CN116482541A - Method, device, and computer-readable storage medium for predicting battery cycle life - Google Patents

Abstract

The invention provides a method, device and computer-readable storage medium for predicting battery cycle life. In the present invention, by obtaining a battery whose cycle life is to be predicted, the charge-discharge cycle test is performed on the battery, the relationship curve between the capacity retention rate and the number of cycles of the battery is obtained according to the charge-discharge cycle test result, the capacity decay model of the battery is obtained according to the relationship curve, and the battery cycle life of the battery is predicted according to the capacity decay model.

Description

Method, device, and computer-readable storage medium for predicting battery cycle life

technical field

The invention relates to the technical field of batteries, in particular to a method, a device and a computer-readable storage medium for predicting battery cycle life.

Background technique

The cycle life of the battery is an important performance evaluation index, which directly affects the service time and quality of the battery. The battery life is affected by various factors. In the related art, the method for evaluating the cycle life of the battery is mainly through experimental testing. However, for batteries with long cycle performance, the experimental test period is long and the cost is high.

Contents of the invention

The invention provides a method, a device and a computer-readable storage medium for predicting the cycle life of a battery, which can quickly evaluate the cycle life of the battery and reduce testing costs.

In a first aspect, the present invention provides a method for predicting battery cycle life.

The method for predicting battery cycle life includes:

Obtaining a battery whose cycle life is to be predicted, and performing a charge-discharge cycle test on the battery;

Obtain a relationship curve between the capacity retention rate of the battery and the number of cycles according to the charge-discharge cycle test results;

Obtaining the capacity fading model of the battery according to the relationship curve;

The battery cycle life of the battery is predicted based on the capacity fade model.

In one embodiment, the step of obtaining a battery whose cycle life is to be predicted, and performing a charge-discharge cycle test on the battery includes:

Obtaining the battery whose cycle life is to be predicted, and selecting a plurality of test temperatures, the test temperature at least including the lowest threshold temperature, the highest threshold temperature and a use temperature of the battery;

The battery is subjected to a charge-discharge cycle test at a plurality of the test temperatures.

In one embodiment, the step of obtaining the relationship curve between the capacity retention rate of the battery and the number of cycles according to the charge-discharge cycle test results includes:

Obtaining the number of cycles of the battery and the experimental data of the capacity retention rate corresponding to the number of cycles;

The experimental data is processed, and a relationship curve between the capacity retention rate of the battery and the number of cycles is established according to the processed experimental data.

In one embodiment, the step of obtaining the capacity fading model of the battery according to the relationship curve includes:

Establish a prediction model as shown in formula 1:

Qloss=f(T, n) Formula 1;

Among them, Qloss represents the amount of capacity decay, T represents the test temperature, and n represents the number of cycles;

Obtain the relationship between capacity decay and capacity retention:

Qloss＝1-Q _i /Q _o Formula 2;

Among them, Q _o is the initial capacity of the battery, Q _i is the corresponding capacity when the number of cycles of the battery is i, Q _i /Q _o is the capacity retention rate of the battery, and Qloss is the capacity attenuation of the battery;

The relational curve is fitted by formula 1 and formula 2, and the fitting result is optimized to obtain a capacity fading model suitable for the battery:

Among them, Qloss represents the capacity decay, T represents the test temperature, n represents the number of cycles, and A, E _a , k ₁ , k ₂ , k ₃ , and k ₄ are model parameters.

In one embodiment, the step of fitting the relational curve using Equation 1 and Equation 2, optimizing the fitting result, and obtaining a capacity fading model suitable for the battery further includes:

Obtaining the predicted capacity retention rate of the battery when the number of cycles is i according to the capacity decay model, and obtaining the measured capacity retention rate of the battery when the number of cycles is i according to the charge-discharge cycle test;

Comparing the relative error between the predicted capacity retention rate and the measured capacity retention rate, if the relative error is less than or equal to a preset threshold, the capacity fading model is obtained; if the relative error is greater than the preset threshold, the capacity fading model is optimized.

