CN117630716A - A real-time prediction method and device for battery life - Google Patents

Abstract

This application relates to the technical field of power batteries and provides a real-time prediction method and device for battery life. The method includes: obtaining the single theoretical capacity loss of the target battery; obtaining the usage conditions of the target battery; determining the correction coefficient corresponding to the usage condition based on the working condition coefficient relationship between the usage conditions and the correction coefficient; converting the correction coefficient into Multiply the single theoretical capacity loss to determine the single predicted capacity loss; determine the predicted number of charges and discharges based on the single predicted capacity loss and the actual capacity of the target battery. This application determines the corresponding correction coefficient by obtaining the current usage conditions of the target battery, and multiplies the correction coefficient with the single theoretical capacity loss to obtain the single predicted capacity loss, which is used to predict the next predicted number of charges and discharges of the target battery. Since the impact of actual usage conditions on the target battery is taken into account, the calculated predicted number of charge and discharge times is more accurate, thereby providing users with more effective information reference.

Description

A real-time prediction method and device for battery life

Technical field

The present application relates to the technical field of power batteries, and in particular to a real-time prediction method and device for battery life.

Background technique

At present, the life prediction of the power battery of new energy vehicles is generally based on experiments, that is, testing or modeling under standard environments and standard operations, and the use of the power battery is determined by charging and discharging the power battery multiple times over a long period of time. life.

This prediction method is relatively vague and general, and can only provide a rough life range. However, when implemented in actual vehicle use, different vehicle conditions and different driving habits lead to different usage conditions of power batteries. The prediction results of standard tests The service life deviates greatly from the actual operating conditions and cannot provide effective reference for users.

Therefore, how to provide a solution to the above technical problems is currently a problem that those skilled in the art need to solve.

Contents of the invention

In view of this, embodiments of the present application provide a real-time battery life prediction method and device to solve the problem that the existing technology cannot provide users with effective battery life prediction.

The first aspect of the embodiments of this application provides a real-time prediction method for battery life, including:

Obtain the single theoretical capacity loss of the target battery; the single theoretical capacity loss is the ratio of the total theoretical capacity loss to the theoretical number of charge and discharge cycles;

Obtain the usage conditions of the target battery; the usage conditions include charge and discharge conditions and temperature conditions;

Based on the working condition coefficient relationship between the working condition and the correction coefficient, determine the correction coefficient corresponding to the working condition; in the working condition coefficient relationship, when the working condition is the standard working condition, the correction coefficient is the minimum value, and the minimum value is 1;

Multiply the correction coefficient with the single theoretical capacity loss to determine the single predicted capacity loss;

Based on the single predicted capacity loss and the actual capacity of the target battery, the predicted number of charges and discharges is determined.

A second aspect of the embodiment of the present application provides a real-time prediction device for battery life, including:

The theoretical value acquisition module is used to obtain the single theoretical capacity loss of the target battery; the single theoretical capacity loss is the ratio of the total theoretical capacity loss to the theoretical number of charge and discharge cycles;

The working condition acquisition module is used to obtain the usage conditions of the target battery; the usage conditions include charge and discharge conditions and temperature conditions;

The correction coefficient acquisition module is used to determine the correction coefficient corresponding to the operating condition based on the operating condition coefficient relationship between the operating conditions and the correction coefficient; in the operating condition coefficient relationship, when the operating condition is the standard operating condition, the correction coefficient is the minimum value, the minimum value is 1;

The calculation module is used to multiply the correction coefficient and the single theoretical capacity loss to determine the single predicted capacity loss, and determine the predicted number of charges and discharges based on the single predicted capacity loss and the actual capacity of the target battery.

A third aspect of the embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps of the above method are implemented.

A fourth aspect of the embodiments of the present application provides a readable storage medium that stores a computer program, and when the computer program is executed by a processor, the steps of the above method are implemented.

Compared with the prior art, the beneficial effects of the embodiments of the present application at least include: the embodiments of the present application determine the corresponding correction coefficient by obtaining the current usage conditions of the target battery, and multiply the correction coefficient by the single theoretical capacity loss to obtain a single The predicted capacity loss is used to predict the next predicted charge and discharge times of the target battery. Since the impact of actual usage conditions on the target battery is taken into account, the calculated predicted number of charges and discharges is more in line with the current usage conditions of the target battery. The battery life under the conditions is more accurate than the test results under standard environment and standard working conditions, thereby providing users with more effective information reference and improving user experience.

