Battery life prediction method and device and storage medium

Abstract

The invention discloses a battery life prediction method, device and system and a storage medium. The battery life prediction method can comprise the following steps: predicting the life of a battery; respectively acquiring first test data and second test data of a to-be-tested battery subjected to the same round of charge-discharge cycle test under a comparison test condition and a target test condition, and determining the activation energy of the to-be-tested battery based on the first test data and the second test data; determining an acceleration factor of the to-be-tested battery based on the activation energy; and determining the battery life of the to-be-tested battery based on the activation energy. According to the battery life prediction method disclosed by the invention, battery life prediction is efficiently and accurately carried out through cycle tests based on a small number of times in a high-temperature accelerated degradation environment.

Description

Technical Field

[0001] The present application relates to the field of battery technology, and in particular to a method, device and storage medium for predicting the battery life of a normal temperature battery through a high temperature accelerated decay process. Background Art

[0002] With the rapid development of electric vehicles, energy storage systems and other fields, the performance and life of power batteries are crucial to the reliability and economy of the equipment. At present, the prediction of battery life is usually based on long-term measured data or theoretical models, which has problems such as long cycle, high testing cost, and difficulty in data acquisition. In order to efficiently predict battery life, there is currently a battery life prediction method based on accelerated aging tests (such as HALT). By simulating the battery's decay process under accelerated conditions, reliable battery life prediction results can be obtained in a short time. However, existing methods still have problems such as requiring a lot of data and long experimental time. Summary of the invention

[0003] In order to solve the above problems, the present application discloses a battery life prediction method, device and storage medium. The battery life prediction method efficiently and accurately predicts the battery life based on a small number of cycle tests in a high temperature accelerated decay environment.

[0004] In a first aspect, the present application provides a method for predicting battery life. The method may include: respectively obtaining first test data and second test data of a battery under test that undergoes the same round of charge-discharge cycle tests under comparative test conditions and target test conditions; determining activation energy of the battery under test based on the first test data and the second test data; determining an acceleration factor of the battery under test based on the activation energy; and determining the battery life of the battery under test based on the acceleration factor.

[0005] According to some embodiments of the present application, the plurality of charge and discharge cycles may not exceed 5 times.

[0006] According to some embodiments of the present application, the comparison test conditions may include normal temperature and a fixed charge and discharge rate; the target test conditions may include a test temperature exceeding the normal temperature and the fixed charge and discharge rate; wherein the normal temperature may be 25°C, and the test temperature may exceed the operating temperature range of the battery to be tested; or, the test temperature is 45°C.

[0007] According to some embodiments of the present application, the first test data may include at least a first capacity decay rate of the battery to be tested, and the second test data may include at least a second capacity decay rate of the battery to be tested; determining the activation energy of the battery to be tested may include: constructing a first model based on the normal temperature, the first capacity decay rate, the test temperature and the second capacity decay rate to calculate the activation energy.

[0008] According to some embodiments of the present application, the expression of the first model is: ;in, is the activation energy, is the first capacity decay rate, is the second capacity decay rate, is the normal temperature, is the test temperature, is Avogadro's constant.

[0009] According to some embodiments of the present application, determining the life attenuation parameter of the battery to be tested may include: constructing a second model based on the normal temperature, the test temperature and the material characteristic parameters to calculate the acceleration factor.

[0010] According to some embodiments of the present application, the expression of the second model is: ;in, is the acceleration factor, is the activation energy, is the normal temperature, is the test temperature, is Avogadro's constant.

[0011] According to some embodiments of the present application, determining the battery life of the battery to be tested may include: performing simulation calculation based on the acceleration factor and the second test data to determine the battery life of the battery to be tested at normal temperature.

[0012] According to a second aspect of the present application, there is provided a battery life prediction device, which may include: an acquisition module configured to respectively acquire first test data and second test data of a battery to be tested that undergoes the same round of charge and discharge cycle tests under comparative test conditions and target test conditions; a first determination module configured to determine the activation energy of the battery to be tested based on the first test data and the second test data; a second determination module configured to determine an acceleration factor of the battery to be tested based on the activation energy; and a prediction module configured to determine the battery life of the battery to be tested based on the acceleration factor.

