Lithium-ion battery life prediction method, device, equipment, medium and program product

Abstract

The application provides a lithium ion battery life prediction method, device, equipment, medium and program product. Cycle life tests are respectively performed on sample lithium ion batteries under different test temperatures and different charge rates, and cycle numbers and corresponding capacity retention rates are obtained. Different cycle life prediction models are obtained through function fitting according to the cycle numbers and corresponding capacity retention rates obtained under different test temperatures and different charge rates. A cycle life prediction model applicable to a lithium ion battery to be tested is determined according to the test temperature and the charge rate of the lithium ion battery to be tested. The cycle life prediction model is determined, and the cycle number of the lithium ion battery to be tested is used to predict the life of the lithium ion battery. The battery capacity retention rate is quickly and accurately obtained, which has guiding significance for optimizing long-term cycle conditions of the battery and prolonging the endurance life of the battery.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of lithium batteries, and particularly relates to a lithium ion battery life prediction method, device, equipment, medium and program product. BACKGROUND

[0002] The statements in this section merely provide background information related to the present application and do not necessarily constitute the prior art.

[0003] Lithium ion batteries are one of the most important electrochemical energy storage devices in today's society, and are widely used in portable electronic devices, electric vehicles, large-scale energy storage and other fields. However, the battery capacity decreases to varying degrees during use, directly affecting the performance of the battery. Therefore, studying the prediction algorithm of the cycle life of lithium ion batteries has guiding significance for saving the cost of battery manufacturing and manpower investment, shortening the test period of the battery, and predicting the service life SOH of the battery.

[0004] Currently, there are many methods and tests for predicting the cycle life of lithium-ion batteries. For example, CN116774042A discloses a lithium-ion battery model working condition identification method and system, which includes the following steps: first, obtaining the first internal characteristic parameters of the lithium-ion battery measured by experiment and the external characteristic parameters during actual work, establishing a coupling model of the lithium-ion battery according to the partial differential equation, parameter equation and thermodynamic calculation, calculating the battery capacity according to the external characteristic parameters and combining the electrochemical model in the coupling model, then calculating the second internal characteristic parameters of the lithium-ion battery, identifying the numerical value of the second internal characteristic parameters and comparing it with the numerical value of the first internal characteristic parameters to obtain the second internal characteristic parameter value, finally, building a virtual lithium-ion battery model using digital twin technology, and simulating the actual working condition of the lithium-ion battery by combining the second internal characteristic parameter value, and verifying the simulation results with the actual external characteristic parameters during work to obtain the working condition of the lithium-ion battery under the current actual working condition. CN116956667A discloses a lithium-ion battery maximum available energy calculation method and system, which includes the following steps: considering the influence of working condition, heating energy consumption and temperature on available energy, establishing a battery working condition prediction model based on traffic signal timing information, simplifying the modeling of key components according to the configuration of the vehicle power system to establish a vehicle energy consumption prediction model, and establishing a temperature prediction model based on the multi-physical field coupling model reduction, which can output the predicted available energy under given working conditions, and can improve the estimation accuracy of the available energy of the lithium-ion battery and be used to guide the target temperature calculation of the battery thermal management system. CN117092520A discloses a method for estimating SOH using a fractional order model considering the aging mechanism of lithium batteries, which simplifies the solid phase diffusion of lithium-ion batteries using fractional order Padé approximation method, proposes a fractional order lithium-ion battery model, and establishes a fractional order lithium-ion battery model considering the aging mechanism by considering the generation of battery electrolyte interface film and lithium dendrite side reactions.

[0005] However, in the above related technology, many parameters are used, the evaluation period is long, and the related model obtained is relatively complex, which has poor universal applicability and cannot quickly and accurately predict the cycle life of lithium batteries. SUMMARY

[0006] To overcome the shortcomings of the above prior art, the present application provides a lithium-ion battery life prediction method, device, equipment, medium and program product, which can quickly and accurately predict the cycle life of lithium batteries. The technical solution is as follows:

[0007] In one aspect, a lithium-ion battery life prediction method is provided, which includes:

[0008] The sample lithium ion battery is subjected to cycle life tests at different test temperatures and different charge rates, respectively, to obtain cycle numbers and corresponding capacity retention rates;

[0009] The obtained cycle numbers and corresponding capacity retention rates are subjected to function fitting to obtain a plurality of cycle life prediction models corresponding to different test temperatures and different charge rates;

[0010] Based on the test temperature and the charge rate of the lithium ion battery to be predicted, a cycle life prediction model applicable to the lithium ion battery to be predicted is determined, and cycle life prediction is performed according to the determined cycle life prediction model.

