Battery abnormality diagnosis device and method

Abstract

Provided is a battery abnormality diagnosing device including a sensing unit that measures a voltage and a current of a battery cell, a differential data calculating unit that calculates differential data of a capacity and a voltage of the battery cell, and an abnormality diagnosing unit that diagnoses an abnormality of the battery cell based on the differential data and classifies a type of the abnormality.

Description

Technical Field

[0001] Cross-references to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2020-0100130, filed on August 10, 2020, with the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. Technical Field

[0004] This invention relates to a battery anomaly diagnosis apparatus and method for diagnosing abnormal battery behavior and classifying anomaly types by using the capacity-voltage differential curve of the battery. Background Technology

[0005] Recently, research and development of rechargeable batteries have been actively pursued. In this paper, rechargeable batteries, as rechargeable / dischargeable batteries, can include all conventional nickel (Ni) / cadmium (Cd) batteries, Ni / metal hydride (MH) batteries, and more recently, lithium-ion batteries. Among rechargeable batteries, lithium-ion batteries have a significantly higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured to be small and lightweight, making them suitable for use as power sources in mobile devices. Additionally, lithium-ion batteries are attracting attention as a next-generation energy storage medium as their applications expand to power electric vehicles.

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

[0007] When such rechargeable batteries are used continuously, a degradation phenomenon may occur, where the usable capacity of the battery gradually decreases. Conventionally, the detection of unstable behavior in measurement data has been used to detect battery degradation, but degradation cannot be detected when the battery is operating normally. Furthermore, conventionally, only battery degradation is detected, but it is difficult to identify the detailed type of degradation. Summary of the Invention

[0008] [Technical Issues]

[0009] The present invention has been designed to solve the above-mentioned problems and aims to provide a battery anomaly diagnosis device and method, wherein abnormal battery behavior is diagnosed and anomaly types are classified by analyzing differential data of battery capacity and voltage.

[0010] [Technical Solution]

[0011] A battery anomaly diagnosis device according to an embodiment of the present invention includes: a sensing unit that measures the voltage and current of a battery cell; a differential data calculation unit that calculates differential data of the capacity and voltage of the battery cell; and an anomaly diagnosis unit that diagnoses anomalies of the battery cell based on the differential data and classifies the types of anomalies.

[0012] A battery anomaly diagnosis method according to an embodiment of the present invention includes: measuring the voltage and current of a battery cell; calculating differential data of the capacity and voltage of the battery cell; and diagnosing anomalies of the battery cell based on the differential data and classifying the types of anomalies.

[0013] [Beneficial Effects]

[0014] According to the battery anomaly diagnosis apparatus and method of the present invention, abnormal battery behavior can be diagnosed and the anomaly type can be classified by analyzing the differential data of battery capacity and voltage. Attached Figure Description

[0015] Figure 1 This is a block diagram of a typical battery holder.

[0016] Figure 2 This is a block diagram illustrating the structure of a battery malfunction diagnostic device according to an embodiment of the present invention.

[0017] Figure 3 It is a graph showing the general shape of the differential data calculated by the battery anomaly diagnostic device according to an embodiment of the present invention.

[0018] Figure 4a This is a view showing the maximum peak voltage and peak intensity calculated by the battery anomaly diagnostic device according to an embodiment of the present invention.

[0019] Figure 4b This is a view showing the peak voltage and peak intensity at a specific point during charging, calculated by a battery anomaly diagnostic device according to an embodiment of the present invention.

[0020] Figure 5 This is a view showing the types of abnormalities classified by the battery abnormality diagnostic device according to an embodiment of the present invention.

[0021] Figure 6 This is a flowchart illustrating a battery anomaly diagnosis method according to an embodiment of the present invention.

[0022] Figure 7 This is a block diagram illustrating the hardware structure of a battery malfunction diagnostic device according to an embodiment of the present invention. Detailed Implementation

[0023] In the following, various embodiments of the invention will 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 the same components will be described without redundancy.

