Method and apparatus for predicting remaining battery life, computer device, and storage medium

Abstract

A method and apparatus for predicting remaining battery life, a computer device, and a storage medium are provided.SOLUTION: Determining a second prediction model according to the predicted battery capacity and the actual battery capacity respectively corresponding to different numbers of cycles of the battery in the initial cycle stage, where the second prediction model is used for representing an association relationship between the number of cycles of the to-be-predicted battery in the cycle stage and the capacity prediction accuracy, and determining the capacity prediction accuracy respectively corresponding to different numbers of cycles in the prediction cycle substage according to the second prediction model; The battery capacity of the prediction cycle sub-stage of the first prediction model is corrected based on the determined capacity prediction accuracy to obtain a corrected first prediction model, and the remaining life prediction result of the battery to be predicted is determined based on the corrected first prediction model and the number of cycles of the battery to be predicted. This effectively ensures the accuracy of the prediction result of the remaining service life of the battery to be predicted.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to the field of electronics, and more particularly to a method, apparatus, computer device and storage medium for predicting the remaining life of a battery. [Background technology]

[0002] In the field of electrochemical energy storage, estimating the remaining life of batteries has always been a focus of research. Traditional estimation methods primarily predict the remaining life based on the battery's capacity fade. Testing a battery involves establishing a relationship model between the number of battery cycles and the battery's capacity fade using the test data. The remaining life of the battery is then determined based on the relationship model and the number of cycles already performed by the battery. However, one major challenge facing current battery life prediction methods is that their prediction accuracy dynamically changes with the number of predicted cycles. However, these methods often fail to update and display this change in accuracy in real time, thereby failing to accurately reflect the underlying uncertainty inherent in the use of predicted life by power plants. This uncertainty is crucial for the long-term stable operation and maintenance of power plants. Building a relationship model based solely on data from the battery testing phase and calculating the battery life based on the established model without considering the cycle data during battery operation results in insufficient accuracy in the remaining life prediction results. Summary of the Invention [Problem to be solved by the invention]

[0003] In view of the above circumstances, the present invention provides a method, apparatus, computer device and storage medium for predicting the remaining life of a battery to solve the problem in the related art that the accuracy of the prediction results is insufficient when predicting the remaining life of a battery during operation using battery test data. [Means for solving the problem]

[0004] In a first aspect, the present invention provides a method for determining a battery capacity prediction accuracy based on a predicted battery capacity and an actual battery capacity corresponding to different cycle numbers in the initial cycle sub-stage, and a step of obtaining a number of cycles of a battery to be predicted and a first prediction model and a second prediction model, the first prediction model being used to represent a relationship between the capacity and the number of cycles in a cycle stage of the battery to be predicted, the cycle stage including an initial cycle sub-stage and a predicted cycle sub-stage, the second prediction model being used to represent a relationship between the number of cycles in the cycle stage of the battery to be predicted and the accuracy of the capacity prediction, the second prediction model being determined based on predicted battery capacity and actual battery capacity corresponding to different cycle numbers in the initial cycle sub-stage, and a step of determining a capacity prediction accuracy based on the second prediction model and a second prediction model. The present invention provides a method for predicting the remaining life of a battery, the method comprising: a step of determining a capacity prediction accuracy corresponding to each cycle count; a step of correcting the battery capacity of the prediction cycle sub-stage of the first prediction model based on the capacity prediction accuracy corresponding to each different cycle count in the prediction cycle sub-stage to obtain a corrected first prediction model; a step of determining the number of cycles at a target capacity of the battery to be predicted based on the corrected first prediction model, wherein the target capacity is determined based on the initial capacity of the battery to be predicted; and a step of determining a remaining life prediction result of the battery to be predicted based on the number of cycles already performed of the battery to be predicted and the number of cycles at the target capacity of the battery to be predicted.

