Battery life prediction method, electronic equipment and storage medium

Abstract

The invention discloses a battery life prediction method, which comprises the following steps: obtaining an initial capacity and an inflection point capacity corresponding to a to-be-detected battery, and a first capacity loss rate of each cycle of capacity loss of the to-be-detected battery in a cyclic charging and discharging process, the inflection point capacity being determined based on a critical inflection point liquid injection parameter; based on the initial capacity, the inflection point capacity and the first capacity loss rate, determining the total number of cycles of the to-be-tested battery when the capacity is attenuated to the inflection point capacity; and inputting the total number of cycles and the first capacity loss rate into a battery life prediction model for life prediction to obtain a predicted battery life corresponding to the to-be-detected battery.

Description

Battery life prediction methods, electronic devices and storage media Technical Field

[0001] This application relates to the field of battery technology, specifically to battery life prediction methods, electronic devices, and storage media. Background Technology

[0002] Battery life prediction is one of the core technologies of battery management systems. With the development of the industry, such as the large-scale application of various lithium-ion batteries in electric vehicles, energy storage power stations and consumer electronics, battery life prediction has become the key to ensuring system safety and optimizing operation and maintenance strategies.

[0003] Traditional methods mainly rely on experimental data and establish empirical models through accelerated aging tests, but they are easily affected by individual differences in batteries, resulting in large errors in battery life prediction. Summary of the Invention

[0004] A battery life prediction method is provided to improve the accuracy of battery life prediction.

[0005] In a first aspect, a battery life prediction method is provided, comprising the following steps: obtaining the initial capacity, inflection point capacity, and first capacity loss rate per cycle during cyclic charging and discharging of the battery under test, wherein the inflection point capacity is determined based on critical inflection point liquid injection parameters; determining the total number of cycles of the battery under test when its capacity decays to the inflection point capacity based on the initial capacity, inflection point capacity, and first capacity loss rate; and inputting the total number of cycles and the first capacity loss rate into a battery life prediction model to predict the battery life, thereby obtaining the predicted battery life corresponding to the battery under test.

[0006] In an exemplary embodiment, obtaining the inflection point capacity of the battery under test includes: obtaining the initial capacity of the battery under test, the critical inflection point liquid injection parameters, the initial liquid injection amount of the battery under test before cyclic charging and discharging, and the second capacity loss rate of the battery under test during cyclic charging and discharging; predicting the critical inflection point capacity loss of the battery under test based on the initial liquid injection amount, the critical inflection point liquid injection parameters, and the second capacity loss rate; and determining the inflection point capacity of the battery under test based on the initial capacity and the critical inflection point capacity loss.

[0007] In this embodiment, the critical inflection point electrolyte injection parameter can accurately reflect the electrolyte content when the battery under test experiences a capacity drop. Therefore, by using the initial electrolyte injection volume, the critical inflection point electrolyte injection parameter, and the second capacity loss rate of the battery under test, the capacity loss at the time of capacity drop—that is, the critical inflection point capacity loss—can be predicted, ensuring the accuracy of the critical inflection point capacity loss. Furthermore, based on the initial capacity and critical inflection point capacity loss of the battery under test, the inflection point capacity at the time of capacity drop can be accurately obtained, improving the prediction accuracy of the battery's inflection point capacity.

[0008] In an exemplary embodiment, obtaining the first capacity loss rate per cycle of the battery under test during cyclic charging and discharging includes: obtaining the cyclic capacity loss and the number of cycles of the battery under test during cyclic charging and discharging; and determining the first capacity loss rate per cycle of the battery under test based on the cyclic capacity loss and the number of cycles.

[0009] In this embodiment, by determining the first capacity loss rate of the battery under test during the linear capacity decay stage, the accuracy of the first capacity loss rate obtained based on the linear capacity decay of the battery under test can be guaranteed, thereby improving the accuracy of the life prediction of the battery under test.

[0010] In an exemplary embodiment, determining the total number of cycles of the battery under test when its capacity decays to the inflection point capacity, based on the initial capacity, the inflection point capacity, and the first capacity loss rate, includes: determining the target capacity loss corresponding to the battery under test based on the initial capacity and the inflection point capacity; and obtaining the total number of cycles of the battery under test when its capacity decays to the inflection point capacity based on the ratio of the target capacity loss to the first capacity loss rate.

[0011] In this embodiment, by determining the target capacity loss based on the initial capacity and the inflection point capacity, and obtaining the total number of cycles based on the target capacity loss and the first capacity loss rate, a functional relationship between battery capacity and the number of cycles can be constructed. This allows for the accurate determination of the number of cycles the battery under test can cycle when its capacity decays to the inflection point capacity, thus improving the accuracy of the battery life prediction.

[0012] In an exemplary embodiment, the total number of cycles and the first capacity loss rate are input into a battery life prediction model to predict the battery life of the battery under test. This includes: inputting the total number of cycles and the first capacity loss rate into the battery life prediction model to predict the charge-discharge cycle duration of each cycle in the total number of cycles; and obtaining the predicted battery life of the battery under test based on the multiple charge-discharge cycle durations corresponding to the total number of cycles.

[0013] In this embodiment, the battery life of the battery under test is predicted by the battery life prediction model, which can ensure the accuracy of the battery life prediction.

[0014] In an exemplary embodiment, predicting the charge-discharge cycle duration for each cycle in the total number of cycles includes: for the charge-discharge cycle corresponding to the current cycle number in the total number of cycles, predicting the charging duration and discharging duration of the battery under test within the charge-discharge cycle based on the first capacity loss rate and the current cycle number; and obtaining the charge-discharge cycle duration of the charge-discharge cycle based on the total duration of the charging duration, discharging duration, and preset rest time, thereby obtaining the charge-discharge cycle duration for each cycle.

[0015] In this embodiment, by incorporating the charge / discharge time plus the rest time into the lifespan calculation, the accuracy and effectiveness of the battery lifespan prediction can be guaranteed.

[0016] In an exemplary embodiment, predicting the charging duration and discharging duration of the battery under test within a charge-discharge cycle based on a first capacity loss rate and the current cycle number includes: acquiring the charging current and discharging current corresponding to the battery under test; determining the charging capacity and discharging capacity of the battery under test within a charge-discharge cycle at the current cycle number based on the initial capacity, the first capacity loss rate, and the current cycle number; and determining the charging duration and discharging duration of the battery under test within a charge-discharge cycle based on the charging capacity, discharging capacity, charging current, and discharging current.

