Battery management device and method

Abstract

The battery management device disclosed in this document may include a measurement unit that measures the current and voltage of a battery cell, an analysis unit that classifies each of the battery cells into a plurality of classes based on characteristic values ​​obtained from the current and voltage of the battery cell and the deterioration behavior of each of the battery cells, and a judgment unit that judges the life of the battery cell using a method previously set for each of the plurality of classes.

Description

[Technical field]

[0001] The present invention claims the benefit of priority based on Korean Patent Application No. 10-2021-0002907, filed on January 8, 2021, and all contents disclosed in the documents of the Korean patent application are incorporated as part of this specification. SUMMARY OF THE DISCLOSURE The embodiments disclosed herein relate to battery management apparatus and methods. [Background technology]

[0002] 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 latest 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, and are therefore used as power sources for mobile devices. The range of use of lithium-ion batteries has also been expanded to include power sources for electric vehicles, and they are attracting attention as a next-generation energy storage medium.

[0003] Moreover, secondary batteries are generally used as battery packs including battery modules in which a plurality of battery cells are connected in series and / or parallel, and the state and operation of the battery packs are managed and controlled by a battery management system.

[0004] In order to calculate the capacity of such a battery cell, a formula such as Ah counting is usually used after all charging and discharging of the battery cell is completed. Also, when shipping such a battery cell, the battery cell is charged and discharged up to 300 cycles in advance and the capacity degradation rate is checked before shipping. However, charging and discharging the battery cell up to 300 cycles is problematic in that it takes a lot of time and costs a lot of money. Summary of the Invention [Problem to be solved by the invention]

[0005] One objective of the embodiments disclosed herein is to provide a battery management device and method that can predict the lifespan of a battery cell early by classifying the battery cells into classes based on data indicating the deterioration characteristics of the battery cells and estimating the capacity.

[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] The battery management device disclosed in this document may include a measurement unit that measures the current and voltage of a battery cell, an analysis unit that classifies each of the battery cells into a plurality of classes based on characteristic values ​​obtained from the current and voltage of the battery cell and the deterioration behavior of each of the battery cells, and a judgment unit that judges the life of the battery cell using a method previously set for each of the plurality of classes.

[0008] According to an embodiment, the analysis unit can exclude a battery cell having a discontinuity in a characteristic value from among the battery cells, from targets for lifespan determination. In one embodiment, the analysis unit can classify each of the battery cells into a plurality of classes based on how the characteristic values ​​change. According to one embodiment, the manner in which the characteristic value changes can include an initial value of the characteristic value and a rate of change with respect to the cycle.

[0009] In one embodiment, the analysis unit can classify a battery cell that can be determined to have a normal life based on a characteristic value of the battery cell when the battery cell has been charged and discharged up to a first cycle into a first class.

[0010] In one embodiment, the analysis unit classifies a battery cell that does not belong to the first class into a second class in which the life of the battery cell can be determined after charging and discharging up to a second cycle, and the second cycle can have a value greater than that of the first cycle.

[0011] In one embodiment, the judgment unit can judge the life of a battery cell by applying characteristic values ​​of the battery cells included in the second class to a regression model in a state where the battery cells included in the second class are charged and discharged for only the second cycle.

[0012] In one embodiment, the analysis unit may classify each of the battery cells into the plurality of classes based on a coefficient of an approximation equation for the characteristic value. In one embodiment, the analysis unit can perform K-means clustering on the coefficients of the approximation formula.

[0013] The battery management method disclosed in this document may include steps of measuring a current and a voltage of a battery cell, classifying each of the battery cells into a plurality of classes based on characteristic values ​​obtained from the current and voltage of the battery cell and the deterioration behavior of each of the battery cells, and determining the lifespan of the battery cells using a method previously set for each of the plurality of classes.

[0014] According to an embodiment, the method may further include excluding a battery cell having a discontinuity in a characteristic value from among the battery cells, the battery cell being a target for lifespan determination. In one embodiment, the step of classifying each of the battery cells into a plurality of classes may classify each of the battery cells into a plurality of classes based on a change in the characteristic value.

