Data processing method and device based on lithium battery pack

Abstract

The invention discloses a data processing method and device based on a lithium battery pack. Relates to the technical field of lithium batteries. Obtaining a target cycle attenuation prediction model of the target lithium battery pack and obtaining a target calendar attenuation prediction model of the target lithium battery pack; calculating the attenuation amount of the target lithium battery pack based on the historical attenuation amount through the target cycle attenuation amount prediction model and the target calendar attenuation amount prediction model to obtain a target attenuation amount; and determining residual life information of the target lithium battery pack according to the target attenuation amount. Through the method and the device, the problem of relatively low life prediction efficiency caused by constructing an electrochemical model to predict the service life of the lithium battery pack in related technologies is solved.

Description

Technical Field

[0001] The present application relates to the field of lithium battery technology, and in particular to a data processing method and device based on a lithium battery pack. Background Art

[0002] In the current energy transition and electrification development, lithium-ion batteries, as core energy storage components, play a key role in electric vehicles, energy storage systems, and other fields. However, battery performance degradation during service is a complex and unpredictable phenomenon that directly affects the reliability and service life of equipment. In particular, comprehensive performance evaluation and prediction of battery packs is a major challenge within the industry.

[0003] As the scope of battery applications continues to expand, the market and users have placed higher demands on battery pack life prediction. Accurately predicting the attenuation of battery packs under complex operating conditions not only helps optimize the strategy of the battery management system (BMS) and reduce maintenance costs, but also provides early warning of potential safety risks, improving user experience and the overall performance of the device. In existing technologies, battery life prediction methods mainly rely on the construction and simulation analysis of electrochemical models. Although such models can provide detailed analysis of electrochemical processes, due to their high complexity, they require a lot of computing resources and time, especially when dealing with battery pack-level predictions, the efficiency is significantly low. In addition, electrochemical models often find it difficult to accurately reflect the changing environments and operating conditions encountered by batteries during actual use, such as temperature fluctuations, changes in charge and discharge depth, etc., resulting in a large deviation between the predicted results and the actual life.

[0004] Currently, no effective solution has been proposed to the problem that the service life of lithium battery packs is predicted by constructing electrochemical models in related technologies, resulting in relatively low efficiency in life prediction. Summary of the Invention

[0005] The main purpose of this application is to provide a data processing method and device based on a lithium battery pack to solve the problem in the related art of predicting the service life of a lithium battery pack by constructing an electrochemical model, resulting in relatively low efficiency in life prediction.

[0006] To achieve the above objectives, according to one aspect of the present application, a data processing method based on a lithium battery pack is provided. The method includes: obtaining historical attenuation of the charge of a target lithium battery pack; obtaining a target cycle attenuation prediction model for the target lithium battery pack and obtaining a target calendar attenuation prediction model for the target lithium battery pack; calculating the attenuation of the target lithium battery pack based on the historical attenuation using the target cycle attenuation prediction model and the target calendar attenuation prediction model to obtain a target attenuation; and determining the remaining life information of the target lithium battery pack based on the target attenuation.

[0007] Furthermore, obtaining the target cycle attenuation prediction model of the target lithium battery pack and obtaining the target calendar attenuation prediction model of the target lithium battery pack include: obtaining the predicted cycle attenuation prediction model corresponding to the single lithium battery in the historical lithium battery pack; determining the target cycle attenuation prediction model based on the predicted cycle attenuation prediction model; obtaining the initial calendar attenuation prediction model corresponding to the single lithium battery in the historical lithium battery pack; and determining the target calendar attenuation prediction model based on the initial calendar attenuation prediction model.

[0008] Furthermore, based on the predicted cycle attenuation prediction model, determining the target cycle attenuation prediction model includes: constructing a second cycle attenuation prediction model based on the target test data of the cycle aging of the historical lithium battery pack; obtaining the attenuation loss coefficient between the historical lithium battery pack and the single lithium battery based on the predicted cycle attenuation prediction model and the second cycle attenuation prediction model; and obtaining the target cycle attenuation prediction model based on the attenuation loss coefficient and the second cycle attenuation prediction model.

[0009] Furthermore, according to the predicted cycle attenuation prediction model and the second cycle attenuation prediction model, the attenuation loss coefficient between the historical lithium battery group and the single lithium battery is obtained, including: determining the total number of first charge and discharge cycles corresponding to the power of the single lithium battery according to the predicted cycle attenuation prediction model; determining the total number of second charge and discharge cycles corresponding to the power of the historical lithium battery group according to the second cycle attenuation prediction model; and calculating according to the first total number of charge and discharge cycles and the second total number of charge and discharge cycles to obtain the attenuation loss coefficient between the historical lithium battery group and the single lithium battery.

[0010] Furthermore, the attenuation of the target lithium battery pack is calculated based on the historical attenuation by the target cyclic attenuation prediction model and the target calendar attenuation prediction model to obtain the target attenuation, including: for a target time period, calculating based on the historical attenuation by the target cyclic attenuation prediction model to obtain the predicted cyclic attenuation of the target lithium battery pack within the target time period; calculating based on the predicted cyclic attenuation and the historical attenuation by the target calendar attenuation prediction model to obtain the predicted calendar attenuation of the target lithium battery pack within the target time period; calculating based on the predicted cyclic attenuation and the predicted calendar attenuation to obtain the target attenuation.

[0011] Furthermore, the target calendar attenuation prediction model is used to calculate based on the predicted cyclic attenuation and the historical attenuation to obtain the predicted calendar attenuation of the target lithium battery pack within the target time period, including: calculating the predicted cyclic attenuation and the historical attenuation to obtain a total attenuation value; calculating the total attenuation value and the duration corresponding to the target time period through the target calendar attenuation prediction model to obtain a predicted calendar attenuation rate of the target lithium battery pack in the target time period; and calculating according to the predicted calendar attenuation rate and the storage duration in the target time period to obtain the predicted calendar attenuation.

[0012] Furthermore, based on the target attenuation amount, determining the remaining life information of the target lithium battery pack includes: calculating based on the target attenuation amount and the historical attenuation amount to obtain a cumulative attenuation amount; judging whether the cumulative attenuation amount is greater than or equal to a preset threshold; if the cumulative attenuation amount is greater than or equal to the preset threshold, determining the remaining life information based on the target time period corresponding to the target attenuation amount.

[0013] Furthermore, after determining whether the cumulative attenuation is greater than or equal to a preset threshold, the method also includes: if the cumulative attenuation is less than the preset threshold, repeatedly executing the step of calculating the attenuation of the target lithium battery pack based on the historical attenuation through the target cycle attenuation prediction model and the target calendar attenuation prediction model until the cumulative attenuation is greater than or equal to the preset threshold, and determining the remaining life information based on the time length corresponding to the cumulative attenuation.

[0014] To achieve the above objectives, according to another aspect of the present application, a data processing device based on a lithium battery pack is provided. The device includes: a first acquisition unit for acquiring the historical attenuation of the power of a target lithium battery pack; a second acquisition unit for acquiring a target cycle attenuation prediction model for the target lithium battery pack and a target calendar attenuation prediction model for the target lithium battery pack; a calculation unit for calculating the attenuation of the target lithium battery pack based on the historical attenuation using the target cycle attenuation prediction model and the target calendar attenuation prediction model to obtain a target attenuation; and a determination unit for determining the remaining life information of the target lithium battery pack based on the target attenuation.

[0015] Furthermore, the second acquisition unit includes: a first acquisition sub-unit, used to obtain a predicted cycle attenuation prediction model corresponding to a single lithium battery in a historical lithium battery pack; a first determination sub-unit, used to determine the target cycle attenuation prediction model based on the predicted cycle attenuation prediction model; a second acquisition sub-unit, used to obtain an initial calendar attenuation prediction model corresponding to a single lithium battery in the historical lithium battery pack; a second determination sub-unit, used to determine the target calendar attenuation prediction model based on the initial calendar attenuation prediction model.

