Method and system for analyzing hydrogen refueling action of fuel cell vehicle based on big data platform

Abstract

To provide a method and system for analyzing hydrogen refueling action of a fuel cell vehicle based on a big data platform.SOLUTION: By extracting a structural design parameter set of a hydrogen system of a fuel cell vehicle and a driving data set of the fuel cell vehicle in a preset time period and further analyzing the obtained hydrogen refueling action data, the method and system calculate and obtain a first hydrogen refueling feature set and a second hydrogen refueling feature set of the fuel cell vehicle, and finally obtain hydrogen refueling action features corresponding to driving of the fuel cell vehicle in a preset time period. In the present disclosure, the method and system achieve analysis of hydrogen refueling features of a fuel cell vehicle based on resources of the big data platform, so that it can assess an economy and a technical level of the fuel cell vehicle when running on an actual road. The method and system have an important guiding significance for planning and layout of geographic locations of hydrogen stations and hydrogen refueling capability in an urban group, and are applicable to an analysis requirement of various fuel cell vehicle models with high coverage, simple operation, and low costs.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] This application relates to fuel cell technology, and particularly to a method and system for analyzing the hydrogen filling behavior of fuel cell vehicles based on a big data platform.

Background Art

[0002] Proton exchange membrane fuel cells have advantages such as high power density, high energy conversion efficiency, and zero emissions, and are considered as one of the clean power sources with broad application prospects in the future transportation field.

[0003] The fuels of conventional internal combustion engine vehicles are gasoline, diesel, natural gas, etc., while the fuel of fuel cell vehicles is hydrogen, and it is necessary to fill hydrogen in a timely manner to support the driving needs of the vehicle. On the other hand, the current construction of hydrogen stations is not highly popular, and the distance between hydrogen stations is relatively far, which affects the daily driving needs and commercial expansion of fuel cell vehicles. On the other hand, hydrogen filling characteristics such as the hydrogen filling mass and average hydrogen consumption of fuel cell vehicles also affect the user's usage cost. Therefore, combining with big data platform resources to realize the analysis of the vehicle's hydrogen filling characteristics can not only obtain the dependence of fuel cell vehicles on using the fuel cell system as the power source in the actual driving process, but also evaluate the maturity of its technology, and has important guiding significance for the geographical location of hydrogen stations, the planning and layout of hydrogen filling capabilities. This method can cover a large number of fuel cell vehicle samples, has a wide vehicle distribution area, and has a low analysis cost.

Summary of the Invention

Means for Solving the Problems

[0004] According to the first aspect of the present invention, the present invention claims the protection of a method for analyzing the hydrogen filling behavior of fuel cell vehicles based on a big data platform. Extracting a set of parameters for the structural design of the hydrogen system of a fuel cell vehicle and a set of driving data A1 of the fuel cell vehicle within a preset time interval from the big data platform of the fuel cell vehicle, Based on the set of driving data A1, extracting a set of hydrogen filling data A2 regarding the hydrogen filling behavior of the fuel cell vehicle, Based on the set of hydrogen filling data A2, identifying the occurrence of the hydrogen filling behavior of the fuel cell vehicle according to preset logical judgment conditions, and determining the data rows corresponding to before and after the hydrogen filling of the fuel cell vehicle, Based on the data rows corresponding to before and after the hydrogen filling of the fuel cell vehicle, obtaining the attribute change value after the hydrogen filling behavior of the fuel cell vehicle, and calculating the hydrogen filling mass of the fuel cell vehicle, Based on all the hydrogen filling behavior data in the set of hydrogen filling data A2, calculating the first hydrogen filling feature set of the fuel cell vehicle, Based on the hydrogen filling mass and the first hydrogen filling feature set of the fuel cell vehicle, obtaining a second hydrogen filling feature set corresponding to when all the hydrogen filling behaviors in the set of hydrogen filling data A2 occur, and creating the hydrogen filling behavior characteristics corresponding to when the fuel cell vehicle travels in a preset time interval, characterized by including the above.

