Battery capacity estimation device and method

Abstract

A battery capacity estimation device is provided, including: a voltage measurement unit configured to measure a voltage of a battery cell; a filtering unit configured to determine voltage data as first data when a recording pattern of the voltage data of the battery cell deviates from a preset reference range; a statistical analysis unit configured to determine second data by statistically analyzing the voltage data of the battery cell; and a capacity estimation unit configured to estimate a capacity by applying data classified by the filtering unit or the statistical analysis unit for the battery cell as a measurement target to a capacity estimation model generated for the first data and the second data, respectively.

Description

Technical Field

[0001] Cross-references to related applications

[0002] This application claims priority to Korean Patent Application No. 10-2020-0101866, filed on August 13, 2020, with the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. Technical Field

[0004] The present invention relates to an apparatus and method for estimating battery capacity by configuring models for easily analyzable data and difficult-to-analyze data respectively. Background Technology

[0005] In recent years, research and development of rechargeable batteries have been actively underway. Rechargeable batteries, as rechargeable / dischargeable batteries, include all conventional nickel (Ni) / cadmium (Cd) batteries, nickel / metal hydride (MH) batteries, and more recently, lithium-ion batteries. Among rechargeable batteries, lithium-ion batteries have a significantly higher energy density than traditional nickel / cadmium and nickel / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a small and lightweight manner, making them suitable for use as power sources in mobile devices. Moreover, as the application of lithium-ion batteries expands to powering electric vehicles, they are attracting attention as a next-generation energy storage medium.

[0006] In addition, secondary batteries are typically used as battery packs that include battery modules, in which multiple battery cells are connected in series and / or parallel. The battery pack can be managed and controlled in terms of state and operation by a battery management system.

[0007] These secondary batteries undergo batch assembly testing (LAT) during production to determine the quality of each cell in production. Typically, a sample from each batch—one cell out of multiple cells—is selected for LAT. The purpose of LAT is to check whether the capacity of the secondary battery is greater than or equal to a specific level, even after 300 cycles of accelerated degradation by the secondary battery.

[0008] LAT allows for full charge and discharge cycles of 1 to 300 times under the same experimental conditions, and determines whether a monomer passes the test based on the state-of-health (SOH) lifetime over 300 cycles. However, the data recorded in LAT may include both easily analyzable and difficult-to-analyze data, making data analysis potentially challenging and reducing accuracy. Summary of the Invention

[0009] [Technical Issues]

[0010] The present invention is made to solve the above-mentioned problems, and the object of the present invention is to provide a battery capacity estimation device and method, wherein the recorded data of a battery cell are distinguished into data that is easy to analyze and data that is not easy to analyze, and a capacity estimation model is applied to each data to accurately and effectively estimate the capacity of the battery cell.

[0011] [Technical Solutions]

[0012] A battery capacity estimation apparatus according to an embodiment of the present invention includes: a voltage measurement unit that measures the voltage of a battery cell; a filtering unit that determines the voltage data as first data when the recording pattern of the voltage data of the battery cell deviates from a preset reference range; a statistical analysis unit that determines second data by performing statistical analysis on the voltage data of the battery cell; and a capacity estimation unit that estimates the capacity by applying data classified by the filtering unit or the statistical analysis unit for the battery cell as the measurement target to a capacity estimation model generated for the first data and the second data, respectively.

[0013] The battery capacity estimation method according to an embodiment of the present invention includes: measuring the voltage of a battery cell; determining the voltage data as first data when the recording pattern of the voltage data of the battery cell deviates from a preset reference range; determining second data by performing statistical analysis on the voltage data of the battery cell; and estimating the capacity of the battery cell by applying the first data and the second data to a capacity estimation model respectively.

[0014] [Beneficial Effects]

[0015] Using the battery capacity estimation apparatus and method according to the present invention, it is possible to distinguish between easily analyzable and difficult-to-analyze data in the recorded data of a battery cell, and apply a capacity estimation model to each data point, thereby accurately and effectively estimating the capacity of the battery cell. Attached Figure Description

[0016] Figure 1 This is a block diagram of a universal battery holder.

[0017] Figure 2 This is a block diagram illustrating the structure of a battery capacity estimation device according to an embodiment of the present invention.

[0018] Figure 3 This is a flowchart illustrating the operation of classifying data performed by a battery capacity estimation device according to an embodiment of the present invention.

[0019] Figure 4 This is a schematic diagram illustrating the correlation between recorded data and the capacity of individual battery cells.