[0015] In one embodiment, the step of classifying each of the battery cells into a plurality of classes may classify each of the battery cells into a plurality of classes based on a gradient of the characteristic value.

[0016] In one embodiment, the step of classifying each of the battery cells into a plurality of classes can classify into a first class those battery cells whose life can be determined to be normal based on characteristic values ​​of the battery cells when the battery cells have been charged and discharged up to a first cycle.

[0017] In one embodiment, the step of classifying each of the battery cells into a plurality of classes classifies a battery cell not belonging to the first class into a second class in which the life of the battery cell can be determined after charging and discharging up to a second cycle, and the second cycle can have a value greater than that of the first cycle.

[0018] In one embodiment, the step of determining the life of the battery cell can determine the life of the battery cell by applying characteristic values ​​of the battery cells included in the second class to a regression model while the battery cells included in the second class are charged and discharged for only the second cycle. Effect of the Invention

[0019] The battery management device and method according to one embodiment disclosed in this document can predict the lifespan of a battery cell early by classifying the battery cells into classes based on data indicating the deterioration characteristics of the battery cells and estimating the capacity. [Brief description of the drawings]

[0020] [Figure 1] FIG. 1 is a block diagram showing the configuration of a typical battery pack including a battery management device according to an embodiment disclosed herein. [Diagram 2] 1 is a block diagram showing a configuration of a battery management device according to an embodiment disclosed in this document. [Diagram 3] FIG. 13 is a diagram illustrating filtering characteristic value data of a battery cell in a battery management device according to an embodiment disclosed herein. [Figure 4]FIG. 2 is a diagram illustrating an example of classifying a battery cell into classes based on characteristic value data of the battery cell in a battery management device according to an embodiment disclosed in this document. [Diagram 5] FIG. 13 is a diagram illustrating an example of a result of determining the life span of a battery cell via a battery management device according to an embodiment disclosed in this document. [Figure 6] 1 is a flow chart illustrating a battery management method according to one embodiment disclosed herein. [Figure 7] FIG. 1 is a block diagram illustrating a computing system that can execute an embodiment of a battery management method disclosed herein. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0021] Hereinafter, various embodiments disclosed in the present document will be described in detail with reference to the accompanying drawings. In this document, the same components in the drawings are designated by the same reference numerals, and duplicated descriptions of the same components will be omitted.

[0022] For the various embodiments disclosed in this document, specific structural or functional descriptions are provided merely for purposes of describing the embodiments, and the various embodiments disclosed in this document may be embodied in various forms and should not be construed as being limited to the embodiments described in this document.

[0023] The terms "first," "second," "first," or "second" used in the various embodiments may modify various elements without regard to order and / or importance and do not limit such elements. For example, a first element may be named a second element, and similarly, the second element may be renamed to the first element without departing from the scope of the embodiments disclosed herein.

[0024] The terms used in this document are merely used to describe a particular embodiment and are not intended to limit the scope of other embodiments. A singular expression may include a plural expression unless otherwise clearly indicated in the context.

[0025] All terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by a person of ordinary skill in the art of the embodiments disclosed herein. Terms defined in commonly used dictionaries may be interpreted to have the same or similar meaning in the context of the relevant art, and are not interpreted in an ideal or overly formal sense unless expressly defined in this document. In some cases, even terms defined in this document should not be interpreted to exclude the embodiments disclosed herein.

[0026] FIG. 1 is a block diagram showing the configuration of a typical battery pack including a battery management device according to an embodiment disclosed herein. Specifically, FIG. 1 illustrates a schematic diagram of a battery control system 1 including a battery pack 10 according to an embodiment disclosed herein and a host controller 20 included in the host system.

[0027] 1, the battery pack 10 may include a plurality of battery modules 12, a sensor 14, a switching unit 16, and a battery management system 100. In this case, the battery pack 10 may include a plurality of battery modules 12, sensors 14, switching units 16, and battery management systems 100.