[0005] According to a second aspect of the present invention, the present invention claims the protection of an analysis system for the hydrogen filling behavior of a fuel cell vehicle based on a big data platform, a memory for non-temporarily storing computer-readable instructions, a processor for historically recording the computer-readable instructions so as to implement an analysis method for the hydrogen filling behavior of a fuel cell vehicle based on the big data platform when executed, characterized by comprising the above.

[0006] The present invention relates to fuel cell technology, and specifically to a method and system for analyzing the hydrogen filling behavior of fuel cell vehicles based on a big data platform. By extracting a set of parameters for the structural design of the hydrogen system of a fuel cell vehicle and a set of driving data of the fuel cell vehicle within a preset time interval, and further calculating and obtaining a first hydrogen filling feature set and a second hydrogen filling feature set of the fuel cell vehicle based on the analyzed hydrogen filling behavior data, finally, the hydrogen filling behavior characteristics corresponding to when the fuel cell vehicle travels within the preset time interval are obtained. The present invention realizes the analysis of the hydrogen filling characteristics of fuel cell vehicles based on big data platform resources, can not only evaluate the economy and technical level of fuel cell vehicles during actual road driving, but also has important guiding significance for the geographical location of hydrogen stations, the planning and layout of hydrogen filling capabilities in urban agglomerations, has a high coverage rate, is easy to operate, has a low cost, and is applicable to the analysis requirements of various vehicle types of fuel cells.

Brief Description of the Drawings

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DETAILED DESCRIPTION OF THE INVENTION

[0008] According to the first embodiment of the present invention, referring to FIG. 1, the analysis method of the hydrogen filling behavior of a fuel cell vehicle based on the big data platform of the present invention includes: extracting a parameter set of the structural design of the hydrogen system of the fuel cell vehicle and a set of driving data A1 of the fuel cell vehicle within a preset time interval from the big data platform of the fuel cell vehicle; extracting a hydrogen filling data set A2 related to the hydrogen filling behavior of the fuel cell vehicle based on the driving data set A1; identifying the occurrence of the hydrogen filling behavior of the fuel cell vehicle according to a preset logical judgment condition based on the hydrogen filling data set A2, and determining the data rows corresponding to before and after hydrogen filling of the fuel cell vehicle; By means of data rows corresponding to before hydrogen filling of the fuel cell vehicle and after hydrogen filling of the fuel cell vehicle, obtaining the attribute change value after the hydrogen filling behavior of the fuel cell vehicle, and calculating the hydrogen filling mass of the fuel cell vehicle, Calculating a first hydrogen filling feature set of the fuel cell vehicle based on all hydrogen filling behavior data in the hydrogen filling data set A2, Based on the hydrogen filling mass and the first hydrogen filling feature set of the fuel cell vehicle, a second hydrogen filling feature set corresponding to when all hydrogen filling behaviors in the hydrogen filling data set A2 occur is obtained, and making hydrogen filling behavior features corresponding to when the fuel cell vehicle travels in a preset time interval, Characterized by including.

[0009] Furthermore, the set of parameters for the hydrogen system structure design of the fuel cell vehicle includes at least the number n_tank of hydrogen storage gas bottles, the nominal water volume V_tank of the hydrogen storage gas bottles, and the nominal operating pressure of the hydrogen storage gas bottles.

[0010] Based on the big data platform of the fuel cell vehicle, obtaining the structure design parameters of the hydrogen system of a certain large truck, among which the number n_tank of hydrogen storage gas bottles is 9, the nominal water volume V_tank of the hydrogen storage gas bottles is 165L, and the nominal operating pressure of the hydrogen storage gas bottles is 35.0 MPa.

[0011] The hydrogen filling data set A2 of the hydrogen filling behavior of the fuel cell vehicle includes at least information transmission time, cumulative driving distance, the highest temperature of the hydrogen system, and the highest hydrogen pressure.