[0020] Figure 5This is a schematic diagram illustrating how a battery capacity estimation device according to an embodiment of the present invention classifies recorded data of individual battery cells into multiple clusters.

[0021] Figure 6 This is a diagram illustrating how the battery capacity estimation device according to an embodiment of the present invention classifies the recorded data of all battery cells into multiple clusters.

[0022] Figure 7 An example of a capacity estimation model (Long Short-Term Memory Network (LSTM)) for a battery capacity estimation device according to an embodiment of the present invention is shown.

[0023] Figure 8 This is a flowchart illustrating a battery capacity estimation method according to an embodiment of the present invention.

[0024] Figure 9 This is a block diagram illustrating the hardware structure of a battery capacity estimation device according to an embodiment of the present invention. Detailed Implementation

[0025] Various embodiments of the present invention will now be described in detail with reference to the accompanying drawings. Throughout this document, the same reference numerals will be used for the same components in the drawings, and redundant descriptions of the same components will be avoided.

[0026] The various embodiments of the present invention disclosed herein are merely illustrative examples of specific structures or functions for describing embodiments of the present invention, and the various embodiments of the present invention can be implemented in various forms and should not be construed as being limited to the embodiments described herein.

[0027] As used in various embodiments, the terms "first," "second," "first," "second," etc., may modify various components regardless of their order and / or importance, and do not limit these components. For example, a first component may be named a second component without departing from the proper scope of this disclosure, and similarly, a second component may be named a first component.

[0028] The terminology used herein is for the purpose of describing specific exemplary embodiments of the invention only and is not intended to limit the scope of other exemplary embodiments of this disclosure. It should be understood that, unless the context clearly specifies otherwise, singular expressions encompass plural expressions.

[0029] All terms used herein, including technical or scientific terms, have the same meaning as commonly understood by those skilled in the art. Furthermore, it should be understood that, unless expressly defined herein, terms such as those defined in common dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant art and not in an idealized or overly formal sense. In some cases, terms defined herein may be interpreted as not including embodiments of the invention.

[0030] Figure 1 This is a block diagram of a universal battery holder.

[0031] Reference Figure 1 The diagram schematically illustrates a battery control system according to an embodiment of the present invention, which includes a battery rack 1 and an upper controller 2 included in an upper system.

[0032] like Figure 1 As shown, the battery rack 1 may include: a battery module 10, which includes one or more battery cells and is rechargeable / dischargeable; a switching unit 14 connected in series to the positive (+) terminal or negative (-) terminal of the battery module 10 to control the charging / discharging current flow of the battery module 10; and a battery management system (e.g., RBMS) 20 for controlling and managing the battery rack 1 by monitoring its voltage, current, temperature, etc., to prevent overcharging and over-discharging. The battery rack 1 may include multiple battery modules 10, sensors 12, switching units 14, and a battery management system 20.

[0033] Here, the switching unit 14, which is used to control the flow of current for charging or discharging the multiple battery modules 10, may, for example, use at least one relay, magnetic contactor, etc., according to the specifications of the battery rack 1.

[0034] The battery management system 20, which serves as an interface for receiving measured values ​​of the various parameters mentioned above, may include multiple terminals and circuitry connected thereto for processing input values. The battery management system 20 can control the on / off switching of switching units 14, such as relays and contactors, and can be connected to battery modules 10 to monitor the status of each battery module 10.

[0035] Meanwhile, the battery management system 20 according to the present invention can perform regression analysis on the voltage of individual battery cells through a separate program, as described below. The calculated regression equations can be used to classify abnormal types of battery cells.

[0036] The upper-level controller 2 can transmit control signals for the battery module 10 to the battery management system 20. Therefore, the battery management system 20 can also be controlled operationally based on signals applied from the upper-level controller 2. Meanwhile, the individual battery cells according to the invention can be included in the battery module 10 for an energy storage system (ESS). In this case, the upper-level controller 2 can be a controller for a battery bank (BBMS) comprising multiple racks, or an ESS controller for controlling the entire ESS comprising multiple racks. However, the battery rack 1 is not limited to this purpose.

[0037] This configuration of the battery holder 1 and the battery management system 20 is a well-known configuration and will not be described in detail.

[0038] Figure 2 This is a block diagram illustrating the structure of a battery capacity estimation device according to an embodiment of the present invention.