[0016] Furthermore, the first determination subunit includes: a construction module for constructing a second cycle attenuation prediction model based on the target test data of the cycle aging of the historical lithium battery pack; a first determination module for obtaining the attenuation loss coefficient between the historical lithium battery pack and the single lithium battery based on the predicted cycle attenuation prediction model and the second cycle attenuation prediction model; and a second determination module for obtaining the target cycle attenuation prediction model based on the attenuation loss coefficient and the second cycle attenuation prediction model.

[0017] Furthermore, the first determination module includes: a first determination submodule, used to determine the total number of first charge and discharge cycles corresponding to the power of the single lithium battery based on the predicted cyclic attenuation prediction model; a second determination submodule, used to determine the total number of second charge and discharge cycles corresponding to the power of the historical lithium battery group based on the second cyclic attenuation prediction model; a calculation submodule, used to calculate based on the first total number of charge and discharge cycles and the second total number of charge and discharge cycles to obtain the attenuation loss coefficient between the historical lithium battery group and the single lithium battery.

[0018] Furthermore, the calculation unit includes: a first calculation subunit, for calculating, for a target time period, based on the historical attenuation through the target cyclic attenuation prediction model, to obtain the predicted cyclic attenuation of the target lithium battery pack within the target time period; a second calculation subunit, for calculating, based on the predicted cyclic attenuation and the historical attenuation through the target calendar attenuation prediction model, to obtain the predicted calendar attenuation of the target lithium battery pack within the target time period; and a third calculation subunit, for calculating based on the predicted cyclic attenuation and the predicted calendar attenuation to obtain the target attenuation.

[0019] Furthermore, the second calculation subunit includes: a first calculation module, used to calculate the predicted cyclic attenuation and the historical attenuation to obtain a total attenuation value; a second calculation module, used to calculate the total attenuation value and the duration corresponding to the target time period through the target calendar attenuation prediction model to obtain the predicted calendar attenuation rate of the target lithium battery pack in the target time period; a third calculation module, used to calculate based on the predicted calendar attenuation rate and the storage duration in the target time period to obtain the predicted calendar attenuation.

[0020] Furthermore, the determination unit includes: a fourth calculation subunit, used to calculate based on the target attenuation and the historical attenuation to obtain a cumulative attenuation; a judgment subunit, used to judge whether the cumulative attenuation is greater than or equal to a preset threshold; and a third determination subunit, used to determine the remaining life information based on the target time period corresponding to the target attenuation if the cumulative attenuation is greater than or equal to the preset threshold.

[0021] Furthermore, the device also includes: an execution unit, which is used to repeatedly execute the step of calculating the attenuation of the target lithium battery pack based on the historical attenuation through the target cycle attenuation prediction model and the target calendar attenuation prediction model after determining whether the cumulative attenuation is greater than or equal to a preset threshold, if the cumulative attenuation is less than the preset threshold, until the cumulative attenuation is greater than or equal to the preset threshold, and determine the remaining life information based on the time corresponding to the cumulative attenuation.

[0022] According to another aspect of an embodiment of the present invention, an electronic device is provided, comprising: a memory storing an executable program; and a processor for running the program, wherein when the program is run, any one of the above-mentioned data processing methods based on a lithium battery pack is executed.

[0023] According to another aspect of an embodiment of the present invention, a computer-readable storage medium is provided, which stores a program. When the program is running, the device where the storage medium is located is controlled to execute any of the above-mentioned data processing methods based on lithium battery packs.

[0024] According to another aspect of an embodiment of the present invention, a computer program product is provided, including a computer program or instructions. When the computer program or instructions are executed by a processor, any one of the above-mentioned data processing methods based on a lithium battery pack is implemented.

[0025] In an embodiment of the present application, the following steps are adopted: obtaining the historical attenuation of the power of the target lithium battery pack; obtaining a target cycle attenuation prediction model for the target lithium battery pack and obtaining a target calendar attenuation prediction model for the target lithium battery pack; calculating the attenuation of the target lithium battery pack based on the historical attenuation using the target cycle attenuation prediction model and the target calendar attenuation prediction model to obtain a target attenuation; determining the remaining life information of the target lithium battery pack based on the target attenuation, thereby solving the technical problem in the related art of predicting the service life of the lithium battery pack by constructing an electrochemical model, resulting in relatively low efficiency in life prediction.

[0026] In this scheme, by using the target cycle attenuation prediction model and the target calendar attenuation prediction model, combined with the historical attenuation, the attenuation of the target lithium battery pack can be quickly calculated, avoiding the detailed simulation of the electrochemical behavior of each battery cell in the traditional method, shortening the calculation time, and at the same time using the target cycle attenuation prediction model and the target calendar attenuation prediction model to comprehensively consider the attenuation characteristics of the battery under different usage conditions (such as charge and discharge cycles and static), thereby more accurately predicting the remaining life information of the battery pack. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0028] Figure 1 A hardware structure block diagram of a computer terminal for implementing a data processing method based on a lithium battery pack is shown;

[0029] Figure 2 is a flow chart of a data processing method based on a lithium battery pack provided in an embodiment of the present application;

[0030] Figure 3 This is a schematic diagram of the power attenuation curve provided in the embodiment of the present application. Figure 1 ;

[0031] Figure 4 This is a schematic diagram of the power attenuation curve provided in the embodiment of the present application. Figure 2 ;

[0032] Figure 5 This is a schematic diagram of the power attenuation curve provided in the embodiment of the present application. Figure 3 ;

[0033] Figure 6 This is a schematic diagram of the power attenuation curve provided in the embodiment of the present application. Figure 4 ;

[0034] Figure 7This is a schematic diagram of the power attenuation curve provided in the embodiment of the present application. Figure 5 ;

[0035] Figure 8 This is a schematic diagram of the power attenuation curve provided in the embodiment of the present application. Figure 6 ;

[0036] Figure 9 is a schematic diagram of a data processing device based on a lithium battery pack according to an embodiment of the present application;

[0037] Figure 10 This is a structural block diagram of an electronic device according to an embodiment of the present application. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the present invention, the following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of this application.

[0039] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in a sequence other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0040] First, some nouns or terms that appear in the description of the embodiments of the present application are subject to the following interpretations:

[0041] SOC, State of Charge, battery state of charge;

[0042] Q loss , Cumulative capacity loss battery capacity loss;

[0043] Q loss,cal , Calendar capacity loss storage capacity loss;

[0044] Q loss,cyc , Cycle capacity loss cycle capacity loss;

[0045] DOD, Depth of Discharge.

[0046] It should be noted that the collected information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data for display, data for analysis, etc.) involved in this application are information and data authorized by the user or fully authorized by all parties, and the collection, storage, use, processing, transmission, provision, disclosure and application of the relevant data comply with relevant laws, regulations and standards, take necessary confidentiality measures, do not violate public order and good morals, and provide corresponding operation portals for users to choose to authorize or refuse. For example, an interface is set up between this system and relevant users or institutions to provide users with corresponding operation portals for users to choose to agree or refuse the automated decision-making results; if the user chooses to refuse, the expert decision-making process will be entered.

[0047] Example 1

[0048] According to an embodiment of the present application, an embodiment of a method for data processing based on a lithium battery pack is also provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0049] The method embodiment provided in the first embodiment of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Figure 1 The hardware structure block diagram of a computer terminal (or mobile device) for implementing a data processing method based on a lithium battery pack is shown. Figure 1 As shown, the computer terminal 10 (or mobile device) may include one or more (illustrated as 102a, 102b, ..., 102n in the figure) processors 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA), a memory 104 for storing data, and a transmission device 106 for communication functions. In addition, it may also include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the BUS bus), a network interface, a power supply and / or a camera. It will be understood by those skilled in the art that Figure 1 The structure shown is only for illustration and does not limit the structure of the above electronic device. Figure 1 More or fewer components than shown, or with Figure 1 Different configurations shown.

