CN117074953A

CN117074953A - Method and device for predicting cycle life of battery and computer storage medium

Info

Publication number: CN117074953A
Application number: CN202310892628.6A
Authority: CN
Inventors: 韩永琪; 邓力; 刘范芬
Original assignee: Hubei Eve Power Co Ltd
Current assignee: Hubei Eve Power Co Ltd
Priority date: 2023-07-18
Filing date: 2023-07-18
Publication date: 2023-11-17

Abstract

The invention discloses a battery cycle life prediction method and device and a computer storage medium, wherein the method comprises the following steps: executing 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, wherein 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 the target capacity retention rate of the target battery after the second charge-discharge cycle operation of the first preset cycle number is executed according to the capacity retention rate prediction relation and the test capacity retention rate corresponding to the target battery, wherein the target SOC interval is in the interval range of the SOC interval corresponding to the second charge-discharge cycle operation, and the target capacity retention rate is used for determining the cycle life of the target battery. Therefore, the invention can improve the prediction efficiency of the battery cycle life and is beneficial to improving the development efficiency of the battery.

Description

Method and device for predicting cycle life of battery and computer storage medium

Technical Field

The present invention relates to the field of battery technologies, and in particular, to a method and apparatus for predicting battery cycle life, and a computer storage medium.

Background

The service life of a lithium iron phosphate battery generally refers to the cycle life, i.e., the number of times the battery can be charged and discharged cyclically. A complete cycle refers to a battery undergoing a complete charge-discharge process, discharging a battery from 100% charge to 0% to 100% recharging, i.e., a cycle.

Currently, life prediction models of lithium iron phosphate batteries can be divided into three main categories: experience models, semi-experience models, and electrochemical models. The empirical model is a data-driven prediction function, the service life of the battery needs to be predicted based on a large amount of past data, and the empirical model cannot explore the chemical reaction process in the lithium iron phosphate battery; the semi-empirical model requires operators to master the physicochemical mechanism of the prediction system, and has higher requirements for the operators; the electrochemical model is a life model constructed based on the reaction mechanism inside the battery, and requires a large amount of computing resources. Therefore, the prediction time required by the battery life prediction by the existing prediction method is longer or the cost is higher, and the development speed of the battery is restricted. Therefore, it is important to provide a technical solution capable of improving the prediction efficiency of the battery cycle life.

Disclosure of Invention

The invention aims to solve the technical problem of providing a battery cycle life prediction method and device, which can improve the battery cycle life prediction efficiency.

To solve the above technical problem, a first aspect of the present invention discloses a method for predicting a cycle life of a battery, the method comprising:

executing 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, wherein the target SOC interval is determined by battery parameters of the target battery, and the battery parameters of the target battery comprise SEI (solid electrolyte interface) 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 the second charge-discharge cycle operation of the first preset cycle number is executed according to a capacity retention rate prediction relation corresponding to the target battery and the test capacity retention rate, wherein the target SOC interval is in an interval range of the SOC interval corresponding to the second charge-discharge cycle operation, and the target capacity retention rate is used for determining the cycle life of the target battery.

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

collecting battery parameters of the target battery corresponding to each preset SOC value in a predicted SOC interval, wherein the predicted SOC interval is the SOC interval corresponding to the second charge-discharge cycle operation, and the predicted SOC interval comprises a plurality of preset SOC values;

analyzing battery parameters of the target battery corresponding to all the preset SOC values to obtain battery parameter variation trends corresponding to the predicted SOC intervals;

and screening the SOC subintervals meeting the preset change trend condition from the predicted SOC intervals to serve as target SOC intervals according to the change trend of the battery parameters.

As an optional embodiment, in the first aspect of the present invention, the battery parameter variation trend includes an SEI film resistance variation trend and an expansion force variation trend;

the step of screening the SOC subinterval meeting the preset variation trend condition from the predicted SOC interval as the target SOC interval according to the variation trend of the battery parameter includes:

Screening at least one SOC subinterval with the expansion force change trend being an ascending trend from the predicted SOC interval as a first candidate SOC subinterval;

determining trend change amplitude of the battery parameter change trend corresponding to each first candidate SOC subinterval, wherein the trend change amplitude comprises SEI film resistance trend change amplitude and expansion force trend change amplitude;

screening at least one second candidate SOC subinterval of which the SEI film resistance trend change amplitude is smaller than a preset resistance amplitude and the expansion force trend change amplitude is larger than a preset expansion force amplitude from all the first candidate SOC subintervals;

when there is only one of the second candidate SOC subintervals, the second candidate SOC subinterval is determined as a target SOC interval.

In an optional implementation manner, in a first aspect of the present invention, the screening, according to the battery parameter variation trend, from the predicted SOC intervals, SOC subintervals that meet a preset variation trend condition as target SOC intervals further includes:

when at least two second candidate SOC subintervals exist, determining a storage life decay rate corresponding to the target battery corresponding to each second candidate SOC subinterval;

Counting the cycle duration corresponding to each second candidate SOC subinterval, wherein the cycle duration is used for indicating the duration of completing one circle of first charge-discharge cycle operation of the battery which is of the same battery type as the target battery in a certain SOC interval;

and screening one second candidate SOC subinterval meeting the preset decay rate condition and the preset cycle duration condition from all second candidate SOC subintervals as a target SOC interval according to the storage life decay rate and the cycle duration.

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

before predicting the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation of the first preset number of cycles according to the capacity retention rate prediction relation corresponding to the target battery and the test capacity retention rate, the method further includes:

in the target SOC interval, performing a first charge-discharge cycle operation of a second preset cycle number on a first sampling battery to obtain a first sampling capacity retention rate corresponding to the target type, wherein the battery type of the first sampling battery is the target type;

Executing a second charge-discharge cycle operation of the second preset cycle number on a second sampling battery to obtain a second sampling capacity retention rate corresponding to the target type, wherein the battery type of the second sampling battery is the target type;

calculating a retention rate difference between the second sample volume retention rate and the first sample volume retention rate;

and establishing a capacity retention rate prediction relation corresponding to the target battery based on the first sampling capacity retention rate, the second sampling capacity retention rate and the retention rate difference.

