Battery life prediction device and operation method thereof

Abstract

A battery life prediction device according to one embodiment disclosed in this document may include a data acquisition unit that acquires operating data of a battery, and a controller that uses the operating data to predict the life of the battery based on a current integration method to generate a first result, uses the operating data to predict the life of the battery based on a life prediction function to generate a second result, and manages the life prediction function based on the first result and the second result.

Description

[Technical field]

[0001] The present invention claims the benefit of priority based on Korean Patent Application No. 10-2022-0006879, filed on January 17, 2022, and all contents disclosed in the documents of the Korean patent application are incorporated by reference into this specification.

[0002] SUMMARY OF THE DISCLOSURE The embodiments disclosed herein relate to a battery life prediction apparatus and method of operation. [Background technology]

[0003] In recent years, research and development into secondary batteries has been actively conducted. Here, secondary batteries are batteries that can be charged and discharged, and include both conventional Ni / Cd batteries, Ni / MH batteries, and the more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of having a much higher energy density than conventional Ni / Cd batteries, Ni / MH batteries, and the like. In addition, lithium-ion batteries can be manufactured to be small and lightweight, so they are used as power sources for mobile devices, and in recent years their range of use has expanded to include power sources for electric vehicles, drawing attention as a next-generation energy storage medium.

[0004] Meanwhile, an energy storage system (ESS) is a device that improves the efficiency of power usage by storing generated electricity in a storage device such as a battery and supplying it when electricity is needed. Generally, there are various methods for predicting the lifespan of such energy storage systems, and various researches are being conducted to improve the accuracy of the prediction. Summary of the Invention [Problem to be solved by the invention]

[0005] An object of the embodiments disclosed herein is to provide a battery life prediction device and an operating method thereof that can improve the accuracy of battery life prediction.

[0006] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0007] A battery life prediction device according to one embodiment disclosed in this document may include a data acquisition unit that acquires operating data of a battery, and a controller that uses the operating data to predict the life of the battery based on a current integration method to generate a first result, uses the operating data to predict the life of the battery based on a life prediction function to generate a second result, and manages the life prediction function based on the first result and the second result.

[0008] In one embodiment, the controller is capable of managing the life prediction function such that the second result has a value that corresponds to the first result.

[0009] In one embodiment, the controller is capable of managing the life prediction function such that a difference between the second result and the first result is less than a reference value.

[0010] In one embodiment, the controller is capable of correcting a value of at least one parameter of the life prediction function.

[0011] In one embodiment, the at least one parameter may include at least one coefficient of SOC, CP-rate, and temperature that constitute the life prediction function.

[0012] In one embodiment, the controller is capable of correcting a value of the at least one parameter such that the second result has a value that corresponds to the first result.

[0013] In one embodiment, the controller may update the life prediction function with a value of the at least one parameter when a difference between the second result and the first result is less than a reference value.

[0014] In one embodiment, the controller can generate the first result and the second result based on the operational data obtained on a daily basis, and manage the life prediction function based on the first result and the second result.

[0015] An operating method of a battery life prediction device according to one embodiment disclosed in this document may include the steps of acquiring operational data of a battery, using the operational data to predict the life of the battery based on a current integration method to generate a first result, using the operational data to predict the life of the battery based on a life prediction function to generate a second result, and managing the life prediction function based on the first result and the second result.

[0016] In one embodiment, the step of managing the life prediction function based on the first result and the second result can manage the life prediction function so that the second result has a value corresponding to the first result.

[0017] In one embodiment, the step of managing the life prediction function based on the first result and the second result may include a step of comparing a difference between the second result and the first result with a reference value, and a step of correcting a value of at least one parameter of the life prediction function based on the result of comparing the difference with the reference value.

[0018] In one embodiment, the method may further include updating the life prediction function based on a comparison of the difference to the reference value.

[0019] In one embodiment, the at least one parameter may include at least one coefficient of SOC, CP-rate, and temperature that constitute the life prediction function. Effect of the Invention

[0020] The battery life prediction device and its operating method according to an embodiment disclosed herein can improve the accuracy of battery life prediction.

