Battery life prediction methods, systems and storage media

Abstract

The present application relates to a battery life prediction method, system and storage medium, wherein the method obtains first test data of a battery to be tested; when power data of a current test time period meets preset conditions, the capacity loss value corresponding to the previous test time period and the temperature data corresponding to the current test time period are input into a preset calendar decay model for cyclic processing to obtain the current cycle number; when the power data does not meet the preset conditions, the capacity loss value corresponding to the previous test time period, the discharge depth and temperature data corresponding to the current test time period are input into a preset cycle decay model for cyclic processing to obtain the current cycle number; when the SOC value of the current test time period reaches a preset value, the life value of the battery to be tested is determined according to the current cycle number and cycle number, thereby achieving accurate and convenient life prediction simulation of the battery to be tested, improving the comprehensiveness of the selected parameter settings, and improving the reliability of the battery life prediction.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a battery life prediction method, system and storage medium. Background Art

[0002] With the development of battery technology and the gradual expansion of battery application areas, the issue of battery life has gradually become prominent and has become one of the focuses of battery performance that people are concerned about. Batteries can be classified into power batteries and energy storage batteries. For energy storage batteries, life prediction is one of the important links in the health management of energy storage batteries.

[0003] At present, battery life prediction simulations are mostly based on constant rate charge and discharge or high rate charge and discharge methods to predict the life of power batteries. However, energy storage batteries have low charge and discharge power and long cycle life. If the power battery life prediction simulation method is used, few parameters are selected in the battery life prediction simulation, and the parameter settings are not comprehensive enough, which can easily lead to large battery life prediction errors. In addition, most of the existing energy storage battery life prediction simulations are based on data algorithms such as life genetics algorithms, long short-term memory networks or Gaussian regression processes for calculations, but they cannot fit the test data well, which still leads to large battery life prediction errors. Summary of the invention

[0004] Based on this, it is necessary to provide a battery life prediction method, system and storage medium that can accurately and conveniently perform battery life prediction simulation in response to the technical problems existing in the above-mentioned existing battery life prediction simulation, with comprehensive parameter settings selected in the battery life prediction simulation, small battery life prediction error and strong reliability.

[0005] In a first aspect, the present application provides a battery life prediction method, comprising the following steps:

[0006] Acquire first test data of the battery to be tested; the first test data includes capacity loss values, power data, temperature data, discharge depth and SOC value in several different test time periods;

[0007] When the power data of the current test period meets the preset conditions, the capacity loss value corresponding to the previous test period and the temperature data corresponding to the current test period are input into the preset calendar decay model for cyclic processing to obtain the current cycle days;

[0008] When the power data of the current test time period does not meet the preset conditions, the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period and the temperature data corresponding to the current test time period are input into the preset cycle decay model for cyclic processing to obtain the current cycle number;

[0009] When the SOC value of the current test time period reaches a preset value, the life value of the battery to be tested is determined according to the current cycle days and the current cycle number.

[0010] In one embodiment, the preset calendar decay model is:

[0011] Q1=exp(G1+F1 / T)*t^f(T)

[0012] Among them, Q1 is the capacity loss value of the previous test time period, G1 is the first influencing factor, F1 is the second influencing factor, T is the temperature data of the current test time period, t is the number of days of the current cycle, and f(T) is the temperature influencing factor.

[0013] In one embodiment, the steps of obtaining the first impact factor and the second impact factor are:

[0014] Acquire second test data of the battery to be tested, the second test data including first historical temperature data, first historical capacity loss values, and first historical cycle days in a plurality of different test time periods;

[0015] Based on the first calendar decay sub-model, fitting processing is performed on the first historical capacity loss values ​​and the first historical cycle days of each different test time period to obtain a first intermediate parameter;

[0016] Based on the second calendar decay sub-model, the first intermediate parameter and the first historical temperature data of each different test time period are fitted to obtain a first influencing factor and a second influencing factor.

[0017] In one embodiment, the first calendar decay sub-model is:

[0018] y1=A1*x1^B1

[0019] Among them, y1 is the first historical capacity loss value, x1 is the first historical cycle days, A1 is the first intermediate parameter, and B1 is the second intermediate parameter;

[0020] The second calendar decay submodel is:

[0021] exp(G1+F1 / T1)=A1

[0022] Wherein, T1 is the first historical temperature data.

[0023] In one embodiment, the step of obtaining the temperature influence factor includes:

[0024] Acquire second test data of the battery to be tested, the second test data also including a historical SOC value;

[0025] The historical SOC values ​​and each first historical temperature data are interpolated to obtain a temperature influence factor.

[0026] In one embodiment, the preset cyclic attenuation model is:

[0027] Q2=exp(G2+F2 / T)*N^B2*f(DOD)

[0028] Among them, Q2 is the capacity loss value of the previous test time period, G2 is the third influencing factor, F2 is the fourth influencing factor, T is the temperature data of the current test time period, N is the current number of cycles, B2 is the fifth influencing factor, and f(DOD) is the discharge depth of the current test time period.