As an optional implementation manner, in the first aspect of the present invention, the predicting, according to the capacity retention rate prediction relation 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 of the first preset number of cycles includes:

calculating the difference between the test capacity retention rate and the determined correction coefficient based on a capacity retention rate prediction relational expression corresponding to the target battery to obtain a target capacity retention rate of the target battery after a second charge-discharge cycle operation of the first preset cycle number;

Wherein the correction parameters are determined by:

acquiring electrode material parameters of the target battery, wherein the electrode material parameters comprise electrode material types and electrode material weights corresponding to each electrode material type;

calculating the weight ratio of the electrode material corresponding to each electrode material type, wherein the weight ratio of the electrode material is used for representing the ratio of the weight of the electrode material corresponding to a certain electrode material type in the total weight of the target battery;

and determining correction parameters corresponding to the capacity retention prediction relational expression according to the retention difference, all the electrode material types and all the electrode material weight ratios.

As an optional implementation manner, in the first aspect of the present invention, after the predicting the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation of the first preset number of cycles according to the capacity retention rate prediction relation corresponding to the target battery and the test capacity retention rate, the method further includes:

detecting a self-discharge rate of the target battery and an environmental parameter corresponding to the target battery, wherein the environmental parameter comprises one or more of combination of environmental humidity, environmental temperature, environmental dust concentration and dust particle size;

And calibrating a target capacity retention rate of the target battery according to the self-discharge rate, the environmental parameter and the target capacity retention rate.

The second aspect of the present invention discloses a battery cycle life prediction apparatus, the apparatus comprising:

the charging and discharging module is used for executing first charging and discharging circulation operation of a first preset circulation number on a target battery in a determined target SOC interval to obtain a test capacity retention rate of the target battery, wherein 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;

the prediction module is configured to predict a target capacity retention rate of the target battery after the second charge-discharge cycle operation of the first preset cycle number is performed according to a capacity retention rate prediction relation corresponding to the target battery and the test capacity retention rate, where the target SOC interval is within an interval range of the SOC interval corresponding to the second charge-discharge cycle operation, and the target capacity retention rate is used to determine a cycle life of the target battery.

As an alternative embodiment, in the second aspect of the present invention, the apparatus further includes:

The acquisition module is used for acquiring battery parameters of the target battery corresponding to each preset SOC value in a predicted SOC interval before the first charge-discharge cycle operation of a first preset cycle number is carried out on the target battery in the determined target SOC interval by the charge-discharge module to obtain the test capacity retention rate of the target battery, wherein the predicted SOC interval is an SOC interval corresponding to the second charge-discharge cycle operation, and the predicted SOC interval comprises a plurality of preset SOC values;

the analysis module is used for analyzing battery parameters of the target battery corresponding to all the preset SOC values to obtain battery parameter change trends corresponding to the predicted SOC intervals;

and the screening module is used for screening the SOC subintervals meeting the preset change trend condition from the predicted SOC intervals to serve as target SOC intervals according to the change trend of the battery parameters.

As an alternative embodiment, in the second aspect of the present invention, the battery parameter variation trend includes an SEI film resistance variation trend and an expansion force variation trend;

the specific way of screening the SOC subinterval meeting the preset variation trend condition from the predicted SOC interval as the target SOC interval by the screening module according to the variation trend of the battery parameter includes:

In an optional implementation manner, in the second aspect of the present invention, the specific manner of screening, according to the battery parameter variation trend, the SOC sub-interval that meets the preset variation trend condition from the predicted SOC interval as the target SOC interval further includes:

As an optional embodiment, in the second aspect of the present invention, the battery type of the target battery is a target type;

the charge-discharge module is further configured to, before the prediction module predicts the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation of the first preset cycle number according to the capacity retention rate prediction relation and the test capacity retention rate corresponding to the target battery, perform the first charge-discharge cycle operation of the second preset cycle number on the first sampling battery in the target SOC interval, to obtain a first sampling capacity retention rate corresponding to the target type, where the battery type of the first sampling battery is the target type;

The charge-discharge module is further configured to perform a second charge-discharge cycle operation of the second preset cycle number on a second sampling battery, to obtain a second sample capacity retention rate corresponding to the target type, where the battery type of the second sampling battery is the target type;

wherein the apparatus further comprises:

a calculation module for calculating a retention rate difference between the second sample volume retention rate and the first sample volume retention rate;

and the relation establishing module is used for establishing a capacity holding rate prediction relation corresponding to the target battery based on the first sampling capacity holding rate, the second sampling capacity holding rate and the holding rate difference value.

As an alternative embodiment, in the second aspect of the present invention, the specific manner of predicting, by the prediction module, the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation of the first preset number of cycles according to the capacity retention rate prediction relation corresponding to the target battery and the test capacity retention rate includes:

Wherein the correction parameters are determined by:

a detection module, configured to detect a self-discharge rate of the target battery and an environmental parameter corresponding to the target battery after the prediction module predicts a target capacity retention rate of the target battery after performing a second charge-discharge cycle operation of the first preset number of cycles according to a capacity retention rate prediction relation corresponding to the target battery and the test capacity retention rate, where the environmental parameter includes a combination of one or more of an environmental humidity, an environmental temperature, an environmental dust concentration, and a dust particle size;

And the calibration module is used for calibrating the target capacity retention rate of the target battery according to the self-discharge rate, the environment parameter and the target capacity retention rate.

In a third aspect, the present invention discloses another battery cycle life prediction apparatus, the apparatus comprising:

a memory storing executable program code;

a processor coupled to the memory;

the processor invokes the executable program code stored in the memory to perform the battery cycle life prediction method disclosed in the first aspect of the present invention.

A fourth aspect of the present invention discloses a computer storage medium storing computer instructions that, when invoked, are adapted to perform the method of predicting battery cycle life disclosed in the first aspect of the present invention.

Compared with the prior art, the embodiment of the invention has the following beneficial effects:

in the embodiment of the invention, in a determined target SOC interval, a first charge-discharge cycle operation of a first preset cycle number is performed on a target battery to obtain a test capacity retention rate of the target battery, wherein 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 the target capacity retention rate of the target battery after the second charge-discharge cycle operation of the first preset cycle number is executed according to the capacity retention rate prediction relation and the test capacity retention rate corresponding to the target battery, wherein the target SOC interval is in the interval range of the SOC interval corresponding to the second charge-discharge cycle operation, and the target capacity retention rate is used for determining the cycle life of the target battery. Therefore, the method and the device can execute the first charge-discharge cycle operation of the first preset cycle number on the target battery in the determined target SOC interval to obtain the test capacity retention rate of the battery, predict the target capacity retention rate of the battery after executing the second charge-discharge cycle operation of the same cycle number based on the corresponding capacity retention rate prediction relation and the test capacity retention rate of the battery, and can improve the prediction efficiency of the capacity retention rate of the battery, thereby improving the prediction efficiency of the battery cycle life, further shortening the time of the battery cycle life test, reducing the cost of the battery cycle life test and being beneficial to improving the development efficiency of the battery.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.