[0012] The first hydrogen filling feature collection of the fuel cell vehicle includes at least the hydrogen storage gas bottle pressure before hydrogen filling of the fuel cell vehicle, the hydrogen storage gas bottle pressure after hydrogen filling of the fuel cell vehicle, the hydrogen storage gas bottle temperature before hydrogen filling of the fuel cell vehicle, and the hydrogen storage gas bottle temperature after hydrogen filling of the fuel cell vehicle.

[0013] The hydrogen filling second feature set includes at least the interval driving distance of hydrogen filling, the interval time of hydrogen filling, the number of hydrogen filling times, the mass of hydrogen filling, and the average hydrogen consumption.

[0014] Furthermore, extracting the hydrogen filling data set A2 regarding the hydrogen filling behavior of the fuel cell vehicle based on the driving data set A1 includes extracting, as the hydrogen filling data set A2, the data regarding the hydrogen filling behavior of the fuel cell vehicle in the n rows of the driving data set A1 sorted in the order before and after the transmission time.

[0015] Based on the big data platform of the fuel cell vehicle, obtain the hydrogen filling data set A2 of a certain large truck in August and sort it into 56162 rows of data according to the order before and after the transmission time.

[0016] The data in the m-th row of the hydrogen filling data set A2 sets the information transmission time as Time_m, the cumulative driving distance as S_m, the highest temperature in the hydrogen system as Temp_m, and the highest hydrogen pressure as P_m. Here, the numerical values of Time_m, S_m, Temp_m, and P_m are all not empty sets and not zero.

[0017] The data in the 2565th row of the hydrogen filling data set A2, the information transmission time is 2022-08-26 01:48:40, the cumulative driving distance is 6269.6 km, the highest temperature in the hydrogen system is 38 °C, and the highest hydrogen pressure is 34.7 MPa.

[0018] The data in the (m - 1)-th row of the hydrogen filling data set A2 sets the information transmission time as Time_m - 1, the cumulative driving distance as S_m - 1, the highest temperature in the hydrogen system as Temp_m - 1, and the highest hydrogen pressure as P_m - 1.

[0019] The data in the 2564th row of the hydrogen filling data set A2, the information transmission time is 2022-08-25 15:50:07, the cumulative driving distance is an empty set, the highest temperature in the hydrogen system is an empty set, and the highest hydrogen pressure is an empty set.

[0020] Furthermore, based on the hydrogen filling data set A2, identifying the occurrence of the hydrogen filling behavior of a fuel cell vehicle according to preset logical judgment conditions and determining the data rows corresponding to before and after the hydrogen filling of the fuel cell vehicle, if the values of Time_m-1, S_m-1, Temp_m-1, and P_m-1 of the data in the (m - 1)-th row of the hydrogen filling data set A2 are all non-empty and non-zero, calculate from the data in the m-th row and the data in the (m - 1)-th row, if the values of Time_m-1, S_m-1, Temp_m-1, and P_m-1 of the data in the (m - 1)-th row of the hydrogen filling data set A2 are empty sets or zero, trace back to the previous valid data row according to the transmission time until a valid data row is found, denote this data row as the (m - a)-th row, where a belongs to the range [1, 2, ··· m - 1], and denote the information transmission time of the (m - a)-th row as Time_m-a, the cumulative driving distance as S_m-a, the maximum temperature in the hydrogen system as Temp_m-a, and the maximum hydrogen pressure as P_m-a, further includes.

[0021] The data in the 2563rd row of the hydrogen filling data set A2, where the information transmission time is 2022-08-25 15:49:57, the cumulative driving distance is 6269.6 km, the maximum temperature in the hydrogen system is 18 °C, and the maximum hydrogen pressure is 13.9 MPa.

[0022] If the data in the m-th row and the data in the (m - a)-th row of the hydrogen filling data set A2 satisfy the following logical judgment conditions, it is considered that the fuel cell vehicle generates a hydrogen filling behavior, otherwise it is considered that the fuel cell vehicle does not generate a hydrogen filling behavior. P_m > P_m-a + B

[0023] Here, B is the pressure change threshold value before and after preset hydrogen filling. The logical judgment condition is that when the pressure change range of the hydrogen storage cylinder of the fuel cell vehicle in adjacent data rows exceeds the pressure change threshold value before and after preset hydrogen filling, the hydrogen filling behavior of the fuel cell vehicle occurs. That is, the hydrogen filling behavior of the fuel cell vehicle causes an increase in the pressure of the hydrogen storage cylinder of the fuel cell vehicle.

