A battery cycle life prediction method and device, and a computer storage medium

Abstract

The application discloses a battery cycle life prediction method and device, and a computer storage medium, and the method comprises the following steps: performing a first charge-discharge cycle operation of a first preset cycle number on a target battery in a determined target SOC interval to obtain a test capacity retention rate of the target battery, the target SOC interval is determined by battery parameters of the target battery, and the battery parameters of the target battery comprise SEI film impedance of the target battery and expansion force of the target battery; and predicting a target capacity retention rate of the target battery after performing a second charge-discharge cycle operation of the first preset cycle number according to a capacity retention rate prediction relationship of the target battery and the test capacity retention rate, the target SOC interval is within an interval range of an SOC interval corresponding to the second charge-discharge cycle operation, and the target capacity retention rate is used for determining a cycle life of the target battery. It can be seen that the application can improve the prediction efficiency of the battery cycle life and is beneficial to improving the development efficiency of the battery.

Description

Technical Field

[0001] This invention relates to the field of battery technology, and in particular to a method and apparatus for predicting battery cycle life, and a computer storage medium. Background Technology

[0002] The lifespan of a lithium iron phosphate battery generally refers to its cycle life, which is the number of times the battery can be charged and discharged. A complete cycle refers to the battery undergoing one complete charge and discharge process, from 100% to 0% and then back to 100%.

[0003] Currently, lithium iron phosphate (LFP) battery life prediction models can be categorized into three main types: empirical models, semi-empirical models, and electrochemical models. Empirical models are data-driven prediction functions that require extensive historical data to predict battery life, and they cannot investigate the internal chemical reaction processes of LFP batteries. Semi-empirical models require operators to understand the physicochemical mechanisms of the prediction system, placing high demands on operators. Electrochemical models are based on the internal reaction mechanisms of the battery and require significant computational resources. It is evident that existing prediction methods for battery life are time-consuming or costly, hindering battery development. Therefore, proposing a technical solution that improves the prediction efficiency of battery cycle life is crucial. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a method and apparatus for predicting battery cycle life, which can improve the prediction efficiency of battery cycle life.

[0005] To address the aforementioned technical problems, the first aspect of this invention discloses a method for predicting battery cycle life, the method comprising:

[0006] Within the determined target SOC range, a first charge-discharge cycle operation of a first preset number of cycles is performed on the target battery to obtain the test capacity retention rate of the target battery. The target SOC range is determined by the battery parameters of the target battery, including the SEI film impedance and the expansion force of the target battery.

[0007] Based on the capacity retention rate prediction formula corresponding to the target battery and the test capacity retention rate, the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation with the first preset number of cycles is predicted. The target SOC range is within the range of the SOC range corresponding to the second charge-discharge cycle operation. The target capacity retention rate is used to determine the cycle life of the target battery.

[0008] As an optional implementation, in the first aspect of the invention, before performing a first charge-discharge cycle operation of a first preset number of cycles on the target battery within the determined target SOC range to obtain the test capacity retention rate of the target battery, the method further includes:

[0009] The battery parameters of the target battery corresponding to each preset SOC value within the predicted SOC range are collected. The predicted SOC range is the SOC range corresponding to the second charge-discharge cycle operation. The predicted SOC range includes multiple preset SOC values.

[0010] Analyze the battery parameters of the target battery corresponding to all the preset SOC values ​​to obtain the battery parameter change trend corresponding to the predicted SOC range;

[0011] Based on the battery parameter change trend, the SOC sub-interval that meets the preset change trend conditions is selected from the predicted SOC interval as the target SOC interval.

[0012] As an optional implementation, in the first aspect of the present invention, the battery parameter variation trend includes the SEI film impedance variation trend and the expansion force variation trend;

[0013] The step of selecting SOC sub-intervals that meet preset trend conditions from the predicted SOC intervals based on the battery parameter change trend includes:

[0014] At least one SOC sub-interval with an upward trend in the expansion force change is selected from the predicted SOC intervals as the first candidate SOC sub-interval;

[0015] Determine the trend change magnitude of the battery parameter change trend corresponding to each first candidate SOC sub-interval, wherein the trend change magnitude includes the trend change magnitude of SEI film impedance and the trend change magnitude of expansion force;

[0016] From all the first candidate SOC sub-intervals, at least one second candidate SOC sub-interval is selected where the change in the SEI membrane impedance trend is less than a preset impedance value and the change in the expansion force trend is greater than a preset expansion force value.

[0017] When there is only one second candidate SOC sub-interval, the second candidate SOC sub-interval is determined as the target SOC interval.

[0018] As an optional implementation, in the first aspect of the present invention, the step of selecting a sub-interval of SOC that meets a preset trend condition from the predicted SOC interval as the target SOC interval based on the battery parameter change trend further includes:

[0019] When there are at least two second candidate SOC sub-intervals, determine the storage lifetime decay rate corresponding to the target battery for each second candidate SOC sub-interval;

[0020] The cycle time corresponding to each second candidate SOC sub-interval is calculated. The cycle time is used to represent the time it takes for a battery of the same type as the target battery to complete one first charge-discharge cycle within a certain SOC interval.

[0021] Based on the storage lifetime decay rate and the cycle duration, select one second candidate SOC sub-interval from all second candidate SOC sub-intervals that meets the preset decay rate condition and the preset cycle duration condition as the target SOC interval.

[0022] As an optional implementation, in the first aspect of the present invention, the battery type of the target battery is a target type;

[0023] Before predicting the target capacity retention rate of the target battery after performing a second charge-discharge cycle of the first preset number of cycles based on the capacity retention rate prediction formula corresponding to the target battery and the test capacity retention rate, the method further includes:

[0024] Within the target SOC range, a first charge-discharge cycle operation with a second preset number of cycles is performed on the first sampled battery to obtain the first sampled capacity retention rate corresponding to the target type, wherein the battery type of the first sampled battery is the target type;

[0025] Perform a second charge-discharge cycle operation with the second preset number of cycles on the second sampled battery to obtain the second sampled capacity retention rate corresponding to the target type, wherein the battery type of the second sampled battery is the target type;

[0026] Calculate the retention rate difference between the second sampling capacity retention rate and the first sampling capacity retention rate;

[0027] Based on the first sampled capacity retention rate, the second sampled capacity retention rate, and the retention rate difference, a prediction formula for the capacity retention rate of the target battery is established.

[0028] As an optional implementation, in the first aspect of the present invention, predicting the target capacity retention rate of the target battery after performing a second charge-discharge cycle of the first preset number of cycles, based on the capacity retention rate prediction formula corresponding to the target battery and the test capacity retention rate, includes:

[0029] Based on the capacity retention rate prediction formula corresponding to the target battery, the difference between the test capacity retention rate and the determined correction coefficient is calculated to obtain the target capacity retention rate of the target battery after the second charge-discharge cycle operation of the first preset number of cycles.

[0030] The correction parameters are determined in the following manner:

[0031] Obtain the electrode material parameters of the target battery, wherein the electrode material parameters include the electrode material type and the electrode material weight corresponding to each electrode material type;

[0032] Calculate the weight percentage of the electrode material corresponding to each of the electrode material types, whereby the weight percentage of the electrode material corresponding to a certain electrode material type is used to represent the proportion of the weight of the electrode material corresponding to a certain electrode material type in the total weight of the target battery;

[0033] Based on the retention rate difference, all electrode material types, and the weight percentage of all electrode materials, the correction parameters corresponding to the capacity retention rate prediction formula are determined.

[0034] As an optional implementation, in a first aspect of the invention, after predicting the target capacity retention rate of the target battery after performing a second charge-discharge cycle of the first preset number of cycles based on the capacity retention rate prediction formula corresponding to the target battery and the test capacity retention rate, the method further includes:

[0035] The self-discharge rate of the target battery and the corresponding environmental parameters of the target battery are detected. The environmental parameters include one or more combinations of environmental humidity, environmental temperature, environmental dust concentration and dust particle size.

