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

Abstract

The invention relates to the technical field of battery management, in particular to an energy attenuation determination method and device of a power battery and a server. The method comprises the following steps: acquiring vehicle big data, wherein the vehicle big data comprises mileage data and charging record data of each vehicle; according to the charging record data of each vehicle, determining an average charging amount Es of each vehicle in a starting time period of a statistical period and an average charging amount Ee of each vehicle in an ending time period of the statistical period; determining an energy attenuation value ed of each vehicle in the statistical period according to the average charging amount Es and the average charging amount Ee of each vehicle and the driving mileage delta D of the vehicle in the statistical period; and determining the power battery energy attenuation index of the corresponding vehicle type according to the energy attenuation value ed of each vehicle of the same vehicle type in the statistical period. According to the scheme of the embodiment of the invention, more accurate energy attenuation indexes of the power battery can be provided.

Description

Technical Field

[0001] The present application relates to the field of battery management technology, and specifically to a method, device and server for determining energy attenuation of a power battery. Background Art

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

[0003] At present, the energy attenuation of power batteries after a period of use is mostly estimated by the battery management system in the vehicle. The estimated value may be inaccurate and may also differ from the actual vehicle usage of the user. Therefore, how to more accurately determine the energy attenuation of power batteries has become a technical problem that needs to be solved. Summary of the invention

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

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

[0006] Acquire vehicle big data, the vehicle big data including: mileage data and charging record data of each vehicle;

[0007] According to the charging record data of each vehicle, the average charging amount E of each vehicle in the starting time period of the statistical period is determined. s , and the average charge capacity E at the end of the statistical period e ;

[0008] According to the average charge amount E of each vehicle s , the average charge capacity E e and the mileage ΔD of the vehicle in the statistical period, and determine the energy decay value e of each vehicle in the statistical period d ;

[0009] According to the energy attenuation value e of each vehicle of the same model in the statistical period d , determine the power battery energy attenuation index of the corresponding vehicle model.

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

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

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

[0013] In some embodiments, based on the charging record data of each vehicle, the average charging amount E of each vehicle in the starting time period of the statistical period is determined. s ,include:

[0014] Determine, based on the charging record data of each vehicle, the effective charging data of each vehicle within the starting time period of the statistical period;

[0015] Determining the effective charging amount of each vehicle during each effective charging period according to the effective charging data;

[0016] According to the charge amount of each vehicle effectively charged in the starting time period, the average charge amount E of each vehicle is determined. s .

[0017] In some embodiments, based on the charging record data of each vehicle, the average charging capacity E of each vehicle at the end time period of the statistical period is determined. e ,include:

[0018] Determine, based on the charging record data of each vehicle, the effective charging data of each vehicle within the end time period of the statistical period;

[0019] Determining the effective charging amount of each vehicle during each effective charging period according to the effective charging data of each vehicle;

[0020] According to the charging amount of each vehicle effectively charged in the end time period, the average charging amount E of each vehicle is determined. e .

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

[0022] The charging gun is in connected state;

[0023] The charging current is displayed as the charging status;

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

[0025] In some embodiments, the average charge E of each vehicle s , the average charge capacity E eand the mileage ΔD of the vehicle in the statistical period, and determine the energy decay value e of each vehicle in the statistical period d ,include:

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

[0027] The energy decay value e of each vehicle in the statistical period is determined according to the ratio of the charge decay value of each vehicle in the preset unit mileage to the nominal energy of the power battery. d .

[0028] In some embodiments, the energy attenuation value e of each vehicle of the same vehicle type during the statistical period is d , determine the power battery energy attenuation index of the corresponding vehicle model, including:

[0029] According to the energy attenuation value e of each vehicle of the same model in the statistical period d The average of the power battery energy attenuation index of the corresponding vehicle model is determined.

