Battery management apparatus and method

Abstract

A battery management device is provided, including: a calculation unit configured to calculate a differential value of a capacity of a battery cell with respect to a voltage of the battery cell; an analysis unit configured to perform a statistical analysis on the differential value; and a determination unit configured to determine the capacity of the battery cell based on the statistical analysis.

Description

TECHNICAL FIELD

[0001] Cross Reference to Related Applications

[0002] This application claims priority to and the benefit of Korean Patent Application No. 10-2020-0183970, filed on December 24, 2020, in the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD

[0004] Embodiments disclosed herein relate to a battery management device and method. BACKGROUND

[0005] Recently, research and development on secondary batteries have been actively conducted. In the present document, the secondary battery, which is a chargeable / dischargeable battery, can include all of conventional nickel (Ni) batteries / cadmium (Cd) batteries, Ni / metal hydride (MH) batteries, etc., and recent lithium ion batteries. Among the secondary batteries, the lithium ion battery has much higher energy density than those of the conventional Ni / Cd battery, Ni / MH battery, etc. Further, the lithium ion battery can be manufactured to be small and light in weight, so that the lithium ion battery has been used as a power source of a mobile device. In addition, the lithium ion battery is attracting attention as a next-generation energy storage medium, as its use range is expanded to a power source of an electric vehicle.

[0006] In addition, the secondary battery is generally used as a battery rack including a battery module in which a plurality of battery cells are connected in series and / or in parallel with each other. The battery rack can be managed and controlled in terms of state and operation by a battery management system.

[0007] In order to calculate the capacity of the battery cell, generally, a calculation equation such as ampere-hour metering, etc. can be used after charging and discharging of the battery cell are all completed. In order to release such a battery cell, the battery cell can be charged or discharged as many as 300 cycles to check a capacity degradation rate before the battery cell is released after that. However, the discharging of the battery cell as many as 300 cycles can consume a large amount of time and cost. SUMMARY

[0008] TECHNICAL PROBLEM

[0009] Embodiments disclosed herein aim to provide a battery management device and method in which the capacity of a battery cell can be predicted early by statistically analyzing state data measured in real time during charging and discharging of the battery cell.

[0010] The technical problems of embodiments disclosed herein are not limited to the above-mentioned technical problems and other unmentioned technical problems will be clearly understood by those skilled in the art from the following description.

[0011] Technical Solution

[0012] A battery management apparatus according to an embodiment disclosed herein includes a calculation unit configured to calculate a differential value of a capacity of a battery cell with respect to a voltage of the battery cell, an analysis unit configured to perform a statistical analysis on the differential value, and a determination unit configured to determine the capacity of the battery cell based on the statistical analysis.

[0013] According to an embodiment, the analysis unit can select a maximum value among deviations of the differential values between charge / discharge cycles of the battery cell as a representative value, and perform the statistical analysis on the representative value.

[0014] According to an embodiment, the analysis unit can calculate an approximate equation for the representative value and perform the statistical analysis on coefficients of the approximate equation.

[0015] According to an embodiment, the analysis unit can perform K-means clustering on the coefficients of the approximate equation.

[0016] According to an embodiment, the determination unit can determine that the capacity of the battery cell is normal when the battery cell belongs to a predetermined cluster among a plurality of clusters.

[0017] According to an embodiment, the predetermined cluster can include a battery cell for which a gradient of the approximate equation for the representative value is less than a reference value.

[0018] According to an embodiment, the battery management apparatus can further include a storage unit storing information about the plurality of clusters.

[0019] According to an embodiment, the determination unit can determine the capacity of the battery cell after performing charge / discharge of the battery cell for up to a preset number of cycles when the battery cell does not belong to the predetermined cluster.

[0020] According to an embodiment, the approximate equation can be a first or second order polynomial.

[0021] A battery management method according to an embodiment disclosed herein includes calculating a differential value of a capacity of a battery cell with respect to a voltage of the battery cell, performing a statistical analysis on the differential value, and determining the capacity of the battery cell based on the statistical analysis.