[0005] In the battery remaining life prediction method of the present invention, capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage is determined based on the second prediction model, the battery capacity of the prediction cycle sub-stage of the first prediction model is corrected based on the capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage, a corrected first prediction model is obtained, and the number of cycles at the target capacity of the battery to be predicted is determined based on the corrected first prediction model, the target capacity is determined based on the initial capacity of the battery to be predicted, and the remaining life prediction result of the battery to be predicted is determined based on the number of cycles already performed of the battery to be predicted and the number of cycles at the target capacity of the battery to be predicted. In the method of the present invention, the second prediction model is determined based on the predicted battery capacity and actual battery capacity corresponding to different cycle numbers in the initial cycle stage of the battery, and is used to represent the relationship between the cycle number and the capacity prediction accuracy in the cycle stage of the battery to be predicted. The capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage can be determined based on the second prediction model. The battery capacity in the prediction cycle sub-stage of the first prediction model is corrected based on the determined capacity prediction accuracy to obtain a corrected first prediction model, thereby ensuring the accuracy of the prediction result of the first prediction model. The cycle number at the target capacity of the battery to be predicted is determined based on the corrected first prediction model. The remaining life prediction result of the battery to be predicted is determined based on the number of cycles already performed by the battery to be predicted and the number of cycles at the target capacity of the battery to be predicted, thereby effectively ensuring the accuracy of the remaining life prediction result of the battery to be predicted.

[0006] In one alternative embodiment, the first prediction model is constructed by the steps of: acquiring battery capacity sequence data during a test phase of the battery to be predicted, wherein the battery capacity sequence data is used to represent battery capacity change data of the battery to be predicted as the number of cycles increases; and constructing a first prediction model of the battery to be predicted based on the battery capacity sequence data.

[0007] In one alternative embodiment, the second prediction model is constructed by the steps of: obtaining actual battery capacities corresponding to different cycle numbers in the initial cycle sub-stage of the battery to be predicted; determining predicted battery capacities corresponding to different cycle numbers in the initial cycle sub-stage of the battery to be predicted based on the first prediction model; calculating capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be predicted based on the predicted battery capacities and actual battery capacities corresponding to different cycle numbers in the initial cycle sub-stage; and constructing a second prediction model based on the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage.

[0008] In one alternative embodiment, the step of calculating a capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be predicted based on the predicted battery capacity and the actual battery capacity corresponding to each different cycle number in the initial cycle sub-stage is a step of calculating a capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be predicted using a target relationship, wherein the target relationship is JPEG2026031417000002.jpg11170, where d indicates the capacity prediction accuracy, and C r indicates the actual battery capacity, and C s includes indicating the predicted battery capacity.

[0009] In a second aspect, the present invention provides an acquisition module for acquiring a number of cycles of a battery to be predicted, and a first prediction model and a second prediction model, wherein the first prediction model is used to represent a relationship between a capacity and a number of cycles at a cycle stage of the battery to be predicted, the cycle stage including a first cycle sub-stage and a prediction cycle sub-stage, the first cycle sub-stage being used to represent an initial cycle sub-stage of the battery to be predicted, and the second prediction model is used to represent a relationship between a number of cycles at a cycle stage of the battery to be predicted and a capacity prediction accuracy at the cycle stage of the battery to be predicted, the second prediction model being determined based on predicted battery capacities and actual battery capacities respectively corresponding to different cycle numbers at the initial cycle sub-stage; and an acquisition module for acquiring a number of cycles of a battery to be predicted, and a first prediction model and a second prediction model, wherein the first prediction model is used to represent a relationship between a capacity and a number of cycles at a cycle stage of the battery to be predicted, the cycle sub-stage including a first cycle sub-stage and a prediction cycle sub-stage, the first cycle sub-stage being used to represent an initial cycle sub-stage of the battery to be predicted, and the second prediction model is used to represent a relationship between a number of cycles at a cycle stage of the battery to be predicted and a capacity prediction accuracy at the cycle sub-stage of the battery to be predicted, the second prediction model being determined based on predicted battery capacities and actual battery capacities respectively corresponding to different cycle numbers at the initial cycle sub-stage; a correction module for correcting the battery capacity of the prediction cycle sub-stage of the first prediction model based on the capacity prediction accuracies corresponding to the different cycle numbers in the prediction cycle sub-stage to obtain a corrected first prediction model; a second determination module for determining the number of cycles at a target capacity of the battery to be predicted based on the corrected first prediction model, where the target capacity is determined based on the initial capacity of the battery to be predicted; and a third determination module for determining a remaining life prediction result of the battery to be predicted based on the number of cycles already performed of the battery to be predicted and the number of cycles at the target capacity of the battery to be predicted.