[0017] In this embodiment, by calculating the duration of each charge-discharge cycle of the battery under test based on the first capacity loss rate of the second stage of capacity decay, the accuracy of the battery life prediction can be guaranteed.

[0018] In an exemplary embodiment, the battery life prediction method further includes: obtaining a first verification capacity loss rate and the number of verification cycles per cycle of capacity loss of the verification battery during the cyclic charge and discharge process, and obtaining the actual battery life of the verification battery during the cyclic charge and discharge process; inputting the first verification capacity loss rate and the number of verification cycles into the battery life prediction model to be verified for life prediction, thereby obtaining the predicted battery life corresponding to the verification battery; and obtaining a verified battery life prediction model when the life error between the predicted battery life and the actual battery life is less than a preset threshold.

[0019] In this embodiment, the cumulative effect of charge / discharge time plus storage time can be quantified through the battery life prediction model (e.g., 2.2 hours of charge / discharge time per cycle and 0.5 hours of storage time, resulting in a total life difference of 500 hours over 1000 cycles). Furthermore, through the error verification mechanism of "predicted value - measured value", the model parameters can be dynamically corrected to achieve a closed loop of "prediction-verification-optimization", thereby improving the accuracy of battery life prediction.

[0020] Secondly, this application also provides a battery life prediction device, which includes: a data acquisition module for acquiring the initial capacity, inflection point capacity, and first capacity loss rate per cycle of the battery under test during cyclic charging and discharging, wherein the inflection point capacity is determined based on critical inflection point liquid injection parameters; a cycle number determination module for determining the total number of cycles of the battery under test when its capacity decays to the inflection point capacity based on the initial capacity, inflection point capacity, and first capacity loss rate; and a life prediction module for inputting the total number of cycles and the first capacity loss rate into a battery life prediction model to predict the life of the battery under test.

[0021] Thirdly, this application also provides an electronic device, including a memory and a processor, the memory storing a computer program for controlling the processor to operate in order to perform the methods in any of the embodiments of any of the above aspects.

[0022] Fourthly, this application also provides a computer-readable storage medium including computer instructions that, when executed by a processor, implement the methods in any of the embodiments described above.

[0023] Fifthly, the present application provides a computer program product that, when executed by a processor, implements the method in any of the above-described embodiments.

[0024] Beneficial effects: By obtaining the inflection point capacity of the battery under test, which is determined based on critical inflection point injection parameters, the battery capacity at which capacity drops can be accurately located. Furthermore, by combining the initial capacity of the battery under test, the first capacity loss rate per cycle during cycling, and the inflection point capacity, the total number of cycles at which the battery capacity decays to the inflection point capacity, i.e., the capacity drop, can be accurately determined. Then, the total number of cycles and the first capacity loss rate are input into the battery life prediction model, which outputs the predicted battery life, thus improving the accuracy of battery life prediction. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0026] Figure 1 is a flowchart illustrating a battery life prediction method provided in an exemplary embodiment of this disclosure; Figure 2 is another flowchart illustrating a battery life prediction method provided in an exemplary embodiment of this disclosure; Figure 3 is another flowchart illustrating a battery life prediction method provided in an exemplary embodiment of this disclosure; Figure 4 is a flowchart illustrating the life prediction and verification process of a battery life prediction model provided in an exemplary embodiment of this disclosure; Figure 5 is a schematic diagram illustrating a battery life prediction device provided in an exemplary embodiment of this disclosure; Figure 6 is an internal structure diagram of an electronic device provided in an exemplary embodiment of this disclosure. Detailed Implementation

[0027] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0028] In the description of this application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0029] This application provides a battery life prediction method, an electronic device, and a storage medium. The electronic device can be a server or a terminal, etc. In an exemplary embodiment, the terminal acquires the initial capacity, inflection point capacity, and the first capacity loss rate per cycle during charge-discharge cycles of the battery under test. The inflection point capacity is determined based on critical inflection point injection parameters. Based on the initial capacity, inflection point capacity, and first capacity loss rate, the terminal determines the total number of cycles required for the battery under test to decay to the inflection point capacity. The terminal inputs the total number of cycles and the first capacity loss rate into a battery life prediction model to predict the battery life, obtaining the predicted battery life. The terminal can then send the predicted battery life to a server or other terminal for further processing. The terminal can include, but is not limited to, computers, laptops, etc. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, big data, and artificial intelligence platforms. The terminal and server can be directly or indirectly connected via wired or wireless communication, which is not limited herein.

[0030] On the one hand, this embodiment provides a battery life prediction method, as shown in Figure 1, including the following steps: S101, obtaining the initial capacity, inflection point capacity, and the first capacity loss rate of the battery under test per cycle during the cyclic charging and discharging process, wherein the inflection point capacity is determined based on the critical inflection point liquid injection parameters.

[0031] S102, based on the initial capacity, inflection point capacity and first capacity loss rate, determine the total number of cycles of the battery under test when the capacity decays to the inflection point capacity.

[0032] The battery under test (BUT) refers to the battery whose lifespan needs to be predicted. Specifically, lifespan prediction can be the prediction of the BUT's cycle life when its capacity decays to the inflection point (i.e., when a capacity drop occurs). In other words, it's the prediction of the BUT's cycle life during repeated charge-discharge cycles. Cycle life also includes the total cycle time of the BUT. A capacity drop refers to the phenomenon where, during repeated charge-discharge use, the battery capacity suddenly changes from a slow decline to a rapid decline.

[0033] Initial capacity refers to the initial capacity of the battery under test before cyclic charging and discharging. Inflection point capacity refers to the capacity value at the inflection point in the battery's capacity decay curve during cyclic charging and discharging, where the battery capacity suddenly changes from slow decay to rapid decay. First capacity loss rate refers to the rate of capacity loss of the battery under test during cyclic charging and discharging, related to the number of cycles; it characterizes the capacity loss of the battery per cycle.

