Method, device and server for determining energy attenuation of power battery

Abstract

This application relates to the field of battery management technology, specifically to a method, apparatus, and server for determining the energy decay of a power battery. The method includes: acquiring vehicle big data, which includes: mileage data and charging record data for each vehicle; and determining the average charging amount E of each vehicle during the initial time period of a statistical period based on the charging record data of each vehicle. s And the average charging amount E during the end period of the statistical period. e According to the average charging amount E of each vehicle s The average charging amount E e And the mileage ΔD of the vehicle during the statistical period, to determine the energy decay value e of each vehicle during the statistical period. d Based on the energy decay value e of each vehicle of the same model within the statistical period. d This invention determines the energy degradation index of the power battery for a given vehicle model. The solution provided in this embodiment offers a more accurate energy degradation index for the power battery.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery management, in particular to a method and device for determining energy attenuation of a power battery and a server. BACKGROUND

[0002] As a source of energy for new energy vehicles, a power battery mainly generates energy through chemical reactions. Influenced by factors such as frequency of use and activity of chemical substances, lithium-ion batteries in the power battery will gradually decrease, the internal resistance of the battery will gradually increase, the cycle life of the power battery will decrease, and the energy of the battery will begin to attenuate.

[0003] At present, the energy attenuation of a power battery after a period of use is mostly estimated by a battery management system in a vehicle, and the estimated value can be inaccurate and can also differ from the actual use of the vehicle by a user. Therefore, how to more accurately determine the energy attenuation of a power battery becomes a technical problem to be solved. SUMMARY

[0004] Therefore, the present application provides a method and device for determining energy attenuation of a power battery and a server, which provides more accurate energy attenuation indicators of a power battery through vehicle big data and statistics of energy attenuation of power batteries of multiple vehicles.

[0005] In a first aspect, an embodiment of the present application provides a method for determining energy attenuation of a power battery, the method comprising:

[0006] obtaining vehicle big data, the vehicle big data comprising: driving mileage data and charging record data of each vehicle;

[0007] determining, according to the charging record data of each vehicle, an average charging amount E s of each vehicle in a starting time period of a statistical period, and an average charging amount E e of each vehicle in an ending time period of the statistical period;

[0008] determining, according to the average charging amount E s , the average charging amount E e of each vehicle, and a driving mileage ΔD of each vehicle in the statistical period, an energy attenuation value e d of each vehicle in the statistical period;

[0009] determining, according to the energy attenuation value e d of each vehicle of the same vehicle model in the statistical period, a power battery energy attenuation indicator of the corresponding vehicle model.

[0010] In some embodiments, the starting time period is a time period with a first length after the start of the statistical period;

[0011] the end time period is a time period with a second length before the end of the statistical period;

[0012] the sum of the start time period and the end time period is less than the statistical period.

[0013] In some embodiments, the average charging amount E s is determined according to the charging record data of each vehicle in a start time period of the statistical period, comprising:

[0014] effective charging data of each vehicle in the start time period of the statistical period is determined according to the charging record data of each vehicle;

[0015] a charging amount of each effective charging of each vehicle in the start time period is determined according to the effective charging data of each vehicle;

[0016] the average charging amount E s of each vehicle is determined according to the charging amount of each effective charging of each vehicle in the start time period.

[0017] In some embodiments, the average charging amount E e of each vehicle in an end time period of the statistical period is determined according to the charging record data of each vehicle, comprising:

[0018] effective charging data of each vehicle in the end time period of the statistical period is determined according to the charging record data of each vehicle;

[0019] a charging amount of each effective charging of each vehicle in the end time period is determined according to the effective charging data of each vehicle;

[0020] the average charging amount E e of each vehicle is determined according to the charging amount of each effective charging of each vehicle in the end time period.

[0021] In some embodiments, the effective charging data in the charging record data satisfies the following conditions:

[0022] the charging gun is in a connected state;

[0023] the charging current is displayed as a charging state;

[0024] a state of charge (SOC) is charged from a first value to a second value, the second value being greater than the first value.