[0021] In addition, the battery life prediction device and its operating method according to an embodiment disclosed in this document can manage a life prediction function for predicting the life of a battery. [Brief description of the drawings]

[0022] [Figure 1] FIG. 1 illustrates a battery system according to one embodiment disclosed herein. [Diagram 2] FIG. 1 illustrates a battery life prediction device according to one embodiment disclosed in this document. [Diagram 3] 1 is a diagram for explaining the operation of a battery life prediction device according to an embodiment disclosed in this document. [Figure 4] 1 is a flowchart illustrating a method of operation of a battery life prediction device according to an embodiment disclosed herein. [Diagram 5] 1 is a flowchart illustrating a method of operation of a battery life prediction device according to an embodiment disclosed herein. [Figure 6] 1 illustrates a computing system that performs an embodiment of a method of operating a battery life prediction device disclosed herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] Hereinafter, the embodiments disclosed in this document will be described in detail with reference to the exemplary drawings. When referring to components in each drawing, it should be noted that the same components are referred to by the same reference numerals as much as possible when they are displayed in other drawings. In addition, when describing the embodiments disclosed in this document, if a detailed description of related publicly known configurations or functions is deemed to hinder understanding of the embodiments disclosed in this document, the detailed description will be omitted.

[0024] When describing components of the embodiments disclosed in this document, terms such as first, second, A, B, (a), (b) and the like may be used. Such terms are merely used to distinguish the components from other components, and do not limit the essence, order, or procedure of the components. In addition, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document belong. Terms as defined in commonly used dictionaries should be interpreted as having a meaning consistent with the contextual meaning of the relevant art, and should not be interpreted in an ideal or overly formal sense unless expressly defined in this application.

[0025] FIG. 1 is a diagram illustrating a battery system according to one embodiment disclosed herein.

[0026] Referring to FIG. 1, a battery system 100 may include a battery 110 and a battery life prediction device 120 .

[0027] The battery 110 can store energy and supply the stored energy to a load (not shown). To this end, the battery 110 can be electrically connected to the load. Here, the load can include an electrical, electronic, or mechanical device that operates by receiving power from the battery 110 including a plurality of battery cells.

[0028] According to an embodiment, the battery 110 may include an energy storage system. For example, the battery 110 may include a battery pack and / or a battery module, and the battery pack and the battery module may include n (n is a natural number equal to or greater than 2) battery cells. Each battery cell may be, but is not limited to, a lithium ion (Li-ion) battery, a lithium ion polymer (Li-ion polymer) battery, a nickel cadmium (Ni-Cd) battery, a nickel metal hydride (Ni-MH) battery, or the like.

[0029] The battery life prediction device 120 can manage the operation and / or state of the battery 110. According to an embodiment, the battery life prediction device 120 can predict the life of the battery 110. Here, the life of the battery 110 can include the remaining life, and can be understood as the degree of degradation, the degree of deterioration, SOH (State of Health), and the like.

[0030] The battery life prediction device 120 may acquire operation data of the battery 110. According to an embodiment, the operation data may include log data of the battery 110, and may include, for example, measured values ​​such as voltage, current, and temperature, and a SOC, a charge / discharge rate (CP-rate), operation time, and the like associated with the use of the battery 110. According to an embodiment, the battery life prediction device 120 may acquire the operation data of the battery 110 from the battery 110 via a wired or wireless communication network, or may acquire the operation data of the battery 110 from another external device.

[0031] The battery life prediction device 120 can predict the life of the battery 110 based on the operation data of the battery 110. According to an embodiment, the battery life prediction device 120 can generate a first result by predicting the life of the battery 110 based on a current integration method. Here, the current integration method can be understood as a life prediction method based on the following Equation 2, where SOH is the remaining life of the battery 110, ΔSOC is the difference in the charge amount due to charging and discharging,

[0032]

number

[0033] may be the integrated current value, and Nominal Capacity may be the nominal capacity of the battery 110.

[0034]

number

[0035] Furthermore, according to an embodiment, the battery life prediction device 120 may generate a second result by predicting the life of the battery 110 based on a life prediction function. Here, the life prediction function may include a mathematical formula modeled based on data experimentally measured in various environments for predicting the life of the battery.