[0034] Critical inflection point electrolyte injection parameters refer to the electrolyte injection volume threshold that prevents the battery from reaching its capacity inflection point. The inflection point capacity represents the battery capacity at which irreversible and rapid capacity decay occurs during cycling, i.e., the battery capacity at which a capacity drop occurs. It can be understood as follows: when the electrolyte injection volume is less than this threshold, the battery's capacity decays to the inflection point during charge-discharge cycles due to insufficient electrolyte injection, resulting in a capacity drop. Conversely, when the electrolyte injection volume is greater than this threshold, the battery can avoid capacity decay to the inflection point during charge-discharge cycles without experiencing a capacity drop. Specifically, critical inflection point electrolyte injection parameters can represent the lower limit of electrolyte injection volume at which battery capacity decay does not reach the inflection point, thus preventing a capacity drop. Total cycle count refers to the cycle life of the battery under test before its capacity decays to the inflection point, i.e., the number of cycles the battery can complete before the capacity drop occurs.

[0035] For example, the battery under test can be a long-cycle battery that has undergone a period of charge-discharge cycles. A long-cycle battery refers to a rechargeable battery with a long cycle life, such as a battery that can achieve 3,000, 5,000, or even tens of thousands or higher cycles with linear capacity decay.

[0036] In response to the battery life prediction command for the battery under test, the terminal obtains the initial capacity, the inflection point capacity, and the first capacity loss rate per cycle during the charge-discharge cycle of the battery under test. The capacity decay of the battery under test during the charge-discharge cycle is linear, and the capacity decay rate is, for example, a first capacity loss rate (e.g., a capacity loss of 0.005 Ah per cycle) or a second capacity loss rate (e.g., a capacity loss of 0.01 Ah per gram of electrolyte consumed).

[0037] Then, the terminal determines the total number of cycles of the battery under test when its capacity drops significantly, based on the initial capacity, the inflection point capacity, and the first capacity loss rate. This can be understood as the total number of cycles when the battery under test's capacity decays to the inflection point capacity during cyclic charging and discharging, which is taken as the cycle life of the battery under test. Specifically, it can determine the capacity loss of the battery under test when its capacity drops significantly based on the initial capacity and the inflection point capacity, and then determine the total number of cycles of the battery under test when its capacity drops significantly based on the capacity loss and the first capacity loss rate.

[0038] In an exemplary embodiment, since the critical inflection point electrolyte injection parameter refers to the electrolyte injection volume threshold to avoid a capacity drop in the battery, the target electrolyte loss of the battery under test can be determined based on the initial electrolyte injection volume and the critical inflection point electrolyte injection parameter. This target electrolyte loss represents the maximum electrolyte consumption allowed to prevent a capacity drop in the battery under test. Then, based on the correlation between electrolyte consumption and capacity loss during the cyclic charge-discharge process of the battery under test, the critical inflection point capacity loss corresponding to the target electrolyte loss can be determined. This critical inflection point capacity loss can be expressed as the maximum capacity loss of the battery under test to prevent capacity decay to the inflection point capacity during the cyclic charge-discharge process without a capacity drop. It can be understood as the capacity loss before the inflection point of the capacity decay curve during the cyclic charge-discharge process. The capacity loss is the amount of capacity change, which is the maximum capacity loss allowed to prevent a capacity drop in the battery under test. Finally, based on the initial capacity and the critical inflection point capacity loss of the battery under test, the corresponding inflection point capacity of the battery under test can be determined.

[0039] S103, input the total number of cycles and the first capacity loss rate into the battery life prediction model to predict the lifespan and obtain the predicted battery lifespan of the battery under test.

[0040] The battery life prediction model is used to predict the total cycle time before a battery experiences a capacity drop. Predicted battery life refers to the total cycle time corresponding to the total number of cycles completed by the tested battery at the point of capacity drop.

[0041] For example, after obtaining the total number of cycles and the first capacity loss rate, the terminal calls a pre-verified battery life prediction model, inputs the total number of cycles and the first capacity loss rate into the battery life prediction model, and predicts the charge-discharge cycle duration of each cycle in the total number of cycles. The charge-discharge cycle duration represents the time required for a single charge-discharge cycle of the battery under test. Then, based on the multiple charge-discharge cycle durations corresponding to the total number of cycles, the predicted battery life corresponding to the battery under test is obtained.

[0042] In this embodiment, by obtaining the inflection point capacity of the battery under test, which is determined based on the critical inflection point injection parameters, the battery capacity at which the battery experiences a capacity drop can be accurately located. Furthermore, by combining the initial capacity of the battery under test, the first capacity loss rate per cycle during cycling, and the inflection point capacity, the total number of cycles at the point of capacity drop can be accurately obtained. Then, the total number of cycles and the first capacity loss rate are input into the battery life prediction model, which outputs the predicted battery life for the battery under test, thus improving the accuracy of battery life prediction.

[0043] In an exemplary embodiment, step S101, obtaining the inflection point capacity of the battery under test, includes: obtaining the initial capacity of the battery under test, the critical inflection point liquid injection parameters, the initial liquid injection amount of the battery under test before cyclic charging and discharging, and the second capacity loss rate of the battery under test during cyclic charging and discharging; predicting the critical inflection point capacity loss of the battery under test based on the initial liquid injection amount, the critical inflection point liquid injection parameters, and the second capacity loss rate; and determining the inflection point capacity of the battery under test based on the initial capacity and the critical inflection point capacity loss.

[0044] The initial electrolyte injection volume refers to the electrolyte parameters injected into the battery under test before cyclic charging and discharging, or it can be the electrolyte content of the battery under test before cyclic charging and discharging. The initial electrolyte injection volume is greater than the critical inflection point electrolyte injection parameters. The second capacity loss rate refers to the rate of capacity loss of the battery under test during cyclic charging and discharging in relation to electrolyte consumption. Specifically, the second capacity loss rate can be a correlation parameter representing the relationship between electrolyte consumption and capacity loss during cyclic charging and discharging, such as the capacity loss corresponding to each unit of electrolyte consumed.

[0045] The critical inflection point capacity loss can be expressed as the maximum capacity loss that the battery under test can avoid during the cycle charge and discharge process to prevent a sudden drop in capacity. It can be understood as the capacity loss before the inflection point of the capacity decay curve during the cycle charge and discharge process. The capacity loss is the amount of capacity change.