[0024] For this large truck, the pressure change threshold value before and after preset hydrogen filling is set to 10 MPa.

[0025] The data in the 2565th row and the 2563rd row of the hydrogen filling data set A2 show that the pressure change value before and after hydrogen filling of the fuel cell vehicle is 20.8 MPa, that is, one hydrogen filling behavior has occurred in the fuel cell vehicle.

[0026] When the hydrogen filling behavior first occurs in the hydrogen filling data set A2, the number of hydrogen fillings is set to 1, and then, each time the hydrogen filling behavior occurs, 1 is added to the number of hydrogen fillings.

[0027] Furthermore, by the data rows corresponding to before hydrogen filling and after hydrogen filling of the fuel cell vehicle, obtaining the attribute change value after the hydrogen filling behavior of the fuel cell vehicle and calculating the hydrogen filling mass of the fuel cell vehicle, The pressure of the hydrogen storage gas cylinder before hydrogen filling of the fuel cell vehicle is denoted as P_before, and P_before = 13.9 MPa, The pressure of the hydrogen storage gas cylinder after hydrogen filling of the fuel cell vehicle is denoted as P_after, and P_after = 34.7 MPa, The temperature of the hydrogen storage gas cylinder before hydrogen filling of the fuel cell vehicle is denoted as Temp_before, and Temp_before = 18 °C, The temperature of the hydrogen storage gas cylinder after hydrogen filling of the fuel cell vehicle is denoted as Temp_after, and Temp_after = 38 °C, Based on the pressure and temperature of the hydrogen storage gas bottle before and after hydrogen filling of the fuel cell vehicle, calculate the hydrogen storage mass m_tank_after after hydrogen filling and the hydrogen storage mass m_tank_before before hydrogen filling of the fuel cell vehicle.

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[0028] Furthermore, based on the hydrogen filling mass and the first hydrogen filling feature set of the fuel cell vehicle, it is possible to obtain the second hydrogen filling feature set corresponding to all hydrogen filling behaviors occurring within the hydrogen filling data set A2, where the interval driving distance during hydrogen filling is equal to the difference in the cumulative driving distance of the fuel cell vehicle corresponding to when two adjacent hydrogen filling behaviors occur,

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[0029] When the above values are substituted and calculated, the average hydrogen consumption is 0.14 kg / km, that is, the average hydrogen consumption per 100 km is 14 kg / 100 km.

[0030] Furthermore, the present invention can create hydrogen filling behavior characteristics corresponding to when the fuel cell vehicle travels in a preset time interval.

[0031] In this embodiment, the interval driving distance distribution of hydrogen filling, the interval time distribution of hydrogen filling, the scatter distribution of interval driving distance - interval time of hydrogen filling, the mass distribution of hydrogen filling, and the distribution of average hydrogen consumption can be created, and the hydrogen filling characteristics corresponding to when the vehicle is traveling within the preset time interval can be obtained, providing a reference for the hydrogen filling rule analysis of the fuel cell vehicle, the geographical location of the urban integrated hydrogen station, the planning and layout of hydrogen filling capacity.

[0032] Referring to FIG. 2, the hydrogen filling behavior characteristics include at least the interval driving distance distribution of the vehicle's hydrogen filling. As can be seen from the figure, the interval driving distance of hydrogen filling and its ratio show a normal distribution as a whole. As the interval driving distance of hydrogen filling increases, the ratio gradually increases, and then, as the interval driving distance of hydrogen filling further increases, the ratio gradually decreases.