[0029] In one embodiment, the steps of obtaining the third impact factor, the fourth impact factor and the fifth impact factor are:

[0030] Acquire second test data of the battery to be tested, the second test data including second historical temperature data, second historical capacity loss values, and second historical cycle numbers in a plurality of different test time periods;

[0031] Based on the first cycle attenuation sub-model, the second historical capacity loss values ​​and the second historical cycle numbers of each different test time period are fitted to obtain the third intermediate parameter and the fifth influencing factor;

[0032] Based on the second cyclic attenuation sub-model, the third intermediate parameter and the second historical temperature data of each different test time period are fitted to obtain the third influencing factor and the fourth influencing factor.

[0033] In one embodiment, the first cycle decay submodel is:

[0034] y2=A2*x2^B2

[0035] Wherein, y2 is the second historical capacity loss value, x2 is the second historical cycle number, A2 is the third intermediate parameter, and B2 is the fifth influencing factor;

[0036] The second cycle decay submodel is:

[0037] exp(G2+F2 / T2)=A2

[0038] Wherein, T2 is the second historical temperature data.

[0039] In one embodiment, when the power data of the current test time period meets the preset conditions, the capacity loss value corresponding to the previous test time period and the temperature data corresponding to the current test time period are input into the preset calendar decay model for cyclic processing, and the step of obtaining the current cycle days includes:

[0040] When the power data of the current test time period is equal to 0, the capacity loss value corresponding to the previous test time period and the temperature data corresponding to the current test time period are input into the preset calendar attenuation model for cyclic processing to obtain the current cycle day.

[0041] In one embodiment, when the power data of the current test time period does not meet the preset conditions, the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period and the temperature data corresponding to the current test time period are input into the preset cycle decay model for cyclic processing, and the step of obtaining the current cycle number includes:

[0042] When the power data of the current test time period is greater than 0, the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period and the temperature data corresponding to the current test time period are input into the preset cycle decay model for cyclic processing to obtain the current cycle number.

[0043] In a second aspect, the present application provides a battery life prediction device, comprising:

[0044] A test data acquisition module is used to acquire first test data of the battery to be tested; the first test data includes capacity loss values, power data, temperature data, discharge depth and SOC value in several different test time periods;

[0045] The calendar decay processing module is used to input the capacity loss value corresponding to the previous test period and the temperature data corresponding to the current test period into the preset calendar decay model for cyclic processing when the power data of the current test period meets the preset conditions, so as to obtain the current cycle days;

[0046] The cycle decay processing module is used to input the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period and the temperature data corresponding to the current test time period into the preset cycle decay model for cycle processing to obtain the current cycle number when the power data of the current test time period does not meet the preset conditions;

[0047] The life prediction module is used to determine the life value of the battery to be tested according to the current cycle days and the current cycle number when the SOC value in the current test time period reaches a preset value.

[0048] In a third aspect, the present application provides a battery life prediction system, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above-mentioned battery life prediction methods when executing the computer program.

[0049] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of any of the above-mentioned battery life prediction methods when the computer program is executed by a processor.

[0050] One of the above technical solutions has the following advantages and beneficial effects:

[0051] In the above-mentioned battery life prediction method, the first test data of the battery to be tested is obtained; the first test data includes capacity loss values, power data, temperature data, discharge depth and SOC values ​​of several different test time periods; when the power data of the current test time period meets the preset conditions, the capacity loss value corresponding to the previous test time period and the temperature data corresponding to the current test time period are input into the preset calendar decay model for cyclic processing to obtain the current cycle number; when the power data of the current test time period does not meet the preset conditions, the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period and the temperature data corresponding to the current test time period are input into the preset cycle decay model for cyclic processing to obtain the current cycle number; when the SOC value of the current test time period reaches the preset value, the life value of the battery to be tested is determined according to the current cycle number and the current cycle number, so as to realize accurate life prediction simulation of the battery to be tested. The present application establishes a preset calendar decay model and a preset cycle decay model in advance, and judges the power data in the test data. If the power data of the current test time period meets the preset conditions, the corresponding influencing factors are input into the preset calendar decay model for cyclic processing, so as to obtain the current cycle days; if the power data of the current test time period does not meet the preset conditions, the corresponding influencing factors are input into the preset cycle decay model for cyclic processing, so as to obtain the current cycle number. When the SOC value of the battery to be tested reaches the preset value, the cyclic processing is terminated, and it is determined whether the current cycle days and the current cycle number meet the standards, so as to determine the life value of the battery to be tested, so as to achieve accurate and convenient life prediction simulation of the battery to be tested. By using the result of the previous cycle processing as the influencing factor in the current cycle processing, the parameter settings selected in the battery life prediction simulation are comprehensive, which reduces the battery life prediction error and improves the reliability of the battery life prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of an application scenario of a battery life prediction method in an embodiment of the present application;

[0053] Figure 2 This is a schematic diagram of a first process of a battery life prediction method in an embodiment of the present application;

[0054] Figure 3This is a flow chart of the step of obtaining the impact factor in calendar decay in the embodiment of the present application;

[0055] Figure 4 This is a flow chart of the step of obtaining the influencing factor in the cyclic attenuation in the embodiment of the present application;

[0056] Figure 5 This is a second flow chart of the battery life prediction method in an embodiment of the present application;

[0057] Figure 6 This is a block diagram of a battery life prediction device in an embodiment of the present application;

[0058] Figure 7 Schematic diagram of the structure of the battery life prediction system in the embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0060] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0061] In addition, the term "plurality" shall mean two or more.