Description of drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only for the purpose of the present application. For some embodiments, for those of ordinary skill in the art, other drawings can be obtained based on these drawings without exerting creative efforts.

Figure 1 is a schematic diagram of an application scenario according to the embodiment of the present application;

Figure 2 is a schematic flowchart of a real-time prediction method for battery life provided by an embodiment of the present application;

Figure 3 is a schematic structural diagram of a device for real-time prediction of battery life provided by an embodiment of the present application;

FIG. 4 is a schematic structural diagram of an electronic device provided by an embodiment of the present application.

Detailed ways

In the following description, for the purpose of explanation rather than limitation, specific details such as specific system structures and technologies are provided to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to those skilled in the art that the present application may be practiced in other embodiments without these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.

A real-time battery life prediction method and device according to embodiments of the present application will be described in detail below with reference to the accompanying drawings.

Figure 1 is a schematic diagram of an application scenario according to an embodiment of the present application. The application scenario may include the first terminal device 101, the second terminal device 102, the third terminal device 103, the server 104 and the network 105.

The first terminal device 101 may be hardware or software. When the first terminal device 101 is hardware, it may be various electronic devices having a display screen and supporting communication with the server 104, including but not limited to vehicle systems, smartphones, tablets, laptop computers, desktop computers, etc. ; When the first terminal device 101 is software, it can be installed in the above electronic device. The first terminal device 101 may be implemented as multiple software or software modules, or may be implemented as a single software or software module, which is not limited in the embodiment of the present application. Further, various applications may be installed on the first terminal device 101, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

The second terminal device 102 may be hardware or software. When the second terminal device 102 is hardware, it may be various electronic devices having a display screen and supporting communication with the server 104, including but not limited to vehicle systems, smartphones, tablets, laptop computers, desktop computers, etc. ; When the second terminal device 102 is software, it can be installed in the above electronic device. The second terminal device 102 may be implemented as multiple software or software modules, or may be implemented as a single software or software module, which is not limited in the embodiment of the present application. Further, various applications may be installed on the second terminal device 102, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

The third terminal device 103 may be hardware or software. When the third terminal device 103 is hardware, it may be various electronic devices having a display screen and supporting communication with the server 104, including but not limited to vehicle systems, smartphones, tablets, laptop computers, desktop computers, etc. ; When the third terminal device 103 is software, it can be installed in the above electronic device. The third terminal device 103 may be implemented as multiple software or software modules, or as a single software or software module, which is not limited in the embodiment of the present application. Further, various applications may be installed on the third terminal device 103, such as data processing applications, instant messaging tools, social platform software, search applications, shopping applications, etc.

The server 104 may be a server that provides various services, for example, a backend server that receives requests sent by terminal devices with which a communication connection is established. The backend server may receive and analyze requests sent by the terminal devices, and generate processing. result. The server 104 may be one server, a server cluster composed of several servers, or a cloud computing service center, which is not limited in the embodiment of the present application.

It should be noted that the server 104 may be hardware or software. When the server 104 is hardware, it may be various electronic devices that provide various services for the first terminal device 101, the second terminal device 102, and the third terminal device 103. When the server 104 is software, it may be multiple software or software modules that provide various services for the first terminal device 101, the second terminal device 102, and the third terminal device 103, or it may be a software module for the first terminal device 101, the second terminal device 102, and the third terminal device 103. The second terminal device 102 and the third terminal device 103 provide individual software or software modules for various services, which are not limited in this embodiment of the present application.

The network 105 can be a wired network connected by coaxial cables, twisted pairs and optical fibers, or a wireless network that can interconnect various communication devices without wiring, such as Bluetooth, Near Field CommunicA Tion, NFC), infrared (Infrared), etc., the embodiments of the present application do not limit this.

It should be noted that the specific types, quantities and combinations of the first terminal device 101, the second terminal device 102, the third terminal device 103, the server 104 and the network 105 can be adjusted according to the actual needs of the application scenario. The embodiments of this application are This is not a restriction.