[0028] Each of the battery modules 12 may include at least one battery cell that is chargeable and dischargeable. In this case, the battery modules 12 may be connected in series or in parallel. The sensor 14 can detect a current flowing through the battery pack 10. At this time, a detection signal can be transmitted to the battery management system 100.

[0029] The switching unit 16 is connected in series to the (+) terminal side or the (-) terminal side of the battery module 12 and can control the flow of charge / discharge current of the battery module 12. For example, the switching unit 16 can use at least one relay, electromagnetic contactor, etc. depending on the specifications of the battery pack 10.

[0030] The battery management system 100 can monitor the voltage, current, temperature, etc. of the battery pack 10 and control and manage the same to prevent overcharging and over-discharging, and can include, for example, an RBMS.

[0031] The battery management system 100 is an interface that receives input of measured values ​​of the various parameters described above, and may include a plurality of terminals, and a circuit connected to these terminals for processing the received values. The battery management system 100 may also control the ON / OFF of a switching unit 16, for example, a relay or a contactor, and may be connected to the battery modules 12 to monitor the state of each battery module 12.

[0032] On the other hand, in the battery management system 100 disclosed in this document, as described below, characteristic values ​​and deterioration behaviors are calculated via a separate program based on the measured voltage and current of the battery cells, and the battery cell lifespan can be estimated by classifying them into multiple classes.

[0033] The upper controller 20 can transmit a control signal for controlling the battery module 12 to the battery management system 100. As a result, the operation of the battery management system 100 can be controlled based on the control signal applied from the upper controller 20. The battery module 12 may be included in an ESS (Energy Storage System). In this case, the upper controller 20 may be a battery bank controller (BBMS) including a plurality of battery packs 10 or an ESS controller that controls the entire ESS including a plurality of banks. However, the battery pack 10 is not limited to such an application. The configuration of the battery pack 10 and the configuration of the battery management system 100 are well known, and therefore a more detailed description thereof will be omitted.

[0034] FIG. 2 is a block diagram showing a configuration of a battery management device according to an embodiment disclosed in this document. Referring to FIG. 2, a battery management device 100 according to an embodiment disclosed in this document may include a measurement unit 110, an analysis unit 120, and a determination unit .

[0035] The measurement unit 110 can measure the current and voltage of the battery cell. In this case, the measurement unit 110 can measure the voltage and current of the battery cell at regular time intervals. The voltage and current measured via the measurement unit 110 can be used to calculate a feature value for each battery cell. For example, the feature value of the battery cell may be a value calculated based on a differential value (dQ / dV) of the capacity with respect to the voltage of the battery cell.

[0036] The analysis unit 120 can classify each of the battery cells into a plurality of classes based on the characteristic values ​​obtained from the current and voltage of the battery cells and the deterioration behavior of each of the battery cells. Specifically, the analysis unit 120 can classify each of the battery cells into a plurality of classes based on the manner in which the characteristic values ​​change. For example, the manner in which the characteristic values ​​change can include the initial value of the characteristic value, the rate of change (slope) of the characteristic value with respect to the cycle, and the like.

[0037] Furthermore, the analysis unit 120 can exclude battery cells having discontinuity in characteristic values ​​from the lifespan judgment target. That is, battery cells having noise due to noise or errors in characteristic value data can be excluded from the analysis target as being unable to predict capacity early, considering that an abnormality occurred during the experiment process or data calculation. In this case, the capacity of the battery cells excluded from the lifespan judgment target can be estimated after charging and discharging up to a set number of cycles (e.g., 300 cycles) as in the conventional case.

[0038] The analysis unit 120 can classify the battery cells that can be determined to have a normal life based on the characteristic value of the battery cell in a state where the battery cell has been charged and discharged up to the first cycle into a first class. For example, the first cycle may be 36 cycles. That is, according to the battery management device disclosed in this document, the capacity can be estimated more quickly than the conventional method of predicting the life after charging and discharging up to 300 cycles.