[0011] In another aspect, a lithium ion battery life prediction device is provided, and the device comprises:

[0012] A test module is configured to subject a sample lithium ion battery to cycle life tests at different test temperatures and different charge rates, respectively, to obtain cycle numbers and corresponding capacity retention rates;

[0013] A fitting module is configured to subject the obtained cycle numbers and corresponding capacity retention rates to function fitting to obtain a plurality of cycle life prediction models corresponding to different test temperatures and different charge rates;

[0014] A prediction module is configured to perform cycle life prediction on a lithium ion battery to be predicted based on the cycle life prediction model.

[0015] In another aspect, a computer device is provided, and the computer device comprises a processor and a memory, and the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by the processor to implement the lithium ion battery life prediction method according to any one of the above embodiments of the present application.

[0016] In another aspect, a computer readable storage medium is provided, and the storage medium stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by a processor to implement the lithium ion battery life prediction method according to any one of the above embodiments of the present application.

[0017] In another aspect, a computer program product or a computer program is provided, and the computer program product or the computer program comprises computer instructions stored in a computer readable storage medium. A processor of an electric vehicle reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions to enable the electric vehicle to perform the lithium ion battery life prediction method according to any one of the above embodiments.

[0018] The one or more technical solutions have the following beneficial effects:

[0019] The application obtains the cycle number and the corresponding capacity retention rate by respectively performing cycle life tests on the sample lithium ion battery at different test temperatures and different charge rates, performs function fitting according to the cycle number and the corresponding capacity retention rate at different test temperatures and different charge rates, obtains a plurality of different cycle life prediction models, determines the applicable cycle life prediction model according to the test temperature and the charge rate of the lithium ion battery to be tested, and performs life prediction of the lithium ion battery by using the cycle number of the lithium ion battery to be tested based on the determined cycle life prediction model.

[0020] Advantages of the additional aspects of the application will be partially given in the following description, partially become obvious from the following description, or be understood by the practice of the application. BRIEF DESCRIPTION OF DRAWINGS

[0021] The accompanying drawings, which form a part of the specification, are included to provide a further understanding of the application, and are incorporated herein by reference. The illustrations are shown to explain the application and are not intended to limit the application in any way.

[0022] Figure 1 is a whole flow chart of a lithium ion battery life prediction method provided by an example embodiment of the application;

[0023] Figure 2 is a flow chart of a life prediction process provided by an example embodiment of the application;

[0024] Figure 3 is a flow chart of a cycle life test method at a first charge rate provided by another example embodiment of the application;

[0025] Figure 4 is a flow chart of a cycle life test method at a step cycle charge rate provided by an example embodiment of the application;

[0026] Figure 5 is a structural block diagram of a lithium ion battery life prediction device provided by an example embodiment of the application;

[0027] Figure 6 is a structural block diagram of a computer device provided by an example embodiment of the application. DETAILED DESCRIPTION

[0028] It should be noted that the following detailed description is exemplary in nature and is intended to provide further description of the application. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0029] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present application.

[0030] In the case of no conflict, the embodiments in the application and the features in the embodiments can be combined with each other.

[0031] First, the terms involved in the present application are explained.

[0032] Battery cycle number: refers to one cycle between complete charging and complete discharging. When the battery reaches a complete charging and discharging cycle, the battery cycle number will increase by one.

[0033] Capacity retention rate: used to represent the retention rate of the discharge capacity of the battery relative to the initial capacity. The capacity retention rate corresponding to a certain cycle number can be used to represent the retention rate of the discharge capacity of the sample lithium ion battery at the cycle number relative to the initial capacity.

[0034] Charge and discharge rate: refers to the current value required by the battery to discharge its rated capacity within a specified time, usually represented by the letter C. The charge and discharge rate is also called battery charge and discharge C rate, which is a measure of the speed of charge and discharge; 1C represents the current intensity when the battery is completely discharged in one hour.

