Lithium battery life prediction method and apparatus, and electronic device

Abstract

The embodiment of the invention discloses a battery life prediction method and device of a lithium battery and electronic equipment. The method comprises the steps of firstly obtaining target charging voltage data of a target lithium battery, carrying out initial polarization processing on the target charging voltage data, obtaining initial polarization data, then carrying out data preprocessing on the initial polarization data to obtain standard sequence type data, and finally predicting the standard sequence type data based on a trained Transform algorithm model. And obtaining a battery life prediction result of the target lithium battery. By performing polarization processing on the target voltage data, the polarization phenomenon generated in the lithium battery recycling process is considered, the accuracy of predicting the service life of the target lithium battery is improved, only part of voltage data of a small number of target lithium batteries needs to be sampled, the data processing time and computing resources are saved, and the practicability of the prediction method is improved.

Description

Technical Field

[0001] One or more embodiments of the present specification relate to the field of battery technology, and in particular, to a method, device, and electronic device for predicting the battery life of a lithium battery. Background Art

[0002] Lithium-ion battery is a long-life, high-capacity, environmentally friendly battery with no memory effect. It is used in power vehicles and energy storage due to its high voltage, good safety performance, fast charging and discharging, and low self-discharge. The service life of power batteries can reach several thousand cycles, while the service life of energy storage batteries can reach tens of thousands of cycles. If the battery life is to be tested periodically, it will consume a lot of manpower and material resources. Therefore, a battery life prediction method for lithium batteries is of great significance. Existing prediction methods often use equivalent circuit model method and electrochemical model method, but the equivalent circuit model method ignores some physical phenomena, such as lithium precipitation and stress effect, resulting in low model accuracy. The electrochemical model method often requires testing a large number of electrochemical parameters in the entire charging and discharging stage before prediction, which not only increases the calculation cost and consumes a lot of time, but also has low practicality. Summary of the invention

[0003] The embodiments of this specification provide a method, device and electronic device for predicting the battery life of a lithium battery, and the technical solution thereof is as follows: In a first aspect, an embodiment of the present specification provides a method for predicting the battery life of a lithium battery, the method comprising: Obtain target charging voltage data of a target lithium battery, wherein the target charging voltage data is voltage data corresponding to an initial charging stage of the target lithium battery in each cycle of use; Performing initial polarization processing on each of the target charging voltage data to obtain initial polarization data corresponding to each of the cycle use periods, wherein the initial polarization data is used to characterize the voltage change rate of the target lithium battery in the initial charging stage; Performing data preprocessing on each of the initial polarization data to obtain standard sequence data, wherein the data preprocessing at least includes smoothing processing or normalization processing; The standard sequence data is predicted based on the trained Transformer algorithm prediction model to obtain the battery life prediction result of the target lithium battery.

[0004] In a second aspect, a battery life prediction device for a lithium battery is provided, the device comprising: An acquisition module, used to acquire target charging voltage data of a target lithium battery, wherein the target charging voltage data is voltage data corresponding to an initial charging stage of the target lithium battery in each cycle of use; A polarization module, used to perform initial polarization processing on each of the target charging voltage data to obtain initial polarization data corresponding to each of the cycle use periods, wherein the initial polarization data is used to characterize the voltage change rate of the target lithium battery in the initial charging stage; A processing module, used for performing data preprocessing on each of the initial polarization data to obtain standard sequence data, wherein the data preprocessing at least includes smoothing processing or normalization processing; The prediction module is used to predict the standard sequence data based on the trained Transformer algorithm prediction model to obtain the battery life prediction result of the target lithium battery.

[0005] In a third aspect, an electronic device is provided, including a device processor and a memory; The device processor is connected to the memory; The memory is used to store executable program code; The device processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, so as to execute the steps of the method provided in the first aspect or any possible implementation manner of the first aspect.

