Battery life prediction method and apparatus, electronic device, and readable storage medium

Abstract

Embodiments of the present application disclose a battery life prediction method and device, electronic equipment and a readable storage medium. The method comprises: obtaining a first curve of current change over time of a first battery, a second curve of temperature change over time; generating real-time temperature, state of charge, calendar time, discharge depth, rate and cycle number of the first battery under each stage according to the first curve and the second curve; generating a first capacity attenuation rate and a second capacity attenuation rate in the calendar aging model and the cycle aging model under constant working conditions according to the real-time temperature, state of charge, calendar time, discharge depth, rate and cycle number under each stage; and generating a total attenuation rate according to the first capacity attenuation rate and the second capacity attenuation rate to determine a life prediction curve of the first battery. The present application can accurately predict the battery life, and the life prediction curve can more truly reflect the service life of the battery in the actual application process.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of battery life evaluation, and in particular to a battery life prediction method and device, electronic equipment and a readable storage medium. BACKGROUND

[0002] During the use of a lithium ion battery, a series of physical and chemical changes occur inside the battery, causing its performance and capacity to show a declining trend, and changes in the external environment also affect its health state, ultimately leading to the battery reaching its service life. As an important part of an electric vehicle system, damage to the battery can cause the entire system to malfunction. Therefore, timely prediction of the battery life and taking appropriate measures can ensure the safe and reliable operation of the battery and its application system.

[0003] Currently, when predicting the battery life under driving conditions of an electric vehicle, a fixed discharge depth is usually used to predict the battery life under known driving current conditions. However, fixed discharge depth prediction of battery life ignores the influence of different discharge depths on battery life, thereby causing a large deviation in the prediction of battery life under driving conditions. At the same time, due to the complex current changes and large charge / discharge rate changes during driving of an electric vehicle, the fixed discharge depth cannot truly reflect the battery life decay under real-time conditions during driving. SUMMARY

[0004] The present application provides a battery life prediction method, device, electronic equipment and readable storage medium, which can accurately predict the battery life without using a fixed discharge depth, and can more truly reflect the service life of the battery in actual application.

[0005] In a first aspect, the present application provides a battery life prediction method, which includes:

[0006] obtaining a first curve of the current change of a first battery over time and a second curve of the temperature change over time;

[0007] generating real-time temperature, state of charge, calendar time, discharge depth, rate and cycle number of the first battery at each stage according to the first curve and the second curve;

[0008] generating a first capacity decay rate of the first battery at each stage in a calendar aging model under constant conditions according to the real-time temperature, state of charge and calendar time at each stage;

[0009] generating a second capacity decay rate of the first battery at each stage in a cycle aging model under constant conditions according to the real-time temperature, discharge depth, rate and cycle number at each stage;

[0010] generating a total decay rate of the first battery at each stage according to the first capacity decay rate and the second capacity decay rate;

[0011] determining a life prediction curve of the first battery according to the total decay rate at each stage.

[0012] In a second aspect, an embodiment of the present application provides a battery life prediction device, which comprises:

[0013] a first obtaining unit, configured to obtain a first curve of current change over time and a second curve of temperature change over time of a first battery;

[0014] a first generating unit, configured to generate real-time temperature, state of charge, calendar time, depth of discharge, rate and cycle number of the first battery at each stage according to the first curve and the second curve;

[0015] a second generating unit, configured to generate a first capacity decay rate of the first battery at each stage in a calendar aging model under constant working conditions according to real-time temperature, state of charge and calendar time at each stage;

[0016] a third generating unit, configured to generate a second capacity decay rate of the first battery at each stage in a cycle aging model under constant working conditions according to real-time temperature, depth of discharge, rate and cycle number at each stage;

[0017] a fourth generating unit, configured to generate a total decay rate of the first battery at each stage according to the first capacity decay rate and the second capacity decay rate;

[0018] a first determining unit, configured to determine a life prediction curve of the first battery according to the total decay rate at each stage.

[0019] In a third aspect, an embodiment of the present application further provides an electronic device, which comprises a memory, a processor and a computer program stored in the memory and capable of running on the processor, and the processor implements the battery life prediction method of the first aspect when executing the computer program.

[0020] In a fourth aspect, an embodiment of the present application further provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program causes a processor to execute the battery life prediction method of the first aspect when the computer program is executed by the processor.