[0036] The battery life prediction device 120 can manage a life prediction function based on a first result and a second result of predicting the life of the battery 110. According to an embodiment, the battery life prediction device 120 can correct the life prediction function so that the second result has a value corresponding to the first result or so that the difference between the second result and the first result is smaller than a reference value.

[0037] The battery life prediction device 120 can predict the life of the battery 110 based on the corrected life prediction function. Therefore, the accuracy of the life prediction of the battery 110 can be improved.

[0038] That is, since the life prediction function is a mathematical formula modeled based on stable conditions such as a laboratory environment, the second result of predicting the life of the battery 110 based on the life prediction function based on the actual use environment conditions of the battery 110 may have low accuracy. Therefore, the accuracy of the life prediction can be improved by correcting the parameter values ​​of the life prediction function using the first result based on the current integration method, which has a relatively high accuracy of life prediction. This will be described in more detail with reference to Figures 2 to 5 below.

[0039] Meanwhile, according to an embodiment, the battery life prediction device 120 may include a battery management system. In this case, the battery life prediction device 120 may manage and / or control the state and / or operation of the battery 110. For example, the battery life prediction device 120 may manage and / or control the state and / or operation of a plurality of battery cells included in the battery 110. The battery life prediction device 120 may manage the charging and / or discharging of the battery 110.

[0040] Fig. 2 is a diagram showing a battery life prediction device according to an embodiment disclosed in the present document. Fig. 3 is a diagram for explaining the operation of the battery life prediction device according to an embodiment disclosed in the present document.

[0041] First, referring to FIG. 2, the battery life prediction device 120 may include a data acquisition unit 121 and a controller 122 .

[0042] The data acquiring unit 121 may acquire operation data from the battery 110. According to an embodiment, the operation data may include log data of the battery 110, and may include measured values ​​such as voltage, current, and temperature, and SOC, charge / discharge rate (CP-rate), operation time, and the like, related to the use of the battery 110. The data acquiring unit 121 may acquire the operation data from the battery 110 in real time. The data acquiring unit 121 may acquire the operation data via a wired or wireless communication network, and may transmit the acquired operation data to the controller 122.

[0043] The controller 122 may use the operational data to predict a lifespan of the battery 110. According to an embodiment, the controller 122 may use the operational data to predict a lifespan of the battery 110 based on a current integration method to generate a first result.

[0044] The controller 122 may use the operation data to predict a lifespan of the battery 110 based on a lifespan prediction function to generate a second result. For example, the controller 122 may use the operation data to analyze a usage pattern of the battery 110, and predict a lifespan of the battery 110 based on a lifespan prediction function corresponding to the usage pattern to generate a second result. The controller 122 may analyze a usage pattern of the battery 110 on a daily basis, and predict a lifespan of the battery 110 on a daily basis based on the analyzed usage pattern.

[0045] The controller 122 can manage a life prediction function based on the first result and the second result. According to an embodiment, the life prediction function can include a formula composed of at least one factor for predicting the life of the battery and at least one parameter that is a coefficient of each factor, for example, the at least one factor can include, but is not limited to, SOC, CP-rate, temperature, usage time, etc.

[0046] According to an embodiment, the controller 122 may manage the life prediction function such that the second result has a value corresponding to the first result. Also, according to an embodiment, the controller 122 may manage the life prediction function such that the difference between the second result and the first result is smaller than a reference value. For example, the reference value may be 0.5% point, but this is an exemplary value and may be changed according to various environments and conditions.

[0047] According to an embodiment, the controller 122 can compare the first result with the second result. The controller 122 can manage the life prediction function when the difference between the first result and the second result is equal to or greater than a reference value. For example, the controller 122 can correct the value of at least one parameter of the life prediction function when the difference between the second result and the first result is equal to or greater than a reference value. Here, the at least one parameter can mean a coefficient of a factor (e.g., SOC, CP-rate, temperature, etc.) constituting the life prediction function.

[0048] For example, the controller 122 can obtain values ​​of at least one parameter of the life prediction function to generate a second result corresponding to the first result. Also, for example, the controller 122 can obtain values ​​of at least one parameter of the life prediction function to generate a second result such that a difference between the second result and the first result is smaller than a reference value (or such that the difference is minimized). According to an embodiment, the controller 122 can search for the value of the at least one parameter using a Generalized Reduced Gradient (GRG) non-linear engine. The controller 122 can iteratively manage and / or correct the life prediction function until the difference between the first result and the second result is smaller than a reference value.

