CN118625154A - Battery remaining life prediction method, device, computer equipment and storage medium - Google Patents

Abstract

The invention relates to the field of electronic technology, and discloses a battery remaining life prediction method, a device, a computer device and a storage medium, wherein the method provided by the invention is characterized in that a second prediction model is determined based on the predicted battery capacity and the actual battery capacity respectively corresponding to different cycle times of a battery in an initial cycle stage and is used for representing the association relation between the cycle times of the battery to be detected in the cycle stage and the capacity prediction accuracy, the capacity prediction accuracy corresponding to different circulation times in the prediction circulation sub-stage can be determined based on the second prediction model, the battery capacity in the prediction circulation sub-stage in the first prediction model is corrected based on the determined capacity prediction accuracy, a corrected first prediction model is obtained, the residual life prediction result of the battery to be detected is determined based on the corrected first prediction model and the circulation times of the battery to be detected, and the accuracy of the residual life prediction result of the battery to be detected is effectively ensured.

Description

Battery remaining life prediction method, device, computer equipment and storage medium

Technical Field

The present invention relates to the field of electronic technology, and in particular to a method, device, computer equipment and storage medium for predicting remaining battery life.

Background Art

In the field of electrochemical energy storage, the estimation of the remaining life of batteries has always been a hot topic of research. Traditional estimation methods mainly predict the remaining life based on the capacity decay of batteries. By testing the battery, the test data is used to determine the relationship model between the number of battery cycles and the battery capacity decay, and the remaining life of the battery is determined based on the relationship model and the number of cycles the battery has undergone. However, a major challenge facing the current battery life prediction methods is that their prediction accuracy changes dynamically with the predicted number of cycles. However, these methods often fail to update and display this change in accuracy in real time, and therefore fail to accurately reflect the potential uncertainty behind the predicted life of the power station. This uncertainty is crucial for the long-term stable operation and maintenance of the power station. If the relationship model is only built based on the data of the battery in the test phase, and the battery life is calculated based on the built model, without considering the cycle data of the battery during operation, the prediction result of the remaining battery life will be inaccurate.

Summary of the invention

In view of this, the present invention provides a battery remaining life prediction method, device, computer equipment and storage medium to solve the problem of inaccurate prediction results when predicting the remaining life of the battery during operation through battery test data in the related art.

In a first aspect, the present invention provides a method for predicting the remaining life of a battery, the method comprising: obtaining the number of cycles of a battery to be tested and a first prediction model and a second prediction model, the first prediction model being used to characterize the correlation between the capacity of the battery to be tested and the number of cycles in a cycle stage, the cycle stage comprising an initial cycle sub-stage and a prediction cycle sub-stage, the second prediction model being used to characterize the correlation between the number of cycles of the battery to be tested and the capacity prediction accuracy in the cycle stage, the second prediction model being determined based on the predicted battery capacity and the actual battery capacity corresponding to different numbers of cycles in the initial cycle sub-stage; determining the capacity prediction accuracy corresponding to different numbers of cycles in the prediction cycle sub-stage based on the second prediction model; correcting the battery capacity of the prediction cycle sub-stage in the first prediction model based on the capacity prediction accuracy corresponding to different numbers of cycles in the prediction cycle sub-stage to obtain a corrected first prediction model; determining the number of cycles of the battery to be tested at a target capacity based on the corrected first prediction model, the target capacity being determined based on the initial capacity of the battery to be tested; determining the remaining life prediction result of the battery to be tested based on the number of cycles of the battery to be tested and the number of cycles of the battery to be tested at the target capacity.