FIG. 2 is a schematic flowchart of a real-time prediction method for battery life provided by an embodiment of the present application. The real-time prediction method of battery life in FIG. 2 may be executed by the first terminal device, the second terminal device, the third terminal device or the server in FIG. 1 . As shown in Figure 2, the real-time prediction method of battery life includes:

S201: Obtain the single theoretical capacity loss of the target battery; the single theoretical capacity loss is the ratio of the total theoretical capacity loss to the theoretical number of charge and discharge cycles;

S202: Obtain the usage conditions of the target battery; the usage conditions include charge and discharge conditions and temperature conditions;

S203: Based on the working condition coefficient relationship between the working condition and the correction coefficient, determine the correction coefficient corresponding to the working condition; in the working condition coefficient relationship, when the working condition is the standard working condition, the correction coefficient is the minimum value, and the minimum value is 1;

S204: Multiply the correction coefficient and the single theoretical capacity loss to determine the single predicted capacity loss;

S205: Determine the predicted number of charges and discharges based on the single predicted capacity loss and the actual capacity of the target battery.

It can be understood that the traditional battery life test on the target battery is conducted under standard working conditions to obtain the theoretical number of charge and discharge cycles corresponding to the total theoretical capacity loss. Since the actual use conditions of the target battery are different from the standard working conditions, Difference, so the actual capacity loss of a single charge and discharge cycle is different from that under standard operating conditions, which in turn causes the actual number of charge and discharge cycles to be different from the theoretical number of charge and discharge cycles. For this reason, this embodiment determines the correction coefficient based on the usage conditions. The correction The minimum value of the coefficient is 1, corresponding to standard operating conditions. When the operating conditions are not standard operating conditions, the correction coefficient changes to a value not less than 1. Based on the correction coefficient, the single predicted capacity loss under the current operating conditions is determined. capacity, and use this to calculate the remaining battery life under current usage conditions, that is, to predict the number of charges and discharges. The number of charges and discharges is not intuitive enough for users, and can be further converted into time or mileage that is more intuitive for users.

It can be understood that in this embodiment, the capacity loss, capacity, etc. of the target battery are calculated based on the charging capacity of the battery or the percentage of the charging capacity. The actual capacity of the target battery refers to the maximum charging capacity of the target battery at the current moment. When the target battery leaves the factory, its maximum charging capacity is the initial capacity; as the number of charge-discharge cycles and usage time increases, the comprehensive performance of the target battery decreases. During the charge-discharge cycle, the battery inserts and removes lithium, causing the anode to expand and shrink. Cracks on the anode surface will expand, and the newly exposed cracks will form a new SEI film, accelerating irreversible capacity loss; when the battery capacity reaches the minimum working capacity, the target battery is considered to have exhausted its life and is no longer suitable as a power battery for automobiles. Therefore, the total theoretical capacity loss is the difference between the initial capacity and the minimum working capacity of the target battery.

It can be understood that a charge-discharge cycle refers to the process of battery consumption from 100% to 0%. During this process, the action of battery charging is allowed. For example, after the first full charge to 100%, 75% is consumed and then charged. When it reaches 100% and consumes another 25%, it is considered that a charge and discharge cycle is completed at this time. As the number of charge-discharge cycles increases, the battery capacity undergoes irreversible capacity loss, and the battery capacity decreases.

The method of this embodiment is implemented based on the specific process of the target battery. Different target batteries correspond to different total theoretical capacity losses and theoretical number of charge and discharge cycles. In current power battery technology, the minimum working capacity is usually set between 70% and 80% of the initial capacity, and the theoretical number of charge and discharge cycles is generally between 1,500 and 2,000.

It can be understood that since the total theoretical capacity loss is the difference between the initial capacity and the minimum working capacity of the target battery; further, based on the single predicted capacity loss and the actual capacity of the target battery, the process of determining the predicted number of charges and discharges is determined. include:

Calculate the difference between the actual capacity and the minimum working capacity of the target battery to obtain the remaining capacity loss;

Calculate the ratio of the remaining capacity loss to the single predicted capacity loss to obtain the predicted number of charge and discharge cycles.

Specifically, the capacity of the target battery is described in the form of a percentage. The initial capacity of the target battery is 100%. Assuming that the minimum working capacity is 80%, the total theoretical capacity loss is 20%; assuming that the theoretical number of charge and discharge cycles is 1500, Then the single theoretical capacity loss is 20%/1500≈0.0133%; assuming the correction coefficient is 1.2, the single predicted capacity loss is 0.0133% × 1.2 = 0.016%; assuming that the current actual capacity of the target battery is 96%, it can The calculated number of predicted charges and discharges is (96%-80%)/0.016%=1000. That is to say, the predicted value of the remaining number of charges and discharges of the current power battery is 1000.

