Fuel cell performance degradation analysis method based on big data and polarization curve fitting

Abstract

The present application relates to the field of fuel cell testing technology, and in particular to a fuel cell performance attenuation analysis method based on big data and polarization curve fitting. Combining the actual road driving data of fuel cell vehicles in the big data platform with the digital twin modeling method, the performance attenuation amplitude and attenuation rate of the fuel cell stack under different reference currents and different cumulative total operating times can be calculated, thereby accurately reflecting the performance changes of the fuel cell stack under actual road conditions; the present invention is based on the uploaded data of fuel cell vehicles in the big data platform and the polarization curve fitting method, and can obtain the performance attenuation of the fuel cell stack, without the need to carry out durability bench tests, which greatly saves the test cycle and labor costs. The present invention can realize the attenuation analysis of the output voltage and output power of the fuel cell stack at any operating current within the rated power range, with wide applicability, simple operation and high flexibility.

Description

Technical Field

[0001] The present application relates to the field of fuel cell testing technology, and in particular to a fuel cell performance attenuation analysis method based on big data and polarization curve fitting. Background Art

[0002] During actual road driving, fuel cell vehicles are subject to the combined influence of road vibration excitation, driving conditions such as start-stop / acceleration / deceleration, and environmental factors such as high cold / humid heat / air pollutants, which causes the performance of the fuel cell stack to decay as the cumulative operating time increases, affecting the service life of the engine and the service life of the vehicle. The durability performance evaluation of the fuel cell stack is usually carried out in the form of bench testing, which requires a long period of physical testing, requires large test resources, and has high test costs. According to the requirements of the national standard GB / T 32960, the data of the fuel cell vehicle and each component needs to be sent to the big data platform, covering parameters such as vehicle status, vehicle speed, cumulative mileage, fuel cell current, fuel cell voltage, maximum hydrogen pressure, and maximum hydrogen system temperature. Combined with the vehicle operation data in the big data platform, the performance decay analysis and life prediction of the fuel cell stack can be realized, which can greatly save test resources.

[0003] Invention patent CN117236082B proposes a method for performance degradation analysis and prediction based on actual vehicle driving data in a big data platform. This method can evaluate the attenuation amplitude of fuel cell current and power under specific operating conditions. Since the actual working current of a fuel cell vehicle during road driving usually cannot cover the entire rated working current range, such as the rated current of the engine is 600A, and the maximum working current during actual operation is 400A, there are no actual data points in the 401-600A current range, resulting in the above method being unable to perform engine performance degradation analysis in this range. Therefore, it is necessary to establish a more widely applicable degradation analysis algorithm that can meet the performance degradation calculation requirements of fuel cell engines in all rated current ranges. Summary of the invention

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] According to a first aspect of the present invention, the present invention claims a method for analyzing fuel cell performance attenuation based on big data and polarization curve fitting, comprising:

[0006] Acquire the operating data of the fuel cell to be analyzed of the experimental vehicle and the configuration parameters of the experimental vehicle in the big data platform, and arrange the operating data in time series to form an initial data set;

[0007] Preprocessing the initial data set to obtain a valid data set, and calculating the total cumulative operating time of the fuel cell to be analyzed in combination with the uploading frequency of the operating data;

[0008] According to the valid data set, a time span interval is set to sequentially acquire a first vehicle operation data set and a second vehicle operation data set of the experimental vehicle;

[0009] Based on the first vehicle operation data set and the second vehicle operation data set, respectively plotting a first polarization curve scatter plot and a second polarization curve scatter plot of the fuel cell to be analyzed, and obtaining a relationship function between the fuel cell voltage and the fuel cell current;

[0010] Based on the first polarization curve scatter plot and the second polarization curve scatter plot, a first interval performance fitting formula and a second interval performance fitting formula are obtained by fitting the relationship function between the fuel cell voltage and the fuel cell current;

[0011] Setting a reference current operating condition for the fuel cell attenuation analysis to be analyzed, substituting the first interval performance fitting formula and the second interval performance fitting formula into the first interval performance fitting formula, and respectively calculating a first interval reference voltage and a second interval reference voltage;

[0012] The performance decay amplitude and the performance decay rate of the total accumulated operation time of the fuel cell to be analyzed are calculated according to the first interval reference voltage and the second interval reference voltage.

