Method, device and storage medium for predicting battery capacity drop

Abstract

The application provides a battery capacity diving prediction method, device and storage medium, the prediction method comprises the following steps: a) performing charge-discharge cycle on a to-be-tested battery; b) obtaining a capacity loss rate of the to-be-tested battery after the cycle charge-discharge; c) establishing a correlation between the capacity loss rate and the cycle number of the to-be-tested battery charge-discharge; and d) predicting the diving risk of the to-be-tested battery according to the correlation. According to the application, the correlation between the charge-discharge data and the cycle number of the to-be-tested battery is established, and the risk of battery diving can be quickly predicted according to the change trend of the correlation.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery, in particular to a battery capacity diving prediction method and device and storage medium. BACKGROUND

[0002] With the development of science and technology, lithium ion batteries (lithium batteries for short) are widely used in many fields due to their high energy density and long cycle life. During the aging process of lithium ion batteries, the battery capacity gradually decreases as the aging process continues. The aging of the battery can be divided into two stages. In the early stage of aging, the capacity attenuation of the battery is linearly related to the cycle number, which is called the linear aging zone. When the battery ages to a certain extent, the aging mechanism inside the battery changes, causing the capacity attenuation rate to suddenly accelerate, which seriously affects the normal use of the battery. Therefore, the prediction of the capacity attenuation diving point of the battery is very important for battery management.

[0003] In the prior art, for the prediction of lithium battery cycle diving, some establish the relationship between the swelling force, internal resistance and capacity attenuation, some determine whether to dive by testing the remaining amount of electrolyte, and some use the slope of capacity and cycle number as the judgment standard for lithium battery cycle diving, and determine the degradation feature angle by using the bending degree of the lithium battery curve, compare the lithium battery degradation curve feature angle with the feature angle alarm threshold and the feature angle diving threshold, and determine whether the lithium battery capacity diving occurs according to the comparison result.

[0004] It can be understood that the above-mentioned prediction method for lithium battery cycle diving needs to establish a direct correspondence between various parameters and capacity or establish a reference standard, and the process is relatively complex and cannot determine whether the lithium battery has cycle diving only according to its own cycle data. SUMMARY

[0005] The purpose of the embodiments of the present application is to provide a battery capacity diving prediction method, device and storage medium, which can solve the problem that the prediction method for lithium battery cycle diving in the prior art needs to establish a direct correspondence between various parameters and capacity or establish a reference standard, the process is relatively complex, and only according to its own cycle data, it cannot determine whether the lithium battery has cycle diving.

[0006] In order to solve the above technical problems, the embodiments of the present application provide a battery capacity diving prediction method, which comprises the following steps: a) performing charge-discharge cycling on a battery to be tested; b) obtaining the capacity loss rate of the battery to be tested after the cycling; c) establishing a correlation between the capacity loss rate and the cycle number of the battery to be tested; and d) predicting the diving risk of the battery to be tested according to the correlation.

[0007] The step d comprises the following sub-steps: d1) obtaining a first correlation between the capacity loss rate and the charge-discharge cycle number of the battery to be tested by data processing of the correlation, wherein the first correlation comprises a first function related to the cycle number; and d2) predicting the diving risk of the battery to be tested according to the value of the first function.

[0008] The step d2 comprises the following sub-steps: d21) dividing the charge-discharge cycle number of the battery to be tested into a plurality of gradients according to a first preset cycle number; d22) obtaining the value of the first function corresponding to each gradient respectively, and confirming a risk gradient interval of diving of the battery to be tested according to the value variation trend of the first function; and d23) predicting the diving risk of the battery to be tested according to the value variation of the first function in the risk gradient interval.

[0009] The step d23 comprises the following sub-steps: d231) dividing the charge-discharge cycle number in the risk gradient interval into a plurality of gradients according to a second preset cycle number; d232) obtaining the value difference of the first function corresponding to adjacent gradients respectively; and d233) determining the diving risk of the battery to be tested according to the probability of the value difference of the first function being positive or negative.

