Apparatus for diagnosing battery and operating method thereof

Abstract

A battery diagnostic device according to one embodiment disclosed in this document may include: an acquisition unit for acquiring time series data related to the state of a plurality of battery cells included in a battery module; a voltage diagnostic unit for calculating a long-term and short-term voltage moving average difference of each battery cell based on the time series data and diagnosing a voltage abnormality of each battery cell based on the long-term and short-term voltage moving average difference; a capacity diagnostic unit for calculating a State of Health (SOH) deviation of each battery cell based on the time series data and diagnosing a capacity abnormality of each battery cell based on the SOH deviation; and a detection unit for detecting an abnormal battery cell based on the diagnosis result of at least one of the voltage diagnostic unit or the capacity diagnostic unit.

Description

Technology Field

[0001] The embodiments disclosed in this document relate to a battery diagnostic device and a method of operating the same. Background Technology

[0002] Recently, active research and development on secondary batteries has been underway. Here, the term "secondary battery" refers to a rechargeable battery, encompassing conventional Ni / Cd and Ni / MH batteries as well as the more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of significantly higher energy density compared to conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight manner, making them suitable for use as power sources for mobile devices. Recently, their scope of application has expanded to include electric vehicles, drawing attention as a next-generation energy storage medium.

[0003] In addition, the secondary battery can generally be used as a battery pack comprising a battery module in which a plurality of battery cells are connected in series and / or parallel. Also, the secondary battery can be used as a battery rack comprising a plurality of battery modules and a rack frame that accommodates these battery modules.

[0004] Such battery cells, battery modules, battery packs, or battery racks can be utilized in various devices. For example, batteries can be used in mobile devices such as mobile phones, laptop computers, smartphones, and smart pads, as well as in fields such as electric vehicles (EVs, HEVs, PHEVs) and large-capacity energy storage systems (ESS).

[0005] The status and operation of these batteries can be managed and controlled by a battery management system (BMS). The battery management system can be included together with the batteries within a single device.

[0006] In addition, the battery management system can manage and control the battery while separated from the device containing the battery. For example, the battery management system can be implemented as a separate server device. In this case, the battery management system can collect battery data and vehicle data from a vehicle, etc., and manage and control the battery by utilizing the collected data.

[0007] Meanwhile, if a battery is defective, the possibility of damage to devices containing the battery (e.g., EV, ESS) may increase. Accordingly, a method is required to detect an abnormal state of the battery and reduce the possibility of damage to devices containing the battery. Reference may be made to the technology forming the background of the present invention, including Korean Patent Publication No. 10-2023-0161377, Japanese Patent Publication No. JP2017-227539, and Korean Patent Publication No. 10-2022-0060931. The problem to be solved

[0008] Conventionally, there have been attempts to diagnose voltage abnormalities by detecting instantaneous changes in battery cell voltage as a method for diagnosing battery defects. For example, a method has been used to diagnose battery cells exhibiting instantaneous voltage changes as abnormal voltage cells based on the cell voltage moving average.

[0009] However, instantaneous voltage fluctuations in a battery cell may be caused by the separation or contact of the cell's negative or positive tab, but they may also be caused by voltage measurement noise. In other words, relying solely on voltage anomaly diagnosis carries the possibility of false detections due to noise.

[0010] In this regard, in the case of a defect caused by separation or contact of the negative or positive tab of a battery cell, it may lead to a momentary decrease in capacity or a deviation in capacity, and in the case of diagnosing a capacity anomaly, the impact of voltage measurement noise may be lower compared to diagnosing a voltage anomaly.

[0011] The embodiments disclosed in this document are conceived to address the problems of voltage abnormality diagnosis methods and can provide a battery diagnosis device and a method of operation thereof capable of detecting abnormal battery cells by performing capacity abnormality diagnosis in addition to voltage abnormality diagnosis for battery cells in order to reduce the false detection rate caused by voltage measurement noise.

[0012] The technical problems of the embodiments disclosed in this document are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0013] A battery diagnostic device according to one embodiment disclosed in this document may include: an acquisition unit for acquiring time series data related to the state of a plurality of battery cells included in a battery module; a voltage diagnostic unit for calculating a long-term and short-term voltage moving average difference of each battery cell based on the time series data and diagnosing a voltage abnormality of each battery cell based on the long-term and short-term voltage moving average difference; a capacity diagnostic unit for calculating a State of Health (SOH) deviation of each battery cell based on the time series data and diagnosing a capacity abnormality of each battery cell based on the SOH deviation; and a detection unit for detecting an abnormal battery cell based on the diagnosis result of at least one of the voltage diagnostic unit or the capacity diagnostic unit.

[0014] In a battery diagnostic device according to one embodiment disclosed in this document, the voltage diagnostic unit calculates a short-term voltage moving average of each battery cell based on a first time window, calculates a long-term voltage moving average of each battery cell based on a second time window having a longer time length than the first time window, and for each battery cell, calculates the difference between the short-term and long-term voltage moving averages corresponding to the difference between the short-term voltage moving average and the long-term voltage moving average.

[0015] In a battery diagnostic device according to one embodiment disclosed in this document, the voltage diagnostic unit calculates, for each battery cell, a voltage diagnostic deviation corresponding to the average value of the long-term and short-term voltage moving average differences of the plurality of battery cells and the deviation of the long-term and short-term voltage moving average difference of each battery cell, and can diagnose a voltage abnormality of each battery cell based on the voltage diagnostic deviation.

[0016] In a battery diagnostic device according to one embodiment disclosed in this document, the voltage diagnostic unit determines a statistical variable threshold value based on a standard deviation of voltage diagnostic deviations of a plurality of battery cells, and for each battery cell, filters the voltage diagnostic deviation based on the statistical variable threshold value to calculate a filter diagnostic value, and can diagnose a voltage abnormality of each battery cell based on the filter diagnostic value.

[0017] In a battery diagnostic device according to one embodiment disclosed in this document, the voltage diagnostic unit calculates a normalized value of the difference between the long-term and short-term voltage moving averages for each battery cell as a normalized voltage diagnostic deviation, and can diagnose a voltage abnormality of each battery cell based on the normalized voltage diagnostic deviation.

[0018] In a battery diagnostic device according to one embodiment disclosed in this document, the voltage diagnostic unit determines a statistical variable threshold value based on the standard deviation of the normalized voltage diagnostic deviations of the plurality of battery cells, and for each battery cell, filters the normalized voltage diagnostic deviation based on the statistical variable threshold value to calculate a filter diagnostic value, and can diagnose a voltage abnormality of each battery cell based on the filter diagnostic value.

[0019] In a battery diagnostic device according to one embodiment disclosed in this document, the voltage diagnostic unit calculates a moving average diagnostic value by recursively repeating the following (i) to (iii) at least once for each battery cell, (i) calculates a first moving average corresponding to a short-term moving average of the normalized voltage diagnostic deviation of each battery cell and a second moving average corresponding to a long-term moving average, (ii) calculates a difference between the first moving average and the second moving average for each battery cell, (iii) calculates a normalized value of the difference between the long-term and short-term moving averages for each battery cell as a moving average diagnostic value, and can diagnose a voltage abnormality of each battery cell based on the moving average diagnostic value.

[0020] In a battery diagnostic device according to one embodiment disclosed in this document, the capacity diagnostic unit calculates the SOH of each battery cell based on the time series data, and for each battery cell, calculates the SOH deviation corresponding to the deviation of the SOH of each battery cell from the median SOH value or average SOH value of the plurality of battery cells.