[0022] According to an embodiment, the battery management method can further include selecting a maximum value among deviations of the differential values between charge / discharge cycles of the battery cell as a representative value, wherein performing the statistical analysis includes performing the statistical analysis on the representative value.

[0023] According to an embodiment, the battery management method can further include calculating an approximate equation for the representative value, wherein performing the statistical analysis includes performing the statistical analysis on coefficients of the approximate equation.

[0024] According to an embodiment, the battery management method can further include performing K-means clustering on coefficients of the approximation equation.

[0025] According to an embodiment, determining the capacity of the battery cell can include determining that the capacity of the battery cell is normal when the battery cell belongs to a predetermined cluster among the plurality of clusters.

[0026] According to an embodiment, the predetermined cluster can include a battery cell for which a gradient of the approximation equation with respect to the representative value is less than a reference value.

[0027] According to an embodiment, the battery management method can further include storing information about the plurality of clusters.

[0028] According to an embodiment, determining the capacity of the battery cell can include determining the capacity of the battery cell after performing charging / discharging of the battery cell for up to a preset number of cycles when the battery cell does not belong to the predetermined cluster.

[0029] According to an embodiment, the approximation equation can be a first or second order polynomial.

[0030] Advantageous effects

[0031] The battery management apparatus and method according to the embodiments disclosed herein can predict the capacity of the battery cell early by statistically analyzing state data measured in real time during charging and discharging of the battery cell. BRIEF DESCRIPTION OF DRAWINGS

[0032] Figure 1 is a block diagram of a general battery rack.

[0033] Figure 2 is a block diagram illustrating a structure of a battery management apparatus according to the embodiments disclosed herein.

[0034] Figure 3 is a view for describing a representative value selection process of a battery cell according to the embodiments disclosed herein.

[0035] Figure 4 is a view showing an approximation equation calculated for a representative value of a battery cell according to the embodiments disclosed herein.

[0036] Figure 5 is a view showing K-means clustering performed on coefficients of an approximation equation for a calculated representative value of a battery cell according to the embodiments disclosed herein.

[0037] Figure 6 is a view showing a state of health (SOH) and a representative value of a battery cell included in each cluster classified by K-means clustering according to the embodiments disclosed herein.

[0038] Figure 7 is a view illustrating a graph of representative values of battery cells classified by clustering according to the embodiments disclosed herein.

[0039] Figure 8 is a view illustrating a calculated capacity degradation degree of battery cells included in each cluster with respect to cycles according to the embodiments disclosed herein.

[0040] Figure 9 is a flowchart illustrating a battery management method according to the embodiments disclosed herein.

[0041] Figure 10 is a block diagram illustrating a computing system performing a battery management method according to the embodiments disclosed herein. DETAILED DESCRIPTION

[0042] Hereinafter, various embodiments disclosed herein will be described in detail with reference to the accompanying drawings. In this document, the same drawing reference numerals are used for the same components throughout the several drawings, and redundant descriptions will not be repeated.

[0043] For various embodiments disclosed in this document, a specific structural or functional description is exemplified only for the purpose of describing the embodiments, and various embodiments disclosed herein can be implemented in various forms, and should not be understood as being limited to the embodiments described in this document.

[0044] As used in various embodiments, the terms "1st", "2nd", "first", "second", and the like can modify various components regardless of importance and do not limit the components. For example, a first component can be named a second component without departing from the scope of the embodiments disclosed herein, and similarly, a second component can be named a first component.

[0045] The terms used in this document are used only to describe specific exemplary embodiments of the disclosure and can not have the intent to limit the scope of other exemplary embodiments of the disclosure. It should be understood that the singular form includes a plural reference unless the context clearly specifies otherwise.

[0046] All terms used herein including technical and scientific terms have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments disclosed herein belong. It will be further understood that terms such as those defined in generally used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and should not be interpreted in an ideal or overly formal sense unless explicitly defined herein. In some cases, the terms defined herein can be interpreted as not including the embodiments disclosed herein.