[0013] The third aspect of the present application provides a computing system, which may include: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program can implement the steps of the battery life prediction method as described above when executed by the processor.

[0014] A fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the steps of the battery life prediction method as described above can be implemented.

[0015] A fifth aspect of the present application provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the battery life prediction method as described above can be implemented.

[0016] A sixth aspect of the present application provides a battery equivalent simulation model establishment device, which may include the battery life prediction method device or computing system as described above.

[0017] The battery life prediction method disclosed in the present application can efficiently and accurately predict the battery life based on a smaller number of cycle tests by setting a high temperature accelerated decay environment.

[0018] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through the practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The present application will be further described in the form of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not restrictive, and in these embodiments, the same number represents the same structure, wherein: Figure 1 is an exemplary flow chart of a battery life prediction method according to some embodiments of the present application; Figure 2 is an exemplary comparison diagram between the predicted life and the actual tested life of the battery according to some embodiments of the present application; Figure 3 is an exemplary module diagram of a battery monomer equivalent simulation model establishment device according to some embodiments of the present application; Figure 4 is an exemplary block diagram of a computing device according to some embodiments of the present application. DETAILED DESCRIPTION

[0020] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are described in detail below. In the following description, many specific details are set forth to facilitate a full understanding of the present application. However, the present application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present application, so the present application is not limited by the specific embodiments disclosed below.

[0021] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as those commonly understood by technicians in the technical field of this application. The terms used in this application and in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. The words "including" or "comprising" and the like used in this application mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. The terms "and / or" or "and / or" used in this application include any and all combinations of one or more related listed items.

[0022] The terms "including", "having" and their cognates used in this application are intended only to indicate specific features, numbers, steps, operations, elements, components or combinations of the foregoing items, and should not be understood as first excluding the existence of one or more other features, numbers, steps, operations, elements, components or combinations of the foregoing items or the possibility of adding one or more features, numbers, steps, operations, elements, components or combinations of the foregoing items.

[0023] It should be noted that the terms "first", "second", "third", etc. used in this application are only used to distinguish descriptions and should not be understood as indicating or implying relative importance. When a component is referred to as being "fixed to", "mounted on" or "disposed on" another component, it can be directly on the other component or there can also be other components centered. When a component is considered to be "connected to" another component, it can be directly connected to the other component or there can be other components centered at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0024] Some preferred embodiments of the present application are described below. It should be noted that the following description is for the purpose of illustration and is not intended to limit the scope of protection of the present application. The steps involved in the present application can be performed accurately in sequence, or various steps can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more operations can be removed from these processes.

[0025] In response to the shortcomings of the prior art, this application proposes a highly accelerated battery life prediction method, which can efficiently and accurately predict the battery life of the battery by setting a high temperature to accelerate the decay process, conducting cycle tests and extracting battery decay characteristics in a relatively short period of time, and using the Arrhenius equation to analyze the life acceleration factor.

[0026] Figure 1 is an exemplary flow chart of a battery life prediction method according to some embodiments of the present application. The battery may be a power battery, which may include but is not limited to lithium-ion batteries (such as lithium cobalt oxide, ternary lithium, lithium iron phosphate, etc.), lead-acid batteries, nickel-metal hydride batteries, sodium-ion batteries, solid-state batteries, etc. Alternatively, the battery may be a battery used to provide power for electric vehicles, energy storage systems, consumer electronics, feature vehicles, aerospace / deep-sea equipment, etc. Optionally or preferably, the battery may be a battery used in electric vehicles or energy storage systems. In some embodiments, Figure 1 The process 100 shown in the figure can be implemented in a computing device, such as an industrial computer, a server, a computer, a tablet, a smart mobile device, etc. For example, the process 100 can be stored in a storage device (such as a storage unit of the computing device or an external storage device) in the form of a program or instruction, and the program or instruction can implement the process 100 when executed. Figure 1 As shown, process 100 may include the following operations.