[0021] The embodiment of the present application provides a battery life prediction method, device, electronic equipment and readable storage medium, the method obtains a first curve of current change with time and a second curve of temperature change with time of a battery in an actual application process, generates real-time temperature, state of charge, calendar time, discharge depth, rate and cycle number of the battery in each stage according to the first curve and the second curve, then calculates a first capacity attenuation rate and a second capacity attenuation rate of the battery in each stage in the actual application process in a calendar aging model and a cycle aging model under constant working conditions respectively, and finally generates a total attenuation rate of the first battery in each stage according to the first capacity attenuation rate and the second capacity attenuation rate, so that the life prediction curve of the battery in the actual application process can be determined, the life of the battery can be predicted accurately without using a fixed value discharge depth, and the generated life prediction curve can more truly reflect the service life of the battery in the actual application process. BRIEF DESCRIPTION OF DRAWINGS

[0022] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. 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 creative labor.

[0023] Figure 1 The flowchart of the battery life prediction method provided by the embodiment of the present application is shown.

[0024] Figure 2 The sub-flowchart of the battery life prediction method provided by the embodiment of the present application is shown.

[0025] Figure 3 Another flowchart of the battery life prediction method provided by the embodiment of the present application is shown.

[0026] Figure 4 The distribution diagram of the state of charge of the battery under variable working conditions provided by the embodiment of the present application is shown.

[0027] Figure 5 Another flowchart of the battery life prediction method provided by the embodiment of the present application is shown.

[0028] Figure 6 Another flowchart of the battery life prediction method provided by the embodiment of the present application is shown.

[0029] Figure 7 The schematic block diagram of the battery life prediction device provided by the embodiment of the present application is shown.

[0030] Figure 8A schematic block diagram of an electronic device according to an embodiment of the present application is provided. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort should fall within the protection scope of the present application.

[0032] It should be understood that, when used in the specification and the appended claims, the terms "comprise" and "include" indicate the presence of the described features, integers, steps, operations, elements, and / or components, but do not exclude one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0033] It should also be understood that the terms used in the specification of the present application are only for the purpose of describing specific embodiments and are not intended to limit the present application. As used in the specification and the appended claims of the present application, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise.

[0034] It should be further understood that the term "and / or" used in the specification and the appended claims of the present application means any combination of one or more of the associated listed items and all possible combinations, and includes these combinations.

[0035] Referring to Figure 1 , Figure 1 A flowchart of a battery life prediction method according to an embodiment of the present application is provided. The battery life prediction method according to the present embodiment is applied to a terminal device, and is executed by application software installed in the terminal device. The terminal device can be a notebook computer, a tablet computer, a mobile phone, an electric vehicle, etc.

[0036] The battery life prediction method will be described in detail below.

[0037] As shown in Figure 1 , the method comprises the following steps S110-S160.

[0038] S110, a first curve of a current of a first battery changing with time and a second curve of a temperature changing with time are obtained.

[0039] The first curve is a distribution curve of the real-time current of the first battery in an actual application process, and the second curve is a distribution curve of the real-time temperature of the first battery in the actual application process. The actual application scenario of the first battery can be a scenario in which the first battery is configured in a terminal device such as a vehicle for use.

[0040] In S120, the real-time temperature, state of charge, calendar time, discharge depth, rate, and cycle number of the first battery in each stage are generated according to the first curve and the second curve.

[0041] In this embodiment, the first curve and the second curve are divided into multiple stages, each stage including multiple time points, and each stage can be understood as a constant working condition of the first battery. The shorter the time of each stage, the closer it is to the working condition of the actual application of the first battery. After obtaining the first curve and the second curve, the state of charge, calendar time, discharge depth, rate, and cycle number of the first battery in each stage can be calculated according to the real-time current and real-time temperature in each stage.

[0042] In another embodiment, as shown in Figure 2 S120 includes steps S121, S122, S123, and S124.

[0043] In S121, the real-time current, real-time temperature, and calendar time of the first battery in each stage are determined according to the first curve and the second curve.

[0044] In S122, the state of charge of the first battery in each stage is generated according to the initial time of the first battery and the real-time current in each stage.

[0045] In S123, the rate of the first battery in each stage is determined according to the state of charge and the real-time current in each stage.