[0047] Figure 1is a block diagram of a general battery rack.

[0048] More specifically, Figure 1 A battery control system 1 including a battery rack 10 and a superior controller 20 included in a superior system according to embodiments disclosed herein is schematically shown.

[0049] As Figure 1 As shown in the middle, the battery rack 10 can include a plurality of battery modules 12, sensors 14, a switching unit 16, and a battery management system (BMS) 100. The battery rack 10 can include the battery modules 12, the sensors 14, the switching unit 16, and the BMS 100 provided in plural.

[0050] The plurality of battery modules 12 can include at least one chargeable / dischargeable battery cell.

[0051] The sensors 14 can detect a current flowing in the battery rack 10. In this case, the detected signal can be transmitted to the BMS 100.

[0052] The switching unit 16 can be connected in series to a (+) terminal side or a (-) terminal side of the battery modules 12 to control a charge / discharge current flow of the battery modules 12. For example, the switching unit 16 can use at least one relay, a magnetic contactor, or the like, according to a specification of the battery rack 10.

[0053] The BMS 100 can monitor a voltage, a current, a temperature, or the like of the battery rack 10 to perform control and management to prevent overcharging and overdischarging, or the like, and can include, for example, an RBMS.

[0054] The BMS 100, which is an interface for receiving measured values of various parameter values described above, can include a plurality of terminals and a circuit or the like connected thereto to process input values. The BMS 100 can control turning on / off of the switching unit 16, for example, a relay, a contactor, or the like, and can be connected to the battery modules 12 to monitor a state of each of the battery modules 12.

[0055] Meanwhile, in the BMS 100 disclosed herein, as will be described later, a differential value of a capacity with respect to a voltage of a battery cell measured through a separate program can be calculated, and the capacity of the battery cell can be predicted by statistically analyzing the differential value.

[0056] The superior controller 20 can transmit a control signal for controlling the battery module 12 to the BMS 100. Accordingly, the BMS 100 can be controlled in its operation based on the signal applied from the superior controller 20. Also, the battery module 12 can be a component included in an energy storage system (ESS). In this case, the superior controller 20 can be a battery bank controller (BBMS) of a battery bank including a plurality of racks 10 or an ESS controller for controlling the entire ESS including a plurality of banks. However, the battery rack 10 is not limited to such a purpose.

[0057] Such a configuration of the battery rack 10 and the battery management system 100 is a well-known configuration, and thus will not be described in detail.

[0058] Figure 2 is a block diagram illustrating a structure of a battery management apparatus according to embodiments disclosed herein.

[0059] Referring to Figure 2 The battery management apparatus 100 according to the embodiments disclosed herein can include a calculation unit 110, an analysis unit 120, a determination unit 130, and a storage unit 140.

[0060] The calculation unit 110 can calculate a differential value of a capacity of a battery cell with respect to a voltage of the battery cell. More specifically, the calculation unit 110 can calculate the differential value dQ / dV of the capacity of the battery cell with respect to the voltage of the battery cell based on the voltage and the current of each battery cell for each charge / discharge cycle. In addition, the calculation unit 110 can store the dQ / dV for each charge / discharge cycle calculated for each battery cell in the storage unit 140.

[0061] The analysis unit 120 can perform a statistical analysis on the differential values calculated by the calculation unit 110. More specifically, the analysis unit 120 can select a maximum value among the deviations of the differential values between the charge / discharge cycles of the battery cell as a representative value (hereinafter, described as nonfixV dQ / dV), and perform a statistical analysis on the selected representative value. In addition, the analysis unit 120 can store each representative value calculated for each battery cell in each charge / discharge cycle in the storage unit 140.