[0046] For example, the terminal acquires the initial capacity of the battery under test, the critical inflection point liquid injection parameters, the initial liquid injection volume of the battery under test before cyclic charging and discharging, and the capacity loss rate of the battery under test during cyclic charging and discharging.

[0047] The target electrolyte loss is determined based on the difference between the initial electrolyte injection volume and the critical inflection point electrolyte injection parameters. Then, the critical inflection point capacity loss is determined based on the target electrolyte loss and the second capacity loss rate of the battery under test during the cyclic charge and discharge process. For example, the critical inflection point capacity loss is obtained by calculating the product of the target electrolyte loss and the second capacity loss rate.

[0048] After obtaining the critical inflection point capacity loss, the terminal calculates the difference between the initial capacity and the critical inflection point capacity loss to obtain the inflection point capacity corresponding to the battery under test.

[0049] In this embodiment, the critical inflection point electrolyte injection parameter can accurately reflect the electrolyte content when the battery under test experiences a capacity drop. Therefore, by using the initial electrolyte injection volume, the critical inflection point electrolyte injection parameter, and the second capacity loss rate of the battery under test, the capacity loss at the time of capacity drop, i.e., the critical inflection point capacity loss, can be predicted, ensuring the accuracy of the critical inflection point capacity loss. Furthermore, based on the initial capacity and critical inflection point capacity loss of the battery under test, the inflection point capacity of the battery at the time of capacity drop can be accurately obtained, improving the prediction accuracy of the battery's inflection point capacity.

[0050] In an exemplary embodiment, the battery under test is a long-cycle battery, then the critical inflection point injection parameter V 跳 This is the minimum electrolyte injection threshold (minimum injection volume) for long-cycle batteries to prevent capacity drops. For example, when the electrolyte content falls below this threshold, the battery is highly susceptible to capacity drops. Therefore, the first setting is: the battery's electrolyte content reaches V... 跳 After that, the battery capacity dropped drastically.

[0051] Furthermore, since the cycle life of a long-cycle battery is sufficiently long, and the number of cycles in the first stage of capacity decay (non-linear electrolyte consumption) is relatively small compared to the total lifespan of the long-cycle cell, the second setting is that the electrolyte consumption in the first stage can be ignored. Therefore, the third setting is that under stable long-cycle test conditions (e.g., room temperature conditions), the capacity loss rate in the second stage of capacity decay (uniform linear electrolyte consumption during the cycle test) remains constant v, including the first capacity loss rate (capacity loss rate per cycle) and the second capacity loss rate (capacity loss rate per gram of electrolyte) in the second stage.

[0052] The first capacity loss rate v0 is calculated as shown in formula (1), and the second capacity loss rate v n The calculation is shown in formula (2).

[0053] v0=△Q / n (unit mAh / n) (1) v n =△Q / △M (unit: mAh / g) (2) Where, △Q represents the cycle capacity loss of the battery under test during the cyclic charging and discharging process, which can be obtained from the difference between the initial capacity Q0 (mAh) of the battery under test and the final capacity Q1 (mAh) of the battery under test when the cyclic charging stops, i.e., △Q=Q0-Q1; △M represents the electrolyte consumption of the battery under test during the cyclic charging and discharging process, which can be obtained from the difference between the initial total electrolyte injection volume V0 of the battery under test, the remaining electrolyte content V2 of the battery under test when the cyclic charging stops, and the average electrolyte loss x0 (g) before the new battery is removed from the production line, i.e., △M=V0-x0-V2. n represents the number of cycles of the battery. The average electrolyte loss x0 can be obtained from the average value of the basic electrolyte loss of multiple new batteries before they are removed from the production line. Electrolyte base loss refers to the electrolyte loss that occurs after a single new battery is produced and before it undergoes cycle charging and discharging. For example, in the actual production process, the electrolyte loss occurs during the formation of the SEI film (solid electrolyte interface film), side reactions such as electrolyte decomposition, and the aging process after the SEI film is formed.

[0054] Based on the above three settings, the inflection point capacity Q of the long-cycle battery is set. 跳 The calculation is shown in formula (3).

[0055] (3) Wherein, V0-x0 represents the initial electrolyte volume of the battery under test before cyclic charging and discharging; V0-X0-V 跳 Indicates the loss of the target electrolyte; This indicates the capacity loss at the critical inflection point.

[0056] In this embodiment, the initial electrolyte injection volume before cycle charging and discharging is determined by the initial electrolyte injection volume and the average electrolyte loss of the battery under test. Based on the initial electrolyte injection volume, the critical inflection point electrolyte injection parameters, and the second capacity loss rate, the critical inflection point capacity loss at which the battery under test experiences a capacity drop is predicted. This approach can take into account the dynamic feedback of electrode expansion-electrolyte retention changes and SEI film growth-electrolyte consumption rate fluctuations during cycling, ensuring the accuracy of the critical inflection point capacity loss. Furthermore, the critical inflection point electrolyte injection parameters can distinguish between inflection point decay caused by electrolyte consumption and intrinsic material aging decay, avoiding attributing the capacity drop caused by electrolyte depletion to cathode pulverization, thus improving the accuracy of the inflection point capacity.

[0057] In an exemplary embodiment, step S101, obtaining the first capacity loss rate per cycle of the battery under test during the cyclic charge and discharge process, includes: obtaining the cyclic capacity loss and the number of cycles of the battery under test during the cyclic charge and discharge process; and determining the first capacity loss rate per cycle of the battery under test during the cyclic charge and discharge process based on the cyclic capacity loss and the number of cycles.

[0058] Cycle capacity loss refers to the capacity loss of the sample battery during cyclic charging and discharging, i.e., the change in capacity. Cycle count refers to the number of times the sample battery is charged and discharged during cyclic charging and discharging.

[0059] For example, the terminal obtains the cycle capacity loss and number of cycles of the battery under test during the cycle charge and discharge process. The cycle capacity loss can be obtained based on the difference between the initial capacity Q0 (mAh) of the battery under test and the final capacity Q1 (mAh) of the battery under test when the cycle charge stops, i.e., ΔQ=Q0-Q1. And the number of cycles is n. Then the terminal calculates the ratio of the cycle capacity loss to the number of cycles to obtain the first capacity loss rate v0 of the battery under test per cycle during the cycle charge and discharge process, as shown in formula (1): v0=ΔQ / n (unit mAh / n).