[0039] Furthermore, the analysis unit 120 can classify battery cells not belonging to the first class into a second class in which the lifespan of the battery cell can be determined after charging and discharging up to the second cycle. In this case, the second cycle is a value greater than the above-mentioned first cycle, and may be 100 cycles. That is, the analysis unit 120 cannot determine the capacity based on only data up to the first cycle (e.g., 36 cycles), but for battery cells whose capacity can be estimated based on data up to the second cycle, which is greater than the first cycle, the analysis unit 120 can estimate the capacity via a different estimation model without charging and discharging up to 300 cycles.

[0040] Then, the analysis unit 120 can calculate an approximation equation for the obtained characteristic value data and classify each battery cell into a plurality of classes based on the coefficients of the approximation equation. In this case, the approximation equation may be a linear or quadratic polynomial. For example, the analysis unit 120 can classify the battery cells into a plurality of classes (clusters) according to the deterioration behavior by performing K-means clustering on the coefficients of the approximation equation.

[0041] The determination unit 130 may determine the lifespan of the battery cells in a method previously set for each of the multiple classes. Specifically, the determination unit 130 may determine that the deterioration level of the battery cells classified into the first class through the analysis unit 120 is normal early on after performing charging and discharging for only a first cycle. In addition, the determination unit 130 may determine the lifespan of the battery cells classified into the second class through the analysis unit 120 by applying the extracted characteristic values ​​of the battery cells to a regression model after performing charging and discharging for only a second cycle.

[0042] Here, the regression model may be a capacity estimation model capable of calculating the SOH value after up to 300 cycles based on data from cycles 1 to 100. For example, the regression model may be an estimation model such as Elastic Net, which is one of neural network techniques via machine learning.

[0043] Meanwhile, although not shown in Fig. 2, the battery management device 100 disclosed herein may include a storage unit. In this case, the storage unit may store various data such as the voltage and current of the battery cell, characteristic values, and data related to the first and second classes. Meanwhile, the battery management device 100 of Fig. 1 may include a communication unit (not shown) instead of the storage unit. In this case, the battery management device 100 may operate in a manner in which various data related to each battery cell is stored in an external server and transmitted and received via the communication unit.

[0044] In this way, the battery management device according to one embodiment disclosed in this document can predict the lifespan of a battery cell early by classifying the battery cells into classes based on data indicating the deterioration characteristics of the battery cells and estimating the capacity.

[0045] FIG. 3 is a diagram illustrating filtering of characteristic value data of a battery cell in a battery management device according to an embodiment disclosed in this document. Referring to Fig. 3, a graph is shown showing changes in characteristic values ​​for each of a plurality of battery cells with respect to the cycles. In this case, the horizontal axis of Fig. 3 shows the number of charge / discharge cycles of the battery cell, and the vertical axis shows the characteristic value of the battery cell. For example, the characteristic value may be a differential value (dQ / dV) of the capacity with respect to the voltage of the battery cell.

[0046] As shown in Figure 3, characteristic value data calculated for each of multiple battery cells usually contains a mixture of data that appears smoothly as continuous values ​​and data that deviates from the trend as discontinuous values ​​due to noise, etc. It is difficult to obtain results from statistical analysis of such discontinuous characteristic value data unless correction processing is performed to smooth the data.

[0047] In this way, in the battery management device disclosed in this document, battery cells that have discontinuities in their characteristic values ​​are classified into a different class in advance and excluded from the lifespan judgment, making it possible to more accurately and easily analyze the characteristic values.

[0048] FIG. 4 is a diagram illustrating an example of classifying battery cells into classes based on characteristic value data of the battery cells in a battery management device according to an embodiment disclosed in this document.

[0049] Referring to FIG. 4, the change in characteristic value with respect to the charge / discharge cycle of the battery cell is shown divided into a plurality of classes. In this case, Class1-1 indicates the first class described above, and Class1-2 and 1-3 indicate the second class described above. Moreover, each class shown in FIG. 4 may be classified by K-means clustering analysis. For example, the characteristic value in FIG. 4 may be a differential value of the capacity with respect to the voltage of the battery cell.

