New energy automobile battery life cycle file generation method and device and storage medium

Abstract

The invention provides a new energy automobile battery life cycle file generation method and device and a storage medium, and the generation method comprises the following steps: S1, obtaining real-time monitoring data of a new energy automobile, and cleaning the real-time monitoring data; s2, the cleaned data is divided into a charging data set and a driving data set based on the charging state signal, the charging data set is a set of data in the charging process, and the driving data set is a set of data in the discharging process. And S3, constructing a plurality of data feature factors. And S4, respectively calculating each data characteristic factor based on the charging data set and the driving data set, and generating a battery life cycle file. The generated battery life cycle file can complete the functions of user charging / driving behavior analysis, fault diagnosis, battery state evaluation, equivalent all-electric mileage calculation and the like.

Description

Technical Field

[0001] The present invention relates to the field of new energy vehicle batteries, and in particular to a method, device and storage medium for generating a life cycle archive of a new energy vehicle battery. Background Art

[0002] By the end of June 2024, the number of new energy vehicles in my country has exceeded 440 million, and the Chinese market has become the world's largest new energy vehicle market. With the popularization of new energy vehicles, batteries, as their core components, have a direct impact on the vehicle's endurance and safety.

[0003] Traditional battery management methods are often limited to monitoring a single parameter, which makes it difficult to fully reflect the actual working status of the battery and the user's usage habits. In the traditional new energy vehicle big data workflow, data preprocessing and user behavior analysis are run independently in each big data application. This workflow has a long data acquisition cycle, and repeated data acquisition for different applications is even more time-consuming. During the analysis process, characteristic factors are repeatedly calculated, wasting computing resources. The result output may also result in inconsistent calculation results for the same characteristic factor in different applications due to slight differences in the algorithm.

[0004] Therefore, it is particularly important to develop an archival system that can comprehensively record and analyze information about the entire life cycle of batteries. Summary of the invention

[0005] The purpose of the present invention is to propose a method, device and storage medium for generating a new energy vehicle battery life cycle archive based on big data. By analyzing the vehicle RTM historical data, 37 characteristic factors are constructed from the three dimensions of charging, discharging and temperature to generate a battery life cycle archive. The generated battery life cycle archive covers the development needs of many big data applications, standardizes the calculation logic of data preprocessing and various characteristic factors, improves the calculation efficiency of various functional applications, and saves the storage space of big data.

[0006] In order to achieve the above object, the present invention proposes a method for generating a new energy vehicle battery life cycle archive, comprising the following steps:

[0007] S1. Obtaining real-time monitoring data of new energy vehicles and cleaning the real-time monitoring data;

[0008] S2. Based on the charging state signal, the cleaned data is divided into a charging data set and a driving data set, wherein the charging data set is a collection of data during the charging process, and the driving data set is a collection of data during the discharging process;

[0009] S3. Construct multiple data feature factors;

[0010] S4. Calculate each data characteristic factor based on the charging data set and the driving data set to generate a battery life cycle profile.

[0011] In one embodiment, the step S2 of dividing the cleaned data into a charging data set and a driving data set based on the charging state signal comprises the following steps:

[0012] S21. According to the charging status signal of the vehicle, the data is divided into data during the charging process and data during the discharging process;

[0013] S22. During the charging or discharging process, if the time interval between the uploading times of two adjacent frames of signals exceeds the preset time interval, the first frame of the two frames of signals is taken as the end of the previous charging or discharging process, and the second frame of the two frames of signals is taken as the beginning of the next charging or discharging process. The data of each charging process is put into a charging data set, and the data of each discharging process is put into a driving data set, so as to obtain several charging data sets and several driving data sets.

[0014] In one embodiment, S3 is to construct 37 data characteristic factors, and the 37 data characteristic factors include frame number, vehicle status, start time, end time, start SOC, end SOC, start battery pack voltage, end battery pack voltage, maximum battery pack voltage during the process, minimum battery pack voltage during the process, maximum cell voltage during the process, minimum cell voltage during the process, maximum cell voltage difference, start battery pack temperature, end battery pack temperature, average battery pack temperature, maximum cell temperature during the process, minimum cell temperature during the process, maximum cell temperature difference, average current, maximum current, duration, charge / consumption SOC, maximum battery power, average battery power, maximum motor power, average motor power, cumulative mileage at the start, cumulative mileage at the end, mileage, idling time, maximum vehicle speed, average speed, average running speed, energy consumption per 100 kilometers, speed distribution and charging mode.

