be implemented in software or hardware. In some cases, the name of a unit does not limit the unit itself. For example, the first acquisition unit may also be described as a "unit for acquiring at least two Internet Protocol addresses."

[0178] The functions described above herein may be performed, at least in part, by one or more hardware logic components. For example, and 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 chip (SOCs), complex programmable logic devices (CPLDs), and the like.

[0179] Example 3

[0180] Figure 7 This is a block diagram of an electronic device according to an embodiment of the present application.

[0181] Reference below Figure 7 , which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The terminal devices in the embodiments of the present disclosure may include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), in-vehicle terminals (such as in-vehicle navigation terminals), and fixed terminals such as digital TVs and desktop computers. Figure 7 The electronic device shown is only an example and should not limit the functions and scope of use of the embodiments of the present disclosure.

[0182] like Figure 7 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from a storage device 606 into a random access memory (RAM) 603. Various programs and data required for the operation of the electronic device are also stored in the RAM 603. The processing device 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0183] Typically, the following devices may be connected to the I / O interface 605: an input device 606 including, for example, a touch screen, a touchpad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 607 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 606 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 609. The communication device 609 may allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although Figure 7 The electronic device is shown with various devices, but it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed instead.

[0184] Example 4

[0185] This embodiment provides a computer-readable storage medium carrying one or more programs, which, when executed by an electronic device, enables the electronic device to perform the sensitivity calculation method described in the first embodiment.

[0186] It should be noted that the computer-readable medium mentioned above in the present disclosure may be a computer-readable signal medium or a computer-readable storage medium, or any combination of the two. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or component, 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 that contains or stores a program that can be used by or in conjunction with an instruction execution system, device, or component. In the present disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transport 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 thereof.

[0187] 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.

[0188] 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 are aware of the basic creative concepts. 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.

[0189] Finally, it should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or terminal device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or terminal device that includes the element.

[0190] The technical solution provided by the present invention is introduced in detail above. Specific examples are used herein to illustrate the principles and implementation methods 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 may be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A sensitivity calculation method, applied to electronic equipment, for calculating the sensitivity of battery performance of a fuel cell system to operating conditions, characterized in that: The sensitivity calculation method comprises the steps of: obtaining a plurality of operating conditions of the fuel cell system; Dividing the value range of each working condition into intervals to obtain a plurality of partition data; calculating a specified performance of the fuel cell system in each partition data of each operating condition to obtain a performance fluctuation index of the specified performance; Calculating the sensitivity of the battery performance to each of the operating conditions according to the performance fluctuation index; Selecting the working condition with the greatest sensitivity from the plurality of working conditions as the target working condition; The performance fluctuation fusion index of each interval partition of the target working condition is obtained by calculating based on the following formula according to the multiple performance fluctuation indicators of the target working condition and the performance average of the specified performance, where the performance average is the ensemble average of the working voltage: in, A set of performance fluctuation indicators for each partition The average value of is the working voltage set of each partition { }, is the performance fluctuation fusion index; Calculate the performance fluctuation fusion index to obtain a threshold judgment index; Calculating the interval width of each of the interval partitions according to the threshold judgment index; Select the widest interval width from all the interval widths as the target interval width; An interval partition corresponding to the target interval width is selected as an optimal control parameter interval for the fuel cell system.

2. The sensitivity calculation method according to claim 1, wherein: The multiple working conditions include air flow, hydrogen flow, coolant flow, air outlet pressure, air inlet pressure, hydrogen outlet pressure, hydrogen inlet pressure, coolant outlet pressure, coolant inlet pressure, air outlet temperature, air inlet temperature, air humidity, air dew point temperature, hydrogen outlet temperature, hydrogen inlet temperature, hydrogen humidity, hydrogen dew point temperature, coolant outlet temperature and coolant inlet temperature and some or all of the variables derived from the above working conditions.

3. The sensitivity calculation method according to claim 1, wherein: The value range of each working condition is divided into intervals to obtain multiple partition data, including the steps of: Based on a preset confidence level, the value range of the standardized working condition is limited to obtain a limited value range; Dividing the limited value range into intervals based on a preset interval step size to obtain a plurality of interval partitions; Data is extracted from a value range based on each of the interval partitions to obtain a plurality of the partition data.

4. The sensitivity calculation method according to claim 3, wherein: The step of dividing the value range of each working condition into intervals to obtain a plurality of partition data further includes the following steps: Before data extraction, verification is performed according to a preset number of samples, and the interval partition is corrected according to the verification result.

5. A sensitivity calculation device, applied to electronic equipment, for calculating the sensitivity of battery performance of a fuel cell system to operating conditions, characterized in that: The sensitivity calculation device includes: a data acquisition module, configured to acquire a plurality of operating conditions of the fuel cell system; An interval division module is configured to divide the value range of each working condition into intervals to obtain a plurality of partition data; a first calculation module configured to calculate a specified performance of the fuel cell system in each partition data of each operating condition to obtain a performance fluctuation index of the specified performance; a second calculation module, configured to calculate the sensitivity of the battery performance to each of the operating conditions according to the performance fluctuation index; a target selection module configured to select the working condition with the greatest sensitivity from the plurality of working conditions as a target working condition; The third calculation module is configured to calculate, based on the following formula, the multiple performance fluctuation indicators of the target working condition and the performance average of the specified performance to obtain a performance fluctuation fusion indicator for each interval partition of the target working condition, where the performance average is an ensemble average of the working voltage: in, is the set of performance fluctuation indicators for each partition { }, is the working voltage set of each partition { }, is the performance fluctuation fusion index; a fourth calculation module, configured to perform calculations based on the performance fluctuation fusion index to obtain a threshold judgment index; a fifth calculation module, configured to calculate the interval width of each of the interval partitions according to the threshold judgment indicator; an interval selection module configured to select the widest interval width from all the interval widths as the target interval width; The data selection module is configured to select the interval partition corresponding to the target interval width as the optimal control parameter of the fuel cell system.

6. The sensitivity calculation device according to claim 5, wherein the multiple operating conditions include air flow rate, hydrogen flow rate, coolant flow rate, air outlet pressure, air inlet pressure, hydrogen outlet pressure, hydrogen inlet pressure, coolant outlet pressure, coolant inlet pressure, air outlet temperature, air inlet temperature, air humidity, air dew point temperature, hydrogen outlet temperature, hydrogen inlet temperature, hydrogen humidity, hydrogen dew point temperature, coolant outlet temperature and coolant inlet temperature, and some or all of the variables derived from the above operating conditions.

7. The sensitivity calculation device according to claim 5, wherein: The interval division module includes: an interval limiting unit configured to limit the range of values ​​of the working conditions after the standardization process based on a preset confidence level to obtain a limited value range; a partitioning execution unit configured to partition the limited value range into intervals based on a preset interval step size to obtain a plurality of interval partitions; The data extraction unit is configured to extract data from a value range based on each of the interval partitions to obtain a plurality of the partition data.

8. The sensitivity calculation device according to claim 5, wherein: The interval division module also includes: The interval correction unit is configured to perform verification according to a preset sample quantity before the data extraction unit performs data extraction, and perform correction processing on the interval partition according to the verification result.

9. An electronic device, applied to a fuel cell system, 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 configured to execute the computer program or instruction so that the electronic device implements the sensitivity calculation method according to any one of claims 1 to 4.

10. A computer-readable storage medium, applied to an electronic device, characterized in that: The computer-readable storage medium carries one or more computer programs. When the electronic device executes the one or more computer programs, it enables the electronic device to implement the sensitivity calculation method as described in any one of claims 1 to 4.

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