[0062] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0063] The battery life prediction method provided in this application can be applied to Figure 1In the application environment shown. Among them, the processing device may include a processor 102 and a memory 104, and the memory 104 may be used to store data such as first test data, cycle days, number of cycles and life value. The processor 102 may be used to obtain the first test data of the battery to be tested; the first test data includes capacity loss values, power data, temperature data, discharge depth and SOC values ​​of several different test time periods; when the power data of the current test time period meets the preset conditions, the capacity loss value corresponding to the previous test time period and the temperature data corresponding to the current test time period are input into the preset calendar decay model for cyclic processing to obtain the current cycle days; when the power data of the current test time period does not meet the preset conditions, the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period and the temperature data corresponding to the current test time period are input into the preset cycle decay model for cyclic processing to obtain the current cycle number; when the SOC value of the current test time period reaches the preset value, the life value of the battery to be tested is determined according to the current cycle days and the current cycle number. The processing device may further include a display 106 , which may display data such as the first test data, cycle days, cycle times, and life value through a graphical interface.

[0064] In one embodiment, Figure 2 As shown, a battery life prediction method is provided, which is applied to Figure 1 The processor 102 in the embodiment is taken as an example to illustrate, and the following steps are included:

[0065] Step S210, obtaining first test data of the battery to be tested; the first test data includes capacity loss values, power data, temperature data, discharge depth and SOC value in several different test time periods.

[0066] Among them, the battery to be tested may be an energy storage battery. The first test data refers to the test data obtained by performing a charge and discharge test on the battery to be tested. Exemplarily, the test data corresponding to multiple charge and discharge tests can be obtained by performing a constant power charge and discharge test on the battery to be tested in different test time periods. In the battery life prediction simulation process, the first test data of the battery to be tested can be divided into capacity loss values, power data, temperature data, discharge depth and SOC values ​​of several different test time periods based on the delayed discrete unit. It should be noted that the test time period can be a complete time period corresponding to a charge and discharge test process, and the test time period can also be a partial time period corresponding to a charge and discharge test process.

[0067] The capacity loss value refers to the capacity attenuation value. The power data can be the charging power and / or discharging power of the battery to be tested. If the power data of the battery to be tested is 0 during a certain test period, it is determined that the battery to be tested is not charged or discharged. The temperature data refers to the test environment temperature during the test of the battery to be tested. The depth of discharge (DOD) refers to the percentage of the discharge amount of the battery to be tested to the rated capacity of the battery to be tested. The SOC value refers to the state of charge of the battery to be tested. The SOC value can be expressed in the form of a percentage, such as 100% indicates that the state of charge is full, and 0 indicates that the state of charge is empty.

[0068] Step S220, when the power data of the current test time period meets the preset conditions, the capacity loss value corresponding to the previous test time period and the temperature data corresponding to the current test time period are input into the preset calendar decay model for cyclic processing to obtain the current cycle day.

[0069] The capacity loss value of the last test period refers to the capacity loss value obtained by performing charge and discharge tests on the battery under test during the last test period. The current cycle days refer to the cycle days obtained by performing the current cycle processing based on the preset calendar decay model. The preset calendar decay model can be pre-established by the system. Exemplarily, the preset calendar decay model can be established based on the Arrhenius model.

[0070] For example, during the current cycle processing, the processor can obtain the power data of the current test time period, and compare the power data of the current test time period with the preset conditions. If the power data of the current test time period meets the preset conditions, the capacity loss value corresponding to the previous test time period and the temperature data corresponding to the current test time period are obtained, and the capacity loss value corresponding to the previous test time period and the temperature data corresponding to the current test time period are input into the preset calendar decay model for the current cycle processing, and then the current cycle days are output. During the next cycle processing, the processor can obtain the power data of the next test time period, and compare the power data of the next test time period with the preset conditions. If the power data of the next test time period meets the preset conditions, the capacity loss value corresponding to the current test time period and the temperature data corresponding to the next test time period are obtained, and the capacity loss value corresponding to the current test time period and the temperature data corresponding to the next test time period are input into the preset calendar decay model for the next cycle processing, and then the next cycle days are output, and so on, thereby realizing the cycle processing process based on the preset calendar decay model.

[0071] Step S230, when the power data of the current test time period does not meet the preset conditions, the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period and the temperature data corresponding to the current test time period are input into the preset cycle decay model for cyclic processing to obtain the current cycle number.

[0072] The current number of cycles refers to the number of cycles obtained by executing the current cycle processing based on a preset cycle attenuation model. The preset cycle attenuation model can be pre-established by the system. Exemplarily, the preset cycle attenuation model can be established based on the Arrhenius model.

[0073] For example, during the current cycle processing, the processor can obtain the power data of the current test time period, and compare the power data of the current test time period with the preset conditions. If the power data of the current test time period does not meet the preset conditions, the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period and the temperature data corresponding to the current test time period are input into the preset cycle attenuation model for the current cycle processing, and then the current cycle number is output. During the next cycle processing, the processor can obtain the power data of the next test time period, and compare the power data of the next test time period with the preset conditions. If the power data of the next test time period meets the preset conditions, the capacity loss value corresponding to the current test time period, the discharge depth corresponding to the next test time period and the temperature data corresponding to the next test time period are obtained, and the capacity loss value corresponding to the current test time period, the discharge depth corresponding to the next test time period and the temperature data corresponding to the next test time period are input into the preset cycle attenuation model for the next cycle processing, and then the next cycle number is output, and so on, thereby realizing the cycle processing process based on the preset cycle attenuation model.