[0035] SOH: used to represent the battery performance state of the battery, that is, the percentage between the current capacity of the battery and the factory capacity. The factory capacity is generally 100%, and the battery capacity is 0% when the battery is completely scrapped.

[0036] As an energy storage device, batteries are widely used in mobile phones, electric vehicles, power grid assistance, high-rate fast charging stations and other scenarios. Among them, in the field of intelligent terminals, charging batteries are often used with chargers to charge equipment. Charging batteries include nickel-cadmium batteries, nickel-hydrogen batteries, lithium-ion batteries, lead-acid batteries and iron lithium batteries. Therefore, the charging battery is built into the above specified device.

[0037] For example, in the field of intelligent terminals (such as the field of smart phones), how to predict the life of a lithium ion battery has always been a hot topic of research. The current life prediction of a lithium ion battery is based on the chemical mechanism of the battery, and is realized by measuring the cycle life of the battery under test conditions through a multi-model and multi-parameter method. However, in actual use of the battery, the battery is affected by many factors during the continuous charging and discharging process, mainly including temperature, charging rate, material system, and discharge cutoff voltage. Therefore, a single model fitted for multiple factors is not only difficult to realize, but also inconvenient. The existing method focuses on multiple factors affecting the battery, but ignores the fact that data is the basis during testing. The prediction error of the battery life is large, or the model is not complete, which causes the cycle life of the battery to be unable to be quickly and accurately predicted.

[0038] In the embodiment of the present application, a lithium ion battery life prediction method is proposed. The cycle number and the corresponding capacity retention rate are obtained by respectively performing cycle life tests on a sample lithium ion battery under different test temperatures and different charging rates. A plurality of different cycle life prediction models are obtained by function fitting according to the cycle number and the corresponding capacity retention rate obtained under different test temperatures and different charging rates. The applicable cycle life prediction model is determined according to the test temperature and the charging rate of the lithium ion battery to be tested. Based on the determined cycle life prediction model, the cycle life of the lithium ion battery to be tested can be predicted by using the cycle number. The method of the present application does not need to occupy a large amount of test resources and time, and can greatly save test cost and improve efficiency. Only by using the cycle number, the capacity retention rate of the battery can be quickly and accurately obtained, which has guiding significance for optimizing the long-term cycle conditions of the battery and prolonging the endurance life of the battery.

[0039] In combination with the above-mentioned name introduction and application scenarios, the lithium ion battery life prediction method provided by the present application is described. As shown in Figure 1 , the method includes the following steps.

[0040] Step 110: The cycle number and the corresponding capacity retention rate are obtained by respectively performing cycle life tests on a sample lithium ion battery under different test temperatures and different charging rates.

[0041] Among them, the capacity retention rate corresponding to different cycle numbers of the sample lithium ion battery obtained under different test temperatures and different charging rates is used as sample data, and the sample data is used for function model fitting in the following.

[0042] Optionally, the sample data can be obtained by performing cycle life tests on the sample lithium ion battery at different test temperatures and different charge rates. Optionally, the test temperature range is 25-45°C, and the charge rate is optionally 1C and a stepped charge rate.

[0043] Step 120: fitting the obtained cycle number and corresponding capacity retention rate to a function to obtain a plurality of cycle life prediction models corresponding to different test temperatures and different charge rates;

[0044] The cycle life prediction model is used to represent the relationship between the capacity retention rate and the cycle number. Optionally, the cycle life prediction model can include a mapping relationship between the cycle number and the capacity retention rate, a functional relationship, etc.

[0045] Step 130: based on the test temperature and charge rate of the predicted lithium ion battery, determine the applicable cycle life prediction model, and perform cycle life prediction according to the determined cycle life prediction model.