[0039] Reference Figure 2 The battery capacity estimation device 200 according to an embodiment of the present invention may include a voltage measurement unit 210, a filtering unit 220, a static analysis unit 230, and a capacity estimation unit 240. For example, the battery capacity estimation device 200 according to an embodiment of the present invention can estimate the capacity of a single battery cell by analyzing the charging and discharging data of the battery in real time, or it can perform testing by sampling faults before installing the battery cell into the module during manufacturing.

[0040] The voltage measurement unit 210 can measure the voltage of a single battery cell. In this case, the voltage measurement unit 210 can measure the voltage of the battery cell at specific time intervals. For example, the voltage measurement unit 210 can measure the voltage during a rest period after the battery cell has been charged or discharged. Furthermore, the voltage measurement unit 210 can measure the current flowing in the battery cell.

[0041] When the recording pattern of the voltage data of a battery cell deviates from a preset reference range, the filtering unit 220 can determine the voltage data as the first data. More specifically, when at least one of the recording time and quantity of the voltage data of a battery cell deviates from the reference range, the filtering unit 220 can determine the voltage data as the first data.

[0042] For example, the recording pattern for battery cell voltage data could be designed to extract four voltage data points in 5-minute increments. In this case, there might be errors in the recording time, such as when the recording time for the battery cell voltage data is (59, 10, 20, 5 minutes); or specific recorded data might be omitted, such as when the recording time is (5, 10, 20 minutes) or (10, 15, 20 minutes), etc. In this case, the filtering unit 220 can classify the corresponding data as the first data.

[0043] The statistical analysis unit 230 can determine the second data by performing statistical analysis on the voltage data of the individual battery cells. In this case, the statistical analysis unit 230 can perform statistical analysis on data other than the data determined as the first data by the filtering unit 220. For each individual battery cell, the statistical analysis unit 230 can calculate the differential voltage data dV / dt relative to time. As described below, the differential voltage data relative to time shows a more significant trend than the recorded voltage data, allowing the statistical analysis unit 230 to use the differential voltage data relative to time.

[0044] More specifically, the statistical analysis unit 230 can extract principal component data of the voltage data by performing principal component analysis (PCA) on the differential data.

[0045] The statistical analysis unit 230 can calculate multiple clusters by performing k-means clustering on the principal component data, and determine the differential data included in a specific cluster as the second data, and the differential data not included in the cluster as the first data.

[0046] For example, the first data may be discontinuous voltage data of individual battery cells, while the second data may be continuous voltage data of individual battery cells. In other words, the first data may be data that is difficult to analyze, while the second data may be data that is easy to analyze.

[0047] Therefore, the battery capacity estimation device 200 according to an embodiment of the present invention can apply capacity estimation models to first data and second data with different general shapes respectively, thereby improving accuracy compared to conventionally using data that combines the first and second data. For example, using the battery capacity estimation device 200 according to an embodiment of the present invention, when predicting the capacity for 300 cycles using data about the initial 100 cycles during battery charging and discharging, accuracy can be improved by applying the data separately to the capacity estimation model.

[0048] The capacity estimation unit 240 estimates capacity by applying data classified by the filtering unit 220 or the statistical analysis unit 230 for the battery cells as the measurement target to capacity estimation models generated for the first and second data, respectively. For example, the capacity estimation model could be a Long Short-Term Memory Network (LSTM) model. In this case, the capacity estimation model generated for each of the first and second data sets could include different parameter values.

[0049] At the same time, although Figure 2 Not shown, the battery capacity estimation device 200 according to an embodiment of the present invention may include a storage unit. In this case, the storage unit may store various data—such as voltage data measured by the voltage measurement unit 210, voltage data classified by the filtering unit 220 and the statistical analysis unit 230, capacity estimation programs, etc. Instead of including a storage unit, the battery capacity estimation device 200 according to an embodiment of the present invention may operate by communicating with an external server via a communication unit (not shown) to send and receive the aforementioned data.

[0050] Therefore, by using the battery capacity estimation device 200 according to the present invention, it is possible to distinguish between data that is easy to analyze and data that is not easy to analyze from the recorded data of the battery cell, and to apply a capacity estimation model to each data, thereby accurately and effectively estimating the capacity of the battery cell.

[0051] Figure 3 This is a flowchart illustrating the operation of classifying data performed by a battery capacity estimation device according to an embodiment of the present invention.