[0025] In some embodiments, the average charging amount E s , the average charging amount E eAnd the mileage ΔD of the vehicle during the statistical period, to determine the energy decay value e of each vehicle during the statistical period. d ,include:

[0026] According to the average charging amount E of each vehicle s The average charging amount E e And the vehicle's mileage ΔD within the statistical period, to determine the charging capacity attenuation value of each vehicle within a preset unit mileage;

[0027] Based on the ratio of the charge decay value of each vehicle within the preset unit mileage to the nominal energy of the power battery, the energy decay value e of each vehicle within the statistical period is determined. d .

[0028] In some embodiments, the energy decay value e of each vehicle of the same model within the statistical period is... d To determine the energy degradation index of the power battery for the corresponding vehicle model, including:

[0029] Based on the energy decay value e of each vehicle of the same model within the statistical period d The average value is used to determine the energy decay index of the power battery for the corresponding vehicle model.

[0030] In some implementations, the energy decay value e of each vehicle of the same model within the statistical period is used. d To determine the energy degradation index of the power battery for the corresponding vehicle model, including:

[0031] Several regions are defined, each corresponding to its own weighting coefficient;

[0032] Determine the energy decay value e for each vehicle of the same model within each region during the statistical period. d mean

[0033] The energy attenuation value e for each region is calculated using weighting factors. d mean By weighting the values, the energy degradation index of the power battery for the corresponding vehicle model can be obtained.

[0034] Secondly, embodiments of the present invention provide a device for determining the energy decay of a power battery, the device comprising:

[0035] The acquisition module is used to acquire vehicle big data, which includes: mileage data and charging record data of each vehicle.

[0036] The charging amount determination module is used to determine the average charging amount E of each vehicle during the initial time period of the statistical period based on the charging record data of each vehicle. sand the average charging amount E of the end time period of the statistical cycle e ;

[0037] an energy attenuation value determination module configured to determine an energy attenuation value e of each vehicle in the statistical cycle according to the average charging amount E of each vehicle s , the average charging amount E e , and the driving mileage AD of the vehicle in the statistical cycle d ;

[0038] an attenuation index determination module configured to determine an energy battery energy attenuation index of the corresponding vehicle type according to the energy attenuation value e of each vehicle of the same vehicle type in the statistical cycle d .

[0039] In a third aspect, an embodiment of the present application provides a server, comprising a memory for storing computer program instructions and a processor for executing the program instructions, wherein when the computer program instructions are executed by the processor, the server executes the method in the first aspect or any one of the first aspect.

[0040] In a fourth aspect, an embodiment of the present application provides a computer readable storage medium, comprising a stored program, wherein when the program runs, the computer readable storage medium controls the device where the computer readable storage medium is located to execute the method in the first aspect or any one of the first aspect.

[0041] The method, device and server for determining the energy attenuation of the power battery provided by the embodiments of the present application have at least the following beneficial effects:

[0042] In the embodiments of the present application, the energy battery energy attenuation index of the same vehicle type is determined by vehicle big data statistics, compared with the method of estimating the energy attenuation value of the power battery of a single vehicle, the index can better indicate the energy attenuation of the power battery of the vehicle of the same vehicle type after running for a period of time, and the smaller the index value is, the higher the durability of the power battery of the vehicle is. Compared with the way of calculating the battery attenuation value of a single vehicle, the attenuation index of the power battery calculated by the embodiments of the present application is more accurate and can better reflect the energy attenuation of the power battery. BRIEF DESCRIPTION OF DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0044] Figure 1A flow chart of a method for determining energy attenuation of a power battery according to an embodiment of the present application is provided.

[0045] Figure 2 A flow chart of another method for determining energy attenuation of a power battery according to an embodiment of the present application is provided.

[0046] Figure 3 A structural schematic diagram of a device for determining energy attenuation of a power battery according to an embodiment of the present application is provided.

[0047] Figure 4 A structural schematic diagram of a server according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0048] In order to better understand the technical solutions of the present application, the embodiments of the present application are described in detail below with reference to the accompanying drawings.