[0030] In some implementations, the energy decay value e of each vehicle of the same vehicle type during the statistical period is d , determine the power battery energy attenuation index of the corresponding vehicle model, including:

[0031] Determine a number of regions, each region corresponding to its own weighting coefficient;

[0032] Determine the energy decay value e of each vehicle of the same model in each area during the statistical period d The mean

[0033] Use the weighting coefficient to calculate the energy attenuation value e of each area d The mean By weighting, the power battery energy attenuation index of the corresponding vehicle model is obtained.

[0034] In a second aspect, an embodiment of the present invention provides a device for determining energy decay of a power battery, the device comprising:

[0035] An acquisition module, used to acquire vehicle big data, wherein the vehicle big data includes: mileage data and charging record data of each vehicle;

[0036] The charging capacity determination module is used to determine the average charging capacity E of each vehicle in the starting time period of the statistical period according to the charging record data of each vehicle. s, and the average charge capacity E at the end of the statistical period e ;

[0037] The energy decay value determination module is used to determine the average charge amount E of each vehicle according to the average charge amount E of each vehicle. s , the average charge capacity E e and the mileage ΔD of the vehicle in the statistical period, and determine the energy decay value e of each vehicle in the statistical period d ;

[0038] The attenuation index determination module is used to determine the energy attenuation value e of each vehicle of the same model within the statistical period. d , determine the power battery energy attenuation index of the corresponding vehicle model.

[0039] In a third aspect, an embodiment of the present invention provides a server, comprising: 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 described in the first aspect or any one of the first aspects.

[0040] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is running, the device where the computer-readable storage medium is located is controlled to execute the method described in the first aspect or any one of the first aspects.

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

[0042] In the embodiment of the present invention, the energy attenuation index of the power battery of the same vehicle model is calculated by using vehicle big data. Compared with the method of estimating the energy attenuation value of the power battery of a single vehicle, this index can better indicate the energy attenuation of the power battery of the same vehicle model after a period of operation. The smaller the value of this index, the higher the durability of the power battery of the vehicle. 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 present invention is more accurate and can better reflect the power attenuation of the power battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0044] Figure 1A flow chart of a method for determining energy attenuation of a power battery provided by an embodiment of the present invention;

[0045] Figure 2 A flow chart of another method for determining energy attenuation of a power battery provided by an embodiment of the present invention;

[0046] Figure 3 A schematic diagram of the structure of a device for determining energy decay of a power battery provided by an embodiment of the present invention;

[0047] Figure 4 A schematic diagram of the structure of a server provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0048] In order to better understand the technical solution 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 part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in the field without creative work are 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 specific embodiments, and are not intended to limit the present application. The singular forms "a", "said" and "the" used in the embodiments of the present application and the appended claims are also intended to include plural forms, unless the context clearly indicates other meanings.

[0051] It should be understood that the term "and / or" used in this article is only a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0052] After an electric vehicle has been used for a period of time, the vehicle's power battery will decay to a certain extent. In related technologies, the energy decay value of the vehicle's power battery is mainly estimated through the vehicle's own BMS. The estimated value of the energy decay value of the vehicle's own power battery through the vehicle's own BMS may be inaccurate, and the energy decay value estimated using a single data may not reflect the actual available power of the power battery. In addition, as the usage time and mileage change, the estimated energy decay value of the power battery may also differ from the actual vehicle usage.

[0053] In order to solve the above technical problems, an embodiment of the present invention provides a method for determining the energy attenuation of a power battery. The method of the embodiment of the present invention does not use the method of calculating a single vehicle individually in the related art to determine the energy attenuation value of the power battery, but uses vehicle big data to count the energy attenuation index of the power battery of a certain model of vehicle. Compared with the method of estimating the energy attenuation value of the power battery of a single vehicle individually, the attenuation index of the power battery of a certain model calculated based on vehicle big data can better reflect the energy attenuation of the battery. The smaller the value of the index, the higher the durability of the vehicle's power battery.