[0033] Referring to Fig. 3, the characteristics of the hydrogen filling behavior further include at least the interval time distribution of vehicle hydrogen filling. It can be seen that the ratios of the interval driving distances of hydrogen filling within 24 hours, 24 to 48 hours, and less than 48 to 72 hours are 28.4%, 45%, and 13.5% respectively, with a total of approximately 86.9%. In particular, the interval time during which 3.7% of the hydrogen filling behaviors occur exceeds 120h. This indicates that some vehicles may not be in a driving state on a certain day, or some vehicles may highly depend on the power battery system during driving, resulting in a longer interval time for hydrogen filling.

[0034] Referring to Fig. 4, the characteristics of the hydrogen filling behavior further include at least the interval distance-interval time distribution status of vehicle hydrogen filling. In the figure, the interval distances of hydrogen filling are mainly distributed within the range of 50km to 300km, the interval times of hydrogen filling mainly concentrate within the range of 0 to 96h, and the interval distances of hydrogen filling for extremely few vehicles with hydrogen filling behaviors are greater than 350km, or the interval times of hydrogen filling are greater than 192h.

[0035] Referring to Fig. 5, the characteristics of the hydrogen filling behavior include at least the probability distribution status of the interval driving distance-interval time of vehicle hydrogen filling. In the figure, the range where the hydrogen filling behavior is most intensive is that the interval driving distance of hydrogen filling is 150km to 200km and the interval time of hydrogen filling is 24h to 48h, and the occupied ratio reaches 18%. Next, the interval driving distance of hydrogen filling is 200km to 250km and the interval time of hydrogen filling is 24h to 48h, and the occupied ratio reaches 14%. Considering that the pure hydrogen driving distance of the vehicle is 340km, the area where the hydrogen filling behavior in the vehicle column is most intensive is that the interval driving distance of hydrogen filling is about 50% of the pure hydrogen driving distance, which has a certain relationship with the construction of local hydrogen stations and the layout of geographical locations.

[0036] Referring to Fig. 6, the characteristics of the hydrogen filling behavior include at least the pressure distribution of the hydrogen gas tank before and after hydrogen filling of the vehicle. As shown in the figure, the pressure distribution of the hydrogen gas tank before hydrogen filling is relatively wide, with a certain distribution between 3 MPa and 20 MPa. After hydrogen filling, the pressure of the hydrogen gas tank mainly concentrates between 25 MPa and 35 MPa, and there is a small scattered distribution in the remaining area. The pressure after hydrogen filling is most concentrated near 35 MPa, and the hydrogen filling behavior in this part is in a full-hydrogen state. There is also a certain distribution near 30 MPa.

[0037] Referring to Fig. 7, the characteristics of the hydrogen filling behavior further include at least the mass distribution of a single hydrogen filling of the vehicle. As can be seen from the figure, as the mass of a single hydrogen filling increases, the ratio shows a tendency of "first increasing and then decreasing". The mass of a single hydrogen filling mainly concentrates on 18 kg - 21 kg, 21 kg - 24 kg, and 24 kg - 27 kg, and the ratios are 21.6%, 27.2%, and 20.4% respectively. The ratio of the mass of a single hydrogen filling being less than 12 kg is only 4.4%.

[0038] Referring to Fig. 8, the characteristics of the hydrogen filling behavior also include at least the situation of the average hydrogen consumption distribution per 100 kilometers of the vehicle. As can be seen from the figure, the proportion of the average hydrogen consumption distribution in the range of 10 kg / 100 km - 12 kg / 100 km is the highest, reaching 35.1%, followed by the interval of 12 kg / 100 km - 14 kg / 100 km. When comprehensively comparing the hydrogen consumption of different vehicles, the hydrogen consumption is related to the differences in vehicle types, the load of transportation operations, environmental temperatures, and the differences in respective technologies.

[0039] According to the second embodiment of the present invention, referring to Fig. 9, the present invention claims the protection of an analysis system for the hydrogen filling behavior of a fuel cell vehicle based on a big data platform. A memory for non-temporarily storing computer-readable instructions, A processor for historically recording the computer-readable instructions so as to implement an analysis method for the hydrogen filling behavior of a fuel cell vehicle based on a big data platform when executed. It is characterized by comprising.