[0036] The target capacity retention rate of the target battery is calibrated based on the self-discharge rate, the environmental parameters, and the target capacity retention rate.

[0037] A second aspect of the present invention discloses a battery cycle life prediction device, the device comprising:

[0038] The charge-discharge module is used to perform a first charge-discharge cycle operation on the target battery within a determined target SOC range, and obtain the test capacity retention rate of the target battery. The target SOC range is determined by the battery parameters of the target battery, including the SEI film impedance and the expansion force of the target battery.

[0039] The prediction module is used to predict the target capacity retention rate of the target battery after performing a second charge-discharge cycle operation with the first preset number of cycles, based on the capacity retention rate prediction formula corresponding to the target battery and the test capacity retention rate. The target SOC range is within the range of the SOC range corresponding to the second charge-discharge cycle operation. The target capacity retention rate is used to determine the cycle life of the target battery.

[0040] As an optional implementation, in a second aspect of the invention, the apparatus further includes:

[0041] The acquisition module is used to acquire battery parameters of the target battery corresponding to each preset SOC value in the predicted SOC range before the charge-discharge module performs a first charge-discharge cycle operation of a first preset number of cycles on the target battery within the determined target SOC range and obtains the test capacity retention rate of the target battery. The predicted SOC range is the SOC range corresponding to the second charge-discharge cycle operation, and the predicted SOC range includes multiple preset SOC values.

[0042] The analysis module is used to analyze the battery parameters of the target battery corresponding to all the preset SOC values, and to obtain the battery parameter change trend corresponding to the predicted SOC range;

[0043] The filtering module is used to filter out SOC sub-intervals that meet preset trend conditions from the predicted SOC intervals based on the battery parameter change trend.

[0044] As an optional implementation, in the second aspect of the present invention, the battery parameter variation trend includes the SEI film impedance variation trend and the expansion force variation trend;

[0045] The specific method by which the filtering module selects SOC sub-intervals that meet preset trend conditions from the predicted SOC intervals as target SOC intervals based on the battery parameter change trend includes:

[0046] At least one SOC sub-interval with an upward trend in the expansion force change is selected from the predicted SOC intervals as the first candidate SOC sub-interval;

[0047] Determine the trend change magnitude of the battery parameter change trend corresponding to each first candidate SOC sub-interval, wherein the trend change magnitude includes the trend change magnitude of SEI film impedance and the trend change magnitude of expansion force;

[0048] From all the first candidate SOC sub-intervals, at least one second candidate SOC sub-interval is selected where the change in the SEI membrane impedance trend is less than a preset impedance value and the change in the expansion force trend is greater than a preset expansion force value.

[0049] When there is only one second candidate SOC sub-interval, the second candidate SOC sub-interval is determined as the target SOC interval.

[0050] As an optional implementation, in the second aspect of the present invention, the specific method by which the screening module selects SOC sub-intervals that meet preset trend conditions from the predicted SOC interval as target SOC intervals based on the battery parameter change trend further includes:

[0051] When there are at least two second candidate SOC sub-intervals, determine the storage lifetime decay rate corresponding to the target battery for each second candidate SOC sub-interval;

[0052] The cycle time corresponding to each second candidate SOC sub-interval is calculated. The cycle time is used to represent the time it takes for a battery of the same type as the target battery to complete one first charge-discharge cycle within a certain SOC interval.

[0053] Based on the storage lifetime decay rate and the cycle duration, select one second candidate SOC sub-interval from all second candidate SOC sub-intervals that meets the preset decay rate condition and the preset cycle duration condition as the target SOC interval.

[0054] As an optional implementation, in the second aspect of the present invention, the battery type of the target battery is a target type;

[0055] The charge-discharge module is further configured to perform a first charge-discharge cycle operation with a second preset number of cycles on the first sampled battery within the target SOC range before the prediction module predicts the target capacity retention rate of the target battery after performing a second charge-discharge cycle operation with the first preset number of cycles based on the capacity retention rate prediction formula corresponding to the target battery and the test capacity retention rate, thereby obtaining a first sampled capacity retention rate corresponding to the target type, wherein the battery type of the first sampled battery is the target type;

[0056] The charging and discharging module is further configured to perform a second charging and discharging cycle operation on the second sampled battery with the second preset number of cycles to obtain a second sampled capacity retention rate corresponding to the target type, wherein the battery type of the second sampled battery is the target type;

[0057] The device further includes:

[0058] The calculation module is used to calculate the difference in retention rates between the second sampling capacity retention rate and the first sampling capacity retention rate;

[0059] The relation establishment module is used to establish a capacity retention rate prediction relation for the target battery based on the first sampled capacity retention rate, the second sampled capacity retention rate, and the retention rate difference.

[0060] As an optional implementation, in a second aspect of the present invention, the specific method by which the prediction module predicts the target capacity retention rate of the target battery after performing a second charge-discharge cycle operation with the first preset number of cycles, based on the capacity retention rate prediction formula corresponding to the target battery and the test capacity retention rate, includes:

[0061] Based on the capacity retention rate prediction formula corresponding to the target battery, the difference between the test capacity retention rate and the determined correction coefficient is calculated to obtain the target capacity retention rate of the target battery after the second charge-discharge cycle operation of the first preset number of cycles.

[0062] The correction parameters are determined in the following manner:

[0063] Obtain the electrode material parameters of the target battery, wherein the electrode material parameters include the electrode material type and the electrode material weight corresponding to each electrode material type;

[0064] Calculate the weight percentage of the electrode material corresponding to each of the electrode material types, whereby the weight percentage of the electrode material corresponding to a certain electrode material type is used to represent the proportion of the weight of the electrode material corresponding to a certain electrode material type in the total weight of the target battery;

[0065] Based on the retention rate difference, all electrode material types, and the weight percentage of all electrode materials, the correction parameters corresponding to the capacity retention rate prediction formula are determined.

[0066] As an optional implementation, in a second aspect of the invention, the apparatus further includes:

[0067] The detection module is used to detect the self-discharge rate of the target battery and the environmental parameters corresponding to the target battery after the prediction module predicts the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation with the first preset number of cycles, based on the capacity retention rate prediction formula corresponding to the target battery and the test capacity retention rate. The environmental parameters include one or more combinations of environmental humidity, environmental temperature, environmental dust concentration and dust particle size.

[0068] A calibration module is used to calibrate the target capacity retention rate of the target battery based on the self-discharge rate, the environmental parameters, and the target capacity retention rate.

[0069] A third aspect of the present invention discloses another device for predicting battery cycle life, the device comprising:

[0070] Memory containing executable program code;

[0071] A processor coupled to the memory;

[0072] The processor calls the executable program code stored in the memory to execute the battery cycle life prediction method disclosed in the first aspect of the present invention.

[0073] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute the battery cycle life prediction method disclosed in the first aspect of the present invention.

[0074] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0075] In this embodiment of the invention, within the determined target SOC range, a first charge-discharge cycle operation of a first preset number of cycles is performed on the target battery to obtain the test capacity retention rate of the target battery. The target SOC range is determined by the battery parameters of the target battery, including the SEI film impedance and the expansion force of the target battery. Based on the capacity retention rate prediction formula corresponding to the target battery and the test capacity retention rate, the target capacity retention rate of the target battery after performing a second charge-discharge cycle operation of the first preset number of cycles is predicted. The target SOC range is within the range of the SOC range corresponding to the second charge-discharge cycle operation. The target capacity retention rate is used to determine the cycle life of the target battery. It is evident that implementing this invention enables the target battery to undergo a first charge-discharge cycle operation with a first preset number of cycles within a determined target SOC range, thereby obtaining the battery's test capacity retention rate. Based on the corresponding capacity retention rate prediction formula and the test capacity retention rate, the target capacity retention rate of the battery after performing a second charge-discharge cycle operation with the same number of cycles can be predicted. This improves the prediction efficiency of the battery's capacity retention rate, thereby improving the prediction efficiency of the battery's cycle life, and further shortening the battery cycle life testing time and reducing the cost of battery cycle life testing, which is beneficial to improving the battery development efficiency. Attached Figure Description

[0076] To more clearly illustrate the technical solutions in the embodiments of the present invention, 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 the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0077] Figure 1 This is a schematic flowchart of a method for predicting battery cycle life disclosed in an embodiment of the present invention;

[0078] Figure 2 This is a flowchart illustrating another method for predicting battery cycle life disclosed in an embodiment of the present invention.