[0024] The specific structural or functional descriptions of the various embodiments of the present invention disclosed in this document are merely illustrative for the purpose of 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 limited to the embodiments described in this document.

[0025] As used in various embodiments, the terms "first," "second," "first," "second," etc., may modify various components regardless of order and / or importance, without limiting the components. For example, a first component may be named a second component without departing from the scope of this disclosure, and similarly, a second component may be named a first component.

[0026] The terminology used in this document is intended to describe only certain exemplary embodiments of this disclosure and is not intended to limit the scope of other exemplary embodiments of this disclosure. It should be understood that, unless the context clearly indicates otherwise, singular expressions include plural expressions.

[0027] All terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. It should be further understood that, unless expressly defined herein, terms such as those defined in common dictionaries should be interpreted as having the same meaning as they have in the context of the prior art, and not in an idealized or overly formal sense. In some cases, terms defined herein may be interpreted as excluding embodiments of this disclosure.

[0028] Figure 1 This is a block diagram of a typical battery holder.

[0029] refer to Figure 1 The diagram schematically illustrates a battery control system according to an embodiment of the present invention, including a battery holder 1 and a higher-level controller 2 included in a higher-level system.

[0030] like Figure 1As 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, which is 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., MBMS) 20, for controlling and managing the battery rack 1 by monitoring the 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 battery management system 20.

[0031] In this document, the switching unit 14, which is a semiconductor switching element for controlling the current flow for charging or discharging multiple battery modules 10, may, for example, use at least one metal-oxide-semiconductor field-effect transistor (MOSFET), relay, magnetic contactor, etc., depending on the specifications of the battery rack 1.

[0032] The battery management system 20 can measure or calculate the voltage and current of the gate, source, drain, etc. of the semiconductor switching elements to monitor the voltage, current, temperature, etc. of the battery holder 1. The battery management system 20 can measure the current, voltage, temperature, etc. of the battery holder 1 using a sensor 12 disposed adjacent to the semiconductor switching elements. In this document, the sensor 12 can correspond to the following... Figure 2 The sensing unit.

[0033] 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 switching unit 14—e.g., a MOSFET—on / off and can be connected to the battery modules 10 to monitor the status of each battery module 10.

[0034] Meanwhile, the battery management system 20 according to the present invention can calculate the differential data of the capacity and voltage of a single battery cell through a separate program, as will be described below. The calculated differential data can be used to diagnose abnormalities in the battery cells.

[0035] The upper-level controller 2 can send control signals for the battery module 10 to the battery management system 20. Therefore, the battery management system 20 can also be controlled in its operation based on signals applied from the upper-level controller 2. Simultaneously, 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 an ESS controller. However, the battery rack 1 is not limited to this purpose.

[0036] Such configurations of the battery holder 1 and the battery management system 20 are well-known and will not be described in detail.

[0037] Figure 2 This is a block diagram illustrating the structure of a battery malfunction diagnostic device according to an embodiment of the present invention.

[0038] refer to Figure 2 According to an embodiment of the present invention, the battery abnormality diagnosis device 200 may include a sensing unit 210, a differential data calculation unit 220 and an abnormality diagnosis unit 230.

[0039] The sensing unit 210 can measure the voltage and current of a single battery cell. The sensing unit 210 can measure the voltage and current of a single battery cell at specific time intervals.

[0040] The differential data calculation unit 220 can calculate the differential data of the capacity and voltage of a single battery cell. For example, the differential data calculation unit 220 can calculate the differential curve dQ / dV of the capacity and voltage of a single battery cell.

[0041] In this scenario, when the data values ​​measured or calculated for a single battery cell meet preset conditions, the differential data calculation unit 220 can calculate differential data. For example, when the change in charging current or discharging current of a battery cell at a specific time exceeds a reference value, the differential data calculation unit 220 can calculate the differential data for the battery cell's capacity and voltage. Furthermore, when the change in state of charge (SOC) at a specific time after the battery cell's charging / discharging ends is less than a reference value, the differential data calculation unit 220 can calculate the differential data for the battery cell's capacity and voltage. Such conditions may be necessary for the stable operation of the ESS system.