[0050] As shown in Fig. 4, it can be seen that the slope of the characteristic value with respect to the cycle is gentler for battery cells belonging to Class 1-1 compared to Class 1-2 and 1-3. That is, in the case of battery cells included in Class 1-1, the change in the characteristic value that is highly correlated with the SOH appears relatively small, so that the capacity degradation may occur less compared to other battery cells.

[0051] As a result of performing an analysis using the battery management device disclosed in this document, if the battery cell belongs to Class 1-1, i.e., the first class, it can be determined that the battery cell has a normal degree of deterioration early on without further charging and discharging after a certain number of cycles (e.g., 36 cycles). Also, if the battery cell belongs to Class 1-2 or 1-3, i.e., the second class, it can be determined that the battery cell has a normal degree of deterioration early on after a certain number of cycles (e.g., 100 cycles) or more are charged and discharged, and then the capacity of the battery cell can be predicted using the regression model described above. For example, the regression models applied to Classes 1-2 and 1-3, which show some different deterioration behaviors, can be configured to have different initial values, calculation conditions, etc.

[0052] FIG. 5 is a diagram showing an example of a result of determining the life span of a battery cell via a battery management device according to an embodiment disclosed in this document. 5 shows the SOH value (Prediction) of the battery cell calculated by classifying it into Class 1-2 and 1-3 described in FIG. 4 through the battery management device according to an embodiment disclosed in this document, and the SOH value (All Pass) of the battery cell classified into Class 1-1 and determined to have a normal degree of deterioration. Graph 200 in FIG. 5 also shows an outline of the life value of the battery cell that was actually measured.

[0053] 5, it can be seen that the battery cells classified as Class 1-1 have SOH values ​​that are the same as the actual measured values ​​and are located on graph 200. In contrast, in the case of battery cells classified as Class 1-2 and 1-3, some error occurs with the actually measured SOH values ​​and they are located at a certain distance from graph 200. In this case, the distance from graph 200 indicates the magnitude of the error.

[0054] 5, the error from the actual SOH value (Actual SOH) is shown as the distance (Absolute Error) from the line. That is, the lower part of FIG. 5 shows the magnitude corresponding to the absolute value of the distance from the graph 200 of the points of the battery cells (Prediction) included in Classes 1-2 and 1-3.

[0055] As can be seen from FIG. 5, in the case of battery cells classified as Class 1-1 (first class) in FIG. 4, they can be judged as normal (Pass) even after a certain number of charge / discharge cycles, and in the case of the remaining battery cells of Classes 1-2 and 1-3, although there is some error with the actual SOH value, they tend to be roughly the same.

[0056] FIG. 6 is a flow chart illustrating a battery management method according to one embodiment disclosed herein. Referring to FIG. 6, in a battery management method according to an embodiment disclosed herein, first, the voltage and current of a battery cell are measured (S110). At this time, the voltage and current of the battery cell may be measured at regular time intervals. In addition, the voltage and current measured in step S110 may be used to calculate a characteristic value for each battery cell. For example, the characteristic value of the battery cell may be a value calculated based on a differential value (dQ / dV) of the capacity with respect to the voltage of the battery cell.

[0057] Then, among the battery cells, battery cells having discontinuity in the characteristic values ​​are excluded from the lifespan judgment target (S120). In other words, battery cells having noise due to noise or errors in the characteristic value data are regarded as having an abnormality during the experiment process or data calculation, and can be excluded from the analysis target as being unable to predict the capacity early. At this time, the capacity of the battery cells excluded from the lifespan judgment target can be estimated after charging and discharging up to a set number of cycles (e.g., 300 cycles) as in the conventional case.

[0058] Next, the battery cells are classified into a plurality of classes based on the characteristic values ​​obtained from the current and voltage of the battery cells and the deterioration behavior of each battery cell (S130). Specifically, in step S130, the battery cells can be classified into a plurality of classes based on the manner in which the characteristic values ​​change. For example, the manner in which the characteristic values ​​change can include the initial value of the characteristic values, the rate of change (slope) of the characteristic values ​​with respect to the cycles, and the like.