[0027] Step 110 , respectively obtaining first test data and second test data of the battery to be tested that is subjected to the same round of charge-discharge cycle tests under comparative test conditions and target test conditions.

[0028] In some embodiments, the battery to be tested may be a battery as described above. The comparative test condition may be used to test the battery to be tested at room temperature, and the target test condition may be used to test the battery to be tested at high temperature. In the present application, "normal temperature" may refer to 25°C. Alternatively, "normal temperature" may also be any value between 20°C and 25°C. For example, an increment or decrement of 20°C, 21°C, 22°C, 23°C, 24°C, 25°C or any value above. "High temperature" may refer to a temperature exceeding "normal temperature" and exceeding the operating temperature range of the battery to be tested. Exemplarily, if the operating temperature range of a battery such as the above-mentioned power battery is 0°C-40°C, then "high temperature" may refer to a temperature value exceeding 40°C, such as 45°C, 50°C, 60°C, 70°C, 80°C, etc. Optionally or preferably, "high temperature" may be 45°C. The present application sets a target test condition with "high temperature" to achieve the purpose of increasing the temperature to accelerate the battery's decay process.

[0029] In some embodiments, the comparative test conditions may include normal temperature and a fixed charge and discharge rate, and the target test conditions may include a test temperature exceeding normal temperature (also referred to as "high temperature" in this application) and the same charge and discharge rate. For the convenience of explanation, in this application, the charge and discharge cycle test of the battery to be tested under the comparative test conditions may be referred to as "normal temperature test", and the charge and discharge cycle test of the battery to be tested under the target test conditions may be referred to as "high temperature test". As an example, the charge and discharge cycle test of the battery to be tested under the comparative test conditions (that is, the above-mentioned normal temperature test) may be performed at 25°C at a charge and discharge rate of 1C for multiple rounds of charge and discharge cycles, and the charge and discharge cycle test of the battery to be tested under the target test conditions (that is, the above-mentioned high temperature test) may be performed at 45°C at a charge and discharge rate of 1C for the same round of charge and discharge cycles. In some embodiments, the number of charge and discharge cycles may not exceed 10. For example, the number of charge and discharge cycles may be 10, 9, 8, 7, 6, 5, etc. Optionally or preferably, the number of charge and discharge cycles may not exceed 5. Exemplarily, the number of charge-discharge cycles may be 5. The present application uses a small number of charge-discharge cycles to effectively shorten the test time and improve the test rate and test efficiency.

[0030] In some embodiments, the first test data may include at least the first battery capacity of the battery to be tested after each charge and discharge cycle under normal temperature test. Similarly or similarly, the second test data may include at least the second battery capacity of the battery to be tested after each charge and discharge cycle under high temperature test. The above battery capacity may be presented in the form of percentage (%) or in the form of ampere-hour (Ah). Based on the above data, the battery capacity decay rate of the battery to be tested during the entire test process (including the normal temperature test and the high temperature test) may also be determined. For example, the first capacity decay rate obtained using the test data of the normal temperature test may be included in the first test data, and the second capacity decay rate obtained using the test data obtained by the high temperature test may be included in the second test data. Directly using the endpoint value to calculate the decay rate, or averaging the slopes obtained from the test data obtained from two adjacent cycle tests, or performing a straight line fitting as the decay rate can all be applied in this application. For example, the battery capacity of the battery to be tested measured after the first cycle of charge and discharge and the battery capacity of the battery to be tested measured after the last cycle of charge and discharge are connected between two points, and the slope of the resulting line segment can be used as the above capacity decay rate. Alternatively, the battery capacity of the battery to be tested measured after each two adjacent charge and discharge cycles is averaged after the decay rate is calculated as the above capacity decay rate. Alternatively, the battery capacity of the battery to be tested measured after multiple charge and discharge cycles (for example, five or ten charge and discharge cycles) can be used for linear fitting, such as linear fitting using the least squares method, and the slope of the obtained line segment can be used as the above capacity decay rate.