FIG. 1 is a schematic flow chart of a method for predicting battery cycle life according to an embodiment of the present invention;

FIG. 2 is a flow chart of another battery cycle life prediction method according to an embodiment of the present invention;

FIG. 3 is a schematic illustration of a change in battery expansion force according to an embodiment of the present invention;

FIG. 4 is a flow chart of yet another battery cycle life prediction method disclosed in an embodiment of the present invention;

FIG. 5 is a schematic structural diagram of a battery cycle life prediction apparatus according to an embodiment of the present invention;

FIG. 6 is a schematic structural diagram of another battery cycle life prediction apparatus according to an embodiment of the present invention;

fig. 7 is a schematic structural view of a battery cycle life prediction apparatus according to another embodiment of the present invention.

Detailed Description

In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.

The terms first, second and the like in the description and in the claims and in the above-described figures are used for distinguishing between different objects and not necessarily for describing a sequential or chronological order. Furthermore, the terms "comprise" and "have," as well as any variations thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, apparatus, article, or article that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or article.

Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment may be included in at least one embodiment of the invention. The appearances of such phrases in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments.

The invention discloses a method and a device for predicting the cycle life of a battery, which can execute a first charge-discharge cycle operation of a first preset cycle number on the target battery in a determined target SOC interval to obtain the test capacity retention rate of the battery, predict the target capacity retention rate of the battery after executing a second charge-discharge cycle operation of the same cycle number based on a capacity retention rate prediction relation and the test capacity retention rate corresponding to the battery, and can improve the prediction efficiency of the capacity retention rate of the battery, thereby improving the prediction efficiency of the cycle life of the battery, further shortening the time of the cycle life test of the battery, reducing the cost of the cycle life test of the battery and being beneficial to improving the development efficiency of the battery. The following will describe in detail.

Example 1

Referring to fig. 1, fig. 1 is a flowchart illustrating a method for predicting battery cycle life according to an embodiment of the invention. The method for predicting the battery cycle life described in fig. 1 may be applied to a device for predicting the battery cycle life, where the device may include one of a predicting device, a predicting terminal, a predicting system, and a server, where the server includes a local server or a cloud server, and embodiments of the present invention are not limited. As shown in fig. 1, the battery cycle life prediction method may include the following operations:

101. And in the determined target SOC interval, executing a first charge-discharge cycle operation of a first preset cycle number on the target battery to obtain the test capacity retention rate of the target battery.

In the embodiment of the invention, the target SOC interval is determined by the battery parameters of the target battery, wherein the battery parameters of the target battery comprise SEI film impedance of the target battery and expansion force of the target battery. The target battery may be a lithium battery (e.g., lithium iron phosphate battery), or may be other batteries capable of forming an SEI film and expanding during production, testing or use. Wherein the target SOC interval includes a maximum SOC threshold value and a minimum SOC threshold value, and the first charge-discharge cycle operation includes a charge operation of charging the SOC of the battery from the minimum SOC threshold value to the maximum SOC threshold value and a discharge operation of discharging the SOC of the battery from the maximum SOC threshold value to the minimum SOC threshold value. The first preset cycle number is used for indicating the times of executing the first charge-discharge cycle operation, and the unit of the first preset cycle number is a cycle; the first preset number of cycles may be one of 100 weeks, 200 weeks, 300 weeks, 400 weeks, 500 weeks, 600 weeks, and 700 weeks, or may be another number, which is not limited in the embodiment of the present invention. For example, assuming that the target SOC interval is an 80% -100% SOC interval, the first preset number of cycles is 100 weeks, that is, 100 charge-discharge cycle operations from 80% SOC to 100% SOC and from 100% SOC to 80% are performed on the battery. The test capacity retention rate is used for indicating the ratio of the current available capacity of the target battery to the initial capacity of the target battery after the target battery is subjected to a first charge-discharge cycle operation of a first preset cycle number.

It should be noted that, the SEI film in the embodiment of the present invention is: in the first charge and discharge process of the battery, the electrode material reacts with the electrolyte on the solid-liquid phase interface to form a passivation layer covering the surface of the electrode material, wherein the passivation layer is an interface layer, has the characteristics of solid electrolyte, is an electronic insulator and is also an excellent conductor of Li+ (lithium ions), and Li+ can be freely inserted and removed through the passivation layer, so the passivation layer is called a solid electrolyte interface film (Solid Electrolyte Interface) and is called an SEI film for short. In addition, it should be further noted that the principle of the embodiment of the present invention is as follows: in the cycling process, active lithium is consumed by the damage repair of the SEI film, and the graphite structure is damaged due to the supporting-shrinking (expansion force) of the crystal structure in the charging and discharging processes of the negative electrode graphite, so that the anode dynamic performance of the battery is reduced, and the capacity of the battery is reduced. Accordingly, the target SOC interval may be determined from the SEI film resistance and the expansion force of the target battery.

102. And predicting the target capacity retention rate of the target battery after the second charge-discharge cycle operation of the first preset cycle number is executed according to the capacity retention rate prediction relation corresponding to the target battery and the test capacity retention rate.

In the embodiment of the invention, the target SOC interval is in the interval range of the SOC interval corresponding to the second charge-discharge cycle operation, namely, the target SOC interval is one of the sub-intervals of the SOC interval 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 that the target SOC interval is 80% to 100% SOC interval, the SOC interval corresponding to the second charge-discharge cycle operation may be 20% to 100% SOC interval or 0% to 100% SOC interval, which is not limited in the embodiment of the present invention. The capacity retention rate prediction relational expression corresponding to the target battery may be a relational expression between the test capacity retention rate and the target capacity retention rate.

Therefore, by implementing the method described by the embodiment of the invention, the first charge-discharge cycle operation of the first preset cycle number can be performed on the target battery in the determined target SOC interval, the test capacity retention rate of the battery is obtained, the target capacity retention rate of the battery after the second charge-discharge cycle operation of the same cycle number is predicted based on the capacity retention rate prediction relation and the test capacity retention rate corresponding to the battery, the prediction efficiency of the capacity retention rate of the battery can be improved, the prediction efficiency of the battery cycle life can be improved, the time of the battery cycle life test can be shortened, the cost of the battery cycle life test can be reduced, and the development efficiency of the battery can be improved.