[0042] The differential data calculation unit 220 can calculate differential data by using sampling relative to the voltage data measured by the sensing unit 210. In this context, sampling relative to voltage data can refer to a preprocessing procedure that converts the voltage of a battery cell into data in a monotonically increasing or monotonically decreasing manner. For example, the differential data calculation unit 220 can perform sampling relative to voltage by classifying the capacity values ​​Q of battery cells with equal voltage amplitude V and calculating the average capacity value of the battery cells for each voltage amplitude.

[0043] Additionally, the differential data calculation unit 220 can calculate differential data by using smoothed splines on the voltage data measured by the sensing unit 210. In this way, by transforming to satisfy the continuity between adjacent data, the curve of the differential data of the battery cell can be transformed into a smooth form.

[0044] The anomaly diagnosis unit 230 can diagnose anomalies in individual battery cells based on differential data and classify the types of anomalies. Specifically, the anomaly diagnosis unit 230 can classify the types of anomalies in individual battery cells based on the peak values ​​of the differential data. Furthermore, the anomaly diagnosis unit 230 can classify the types of anomalies in individual battery cells based on the voltage at the peak of the differential data and the intensity of the peak.

[0045] More specifically, the anomaly diagnosis unit 230 can classify anomalies in a battery cell into a first type of anomaly for that battery cell by comparing the sum of voltages at the peaks of the differential data with a preset first reference value. In this case, the first reference value can be expressed as the sum of the mean and standard deviation of the differential data (e.g., μ+3σ). For example, the first type of anomaly for a battery cell may include available lithium loss.

[0046] Furthermore, the anomaly diagnosis unit 230 can classify anomalies in a battery cell into a second type of anomaly for that battery cell by comparing the sum of the peak intensities of the differential data with a preset second reference value. In this case, the second reference value can be expressed as the difference between the mean and the standard deviation (e.g., μ-3σ). For example, the second type of anomaly for a battery cell may include a reduction in the anode reaction area.

[0047] The anomaly diagnosis unit 230 can classify anomalies of a battery cell into a third type of anomaly for that battery cell by comparing a value obtained by dividing the intensity of the peak of the differential data by the voltage at the peak of the differential data with a preset third reference value. In this case, the third reference value can be expressed as the sum of the mean and standard deviation of the differential data (e.g., μ + 3σ). For example, the third type of anomaly for a battery cell may include a reduction in the cathode reaction area.

[0048] At the same time, despite Figure 2 As not shown, the battery anomaly diagnostic device 200 according to an embodiment of the present invention may include a storage unit. The storage unit may store voltage and current measurement data of individual battery cells, differential data of the capacity and voltage of individual battery cells, various data regarding the anomaly types of individual battery cells, etc. However, the storage unit is not necessarily included in the battery anomaly diagnostic device 200, and the storage unit may be included in an external server and send and receive data via a separate communication module (not shown).

[0049] The battery fault diagnosis device 200 according to an embodiment of the present invention may further include a display unit (not shown). Therefore, the battery fault diagnosis device 200 according to an embodiment of the present invention can express to the user, in the form of a graph, the general shape of the differential curve calculated by the differential data calculation unit 220, etc., through the display unit. Additionally, the display unit may include a user interface (e.g., a touchpad, etc.) for receiving user input.

[0050] Therefore, according to the battery anomaly diagnosis device and method of the present invention, by analyzing the differential data of the battery's capacity and voltage, abnormal battery behavior can be diagnosed and the anomaly type can be classified.

[0051] Figure 3 This is a graph showing the general shape of the differential data calculated by the battery anomaly diagnostic device according to an embodiment of the present invention. (Reference) Figure 3 The x-axis indicates the voltage V of a single battery cell, while the y-axis indicates the differential data dQ / dV (Ah / V) related to the capacity and voltage of a single battery cell.