[0031] It should be noted that any method that can determine the capacity decay rate based on the battery capacity of the battery to be tested after each cycle of charge and discharge can be applied to the present application without limitation. In addition, the data acquisition on the battery capacity can be based on existing equipment, such as using a charge and discharge tester.

[0032] Step 120: determining the activation energy of the battery to be tested based on the first test data and the second test data.

[0033] In some embodiments, the material characteristic parameter can be calculated based on a first model. The first model can be constructed based on the Arrhenius equation. Exemplarily, the Arrhenius equation can be expressed as the following formula 1: ; in, Indicates temperature The reaction rate constant under represents the pre-exponential factor, which is usually a constant independent of the reaction rate. represents the activation energy or activation energy, the unit is kJ / mol, represents the gas constant, which is 8.314 J / mol. Indicates temperature in Kelvin ( ). In this application, it is considered that the aging rate of the battery to be tested and the temperature conform to the Arrhenius equation, and the aging rate can be represented by the capacity decay rate of the battery to be tested (capacity decay means that the battery life is shortened). Then The first capacity decay rate included in the first test data or the second capacity decay rate included in the second test data can be used for corresponding replacement. Substituting the two sets of data into Formula 1 respectively, the first model shown in Formula 2 can be obtained: ; Among them, the subscript Associated with the first test data, the subscript is associated with the second test data. represents the second capacity decay rate, represents the first capacity decay rate, Indicates the high temperature, such as 45°C (273.15+45=318.15K), represents the normal temperature, such as 25°C (273.15+25=298.15K). After the relevant data is entered, the activation energy is calculated by the first model. .

[0034] Step 130: determining an acceleration factor of the battery to be tested based on the activation energy.

[0035] In some embodiments, the acceleration factor can be calculated based on a second model. Similarly or similarly, the second model can also be constructed based on the Arrhenius equation. Under the same assumption as in step 120, the ratio of the aging rate of the battery under test determined using the Arrhenius equation under two test conditions can be expressed as the following formula 3: ; Due to the aging rate and battery life (e.g., battery capacity after a certain number of cycles), and an acceleration factor To represent the ratio between the life of the battery under test at room temperature and the life at high temperature, the second model represented by the following formula 4 can be used to calculate the above acceleration factor : ; The material characteristic parameter determined in step 120, that is, the activation energy And the acceleration factor calculated by the second model after the temperature is brought in can be used as the lifetime decay parameter.

[0036] Step 140: Determine the battery life of the battery to be tested based on the acceleration factor.

[0037] In some embodiments, the acceleration factor can be used to perform simulation calculation in combination with the second test data to obtain the battery life of the battery under test at room temperature. A feasible implementation method can be that the acceleration factor and the second test data obtained under high temperature testing can be input into simulation software embedded with the Arrhenius equation for calculation to obtain the battery life of the battery under test at room temperature output by the software.

[0038] In some embodiments, the verification test data of the battery under the comparative test conditions can be obtained to evaluate the accuracy of the battery life. Exemplarily, the battery under the comparative test conditions (that is, the test temperature is room temperature) can continue to perform more charge and discharge cycles, for example, 50 times, 100 times or even more times. Similarly, the number of cycles and the corresponding battery capacity will be collected as the verification test data. The results can be referred to Figure 2 In this way, the straight line can be used as fitting data, and the verification test data can be used as actual data to calculate the degree of fit between the two, such as R 2 (R-Square). Substitute Figure 2 After calculating the data shown in 2 =0.9997. This shows that the fitting degree of the data is very high, and the difference between the predicted value and the actual value is very small. Therefore, the battery life prediction method disclosed in this application is highly reliable, and the prediction result has a high accuracy.

[0039] It should be noted that the above Figure 1 The description of each step in the embodiment is only for example and explanation, and does not limit the scope of application of this specification. For those skilled in the art, under the guidance of this specification, Figure 1 Various modifications and changes may be made to the various steps in the present invention. However, these modifications and changes are still within the scope of this specification.

[0040] The above process is exemplified below with a specific implementation process. It should be noted that the following content is only for illustration and not for limiting the present application.