[0021] In a battery diagnostic device according to one embodiment disclosed in this document, the capacity diagnostic unit can calculate the SOH of each battery cell based on the time series data, calculate the moving average of the SOH of each battery cell based on a third time window, and for each battery cell, calculate the SOH deviation corresponding to the deviation of the moving average of the SOH of each battery cell and the average value of the moving averages of the SOH of the plurality of battery cells.

[0022] In a battery diagnostic device according to one embodiment disclosed in this document, the capacity diagnostic unit calculates the difference in State of Charge (SOC) before and after a charging interval of each battery cell based on the time series data, calculates the current integration value in the charging interval of each battery cell based on the time series data, and for each battery cell, represents the SOH regarding the capacity of each battery cell based on the SOC difference, the current integration value, and the initial capacity of each battery cell. c Calculate and the above SOH c Based on this, the SOH deviation of each battery cell can be calculated.

[0023] In a battery diagnostic device according to one embodiment disclosed in this document, the capacity diagnostic unit can diagnose a capacity abnormality only for at least one battery cell diagnosed as having a voltage abnormality by the voltage diagnostic unit.

[0024] In a battery diagnostic device according to one embodiment disclosed in this document, the detection unit can detect a battery cell diagnosed as having an abnormal capacity by the capacity diagnostic unit as the abnormal battery cell.

[0025] In a battery diagnostic device according to one embodiment disclosed in this document, the detection unit can detect a battery cell that is diagnosed as having a voltage abnormality by the voltage diagnostic unit and diagnosed as having a capacity abnormality by the capacity diagnostic unit as the abnormal battery cell.

[0026] A battery diagnostic method according to one embodiment disclosed in this document may include: acquiring time series data related to the state of a plurality of battery cells included in a battery module; calculating a long-term and short-term voltage moving average difference of each battery cell based on the time series data and diagnosing a voltage abnormality of each battery cell based on the long-term and short-term voltage moving average difference; calculating a State of Health (SOH) deviation of each battery cell based on the time series data and diagnosing a capacity abnormality of each battery cell based on the SOH deviation; and detecting an abnormal battery cell based on the diagnosis result of at least one of the voltage diagnostic unit or the capacity diagnostic unit.

[0027] In a battery diagnostic method according to an embodiment disclosed in this document, the operation of diagnosing a capacity abnormality of each battery cell includes the operation of diagnosing a capacity abnormality only for at least one battery cell diagnosed as having a voltage abnormality, and the operation of detecting the abnormal battery cell may include the operation of detecting the battery cell diagnosed as having a capacity abnormality as the abnormal battery cell. A battery diagnostic device according to an embodiment disclosed in this document includes an acquisition unit that acquires data related to the state of a plurality of battery cells included in a battery module; A processor may be included that obtains information regarding the State of Health (SOH) of the plurality of battery cells based on the above data, calculates an average SOH value of the plurality of battery cells based on the SOH of each battery cell included in the information regarding the SOH, and identifies a battery cell in an abnormal state among the plurality of battery cells based on the average SOH value and the SOH of each battery cell. In a battery diagnostic device according to an embodiment disclosed in this document, the processor may obtain a plurality of SOH deviation values ​​of the plurality of battery cells based on the difference between the average SOH value of the plurality of battery cells and the SOH of each battery cell, and identify a battery cell in an abnormal state based on the plurality of SOH deviation values. In a battery diagnostic device according to an embodiment disclosed in this document, the processor may obtain a plurality of moving average values ​​corresponding to the plurality of SOHs of the plurality of battery cells by applying a moving average filter to the SOH of each battery cell, obtain a plurality of moving average deviation values ​​based on the average value of the plurality of moving average values ​​and the difference between each moving average value, and identify the battery cell in an abnormal state based on the plurality of moving average deviation values. Battery cells can be identified.A battery diagnostic method according to an embodiment disclosed in this document may include: a step of obtaining data related to the state of a plurality of battery cells included in a battery module; a step of obtaining information regarding the state of health (SOH) of the plurality of battery cells based on the data; a step of calculating an average SOH value of the plurality of battery cells based on the SOH of each battery cell included in the information regarding the SOH; and a step of identifying a battery cell in an abnormal state among the plurality of battery cells based on the average SOH value and the SOH of each battery cell. In a battery diagnostic method according to an embodiment disclosed in this document, the step of identifying a battery cell in an abnormal state comprises: a step of obtaining a plurality of SOH deviation values ​​of the plurality of battery cells based on the difference between the average SOH value of the plurality of battery cells and the SOH of each battery cell. and may include a step of identifying the battery cell in an abnormal state based on the plurality of SOH deviation values. In a battery diagnostic method according to an embodiment disclosed herein, the step of identifying the battery cell in an abnormal state may include: a step of obtaining a plurality of moving average values ​​corresponding to the plurality of SOHs of the plurality of battery cells by applying a moving average filter to the SOH of each battery cell; a step of obtaining a plurality of moving average deviation values ​​based on the average value of the plurality of moving average values ​​and the difference between each moving average value; and a step of identifying the battery cell in an abnormal state based on the plurality of moving average deviation values. Effects of the invention

[0028] According to the embodiments disclosed in this document, battery diagnostic accuracy can be improved by reducing false detections caused by voltage measurement noise during the abnormal battery cell detection process.

[0029] In addition, various effects that can be identified directly or indirectly through this document may be provided. Brief explanation of the drawing

[0030] FIG. 1 is a block diagram of a battery diagnostic device according to one embodiment. FIG. 2 is a graph showing voltage time series data of a plurality of battery cells obtained by a battery diagnostic device according to one embodiment. FIGS. 3a to 3g are graphs for explaining the process of a battery diagnostic device according to one embodiment diagnosing voltage abnormalities of each battery cell from voltage time series data of FIG. 2. FIGS. 4a and FIGS. 4b are graphs illustrating the process of a battery diagnostic device according to one embodiment diagnosing a capacity abnormality of each battery cell. FIG. 5 is an operation flowchart of a battery diagnostic device according to one embodiment. FIG. 6 is an operation flowchart of a battery diagnostic device according to one embodiment. FIG. 7 is an operation flowchart of a battery diagnostic device according to one embodiment. FIG. 8 is an operation flowchart of a battery diagnostic device according to one embodiment. FIG. 9 is an operation flowchart of a battery diagnostic device according to one embodiment. FIG. 10 is an operation flowchart of a battery diagnostic device according to one embodiment. Specific details for implementing the invention

[0031] Hereinafter, various embodiments of the present invention are described with reference to the accompanying drawings. However, this is not intended to limit the present invention to specific embodiments and should be understood to include various modifications, equivalents, and / or alternatives of the embodiments of the present invention.

[0032] The various embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various modifications, equivalents, or substitutions of said embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of said items unless the relevant context clearly indicates otherwise.

[0033] In this document, each of the phrases such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B or C,” “at least one of A, B and C,” and “at least one of A, B, or C” may include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as “first,” “second,” “first,” “second,” “A,” “B,” “(a),” or “(b)” may be used simply to distinguish a component from another component and, unless specifically stated otherwise, do not limit the components in any other aspect (e.g., importance or order).

[0034] In this document, where it is mentioned that any (e.g., 1) component is “connected,” “coupled,” or “joined” to another (e.g., 2) component, with or without the terms “functionally” or “communicationly,” or where it is mentioned as “coupled” or “connected,” it means that said component may be connected to said other component directly (e.g., by wire), wirelessly, or through a third component.

[0035] According to various embodiments, each component (e.g., module or program) of the components described above may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to various embodiments, one or more of the components or operations among the aforementioned components may be omitted, or one or more other components or operations may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as those performed by the corresponding component among the multiple components prior to integration. According to various embodiments, operations performed by the module, program, or other components may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0036] FIG. 1 is a block diagram of a battery diagnostic device according to one embodiment.