[0074] Step S240, when the SOC value of the current test time period reaches a preset value, the life value of the battery to be tested is determined according to the current cycle days and the current cycle number.

[0075] During the cycle processing of the battery to be tested, the concentration SOC value of the current test time period can be obtained. If the SOC value of the current test time period reaches a preset value (for example, the SOC value drops to 70%), the battery to be tested is determined to be in a scrap state, and the cycle processing of the battery to be tested is stopped, and the latest number of cycle days and the current number of cycles obtained are judged to be up to standard, thereby determining the life value of the battery to be tested, determining whether the battery to be tested meets the expected life of the product, and realizing accurate and reliable life prediction simulation of the battery to be tested.

[0076] In the above-mentioned embodiment, by pre-establishing a preset calendar decay model and a preset cycle decay model, by judging the power data in the test data, if the power data of the current test time period meets the preset conditions, the corresponding influencing factors are input into the preset calendar decay model for cyclic processing, thereby obtaining the current cycle days; if the power data of the current test time period does not meet the preset conditions, the corresponding influencing factors are input into the preset cycle decay model for cyclic processing, thereby obtaining the current number of cycles. When the SOC value of the battery to be tested reaches the preset value, the cyclic processing is terminated, and it is determined whether the current cycle days and the current number of cycles meet the standards, thereby determining the life value of the battery to be tested, thereby achieving accurate and convenient life prediction simulation of the battery to be tested. By taking the result of the previous cycle processing as the influencing factor in the current cycle processing, the parameter settings selected in the battery life prediction simulation are comprehensive, thereby reducing the battery life prediction error and improving the reliability of the battery life prediction.

[0077] In one embodiment, the preset calendar decay model is: Q1 = exp(G1 + F1 / T) * t^f(T).

[0078] Among them, Q1 is the capacity loss value of the previous test time period, G1 is the first influencing factor, F1 is the second influencing factor, T is the temperature data of the current test time period, t is the number of days of the current cycle, and f(T) is the temperature influencing factor.

[0079] The capacity loss value can be divided into the capacity loss value of the previous test period, the capacity loss value of the current test period, and the capacity loss value of the next test period based on the test time. The first influencing factor G1 is a value obtained by pre-processing, and the second influencing factor F1 is a value obtained by pre-processing. The temperature influencing factor can be obtained by interpolation using the capacity attenuation of the same SOC value at different temperatures.

[0080] For example, during the current cycle processing, the processor can obtain the power data of the current test time period, and compare the power data of the current test time period with the preset conditions. If the power data of the current test time period meets the preset conditions, the capacity loss value Q1 corresponding to the previous test time period and the temperature data T corresponding to the current test time period are obtained, and the capacity loss value Q1 corresponding to the previous test time period and the temperature data T corresponding to the current test time period are input into Q1 = exp(G1+F1 / T)*t^f(T) for the current cycle processing, and then the current cycle number t is output. Similarly, during the next cycle processing, the processor can obtain the power data of the next test time period, and compare the power data of the next test time period with the preset conditions. If the power data of the next test time period meets the preset conditions, the capacity loss value Q1 corresponding to the current test time period and the temperature data T corresponding to the next test time period are obtained, and the capacity loss value Q1 corresponding to the current test time period and the temperature data T corresponding to the next test time period are input into Q1 = exp(G1+F1 / T)*t^f(T) for the next cycle processing, and then the next cycle day t is output, and so on, thereby realizing the cycle processing process based on the preset calendar decay model.

[0081] In one embodiment, Figure 3 As shown, the steps for obtaining the first impact factor and the second impact factor are:

[0082] Step S310, obtaining second test data of the battery to be tested, the second test data including first historical temperature data, first historical capacity loss values ​​and first historical cycle days of several different test time periods.

[0083] Among them, the second test data refers to the test data obtained by performing a charge and discharge test on the battery to be tested. Exemplarily, the constant power charge and discharge test can be performed on the battery to be tested in different test time periods to obtain test data corresponding to multiple charge and discharge tests. The second test data can be divided into the first historical temperature data, the first historical capacity loss value, and the first historical cycle days of several different test time periods based on the test time period. In an example, the first historical temperature data, the first historical capacity loss value, and the first historical cycle days can be stored in the form of a data table based on a mapping relationship.

[0084] Step S320: Based on the first calendar decay sub-model, the first historical capacity loss values ​​and the first historical cycle days of different test time periods are fitted to obtain a first intermediate parameter.

[0085] The first calendar decay sub-model is a model established by the system preset. In one example, the first calendar decay sub-model is: y1 = A1*x1^B1. Among them, y1 is the first historical capacity loss value, x1 is the first historical cycle days, A1 is the first intermediate parameter, and B1 is the second intermediate parameter.

[0086] The first historical capacity loss value y1 of each different test time period and the first historical cycle day x1 of each corresponding first historical capacity loss value y1 are input into the first calendar decay sub-model y1=A1*x1^B1 for fitting processing, and then the first intermediate parameter A1 and the second intermediate parameter B1 are output.