[0049] It should be clear that the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0050] The terms used in the embodiments of the present application are only for the purpose of describing the specific embodiments, and are not intended to limit the present application. The singular forms "a", "an" and "the" used in the embodiments of the present application and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0051] It should be understood that the term "and / or" used herein is only to describe the association relationship of the associated objects, which means that there can be three relationships, for example, A and / or B, which can represent the three cases of A alone, A and B together, and B alone. In addition, the character " / " in this paper generally represents that the front and rear associated objects are a "or" relationship.

[0052] After the electric vehicle is used for a period of time, the power battery of the vehicle will have a certain attenuation. In the related art, the energy attenuation value of the power battery of the vehicle itself is mainly estimated by the BMS of the vehicle itself. The estimated value of the energy attenuation value of the power battery itself by the BMS of the vehicle itself may not be accurate, and the energy attenuation value estimated by using a single data may not reflect the actual available electric energy of the power battery, and as the use time and the driving distance change, the estimated energy attenuation value of the power battery may also have certain differences from the actual use of the vehicle.

[0053] To solve the above technical problems, the embodiment of the present application provides a method for determining the energy attenuation of a power battery. The method does not use the way of single vehicle to determine the energy attenuation value of the power battery in the related art, but uses the vehicle big data to count the power battery energy attenuation index of a vehicle model. Compared with the method of single vehicle to estimate the power battery energy attenuation value, the power battery attenuation index of a vehicle model counted based on the vehicle big data can better reflect the energy attenuation of the battery. The smaller the index value is, the higher the durability of the power battery of the vehicle is.

[0054] Referring to Figure 1 A flow chart of a method for determining the energy attenuation of a power battery is provided in the embodiment of the present application. As shown in the figure Figure 1 The execution subject of the method is a server. The server is used to acquire vehicle big data and estimate the power battery energy attenuation index of the same vehicle model based on the vehicle big data. As shown in the figure Figure 1 The processing steps of the method include:

[0055] 101, acquiring vehicle big data, the vehicle big data including the driving mileage data and the charging record data of each vehicle. The driving mileage data may, for example, include the vehicle driving mileage of the vehicle at different times. The charging record data may, for example, include the charging sampling time, the power battery voltage, the power battery current, the state of charge (SOC) and the charging gun connection state signal, etc. Optionally, the server can acquire the vehicle big data of each vehicle through the vehicle terminal.

[0056] 102, determining the average charging amount E s of each vehicle at the beginning time period of the statistical period according to the charging record data of each vehicle, and determining the average charging amount E e of each vehicle at the end time period of the statistical period.

[0057] The statistical period can be set as needed. In some embodiments, the statistical period can be a period with a set starting point and ending point. For example, for a vehicle model, if the market time is XX year YY month, then XX year YY month is the starting time of the first period, XX year YY month + period length is the first period, the second period is the end of the first period + period length, and so on to obtain each statistical period. In some embodiments, the statistical period can be set according to the use time of the vehicle. For example, after a user purchases a vehicle of a certain model, the purchase time is set as the starting time of the first period, the purchase time + period length is the first period, the second period is the end of the first period + period length. In some embodiments, the period length may, for example, be three years, four years, five years or other lengths.

[0058] The start time period of the statistical period is a time period with a first length after the start of the statistical period, and the end time period of the statistical period is a time period with a second length before the end of the statistical period, and the sum of the start time period and the end time period is less than the statistical period.

[0059] In one example, the statistical period is the first 3 years of use of the vehicle, the start time period of the statistical period is the first month after the vehicle starts to be used, and if there is no charging record in the first month, the next month is counted. Optionally, the start time period of the statistical period can also be calculated in units of days, hours, etc. In the example, the end time period of the statistical period can be one month before the end of the statistical period, and if there is no charging record in the current month, the charging record of the previous month is counted again.

[0060] The average charging amount E s of each vehicle in the start time period of the statistical period is determined according to the charging record data of each vehicle. s .

[0061] Further, the average charging amount E e of each vehicle in the end time period of the statistical period is determined, including: determining the effective charging data of each vehicle in the end time period of the statistical period according to the charging record data of each vehicle; determining the charging amount of each effective charging of each vehicle in the end time period according to the effective charging data of each vehicle; and determining the average charging amount E e of each vehicle according to the charging amount of each effective charging in the end time period.