[0037] The battery diagnostic device (101) described below can be implemented as a Battery Management System (BMS) within an electronic device (102), and can also be implemented as various external devices such as a server, cloud, charger, or charger / discharger.

[0038] Referring to FIG. 1, the battery diagnostic device (101) can be connected to an electronic device (102) and a user terminal (104) via wired and / or wireless connections.

[0039] According to one embodiment, the connection (103) between the battery diagnostic device (101) and the electronic device (102) may be a communication connection via a wired and / or wireless network. In one embodiment, the wired network may be based on a local area network (LAN) communication or power line communication. In one embodiment, the wireless network may be based on a short-range communication network (e.g., Bluetooth, WiFi (wireless fidelity) or IrDA (infrared data association)), or a long-range communication network (cellular network, 4G network, 5G network).

[0040] According to another embodiment, the connection (103) between the battery diagnostic device (101) and the electronic device (102) may be a connection via a device-to-device communication method (e.g., bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0041] According to one embodiment, the electronic device (102) may be a mobile device (e.g., mobile phone, laptop computer, smartphone, smart pad), an electric vehicle (e.g., EV (electric vehicle), HEV (hybrid EV), PHEV (plug-in HEV), FCEV (fuel cell EV)), an energy storage system (ESS), or a battery swapping system (BSS).

[0042] According to one embodiment, the electronic device (102) may include a plurality of battery cells (151, 153, 155). Here, each battery cell (151, 153, 155) may be a single battery cell, or it may be a cell group in which at least two or more battery cells are connected in parallel. According to one embodiment, the electronic device (102) may include at least one battery module and / or at least one battery pack having a plurality of battery cells (151, 153, 155). For example, the plurality of battery cells (151, 153, 155) may be included in one battery module or one battery pack.

[0043] According to one embodiment, the connection (105) between the battery diagnostic device (101) and the user terminal (104) may be a communication connection through a wired and / or wireless network.

[0044] According to one embodiment, the user terminal (104) may be a mobile device (e.g., mobile phone, laptop computer, smartphone, smart pad) or a PC (personal computer). According to one embodiment, the battery diagnostic device (101) may provide information related to the diagnostic results of the battery unit (151, 153, or 155) to the user terminal (104).

[0045] According to one embodiment, the battery diagnostic device (101) may include a communication circuit (110), a sensor (120), a memory (130), and a processor (140). According to an embodiment, the battery diagnostic device (101) illustrated in FIG. 1 may further include at least one component other than the components illustrated in FIG. 1 (e.g., a display, an input device, or an output device), or at least one component among the components illustrated in FIG. 1 (e.g., a sensor (120)) may be omitted. For example, if the battery diagnostic device (101) is implemented as an external electronic device separate from the electronic device (102), such as a server or a cloud, the battery diagnostic device (101) may obtain status information of a plurality of battery cells (151, 153, 155) using the communication circuit (110). In this case, the battery diagnostic device (101) may not include a sensor (120).

[0046] According to one embodiment, the communication circuit (110) establishes a wired communication channel and / or a wireless communication channel between the battery diagnostic device (101) and the electronic device (102) and / or the user terminal (104), and can transmit and receive data with the electronic device (102) and / or the user terminal (104) through the established communication channel.

[0047] According to one embodiment, the sensor (120) can measure information (e.g., voltage, current, temperature, etc.) related to the state of a plurality of battery cells (151, 153, 155) of an electronic device (102). For example, if the battery diagnostic device (101) is implemented as a BMS within the electronic device, the battery diagnostic device (101) can directly measure the state values ​​of a plurality of battery cells (151, 153, 155) using the sensor (120).

[0048] According to one embodiment, the communication circuit (110) and / or sensor (120) can acquire time-series data related to the state of a plurality of battery cells (151, 153, 155). In one embodiment, the time-series data related to the state of the plurality of battery cells (151, 153, 155) may be data representing the voltage, current, resistance, state of charge (SOC), state of health (SOH), and / or temperature of the plurality of battery cells (151, 153, 155) over time.

[0049] According to one embodiment, the memory (130) may include volatile memory and / or non-volatile memory.

[0050] According to one embodiment, the memory (130) may store data used by at least one component (e.g., processor (140)) of the battery diagnostic device (101). For example, the data may include software (or related instructions), input data, or output data. In one embodiment, the instructions may cause the battery fault diagnostic device (101) to perform operations defined by the instructions when executed by the processor (140).

[0051] According to one embodiment, the memory (140) may include one or more software components (e.g., an acquisition unit (131), a voltage diagnosis unit (132), a capacity diagnosis unit (133), a detection unit (134), and an anomaly processing unit (135)).

[0052] According to one embodiment, the processor (140) may include a central processing unit, an application processor, a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor.

[0053] According to one embodiment, the processor (140) can execute software stored in memory (130) (e.g., acquisition unit (131), voltage diagnosis unit (132), capacity diagnosis unit (133), detection unit (134), and anomaly processing unit (135)) to control at least one other component (e.g., hardware or software component) of the battery diagnosis device (101) connected to the processor (140) and can perform various data processing or operations.

[0054] Hereinafter, with reference to FIGS. 2, FIGS. 3a to 3g, FIGS. 4a, and FIGS. 4b, a method for a battery diagnostic device (101) to diagnose abnormalities in a plurality of battery cells (151, 153, 155) through an acquisition unit (131), a voltage diagnostic unit (132), a capacity diagnostic unit (133), a detection unit (134), and an abnormality processing unit (135) will be described. More specifically, with reference to FIGS. 3a to 3g, the voltage abnormality diagnosis process of the battery diagnostic device (101) will be described, and with reference to FIGS. 4a and FIG. 4b, the capacity abnormality diagnosis process of the battery diagnostic device (101) will be described.

[0055] FIG. 2 is a graph showing voltage time series data of a plurality of battery cells obtained by a battery diagnostic device according to one embodiment. FIG. 3a to 3g are graphs for explaining the process of diagnosing a voltage abnormality of each battery cell from the voltage time series data of FIG. 2 by a battery diagnostic device according to one embodiment. FIG. 4a and 4b are graphs for explaining the process of diagnosing a capacity abnormality of each battery cell by a battery diagnostic device according to one embodiment.

[0056] According to one embodiment, the acquisition unit (131) can acquire time series data related to the state of a plurality of battery cells (151, 153, 155). According to one embodiment, the acquisition unit (131) can acquire the time series data using a communication circuit (110) and / or a sensor (120).

[0057] According to one embodiment, the acquisition unit (131) can collect signals related to the state of a plurality of battery cells (151, 153, 155) using a communication circuit (110) and / or a sensor (120) at unit times and record the state values ​​(e.g., voltage value, current value, temperature value, or resistance value, etc.) of the plurality of battery cells (151, 153, 155) in the memory (130). Here, the unit time may be an integer multiple of the state signal reception cycle of the communication circuit (110) or the state measurement cycle of the sensor (120).

[0058] According to one embodiment, the acquisition unit (131) can generate time series data representing changes in the state values ​​of a plurality of battery cells (151, 153, 155) over time based on the state values ​​of a plurality of battery cells (151, 153, 155) recorded in the memory (130). At this time, the number of time series data can increase by 1 each time the state values ​​of the plurality of battery cells (151, 153, 155) are acquired.

[0059] Referring to FIG. 2, a graph (200) can be seen that exemplarily shows voltage time series data representing the voltage change over time of a plurality of battery cells (e.g., 151, 153, 155) acquired by the acquisition unit (131). In the graph (200), the horizontal axis may represent time and the vertical axis may represent voltage.