[0010] In one alternative embodiment, the first prediction model is constructed by the steps of: acquiring battery capacity sequence data during a test phase of the battery to be predicted, wherein the battery capacity sequence data is used to represent battery capacity change data of the battery to be predicted as the number of cycles increases; and constructing a first prediction model of the battery to be predicted based on the battery capacity sequence data.

[0011] In one alternative embodiment, the second prediction model is constructed by the steps of: obtaining actual battery capacities corresponding to different cycle numbers in the initial cycle sub-stage of the battery to be predicted; determining predicted battery capacities corresponding to different cycle numbers in the initial cycle sub-stage of the battery to be predicted based on the first prediction model; calculating capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be predicted based on the predicted battery capacities and actual battery capacities corresponding to different cycle numbers in the initial cycle sub-stage; and constructing a second prediction model based on the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage.

[0012] In a third aspect, the present invention provides a computer device including a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the battery remaining life prediction method of the first aspect or any one of the embodiments corresponding thereto.

[0013] In a fourth aspect, the present invention provides a computer-readable storage medium having stored thereon computer instructions for causing a computer to execute the battery remaining life prediction method of the first aspect or any one of the embodiments corresponding thereto.

[0014] In a fifth aspect, the present invention provides a computer program product including computer instructions for causing a computer to execute the method for predicting remaining battery life according to the first aspect or any one of the embodiments corresponding thereto.

[0015] In order to more clearly describe the specific embodiments of the present invention or the technical solutions of the prior art, the following will briefly describe the drawings that need to be used to describe the specific embodiments or the prior art. It is obvious that the drawings described below are some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without any creative work. [Brief explanation of the drawings]

[0016] [Figure 1] 2 is a flowchart of a method for predicting the remaining life of a battery according to an embodiment of the present invention. [Figure 2] 1 is a structural block diagram of a battery remaining life prediction device according to an embodiment of the present invention; [Figure 3] FIG. 1 is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0017] In order to clarify the objectives, technical solutions and advantages of the embodiments of the present invention, the technical solutions of the embodiments of the present invention will be described clearly and completely below with reference to the drawings of the embodiments of the present invention, and it is obvious that the described embodiments are only some of the embodiments of the present invention, and not all of the embodiments. All other embodiments that a person skilled in the art can obtain based on the embodiments of the present invention without any creative work fall within the scope of protection of the present invention.

[0018] In related art, batteries are tested, and the test data is used to determine a relationship model between the number of battery cycles and the battery's capacity fade. The remaining battery life is then determined based on the relationship model and the number of cycles the battery has already undergone. However, one major challenge facing current battery life prediction methods is that their prediction accuracy dynamically changes with the number of predicted cycles. However, these methods often cannot update and display this change in accuracy in real time, and therefore cannot accurately reflect the uncertainty inherent in the use of predicted battery life by power plants. This uncertainty is crucial for the long-term stable operation and maintenance of power plants. If a relationship model is built based solely on data from the battery test phase and the battery life is calculated based on the built model, without considering cycle data during battery operation, the accuracy of the remaining battery life prediction results will be insufficient.