[0087] Step S330: Based on the second calendar decay sub-model, fitting processing is performed on the first intermediate parameter and the first historical temperature data of each different test time period to obtain a first influencing factor and a second influencing factor.

[0088] The second calendar decay sub-model is a model established by the system preset. In one example, the second calendar decay sub-model is: exp(G1+F1 / T1)=A1. Wherein, T1 is the first historical temperature data, G1 is the first influencing factor, F1 is the second influencing factor, and A1 is the first intermediate parameter.

[0089] The first intermediate parameter A1 obtained by processing in step S320 and the first historical temperature data T1 of each different test time period are input into the second calendar decay sub-model exp(G1+F1 / T1)=A1 for fitting processing, and then the first influencing factor G1 and the second influencing factor F1 are output. Further, the calculated first influencing factor G1 and the second influencing factor F1 are added to the preset calendar decay model Q1=exp(G1+F1 / T)*t^f(T), which can improve the comprehensiveness of the parameter setting of the cycle days prediction, reduce the cycle days prediction error, and thus improve the reliability of the battery life prediction.

[0090] In one example, the step of obtaining the temperature influence factor includes:

[0091] Acquire second test data of the battery to be tested, the second test data also including a historical SOC value; perform interpolation processing on the historical SOC value and each first historical temperature data to obtain a temperature influence factor.

[0092] Among them, the temperature impact factor f(T) can be used to represent the index at different temperatures in the preset calendar decay model. Exemplarily, considering that different temperatures will affect calendar decay, the capacity decay under different first historical temperature data and the same historical SOC value is interpolated to obtain the temperature impact factor. For example, the historical SOC value is 80%, and the first historical temperature data are 15°C, 25°C and 35°C respectively. The temperature impact factor is obtained by linearly interpolating the historical SOC value and each first historical temperature data.

[0093] In one embodiment, the preset cyclic decay model is: Q2=exp(G2+F2 / T)*N^B2*f(DOD).

[0094] Among them, Q2 is the capacity loss value of the previous test time period, G2 is the third influencing factor, F2 is the fourth influencing factor, T is the temperature data of the current test time period, N is the current number of cycles, B2 is the fifth influencing factor, and f(DOD) is the discharge depth of the current test time period.

[0095] The third influencing factor G2 is a value obtained by pre-processing, the fourth influencing factor F2 is a value obtained by pre-processing, and the fifth influencing factor B2 is a value obtained by pre-processing. The depth of discharge f (DOD) can be calculated from the capacity loss of the current test. For example, when the temperature data of the current test time period is 25°C, the discharge capacity of the battery to be tested is discharged from 100% to 0, then the depth of discharge of the current test time period is 100%; when the temperature data of the current test time period is 25°C, the discharge capacity of the battery to be tested is discharged from 100% to 10%, then the depth of discharge of the current test time period is 90%; when the temperature data of the current test time period is 25°C, the discharge capacity of the battery to be tested is discharged from 95% to 5%, then the depth of discharge of the current test time period is 90%; when the temperature data of the current test time period is 25°C, the discharge capacity of the battery to be tested is discharged from 90% to 10%, then the depth of discharge of the current test time period is 80%; when the temperature data of the current test time period is 25°C, the discharge capacity of the battery to be tested is discharged from 90% to 10%, then the depth of discharge of the current test time period is 80%. The discharge capacity of the battery to be tested is discharged from 95% to 15%, and the discharge depth of the current test time period is 80%; the temperature data in the current test time period is 25°C, and the discharge capacity of the battery to be tested is discharged from 90% to 30%, and the discharge depth of the current test time period is 60%; the temperature data in the current test time period is 25°C, and the discharge capacity of the battery to be tested is discharged from 70% to 10%, and the discharge depth of the current test time period is 60%; the temperature data in the current test time period is 35°C, and the discharge capacity of the battery to be tested is discharged from 100% to 0, and the discharge depth of the current test time period is 100%; the temperature data in the current test time period is 45°C, and the discharge capacity of the battery to be tested is discharged from 100% to 0, and the discharge depth of the current test time period is 100%.

[0096] During the current cycle processing, the processor can obtain the power data of the current test time period, and compare the power data of the current test time period with the preset conditions. If the power data of the current test time period does not meet the preset conditions, the capacity loss value Q2 corresponding to the previous test time period, the discharge depth f(DOD) corresponding to the current test time period and the temperature data T corresponding to the current test time period are obtained, and the capacity loss value Q2 corresponding to the previous test time period, the discharge depth f(DOD) corresponding to the current test time period and the temperature data T corresponding to the current test time period are input into Q2=exp(G2+F2 / T)*N^B2*f(DOD) for the current cycle processing, and then the current number of cycles N is output. Similarly, during the next cycle processing, the processor can obtain the power data of the next test time period, and compare the power data of the next test time period with the preset conditions. If the power data of the next test time period does not meet the preset conditions, the capacity loss value Q2 corresponding to the current test time period, the discharge depth f(DOD) corresponding to the next test time period and the temperature data T corresponding to the next test time period are obtained, and the capacity loss value Q2 corresponding to the current test time period, the discharge depth f(DOD) corresponding to the next test time period and the temperature data T corresponding to the next test time period are input into Q2=exp(G2+F2 / T)*N^B2*f(DOD) for the next cycle processing, and then the next cycle number N is output, and so on, thereby realizing the cycle processing process based on the preset cycle attenuation model.