[0052] like Figure 3 As shown, the battery anomaly diagnostic device according to an embodiment of the present invention can determine the anomaly of a battery cell by analyzing the general shape of the differential data of the capacity and voltage of the battery cell. For example, in Figure 3 The peaks of the differential curve appear at points Ea(1), Ea(5), Ec(2), and Ea(6). pre An abnormality may occur in a single battery cell.

[0053] In addition, through the Figure 3 By applying statistical methods to differential data, anomalies in individual battery cells can be diagnosed. For example, statistical values ​​such as the mean and standard deviation of differential data for individual battery cells can be compared with preset thresholds to detect anomalies. In this case, anomalies can be diagnosed by using analysis schemes such as a sliding window for the mean and standard deviation.

[0054] Figure 4a This is a view showing the maximum peak voltage and peak intensity calculated by the battery anomaly diagnostic device according to an embodiment of the present invention.

[0055] refer to Figure 4a In the graph above, the x-axis indicates time and the y-axis indicates voltage V (e.g., Figure 3 (x-axis), where the maximum peak appears Figure 3 In the differential data. Figure 4a In the lower graph, the x-axis indicates time and the y-axis indicates... Figure 3 The intensity (Ah / V) of the maximum peak in the differential data. In this case, the charging / discharging time obtained for a single battery cell. Figure 4a The data can correspond to Figure 3 Ea(5) and Ec(2).

[0056] refer to Figure 4a The upper and lower curves of the battery cell show that the general shape of the maximum peak voltage and maximum peak intensity has a peak that falls within a specific range and then leaves the upper or lower end during a specific time period. For example, it can be seen from... Figure 4a As can be seen, peaks were generated on December 2, 2019, December 10, and December 26, 2019, in both the upper and lower curves. Therefore, the battery anomaly diagnosis device according to an embodiment of the present invention can determine the anomaly of a battery cell by detecting the peak value in the curves of the maximum peak voltage and the maximum peak intensity of the battery cell.

[0057] Figure 4b This is a view showing the peak voltage and peak intensity at a specific point during charging, calculated by a battery anomaly diagnostic device according to an embodiment of the present invention.

[0058] refer to Figure 4b In the graph above, the x-axis indicates time and the y-axis indicates voltage V (e.g., Figure 3 (x-axis), where the maximum peak appears Figure 3 In the differential data. Figure 4a In the lower graph, the x-axis indicates time and the y-axis indicates... Figure 3 The intensity (Ah / V) of the maximum peak in the differential data. In this case, the charging / discharging time obtained for a single battery cell. Figure 4b The data can correspond to Figure 3 Ea(6) pre .

[0059] refer to Figure 4b The upper and lower curves, like in Figure 4a As in the case of a single battery cell, the general shape of its maximum peak voltage and maximum peak intensity is such that it falls within a specific range and then moves away from the upper or lower end during a specific time period. For example, in Figure 4b The upper curve graph shows abnormal data generated on December 5, 2019, while the lower curve graph shows abnormal data generated on December 5, December 10, and December 13. Therefore, the battery anomaly diagnosis device according to an embodiment of the present invention can determine the anomaly of a battery cell by detecting abnormal data deviating from a preset range from the curves of the maximum peak voltage and maximum peak intensity of the battery cell.

[0060] Figure 5 This is a view showing the types of abnormalities classified by the battery abnormality diagnostic device according to an embodiment of the present invention.

[0061] refer to Figure 5The x-axis indicates the ID of the individual battery cells included in the battery rack, while the y-axis, from the left curve, sequentially indicates the sum of peak voltages, the sum of peak intensities, and the value obtained by dividing the peak intensity by the voltage (e.g., ...). Figure 3 Ea(6) pre ).

[0062] Therefore, as Figure 5 As shown, the battery anomaly diagnosis device according to an embodiment of the present invention can diagnose anomalies in a battery cell based on values ​​calculated from differential data and classify the types of anomalies.