[0062] The analysis unit 120 can calculate an approximation equation for the representative values and perform a statistical analysis on coefficients of the approximation equation. For example, the approximation equation for the representative values can be a first or second order polynomial. The approximation equation can be calculated through an approximation to any number of charge / discharge cycles. For example, the approximation equation can be calculated for each battery cell based on data from 1 to 4 cycles or data from 1 to 100 cycles. When the approximation equation calculated by the analysis unit 120 is a second order equation, an approximation closer to the general shape of the representative values can be possible, thus allowing for an analysis focusing on the general shape, and when the approximation equation is a first order equation, an approximation in a manner such that the gradient of the representative values is emphasized, allowing for an analysis focusing on the amount of change, is possible.

[0063] In addition, the analysis unit 120 can perform K-means clustering on the calculated coefficients of the approximation equation to classify the calculated coefficients into a preset number of clusters. At this time, each cluster can be classified based on the gradient of the representative values of the battery cells. This will be described with reference to FIG. 2. Figure 5

[0064] The determination unit 130 can determine the capacity of the battery cell based on the statistical analysis. More specifically, when the battery cell belongs to a predetermined cluster among the plurality of clusters calculated by the analysis unit 120, the determination unit 130 can determine that the capacity of the battery cell is normal. In this case, the predetermined cluster can include battery cells in which the gradient of the representative values is less than a reference value. Meanwhile, when the battery cell does not belong to the predetermined cluster, the determination unit 130 can determine the capacity of the battery cell after charging / discharging the battery cell up to a preset number of cycles (e.g., 300 cycles).

[0065] The storage unit 140 can store information about the plurality of clusters. For example, the storage unit 140 can store information about the clusters previously calculated by the analysis unit 120 through K-means clustering. In addition, the storage unit 140 can store data about the differential values and the representative values calculated for each battery cell.

[0066] Meanwhile, the battery management apparatus 100 according to the embodiments disclosed herein is described as including the storage unit 140 in the above-described configuration, but the battery management apparatus 100 can include a communication unit (not shown) instead of the storage unit 140. In this case, the battery management apparatus 100 can operate by storing various data such as the differential data or the representative values for each battery cell, information about the plurality of clusters, etc., in an external server and transmitting and receiving data through the communication unit. Figure 1

[0067] ​​Accordingly, the battery management device 100 according to the embodiments disclosed herein can predict the capacity of the battery cell early by statistically analyzing the state data measured in real time during charging and discharging of the battery cell.

[0068] Figure 3 is a view for describing a maximum value among deviations of differential values between charging / discharging cycles of the battery cell as a representative value.

[0069] Referring to Figure 3 , the x-axis indicates the voltage V of the battery cell, and the y-axis indicates the differential data dQ / dV of the capacity with respect to the voltage of the battery cell. Figure 3 Each graph of shows the differential value calculated for each charging / discharging cycle of the battery cell.

[0070] As shown in Figure 3 , the battery management device 100 according to the embodiments disclosed herein can select a maximum value among deviations of differential values between charging / discharging cycles of the battery cell as a representative value. As will be described below, the representative value calculated in this way has a high correlation with the capacity of the battery, so that it can be suitable for predicting the capacity of the battery cell early without repeating the charging / discharging cycle of the battery cell several times.

[0071] Figure 4 is a view illustrating an approximate equation calculated for the representative value of the battery cell.

[0072] Referring to Figure 4 , the x-axis indicates the number of charging / discharging cycles of the battery cell, and the y-axis indicates the representative value nonfixV dQ / dV of the battery cell. At this time, the representative value of the y-axis can be a value corresponding to the maximum deviation between charging / discharging cycles in dQ / dV of the battery cell. Figure 4 shows a graph A of the representative value of the battery cell and a graph B of an approximate equation calculated using a quadratic polynomial (ax 2 +bx+c). As shown in Figure 4 , it can be seen that the representative value decreases and a form similar to the approximate equation appears as the charging / discharging cycle proceeds.

[0073] Figure 5 is a view showing K-means clustering performed on coefficients of the approximate equation for the representative value of the battery cell.