[0079] Figure 3 This is a schematic diagram of the change in battery expansion force disclosed in an embodiment of the present invention;

[0080] Figure 4 This is a flowchart illustrating another method for predicting battery cycle life disclosed in an embodiment of the present invention.

[0081] Figure 5 This is a schematic diagram of the structure of a battery cycle life prediction device disclosed in an embodiment of the present invention;

[0082] Figure 6 This is a schematic diagram of another battery cycle life prediction device disclosed in an embodiment of the present invention;

[0083] Figure 7 This is a schematic diagram of the structure of another battery cycle life prediction device disclosed in an embodiment of the present invention. Detailed Implementation

[0084] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0085] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or end that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or ends.

[0086] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0087] This invention discloses a method and apparatus for predicting battery cycle life. It can perform a first charge-discharge cycle operation with a first preset number of cycles on a target battery within a determined target SOC range to obtain the battery's test capacity retention rate. Based on the corresponding capacity retention rate prediction formula and the test capacity retention rate, it predicts the target capacity retention rate after performing a second charge-discharge cycle operation with the same number of cycles. This improves the prediction efficiency of battery capacity retention rate, thereby improving the prediction efficiency of battery cycle life, shortening the battery cycle life testing time, reducing the cost of battery cycle life testing, and contributing to improved battery development efficiency. Detailed descriptions follow.

[0088] Example 1

[0089] Please see Figure 1 , Figure 1 This is a schematic flowchart of a method for predicting battery cycle life disclosed in an embodiment of the present invention. Wherein, Figure 1 The described method for predicting battery cycle life can be applied to a battery cycle life prediction device, which may include one of a prediction device, a prediction terminal, a prediction system, and a server, wherein the server may be a local server or a cloud server, and this embodiment of the invention is not limited thereto. Figure 1 As shown, the method for predicting the cycle life of this battery may include the following operations:

[0090] 101. Within the determined target SOC range, perform a first charge-discharge cycle operation on the target battery for a first preset number of cycles to obtain the test capacity retention rate of the target battery.

[0091] In this embodiment of the invention, the target SOC range is determined by the battery parameters of the target battery, including the SEI film impedance and the expansion force of the target battery. The target battery can be a lithium battery (e.g., lithium iron phosphate battery), or other batteries capable of forming an SEI film and expanding during production, testing, or use; this embodiment of the invention does not limit the type of battery. The target SOC range includes a maximum SOC threshold and a minimum SOC threshold. The first charge-discharge cycle operation includes a charging operation to charge the battery's SOC from the minimum SOC threshold to the maximum SOC threshold and a discharging operation to discharge the battery's SOC from the maximum SOC threshold to the minimum SOC threshold. The first preset cycle number represents the number of times the first charge-discharge cycle operation is performed, and the unit of the first preset cycle number is cycles; for example, the first preset cycle number can be one of 100 cycles, 200 cycles, 300 cycles, 400 cycles, 500 cycles, 600 cycles, and 700 cycles, or other values; this embodiment of the invention does not limit the type of cycle number. For example, assuming the target SOC range is 80% to 100% SOC, and the first preset number of cycles is 100 cycles, that is, performing 100 charge-discharge cycles from 80% SOC to 100% SOC and from 100% SOC to 80% SOC on the battery. The test capacity retention rate is used to represent the ratio of the target battery's current usable capacity to its initial capacity after the first preset number of charge-discharge cycles.

[0092] It should be noted that the SEI film in this embodiment of the invention is as follows: During the first charge and discharge of the battery, the electrode material and the electrolyte react at the solid-liquid interface to form a passivation layer covering the surface of the electrode material. This passivation layer is an interface layer with the characteristics of a solid electrolyte. It is an electronic insulator but also an excellent conductor of Li+ (lithium ions). Li+ can freely insert and extract through this passivation layer. Therefore, this passivation film is called a "Solid Electrolyte Interface" (SEI film). Furthermore, it should be noted that the principle of this embodiment of the invention is as follows: During cycling, the damage and repair of the SEI film consumes active lithium, and the expansion-contraction (expansion force) of the crystal structure during the charge and discharge of the negative electrode graphite leads to damage to the graphite structure, thereby causing a decrease in the anodic dynamic performance of the battery and resulting in a capacity decay. Therefore, the target SOC range can be determined by the SEI film impedance and expansion force of the target battery.

[0093] 102. Based on the capacity retention rate prediction formula and the test capacity retention rate of the target battery, predict the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation with the first preset number of cycles.

[0094] In this embodiment of the invention, the target SOC range is within the range of the SOC range corresponding to the second charge-discharge cycle operation; that is, the target SOC range is a sub-range of the SOC range corresponding to the second charge-discharge cycle operation. The target capacity retention rate is used to determine the cycle life of the target battery. For example, assuming the target SOC range is 80%–100% SOC, the SOC range corresponding to the second charge-discharge cycle operation can be 20%–100% SOC or 0%–100% SOC; this embodiment of the invention does not impose any limitations. The capacity retention rate prediction formula corresponding to the target battery can be the relationship between the test capacity retention rate and the target capacity retention rate.

[0095] As can be seen, implementing the method described in the embodiments of the present invention can perform a first charge-discharge cycle operation with a first preset number of cycles on the target battery within the determined target SOC range, obtain the test capacity retention rate of the battery, and predict the target capacity retention rate of the battery after performing a second charge-discharge cycle operation with the same number of cycles based on the corresponding capacity retention rate prediction formula and the test capacity retention rate. This can improve the prediction efficiency of the battery capacity retention rate, thereby improving the prediction efficiency of the battery cycle life, and thus shortening the battery cycle life test time, reducing the cost of battery cycle life test, and helping to improve the battery development efficiency.

[0096] In an optional embodiment, before performing a first charge-discharge cycle operation of a first preset number of cycles on the target battery within the determined target SOC range to obtain the test capacity retention rate of the target battery, the method may further include the following operations:

[0097] The battery parameters of the target battery corresponding to each preset SOC value within the predicted SOC range are collected. The predicted SOC range is the SOC range corresponding to the second charge-discharge cycle operation. The predicted SOC range includes multiple preset SOC values.

[0098] Analyze the battery parameters of the target battery corresponding to all preset SOC values ​​to obtain the battery parameter change trend corresponding to the predicted SOC range;

[0099] Based on the trend of battery parameter changes, the SOC sub-intervals that meet the preset trend conditions are selected from the predicted SOC intervals as the target SOC intervals.

[0100] The battery parameters of the target battery corresponding to each preset SOC value can be obtained by the target battery undergoing a charge-discharge cycle test, or by a battery of the same type as the target battery undergoing a charge-discharge cycle test. This embodiment of the invention does not limit the parameters.

[0101] Optionally, the battery parameter change trend can be obtained by analyzing the changes of all battery parameters through statistical charts. The statistical charts may include one or more combinations of line charts, scatter plots, bar charts, and histograms. The line chart can be a curve or other types of line charts, and this embodiment of the invention is not limited thereto. The battery parameter change trend can also be obtained by analyzing the battery parameters through a data analysis model, and this embodiment of the invention is not limited thereto.