In an alternative embodiment, before performing the first charge-discharge cycle operation of the first preset number of cycles on the target battery in the determined target SOC interval to obtain the test capacity retention rate of the target battery, the method may further include the following operations:

acquiring battery parameters of a target battery corresponding to each preset SOC value in a predicted SOC interval, wherein the predicted SOC interval is an SOC interval corresponding to a second charge-discharge cycle operation, and the predicted SOC interval comprises a plurality of preset SOC values;

analyzing battery parameters of the target battery corresponding to all preset SOC values to obtain battery parameter change trends corresponding to the predicted SOC intervals;

The battery parameter of the target battery corresponding to each preset SOC value may be obtained by performing a charge-discharge cycle test on the target battery in advance, or may be obtained by performing a charge-discharge cycle test on a battery of the same battery type as the target battery, which is not limited in the embodiment of the present invention.

Wherein, optionally, the battery parameter change trend may be obtained by analyzing the change situation of all battery parameters through a statistical chart, where the statistical chart may include one or more combinations of a line graph, a scatter graph, a bar graph and a histogram, and the line graph may be a graph, or may be any other type of line graph; the battery parameter change trend can also be obtained by analyzing the battery parameter through a data analysis model, and the embodiment of the invention is not limited.

Therefore, according to the alternative embodiment, the battery parameter change trend corresponding to the predicted SOC interval can be obtained by collecting and analyzing the battery parameters corresponding to all the preset SOC values in the predicted SOC interval, and the SOC subinterval meeting the preset change trend condition predicted SOC interval is screened out as the target SOC interval according to the battery parameter change trend, so that the analysis accuracy of the battery parameter change trend can be improved, the determination accuracy of the SOC interval of the battery cycle life test can be improved, the determination accuracy and reliability of the test capacity retention rate of the battery can be improved, and the prediction accuracy of the capacity retention rate of the battery can be improved.

In this alternative embodiment, optionally, the battery parameter variation trend includes an SEI film resistance variation trend and an expansion force variation trend;

according to the variation trend of the battery parameters, the SOC subinterval meeting the preset variation trend condition is screened out from the predicted SOC interval to serve as a target SOC interval, and the method can comprise the following operations:

determining trend change amplitude of battery parameter change trend corresponding to each first candidate SOC subinterval, wherein the trend change amplitude comprises SEI film resistance trend change amplitude and expansion force trend change amplitude;

Screening at least one second candidate SOC subinterval of which the SEI film resistance trend variation amplitude is smaller than a preset resistance amplitude and the expansion force trend variation amplitude is larger than a preset expansion force amplitude from all the first candidate SOC subintervals;

when there is only one second candidate SOC subinterval, the second candidate SOC subinterval is determined as the target SOC interval.

When the statistical graph for analysis is a graph, the trend change amplitude of the battery parameter change trend can be represented by the slope of the graph.

For example, it is assumed that a battery parameter variation trend is analyzed based on a graph, and if in a predicted SOC interval, the SEI film resistance of a target battery remains almost unchanged only in 80% -100% SOC subintervals, the expansion force variation trend of the target battery is an upward trend, and the slope of the expansion force curve is greater than a preset slope threshold, the 80% -100% SOC subintervals are determined as target SOC intervals.

Therefore, according to the alternative embodiment, the candidate SOC subinterval with the higher expansion force trend and the smaller expansion force trend change amplitude can be selected from the predicted SOC interval as the target SOC interval according to the SEI film resistance change condition and the expansion force change condition, so that the numerical range of the accurate target SOC interval determined based on the battery structure change condition is realized, the analysis accuracy of the battery structure change can be improved, the determination accuracy of the SOC interval of the battery cycle life test is further improved, and the determination accuracy and reliability of the test capacity retention rate of the battery are further facilitated.

In this optional embodiment, further optionally, according to the battery parameter variation trend, an SOC sub-interval satisfying a preset variation trend condition is selected from the predicted SOC intervals as the target SOC interval, and the following operations may be further included:

when at least two second candidate SOC subintervals exist, determining a storage life decay rate corresponding to a target battery corresponding to each second candidate SOC subinterval;

The storage life of a battery is used to represent the time period required for the battery to be stored under a certain environmental condition and a state condition (such as a state of charge) of the battery itself, the capacity of the battery is irreversibly lost, and the capacity of the battery is reduced to a certain capacity threshold.

It should be noted that, the storage life decay rate corresponding to the target battery may be determined by detecting the storage life decay rate of the same type of battery in the second candidate subinterval; the cycle duration corresponding to the second candidate SOC subinterval may be determined by counting cycle durations of the first charge-discharge cycle operation performed by the same type of battery in the second candidate SOC subinterval, where the same type of battery is another battery of the same battery type as the target battery.

Optionally, screening one second candidate SOC subinterval satisfying the preset decay rate condition and the preset cycle duration condition from all the second candidate SOC subintervals as the target SOC interval according to the storage life decay rate and the cycle duration, may include the following operations:

and screening one second candidate SOC subinterval with the storage life decay rate larger than a preset decay rate threshold and the cycle duration smaller than a preset cycle duration threshold from all the second candidate SOC subintervals as a target SOC interval.

For example, assuming that the second candidate SOC subinterval includes 20% to 30% SOC subinterval and 80% to 100% subinterval, the battery stored with the 100% SOC value has the maximum storage life decay rate under the same temperature condition, and the cycle duration corresponding to the 80% to 100% subinterval is smaller than the cycle duration corresponding to the 20% to 30% SOC subinterval, the 80% to 100% subinterval is determined as the target SOC interval.

It can be seen that, in this alternative embodiment, when there are at least two second candidate SOC subintervals, the storage life decay rate and the cycle duration corresponding to the target battery corresponding to each second candidate SOC subinterval are obtained, and one candidate SOC subinterval satisfying the preset decay rate condition and the preset cycle duration condition is taken as the SOC interval, so that the numerical range of the target SOC interval is determined by combining the storage performance decay condition of the battery and the time cost of the cycle life test on the basis of the battery structure change condition, the determination accuracy of the SOC interval of the battery cycle life test can be further improved, thereby improving the prediction accuracy of the capacity retention rate of the battery, and also shortening the duration of the battery cycle life test, and being beneficial to improving the prediction efficiency of the battery cycle life.