[0046] The cycle life prediction model established at different test temperatures and different charge rates can be used to predict the battery life of the lithium battery at different cycle numbers. Since the cycle life prediction model is the relationship between the capacity retention rate and the cycle number at different test temperatures and different charge rates. Therefore, by determining the test temperature and the charge rate, the cycle life prediction model is determined, and the cycle number can be used to determine the corresponding capacity retention rate using the determined cycle life prediction model. It represents that the capacity of the battery is reduced to the capacity retention rate after the specified cycle number at the test temperature and the charge rate. Alternatively, by specifying a capacity retention rate, the cycle life prediction model can be used to determine the corresponding cycle number as the cycle life of the battery. It represents that the capacity of the battery is reduced to the specified capacity retention rate after the cycle number at the test temperature and the charge rate.

[0047] As shown in FIG. 1, it shows a lithium ion battery life prediction method provided by the present application, and the current method includes the following steps: Figure 2

[0048] Step 210: performing cycle life tests on the sample lithium ion battery at different test temperatures and at a 1C charge rate to obtain cycle numbers and corresponding capacity retention rates;

[0049] Step 220: performing cycle life tests on the sample lithium ion battery at different test temperatures and at a stepped cycle charge rate to obtain cycle numbers and corresponding capacity retention rates.

[0050] ​In the embodiment, during the cycle life test of the sample lithium ion battery, the same size and material fixture is fixed on the sample lithium ion battery, and the same torsion (300 Kgf) is used for subsequent test experiments.

[0051] In the embodiment, as shown in Figure 3 step 210 specifically includes:

[0052] Discharge the sample lithium ion battery at a first discharge rate to a first set voltage under different set test temperatures;

[0053] Rest for a first preset time;

[0054] Charge the sample lithium ion battery at a first charge rate to a second set voltage;

[0055] Rest for a second preset time;

[0056] Discharge the sample lithium ion battery at a first discharge rate to a first set voltage;

[0057] Repeat the above steps for multiple cycles to obtain the capacity retention rate of the sample lithium ion battery under different cycle numbers.

[0058] For example, taking the cycle life test of the sample lithium ion battery at 25°C and a 1c charge rate as an example, the embodiment specifically includes:

[0059] Step 211: Take two 93 Ah fresh square lithium iron phosphate batteries 1# and 2#;

[0060] Step 212: Rest for a third preset time, which is 5 min. optionally;

[0061] Step 213: Discharge the two lithium ion batteries at a first discharge rate to a first set voltage; optionally, the first set voltage is 2.5 V;

[0062] Step 214: Rest for a first preset time, which is 30 min. optionally;

[0063] Step 215: Charge the two lithium ion batteries at a first charge rate to a second set voltage; optionally, the second set voltage is 3.65 V;

[0064] Step 216: Rest for a second preset time, which is 30 min. optionally;

[0065] Step 217: Discharge the two lithium ion batteries at a first discharge rate to a first set voltage;

[0066] Step 218: Repeat steps 214-217 multiple times, optionally, 2000 times.

[0067] The test data shown in Tables 1 and 2 were obtained using the methods described above.

[0068] Table 1:

[0069]

[0070] Table 2:

[0071]

[0072]

[0073] For example, taking a sample lithium-ion battery undergoing a cycle life test at 45°C and a 1C charging rate as an example, the following is specifically included:

[0074] Step 211: Take two fresh 93Ah square lithium iron phosphate batteries, #5 and #6;

[0075] Step 212: Let it stand for the third preset time, which is optional and is 5 minutes.

[0076] Step 213: Discharge the two lithium-ion batteries at a constant current at a first discharge rate to a first set voltage; optionally, the first set voltage is 2.5V;

[0077] Step 214: Let stand for the first preset time, which is optional and can be 30 minutes;

[0078] Step 215: Charge the two lithium-ion batteries at a constant current and constant voltage at a first charging rate to a second set voltage; optionally, the second set voltage is 3.65V;

[0079] Step 216: Let stand for the second preset time, which is optional and can be 30 minutes;

[0080] Step 217: Discharge the two lithium-ion batteries at a constant current at the first discharge rate to the first set voltage;

[0081] Step 218: Repeat steps 214-217 multiple times, optionally, 2000 times.

[0082] The test data shown in Tables 3 and 4 were obtained using the methods described above.