[0010] The step d23 comprises the following sub-steps: d231) dividing the charge-discharge cycle number in the risk gradient interval into a plurality of gradients according to a second preset cycle number; d232) obtaining the value difference of the first function corresponding to adjacent gradients respectively; and d233) determining the diving risk of the battery to be tested according to the probability of the value difference of the first function being positive or negative.

[0011] The step d22 comprises the following sub-steps: d221) judging whether the value of the first function corresponding to all gradients is continuously decreasing; d222) if the judgment is no, determining the gradient interval corresponding to the value jump of the first function; and d223) confirming the risk gradient interval of diving of the battery to be tested according to the gradient interval.

[0012] The correlation between the capacity loss rate and the charge-discharge cycle number of the battery to be tested is as follows:

[0013] Qloss = B * exp (-Ea / RT) * n Z

[0014] The first correlation between the capacity loss rate and the cycle number of the battery under test is:

[0015] ln(Qloss)=A+Zln(n)

[0016] Wherein, the Qloss is the capacity loss rate of the battery under test, B is a constant, A is a function related to temperature, Z is the first function related to the cycle number, n is the cycle number, R is a gas constant, T is temperature, and Ea is the Arrhenius formula and activation energy.

[0017] Wherein, the step b comprises the following sub-steps: b1) obtaining the initial cycle discharge standard capacity of the battery under test and the cycle capacity after cycle charging and discharging; b2) determining the capacity retention rate of the battery under test according to the initial cycle discharge standard capacity and the cycle capacity; and b3) determining the capacity loss rate of the battery under test according to the capacity retention rate.

[0018] To solve the above technical problems, the embodiment of the present application also provides a battery capacity diving prediction device, which comprises: a charging and discharging cycle module for performing charging and discharging cycle on a battery under test; an acquisition module for acquiring the capacity loss rate of the battery under test after cycle charging and discharging; an establishment module for establishing the correlation between the capacity loss rate and the cycle number of the battery under test; and a prediction module for predicting the diving risk of the battery under test according to the correlation.

[0019] To solve the above technical problems, the embodiment of the present application also provides a computer readable storage medium, which adopts the following technical solution: the computer readable storage medium stores computer readable instructions, and the computer readable instructions are executed by a processor to realize the steps of the battery capacity diving prediction method.

[0020] Compared with the prior art, the embodiment of the present application has the following beneficial effects:

[0021] The present application provides a battery capacity diving prediction method, device and storage medium, by establishing the correlation between the charging and discharging data of the battery under test and the cycle number of the charging and discharging, the change trend of the correlation can be used to quickly predict the risk of battery diving, and various complex curve relationships do not need to be established, thereby improving the prediction efficiency of battery capacity diving risk. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the solutions in the present application, the drawings needed to be used in the embodiments of the present application will be briefly introduced as follows. Obviously, the drawings in the following description are some embodiments of the present application, and other drawings can be obtained by those skilled in the art without any creative effort based on these drawings.

[0023] Figure 1 is a flowchart of an embodiment of the method for predicting battery capacity drop in the present application;

[0024] Figure 2 is a flowchart of an embodiment of step b in the present application;

[0025] Figure 3 is a flowchart of an embodiment of step d in the present application;

[0026] Figure 4 is a flowchart of an embodiment of step d2 in the present application;

[0027] Figure 5 is a flowchart of an embodiment of step d22 in the present application;

[0028] Figure 6 is a flowchart of an embodiment of step d23 in the present application;

[0029] Figure 7 is a structural diagram of an embodiment of the device for predicting battery capacity drop in the present application;

[0030] Figure 8 is a structural diagram of an embodiment of the computer device according to the present application. DETAILED DESCRIPTION

[0031] 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 the present application belongs. The terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The description and claims of the application as well as the above drawings, illustrate and set forth rather than limit the application. The terms "comprises", "comprising", "includes", "including" and "has", "having" as used herein, are specifically intended to be construed as open-ended terms (i.e., the terms do not allow for exclusion of any other items, components, elements, etc. not explicitly stated). The terms "first", "second" and the like, as used herein do not have any specific meaning, and are used only to distinguish one element from another.