In one embodiment, the step of predicting the battery cycle life of the battery according to the capacity fading model includes:

Obtaining a battery whose cycle life is to be predicted, and performing a preset number of charge-discharge cycle tests on the battery;

Obtain a relationship curve between the capacity retention rate of the battery and the number of cycles according to the preset number of charge-discharge cycle tests;

Obtaining the model parameters A, E _a , k ₁ , k ₂ , k ₃ , and k ₄ according to the relationship curve and the capacity fading model;

Substituting the model parameters into formula 3 to obtain the first capacity fading model applicable to the battery;

According to the first capacity fading model, the battery cycle life of the battery is predicted.

In one embodiment, the step of obtaining a battery whose cycle life is to be predicted, and performing a preset number of charge-discharge cycle tests on the battery includes:

Obtain the battery whose cycle life is to be predicted;

determining the preset number of times according to the type of the battery;

The battery is subjected to a predetermined number of charge-discharge cycle tests.

In a second aspect, the present invention provides a device for predicting battery cycle life.

The device for predicting battery cycle life includes:

A charge-discharge cycle test unit, configured to obtain a battery whose cycle life is to be predicted, and perform a charge-discharge cycle test on the battery;

a relationship curve generating unit, configured to obtain a relationship curve between the capacity retention rate of the battery and the number of cycles according to the test result of the charge-discharge cycle;

A capacity fading model acquisition unit, configured to obtain a capacity fading model of the battery according to the relationship curve;

A battery cycle life predicting unit, configured to predict the battery cycle life of the battery according to the capacity fading model.

In an embodiment, the capacity fading model acquisition unit includes:

The prediction model acquisition module is used to establish the prediction model shown in formula 1:

Qloss=f(T, n) Formula 1;

Among them, Qloss represents the amount of capacity decay, T represents the test temperature, and n represents the number of cycles;

The predictive model acquiring unit is also used to acquire the relationship between the amount of capacity decay and the capacity retention rate:

Qloss＝1-Q _i /Q _o Formula 2;

Among them, Q _o is the initial capacity of the battery, Q _i is the corresponding capacity when the number of cycles of the battery is i, Q _i /Q _o is the capacity retention rate of the battery, and Qloss is the capacity attenuation of the battery;

The capacity fading model processing module is used to fit the relationship curve by using formula 1 and formula 2, optimize the fitting result, and obtain the capacity fading model suitable for the battery:

Among them, Qloss represents the capacity decay, T represents the test temperature, n represents the number of cycles, and A, E _a , k ₁ , k ₂ , k ₃ , and k ₄ are model parameters.

In a third aspect, the present invention provides a computer-readable storage medium, where a computer program is stored on the computer-readable storage medium, and the computer program is loaded by a processor to execute the steps in the above-mentioned method for predicting battery cycle life.

Beneficial effects of the present invention:

In the present invention, by obtaining a battery whose cycle life is to be predicted, the charge-discharge cycle test is performed on the battery, the relationship curve between the capacity retention rate of the battery and the number of cycles is obtained according to the charge-discharge cycle test result, the capacity decay model of the battery is obtained according to the relationship curve, and the cycle life of the battery is predicted according to the capacity decay model, which can greatly shorten the cycle life test cycle, realize rapid evaluation of the cycle life of the battery, and reduce the test cost, thereby improving the technical problems of long cycle life test experiments and high costs in the existing battery cycle life.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those skilled in the art, other drawings can also be obtained according to these drawings without creative work.

Fig. 1 is a schematic flow chart of the method for predicting battery cycle life provided by the present invention;

Fig. 2 is the relationship curve between the capacity retention rate and the number of cycles of the measured battery provided by the present invention;

Fig. 3 is the relational curve of the capacity retention rate and cycle times of predicted battery provided by the present invention;

Fig. 4 is the comparison of the relationship curve between the capacity retention rate of the measured battery and the number of cycles and the relationship curve between the capacity retention rate of the predicted battery and the number of cycles provided by the present invention;

Fig. 5 is a structural schematic diagram of a device for predicting battery cycle life provided by the present invention;

Fig. 6 is a schematic structural diagram of a capacity fading model acquisition unit provided by the present invention.

Explanation of reference signs:

A device 100 for predicting battery cycle life, a charge-discharge cycle test unit 10 , a relationship curve generation unit 20 , a capacity decay model acquisition unit 30 , a prediction model acquisition module 31 , a capacity decay model processing module 32 , and a battery cycle life prediction unit 40 .