Furthermore, since the predicted number of charge and discharge times is not intuitive enough for users, the single time length of a single charge and discharge cycle can be further determined based on the current usage conditions, and the product of the single time length and the predicted number of charge and discharge times is used as the predicted remaining Length of use, similarly, the single mileage of a single charge-discharge cycle can also be determined based on the current usage conditions, and the product of the single mileage and the predicted number of charges and discharges is used as the predicted remaining mileage. Therefore, the method of this embodiment may also include:

Obtain the historical time length of each charge and discharge cycle according to the charge and discharge conditions;

Calculate the average of all historical time lengths to obtain the single time length of a single charge and discharge cycle;

Based on the predicted number of charge and discharge cycles and the length of a single cycle, the predicted remaining usage time is determined.

It is understandable that the attenuation of power batteries, in addition to the attenuation during the charge and discharge cycle, also includes the attenuation during storage. When the production is off the production line, the battery will undergo the first cycle to form an initial SEI film, and then during long-term storage, The SEI film will continue to grow on the anode surface, causing irreversible capacity loss. This loss is also a continuous, irreversible chronic attenuation. Based on this chronic attenuation, the length of time from production to the end of the battery's life is called the calendar life. It is understandable that the calendar life of batteries with different chemical systems is different, but the difference is small. Calendar life is used to calculate the capacity loss when the battery is left alone for a unit period of time, which is called the calendar capacity loss. Usually the calendar capacity loss is expressed in years. The unit is, a rough reference is 1% ~ 1.5%/year, the specific calculation needs to be based on standard working conditions.

When considering long-term battery life, the cumulative impact of calendar life is more obvious, which can be included in the prediction factors of battery life. This embodiment will set the calendar capacity loss based on the concept of calendar life, that is, the calendar life corresponding to the unit time Therefore, based on the single predicted capacity loss and the actual capacity of the target battery, the process of determining the predicted number of charge and discharge times includes:

Obtain the historical time length of each charge and discharge cycle according to the charge and discharge conditions;

Calculate the average of all historical time lengths to obtain the single time length of a single charge and discharge cycle;

Based on the life relationship of the target battery, determine the predicted remaining usage time;

The life relationship formula is A×T/N+B×T=SOH_r-SOH_min;

Among them, A is the single predicted capacity loss, T is the predicted remaining usage time, N is the single time length, B is the calendar capacity loss of the target battery, SOH_r is the actual capacity of the target battery, and SOH_min is the minimum operating time of the target battery. capacity;

Based on the predicted remaining usage time and single time length, the predicted number of charge and discharge times is determined.

It can be understood that in the life relationship formula, the right side of the equal sign is the remaining capacity loss, A×T/N is the capacity loss caused by charge and discharge cycles, and B×T is the capacity generated by the calendar capacity loss over time. Loss amount, by solving the equation according to the life relationship, the predicted remaining usage time can be obtained, providing users with a more accurate information reference.

Specifically, the usage conditions of the target battery refer to the historical usage conditions during operation of the target battery after it is installed on the current vehicle. The usage conditions used in this embodiment may be all historical usage conditions. , or it can be the recent usage conditions, such as the usage conditions within the most recent preset time period, or the usage conditions of the most recent preset number of charge and discharge cycles, the specific preset time period such as half a year or 3 months , the preset number of charge and discharge cycles, such as 10 or 20 times, can be set and adjusted according to the actual situation. On the one hand, the usage conditions used in this embodiment should be close to the current moment in terms of timeliness, so as to reflect the current user's usage habits. On the other hand, the usage conditions should be relatively stable. If there are obvious abnormalities in the usage conditions, value needs to be removed from it.