[0097] In one embodiment, Figure 4 As shown, the steps for obtaining the third impact factor, the fourth impact factor and the fifth impact factor are:

[0098] Step S410, obtaining second test data of the battery to be tested, the second test data including second historical temperature data, second historical capacity loss values ​​and second historical cycle numbers in several different test time periods.

[0099] The second test data may also be divided into second historical temperature data, second historical capacity loss values, and second historical cycle days of several different test time periods based on the test time period. In one example, the second historical temperature data, the second historical capacity loss values, and the second historical cycle days may be stored in the form of a data table based on a mapping relationship. In another example, the test data may also be represented in the form of a curve, for example, by converting the second test data curve graph to obtain a test change curve that changes with the test time period.

[0100] Step S420: Based on the first cycle attenuation sub-model, the second historical capacity loss values ​​of different test time periods and the second historical cycle numbers are fitted to obtain a third intermediate parameter and a fifth influencing factor.

[0101] The first cycle attenuation submodel is a model established by the system preset. In one example, the first cycle attenuation submodel is: y2 = A2*x2^B2. Where y2 is the second historical capacity loss value, x2 is the second historical cycle number, A2 is the third intermediate parameter, and B2 is the fifth influencing factor.

[0102] The second historical capacity loss value y2 of each different test time period and the second historical cycle number x2 of each corresponding second historical capacity loss value y2 are input into the first cycle attenuation sub-model y2=A2*x2^B2 for fitting processing, and then the third intermediate parameter A2 and the fifth influencing factor B2 are output.

[0103] Step S430: Based on the second cyclic attenuation sub-model, fitting processing is performed on the third intermediate parameter and the second historical temperature data of each different test time period to obtain a third influencing factor and a fourth influencing factor.

[0104] The second cyclic attenuation submodel is a model established by the system preset. In one example, the second cyclic attenuation submodel is: exp(G2+F2 / T2)=A2. Among them, T2 is the second historical temperature data, G2 is the third influencing factor, F2 is the fourth influencing factor, and A2 is the third intermediate parameter.

[0105] The third intermediate parameter A2 obtained by processing in step S420 and the second historical temperature data T2 of each different test time period are input into the second cycle decay sub-model exp(G2+F2 / T2)=A2 for fitting processing, and then the third influencing factor G2 and the fourth influencing factor F2 are output. Further, the calculated third influencing factor G2, the fourth influencing factor F2 and the fifth influencing factor B2 are added to the preset cycle decay model Q2=exp(G2+F2 / T)*N^B2*f(DOD), which can improve the comprehensiveness of the parameter setting of the cycle number prediction, reduce the cycle number prediction error, and thus improve the reliability of the battery life prediction.

[0106] In one embodiment, Figure 5 As shown, a battery life prediction method is provided, which is applied to Figure 1 The processor 102 in the embodiment is taken as an example to illustrate, and the following steps are included:

[0107] Step S510, obtaining first test data of the battery to be tested; the first test data includes capacity loss values, power data, temperature data, discharge depth and SOC value in several different test time periods.

[0108] For the specific description of the above step S510, please refer to the description of the above embodiment, which will not be repeated here.

[0109] Step S520, when the power data of the current test time period is equal to 0, the capacity loss value corresponding to the previous test time period and the temperature data corresponding to the current test time period are input into the preset calendar decay model for cyclic processing to obtain the current cycle day.

[0110] For example, the charge and discharge power can be obtained by performing a charge and discharge test on the battery to be tested, and then the charge and discharge power can be confirmed as power data. Exemplarily, the power data can be stored by forming a power table. The processor can obtain the power data corresponding to the current test time period by identifying the power table, and judge the numerical value of the power data of the current test time period. If the power data of the current test time period is equal to 0, it is judged that the battery to be tested is not charged or discharged in the current test time period, and then the current test time period is confirmed as a calendar decay time period.

[0111] During the current cycle processing, the processor can obtain the power data of the current test time period. If the power data of the current test time period is equal to 0, the capacity loss value corresponding to the previous test time period and the temperature data corresponding to the current test time period are obtained, and the capacity loss value corresponding to the previous test time period and the temperature data corresponding to the current test time period are input into the preset calendar decay model for the current cycle processing, and then the number of days of the current cycle is output. During the next cycle processing, the processor can obtain the power data of the next test time period. If the power data of the next test time period is still equal to 0, the capacity loss value corresponding to the current test time period and the temperature data corresponding to the next test time period are obtained, and the capacity loss value corresponding to the current test time period and the temperature data corresponding to the next test time period are input into the preset calendar decay model for the next cycle processing, and then the number of days of the next cycle is output, and so on, thereby realizing the cycle processing process based on the preset calendar decay model.

[0112] Step S530, when the power data of the current test time period is greater than 0, the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period and the temperature data corresponding to the current test time period are input into the preset cycle decay model for cyclic processing to obtain the current cycle number.

[0113] For example, power data can be stored in the form of a power meter. The processor can identify the power meter, obtain the power data corresponding to the current test time period, and judge the numerical size of the power data of the current test time period. If the power data of the current test time period is greater than 0, it is judged that the battery to be tested is charged and discharged in the current test time period, and then the current test time period is confirmed as a time period of cyclic attenuation.