[0059] In step S130, an approximation equation for the obtained characteristic value data is calculated, and the battery cells can be classified into a plurality of classes based on the coefficients of the approximation equation. In this case, the approximation equation may be a linear or quadratic polynomial. For example, by performing K-means clustering on the coefficients of the approximation equation, the battery cells can be classified into a plurality of classes (clusters) according to the deterioration behavior.

[0060] If the battery cell is included in the first class in step S140 (YES), the battery cell is determined to have a normal life based on the characteristic value of the battery cell after charging and discharging up to the first cycle (S150). For example, the first cycle may be 36 cycles.

[0061] On the other hand, if the battery cell is not included in the first class (NO) in step S140, the battery cell is classified into a second class (S160). In addition, in a state where charging and discharging are performed for only the second cycle, the characteristic value of the battery cell is applied to a regression model to evaluate the life of the battery cell (S170). For example, the second cycle is a value larger than the above-mentioned first cycle, and may be 100 cycles.

[0062] Here, the regression model may be a capacity estimation model capable of calculating the SOH value after up to 300 cycles based on data from cycles 1 to 100. For example, the regression model may be an estimation model such as Elastic Net, which is one of the neural network techniques via machine learning.

[0063] In this manner, the battery management method according to one embodiment disclosed in this document can predict the lifespan of a battery cell early by classifying the battery cells into classes based on data indicating the deterioration characteristics of the battery cells and estimating the capacity.

[0064] FIG. 7 is a block diagram illustrating a computing system that executes one embodiment of the battery management method disclosed herein. Referring to FIG. 7, a computing system (battery management device or battery management system) 30 according to one embodiment disclosed in this document may include an MCU 32, a memory 34, an input / output I / F 36, and a communication I / F 38.

[0065] The MCU 32 may be a processor that executes various programs (e.g., a characteristic value calculation program, a class classification and life estimation program, etc.) stored in the memory 34, processes various data including the voltage, current, etc. of the battery cells via such programs, and performs the functions of the battery management device shown in FIG. 2 described above.

[0066] The memory 34 can store various programs related to calculation of characteristic values ​​of the battery cells, classification, and life estimation, as well as various data such as the voltage, current, and characteristic value data of each battery cell.

[0067] A plurality of such memories 34 may be provided as necessary. The memories 34 may be volatile memories or nonvolatile memories. The memory 34 as a volatile memory may be a RAM, a DRAM, an SRAM, or the like. The memory 34 as a nonvolatile memory may be a ROM, a PROM, an EAROM, an EPROM, an EEPROM, a flash memory, or the like. The examples of the memory 34 listed above are merely illustrative and are not limited to these examples.

[0068] The input / output I / F 36 can 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 32 to transmit and receive data.

[0069] The communication I / F 38 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, programs and various data for calculating characteristic values ​​of battery cells, classifying them, and estimating their life spans can be transmitted and received from a separately provided external server via the communication I / F 38.

[0070] In this manner, a computer program according to one embodiment disclosed in this document may be recorded in memory 34 and processed by MCU 32 to be realized, for example, as a module performing each function shown in FIG. 2.

[0071] Although it has been described above that all components constituting the embodiments disclosed in this document are combined or operate in combination, the embodiments disclosed in this document are not necessarily limited to such embodiments. In other words, within the scope of the purpose of the embodiments disclosed in this document, all components may be selectively combined and operate in one or more combinations.

[0072] In addition, the terms "comprise", "constitute", or "have" described above mean that the relevant element can be contained, unless otherwise specified, and should be interpreted as meaning that other elements may be included, rather than excluding other elements. All terms, 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, unless otherwise defined. Commonly used terms, such as terms defined in a dictionary, should be interpreted as consistent with the contextual meaning of the relevant art, and should not be interpreted in an idealized or overly formal sense, unless expressly defined in this document.