[0054] See also Figure 1 , is a flow chart of a method for determining energy attenuation of a power battery provided by an embodiment of the present invention. Figure 1 The execution subject of the method shown is a server, which is used to obtain vehicle big data and estimate the power battery energy attenuation index of the same vehicle model based on the vehicle big data. Figure 1 As shown, the processing steps of the method include:

[0055] 101, obtain vehicle big data, the vehicle big data includes: mileage data and charging record data of each vehicle. The mileage data may include, for example, the vehicle mileage of the vehicle at different times. The charging record data may include, for example, charging sampling time, power battery voltage, power battery current, state of charge SOC, and charging gun connection status signal. Optionally, the server may obtain the vehicle big data of each vehicle through the vehicle terminal.

[0056] 102, according to the charging record data of each vehicle, determine the average charging amount E of each vehicle in the starting time period of the statistical period s , and the average charge capacity E at the end of the statistical period e .

[0057] Among them, the statistical cycle can be set as needed. In some embodiments, the statistical cycle can be a cycle with a set starting point and an end point. For example, for a certain model, its launch time is XX year YY month, then XX year YY month is the starting time of the first cycle, XX year YY month + cycle duration is the first cycle, the second cycle is the end of the first cycle + cycle duration, and so on to obtain each statistical cycle. In some embodiments, the statistical cycle 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 the starting time of the first cycle, the purchase time + cycle duration is the first cycle, and the second cycle is the end of the first cycle + cycle duration. In some embodiments, the cycle duration can be, for example, three years, four years, five years or other durations.

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

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

[0060] Among them, the average charge amount E of each vehicle at the beginning time period of the statistical period is determined s The method may include: determining the effective charging data of each vehicle in the starting time period of the statistical period according to the charging record data of each vehicle; determining the charging amount of each vehicle effectively charged in the starting time period according to the effective charging data; determining the average charging amount E of each vehicle according to the charging amount of each vehicle effectively charged in the starting time period. s .

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

[0062] Among them, the valid charging data in the charging record data meets the following conditions: the charging gun is in a connected state, the charging current shows an uncharged state, the SOC of the power battery is charged from below a first value to above a second value, and the second value is greater than the first value.

[0063] 103, based on the average charge amount E of each vehicle during the statistical period s , average charge capacity E e and the mileage ΔD of the vehicle in the statistical period, and determine the energy decay value e of each vehicle in the statistical period d .

[0064] For a single vehicle, the average charge amount E in the statistical cycle can be s , average charge capacity E eand the mileage ΔD of the vehicle in the statistical period, determine the charge attenuation value of the vehicle in the preset unit mileage; 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, determine the energy attenuation value e of the vehicle in the statistical period d The charge attenuation value of the vehicle within a preset unit mileage may be, for example, a charge attenuation value of the vehicle per 10,000 kilometers.

[0065] 104, based on the energy decay value e of each vehicle of the same model within the statistical period d , determine the power battery energy attenuation index of the corresponding vehicle model.

[0066] Calculate the energy attenuation value e of each vehicle in the statistical period based on the vehicle big data d After that, the e d , determine the energy decay index of the corresponding vehicle model, and the energy decay index can be used for each vehicle of the corresponding vehicle model.

[0067] In the embodiment of the present invention, the energy attenuation index of the power battery of the same vehicle model is calculated by using vehicle big data. Compared with the method of estimating the energy attenuation value of the power battery of a single vehicle, this index can better indicate the energy attenuation of the power battery of the same vehicle model after a period of operation. The smaller the value of this index, the higher the durability of the power battery of the vehicle. 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 present invention is more accurate and can better reflect the power attenuation of the power battery.

[0068] See also Figure 2 , is a flow chart of another method for determining energy attenuation of a power battery provided by an embodiment of the present invention. Figure 2 As shown, the processing steps of the method include:

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

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

[0071] Among them, the valid charging data used to determine a valid charging behavior meets the following conditions: the charging gun is in a connected state, the charging current is displayed as a charging state, and the SOC of the power battery is charged from below a first value to above a second value, for example, the power battery is charged from below 30% to above 90% in one charge.