[0041] Assume that the battery parameters of the battery to be tested are: positive electrode: lithium iron phosphate; negative electrode: graphite; electrolyte: containing LiPF 6 Organic solvent for salt (EC+DMC); Capacity: 50Ah.

[0042] 1. Set comparative test conditions: perform 5 charge and discharge cycles at a charge and discharge rate of 1C at 25°C, collect capacity data of the battery to be tested, and calculate the capacity decay rate (that is, the second decay rate) under the comparative test conditions to be 0.0127, see Table 1 below.

[0043] Table 1 Battery capacity parameters under comparative test conditions ; II. Setting target test conditions: Perform 5 charge and discharge cycles at 1C charge and discharge rate at 45°C, collect the capacity data of the battery to be tested, and calculate the capacity decay rate (i.e., the second decay rate) under the target test conditions to be 0.04175. See Table 2 below.

[0044] Table 2 Battery capacity parameters under target test conditions ; 3. Determine the material characteristic parameter - activation energy ; Based on the test data of the battery under test in step 1> and step 2>, the activation energy is determined by calculation using formula (2): It is 43.87 KJ / mol.

[0045] ; 4. Determine the life decay parameter - acceleration factor ; Based on the activation energy determined in 3> , calculate through formula (4) to determine the acceleration factor is 3.04.

[0046] ; 5. Based on the acceleration factor Perform simulation to obtain the battery life of the battery under test at 25°C. Figure 2 .

[0047] VI>, the battery to be tested is charged and discharged for 100 times under the comparative test conditions, and the capacity data of the battery to be tested is collected. Figure 2 The circular points shown in .

[0048] 7. Based on the capacity data obtained in 6, we can get R 2 is 0.9997.

[0049] The battery life prediction method disclosed in the present application can efficiently and accurately predict the battery life based on a smaller number of cycle tests by setting a high temperature accelerated decay environment.

[0050] The present application also discloses a battery life prediction device. The battery life prediction model establishment device can be used to perform the following steps: Figure 1 For details of the steps shown in FIG. 1 , please refer to the corresponding drawings. Figure 3 is an exemplary module diagram of a battery life prediction device according to some embodiments of the present application, such as Figure 3 As shown, the battery life prediction device 300 may include an acquisition module 310 , a first determination module 320 , a second determination module 330 and a prediction module 340 .

[0051] The acquisition module 310 may be configured to respectively acquire the first test data and the second test data of the same round of charge and discharge cycle tests of the battery to be tested under the comparative test conditions and the target test conditions. Among them, the charge and discharge cycle test under the comparative test conditions may be referred to as a normal temperature test, and the charge and discharge cycle test under the target test conditions may be referred to as a high temperature test. The number of rounds of the charge and discharge cycle may not exceed 10 times. Optionally or preferably, the number of rounds of the charge and discharge cycle may not exceed 5 times. Exemplarily, the number of rounds of the charge and discharge cycle may be 5 times. The first test data may at least include the first battery capacity of the battery to be tested after each charge and discharge cycle under the normal temperature test. The second test data may at least include the second battery capacity of the battery to be tested after each charge and discharge cycle under the high temperature test. The above data can be captured by, for example, a charge and discharge tester. The acquisition module 310 can be obtained by communicating with the device as a component of the first test data and the second test data. The first capacity decay rate and the second capacity decay rate can also be determined by the first battery capacity and the second battery capacity, which are also respectively included in the first test data and the second test data.

[0052] The first determination module 320 may be configured to determine the activation energy of the battery to be tested based on the first test data and the second test data. The activation energy may be calculated based on the first model. The first determination module 320 may construct the first model based on the Arrhenius equation. The activation energy is obtained by substituting relevant data (including but not limited to test temperature, first capacity decay rate, and second capacity decay rate, etc.) into the first model for calculation.

[0053] The second determination module 330 may be configured to determine an acceleration factor of the battery to be tested based on the activation energy. The acceleration factor may be calculated based on a second model. The second determination module 330 may construct the second model based on the Arrhenius equation. The acceleration factor is obtained by substituting relevant data (including but not limited to test temperature and activation energy) into the second model for calculation.