The battery remaining life prediction method provided by the present invention determines the capacity prediction accuracy corresponding to different numbers of cycles in a prediction cycle sub-stage based on a second prediction model; corrects the battery capacity of the prediction cycle sub-stage in the first prediction model based on the capacity prediction accuracy corresponding to different numbers of cycles in the prediction cycle sub-stage to obtain a corrected first prediction model; determines the number of cycles of a battery to be tested at a target capacity based on the corrected first prediction model, wherein the target capacity is determined based on the initial capacity of the battery to be tested; and determines the remaining life prediction result of the battery to be tested based on the number of cycles of the battery to be tested and the number of cycles of the battery to be tested at the target capacity. The method provided by the present invention, the second prediction model is determined based on the predicted battery capacity and the actual battery capacity corresponding to different cycle numbers of the battery in the initial cycle stage, and is used to characterize the correlation between the cycle number of the battery to be tested in the cycle stage and the capacity prediction accuracy. Based on the second prediction model, the capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage can be determined, and the battery capacity of the prediction cycle sub-stage in the first prediction model is corrected based on the determined capacity prediction accuracy to obtain the corrected first prediction model, thereby ensuring the accuracy of the prediction result of the first prediction model, and determining the cycle number of the battery to be tested at the target capacity based on the corrected first prediction model, and determining the remaining life prediction result of the battery to be tested based on the number of cycles of the battery to be tested and the number of cycles of the battery to be tested at the target capacity, thereby effectively ensuring the accuracy of the remaining life prediction result of the battery to be tested.

In an optional embodiment, the first prediction model is constructed by the following steps: obtaining battery capacity sequence data of the battery to be tested during the test phase, the battery capacity sequence data being used to characterize the battery capacity change data of the battery to be tested as the number of cycles increases; and constructing a first prediction model for the battery to be tested based on the battery capacity sequence data.

In an optional embodiment, the second prediction model is constructed by the following steps: obtaining the actual battery capacity corresponding to different cycle numbers of the battery to be tested in the initial cycle sub-stage; determining the predicted battery capacity corresponding to different cycle numbers of the battery to be tested in the initial cycle sub-stage based on the first prediction model; calculating the capacity prediction accuracy corresponding to each cycle number of the initial cycle sub-stage of the battery to be tested based on the predicted battery capacity corresponding to different cycle numbers of the initial cycle sub-stage and the actual battery capacity; constructing a second prediction model based on the capacity prediction accuracy corresponding to each cycle number of the initial cycle sub-stage.

In an optional embodiment, the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be tested is calculated based on the predicted battery capacity corresponding to different cycle numbers in the initial cycle sub-stage and the actual battery capacity, including: calculating the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be tested by a target relationship, and the target relationship is:

in, represents the capacity forecast accuracy, Indicates the actual battery capacity. Represents the predicted battery capacity.

In a second aspect, the present invention provides a battery remaining life prediction device, the device comprising: an acquisition module, used to obtain the number of cycles of a battery to be tested and a first prediction model and a second prediction model, the first prediction model is used to characterize the correlation between the capacity of the battery to be tested in a cycle stage and the number of cycles, the cycle stage includes a first cycle sub-stage and a prediction cycle sub-stage, the first cycle sub-stage is used to characterize the initial cycle sub-stage of the battery to be tested, the second prediction model is used to characterize the correlation between the number of cycles of the battery to be tested in the cycle stage and the accuracy of capacity prediction, the second prediction model is determined based on the predicted battery capacity and the actual battery capacity corresponding to different cycle numbers in the initial cycle sub-stage; The first determination module is used to determine the capacity prediction accuracy corresponding to different numbers of cycles in the prediction cycle sub-stage based on the second prediction model; the correction module is used to correct the battery capacity of the prediction cycle sub-stage in the first prediction model based on the capacity prediction accuracy corresponding to different numbers of cycles in the prediction cycle sub-stage to obtain the corrected first prediction model; the second determination module is used to determine the number of cycles of the battery to be tested at the target capacity based on the corrected first prediction model, and the target capacity is determined based on the initial capacity of the battery to be tested; the third determination module is used to determine the remaining life prediction result of the battery to be tested based on the number of cycles of the battery to be tested and the number of cycles of the battery to be tested at the target capacity.