[0013] Furthermore, the step of acquiring the operating data of the fuel cell to be analyzed of the experimental vehicle and the configuration parameters of the experimental vehicle in the big data platform, and arranging the operating data in time series to form an initial data set also includes:

[0014] The operating data of the fuel cell to be analyzed includes at least:

[0015] Information sending time, fuel cell current, fuel cell voltage;

[0016] The configuration parameters of the experimental vehicle include at least:

[0017] The number of single cells in the fuel cell stack, the effective reaction area of ​​single cells in the fuel cell stack, and the rated operating current of the fuel cell stack.

[0018] Further, the preprocessing of the initial data set to obtain a valid data set, and the calculation of the total cumulative operation time of the fuel cell to be analyzed in combination with the upload frequency of the operation data, further includes:

[0019] Preprocessing the initial data set, deleting default rows, rows with zero values, and rows with abnormal values, to obtain a valid data set;

[0020] The effective data set is described in a matrix form, with a total number of rows being M and a total number of columns being 3, wherein the first column is the information sending time, the second column is the fuel cell current, and the third column is the fuel cell voltage.

[0021] Furthermore, the step of setting a time span interval to sequentially acquire a first vehicle operation data set and a second vehicle operation data set of the experimental vehicle based on the valid data set also includes:

[0022] Based on the valid data set, the time span interval is extracted as follows: Hours of fuel cell vehicle operation data, 0- The time interval is recorded as a first time span interval, and the corresponding vehicle operation data set is recorded as a first vehicle operation data set;

[0023] Based on the above valid data set, the total cumulative operating time of the fuel cell stack is extracted to be the preset number of hours and the time span interval traced back is The corresponding time interval is recorded as the second time span interval, and the corresponding operation data set is recorded as the second vehicle operation data set.

[0024] Further, based on the first vehicle operation data set and the second vehicle operation data set, respectively drawing a first polarization curve scatter plot and a second polarization curve scatter plot of the fuel cell to be analyzed, and obtaining a relationship function between the fuel cell voltage and the fuel cell current, further includes:

[0025] Based on the first vehicle operation data set, a polarization curve scatter plot of the fuel cell stack is drawn with the fuel cell current as the abscissa and the fuel cell voltage as the ordinate, which is recorded as a first polarization curve scatter plot;

[0026] Based on the second vehicle operation data set, a polarization curve scatter plot of the fuel cell stack is plotted with the fuel cell current as the abscissa and the fuel cell voltage as the ordinate, which is recorded as a second polarization curve scatter plot;

[0027] Based on the Tafel equation, the fuel cell voltage of the fuel cell to be analyzed is expressed as a function of the fuel cell current.

[0028] Furthermore, the first interval performance fitting formula and the second interval performance fitting formula are obtained by fitting the relationship function between the fuel cell voltage and the fuel cell current based on the first polarization curve scatter diagram and the second polarization curve scatter diagram, and further include:

[0029] Based on the first polarization curve scatter plot, data fitting is performed using a relational function through a least squares method to obtain an interval performance fitting formula under a first time span interval, which is recorded as a first interval performance fitting formula;

[0030] Based on the second polarization curve scatter plot, data fitting is performed using a relationship function through the least squares method to obtain an interval performance fitting formula in a second time span interval, which is recorded as a second interval performance fitting formula.

[0031] The present application relates to the field of fuel cell testing technology, and in particular to a fuel cell performance attenuation analysis method based on big data and polarization curve fitting. Combining the actual road driving data of fuel cell vehicles in the big data platform with the digital twin modeling method, the performance attenuation amplitude and attenuation rate of the fuel cell stack under different reference currents and different cumulative total operating times can be calculated, thereby accurately reflecting the performance changes of the fuel cell stack under actual road conditions; the present invention is based on the uploaded data of fuel cell vehicles in the big data platform and the polarization curve fitting method, and can obtain the performance attenuation of the fuel cell stack, without the need to carry out durability bench tests, which greatly saves the test cycle and labor costs. The present invention can realize the attenuation analysis of the output voltage and output power of the fuel cell stack at any operating current within the rated power range, with wide applicability, simple operation and high flexibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 A flowchart of a fuel cell performance attenuation analysis method based on big data and polarization curve fitting as claimed in an embodiment of the present application;

[0033] Figure 2 A schematic diagram of polarization curve fitting for a fuel cell performance attenuation analysis method based on big data and polarization curve fitting as claimed in an embodiment of the present application. DETAILED DESCRIPTION

[0034] 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.