[0046] In S124, the discharge depth and cycle number of the first battery in each stage are determined according to the state of charge in each stage.

[0047] In this embodiment, the calendar time in each stage can be determined according to the time in the corresponding stage and the factory time of the first battery. When determining the time in the corresponding stage, if the time in the corresponding stage is short, any time point in the corresponding stage can be used as an end point of the calendar time; if the time in the corresponding stage is long, a middle time point in the corresponding stage can be used as an end point of the calendar time.

[0048] The real-time current and the real-time temperature of each stage can be directly obtained by the first curve and the second curve respectively. When generating the state of charge of the first battery at each stage, if the previous stage of the current stage is the initial moment, the real-time current of the current stage can be directly practically integrated and added to the state of charge of the initial moment, so as to obtain the state of charge of the first battery at the current stage; similarly, if the previous stage of the current stage is not the initial moment, the real-time current of the current moment can be directly practically integrated and added to the state of charge of the previous stage, so as to obtain the state of charge of the first battery at the current stage, and so on, and thus the state of charge of the first battery at each stage can be obtained. The calculation formula of the state of charge of the first battery at each stage is:

[0049]

[0050] Wherein, SOC0 is the state of charge of the first battery at the initial moment, n is the micro-time of each stage, and i is the real-time current corresponding to each stage.

[0051] After determining the state of charge of the first battery at each stage, the remaining capacity of the first battery at each stage can be determined, and then the rate of the first battery at each stage can be obtained through the real-time current of the first battery at each stage, and the specific calculation formula can be:

[0052]

[0053] Wherein, i is the real-time current of the current stage, and Qi is the remaining capacity of the first battery at the current stage.

[0054] In addition, after determining the state of charge of the first battery at each stage, the distribution of the state of charge of the first battery at each stage can be obtained as shown in Figure 4 The discharge depth and the cycle number of the first battery at each stage can be determined through the distribution diagram of the state of charge of the first battery.

[0055] In another embodiment, as shown in Figure 3 Step S123 includes steps S1231 and S1232.

[0056] S1231, generating the state of charge distribution of the first battery according to the state of charge at each stage, and determining the cycle number of each first battery at each stage according to the state of charge distribution;

[0057] S1232, generating the discharge depth of the first battery at the corresponding stage according to the difference between the maximum state of charge and the minimum state of charge of the first battery at each stage.

[0058] In one embodiment, such as Figure 4 As shown, the first battery exists in three stages: A to B, B to C, and C to D. These three stages can be considered as one stage in the actual use of the first battery. From... Figure 4 As can be seen, stages A to B represent the discharge stage of the first battery, B to C represent the charging stage, and C to D represent the discharge stage. To more accurately predict the lifespan of the first battery, A to B, B to C, and C to D can each be considered one cycle of the first battery. That is, each charging or discharging stage in the actual application process is considered one cycle of the first battery. Therefore, after determining the state of charge (SOC) of the first battery in each stage, the number of cycles in each stage can be directly determined by the distribution of the SOC in each stage.

[0059] It is understood that this application may also directly use stages A to C as one cycle of the first battery, i.e., a charge-discharge cycle, or it may use stages A to D as one cycle of the first battery, i.e., two charge-discharge cycles as one cycle of the first battery. The choice can be made according to the actual application, and this embodiment does not make specific limitations.

[0060] In this embodiment, each stage is considered as a constant operating condition time for the first battery. Therefore, after determining the state of charge (SOC) of the first battery in each stage, the depth of discharge (DDR) of the first battery in each stage can be directly calculated using the difference between the maximum and minimum SOC of that stage. The specific calculation formula is as follows:

[0061]

[0062] Among them, SOC max The maximum state of charge (SOC) of the first cell in each stage min Let t1 be the minimum state of charge of the first battery in the corresponding stage, and t1 be the initial time of the corresponding stage. n This represents the end time of the corresponding stage.

[0063] In addition, after generating the depth of discharge of the first battery at each stage, the following program can be programmed in Matlab to solve for the DOD distribution over time.

[0064]

[0065]

[0066] S130. Generate the first capacity decay rate of the first battery in each stage based on the real-time temperature, state of charge and calendar time under constant operating conditions in the calendar aging model.