[0015] In one embodiment, the temperature of the battery cell is obtained by a temperature probe, and the temperature of the battery pack is calculated by the temperature of the battery cell. The method for calculating the temperature of the battery pack includes:

[0016] Obtaining maximum and minimum values ​​of all temperature probes, and calculating an average value based on the maximum and minimum values;

[0017] Corrected the maximum and minimum values ​​of all temperature probes;

[0018] Calculate the weight coefficient based on the maximum and minimum values ​​of all temperature probes;

[0019] The battery pack temperature is calculated based on the weight coefficient, the maximum value, the minimum value and the average value.

[0020] In one embodiment, the method for correcting the minimum value of all temperature probes is:

[0021]

[0022] Among them, T min is the minimum value of all temperature probes, T avg is the average value of all temperature probes, T max is the maximum value of all temperature probes.

[0023] In one embodiment, the method for correcting the maximum value of all temperature probes is:

[0024]

[0025] Among them, T min is the minimum value of all temperature probes, T avg is the average value of all temperature probes, T max is the maximum value of all temperature probes.

[0026] In one embodiment, the calculation formula of the battery pack temperature is:

[0027]

[0028] Among them, T pack is the battery pack temperature, a, b and c are weight coefficients, T min is the minimum value of all temperature probes, T avg is the average value of all temperature probes, T max is the maximum value of all temperature probes.

[0029] In one embodiment, the weight coefficients include a, b and c, and the weight coefficient b is calculated as follows:

[0030]

[0031] The calculation method of the weight coefficient c is:

[0032]

[0033] The calculation method of the weight coefficient a is:

[0034]

[0035] Among them, T min is the minimum value of all temperature probes, T max is the maximum value of all temperature probes.

[0036] In order to achieve the above-mentioned object, the present invention also proposes a new energy vehicle battery life cycle archive generation device, which is used to implement the new energy vehicle battery life cycle archive generation method as described above, and the generation device includes:

[0037] Acquisition module, used to obtain real-time monitoring data of new energy vehicles;

[0038] A preprocessing module is used to clean the acquired real-time monitoring data and divide the cleaned data into a charging data set and a driving data set;

[0039] A building module is used to build multiple data feature factors;

[0040] The calculation module is used to calculate each data characteristic factor based on the charging data set and the driving data set to form a battery life cycle profile.

[0041] In order to achieve the above-mentioned purpose, the present invention also proposes a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for generating a new energy vehicle battery life cycle archive as described above is implemented.

[0042] The method, device and storage medium for generating a new energy vehicle battery life cycle archive of the present invention have the following beneficial effects:

[0043] 1. The establishment of a battery life cycle archive provides a rich and unified data source for the development of new energy vehicle big data. The 37 characteristic factors constructed by the battery life cycle archive can complete functions including user charging / driving behavior analysis, fault diagnosis, battery status evaluation, equivalent full-electric mileage calculation, etc., support accurate prediction of battery performance change trends and potential fault points, and efficiently provide battery usage data for new energy vehicle manufacturers, promoting continuous innovation and improvement of battery technology. The battery life cycle archive standardizes the data preprocessing and calculation process of each characteristic factor to ensure that the calculation results between different applications remain consistent. The present invention provides users with personalized battery life cycle archive reports, provides targeted maintenance suggestions, extends battery life, and reduces user usage costs. The battery life cycle archive is simple to calculate and has a short running time. Daily scheduled operation can greatly reduce the calculation time of big data applications, improve overall operating efficiency, and improve vehicle safety and user satisfaction.

[0044] 2. The present invention uses the battery life cycle archive to compile data into a more compact form, which greatly saves storage space and improves data storage efficiency. For example, a new energy vehicle generates about 90 million RTM data every day, which takes up a lot of memory space. The battery life cycle archive compiles the daily RTM data into 500,000 lines, saving 99% of the space.