[0052] Reference Figure 3 In operation S310, the battery capacity estimation device according to an embodiment of the present invention can determine whether a battery cell is currently in a dormant state (i.e., current = 0). When a battery cell is in a dormant state, in operation S320, it can be determined whether voltage data is recorded according to a preset scheme. For example, the recording scheme may be to record four voltage data points at 5-minute intervals.

[0053] When the voltage data is outside the preset recording scheme (No), the voltage data can be classified as first data in operation S330. On the other hand, when the voltage data is recorded normally according to the preset scheme (Yes), the differential data dV / dt of the voltage data with respect to time can be calculated in operation S340.

[0054] Then, feature (principal component) data can be extracted from the differential data relative to voltage using PCA. In this case, the dimensionality of the data can be reduced by performing PCA on the differential data. In operation S360, the data can be classified into multiple clusters by applying k-means clustering to the principal component data.

[0055] When the data being categorized is not included in a specific cluster (e.g., cluster 2) (no), the data can be categorized as first data in operation S370. On the other hand, when the data is included in a specific cluster (yes), the data can be categorized as second data in operation S380.

[0056] Therefore, using the battery capacity estimation apparatus according to embodiments of the present invention, voltage data can be identified by examining the time unit of the recorded data and using PCA and k-means clustering algorithms. Thus, data with similar characteristics can be classified, thereby performing battery capacity estimation accurately and efficiently.

[0057] Figure 4 This is a schematic diagram illustrating the correlation between recorded data and the capacity of individual battery cells.

[0058] exist Figure 4In the middle, the left curve plot can indicate the correlation between each (dV1, dV2, dV3, and dV4) (x-axis) of the voltage relative to time (e.g., SOH) (y-axis) after charging and discharging, and the right table can indicate the recorded voltage data after the battery is charged and discharged.

[0059] Reference Figure 4 The x-axis indicates the differential voltage data dV1, dV2, dV3, and dV4 after charging, and the differential voltage data dV1, dV2, dV3, and dV4 after discharging. In this case, in Figure 4 In the graph, when the y-axis value is close to 1 or -1 and the vertical bars are short, the correlation with battery capacity can be relatively high. In other words, it can be seen that the data immediately following charging and discharging (25 and 29) have a higher correlation with battery capacity values ​​than other data. This is because the voltage changes immediately following charging and discharging are more pronounced than those following them.

[0060] Figure 5 This is a schematic diagram illustrating how a battery capacity estimation device according to an embodiment of the present invention classifies recorded data of individual battery cells into multiple clusters.

[0061] Reference Figure 5 The x-axis indicates the number of charge and discharge cycles of a single battery cell, and the y-axis indicates the differential voltage data dV1 to dV4 of the voltage relative to time during the rest period after charging and discharging of a single battery cell. Cluster 1 indicates the first data mentioned above, and cluster 2 indicates the second data mentioned above.

[0062] like Figure 5 As shown, the first data point belonging to cluster 1 can indicate that the data is relatively discontinuous. That is, the first data point may be inherently irregular and therefore may not be easy to perform battery capacity analysis on.

[0063] Meanwhile, when compared with the first data, the second data belonging to cluster 2 can be continuous and have a flat curve. That is to say, the second data can have a more constant trend than the first data and is easier to analyze.

[0064] Figure 6 This is a diagram illustrating how the battery capacity estimation device according to an embodiment of the present invention classifies the recorded data of all battery cells into multiple clusters.

[0065] Reference Figure 6 The x-axis indicates the number of charge and discharge cycles of a single battery cell, while the y-axis indicates the differential voltage data dV2 relative to time during the rest period after the entire battery cell has been charged and discharged.

[0066] and Figure 5Similarly, even when summing the data for the entire battery cell, data belonging to cluster 1 may often jut out irregularly from the top or bottom. On the other hand, data belonging to cluster 2 can have a more gradual form compared to cluster 1. Therefore, more accurate results can be calculated in battery capacity estimation.

[0067] Figure 7 An example of an LSTM battery capacity estimation device according to an embodiment of the present invention is shown.

[0068] Figure 7 The capacity estimation model shown can be used to instruct LSTM. In Figure 7 In this context, C can indicate long-term information, h can indicate information from the previous step, σ and tanh can indicate activation functions, W can indicate weight values, and b can indicate noise. Figure 7 An LSTM can include a forget gate layer, a decision layer, a new state value update operation, and an output value decision operation.