Detailed ways

The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present invention. In addition, it should be understood that the specific embodiments described here are only used to illustrate and explain the present invention, and are not intended to limit the present invention. In the present invention, unless stated to the contrary, the used orientation words such as "up" and "down" generally refer to the up and down of the device in actual use or working state, specifically the direction of the drawing in the drawings; while "inside" and "outside" refer to the outline of the device.

The cycle life of the battery is an important performance evaluation index, which directly affects the service time and quality of the battery. The battery life is affected by various factors. In the related art, the method for evaluating the cycle life of the battery is mainly through experimental testing. However, for batteries with long cycle performance, the experimental test period is long and the cost is high.

As shown in Figure 1, Figure 1 is a schematic flow chart of the method for predicting battery cycle life provided by the present invention. The present invention provides a method for predicting battery cycle life, which can quickly evaluate the battery cycle life and reduce testing costs.

The invention provides a method for predicting battery cycle life, the method comprising:

S10, acquiring a battery whose cycle life is to be predicted, and performing a charge-discharge cycle test on the battery;

S20. Obtain a relationship curve between the capacity retention rate of the battery and the number of cycles according to the test result of the charge-discharge cycle;

S30, obtaining a capacity decay model of the battery according to the relationship curve;

S40. Predict the battery cycle life of the battery according to the capacity decay model.

In the present invention, by obtaining a battery whose cycle life is to be predicted, the charge-discharge cycle test is performed on the battery, the relationship curve between the capacity retention rate of the battery and the number of cycles is obtained according to the charge-discharge cycle test result, the capacity decay model of the battery is obtained according to the relationship curve, and the cycle life of the battery is predicted according to the capacity decay model, which can greatly shorten the cycle life test cycle, realize rapid evaluation of the cycle life of the battery, and reduce the test cost, thereby improving the technical problems of long cycle life test experiments and high costs in the existing battery cycle life.

The method for predicting battery cycle life of the present application is applicable to various batteries, such as lithium batteries. The capacity fading model obtained from one type of charge-discharge cycle test is applicable to the same type of battery. For example, the capacity fading model obtained from the charge-discharge cycle test of lithium iron-aluminum phosphate batteries is suitable for lithium iron-aluminum phosphate batteries. When it is necessary to predict the battery cycle life of a specific type of lithium battery, the charge-discharge cycle test can be carried out first to obtain the corresponding capacity fading model.

It should be noted that although the capacity fading models used to predict the cycle life are different for different types of batteries, this method is not limited by the battery type, and the cycle life of various types of batteries can be predicted by this method.

In step S10, performing charge-discharge cycle test on the battery refers to placing the battery in an incubator for repeated charge and discharge behaviors, and the number of times the battery is charged and discharged is called the number of cycles. The capacity of the battery will decay after charge and discharge cycles, and the decayed capacity is less than the initial capacity. Through the charge-discharge cycle test, the remaining capacity of the battery after the charge-discharge cycle can be obtained, so as to analyze the performance of the battery according to the corresponding relationship between the number of cycles and the remaining capacity.

Specifically, the battery can be subjected to charge and discharge cycle tests at different rates and temperatures. For example, 1C rate can be used for charge and discharge cycle test. 1C rate refers to the current intensity when the battery is fully discharged for one hour. It is worth noting that the charge-discharge cycle test can also be performed at a low rate, which will lengthen the test time and increase the test cost. This application does not limit the magnification of the charge-discharge cycle test.

In one embodiment, the battery is subjected to a charge-discharge cycle test at a rate of 1C.

In step S20, a relationship curve between the capacity retention rate of the battery and the number of cycles is obtained according to the test result of the charge-discharge cycle.

For example, the initial capacity of the battery is Q _o , after a charge-discharge cycle test is performed on the battery, the cycle number is 1, the corresponding remaining capacity can be recorded as Q ₁ , and the capacity retention rate when the cycle number is 1 is Q ₁ /Q _o . Through a charge-discharge cycle test, a set of data on capacity retention and cycle times can be obtained. In the same way, after i times of charge-discharge cycle test, the data of i group capacity retention rate and number of cycles can be obtained.