[0083] Table 3:

[0084]

[0085]

[0086] Table 4:

[0087]

[0088] In the present embodiment, as shown in Figure 4 step 220 specifically comprises:

[0089] discharging the sample lithium ion battery at a first discharge rate to a first set voltage at different set test temperatures;

[0090] resting for a first preset time;

[0091] charging the sample lithium ion battery to a second set voltage in steps;

[0092] resting for a second preset time;

[0093] discharging the sample lithium ion battery at a first discharge rate to a first set voltage;

[0094] repeating the above steps multiple times to obtain the capacity retention rate of the sample lithium ion battery at different cycle numbers.

[0095] For example, taking the cycle life test of the sample lithium ion battery at 25°C at a step charging rate as an example, the specific steps include:

[0096] Step 221: Take two 93 Ah fresh square lithium iron phosphate batteries 3# and 4#;

[0097] Step 222: Rest for a third preset time, which is 5 minutes, optionally;

[0098] Step 223: Discharge the two lithium ion batteries at a first discharge rate to a first set voltage; optionally, the first set voltage is 2.5V;

[0099] Step 224: Rest for a first preset time, which is 30 minutes, optionally;

[0100] Step 225: Charge the two lithium ion batteries to a second set voltage in steps; optionally, the second set voltage is 3.65V;

[0101] Step 226: Rest for a second preset time, which is 30 minutes, optionally;

[0102] Step 227: Discharge the two lithium ion batteries at a first discharge rate to a first set voltage;

[0103] Step 228: Repeat steps 224-227 multiple times, optionally 2000 times.

[0104] The test data shown in Tables 5 and 6 were obtained according to the above method.

[0105] Table 5:

[0106]

[0107] Table 6:

[0108]

[0109] Exemplary, taking the cycle life test of a sample lithium ion battery at 45℃ with a step charge rate as an example, specifically including:

[0110] Step 221: Take two 93 Ah fresh square lithium iron phosphate batteries 7#, 8#;

[0111] Step 222: Stand for a third preset time, and optionally, the third preset time is 5 min;

[0112] Step 223: Discharge the two lithium ion batteries at a first discharge rate to a first set voltage; and optionally, the first set voltage is 2.5 V;

[0113] Step 224: Stand for a first preset time, and optionally, the first preset time is 30 min;

[0114] Step 225: Charge the two lithium ion batteries at a step to a second set voltage; and optionally, the second set voltage is 3.65 V;

[0115] Step 226: Stand for a second preset time, and optionally, the second preset time is 30 min;

[0116] Step 227: Discharge the two lithium ion batteries at a first discharge rate to a first set voltage;

[0117] Step 228: Repeat steps 224-227 for multiple times, and optionally, 2000 times.

[0118] The test data shown in Tables 7 and 8 were obtained according to the above method.

[0119] Table 7:

[0120]

[0121] Table 8:

[0122]

[0123]

[0124] Optionally, the acquisition of the above experimental data can be calculated according to 5-7 cycles of data per day, and the cycle data is exported every half month and recorded in the table, and the capacity retention rate is calculated.

[0125] Optionally, during the cycle life test, if the capacity of the lithium ion battery is attenuated to below 80% in the middle, the test is stopped, and only the current cycle data is selected for subsequent cycle life prediction model fitting.

[0126] In this embodiment, the above obtained cycle number and corresponding capacity retention rate are functionally fitted to obtain a plurality of cycle life prediction models corresponding to different test temperatures and different charge rates.

[0127] Optionally, the above data is fitted by a function model y=ax^3+bx^2+cx+d, wherein a, b, c, d are parameters, x represents the cycle number, and y is the corresponding battery capacity retention rate.

[0128] In this embodiment, the above obtained experimental data is used to obtain a cycle life prediction model under the condition of 25℃-1C cycle, which is y=-0.0001x^3+0.0011x^2-0.0121x+0.9984.

[0129] Under the condition of 25℃-step cycle, the cycle life prediction model is y=-0.00005x^3+0.0008x^2-0.012x+0.998.

[0130] Under the condition of 45℃-1C cycle, the cycle life prediction model is y=0.000003x^5-0.0001x^4+0.0011x^3-0.0056x^2+0.0064x+0.99.

[0131] Under the condition of 45℃-step cycle, the step cycle life prediction model is y=0.0000003x^5-0.000009x^4+0.00009x^3-0.0006x^2-0.0081x+0.9729.