[0060] In this embodiment, by determining the first capacity loss rate of the battery under test during the linear capacity decay stage, the accuracy of the first capacity loss rate obtained based on the linear capacity decay of the battery under test can be guaranteed, thereby improving the accuracy of the life prediction of the battery under test.

[0061] In an exemplary embodiment, step S102, determining the total number of cycles of the battery under test when its capacity decays to the inflection point capacity based on the initial capacity, the inflection point capacity, and the first capacity loss rate, includes: determining the target capacity loss corresponding to the battery under test based on the initial capacity and the inflection point capacity; and obtaining the total number of cycles of the battery under test when its capacity decays to the inflection point capacity based on the ratio of the target capacity loss to the first capacity loss rate.

[0062] For example, the terminal calculates the difference between the initial capacity and the inflection point capacity to obtain the target capacity loss corresponding to the battery under test. The target capacity loss represents the maximum capacity loss allowed for the battery under test to avoid capacity drop, and can also be expressed as the critical inflection point capacity loss. Then, the ratio of the target capacity loss to the first capacity loss rate is calculated to obtain the total number of cycles when the battery under test decays to the inflection point capacity, that is, when capacity drop occurs. The calculation of the total number of cycles n is shown in formula (4).

[0063] n = (Q0 - Q) 跳 ) / v0(4) where Q0 represents the initial capacity of the battery under test; Q 跳v0 represents the inflection point capacity of the battery under test; v0 represents the first capacity loss rate per cycle during the charge-discharge cycle of the battery under test.

[0064] In this embodiment, by determining the target capacity loss based on the initial capacity and the inflection point capacity, and obtaining the total number of cycles based on the target capacity loss and the first capacity loss rate, a functional relationship between battery capacity and the number of cycles can be constructed. This allows for the accurate determination of the number of cycles the battery under test can cycle when its capacity drops, thus improving the accuracy of life prediction for the battery under test.

[0065] In an exemplary embodiment, as shown in FIG2, step S103, inputting the total number of cycles and the first capacity loss rate into the battery life prediction model to predict the lifespan and obtain the predicted battery lifespan corresponding to the battery under test, includes: step S201, inputting the total number of cycles and the first capacity loss rate into the battery life prediction model to predict the charge-discharge cycle duration of each cycle in the total number of cycles; step S202, obtaining the predicted battery lifespan corresponding to the battery under test based on the multiple charge-discharge cycle durations corresponding to the total number of cycles.

[0066] The charge / discharge cycle duration refers to the time required for a single charge / discharge cycle of the battery under test, including the charging time, discharging time, and resting time of the battery under test.

[0067] For example, the terminal inputs the total number of cycles and the first capacity loss rate into the battery life prediction model. The battery life prediction model predicts the charge-discharge cycle duration of each cycle in the total number of cycles based on the first capacity loss rate. The multiple charge-discharge cycle durations corresponding to the total number of cycles are combined and calculated, such as by summation or integration, to obtain the predicted battery life corresponding to the battery under test, that is, the total cycle duration corresponding to the total number of cycles.

[0068] In this embodiment, the battery life of the battery under test is predicted by the battery life prediction model, which can ensure the accuracy of the battery life prediction.

[0069] In an exemplary embodiment, as shown in FIG3, step S201, predicting the charge-discharge cycle duration of each cycle in the total number of cycles, includes: step S301, for the charge-discharge cycle corresponding to the current cycle number in the total number of cycles, predicting the charging duration and discharging duration of the battery under test in the charge-discharge cycle based on the first capacity loss rate and the current cycle number; step S302, obtaining the charge-discharge cycle duration of the charge-discharge cycle based on the total duration of the charging duration, discharging duration and preset rest duration, thereby obtaining the charge-discharge cycle duration of each cycle.

[0070] For example, the terminal inputs the total number of battery cycles and the first capacity loss rate into the battery life prediction model. The battery life prediction model then calculates the charging and discharging times of the battery under test within that charge / discharge cycle for the current cycle number, based on the first capacity loss rate and the current cycle number. Finally, it calculates the total duration of the charging and discharging times, along with a preset rest period, to obtain the charge / discharge cycle duration for the current cycle number. The rest period refers to the interval between charging and discharging of the battery under test.

[0071] Then, the duration of multiple charge-discharge cycles corresponding to the total number of cycles is integrated to obtain the predicted battery life corresponding to the battery under test, that is, the total cycle time corresponding to the total number of cycles.

[0072] In this embodiment, by incorporating the charge / discharge time plus the rest time into the lifespan calculation, the accuracy and effectiveness of the battery lifespan prediction can be guaranteed.

[0073] In an exemplary embodiment, step S301, predicting the charging duration and discharging duration of the battery under test in a charge-discharge cycle based on the first capacity loss rate and the current cycle number, includes: obtaining the charging current and discharging current corresponding to the battery under test; determining the charging capacity and discharging capacity of the battery under test in a charge-discharge cycle at the current cycle number based on the initial capacity, the first capacity loss rate, and the current cycle number; and determining the charging duration and discharging duration of the battery under test in a charge-discharge cycle based on the charging capacity, discharging capacity, charging current, and discharging current.

[0074] Here, charging capacity refers to the charging capacity of the battery under test during the current charge-discharge cycle. Discharge capacity refers to the discharge capacity of the battery under test during the current charge-discharge cycle.

[0075] For example, when the terminal calculates the charging and discharging durations of the battery under test in each charge-discharge cycle using a battery life prediction model, it needs to calculate the charging and discharging capacities of the battery under test in each charge-discharge cycle. Generally, the charging and discharging capacities of the battery under test differ in each charge-discharge cycle, but the charging and discharging capacities are the same within the same charge-discharge cycle.

[0076] Specifically, the charging and discharging currents of the battery under test can be obtained through a battery life prediction model. The charging and discharging currents can be set according to the actual cycle steps. Generally, the charging current and discharging current of the battery under test are the same in each charge and discharge cycle, and the charging current and discharging current are the same.

[0077] Then, based on the first capacity loss rate and the current number of cycles, determine the current capacity loss of the battery under test when it reaches the current number of cycles. This can be done by calculating the product of the first capacity loss rate and the current number of cycles to obtain the current capacity loss of the battery under test, and calculating the difference between the initial capacity and the current capacity loss of the battery under test to obtain the charging capacity and discharging capacity of the battery under test in the charge and discharge cycle corresponding to the current number of cycles, as shown in formula (6).