[0019] In view of the above, a battery remaining life prediction method according to an embodiment of the present application can be applied to a server to predict the remaining life of a battery. In the method according to the present invention, a second prediction model is determined based on predicted battery capacities and actual battery capacities corresponding to different cycle counts in an initial cycle stage of the battery, and is used to represent the relationship between the cycle count and capacity prediction accuracy in the cycle stage of the battery to be predicted. Capacity prediction accuracy corresponding to different cycle counts in the prediction cycle sub-stage can be determined based on the second prediction model. The battery capacity in the prediction cycle sub-stage of the first prediction model is modified based on the determined capacity prediction accuracy to obtain a modified first prediction model, thereby ensuring the accuracy of the prediction result of the first prediction model. The number of cycles at the target capacity of the battery to be predicted is determined based on the modified first prediction model. A remaining life prediction result of the battery to be predicted is determined based on the number of cycles already performed by the battery to be predicted and the number of cycles at the target capacity of the battery to be predicted, thereby effectively ensuring the accuracy of the remaining life prediction result of the battery to be predicted.

[0020] According to an embodiment of the present invention, an embodiment of a method for predicting remaining battery life is provided, and it is noted that the steps illustrated in the flowcharts of the drawings may be performed, for example, by a computer system having a set of computer-executable instructions, and that although a logical order is shown in the flowchart, in some cases the steps shown or described may be performed in an order different from that shown here.

[0021] In this embodiment, a method for predicting remaining battery life is provided, which can be applied to the above-mentioned server. FIG. 1 is a flowchart of the method for predicting remaining battery life according to an embodiment of the present invention. As shown in FIG. 1, the flow includes the following steps:

[0022] Step S101: The number of cycles already performed for the battery to be predicted, the first prediction model, and the second prediction model are obtained.

[0023] For example, the first prediction model represents the relationship between the capacity and the number of cycles in a cycle stage of the battery to be predicted, where the cycle stage includes an initial cycle sub-stage and a predicted cycle sub-stage. The second prediction model represents the relationship between the number of cycles in the cycle stage of the battery to be predicted and the accuracy of the 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 needs to be performed. The battery to be predicted may be tested for N cycles (e.g., 500 to 1000) in the initial stage of operation to obtain test data, and the first prediction model may be obtained by fitting based on the test data. The number of cycles used represents the sum of the test cycle number of the battery to be predicted and the cycle number in the operating stage of the battery.

[0024] In some alternative embodiments, the first prediction model is constructed by steps a1 to a2.

[0025] Step a1: Obtain battery capacity sequence data of the battery to be predicted during the test stage, and the battery capacity sequence data is used to represent the battery capacity change data of the battery to be predicted with the increase in the number of cycles.

[0026] Step a2: Build a first prediction model of the battery to be predicted based on the battery capacity sequence data. Illustratively, in the present embodiment, the first prediction model can be generated by fitting the battery capacity sequence data, and the fitting curve is used to represent the relationship between the capacity and the cycle number at the cycle stage of the battery to be predicted.

[0027] In some alternative embodiments, the second prediction model is constructed by steps b1 to b4.

[0028] Step b1: Obtain actual battery capacities corresponding to different cycle numbers in the initial cycle sub-stage of the battery to be predicted. Illustratively, in the present embodiment, the initial cycle sub-stage can be understood as the beginning of the cycle stage of the battery, and every time the first cycle passes, the actual battery capacity of the corresponding cycle can be obtained, and the actual battery capacity is uniformly represented by the discharge capacity.

[0029] Step b2: determining predicted battery capacities corresponding to different cycle numbers in the initial cycle sub-stage of the battery to be predicted based on the first prediction model;

[0030] Step b3: Based on the predicted battery capacities and actual battery capacities corresponding to different cycle numbers in the initial cycle sub-stage, calculate the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be predicted.

[0031] For example, in the embodiment of the present application, a target relationship is used to calculate the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be predicted, and the target relationship is: JPEG2026031417000003.jpg11170, where d indicates the capacity prediction accuracy, and C r indicates the actual battery capacity, and C s indicates the predicted battery capacity.

[0032] Step b4: A second prediction model is constructed based on the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage.

[0033] Illustratively, in the embodiment of the present application, the second prediction model can be expressed by the following formula:

[0034] JPEG2026031417000004.jpg7170Here, a, b, and c represent fitting coefficients, and N represents the number of cycles.