In an optional embodiment, the first prediction model is constructed by the following steps: obtaining battery capacity sequence data of the battery to be tested during the test phase, the battery capacity sequence data being used to characterize the battery capacity change data of the battery to be tested as the number of cycles increases; and constructing a first prediction model for the battery to be tested based on the battery capacity sequence data.

In an optional embodiment, the second prediction model is constructed by the following steps: obtaining the actual battery capacity corresponding to different cycle numbers of the battery to be tested in the initial cycle sub-stage; determining the predicted battery capacity corresponding to different cycle numbers of the battery to be tested in the initial cycle sub-stage based on the first prediction model; calculating the capacity prediction accuracy corresponding to each cycle number of the initial cycle sub-stage of the battery to be tested based on the predicted battery capacity corresponding to different cycle numbers of the initial cycle sub-stage and the actual battery capacity; constructing a second prediction model based on the capacity prediction accuracy corresponding to each cycle number of the initial cycle sub-stage.

In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the battery remaining life prediction method of the above-mentioned first aspect or any corresponding embodiment thereof by executing the computer instructions.

In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the battery remaining life prediction method of the first aspect or any corresponding embodiment thereof.

In a fifth aspect, the present invention provides a computer program product, comprising computer instructions for causing a computer to execute the battery remaining life prediction method of the first aspect or any corresponding embodiment thereof.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

FIG1 is a schematic diagram of a flow chart of a method for predicting remaining battery life according to an embodiment of the present invention;

2 is a structural block diagram of a device for predicting remaining battery life according to an embodiment of the present invention;

FIG. 3 is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention.

DETAILED DESCRIPTION

In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

In the related art, the battery is tested and the test data is used to determine the relationship model between the number of battery cycles and the battery capacity attenuation, and the remaining life of the battery is determined based on the relationship model and the number of cycles the battery has undergone. However, a major challenge facing current battery life prediction methods is that their prediction accuracy changes dynamically with the predicted number of cycles. However, these methods are often unable to update and display this change in accuracy in real time, and therefore cannot accurately reflect the potential uncertainty behind the predicted life of the power station. This uncertainty is critical to the long-term stable operation and maintenance of the power station. If a relationship model is constructed based only on the data of the battery during the testing phase, and the battery life is calculated based on the constructed model, without considering the cycle data of the battery during operation, the prediction result of the remaining battery life will be inaccurate.

In view of this, a battery remaining life prediction method provided in an embodiment of the present application can be applied to a server to realize the prediction of the remaining life of the battery. In the method provided by the present invention, the second prediction model is determined based on the predicted battery capacity and the actual battery capacity corresponding to different cycle numbers of the battery in the initial cycle stage, and is used to characterize the correlation between the number of cycles of the battery to be tested in the cycle stage and the capacity prediction accuracy. Based on the second prediction model, the capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage can be determined. Based on the determined capacity prediction accuracy, the battery capacity of the prediction cycle sub-stage in the first prediction model is corrected to obtain the corrected first prediction model, which ensures the accuracy of the prediction result of the first prediction model, determines the number of cycles of the battery to be tested at the target capacity based on the corrected first prediction model, and determines the remaining life prediction result of the battery to be tested based on the number of cycles of the battery to be tested and the number of cycles of the battery to be tested at the target capacity, which effectively ensures the accuracy of the remaining life prediction result of the battery to be tested.

According to an embodiment of the present invention, an embodiment of a battery remaining life prediction method is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

In this embodiment, a method for predicting the remaining battery life is provided, which can be used for the above-mentioned server. FIG. 1 is a flow chart of the method for predicting the remaining battery life according to an embodiment of the present invention. As shown in FIG. 1 , the process includes the following steps:

Step S101, obtaining the number of cycles of the battery to be tested and the first prediction model and the second prediction model.