[0032] Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are they necessarily mutually exclusive of one another. As will be apparent to those of ordinary skill in the art, embodiments described herein can be combined with other embodiments.

[0033] In order to better understand the technical scheme of the present application, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings.

[0034] Please combine Figure 1 , Figure 1 The flowchart of an embodiment of the battery capacity jump prediction method of the present application is shown in FIG. 1. Figure 1 The battery capacity jump prediction method provided by the present application includes the following steps:

[0035] Step a, performing a charge-discharge cycle on the battery to be tested.

[0036] It can be understood that, in order to ensure the stability of the test temperature, the battery to be tested needs to be further placed at a specific temperature (which can be 25°C in the present application) for a preset time before the charge-discharge cycle in step a is performed. In the present application, the time range for placing the battery to be tested at a specific temperature can be 10-120 min, and of course it can also be other time ranges in other embodiments, which are not specifically limited here.

[0037] Further, in a specific application scenario of the present application, a 801444 battery of a lithium cobalt oxide system can be used as the battery to be tested (representative model), wherein the raw material ratio of the battery to be tested is 98wt% LiCoO2, 0.5wt% conductive agent 1, 0.5% conductive agent 2, and 1% PVDF, and the negative electrode of the battery to be tested uses 97wt% graphite, 1wt% conductive agent 1, 1% CMC, and 1% SBR. After the positive and negative electrodes are wound with a separator, packaged, and liquid injected to make the battery to be tested, the parameters of the battery to be tested after pressure formation are: 80°C 1.1Mpa 120min 1C, and the cycle performance test is performed after 72h of static test at room temperature after the capacity is divided. It can be understood that other batteries of other systems can also be used as representative signals for testing in the embodiments of the present application, which are not specifically limited here.

[0038] Further, performing a charge-discharge cycle on the battery to be tested includes the following sub-steps:

[0039] 1. Constant current constant voltage charging, charging the battery to be tested at 1C constant current to 4.4V charging limit voltage, and then converting to constant voltage charging until the charging current of the battery to be tested is less than or equal to 0.02C;

[0040] 2. Rest, rest the battery under test filled with electricity for 10 min;

[0041] 3. Constant current discharge, discharge the battery under test at 1C constant current to the end voltage 3.0V;

[0042] 4. Rest, rest the battery under test discharged for 10 min;

[0043] Further, the battery under test is subjected to the charge-discharge cycle of steps 1 to 4, and the cycle number is greater than 300 times, and the cycle capacity of the battery under test is counted. Alternatively, the cycle number in the embodiment of the application can be 600 times, and in other embodiments it can also be other cycle times, which are not limited here.

[0044] Step b, obtaining the capacity loss rate of the battery under test after the cycle charge-discharge.

[0045] Please further refer to Figure 2 , Figure 2 is a flowchart of an embodiment of step b of the application, as Figure 2 , step b further comprises the following sub-steps:

[0046] Step b1, obtaining the standard initial cycle discharge capacity of the battery under test and the cycle capacity after cycle charge-discharge.

[0047] The initial cycle discharge standard capacity of the battery under test and the cycle capacity after cycle charge-discharge are obtained before the battery under test is subjected to the charge-discharge cycle. Specifically, the initial cycle discharge capacity of the battery under test is Q0, and the cycle capacity of the battery under test after cycle charge-discharge is Q, of course Q can be the cycle capacity after any cycle number.

[0048] Step b2, determining the capacity retention rate of the battery under test according to the initial cycle discharge capacity standard capacity and the cycle capacity.

[0049] Specifically, the capacity retention rate Q1 of the battery under test is calculated as follows:

[0050] Q1 = Q / Q0

[0051] Step b3, determining the capacity loss rate of the battery under test according to the capacity retention rate.

[0052] According to the capacity retention rate Q1 of the battery under test, the capacity loss rate Qloss of the battery under test is obtained:

[0053] Qloss = 1-Q1 = 1-Q / Q0 (1)

[0054] Step c, establishing the correlation between the capacity loss rate and the cycle number of the battery under test.