[0049] The controller 122 may update the life prediction function when the difference between the first result and the second result is less than a reference value. For example, the controller 122 may update the value of at least one parameter of the life prediction function.

[0050] Therefore, the controller 122 can accurately predict the lifespan of the battery 110 based on the updated lifespan prediction function.

[0051] That is, referring to FIG. 3, the controller 122 can improve the accuracy of the life prediction by correcting the life prediction function to generate a second result that minimizes the difference between the first result (A) and the second result (B), and predicting (C) the life of the battery 110 based on the corrected life prediction function.

[0052] 4 and 5 are flow charts illustrating a method of operation of a battery life prediction device according to an embodiment disclosed herein.

[0053] First, referring to FIG. 4, an operating method of a battery life prediction device according to one embodiment disclosed in this document may include a step of acquiring battery operation data (S110), a step of using the operation data to predict the battery life based on a current accumulation method to generate a first result (S120), a step of using the operation data to predict the battery life based on a life prediction function to generate a second result (S130), and a step of managing the life prediction function based on the first result and the second result (S140).

[0054] Hereinafter, steps S110 to S140 will be specifically described with reference to FIG.

[0055] In step S110, the data acquisition unit 121 may acquire operation data from the battery 110. According to an embodiment, the operation data may include log data of the battery 110, and may include measurement values ​​such as voltage, current, and temperature, as well as SOC, charge / discharge rate (CP-rate), operation time, and the like related to the use of the battery 110.

[0056] In step S120, the controller 122 can use the operational data to predict the lifespan of the battery 110 based on the current integration method to generate a first result.

[0057] In step S130, the controller 122 may use the operational data to predict the lifespan of the battery 110 based on the lifespan prediction function to generate a second result. According to an embodiment, step S130 may be performed simultaneously with step S120.

[0058] In step S140, the controller 122 can manage a life prediction function based on the first result and the second result.

[0059] Referring to FIG. 5, step S140 may include a step (S141) of comparing the difference between the first result and the second result with a reference value, a step (S142) of correcting the value of at least one parameter of the life prediction function, and a step (S143) of updating the life prediction function.

[0060] In step S141, the controller 122 may determine whether the difference between the first result and the second result is smaller than a reference value. According to an embodiment, if the difference between the first result and the second result is equal to or larger than the reference value, step S142 may be performed, and if the difference between the first result and the second result is smaller than the reference value, step S143 may be performed.

[0061] In step S142, the controller 122 can manage the life prediction function when the difference between the first result and the second result is equal to or greater than a reference value. For example, the controller 122 can correct the value of at least one parameter of the life prediction function when the difference between the second result and the first result is equal to or greater than a reference value.

[0062] For example, the controller 122 can obtain values ​​of at least one parameter of a lifespan prediction function that generates a second result corresponding to the first result, and for example, the controller 122 can obtain values ​​of at least one parameter of a lifespan prediction function that generates a second result such that a difference between the second result and the first result is less than a reference value (or such that the difference is minimized).

[0063] In step S143, the controller 122 may update the life prediction function when the difference between the first result and the second result is smaller than the reference value. For example, the controller 122 may update the value of at least one parameter of the life prediction function.

[0064] FIG. 6 illustrates a computing system that performs the method of operation of a battery life prediction device according to one embodiment disclosed herein.

[0065] Referring to FIG. 6, a computing system 200 according to one embodiment disclosed in this document may include an MCU 210, a memory 220, an input / output I / F 230, and a communication I / F 240.

[0066] The MCU 210 may be a processor that executes various programs stored in the memory 220, processes various operational data including the current, voltage, temperature, SOC, etc. of the battery 110 through such programs, and performs the functions of the battery life prediction device 120 described with reference to Figures 1 to 3, or a processor that executes the operating method of the battery life prediction device described with reference to Figures 4 and 5.

[0067] The memory 220 can store various programs related to predicting the life span of the battery 110. The memory 220 can also store various operational data of the battery 110, such as SOC and SOH data.