A corresponding relationship curve can be established through multiple sets of data corresponding to the capacity retention rate and the number of cycles. For example, a corresponding relationship curve can be established with the number of cycles as the abscissa and the capacity retention rate as the ordinate. 2 to 4 show the relationship curves of capacity retention and cycle times in some embodiments.

In step S30, a capacity fading model of the battery is obtained according to the relationship curve. By establishing a basic cycle life prediction model and optimizing the model so that the model can reflect the law in the relationship curve, an accurate capacity fading model can be obtained.

In some embodiments, when the relative error X between the predicted result of the capacity fading model and the measured relationship curve is smaller than a preset threshold Y, the capacity fading model can be determined to be an accurate capacity fading model. The preset threshold Y can be set according to actual needs, which is not limited in this application.

In step S40, the battery cycle life of the battery is predicted according to the capacity fading model. After obtaining an accurate capacity fading model, the battery can be tested for a preset number of charge-discharge cycles to obtain the relevant model parameters of the battery, and then predict the cycle life of the battery through the relevant model parameters.

For example, for a lithium battery used in a vehicle, usually when the capacity retention rate of the battery drops to 80%, it is judged that it can no longer be used in an electric vehicle. According to the number of cycles of the battery, the corresponding capacity retention rate can be calculated to predict the cycle life of the battery.

In one embodiment, step S10 also includes:

S11. Acquire a battery whose cycle life is to be predicted, and select a plurality of test temperatures, where the test temperature includes at least a minimum threshold temperature, a maximum threshold temperature, and an operating temperature of the battery;

The inventors of the present application found that the number of cycles of the battery is greatly affected by temperature, and by testing the battery at multiple different temperatures, the relationship between the capacity retention rate of the battery and the number of cycles can be obtained more comprehensively, thereby predicting the cycle life of the battery more accurately.

In one embodiment, multiple test temperatures may be selected. The test temperature includes at least the lowest threshold temperature of the battery in actual operation, the highest threshold test and one other temperature. For example, you can choose 25°C 1C charge-discharge cycle working conditions, 35°C 1C charge-discharge cycle work conditions, 45°C 1C charge-discharge cycle work conditions for testing, so as to obtain the test results corresponding to the temperature.

It should be noted that other test temperatures can be selected according to the actual working conditions of the battery, and this application does not limit the actual test temperature selected.

S12, performing a charge-discharge cycle test on the battery at multiple test temperatures.

The incubator for the charge-discharge cycle test is set to the corresponding test temperature, and the charge-discharge cycle test is performed on the battery at the test temperature. It should be noted that the test temperature is the initial temperature set by the incubator, and the actual temperature of the battery will rise during the test. In some embodiments, the temperature rise is about 10°C.

In one embodiment, step S20 also includes:

S21, acquiring the number of cycles of the battery and experimental data of a capacity retention rate corresponding to the number of cycles;

After several charge and discharge cycle tests, the capacity of the battery will gradually decrease. Through the test, the experimental data of the number of cycles and the capacity retention rate corresponding to the number of cycles can be obtained.

S22. Process the experimental data, and establish a relationship curve between the capacity retention rate of the battery and the number of cycles according to the processed experimental data.

The processing of experimental data includes data screening and impurity removal. For example, remove obviously abnormal data. According to the processed experimental data, the relationship curve between the capacity retention rate of the battery and the number of cycles is established.

As shown in FIG. 2 , FIG. 2 is a relationship curve between the capacity retention rate and the number of cycles of the measured battery provided by the present invention. The figure shows the relationship curve of the capacity retention rate corresponding to 1500 cycles.

Among them, curve 1 corresponds to the 25°C 1C charge-discharge cycle working condition, curve 2 corresponds to the 35°C 1C charge-discharge cycle work condition, and curve 3 corresponds to the 45°C 1C charge-discharge cycle work condition. When the temperature is higher, the capacity retention rate of the battery drops faster.

In one embodiment, step S30 includes:

S31, establish a prediction model as shown in formula 1:

Qloss=f(T, n) Formula 1;

Among them, Qloss represents the amount of capacity decay, T represents the test temperature, and n represents the number of cycles;

Obtain the relationship between capacity decay and capacity retention:

Qloss＝1-Q _i /Q _o Formula 2;

Among them, Q _o is the initial capacity of the battery, Q _i is the corresponding capacity when the number of cycles of the battery is i, Q _i /Q _o is the capacity retention rate of the battery, and Qloss is the capacity attenuation of the battery;

It should be noted that the prediction model can be based on the Arrhenius formula, that is, the prediction model includes a temperature pre-exponential factor A, a temperature coefficient E _a , and a power function with e as the base. The prediction model is the basic model. On this basis, combined with the experimental data obtained from the charge-discharge cycle test, the prediction model is revised several times.