[0050] It should be noted that the one or more processors 102 and / or other data processing circuits described above may generally be referred to herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part as software, hardware, firmware, or any other combination thereof. In addition, the data processing circuitry may be a single independent processing module, or may be incorporated in whole or in part into any of the other components of the computer terminal 10 (or mobile device). As described in the embodiments of the present application, the data processing circuitry serves as a processor control (e.g., selection of a variable resistor terminal path connected to an interface).

[0051] The memory 104 can be used to store software programs and modules of application software, such as the program instructions / data storage device corresponding to the data processing method based on the lithium battery pack in the embodiment of the present application. The processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, that is, implementing the above-mentioned data processing method based on the lithium battery pack. The memory 104 may include a high-speed random access memory and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely located relative to the processor 102, and these remote memories may be connected to the computer terminal 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0052] The transmission device 106 is configured to receive or transmit data via a network. A specific example of the aforementioned network may include a wireless network provided by the communications provider of the computer terminal 10. In one embodiment, the transmission device 106 includes a network interface controller (NIC), which can be connected to other network devices via a base station to enable communication with the Internet. In another embodiment, the transmission device 106 may be a radio frequency (RF) module, which is configured to communicate with the Internet wirelessly.

[0053] The display may be, for example, a touch screen liquid crystal display (LCD) that enables a user to interact with a user interface of the computer terminal 10 (or mobile device).

[0054] Under the above operating environment, this application provides Figure 2 The data processing method based on lithium battery pack is shown. Figure 2 This is a flow chart of a data processing method based on a lithium battery pack according to the first embodiment of the present application.

[0055] Step S201: Obtain the historical attenuation of the power of the target lithium battery pack.

[0056] Optionally, the attenuation refers to the maximum amount of electricity or capacity that can be released by the battery during use, relative to the reduction in initial capacity. It is usually expressed as a percentage, that is, the percentage by which the current battery capacity has decreased compared to the brand new state of the battery. If the target lithium battery pack to be predicted is a used lithium battery, the battery management system can indirectly calculate the current capacity attenuation by monitoring the charging and discharging process of the battery. The historical attenuation (that is, the historical attenuation mentioned above) can also be calculated by the target cycle attenuation prediction model and the target calendar attenuation prediction model proposed in this application. If the single lithium battery to be predicted is a fresh, unused battery, the corresponding historical attenuation is 0. When predicting battery life, the historical attenuation serves as a starting point and reference benchmark to help the prediction model evaluate the subsequent aging rate.

[0057] Step S202 , obtaining a target cycle attenuation prediction model of a target lithium battery pack and obtaining a target calendar attenuation prediction model of a target lithium battery pack.

[0058] Optionally, a target cycle decay prediction model and a target calendar decay prediction model can be obtained by fitting the laboratory aging history data of the target lithium battery pack. It should be noted that the target cycle decay prediction model can be a model used to describe the relationship between cycle decay and cumulative throughput, and the target calendar decay prediction model can be a model used to describe the relationship between calendar decay and storage duration.

[0059] For example, the charge and discharge data and storage data of the target lithium battery pack under different usage conditions in the past are collected, and statistical analysis tools are used to fit and analyze the data to obtain the target cycle attenuation prediction model and the target calendar attenuation prediction model.

[0060] Step S203 , calculating the attenuation of the target lithium battery pack based on the historical attenuation using the target cycle attenuation prediction model and the target calendar attenuation prediction model to obtain a target attenuation.

[0061] Optionally, the target cycle attenuation prediction model is fitted based on the historical data of the battery under different operating conditions, wherein the operating condition data includes but is not limited to the battery's charge and discharge rate (Crate), the battery's thermodynamic temperature, the battery's cumulative throughput (Ah), etc. Parameters such as the current number of charge and discharge times, charge and discharge rate, temperature, and historical attenuation are then input into the target cycle attenuation prediction model to obtain the cycle attenuation of the target lithium battery pack in the next usage cycle. For the calendar attenuation of the battery pack during the expected unused (stationary) time, parameters such as the stationary temperature, expected stationary time, state of charge, and historical attenuation are input into the target calendar attenuation prediction model to obtain the possible calendar attenuation of the battery during the inactive period. Finally, the calculated target cycle attenuation and target calendar attenuation are added to obtain the total attenuation of the target lithium battery pack in the expected future period.

[0062] It should be noted that the battery management system can obtain historical operating condition data for the target lithium battery pack. This historical operating condition data includes but is not limited to operating time characteristics, charge and discharge rates, duration, temperature, SOC, discharge capacity, cycle count, and other data. These parameters directly reflect the battery's operating intensity and usage environment and are the main factors affecting battery life.

[0063] If the target lithium battery pack to be predicted is a fresh battery, that is, an unused battery pack, the operating condition data of the target lithium battery pack to be predicted can be determined based on operating condition data similar to the operating condition of the target lithium battery pack.

[0064] Step S204 : determining the remaining life information of the target lithium battery pack according to the target attenuation amount.

[0065] Optionally, the remaining life information refers to the length of time or number of cycles that the target lithium battery pack is expected to continue to operate normally under given usage conditions until its capacity decays to a certain critical value, usually 70% or less of the rated capacity, which marks the end of the effective life of the battery pack.

[0066] First, determine a capacity threshold (such as 70% of the rated capacity). When the capacity of the battery pack is lower than this threshold, it is considered that its life is about to end. The choice of this threshold can be set according to specific usage scenarios and needs, and is not specifically limited here. Then, the current effective capacity of the battery pack is calculated based on the historical attenuation. Based on the target cycle attenuation and the target calendar attenuation, the target attenuation is accumulated within the expected usage cycle to obtain the total expected capacity loss. Subtract the total expected capacity loss from the current capacity to estimate the remaining capacity of the battery pack after experiencing the predicted operating conditions. Finally, after the remaining capacity is less than or equal to the preset capacity threshold, the remaining life information of the target lithium battery pack is obtained based on the expected usage cycle.

[0067] In summary, by using the target cycle attenuation prediction model and the target calendar attenuation prediction model, combined with the historical attenuation, the attenuation of the target lithium battery pack can be quickly calculated, avoiding the detailed simulation of the electrochemical behavior of each battery cell in the traditional method, shortening the calculation time, and at the same time using the target cycle attenuation prediction model and the target calendar attenuation prediction model can comprehensively consider the attenuation characteristics of the battery under different usage conditions (such as charge and discharge cycles and static), thereby more accurately predicting the remaining life information of the battery pack.

[0068] Optionally, in the data processing method based on the lithium battery pack provided in the embodiment of the present application, obtaining the target cycle attenuation prediction model of the target lithium battery pack and obtaining the target calendar attenuation prediction model of the target lithium battery pack include: obtaining the predicted cycle attenuation prediction model corresponding to the single lithium battery in the historical lithium battery pack; determining the target cycle attenuation prediction model based on the predicted cycle attenuation prediction model; obtaining the initial calendar attenuation prediction model corresponding to the single lithium battery in the historical lithium battery pack; and determining the target calendar attenuation prediction model based on the initial calendar attenuation prediction model.

[0069] Determining a target cycle attenuation prediction model based on the predicted cycle attenuation prediction model includes: constructing a second cycle attenuation prediction model based on target test data of cycle aging of historical lithium battery packs; obtaining an attenuation loss coefficient between the historical lithium battery pack and a single lithium battery based on the predicted cycle attenuation prediction model and the second cycle attenuation prediction model; and obtaining a target cycle attenuation prediction model based on the attenuation loss coefficient and the second cycle attenuation prediction model.

[0070] In an optional embodiment, in the lithium battery pack data processing method provided in the embodiment of the present application, the process of obtaining the target cycle attenuation prediction model and the target calendar attenuation prediction model for the target lithium battery pack is achieved through a two-step modeling and the introduction of a depreciation coefficient. First, a predicted cycle attenuation prediction model is constructed based on the historical data of single lithium batteries in the lithium battery pack. This model reflects the capacity attenuation law of single batteries under normal cyclic charge and discharge conditions.