Exemplarily, the first prediction model is used to characterize the correlation between the capacity and the number of cycles of the battery to be tested in the cycle stage, and the cycle stage includes an initial cycle sub-stage and a prediction cycle sub-stage. The second prediction model is used to characterize the correlation between the number of cycles of the battery to be tested in the cycle stage and the accuracy of capacity prediction. The second prediction model is determined based on the predicted battery capacity and the actual battery capacity corresponding to different cycle numbers in the initial cycle sub-stage. In an embodiment of the present application, the battery to be predicted may be an energy storage battery for which a remaining life prediction is required. The battery to be predicted is tested N times (for example, 500 to 1000 times) in the early stage of operation to obtain test data, and a first prediction model can be fitted based on the test data. The number of cycles is used to characterize the sum of the number of test cycles of the battery to be tested and the number of cycles in the battery operation stage.

In some optional implementations, the first prediction model is constructed by the following steps:

Step a1, obtaining battery capacity sequence data of the battery to be tested during the test phase, wherein the battery capacity sequence data is used to characterize battery capacity change data of the battery to be tested as the number of cycles increases.

Step a2, constructing a first prediction model of the battery to be tested based on the battery capacity sequence data. Exemplarily, in the embodiment of the present application, the battery capacity sequence data can be used to fit and generate a first prediction model, and the fitting curve is used to characterize the correlation between the capacity of the battery to be tested and the number of cycles in the cycle stage.

In some optional implementations, the second prediction model is constructed by the following steps:

Step b1, obtaining the actual battery capacity corresponding to different cycle numbers of the battery under test in the initial cycle sub-stage. For example, in the embodiment of the present application, the initial cycle sub-stage can be understood as the initial stage of the battery cycle stage. After each first cycle, the actual battery capacity of the corresponding cycle can be obtained, and the actual battery capacity is uniformly represented by the discharge capacity.

Step b2: determining predicted battery capacities corresponding to different cycle numbers of the battery to be tested in the initial cycle sub-stage based on the first prediction model.

Step b3, calculating the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be tested based on the predicted battery capacity corresponding to different cycle numbers in the initial cycle sub-stage and the actual battery capacity.

For example, in the embodiment of the present application, the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be tested is calculated by a target relationship, and the target relationship is:

in, represents the capacity forecast accuracy, Indicates the actual battery capacity. Represents the predicted battery capacity.

Step b4: constructing a second prediction model based on the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage.

Exemplarily, in the embodiment of the present application, the second prediction model can be expressed by the following formula:

Where a , b , and c represent the fitting coefficients, and N represents the number of cycles.

Step S102: determining the capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-phase based on the second prediction model.

Illustratively, in the embodiment of the present application, the capacity prediction accuracy corresponding to different numbers of cycles in the cycle sub-stage is predicted based on the second prediction model to obtain a prediction result.

Step S103, based on the capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage, the battery capacity of the prediction cycle sub-stage in the first prediction model is corrected to obtain a corrected first prediction model.

Exemplarily, the capacity prediction accuracy can characterize the deviation between the predicted battery capacity and the actual battery capacity. Based on the capacity prediction accuracy corresponding to different numbers of cycles in the prediction cycle sub-stage, the battery capacity of the prediction cycle sub-stage in the first prediction model is corrected to obtain a corrected first prediction model.

Step S104, determining the number of cycles of the battery to be tested at a target capacity based on the modified first prediction model, wherein the target capacity is determined based on an initial capacity of the battery to be tested.

Exemplarily, the number of cycles of the battery to be tested at the target capacity can be used to characterize the predicted cycle life of the battery to be tested. In the embodiment of the present application, the target capacity can be 80% of the initial capacity, and the corresponding number of cycles when the capacity of the battery to be tested is 80% of the initial capacity is the predicted cycle life of the battery to be tested.

Step S105 , determining a remaining life prediction result of the battery to be tested based on the number of cycles of the battery to be tested and the number of cycles of the battery to be tested at a target capacity.