S32. Fit the relational curve using Formula 1 and Formula 2, optimize the fitting result, and obtain a capacity fading model suitable for the battery:

Among them, Qloss represents the amount of capacity decay, T represents the test temperature, n represents the number of cycles, and A, E _a , k ₁ , k ₂ , k ₃ , and k ₄ are model parameters.

As shown in Equation 3, Equation 3 is a capacity fading model obtained after multiple iterative corrections, and this capacity fading model can be used to predict the cycle life of a lithium iron aluminum phosphate battery.

In one embodiment, in step S32, the relationship curve is fitted using formula 1 and formula 2, the fitting result is optimized, and the step of obtaining a capacity fading model suitable for the battery further includes:

S321. Obtain the predicted capacity retention rate _Qb of the battery when the number of cycles is i according to the capacity decay model;

As shown in Figure 3, the cycle life of the battery is predicted under the 25°C 1C charge-discharge cycle condition, 35°C 1C charge-discharge cycle condition, and 45°C 1C charge-discharge cycle condition. According to the capacity decay model, the predicted capacity retention rate _Qb of the battery when the number of cycles is i can be obtained. For example, the predicted capacity retention rate _Qb corresponding to the number of cycles from 1 to 6000 can be obtained.

Among them, curve 4 corresponds to the relationship between the number of cycles of the 25°C 1C charge-discharge cycle and the predicted capacity retention rate _Qb , curve 5 corresponds to the relationship between the cycle number of the 35°C 1C charge-discharge cycle condition and the predicted capacity retention rate _Qb , and curve 6 corresponds to the relationship between the cycle number of the 45°C 1C charge-discharge cycle condition and the predicted capacity retention rate _Qb .

According to the charge-discharge cycle test, the measured capacity retention rate Q _a of the battery when the number of cycles is i;

As shown in Figure 4, select the 25°C 1C charge-discharge cycle working condition, 35°C 1C charge-discharge cycle work condition, and 45°C 1C charge-discharge cycle work condition to conduct the charge-discharge cycle test of the battery, and continue the test until the capacity retention rate of the battery drops to 80%.

Among them, curve 7 corresponds to the relationship between the cycle number of the 25°C 1C charge-discharge cycle condition and the measured capacity retention rate _Qa , curve 8 corresponds to the relationship between the cycle number of the 35°C 1C charge-discharge cycle condition and the measured capacity retention rate _Qa , and curve 9 corresponds to the relationship between the cycle number of the 45°C 1C charge-discharge cycle condition and the measured capacity retention rate _Qa .

For example, for the 25°C 1C charge-discharge cycle working condition, the value of i ranges from 1 to 6000, and the capacity retention rate corresponding to the cycle number i is obtained in the range of 100% to 80%, wherein each i corresponds to a capacity retention rate, so that the measured capacity retention rate Q _a of the battery when the cycle number is i is obtained according to the capacity decay model.

S322. Comparing the relative error X of the predicted capacity retention rate _Qb and the measured capacity retention rate _Qa , if the relative error X is less than or equal to a preset threshold Y, obtain the capacity fading model; if the relative error X is greater than the preset threshold Y, optimize the capacity fading model.

The measured capacity retention rate Q _a is compared with the predicted capacity retention rate Q _b to obtain the relative error X. For example, relative error X=|Q _a −Q _b |/Q _a *100%. The value of the preset threshold Y can be set as required. For example, the preset threshold Y=3% can be set. When X≤Y, the capacity fading model is accurate; when X>Y, the capacity fading model is optimized.

Wherein, Equation 3 is the capacity fading model optimized by multiple iterations in this application, and the relative error X of the capacity retention ratio predicted by Equation 3 is less than 3%.