In the embodiment of the invention, an electrochemical impedance of the LF280K battery at 10% -90% SOC (once per 10% SOC test) is tested by taking the battery with the battery model of LF280K as a target battery, wherein Rs (ohmic impedance) of the battery fluctuates between 0.0008 and 0.00083 omega under different SOCs, rsei (impedance of SEI film) of the battery is basically kept at 0.0012 omega, and Rct (electrode polarization impedance, impedance of lithium ions penetrating through the SEI film and a graphite contact layer) of the battery is reduced from 0.016 omega to 0.014 omega along with the increase of the SOCs. As can be seen, the size of Rsei is less affected by SOC variations;

and taking the battery with the battery model of LF280K as a target battery, the expansion force change curves corresponding to different SOC values of the battery can be shown as shown in fig. 3, wherein (1) in fig. 3 is the expansion force change curve of the battery in the 1 st charge, (2) is the expansion force change curve of the battery in the 100 th charge, (3) is the expansion force change curve of the battery in the 200 th charge, (4) is the expansion force change curve of the battery in the 300 th charge, (5) is the expansion force change curve of the battery in the 400 th charge, and (6) is the expansion force change curve of the battery in the 500 th charge. It can be seen that the expansion force of the battery increases with the increase of the number of charging times, and the expansion force of two SOC intervals in the same curve tends to increase. The battery expansion force increases greatly in the 20% -30% SOC interval and the 80% -100% SOC interval, which shows that the battery expansion force is the main SOC interval with the changed negative electrode graphite structure in the two SOC intervals.

In addition, in the 80% -100% SOC interval, the negative electrode graphite structure is changed quickly, the storage life decay rate of the battery is also quick in a full-charge state (100% SOC), and the cycle duration is short; the storage life of the battery is basically not attenuated in the SOC interval of 20% -30%, the cycle only has influence on the battery capacity, and the cycle duration is long. Therefore, the 80% -100% SOC interval is determined as the target SOC interval by combining the battery structure change, the storage life decay condition and the cycle time cost.

In another alternative embodiment, after predicting the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation of the first preset number of cycles according to the capacity retention rate prediction relation and the test capacity retention rate corresponding to the target battery, the method may further include:

judging whether the target capacity retention rate is greater than or equal to a capacity retention rate threshold;

when the target capacity retention rate is judged to be smaller than the capacity retention rate threshold, judging whether the first preset cycle number is larger than or equal to the cycle number threshold or not;

when the first preset cycle number is smaller than the cycle number threshold value, determining the target battery as a defective product, and detecting production parameters corresponding to the defective product, wherein the production parameters comprise identification of production equipment, working state of the production equipment and production qualification rate of the production equipment;

Judging whether the production parameters corresponding to the unqualified products reach the preset production parameter standard or not;

when the production parameters corresponding to the unqualified products reach the preset production parameter standard, adjusting the construction parameters of the battery with the same battery type as the unqualified products, wherein the construction parameters of the battery comprise the physical structure of the battery and the electrode material parameters of the battery;

when judging that the production parameters corresponding to the unqualified products do not reach the preset production parameter standard, adjusting the production parameters corresponding to the unqualified products;

and when the target capacity retention rate is judged to be greater than or equal to the capacity retention rate threshold, determining the target battery as a qualified product.

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; the capacity retention threshold may be 80% or other values, which are not limited by the embodiments of the present invention.

It can be seen that this alternative embodiment can also be when the target capacity retention rate that predicts is less than capacity retention rate threshold and the number of cycles is less than the threshold of cycle number, consider this battery as the disqualified product to whether the production parameter that the inspection battery corresponds up to standard, if production parameter up to standard, adjust the structure of battery, if production parameter is not up to standard, adjust production parameter, can improve the accuracy of detecting battery quality based on battery cycle life, thereby improve the adjustment accuracy of production condition or battery product, and then be favorable to improving the development reliability of battery.

Example two

Referring to fig. 2, fig. 2 is a flowchart illustrating a method for predicting battery cycle life according to an embodiment of the invention. The method for predicting the battery cycle life described in fig. 2 may be applied to a device for predicting the battery cycle life, where the device may include one of a predicting device, a predicting terminal, a predicting system, and a server, where the server includes a local server or a cloud server, and embodiments of the present invention are not limited. As shown in fig. 2, the battery cycle life prediction method may include the following operations:

201. and in the determined target SOC interval, executing a first charge-discharge cycle operation of a first preset cycle number on the target battery to obtain the test capacity retention rate of the target battery.

In the embodiment of the invention, the battery type of the target battery is the target type; the battery type of the battery can be divided according to the type of the battery, the type of electrolyte of the battery, and the positive and negative electrode materials of the battery, and the embodiment of the invention is not limited.

202. And in the target SOC interval, performing first charge-discharge cycle operation of a second preset cycle number on the first sampling battery to obtain a first sampling capacity retention rate corresponding to the target type.

In the embodiment of the invention, the battery type of the first sampling battery is the target type. The number of the second preset cycle number may be the same as the number of the first preset cycle number, or may be other values, which is not limited in the embodiment of the present invention. Wherein the number of the first sampling batteries is at least one.

203. And executing a second charge-discharge cycle operation of a second preset cycle number on the second sampling battery to obtain a second sampling capacity retention rate corresponding to the target type.

In the embodiment of the invention, the battery type of the second sampling battery is the target type. Wherein the number of the second sampling batteries is at least one.

The temperature conditions and the charge-discharge current conditions corresponding to the first charge-discharge cycle operation performed on the first sampling battery and the second charge-discharge cycle operation performed on the second sampling battery are the same, respectively. For example, 3 LF280K batteries were subjected to a 25 ℃ 0.5C/0.5C 80-100% soc cycle (first charge-discharge cycle operation of the first sampling battery), and 3 LF280K batteries of the same batch were additionally subjected to a 25 ℃ 0.5C/0.5C 0-100% soc cycle (second charge-discharge cycle operation of the second sampling battery), i.e., the batteries were charged to 3.65V by a 0.5C constant current constant voltage and then discharged to 2.5V by a 0.5C constant current at 25 ℃ and within a 80-100% soc interval or 0-100% soc interval.

It should be noted that, step 202 and step 203 have no precedence relationship, that is, step 202 may occur before or after step 203 or simultaneously with step 203, which is not limited by the embodiments of the present invention.

204. A retention rate difference between the second sample-and-hold rate and the first sample-and-hold rate is calculated.

205. And establishing a capacity retention rate prediction relation corresponding to the target battery based on the first sampling capacity retention rate, the second sampling capacity retention rate and the retention rate difference.

It should be noted that, step 201 is not related to any of steps 202 to 205, that is, step 301 may occur before or after any of steps 202 to 205 or may occur simultaneously with any of steps 202 to 205, which is not limited by the embodiment of the present invention.