[0055] Further, the correlation between the capacity loss rate and the cycle number of the battery to be measured is established according to the Arrhenius formula, and the specific process is as follows:

[0056] Qloss = B * exp (-Ea / RT) * n Z (2)

[0057] wherein the Qloss is the capacity loss rate of the battery to be measured, B is a constant, Z is the first function related to the cycle number, n is the cycle number, R is a gas constant, T is temperature, and Ea is the Arrhenius activation energy.

[0058] It can be understood that, in the present application, the correlation between the charge-discharge data of the battery to be measured and the cycle number is established, and the risk of the battery diving can be quickly predicted through the correlation, without the need to establish various complex curve relationships, thereby improving the prediction efficiency of the battery capacity diving risk.

[0059] Step d, predicting the diving risk of the battery to be measured according to the correlation.

[0060] Please further refer to Figure 3 , Figure 3 The flowchart of an embodiment of step d of the present application is shown in FIG. 2. Figure 3 Step d further includes the following sub-steps:

[0061] Step d1, data processing of the correlation to obtain the first correlation between the capacity loss rate and the cycle number of the battery to be measured, wherein the first correlation includes the first function related to the cycle number.

[0062] Specifically, the correlation between the capacity loss rate and the cycle number of the battery to be measured is data processed (i.e., the above correlation is simplified), and the specific process is as follows:

[0063] Let G = lnB, A = G - Ea / RT, and bring them into the correlation Qloss = B * exp (-Ea / RT) * n Z , to obtain the following expression:

[0064] Qloss = exp (A) * n Z (3)

[0065] Further, the parameters on both sides of the above first correlation (formula 3) are taken as logarithms to obtain the first correlation between the logarithm of the capacity decay rate of the battery to be measured and the logarithm of the cycle number, and the expression is as follows:

[0066] ln (Qloss) = A + Z ln (n) (4)

[0067] wherein A is a temperature dependent function.

[0068] It can be understood that by simplifying the correlation between the charge-discharge data of the battery to be measured and the charge-discharge cycle number, a first correlation between the logarithm of the capacity decay rate and the logarithm of the cycle number is obtained, and the application only needs to detect the value of the first function Z in the first correlation, that is, the risk of the battery capacity diving can be predicted, which is described in detail as follows.

[0069] Step d2, predicting the diving risk of the battery to be measured according to the value of the first function.

[0070] Please further refer to Figure 4 , Figure 4 The flowchart of an embodiment of step d2 of the application is shown in FIG. 2. Figure 4 Step d2 further includes the following sub-steps:

[0071] Step d21, dividing the charge-discharge cycle number of the battery to be measured into multiple gradients according to a first preset cycle number.

[0072] Optionally, the charge-discharge cycle number n of the battery to be measured can be divided into multiple gradients according to a first preset cycle number. In a specific application scenario of the application, the charge-discharge cycle number n is 600, and the first preset cycle number can be 50, that is, the charge-discharge cycle number 600 is divided into multiple gradients according to the cycle number 50 as a gradient. Of course, in other embodiments, other preset cycle numbers can also be used, such as 30, 60, and 90, which are not limited here.

[0073] Step d22, obtaining the value of the first function corresponding to each gradient respectively, and confirming the risk gradient interval of the battery to be measured from the change trend of the value of the first function.

[0074] Please refer to Figure 5 , Figure 5 The flowchart of an embodiment of step d22 of the application is shown in FIG. 3. Figure 5 Step d22 further includes the following sub-steps:

[0075] Step d221, judging whether the value of the first function corresponding to all gradients is continuously decreasing.

[0076] Please refer to Table 1, which is a relationship table between different gradients of the charge-discharge cycle number of the battery to be measured and the value of the corresponding first function, as shown in Table 1:

[0077] Table 1 Relationship table between different gradients of the charge-discharge cycle number of the battery to be measured and the value of the corresponding first function

[0078]

[0079] It can be understood that the number of charge-discharge cycles in the above table of the present application is taken from 150, each gradient increases by 50 cycles, and the value of the first function corresponding to each gradient is calculated (i.e. Z value), to determine whether the value of the first function continues to decrease, if not, then enter step S4222, otherwise if the value of the first function continues to decrease, for example, the value of the first function in group B is continuously decreasing, indicating that the battery to be tested in group B does not occur diving phenomenon, then end.