[0132] In this application, the model algorithm is constructed by python, specifically:

[0133] import numpy as np;

[0134] string1=input("Please enter the test temperature:")

[0135] string2=input("Please enter the battery charge rate:")

[0136] # give x a range, the range is 1-10, the difference is given, a total of 10, 1-10 corresponds to the number of cycles 100-1000 circles;

[0137] x = np.linspace(1, 10, 10);

[0138] if string1 == "25℃" and string2 == "step charging":

[0139] SOH1 = -0.00005 * x ** 3 + 0.0008 * x ** 2-0.012 * x + 0.998

[0140] print('SOH1 =', SOH1);

[0141] elif string1 == "25℃" and string2 == "1C charging":

[0142] SOH2 = -0.0001 * x ** 3 + 0.0011 * x ** 2-0.0121 * x + 0.9984

[0143] print('SOH2 =', SOH2);

[0144] elif string1 == "45℃" and string2 == "step charging":

[0145] SOH3 = 0.0000003 * x ** 5-0.000009 * x ** 4+0.00009 * x ** 3-0.0006 * x ** 2-0.0081 * x + 0.9729;

[0146] print('SOH3 =', SOH3);

[0147] else:

[0148] SOH4 = 0.000003 * x ** 5-0.0001 * x ** 4+0.0011 * x ** 3-0.0056 * x ** 2+0.0064 * x + 0.99

[0149] print('SOH4 =', SOH4).

[0150] The present application constructs two factors affecting the cycle life of the battery as two variables. In the case of the material system of lithium iron phosphate, there are four different combinations of test temperature and battery charging rate (variables 1 and 2), corresponding to four test conditions in the cycle test, and the data model of each condition is a mathematical model that can be determined by test data.

[0151] Figure 5 The lithium-ion battery life prediction device is provided, and the device comprises:

[0152] A test module is configured to perform cycle life tests on sample lithium-ion batteries at different test temperatures and different charge rates respectively, and obtain cycle numbers and corresponding capacity retention rates.

[0153] A fitting module is configured to perform function fitting on the obtained cycle numbers and corresponding capacity retention rates, and obtain a plurality of cycle life prediction models corresponding to different test temperatures and different charge rates.

[0154] A prediction module is configured to perform cycle life prediction on a lithium-ion battery to be predicted based on the cycle life prediction models.

[0155] In the test module, specifically comprising:

[0156] A first sub-test module is configured to perform cycle life tests on sample lithium-ion batteries at different test temperatures at a 1C charge rate, and obtain cycle numbers and corresponding capacity retention rates.

[0157] A second sub-test module is configured to perform cycle life tests on sample lithium-ion batteries at different test temperatures at a step cycle charge rate, and obtain cycle numbers and corresponding capacity retention rates.

[0158] In the first sub-test module, comprising:

[0159] A first discharging sub-module is configured to discharge the sample lithium-ion battery at a first discharging rate to a first set voltage at different set test temperatures.

[0160] A first resting sub-module is configured to rest for a first preset time.

[0161] A first charging sub-module is configured to charge the sample lithium-ion battery at a first charging rate to a second set voltage.

[0162] A second resting sub-module is configured to rest for a second preset time.

[0163] A second discharging sub-module is configured to discharge the sample lithium-ion battery at a first discharging rate to a first set voltage.

[0164] A first cycle sub-module is configured to repeat the above steps multiple times to obtain the capacity retention rates of the sample lithium-ion battery at different cycle numbers.

[0165] In the second sub-test module, comprising:

[0166] a third discharging sub-module, configured to discharge the sample lithium ion battery at a first preset current and a first preset voltage;

[0167] a third resting sub-module, configured to rest for a first preset time;

[0168] a second charging sub-module, configured to charge the sample lithium ion battery to a second preset voltage;

[0169] a fourth resting sub-module, configured to rest for a second preset time;

[0170] a fourth discharging sub-module, configured to discharge the sample lithium ion battery at the first preset current and the first preset voltage;

[0171] a second cycling sub-module, configured to repeatedly cycle the above steps for a plurality of times to obtain the capacity retention rate of the sample lithium ion battery under different cycle numbers.