[0074] In the coordinate space shown in Figure 5 , each coordinate axis indicates each of an a-axis, a b-axis, and a c-axis corresponding to coefficients of the approximate equation of the representative value. In addition, Figure 5 each point can indicate the coefficients of the approximate equation of the representative value for each battery cell. InFigure 5 In the example of FIG. 18, each battery cell is classified into three clusters: cluster 1 to cluster 3 by K-means clustering. However, the present embodiment can not be limited thereto, and the cluster of each battery cell can be determined as any number.

[0075] Figure 6 FIG. 18 is a view illustrating a state of health (SOH) and a representative value of a battery cell included in each cluster classified by K-means clustering.

[0076] Referring to Figure 6 , a graph of cluster 1 to cluster 3 in the coordinate space illustrated in Figure 5 is shown. In addition, the graph indicated more lightly in cluster 1 to cluster 3 of Figure 6 may illustrate a case in which the capacity of a battery cell falls within a normal range (pass), and the graph indicated more thickly can illustrate a case in which the capacity of a battery cell is abnormal (failure).

[0077] More specifically, Figure 6 it is illustrated that, after the above-described representative value is classified by K-means clustering, a capacity degradation test can be performed for each cluster to determine normal or abnormal. At this time, in Figure 6 , for cluster 1, 226 battery cells among 242 battery cells are normal (pass), and 16 battery cells are abnormal (failure), and for cluster 2, 154 batteries among 197 battery cells are normal (pass), and 43 battery cells are abnormal (failure). On the other hand, for cluster 3, all 11 battery cells are normal.

[0078] Therefore, for cluster 3 classified by K-means clustering, the gradient of the representative value indicating the maximum value among the differential values of the battery cells is less than that of another cluster, such that the amount of change with respect to the initial representative value is small after a certain cycle. Therefore, with the battery management device according to the embodiments disclosed herein, after each cluster is calculated in advance by performing K-means clustering on the representative value with respect to the differential value of the battery cell, when the battery cell belongs to a certain cluster (for example, cluster 3), the degree of degradation is determined to fall within a normal range (pass), thereby estimating the capacity of the battery cell early without repeating up to 300 cycles as in the conventional art.

[0079] Figure 7 FIG. 19 is a view illustrating a graph of representative values of battery cells classified by clustering.

[0080] Referring to Figure 7 , the x-axis indicates the number of charge / discharge cycles of a battery cell, and the y-axis indicates a representative value with respect to a differential value of the battery cell. From Figure 7It can be seen that, when compared with Cluster 1 and Cluster 2, the representative value regarding the differential value appears gently with respect to the gradient of cycles for Cluster 3. That is, for the battery cell belonging to Cluster 3, the change in the representative value having a high correlation with SOH is small, thereby resulting in a relatively small anode capacity deterioration. Accordingly, when the specific battery cell belongs to Cluster 3 as a result of statistical analysis using the battery management device disclosed herein, the charge / discharge test can be immediately stopped, and the battery cell can be determined as a battery cell having a normal degree of deterioration.

[0081] Figure 8 is a view showing the degree of capacity deterioration of the battery cell included in each cluster with respect to cycles.

[0082] Referring to Figure 8 , for the representative value that is the maximum deviation value between cycles regarding the differential value of the capacity of the battery cell with respect to the voltage of the battery cell, after being approximated to a first order polynomial and a second order polynomial, respectively, the normal (pass) or abnormal (fail) of the degree of capacity deterioration is indicated. As Figure 8 shown in FIG. 6, at the beginning of the charge / discharge cycles of the battery cell, the normal / abnormal determination result diverges or vibrates, and after a certain number of cycles, the result gradually converges.

[0083] In detail, referring to Figure 8 , it can be seen that, when the representative value of the battery cell is approximated to a second order polynomial, the result value of the degree of capacity deterioration diverges or vibrates on both sides of normal and abnormal, focusing on the general shape. On the other hand, in the case of being approximated to a first order polynomial that maximizes the gradient of the representative value of the battery, the result value of the degree of capacity deterioration converges from the initial stage. That is, as Figure 8 shown in FIG. 6, for the first order polynomial, in the case of Cluster 3, normal and abnormal converge from about 36 cycles, and in particular, the abnormal (failure) case is zero (0) after 36 cycles, so that the capacity deterioration can be stably identified when compared with the second order polynomial.