[0078] (6) Where Q 充 Indicates charging capacity; Q 放 Indicates discharge capacity; Q0 ​​represents the initial capacity of the battery under test; v n The second capacity loss rate of the battery under test is represented by ; i represents the current cycle number.

[0079] Then, the ratio of charging capacity to charging current is calculated to obtain the charging time within the charge-discharge cycle, and the ratio of discharging capacity to discharging current is calculated to obtain the discharging time within the charge-discharge cycle. Finally, the total duration of the charging time, discharging time, and preset rest time is calculated to obtain the charge-discharge cycle duration corresponding to the charge-discharge cycle.

[0080] Then, the duration of multiple charge-discharge cycles corresponding to the total number of cycles is integrated to obtain the predicted battery life corresponding to the battery under test, that is, the total cycle time corresponding to the total number of cycles.

[0081] The battery life prediction model calculates the battery life T as shown in formula (7).

[0082] (7) Where n represents the total number of cycles; i represents the current cycle number; Indicates charging time; Indicates the duration of discharge; Indicates the preset set-off time; This indicates the duration of a single charge-discharge cycle; Indicates the charging current; This represents the discharge current.

[0083] In this embodiment, by calculating the duration of each charge-discharge cycle of the battery under test based on the first capacity loss rate of the second stage of capacity decay, the accuracy of the battery life prediction can be guaranteed.

[0084] In an exemplary embodiment, the battery life prediction method further includes: obtaining a first verification capacity loss rate and the number of verification cycles per cycle of capacity loss of the verification battery during the cyclic charge and discharge process, and obtaining the actual battery life of the verification battery during the cyclic charge and discharge process; inputting the first verification capacity loss rate and the number of verification cycles into the battery life prediction model to be verified for life prediction, thereby obtaining the predicted battery life corresponding to the verification battery; and obtaining a verified battery life prediction model when the life error between the predicted battery life and the actual battery life is less than a preset threshold.

[0085] The battery life prediction model to be verified is used to verify the accuracy of life prediction. Verifying the first capacity loss rate refers to verifying the first capacity loss rate of the battery during cyclic charge-discharge. Verifying the number of cycles refers to verifying the actual number of cycles the battery completes during cyclic charge-discharge. Actual battery life refers to the actual cycle time corresponding to the verified number of cycles. Predicted battery life is the predicted cycle time corresponding to the verified number of cycles output by the battery life prediction model to be verified.

[0086] For example, a terminal can use a battery life prediction model that has passed accuracy verification to predict the lifespan of the battery under test. Accuracy verification is used to verify whether the battery life prediction model accurately predicts the battery lifespan compared to the actual battery lifespan.

[0087] Specific accuracy verification can involve the terminal calling the battery life prediction model to be verified and obtaining the first verification capacity loss rate and the number of verification cycles per cycle during the battery's charge-discharge cycle. The number of verification cycles refers to the actual number of cycles the battery completes during the charge-discharge cycle. The actual battery life during the charge-discharge cycle is then obtained, i.e., the actual cycle time corresponding to the number of verification cycles. The first verification capacity loss rate can be obtained as the ratio of the capacity loss to the number of verification cycles. The verification battery can be the battery under test.

[0088] Then, the first capacity loss rate and the number of verification cycles of the verification battery are input into the battery life prediction model to predict the battery life, and the predicted battery life corresponding to the verification battery is obtained. Then, the life error between the predicted battery life and the actual battery life is calculated, and when the life error is less than a preset threshold, the battery life prediction model that has passed the verification is obtained. The life error is shown in formula (8).

[0089] ε = |(T0-T2) / T0|×100% (8) where ε represents the life error; T0 represents the actual battery life; and T2 represents the predicted battery life.

[0090] When the lifespan error exceeds a preset threshold, the battery lifespan prediction model to be validated is optimized. For example, a fine-tuning factor is introduced to weight the calculations of charging and discharging times, fine-tuning the charging and discharging times within a charge-discharge cycle to make the predicted charging and discharging times closer to the actual charging and discharging times. The fine-tuning factor can be determined based on the battery's cycle conditions, such as the fine-tuning factor corresponding to a 35°C high-temperature condition or a charge-discharge current condition.

[0091] In an exemplary embodiment, the accuracy of the battery life prediction model under different cyclic operating conditions can also be verified. For example, the verification battery is subjected to cyclic charging and discharging under different cyclic operating conditions, and the predicted battery life of the verification battery under different cyclic operating conditions is predicted by the life prediction model. Based on the life error between the predicted battery life and the actual battery life, the accuracy of the life prediction model of the battery life to be verified under different cyclic operating conditions is determined. Then, based on the accuracy of the life prediction model of the battery life to be verified, it is determined whether the battery life prediction model of the battery to be verified is applicable to the life prediction of the battery under test under different cyclic operating conditions.

[0092] For example, six verification batteries were set up. Three of the verification batteries were cycled 2200 times under normal temperature and 0.5C conditions, and the other three verification batteries were cycled 4400 times under normal temperature and 1C conditions. The key data recorded are shown in Table 1.

[0093] Table 1

[0094] As shown in Table 1, the lifespan error between the predicted and actual lifespans of the six verified batteries under the corresponding cycle conditions is less than the preset threshold, such as 10%. This indicates that the battery lifespan prediction model is applicable to predicting the lifespan of the batteries under test under both room temperature and 0.5C cycle conditions, as well as room temperature and 1C cycle conditions. The verification error of the 0.5C 2200-cycle battery is less than 2.2%, and the verification error of the 1C 4400-cycle battery is less than 8%. This demonstrates that the above cycle conditions are applicable to the lifespan prediction model.

[0095] In this embodiment, the cumulative effect of charge / discharge time plus storage time can be quantified through the battery life prediction model. Furthermore, through the error verification mechanism of "predicted value - measured value", the model parameters can be dynamically corrected to achieve a closed loop of "prediction-verification-optimization", thereby improving the accuracy of battery life prediction.

[0096] In an exemplary embodiment, after the predicted battery life of the battery under test is predicted by the battery life prediction model, the accuracy of the battery life prediction model can be verified using a verification battery. After the accuracy verification of the battery life prediction model is passed, the predicted battery life of the battery under test is taken as the target predicted battery life corresponding to the battery under test, representing a more reliable predicted battery life.