[0080] Step d222, if not, determine the gradient interval corresponding to the jump of the value of the first function.

[0081] Alternatively, if the value of the first function does not continue to decrease, for example, the Z value in group A, the trend of change is first decreasing and then increasing, it can be seen that the battery to be tested in group A occurs diving phenomenon. Further, determine the cycle number corresponding to the jump of the Z value, and then determine the corresponding gradient interval according to the cycle number corresponding to the jump of the Z value, specifically, the cycle number corresponding to the jump of the Z value can be taken as the reference gradient, and the reference gradient and the last gradient of the reference gradient can be taken as the corresponding gradient interval when the value of the first function jumps. Further, combined with Table 1, and in group A, when the cycle number is 400, the corresponding Z value has already jumped, so it can be determined that group A has occurred slight diving between cycle numbers 350-400, and it can be seen from Table 1 that the cycle number is between 400-450, the battery to be tested occurs large cycle diving, so it can be determined that the corresponding gradient interval of the value of the first function in group A when it jumps is 350-400.

[0082] Step d223, determine the risk gradient interval of the battery to be tested from diving according to the gradient interval.

[0083] Further, determine the risk gradient interval of the battery to be tested from diving according to the gradient interval corresponding to the jump of the value of the first function of the battery to be tested. It can be understood that the cycle number of one gradient on the left and right of the gradient interval in the present application is selected as the risk gradient interval.

[0084] Specifically, combined with Table 1, the corresponding gradient interval of the value of the first function in group A when it jumps is 350-400, and the cycle number of one gradient on the left and right of the gradient interval is taken as the risk gradient interval, i.e. 300-450.

[0085] It can be understood that the present application divides the charge-discharge cycle number into different gradients according to the first preset cycle number, and the approximate range of the battery to be tested from cycle diving can be determined by detecting the trend of the value of the first function.

[0086] determining the risk of the battery under test from diving according to the variation of the first function in the risk gradient interval

[0087] Referring to Figure 6 , Figure 6 The flowchart of an embodiment of step d23 is shown in FIG. 3. Figure 6 Step d23 further comprises the following sub-steps:

[0088] Step d231, dividing the number of charge-discharge cycles in the risk gradient interval into multiple gradients according to a second preset cycle number.

[0089] It can be understood that after determining the approximate range of the battery under test from diving, the specific cycle number of the battery under test from diving needs to be further determined. In this way, the number of charge-discharge cycles in the risk gradient interval can be divided into multiple gradients according to a second preset cycle number. In a specific embodiment, the second preset cycle number can be 5, i.e. the risk gradient interval 300-450 is divided into multiple gradients according to cycle number 5 as a gradient. Of course, in other embodiments, it can also be other preset cycle numbers, such as 10, 15, 20, which are not limited here.

[0090] Step d232, respectively obtaining the numerical difference of the first function corresponding between adjacent gradients.

[0091] Please further refer to Table 2, which is a data table of the numerical value of the first function in the risk gradient interval, as shown in Table 2:

[0092] Table 2 Data table of the numerical value of the first function in the risk gradient interval

[0093]

[0094] Further, the difference of the first function corresponding between adjacent gradients is obtained, i.e. the difference between adjacent Z values in Table 2.

[0095] Step d233, determining the diving risk of the battery under test according to the probability of the numerical difference of the first function being positive or negative.

[0096] Specifically, if the numerical difference of the first function is all negative, it is determined that the battery under test has not occurred from diving;

[0097] If the probability of the numerical difference of the first function being negative is greater than the probability of being positive, it is determined that the battery under test is about to occur from diving;

[0098] If the probability of the numerical difference of the first function being negative is less than the probability of being positive, it is determined that the amplitude of the battery under test from diving is low;

[0099] If the numerical difference of the first function is all positive and gradually increases, it is determined that the cycle diving of the battery to be tested increases in amplitude.