[0063] More specifically, see reference Figure 5 The first curve shows that when the sum of the voltages at the peaks of the differential data exceeds a first reference value (e.g., μ+3σ), the anomaly of the battery cell can be classified into a first type of anomaly for that battery cell. In this case, the first type could be the loss of available lithium.

[0064] Additionally, refer to Figure 5 The second curve shows that when the sum of the peak intensities of the differential data is less than a preset second reference value (e.g., μ-3σ), the anomaly of the battery cell can be classified into a second type of anomaly for that battery cell. In this case, the second type could be a reduction in the anode reaction area.

[0065] In addition, refer to Figure 5 The third curve shows that when the value obtained by dividing the peak intensity of the differential data by the voltage at the peak of the differential data is greater than a preset third reference value (e.g., μ+3σ), the anomaly of the battery cell can be classified into a third type of anomaly for the battery cell. In this case, the third type could be a reduction in the cathode reaction area.

[0066] Meanwhile, it has been referenced Figure 5 The first to third types of abnormalities described indicate the loss of available lithium, reduction of anode reaction area, and reduction of cathode reaction area, respectively. The present invention is not limited thereto and may include different types of abnormalities that can be detected by the battery abnormality diagnostic device according to the present invention.

[0067] In addition, it has been referenced Figure 5 The invention describes the sum of voltages at the peaks of the differential data, the sum of peak intensities of the differential data, and a comparison of the value obtained by dividing the peak intensity of the differential data by the voltage at the peaks of the differential data with a reference value. However, the invention is not limited to this, allowing the use of other calculated values ​​derived from the differential data besides those described above, and enabling the use of various diagnostic schemes as needed. While a reference value has been set based on the sum of the average value and standard deviation of the differential data of the battery cells, or the difference between them, the first to third reference values ​​can be set differently depending on the requirements.

[0068] Figure 6 This is a flowchart illustrating a battery anomaly diagnosis method according to an embodiment of the present invention.

[0069] refer to Figure 6 The voltage and current of a single battery cell can be measured during operation S610. In this case, the voltage and current of a single battery cell can be measured at preset specific time intervals during operation S610.

[0070] The process in S620 also determines whether the measured data values ​​meet preset conditions. In this document, preset conditions may include situations where the change in charging current or discharging current of a battery cell is greater than a reference value within a specific time period, and the change in SOC (State of Charge) of the battery cell is less than a reference value within a specific time period after the end of charging / discharging. This condition may be necessary for the stable operation of the ESS (Emergency Storage System).

[0071] When the measured data value does not meet the preset condition (No), non-classification processing can be performed in operation S680. On the other hand, when the measured data value meets the preset condition (Yes), operation S630 can be performed.

[0072] In operation S630, the differential data dQ / dV of the capacity and voltage of a single battery cell can be calculated. In this case, the differential data can be calculated by sampling relative to the measured voltage data. For example, sampling relative to voltage can be performed by classifying capacity values ​​with equal voltage amplitudes of the battery cells and calculating the average capacity value of the battery cells for each voltage amplitude, so that the voltage of the battery cells can be converted into data in a monotonically increasing or monotonically decreasing form. In operation S630, the curve of the differential data of the battery cells can be converted into a smooth form by using a smooth spline relative to the measured voltage data to satisfy the continuity between adjacent data.

[0073] Next, in operation S640, the voltage V at the peak of the differential data can be calculated. peak And the intensity of the peaks in the differential data. In operation S650, the sum of the voltages at the peaks of the differential data, ∑V, can be calculated. peak The value is compared with a preset first reference value (μ+3σ). When the sum of the voltages at the peaks of the differential data exceeds the first reference value (yes), it is determined in operation S655 that a loss of usable lithium has occurred in the battery cell.