As shown in Figure 4, curve 4 is close to curve 7, curve 5 is close to curve 8, and curve 6 is close to curve 9, indicating that the relative error X between the capacity retention rate predicted by formula 3 and the measured capacity retention rate is less than 3%, and the relationship curve between the predicted capacity retention rate _Qb of the battery and the number of cycles is approximately matched with the curve fitting result of the relationship between the measured capacity retention rate _Qa of the battery and the number of cycles.

After actual measurement and verification, the predicted data under the 25°C 1C, 35°C 1C, and 45°C 1C charge-discharge cycle conditions are consistent with the attenuation trend of the measured data in the entire life cycle, and the prediction accuracy is high, and the method is reliable. It can quickly evaluate the cycle life of the battery and reduce the test cost, thereby improving the existing technical problems of long cycle life test experiments and high costs.

When the relative error X is greater than 3% after the actual measurement, it is necessary to adjust the formula 3 so that it is constantly approaching the measured capacity retention result. The manner of adjusting the formula 3 includes adjusting the algorithm of each parameter in the formula 3, and the like.

In one embodiment, step S40 includes:

S41. Obtain a battery whose cycle life is to be predicted, and perform a preset number of charge-discharge cycle tests on the battery.

Further, step S41 includes:

S411. Acquire a battery whose cycle life is to be predicted.

S412. Determine the preset times according to the type of the battery.

For example, the preset number of times may be 1500 times.

S413, performing a charge-discharge cycle test on the battery for a preset number of times.

The preset number of times can be set as required, and the preset number of times is smaller than the number of cycles when the capacity retention rate of the battery is reduced to 80%. Taking the battery corresponding to Figure 4 as an example, its cycle count under the 25°C 1C charge-discharge cycle condition is 6000, and you can select a preset number of times within 6000 for the charge-discharge cycle test. For example, 1000 times, 1500 times, 2000 times, 2500 times and other preset times can be selected. When the selected preset times are larger, the model parameters of the capacity decay model established by the preset times are more accurate; when the selected preset times are smaller, the accuracy of the model parameters of the capacity decay model established by the preset times is reduced.

Therefore, in order to balance the accuracy of model parameters and save time, the corresponding preset times can be reasonably selected. This application does not limit the preset number of selections.

S42. Obtain a relationship curve between the capacity retention rate of the battery and the number of cycles according to the preset number of charge-discharge cycle tests;

As shown in FIG. 2 , the preset number of times selected in FIG. 2 is 1500 times, and the relationship curve between the capacity retention rate of the battery and the number of cycles is obtained by performing 1500 charge-discharge cycle tests on the battery. In this embodiment, the time required to perform 1500 charge and discharge cycles is about 0.5 years.

S43. Obtain the model parameters A, E _a , k ₁ , k ₂ , k ₃ , and k ₄ according to the relationship curve and the capacity fading model;

According to the relationship curve between the capacity retention rate and the number of cycles in Figure 2 and Formula 3, the model parameters A, E _a , k ₁ , k ₂ , k ₃ , and k ₄ in Formula 3 are solved.

For example, according to the relationship curve in Figure 2 and Equation 3, the values of each model parameter obtained are as follows: A is 2.72206E-07, E _a is 34.23058, k ₁ is 0.5, k ₂ is 1.60526E-04, k ₃ is 7E-05, and k ₄ is 7E-06.

S44, substituting the model parameters A=2.72206E-07, E _a =34.23058, k ₁ =0.5, k ₂ =1.60526E-04, k ₃ =7E-05, k ₄ =7E-06 into Equation 3 to obtain a first capacity fading model suitable for the battery;

S45. Predict the battery cycle life of the battery according to the first capacity fading model.

Through the above method, the battery can only be tested for 1,500 charge-discharge cycles to obtain its capacity retention rate at 1,500 to 6,000 or more times. In this embodiment, the time for the battery to be tested for 1,500 charge-discharge cycles is 0.5 years, and the time for the battery to be tested for 6,000 charge-discharge cycles is 2 years. By using the capacity decay model, the test time can be shortened from 2 years to 0.5 years, greatly shortening the cycle life test period, saving test time, reducing test costs, and achieving Rapid evaluation of the cycle life of the battery can provide a basis for whether the battery meets the design specifications, thereby improving the technical problems of long test cycle and high cost of the existing battery cycle life test.