[0100] In combination with Table 2, first, the Z value trends of Group A (diving occurs) and Group B (diving does not occur) are obviously different, as follows:

[0101] Between 300-360 cycles, the Z value of Group A is in a fluctuation stage, and the probability of the difference between adjacent Z values being negative is more than that of being positive. This stage can be used as a warning that the battery will soon experience cycle diving. After cycle number 365 (because the test data fluctuates, the Z values of two groups of data are negative, at 385 and 405 cycles, respectively), the probability of the difference between Z values of Group A being positive is much greater than that of being negative. This trend indicates that cycle diving has occurred slightly. After cycle number 410, the difference between Z values of Group A is all positive, and the difference gradually increases, indicating that the amplitude of cycle diving increases. In combination with the data of Group B, if the difference between Z values is all negative and in a continuous downward trend, it is determined that the battery to be tested does not experience cycle diving. Therefore, whether the battery to be tested experiences cycle diving and the degree of cycle diving can be predicted in advance according to the change in Z value.

[0102] In the above embodiment, by establishing the correlation between the charge-discharge data of the battery to be tested and the charge-discharge cycle number, the risk of battery diving can be quickly predicted according to the change trend of the correlation, and various complex curve relationships do not need to be established, thereby improving the prediction efficiency of the risk of battery capacity diving.

[0103] Please refer to Figure 7 , Figure 7 is a structural schematic diagram of an embodiment of the battery capacity diving prediction device of the present application, like Figure 7 The battery capacity diving prediction device 100 provided by the present application comprises a charge-discharge cycle module 110, an acquisition module 120, an establishment module 130, and a prediction module 140.

[0104] The charge-discharge cycle module 110 is configured to perform charge-discharge cycling on the battery to be tested.

[0105] The acquisition module 120 is configured to acquire the capacity loss rate of the battery to be tested after cycle charge-discharge. The acquisition module 120 is further configured to acquire the initial cycle discharge standard capacity of the battery to be tested and the cycle capacity after cycle charge-discharge; to determine the capacity retention rate of the battery to be tested according to the initial cycle discharge standard capacity and the cycle capacity; and to determine the capacity loss rate of the battery to be tested according to the capacity retention rate.

[0106] The establishment module 130 is configured to establish the correlation between the capacity loss rate and the charge-discharge cycle number of the battery to be tested.

[0107] The prediction module 140 is configured to predict the risk of diving of the battery to be measured according to the correlation. The prediction module 140 is further configured to divide the charge-discharge cycle number of the battery to be measured into a plurality of gradients according to a first preset cycle number; obtain the value of the first function corresponding to each gradient, and determine the risk gradient interval of the battery to be measured from the value of the first function, including judging whether the value of the first function corresponding to all gradients is continuously decreasing; if the judgment is no, the gradient interval corresponding to the value jump of the first function is determined; and the risk gradient interval of the battery to be measured from the value jump of the first function is determined.

[0108] The risk of diving of the battery to be measured is predicted according to the value change of the first function in the risk gradient interval, including: dividing the charge-discharge cycle number in the risk gradient interval into a plurality of gradients according to a second preset cycle number; obtaining the value difference of the first function between adjacent gradients; determining the risk of diving of the battery to be measured according to the probability of the value difference of the first function being positive or negative, including: if the value difference of the first function is all negative, it is determined that the battery to be measured does not dive; if the probability of the value difference of the first function being negative is greater than the probability of the value difference of the first function being positive, it is determined that the battery to be measured is about to dive; if the probability of the value difference of the first function being negative is less than the probability of the value difference of the first function being positive, it is determined that the amplitude of the battery to be measured diving is low; and if the value difference of the first function is all positive and gradually increases, it is determined that the amplitude of the battery to be measured diving increases.

[0109] In the above embodiment, by establishing the correlation between the charge-discharge data of the battery to be measured and the charge-discharge cycle number, the risk of diving of the battery can be quickly predicted according to the change trend of the correlation, and various complex curve relationships do not need to be established, thereby improving the prediction efficiency of the risk of diving of the battery.