[0045] 3. The present invention can comprehensively and efficiently process the RTM data of new energy vehicles, explore the intrinsic value of the data, and provide a data basis for accurately predicting battery performance change trends and potential failure points. With the continuous improvement of data collection frequency, feedback speed, statistical accuracy, and continuous breakthroughs in big data processing technology and server computing capabilities, the realization of digital twins and cloud computing will become possible. The battery life cycle archive can serve as the data basis for the battery digital twin model, which can accurately reflect the historical status of the physical battery and input the attenuation history of the user's battery into the battery digital twin model. This makes it possible to calculate the current status of the digital twin model battery and realize the cloud-based BMS. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 A schematic diagram of a process for generating a new energy vehicle battery life cycle archive according to an embodiment of the present invention;

[0047] Figure 2 Detailed information diagram of 37 data feature factors according to an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the invention and are not used to limit the invention.

[0049] The battery life cycle archive is a record of static and dynamic information of the entire life cycle of new energy vehicle battery packs. Static information includes basic information such as vehicle model, number of cells, rated capacity, etc. Dynamic information includes historical battery charging and discharging characteristics. Figure 1 As shown, the present invention proposes a method for generating a new energy vehicle battery life cycle archive, comprising the following steps:

[0050] S1. Acquire the real-time monitoring data of new energy vehicles (RTM data for short, i.e., data collected by the real-time monitoring system of new energy vehicles, Real Time Monitor) and clean the collected real-time monitoring data.

[0051] The big data of new energy vehicle operation required by the national standard GB / T 32960 covers two categories: static vehicle data and real-time data. Among them, static data includes a unique identification code (VIN, i.e., vehicle frame number). When transmitting vehicle data, VIN should be used, and its character code should comply with the provisions of GB 16735. RTM data is the real-time data part of the big data of new energy vehicle operation required by the national standard GB / T 32960, including: vehicle status, time, battery SOC, battery pack voltage and current, battery cell temperature, battery power, motor power, cumulative mileage, vehicle speed and charging mode, etc.

[0052] These data are described as follows:

[0053] Vehicle status: There are two states: charging and discharging.

[0054] Time: records the timestamp of data collection, which can be used to perform time series analysis on vehicle driving data.

[0055] Battery SOC (State of Charge): Indicates the battery's state of charge, that is, the percentage of remaining available energy in the battery.

[0056] Battery pack current and voltage: reflect the charge and discharge status and health of the battery.

[0057] Battery cell temperature: The battery cell temperature is measured by a temperature probe. The temperature probe is placed on a single battery cell. The temperature value of the temperature probe represents the temperature value of a single battery cell. The battery cell temperature can be converted to the battery pack temperature required later. Temperature affects both battery performance and safety.

[0058] Motor power: It is crucial to analyze the motor performance.

[0059] Cumulative mileage: Recording the total mileage of the vehicle is very important for predicting the remaining range.

[0060] Vehicle speed: records the actual driving speed of the vehicle, which is one of the important parameters affecting energy consumption and endurance.

[0061] Charging modes: DC charging, AC charging and not charging.

[0062] Specifically, S1 includes:

[0063] S10, obtaining RTM data;

[0064] S11, data cleaning is performed on the acquired RTM data. The data cleaning steps include filling missing data values, identifying and deleting outliers, and data format conversion. The details are as follows:

[0065] S111. Determine whether there are missing values. If so, fill in the missing values ​​and proceed to the next step. Otherwise, proceed directly to the next step. During the data collection process, some parameters may be missing due to sensor failure or other reasons. In the data cleaning stage, the missing values ​​need to be processed, and the missing data can be filled by interpolation and other methods. For example, when the data of a temperature probe is missing, it is filled with the average value of the adjacent temperature probes.

[0066] S112. Determine whether there are abnormal values. If so, delete the abnormal values ​​and proceed to the next step, otherwise proceed directly to the next step. During the data collection process, some abnormal data points may appear. These abnormal values ​​may be caused by sensor failure, data collection errors or other reasons. In the data cleaning stage, these abnormal values ​​need to be identified and eliminated to ensure the accuracy and reliability of the data. In addition to eliminating abnormal values, it is also necessary to analyze and process possible abnormal situations in the data, such as sudden data jumps, unreasonable values, etc., to ensure the consistency and rationality of the data. For example, when the mileage and voltage values ​​are negative and the mileage value is 999999.9, these values ​​need to be deleted.