[0035] The terms "first", "second" and "third" in this application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of this application are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0036] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0037] According to the first embodiment of the present invention, the present invention claims a fuel cell performance attenuation analysis method based on big data and polarization curve fitting, referring to Figure 1 ,include:

[0038] Obtaining the operating data of the fuel cell to be analyzed of the experimental vehicle and the configuration parameters of the experimental vehicle in the big data platform, and arranging the operating data in time series to form an initial data set;

[0039] Preprocess the initial data set to obtain a valid data set, and calculate the total cumulative operating time of the fuel cell to be analyzed based on the upload frequency of the operating data;

[0040] According to the valid data set, a time span interval is set to sequentially obtain a first vehicle operation data set and a second vehicle operation data set of the experimental vehicle;

[0041] Based on the first vehicle operation data set and the second vehicle operation data set, respectively draw a first polarization curve scatter plot and a second polarization curve scatter plot of the fuel cell to be analyzed, and obtain a relationship function between the fuel cell voltage and the fuel cell current;

[0042] Based on the first polarization curve scatter diagram and the second polarization curve scatter diagram, a first interval performance fitting formula and a second interval performance fitting formula are obtained by fitting a relationship function between the fuel cell voltage and the fuel cell current;

[0043] Set the reference current working condition of the fuel cell attenuation analysis to be analyzed, substitute the first interval performance fitting formula and the second interval performance fitting formula, and calculate the first interval reference voltage and the second interval reference voltage respectively;

[0044] The performance attenuation amplitude and the performance attenuation rate of the total accumulated operation time of the fuel cell to be analyzed are calculated according to the first interval reference voltage and the second interval reference voltage.

[0045] Furthermore, the operating data of the fuel cell to be analyzed of the experimental vehicle and the configuration parameters of the experimental vehicle are obtained in the big data platform, and the operating data are arranged in time series to form an initial data set, which also includes:

[0046] The operating data of the fuel cell to be analyzed includes at least:

[0047] Information sending time, fuel cell current, fuel cell voltage;

[0048] The configuration parameters of the experimental vehicle include at least:

[0049] The number of single cells in the fuel cell stack, the effective reaction area of ​​single cells in the fuel cell stack, and the rated operating current of the fuel cell stack.

[0050] In this embodiment, the fuel cell vehicle operation data (including information sending time, fuel cell current, fuel cell voltage) from the first time the vehicle data is uploaded to the current moment in the big data platform is extracted to form an initial data set S_total_ini; the configuration parameters of the vehicle in the big data platform are extracted, including the number of single cells in the fuel cell stack , the effective reaction area of ​​a single cell in a fuel cell stack , fuel cell stack rated operating current .

[0051] Furthermore, the initial data set is preprocessed to obtain a valid data set, and the total cumulative operating time of the fuel cell to be analyzed is calculated in combination with the upload frequency of the operating data, which also includes:

[0052] Preprocess the initial data set, delete the default rows, rows with zero values, and rows with abnormal values, and obtain a valid data set;

[0053] The effective data set is described in a matrix form, with a total number of rows being M and a total number of columns being 3, wherein the first column is the information sending time, the second column is the fuel cell current, and the third column is the fuel cell voltage.

[0054] In this embodiment, the initial data set S_total_ini is preprocessed, and the default rows, rows with zero values ​​(either the fuel cell voltage or the fuel cell current is 0), and rows with abnormal values ​​(such as the fuel cell voltage>1000V, the fuel cell current>1000A, and the judgment threshold can be adjusted) are deleted to obtain the valid data set S_total (explained in matrix form, the total number of rows is M, the total number of columns is 3, wherein the first column is the information sending time, the second column is the fuel cell current, and the third column is the fuel cell voltage);

[0055] (1)

[0056] in , , , Respectively represent the information sending time of the 1st, 2nd, 3rd and Mth rows, , , , Represents the fuel cell current (A) of the 1st, 2nd, 3rd, and Mth rows respectively, , , , Represent the fuel cell voltages (V) of the 1st row, the 2nd row, the 3rd row, and the Mth row respectively.