[0071] For example, laboratory aging history data collection: Collecting single-cell lithium battery cycle aging test data and calendar aging test data, including but not limited to battery voltage, temperature, charge and discharge rate, SOC (state of charge), SOH (state of health), discharge depth, SOC cycle range, charge and discharge capacity and other parameters. Based on the cycle aging test data, a cycle attenuation prediction model is constructed based on the relationship between cycle attenuation and cumulative throughput. For example, using statistical or machine learning methods (such as linear regression, etc.), a model that can characterize the relationship between cycle attenuation and cumulative throughput is fitted, that is, a predicted cycle attenuation prediction model.

[0072] In an optional embodiment, the cyclic attenuation prediction model is shown in formula (1):

[0073]

[0074] Among them, T1 is the thermodynamic temperature, that is, the thermodynamic temperature of the battery cycle aging test; Ea1 is the first activation energy; R is the ideal gas constant (constant value); Crate is the charge and discharge rate; m is a parameter related to the depth of discharge and SOC cycle range, k is the weight of the charge and discharge rate, which can be obtained by fitting laboratory data; Ah is the cumulative throughput of the battery (which can be equivalent to the number of cycles); b1 and z1 are parameters to be fitted, that is, the first parameter and the second parameter respectively.

[0075] In an optional embodiment, the schematic diagram of the cycle attenuation curve of the single cell is as follows: Figure 3 As shown in FIG, Cycle NUM is the number of cycles, which is equivalent to the cumulative throughput of the battery. Figure 3 The experimental and predicted cyclic decay curves at different temperatures are shown.

[0076] Then, according to the working condition characteristics of the lithium battery pack, the average temperature of the battery pack, the charge and discharge rate of the battery pack, the cumulative throughput of the battery pack and the Q loss,cyc,pack (capacity attenuation value), build a cycle attenuation model for the lithium battery pack, use the historical cycle data of the battery pack collected at the beginning, bring the historical cycle-related data into the initial cycle attenuation formula, fit the parameters to be fitted in the attenuation model, and then obtain the second cycle attenuation prediction model corresponding to the battery pack.

[0077] For example, the second cycle attenuation prediction model shown in formula (2) is:

[0078]

[0079] Among them, Q loss,cyc,pack represents the battery pack cycle attenuation, T3 is the thermodynamic temperature, that is, the thermodynamic average temperature of the battery pack cycle aging test; Ea3 is the third activation energy (which can be obtained by fitting in the laboratory); R is the ideal gas constant (constant value); Crate is the charge and discharge rate; Ah is the cumulative throughput of the battery pack; b3 and z3 are parameters to be fitted.

[0080] Subtle differences in process parameters during battery manufacturing lead to varying performance of individual cells, such as capacity, internal resistance, and self-discharge rate. These differences in individual cell performance can lead to varying aging states among individual cells within the system after assembly. Therefore, it is necessary to determine the attenuation loss factor n between the lithium battery pack and individual lithium battery cells. For example, the attenuation loss factor n can be calculated by calculating the ratio of the number of cycles of the battery pack to the number of cycles of the individual cells.

[0081] Finally, the second cyclic attenuation prediction model is modified according to the attenuation loss coefficient to obtain the target cyclic attenuation prediction model. For example, the target cyclic attenuation prediction model is shown in formula (3):

[0082]

[0083] In an optional embodiment, a schematic diagram of a cycle decay curve of a battery pack is shown as follows: Figure 4 As shown, Cycle NUM is the number of cycles, equivalent to the cumulative throughput of the battery pack. At a specific Qloss, the cycle number of the battery pack module data is divided by the number of cycles of the battery cell in the previous step at the corresponding temperature of the battery pack to calculate the cycle life conversion factor from the single cell to the battery pack. Here, it is 0.9. Figure 4 The experimental cycle attenuation curve and predicted cycle attenuation curve of the battery pack, as well as the cycle attenuation curve corresponding to a single cell*0.9 are displayed.

[0084] In an optional embodiment, determining the target calendar decay prediction model according to the initial calendar decay prediction model includes:

[0085] Based on the calendar aging test data of single lithium batteries, an initial calendar decay prediction model is constructed regarding the relationship between calendar decay and storage time.

[0086] In an optional embodiment, the initial calendar attenuation prediction model is shown in formula (4):

[0087]

[0088] Among them, Q loss,cal represents the calendar attenuation of the single cell battery, T2 is the thermodynamic temperature, that is, the thermodynamic temperature of the single cell calendar aging test environment; Ea2 is the second activation energy; R is the ideal gas constant (constant value); t is the standing time (that is, storage time), the unit is second; f(SOC) is a function related to the standing SOC; b2 and z2 are parameters to be fitted, that is, the third parameter and the fourth parameter respectively.

[0089] It should be noted that f1(SOC) and f2(SOC) are both functions related to the static SOC, that is, functions of the influence of different SOCs on storage capacity loss, which can be obtained by fitting laboratory test data. For example, f1(SOC) and f2(SOC) are functions after fitting two different curves, for example, f1(SOC) is a linear curve and f2(SOC) is a quadratic curve.

[0090] In an optional embodiment, the schematic diagram of the calendar decay curve of a single battery is as follows: Figure 5 As shown, Figure 5The experimental calendar decay curves and calendar cycle decay curves at different temperatures are shown.

[0091] Since storage attenuation does not produce much difference between single cells and battery packs, the initial calendar attenuation prediction model can be directly determined as the target calendar attenuation prediction model for the lithium battery pack.

[0092] By introducing the attenuation loss coefficient, the difference in cycle aging between battery cells and battery packs can be quantified and corrected, making the target cycle attenuation prediction model closer to the actual attenuation of the battery pack and improving the accuracy of the prediction.

[0093] Optionally, in the data processing method based on the lithium battery pack provided in the embodiment of the present application, obtaining the attenuation loss coefficient between the historical lithium battery pack and the single lithium battery according to the predicted cycle attenuation prediction model and the second cycle attenuation prediction model includes: determining the total number of first charge and discharge cycles corresponding to the power of the single lithium battery according to the predicted cycle attenuation prediction model; determining the total number of second charge and discharge cycles corresponding to the power of the historical lithium battery pack according to the second cycle attenuation prediction model; and calculating based on the total number of first charge and discharge cycles and the total number of second charge and discharge cycles to obtain the attenuation loss coefficient between the historical lithium battery pack and the single lithium battery.

[0094] In an optional embodiment, based on the single cell cycle attenuation formula (i.e., the first cycle attenuation prediction model) and the battery pack cycle attenuation formula (i.e., the second cycle attenuation prediction model), the attenuation loss caused by single cell inconsistency under different Qloss conditions can be calculated, and the calculation method is as shown in formula (5).

[0095]

[0096] Among them, Cyc Qloss0_Pack Cyc is the number of cycles of the battery pack under a certain Qloss. Qloss0_cell = is the number of cycles per cell under the same Qloss as the battery pack. It should be noted that the cumulative throughput of a cell can be calculated based on Qloss and the first-cycle attenuation prediction model, and the corresponding number of cycles per cell can be calculated based on the cumulative throughput. The calculation method for battery pack cycles is the same as that for individual cells and is not repeated here.

[0097] By introducing the attenuation loss coefficient, the difference in cycle aging between battery cells and battery packs can be quantified and corrected, making the target cycle attenuation prediction model closer to the actual attenuation of the battery pack and improving the accuracy of the prediction.

[0098] Optionally, in the data processing method based on the lithium battery pack provided in the embodiment of the present application, the attenuation of the target lithium battery pack is calculated based on the historical attenuation by the target cycle attenuation prediction model and the target calendar attenuation prediction model, and the target attenuation is obtained, including: for the target time period, the target cycle attenuation prediction model is used to calculate based on the historical attenuation to obtain the predicted cycle attenuation of the target lithium battery pack within the target time period; the target calendar attenuation prediction model is used to calculate based on the predicted cycle attenuation and the historical attenuation to obtain the predicted calendar attenuation of the target lithium battery pack within the target time period; and the target attenuation is obtained by calculation based on the predicted cycle attenuation and the predicted calendar attenuation.