[0060] Voltage anomaly diagnosis

[0061] According to one embodiment, the voltage diagnosis unit (132) can calculate the long-term and short-term voltage moving average difference of each battery cell (151, 153, 155) based on time series data acquired by the acquisition unit (131).

[0062] According to one embodiment, the voltage diagnostic unit (132) can calculate the voltage moving average of each of the plurality of battery cells (151, 153, 155) per unit time using one time window or two time windows. When two time windows are used, the time length for one time window may be different from the time length for the other time window.

[0063] Here, the time length of each time window is an integer multiple of a unit time, the end point of each time window is the current point in time, and the start point of each time window can be a point in time preceding the current point in time by a predetermined time length.

[0064] For convenience of explanation, the time window with the shorter time length among the two time windows will be referred to as the first time window, and the time window with the longer time length will be referred to as the second time window.

[0065] According to one embodiment, the voltage diagnostic unit (132) can calculate the short-term voltage moving average of each battery cell (151, 153, 155) per unit time based on a first time window. Additionally, the voltage diagnostic unit (132) can calculate the long-term voltage moving average of each battery cell (151, 153, 155) per unit time based on a second time window. Here, the moving average may refer to any one of a Simple Moving Average (SMA), a Weighted Moving Average (WMA), or an Exponential Moving Average (EMA).

[0066] For example, the input factors for calculating the voltage moving average may be the voltage values ​​of each battery cell (151, 153, 155) in a specific time window (e.g., a first time window or a second time window). In this case, the calculated voltage moving average may represent the short-term and long-term moving averages of the voltage values ​​of each battery cell (151, 153, 155).

[0067] As another example, the input factor when calculating the voltage moving average may be the deviation between the reference voltage of multiple battery cells (151, 153, 155) and the voltage of each battery cell (151, 153, 155) in a specific time window (e.g., a first time window or a second time window). Here, the reference voltage is a value determined from the multiple battery cells (151, 153, 155) and may be determined as the average value or the median value of the voltage values ​​of the multiple battery cells (151, 153, 155). In this case, the calculated voltage moving average may represent the short-term and long-term moving average of the voltage deviation of each battery cell (151, 153, 155).

[0068] According to one embodiment, the voltage diagnostic unit (132) can compare the short-term change trend and the long-term change trend of the cell voltage at each unit time based on the short-term voltage moving average and the long-term voltage moving average of each battery cell (151, 153, 155) calculated at each unit time.

[0069] Referring to FIG. 3a, a graph (310) can be seen that exemplarily shows the short-term voltage moving average line and the long-term voltage moving average line of a plurality of battery cells (e.g., 151, 153, 155) calculated by the voltage diagnostic unit (132) from the voltage time series data of FIG. 2. In the graph (310), the horizontal axis may represent time, and the vertical axis may represent the voltage moving average value.

[0070] In the graph (310), the two moving average lines, which are of the same shape but different thicknesses, are the short-term voltage moving average line and the long-term voltage moving average line of a specific battery cell, respectively, and can represent the history of change over time of the short-term voltage moving average and the long-term voltage moving average of the battery cell. At this time, the time length of the first time window used when calculating the moving average of the graph (310) is 10 seconds and the time length of the second time window is 100 seconds, but is not limited thereto.

[0071] According to one embodiment, the voltage diagnostic unit (132) can calculate the difference between the short-term and long-term voltage moving averages, which corresponds to the difference between the short-term and long-term voltage moving averages, for each battery cell (151, 153, 155) at a unit time. Here, the difference between the short-term and long-term voltage moving averages may be the value obtained by subtracting the smaller one from the larger one between the short-term and long-term voltage moving averages.

[0072] Referring to FIG. 3b, a graph (320) can be seen that exemplarily shows the difference between the long-term and short-term voltage moving averages of a plurality of battery cells (e.g., 151, 153, 155) calculated by the voltage diagnostic unit (132) from the voltage moving average line of FIG. 3a. In the graph (320), the horizontal axis represents time, and the vertical axis represents the value of the difference between the long-term and short-term voltage moving averages.

[0073] The difference between the short-term and long-term moving average voltages of a battery cell depends on the short-term and long-term change history of the cell voltage.

[0074] Battery cell temperature and State of Health (SOH) consistently affect cell voltage in both the short and long term. Therefore, the difference between the short-term and long-term moving average voltages of battery cells without voltage abnormalities does not show a significant difference compared to the difference between the short-term and long-term moving average voltages of the remaining battery cells.

[0075] On the other hand, sudden voltage anomalies caused by internal and / or external short circuits in battery cells have a greater impact on the short-term moving average than on the long-term moving average. As a result, the difference between the long-term and short-term moving averages of a battery cell differs significantly from the difference between the long-term and short-term moving averages of the remaining battery cells that are free from voltage anomalies.

[0076] Therefore, the difference between the short-term and long-term moving average voltages of a battery cell can serve as an indicator necessary for diagnosing voltage anomalies.

[0077] According to one embodiment, the voltage diagnosis unit (132) can diagnose voltage abnormalities of each battery cell (151, 153, 155) based on the difference between the short-term and long-term voltage moving averages of each battery cell (151, 153, 155).

[0078] According to one embodiment, the voltage diagnosis unit (132) can calculate the voltage diagnosis deviation of each battery cell (151, 153, 155) per unit time based on the difference between the long-term and short-term voltage moving averages.

[0079] According to one embodiment, the voltage diagnostic unit (132) can calculate, for each battery cell (151, 153, 155), a voltage diagnostic deviation corresponding to the average value of the long-term and short-term voltage moving average differences of a plurality of battery cells (151, 153, 155) and the deviation of the long-term and short-term voltage moving average difference of each battery cell (151, 153, 155). In this case, the voltage diagnostic unit (132) can diagnose a voltage abnormality of each battery cell (151, 153, 155) based on the calculated voltage diagnostic deviation. For example, the voltage diagnostic unit (132) can diagnose a battery cell as having a voltage abnormality if the calculated voltage diagnostic deviation exceeds a preset threshold value (e.g., 0.015).

[0080] According to one embodiment, the voltage diagnosis unit (132) can calculate the normalized value of the difference between the long and short-term voltage moving averages for each battery cell (151, 153, 155) per unit time as the normalized voltage diagnosis deviation.

[0081] For example, the voltage diagnostic unit (132) can normalize the long-term and short-term moving average voltage difference of each battery cell (151, 153, 155) using the average value of the long-term and short-term moving average voltage differences of a plurality of battery cells (151, 153, 155). For example, the voltage diagnostic unit (132) performs a division operation (e.g., D) of the long-term and short-term moving average voltage difference of each battery cell (151, 153, 155) by the average value. SL / D av , here D SL is the above long-term and short-term voltage moving average difference, D av The difference between the short-term and long-term voltage moving averages can be normalized by the above average value.

[0082] As another example, the voltage diagnostic unit (132) performs a logarithmic operation on the difference between the short-term and long-term voltage moving averages of each battery cell (151, 153, 155) (e.g., Log(D SL ), here D SL It can also be normalized through the difference between the short-term and long-term voltage moving averages.

[0083] Referring to FIG. 3c, a graph (330) can be seen that exemplarily shows the voltage diagnosis deviation of a plurality of battery cells (e.g., 151, 153, 155) calculated by the voltage diagnosis unit (132) based on the long-term and short-term voltage moving average difference of FIG. 3b. In the graph (330), the X-axis represents time, and the vertical axis represents the voltage diagnosis deviation normalized by dividing the long-term and short-term voltage moving average difference of each battery cell (151, 153, 155) by the average value.