[0062] The effective charging data in the charging record data satisfies the following conditions: the charging gun is in a connected state, the charging current display is in an uncharged state, the SOC of the power battery is charged from a first value to a second value, and the second value is greater than the first value.

[0063] 103, according to the average charging amount E s , the average charging amount E e and the driving distance ΔD of the vehicle in the statistical period, the energy attenuation value e d of each vehicle in the statistical period is determined.

[0064] For a single vehicle, the average charging amount E s , the average charging amount E eand the driving mileage of the vehicle in the statistical period, determine the charge attenuation value of the vehicle in a preset unit mileage, and determine the energy attenuation value e of the vehicle in the statistical period according to the ratio of the charge attenuation value of the vehicle in the preset unit mileage to the nominal energy of the power battery d . For example, the charge attenuation value of the vehicle in a preset unit mileage can be the charge attenuation value of the vehicle per 10,000 kilometers.

[0065] 104, according to the energy attenuation value e of each vehicle of the same vehicle model in the statistical period d , determine the power battery energy attenuation index of the vehicle of the corresponding vehicle model.

[0066] According to the vehicle big data, the energy attenuation value e of each vehicle in the statistical period is calculated d . According to the e of each vehicle of the same vehicle model d , the energy attenuation index of the corresponding vehicle model can be determined, which can be used for each vehicle of the corresponding vehicle model.

[0067] In the embodiment of the application, the energy attenuation index of the power battery of the vehicle of the same vehicle model is calculated through vehicle big data, which can better indicate the energy attenuation of the power battery of the vehicle of the same vehicle model after running for a period of time. The smaller the index value is, the higher the durability of the power battery of the vehicle is. Compared with the method of calculating the battery attenuation value of a single vehicle, the attenuation index of the power battery calculated by the embodiment of the application is more accurate and can better reflect the energy attenuation of the power battery.

[0068] Referring to Figure 2 , the flow chart of another method for determining the energy attenuation of the power battery provided by the embodiment of the application. As Figure 2 shown, the processing steps of the method include:

[0069] 201, obtaining vehicle big data of each vehicle of the same vehicle model, the vehicle big data including driving mileage data and charging record data of each vehicle.

[0070] 202, according to the charging record data of each vehicle, determine the effective charging data of each vehicle in the starting time period of the statistical period.

[0071] The effective charging data for determining a one-time effective charging behavior satisfies the following conditions: the charging gun is in a connected state, the charging current display is in a charging state, and the SOC of the power battery is charged from a first value to a second value, for example, the power battery is charged from below 30% to above 90% in one charging.

[0072] 203. Based on the effective charging data of each vehicle, determine the amount of charge for each vehicle during each effective charging period at the beginning of the statistical period.

[0073] Among them, it can be based on the formula Calculate the charge amount E of a single effective charge of the power battery.

[0074] U(t) represents the battery voltage at sampling time t, I(t) represents the battery input current at sampling time t, Δt represents the time difference between adjacent sampling times, and n represents the number of samplings during one effective charging cycle. In one example, the amount of charge a battery can receive during one effective charging cycle, from below 30% to above 90%, can be calculated using formula E.

[0075] 204. Based on the charging amount of each vehicle during each effective charging session within the statistical period, determine the average charging amount E for each vehicle. s .

[0076] For a single vehicle, the average charging amount E during the initial period of the statistical period is... s It can be calculated using the following formula:

[0077]

[0078] Among them, E st This refers to the amount of charge received during the *st*th effective charge, where *st* represents the number of effective charges within the period starting from the beginning of the statistical period. For example, *st* represents the number of effective charges received by the vehicle within one month after the start of the statistical period.

[0079] 205. Based on the charging record data of each vehicle, determine the valid charging data of each vehicle within the end time period of the statistical period. See step 203 for the specific determination method.

[0080] 206. Based on the effective charging data of each vehicle, determine the amount of charge for each vehicle during each effective charging period at the end of the statistical period.

[0081] The amount of charge for each vehicle during each effective charge can be calculated using the formula... Sure.

[0082] 207. Based on the charging amount of each vehicle during each effective charging session within the statistical period ending in the statistical period, determine the average charging amount E for each vehicle. e .