[0099] In an optional embodiment, calculating the attenuation of the target lithium battery pack based on the historical attenuation using the target cycle attenuation prediction model and the target calendar attenuation prediction model includes the following steps:

[0100] For a target time period (e.g., a workday, a week, etc.), the predicted cyclic attenuation due to charge and discharge cycles within the target time period is first calculated using historical attenuation and a target cyclic attenuation prediction model built based on historical operating condition data. Next, the predicted calendar attenuation for the target time period due to non-use or storage is calculated based on the predicted cyclic attenuation, historical operating condition data, and the target calendar attenuation prediction model. Finally, the predicted cyclic attenuation and predicted calendar attenuation are added together to obtain the target attenuation for the target time period.

[0101] In an optional embodiment, the cycle attenuation rate is a proportional factor that describes the degree of capacity loss after each cycle of the battery pack, which is the percentage of battery pack capacity reduction after each charge and discharge cycle. This ratio is affected by many factors, including the depth of charge and discharge (DOD), charge and discharge rate (Crate), battery pack temperature, etc. In the embodiment of the present application, the historical attenuation and the target cycle attenuation prediction model constructed based on historical operating condition data are used for calculation to obtain the predicted cycle attenuation caused by the charge and discharge cycle in the target time period, including the following steps:

[0102] During a target time period, the cyclic decay rate for that time interval can be calculated based on historical decay data, historical operating condition data, and a cyclic decay prediction model. For example, by inputting historical decay data into the target cyclic decay prediction model, the cumulative throughput of the battery pack corresponding to that decay data (i.e., the current cumulative throughput mentioned above) can be calculated in reverse order, i.e., the total charge and discharge volume experienced by the battery pack to date.

[0103] The historical operating condition data includes key information about the battery pack's past charge and discharge cycles, charge and discharge depth, temperature, and charge and discharge rate. Based on this data, the battery pack's cumulative throughput can be predicted after a target time period, yielding the predicted cumulative throughput. This predicted cumulative throughput is then fed into the target cycle decay prediction model, which in turn calculates the derived cycle decay corresponding to this decay.

[0104] The cycle decay rate is then calculated based on the historical decay, current cumulative throughput, predicted cumulative throughput, and derived cycle decay. The cycle decay rate reflects the ratio between the battery pack's capacity loss and the change in charge and discharge throughput over a target time period. Based on historical operating condition data, the number of charge and discharge cycles the battery pack will complete within a target time period (e.g., one day, one week, etc.) is predicted.

[0105] Finally, combined with the calculated cycle decay rate And the number of cycles cyc of the battery pack in the target time period, the predicted cycle attenuation can be calculated using the following formula:

[0106]

[0107] By calculating the cycle attenuation rate in the target time period and then calculating the predicted cycle attenuation amount based on the cycle attenuation rate, the aging rate of the battery pack can be evaluated more accurately, thereby improving the accuracy of life prediction.

[0108] In an optional embodiment, the target calendar attenuation prediction model is used to calculate based on the predicted cycle attenuation and the historical attenuation to obtain the predicted calendar attenuation of the target lithium battery pack within the target time period, including: calculating the predicted cycle attenuation and the historical attenuation to obtain the total attenuation value; calculating the total attenuation value and the duration corresponding to the target time period through the target calendar attenuation prediction model to obtain the predicted calendar attenuation rate of the target lithium battery pack in the target time period; and calculating based on the predicted calendar attenuation rate and the storage duration in the target time period to obtain the predicted calendar attenuation.

[0109] In an optional embodiment, the following steps can be used to calculate the predicted calendar attenuation: based on the predicted cycle attenuation of the lithium battery pack in the target time period, the calendar attenuation rate is calculated in combination with the calendar attenuation prediction model. The total attenuation value is obtained by summing the predicted cycle attenuation and the current attenuation, and then the total attenuation value is brought into the calendar attenuation prediction model to reversely obtain the corresponding first storage duration. Then, based on the analysis of historical operating data, the actual static or storage time of the battery in the target time period can be estimated. For example, if the target time period is set to one day, and the battery is only used for 6 hours in this day, the remaining 18 hours of storage time will be used for the calculation of subsequent calendar attenuation. The estimated storage time is input into the calendar attenuation prediction model to obtain the corresponding inferred calendar attenuation, and then the calendar attenuation rate can be calculated based on the total attenuation value, the first storage duration, the estimated storage duration and the inferred calendar attenuation. Finally, the calculated calendar attenuation rate is combined with the predicted storage duration to calculate the predicted calendar attenuation corresponding to the target time period, for example, is the calendar decay rate, t2 is the target time period, and t1 is the charge and discharge duration.

[0110] In an optional embodiment, the following steps can be used to calculate the capacity decay Q within the target time: loss,all First, calculate the past cycle attenuation and past storage attenuation of the lithium battery pack in each past time period before the target time; secondly, the sum of all past cycle attenuation and all past storage attenuation is taken as the total past attenuation Q loss,0 ; Using Q loss,0 Combined with the cyclic attenuation prediction model, the current cyclic attenuation rate is calculated and the decay rate of the current cyclic attenuation is determined. The cyclic attenuation in the target time period is calculated by multiplying the current cyclic attenuation rate by the number of cycles in the target cyclic time period. Determine; determine all capacity losses within the target time period before the start of the storage time period Combined with the calendar decay prediction model, the calendar decay rate in the subsequent storage period is calculated The calendar decay amount in the target time period is the product of the current calendar decay rate and the storage time in the target time period. Confirm; the sum of all past total attenuation, cyclic attenuation within the current target time period, and calendar attenuation within the target time period Q loss,all As the total attenuation. For example, the total attenuation

[0111] By calculating the calendar decay rate of the target time period and then calculating the predicted calendar decay amount based on the calendar decay rate, combined with the battery's cycle decay, a more comprehensive and refined battery aging assessment can be provided.

[0112] Optionally, in the data processing method based on the lithium battery pack provided in the embodiment of the present application, determining the remaining life information of the target lithium battery pack based on the target attenuation includes: calculating based on the target attenuation and the historical attenuation to obtain the cumulative attenuation; judging whether the cumulative attenuation is greater than or equal to a preset threshold; if the cumulative attenuation is greater than or equal to the preset threshold, determining the remaining life information based on the target time period corresponding to the target attenuation.

[0113] In an optional embodiment, determining the remaining life information of a target lithium battery pack includes: first, determining the historical attenuation of the battery pack, i.e., the total loss of battery capacity during all past charge / discharge cycles and storage processes from the time the battery pack was first put into use to the current time point. Then, the predicted attenuation during the target time period (the sum of the target cycle attenuation and the target calendar attenuation) is added to the historical attenuation to obtain a cumulative attenuation.

[0114] A preset threshold is a critical value for capacity loss or performance degradation set by the battery manufacturer or end user, typically related to battery performance assurance or safety. For example, a battery may be considered to have reached the end of its life when its capacity drops below 70% of its original capacity. The calculated cumulative decay is compared with the preset threshold to determine whether the battery pack is approaching or has reached the critical point in its life. If the cumulative decay is greater than or equal to the preset threshold, the target time period is used as the remaining life information.

[0115] The cumulative attenuation obtained by comprehensively calculating the target attenuation and the historical attenuation, combined with the judgment of the preset threshold, can accurately evaluate the remaining life of the lithium battery pack.

[0116] Optionally, in the data processing method based on the lithium battery pack provided in the embodiment of the present application, after determining whether the cumulative attenuation is greater than or equal to a preset threshold, the method also includes: if the cumulative attenuation is less than the preset threshold, repeatedly executing the step of calculating the attenuation of the target lithium battery pack based on the historical attenuation through the target cycle attenuation prediction model and the target calendar attenuation prediction model until the cumulative attenuation is greater than or equal to the preset threshold, and determining the remaining life information based on the time corresponding to the cumulative attenuation.