[0102] As can be seen, this optional embodiment can obtain the battery parameter change trend corresponding to the predicted SOC interval by collecting and analyzing the battery parameters corresponding to all preset SOC values ​​in the predicted SOC interval, and select the SOC sub-intervals that meet the preset change trend conditions as the target SOC interval based on the battery parameter change trend. This can improve the accuracy of the analysis of battery parameter change trends, thereby improving the accuracy of determining the SOC interval for battery cycle life testing, which is conducive to improving the accuracy and reliability of determining the battery's test capacity retention rate, and further conducive to improving the prediction accuracy of the battery's capacity retention rate.

[0103] In this optional embodiment, the battery parameter variation trend may optionally include the SEI film impedance variation trend and the expansion force variation trend;

[0104] Based on the battery parameter change trend, select the SOC sub-interval that meets the preset change trend conditions from the predicted SOC interval as the target SOC interval. This can include the following operations:

[0105] At least one SOC sub-interval with an upward trend in expansion force is selected from the predicted SOC intervals as the first candidate SOC sub-interval.

[0106] Determine the trend change magnitude of the battery parameter change trend corresponding to each first candidate SOC sub-interval. The trend change magnitude includes the trend change magnitude of SEI film impedance and the trend change magnitude of expansion force.

[0107] Select at least one second candidate SOC sub-interval from all first candidate SOC sub-intervals where the change in SEI membrane impedance trend is less than the preset impedance value and the change in expansion force trend is greater than the preset expansion force value.

[0108] When there is only one second candidate SOC sub-interval, the second candidate SOC sub-interval is determined as the target SOC interval.

[0109] When the statistical graph used for analysis is a curve, the slope of the curve can represent the trend of battery parameter changes.

[0110] For example, assuming that the battery parameter change trend is analyzed based on the curve, and if the target battery's SEI film impedance remains almost unchanged only in the 80% to 100% SOC sub-interval within the predicted SOC range, and the expansion force of the target battery shows an upward trend with the slope of the expansion force curve greater than a preset slope threshold, then the 80% to 100% SOC sub-interval is determined as the target SOC range.

[0111] As can be seen, this optional embodiment can also select a candidate SOC sub-interval from the predicted SOC interval based on the changes in SEI film impedance and expansion force, where the expansion force trend is upward and the expansion force trend change amplitude is large, while the SEI film impedance trend change amplitude is small. This achieves the determination of an accurate numerical range of the target SOC interval based on changes in battery structure, which can improve the accuracy of battery structure change analysis, thereby further improving the accuracy of determining the SOC interval for battery cycle life testing, and thus contributing to the accuracy and reliability of determining the battery's test capacity retention rate.

[0112] In this optional embodiment, further optionally, selecting the SOC sub-interval that meets the preset trend conditions from the predicted SOC interval as the target SOC interval based on the battery parameter change trend may also include the following operations:

[0113] When there are at least two second candidate SOC sub-intervals, determine the storage lifetime decay rate of the target battery corresponding to each second candidate SOC sub-interval;

[0114] The cycle time corresponding to each second candidate SOC sub-interval is calculated. The cycle time is used to represent the time it takes for a battery of the same type as the target battery to complete one first charge-discharge cycle within a certain SOC interval.

[0115] Based on the storage lifetime decay rate and cycle duration, select one second candidate SOC sub-interval from all second candidate SOC sub-intervals that meets the preset decay rate condition and the preset cycle duration condition as the target SOC interval.

[0116] Among them, the storage life of a battery is used to represent the time required for the battery capacity to irreversibly decrease and drop to a certain capacity threshold when the battery is stored under certain environmental conditions and its own state conditions (e.g., state of charge).

[0117] It should be noted that the storage lifetime decay rate corresponding to the target battery can be determined by detecting the storage lifetime decay rate of the same type of battery in the second candidate sub-interval; the cycle time corresponding to the second candidate SOC sub-interval can be determined by statistically analyzing the cycle time of the first charge-discharge cycle operation performed by the same type of battery in the second candidate SOC sub-interval, wherein the same type of battery refers to other batteries of the same type as the target battery.

[0118] Optionally, based on the storage lifetime decay rate and cycle duration, a second candidate SOC sub-interval that meets the preset decay rate condition and preset cycle duration condition is selected as the target SOC interval from all second candidate SOC sub-intervals. This may include the following operations:

[0119] From all the second candidate SOC sub-intervals, select one second candidate SOC sub-interval whose storage lifetime decay rate is greater than a preset decay rate threshold and whose cycle time is less than a preset cycle time threshold as the target SOC interval.

[0120] For example, suppose the second candidate SOC sub-range includes a 20%–30% SOC sub-range and an 80%–100% SOC sub-range. Under the same temperature conditions, the battery stored at 100% SOC has the highest storage life decay rate, and the cycle time corresponding to the 80%–100% SOC sub-range is less than the cycle time corresponding to the 20%–30% SOC sub-range. Therefore, the 80%–100% SOC sub-range is determined as the target SOC range.

[0121] As can be seen, this optional embodiment can also obtain the storage life decay rate and cycle time of the target battery corresponding to each second candidate SOC sub-interval when there are at least two second candidate SOC sub-intervals. The candidate SOC sub-interval that satisfies the preset decay rate condition and the preset cycle time condition is taken as the SOC interval. This realizes the determination of the numerical range of the target SOC interval based on the battery structure change, combined with the battery storage performance decay and the time cost of cycle life testing. This can further improve the accuracy of determining the SOC interval of battery cycle life testing, thereby improving the accuracy of battery capacity retention prediction, and can also shorten the battery cycle life testing time, which is conducive to improving the prediction efficiency of battery cycle life.

[0122] In this embodiment of the invention, exemplarily, a battery of model LF280K was used as the target battery. The electrochemical impedance of the LF280K battery was tested at 10%–90% SOC (tested every 10% SOC). Under different SOCs, the battery's Rs (ohmic impedance) fluctuated between 0.0008 and 0.00083 Ω, the battery's Rsei (resistance of the SEI film) remained essentially constant at 0.0012 Ω, while the battery's Rct (electrode polarization impedance, the resistance of lithium ions passing through the SEI film and graphite contact layer) decreased from 0.016 Ω to 0.014 Ω as the SOC increased. It is evident that the magnitude of Rsei is relatively unaffected by changes in SOC.

[0123] Furthermore, using the LF280K battery as the target battery, the expansion force variation curves corresponding to different SOC values ​​can be shown as follows: Figure 3 As shown, Figure 3 In the graph, ① represents the expansion force change curve for the battery during the first charge, ② for the 100th charge, ③ for the 200th charge, ④ for the 300th charge, ⑤ for the 400th charge, and ⑥ for the 500th charge. It can be seen that the battery's expansion force increases with the number of charges, and there are two SOC ranges within the same curve where the expansion force shows an upward trend. Specifically, the increase in battery expansion force is larger in the 20%–30% SOC range and the 80%–100% SOC range, indicating that these are the main SOC ranges where the negative electrode graphite structure undergoes changes.

[0124] Furthermore, within the 80%–100% SOC range, not only does the negative electrode graphite structure change rapidly, but the battery's storage life also decays quickly at full charge (100% SOC), with a shorter cycle time. In contrast, within the 20%–30% SOC range, the battery's storage life shows virtually no decay; cycling only affects battery capacity, and the cycle time is longer. Therefore, considering battery structure changes, storage life decay, and cycle time costs, the 80%–100% SOC range is determined as the target SOC range.

[0125] In another optional embodiment, after predicting the target capacity retention rate of the target battery after performing a second charge-discharge cycle of a first preset number of cycles based on the capacity retention rate prediction formula and the test capacity retention rate corresponding to the target battery, the method may further include:

[0126] Determine whether the target capacity retention rate is greater than or equal to the capacity retention rate threshold;

[0127] When it is determined that the target capacity retention rate is less than the capacity retention rate threshold, it is determined whether the first preset number of cycles is greater than or equal to the number of cycles threshold.