[0074] On the other hand, when the sum of the voltages at the peaks of the differential data is less than or equal to the second reference value (No), the sum of the peak intensities ∑Intensity of the differential data can be compared with a preset second reference value (μ-3σ) in operation S660. When the sum of the peak intensities of the differential data is less than the second reference value (Yes), it is determined in operation S665 that a decrease in the anode reaction area has occurred in the battery cell.

[0075] On the other hand, when the sum of the peak intensities of the differential data is greater than or equal to the second reference value (no), the value Intensity / V obtained by dividing the peak intensity of the differential data by the voltage at the peak of the differential data can be used in operation S670. peak The value is compared with a preset third reference value (μ+3σ). When the sum of the peak intensities of the differential data is greater than the third reference value (yes), it is determined in operation S675 that a decrease in the cathode reaction area occurs in the cell.

[0076] Meanwhile, when the sum of the peak intensities of the differential data is less than the third reference value (no), the process can be terminated in operation S680 without classifying the type of abnormality of the battery cell.

[0077] Therefore, according to the battery anomaly diagnosis method of the present invention, by analyzing the differential data of battery capacity and voltage, abnormal battery behavior can be diagnosed and the anomaly type can be classified.

[0078] Figure 7 This is a block diagram illustrating the hardware structure of a battery malfunction diagnostic device according to an embodiment of the present invention.

[0079] refer to Figure 7 According to an embodiment of the present invention, the battery fault diagnosis device 700 may include a microcontroller unit (MCU) 710, a memory 720, an input / output interface (I / F) 730, and a communication I / F 740.

[0080] The MCU 710 can run various programs stored in the memory 720 (e.g., battery diagnostic programs, differential data calculation programs, battery anomaly type classification programs, etc.), process various data through these programs to perform anomaly diagnosis and type classification of individual battery cells, and run... Figure 2 The processor that performs the aforementioned functions.

[0081] The memory 720 can store various programs related to differential data calculation, anomaly diagnosis, and classification of individual battery cells. Furthermore, the memory 720 can store various data, such as measured voltage and current data of individual battery cells, and differential data of individual battery cells.

[0082] Depending on the needs, multiple memories 720 may be provided. Memory 720 may be volatile or non-volatile. For memory 720 as volatile memory, random access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), etc., may be used. For memory 720 as non-volatile memory, read-only memory (ROM), programmable ROM (PROM), electrically changeable ROM (EAROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), flash memory, etc., may be used. The examples of memory 720 listed above are merely examples and are not limited thereto.

[0083] The Input / Output I / F 730 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.

[0084] The Communication I / F 740, as a component capable of sending and receiving various types of data to and from a server, can be any type of device capable of supporting wired or wireless communication. For example, the Communication I / F 740 can send programs or various data for differential data calculation and diagnosis of individual battery cells to and receive programs or various data for differential data calculation and diagnosis of individual battery cells from and from a separately located external server.

[0085] Therefore, the computer program according to an embodiment of the present invention can be recorded in the memory 720 and processed by the MCU 710, and thus implemented to execute. Figure 2 The module of the function block shown.

[0086] Even though all components constituting the embodiments of the present invention have been described above as operating in combination or in combination, the present invention is not necessarily limited to these embodiments. That is, within the scope of the present invention, all components can be operated by being selectively combined into one or more.

[0087] Furthermore, terms such as “comprising,” “constituting,” or “having” as described above may mean that the corresponding component is inherent unless otherwise stated, 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 one of ordinary skill in the art. Terms commonly used, like 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 clearly defined in this invention.