[0073] The above description is merely an illustrative example of the technical ideas disclosed in this document, and a person having ordinary knowledge in the technical field to which the embodiments disclosed in this document belong can make various modifications and variations within the scope of the essential characteristics of the embodiments disclosed in this document. Therefore, the embodiments disclosed in this document are intended to explain, not to limit, the technical ideas of the embodiments 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] 1: Battery control system 10: Battery pack 12: Multiple battery modules 14: Sensor 16: Switching section 20: Upper controller 30:Battery management device 32:MCU 34: Memory 36: Input / Output Interface 38: Communication I / F 100: Battery management device, Battery Management System (BMS) 110: Measuring part 120: Analysis Department 130: Judgment Department

Claims

1. A measurement unit for measuring a current and a voltage of a battery cell; an analysis unit that classifies each of the battery cells into a plurality of classes based on a characteristic value obtained from a current and a voltage of the battery cell and a deterioration behavior of each of the battery cells; a determination unit for determining a life span of the battery cell in a method previously set for each of the plurality of classes; Including, the analysis unit classifies, into a first class, a battery cell that can be determined to have a normal life based on a characteristic value of the battery cell in a state in which the battery cell has been charged and discharged up to a first cycle; The analysis unit classifies a battery cell not belonging to the first class into a second class in which the life of the battery cell can be determined after charging and discharging up to a second cycle, and the number of the second cycles is greater than the number of the first cycles.

2. The battery management device according to claim 1 , wherein the analysis unit excludes, from among the battery cells, battery cells in which noise appears in the characteristic value data, from targets for lifespan determination.

3. 3. The battery management device according to claim 1, wherein the analysis unit classifies each of the battery cells into the plurality of classes based on a change in the characteristic value, and the change in the characteristic value includes an initial value of the characteristic value and a rate of change with respect to a cycle.

4. 4. The battery management device according to claim 1, wherein the determination unit determines the lifetime of the battery cells included in the second class by applying characteristic values ​​of the battery cells included in the second class to a regression model in a state in which the battery cells included in the second class are charged and discharged for only the second cycle.

5. A measurement unit for measuring the current and voltage of a battery cell; an analysis unit that classifies each of the battery cells into a plurality of classes based on a characteristic value obtained from a current and a voltage of the battery cell and a deterioration behavior of each of the battery cells; a determination unit for determining a life span of the battery cell in a method previously set for each of the plurality of classes; Including, the analysis unit classifies each of the battery cells into the plurality of classes based on a coefficient of an approximation equation for the characteristic value; the characteristic value is a value calculated based on a differential value of a capacity with respect to a voltage of the battery cell, The battery management device, wherein the approximation equation is a first-order or second-order polynomial.

6. The battery management device according to claim 5 , wherein the analysis unit performs K-means clustering on the coefficients of the approximation formula.

7. measuring the current and voltage of a battery cell; classifying each of the battery cells into a plurality of classes based on characteristic values ​​obtained from the current and voltage of the battery cells and deterioration behavior of each of the battery cells; determining a life span of the battery cell according to a method previously set for each of the plurality of classes; Including, The step of classifying each of the battery cells into a plurality of classes includes classifying into a first class a battery cell that can be determined to have a normal life based on a characteristic value of the battery cell in a state in which the battery cell has been charged and discharged up to a first cycle; The step of classifying each of the battery cells into a plurality of classes classifies a battery cell not belonging to the first class into a second class in which the life of the battery cell can be determined after charging and discharging up to a second cycle, and the number of the second cycles is greater than the number of the first cycles.

8. 8. The battery management method according to claim 7, further comprising the step of excluding, from among the battery cells, battery cells in which noise appears in the characteristic value data, from targets for lifespan determination.

9. 9. The battery management method according to claim 7 or 8, wherein the step of classifying each of the battery cells into a plurality of classes classifies each of the battery cells into the plurality of classes based on a manner in which the characteristic value changes, and the manner in which the characteristic value changes includes an initial value of the characteristic value and a rate of change with respect to cycles.

10. A battery management method as described in claim 9, wherein the rate of change is the slope of the characteristic value.

11. The battery management method according to any one of claims 7 to 10, wherein the step of determining the life of the battery cell determines the life of the battery cell by applying characteristic values ​​of the battery cells included in the second class to a regression model in a state in which the battery cells included in the second class have been charged and discharged for only the second cycle.

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