[0006] In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer-readable storage medium stores instructions, which, when the instructions are executed on a computer or a device processor, cause the computer or the device processor to execute the method provided in the first aspect or any possible implementation of the first aspect.

[0007] The beneficial effects brought by the technical solutions provided by some embodiments of this specification include at least: In one or more embodiments of the present specification, the target charging voltage data of the target lithium battery is first obtained, and each target charging voltage data is subjected to initial polarization processing. After obtaining the initial polarization data, the data is preprocessed to obtain standard sequence data, and finally the standard sequence data is predicted based on the trained Transformer algorithm prediction model to obtain the battery life prediction result of the target lithium battery. By polarizing the target voltage data, not only the polarization phenomenon generated during the cyclic use of the lithium battery is taken into account, the accuracy of the predicted life of the target lithium battery is improved, and only a small amount of partial voltage data of the target lithium battery needs to be sampled, which saves data processing time and computing resources and improves the practicality of the prediction method. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] In order to more clearly illustrate the technical solutions in 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 work.

[0009] Figure 1 A schematic diagram of the system architecture of a lithium battery life prediction method provided in an embodiment of this specification; Figure 2 A flowchart of a method for predicting the battery life of a lithium battery provided in an embodiment of this specification; Figure 3 A schematic diagram of the structure of a lithium battery life prediction device provided in an embodiment of this specification; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification. DETAILED DESCRIPTION

[0010] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application.

[0011] The terms "first", "second", "third", etc. in the description and claims of this specification and the above-mentioned drawings are used to distinguish different objects, rather than to describe a specific order. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices.

[0012] The following description provides examples and does not limit the scope, applicability or examples set forth in the claims. Changes may be made to the functions and arrangements of the elements described without departing from the scope of the present specification. Various processes or components may be appropriately omitted, substituted or added to each example. For example, the described method may be performed in an order different from the order described, and various steps may be added, omitted or combined. In addition, features described with respect to some examples may be combined in other examples.

[0013] See also Figure 1 , Figure 1 A schematic diagram of the system architecture of a method for predicting the battery life of a lithium battery provided in an embodiment of this specification is shown.

[0014] like Figure 1As shown, the system architecture of the lithium battery life prediction method may include at least a terminal 10 , a server 20 and a network 30 .

[0015] The terminal 10 includes but is not limited to electronic devices such as smart phones, desktop computers, tablet computers, laptops, smart speakers, digital assistants, smart wearable devices, etc., and may also be software running on the above electronic devices, such as applications, etc. Optionally, the operating system running on the electronic device may include but is not limited to Android system, IOS system, Linux, Windows, etc. Optionally, the terminal 10 provides a lithium battery life prediction service to the user, and the terminal 10 may obtain a lithium battery life prediction instruction of an application program interface and send a lithium battery life prediction request to the server 20.

[0016] The server 20 can provide background services for the terminal 10. According to the lithium battery life prediction request sent by the terminal 10, the server 20 will obtain a series of lithium battery life prediction instructions, and the server 20 transmits the lithium battery life prediction instructions to other terminals 10 through the network 30. Specifically, the server 20 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN (Content Delivery Network), and big data and artificial intelligence platforms.

[0017] The network 30 is used to provide a medium for a communication link between the terminal 10 and the server 20. The network 30 may include various connection types, such as wired or wireless communication links or optical fiber cables.

[0018] In addition, it should be noted that Figure 1 What is shown is only one system provided by the present disclosure. In practical applications, other systems may also be included, for example, more terminals may be included.

[0019] In the embodiments of this specification, the terminal 10 and the server 20 may be directly or indirectly connected via wired or wireless communication, which is not limited in the present disclosure.

[0020] See next Figure 2 , Figure 2 The overall flow chart of a lithium battery life prediction method provided in an embodiment of the present specification is shown. The lithium battery life prediction method can be used in the server 20.

[0021] like Figure 2 As shown, the battery life prediction method of the lithium battery may at least include the following steps: Step 201: Obtain target charging voltage data of a target lithium battery.