[0114] During the current cycle processing, the processor can obtain the power data of the current test time period. If the power data of the current test time period is greater than 0, the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period, and the temperature data corresponding to the current test time period are input into the preset cycle decay model for the current cycle processing, and then the current cycle number is output. During the next cycle processing, the processor can obtain the power data of the next test time period. If the power data of the next test time period is greater than 0, the capacity loss value corresponding to the current test time period, the discharge depth corresponding to the next test time period, and the temperature data corresponding to the next test time period are obtained. The capacity loss value corresponding to the current test time period, the discharge depth corresponding to the next test time period, and the temperature data corresponding to the next test time period are input into the preset cycle decay model for the next cycle processing, and then the next cycle number is output, and so on, thereby realizing the cycle processing process based on the preset cycle decay model.

[0115] Step S540, when the SOC value of the current test time period reaches a preset value, the life value of the battery to be tested is determined according to the current cycle day and the current cycle number.

[0116] For the specific description of the above step S540, please refer to the description of the above embodiment, which will not be repeated here.

[0117] In the above-mentioned embodiment, by pre-establishing a preset calendar decay model and a preset cycle decay model, by determining whether the power data in the test data is 0, if the power data of the current test time period is equal to 0, the corresponding influencing factor is input into the preset calendar decay model for cyclic processing, thereby obtaining the current cycle days; if the power data of the current test time period is greater than 0, the corresponding influencing factor is input into the preset cycle decay model for cyclic processing, thereby obtaining the current number of cycles, and when the SOC value of the battery to be tested reaches the preset value, the cyclic processing is terminated, and it is determined whether the current cycle days and the current number of cycles meet the standards, thereby determining the life value of the battery to be tested, thereby achieving accurate and convenient life prediction simulation of the battery to be tested, and by using the result of the previous cycle processing as the influencing factor in the current cycle processing, the parameter settings selected in the battery life prediction simulation are comprehensive, thereby reducing the battery life prediction error and improving the reliability of the battery life prediction.

[0118] It should be understood that although Figures 2 to 5 The steps in the flowchart are shown in sequence as indicated by the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified in this document, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, Figures 2 to 5 At least part of the steps may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed in turn or alternately with other steps or at least part of the sub-steps or stages of other steps.

[0119] In one embodiment, Figure 6 As shown, the present application provides a battery life prediction device, comprising:

[0120] The test data acquisition module 610 is used to acquire first test data of the battery to be tested; the first test data includes capacity loss values, power data, temperature data, discharge depth and SOC value in several different test time periods;

[0121] The calendar decay processing module 620 is used to input the capacity loss value corresponding to the previous test period and the temperature data corresponding to the current test period into a preset calendar decay model for cyclic processing to obtain the current cycle number of days when the power data of the current test period meets the preset conditions;

[0122] The cycle decay processing module 630 is used to input the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period and the temperature data corresponding to the current test time period into a preset cycle decay model for cycle processing to obtain the current cycle number when the power data of the current test time period does not meet the preset conditions;

[0123] The life prediction module 640 is used to determine the life value of the battery to be tested according to the current cycle days and the current cycle number when the SOC value in the current test time period reaches a preset value.

[0124] For the specific definition of the battery life prediction device, please refer to the definition of the battery life prediction method above, which will not be repeated here. Each module in the above-mentioned battery life prediction device can be implemented in whole or in part by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the battery life prediction system in the form of hardware, or can be stored in the memory of the battery life prediction system in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0125] In one embodiment, a battery life prediction system is provided. The battery life prediction system may be, but is not limited to, a computer. The internal structure diagram of the battery life prediction system may be as follows: Figure 7 As shown. The battery life prediction system includes a processor and a memory connected via a system bus; the battery life prediction system may also include a network interface and an input device. Among them, the processor is used to provide computing and control capabilities. The memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a battery life prediction method under high concurrency is implemented.

[0126] Those skilled in the art will understand that Figure 7 The structure shown in the figure is merely a block diagram of a portion of the structure related to the scheme of the present application, and does not constitute a limitation on the battery life prediction system to which the scheme of the present application is applied. The specific battery life prediction system may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0127] In one embodiment, a battery life prediction system is also provided, including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above-mentioned battery life prediction methods when executing the computer program.

[0128] For example, the processor may be configured to execute the following steps of the battery life prediction method:

[0129] Acquire the first test data of the battery to be tested; the first test data includes capacity loss values, power data, temperature data, discharge depth and SOC values ​​of several different test time periods; when the power data of the current test time period meets the preset conditions, the capacity loss value corresponding to the previous test time period and the temperature data corresponding to the current test time period are input into the preset calendar decay model for cyclic processing to obtain the current cycle number; when the power data of the current test time period does not meet the preset conditions, the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period and the temperature data corresponding to the current test time period are input into the preset cycle decay model for cyclic processing to obtain the current cycle number; when the SOC value of the current test time period reaches the preset value, the life value of the battery to be tested is determined according to the current cycle number and the current cycle number, so as to realize accurate life prediction simulation of the battery to be tested.

[0130] In one embodiment, a computer-readable storage medium is further provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned battery life prediction methods are implemented.