[0035] Step S102: Determine the capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage based on the second prediction model.

[0036] Illustratively, in the embodiment of the present application, the capacity prediction accuracy corresponding to different cycle numbers in the cycle sub-stage is predicted based on the second prediction model to obtain a prediction result.

[0037] Step S103: Modify the battery capacity in the predicting cycle sub-stage of the first prediction model according to the capacity prediction accuracy corresponding to different cycle numbers in the predicting cycle sub-stage, to obtain a modified first prediction model.

[0038] For example, the capacity prediction accuracy may represent the deviation between the predicted battery capacity and the actual battery capacity, and the battery capacity of the predicted cycle sub-stage of the first prediction model is corrected based on the capacity prediction accuracy corresponding to each different cycle number in the prediction cycle sub-stage to obtain a corrected first prediction model.

[0039] Step S104: Determine the number of cycles at the target capacity of the battery to be predicted based on the corrected first prediction model, and the target capacity is determined based on the initial capacity of the battery to be predicted.

[0040] For example, the number of cycles at the target capacity of the battery to be predicted can be used to represent the predicted cycle life of the battery to be predicted. In an embodiment of the present application, the target capacity may be 80% of the initial capacity, and the number of cycles corresponding to when the capacity of the battery to be predicted is 80% of the initial capacity is the estimated cycle life of the battery to be predicted.

[0041] Step S105: Determine the remaining life prediction result of the battery to be predicted based on the number of cycles already performed by the battery to be predicted and the number of cycles at the target capacity of the battery to be predicted.

[0042] For example, in the embodiment of the present application, the remaining cycle life of the battery to be predicted is obtained by subtracting the number of cycles already performed from the number of cycles at the target capacity of the battery to be predicted. Specifically, if the number of cycles at the target capacity of the battery to be predicted is L, and it is assumed that the battery has already operated L0 times, the value d corresponding to L-L0 is the accuracy corresponding to the predicted life L, which is updated in real time.

[0043] In the battery remaining life prediction method of this embodiment, the second prediction model is determined based on the predicted battery capacity and actual battery capacity corresponding to different cycle numbers in the initial cycle stage of the battery, and is used to represent the correlation between the cycle number and capacity prediction accuracy in the cycle stage of the battery to be predicted. The capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage can be determined based on the second prediction model. The battery capacity of the prediction cycle sub-stage of the first prediction model is corrected based on the determined capacity prediction accuracy to obtain a corrected first prediction model, thereby ensuring the accuracy of the prediction result of the first prediction model. The cycle number at the target capacity of the battery to be predicted is determined based on the corrected first prediction model. The remaining life prediction result of the battery to be predicted is determined based on the number of cycles already performed by the battery to be predicted and the number of cycles at the target capacity of the battery to be predicted, thereby effectively ensuring the accuracy of the remaining life prediction result of the battery to be predicted.

[0044] The remaining life prediction method for a battery according to the present invention will be described below by way of a specific embodiment.

[0045] Example By conducting multiple tests during the initial operation stage of the battery, an initial cycle-capacity simulation curve (first prediction model) is generated, the accuracy between the actual capacity and the simulated capacity is calculated for each cycle, and a relationship curve (second prediction model) between the number of future cycles and the accuracy is constructed. Then, throughout the entire life cycle of the battery, the second prediction model is used to correct the first prediction model, updating the life prediction results in real time and continuously correcting the capacity prediction value and the accuracy of the life prediction according to actual operating data.

[0046] Assume that a battery undergoes an initial test phase to obtain a cycle-capacity simulation curve. During actual operation, the actual capacity Cr1 is obtained through the first cycle, and the accuracy d1 of the first cycle is calculated. Then, by analogy, the accuracy data from multiple future cycles is used to fit a future cycle-accuracy relationship curve. For subsequent cycles, the capacity prediction curve is updated based on real-time data for each cycle, and the predicted life and its accuracy are adjusted according to the accuracy relationship curve.