[0022] The target charging voltage data is the voltage data corresponding to the initial charging stage of the target lithium battery in each cycle of use.

[0023] In the embodiments of this specification, since the lithium battery will be polarized during the actual charging and discharging process, the internal resistance of the battery will increase and a reverse voltage will appear, causing the battery potential to deviate from the equilibrium potential. As the number of battery cycles increases, the internal resistance of the battery will continue to increase, causing the test voltage of the battery to reach the charging cut-off voltage prematurely, and ultimately causing the lithium battery capacity to decrease. Therefore, the test voltage change rate corresponding to the initial charging stage of the lithium battery is more obvious and is less affected by the ambient temperature. In order to subsequently predict the battery life of the target lithium battery, the target lithium battery can be first subjected to a cyclic charge and discharge test, and the voltage data corresponding to the target lithium battery in the initial charging stage, that is, the target charging voltage data of the target lithium battery, can be obtained, so as to subsequently predict the battery life of the lithium battery.

[0024] Among them, the target charging voltage data of the target lithium battery in the first 100 cycles of use can generally be selected.

[0025] In one possible implementation, the step of obtaining target charging voltage data of a target lithium battery includes: For any cycle of the target lithium battery, obtain the initial charging voltage data and the stage charging voltage data corresponding to the initial charging stage; The initial charging voltage data and the stage charging voltage data are difference calculated to obtain the target charging voltage data of the target lithium battery.

[0026] In the embodiments of this specification, for a target lithium battery in any cycle, the initial charging voltage data corresponding to the moment of charging and the stage charging voltage data corresponding to a fixed charging time can be obtained by the voltage sensor at the initial charging stage. The fixed time is generally set to 30s. Then, the difference between the obtained initial charging voltage data and the stage charging voltage data is calculated to obtain a difference calculation result, which is a voltage change value representing the fixed charging time, and is used as the target charging voltage data of the target lithium battery, so that the target charging voltage data can be polarized later.

[0027] Step 202: Perform initial polarization processing on each of the target charging voltage data to obtain initial polarization data corresponding to each of the cyclic use periods.

[0028] The initial polarization data is used to characterize the voltage change rate of the target lithium battery in the initial charging stage.

[0029] In the embodiments of this specification, after obtaining the target charging voltage data of the target lithium battery, an initial polarization process can be performed on it, that is, the voltage change rate corresponding to the initial charging stage of each cycle of the target lithium battery can be determined by the target charging voltage data. Among them, the charging time can be determined by the slope algorithm first, and then the voltage change rate can be determined. The voltage change rate can also be determined by curve simulation derivation of the target charging voltage data by the curve derivation method, and finally the initial polarization data corresponding to each cycle of use can be obtained.

[0030] In one possible implementation, performing initial polarization processing on each of the target charging voltage data to obtain initial polarization data corresponding to each of the cyclic use periods includes: For any cycle of the target lithium battery, determine the corresponding charging time between each of the initial charging voltage data and the stage charging voltage data; Polarization calculation is performed on the target charging voltage data and the charging duration based on a slope algorithm to obtain initial polarization data corresponding to each of the cyclic use cycles.

[0031] In the embodiments of this specification, for the target lithium battery in any cycle, after obtaining the target charging voltage data, the charging time corresponding to the target charging voltage can be further determined, that is, the time between the two acquisition moments corresponding to the initial charging voltage data and the stage charging voltage data in the target charging voltage. Then, the target charging voltage data and the charging time in a cycle are divided and polarized by the slope algorithm to obtain the voltage change rate corresponding to the initial charging stage of each cycle, that is, the initial polarization data.

[0032] Optionally, the calculation expression of the initial polarization can be defined as , where D is the voltage conversion rate, is the stage charging voltage, is the initial charging voltage, is the stage voltage acquisition time, is the initial voltage acquisition time.