[0172] It should be noted that the lithium ion battery life prediction device provided in the above embodiment is only used as an example to illustrate the division of the above functional modules. In actual applications, the above functions can be completed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to complete all or part of the above described functions. In addition, the lithium ion battery life prediction device and the lithium ion battery life prediction method provided in the above embodiment belong to the same concept, and the specific implementation process is described in the method embodiment, which will not be repeated here.

[0173] Figure 6 A structural schematic diagram of a computer device provided by an example embodiment of the present application is shown.

[0174] Specifically, the computer device 1000 includes a central processing unit (CPU) 1001, a system memory 1004 including a random access memory (RAM) 1002 and a read-only memory (ROM) 1003, and a system bus 1005 connecting the system memory 1004 and the central processing unit 1001. The computer device 1000 also includes a mass storage device 1006 for storing an operating system 1013, application programs 1014, and other program modules 1015.

[0175] The mass storage device 1006 is connected to the central processing unit 1001 through a mass storage controller (not shown) connected to the system bus 1005. The mass storage device 1006 and its associated computer readable media provide nonvolatile storage for the computer device 1000. That is, the mass storage device 1006 can include a computer readable medium (not shown) such as a hard disk or a compact disc read only memory (CD-ROM) drive.

[0176] Without loss of generality, the computer readable medium can include computer storage media and communication media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes RAM, ROM, Erasable Programmable Read Only Memory (EPROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other solid state memory technology, CD-ROM, Digital Versatile Disc (DVD), or other optical storage, magnetic cassettes, magnetic tapes, magnetic disk storage or other magnetic storage devices. Of course, computer storage media does not pertain to the modality of communication, but pertains to the modality of the storage of the information. The system memory 1004 and the mass storage device 1006 described above can be collectively referred to as memory.

[0177] According to various embodiments of the present application, the computer device 1000 can also operate in a networking environment via the network 1012, such as the Internet. That is, the computer device 1000 can be connected to the network 1012 through a network interface unit 1011 connected to the system bus 1005, or can be connected to other types of networks or remote computer systems (not shown) using the network interface unit 1011.

[0178] The above-mentioned memory further includes one or more programs, and the one or more programs are stored in the memory and configured to be executed by the CPU.

[0179] The embodiments of the present application also provide a computer device including a processor and a memory, the memory storing at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set being loaded and executed by the processor to implement the training method of the image recognition model provided by the above-mentioned method embodiments.

[0180] An embodiment of the present application further provides a computer readable storage medium, which stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, the at least one program, the code set or the instruction set are loaded and executed by a processor to implement the training method of the image recognition model provided by any of the above method embodiments.

[0181] An embodiment of the present application further provides a computer program product or a computer program, which comprises computer instructions stored in a computer readable storage medium. A processor of a computer device reads the computer instructions from the computer readable storage medium, and the processor executes the computer instructions, so that the computer device executes the training method of the image recognition model according to any of the above embodiments.

[0182] Optionally, the computer readable storage medium can include a read only memory (ROM), a random access memory (RAM), a solid state disk (SSD) or an optical disk. The random access memory can include a resistance random access memory (ReRAM) and a dynamic random access memory (DRAM). The above-mentioned serial numbers of the embodiments of the present application are only for description, and do not represent the advantages and disadvantages of the embodiments.

[0183] Those skilled in the art can understand that all or part of the steps of the above-mentioned embodiments can be completed by hardware, or by program instructing relevant hardware to complete, and the program can be stored in a computer readable storage medium. The storage medium mentioned above can be a read only memory, a magnetic disk or an optical disk.