In an exemplary embodiment, based on the working condition coefficient relationship between the working condition and the correction coefficient, the process of determining the correction coefficient corresponding to the working condition includes:

Based on the charging and discharging conditions, obtain the average charging and discharging depth;

Based on the first relationship between the discharge depth and the first correction coefficient, a first correction coefficient corresponding to the average charge and discharge depth is determined; in the first relationship, the first correction coefficient is positively correlated with the charge and discharge depth;

Based on the second relationship between the temperature and the second correction coefficient, determine the second correction coefficient corresponding to the temperature working condition; in the second relationship, the second correction coefficient is positively correlated with the absolute value of the temperature difference, and the absolute value of the temperature difference is the temperature and standard The absolute value of the temperature difference;

The correction coefficient corresponding to the usage condition is determined based on the first correction coefficient and the second correction coefficient.

It can be understood that the usage conditions of the target battery include charge and discharge conditions and temperature conditions. The charge and discharge conditions mainly refer to the charge and discharge depth of the target battery. The charge and discharge depth includes the charging depth and the discharge depth. The discharge depth is The one-time discharge process of a power battery is the change in power from one charge to the next charge; the charging depth is the sequential charging process of the power battery, that is, the change in power from before charging to after charging is stopped. Usually the depth of charge and the depth of discharge are related to each other, so both the depth of charge and the depth of discharge can be included in the depth of charge and discharge for calculation. The greater the depth of charge and discharge, the greater the damage to the battery life, so the corresponding first correction coefficient is also greater, that is, the first correction coefficient is positively correlated with the depth of charge and discharge.

It can be understood that the temperature working condition refers to the operating temperature of the target battery. There is a suitable operating temperature range for the power battery. When the operating temperature range is exceeded, such as overheating or undercooling, the life of the power battery will be reduced, that is, the service life of the power battery will not be reached. The lower the working temperature is at the standard temperature, the greater the second correction coefficient is. When the working temperature exceeds the standard temperature, the higher the second correction coefficient is, the greater the second correction coefficient is. Therefore, in this embodiment, the second correction coefficient is set to be positively correlated with the absolute value of the temperature difference. The temperature difference The absolute value of the value is the absolute value of the difference between the temperature corresponding to the temperature working condition and the standard temperature.

It can be understood that after knowing the first correction coefficient and the second correction coefficient, the total correction coefficient can be determined, that is, the process of determining the correction coefficient corresponding to the operating condition based on the first correction coefficient and the second correction coefficient, including:

Determine the correction coefficient corresponding to the first correction coefficient and the second correction coefficient according to the third relationship between the first correction coefficient, the second correction coefficient and the correction coefficient;

In the third relationship, the first correction coefficient is positively correlated with the correction coefficient, and the second correction coefficient is positively correlated with the correction coefficient.

Specifically, the positive correlation mentioned above can be determined through test data and the actual usage conditions of the target battery as feedback. The positive correlation between the first relationship and the second relationship may be linear or exponential. In the third relationship, the correction coefficient It can be a weighted sum of the first correction coefficient and the second correction coefficient, or it can be a multiplication or exponential operation of the first correction coefficient and the second correction coefficient. The specific relational expression can be set according to the actual value.

It can be understood that the correction coefficient in this embodiment can be adjusted at any time according to the usage conditions, that is, the correction coefficient is determined according to the charging and discharging conditions and the temperature condition, and then the single predicted capacity loss is determined, and then the actual capacity loss of the target battery is determined. The actual single capacity loss is obtained through the capacity change process, and the single capacity loss is compared with the single predicted capacity loss and back-reduced to optimize the correction coefficient. Therefore, the method of this embodiment also includes:

Continuously obtain the usage conditions of the target battery;

The correction coefficient is adjusted based on the change process of the charge and discharge conditions, the temperature conditions and the actual capacity of the target battery corresponding to each charge and discharge cycle in the usage conditions.

It is understandable that by observing the usage conditions of the target battery, suggestions for the use of the target battery can be made to the user, such as reminding the user to place the battery at a suitable temperature, controlling the charging and discharging depth of the battery, etc., thereby consciously extending the life of the target battery. service life.

The method of the embodiment of the present application determines the corresponding correction coefficient by obtaining the current usage conditions of the target battery, and multiplies the correction coefficient with the single theoretical capacity loss to obtain the single predicted capacity loss, which is used to predict the subsequent prediction of the target battery. The number of charge and discharge times, since the impact of actual usage conditions on the target battery is taken into account, the calculated predicted number of charge and discharge times is more in line with the battery life under the current usage conditions of the target battery. Compared with the battery life under standard environment and standard working conditions, The test results are more accurate, thereby providing users with more effective information reference and improving user experience.