[0047] This embodiment further provides a battery remaining life prediction device, which is used to realize the above-described embodiments and preferred embodiments, and duplicated explanations of parts already explained will be omitted. As used below, the term "module" refers to a combination of software and / or hardware that realizes a given function. The device described in the following embodiment is preferably realized by software, but it is also possible and envisioned to realize it by hardware or a combination of software and hardware.

[0048] This embodiment provides a battery remaining life prediction device, and as shown in FIG. 2 , the device includes an acquisition module 201 for acquiring the number of cycles of a battery to be predicted, and a first prediction model and a second prediction model, wherein the first prediction model is used to represent a relationship between the capacity and the number of cycles at a cycle stage of the battery to be predicted, the cycle stage including a first cycle sub-stage and a prediction cycle sub-stage, the first cycle sub-stage being used to represent an initial cycle sub-stage of the battery to be predicted, and the second prediction model is used to represent a relationship between the number of cycles at a cycle stage of the battery to be predicted and the accuracy of capacity prediction, and the second prediction model is determined based on predicted battery capacities and actual battery capacities corresponding to different cycle numbers at the initial cycle sub-stage. the first determination module 202 for determining the capacity prediction accuracy corresponding to each different number of cycles in the prediction cycle sub-stage based on the capacity prediction accuracy; a correction module 203 for correcting the battery capacity of the prediction cycle sub-stage of the first prediction model based on the capacity prediction accuracy corresponding to each different number of cycles in the prediction cycle sub-stage to obtain a corrected first prediction model; a second determination module 204 for determining the number of cycles at a target capacity of the battery to be predicted based on the corrected first prediction model, wherein the target capacity is determined based on the initial capacity of the battery to be predicted; and a third determination module 205 for determining a remaining life prediction result of the battery to be predicted based on the number of cycles already performed of the battery to be predicted and the number of cycles at the target capacity of the battery to be predicted.

[0049] In some alternative embodiments, the first prediction model is constructed by: acquiring battery capacity sequence data during a test phase of the battery to be predicted, wherein the battery capacity sequence data is used to represent battery capacity change data of the battery to be predicted as the number of cycles increases; and constructing a first prediction model of the battery to be predicted based on the battery capacity sequence data.

[0050] In some alternative embodiments, the second prediction model is constructed by the steps of: obtaining actual battery capacities corresponding to different cycle numbers in the initial cycle sub-stage of the battery to be predicted; determining predicted battery capacities corresponding to different cycle numbers in the initial cycle sub-stage of the battery to be predicted based on the first prediction model; calculating capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be predicted based on the predicted battery capacities and actual battery capacities corresponding to different cycle numbers in the initial cycle sub-stage; and constructing a second prediction model based on the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage.

[0051] In some alternative embodiments, the step of calculating a capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be predicted based on the predicted battery capacity and the actual battery capacity corresponding to each different cycle number in the initial cycle sub-stage is a step of calculating a capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be predicted using a target relationship, wherein the target relationship is JPEG2026031417000005.jpg11170, where d indicates the capacity prediction accuracy, and C r indicates the actual battery capacity, and C s includes indicating the predicted battery capacity.

[0052] Further functional explanations of the above modules and units are the same as those of the corresponding embodiments, so duplicate explanations will be omitted here.

[0053] The battery remaining life prediction device in this embodiment is presented in the form of a functional unit, where unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory executing one or more software or fixed programs, and / or other device capable of providing the above functionality.

[0054] An embodiment of the present invention further provides a computer device having the battery remaining life prediction device shown in FIG.