[0067] S140, generating the second capacity attenuation rate of the first battery in each stage according to the real-time temperature, the depth of discharge, the rate and the cycle number in the cycle aging model under constant working conditions.

[0068] Specifically, in the actual use process of the battery, there are two states affecting the service life of the battery, one is that the battery is in a storage state without current exchange, and the other is that the battery is in a charging and discharging state. The two states affecting the service life of the battery are respectively equivalent to calendar life attenuation factors and cycle life attenuation factors. Therefore, after obtaining the real-time temperature, the state of charge, the calendar time, the depth of discharge, the rate and the cycle number of the first battery in each stage, the relevant parameters are respectively input into the calendar aging model and the cycle aging model under constant working conditions, so as to generate the first capacity attenuation rate and the second capacity attenuation rate of the first battery in each stage, and then the total capacity attenuation rate of the first battery in each stage can be obtained.

[0069] In another embodiment, as shown in Figure 5 the construction steps of the calendar aging model and the cycle aging model of the first battery under constant working conditions include S210 and S220.

[0070] S210, obtaining calendar aging data of the second battery tested under the first parameter and cycle aging data of the second battery tested under the second parameter; wherein the first parameter includes temperature, state of charge and calendar time, the second parameter includes temperature, depth of discharge, rate and cycle number, and the second battery is the same type of battery as the first battery;

[0071] S220, constructing the calendar aging model and the cycle aging model according to the calendar aging data and the cycle aging data respectively.

[0072] In this embodiment, in order to more accurately predict the service life of the first battery, the second battery used for constructing the calendar aging model and the cycle aging model is the same type of battery as the first battery. The calendar aging data is the capacity attenuation data of the second battery tested under different temperatures, different states of charge and different calendar times, and the cycle aging data is the capacity attenuation data of the second battery tested under different temperatures, different depths of discharge, different rates and different cycle numbers. After obtaining the calendar aging data of the second battery tested under the first parameter and the cycle aging data of the second battery tested under the second parameter, the calendar aging data and the cycle aging data are fitted, and then the calendar aging model and the cycle aging model can be constructed.

[0073] In another embodiment, as shown in Figure 6 step S220 includes steps S221, S222 and S223.

[0074] S221, respectively fitting the calendar aging data and the cycle aging data by using an exponential function to obtain a first fitting factor of the calendar aging data and a second fitting factor of the cycle aging data;

[0075] S222, constructing the calendar aging model according to the first fitting factor and the first parameter;

[0076] S223, constructing the cycle aging model according to the second fitting factor and the second parameter.

[0077] In the embodiment, the first fitting factor is a plurality of values obtained by fitting the calendar aging data by using an exponential function, and the second fitting factor is a plurality of values obtained by fitting the cycle aging data by using an exponential function. The calendar aging data and the cycle aging data can be fitted by using software such as Matlab and Origin, and the specific selection can be made according to the actual application. The exponential function can be y=A*x B When the calendar aging data is fitted by using the exponential function, the capacity attenuation amount is used as the dependent variable y, and the calendar time is used as the independent variable x. The temperature and the state of charge can be used to fit A and B in the exponential function, and then the first fitting factor of the calendar aging data can be obtained. When the cycle aging data is fitted by using the exponential function, the capacity attenuation amount is used as the dependent variable y, and the cycle number is used as the independent variable x. The temperature, the discharge depth, and the rate can be used to fit A and B in the exponential function, and then the second fitting factor of the cycle aging data can be obtained.

[0078] After the first fitting factor and the second fitting factor are obtained by fitting the calendar aging data and the cycle aging data by using the exponential function, the calendar aging equation can be constructed by using the first fitting factor and the first parameter, and the cycle aging equation can be constructed by using the second fitting factor and the second parameter.

[0079] The equation of the calendar aging model can be:

[0080]

[0081] Q1=a*T+b*T loss The first capacity attenuation rate, a, b, c, d, e, f, g, h, i, and j are the first fitting factor, T is the temperature, T ref is the reference temperature, SOC is the state of charge, and t is the calendar time.

[0082] The equation of the cycle aging model is:

[0083]

[0084] Q2=c*T+d*T lossis the second capacity fade rate, k, l, m, n, o, p, q, r, s, u, v, w, x, y, z are the second fitting factors, T is the temperature, T ref is the reference temperature, C rate is the rate, DOD is the depth of discharge, N is the cycle number.