[0033] Step 203: perform data preprocessing on each of the initial polarization data to obtain standard sequence data.

[0034] Wherein, the data preprocessing at least includes smoothing processing or normalization processing.

[0035] In the embodiments of the present specification, since the initial polarization data obtained comes from the target lithium battery recording layer data, and during the charge and discharge process of the target lithium battery, the initial polarization of the battery will change with the increase in the number of cycles, the initial polarization data obtained will contain a large amount of accidental deviation data caused by external environmental factors. It is necessary to perform data preprocessing such as smoothing or normalization on the initial polarization data obtained to obtain standard sequence data, which is convenient for the subsequent algorithm model to predict the battery life.

[0036] In one possible implementation manner, the performing data preprocessing on each of the initial polarization data to obtain standard sequence data includes: Analytically reconstructing each of the initial polarization data to obtain an initial polarization curve; The initial polarization curve is sequentially smoothed and preprocessed in a normalized format to obtain standard sequence data.

[0037] In the embodiments of this specification, when performing data preprocessing on each initial polarization data, the initial polarization data corresponding to each number of cycles can be first analyzed and reconstructed, with the number of cycles used as the horizontal coordinate and the corresponding initial polarization value as the vertical coordinate, and each target point is reconstructed by curve fitting to obtain the initial polarization curve corresponding to the target number of cycles. Then, the initial polarization curve is smoothed to remove noise points, and the smoothed curve data is preprocessed in a normalized format to obtain standard sequence data that meets the format of the subsequent prediction model.

[0038] In one possible implementation, the initial polarization curve is sequentially smoothed and preprocessed in a normalized format to obtain standard sequence data, including: Smoothing each of the initial polarization curves based on a sliding mean-median method to obtain a target smooth curve; Each curve data in the target smooth curve is processed in a normalized format to obtain standard sequence data.

[0039] In the embodiments of the present specification, when the initial polarization curve is smoothed, the sliding average-median method can be used to process it. First, a suitable point window size can be determined according to the data characteristics and noise level for calculating the sliding average and median. Then, the window is moved along the polarization curve data, and in each window, the arithmetic mean of the data in the window is calculated, and this average is used as the new smoothed value of the center point of the window. The same window is moved along the polarization curve data, and in each window, the median of the data in the window is calculated, and this median is used as the new smoothed value of the center point of the window. Furthermore, for each window center point, the weighted average of the sliding average and the median can be taken as the final smoothed value to obtain the target smooth curve. Finally, the curve data corresponding to each curve point in the target smooth curve is processed in a normalized format, and all of its curve data is converted into a standard data structure form for predictive model construction to obtain standard sequence data.

[0040] Step 204: predict the standard sequence data based on the trained Transformer algorithm prediction model to obtain a battery life prediction result of the target lithium battery.

[0041] In the embodiments of this specification, in the trained Transformer algorithm lithium battery life prediction model, the standard sequence data is generally used as input and the battery life prediction result is used as output. Therefore, after obtaining the standard sequence data of the target lithium battery, it can be directly used as input and transmitted to the trained Transformer algorithm prediction model, and the battery life prediction result is obtained after statistical conversion of the model output.

[0042] In one possible implementation, before predicting the standard sequence data based on the trained Transformer algorithm prediction model to obtain the battery life prediction result of the target lithium battery, the method further includes: Acquire at least two groups of historical test data in the historical test records, wherein the historical test data includes historical sequence data and historical battery life corresponding to the historical sequence data; Constructing an initial Transformer algorithm prediction model, and constructing a Transformer algorithm training set based on each group of historical test data; The initial Transformer algorithm prediction model is trained based on the Transformer algorithm training set to obtain a trained Transformer algorithm prediction model.