The present application also provides a device 100 for predicting battery cycle life, as shown in FIG. 5 , which is a schematic structural diagram of the device 100 for predicting battery cycle life provided by the present invention. The device includes:

A charge-discharge cycle test unit 10, configured to obtain a battery whose cycle life is to be predicted, and perform a charge-discharge cycle test on the battery;

A relationship curve generating unit 20, configured to obtain a relationship curve between the capacity retention rate of the battery and the number of cycles according to the test result of the charge-discharge cycle;

A capacity fading model acquisition unit 30, configured to obtain a capacity fading model of the battery according to the relationship curve;

The battery cycle life prediction unit 40 is configured to predict the battery cycle life of the battery according to the capacity fading model.

In one embodiment, as shown in FIG. 6, FIG. 6 is a schematic structural diagram of a capacity fading model acquisition unit 30 provided by the present invention, and the capacity fading model acquisition unit 30 includes:

The predictive model acquisition module 31 is used to establish a predictive model as shown in formula 1:

Qloss=f(T, n) Formula 1;

Among them, Qloss represents the amount of capacity decay, T represents the test temperature, and n represents the number of cycles;

The predictive model acquisition unit 31 is also used to acquire the relationship between the amount of capacity decay and the capacity retention rate:

Qloss＝1-Q _i /Q _o Formula 2;

Among them, Q _o is the initial capacity of the battery, Q _i is the corresponding capacity when the number of cycles of the battery is i, Q _i /Q _o is the capacity retention rate of the battery, and Qloss is the capacity attenuation of the battery;

The capacity fading model processing module 32 is used to fit the relational curve using formula 1 and formula 2, optimize the fitting result, and obtain a capacity fading model suitable for the battery:

Among them, Qloss represents the capacity decay, T represents the test temperature, n represents the number of cycles, and A, E _a , k ₁ , k ₂ , k ₃ , and k ₄ are model parameters.

The present invention also provides a computer-readable storage medium, which may include: a read only memory (ROM, Read Only Memory), a random access memory (RAM, Random Access Memory), a magnetic disk or an optical disk, and the like. A computer program is stored on the computer-readable storage medium, and the computer program is loaded by a processor to execute the steps in the method for predicting battery cycle life of the present invention. For example, the computer program being loaded by the processor may perform the following steps:

Obtaining a battery whose cycle life is to be predicted, and performing a charge-discharge cycle test on the battery;

Obtain a relationship curve between the capacity retention rate of the battery and the number of cycles according to the charge-discharge cycle test results;

Obtaining the capacity fading model of the battery according to the relationship curve;

The battery cycle life of the battery is predicted based on the capacity fade model.

The embodiments of the present invention have been described in detail above, and specific examples have been used herein to illustrate the principles and implementations of the present invention. The descriptions of the above examples are only used to help understand the method of the present invention and its core idea; at the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation and the scope of application. In summary, the content of this description should not be interpreted as limiting the present invention.

Claims (10)

1. A method of predicting battery cycle life comprising:

obtaining a battery with a cycle life to be predicted, and performing charge-discharge cycle test on the battery;

acquiring a relation curve of the capacity retention rate and the cycle number of the battery according to the charge-discharge cycle test result;

obtaining a capacity attenuation model of the battery according to the relation curve;

predicting the battery cycle life of a battery according to the capacity fade model.

2. The method of predicting battery life cycles as defined in claim 1, wherein the step of obtaining a battery to be predicted for life cycles and performing a charge-discharge cycle test on the battery comprises:

obtaining a battery with a cycle life to be predicted, and selecting a plurality of test temperatures, wherein the test temperatures at least comprise a minimum threshold temperature, a maximum threshold temperature and a use temperature of the battery;

and performing charge-discharge cycle test on the battery at a plurality of test temperatures.

3. The method of predicting battery cycle life of claim 1, wherein the step of obtaining a capacity retention rate versus cycle number of the battery from the charge-discharge cycle test result comprises:

acquiring experimental data of the cycle times of the battery and the capacity retention rate corresponding to the cycle times;

and processing the experimental data, and establishing a relation curve of the capacity retention rate and the cycle number of the battery according to the processed experimental data.