[0085] S150, generating a total fade rate of the first battery at each stage according to the first capacity fade rate and the second capacity fade rate.

[0086] S160, determining a life prediction curve of the first battery according to the total fade rate at each stage.

[0087] Specifically, after generating the first capacity fade rate and the second capacity fade rate of the first battery at each stage in the actual application process, the first capacity fade rate and the second capacity fade rate are added to obtain the total fade rate of the first battery at each stage in the actual application process, and finally the total fade rate curve of the first battery is obtained through the total fade rate at each stage, so that the life of the first battery is predicted.

[0088] In the battery life prediction method provided in the embodiment of the application, the first curve of the current of the first battery changing with time and the second curve of the temperature changing with time are obtained; the real-time temperature, state of charge, calendar time, depth of discharge, rate and cycle number of the first battery at each stage are generated according to the first curve and the second curve; the first capacity fade rate of the first battery at each stage is generated in a calendar aging model under constant working conditions according to the real-time temperature, state of charge and calendar time at each stage; the second capacity fade rate of the first battery at each stage is generated in a cycle aging model under constant working conditions according to the real-time temperature, depth of discharge, rate and cycle number at each stage; the total fade rate of the first battery at each stage is generated according to the first capacity fade rate and the second capacity fade rate; and the life prediction curve of the first battery is determined according to the total fade rate at each stage, so that the life of the battery can be accurately predicted under variable working conditions without using a fixed value of the depth of discharge to predict the life of the battery, the depth of discharge of the battery under variable working conditions is redefined and calculated, the service life of the battery in the actual application process can be more truly reflected, and the accuracy of the prediction of the life of the battery is improved.

[0089] The embodiment of the application also provides a battery life prediction device 100, which is used to execute any one of the foregoing embodiments of the battery life prediction method.

[0090] Specifically, refer to Figure 7 , Figure 7is a schematic block diagram of a battery life prediction device 100 provided by an embodiment of the present application.

[0091] As shown in Figure 7 The battery life prediction device 100 comprises a first acquisition unit 110, a first generation unit 120, a second generation unit 130, a third generation unit 140, a fourth generation unit 150, and a first determination unit 160.

[0092] The first acquisition unit 110 is configured to acquire a first curve of current change over time and a second curve of temperature change over time of a first battery.

[0093] The first generation unit 120 is configured to generate real-time temperature, state of charge, calendar time, depth of discharge, rate, and cycle number of the first battery at each stage according to the first curve and the second curve.

[0094] In other embodiments of the present application, the first generation unit 120 comprises a second determination unit, a fifth generation unit, a third determination unit, and a fourth determination unit.

[0095] The second determination unit is configured to determine real-time current, real-time temperature, and calendar time of the first battery at each stage according to the first curve and the second curve; the fifth generation unit is configured to generate state of charge of the first battery at each stage according to initial time state of charge of the first battery and the real-time current at each stage; the third determination unit is configured to determine rate of the first battery at each stage according to state of charge and real-time current at each stage; and the fourth determination unit is configured to determine depth of discharge and cycle number of the first battery at each stage according to the state of charge at each stage.

[0096] In other embodiments of the present application, the fourth determination unit comprises a fifth determination unit and a sixth generation unit.

[0097] The fifth determination unit is configured to generate state of charge distribution of the first battery according to the state of charge at each stage, and determine cycle number of each of the first battery at each stage according to the state of charge distribution; and the sixth generation unit is configured to generate depth of discharge of the first battery at each stage according to a difference between maximum state of charge and minimum state of charge of the first battery at each stage.

[0098] The second generation unit 130 is configured to generate a first capacity decay rate of the first battery at each stage in a calendar aging model under constant working conditions according to real-time temperature, state of charge, and calendar time at each stage.

[0099] The third generating unit 140 is configured to generate a second capacity attenuation rate of the first battery in each stage according to the real-time temperature, the depth of discharge, the rate and the cycle number in a cycle aging model under a constant working condition.

[0100] In other embodiments of the present application, the battery life prediction device 100 further comprises a second obtaining unit and a first constructing unit.