[0040] A person skilled in the art can understand that various changes and improvements can be made to the content disclosed in the present disclosure. For example, the various devices or components described above can be realized by hardware, or by software, firmware, or some or all combinations of the three.

[0041] The flowchart of the present disclosure is used to illustrate the steps of the method according to the embodiments of the present disclosure. It should be noted that the steps before and after are not necessarily exactly performed in order. On the contrary, various steps may be processed in reverse order or simultaneously. In addition, other operations can also be added to these processes.

[0042] A person skilled in the art will understand that all or part of the steps of the method described above may be completed by instructing the related hardware by a computer program, and the program may be stored in a computer-readable storage medium such as a read-only memory, a magnetic disk, or an optical disk. Alternatively, all or part of the steps of the above embodiments may be realized using one or more integrated circuits. Also, each module / unit in the above embodiments may be realized by hardware or by a software function module. The present disclosure is not limited to a specific form combination of hardware and software.

[0043] All terms used in this specification shall have the same meaning as commonly understood by those having ordinary knowledge in the technical field to which the present disclosure belongs, unless otherwise specified. Also, terms defined in a normal dictionary shall not be interpreted by applying an idealized or extremely formal meaning unless explicitly defined in this specification, but shall be interpreted as having a meaning consistent with the meaning in the context of the related art.

[0044] The above is the description of the present disclosure and is not limited thereto. Although several exemplary embodiments of the present disclosure have been described, those skilled in the art will readily understand that many changes can be made to the exemplary embodiments without departing from the novel teachings and advantages of the present disclosure. Therefore, such modifications are included within the scope of the invention as recited in the claims. The above is the description of the present invention and should not be considered limited to the specific embodiments disclosed. It should be understood that changes to the disclosed embodiments and other embodiments are included within the scope of the appended claims. The present disclosure is defined by the claims and their equivalents.

[0045] In the description of this specification, the description of terms such as "one embodiment", "several embodiments", "exemplary embodiment", "example", "specific example", or "several examples" means that the specific features, structures, materials, or characteristics described in relation to the embodiment or example are included in at least one embodiment or example of the present application. It should be noted that in this specification, the schematic expressions of the above terms do not necessarily indicate the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0046] The above description is merely a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included within the protection scope of the present application.