[0057] According to the total number of rows M of the valid data set S_total and the upload frequency F of the vehicle data, the total cumulative operation time t_total of the fuel cell stack is calculated;

[0058] (2);

[0059] in is the total cumulative operating time of the fuel cell stack (h), M is the total number of rows in the valid data set S_total, and F is the upload frequency of vehicle data (Hz);

[0060] Further, according to the valid data set, a time span interval is set to sequentially obtain a first vehicle operation data set and a second vehicle operation data set of the experimental vehicle, further comprising:

[0061] Based on the valid data set, the time span interval is extracted as follows: Hours of fuel cell vehicle operation data, 0- The time interval is recorded as a first time span interval, and the corresponding vehicle operation data set is recorded as a first vehicle operation data set;

[0062] Based on the above valid data set, the total cumulative operating time of the fuel cell stack is extracted to be the preset number of hours and the time span interval traced back is The corresponding time interval is recorded as the second time span interval, and the corresponding operation data set is recorded as the second vehicle operation data set.

[0063] In this embodiment, based on the valid data set S_total, the time span interval is extracted as Hours of fuel cell vehicle operation data, 0- The time interval is recorded as the first time span interval, and the corresponding vehicle operation data set is recorded as S_total_sp1 (the first vehicle operation data set is all data from the 1st row to the sp1th row in S_total);

[0064] (3);

[0065] (4);

[0066] Based on the valid data set S_total, the total cumulative operating time of the fuel cell stack is extracted as Hour( No more than )The time span of the traceback is Hours of fuel cell vehicle operation data will be The time interval is recorded as the second time span interval, and its corresponding operation data set is recorded as S_total_sp2 (the second vehicle operation data set, i.e., all data from the sp2'th row to the sp2th row in S_total);

[0067] (5);

[0068] (6).

[0069] Further, based on the first vehicle operation data set and the second vehicle operation data set, a first polarization curve scatter plot and a second polarization curve scatter plot of the fuel cell to be analyzed are respectively plotted to obtain a relationship function between the fuel cell voltage and the fuel cell current, further comprising:

[0070] Based on the first vehicle operation data set, a polarization curve scatter plot of the fuel cell stack is plotted with the fuel cell current as the abscissa and the fuel cell voltage as the ordinate, which is recorded as a first polarization curve scatter plot;

[0071] Based on the second vehicle operation data set, a polarization curve scatter plot of the fuel cell stack is plotted with the fuel cell current as the abscissa and the fuel cell voltage as the ordinate, which is recorded as a second polarization curve scatter plot;

[0072] Based on the Tafel equation, the fuel cell voltage of the fuel cell to be analyzed is expressed as a function of the fuel cell current.

[0073] In this embodiment, based on the Tafel formula, the fuel cell voltage of the fuel cell stack can be expressed as a function of the fuel cell current:

[0074] (7)

[0075] in represents the fuel cell voltage of the fuel cell stack (V), Indicates the number of cells in the fuel cell stack, represents a constant, represents the activation loss voltage (V), represents the ohmic loss voltage (V), Indicates the concentration loss voltage (V), represents the fuel cell current (A) of the fuel cell stack, a, b, c, d represent constants, represents the natural logarithm operation symbol; since the concentration loss voltage is extremely small relative to the activation loss voltage and the ohmic loss voltage, the function of the fuel cell voltage and the fuel cell current can be further simplified as follows:

[0076] (8).

[0077] Furthermore, based on the first polarization curve scatter diagram and the second polarization curve scatter diagram, the first interval performance fitting formula and the second interval performance fitting formula are obtained by fitting the relationship function between the fuel cell voltage and the fuel cell current, and further comprising:

[0078] Based on the scatter plot of the first polarization curve, the least square method is used to fit the data with the relationship function to obtain the interval performance fitting formula under the first time span interval, which is recorded as the first interval performance fitting formula;

[0079] Based on the scatter plot of the second polarization curve, the data is fitted with a relationship function through the least squares method to obtain an interval performance fitting formula under the second time span interval, which is recorded as the second interval performance fitting formula.

[0080] In this embodiment, based on the scatter plot Scatter_sp1 of the polarization curve in the first interval, the least square method is used to fit the data using formula (8) to obtain the interval performance fitting formula fun_fit_sp1 (referred to as the first interval performance fitting formula) in the first time span interval.