[0097] Specifically, as shown in Figure 4, a schematic diagram of the life prediction and verification process for a battery life prediction model is provided. Taking a lithium-ion pouch battery as an example, by controlling the electrode compaction density (positive electrode 4.0±0.1g / cm³, negative electrode 1.55±0.1g / cm³) and formation process (0.05C→0.1C→0.2C to 100% SOC) to be consistent, and determining the critical parameters of the pouch battery, including the critical inflection point liquid injection parameters and the inflection point capacity, the inflection point capacity Q of the pouch battery can be determined based on the critical inflection point liquid injection parameters. 跳 For example, the minimum electrolyte threshold V that prevents a capacity drop in pouch batteries can be used as an example. 跳 As a critical inflection point injection parameter.

[0098] Then, based on the first capacity loss rate during the second stage of the pouch battery's cyclic charge-discharge process and the target capacity loss determined based on the inflection point capacity, the total number of cycles when the capacity of the pouch battery drops is determined. The total number of cycles and the first capacity loss rate are then input into the battery life prediction model, which predicts the battery life of the pouch battery.

[0099] Then, the first capacity loss rate and the number of verification cycles corresponding to the verification battery are input into the battery life prediction model to predict the battery life, and the predicted battery life is obtained. The battery life prediction model is verified for error based on the predicted battery life and the actual battery life. For example, if the life error between the predicted battery life and the actual battery life is less than a preset threshold, the battery life prediction model is determined to have passed the verification. Or, if the life error between the predicted battery life and the actual battery life is greater than or equal to the preset threshold, the battery life prediction model is determined to have failed the verification. The model is then optimized until the verification conditions are met.

[0100] In this embodiment, the inflection point capacity is determined based on the critical inflection point injection parameters, and the functional relationship between battery capacity and cycle number is derived and constructed based on the inflection point capacity. The battery life is then quantitatively calculated using calculus functions. An error verification component is added, specifically calculating the error ε between the actual battery life and the predicted battery life, to evaluate the accuracy of the life prediction model. This allows for the determination of the critical electrolyte drain threshold, i.e., the critical inflection point injection parameter V. 跳By incorporating lifespan prediction, it addresses the problem that existing technologies only identify inflection points and cannot correlate them with remaining lifespan. Furthermore, it can quantify the cumulative effect of "charge and discharge time + storage time". Through an error verification mechanism of "predicted value - disassembly measured value", it can dynamically correct model parameters, thereby improving the accuracy of battery lifespan prediction.

[0101] On the other hand, this embodiment provides a battery life prediction device. FIG5 is a schematic diagram of a battery life prediction device according to an embodiment of this application. As shown in FIG5, the battery life prediction device 500 includes: a data acquisition module 501, a cycle count determination module 502, and a life prediction module 503. The device will be described below.

[0102] The data acquisition module 501 is used to acquire the initial capacity, inflection point capacity, and first capacity loss rate of the battery under test during the cyclic charging and discharging process. The inflection point capacity is determined based on the critical inflection point liquid injection parameters. The cycle number determination module 502 is used to determine the total number of cycles of the battery under test before its capacity decays to the inflection point capacity based on the initial capacity, inflection point capacity, and first capacity loss rate. The life prediction module 503 is used to input the total number of cycles and the first capacity loss rate into the battery life prediction model to predict the life of the battery under test.

[0103] In an exemplary embodiment, the data acquisition module 501 is further configured to acquire the initial capacity, critical inflection point liquid injection parameters, initial liquid injection volume of the battery under test before cyclic charging and discharging, and second capacity loss rate of the battery under test during cyclic charging and discharging; predict the critical inflection point capacity loss of the battery under test based on the initial liquid injection volume, critical inflection point liquid injection parameters, and second capacity loss rate; and determine the inflection point capacity of the battery under test at a given time based on the initial capacity and critical inflection point capacity loss.

[0104] In an exemplary embodiment, the data acquisition module 501 is further configured to acquire the cycle capacity loss and number of cycles of the battery under test during the cyclic charge and discharge process; and based on the cycle capacity loss and number of cycles, determine the first capacity loss rate of the battery under test per cycle during the cyclic charge and discharge process.

[0105] In an exemplary embodiment, the cycle number determination module 502 is further configured to determine the target capacity loss corresponding to the battery under test based on the initial capacity and the inflection point capacity; and to obtain the total number of cycles of the battery under test when the capacity decays to the inflection point capacity based on the ratio of the target capacity loss to the first capacity loss rate.

[0106] In an exemplary embodiment, the life prediction module 503 is further configured to input the total number of cycles and the first capacity loss rate into the battery life prediction model to predict the charge-discharge cycle duration of each cycle in the total number of cycles; and to obtain the predicted battery life corresponding to the battery under test based on the multiple charge-discharge cycle durations corresponding to the total number of cycles.

[0107] In an exemplary embodiment, the lifetime prediction module 503 is further configured to predict the charging duration and discharging duration of the battery under test in the charging and discharging cycle for the current cycle number in the total number of cycles, based on the first capacity loss rate and the current cycle number; and obtain the charging and discharging cycle duration of the charging and discharging cycle based on the total duration of the charging duration, discharging duration and preset rest duration, thereby obtaining the charging and discharging cycle duration of each cycle.

[0108] In an exemplary embodiment, the lifetime prediction module 503 is further configured to acquire the charging current and discharging current corresponding to the battery under test; determine the charging capacity and discharging capacity of the battery under test in the current charge-discharge cycle based on the initial capacity, the first capacity loss rate and the current cycle number; and determine the charging duration and discharging duration of the battery under test in the charge-discharge cycle based on the charging capacity, discharging capacity, charging current and discharging current.

[0109] In an exemplary embodiment, the life prediction module 503 is further configured to obtain the first verification capacity loss rate and the number of verification cycles per cycle of capacity loss of the verification battery during the cyclic charge and discharge process, and to obtain the actual battery life of the verification battery during the cyclic charge and discharge process; input the first verification capacity loss rate and the number of verification cycles into the battery life prediction model to be verified for life prediction, and obtain the predicted battery life corresponding to the verification battery; when the life error between the predicted battery life and the actual battery life is less than a preset threshold, a verified battery life prediction model is obtained.