[0101] The second obtaining unit is configured to obtain calendar aging data of a second battery tested under a first parameter and cycle aging data of the second battery tested under a second parameter, wherein the first parameter comprises temperature, state of charge and calendar time, the second parameter comprises temperature, depth of discharge, rate and cycle number, and the second battery is the same model as the first battery; and the first constructing unit is configured to construct a calendar aging model and a cycle aging model according to the calendar aging data and the cycle aging data, respectively.

[0102] In other embodiments of the present application, the first constructing unit comprises a fitting unit, a second constructing unit and a third constructing unit.

[0103] The fitting unit is configured to fit the calendar aging data and the cycle aging data by using an exponential function to obtain a first fitting factor of the calendar aging data and a second fitting factor of the cycle aging data; the second constructing unit is configured to construct the calendar aging model according to the first fitting factor and the first parameter; and the third constructing unit is configured to construct the cycle aging model according to the second fitting factor and the second parameter.

[0104] The fourth generating unit 150 is configured to generate a total attenuation rate of the first battery in each stage according to the first capacity attenuation rate and the second capacity attenuation rate.

[0105] The first determining unit 160 is configured to determine a life prediction curve of the first battery according to the total attenuation rate in each stage.

[0106] The battery life prediction device 100 provided by the embodiment of the present application is used to perform the above-mentioned obtaining a first curve of current change over time and a second curve of temperature change over time of a first battery; generating real-time temperature, state of charge, calendar time, discharge depth, rate and cycle number of the first battery at each stage according to the first curve and the second curve; generating a first capacity attenuation rate of the first battery at each stage in a calendar aging model under constant working conditions according to the real-time temperature, state of charge and calendar time at each stage; generating a second capacity attenuation rate of the first battery at each stage in a cycle aging model under constant working conditions according to the real-time temperature, discharge depth, rate and cycle number at each stage; generating a total attenuation rate of the first battery at each stage according to the first capacity attenuation rate and the second capacity attenuation rate; and determining a life prediction curve of the first battery according to the total attenuation rate at each stage.

[0107] Please refer to Figure 8 , Figure 8 is a schematic block diagram of an electronic device provided by the embodiment of the present application.

[0108] Please refer to Figure 8 The device 500 includes a processor 502, a memory and a network interface 505 connected through a system bus 501, wherein the memory can include a storage medium 503 and an internal memory 504.

[0109] The storage medium 503 can store an operating system 5031 and a computer program 5032. The computer program 5032, when executed, can make the processor 502 execute the battery life prediction method.

[0110] The processor 502 is used to provide computing and control capabilities to support the operation of the entire device 500.

[0111] The internal memory 504 provides an environment for the execution of the computer program 5032 in the non-volatile storage medium 503, and the computer program 5032, when executed by the processor 502, can make the processor 502 execute the battery life prediction method.

[0112] The network interface 505 is used for network communication, such as providing transmission of data information, etc. Those skilled in the art can understand that Figure 8 The structure shown in the figure is only a block diagram of part of the structure related to the present application scheme, and does not constitute a limitation on the device 500 to which the present application scheme is applied. The specific device 500 can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0113] The processor 502 is configured to run the computer program 5032 stored in the memory to perform the following functions: obtaining a first curve of current change over time and a second curve of temperature change over time of a first battery; generating real-time temperature, state of charge, calendar time, depth of discharge, rate and cycle number of the first battery at each stage according to the first curve and the second curve; generating a first capacity attenuation rate of the first battery at each stage in a calendar aging model under constant working conditions according to the real-time temperature, state of charge and calendar time at each stage; generating a second capacity attenuation rate of the first battery at each stage in a cycle aging model under constant working conditions according to the real-time temperature, depth of discharge, rate and cycle number at each stage; generating a total attenuation rate of the first battery at each stage according to the first capacity attenuation rate and the second capacity attenuation rate; and determining a life prediction curve of the first battery according to the total attenuation rate at each stage.

[0114] Those skilled in the art can understand that, Figure 8 The embodiments of the device 500 shown in the figures are not intended to limit the specific structure of the device 500, and in other embodiments, the device 500 can include more or fewer components than shown, or combine certain components, or arrange different components. For example, in some embodiments, the device 500 can only include the memory and the processor 502, and in such embodiments, the structure and function of the memory and the processor 502 are consistent with the embodiments shown, and will not be repeated here. Figure 8

[0115] It should be understood that in the embodiments of the present application, the processor 502 can be a central processing unit (CPU), and the processor 502 can also be other general-purpose processors 502, digital signal processors 502 (DSP), application specific integrated circuits (ASIC), ready programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor 502 can be a microprocessor 502 or the processor 502 can also be any conventional processor 502, etc.