[0110] To solve the above technical problems, the embodiment of the present application further provides a computer device. For details, please refer to Figure 8 , Figure 8 The basic structure block diagram of the computer device of the present embodiment is shown in the figure.

[0111] The computer device 300 includes a memory 301, a processor 302 and a network interface 303 which are connected to each other through a system bus. It should be pointed out that, Figure 8The computer device 300 is shown with only components 301-303, but it is understood that all of the illustrated components are not required, and that more or fewer components can be alternatively implemented. As those skilled in the art will appreciate, the computer device is a device capable of automatically processing data and / or information according to pre-set or stored instructions, and its hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, etc.

[0112] The computer device can be a desktop computer, a notebook computer, a palm computer, a cloud server, or the like. The computer device can interact with a user through a keyboard, a mouse, a remote controller, a touchpad, a voice control device, or the like.

[0113] The memory 301 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card-type memory (e.g., an SD or a DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read-Only Memory (ROM), an Electrically Erasable Programmable Read-Only Memory (EEPROM), a Programmable Read-Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 301 can be an internal storage unit of the computer device 300, such as a hard disk or a memory of the computer device 300. In other embodiments, the memory 301 can also be an external storage device of the computer device 300, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) card, a Flash Card, etc. Of course, the memory 301 can include both an internal storage unit and an external storage device of the computer device 300. In this embodiment, the memory 301 is generally used to store an operating system and various application software installed in the computer device 300, such as computer readable instructions of the interface calling method, etc. In addition, the memory 301 can also be used to temporarily store various data that have been output or will be output.

[0114] The processor 302 may, in some embodiments, be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chip. The processor 302 is generally used to control the overall operation of the computer device 300. In the present embodiment, the processor 302 is configured to run computer readable instructions stored in the memory 301 or to process data, such as computer readable instructions of the method for predicting battery capacity drop.

[0115] The network interface 303 may include a wireless network interface or a wired network interface, and is generally used to establish a communication connection between the computer device 300 and other electronic devices.

[0116] In the above embodiments, by establishing the correlation between the charge-discharge data of the battery to be tested and the number of charge-discharge cycles, the risk of battery capacity drop can be quickly predicted according to the change trend of the correlation, and various complex curve relationships do not need to be established, thereby improving the prediction efficiency of the battery capacity drop risk.

[0117] The present application also provides another embodiment, i.e., a computer readable storage medium storing computer readable instructions, which can be executed by at least one processor to make the at least one processor perform the steps of the method for predicting battery capacity drop as described above.

[0118] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software and a general hardware platform, and of course, they can also be implemented by hardware, but in many cases, the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, or an optical disc), and includes a plurality of instructions for making a terminal device (which can be a mobile phone, a computer, a server, an air conditioner, or a network device) execute the methods of the various embodiments of the present application.

[0119] Obviously, the above-described embodiments are only some embodiments but not all the embodiments of the present application, the preferred embodiments of the present application are shown in the drawings, but do not limit the patent scope of the present application. The present application can be implemented in many different forms, and conversely, the purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing specific embodiments, or make equivalent replacements to some technical features therein. Any equivalent structure made by using the content of the specification and drawings, directly or indirectly applied to other related technical fields, is also within the patent protection scope of the present application.

Claims

1. A method of predicting battery capacity drop-out, characterized by, The prediction method comprises the following steps: a) performing a charge-discharge cycle on a battery to be tested; b) obtaining a capacity loss rate of the battery to be tested after the cycle charge-discharge; c) establishing a correlation between the capacity loss rate and the cycle number of the charge-discharge cycle of the battery to be tested; d) predicting the diving risk of the battery to be tested according to the correlation; The step d comprises the following sub-steps: d1) performing data processing on the correlation to obtain a first correlation between the capacity loss rate and the cycle number of the charge-discharge cycle of the battery to be tested, wherein the first correlation comprises a first function related to the cycle number; d2) predicting the diving risk of the battery to be tested according to the numerical value of the first function; The step d2 comprises the following sub-steps: d21) dividing the cycle number of the charge-discharge cycle of the battery to be tested into multiple gradients according to a first preset cycle number; d22) obtaining the numerical value of the first function corresponding to each gradient respectively, and confirming the risk gradient interval of the battery to be tested from the trend of the numerical value of the first function; d23) predicting the risk of the battery to be tested from the trend of the numerical value of the first function in the risk gradient interval.