[0184] The above is only optional embodiments of the present application, and does not limit the present application. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for predicting the lifespan of a lithium-ion battery, characterized in that, The method includes: The sample lithium-ion batteries were subjected to cycle life tests at different test temperatures and charging rates to obtain the number of cycles and the corresponding capacity retention, including: (1) The sample lithium-ion battery was subjected to cycle life test at different test temperatures and at the first charging rate to obtain the number of cycles and the corresponding capacity retention rate; the first charging rate was 1C. (2) The sample lithium-ion batteries were subjected to cycle life tests at different test temperatures and with stepped cycle charging rates to obtain the number of cycles and the corresponding capacity retention rate. The obtained cycle count and corresponding capacity retention rate are fitted by a function to obtain multiple cycle life prediction models corresponding to different test temperatures and different charging rates. Based on the predicted test temperature and charging rate of the lithium-ion battery, the applicable cycle life prediction model is determined, and cycle life is predicted according to the determined cycle life prediction model. The cycle life test includes: The sample lithium-ion batteries were discharged at a constant current at a first discharge rate to a first set voltage under different set test temperatures. Let it stand for the first preset time; The sample lithium-ion battery is charged to the second set voltage at a constant current and constant voltage at a first charging rate. Let it stand for the second preset time; The sample lithium-ion battery is discharged at a constant current at a first discharge rate to a first set voltage. Repeat the above steps multiple times to obtain the capacity retention rate of the sample lithium-ion battery at different cycle numbers; The cycle life test also includes: The sample lithium-ion batteries were discharged at a constant current at a first discharge rate to a first set voltage under different set test temperatures. Let it stand for the first preset time; The sample lithium-ion battery was charged to the second set voltage using a stepped cyclic charging rate. Let it stand for the second preset time; The sample lithium-ion battery is discharged at a constant current at a first discharge rate to a first set voltage. Repeat the above steps multiple times to obtain the capacity retention rate of the sample lithium-ion battery at different cycle numbers.

2. The lithium-ion battery life prediction method as described in claim 1, characterized in that, During the cycle life test, the voltage of the sample lithium-ion battery was controlled within the range of 2.5V to 3.65V.

3. The lithium-ion battery life prediction method as described in claim 1, characterized in that, The cycle life prediction model is determined based on the test temperature and charging rate of the lithium-ion battery to be predicted; the capacity retention rate of the lithium-ion battery to be predicted is obtained based on the number of cycles of the lithium-ion battery to be predicted and the determined cycle life prediction model.

4. A lithium-ion battery life prediction device, characterized in that, The device includes: The testing module is used to perform cycle life tests on sample lithium-ion batteries at different test temperatures and charging rates to obtain the number of cycles and the corresponding capacity retention, including: (1) The sample lithium-ion battery was subjected to cycle life test at different test temperatures and at the first charging rate to obtain the number of cycles and the corresponding capacity retention rate; the first charging rate was 1C. (2) The sample lithium-ion batteries were subjected to cycle life tests at different test temperatures and with stepped cycle charging rates to obtain the number of cycles and the corresponding capacity retention rate. The cycle life test includes: The sample lithium-ion batteries were discharged at a constant current at a first discharge rate to a first set voltage under different set test temperatures. Let it stand for the first preset time; The sample lithium-ion battery is charged to the second set voltage at a constant current and constant voltage at a first charging rate. Let it stand for the second preset time; The sample lithium-ion battery is discharged at a constant current at a first discharge rate to a first set voltage. Repeat the above steps multiple times to obtain the capacity retention rate of the sample lithium-ion battery at different cycle numbers; The cycle life test also includes: The sample lithium-ion batteries were discharged at a constant current at a first discharge rate to a first set voltage under different set test temperatures. Let it stand for the first preset time; The sample lithium-ion battery was charged to the second set voltage using a stepped cyclic charging rate. Let it stand for the second preset time; The sample lithium-ion battery is discharged at a constant current at a first discharge rate to a first set voltage. Repeat the above steps multiple times to obtain the capacity retention rate of the sample lithium-ion battery at different cycle numbers; The fitting module is used to fit the obtained cycle number and corresponding capacity retention rate to a function to obtain multiple cycle life prediction models corresponding to different test temperatures and different charging rates. The prediction module is used to predict the cycle life of the lithium-ion battery to be predicted based on the cycle life prediction model.

5. A computer device, characterized in that, The computer device includes a processor and a memory, the memory storing at least one program, which is loaded and executed by the processor to implement the lithium-ion battery life prediction method as described in any one of claims 1 to 3.

6. A computer-readable storage medium, characterized in that, The storage medium stores at least one program segment, which is loaded and executed by a processor to implement the lithium-ion battery life prediction method as described in any one of claims 1 to 3.

7. A computer program product, characterized in that, It includes a computer program that, when executed by a processor, implements the lithium-ion battery life prediction method as described in any one of claims 1 to 3.

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