[0088] The above description merely illustrates the technical concept of the present invention, and those skilled in the art will be able to make various modifications and variations without departing from the essential characteristics of the invention. Therefore, the embodiments disclosed in this invention are intended for descriptive purposes only and do not limit the technical spirit of the invention, nor is the scope of the technical spirit of the invention limited by these embodiments. The scope of protection of this invention should be interpreted through 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 abnormality diagnosing apparatus comprising: a sensing unit that measures a voltage and a current of a battery cell; a differential data calculating unit that calculates differential data of a capacity and a voltage of the battery cell; and an abnormality diagnosing unit that diagnoses an abnormality of the battery cell based on the differential data and classifies a type of the abnormality of the battery cell based on a peak value of the differential data, wherein the classifying of the type of the abnormality includes one of: comparing a sum of voltages at peaks of the differential data to a preset first reference value, comparing a sum of peak intensities of the differential data to a preset second reference value, or comparing a value obtained by dividing the peak intensities of the differential data by the voltages at peaks of the differential data to a preset third reference value. when the sum of the voltages at the peaks of the differential data is greater than the preset first reference value, classifying the abnormality of the battery cell as a first type of the abnormality for the battery cell, and 2. The battery abnormality diagnosing apparatus according to claim 1, wherein wherein the first type of the abnormality for the battery cell includes a loss of available lithium. when the sum of the peak intensities of the differential data is less than a preset second reference value, classifying the abnormality of the battery cell as a second type of the abnormality for the battery cell, and 3. The battery abnormality diagnosing apparatus according to claim 1, wherein wherein the second type of the abnormality for the battery cell includes a decrease in an anode reaction area. when the value obtained by dividing the peak intensities of the differential data by the voltages at peaks of the differential data is greater than the preset third reference value, classifying the abnormality of the battery cell as a third type of the abnormality for the battery cell, and 4. The battery abnormality diagnosing apparatus according to claim 1, wherein wherein the third type of the abnormality for the battery cell includes a decrease in a cathode reaction area. the differential data calculating unit calculates the differential data of the capacity and the voltage of the battery cell when a change in a charge current or a change in a discharge current of the battery cell within a certain time is greater than a reference value.

5. The battery abnormality diagnosing apparatus according to claim 1, wherein the differential data calculating unit calculates the differential data of the capacity and the voltage of the battery cell when a state of charge (SOC) change within a reference time after the end of charge / discharge of the battery cell is less than a reference value.

6. The battery abnormality diagnosing apparatus according to claim 1, wherein the differential data calculating unit calculates the differential data by using a sample with respect to voltage data measured by the sensing unit.

7. The battery abnormality diagnosing apparatus according to claim 1, wherein the differential data calculating unit calculates the differential data by using a smoothing spline with respect to voltage data measured by the sensing unit.

8. The battery abnormality diagnosing apparatus according to claim 1, wherein 9. A battery abnormality diagnosing method comprising: measuring a voltage and a current of a battery cell; calculating differential data of a capacity and a voltage of the battery cell; and diagnosing an abnormality of the battery cell based on the differential data and classifying a type of the abnormality of the battery cell based on a peak value of the differential data, wherein the classifying of the type of the abnormality includes one of: ​ ​ comparing a sum of voltages at peaks of the differential data with a preset first reference value, comparing a sum of intensities of peaks of the differential data with a preset second reference value, or comparing a value obtained by dividing intensities of peaks of the differential data by voltages at peaks of the differential data with a preset third reference value.

10. The battery abnormality diagnosing method according to claim 9, wherein when the sum of the voltages at the peaks of the differential data is greater than the preset first reference value, classifying the abnormality of the battery cell as a first type of the abnormality for the battery cell, and wherein the first type of the abnormality of the battery cell includes loss of available lithium.

11. The battery abnormality diagnosing method according to claim 9, wherein when the sum of the intensities of the peaks of the differential data is less than a preset second reference value, classifying the abnormality of the battery cell as a second type of the abnormality for the battery cell, and wherein the second type of the abnormality for the battery cell includes reduction of an anode reaction area.

12. The battery abnormality diagnosing method according to claim 9, wherein when the value obtained by dividing the intensities of the peaks of the differential data by the voltages at the peaks of the differential data is greater than the preset third reference value, classifying the abnormality of the battery cell as a third type of the abnormality for the battery cell, and wherein the third type of the abnormality for the battery cell includes reduction of a cathode reaction area.

Images