For example, in the embodiment of the present application, the number of cycles of the battery under test at the target capacity minus the number of cycles completed is used to obtain the remaining cycle life of the battery under test. Specifically, if the number of cycles of the battery under test at the target capacity is L, and it is assumed that it has run L0 times, then the d value corresponding to L-L0 is the accuracy corresponding to the predicted life L updated in real time.

The battery remaining life prediction method provided in this embodiment, the second prediction model is determined based on the predicted battery capacity and the actual battery capacity corresponding to different cycle numbers of the battery in the initial cycle stage, and is used to characterize the correlation between the cycle number of the battery to be tested in the cycle stage and the capacity prediction accuracy. Based on the second prediction model, the capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage can be determined, and the battery capacity of the prediction cycle sub-stage in the first prediction model is corrected based on the determined capacity prediction accuracy to obtain the corrected first prediction model, thereby ensuring the accuracy of the prediction result of the first prediction model, determining the cycle number of the battery to be tested at the target capacity based on the corrected first prediction model, and determining the remaining life prediction result of the battery to be tested based on the number of cycles of the battery to be tested and the number of cycles of the battery to be tested at the target capacity, thereby effectively ensuring the accuracy of the remaining life prediction result of the battery to be tested.

A battery remaining life prediction method provided by the present application is described below through a specific embodiment.

Example:

By performing multiple tests in the initial operation stage of the battery, an initial cycle-capacity simulation curve (first prediction model) is generated, and the accuracy between the actual capacity and the simulated capacity is calculated after each cycle, and a relationship curve between the number of future cycles and accuracy is constructed (second prediction model). Then, during the entire life cycle of the battery, the second prediction model is used to correct the first prediction model, update the life prediction results in real time, and continuously correct the capacity prediction value and the accuracy of the life prediction based on the actual operation data.

Assume that a battery has obtained a cycle-capacity simulation curve after the initial test phase. In the actual operation process, the actual capacity obtained after the first cycle is Cr1, and the accuracy d1 of the first cycle is calculated. Similarly, the accuracy data of several future cycles are used to fit the future cycle number-accuracy relationship curve. In subsequent cycles, the capacity prediction curve is updated based on real-time data after each cycle, and the predicted life and its accuracy are adjusted according to the accuracy relationship curve.

In this embodiment, a battery remaining life prediction device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and/or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

This embodiment provides a battery remaining life prediction device, as shown in FIG2 , comprising:

An acquisition module 201 is used to acquire the number of cycles of the battery to be tested and a first prediction model and a second prediction model, wherein the first prediction model is used to characterize the correlation between the capacity of the battery to be tested and the number of cycles in the cycle stage, the cycle stage includes a first cycle sub-stage and a prediction cycle sub-stage, the first cycle sub-stage is used to characterize the initial cycle sub-stage of the battery to be tested, the second prediction model is used to characterize the correlation between the number of cycles of the battery to be tested and the accuracy of capacity prediction in the cycle stage, and the second prediction model is determined based on the predicted battery capacity and the actual battery capacity corresponding to different cycle numbers in the initial cycle sub-stage;

A first determination module 202 is used to determine the capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage based on the second prediction model;

A correction module 203, configured to correct the battery capacity of the prediction cycle sub-stage in the first prediction model based on the capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage, to obtain a corrected first prediction model;

A second determination module 204 is used to determine the number of cycles of the battery to be tested at a target capacity based on the modified first prediction model, where the target capacity is determined based on an initial capacity of the battery to be tested;

The third determination module 205 is used to determine the remaining life prediction result of the battery to be tested based on the number of cycles of the battery to be tested and the number of cycles of the battery to be tested at the target capacity.

In some optional implementations, the first prediction model is constructed by the following steps:

Obtaining battery capacity sequence data of the battery to be tested during the test phase, where the battery capacity sequence data is used to characterize battery capacity change data of the battery to be tested as the number of cycles increases;

A first prediction model for the battery to be tested is constructed based on the battery capacity sequence data.