4. The method of predicting battery cycle life as set forth in claim 1, wherein said step of deriving a capacity fade model of the battery from said relationship comprises:

establishing a prediction model shown in the formula 1:

qloss=f (T, n) formula 1;

wherein Qloss represents capacity attenuation, T represents test temperature, and n represents cycle number;

acquiring the relation between the capacity attenuation and the capacity retention rate:

Qloss＝1-Q _i /Q _o formula 2;

wherein Q is _o To the initial capacity of the battery, Q _i Q is the corresponding capacity when the cycle number of the battery is i _i /Q _o Qloss is the capacity attenuation of the battery;

fitting the relation curve by adopting the formulas 1 and 2, and optimizing the fitting result to obtain a capacity attenuation model applicable to the battery:

wherein Qloss represents capacity attenuation, T represents test temperature, n represents cycle number, A, E _a 、k ₁ 、k ₂ 、k ₃ 、k ₄ Is a model parameter.

5. The method of predicting battery life cycle of claim 4, wherein the step of fitting the relationship curve using equations 1 and 2 and optimizing the fitting result to obtain a capacity fade model for the battery further comprises:

obtaining a predicted capacity retention rate of the battery when the cycle number is i according to the capacity attenuation model, and obtaining an actual capacity retention rate of the battery when the cycle number is i according to a charge-discharge cycle test;

comparing the relative error of the predicted capacity retention rate and the actual capacity retention rate, and if the relative error is smaller than or equal to a preset threshold value, obtaining the capacity attenuation model; and if the relative error is greater than the preset threshold, optimizing the capacity attenuation model.

6. The method of predicting battery cycle life of claim 4, wherein said step of predicting said battery cycle life of a battery based on said capacity fade model comprises:

obtaining a battery with a cycle life to be predicted, and carrying out charge-discharge cycle test for the battery for preset times;

acquiring a relation curve of the capacity retention rate and the cycle number of the battery according to the charge-discharge cycle test of the preset times;

obtaining the model parameters A, E from the relationship and the capacity fade model _a 、k ₁ 、k ₂ 、k ₃ 、k ₄ ；

Substituting the model parameters into formula 3 to obtain a first capacity attenuation model applicable to the battery;

and predicting the battery cycle life of the battery according to the first capacity fade model.

7. The method for predicting battery life cycle of claim 6, wherein the step of obtaining a battery to be predicted for life cycle and performing a predetermined number of charge-discharge cycle tests on the battery comprises:

obtaining a battery with a cycle life to be predicted;

determining the preset times according to the type of the battery;

and carrying out charge and discharge cycle test for the battery for preset times.

8. An apparatus for predicting battery cycle life, the apparatus comprising:

the charge-discharge cycle test unit is used for obtaining a battery with a cycle life to be predicted and carrying out charge-discharge cycle test on the battery;

the relation curve generating unit is used for acquiring a relation curve of the capacity retention rate and the cycle times of the battery according to the charge-discharge cycle test result;

a capacity attenuation model obtaining unit, configured to obtain a capacity attenuation model of the battery according to the relationship curve;

and the battery cycle life prediction unit is used for predicting the battery cycle life of the battery according to the capacity fading model.

9. The apparatus for predicting battery cycle life as recited in claim 8, wherein the capacity fade model acquisition unit includes:

the prediction model acquisition module is used for establishing a prediction model shown in the formula 1:

qloss=f (T, n) formula 1;

wherein Qloss represents capacity attenuation, T represents test temperature, and n represents cycle number;

the prediction model obtaining unit is further used for obtaining the relation between the capacity attenuation amount and the capacity retention rate:

Qloss＝1-Q _i /Q _o formula 2;

wherein Q is _o To the initial capacity of the battery, Q _i Q is the corresponding capacity when the cycle number of the battery is i _i /Q _o Qloss is the capacity attenuation of the battery;

the capacity attenuation model processing module is used for fitting the relation curve by adopting the formulas 1 and 2, and optimizing the fitting result to obtain a capacity attenuation model applicable to the battery:

wherein Qloss represents capacity attenuation, T represents test temperature, n represents cycle number, A, E _a 、k ₁ 、k ₂ 、k ₃ 、k ₄ Is a model parameter.

10. A computer readable storage medium, having stored thereon a computer program, the computer program being loaded by a processor to perform the steps of the method of predicting battery cycle life of any one of claims 1 to 7.