[0084] According to the graph (330), it can be seen that the change in the long-term and short-term moving average difference of each battery cell is amplified based on the average value as the long-term and short-term moving average difference is normalized. As a result, the diagnosis of voltage abnormalities in the battery cell can be performed more accurately.

[0085] According to one embodiment, the voltage diagnosis unit (132) can diagnose voltage abnormalities of each battery cell (151, 153, 155) by comparing the calculated voltage diagnosis deviation (or, normalized voltage diagnosis deviation) with a statistical variable threshold value.

[0086] According to one embodiment, the voltage diagnostic unit (132) can determine a statistical variable threshold value per unit time that depends on the standard deviation of the voltage diagnostic deviations (or normalized voltage diagnostic deviations) of a plurality of battery cells (151, 153, 155). For example, the voltage diagnostic unit (132) can determine the statistical variable threshold value based on the following mathematical formula 1.

[0087]

[0088] In the above mathematical formula 1, D threshold is the above-mentioned statistical variable threshold value, β is a predetermined constant, and Sig is a function that calculates the standard deviation for voltage diagnostic deviations of a plurality of battery cells (151, 153, 155). β is a factor that determines diagnostic sensitivity and can be determined experimentally. β can be appropriately determined by trial and error so that when the embodiments disclosed herein are performed on a cell group containing a battery cell with an actual voltage abnormality, the battery cell can be detected as a voltage abnormality cell. For example, β can be set to at least 5, at least 6, at least 7, at least 8, or at least 9.

[0089] Meanwhile, battery cells with voltage abnormalities may exhibit a relatively larger voltage diagnostic deviation compared to normal battery cells. Therefore, to improve the accuracy and reliability of the diagnosis, Sig(D diag The maximum value of the voltage diagnostic deviation can be excluded when calculating ).

[0090] According to one embodiment, the voltage diagnostic unit (132) can calculate a filter diagnostic value by filtering the voltage diagnostic deviation (or normalized voltage diagnostic deviation) for each battery cell (151, 153, 155) based on a statistical variable threshold value. For example, the voltage diagnostic unit (132) can calculate one of two values ​​as the filter diagnostic value based on the following mathematical formula 2.

[0091]

[0092]

[0093] That is, the voltage diagnosis unit (132) can calculate the difference between the voltage diagnosis deviation (or normalized voltage diagnosis deviation) and the statistical variable threshold value as the filter diagnosis value if the voltage diagnosis deviation (or normalized voltage diagnosis deviation) is greater than the statistical variable threshold value. On the other hand, the voltage diagnosis unit (132) can calculate the filter diagnosis value as 0 if the voltage diagnosis deviation (or normalized voltage diagnosis deviation) is less than or equal to the statistical variable threshold value.

[0094] Referring to FIG. 3d, a graph (340) can be seen that exemplarily shows the filter diagnostic values ​​of a plurality of battery cells (e.g., 151, 153, 155) calculated by the voltage diagnostic unit (132) based on the voltage diagnostic deviation and statistical variable threshold value of FIG. 3c. In the graph (330), the X-axis may represent time, and the vertical axis may represent the filter diagnostic values.

[0095] According to one embodiment, the voltage diagnosis unit (132) can diagnose voltage abnormalities of each battery cell (151, 153, 155) based on the calculated filter diagnosis value.

[0096] For example, the voltage diagnostic unit (132) can accumulate time intervals for each battery cell (151, 153, 155) in which the filter diagnostic value is greater than (or greater than or equal to) the diagnostic threshold value (e.g., 0), and diagnose a battery cell as having a voltage abnormality if the condition is met where the accumulated time is greater than (or greater than or equal to) a preset reference time. The voltage diagnostic unit (132) can accumulate time intervals in which the condition that the filter diagnostic value is greater than (or greater than or equal to) the diagnostic threshold value is continuously satisfied. If there are multiple such time intervals, the voltage diagnostic unit (132) can independently calculate the accumulated time for each time interval.

[0097] As another example, the voltage diagnostic unit (132) can accumulate the number of data included in time intervals where the filter diagnostic value is greater than (or greater than or equal to) the diagnostic threshold value (e.g., 0) in the time series data of the filter diagnostic value, and diagnose a battery cell as having an abnormal voltage if the condition is met where the accumulated data value is greater than (or greater than or equal to) a preset reference count. The voltage diagnostic unit (132) can accumulate only the number of data included in time intervals where the condition that the filter diagnostic value is greater than (or greater than or equal to) the diagnostic threshold value is continuously satisfied. If there are multiple such time intervals, the voltage diagnostic unit (132) can independently accumulate the number of data for each time interval.

[0098] According to one embodiment, the voltage diagnostic unit (132) can calculate a moving average diagnostic value for each battery cell (151, 153, 155) based on a voltage diagnostic deviation (or, normalized voltage diagnostic deviation). The voltage diagnostic unit (132) can recursively perform the process of (i) calculating a first moving average corresponding to a short-term moving average and a second moving average corresponding to a long-term moving average of the voltage diagnostic deviation (or, normalized voltage diagnostic deviation) of each battery cell (151, 153, 155), (ii) calculating a long-term and short-term moving average difference corresponding to the difference between the first moving average and the second moving average for each battery cell (151, 153, 155), and (iii) calculating a normalized value of the long-term and short-term moving average difference as a moving average diagnostic value for each battery cell (151, 153, 155). That is, the voltage diagnostic unit (132) can replace the voltage time series data of FIG. 2 with the normalized voltage diagnostic deviation of FIG. 3c. That is, the voltage diagnostic unit (132) can be based on the first time window when calculating the first moving average and on the second time window when calculating the second moving average.

[0099] The voltage diagnosis unit (132) can calculate the filter diagnosis value of each battery cell (151, 153, 155) according to the method described above using the calculated moving average diagnosis value, and diagnose voltage abnormalities based on the calculated filter diagnosis value.

[0100] Referring to FIG. 3e, a graph (350) can be seen that exemplarily shows the difference between the long and short-term moving averages of a plurality of battery cells (151, 153, 155) calculated by the voltage diagnostic unit (132) through the process (i) and (ii). In the graph (350), the horizontal axis may represent time, and the vertical axis may represent the difference between the moving averages.

[0101] Referring to FIG. 3f, a graph (360) can be seen that exemplarily shows the moving average diagnostic value of a plurality of battery cells (151, 153, 155) calculated by the voltage diagnostic unit (132) through the process (iii). In the graph (360), the horizontal axis may represent time, and the vertical axis may represent the moving average diagnostic value.

[0102] Referring to FIG. 3g, a graph (370) can be seen that exemplarily shows the filter diagnostic values ​​of a plurality of battery cells (151, 153, 155) calculated by the voltage diagnostic unit (132) from the moving average diagnostic value of FIG. 3f. In the graph (370), the horizontal axis may represent time, and the vertical axis may represent the filter diagnostic value.

[0103] According to one embodiment, the voltage diagnostic unit (132) may recursively repeat the above processes (i) to (iii) at least once. That is, the voltage diagnostic unit (132) may replace the voltage time series data of FIG. 2 with the moving average diagnostic value of FIG. 3f.

[0104] When the above-described recursive operation process is repeated, the diagnosis of voltage abnormalities in battery cells can be performed more precisely. Referring to Fig. 3d, a positive profile pattern is observed in only two time intervals in the time series data of filter diagnostic values ​​of a battery cell with a voltage abnormality. However, referring to Fig. 3g, a positive profile pattern is observed in more time intervals in the time series data of filter diagnostic values ​​of a battery cell with a voltage abnormality than in Fig. 3d. Therefore, when the recursive operation process is repeated, the time at which a voltage abnormality occurs in a battery cell can be detected more accurately.