[0083] For a single vehicle, the average charging amount E at the end of the statistical period is... e It can be calculated using the following formula:

[0084]

[0085] wherein E et is the charging amount of the et-th valid charging, et is the number of valid charging in the time period ending at the statistical cycle. For example, et is the number of valid charging of the vehicle in the month before the end of the statistical cycle.

[0086] 208, determine the driving mileage AD of each vehicle in the statistical cycle.

[0087] AD = D et - D st ;

[0088] D st is the total mileage at the beginning of the statistical cycle, D et is the total mileage at the end of the statistical cycle, in km.

[0089] 209, according to the average charging amount E s , the average charging amount E e and the driving mileage AD of each vehicle in the statistical cycle, determine the energy attenuation value e d of each vehicle in the statistical cycle.

[0090] wherein the energy attenuation value e d of the power battery of a single vehicle can be calculated by the percentage of energy attenuation of the power battery per 10,000 km. Specifically, e d can be calculated according to the following formula, in %.

[0091]

[0092] E0 is the nominal energy of the power battery of the current vehicle model announced and publicized by the Ministry of Industry and Information Technology.

[0093] 210, according to the average of the energy attenuation value e d of each vehicle of the same vehicle model in the statistical cycle, determine the power battery energy attenuation index E

[0094] N is the total number of vehicles of the same vehicle model.

[0095] In some embodiments, according to the number of vehicles of the same vehicle model in different regions or the use environment climate and other factors, a weighting coefficient can be set for each region; then determine the average of the energy attenuation value e d of each vehicle of the same vehicle model in each region in the statistical cycle Finally, the weighting coefficient is used to weight the E of each region to obtain the power battery energy attenuation index E d of the corresponding vehicle model.

[0096] The weighting coefficients for each region satisfy the formula k1 + k2 + ... + k m =1,k m This represents the weighting coefficient for the m-th region.

[0097] For each region The energy degradation index E of the power battery for the corresponding vehicle model is obtained by weighting. d for:

[0098] k i The weighting coefficients for the i-th region are... The energy decay index of the power battery of a certain vehicle model in the i-th region.

[0099] This invention, based on vehicle big data and multi-vehicle analysis and statistics of power battery energy degradation, can comprehensively consider different regions and environments to obtain the power battery energy changes of a certain model within a certain usage time range, providing a more accurate battery energy degradation trend. This is beneficial for understanding and evaluating the stability of product lifespan, and helps companies better understand the actual durability of vehicles used by users, thus providing a reference for power battery lifespan development and design, and reliability and durability testing and verification.

[0100] Corresponding to the above method, this embodiment of the invention provides a device for determining the energy decay of a power battery. This device is used to deploy in a server, such as... Figure 3 As shown, the device includes:

[0101] The acquisition module 301 is used to acquire vehicle big data, which includes: mileage data and charging record data of each vehicle.

[0102] The charging amount determination module 302 is used to determine the average charging amount E of each vehicle during the initial time period of the statistical period based on the charging record data of each vehicle. s And the average charging amount E during the end period of the statistical period. e ;

[0103] The energy decay value determination module 303 is used to determine the energy decay value based on the average charging amount E of each vehicle. s The average charging amount E e And the mileage ΔD of the vehicle during the statistical period, to determine the energy decay value e of each vehicle during the statistical period. d ;

[0104] The attenuation index determination module 304 is used to determine the energy attenuation value e of each vehicle of the same model within the statistical period. d To determine the energy degradation index of the power battery for the corresponding vehicle model.

[0105] The power battery energy attenuation determination device of the embodiment of the present application can execute the power battery energy attenuation determination method of the embodiment shown above. The parts not described in detail in the embodiment of the present application can refer to the related description of the method embodiment. The execution process and technical effects of the technical solution can refer to the description in the method embodiment, which will not be described here.

[0106] Referring to Figure 4 A structural schematic diagram of a server is provided for the embodiment of the present application. As shown in Figure 4 The server 400 can include a processor 401, a memory 402 and a communication unit 403. These components communicate through one or more buses. Those skilled in the art can understand that the structure of the server shown in the figure does not constitute a limitation on the embodiments of the present application. It can be a bus structure, a star structure, or include more or fewer components, or combine certain components, or different component arrangements.