[0116] ​In another embodiment of the present application, a computer storage medium is provided. The storage medium can be a non-volatile computer readable storage medium or a volatile computer readable storage medium. The storage medium stores a computer program 5032, wherein the computer program 5032, when executed by the processor 502, implements the following steps: obtaining a first curve of current change over time and a second curve of temperature change over time of a first battery; generating real-time temperature, state of charge, calendar time, depth of discharge, rate and cycle number of the first battery at each stage according to the first curve and the second curve; generating a first capacity attenuation rate of the first battery at each stage in a calendar aging model under constant working conditions according to the real-time temperature, the state of charge and the calendar time at each stage; generating a second capacity attenuation rate of the first battery at each stage in a cycle aging model under constant working conditions according to the real-time temperature, the depth of discharge, the rate and the cycle number at each stage; generating a total attenuation rate of the first battery at each stage according to the first capacity attenuation rate and the second capacity attenuation rate; and determining a life prediction curve of the first battery according to the total attenuation rate at each stage.

[0117] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described devices, apparatuses and units can refer to the corresponding processes in the foregoing method embodiments, which will not be described here. Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be realized in electronic hardware, computer software or a combination of both. In order to clearly illustrate the interchangeability of hardware and software, the components and steps of the examples have been described in a general manner in the foregoing description. Whether the functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present application.

[0118] In several embodiments provided by the present application, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, or units with the same function can be combined into one unit, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, or can be electrical, mechanical or other forms of connection.

[0119] The units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, i.e. may be located in one place, or may be distributed on multiple network units. Part or all of the units can be selected according to actual needs to achieve the purpose of the embodiment of the present application.

[0120] In addition, each functional unit in each embodiment of the present application can be integrated in one processing unit, or each unit can exist physically, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0121] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a storage medium. Based on this understanding, the technical solutions of the present application essentially or the part of the prior art that contributes to the present application, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for causing an apparatus 500 (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM, Read-Only Memory), a magnetic disk or an optical disk, and various program code storage media.

[0122] The above is only a specific embodiment of the present application, but the protection scope of the present application is not limited thereto. Any skilled person in the art can easily think of various equivalent modifications or replacements within the technical range disclosed by the present application, and these modifications or replacements should be covered within the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for predicting battery life, characterized in that, include: Obtain a first curve showing the change of current in the first battery over time and a second curve showing the change of temperature over time; the first curve is the real-time current distribution curve of the first battery, and the second curve is the real-time temperature distribution curve of the first battery. Based on the first curve and the second curve, the real-time temperature, state of charge, calendar time, depth of discharge, rate, and number of cycles of the first battery at each stage are generated; each stage is when the first battery is in a constant operating condition. The first capacity decay rate of the first battery at each stage is generated based on the real-time temperature, state of charge and calendar time under constant operating conditions in the calendar aging model of the first battery at each stage. The second capacity decay rate of the first battery at each stage is generated in the cyclic aging model under constant operating conditions based on the real-time temperature, depth of discharge, rate and number of cycles at each stage. The total capacity decay rate of the first battery at each stage is generated based on the first capacity decay rate and the second capacity decay rate. The life prediction curve of the first battery is determined based on the total decay rate at each stage; The equation for the cyclic aging model is: Among them, Q2 loss The second capacity decay rate is given by k, l, m, n, o, p, q, r, s, u, v, w, x, y, z, which are the second fitting factors, and T represents temperature. ref For reference temperature, C rate Where DOD is the discharge rate, N is the depth of discharge, and N is the number of cycles. The step of generating the real-time temperature, state of charge, calendar time, depth of discharge, rate of discharge, and number of cycles of the first battery at each stage based on the first curve and the second curve includes: determining the real-time current, real-time temperature, and calendar time of the first battery at each stage based on the first curve and the second curve; generating the state of charge of the first battery at each stage based on the initial state of charge of the first battery and the real-time current at each stage; determining the rate of discharge of the first battery at each stage based on the state of charge and the real-time current at each stage; and determining the depth of discharge and number of cycles of the first battery at each stage based on the state of charge at each stage. The step of determining the depth of discharge and number of cycles of the first battery in each stage based on the state of charge in each stage includes: generating a state of charge distribution of the first battery based on the state of charge in each stage, and determining the number of cycles of the first battery in each stage based on the state of charge distribution; generating the depth of discharge of the first battery in the corresponding stage based on the difference between the maximum state of charge and the minimum state of charge of the first battery in each stage.