[0128] When it is determined that the first preset number of cycles is less than the number of cycles threshold, the target battery is identified as a defective product, and the production parameters corresponding to the defective product are detected. The production parameters include the identification of the production equipment, the working status of the production equipment, and the production pass rate of the production equipment.

[0129] Determine whether the production parameters corresponding to non-conforming products meet the preset production parameter standards;

[0130] When it is determined that the production parameters corresponding to the defective product have reached the preset production parameter standard, the construction parameters of the battery of the same type as the defective product are adjusted. The construction parameters of the battery include the physical structure of the battery and the electrode material parameters of the battery.

[0131] When it is determined that the production parameters corresponding to the non-conforming product have not met the preset production parameter standards, the production parameters corresponding to the non-conforming product are adjusted.

[0132] When the target capacity retention rate is determined to be greater than or equal to the capacity retention rate threshold, the target battery is identified as a qualified product.

[0133] The production qualification rate of the production equipment is determined based on the proportion of qualified products produced by the production equipment to all products produced by the production equipment; for example, the capacity retention rate threshold can be 80%, or other values, and this embodiment of the invention does not limit it.

[0134] As can be seen, this optional embodiment can also identify the battery as a defective product when the predicted target capacity retention rate is lower than the capacity retention rate threshold and the number of cycles is lower than the number of cycles threshold, and check whether the corresponding production parameters of the battery meet the standards. If the production parameters meet the standards, the battery structure is adjusted; if the production parameters do not meet the standards, the production parameters are adjusted. This can improve the accuracy of battery quality detection based on battery cycle life, thereby improving the accuracy of adjusting production conditions or battery products, and thus helping to improve the reliability of battery development.

[0135] Example 2

[0136] Please see Figure 2 , Figure 2 This is a schematic flowchart of a method for predicting battery cycle life disclosed in an embodiment of the present invention. Wherein, Figure 2 The described method for predicting battery cycle life can be applied to a battery cycle life prediction device, which may include one of a prediction device, a prediction terminal, a prediction system, and a server, wherein the server may be a local server or a cloud server, and this embodiment of the invention is not limited thereto. Figure 2As shown, the method for predicting the cycle life of this battery may include the following operations:

[0137] 201. Within the determined target SOC range, perform a first charge-discharge cycle operation on the target battery for a first preset number of cycles to obtain the test capacity retention rate of the target battery.

[0138] In this embodiment of the invention, the battery type of the target battery is the target type; wherein, the battery type can be classified according to the battery model, the type of electrolyte, or the positive and negative electrode materials of the battery, and this embodiment of the invention does not limit it.

[0139] 202. Within the target SOC range, perform a first charge-discharge cycle operation with a second preset number of cycles on the first sampled battery to obtain the first sampled capacity retention rate corresponding to the target type.

[0140] In this embodiment of the invention, the battery type of the first sampling battery is the target type. The value of the second preset number of cycles can be the same as the value of the first preset number of cycles, or it can be another value; this embodiment of the invention does not limit this. The number of first sampling batteries is at least one.

[0141] 203. Perform a second charge-discharge cycle operation with a second preset number of cycles on the second sampled battery to obtain the second sampled capacity retention rate corresponding to the target type.

[0142] In this embodiment of the invention, the battery type of the second sampling battery is the target type. The number of second sampling batteries is at least one.

[0143] It should be noted that the temperature and charge / discharge current conditions corresponding to the first charge / discharge cycle operation performed on the first sampled battery and the second charge / discharge cycle operation performed on the second sampled battery are the same. For example, three LF280K batteries are subjected to a 25℃ 0.5C / 0.5C 80-100% SOC cycle (the first charge / discharge cycle operation of the first sampled battery), and another three LF280K batteries from the same batch are subjected to a 25℃ 0.5C / 0.5C 0-100% SOC cycle (the second charge / discharge cycle operation of the second sampled battery). That is, under the condition of 25℃, and within the 80-100% SOC range or the 0-100% SOC range, the batteries are charged to 3.65V by constant current and constant voltage at 0.5C, and then discharged to 2.5V by constant current at 0.5C.

[0144] It should be noted that steps 202 and 203 are not sequential; that is, step 202 can occur before or after step 203, or occur simultaneously with step 203. This embodiment of the invention does not impose any limitations on this.

[0145] 204. Calculate the difference in retention rate between the second sampling capacity retention rate and the first sampling capacity retention rate.

[0146] 205. Based on the first sampling capacity retention rate, the second sampling capacity retention rate, and the retention rate difference, establish a prediction formula for the capacity retention rate of the target battery.

[0147] It should be noted that step 201 is not sequential with any of steps 202-205. That is, step 301 can occur before or after any of steps 202-205, or simultaneously with any of steps 202-205. This embodiment of the invention does not impose any limitations.

[0148] 206. Based on the capacity retention rate prediction formula and the test capacity retention rate of the target battery, predict the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation with the first preset number of cycles.

[0149] In this embodiment of the invention, for other detailed descriptions of steps 201 and 206, please refer to the detailed description of steps 101-102 in Embodiment 1. These descriptions will not be repeated in this embodiment of the invention.

[0150] As can be seen, the method described in the embodiments of the present invention can perform a first charge-discharge cycle operation with a first preset number of cycles on the target battery within a determined target SOC range, obtain the test capacity retention rate of the battery, and predict the target capacity retention rate of the battery after performing a second charge-discharge cycle operation with the same number of cycles based on the corresponding capacity retention rate prediction formula and the test capacity retention rate. This can improve the prediction efficiency of the battery capacity retention rate, thereby improving the prediction efficiency of the battery cycle life, and thus shortening the battery cycle life test time and reducing the cost of battery cycle life test, which is beneficial to improving the battery development efficiency. In addition, it can also calculate the retention rate difference based on the first sampled capacity retention rate and the second sampled capacity retention rate corresponding to the first charge-discharge cycle operation and the second charge-discharge cycle operation of the sampled battery, and establish a capacity retention rate prediction formula based on the first sampled capacity retention rate, the second sampled capacity retention rate and the retention rate difference. This can improve the determination accuracy of the capacity retention rate prediction formula, thereby improving the prediction accuracy of the capacity retention rate, and thus improving the prediction accuracy of the battery cycle life.

[0151] In an optional embodiment, predicting the target capacity retention rate of the target battery after performing a second charge-discharge cycle of a first preset number of cycles, based on the capacity retention rate prediction formula and the test capacity retention rate corresponding to the target battery, may include the following operations:

[0152] Based on the capacity retention rate prediction formula corresponding to the target battery, the difference between the test capacity retention rate and the determined correction coefficient is calculated to obtain the target capacity retention rate of the target battery after the second charge-discharge cycle operation after the first preset number of cycles.

[0153] The aforementioned correction parameters can be determined in the following ways:

[0154] Obtain the electrode material parameters of the target battery. The electrode material parameters include the electrode material type and the weight of the electrode material corresponding to each electrode material type.

[0155] Calculate the weight percentage of each electrode material type. The weight percentage of the electrode material is used to represent the proportion of the weight of a certain electrode material type in the total weight of the target battery.

[0156] Based on the retention rate difference, all electrode material types, and the weight percentage of all electrode materials, the correction parameters corresponding to the capacity retention rate prediction formula are determined.

[0157] The formula for predicting the capacity retention rate of the target battery can be expressed as follows:

[0158] Target capacity retention = Test capacity retention - Calibration parameter

[0159] For example, when the correction parameter is 1.6, the predicted capacity retention rate of the target battery can be expressed as follows:

[0160] Target capacity retention rate = Test capacity retention rate - 1.6

[0161] The electrode materials may include positive electrode materials and negative electrode materials; the positive electrode materials may include one or more combinations of lithium manganese oxide, lithium cobalt oxide, lithium iron phosphate, ternary materials, lithium nickel oxide and lithium titanate, and the negative electrode materials may include carbon materials and non-carbon materials. The carbon materials may include one or more of artificial graphite, natural graphite, mesophase carbon microspheres (MCMB), petroleum coke, carbon fiber and pyrolytic resin carbon; the electrode materials may also include other materials suitable for lithium batteries, which are not limited in the embodiments of the present invention.