[0055] Referring to FIG. 3, FIG. 3 is a structural diagram of a computer device according to an alternative embodiment of the present invention. As shown in FIG. 3, the computer device includes one or more processors 10, memory 20, and interfaces, including high-speed and low-speed interfaces, for connecting each component. The components are communicatively connected to each other via different buses and may be mounted on a common motherboard or otherwise attached as needed. The processor can process instructions executed within the computer device, including instructions stored in or on memory for displaying GUI graphic information on an external input / output device (e.g., a display device coupled to the interface). In some alternative embodiments, multiple processors and / or multiple buses may be used along with multiple memories as needed. Similarly, multiple computer devices may be connected, each performing a portion of the required operations (e.g., functioning as a server array, a set of blade servers, or a multiprocessor system). FIG. 3 illustrates one processor 10 as an example.

[0056] 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 an application specific integrated circuit, a programmable logic device, or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable logic gate array, a generic array logic, or any combination thereof.

[0057] The memory 20 stores instructions executable by at least one processor 10, causing the at least one processor 10 to execute and realize the methods shown in the above embodiments.

[0058] Memory 20 may include a program storage area capable of storing an operating system and / or application programs required for at least one function, and a data storage area capable of storing data generated in response to use of the computing device. Memory 20 may also include high-speed random access memory and may further include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, memory 20 may optionally include memory located remotely from processor 10, and these remote memories may be connected to the computing device via a network. Examples of such networks include, but are not limited to, the Internet, a corporate intranet, a local area network, a mobile communications network, and combinations thereof.

[0059] Memory 20 may include volatile memory, such as random access memory, or may include non-volatile memory, such as flash memory, a hard disk, or a solid state drive, or memory 20 may include a combination of the above types of memory.

[0060] The computing device further includes a communications interface 30 that allows the computing device to communicate with other devices or communications networks.

[0061] An embodiment of the present invention further provides a computer-readable storage medium, and the methods according to the above-described embodiment of the present invention may be implemented in hardware and firmware, or may be recordable in a storage medium, or may be implemented as computer code downloaded over a network, originally stored in a remote storage medium or a non-transitory machine-readable storage medium, but stored in a local storage medium, whereby the methods described herein may be processed by software stored in a storage medium using a general-purpose computer, a special-purpose processor, or programmable or special-purpose hardware. The storage medium may be a magnetic disk, optical disk, read-only memory, random-access memory, flash memory, hard disk, solid-state drive, etc., and optionally, the storage medium may include a combination of the above types of memory. It will be understood that a computer, processor, microprocessor controller, or programmable hardware may include a storage component capable of storing or receiving software or computer code, and when the software or computer code is accessed and executed by the computer, processor, or hardware, it implements the methods described in the above-described embodiment.

[0062] Some aspects of the present invention may be implemented as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions of the present invention through the operation of the computer. As those skilled in the art will appreciate, the existence of computer program instructions on a computer-readable medium can include, but are not limited to, a source file, an executable file, an installation pack file, etc. Correspondingly, the computer program instructions can be executed by a computer in the following ways: directly executing the instructions; compiling the instructions and then executing a corresponding compiled program; reading and executing the instructions; or reading and installing the instructions and then executing a corresponding installed program. Here, the computer-readable medium may be any available computer-readable storage medium or communication medium accessible by a computer.

[0063] Although the embodiments of the present invention have been described with reference to the drawings, those skilled in the art may make various changes and modifications without departing from the spirit and scope of the present invention, and all such changes and modifications fall within the scope defined by the appended claims.

Claims

1. A method for predicting remaining battery life, comprising: a step of obtaining the number of cycles of the battery to be predicted and a first prediction model and a second prediction model, the first prediction model being used to represent a relationship between the capacity and the number of cycles at a cycle stage of the battery to be predicted, the cycle stage including an initial cycle sub-stage and a predicted cycle sub-stage; the second prediction model being used to represent a relationship between the number of cycles at the cycle stage of the battery to be predicted and capacity prediction accuracy, the second prediction model being determined based on predicted battery capacities and actual battery capacities respectively corresponding to different cycle numbers at the initial cycle sub-stage; determining a capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage based on the second prediction model; modifying the battery capacity of the prediction cycle sub-stage of the first prediction model based on the capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage, thereby obtaining a modified first prediction model; determining a number of cycles at a target capacity of a battery to be predicted based on the corrected first prediction model, the target capacity being determined based on an initial capacity of the battery to be predicted; and determining a remaining life prediction result for the battery to be predicted based on the number of cycles already performed by the battery to be predicted and the number of cycles at the target capacity of the battery to be predicted.