[0131] For example, when the computer program is executed by a processor, the following steps of the battery life prediction method are implemented:

[0132] Acquire the first test data of the battery to be tested; the first test data includes capacity loss values, power data, temperature data, discharge depth and SOC values ​​of several different test time periods; when the power data of the current test time period meets the preset conditions, the capacity loss value corresponding to the previous test time period and the temperature data corresponding to the current test time period are input into the preset calendar decay model for cyclic processing to obtain the current cycle number; when the power data of the current test time period does not meet the preset conditions, the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period and the temperature data corresponding to the current test time period are input into the preset cycle decay model for cyclic processing to obtain the current cycle number; when the SOC value of the current test time period reaches the preset value, the life value of the battery to be tested is determined according to the current cycle number and the current cycle number, so as to realize accurate life prediction simulation of the battery to be tested.

[0133] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing related hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned division operation methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0134] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0135] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be construed as limiting the scope of the patent application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent application shall be subject to the attached claims.

Claims

1. A battery life prediction method, characterized in that: The following steps are involved: Acquire first test data of the battery to be tested; the first test data includes capacity loss values, power data, temperature data, discharge depth and SOC values ​​in several different test time periods; When the power data of the current test period is equal to 0, the capacity loss value corresponding to the previous test period and the temperature data corresponding to the current test period are input into the preset calendar decay model for cyclic processing to obtain the current cycle days; The preset calendar decay model is established based on the Arrhenius model; When the power data of the current test time period is greater than 0, the capacity loss value corresponding to the previous test time period, the discharge depth corresponding to the current test time period and the temperature data corresponding to the current test time period are input into the preset cycle decay model for cyclic processing to obtain the current cycle number; The preset cyclic attenuation model is established based on the Arrhenius model; When the SOC value of the current test time period reaches a preset value, the life value of the battery to be tested is determined according to the current cycle days and the current cycle number.

2. The battery life prediction method according to claim 1, characterized in that: The preset calendar decay model is: Q1=exp(G1+F1 / T)*t^f(T) Among them, Q1 is the capacity loss value of the previous test time period, G1 is the first influencing factor, F1 is the second influencing factor, T is the temperature data of the current test time period, t is the number of days of the current cycle, and f(T) is the temperature influencing factor.

3. The battery life prediction method according to claim 2, characterized in that: The steps of obtaining the first impact factor and the second impact factor are: Acquire second test data of the battery to be tested, wherein the second test data includes first historical temperature data, first historical capacity loss values, and first historical cycle days in a plurality of different test time periods; Based on the first calendar decay sub-model, fitting the first historical capacity loss values ​​and the first historical cycle days in different test time periods to obtain a first intermediate parameter; Based on the second calendar decay sub-model, the first intermediate parameter and the first historical temperature data of different test time periods are fitted to obtain the first influencing factor and the second influencing factor.

4. The battery life prediction method according to claim 3, characterized in that: The first calendar decay submodel is: y1=A1*x1^B1 Among them, y1 is the first historical capacity loss value, x1 is the first historical cycle days, A1 is the first intermediate parameter, and B1 is the second intermediate parameter; The second calendar decay submodel is: exp(G1+F1 / T1)=A1 Wherein, T1 is the first historical temperature data.

5. The battery life prediction method according to claim 3, characterized in that: The step of obtaining the temperature influence factor comprises: Acquire second test data of the battery to be tested, wherein the second test data also includes a historical SOC value; The historical SOC value and each of the first historical temperature data are interpolated to obtain the temperature influence factor.

6. The battery life prediction method according to claim 1, characterized in that: The preset cyclic attenuation model is: Q2=exp(G2+F2 / T)*N^B2*f(DOD) Among them, Q2 is the capacity loss value of the previous test time period, G2 is the third influencing factor, F2 is the fourth influencing factor, T is the temperature data of the current test time period, N is the current number of cycles, B2 is the fifth influencing factor, and f(DOD) is the discharge depth of the current test time period.

7. The battery life prediction method according to claim 6, characterized in that: The steps of obtaining the third impact factor, the fourth impact factor and the fifth impact factor are: Acquire second test data of the battery to be tested, wherein the second test data includes second historical temperature data, second historical capacity loss values, and second historical cycle numbers in a plurality of different test time periods; Based on the first cycle attenuation sub-model, fitting processing is performed on the second historical capacity loss values ​​and each second historical cycle number in each different test time period to obtain a third intermediate parameter and the fifth influencing factor; Based on the second cyclic attenuation sub-model, a fitting process is performed on the third intermediate parameter and the second historical temperature data of each different test time period to obtain the third influencing factor and the fourth influencing factor.

8. The battery life prediction method according to claim 7, characterized in that: The first cycle decay submodel is: y2=A2*x2^B2 Wherein, y2 is the second historical capacity loss value, x2 is the second historical cycle number, A2 is the third intermediate parameter, and B2 is the fifth influencing factor; The second cycle decay submodel is: exp(G2+F2 / T2)=A2 Wherein, T2 is the second historical temperature data.

9. A battery life prediction system, characterized in that: The method comprises a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the battery life prediction method according to any one of claims 1 to 8 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the battery life prediction method according to any one of claims 1 to 8 are implemented.

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