[0107] The communication unit 403 is configured to establish a communication channel, so that the server can communicate with other devices. It receives user data sent by other devices or sends user data to other devices.

[0108] The processor 401 is the control center of the server. It connects various parts of the server through various interfaces and lines, executes software programs, instructions and / or modules stored in the memory 402, and calls data stored in the memory, to perform various functions of the server and / or process data. The processor can be composed of integrated circuits (IC), such as a single packaged IC or multiple packaged ICs with the same or different functions connected together. For example, the processor 401 can include a central processing unit (CPU), a microcontroller unit (MCU), etc.

[0109] The memory 402 is configured to store the execution instructions of the processor 401. The memory 402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0110] When the execution instructions in the memory 402 are executed by the processor 401, the server 400 is enabled to perform the power battery energy attenuation determination method in the embodiments of the present application.

[0111] In specific implementation, the present application further provides a computer storage medium, wherein the computer storage medium can store a program, and the program can include some or all steps in the embodiments of the power battery energy attenuation determination method provided by the present application when the program is executed. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM) or a random access memory (RAM) and the like.

[0112] In specific implementation, the present application further provides a computer program product, wherein the computer program product contains executable instructions, and when the executable instructions are executed on a computer, the computer is enabled to perform some or all steps in the embodiments of the power battery energy attenuation determination method provided by the present application.

[0113] The embodiments of the present application further provide a non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores computer instructions, and the computer instructions enable the computer to perform the power battery energy attenuation determination method provided by the embodiments of the present application.

[0114] The non-transitory computer readable storage medium can adopt any combination of one or more computer readable mediums. The computer readable medium can be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium can be, for example but not limited to, an electrical, a magnetic, an optical, an electromagnetic, an infrared, or a semiconductor system, device or apparatus, or any combination of the above. More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM) or a flash memory, an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, the computer readable storage medium can be any tangible medium that contains or stores a program that can be used by or in connection with an instruction execution system, apparatus or device.

[0115] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including—but not limited to—electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0116] The program code contained on a computer-readable medium may be transmitted using any suitable medium, including—but not limited to—wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.

[0117] Those skilled in the art will clearly understand that the techniques in the embodiments of this application can be implemented using software plus necessary general-purpose hardware platforms. Based on this understanding, the technical solutions in the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments of this application or some parts of the embodiments.

[0118] The same or similar parts between the various embodiments in this specification can be referred to mutually. In particular, the device embodiments and terminal embodiments are basically similar to the method embodiments, so the description is relatively simple, and the relevant parts can be referred to the description in the method embodiments.

Claims

1. A method for determining the energy decay of a power cell, characterized in that The method comprises: obtaining vehicle big data, the vehicle big data comprising: driving mileage data and charging record data of each vehicle; determining an average charge amount E of each vehicle at a start time period of a statistical period based on the charge record data of each vehicle s , and an average charge amount E of each vehicle at an end time period of the statistical period e ; According to the average charge amount E of each vehicle s , the average charge amount E e , and the travel distance ΔD of the vehicle in the statistical period, the energy attenuation value e of each vehicle in the statistical period is determined d ; the energy attenuation values e of the vehicles of the same vehicle model in the statistical period d determining the power battery energy attenuation index of the corresponding vehicle model; determining an average charging amount E of each vehicle at a start time period of a statistical period based on the charging record data of each vehicle s comprising: determining valid charging data of each vehicle in a start time period of the statistical period according to the charging record data of each vehicle; determining charging amount of each valid charging of each vehicle in the start time period according to the valid charging data; determining the average charging amount E of each vehicle based on the charging amount of each valid charging of each vehicle in the start time period s ; determining an average charging amount E of each vehicle at an end time period of the statistical period based on the charging record data of each vehicle e comprising: determining valid charging data of each vehicle in an end time period of the statistical period according to the charging record data of each vehicle; determining charging amount of each valid charging of each vehicle in the end time period according to the valid charging data of each vehicle; determining the average charging amount E of each vehicle based on the charging amount of each valid charging of each vehicle in the end time period e ; The average charge amount E of each vehicle s , the average charge amount E e , and the travel distance ΔD of the vehicle in the statistical period, determine the energy attenuation value e of each vehicle in the statistical period d , comprising: According to the average charge amount E of each vehicle s , the average charge amount E e , and the driving distance ΔD of the vehicle in the statistical period, the charge amount decay value of each vehicle in a preset unit distance is determined; According to the ratio of the charge attenuation value of each vehicle in the preset unit mileage to the nominal energy of the power battery, the energy attenuation value e of each vehicle in the statistical period is determined d .