[0072] 203 , determining the effective charging amount of each vehicle during each effective charging in the starting time period of the statistical period according to the effective charging data of each vehicle.

[0073] Among them, according to the formula Calculate the effective charge capacity E of the power battery.

[0074] U(t) is the power battery voltage at sampling time t, I(t) is the power battery input current at sampling time t, Δt is the time difference between adjacent sampling time points, and n is the number of sampling times during an effective charging behavior. In one example, the charge amount of the battery from less than 30% to more than 90% in an effective charging behavior can be calculated according to Formula E.

[0075] 204, determine the average charge capacity E of each vehicle according to the charge capacity of each vehicle effectively charged in the starting time period of the statistical period s .

[0076] Among them, for a single vehicle, its average charge capacity E at the beginning of the statistical period is s It can be calculated according to the following formula:

[0077]

[0078] Among them, E st is the charge amount of the st-th effective charge, and st is the number of effective charges within the starting time period of the statistical period. For example, st is the number of effective charges of the vehicle within one month after the start of the statistical period.

[0079] 205 , according to the charging record data of each vehicle, determine the effective charging data of each vehicle in the end time period of the statistical cycle. For the specific determination method, see step 203 .

[0080] 206 , determining the effective charging amount of each vehicle during each effective charging within the end time period of the statistical period according to the effective charging data of each vehicle.

[0081] Among them, the effective charging amount of each vehicle each time can be calculated according to the formula Sure.

[0082] 207, according to the charging amount of each vehicle effectively charged in the end time period of the statistical period, determine the average charging amount E of each vehicle e .

[0083] Among them, for a single vehicle, its average charge capacity at the end of the statistical period is E e It can be calculated according to the following formula:

[0084]

[0085] Among them, E et is the charging amount of the et-th effective charging, and et is the number of effective charging times within the end time period of the statistical period. For example, et is the number of effective charging times of the vehicle within one month before the end of the statistical period.

[0086] 208, determining the mileage ΔD of each vehicle in the statistical period.

[0087] ΔD=D et -D st ;

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

[0089] 209, based on the average charge E of each vehicle s , average charge capacity E e And the mileage ΔD of the vehicle in the statistical period, determine the energy decay value e of each vehicle in the statistical period d .

[0090] Among them, the energy decay value of the power battery of a single vehicle is e d The energy attenuation percentage of the power battery per 10,000 kilometers can be used for calculation. Specifically, e can be calculated according to the following formula: d , unit is %.

[0091]

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

[0093] 210, based on the energy decay value e of each vehicle of the same model within the statistical period d The average of the power battery energy attenuation index of the corresponding vehicle model is determined

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

[0095] In some embodiments, a weighting coefficient may be set for each region based on the number of vehicles of the same model in different regions or the environmental climate in which they are used; and then the energy attenuation value e of each vehicle of the same model in each region during the statistical period is determined. d The mean Finally, the weighted coefficients are used to Weighted to obtain the power battery energy attenuation index E of the corresponding vehicle model d .

[0096] Among them, the weighted coefficient of each area satisfies the formula k1+k2+…+k m =1, k m Represents the weighting coefficient of the mth region.

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

[0098] k i is the weighting coefficient of the ith region, The power battery energy attenuation index of a certain vehicle model in the i-th area.

[0099] The embodiments of the present invention perform statistical analysis on the energy attenuation of power batteries based on vehicle big data and multiple vehicles, can comprehensively consider different regions and different environments, obtain the energy changes of power batteries of a certain vehicle model within a certain range of use time, and provide a more accurate battery energy attenuation trend, which is beneficial for understanding and evaluating the stability of product service life, and is beneficial for enterprises to better understand the actual durability of users' vehicles, thereby providing a reference for power battery life development design and reliability endurance test verification.