All the above optional technical solutions can be combined in any way to form optional embodiments of the present application, and will not be described again one by one. It should be understood that the sequence number of each step in the above embodiment does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiment of the present application.

The following are device embodiments of the present application, which can be used to execute method embodiments of the present application. For details not disclosed in the device embodiments of this application, please refer to the method embodiments of this application.

FIG. 3 is a schematic diagram of a real-time prediction device for battery life provided by an embodiment of the present application. As shown in Figure 3, the real-time prediction device of battery life includes:

The theoretical value acquisition module 301 is used to obtain the single theoretical capacity loss of the target battery; the single theoretical capacity loss is the ratio of the total theoretical capacity loss to the theoretical number of charge and discharge cycles;

The operating condition acquisition module 302 is used to obtain the operating conditions of the target battery; the operating conditions include charge and discharge conditions and temperature conditions;

The correction coefficient acquisition module 303 is used to determine the correction coefficient corresponding to the use condition based on the working condition coefficient relationship between the use condition and the correction coefficient; in the working condition coefficient relationship, when the use condition is the standard working condition, the correction coefficient is Minimum value, the minimum value is 1;

The calculation module 304 is used to multiply the correction coefficient by the single theoretical capacity loss to determine the single predicted capacity loss, and determine the predicted number of charges and discharges based on the single predicted capacity loss and the actual capacity of the target battery.

The device of the embodiment of the present application determines the corresponding correction coefficient by obtaining the current usage conditions of the target battery, and multiplies the correction coefficient with the single theoretical capacity loss to obtain the single predicted capacity loss, which is used to predict the subsequent prediction of the target battery. The number of charge and discharge times, since the impact of actual usage conditions on the target battery is taken into account, the calculated predicted number of charge and discharge times is more in line with the battery life under the current usage conditions of the target battery. Compared with the battery life under standard environment and standard working conditions, The test results are more accurate, thereby providing users with more effective information reference and improving user experience.

In an exemplary embodiment, the total theoretical capacity loss is the difference between the initial capacity and the minimum working capacity of the target battery;

Based on the single predicted capacity loss and the actual capacity of the target battery, the process of determining the predicted number of charges and discharges includes:

Calculate the difference between the actual capacity and the minimum working capacity of the target battery to obtain the remaining capacity loss;

Calculate the ratio of the remaining capacity loss to the single predicted capacity loss to obtain the predicted number of charge and discharge cycles.

In an exemplary embodiment, the calculation module 304 is also used to:

Obtain the historical time length of each charge and discharge cycle according to the charge and discharge conditions;

Calculate the average of all historical time lengths to obtain the single time length of a single charge and discharge cycle;

Based on the predicted number of charge and discharge cycles and the length of a single cycle, the predicted remaining usage time is determined.

In an exemplary embodiment, the process of determining the predicted number of charges and discharges based on the single predicted capacity loss and the actual capacity of the target battery includes:

Obtain the historical time length of each charge and discharge cycle according to the charge and discharge conditions;

Calculate the average of all historical time lengths to obtain the single time length of a single charge and discharge cycle;

Based on the life relationship of the target battery, determine the predicted remaining usage time;

The life relationship formula is A×T/N+B×T=SOH_r-SOH_min;

Among them, A is the single predicted capacity loss, T is the predicted remaining usage time, N is the single time length, B is the calendar capacity loss of the target battery, SOH_r is the actual capacity of the target battery, and SOH_min is the minimum operating time of the target battery. capacity;

Based on the predicted remaining usage time and single time length, the predicted number of charge and discharge times is determined.

In an exemplary embodiment, based on the working condition coefficient relationship between the working condition and the correction coefficient, the process of determining the correction coefficient corresponding to the working condition includes:

Based on the charging and discharging conditions, obtain the average charging and discharging depth;

Based on the first relationship between the discharge depth and the first correction coefficient, a first correction coefficient corresponding to the average charge and discharge depth is determined; in the first relationship, the first correction coefficient is positively correlated with the charge and discharge depth;

Based on the second relationship between the temperature and the second correction coefficient, determine the second correction coefficient corresponding to the temperature working condition; in the second relationship, the second correction coefficient is positively correlated with the absolute value of the temperature difference, and the absolute value of the temperature difference is the temperature and standard The absolute value of the temperature difference;

The correction coefficient corresponding to the usage condition is determined based on the first correction coefficient and the second correction coefficient.