[0117] In an optional embodiment, if the cumulative decay value is less than a preset threshold, indicating that the battery pack still has sufficient performance margin, the prediction phase proceeds to the next target time period, repeating the prediction using the target cycle decay prediction model and the target calendar decay prediction model. As time progresses, the cumulative decay value is continuously updated. When the cumulative decay value at a certain moment reaches or exceeds a preset threshold, the prediction cycle is terminated, and the remaining life information is determined based on the duration of the cumulative decay value at that time.

[0118] Predicting the remaining life of a battery pack by dividing it into time periods can more accurately calculate the cumulative attenuation and thus more accurately determine the remaining life information.

[0119] In an optional embodiment, the method for determining the target battery pack operating condition attenuation includes the following steps:

[0120] First, calculate the past cycle attenuation and past storage attenuation of the battery pack in each past time period before the target time; secondly, the sum of all past cycle attenuation and all past storage attenuation is taken as the total past attenuation Q loss,0 ; Using Q loss,0 Combined with the target cyclic attenuation prediction model, the current cyclic attenuation rate is calculated and the attenuation rate of the next cyclic attenuation is determined. The cyclic attenuation in the target time period is calculated by multiplying the current cyclic attenuation rate by the number of cycles in the target cyclic time period. Determine; determine all capacity losses within the target time period before the start of the storage time period Combined with the target calendar decay prediction model, the storage decay rate in the subsequent storage period is calculated The storage decay amount in the target time period is the product of the current storage decay rate and the storage duration in the target time period. Confirm; the sum of all past total attenuation, the cyclic attenuation in the current target time period, and the stored attenuation in the target time period The target battery pack's operating condition degradation is determined by subtracting the total degradation from the target constant. The target battery pack has a certain service life, known as its degradation life. This degradation life can be divided into multiple time periods based on predetermined time units. The divided time periods include the target time period and the previous time periods before it. The duration of the previous time periods may not be the same as the target time period.

[0121] It should be noted that the past cyclic attenuation and the target cyclic attenuation in the past time period are calculated in the same way, and the past storage attenuation and the target storage attenuation are also calculated in the same way, and the specific methods will not be repeated.

[0122] This application addresses the issue of varying cell-to-pack lifespans by discounting cell-to-module and system cycle attenuation. Furthermore, this application can calculate the service lifespan of a battery pack based on cell lifespan, eliminating the need for extensive battery pack testing and reducing experimental costs.

[0123] In an optional embodiment, the operating life aging attenuation curve of the battery pack under different operating conditions is as follows: Figure 6 shown. Figure 6 The Calendarloss is the calendar attenuation curve, the Cycleloss is the cycle attenuation curve, and the operating loss is the operating life aging attenuation curve. The attenuation data of the actual operation of the battery pack (scattered data) is compared with the attenuation trajectory (dashed line data) calculated by the method of this application. Figure 7 and Figure 8 As shown, Figure 7 This is the working cycle attenuation trajectory of a certain liquid-cooled energy storage cabinet. Figure 7 The attenuation data of the actual operation of the battery pack (scattered data) and the attenuation trajectory calculated by the method of this application (dashed line data). Figure 8 This is a schematic diagram of the cumulative discharge curve of a certain liquid-cooled energy storage cabinet. Figure 7 and Figure 8 It can be seen that the decay trajectory calculated by this application is very consistent with the actual one, and can be used to evaluate the life of the actual battery pack under actual operating conditions. The corresponding cumulative discharge amount is also highly consistent. Therefore, the life prediction method provided by this application can accurately predict the life information of lithium battery packs.

[0124] The data processing method based on the lithium battery pack provided in the embodiment of the present application obtains the historical attenuation of the power of the target lithium battery pack; obtains a target cycle attenuation prediction model for the target lithium battery pack and a target calendar attenuation prediction model for the target lithium battery pack; calculates the attenuation of the target lithium battery pack based on the historical attenuation using the target cycle attenuation prediction model and the target calendar attenuation prediction model to obtain a target attenuation; and determines the remaining life information of the target lithium battery pack based on the target attenuation, thereby solving the technical problem in the related art of predicting the service life of the lithium battery pack by constructing an electrochemical model, resulting in relatively low efficiency in life prediction.

[0125] In this scheme, by using the target cycle attenuation prediction model and the target calendar attenuation prediction model, combined with the historical attenuation, the attenuation of the target lithium battery pack can be quickly calculated, avoiding the detailed simulation of the electrochemical behavior of each battery cell in the traditional method, shortening the calculation time, and at the same time using the target cycle attenuation prediction model and the target calendar attenuation prediction model to comprehensively consider the attenuation characteristics of the battery under different usage conditions (such as charge and discharge cycles and static), thereby more accurately predicting the remaining life information of the battery pack.

[0126] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0127] Example 2

[0128] The present application also provides a data processing device based on a lithium battery pack. It should be noted that the data processing device based on a lithium battery pack according to the present application can be used to execute the data processing method for a lithium battery pack according to the present application. The following describes the data processing device based on a lithium battery pack according to the present application.

[0129] According to an embodiment of the present application, a device for implementing the above-mentioned data processing method based on a lithium battery pack is also provided, such as Figure 9 As shown, the device includes: a first acquiring unit 901 , a second acquiring unit 902 , a calculating unit 903 and a determining unit 904 .

[0130] A first acquiring unit 901 is configured to acquire a historical attenuation of the power level of a target lithium battery pack;

[0131] A second acquiring unit 902 is configured to acquire a target cycle attenuation prediction model for a target lithium battery pack and a target calendar attenuation prediction model for a target lithium battery pack;

[0132] A calculation unit 903 is configured to calculate the attenuation of a target lithium battery pack based on historical attenuation using a target cycle attenuation prediction model and a target calendar attenuation prediction model to obtain a target attenuation;

[0133] The determining unit 904 is configured to determine the remaining life information of the target lithium battery pack according to the target attenuation amount.

[0134] The data processing device based on the lithium battery pack provided in the embodiment of the present application is used to obtain the historical attenuation of the power of the target lithium battery pack through the first acquisition unit 901; the second acquisition unit 902 is used to obtain the target cycle attenuation prediction model of the target lithium battery pack and the target calendar attenuation prediction model of the target lithium battery pack; the calculation unit 903 is used to calculate the attenuation of the target lithium battery pack based on the historical attenuation through the target cycle attenuation prediction model and the target calendar attenuation prediction model to obtain the target attenuation; the determination unit 904 is used to determine the remaining life information of the target lithium battery pack based on the target attenuation, which solves the technical problem in the related art of predicting the service life of the lithium battery pack by constructing an electrochemical model, resulting in relatively low efficiency in life prediction.

[0135] In this scheme, by using the target cycle attenuation prediction model and the target calendar attenuation prediction model, combined with the historical attenuation, the attenuation of the target lithium battery pack can be quickly calculated, avoiding the detailed simulation of the electrochemical behavior of each battery cell in the traditional method, shortening the calculation time, and at the same time using the target cycle attenuation prediction model and the target calendar attenuation prediction model to comprehensively consider the attenuation characteristics of the battery under different usage conditions (such as charge and discharge cycles and static), thereby more accurately predicting the remaining life information of the battery pack.

[0136] Optionally, in the data processing device based on the lithium battery pack provided in the embodiment of the present application, the second acquisition unit includes: a first acquisition sub-unit, used to obtain the predicted cycle attenuation prediction model corresponding to the single lithium battery in the historical lithium battery pack; a first determination sub-unit, used to determine the target cycle attenuation prediction model based on the predicted cycle attenuation prediction model; a second acquisition sub-unit, used to obtain the initial calendar attenuation prediction model corresponding to the single lithium battery in the historical lithium battery pack; and a second determination sub-unit, used to determine the target calendar attenuation prediction model based on the initial calendar attenuation prediction model.

[0137] Optionally, in the data processing device based on the lithium battery pack provided in the embodiment of the present application, the first determination subunit includes: a construction module, which is used to construct a second cycle attenuation prediction model based on the target test data of the cycle aging of the historical lithium battery pack; a first determination module, which is used to obtain the attenuation loss coefficient between the historical lithium battery pack and the single lithium battery based on the predicted cycle attenuation prediction model and the second cycle attenuation prediction model; and a second determination module, which is used to obtain the target cycle attenuation prediction model based on the attenuation loss coefficient and the second cycle attenuation prediction model.