[0081] (9)

[0082] in represents the fuel cell voltage of the fuel cell stack (V), represents the fuel cell current of the fuel cell stack (A), Indicates the number of cells in the fuel cell stack, represents the first interval fitting constant;

[0083] Based on the scatter plot Scatter_sp2 of the polarization curve in the second interval, the least square method is used to fit the data using formula (8) to obtain the interval performance fitting formula fun_fit_sp2 (referred to as the second interval performance fitting formula) in the second time span interval. (10).

[0084] in represents the fuel cell voltage of the fuel cell stack (V), represents the fuel cell current of the fuel cell stack (A), Indicates the number of cells in the fuel cell stack, represents the fitting constant of the second interval;

[0085] Set the reference current working condition for the fuel cell stack performance attenuation analysis, substitute the first interval performance fitting formula and the second interval performance fitting formula, and calculate the first interval reference voltage and the second interval reference voltage respectively;

[0086] (11)

[0087] in represents the first interval reference voltage (V), represents the second interval reference voltage (V), Indicates the reference current condition (A);

[0088] According to the first interval reference voltage and the second interval reference voltage, the total cumulative operation time of the fuel cell stack is calculated as follows: The performance degradation amplitude and performance degradation rate per hour;

[0089] (12)

[0090] (13)

[0091] in Indicates that the total cumulative operating time of the fuel cell stack is The performance degradation rate in hours, Indicates that the total cumulative operating time of the fuel cell stack is Average single chip voltage performance decay rate per hour (V / hour).

[0092] In the embodiment of the present invention, the specific implementation experiment examples are as follows:

[0093] Vehicle data: The number of cells in the fuel cell stack is 300, the effective reaction area of ​​a cell in the fuel cell stack is 250cm2, and the rated operating current of the fuel cell stack is 430A;

[0094] The first upload time of vehicle data: June 2022;

[0095] Current upload time: June 2024;

[0096] Vehicle data upload frequency: 0.1Hz.

[0097] Combined with the vehicle upload data, The values ​​are:

[0098] ;

[0099] The total number of rows M is 241519.

[0100] Combined with the vehicle data upload frequency of 0.1Hz, the total cumulative operating time of the fuel cell stack is calculated:

[0101] .

[0102] The time span interval is set to 20 hours, then the first time span interval is the total cumulative operation time of the fuel cell stack 0-20 hours, and its corresponding vehicle operation data set S_total_sp1 is:

[0103] ;

[0104] .

[0105] Will Assuming it is 500h, the second time span interval is the total cumulative operation time of the fuel cell stack 480-500h, and its corresponding vehicle operation data set S_total_sp2 is:

[0106] ;

[0107] .

[0108] Reference Figure 2 , based on the scatter plot of the polarization curve in the first interval Scatter_sp1, the performance fitting formula for the first interval is obtained:

[0109] ;

[0110] Based on the scatter plot of the polarization curve in the second interval Scatter_sp2, the performance fitting formula for the second interval is obtained:

[0111] ;

[0112] The reference current condition is set to 430A, and the reference voltage in the first interval is calculated to be 206.32V, and the reference voltage in the second interval is calculated to be 193.65V;

[0113] It is calculated that the performance degradation of the fuel cell stack with a total cumulative operating time of 500 hours is: ;

[0114] It is calculated that the average single-cell voltage performance decay rate of the fuel cell stack with a total cumulative operating time of 500 hours is: .

[0115] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0116] In addition, each functional unit in each embodiment of the present application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

[0117] The specific implementation methods of the invention are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A fuel cell performance attenuation analysis method based on big data and polarization curve fitting, characterized in that: include: Acquire the operating data of the fuel cell to be analyzed of the experimental vehicle and the configuration parameters of the experimental vehicle in the big data platform, and arrange the operating data in time series to form an initial data set; Preprocessing the initial data set to obtain a valid data set, and calculating the total cumulative operating time of the fuel cell to be analyzed in combination with the uploading frequency of the operating data; According to the valid data set, a time span interval is set to sequentially acquire a first vehicle operation data set and a second vehicle operation data set of the experimental vehicle; Based on the first vehicle operation data set and the second vehicle operation data set, respectively plotting a first polarization curve scatter plot and a second polarization curve scatter plot of the fuel cell to be analyzed, and obtaining a relationship function between the fuel cell voltage and the fuel cell current; Based on the first polarization curve scatter plot and the second polarization curve scatter plot, a first interval performance fitting formula and a second interval performance fitting formula are obtained by fitting the relationship function between the fuel cell voltage and the fuel cell current; Setting a reference current operating condition for the fuel cell attenuation analysis to be analyzed, substituting the first interval performance fitting formula and the second interval performance fitting formula into the first interval performance fitting formula, and respectively calculating a first interval reference voltage and a second interval reference voltage; The performance decay amplitude and the performance decay rate of the total accumulated operation time of the fuel cell to be analyzed are calculated according to the first interval reference voltage and the second interval reference voltage.