[0162] As can be seen, this optional embodiment can use the difference between the tested capacity retention rate and the determined correction coefficient as the target capacity retention rate of the target battery after a second charge-discharge cycle of a first preset number of cycles, based on the capacity retention rate prediction formula. This can improve the prediction efficiency of capacity retention rate, thereby improving the prediction efficiency of battery cycle life and shortening the battery cycle life test time. Furthermore, by combining the battery's electrode material and the retention rate difference to determine the correction parameters, it is possible to determine correction parameters with a higher degree of matching with the target battery, improving the accuracy and reliability of the correction parameters, thereby further improving the prediction accuracy of capacity retention rate and thus contributing to improving the prediction accuracy of battery cycle life.

[0163] In another optional embodiment, after predicting the target capacity retention rate of the target battery after performing a second charge-discharge cycle of a first preset number of cycles based on the capacity retention rate prediction formula and the test capacity retention rate corresponding to the target battery, the method may further include the following operations:

[0164] The self-discharge rate of the target battery and the corresponding environmental parameters of the target battery are detected. The environmental parameters include one or more combinations of environmental humidity, environmental temperature, environmental dust concentration and dust particle size.

[0165] The target capacity retention rate of the target battery is calibrated based on the self-discharge rate, environmental parameters, and target capacity retention rate.

[0166] The self-discharge rate is used to represent the ability of a battery to retain its stored charge under certain conditions when it is in an open-circuit state.

[0167] For example, if the current ambient temperature is higher than a preset temperature threshold, it will reduce the battery's capacity retention rate after cycling. Therefore, the calculated target capacity retention rate can be adjusted according to the current ambient temperature to obtain a calibrated target capacity retention rate.

[0168] As can be seen, this optional embodiment can also calibrate the calculated target capacity retention rate based on the self-discharge rate of the target battery and different environmental conditions, thereby combining multiple factors to calibrate the capacity retention rate, which can further improve the prediction accuracy of the capacity retention rate, and thus further improve the prediction accuracy of the battery cycle life.

[0169] In this embodiment of the invention, an exemplary flowchart of the method for predicting battery cycle life is shown below. Figure 4 As shown, the specific process of the battery cycle life prediction method is as follows:

[0170] The impedance (SEI film impedance) and expansion force of the battery cells (of the same type as the target battery) at different SOC values ​​were tested. Based on the impedance and expansion force data set corresponding to different SOC values, an appropriate SOC range (target SOC range) was selected for accelerated cycling test (first charge-discharge cycle operation). The cycle rate and cycle temperature of the accelerated cycling test were consistent with those of the conventional cycling test (second charge-discharge cycle operation), and the relationship between the accelerated cycling capacity retention rate and the conventional cycling capacity retention rate (the capacity retention rate prediction relationship corresponding to the target battery) was output. The accelerated cycling test was conducted until the capacity retention rate of the battery cells reached 80% (test capacity retention rate). The cycle life with a capacity retention rate of 80% in the conventional cycling test was calculated using the above relationship.

[0171] For example, three LF280K batteries were subjected to 25℃ 0.5C / 0.5C 0~100% SOC cycling, and another three LF280K batteries from the same batch were subjected to 25℃ 0.5C / 0.5C 80~100% SOC cycling (accelerated cycling method). The capacity retention rates of the two cycling methods at the same number of cycles are shown in Table 1:

[0172]

[0173] Table 1 Capacity retention rate under different cycling methods

[0174] The capacity retention rate corresponding to the cycle in the 0-100% SOC range will be used as the target capacity retention rate, and the capacity retention rate corresponding to the cycle in the 80-100% SOC range will be used as the test capacity retention rate.

[0175] As shown in Table 1, under the same number of cycles, the tested capacity retention rate and the target capacity retention rate maintain a fixed difference: tested capacity retention rate - 1.6 = target capacity retention rate. The time taken for a single cycle in the accelerated cycle test is 0.8 hours, while the time taken for a single cycle in the conventional cycle test is 4 hours. After testing the capacity retention rate for 10,000 cycles, the accelerated cycle test only requires 334 days, while the conventional cycle test requires 1667 days. The accelerated cycle test reduces the testing time to 1 / 5 of the original time.

[0176] Example 3

[0177] Please see Figure 5 , Figure 5 This is a schematic diagram of the structure of a battery cycle life prediction device disclosed in an embodiment of the present invention. Figure 5 The described battery cycle life prediction device may include one of a prediction device, a prediction terminal, a prediction system, and a server, wherein the server includes a local server or a cloud server, and the embodiments of the present invention are not limited thereto. Figure 5 As shown, the battery cycle life prediction device may include:

[0178] The charge-discharge module 301 is used to perform a first charge-discharge cycle operation on the target battery within a determined target SOC range, and obtain the test capacity retention rate of the target battery. The target SOC range is determined by the battery parameters of the target battery, including the SEI film impedance and the expansion force of the target battery.

[0179] The prediction module 302 is used to predict the target capacity retention rate of the target battery after performing a second charge-discharge cycle operation with a first preset number of cycles, based on the capacity retention rate prediction formula and the test capacity retention rate corresponding to the target battery. The target SOC range is within the range of the SOC range corresponding to the second charge-discharge cycle operation. The target capacity retention rate is used to determine the cycle life of the target battery.

[0180] As can be seen, the apparatus described in the embodiments of the present invention can perform a first charge-discharge cycle operation with a first preset number of cycles on the target battery within a determined target SOC range, obtain the test capacity retention rate of the battery, and predict the target capacity retention rate of the battery after performing a second charge-discharge cycle operation with the same number of cycles based on the corresponding capacity retention rate prediction formula and the test capacity retention rate. This can improve the prediction efficiency of the battery capacity retention rate, thereby improving the prediction efficiency of the battery cycle life, and thus shortening the battery cycle life test time, reducing the cost of battery cycle life test, and helping to improve the battery development efficiency.

[0181] In an optional embodiment, such as Figure 6 As shown, the device may further include:

[0182] The acquisition module 303 is used to acquire the battery parameters of the target battery corresponding to each preset SOC value in the predicted SOC range before the charge-discharge module 301 performs a first charge-discharge cycle operation with a first preset number of cycles on the target battery within the determined target SOC range and obtains the test capacity retention rate of the target battery. The predicted SOC range is the SOC range corresponding to the second charge-discharge cycle operation, and the predicted SOC range includes multiple preset SOC values.

[0183] Analysis module 304 is used to analyze the battery parameters of the target battery corresponding to all preset SOC values ​​and obtain the battery parameter change trend corresponding to the predicted SOC range.

[0184] The filtering module 305 is used to filter out the SOC sub-intervals that meet the preset change trend conditions from the predicted SOC intervals based on the battery parameter change trend.

[0185] As can be seen, the apparatus described in this optional embodiment can obtain the battery parameter change trend corresponding to the predicted SOC interval by collecting and analyzing the battery parameters corresponding to all preset SOC values ​​in the predicted SOC interval, and select the SOC sub-intervals that meet the preset change trend conditions as the target SOC interval based on the battery parameter change trend. This can improve the accuracy of the analysis of battery parameter change trends, thereby improving the accuracy of determining the SOC interval for battery cycle life testing, which is beneficial to improving the accuracy and reliability of determining the battery's test capacity retention rate, and further beneficial to improving the prediction accuracy of the battery's capacity retention rate.