206. And predicting the target capacity retention rate of the target battery after the second charge-discharge cycle operation of the first preset cycle number is executed according to the capacity retention rate prediction relation corresponding to the target battery and the test capacity retention rate.

In the embodiment of the present invention, for other detailed descriptions of step 201 and step 206, please refer to the detailed descriptions of step 101-step 102 in the first embodiment, and the detailed description of the embodiment of the present invention is omitted.

Therefore, by implementing the method described by the embodiment of the invention, the first charge-discharge cycle operation of the first preset cycle number can be performed on the target battery in the determined target SOC interval, the test capacity retention rate of the battery is obtained, the target capacity retention rate of the battery after the second charge-discharge cycle operation of the same cycle number is predicted based on the capacity retention rate prediction relation and the test capacity retention rate corresponding to the battery, the prediction efficiency of the capacity retention rate of the battery can be improved, the prediction efficiency of the battery cycle life can be improved, the time of the battery cycle life test can be shortened, the cost of the battery cycle life test can be reduced, and the development efficiency of the battery can be improved. In addition, the first charge-discharge cycle operation and the first sample capacity holding rate and the second sample capacity holding rate corresponding to the second charge-discharge cycle operation can be executed based on the sample battery, the holding rate difference value is calculated, a capacity holding rate prediction relational expression is built based on the first sample capacity holding rate, the second sample capacity holding rate and the holding rate difference value, and the determination accuracy of the capacity holding rate prediction relational expression can be improved, so that the prediction accuracy of the capacity holding rate is improved, and the prediction accuracy of the battery cycle life is further improved.

In an alternative embodiment, predicting the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation of the first preset number of cycles according to the capacity retention rate prediction relation and the test capacity retention rate corresponding to the target battery may include the following operations:

calculating a difference value between the test capacity retention rate and the determined correction coefficient based on a capacity retention rate prediction relation corresponding to the target battery to obtain a target capacity retention rate of the target battery after a second charge-discharge cycle operation of a first preset cycle number;

wherein the correction parameters described above may be determined by:

acquiring electrode material parameters of a target battery, wherein the electrode material parameters comprise electrode material types and electrode material weights corresponding to each electrode material type;

and determining correction parameters corresponding to the capacity retention prediction relational expression according to the retention difference, all electrode material types and all electrode material weight ratios.

The capacity retention prediction relation corresponding to the target battery may be as follows:

target capacity retention = test capacity retention-correction parameter

For example, when the correction parameter is 1.6, the capacity retention prediction relation corresponding to the target battery may be as follows:

target capacity retention = test capacity retention-1.6

Wherein the electrode material may include a positive electrode material and a negative electrode material; the positive electrode material may include one or more combinations of lithium manganate, lithium cobaltate, lithium iron phosphate, ternary material, lithium nickelate and lithium titanate, the negative electrode material may include carbon material and non-carbon material, and the carbon material may include one or more of artificial graphite, natural graphite, mesophase Carbon Microsphere (MCMB), petroleum coke, carbon fiber, pyrolytic resin carbon; the electrode material may also include other materials suitable for lithium batteries, and embodiments of the present invention are not limited.

Therefore, the optional embodiment can take the difference between the test capacity retention rate and the determined correction coefficient as the target capacity retention rate of the target battery after the second charge-discharge cycle operation of the first preset cycle number based on the capacity retention rate prediction relational expression, and can improve the prediction efficiency of the capacity retention rate, thereby improving the prediction efficiency of the battery cycle life and further shortening the time of the battery cycle life test; and determining correction parameters by combining electrode materials of the battery and the difference value of the retention rate, so that the correction parameters with higher matching degree with the target battery can be determined, and the accuracy and reliability of the correction parameters are improved, thereby further improving the prediction accuracy of the capacity retention rate and further being beneficial to improving the prediction accuracy of the battery cycle life.

In another alternative embodiment, after predicting the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation of the first preset number of cycles according to the capacity retention rate prediction relation and the test capacity retention rate corresponding to the target battery, the method may further include the operations of:

detecting the self-discharge rate of the target battery and the corresponding environmental parameters of the target battery, wherein the environmental parameters comprise one or more of the combination of environmental humidity, environmental temperature, environmental dust concentration and dust particle size;

and calibrating the target capacity retention rate of the target battery according to the self-discharge rate, the environmental parameter and the target capacity retention rate.

The self-discharge rate is used to indicate the capacity of the battery to hold the stored electric energy under certain conditions in an open state.

For example, if the current ambient temperature is higher than the preset temperature threshold, the capacity retention rate of the battery after circulation is reduced, so that the calculated target capacity retention rate can be adjusted according to the current ambient temperature to obtain the calibrated target capacity retention rate.

Therefore, the optional embodiment can calibrate the calculated target capacity retention rate according to the self-discharge rate of the target battery and different environmental conditions, so that the capacity retention rate is calibrated by combining multiple factors, the prediction accuracy of the capacity retention rate can be further improved, and the prediction accuracy of the battery cycle life is further improved.

In the embodiment of the present invention, when the schematic flow chart of the method for predicting the battery cycle life is shown in fig. 4, the flow chart of the method for predicting the battery cycle life specifically includes:

testing the impedance (SEI film impedance) and expansion force of the corresponding battery cells (the battery of the same type as the target battery) at different SOC values; based on the impedance and expansion force data sets corresponding to different SOC values, selecting a proper SOC interval (a target SOC interval) for acceleration cycle test (first charge-discharge cycle operation), wherein the cycle rate and the cycle temperature of the acceleration cycle test are consistent with those of a conventional cycle test (second charge-discharge cycle operation), and outputting a relational expression (a capacity retention rate prediction relational expression corresponding to a target battery) of the acceleration cycle capacity retention rate and the conventional cycle capacity retention rate; and (3) through accelerating the cycle test until the capacity retention rate of the battery cell is 80% (test capacity retention rate), and calculating through the relational expression, obtaining the cycle life of which the capacity retention rate of the battery cell is 80% in the conventional cycle test.