2. A fuel cell performance attenuation analysis method based on big data and polarization curve fitting as claimed in claim 1, characterized in that: The step of obtaining the operating data of the fuel cell to be analyzed of the experimental vehicle and the configuration parameters of the experimental vehicle in the big data platform, and arranging the operating data in time series to form an initial data set, further includes: The operating data of the fuel cell to be analyzed includes at least: Information sending time, fuel cell current, fuel cell voltage; The configuration parameters of the experimental vehicle include at least: The number of single cells in the fuel cell stack, the effective reaction area of ​​single cells in the fuel cell stack, and the rated operating current of the fuel cell stack.

3. A fuel cell performance attenuation analysis method based on big data and polarization curve fitting as claimed in claim 1, characterized in that: The preprocessing of the initial data set to obtain a valid data set, and the calculation of the total cumulative operation time of the fuel cell to be analyzed in combination with the upload frequency of the operation data, further includes: Preprocessing the initial data set, deleting default rows, rows with zero values, and rows with abnormal values, to obtain a valid data set; The effective data set is described in a matrix form, with a total number of rows being M and a total number of columns being 3, wherein the first column is the information sending time, the second column is the fuel cell current, and the third column is the fuel cell voltage.

4. A fuel cell performance attenuation analysis method based on big data and polarization curve fitting as claimed in claim 1, characterized in that: The step of setting a time span interval to sequentially acquire a first vehicle operation data set and a second vehicle operation data set of the experimental vehicle according to the valid data set also includes: Based on the valid data set, the time span interval is extracted as follows: Hours of fuel cell vehicle operation data, 0- The time interval is recorded as a first time span interval, and the corresponding vehicle operation data set is recorded as a first vehicle operation data set; Based on the above valid data set, the total cumulative operating time of the fuel cell stack is extracted to be the preset number of hours and the time span interval traced back is The corresponding time interval is recorded as the second time span interval, and the corresponding operation data set is recorded as the second vehicle operation data set.

5. The fuel cell performance attenuation analysis method based on big data and polarization curve fitting according to claim 1, characterized in that: The method of obtaining a first polarization curve scatter plot and a second polarization curve scatter plot of the fuel cell to be analyzed based on the first vehicle operation data set and the second vehicle operation data set, and obtaining a relationship function between the fuel cell voltage and the fuel cell current, further includes: Based on the first vehicle operation data set, a polarization curve scatter plot of the fuel cell stack is drawn with the fuel cell current as the abscissa and the fuel cell voltage as the ordinate, which is recorded as a first polarization curve scatter plot; Based on the second vehicle operation data set, a polarization curve scatter plot of the fuel cell stack is plotted with the fuel cell current as the abscissa and the fuel cell voltage as the ordinate, which is recorded as a second polarization curve scatter plot; Based on the Tafel equation, the fuel cell voltage of the fuel cell to be analyzed is expressed as a function of the fuel cell current.

6. A fuel cell performance attenuation analysis method based on big data and polarization curve fitting as claimed in claim 1, characterized in that: The method of obtaining a first interval performance fitting formula and a second interval performance fitting formula by fitting the relationship function between the fuel cell voltage and the fuel cell current based on the first polarization curve scatter diagram and the second polarization curve scatter diagram further includes: Based on the first polarization curve scatter plot, data fitting is performed using a relational function through a least squares method to obtain an interval performance fitting formula under a first time span interval, which is recorded as a first interval performance fitting formula; Based on the second polarization curve scatter plot, data fitting is performed using a relationship function through the least squares method to obtain an interval performance fitting formula in a second time span interval, which is recorded as a second interval performance fitting formula.

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