[0067] S113. Data format conversion: Ensure that the data formats of different parameters are consistent, such as the format of time, the unit of value, etc., so that subsequent data analysis and modeling can proceed smoothly. For example, the upload time is converted from the unix timestamp format to the ISO 8601 time format.

[0068] S2. Based on the charging state signal, the cleaned data is divided into a charging data set and a driving data set, wherein the charging data set is a collection of data during the charging process, and the driving data set is a collection of data during the discharging process. The charging state signal indicates whether the vehicle is currently in a charging state or a discharging (driving) state. The data set division specifically includes the following process:

[0069] S21. According to the charging status signal of the vehicle, the data is divided into data during the charging process and data during the discharging process;

[0070] S22. During the charging or discharging process, if the time interval between the uploading times of two adjacent frames of signals exceeds the preset time interval, the previous frame of the two frames of signals is taken as the end of the previous charging or discharging process, and the next frame of the two frames of signals is taken as the beginning of the next charging or discharging process, and the data of each charging process is put into a charging data set, and the data of each discharging process is put into a driving data set, and a plurality of charging data sets and a plurality of driving data sets are obtained. In this embodiment, the preset time interval is preferably five minutes.

[0071] S3. Construct multiple data feature factors.

[0072] In this embodiment, based on the RTM upload data fields and function development requirements, 37 data feature factors are constructed to reflect the battery status changes and user behaviors of the vehicle. Figure 2 As shown, the 37 data characteristic factors are:

[0073] (1) The vehicle frame number, vehicle status and charging mode are the same as described above.

[0074] The maximum cell voltage difference indicates the maximum state reached by the difference between the maximum and minimum cell voltages at a certain moment during the charge / discharge process of the battery.

[0075] The maximum cell temperature difference refers to the maximum state reached at a certain moment by the difference between the maximum and minimum temperature values ​​measured by the temperature probe during the charge / discharge process of the battery.

[0076] Idle time refers to the time consumed when the speed is 0km / h during the charging / discharging process.

[0077] The speed distribution feature is a list that indicates the time proportion of the vehicle in different speed ranges.

[0078] (2) Start time, end time, start SOC, end SOC, start battery pack voltage, end battery pack voltage, start battery pack temperature, end battery pack temperature, cumulative mileage at start, and cumulative mileage at end indicate that only the data corresponding to the start and end times of charge / discharge need to be recorded.

[0079] The start time is the start time of the charge / discharge process.

[0080] The end time is the end time of the charge / discharge process.

[0081] Starting SOC: The battery state of charge (State of Charge) at the beginning of charging / discharging.

[0082] End SOC: The battery state of charge at the end of charging / discharging.

[0083] Starting battery pack voltage: The battery pack voltage at the start of charge / discharge.

[0084] End battery pack voltage: The battery pack voltage at the end of charge / discharge.

[0085] Start battery pack temperature: The battery pack temperature when charge / discharge starts.

[0086] End battery pack temperature: The battery pack temperature at the end of charge / discharge.

[0087] Cumulative mileage at start: The vehicle’s cumulative mileage at the start of charging / discharging.

[0088] Cumulative mileage at the end: The cumulative mileage of the vehicle at the end of charging / discharging.

[0089] (3) The maximum value of the battery pack voltage during the process, the minimum value of the battery pack voltage during the process, the maximum value of the battery cell voltage during the process, the minimum value of the battery cell voltage during the process, the average temperature of the battery pack, the maximum value of the battery cell temperature during the process, the minimum value of the battery cell temperature during the process, the average current, the maximum current, the maximum power of the battery, the average power of the battery, the maximum power of the motor, the average power of the motor, the maximum vehicle speed, and the average speed indicate that the maximum value, minimum value, and average value of the corresponding data during the entire charging / discharging process need to be calculated.

[0090] Among them, the maximum value of the battery pack voltage during the process: the maximum value of the battery pack voltage during the charge / discharge process.