[0138] Optionally, in the data processing device based on the lithium battery pack provided in the embodiment of the present application, the first determination module includes: a first determination submodule, used to determine the total number of first charge and discharge cycles corresponding to the power of the single lithium battery based on the predicted cycle attenuation prediction model; a second determination submodule, used to determine the total number of second charge and discharge cycles corresponding to the power of the historical lithium battery pack based on the second cycle attenuation prediction model; a calculation submodule, used to calculate based on the total number of the first charge and discharge cycles and the total number of the second charge and discharge cycles to obtain the attenuation loss coefficient between the historical lithium battery pack and the single lithium battery.

[0139] Optionally, in the data processing device based on the lithium battery pack provided in the embodiment of the present application, the calculation unit includes: a first calculation subunit, which is used to calculate, for a target time period, based on the historical attenuation through a target cycle attenuation prediction model, to obtain the predicted cycle attenuation of the target lithium battery pack within the target time period; a second calculation subunit, which is used to calculate based on the predicted cycle attenuation and the historical attenuation through a target calendar attenuation prediction model, to obtain the predicted calendar attenuation of the target lithium battery pack within the target time period; and a third calculation subunit, which is used to calculate based on the predicted cycle attenuation and the predicted calendar attenuation to obtain the target attenuation.

[0140] Optionally, in the data processing device based on the lithium battery pack provided in the embodiment of the present application, the second calculation subunit includes: a first calculation module, used to calculate the predicted cyclic attenuation and the historical attenuation to obtain the total attenuation value; a second calculation module, used to calculate the total attenuation value and the duration corresponding to the target time period through the target calendar attenuation prediction model to obtain the predicted calendar attenuation rate of the target lithium battery pack in the target time period; a third calculation module, used to calculate based on the predicted calendar attenuation rate and the storage duration in the target time period to obtain the predicted calendar attenuation.

[0141] Optionally, in the data processing device based on the lithium battery pack provided in the embodiment of the present application, the determination unit includes: a fourth calculation subunit, used to calculate based on the target attenuation amount and the historical attenuation amount to obtain the cumulative attenuation amount; a judgment subunit, used to judge whether the cumulative attenuation amount is greater than or equal to a preset threshold; a third determination subunit, used to determine the remaining life information based on the target time period corresponding to the target attenuation amount if the cumulative attenuation amount is greater than or equal to the preset threshold.

[0142] Optionally, in the data processing device based on the lithium battery pack provided in the embodiment of the present application, the device also includes: an execution unit, which is used to repeatedly execute the step of calculating the attenuation of the target lithium battery pack based on the historical attenuation through the target cycle attenuation prediction model and the target calendar attenuation prediction model after determining whether the cumulative attenuation is greater than or equal to the preset threshold, if the cumulative attenuation is less than the preset threshold, until the cumulative attenuation is greater than or equal to the preset threshold, and determine the remaining life information based on the time corresponding to the cumulative attenuation.

[0143] It should be noted that the first acquisition unit 901, the second acquisition unit 902, the calculation unit 903, and the determination unit 904 described above correspond to steps S201 to S204 in the first embodiment. The examples and application scenarios implemented by the four units and the corresponding steps are the same, but are not limited to the contents disclosed in the first embodiment. It should be noted that the above modules or units can be hardware components or software components stored in a memory (e.g., memory 104) and processed by one or more processors (e.g., processors 102a, 102b, ..., 102n). The above modules can also be part of a device and can be run in the computer terminal 10 provided in the first embodiment.

[0144] Example 3

[0145] An embodiment of the present application may provide an electronic device, Figure 10 This is a structural block diagram of an electronic device according to an embodiment of the present application. Figure 10 As shown, the electronic device may include: one or more ( Figure 10 Only one is shown) processor 1002, memory 1004, storage controller, and peripheral interface, wherein the peripheral interface is connected to the radio frequency module, audio module and display.

[0146] Among them, the memory can be used to store software programs and modules, such as program instructions / modules corresponding to the methods and devices in the embodiments of the present application. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implementing the above-mentioned method. The memory may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory may further include a memory remotely arranged relative to the processor, and these remote memories may be connected to the terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network and a combination thereof.

[0147] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: obtain the historical attenuation of the power of the target lithium battery pack; obtain the target cycle attenuation prediction model of the target lithium battery pack and obtain the target calendar attenuation prediction model of the target lithium battery pack; calculate the attenuation of the target lithium battery pack based on the historical attenuation through the target cycle attenuation prediction model and the target calendar attenuation prediction model to obtain the target attenuation; determine the remaining life information of the target lithium battery pack based on the target attenuation.

[0148] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: obtaining the target cycle attenuation prediction model of the target lithium battery pack and obtaining the target calendar attenuation prediction model of the target lithium battery pack include: obtaining the predicted cycle attenuation prediction model corresponding to the single lithium battery in the historical lithium battery pack; determining the target cycle attenuation prediction model based on the predicted cycle attenuation prediction model; obtaining the initial calendar attenuation prediction model corresponding to the single lithium battery in the historical lithium battery pack; determining the target calendar attenuation prediction model based on the initial calendar attenuation prediction model.

[0149] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: determining the target cycle attenuation prediction model based on the predicted cycle attenuation prediction model, including: constructing a second cycle attenuation prediction model based on the target test data of the cycle aging of the historical lithium battery pack; obtaining the attenuation loss coefficient between the historical lithium battery pack and the single lithium battery based on the predicted cycle attenuation prediction model and the second cycle attenuation prediction model; obtaining the target cycle attenuation prediction model based on the attenuation loss coefficient and the second cycle attenuation prediction model.

[0150] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: based on the predicted cycle attenuation prediction model and the second cycle attenuation prediction model, obtain the attenuation loss coefficient between the historical lithium battery group and the single lithium battery, including: based on the predicted cycle attenuation prediction model, determine the total number of first charge and discharge cycles corresponding to the power of the single lithium battery; based on the second cycle attenuation prediction model, determine the total number of second charge and discharge cycles corresponding to the power of the historical lithium battery group; calculate based on the total number of first charge and discharge cycles and the total number of second charge and discharge cycles to obtain the attenuation loss coefficient between the historical lithium battery group and the single lithium battery.

[0151] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: calculate the attenuation of the target lithium battery pack based on the historical attenuation through the target cycle attenuation prediction model and the target calendar attenuation prediction model to obtain the target attenuation, including: for the target time period, calculate based on the historical attenuation through the target cycle attenuation prediction model to obtain the predicted cycle attenuation of the target lithium battery pack within the target time period; calculate based on the predicted cycle attenuation and the historical attenuation through the target calendar attenuation prediction model to obtain the predicted calendar attenuation of the target lithium battery pack within the target time period; calculate based on the predicted cycle attenuation and the historical attenuation; calculate according to the predicted cycle attenuation and the predicted calendar attenuation to obtain the target attenuation.

[0152] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: calculate based on the predicted cycle attenuation and the historical attenuation through the target calendar attenuation prediction model to obtain the predicted calendar attenuation of the target lithium battery pack within the target time period, including: calculating the predicted cycle attenuation and the historical attenuation to obtain the total attenuation value; calculating the total attenuation value and the duration corresponding to the target time period through the target calendar attenuation prediction model to obtain the predicted calendar attenuation rate of the target lithium battery pack in the target time period; calculating according to the predicted calendar attenuation rate and the storage duration in the target time period to obtain the predicted calendar attenuation.

[0153] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: determine the remaining life information of the target lithium battery pack based on the target attenuation amount, including: calculating based on the target attenuation amount and the historical attenuation amount to obtain the cumulative attenuation amount; judging whether the cumulative attenuation amount is greater than or equal to a preset threshold; if the cumulative attenuation amount is greater than or equal to the preset threshold, then determining the remaining life information based on the target time period corresponding to the target attenuation amount.