[0043] In the embodiments of this specification, before predicting the battery life through standard sequence data, the prediction algorithm model needs to be trained. First, an initial Transformer algorithm prediction model can be constructed, and then multiple groups of historical test data in the historical test records can be obtained, and the historical sequence data in the historical test data can be used as input, and the corresponding historical battery life can be used as output to construct a Transformer algorithm training set. Furthermore, the obtained Transformer algorithm training set is used to predict the constructed initial Transformer algorithm prediction model to obtain a trained Transformer algorithm prediction model.

[0044] In one possible implementation, the method of predicting the standard sequence data based on the trained Transformer algorithm prediction model to obtain the battery life prediction result of the target lithium battery includes: Predicting the standard sequence data based on the trained Transformer algorithm prediction model to obtain a model output; The model output is de-normalized to obtain a battery life prediction result of the target lithium battery.

[0045] In the embodiments of this specification, standard sequence data is input into the trained Transformer algorithm prediction model for prediction. After obtaining the model output, the model output can be further de-normalized, wherein the corresponding de-normalization can be performed for the normalization step performed before data preprocessing. As an example, after the maximum and minimum normalization processing, the de-normalization formula is used for processing, after the Z-score normalization processing, the corresponding de-Z-score formula is used for de-normalization processing, and the processing result is interpreted as an actual numerical value to obtain the battery life prediction result of the target lithium battery.

[0046] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0047] See next Figure 3 , Figure 3 The schematic diagram of the structure of a lithium battery life prediction device provided in the embodiment of this specification is shown. It should be noted that: Figure 3 The battery life prediction device of the lithium battery shown is used to execute the present application Figure 2 For the convenience of explanation, only the part related to the embodiment of the present application is shown. For the specific technical details not disclosed, please refer to the present application. Figure 2 The embodiment shown.

[0048] like Figure 3 As shown, the battery life prediction device of the lithium battery may at least include: An acquisition module 301 is used to acquire target charging voltage data of a target lithium battery, wherein the target charging voltage data is voltage data corresponding to an initial charging stage of the target lithium battery in each cycle of use; The polarization module 302 is used to perform initial polarization processing on each of the target charging voltage data to obtain initial polarization data corresponding to each of the cycle use periods, wherein the initial polarization data is used to characterize the voltage change rate of the target lithium battery in the initial charging stage; A processing module 303 is used to perform data preprocessing on each of the initial polarization data to obtain standard sequence data, wherein the data preprocessing at least includes smoothing processing or normalization processing; The prediction module 304 is used to predict the standard sequence data based on the trained Transformer algorithm prediction model to obtain the battery life prediction result of the target lithium battery.

[0049] In one possible implementation, the acquisition module 301 is specifically used for: For any cycle of the target lithium battery, obtain the initial charging voltage data and the stage charging voltage data corresponding to the initial charging stage; The initial charging voltage data and the stage charging voltage data are difference calculated to obtain the target charging voltage data of the target lithium battery.

[0050] In one possible implementation, the polarization module 302 is specifically used for: For any cycle of the target lithium battery, determine the corresponding charging time between each of the initial charging voltage data and the stage charging voltage data; Polarization calculation is performed on the target charging voltage data and the charging duration based on a slope algorithm to obtain initial polarization data corresponding to each of the cyclic use cycles.

[0051] In one possible implementation, the processing module 303 is specifically used for: Analytically reconstructing each of the initial polarization data to obtain an initial polarization curve; The initial polarization curve is sequentially smoothed and preprocessed in a normalized format to obtain standard sequence data.

[0052] In one possible implementation, the processing module 303 is further configured to: Smoothing each of the initial polarization curves based on a sliding mean-median method to obtain a target smooth curve; Each curve data in the target smooth curve is processed in a normalized format to obtain standard sequence data.

[0053] In one possible implementation, the prediction module 304 is specifically used to: Acquire at least two groups of historical test data in the historical test records, wherein the historical test data includes historical sequence data and historical battery life corresponding to the historical sequence data; Constructing an initial Transformer algorithm prediction model, and constructing a Transformer algorithm training set based on each group of historical test data; The initial Transformer algorithm prediction model is trained based on the Transformer algorithm training set to obtain a trained Transformer algorithm prediction model.