In some optional implementations, the second prediction model is constructed by the following steps:

Obtain the actual battery capacity of the battery under test corresponding to different cycle numbers in the initial cycle sub-stage;

Determine the predicted battery capacities corresponding to different cycle numbers of the battery to be tested in the initial cycle sub-stage based on the first prediction model;

Based on the predicted battery capacities and actual battery capacities corresponding to different cycle numbers in the initial cycle sub-stage, the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be tested is calculated;

A second prediction model is constructed based on the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage.

In some optional implementations, the capacity prediction accuracy corresponding to each number of cycles in the initial cycle sub-stage of the battery to be tested is calculated based on the predicted battery capacity corresponding to different numbers of cycles in the initial cycle sub-stage and the actual battery capacity, including:

The capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be tested is calculated by the target relationship, and the target relationship is:

in, represents the capacity forecast accuracy, Indicates the actual battery capacity. Represents the predicted battery capacity.

The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

The battery remaining life prediction device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and/or other devices that can provide the above functions.

An embodiment of the present invention further provides a computer device having the battery remaining life prediction device shown in FIG. 2 .

Please refer to FIG. 3, which is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention. As shown in FIG. 3, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. The various components are connected to each other using different buses for communication, and can be installed on a common motherboard or installed in other ways as needed. The processor can process instructions executed in the computer device, including instructions stored in or on the memory to display graphical information of the GUI on an external input/output device (such as a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and/or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). In FIG. 3, a processor 10 is taken as an example.

The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

The memory 20 stores instructions executable by at least one processor 10, so that the at least one processor 10 executes the method shown in the above embodiment.

The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor or hardware, the method shown in the above embodiment is implemented.

A part of the present invention may be applied as a computer program product, such as a computer program instruction, which, when executed by a computer, can call or provide the method and/or technical solution according to the present invention through the operation of the computer. Those skilled in the art should understand that the existence of the computer program instruction in a computer-readable medium includes, but is not limited to, a source file, an executable file, an installation package file, etc., and accordingly, the way in which the computer program instruction is executed by the computer includes, but is not limited to: the computer directly executes the instruction, or the computer compiles the instruction and then executes the corresponding compiled program, or the computer reads and executes the instruction, or the computer reads and installs the instruction and then executes the corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible to the computer.

Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims (10)

1.A method for predicting remaining life of a battery, the method comprising:

Acquiring the circulation times of a battery to be tested, a first prediction model and a second prediction model, wherein the first prediction model is used for representing the association relation between the capacity of the battery to be tested in a circulation stage and the circulation times, the circulation stage comprises an initial circulation sub-stage and a prediction circulation sub-stage, the second prediction model is used for representing the association relation between the circulation times of the battery to be tested in the circulation stage and the capacity prediction accuracy, and the second prediction model is determined based on the prediction battery capacity and the actual battery capacity which correspond to different circulation times of the initial circulation sub-stage respectively;

determining capacity prediction accuracy corresponding to different circulation times in a prediction circulation sub-stage based on the second prediction model;

Correcting the battery capacity of the prediction circulation sub-stage in the first prediction model based on capacity prediction accuracy corresponding to different circulation times in the prediction circulation sub-stage respectively, so as to obtain a corrected first prediction model;

Determining the cycle times of the battery to be tested under the target capacity based on the corrected first prediction model, wherein the target capacity is determined based on the initial capacity of the battery to be tested;

And determining a residual life prediction result of the battery to be tested based on the circulating times of the battery to be tested and the circulating times of the battery to be tested under the target capacity.

2. The method of claim 1, wherein the first predictive model is constructed by:

Acquiring battery capacity sequence data of a battery to be tested in a test stage, wherein the battery capacity sequence data is used for representing battery capacity change data of the battery to be tested along with the increase of cycle times;

and constructing a first prediction model of the battery to be tested based on the battery capacity sequence data.