[0091] Minimum battery pack voltage during the process: The minimum value of the battery pack voltage during the charge / discharge process.

[0092] Maximum cell voltage during the process: The maximum cell voltage during the charge / discharge process.

[0093] Minimum cell voltage during the process: the minimum cell voltage during the charge / discharge process.

[0094] Maximum cell temperature during the process: The maximum cell temperature during the charge / discharge process.

[0095] Minimum cell temperature during the process: the minimum cell temperature during the charge / discharge process.

[0096] Average battery pack temperature: The average temperature of the battery pack during the charge / discharge process.

[0097] Average current: The average current during the charge / discharge process.

[0098] Maximum current: The maximum current during the charge / discharge process.

[0099] Battery maximum power: The maximum power of the battery during the charge / discharge process.

[0100] Battery average power: The average power of the battery during the charge / discharge process.

[0101] Motor maximum power: The maximum power of the motor during charging / discharging.

[0102] Motor average power: The average power of the motor during the charging / discharging process.

[0103] Maximum vehicle speed: The maximum vehicle speed during charging / discharging.

[0104] Average speed: The average speed during the charge / discharge process (total distance traveled divided by total time).

[0105] (4) Duration, charging / consumption SOC, and mileage are expressed by subtracting the data corresponding to the start time from the data corresponding to the end time of charging / discharging.

[0106] The duration is the duration of the charge / discharge process, which can be calculated by subtracting the start time from the end time.

[0107] Charge / discharge SOC: The change in SOC during the charge / discharge process, which can be obtained by subtracting the start SOC from the end SOC.

[0108] Mileage: The mileage during the charging / discharging process can be obtained by subtracting the accumulated mileage at the beginning from the accumulated mileage at the end.

[0109] In addition, there are two data characterization factors:

[0110] The average operating speed is the average speed of the vehicle during its driving process.

[0111] The energy consumption per 100 kilometers is the amount of electricity consumed by the vehicle when it travels 100 kilometers. In this embodiment, the method for calculating the energy consumption per 100 kilometers includes:

[0112] Through the corresponding table of vehicle frame number and battery pack capacity (usually provided by the automobile manufacturer or battery supplier in the vehicle manual, battery specification table or related query platform), query and obtain the battery pack capacity Q nom .

[0113] The energy consumption Q per 100 kilometers is calculated by the following formula csp :

[0114]

[0115] Among them, SOC start Indicates the SOC at the start time (i.e., starting SOC), SOC end represents the SOC at the end of the time (i.e., the end SOC), V avg It represents the average voltage of the battery pack during the charging / discharging process (i.e., the average voltage of the battery pack during the charging / discharging process), and l represents the mileage.

[0116] Among all data characteristic factors, the battery pack temperature involved needs to be converted from the temperature value of the battery cell. In this embodiment, the temperature probe is placed on a single battery cell, and the temperature value of the temperature probe represents the temperature value of the single battery cell. The calculation method of the battery pack temperature includes:

[0117] Get the maximum and minimum values ​​of all temperature probes and calculate the average value based on the maximum and minimum values;

[0118] Corrected the maximum and minimum values ​​of all temperature probes;

[0119] Calculate the weight coefficient based on the maximum and minimum values ​​of all temperature probes;

[0120] The battery pack temperature value is calculated based on the weight coefficient, maximum value, minimum value and average value.

[0121] Among them, the method for correcting the minimum value of all temperature probes is:

[0122]

[0123] The method to correct the maximum value of all temperature probes is:

[0124]

[0125] The weight coefficients include a, b and c. The calculation method of weight coefficient b is:

[0126]

[0127] The calculation method of weight coefficient c is:

[0128]

[0129] The calculation method of weight coefficient a is:

[0130]

[0131] The calculation formula for the battery pack temperature is:

[0132]

[0133] Among them, a, b and c are weight coefficients, T pack is the battery pack temperature, T min is the minimum value of all temperature probes, T avg is the average value of all temperature probes, T max It is the maximum value of all temperature probes. All temperature references (including specific numerical values ​​representing the temperature) are in degrees Celsius.

[0134] S4. Calculate each data characteristic factor based on the charging data set and the driving data set to generate a battery life cycle profile.