[0110] Each module in the aforementioned battery life prediction device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device, or stored in the memory of a computer device as software, so that the processor can call and execute the corresponding operations of each module.

[0111] Thirdly, this embodiment provides an electronic device, including a memory and a processor. The memory stores computer instructions, and when the computer instructions are executed by the processor, they implement the method of any of the above embodiments.

[0112] In one embodiment, this embodiment also provides an electronic device, which can be a server, and its internal structure diagram is shown in Figure 6. The electronic device includes a processor, memory, input / output interfaces (I / O), and a communication interface. The processor, memory, and I / O interfaces are connected via a system bus, and the communication interface is connected to the system bus via the I / O interfaces. The processor of the electronic device provides computing and control capabilities. The memory of the electronic device includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer instructions, and a database. The internal memory provides an environment for the operation of the operating system and computer instructions in the non-volatile storage medium. The database of the electronic device stores data involved in business data processing methods. The I / O interfaces of the electronic device are used for exchanging information between the processor and external devices. The communication interface of the electronic device is used for communicating with external terminals via a network connection. When the computer instructions are executed by the processor, a battery life prediction method is implemented.

[0113] Those skilled in the art will understand that the structure shown in Figure 6 is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0114] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer instructions thereon, which are loaded by a processor to execute the arrangements in any of the methods described above. In embodiments of this application, the storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM), etc.

[0115] Fifthly, embodiments of this application provide a computer program product, including a computer program or instructions, which are executed by a processor to implement the steps of any of the methods described above.

[0116] In the above embodiments, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0117] The battery life prediction method, electronic device, and computer-readable storage medium provided in the embodiments of this application have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of this application. The description of the above embodiments is only for the purpose of helping to understand the method and core ideas of this application. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this application. Therefore, the content of this specification should not be construed as a limitation of this application.

Claims

1. A method for predicting battery life, characterized in that, The process includes the following steps: obtaining the initial capacity, inflection point capacity, and first capacity loss rate per cycle during the charge-discharge cycle of the battery under test, wherein the inflection point capacity is determined based on critical inflection point liquid injection parameters; and determining the total number of cycles of the battery under test when its capacity decays to the inflection point capacity based on the initial capacity, the inflection point capacity, and the first capacity loss rate. The total number of cycles and the first capacity loss rate are input into the battery life prediction model to predict the battery life, thereby obtaining the predicted battery life corresponding to the battery under test.

2. The method according to claim 1, characterized in that, Obtaining the inflection point capacity of the battery under test includes: obtaining the initial capacity of the battery under test, the critical inflection point electrolyte injection parameters, the initial electrolyte injection volume of the battery under test before cyclic charging and discharging, and the second capacity loss rate of the battery under test during cyclic charging and discharging; predicting the critical inflection point capacity loss of the battery under test based on the initial electrolyte injection volume, the critical inflection point electrolyte injection parameters, and the second capacity loss rate; and determining the inflection point capacity of the battery under test based on the initial capacity and the critical inflection point capacity loss.

3. The method according to claim 1, characterized in that, Obtaining the first capacity loss rate per cycle of the battery under test during cyclic charging and discharging includes: obtaining the cyclic capacity loss and the number of cycles of the battery under test during cyclic charging and discharging; and determining the first capacity loss rate per cycle of the battery under test based on the cyclic capacity loss and the number of cycles.

4. The method according to claim 1, characterized in that, The step of determining the total number of cycles of the battery under test when its capacity decays to the inflection point capacity based on the initial capacity, the inflection point capacity, and the first capacity loss rate includes: determining the target capacity loss corresponding to the battery under test based on the initial capacity and the inflection point capacity; and obtaining the total number of cycles of the battery under test when its capacity decays to the inflection point capacity based on the ratio of the target capacity loss to the first capacity loss rate.

5. The method according to any one of claims 1-4, characterized in that, The step of inputting the total number of cycles and the first capacity loss rate into the battery life prediction model to predict the battery life of the battery under test includes: inputting the total number of cycles and the first capacity loss rate into the battery life prediction model to predict the charge-discharge cycle duration of each cycle in the total number of cycles; and obtaining the predicted battery life of the battery under test based on the multiple charge-discharge cycle durations corresponding to the total number of cycles.

6. The method according to claim 5, characterized in that, The method of predicting the charge-discharge cycle duration for each cycle in the total number of cycles includes: for the charge-discharge cycle corresponding to the current cycle number in the total number of cycles, predicting the charging duration and discharging duration of the battery under test in the charge-discharge cycle based on the first capacity loss rate and the current cycle number; and obtaining the charge-discharge cycle duration of the charge-discharge cycle based on the total duration of the charging duration, the discharging duration, and the preset rest period, thereby obtaining the charge-discharge cycle duration for each cycle.

7. The method according to claim 6, characterized in that, The step of predicting the charging and discharging duration of the battery under test within the charge-discharge cycle based on the first capacity loss rate and the current cycle number includes: obtaining the charging current and discharging current corresponding to the battery under test; determining the charging capacity and discharging capacity of the battery under test within the current cycle number of charge-discharge cycles based on the initial capacity, the first capacity loss rate, and the current cycle number; and determining the charging and discharging duration of the battery under test within the charge-discharge cycle based on the charging capacity, the discharging capacity, the charging current, and the discharging current.

8. The method according to any one of claims 1-7, characterized in that, The method further includes: obtaining the first verification capacity loss rate and the number of verification cycles per cycle of the verification battery during the cyclic charge and discharge process, and obtaining the actual battery life of the verification battery during the cyclic charge and discharge process; inputting the first verification capacity loss rate and the number of verification cycles into the battery life prediction model to be verified for life prediction, and obtaining the predicted battery life corresponding to the verification battery; when the life error between the predicted battery life and the actual battery life is less than a preset threshold, the battery life prediction model that has passed verification is obtained.

9. An electronic device, characterized in that, The electronic device includes a processor and a memory, the memory storing a computer program configured to be executed by the processor to implement the steps of the method according to any one of claims 1 to 8.

10. A computer storage medium, characterized in that, The computer storage medium stores a computer program configured to be executed by a processor to implement the method of any one of claims 1 to 8.

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