2. The prediction method as claimed in claim 1, characterized in that The step d23 comprises the following sub-steps: d231) dividing the cycle number of the charge-discharge cycle in the risk gradient interval into multiple gradients according to a second preset cycle number; d232) obtaining the numerical difference value of the first function between adjacent gradients respectively; d233) determining the diving risk of the battery to be tested according to the probability that the numerical difference value of the first function is positive or negative.

3. The prediction method as claimed in claim 1, characterized in that, Determining the diving risk of the battery to be tested according to the probability that the numerical difference value of the first function is positive or negative comprises: If all the numerical difference values of the first function are negative, it is determined that the battery to be tested has not occurred cycle diving; If the probability that the numerical difference value of the first function is negative is greater than the probability that the numerical difference value of the first function is positive, it is determined that the battery to be tested is about to occur cycle diving; If the probability that the numerical difference value of the first function is negative is less than the probability that the numerical difference value of the first function is positive, it is determined that the amplitude of cycle diving of the battery to be tested is low; If all the numerical difference values of the first function are positive and the difference values gradually increase, it is determined that the amplitude of cycle diving of the battery to be tested increases.

4. The prediction method of claim 1, wherein, The step d22 comprises the following sub-steps: d221) determining whether the numerical value of the first function corresponding to all gradients is continuously decreasing; d222) if the determination is no, determining the gradient interval corresponding to the jump of the numerical value of the first function; d223) confirming the risk gradient interval of the battery to be tested from the trend of the numerical value of the first function in the gradient interval.

5. The prediction method of claim 1, wherein, The correlation between the capacity loss rate and the cycle number of the charge-discharge cycle of the battery to be tested is: Qloss = B * exp(-Ea / RT) * n Z The first correlation between the capacity loss rate and the cycle number of the charge-discharge cycle of the battery to be tested is: ln(Qloss)=A+Zln(n) Wherein, the Qloss is the capacity loss rate of the battery to be measured, B is a constant, A is a function related to temperature, Z is the first function related to the cycle number, n is the cycle number, R is a gas constant, T is temperature, Ea is the Arrhenius formula and activation energy.

6. The prediction method of claim 1, wherein, The step b comprises the following sub-steps: b1) obtaining the initial cycle discharge standard capacity of the battery to be measured and the cycle capacity after cycle charging and discharging; b2) determining the capacity retention rate of the battery to be measured according to the initial cycle discharge standard capacity and the cycle capacity; b3) determining the capacity loss rate of the battery to be measured according to the capacity retention rate.

7. A device for predicting battery capacity dive, characterized by, The prediction device of the battery capacity drop comprises: a charging and discharging cycle module for charging and discharging the battery to be measured; an acquisition module for acquiring the capacity loss rate of the battery to be measured after cycle charging and discharging; an establishment module for establishing the correlation between the capacity loss rate and the cycle number of the battery to be measured; a prediction module for predicting the risk of the battery to be measured according to the correlation, comprising: data processing the correlation to obtain the first correlation between the capacity loss rate and the cycle number of the battery to be measured, wherein the first correlation comprises a first function related to the cycle number; dividing the cycle number of the battery to be measured into multiple gradients according to a first preset cycle number, respectively acquiring the numerical value of the first function corresponding to each gradient, and confirming the risk gradient interval of the battery to be measured according to the numerical value variation trend of the first function, and predicting the risk of the battery to be measured according to the numerical value variation of the first function in the risk gradient interval.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores computer readable instructions, and the computer readable instructions are executed by the processor to realize the steps of the battery capacity drop prediction method in any one of claims 1 to 6. The computer readable storage medium stores computer readable instructions, and the computer readable instructions are executed by the processor to realize the steps of the battery capacity drop prediction method in any one of claims 1 to 6.

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