[0135] In this embodiment, 37 characteristic factors need to be calculated for each charging data set and each driving data set to form a complete battery life cycle profile containing basic vehicle information, battery status information and user behavior patterns. Each data set generates a unique battery life cycle profile, which reflects the battery performance or status of the vehicle during charging and driving.

[0136] It is important to note that although each data set corresponds to a battery life cycle profile, these battery life cycle profiles are not isolated. Together, they form a comprehensive description of the vehicle battery performance. In practical applications, it may be necessary to combine these battery life cycle profiles for analysis to more accurately assess the health of the battery or predict its future performance.

[0137] The present invention also provides a new energy vehicle battery life cycle archive generation device, which is used to implement the new energy vehicle battery life cycle archive generation method as described above, and the generation device includes:

[0138] Acquisition module, used to obtain real-time monitoring data of new energy vehicles;

[0139] A preprocessing module is used to clean the acquired real-time monitoring data and divide the cleaned data into a charging data set and a driving data set;

[0140] A building module is used to build multiple data feature factors;

[0141] The calculation module is used to calculate each data characteristic factor based on the charging data set and the driving data set to form a battery life cycle profile.

[0142] The present invention also provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the method for generating a new energy vehicle battery life cycle archive as described above is implemented.

[0143] The method, device and storage medium for generating a new energy vehicle battery life cycle archive of the present invention have the following beneficial effects:

[0144] 1. The establishment of a battery life cycle archive provides a rich and unified data source for the development of new energy vehicle big data. The 37 characteristic factors constructed by the battery life cycle archive can complete functions including user charging / driving behavior analysis, fault diagnosis, battery status evaluation, equivalent full-electric mileage calculation, etc., support accurate prediction of battery performance change trends and potential fault points, and efficiently provide battery usage data for new energy vehicle manufacturers, promoting continuous innovation and improvement of battery technology. The battery life cycle archive standardizes the data preprocessing and calculation process of each characteristic factor to ensure that the calculation results between different applications remain consistent. The present invention provides users with personalized battery life cycle archive reports, provides targeted maintenance suggestions, extends battery life, and reduces user usage costs. The battery life cycle archive is simple to calculate and has a short running time. Daily scheduled operation can greatly reduce the calculation time of big data applications, improve overall operating efficiency, and improve vehicle safety and user satisfaction.

[0145] 2. The present invention uses the battery life cycle archive to compile data into a more compact form, which greatly saves storage space and improves data storage efficiency. For example, a new energy vehicle generates about 90 million RTM data every day, which takes up a lot of memory space. The battery life cycle archive compiles the daily RTM data into 500,000 lines, saving 99% of the space.

[0146] 3. The present invention can comprehensively and efficiently process the RTM data of new energy vehicles, explore the intrinsic value of the data, and provide a data basis for accurately predicting battery performance change trends and potential failure points. With the continuous improvement of data collection frequency, feedback speed, statistical accuracy, and continuous breakthroughs in big data processing technology and server computing capabilities, the realization of digital twins and cloud computing will become possible. The battery life cycle archive can serve as the data basis for the battery digital twin model, which can accurately reflect the historical status of the physical battery and input the attenuation history of the user's battery into the battery digital twin model. This makes it possible to calculate the current status of the digital twin model battery and realize the cloud-based BMS.

[0147] The above-described embodiments are only further explanations of the present invention, and are not intended to limit the present invention in other forms. The present invention may also have other various embodiments. Without departing from the spirit and essence of the present invention, those skilled in the art may make various corresponding modifications and changes according to the present invention, but these corresponding modifications and changes should all fall within the protection scope of the present invention.

Claims

1. A method for generating a new energy vehicle battery life cycle archive, characterized in that: The following steps are involved: S1. Obtaining real-time monitoring data of new energy vehicles and cleaning the real-time monitoring data; S2. Based on the charging state signal, the cleaned data is divided into a charging data set and a driving data set, wherein the charging data set is a collection of data during the charging process, and the driving data set is a collection of data during the discharging process; S3. Construct multiple data feature factors; S4. Calculate each data characteristic factor based on the charging data set and the driving data set to generate a battery life cycle profile.