[0100] Corresponding to the above method, an embodiment of the present invention provides a device for determining energy attenuation of a power battery. The device is used to be deployed in a server, such as Figure 3 As shown, the device comprises:

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

[0102] The charging capacity determination module 302 is used to determine the average charging capacity E of each vehicle in the starting time period of the statistical period according to the charging record data of each vehicle. s , and the average charge capacity E at the end of the statistical period e ;

[0103] The energy decay value determination module 303 is used to determine the average charge amount E of each vehicle according to the average charge amount E of each vehicle. s , the average charge capacity E e and the mileage ΔD of the vehicle in the statistical period, and determine the energy decay value e of each vehicle in 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 vehicle type within the statistical period. d , determine the power battery energy attenuation index of the corresponding vehicle model.

[0105] The energy decay determination device of the power battery of the embodiment of the present invention can execute the energy decay determination method of the power battery of the embodiment shown above. For the parts not described in detail in the embodiment of the present invention, reference can be made to the relevant description of the method embodiment. The execution process and technical effects of the technical solution can be referred to the description in the method embodiment, which will not be repeated here.

[0106] See also Figure 4 , is a schematic diagram of the structure of a server provided by an embodiment of the present invention. Figure 4 As shown, the server 400 may 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 will appreciate that the structure of the server shown in the figure does not constitute a limitation on the embodiments of the present application. It may be a bus structure or a star structure, and may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

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

[0108] The processor 401 is the control center of the server, and uses various interfaces and lines to connect various parts of the entire server. It runs or 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 an integrated circuit (IC), for example, it can be composed of a single packaged IC, or it can be composed of multiple packaged ICs with the same or different functions. For example, the processor 401 may include a central processing unit (CPU), a microcontroller unit (MCU), etc.

[0109] The memory 402 is used 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 instruction in the memory 402 is executed by the processor 401 , the server 400 is enabled to execute the method for determining energy decay of a power battery in the embodiment of the present invention.

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

[0112] In a specific implementation, the present application also provides a computer program product, wherein the computer program product includes executable instructions, and when the executable instructions are executed on a computer, the computer executes part or all of the steps in each embodiment of the method for determining the energy decay of a power battery provided in the present application.

[0113] An embodiment of the present application also provides a non-transitory computer-readable storage medium, which stores computer instructions. The computer instructions enable the computer to execute the method for determining energy decay of a power battery provided in an embodiment of the present application.

[0114] The above-mentioned non-temporary computer-readable storage medium can adopt any combination of one or more computer-readable media. 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, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (Read Only Memory; hereinafter referred to as: ROM), an erasable programmable read-only memory (ErasableProgrammable Read Only Memory; hereinafter referred to as: EPROM) or flash memory, 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, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0115] Computer-readable signal media may include a data signal propagated in baseband or as part of a carrier wave, which carries a computer-readable program code. Such propagated data signals may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0116] Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0117] Those skilled in the art can clearly understand that the technology in the embodiments of the present application can be implemented by means of software plus a necessary general hardware platform. Based on this understanding, the technical solution in the embodiments of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, a disk, an optical disk, etc., including several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment of the present application or some parts of the embodiments.

[0118] In this specification, the same or similar parts between the various embodiments can be referred to each other. In particular, for the device embodiment and the terminal embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the description in the method embodiment.

Claims

1. A method for determining energy attenuation of a power battery, characterized in that: The method comprises: Acquire vehicle big data, the vehicle big data including: mileage data and charging record data of each vehicle; According to the charging record data of each vehicle, the average charging amount E of each vehicle in the starting time period of the statistical period is determined. s , and the average charge capacity E at the end of the statistical period e ; According to the average charge amount E of each vehicle s , the average charge capacity E e and the mileage ΔD of the vehicle in the statistical period, and determine the energy decay value e of each vehicle in the statistical period d ; According to the energy attenuation value e of each vehicle of the same model in the statistical period d , determine the power battery energy attenuation index of the corresponding vehicle model.