[0069] More specifically, forgetting the gate layer can be achieved by using h with output values ​​between 0 and 1. t-1 and X t The input determines whether to retain (1) or discard (0) some information. Furthermore, in the decision layer, the value to be updated for storing the new state can be determined. In this case, the value to be updated can be determined in the input gate layer, and a vector C of new candidate values ​​to be added to the individual state can be generated in the tanh layer.

[0070] Furthermore, in the new single-entity state update operation, the old single-entity state C can be updated. t-1 Updated to the new single-unit state C t In the output value determination operation, the value to be output can be determined, and the value between -1 and 1 of the single-state can be extracted through the tanh function. Then the output value can be multiplied by the output value of the forget gate layer.

[0071] Therefore, when the aforementioned timing voltage data is input to Figure 7 When using the capacity estimation model shown, the aforementioned processing can be used to output the capacity value of a single battery cell (e.g., percentage capacity). Meanwhile, Figure 7 The LSTM model has a well-known configuration, so it will not be described in detail.

[0072] but, Figure 7 The LSTM is just an example, therefore the capacity estimation model according to the present invention is not limited to this, and various estimation models can be used.

[0073] Figure 8 This is a flowchart illustrating a battery capacity estimation method according to an embodiment of the present invention.

[0074] Reference Figure 8According to an embodiment of the present invention, the battery capacity estimation method measures the voltage of a single battery cell in operation S810. In this case, the voltage of a single battery cell can be measured at specific time intervals in operation S810. For example, the voltage can be measured during a rest period after the charging or discharging of a single battery cell.

[0075] In operation S820, when the recording pattern of the voltage data of a battery cell deviates from a preset reference range, the voltage data can be determined as the first data. More specifically, when at least one of the recording time and the number of voltage data of a battery cell deviates from the reference range, the voltage data can be determined as the first data. For example, the recording pattern of the voltage data of a battery cell may be designed to extract four voltage data points in 5-minute increments.

[0076] Next, in operation S830, the second data can be determined through statistical analysis of the voltage data of the individual battery cells. In operation S830, the differential voltage data dV / dt relative to time can be calculated for each individual battery cell. More specifically, in operation S830, principal component data of the voltage data can be extracted by performing PCA on the differential data. Multiple clusters can be calculated by performing k-means clustering on the principal component data, and the differential data included in a specific cluster can be determined as the second data, while the differential data not included in any cluster can be determined as the first data.

[0077] Finally, the first and second data extracted in operations S820 and S830 can be applied to the capacity estimation model respectively, thereby estimating the capacity of the battery cell in operation S840. For example, the capacity estimation model can be an LSTM model. In this case, the capacity estimation model for the first and second data may be the same, but the parameter values ​​input to the capacity estimation model may be different.

[0078] Therefore, by using the battery capacity estimation method according to the present invention, it is possible to distinguish between easily analyzable and difficult-to-analyze data from the recorded data of individual battery cells, and to apply the capacity estimation model to each data point, thereby accurately and effectively estimating the capacity of individual battery cells.

[0079] Figure 9 This is a block diagram illustrating the hardware structure of a battery capacity estimation device according to an embodiment of the present invention.

[0080] Reference Figure 9 According to an embodiment of the present invention, the battery capacity estimation device 900 may include a microcontroller unit (MCU) 910, a memory 920, an input / output interface (I / F) 930, and a communication I / F 940.

[0081] The MCU 910 can execute various programs stored in the memory 920 (e.g., battery capacity estimation program, principal component analysis program, k-means clustering program, etc.), process various data for battery cell classification, capacity estimation, etc., through these programs, and perform... Figure 2 The processor that performs the aforementioned functions.

[0082] The memory 920 can store various programs related to statistical analysis and capacity estimation of individual battery cells. Furthermore, the memory 920 can store various data, such as voltage data of individual battery cells and differential voltage data of individual battery cells.

[0083] The memory 920 may be provided in multiples as needed. The memory 920 may be volatile or non-volatile. For the memory 920 as volatile memory, random access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), etc., may be used. For the memory 920 as non-volatile memory, read-only memory (ROM), programmable ROM (PROM), electrically modifiable ROM (EAROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), flash memory, etc., may be used. The above examples of memory 720 are merely examples and are not limited thereto.

[0084] The Input / Output I / F 930 provides an interface for sending and receiving data by connecting input devices (not shown) such as a keyboard, mouse, touch panel, etc., and output devices such as a display (not shown) to the MCU 710.