[0054] The prediction module 340 may be configured to determine the battery life of the battery under test based on the acceleration factor. The prediction module 340 may call a simulation program / software embedded with the Arrhenius equation, and input the acceleration factor and the second test data obtained under the high temperature test into the simulation program / software for calculation, so as to obtain the battery life of the battery under test at normal temperature output by the program / software.

[0055] For other descriptions of the above components, please refer to this application Figure 1-Figure 2 part.

[0056] It should be understood that Figure 3 The system and its modules shown can be implemented in various ways. For example, in some embodiments, the system and its modules can be implemented by hardware, software, or a combination of software and hardware. Among them, the hardware part can be implemented using dedicated logic; the software part can be stored in a memory and executed by an appropriate instruction execution system, such as a microprocessor or a dedicated design hardware. Those skilled in the art will understand that the above methods and systems can be implemented using computer executable instructions and / or included in processor control codes, such as carrier media such as disks, CDs or DVD-ROMs, programmable memories such as read-only memories (firmware), or data carriers such as optical or electronic signal carriers. Such codes are provided on. The system and its modules of the present application can be implemented not only by hardware circuits such as ultra-large-scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, etc., or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but can also be implemented by software executed by various types of processors, and can also be implemented by a combination of the above hardware circuits and software (e.g., firmware).

[0057] It should be noted that the above description of the modules is only for convenience of description and does not limit the present application to the scope of the embodiments. It is understandable that, for those skilled in the art, after understanding the principle of the system, it is possible to arbitrarily combine the modules or form a subsystem connected to other modules without deviating from this principle. For example, the modules may share a storage module, or each module may have its own storage module. Such variations are all within the scope of protection of the present application.

[0058] The present application also provides a computing device. Figure 4 The computing device 400 may include a computer program product for implementing the process described in the embodiments of the present application (for example, Figure 1 ) or systems (e.g. Figure 3 ) of any component. Exemplarily, the computing device 400 may be implemented using hardware, software programs, firmware, or a combination thereof. For convenience, Figure 4 Only one computing device is drawn in the figure, but the computing functions related to the process and / or system / apparatus described in the embodiments of the present application can be implemented in a distributed manner by a group of similar platforms to disperse the processing load of the system.

[0059] In some embodiments, the computing device 400 may include a processor 410, a memory 420, an input / output 430, and a communication port 440. In some embodiments, the processor (e.g., CPU) 410 may execute program instructions in the form of one or more processors. In some embodiments, the memory 420 includes different forms of program memory and data memory, such as a hard disk, a read-only memory (ROM), a random access memory (RAM), etc., for storing various data files processed and / or transmitted by the computer. In some embodiments, the input / output 430 may be used to support input / output between the computing device 400 and other components. In some embodiments, the communication port 440 may be connected to a network for data communication. An exemplary computing device may include program instructions executed by the processor 410 stored in a read-only memory (ROM), a random access memory (RAM), and / or other types of non-temporary storage media. The method and / or process of the embodiment of the present application may be implemented in the form of program instructions. The computing device 400 may also receive the programs and data disclosed in the present application through network communication.

[0060] For ease of understanding, Figure 4 Only one processor is drawn as an example. However, it should be noted that the computing device 400 in the embodiment of the present application may include multiple processors, so the operations and / or methods implemented by one processor described in the embodiment of the present application may also be implemented jointly or independently by multiple processors. For example, if in the present application, the processor of the computing device 400 performs operations A and B, it should be understood that operations A and B may also be performed jointly or independently by two different processors of the computing device 400 (for example, the first processor performs operation A, the second processor performs operation B, or the first and second processors perform operations A and B jointly).

[0061] The present application has described the basic concepts. Obviously, for those skilled in the art, the above detailed disclosure is only for example and does not constitute a limitation of the present application. Although not explicitly stated herein, those skilled in the art may make various modifications, improvements and amendments to the present application. Such modifications, improvements and amendments are suggested in the present application, so such modifications, improvements and amendments still belong to the spirit and scope of the exemplary embodiments of the present application.