[0154] The processor can call the information and application stored in the memory through the transmission device to perform the following steps: After determining whether the cumulative attenuation is greater than or equal to a preset threshold, the method also includes: if the cumulative attenuation is less than the preset threshold, then repeatedly executing the step of calculating the attenuation of the target lithium battery pack based on the historical attenuation through the target cycle attenuation prediction model and the target calendar attenuation prediction model until the cumulative attenuation is greater than or equal to the preset threshold, and determining the remaining life information based on the time corresponding to the cumulative attenuation.

[0155] It can be understood by those skilled in the art that Figure 10 The structure shown is for illustration only, and the electronic device may also be a terminal device such as a smart phone, a tablet computer, a PDA, a mobile Internet device (MID), or a PAD. Figure 10 It does not limit the structure of the above electronic device. For example, the electronic device may also include Figure 10 More or fewer components (such as network interfaces, display devices, etc.) shown in, or with Figure 10 Different configurations shown.

[0156] A person skilled in the art will understand that all or part of the steps in the various methods of the above embodiments can be completed by instructing the hardware related to the terminal device through a program, and the program can be stored in a computer-readable storage medium, which may include: a flash drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0157] Example 4

[0158] The embodiment of the present application further provides a computer-readable storage medium. Optionally, in this embodiment, the storage medium can be used to store the program code executed by the data processing method based on the lithium battery pack provided in the first embodiment.

[0159] Optionally, in this embodiment, the storage medium may be located in any computer terminal in a computer terminal group in a computer network, or in any mobile terminal in a mobile terminal group.

[0160] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing the steps of the data processing method based on a lithium battery pack.

[0161] The serial numbers of the above embodiments of the present application are for description only and do not represent the advantages or disadvantages of the embodiments.

[0162] In the above embodiments of the present application, the description of each embodiment has its own focus. For parts that are not described in detail in a certain embodiment, please refer to the relevant description of other embodiments.

[0163] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, 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 interfaces, indirect coupling or communication connection of units or modules, which can be electrical or other forms.

[0164] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0165] In addition, the functional units in the various embodiments of the present application may be integrated into a single processing unit, or each unit may exist physically separately, or two or more units may be integrated into a single unit. The aforementioned integrated units may be implemented in the form of hardware or software functional units.

[0166] 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 storage medium. Based on this understanding, the technical solution of the present application is essentially 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, and the computer software product is stored in a storage medium, including a number of instructions for enabling a computer device (which can be a personal computer, a server or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk or an optical disk.

[0167] The above is only a preferred embodiment of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A data processing method based on a lithium battery pack, characterized in that: include: Obtain the historical power attenuation of the target lithium battery pack; Obtaining a target cycle attenuation prediction model for the target lithium battery pack and obtaining a target calendar attenuation prediction model for the target lithium battery pack; Calculating the attenuation of the target lithium battery pack based on the historical attenuation using the target cycle attenuation prediction model and the target calendar attenuation prediction model to obtain a target attenuation; The remaining life information of the target lithium battery pack is determined according to the target attenuation amount.

2. The method according to claim 1, characterized in that Obtaining a target cycle attenuation prediction model for the target lithium battery pack and obtaining a target calendar attenuation prediction model for the target lithium battery pack include: Obtain a predicted cycle attenuation prediction model corresponding to a single lithium battery in a historical lithium battery pack; Determining the target cyclic attenuation prediction model according to the predicted cyclic attenuation prediction model; Obtaining an initial calendar attenuation prediction model corresponding to a single lithium battery in the historical lithium battery pack; The target calendar decay prediction model is determined based on the initial calendar decay prediction model.

3. The method according to claim 2, characterized in that Determining the target cyclic attenuation prediction model according to the predicted cyclic attenuation prediction model includes: Constructing a second cycle attenuation prediction model based on the target test data of the historical cycle aging of the lithium battery pack; Obtaining a decay loss coefficient between the historical lithium battery pack and the single lithium battery according to the predicted cycle decay prediction model and the second cycle decay prediction model; The target cyclic attenuation prediction model is obtained according to the attenuation loss coefficient and the second cyclic attenuation prediction model.

4. The method according to claim 3, characterized in that Obtaining the attenuation loss coefficient between the historical lithium battery pack and the single lithium battery according to the predicted cycle attenuation prediction model and the second cycle attenuation prediction model includes: Determining the total number of first charge-discharge cycles corresponding to the power of the single lithium battery according to the predicted cycle attenuation prediction model; Determining the total number of second charge-discharge cycles corresponding to the power level of the historical lithium battery pack according to the second cycle attenuation prediction model; The attenuation loss coefficient between the historical lithium battery pack and the single lithium battery is calculated based on the total number of the first charge and discharge cycles and the total number of the second charge and discharge cycles.

5. The method according to claim 1, wherein Calculating the attenuation of the target lithium battery pack based on the historical attenuation using the target cycle attenuation prediction model and the target calendar attenuation prediction model to obtain the target attenuation includes: For a target time period, the target cycle attenuation prediction model is used to calculate based on the historical attenuation to obtain a predicted cycle attenuation of the target lithium battery pack within the target time period; Calculating the predicted cyclic attenuation and the historical attenuation using the target calendar attenuation prediction model to obtain the predicted calendar attenuation of the target lithium battery pack within the target time period; The target attenuation is obtained by performing calculation based on the predicted cyclic attenuation and the predicted calendar attenuation.

6. The method according to claim 5, characterized in that Calculating the target calendar attenuation prediction model based on the predicted cycle attenuation and the historical attenuation to obtain the predicted calendar attenuation of the target lithium battery pack within the target time period includes: Calculating the predicted cyclic attenuation and the historical attenuation to obtain a total attenuation value; Calculating the total attenuation value and the duration corresponding to the target time period using the target calendar attenuation prediction model to obtain a predicted calendar attenuation rate of the target lithium battery pack in the target time period; The predicted calendar decay amount is obtained by performing calculation based on the predicted calendar decay rate and the storage duration in the target time period.

7. The method according to claim 1, characterized in that Determining the remaining life information of the target lithium battery pack according to the target attenuation includes: Calculating according to the target attenuation and the historical attenuation to obtain a cumulative attenuation; Determining whether the accumulated attenuation is greater than or equal to a preset threshold; If the accumulated attenuation is greater than or equal to the preset threshold, the remaining life information is determined according to the target time period corresponding to the target attenuation.

8. The method according to claim 7, characterized in that After determining whether the accumulated attenuation is greater than or equal to a preset threshold, the method further includes: If the cumulative attenuation is less than the preset threshold, the step of calculating the attenuation of the target lithium battery pack based on the historical attenuation through the target cycle attenuation prediction model and the target calendar attenuation prediction model is repeated until the cumulative attenuation is greater than or equal to the preset threshold, and the remaining life information is determined based on the time corresponding to the cumulative attenuation.

9. A data processing device based on a lithium battery pack, characterized in that: include: A first acquiring unit is used to acquire a historical attenuation of the power of a target lithium battery pack; A second acquisition unit, configured to acquire a target cycle attenuation prediction model of the target lithium battery pack and a target calendar attenuation prediction model of the target lithium battery pack; a calculation unit, configured to calculate the attenuation of the target lithium battery pack based on the historical attenuation using the target cycle attenuation prediction model and the target calendar attenuation prediction model to obtain a target attenuation; A determination unit is used to determine the remaining life information of the target lithium battery pack according to the target attenuation amount.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium includes a stored executable program, wherein when the executable program is running, the device where the computer-readable storage medium is located is controlled to execute the data processing method based on a lithium battery pack according to any one of claims 1 to 8.

11. An electronic device, characterized in that: include: a memory storing an executable program; A processor is used to run the program, wherein when the program is run, the data processing method based on the lithium battery pack according to any one of claims 1 to 8 is executed.

12. A computer program product comprising computer instructions, characterized in that When the computer instructions are executed by a processor, the steps of the lithium battery pack-based data processing method according to any one of claims 1 to 8 are implemented.

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