In which, for example, 3 LF280K batteries are subjected to 25 ℃ 0.5C/0.5C 0-100% SOC circulation, and 3 LF280K batteries in the same batch are subjected to 25 ℃ 0.5C/0.5C 80-100% SOC circulation (accelerating circulation method). The capacity retention at the same number of turns for both cycling modes is shown in table 1:

TABLE 1 Capacity Retention Rate for different cycling modes

Taking the capacity retention rate corresponding to the cycle in the 0-100% SOC interval as a target capacity retention rate, and taking the capacity retention rate corresponding to the cycle in the 80-100% SOC interval as a test capacity retention rate;

as can be seen from table 1, the test capacity retention rate and the target capacity retention rate maintain a fixed difference at the same number of cycles, the test capacity retention rate-1.6=target capacity retention rate, the time taken for the accelerated cycle test single cycle is 0.8h, and the conventional cycle test single cycle time is 4h. After 10000 cycles, the capacity retention rate is tested, the accelerated cycle test only needs 334 days, and the conventional cycle test only needs 1667 days, and the accelerated cycle test shortens the test time to 1/5 of the original test time.

Example III

Referring to fig. 5, fig. 5 is a schematic structural diagram of a battery cycle life prediction apparatus according to an embodiment of the invention. The prediction apparatus for the battery cycle life described in fig. 5 may include one of a prediction device, a prediction terminal, a prediction system, and a server, where the server includes a local server or a cloud server, and embodiments of the present invention are not limited. As shown in fig. 5, the battery cycle life prediction apparatus may include:

The charge-discharge module 301 is configured to perform a first charge-discharge cycle operation of a first preset cycle number on a target battery in a determined target SOC interval, so as to obtain a test capacity retention rate of the target battery, where the target SOC interval is determined by battery parameters of the target battery, and the battery parameters of the target battery include an SEI film impedance of the target battery and an expansion force of the target battery;

the prediction module 302 is configured to predict, according to a capacity retention prediction relation and a test capacity retention rate corresponding to the target battery, a target capacity retention rate of the target battery after the second charge-discharge cycle operation of the first preset number of cycles is performed, where the target SOC interval is within an interval range of the SOC interval corresponding to the second charge-discharge cycle operation, and the target capacity retention rate is used to determine a cycle life of the target battery.

Therefore, the device described by the embodiment of the invention can execute the first charge-discharge cycle operation of the first preset cycle number on the target battery in the determined target SOC interval to obtain the test capacity retention rate of the battery, and predict the target capacity retention rate of the battery after executing the second charge-discharge cycle operation of the same cycle number based on the capacity retention rate prediction relation and the test capacity retention rate corresponding to the battery, thereby improving the prediction efficiency of the capacity retention rate of the battery, improving the prediction efficiency of the battery cycle life, shortening the time of the battery cycle life test, reducing the cost of the battery cycle life test, and being beneficial to improving the development efficiency of the battery.

In an alternative embodiment, as shown in fig. 6, the apparatus may further include:

the acquisition module 303 is configured to acquire, in the determined target SOC interval, battery parameters of the target battery corresponding to each preset SOC value in a predicted SOC interval, where the predicted SOC interval is an SOC interval corresponding to the second charge-discharge cycle operation, and the predicted SOC interval includes a plurality of preset SOC values, before the charge-discharge module 301 performs a first charge-discharge cycle operation of a first preset number of cycles on the target battery to obtain a test capacity retention rate of the target battery;

the analysis module 304 is configured to analyze battery parameters of the target battery corresponding to all preset SOC values, and obtain a battery parameter variation trend corresponding to the predicted SOC interval;

the screening module 305 is configured to screen, according to the variation trend of the battery parameter, an SOC subinterval satisfying a preset variation trend condition from the predicted SOC interval as a target SOC interval.

Therefore, the device described by implementing the alternative embodiment can acquire and analyze the battery parameters corresponding to all the preset SOC values in the predicted SOC interval to obtain the battery parameter variation trend corresponding to the predicted SOC interval, and screen out the SOC subinterval meeting the preset variation trend condition predicted SOC interval as the target SOC interval according to the battery parameter variation trend, so that the analysis accuracy of the battery parameter variation trend can be improved, the determination accuracy of the SOC interval of the battery cycle life test can be improved, the determination accuracy and reliability of the test capacity retention rate of the battery can be improved, and the prediction accuracy of the capacity retention rate of the battery can be improved.

the specific way of screening, by the screening module 305, the SOC sub-interval satisfying the preset variation trend condition from the predicted SOC interval as the target SOC interval according to the variation trend of the battery parameter may include:

Therefore, the device described by implementing the alternative embodiment can also screen out a candidate SOC subinterval with a larger expansion force trend change trend and a smaller expansion force trend change trend from the predicted SOC interval as a target SOC interval according to the SEI film resistance change condition and the expansion force change condition, so that the numerical range of the target SOC interval which is determined accurately based on the battery structure change condition is realized, the analysis accuracy of the battery structure change can be improved, the determination accuracy of the SOC interval of the battery cycle life test is further improved, and the determination accuracy and reliability of the test capacity retention rate of the battery are further facilitated.

In this optional embodiment, further optionally, the specific manner of screening, by the screening module 305, the SOC sub-interval satisfying the preset variation trend condition from the predicted SOC interval according to the variation trend of the battery parameter as the target SOC interval may further include:

It can be seen that the device described in this alternative embodiment can also obtain, when there are at least two second candidate SOC subintervals, a storage life decay rate and a cycle duration corresponding to the target battery corresponding to each second candidate SOC subinterval, and use, as the SOC interval, one candidate SOC subinterval satisfying the preset decay rate condition and the preset cycle duration condition, so as to determine, based on the battery structure change condition, a numerical range of the target SOC interval in combination with the storage performance decay condition of the battery and the time cost of the cycle life test, and further improve accuracy in determining the SOC interval of the battery cycle life test, thereby improving accuracy in predicting the capacity retention rate of the battery, and also shortening the duration of the battery cycle life test, so as to be beneficial to improving prediction efficiency of the battery cycle life.

In another alternative embodiment, the battery type of the target battery is the target type;

the charge-discharge module 301 is further configured to, before the prediction module 302 predicts the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation of the first preset number of cycles according to the capacity retention rate prediction relation and the test capacity retention rate corresponding to the target battery, perform the first charge-discharge cycle operation of the second preset number of cycles on the first sampling battery in the target SOC interval, to obtain a first sample capacity retention rate corresponding to the target type, where the battery type of the first sampling battery is the target type;

the charge-discharge module 301 is further configured to perform a second charge-discharge cycle operation of a second preset cycle number on a second sampling battery, to obtain a second sample capacity retention rate corresponding to the target type, where the battery type of the second sampling battery is the target type;

wherein, as shown in fig. 6, the device may further include:

a calculation module 306 for calculating a retention rate difference between the second sample-and-hold rate and the first sample-and-hold rate;

a relation establishing module 307, configured to establish a capacity retention prediction relation corresponding to the target battery based on the first sample capacity retention rate, the second sample capacity retention rate, and the retention rate difference.