[0054] In one possible implementation, the prediction module 304 is further configured to: Predicting the standard sequence data based on the trained Transformer algorithm prediction model to obtain a model output; The model output is de-normalized to obtain a battery life prediction result of the target lithium battery.

[0055] Those skilled in the art can clearly understand that the technical solutions of the embodiments of the present application can be implemented with the help of software and / or hardware. The "unit" and "module" in this specification refer to software and / or hardware that can independently complete or cooperate with other components to complete specific functions, where the hardware can be, for example, a field programmable gate array (FPGA), an integrated circuit (IC), etc.

[0056] Each processing unit and / or module of the embodiments of the present application may be implemented by an analog circuit that implements the functions described in the embodiments of the present application, or may be implemented by software that executes the functions described in the embodiments of the present application.

[0057] See next Figure 4 , Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of this specification is shown.

[0058] like Figure 4As shown, the electronic device 400 may include: at least one device processor 401 , at least one network interface 404 , a user interface 403 , a memory 405 , and at least one communication bus 402 .

[0059] The communication bus 402 may be used to realize the connection and communication among the above-mentioned components.

[0060] The user interface 403 may include buttons, and the optional user interface may also include a standard wired interface or a wireless interface.

[0061] The network interface 404 may include, but is not limited to, a Bluetooth module, an NFC module, a Wi-Fi module, etc.

[0062] Among them, the device processor 401 may include one or more processing cores. The device processor 401 uses various interfaces and lines to connect various parts within the entire electronic device 400, and executes various functions and processes data of the electronic device 400 by running or executing instructions, programs, code sets or instruction sets stored in the memory 405, and calling data stored in the memory 405. Optionally, the device processor 401 can be implemented in at least one hardware form of DSP, FPGA, and PLA. The device processor 401 can integrate one or a combination of CPU, GPU, modem, etc. Among them, the CPU mainly processes the operating system, user interface, and application programs; the GPU is responsible for rendering and drawing the content to be displayed on the display screen; the modem is used to handle wireless communications. It can be understood that the above-mentioned modem may not be integrated into the device processor 401, and it can be implemented separately through a chip.

[0063] The memory 405 may include a RAM or a ROM. Optionally, the memory 405 includes a non-transitory computer-readable medium. The memory 405 may be used to store instructions, programs, codes, code sets or instruction sets. The memory 405 may include a program storage area and a data storage area, wherein the program storage area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playback function, an image playback function, etc.), instructions for implementing the above-mentioned method embodiments, etc.; the data storage area may store data involved in the above-mentioned method embodiments, etc. The memory 405 may optionally be at least one storage device located away from the aforementioned device processor 401. As Figure 4 As shown, the memory 405 as a computer storage medium may include an operating system, a network communication module, a user interface module, and program instructions.

[0064] Specifically, the device processor 401 may be used to call the battery life prediction application of the lithium battery stored in the memory 405, and specifically perform the following operations: Obtain target charging voltage data of a target lithium battery, wherein the target charging voltage data is voltage data corresponding to an initial charging stage of the target lithium battery in each cycle of use; Performing initial polarization processing on each of the target charging voltage data to obtain initial polarization data corresponding to each of the cycle use periods, wherein the initial polarization data is used to characterize the voltage change rate of the target lithium battery in the initial charging stage; Performing data preprocessing on each of the initial polarization data to obtain standard sequence data, wherein the data preprocessing at least includes smoothing processing or normalization processing; The standard sequence data is predicted based on the trained Transformer algorithm prediction model to obtain the battery life prediction result of the target lithium battery.

[0065] As an option of the embodiment of this specification, the step of obtaining target charging voltage data of the target lithium battery includes: For any cycle of the target lithium battery, obtain the initial charging voltage data and the stage charging voltage data corresponding to the initial charging stage; The initial charging voltage data and the stage charging voltage data are difference calculated to obtain the target charging voltage data of the target lithium battery.