Claims

1. A method for analyzing hydrogen filling behavior of a fuel cell vehicle based on a big data platform, comprising: Extracting a parameter set for the structural design of the hydrogen system of the fuel cell vehicle and a running data set A1 of the fuel cell vehicle within a preset time interval from a big data platform of the fuel cell vehicle; Extracting a hydrogen filling data set A2 relating to a hydrogen filling behavior of the fuel cell vehicle based on the driving data set A1; According to the hydrogen filling data set A2, identify the occurrence of the hydrogen filling behavior of the fuel cell vehicle according to preset logical judgment conditions, and determine data rows corresponding to the hydrogen filling behavior of the fuel cell vehicle before hydrogen filling and the hydrogen filling behavior of the fuel cell vehicle after hydrogen filling; According to the data rows corresponding to the fuel cell vehicle before hydrogen filling and the fuel cell vehicle after hydrogen filling, an attribute change value after hydrogen filling behavior of the fuel cell vehicle is obtained, and a hydrogen filling mass of the fuel cell vehicle is calculated; Calculating a first hydrogen filling characteristic set of the fuel cell vehicle based on all hydrogen filling behavior data of the hydrogen filling data set A2; According to the hydrogen filling mass of the fuel cell vehicle and the first hydrogen filling feature set, a second hydrogen filling feature set corresponding to when all hydrogen filling behaviors in the hydrogen filling data set A2 occur is obtained, and a hydrogen filling behavior feature corresponding to when the fuel cell vehicle runs in a preset time interval is produced; Including, The set of parameters for the hydrogen system structure design of the fuel cell vehicle includes at least the number n_tank of hydrogen storage gas bottles, the nominal water volume V_tank of the hydrogen storage gas bottles, and the nominal operating pressure of the hydrogen storage gas bottles; The hydrogen filling data set A2 of the hydrogen filling behavior of the fuel cell vehicle includes at least an information transmission time, an accumulated mileage, a maximum temperature of the hydrogen system, and a maximum hydrogen pressure; The first characteristic collection of hydrogen filling of the fuel cell vehicle includes at least a pressure of the hydrogen storage gas bottle before hydrogen filling of the fuel cell vehicle, a pressure of the hydrogen storage gas bottle after hydrogen filling of the fuel cell vehicle, a temperature of the hydrogen storage gas bottle before hydrogen filling of the fuel cell vehicle, and a temperature of the hydrogen storage gas bottle after hydrogen filling of the fuel cell vehicle; The second set of hydrogen charging characteristics includes at least a hydrogen charging interval mileage, a hydrogen charging interval time, a hydrogen charging frequency, a hydrogen charging mass, and an average hydrogen consumption; A second feature set of hydrogen filling corresponding to all hydrogen filling behaviors in the hydrogen filling data set A2 is obtained based on the hydrogen filling mass of the fuel cell vehicle and the first feature set of hydrogen filling. The hydrogen filling interval mileage is equal to the difference between the cumulative mileage of the fuel cell vehicle corresponding to the occurrence of two adjacent hydrogen filling events. [0070] Here, H2_add_int_distance represents the hydrogen filling interval mileage, S_m' represents the cumulative mileage of the fuel cell vehicle corresponding to the occurrence of the next hydrogen filling behavior, and S_m represents the cumulative mileage of the fuel cell vehicle corresponding to the occurrence of the current hydrogen filling behavior, The average hydrogen consumption is equal to the mass of hydrogen consumed between the current hydrogen filling operation of the fuel cell vehicle and the next hydrogen filling operation divided by the distance traveled between hydrogen filling operations. [0080] Here, H2_add_int_time represents the interval time between hydrogen filling, Time_m' represents the information transmission time of the fuel cell vehicle corresponding to the occurrence of the next hydrogen filling behavior, and Time_m represents the information transmission time of the fuel cell vehicle corresponding to the occurrence of the current hydrogen filling behavior; The average hydrogen consumption is equal to the mass of hydrogen consumed between the current hydrogen filling operation of the fuel cell vehicle and the next hydrogen filling operation divided by the distance traveled between hydrogen filling operations. [0090] Here, H2_comp_rate represents the average hydrogen consumption, m_tank_mass_comp represents the mass of hydrogen consumed between the current hydrogen filling behavior and the next hydrogen filling behavior, and this value is obtained by subtracting the hydrogen storage mass of the fuel cell vehicle after hydrogen filling when the current hydrogen filling behavior occurs from the hydrogen storage mass of the fuel cell vehicle before hydrogen filling when the next hydrogen filling behavior occurs, and H2_add_int_distance represents the hydrogen filling interval driving distance between the current hydrogen filling behavior and the next hydrogen filling behavior, The method for analysis further comprises:

2. Extracting a hydrogen filling data set A2 relating to the hydrogen filling behavior of the fuel cell vehicle based on the travel data set A1 includes extracting data relating to the hydrogen filling behavior of the fuel cell vehicle from the n rows of the travel data set A1 sorted in order of before and after the transmission time as the hydrogen filling data set A2; The data in the m-th row in the hydrogen filling data set A2 is the information transmission time Time_m, the accumulated mileage S_m, the maximum temperature in the hydrogen system Temp_m, and the maximum hydrogen pressure P_m, where the values ​​of Time_m, S_m, Temp_m, and P_m are not an empty set and are not zero, The data in the (m-1)th row in the hydrogen filling data set A2 is: information transmission time is Time_m-1, cumulative mileage is S_m-1, maximum temperature in the hydrogen system is Temp_m-1, and maximum hydrogen pressure is P_m-1. The method for analyzing hydrogen filling behavior of a fuel cell vehicle based on a big data platform as claimed in claim 1.