It can be seen that the apparatus described in implementing this alternative embodiment can calculate the holding rate difference based on the first sample capacity holding rate and the second sample capacity holding rate corresponding to the first charge-discharge cycle operation and the second charge-discharge cycle operation performed by the sample battery, and establish the capacity holding rate prediction relational expression based on the first sample capacity holding rate, the second sample capacity holding rate, and the holding rate difference, so that the accuracy of determining the capacity holding rate prediction relational expression can be improved, thereby improving the accuracy of predicting the capacity holding rate, and further improving the accuracy of predicting the battery cycle life.

In this optional embodiment, optionally, the specific manner of predicting, by the prediction module 302, the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation of the first preset number of cycles according to the capacity retention rate prediction relation and the test capacity retention rate corresponding to the target battery may include:

Wherein the correction parameters described above may be determined by:

It can be seen that the device described in this alternative embodiment can also be implemented to use the difference between the test capacity retention rate and the determined correction coefficient as the target capacity retention rate of the target battery after the second charge-discharge cycle operation of the first preset cycle number based on the capacity retention rate prediction relational expression, so that the prediction efficiency of the capacity retention rate can be improved, thereby improving the prediction efficiency of the battery cycle life, and further shortening the time of the battery cycle life test; and determining correction parameters by combining electrode materials of the battery and the difference value of the retention rate, so that the correction parameters with higher matching degree with the target battery can be determined, and the accuracy and reliability of the correction parameters are improved, thereby further improving the prediction accuracy of the capacity retention rate and further being beneficial to improving the prediction accuracy of the battery cycle life.

In yet another alternative embodiment, as shown in fig. 6, the apparatus may further include:

a detection module 308, configured to detect a self-discharge rate of the target battery and an environmental parameter corresponding to the target battery after the prediction module 302 predicts the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation of the first preset number of cycles according to the capacity retention rate prediction relation and the test capacity retention rate corresponding to the target battery, where the environmental parameter includes one or more of an environmental humidity, an environmental temperature, an environmental dust concentration, and a dust particle size;

a calibration module 309 for calibrating the target capacity retention rate of the target battery based on the self-discharge rate, the environmental parameter, and the target capacity retention rate.

Therefore, the device described by implementing the alternative embodiment can calibrate the calculated target capacity retention rate according to the self-discharge rate of the target battery and different environmental conditions, and calibrate the capacity retention rate by combining multiple factors, so that the prediction accuracy of the capacity retention rate can be further improved, and the prediction accuracy of the battery cycle life is further improved.

Example IV

Referring to fig. 7, fig. 7 is a schematic structural diagram of a battery cycle life prediction apparatus according to another embodiment of the present invention. As shown in fig. 7, the battery cycle life prediction apparatus may include:

A memory 401 storing executable program codes;

a processor 402 coupled with the memory 401;

the processor 402 invokes executable program codes stored in the memory 401 to perform the steps in the battery cycle life prediction method described in the first or second embodiment of the present invention.

Example five

The embodiment of the invention discloses a computer storage medium which stores computer instructions for executing the steps in the battery cycle life prediction method described in the first or second embodiment of the invention when the computer instructions are called.

Example six

An embodiment of the present invention discloses a computer program product comprising a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the method for predicting battery cycle life described in embodiment one or embodiment two.

The apparatus embodiments described above are merely illustrative, wherein the modules illustrated as separate components may or may not be physically separate, and the components shown as modules may or may not be physical, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.

From the above detailed description of the embodiments, it will be apparent to those skilled in the art that the embodiments may be implemented by means of software plus necessary general hardware platforms, or of course by means of hardware. Based on such understanding, the foregoing technical solutions may be embodied essentially or in part in the form of a software product that may be stored in a computer-readable storage medium including Read-Only Memory (ROM), random-access Memory (Random Access Memory, RAM), programmable Read-Only Memory (Programmable Read-Only Memory, PROM), erasable programmable Read-Only Memory (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 (Compact Disc Read-Only Memory, CD-ROM) or other optical disc Memory, magnetic disc Memory, tape Memory, or any other medium that can be used for computer-readable carrying or storing data.

Finally, it should be noted that: the embodiment of the invention discloses a battery cycle life prediction method and device, which are disclosed as preferred embodiments of the invention, and are only used for illustrating the technical scheme of the invention, but not limiting the technical scheme; although the invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art will understand that; the technical scheme recorded in the various embodiments can be modified or part of technical features in the technical scheme can be replaced equivalently; such modifications and substitutions do not depart from the spirit and scope of the corresponding technical solutions.

Claims

1. A method of predicting battery cycle life, the method comprising:

2. The method according to claim 1, wherein before the first charge-discharge cycle operation of a first preset number of cycles is performed on a target battery in the determined target SOC interval to obtain a test capacity retention rate of the target battery, the method further comprises:

3. The method for predicting the cycle life of a battery according to claim 2, wherein the battery parameter variation trend includes an SEI film resistance variation trend and an expansion force variation trend;

4. The battery cycle life prediction method according to claim 3, wherein the step of screening, from the predicted SOC intervals, SOC subintervals satisfying a preset trend condition as target SOC intervals according to the battery parameter trend, further comprises:

5. The method for predicting battery cycle life as claimed in claim 1, wherein the battery type of the target battery is a target type;

6. The method according to claim 5, wherein predicting the target capacity retention rate of the target battery after performing the second charge-discharge cycle operation of the first preset number of cycles according to the capacity retention rate prediction relation corresponding to the target battery and the test capacity retention rate, comprises:

Wherein the correction parameters are determined by:

7. The method according to any one of claims 1 to 6, characterized in that, after the target battery is predicted for a target capacity retention rate after performing a second charge-discharge cycle operation of the first preset number of cycles according to the capacity retention rate prediction relation corresponding to the target battery and the test capacity retention rate, the method further comprises:

8. A battery cycle life prediction apparatus, the apparatus comprising:

9. A battery cycle life prediction apparatus, the apparatus comprising:

A memory storing executable program code;

a processor coupled to the memory;

the processor invokes the executable program code stored in the memory to perform the battery cycle life prediction method of any one of claims 1-7.

10. A computer storage medium storing computer instructions which, when invoked, are adapted to perform the method of predicting battery cycle life of any one of claims 1-7.