[0105] Diagnosis of overdose

[0106] According to one embodiment, the capacity diagnosis unit (133) can calculate the SOH of a plurality of battery cells (151, 153, 155) per unit time based on time series data acquired by the acquisition unit (131). Here, SOH is a lifespan-related parameter of the battery cells (151, 153, and / or 155), and represents the SOH related to the capacity of the battery. c or SOH representing the SOH regarding the resistance growth of the battery r It may include at least one of the following. In the following, the dosage diagnosis unit (133) is SOH c An example of calculating is described. However, this is only one example, and the dosage diagnosis unit (133) is SOH c , SOH r Various SOHs can be calculated, including the final SOH calculated based on these.

[0107] According to one embodiment, the capacity diagnosis unit (133) can calculate the difference in SOC before and after the charging interval of a plurality of battery cells (151, 153, 155) at each unit time based on the time series data.

[0108] For example, the capacity diagnostic unit (133) can calculate the SOC difference based on the SOC data included in the time series data, using the SOC value at a first point in time before the charging section and the SOC value at a second point in time after the charging section.

[0109] As another example, the capacity diagnostic unit (133) can calculate the Open Circuit Voltage (OCV) of a plurality of battery cells (151, 153, 155) based on at least one of the time series voltage data or time series current data included in the time series data, and calculate the difference in SOC after converting the calculated OCV into SOC using a SOC-OCV table stored in advance in the memory (130).

[0110] According to one embodiment, the capacity diagnostic unit (133) can calculate the current integration value in the charging section of a plurality of battery cells (151, 153, 155) per unit time based on the time series data. For example, the capacity diagnostic unit (133) can calculate the current integration value in the charging section based on the current value of the charging section included in the time series data and the time length of the charging section. Here, the unit of the current integration value may be 'Ah (Ampere hour)'.

[0111] According to another embodiment, the capacity diagnostic unit (133) may calculate the current integration value in the charging section if the calculated SOC difference is greater than or equal to a specified value. For example, if the SOC difference of the battery cell (151, 153, or 155) calculated in the current cycle is less than or equal to a specified value, the capacity diagnostic unit (133) may not calculate the current integration value of the battery cell (151, 153, or 155) in the current cycle. In this case, since the current integration value of the battery cell (151, 153, or 155) is not calculated, the capacity diagnostic unit (133) [calculates] the SOH of the corresponding battery cell (151, 153, or 155 c It may also not be calculated. As another example, the capacity diagnostic unit (133) may calculate the current integrated value of the battery cell (151, 153, or 155) in the current cycle if the difference in SOC of the battery cell (151, 153, or 155) calculated in the current cycle is greater than (or exceeds) a specified value. In this case, the capacity diagnostic unit (133) uses the current integrated value of the battery cell (151, 153, or 155) to calculate the SOH of the corresponding battery cell (151, 153, or 155). c It can produce.

[0112] According to one embodiment, the capacity diagnostic unit (133) determines the SOH of the battery cells (151, 153, and / or 155) based on the SOC difference, current integration value, and initial capacity of the battery cells (151, 153, and / or 155). c It can be calculated. Here, the unit of the initial capacity of the battery cells (151, 153, and / or 155) may be 'Ah', which is the same as the unit of the current integration value.

[0113] For example, the capacity diagnostic unit (133) determines the SOH of the battery cell (151, 153, and / or 155) based on the following mathematical formula 3. c It can produce.

[0114]

[0115] In the above mathematical formula 3, IΔT is the current integration value of the battery cells (151, 153, and / or 155), ΔSOC is the SOC difference of the battery cells (151, 153, and / or 155), and C is the initial capacity of the battery cells (151, 153, and / or 155).

[0116] Referring to FIG. 4a, the SOH of a plurality of battery cells (e.g., 151, 153, 155) calculated by the capacity diagnostic unit (133). c A graph (410) representing can be observed. In the graph (410), the horizontal axis represents time, and the vertical axis represents SOH c It can represent.

[0117] According to one embodiment, the capacity diagnosis unit (133) diagnoses the SOH of the battery cells to be diagnosed per unit time. c It can calculate the SOH calculated by the capacity diagnosis unit (133). The battery diagnostic device (101) can calculate the SOH. c By accumulatingly storing in memory (130) for a specified period of time, SOH such as graph (410) c You can manage data.

[0118] According to one embodiment, the capacity diagnostic unit (133) [describes] the SOH of each battery cell (151, 153, 155) (e.g., calculated SOH c The SOH deviation can be calculated based on ). For example, the SOH deviation may be the deviation of the SOH of each battery cell (151, 153, 155) from the median SOH of the plurality of battery cells (151, 153, 155), the deviation of the SOH of each battery cell (151, 153, 155) from the average SOH of the plurality of battery cells (151, 153, 155), or the deviation of the SOH of each battery cell (151, 153, 155) from the average value of the moving averages of the SOH of the plurality of battery cells (151, 153, 155).

[0119] According to one embodiment, the capacity diagnostic unit (133) can calculate the moving average of the SOH of each battery cell (151, 153, 155) at each unit time based on a third time window. Here, the time length of the third time window is an integer multiple of the unit time, the end point of the third time window is the current time, and the starting point may be a time point preceding the current time by a predetermined time length. Additionally, the moving average may refer to any one of a simple moving average, a weighted moving average, or an exponential moving average.

[0120] Below, an example is described in which the capacity diagnostic unit (133) calculates an SOH index moving average. However, this is merely one example, and the capacity diagnostic unit (133) can calculate various moving average values, such as a simple SOH moving average, a weighted SOH moving average, or an SOH index moving average.

[0121] According to one embodiment, the capacity diagnostic unit (133) can calculate the SOH exponential moving average at the current time by inputting the SOH of each battery cell (151, 153, 155) into the following mathematical formula 4.

[0122]

[0123] In Equation 4, EMA t is the exponential moving average of the current SOH of the battery cell (151, 153, or 155), α is the weight, SOH is the current SOH of the battery cell (151, 153, or 155), EMA t-1 is the moving average of the SOH exponential of the battery cell (151, 153, or 155) at a previous point in time. Here, the weight can be set differently depending on the specifications of the plurality of battery cells (151, 153, 155). For example, the weight can be set to 0.05, but is not limited thereto.

[0124] Referring to FIG. 4b, a graph (420) showing the moving average of the SOH index over time of a plurality of battery cells (151, 153, 155) calculated by the capacity diagnostic unit (133) can be seen. In the graph (420), the horizontal axis represents time, and the vertical axis represents the moving average of the SOH index.

[0125] According to one embodiment, the capacity diagnostic unit (133) can calculate the moving average of the SOH of the battery cells to be diagnosed at each unit time. The battery diagnostic device (101) can manage moving average data such as the graph (420) by accumulating the SOH moving average calculated by the capacity diagnostic unit (133) in the memory (130) for a specified time. According to one embodiment, the capacity diagnostic unit (133) may obtain the moving average data of the battery cells to be diagnosed by applying the SOH data stored in the memory (130) (e.g., the graph (410) of FIG. 4a) to a moving average filter.

[0126] According to one embodiment, the capacity diagnostic unit (133) can calculate, for each battery cell (151, 153, 155), the average value of multiple SOH moving averages of multiple battery cells (151, 153, 155) and the SOH deviation corresponding to the deviation of the SOH moving average of each battery cell (151, 153, 155) per unit time.

[0127] According to one embodiment, the capacity diagnosis unit (133) can diagnose a capacity abnormality of each battery cell (151, 153, 155) based on the SOH deviation. For example, the capacity diagnosis unit (133) can diagnose whether there is a defect due to a decrease in the capacity of each battery cell (151, 153, 155).