3. Identifying the occurrence of hydrogen filling behavior of the fuel cell vehicle according to the preset logical judgment conditions based on the hydrogen filling data set A2, and determining the data rows corresponding to the before hydrogen filling of the fuel cell vehicle and the after hydrogen filling of the fuel cell vehicle; If the values ​​of Time_m-1, S_m-1, Temp_m-1, and P_m-1 of the data in the (m-1)th row of the hydrogen filling data set A2 are not an empty set and are not zero, calculate them from the data in the (m-1)th row and the data in the (m-1)th row; If the values ​​of Time_m-1, S_m-1, Temp_m-1, and P_m-1 of the data in the (m-1)th row of the hydrogen filling data set A2 are an empty set or zero, go back to the previous valid data rows according to the transmission time until a valid data row is found, and denote this data row as the (m-a)th row, where a belongs to the range [1, 2, ..., (m-1)], and denote the information transmission time of the (m-a)th row as Time_m-a, the accumulated mileage as S_m-a, the maximum temperature in the hydrogen system as Temp_m-a, and the maximum hydrogen pressure as P_m-a; Further comprising: If the data in the m-th row and the m-a-th row of the hydrogen filling data set A2 satisfy the following logical judgment condition, the fuel cell vehicle is deemed to have generated a hydrogen filling behavior, and if not, it is deemed to have not generated a hydrogen filling behavior. P_m>P_m-a+B where B is the preset pressure change threshold before and after hydrogen filling, and the logical judgment condition is that when the pressure change range of the hydrogen storage cylinder of the fuel cell vehicle in the adjacent data row exceeds the preset pressure change threshold before and after hydrogen filling, the hydrogen filling behavior of the fuel cell vehicle occurs; When a hydrogen filling behavior occurs for the first time in the hydrogen filling data set A2, the number of hydrogen fillings is set to 1, and thereafter, the number of hydrogen fillings is incremented by 1 each time a hydrogen filling behavior occurs. The method for analyzing hydrogen filling behavior of a fuel cell vehicle based on a big data platform as claimed in claim 2.

4. According to the data rows corresponding to the fuel cell vehicle before hydrogen filling and the fuel cell vehicle after hydrogen filling, the attribute change value after hydrogen filling behavior of the fuel cell vehicle is obtained, and the hydrogen filling mass of the fuel cell vehicle is calculated. The pressure of the hydrogen storage gas bottle of the fuel cell vehicle before hydrogen filling is expressed as P_before, and P_before=P_m-a; The pressure of the hydrogen storage gas bottle after filling the fuel cell vehicle with hydrogen is denoted as P_after, and P_after=P_m; The temperature of the hydrogen storage gas bottle before hydrogen filling of the fuel cell vehicle is expressed as Temp_before, and Temp_before=Temp_m-a; The temperature of the hydrogen storage gas bottle after hydrogen filling of the fuel cell vehicle is expressed as Temp_after, and Temp_after=Temp_m; calculating a hydrogen storage mass m_tank_after of the fuel cell vehicle after hydrogen filling and a hydrogen storage mass m_tank_before of the fuel cell vehicle before hydrogen filling based on the pressure and temperature of the hydrogen storage gas bottle before and after hydrogen filling of the fuel cell vehicle; [0010] where V_tank is the nominal water volume of the hydrogen storage gas bottle, n_tank is the number of hydrogen storage gas bottles, and M Ｈ２ is the molar mass of hydrogen, R is the ideal gas constant, ##EQU00011## The hydrogen filling mass of the fuel cell vehicle is calculated based on the hydrogen storage mass after hydrogen filling of the fuel cell vehicle and the hydrogen storage mass before hydrogen filling of the fuel cell vehicle; ##EQU00012## The method for analyzing hydrogen filling behavior of a fuel cell vehicle based on a big data platform as claimed in claim 3, further comprising:

5. A system for analyzing hydrogen filling behavior of a fuel cell vehicle based on a big data platform, comprising: a memory for non-transitory storage of computer readable instructions; A processor for historically recording the computer readable instructions, when executed, to implement the method for analyzing hydrogen filling behavior of a fuel cell vehicle based on a big data platform according to any one of claims 1 to 4; An analysis system comprising:

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