2. The method for generating a new energy vehicle battery life cycle archive according to claim 1, characterized in that: The S2 divides the cleaned data into a charging data set and a driving data set based on the charging state signal, including the following steps: S21. According to the charging status signal of the vehicle, the data is divided into data during the charging process and data during the discharging process; S22. During the charging or discharging process, if the time interval between the uploading times of two adjacent frames of signals exceeds the preset time interval, the first frame of the two frames of signals is taken as the end of the previous charging or discharging process, and the second frame of the two frames of signals is taken as the beginning of the next charging or discharging process. The data of each charging process is put into a charging data set, and the data of each discharging process is put into a driving data set, so as to obtain several charging data sets and several driving data sets.

3. The method for generating a new energy vehicle battery life cycle archive according to claim 1, characterized in that: The S3 is to construct 37 data characteristic factors, and the 37 data characteristic factors include frame number, vehicle status, start time, end time, start SOC, end SOC, start battery pack voltage, end battery pack voltage, maximum battery pack voltage during the process, minimum battery pack voltage during the process, maximum cell voltage during the process, minimum cell voltage during the process, maximum cell voltage difference, start battery pack temperature, end battery pack temperature, average battery pack temperature, maximum cell temperature during the process, minimum cell temperature during the process, maximum cell temperature difference, average current, maximum current, duration, charge / consumption SOC, maximum battery power, average battery power, maximum motor power, average motor power, cumulative mileage at the start, cumulative mileage at the end, mileage, idling time, maximum vehicle speed, average speed, average running speed, energy consumption per 100 kilometers, speed distribution and charging mode.

4. The method for generating a new energy vehicle battery life cycle archive according to claim 3, characterized in that: The cell temperature is obtained by a temperature probe, and the battery pack temperature is calculated by the cell temperature. The calculation method of the battery pack temperature includes: Obtaining maximum and minimum values ​​of all temperature probes, and calculating an average value based on the maximum and minimum values; Corrected the maximum and minimum values ​​of all temperature probes; Calculate the weight coefficient based on the maximum and minimum values ​​of all temperature probes; The battery pack temperature is calculated based on the weight coefficient, the maximum value, the minimum value and the average value.

5. The method for generating a new energy vehicle battery life cycle archive according to claim 4, characterized in that: The method to correct the minimum value of all temperature probes is: Among them, T min is the minimum value of all temperature probes, T avg is the average value of all temperature probes, T max is the maximum value of all temperature probes.

6. The method for generating a new energy vehicle battery life cycle archive according to claim 4, characterized in that: The method to correct the maximum value of all temperature probes is: Among them, T min is the minimum value of all temperature probes, T avg is the average value of all temperature probes, T max is the maximum value of all temperature probes.

7. The method for generating a new energy vehicle battery life cycle archive according to claim 4, characterized in that: The calculation formula of the battery pack temperature is: Among them, T pack is the battery pack temperature, a, b and c are weight coefficients, T min is the minimum value of all temperature probes, T avg is the average value of all temperature probes, T max is the maximum value of all temperature probes.

8. The method for generating a new energy vehicle battery life cycle archive according to claim 4, characterized in that: The weight coefficients include a, b and c, and the weight coefficient b is calculated as follows: The calculation method of the weight coefficient c is: The calculation method of the weight coefficient a is: Among them, T min is the minimum value of all temperature probes, T max is the maximum value of all temperature probes.

9. A device for generating a life cycle archive of a new energy vehicle battery, used to implement the method for generating a life cycle archive of a new energy vehicle battery as claimed in any one of claims 1 to 8, characterized in that: The generating device comprises: Acquisition module, used to obtain real-time monitoring data of new energy vehicles; A preprocessing module is used to clean the acquired real-time monitoring data and divide the cleaned data into a charging data set and a driving data set; A building module is used to build multiple data feature factors; The calculation module is used to calculate each data characteristic factor based on the charging data set and the driving data set to form a battery life cycle profile.

10. A computer-readable storage medium, characterized in that: The storage medium stores a computer program, and when the computer program is executed by the processor, the method for generating a new energy vehicle battery life cycle archive as described in any one of claims 1 to 8 is implemented.

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