[0186] In this optional embodiment, the battery parameter variation trend may optionally include the SEI film impedance variation trend and the expansion force variation trend;

[0187] The specific methods by which the filtering module 305 selects SOC sub-intervals that meet preset trend conditions from the predicted SOC intervals as target SOC intervals based on the battery parameter change trend can include:

[0188] At least one SOC sub-interval with an upward trend in expansion force is selected from the predicted SOC intervals as the first candidate SOC sub-interval.

[0189] Determine the trend change magnitude of the battery parameter change trend corresponding to each first candidate SOC sub-interval. The trend change magnitude includes the trend change magnitude of SEI film impedance and the trend change magnitude of expansion force.

[0190] Select at least one second candidate SOC sub-interval from all first candidate SOC sub-intervals where the change in SEI membrane impedance trend is less than the preset impedance value and the change in expansion force trend is greater than the preset expansion force value.

[0191] When there is only one second candidate SOC sub-interval, the second candidate SOC sub-interval is determined as the target SOC interval.

[0192] As can be seen, the apparatus described in this optional embodiment can also select a candidate SOC sub-interval from the predicted SOC interval based on the changes in SEI film impedance and expansion force, where the expansion force trend is upward and the expansion force trend change amplitude is large, while the SEI film impedance trend change amplitude is small. This achieves the determination of an accurate numerical range of the target SOC interval based on changes in battery structure, which can improve the accuracy of battery structure change analysis, thereby further improving the accuracy of determining the SOC interval for battery cycle life testing, and thus contributing to the accuracy and reliability of determining the battery's test capacity retention rate.

[0193] In this optional embodiment, further optionally, the specific method by which the filtering module 305 filters out the SOC sub-intervals that meet the preset trend conditions from the predicted SOC intervals as the target SOC intervals based on the battery parameter change trend may also include:

[0194] When there are at least two second candidate SOC sub-intervals, determine the storage lifetime decay rate of the target battery corresponding to each second candidate SOC sub-interval;

[0195] The cycle time corresponding to each second candidate SOC sub-interval is calculated. The cycle time is used to represent the time it takes for a battery of the same type as the target battery to complete one first charge-discharge cycle within a certain SOC interval.

[0196] Based on the storage lifetime decay rate and cycle duration, select one second candidate SOC sub-interval from all second candidate SOC sub-intervals that meets the preset decay rate condition and the preset cycle duration condition as the target SOC interval.

[0197] As can be seen, the apparatus described in this optional embodiment can also obtain the storage life decay rate and cycle time of the target battery corresponding to each second candidate SOC sub-interval when there are at least two second candidate SOC sub-intervals. It can also take a candidate SOC sub-interval that satisfies the preset decay rate condition and the preset cycle time condition as the SOC interval. This realizes the determination of the numerical range of the target SOC interval based on the battery structure change, combined with the battery storage performance decay and the time cost of cycle life testing. This can further improve the accuracy of determining the SOC interval of battery cycle life testing, thereby improving the accuracy of battery capacity retention prediction, and can also shorten the battery cycle life testing time, which is beneficial to improving the prediction efficiency of battery cycle life.

[0198] In another optional embodiment, the battery type of the target battery is the target type;

[0199] The charge-discharge module 301 is also used to perform a first charge-discharge cycle operation with a second preset number of cycles on the first sampled battery within the target SOC range before the prediction module 302 predicts the target capacity retention rate of the target battery after performing a second charge-discharge cycle operation with a first preset number of cycles based on the capacity retention rate prediction formula and the test capacity retention rate corresponding to the target battery, thereby obtaining the first sampled capacity retention rate corresponding to the target type, wherein the battery type of the first sampled battery is the target type.

[0200] The charge-discharge module 301 is also used to perform a second charge-discharge cycle operation with a second preset number of cycles on the second sampled battery to obtain the second sampled capacity retention rate corresponding to the target type, wherein the battery type of the second sampled battery is the target type;

[0201] Among them, such as Figure 6 As shown, the device may further include:

[0202] Calculation module 306 is used to calculate the difference in retention rate between the second sampling capacity retention rate and the first sampling capacity retention rate;

[0203] The relation establishment module 307 is used to establish a prediction relation for the capacity retention rate of the target battery based on the first sampled capacity retention rate, the second sampled capacity retention rate, and the retention rate difference.

[0204] As can be seen, the apparatus described in this optional embodiment can calculate the retention rate difference based on the first and second sampled capacity retention rates corresponding to the first and second charge-discharge cycles performed by the sampled battery, and establish a capacity retention rate prediction formula based on the first sampled capacity retention rate, the second sampled capacity retention rate, and the retention rate difference. This can improve the accuracy of determining the capacity retention rate prediction formula, thereby improving the accuracy of capacity retention rate prediction and, consequently, improving the accuracy of battery cycle life prediction.

[0205] In this optional embodiment, optionally, the prediction module 302 predicts the target capacity retention rate of the target battery after performing a second charge-discharge cycle operation with a first preset number of cycles based on the capacity retention rate prediction formula and the test capacity retention rate corresponding to the target battery. The specific method may include:

[0206] Based on the capacity retention rate prediction formula corresponding to the target battery, the difference between the test capacity retention rate and the determined correction coefficient is calculated to obtain the target capacity retention rate of the target battery after the second charge-discharge cycle operation after the first preset number of cycles.

[0207] The aforementioned correction parameters can be determined in the following ways:

[0208] Obtain the electrode material parameters of the target battery. The electrode material parameters include the electrode material type and the weight of the electrode material corresponding to each electrode material type.

[0209] Calculate the weight percentage of each electrode material type. The weight percentage of the electrode material is used to represent the proportion of the weight of a certain electrode material type in the total weight of the target battery.

[0210] Based on the retention rate difference, all electrode material types, and the weight percentage of all electrode materials, the correction parameters corresponding to the capacity retention rate prediction formula are determined.

[0211] As can be seen, the apparatus described in this optional embodiment can also use the difference between the tested capacity retention rate and the determined correction coefficient as the target capacity retention rate of the target battery after a second charge-discharge cycle operation with a first preset number of cycles, based on the capacity retention rate prediction formula. This can improve the prediction efficiency of capacity retention rate, thereby improving the prediction efficiency of battery cycle life and shortening the battery cycle life test time. Furthermore, by combining the battery's electrode material and the retention rate difference to determine the correction parameters, it is possible to determine correction parameters with a higher degree of matching with the target battery, improving the accuracy and reliability of the correction parameters, thereby further improving the prediction accuracy of capacity retention rate and thus contributing to improving the prediction accuracy of battery cycle life.

[0212] In yet another alternative embodiment, such as Figure 6 As shown, the device may further include:

[0213] The detection module 308 is used to detect the self-discharge rate of the target battery and the environmental parameters corresponding to the target battery after the prediction module 302 predicts the target capacity retention rate of the target battery after performing a second charge-discharge cycle operation with a first preset number of cycles based on the capacity retention rate prediction formula and the test capacity retention rate of the target battery. The environmental parameters include one or more combinations of environmental humidity, environmental temperature, environmental dust concentration and dust particle size.

[0214] The calibration module 309 is used to calibrate the target capacity retention rate of the target battery based on the self-discharge rate, environmental parameters, and target capacity retention rate.

[0215] As can be seen, the apparatus described in this optional embodiment can calibrate the calculated target capacity retention rate based on the self-discharge rate of the target battery and different environmental conditions, thereby calibrating the capacity retention rate by combining multiple factors, which can further improve the prediction accuracy of the capacity retention rate, and thus further improve the prediction accuracy of the battery cycle life.

[0216] Example 4

[0217] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of another battery cycle life prediction device disclosed in an embodiment of the present invention. Figure 7 As shown, the battery cycle life prediction device may include:

[0218] Memory 401 storing executable program code;

[0219] Processor 402 coupled to memory 401;

[0220] The processor 402 calls the executable program code stored in the memory 401 to execute the steps in the battery cycle life prediction method described in Embodiment 1 or Embodiment 2 of the present invention.