[0068] A plurality of such memories 220 may be provided as necessary. The memories 220 may be volatile memories or non-volatile memories. The memory 220 as a volatile memory may be a RAM, a DRAM, an SRAM, or the like. The memory 220 as a non-volatile memory may be a ROM, a PROM, an EAROM, an EPROM, an EEPROM, a flash memory, or the like. The examples of the memory 220 listed above are merely illustrative and are not limited to these examples.

[0069] The input / output I / F 230 may provide an interface that connects input devices (not shown) such as a keyboard, mouse, or touch panel, and output devices such as a display (not shown), to the MCU 210 to transmit and receive data.

[0070] The communication I / F 240 is configured to be able to transmit and receive various data to and from a server, and may be any of various devices capable of supporting wired or wireless communication. For example, a program for predicting the life of the battery 110 and various operational data can be transmitted and received from a separately provided external server via the communication I / F 240.

[0071] In this manner, the method of operation of the battery life prediction device according to an embodiment disclosed herein can be stored in the memory 220 and executed by the MCU 210 .

[0072] The above description is merely an illustrative example of the technical ideas disclosed in this document, and various modifications and variations may be made by a person having ordinary skill in the art to which the embodiments disclosed in this document pertain without departing from the essential characteristics of the embodiments disclosed in this document.

[0073] Therefore, the embodiments disclosed in this document are intended to explain, not to limit, the technical ideas disclosed in this document, and such embodiments do not limit the scope of the technical ideas disclosed in this document. The scope of protection of the technical ideas disclosed in this document should be interpreted according to the claims below, and all technical ideas within the equivalent scope should be interpreted as being included in the scope of rights of this document. [Explanation of symbols]

[0074] 100 Battery System 110 Batteries 120 Battery life prediction device 121 Data Acquisition Section 122 Controller 200 Computing Systems 210 MCU 220 Memory 230 Input / Output Interface 240 Communication I / F

Claims

1. A data acquisition unit for acquiring operation data of the battery; a controller that uses the operational data to predict a life of the battery based on a current integration method to generate a first result, and uses the operational data to predict a life of the battery based on a life prediction function to generate a second result, and manages the life prediction function based on the first result and the second result; A battery life prediction device comprising:

2. 2. The battery life prediction device according to claim 1, wherein the controller manages the life prediction function so that the second result has a value corresponding to the first result.

3. 2 . The battery life prediction device according to claim 1 , wherein the controller manages the life prediction function so that a difference between the second result and the first result becomes smaller than a reference value.

4. 2. The battery life prediction device according to claim 1, wherein the controller corrects a value of at least one parameter of the life prediction function.

5. 5. The battery life prediction device according to claim 4, wherein the at least one parameter includes at least one coefficient of SOC, CP-rate, and temperature that constitute the life prediction function.

6. 6. The battery life prediction device according to claim 5, wherein the controller corrects a value of the at least one parameter such that the second result has a value corresponding to the first result.

7. The battery life prediction device of claim 4, characterized in that the controller updates the life prediction function using the value of the at least one parameter when the difference between the second result and the first result is smaller than a reference value.

8. The battery life prediction device of claim 1 , characterized in that the controller generates the first result and the second result based on the operational data acquired on a daily basis, and manages the life prediction function based on the first result and the second result.

9. acquiring operational data of the battery; using the operational data to predict a lifespan of the battery based on a current integration method to generate a first result; using the operational data to predict a lifetime of the battery based on a lifetime prediction function to generate a second result; managing the life prediction function based on the first result and the second result; A method of operating a battery life prediction device, comprising:

10. The method of operating a battery life prediction device as described in claim 9, characterized in that the step of managing the life prediction function based on the first result and the second result manages the life prediction function so that the second result has a value corresponding to the first result.

11. Managing the life prediction function based on the first result and the second result includes comparing a difference between the second result and the first result with a reference value; and correcting a value of at least one parameter of the life prediction function based on a result of comparing the difference with the reference value.

12. 12. The method of claim 11, further comprising the step of updating the life prediction function based on a result of comparing the difference with the reference value.

13. 12. The method of claim 11, wherein the at least one parameter includes at least one coefficient of SOC, CP-rate, and temperature that constitute the battery life prediction function.

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