2. The method of claim 1, wherein: the start time period is a time period with a first length after the start of the statistical period; the end time period is a time period with a second length before the end of the statistical period; the sum of the start time period and the end time period is less than the statistical period.

3. The method of claim 1, wherein: the valid charging data in the charging record data satisfies the following conditions: the charging gun is in a connected state; the charging current is displayed as a charging state; the state of charge (SOC) is charged from a first value to a second value, and the second value is greater than the first value.

4. The method of claim 1, wherein: the energy attenuation values e of the vehicles of the same vehicle model in the statistical period d determining the power battery energy attenuation index of the corresponding vehicle model, comprising: a mean value of the energy attenuation values e of the vehicles of the same vehicle model within the statistical period d determines the power battery energy attenuation index of the corresponding vehicle model.

5. The method of claim 1, wherein: the energy attenuation values e of the vehicles of the same vehicle model in the statistical period d determining the power battery energy attenuation index of the corresponding vehicle model, comprising: a plurality of regions are determined, each region corresponding to a respective weighting coefficient; determining the average value of the energy decay values e of the vehicles of the same model in each zone in the statistical period d of the vehicles of the same model in each zone in the statistical period The energy attenuation values e of the respective regions are weighted using weighting coefficients d The mean value of the energy attenuation values e of the respective regions is determined The power battery energy attenuation index of the corresponding vehicle model is obtained by weighting.

6. A device for determining the energy decay of a power cell, characterized by The device comprises: an acquisition module for acquiring vehicle big data, the vehicle big data comprising: driving mileage data and charging record data of each vehicle; The charging amount determination module is configured to determine, according to the charging record data of each vehicle, an average charging amount E of each vehicle in a starting time period of a statistical period s , and an average charging amount E in an ending time period of the statistical period e . an energy attenuation value determination module configured to determine an energy attenuation value e of each vehicle in the statistical period based on the average charge amount E of each vehicle s , the average charge amount E e , and a driving distance ΔD of the vehicle in the statistical period d ; The attenuation index determination module is configured to determine the power battery energy attenuation index of the corresponding vehicle model according to the energy attenuation values e of each vehicle of the same vehicle model in the statistical period. d , determine the power battery energy attenuation index of the corresponding vehicle model; wherein the charging amount determination module is specifically configured to: determine valid charging data of each vehicle in a start time period of the statistical period according to the charging record data of each vehicle; determine charging amount of each valid charging of each vehicle in the start time period according to the valid charging data; determining the average charging amount E of each vehicle based on the charging amount of each valid charging of each vehicle in the start time period s ; determine valid charging data of each vehicle in an end time period of the statistical period according to the charging record data of each vehicle; determine charging amount of each valid charging of each vehicle in the end time period according to the valid charging data of each vehicle; determining the average charging amount E of each vehicle based on the charging amount of each valid charging of each vehicle in the end time period e ; The energy attenuation value determination module is specifically configured to: determining a charging amount decay value of each vehicle in a preset unit distance based on the average charging amount E of each vehicle s , the average charging amount E e , and the driving distance ΔD of the vehicle in the statistical period According to the ratio of the charge attenuation value of each vehicle in the preset unit mileage to the nominal energy of the power battery, the energy attenuation value e of each vehicle in the statistical period is determined d .

7. A server, characterized by comprise: a memory for storing computer program instructions and a processor for executing program instructions, wherein when the computer program instructions are executed by the processor, the server executes the method of any one of claims 1 to 5.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium comprises a stored program, wherein the program controls the device where the computer readable storage medium is located to execute the method of any one of claims 1 to 5 when the program is running.

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