[0221] Example 5

[0222] This invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute steps in the battery cycle life prediction method described in Embodiment 1 or Embodiment 2 of this invention.

[0223] Example 6

[0224] This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program operable to cause a computer to perform the steps in the battery cycle life prediction method described in Embodiment 1 or Embodiment 2.

[0225] The device embodiments described above are merely illustrative. The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; that is, they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0226] Through the detailed description of the above embodiments, those skilled in the art can clearly understand that each implementation method can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, including read-only memory (ROM), random access memory (RAM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), one-time programmable read-only memory (OTPROM), electrically-Erasable Programmable Read-Only Memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0227] Finally, it should be noted that the battery cycle life prediction method and apparatus disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting battery cycle life, characterized in that, The method includes: Within the determined target SOC range, a first charge-discharge cycle operation of a first preset number of cycles is performed on the target battery to obtain the test capacity retention rate of the target battery. The target SOC range is determined by the battery parameters of the target battery, including the SEI film impedance and the expansion force of the target battery. The battery type of the target battery is the target type. Within the target SOC range, a first charge-discharge cycle operation with a second preset number of cycles is performed on the first sampled battery to obtain the first sampled capacity retention rate corresponding to the target type, wherein the battery type of the first sampled battery is the target type; Perform a second charge-discharge cycle operation with the second preset number of cycles on the second sampled battery to obtain the second sampled capacity retention rate corresponding to the target type, wherein the battery type of the second sampled battery is the target type; Calculate the retention rate difference between the second sampling capacity retention rate and the first sampling capacity retention rate; Based on the first sampled capacity retention rate, the second sampled capacity retention rate, and the retention rate difference, a prediction formula for the capacity retention rate of the target battery is established. Based on the capacity retention rate prediction formula corresponding to the target battery and the test capacity retention rate, the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation with the first preset number of cycles is predicted. The target SOC range is within the range of the SOC range corresponding to the second charge-discharge cycle operation. The target capacity retention rate is used to determine the cycle life of the target battery.

2. The method for predicting battery cycle life according to claim 1, characterized in that, Before performing a first charge-discharge cycle operation on the target battery within the determined target SOC range for a first preset number of cycles to obtain the test capacity retention rate of the target battery, the method further includes: The battery parameters of the target battery corresponding to each preset SOC value within the predicted SOC range are collected. The predicted SOC range is the SOC range corresponding to the second charge-discharge cycle operation. The predicted SOC range includes multiple preset SOC values. Analyze the battery parameters of the target battery corresponding to all the preset SOC values ​​to obtain the battery parameter change trend corresponding to the predicted SOC range; Based on the battery parameter change trend, the SOC sub-interval that meets the preset change trend conditions is selected from the predicted SOC interval as the target SOC interval.

3. The method for predicting battery cycle life according to claim 2, characterized in that, The battery parameter variation trends include the SEI film impedance variation trend and the expansion force variation trend; The step of selecting a sub-interval of SOC that meets a preset trend condition from the predicted SOC interval based on the battery parameter change trend as the target SOC interval includes: At least one SOC sub-interval with an upward trend in the expansion force change is selected from the predicted SOC intervals as the first candidate SOC sub-interval; Determine the trend change magnitude of the battery parameter change trend corresponding to each first candidate SOC sub-interval, wherein the trend change magnitude includes the trend change magnitude of SEI film impedance and the trend change magnitude of expansion force; From all the first candidate SOC sub-intervals, at least one second candidate SOC sub-interval is selected where the change in the SEI membrane impedance trend is less than a preset impedance value and the change in the expansion force trend is greater than a preset expansion force value. When there is only one second candidate SOC sub-interval, the second candidate SOC sub-interval is determined as the target SOC interval.

4. The method for predicting battery cycle life according to claim 3, characterized in that, The step of selecting a sub-interval of SOC that meets a preset trend condition from the predicted SOC interval based on the battery parameter change trend as the target SOC interval further includes: When there are at least two second candidate SOC sub-intervals, determine the storage lifetime decay rate corresponding to the target battery for each second candidate SOC sub-interval; The cycle time corresponding to each second candidate SOC sub-interval is calculated. The cycle time is used to represent the time it takes for a battery of the same type as the target battery to complete one first charge-discharge cycle within a certain SOC interval. Based on the storage lifetime decay rate and the cycle duration, select one second candidate SOC sub-interval from all second candidate SOC sub-intervals that satisfies the preset decay rate condition and the preset cycle duration condition as the target SOC interval.

5. The method for predicting battery cycle life according to claim 1, characterized in that, The step of predicting the target capacity retention rate of the target battery after performing a second charge-discharge cycle of the first preset number of cycles, based on the capacity retention rate prediction formula corresponding to the target battery and the test capacity retention rate, includes: Based on the capacity retention rate prediction formula corresponding to the target battery, the difference between the test capacity retention rate and the determined correction parameter is calculated to obtain the target capacity retention rate of the target battery after the second charge-discharge cycle operation after the first preset number of cycles. The correction parameters are determined in the following manner: Obtain the electrode material parameters of the target battery, wherein the electrode material parameters include the electrode material type and the electrode material weight corresponding to each electrode material type; Calculate the weight percentage of the electrode material corresponding to each of the electrode material types, whereby the weight percentage of the electrode material corresponding to a certain electrode material type is used to represent the proportion of the weight of the electrode material corresponding to a certain electrode material type in the total weight of the target battery; Based on the retention rate difference, all electrode material types, and the weight percentage of all electrode materials, the correction parameters corresponding to the capacity retention rate prediction formula are determined.

6. The method for predicting battery cycle life according to any one of claims 1-5, characterized in that, After predicting the target capacity retention rate of the target battery after performing a second charge-discharge cycle of the first preset number of cycles based on the capacity retention rate prediction formula corresponding to the target battery and the test capacity retention rate, the method further includes: The self-discharge rate of the target battery and the corresponding environmental parameters of the target battery are detected. The environmental parameters include one or more combinations of environmental humidity, environmental temperature, environmental dust concentration and dust particle size. The target capacity retention rate of the target battery is calibrated based on the self-discharge rate, the environmental parameters, and the target capacity retention rate.

7. A device for predicting battery cycle life, characterized in that, The device includes: The charge-discharge module is used to perform a first charge-discharge cycle operation with a first preset number of cycles on a target battery within a determined target SOC range to obtain the test capacity retention rate of the target battery. The target SOC range is determined by the battery parameters of the target battery, including the SEI film impedance and the expansion force of the target battery. The battery type of the target battery is a target type. The module is also used to perform a first charge-discharge cycle operation with a second preset number of cycles on a first sampled battery within the target SOC range to obtain a first sampled capacity retention rate corresponding to the target type, where the battery type of the first sampled battery is the target type. Furthermore, it performs a second charge-discharge cycle operation with the second preset number of cycles on a second sampled battery to obtain a second sampled capacity retention rate corresponding to the target type, where the battery type of the second sampled battery is the target type. The calculation module is used to calculate the difference in retention rate between the second sampling capacity retention rate and the first sampling capacity retention rate; The relation establishment module is used to establish a prediction relation for the capacity retention rate of the target battery based on the first sampled capacity retention rate, the second sampled capacity retention rate, and the retention rate difference. The prediction module is used to predict the target capacity retention rate of the target battery after performing a second charge-discharge cycle operation with the first preset number of cycles, based on the capacity retention rate prediction formula corresponding to the target battery and the test capacity retention rate. The target SOC range is within the range of the SOC range corresponding to the second charge-discharge cycle operation. The target capacity retention rate is used to determine the cycle life of the target battery.

8. A device for predicting battery cycle life, characterized in that, The device includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the battery cycle life prediction method as described in any one of claims 1-6.

9. A computer storage medium, characterized in that, The computer storage medium stores computer instructions, which, when invoked, are used to execute the battery cycle life prediction method as described in any one of claims 1-6.

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