2. The method for predicting battery life according to claim 1, characterized in that, The methods for constructing the calendar aging model and the cyclic aging model include: Acquire calendar aging data and cycle aging data of the second battery under the first parameter and the second parameter, respectively; wherein the first parameter includes temperature, state of charge and calendar time, and the second parameter includes temperature, depth of discharge, rate and number of cycles, and the second battery is the same type of battery as the first battery; The calendar aging model and the cyclic aging model are constructed based on the calendar aging data and the cyclic aging data, respectively.

3. The method for predicting battery life according to claim 2, characterized in that, The step of constructing the calendar aging model and the cyclic aging model based on the calendar aging data and the cyclic aging data, respectively, includes: An exponential function is used to fit the calendar aging data and the cyclic aging data respectively to obtain a first fitting factor for the calendar aging data and a second fitting factor for the cyclic aging data. The calendar aging model is constructed based on the first fitting factor and the first parameter. The cyclic aging model is constructed based on the second fitting factor and the second parameter.

4. The method for predicting battery life according to claim 3, characterized in that, The equation for the calendar aging model is: Among them, Q1 loss Let be the first capacity decay rate, and let a, b, c, d, e, f, g, h, i, j be the first fitting factors, and T be the temperature. ref The reference temperature is t, the state of charge (SOC) is t, and the calendar time is t.

5. A device for predicting battery life, characterized in that, include: The first acquisition unit is used to acquire a first curve of the current of the first battery changing over time and a second curve of the temperature changing over time; the first curve is the real-time current distribution curve of the first battery, and the second curve is the real-time temperature distribution curve of the first battery. The first generation unit is used to generate the real-time temperature, state of charge, calendar time, depth of discharge, rate and number of cycles of the first battery at each stage based on the first curve and the second curve; each stage is when the first battery is in a constant operating condition. The second generation unit is used to generate the first capacity decay rate of the first battery in each stage based on the real-time temperature, state of charge and calendar time under constant operating conditions in the calendar aging model of the first battery in each stage. The third generation unit is used to generate the second capacity decay rate of the first battery in each stage based on the real-time temperature, depth of discharge, rate and number of cycles in the constant operating condition cycle aging model of each stage. The fourth generation unit is used to generate the total capacity decay rate of the first battery at each stage based on the first capacity decay rate and the second capacity decay rate. The first determining unit is used to determine the life prediction curve of the first battery based on the total decay rate at each stage. The equation for the cyclic aging model is: Among them, Q2 loss The second capacity decay rate is given by k, l, m, n, o, p, q, r, s, u, v, w, x, y, z, which are the second fitting factors, and T represents temperature. ref For reference temperature, C rate Where DOD is the discharge rate, N is the depth of discharge, and N is the number of cycles. The first generation unit includes a second determining unit, a fifth generation unit, a third determining unit, and a fourth determining unit; The second determining unit is used to determine the real-time current, real-time temperature, and calendar time of the first battery at each stage based on the first curve and the second curve; the fifth generating unit is used to generate the state of charge of the first battery at each stage based on the initial state of charge of the first battery and the real-time current at each stage; the third determining unit is used to determine the rate of charge of the first battery at each stage based on the state of charge and the real-time current at each stage; the fourth determining unit is used to determine the depth of discharge and number of cycles of the first battery at each stage based on the state of charge at each stage. The fourth determining unit includes a fifth determining unit and a sixth generating unit; The fifth determining unit is used to generate the state of charge distribution of the first battery according to the state of charge in each stage, and to determine the number of cycles of the first battery in each stage according to the state of charge distribution; the sixth generating unit is used to generate the depth of discharge of the first battery in the corresponding stage according to the difference between the maximum state of charge and the minimum state of charge of the first battery in each stage.

6. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method for predicting battery life as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, causes the processor to perform the battery life prediction method as described in any one of claims 1 to 4.

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