3. The method according to claim 2, wherein the second predictive model is constructed by:

Acquiring actual battery capacities of the battery to be tested, which correspond to different cycle times in an initial cycle sub-stage;

determining predicted battery capacities of the battery to be measured, which correspond to different cycle times of the battery to be measured in an initial cycle sub-stage, based on the first prediction model;

calculating capacity prediction accuracy corresponding to each cycle number of the initial cycle sub-stage of the battery to be detected respectively based on the predicted battery capacity and the actual battery capacity corresponding to different cycle numbers of the initial cycle sub-stage respectively;

And constructing a second prediction model based on capacity prediction accuracy corresponding to each cycle number of the initial cycle sub-stage.

4. The method according to claim 3, wherein calculating the capacity prediction accuracy of the battery to be measured for each cycle number of the initial cycle sub-stage based on the predicted battery capacity and the actual battery capacity for each cycle number of the initial cycle sub-stage, respectively, comprises:

calculating capacity prediction accuracy corresponding to each cycle time of the initial cycle sub-stage of the battery to be detected through a target relational expression, wherein the target relational expression is as follows:

wherein, Representing the accuracy of the capacity prediction,Indicating the actual capacity of the battery,Representing the predicted battery capacity.

5. A battery remaining life prediction apparatus, characterized by comprising:

The system comprises an acquisition module, a first prediction module and a second prediction module, wherein the acquisition module is used for acquiring the circulation times of a battery to be tested, the first prediction module is used for representing the association relation between the capacity of the battery to be tested in a circulation stage and the circulation times, the circulation stage comprises a first circulation sub-stage and a prediction circulation sub-stage, the first circulation sub-stage is used for representing the initial circulation sub-stage of the battery to be tested, the second prediction module is used for representing the association relation between the circulation times of the battery to be tested in the circulation stage and the capacity prediction accuracy, and the second prediction module is determined based on the predicted battery capacity and the actual battery capacity which correspond to different circulation times of the initial circulation sub-stage respectively;

The first determining module is used for determining capacity prediction accuracy corresponding to different circulation times in a prediction circulation sub-stage based on the second prediction model;

The correction module is used for correcting the battery capacity of the prediction circulation sub-stage in the first prediction model based on the capacity prediction accuracy corresponding to different circulation times in the prediction circulation sub-stage, so as to obtain a corrected first prediction model;

The second determining module is used for determining the circulation times of the battery to be tested under the target capacity based on the corrected first prediction model, and the target capacity is determined based on the initial capacity of the battery to be tested;

And the third determining module is used for determining a residual life prediction result of the battery to be tested based on the circulated times of the battery to be tested and the circulated times of the battery to be tested under the target capacity.

6. The apparatus of claim 5, wherein the first predictive model is constructed by:

Acquiring battery capacity sequence data of a battery to be tested in a test stage, wherein the battery capacity sequence data is used for representing battery capacity change data of the battery to be tested along with the increase of cycle times;

and constructing a first prediction model of the battery to be tested based on the battery capacity sequence data.

7. The apparatus of claim 6, wherein the second predictive model is constructed by:

Acquiring actual battery capacities of the battery to be tested, which correspond to different cycle times in an initial cycle sub-stage;

determining predicted battery capacities of the battery to be measured, which correspond to different cycle times of the battery to be measured in an initial cycle sub-stage, based on the first prediction model;

calculating capacity prediction accuracy corresponding to each cycle number of the initial cycle sub-stage of the battery to be detected respectively based on the predicted battery capacity and the actual battery capacity corresponding to different cycle numbers of the initial cycle sub-stage respectively;

And constructing a second prediction model based on capacity prediction accuracy corresponding to each cycle number of the initial cycle sub-stage.

8. A computer device, comprising:

A memory and a processor in communication with each other, the memory having stored therein computer instructions, the processor executing the computer instructions to perform the battery remaining life prediction method of any one of claims 1 to 4.

9. A computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the battery remaining life prediction method according to any one of claims 1 to 4.

10. A computer program product comprising computer instructions for causing a computer to perform the battery remaining life prediction method of any one of claims 1 to 4.