In an exemplary embodiment, the process of determining the correction coefficient corresponding to the operating condition based on the first correction coefficient and the second correction coefficient includes:

Determine the correction coefficient corresponding to the first correction coefficient and the second correction coefficient according to the third relationship between the first correction coefficient, the second correction coefficient and the correction coefficient;

In the third relationship, the first correction coefficient is positively correlated with the correction coefficient, and the second correction coefficient is positively correlated with the correction coefficient.

In an exemplary embodiment, the working condition acquisition module 302 is also used to:

Continuously obtain the usage conditions of the target battery;

The correction coefficient is adjusted based on the change process of the charge and discharge conditions, the temperature conditions and the actual capacity of the target battery corresponding to each charge and discharge cycle in the usage conditions.

FIG. 4 is a schematic diagram of the electronic device 4 provided by the embodiment of the present application. As shown in FIG. 4 , the electronic device 4 of this embodiment includes: a processor 401 , a memory 402 , and a computer program 403 stored in the memory 402 and executable on the processor 401 . When the processor 401 executes the computer program 403, the steps in each of the above method embodiments are implemented. Alternatively, when the processor 401 executes the computer program 403, the functions of each module/unit in each of the above device embodiments are implemented.

The electronic device 4 may be a desktop computer, a notebook, a handheld computer, a cloud server and other electronic devices. The electronic device 4 may include, but is not limited to, a processor 401 and a memory 402. Those skilled in the art can understand that FIG. 4 is only an example of the electronic device 4 and does not constitute a limitation on the electronic device 4. The electronic device 4 may include more or fewer components than shown in the figure, or different components.

The processor 401 can be a central processing unit (Central Processing Unit, CPU), or other general-purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (ApplicA×Tion Specific IntegrA×Ted Circuit, ASIC ), Field-Programmable GA×Te Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

The memory 402 may be an internal storage unit of the electronic device 4 , for example, a hard disk or memory of the electronic device 4 . The memory 402 can also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SD) card, a flash memory card ( Flash Card), etc. The memory 402 may also include both an internal storage unit of the electronic device 4 and an external storage device. Memory 402 is used to store computer programs and other programs and data required by the electronic device.

Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above functional units and modules is used as an example. In actual applications, the above functions can be allocated to different functional units and modules according to needs. Module completion means dividing the internal structure of the device into different functional units or modules to complete all or part of the functions described above. Each functional unit and module in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be hardware-based. It can also be implemented in the form of software functional units.

Integrated modules/units, if implemented in the form of software functional units and sold or used as independent products, can be stored in a readable storage medium, such as a computer-readable storage medium. Based on this understanding, this application can implement all or part of the processes in the methods of the above embodiments. It can also be completed by instructing relevant hardware through a computer program. The computer program can be stored in a readable storage medium. The computer program can be processed by a processor. When executed, the steps of each of the above method embodiments may be implemented. A computer program may include computer program code, and the computer program code may be in the form of source code, object code, executable file or some intermediate form. Readable storage media may include: any entity or device that can carry computer program code, recording media, USB flash drives, mobile hard drives, magnetic disks, optical disks, computer memory, read-only memory (Read-Only Memory, ROM), random access Memory (Random Access Memory, RAM), electrical carrier signals, telecommunications signals, and software distribution media, etc.

The above embodiments are only used to illustrate the technical solutions of the present application, but are not intended to limit them. Although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments. Modifications are made to the recorded technical solutions, or equivalent substitutions are made to some of the technical features; these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application, and shall be included in this application. within the scope of protection.

Claims (10)