[0062] At the same time, the present application uses specific words to describe the embodiments of the present application. For example, "one embodiment", "an embodiment", and / or "some embodiments" refer to a certain feature, structure or characteristic related to at least one embodiment of the present application. Therefore, it should be emphasized and noted that "one embodiment" or "an embodiment" or "an alternative embodiment" mentioned twice or multiple times in different positions in the present application does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present application can be appropriately combined.

[0063] Similarly, it should be noted that in order to simplify the description of the disclosure of this application and thus help understand one or more embodiments of the invention, in the above description of the embodiments of this application, multiple features are sometimes combined into one embodiment or its description. However, this disclosure method does not mean that the features required by the object of this application are more than the features mentioned in the claims. In fact, the features of the embodiments are less than all the features of the single embodiment disclosed above.

[0064] Finally, it should be understood that the embodiments described in this application are only used to illustrate the principles of the embodiments of the present application. Other variations may also fall within the scope of the present application. Therefore, as an example and not a limitation, the alternative configurations of the embodiments of the present application may be considered to be consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to the embodiments explicitly introduced and described in the present application.

Claims

1. A battery life prediction method, characterized in that: The method comprises: Respectively obtaining first test data and second test data of the same round of charge-discharge cycle test of the battery to be tested under the comparison test conditions and the target test conditions; the number of rounds of charge-discharge cycle does not exceed 10 times; Determine the activation energy of the battery to be tested based on the first test data and the second test data; Determining an acceleration factor of the battery to be tested based on the activation energy; Determining the battery life of the battery to be tested based on the acceleration factor; The expression of the acceleration factor is: ;in, is the acceleration factor, is the activation energy, For normal temperature, To test the temperature, is Avogadro's constant.

2. The battery life prediction method according to claim 1, characterized in that: The number of charge and discharge cycles does not exceed 5 times.

3. The battery life prediction method according to claim 1, characterized in that: The comparison test conditions include normal temperature and a fixed charge and discharge rate; the target test conditions include a test temperature exceeding the normal temperature and the fixed charge and discharge rate; wherein the normal temperature is 25°C and the test temperature exceeds the operating temperature range of the battery to be tested; or, the test temperature is 45°C.

4. The battery life prediction method according to claim 3, characterized in that: The first test data at least includes a first capacity decay rate of the battery to be tested, and the second test data at least includes a second capacity decay rate of the battery to be tested; and determining the activation energy of the battery to be tested includes: A first model is constructed based on the normal temperature, the first capacity fade rate, the test temperature, and the second capacity fade rate to calculate the activation energy.

5. The battery life prediction method according to claim 4, characterized in that: The expression of the first model is: ;in, is the activation energy, is the first capacity decay rate, is the second capacity decay rate, is the normal temperature, is the test temperature, is Avogadro's constant.

6. The battery life prediction method according to claim 4, characterized in that: The step of determining the acceleration factor of the battery to be tested includes: A second model is constructed based on the normal temperature, the test temperature and the activation energy to calculate the acceleration factor.

7. The battery life prediction method according to claim 1, characterized in that: The determining the battery life of the battery to be tested includes: A simulation calculation is performed based on the acceleration factor and the second test data to determine the battery life of the battery to be tested at normal temperature.

8. A battery life prediction device, characterized in that: The device predicts battery life using the battery life prediction method according to any one of claims 1 to 7, including: An acquisition module is configured to respectively acquire first test data and second test data of the battery to be tested subjected to the same round of charge-discharge cycle test under the comparison test conditions and the target test conditions; the number of the charge-discharge cycle does not exceed 10 times; A first determination module is configured to determine the activation energy of the battery to be tested based on the first test data and the second test data; A second determination module is configured to determine an acceleration factor of the battery to be tested based on the activation energy; The prediction module is configured to determine the battery life of the battery to be tested based on the acceleration factor.

9. A computer-readable storage medium, characterized in that: The 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 7 are implemented.

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