[0085] The Communication I / F 940 is a component capable of sending and receiving various types of data to and from a server, and can be any type of device capable of supporting wired or wireless communication. For example, the Communication I / F 940 can be used to send and receive programs or various data for statistical analysis and capacity estimation to and from a separately provided external server.

[0086] Therefore, the computer program according to embodiments of the present invention can be recorded in memory 920 and processed by MCU 910, thereby being implemented for execution. Figure 2 The module of the function block shown.

[0087] Although all components constituting an embodiment of the present invention are described above as operating in combination or in combination, the present invention is not necessarily limited to this embodiment. That is, within the scope of the present invention, all components can be operated by selectively combining them into one or more.

[0088] Furthermore, terms such as “comprising,” “constituting,” or “having” as described above may mean that, unless otherwise stated, the corresponding component may be inherent and should therefore be interpreted as further including, rather than excluding, other components. Unless otherwise defined, all terms, including technical or scientific terms, have the same meaning as commonly understood by those skilled in the art. Commonly used terms, such as those defined in dictionaries, should be interpreted as having the same meaning as in the context of the relevant art and should not be interpreted as having an ideal or overly formal meaning unless they are explicitly defined in this invention.

[0089] The above description merely illustrates the technical concept of the present invention. Those skilled in the art can make various modifications and variations without departing from the essential characteristics of the invention. Therefore, the embodiments disclosed in this invention are intended to describe, not limit, the technical spirit of the invention, and the scope of the technical spirit of the invention is not limited by these embodiments. The scope of protection of this invention should be interpreted by the following claims, and all technical spirit within the same scope should be understood to be included within the scope of this invention.

Claims

1. A battery capacity estimation device, comprising: A voltage measuring unit that measures the voltage of a single battery cell; The filtering unit determines the voltage data as the first data when the recording mode of the voltage data of the battery cell deviates from the preset reference range. A statistical analysis unit determines second data by performing statistical analysis on the voltage data of the battery cells other than the data determined as the first data by the filtering unit; as well as The capacity estimation unit estimates capacity by applying data classified by the filtering unit or the statistical analysis unit for the battery cell as the measurement target to capacity estimation models generated for the first data and the second data, respectively.

2. The battery capacity estimation device according to claim 1, wherein, The statistical analysis unit calculates the differential data of the voltage of the battery cell with respect to time.

3. The battery capacity estimation device according to claim 2, wherein, The statistical analysis unit extracts a principal component data set by performing principal component analysis (PCA) on the multiple differential data sets.

4. The battery capacity estimation device according to claim 3, wherein, The statistical analysis unit calculates multiple clusters by performing k-means clustering on the principal component data, and when the differential data is included in a preset cluster, the differential data is identified as the second data.

5. The battery capacity estimation device according to claim 4, wherein, The statistical analysis unit identifies the differential data not included in the cluster as the first data.

6. The battery capacity estimation device according to claim 1, wherein, The capacity estimation model is a Long Short-Term Memory (LSTM) network model.

7. The battery capacity estimation device according to claim 1, wherein, The voltage measurement unit measures the voltage of the battery cell during the rest period after charging or discharging.

8. The battery capacity estimation device according to claim 1, wherein, When at least one of the recording time and number of voltage data of the battery cell deviates from the reference range, the filtering unit determines the voltage data as the first data.

9. The battery capacity estimation device according to claim 1, wherein, The first data is the voltage data of the battery cell in a discontinuous form, and the second data is the voltage data of the battery cell in a continuous form.

10. A battery capacity estimation method, comprising: Measure the voltage of individual battery cells; When the recording mode of the voltage data of the battery cell deviates from the preset reference range, the voltage data is determined as the first data; The second data is determined by statistical analysis of the voltage data of the battery cells other than the data identified as the first data; as well as The capacity of the battery cell is estimated by applying the first data and the second data to the capacity estimation model, respectively.

11. The battery capacity estimation method according to claim 10, further comprising calculating the differential data of voltage relative to time for the individual battery cells.

12. The battery capacity estimation method according to claim 11 further includes extracting the plurality of differential data as a principal component data by performing principal component analysis (PCA) on the plurality of differential data.

13. The battery capacity estimation method according to claim 12, wherein, Determining the second data includes: calculating multiple clusters by performing k-means clustering on the principal component data, and determining the differential data as the second data when the differential data is included in a preset cluster.

14. The battery capacity estimation method according to claim 13, further comprising: Differential data not included in the cluster are identified as the first data.

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