1. A real-time prediction method for battery life, characterized by including: Obtain the single theoretical capacity loss of the target battery; the single theoretical capacity loss is the ratio of the total theoretical capacity loss to the theoretical number of charge and discharge cycles; Obtain the usage conditions of the target battery; the usage conditions include charge and discharge conditions and temperature conditions; Based on the working condition coefficient relationship between the working condition and the correction coefficient, the correction coefficient corresponding to the working condition is determined; in the working condition coefficient relationship, when the using condition is a standard working condition, the correction coefficient The coefficient is the minimum value, and the minimum value is 1; Multiply the correction coefficient and the single theoretical capacity loss to determine the single predicted capacity loss; Based on the single predicted capacity loss and the actual capacity of the target battery, the predicted number of charges and discharges is determined. 2. The method according to claim 1, wherein the total theoretical capacity loss is the difference between the initial capacity and the minimum working capacity of the target battery; Based on the single predicted capacity loss and the actual capacity of the target battery, the process of determining the predicted number of charges and discharges includes: Calculate the difference between the actual capacity of the target battery and the minimum working capacity to obtain the remaining capacity loss; Calculate the ratio of the remaining capacity loss to the single predicted capacity loss to obtain the predicted number of charge and discharge cycles. 3. The method of claim 2, further comprising: Obtain the historical time length of each charge and discharge cycle according to the charge and discharge conditions; Calculate the average of all the historical time lengths to obtain the single time length of a single charge and discharge cycle; The predicted remaining usage time is determined based on the predicted number of charge and discharge cycles and the single time length. 4. The method according to claim 1, characterized in that, based on the single predicted capacity loss and the actual capacity of the target battery, the process of determining the predicted number of charges and discharges includes: Obtain the historical time length of each charge and discharge cycle according to the charge and discharge conditions; Calculate the average of all the historical time lengths to obtain the single time length of a single charge and discharge cycle; Based on the life relationship of the target battery, determine the predicted remaining usage time; The life span relationship formula is A×T/N+B×T=SOH_r-SOH_min; Wherein, A is the single predicted capacity loss, T is the predicted remaining usage time, N is the single time length, B is the calendar capacity loss of the target battery, and SOH_r is the target battery's Actual capacity, SOH_min is the minimum working capacity of the target battery; Based on the predicted remaining usage time and the single time length, the predicted number of charge and discharge times is determined. 5. The method according to claim 1, characterized in that, based on the working condition coefficient relationship between the working conditions and the correction coefficient, the process of determining the correction coefficient corresponding to the working condition includes: Based on the charging and discharging conditions, obtain the average charging and discharging depth; Based on the first relationship between the discharge depth and the first correction coefficient, the first correction coefficient corresponding to the average charge and discharge depth is determined; in the first relationship, the first correction coefficient is positively correlated with the charge and discharge depth; Based on the second relationship between temperature and the second correction coefficient, the second correction coefficient corresponding to the temperature operating condition is determined; in the second relationship, the second correction coefficient is positively correlated with the absolute value of the temperature difference, so The absolute value of the temperature difference is the absolute value of the difference between the temperature and the standard temperature; The correction coefficient corresponding to the usage condition is determined according to the first correction coefficient and the second correction coefficient. 6. The method according to claim 5, characterized in that the process of determining the correction coefficient corresponding to the usage condition based on the first correction coefficient and the second correction coefficient includes: Determine the correction coefficient corresponding to the first correction coefficient and the second correction coefficient according to the third relationship between the first correction coefficient, the second correction coefficient and the correction coefficient; In the third relationship, the first correction coefficient is positively correlated with the correction coefficient, and the second correction coefficient is positively correlated with the correction coefficient. 7. The method according to any one of claims 1 to 6, further comprising: Continuously obtain the usage conditions of the target battery; The correction coefficient is adjusted based on the change process of the charge and discharge conditions, the temperature conditions and the actual capacity of the target battery corresponding to each charge and discharge cycle in the use conditions. 8. A real-time prediction device for battery life, characterized in that it includes: The theoretical value acquisition module is used to obtain the single theoretical capacity loss of the target battery; the single theoretical capacity loss is the ratio of the total theoretical capacity loss to the theoretical number of charge and discharge cycles; A working condition acquisition module, used to acquire the usage conditions of the target battery; the usage conditions include charge and discharge conditions and temperature conditions; A correction coefficient acquisition module is used to determine the correction coefficient corresponding to the usage condition based on the working condition coefficient relationship between the usage condition and the correction coefficient; in the working condition coefficient relationship, when the usage condition is Under standard working conditions, the correction coefficient is the minimum value, and the minimum value is 1; A calculation module configured to multiply the correction coefficient by the single theoretical capacity loss to determine the single predicted capacity loss, and determine the predicted capacity based on the single predicted capacity loss and the actual capacity of the target battery. Number of charge and discharge times. 9. 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, the processor implements the claims as claimed in The steps of the method of any one of 1 to 7. 10. A readable storage medium storing a computer program, characterized in that when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are implemented.