[0066] As an option of the embodiment of this specification, the initial polarization processing is performed on each of the target charging voltage data to obtain the initial polarization data corresponding to each of the cyclic use periods, including: For any cycle of the target lithium battery, determine the corresponding charging time between each of the initial charging voltage data and the stage charging voltage data; Polarization calculation is performed on the target charging voltage data and the charging duration based on a slope algorithm to obtain initial polarization data corresponding to each of the cyclic use cycles.

[0067] As an option of an embodiment of this specification, performing data preprocessing on each of the initial polarization data to obtain standard sequence data includes: Analytically reconstructing each of the initial polarization data to obtain an initial polarization curve; The initial polarization curve is sequentially smoothed and preprocessed in a normalized format to obtain standard sequence data.

[0068] As an option of the embodiment of this specification, the initial polarization curve is smoothed and preprocessed in a normalized format in sequence to obtain standard sequence data, including: Smoothing each of the initial polarization curves based on a sliding mean-median method to obtain a target smooth curve; Each curve data in the target smooth curve is processed in a normalized format to obtain standard sequence data.

[0069] As an option of the embodiment of this specification, before predicting the standard sequence data based on the trained Transformer algorithm prediction model to obtain the battery life prediction result of the target lithium battery, it also includes: Acquire at least two groups of historical test data in the historical test records, wherein the historical test data includes historical sequence data and historical battery life corresponding to the historical sequence data; Constructing an initial Transformer algorithm prediction model, and constructing a Transformer algorithm training set based on each group of historical test data; The initial Transformer algorithm prediction model is trained based on the Transformer algorithm training set to obtain a trained Transformer algorithm prediction model.

[0070] As an optional embodiment of the present specification, the method of predicting the standard sequence data based on the trained Transformer algorithm prediction model to obtain the battery life prediction result of the target lithium battery includes: Predicting the standard sequence data based on the trained Transformer algorithm prediction model to obtain a model output; The model output is de-normalized to obtain a battery life prediction result of the target lithium battery.

[0071] The embodiments of this specification also provide a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the steps of the above method are implemented. The computer-readable storage medium may include, but is not limited to, any type of disk, including a floppy disk, an optical disk, a DVD, a CD-ROM, a micro drive, and a magneto-optical disk, a ROM, a RAM, an EPROM, an EEPROM, a DRAM, a VRAM, a flash memory device, a magnetic card or an optical card, a nanosystem (including a molecular memory IC), or any type of medium or device suitable for storing instructions and / or data.

[0072] It should be noted that, for the aforementioned method embodiments, for the sake of simplicity, they are all expressed as a series of action combinations, but those skilled in the art should be aware that the present application is not limited by the described order of actions, because according to the present application, certain steps can be performed in other orders or simultaneously. Secondly, those skilled in the art should also be aware that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily required by the present application.

[0073] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0074] In the several embodiments provided in the present application, it should be understood that the disclosed devices can be implemented in other ways. For example, the device embodiments described above are only schematic, such as the division of the units, which is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some service interfaces, and the indirect coupling or communication connection of devices or units can be electrical or other forms.

[0075] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0076] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0077] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable memory. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a memory, including a number of instructions to enable a computer device (which can be a personal computer, server or network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned memory includes: U disk, read-only memory (ROM), random access memory (RAM), mobile hard disk, disk or optical disk and other media that can store program codes.