[0128] According to one embodiment, the capacity diagnosis unit (133) can diagnose a capacity abnormality of each battery cell (151, 153, 155) by comparing the SOH deviation with a preset threshold value. For example, the capacity diagnosis unit (133) can diagnose a battery cell with a capacity abnormality as a battery cell in which the SOH deviation exceeds (or is greater than) the threshold value.

[0129] For example, the capacity diagnostic unit (133) can accumulate time intervals in which the SOH deviation is greater than (or greater than or equal to) the threshold value for each battery cell (151, 153, 155), and diagnose a battery cell as having a capacity exceeding the threshold value if the condition is met where the accumulated time is greater than (or greater than or equal to) a preset reference time. The battery diagnostic device (101) can accumulate time intervals in which the condition that the SOH deviation is greater than (or greater than or equal to) the threshold value is continuously satisfied. If there are multiple such time intervals, the battery diagnostic device (101) can independently calculate the accumulated time for each time interval.

[0130] As another example, the battery diagnostic device (101) can accumulate the number of data included in time intervals where the SOH deviation is greater than (or greater than or equal to) the threshold value in the time series data of the SOH deviation, and diagnose a battery cell as having a capacity greater than that of a battery cell in which the condition is satisfied where the accumulated data value is greater than (or greater than or equal to) a preset reference count. The battery diagnostic device (101) can accumulate only the number of data included in time intervals where the condition that the SOH deviation is greater than (or greater than or equal to) the threshold value is continuously satisfied. If there are multiple such time intervals, the battery diagnostic device (101) can independently accumulate the number of data for each time interval.

[0131] According to one embodiment, the detection unit (134) can detect an abnormal battery cell based on at least one of the voltage abnormality diagnosis result of the voltage diagnosis unit (132) or the capacity abnormality diagnosis result of the capacity diagnosis unit (133).

[0132] For example, the detection unit (134) can detect a battery cell as an abnormal battery cell that is diagnosed as having a voltage abnormality by the voltage diagnosis unit (132) and diagnosed as having a capacity abnormality by the capacity diagnosis unit (133).

[0133] In another example, the capacity diagnosis unit (133) can diagnose a capacity abnormality only for at least one battery cell diagnosed as having a voltage abnormality by the voltage diagnosis unit (132). In this case, the detection unit (134) can detect the battery cell diagnosed as having a capacity abnormality by the capacity diagnosis unit (133) as an abnormal battery cell. That is, in the example, the battery diagnosis device (101) can first diagnose a voltage abnormality of each battery cell (151, 153, 155) using the voltage diagnosis unit (132), and secondarily diagnose a capacity abnormality for at least one battery cell with a voltage abnormality using the capacity diagnosis unit (133), and finally detect the battery cell with a capacity abnormality as an abnormal battery cell.

[0134] According to one embodiment, the abnormality processing unit (135) can perform an abnormality processing function based on the abnormality diagnosis results of a plurality of battery cells (151, 153, 155). Here, the abnormality processing function may include a notification function or a short-circuit function.

[0135] According to one embodiment, the abnormality processing unit (135) can transmit the abnormality diagnosis results of the plurality of battery cells (151, 153, 155) to a user terminal (104) connected via a wired and / or wireless network.

[0136] According to one embodiment, the abnormality processing unit (135) may isolate the abnormal battery cell from the electronic device (102) based on the abnormality diagnosis results of a plurality of battery cells (151, 153, 155). Here, the isolation may include electrical and / or mechanical isolation.

[0137] FIG. 5 is an operation flowchart of a battery diagnostic device according to one embodiment. FIG. 5 can be explained using the configurations of FIG. 1.

[0138] The embodiment illustrated in FIG. 5 is merely one example, and the order of steps according to various embodiments of the present invention may differ from that illustrated in FIG. 5, and some steps illustrated in FIG. 5 may be omitted, the order of steps may be changed, or steps may be merged.

[0139] Referring to FIG. 5, in operation 505, the battery diagnostic device (101) can acquire time-series data related to the status of a plurality of battery cells (151, 153, 155). According to one embodiment, the battery diagnostic device (101) can acquire the time-series data using a communication circuit (110) and / or a sensor (120).

[0140] In operation 510, the battery diagnostic device (101) can diagnose voltage abnormalities of each battery cell (151, 153, 155) based on time series data obtained in operation 505. More specifically, the battery diagnostic device (101) can diagnose voltage abnormalities of each battery cell (151, 153, 155) based on voltage time series data included in the time series data.

[0141] According to one embodiment, the battery diagnostic device (101) calculates the long-term and short-term voltage moving average difference of each battery cell (151, 153, 155) based on time series data, and can diagnose voltage abnormalities of each battery cell (151, 153, 155) based on the calculated long-term and short-term voltage moving average difference.

[0142] The operation 510 in which the battery diagnostic device (101) diagnoses voltage abnormalities of each battery cell (151, 153, 155) can be specifically explained through FIGS. 6 to 8, which will be described later.

[0143] In operation 515, the battery diagnostic device (101) can diagnose the capacity abnormality of each battery cell (151, 153, 155) based on the time series data obtained in operation 505.

[0144] According to one embodiment, the battery diagnostic device (101) calculates the SOH deviation of each battery cell (151, 153, 155) based on time series data and can diagnose the capacity abnormality of each battery cell (151, 153, 155) based on the calculated SOH deviation.

[0145] The operation 515 in which the battery diagnostic device (101) diagnoses an excess capacity of each battery cell (151, 153, 155) can be specifically explained through FIG. 9, which will be described later.

[0146] In operation 520, the battery diagnostic device (101) can detect an abnormal battery cell among a plurality of battery cells (151, 153, 155). According to one embodiment, the battery diagnostic device (101) can detect an abnormal battery cell based on at least one of the voltage abnormality diagnosis result of operation 510 or the capacity abnormality diagnosis result of operation 515.

[0147] For example, the battery diagnostic device (101) can detect a battery cell as an abnormal battery cell that is diagnosed as having a voltage abnormality in operation 510 and diagnosed as having a capacity abnormality in operation 515.

[0148] Hereinafter, with reference to FIGS. 6 to 8, the operation of a battery diagnostic device (101) diagnosing a voltage abnormality of each battery cell (151, 153, 155) will be described.

[0149] FIG. 6 is an operation flowchart of a battery diagnostic device according to one embodiment. FIG. 6 can be described using the configurations of FIG. 1.

[0150] The embodiment illustrated in FIG. 6 is merely one example, and the order of steps according to various embodiments of the present invention may differ from that illustrated in FIG. 6, and some steps illustrated in FIG. 6 may be omitted, the order of steps may be changed, or steps may be merged.

[0151] Referring to FIG. 6, in operation 605, the battery diagnostic device (101) can calculate the long-term and short-term voltage moving average difference of each battery cell (151, 153, 155) based on the time series data obtained in operation 505 of FIG. 5.

[0152] According to one embodiment, the battery diagnostic device (101) can calculate the short-term voltage moving average of each battery cell (151, 153, 155) per unit time based on a first time window. Additionally, the battery diagnostic device (101) can calculate the long-term voltage moving average of each battery cell (151, 153, 155) per unit time based on a second time window.

[0153] For example, the input factors for calculating the voltage moving average may be the voltage values ​​of each battery cell (151, 153, 155) in a specific time window (e.g., a first time window or a second time window). In this case, the calculated voltage moving average may represent the short-term and long-term moving averages of the voltage values ​​of each battery cell (151, 153, 155).