[0084] Accordingly, with the battery management device according to the embodiments disclosed herein, whether the capacity caused by anode deterioration of the battery cell is normal can be determined early through statistical analysis of the representative value of the battery cell.

[0085] Figure 9 is a flowchart illustrating a battery management method according to the embodiments disclosed herein.

[0086] Referring to Figure 9According to the battery management method according to the embodiments disclosed herein, the differential value of the capacity of the battery cell with respect to the voltage of the battery cell can be calculated in operation S110. More specifically, in operation S110, the differential value dQ / dV of the capacity of the battery cell with respect to the voltage of the battery cell can be calculated based on the voltage and the current of each battery cell for each charge / discharge cycle.

[0087] In operation S120, a maximum value among the deviations of the differential values between the charge / discharge cycles of the battery cells can be selected as a representative value, and in operation S130, an approximation equation for the selected representative value can be calculated. At this time, the approximation equation can be a first or second order polynomial. As described above, when the approximation method is a second order equation, approximation of the general shape to the representative value can be possible, so that analysis of the general shape is useful, and when the approximation equation is a first order equation, analysis of the amount of change can be performed.

[0088] Next, in operation S140, K-means clustering can be performed on the coefficients of the approximation equation calculated in operation S130. Accordingly, each battery cell can be classified for each cluster according to a preset number of clusters. Further, in operation S150, it is determined whether the battery cell belongs to a predetermined cluster.

[0089] In operation S160, when the battery cell belongs to the predetermined cluster (Yes), the capacity of the battery cell is determined to be normal. In this case, the predetermined cluster can include battery cells in which the gradient of the approximation equation for the representative value is less than a reference value. For example, the predetermined cluster can correspond to cluster 3 described above. On the other hand, in operation S170, when the battery cell does not belong to the predetermined cluster (No), the capacity of the battery cell can be determined after charging / discharging the battery cell up to a preset number of cycles (for example, 300 cycles).

[0090] Accordingly, with the battery management method according to the embodiments disclosed herein, it is possible to predict the capacity of the battery cell early by statistically analyzing the state data measured in real time during charging and discharging of the battery cell.

[0091] Figure 10 is a block diagram illustrating a computing system that performs a battery management method according to the embodiments disclosed herein.

[0092] Reference Figure 10 The computing system 30 according to the embodiments disclosed herein can include an MCU 32, a memory 34, an input / output interface (I / F) 36, and a communication I / F 38.

[0093] The MCU 32 can be a processor that executes various programs (e.g., a differential value calculation program, a capacity prediction program, etc.) stored in the memory 34, processes various data including the voltage, current, capacity, etc. of the battery cells by these programs, and performs Figure 2 the above-described functions of the battery management device illustrated in FIG. 1.

[0094] The memory 34 can store various programs regarding the differential value calculation and capacity prediction of the battery cells. Also, the memory 34 can store various data of each battery cell such as the voltage, current, differential value, representative value data, etc.

[0095] The memory 34 can be provided in plural depending on the need. The memory 34 can be a volatile memory or a non-volatile memory. For the memory 34 as a volatile memory, a random access memory (RAM), a dynamic RAM (DRAM), a static RAM (SRAM), etc. can be used. For the memory 34 as a non-volatile memory, a read only memory (ROM), a programmable ROM (PROM), an electrically alterable ROM (EAROM), an erasable ROM (EPROM), an electrically erasable ROM (EEPROM), a flash memory, etc. can be used. The above-listed examples of the memory 34 are merely examples and are not limited thereto.

[0096] The input / output I / F 36 can provide an interface for transmitting and receiving data by connecting an input device (not shown) such as a keyboard, a mouse, a touch panel, etc. and an output device such as a display (not shown) to the MCU 32.