[0078] A person skilled in the art may understand that all or part of the steps in the various methods of the above embodiments may be completed by entering a program to instruct related hardware. The program may be stored in a computer-readable memory, which may include a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0079] The above is a description of a specific embodiment of the specification. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the embodiments and still achieve the desired results. In addition, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Claims

1. A method for predicting the battery life of a lithium battery, characterized in that: The method comprises: Obtain target charging voltage data of a target lithium battery, wherein the target charging voltage data is voltage data corresponding to an initial charging stage of the target lithium battery in each cycle of use; Performing initial polarization processing on each of the target charging voltage data to obtain initial polarization data corresponding to each of the cycle use periods, wherein the initial polarization data is used to characterize the voltage change rate of the target lithium battery in the initial charging stage; Performing data preprocessing on each of the initial polarization data to obtain standard sequence data, wherein the data preprocessing at least includes smoothing processing or normalization processing; The standard sequence data is predicted based on the trained Transformer algorithm prediction model to obtain the battery life prediction result of the target lithium battery.

2. The method according to claim 1, characterized in that The step of obtaining target charging voltage data of a target lithium battery includes: For any cycle of the target lithium battery, obtain the initial charging voltage data and the stage charging voltage data corresponding to the initial charging stage; The initial charging voltage data and the stage charging voltage data are difference calculated to obtain the target charging voltage data of the target lithium battery.

3. The method according to claim 2, characterized in that The performing initial polarization processing on each of the target charging voltage data to obtain initial polarization data corresponding to each of the cyclic use periods includes: For any cycle of the target lithium battery, determine the corresponding charging time between each of the initial charging voltage data and the stage charging voltage data; Polarization calculation is performed on the target charging voltage data and the charging duration based on a slope algorithm to obtain initial polarization data corresponding to each of the cyclic use cycles.

4. The method according to claim 1, characterized in that: The performing data preprocessing on each of the initial polarization data to obtain standard sequence data includes: Analytically reconstructing each of the initial polarization data to obtain an initial polarization curve; The initial polarization curve is sequentially smoothed and preprocessed in a normalized format to obtain standard sequence data.

5. The method according to claim 4, characterized in that The initial polarization curve is subjected to smoothing processing and normalization format preprocessing in sequence to obtain standard sequence data, including: Smoothing each of the initial polarization curves based on a sliding mean-median method to obtain a target smooth curve; Each curve data in the target smooth curve is processed in a normalized format to obtain standard sequence data.

6. The method according to claim 1, characterized in that Before predicting the standard sequence data based on the trained Transformer algorithm prediction model to obtain the battery life prediction result of the target lithium battery, the method further includes: Acquire at least two groups of historical test data in the historical test records, wherein the historical test data includes historical sequence data and historical battery life corresponding to the historical sequence data; Constructing an initial Transformer algorithm prediction model, and constructing a Transformer algorithm training set based on each group of historical test data; The initial Transformer algorithm prediction model is trained based on the Transformer algorithm training set to obtain a trained Transformer algorithm prediction model.

7. The method according to claim 1, characterized in that The method of predicting the standard sequence data based on the trained Transformer algorithm prediction model to obtain the battery life prediction result of the target lithium battery includes: Predicting the standard sequence data based on the trained Transformer algorithm prediction model to obtain a model output; The model output is de-normalized to obtain a battery life prediction result of the target lithium battery.

8. A battery life prediction device for a lithium battery, characterized in that: The device comprises: An acquisition module, used to acquire target charging voltage data of a target lithium battery, wherein the target charging voltage data is voltage data corresponding to an initial charging stage of the target lithium battery in each cycle of use; A polarization module, used to perform initial polarization processing on each of the target charging voltage data to obtain initial polarization data corresponding to each of the cycle use periods, wherein the initial polarization data is used to characterize the voltage change rate of the target lithium battery in the initial charging stage; A processing module, used for performing data preprocessing on each of the initial polarization data to obtain standard sequence data, wherein the data preprocessing at least includes smoothing processing or normalization processing; The prediction module is used to predict the standard sequence data based on the trained Transformer algorithm prediction model to obtain the battery life prediction result of the target lithium battery.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, wherein the computer-readable storage medium has instructions stored therein, and when the instructions are executed on a computer or a processor, the computer or the processor executes the steps of the method according to any one of claims 1 to 7.

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