2. The method according to claim 1, characterized in that 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 of a second length before the end of the statistical period; The sum of the start time period and the end time period is smaller than the statistical period.

3. The method according to claim 1, characterized in that According to the charging record data of each vehicle, the average charging amount E of each vehicle in the starting time period of the statistical period is determined. s ,include: Determine, based on the charging record data of each vehicle, the effective charging data of each vehicle within the starting time period of the statistical period; Determining the effective charging amount of each vehicle during each effective charging period according to the effective charging data; According to the charge amount of each vehicle effectively charged in the starting time period, the average charge amount E of each vehicle is determined. s .

4. The method according to claim 1, characterized in that: According to the charging record data of each vehicle, the average charging amount E of each vehicle at the end time period of the statistical period is determined. e ,include: Determine the effective charging data of each vehicle within the end time period of the statistical period according to the charging record data of each vehicle; Determining the effective charging amount of each vehicle during each effective charging period according to the effective charging data of each vehicle; According to the charging amount of each vehicle effectively charged in the end time period, the average charging amount E of each vehicle is determined. e .

5. The method according to claim 3 or 4, characterized in that: The valid charging data in the charging record data meets the following conditions: The charging gun is in connected state; The charging current is displayed as the charging status; The state of charge (SOC) is charged from below a first value to above a second value, wherein the second value is greater than the first value.

6. The method according to claim 1, characterized in that The average charge amount E of each vehicle s , the average charge capacity E e and the mileage ΔD of the vehicle in the statistical period, and determine the energy decay value e of each vehicle in the statistical period d ,include: According to the average charge amount E of each vehicle s , the average charge capacity E e and the driving mileage ΔD of the vehicle in the statistical period, to determine the charging amount attenuation value of each vehicle within a preset unit mileage; The energy decay value e of each vehicle in the statistical period is determined according to the ratio of the charge decay value of each vehicle in the preset unit mileage to the nominal energy of the power battery. d .

7. The method according to claim 1, characterized in that The energy attenuation value e of each vehicle of the same model in the statistical period d , determine the power battery energy attenuation index of the corresponding vehicle model, including: According to the energy attenuation value e of each vehicle of the same model in the statistical period d The average of the power battery energy attenuation index of the corresponding vehicle model is determined.

8. The method according to claim 1, characterized in that The energy attenuation value e of each vehicle of the same model in the statistical period d , determine the power battery energy attenuation index of the corresponding vehicle model, including: Determine a number of regions, each region corresponding to its own weighting coefficient; Determine the energy decay value e of each vehicle of the same model in each area during the statistical period d The mean Use the weighting coefficient to calculate the energy attenuation value e of each area d The mean By weighting, the power battery energy attenuation index of the corresponding vehicle model is obtained.

9. A device for determining energy attenuation of a power battery, characterized in that: The device comprises: An acquisition module, used to acquire vehicle big data, wherein the vehicle big data includes: mileage data and charging record data of each vehicle; The charging capacity determination module is used to determine the average charging capacity E of each vehicle in the starting time period of the statistical period according to the charging record data of each vehicle. s , and the average charge capacity E at the end of the statistical period e ; The energy decay value determination module is used to determine the average charge amount E of each vehicle according to the average charge amount E of each vehicle. s , the average charge capacity E e and the mileage ΔD of the vehicle in the statistical period, and determine the energy decay value e of each vehicle in the statistical period d ; The attenuation index determination module is used to determine the energy attenuation value e of each vehicle of the same model within the statistical period. d , determine the power battery energy attenuation index of the corresponding vehicle model.

10. A server, characterized in that: include: 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 is caused to execute the method according to any one of claims 1 to 8.

11. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method according to any one of claims 1 to 8.

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