2. The first prediction model is Obtaining battery capacity sequence data during a test phase of the battery to be predicted, the battery capacity sequence data being used to represent battery capacity change data of the battery to be predicted with an increase in the number of cycles; and constructing a first prediction model of the battery to be predicted based on the battery capacity sequence data.

3. The second prediction model is obtaining actual battery capacities corresponding to different cycle numbers in an initial cycle sub-phase of the battery to be predicted; determining predicted battery capacities corresponding to different cycle numbers in an initial cycle sub-phase of the battery to be predicted based on the first prediction model; Calculating a capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be predicted based on the predicted battery capacity and the actual battery capacity corresponding to each different cycle number in the initial cycle sub-stage; and constructing a second prediction model based on the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage.

4. The step of calculating the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be predicted based on the predicted battery capacity and the actual battery capacity corresponding to each different cycle number in the initial cycle sub-stage, A step of calculating a capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be predicted using a target relational expression, wherein the target relational expression is: and where d indicates the capacity prediction accuracy, and C ｒ indicates the actual battery capacity, and C ｓ 4. The method of claim 3, further comprising the step of indicating a predicted battery capacity.

5. A battery remaining life prediction device, an acquisition module for acquiring the number of cycles of the battery to be predicted and a first prediction model and a second prediction model, the first prediction model being used to represent a relationship between the capacity and the number of cycles at a cycle stage of the battery to be predicted, the cycle stage including a first cycle sub-stage and a prediction cycle sub-stage, the first cycle sub-stage being used to represent an initial cycle sub-stage of the battery to be predicted; the second prediction model being used to represent a relationship between the number of cycles at the cycle stage of the battery to be predicted and capacity prediction accuracy, the second prediction model being determined based on predicted battery capacities and actual battery capacities respectively corresponding to different cycle numbers at the initial cycle sub-stage; a first determination module for determining capacity prediction accuracy corresponding to different cycle numbers in the prediction cycle sub-stage based on the second prediction model; a correction module for correcting the battery capacity of the prediction cycle sub-stage of the first prediction model according to the capacity prediction accuracy corresponding to each different cycle number in the prediction cycle sub-stage, thereby obtaining a corrected first prediction model; a second determination module for determining a number of cycles at a target capacity of the battery to be predicted based on the modified first prediction model, the target capacity being determined based on an initial capacity of the battery to be predicted; and a third determination module for determining a remaining life prediction result of the battery to be predicted based on the number of cycles already performed by the battery to be predicted and the number of cycles at a target capacity of the battery to be predicted.

6. The first prediction model is Obtaining battery capacity sequence data during a test phase of the battery to be predicted, the battery capacity sequence data being used to represent battery capacity change data of the battery to be predicted with an increase in the number of cycles; and constructing a first prediction model of the battery to be predicted based on the battery capacity sequence data.

7. The second prediction model is obtaining actual battery capacities corresponding to different cycle numbers in an initial cycle sub-phase of the battery to be predicted; determining predicted battery capacities corresponding to different cycle numbers in an initial cycle sub-phase of the battery to be predicted based on the first prediction model; Calculating a capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage of the battery to be predicted based on the predicted battery capacity and the actual battery capacity corresponding to each different cycle number in the initial cycle sub-stage; and constructing a second prediction model based on the capacity prediction accuracy corresponding to each cycle number in the initial cycle sub-stage.

8. A computer device comprising: A computer device comprising: a memory; and a processor, the memory and the processor being communicatively connected to each other; computer instructions stored in the memory; and 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 method for predicting the remaining life of a battery according to any one of claims 1 to 4.

10. A computer program product, comprising computer instructions for causing a computer to execute the method for predicting the remaining life of a battery according to any one of claims 1 to 4.

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