[0097] The communication I / F 38, which is a component capable of transmitting and receiving various data to and from a server, can be various types of devices capable of supporting wired or wireless communication. For example, programs or various data for the differential value and representative value calculation or capacity prediction of the battery cells can be transmitted to and received from an externally provided server through the communication I / F 38.

[0098] Accordingly, the computer program according to the embodiments disclosed herein can be recorded in the memory 34 and processed by the MCU 32, thus being implemented as a module that performs the functions illustrated in FIG. 1. Figure 2

[0099] Although all components constituting the embodiments disclosed herein have been described above as being combined into one or combined operations, the embodiments disclosed herein are not necessarily limited to these embodiments. That is, within the scope of the objects of the embodiments disclosed herein, all components can be operated by being selectively combined into one or more.

[0100] ​Also, the aforementioned terms such as "include," "constitute," or "have" can mean that a corresponding component can be inherent, unless otherwise stated, and thus should be understood to further include other components rather than excluding other components. Unless otherwise defined, all terms including technical and scientific terms used herein have the same meanings as those generally understood by those skilled in the art to which the embodiments disclosed herein relate. Generally used terms, like those defined in a dictionary, should be interpreted to have the same meanings as those in the context of relevant technology and should not be interpreted to have ideal or overly formal meanings, unless they are clearly defined in this document.

[0101] The above description is merely illustrative of the technical idea of the present disclosure, and those of ordinary skill in the art to which the embodiments disclosed herein relate will be able to make various modifications and variations without departing from the essential characteristics of the embodiments of the present disclosure. Accordingly, the embodiments disclosed herein are intended to be illustrative of and not limiting of the technical spirit of the embodiments disclosed herein, and the scope of the technical spirit of the present disclosure is not limited by these embodiments disclosed herein. The scope of protection of the technical spirit disclosed herein should be interpreted by the appended claims, and all technical spirits within the same scope should be understood to be included in the scope of this document.

Claims

1. A battery management device, comprising: A calculation unit configured to calculate the differential value of the capacity of a single battery cell relative to the voltage of the single battery cell; An analysis unit is configured to select the maximum value among the deviations of the differential values ​​between the charge / discharge cycles of the battery cell as a representative value, calculate an approximate equation for the representative value, and perform statistical analysis on the coefficients of the approximate equation. as well as A determining unit is configured to determine the capacity of the battery cell based on the statistical analysis.

2. The battery management device according to claim 1, wherein, The analysis unit is configured to: K-means clustering is performed on the coefficients of the approximate equation.

3. The battery management device according to claim 2, wherein, The determining unit is configured to: When a battery cell belongs to a predetermined cluster among multiple clusters, the capacity of the battery cell is determined to be normal.

4. The battery management device according to claim 3, wherein, The predetermined clustering includes battery cells whose gradients for the approximate equation for the representative value are less than those for the reference value.

5. The battery management device of claim 3, further comprising a storage unit configured to store information relating to the plurality of clusters.

6. The battery management device according to claim 3, wherein, The determining unit is configured to: When the battery cell does not belong to the predetermined cluster, the capacity of the battery cell is determined after performing up to a preset number of charging / discharging cycles on the battery cell.

7. The battery management device according to claim 1, wherein, The approximate equation is a first-order or second-order polynomial.

8. A battery management method, comprising: Calculate the differential value of the capacity of a single battery cell relative to the voltage of that single battery cell; The maximum value among the deviations of the differential values ​​between the charge / discharge cycles of the battery cell is selected as the representative value; Calculate the approximate equation for the representative value; Perform statistical analysis on the coefficients of the approximate equation; and The capacity of the battery cell is determined based on the statistical analysis.

9. The battery management method according to claim 8, further comprising performing K-means clustering on the coefficients of the approximate equation.

10. The battery management method according to claim 9, wherein, Determining the capacity of a single battery cell includes: when the battery cell belongs to a predetermined cluster among multiple clusters, the capacity of the battery cell is determined to be normal.

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