[0154] As another example, the input factor when calculating the voltage moving average may be the deviation between the reference voltage of multiple battery cells (151, 153, 155) and the voltage of each battery cell (151, 153, 155) in a specific time window (e.g., a first time window or a second time window). Here, the reference voltage is a value determined from the multiple battery cells (151, 153, 155) and may be determined as the average value or the median value of the voltage values ​​of the multiple battery cells (151, 153, 155). In this case, the calculated voltage moving average may represent the short-term and long-term moving average of the voltage deviation of each battery cell (151, 153, 155).

[0155] In operation 610, the battery diagnostic device (101) can calculate the difference between the short-term and long-term voltage moving averages, which corresponds to the difference between the short-term and long-term voltage moving averages for each battery cell (151, 153, 155), at each unit time. Here, the difference between the short-term and long-term voltage moving averages may be the value obtained by subtracting the smaller of the short-term and long-term voltage moving averages calculated in operation 605 from the larger one.

[0156] According to one embodiment, the battery diagnostic device (101) may diagnose voltage abnormalities in each battery cell (151, 153, 155) based on the difference between the short-term and long-term voltage moving averages of each battery cell (151, 153, 155).

[0157] In operation 615, the battery diagnostic device (101) can calculate the voltage diagnostic deviation of each battery cell (151, 153, 155) per unit time based on the voltage moving average difference calculated in operation 610.

[0158] According to one embodiment, the battery diagnostic device (101) can calculate, for each battery cell (151, 153, 155), the average value of the long-term and short-term voltage moving average differences of the plurality of battery cells (151, 153, 155) and the voltage diagnostic deviation corresponding to the deviation of the long-term and short-term voltage moving average difference of each battery cell (151, 153, 155).

[0159] According to one embodiment, the battery diagnostic device (101) can calculate, for each battery cell (151, 153, 155), a normalized value of the difference between the long and short-term voltage moving averages per unit time as a normalized voltage diagnostic deviation.

[0160] For example, a battery diagnostic device (101) can normalize the long-term and short-term voltage moving average difference of each battery cell (151, 153, 155) using the average value of the long-term and short-term voltage moving average differences of a plurality of battery cells (151, 153, 155). For example, the battery diagnostic device (101) can perform a division operation (e.g., D) on the long-term and short-term voltage moving average difference of each battery cell (151, 153, 155) by the average value. SL / D av , here D SL is the above long-term and short-term voltage moving average difference, D av The difference between the short-term and long-term voltage moving averages can be normalized by the above average value.

[0161] As another example, the battery diagnostic device (101) performs a logarithmic operation on the difference between the short-term and long-term voltage moving averages of each battery cell (151, 153, 155) (e.g., Log(D SL ), here D SL It can also be normalized through the difference between the short-term and long-term voltage moving averages.

[0162] In operation 620, the battery diagnostic device (101) can diagnose a voltage abnormality of each battery cell (151, 153, 155) based on the voltage diagnostic deviation calculated in operation 615. For example, the battery diagnostic device (101) can diagnose a voltage abnormality in a battery cell where the calculated voltage diagnostic deviation exceeds a preset threshold value (e.g., 0.015).

[0163] According to one embodiment, the battery diagnostic device (101) can diagnose a voltage abnormality of each battery cell (151, 153, 155) by comparing the voltage diagnostic deviation (or normalized voltage diagnostic deviation) calculated in operation 615 with a statistical variable threshold value. The operation of the battery diagnostic device (101) diagnosing a voltage abnormality by comparing the voltage diagnostic deviation with a statistical variable threshold value can be specifically explained through FIG. 7, which will be described later.

[0164] FIG. 7 is an operation flowchart of a battery diagnostic device according to one embodiment. FIG. 7 can be described using the configurations of FIG. 1.

[0165] The embodiment illustrated in FIG. 7 is merely one example, and the order of steps according to various embodiments of the present invention may differ from that illustrated in FIG. 7, and some steps illustrated in FIG. 7 may be omitted, the order of steps may be changed, or steps may be merged.

[0166] Referring to FIG. 7, in operation 705, the battery diagnostic device (101) can determine a statistical variable threshold value per unit time that depends on the standard deviation of the voltage diagnostic deviations (or normalized voltage diagnostic deviations) of a plurality of battery cells (151, 153, 155). For example, the voltage diagnostic unit (132) can determine the statistical variable threshold value based on the above mathematical formula 1.

[0167] In operation 710, the battery diagnostic device (101) can calculate a filter diagnostic value by filtering the voltage diagnostic deviation (or normalized voltage diagnostic deviation) for each battery cell (151, 153, 155) based on a statistical variable threshold value. For example, the battery diagnostic device (101) can calculate one of two values ​​as the filter diagnostic value based on the above mathematical formula 2.

[0168] In operation 715, the battery diagnostic device (101) can diagnose voltage abnormalities of each battery cell (151, 153, 155) based on the filter diagnostic value calculated in operation 710.

[0169] For example, a battery diagnostic device (101) can accumulate time intervals for each battery cell (151, 153, 155) in which the filter diagnostic value is greater than (or greater than or equal to) a diagnostic threshold value (e.g., 0), and diagnose a battery cell as having an abnormal voltage if the condition is met where the accumulated time is greater than (or greater than or equal to) a preset reference time. The battery diagnostic device (101) can accumulate time intervals in which the condition that the filter diagnostic value is greater than (or greater than or equal to) a diagnostic threshold value is continuously satisfied. If there are multiple such time intervals, the battery diagnostic device (101) can independently calculate the accumulated time for each time interval.

[0170] As another example, the battery diagnostic device (101) can accumulate the number of data included in time intervals where the filter diagnostic value is greater than (or greater than or equal to) the diagnostic threshold (e.g., 0) in the time series data of the filter diagnostic value, and diagnose a battery cell as having an abnormal voltage when the condition is met where the accumulated data value is greater than (or greater than or equal to) a preset reference count. The battery diagnostic device (101) can accumulate only the number of data included in time intervals where the condition that the filter diagnostic value is greater than (or greater than or equal to) the diagnostic threshold is continuously satisfied. If there are multiple such time intervals, the battery diagnostic device (101) can independently accumulate the number of data for each time interval.

[0171] FIG. 8 is an operation flowchart of a battery diagnostic device according to one embodiment. FIG. 8 can be explained using the configurations of FIG. 1.

[0172] The embodiment illustrated in FIG. 8 is merely one example, and the order of steps according to various embodiments of the present invention may differ from that illustrated in FIG. 8, and some steps illustrated in FIG. 8 may be omitted, the order of steps may be changed, or steps may be merged.

[0173] Operations 805 to 815 of FIG. 8 are identical to operations 605 to 615 of FIG. 6, so the explanation will be omitted.

[0174] Referring to FIG. 8, in operation 820, the battery diagnostic device (101) can calculate a first moving average corresponding to a short-term moving average of the voltage diagnostic deviation (or normalized voltage diagnostic deviation) calculated in operation 815. The battery diagnostic device (101) may base the calculation of the first moving average on a first time window.

[0175] In operation 825, the battery diagnostic device (101) may calculate a second moving average corresponding to the long-term moving average of the voltage diagnostic deviation (or normalized voltage diagnostic deviation) calculated in operation 815. The battery diagnostic device (101) may base the second moving average calculation on a second time window.

[0176] In operation 830, the battery diagnostic device (101) can calculate a long-term and short-term moving average difference corresponding to the difference between the first moving average calculated in operation 820 and the second moving average calculated in operation 825 for each battery cell (151, 153, 155).

[0177] In operation 835, the battery diagnostic device (101) can calculate a moving average diagnostic value for each battery cell (151, 153, 155). According to one embodiment, the battery diagnostic device (101) can calculate, for each battery cell (151, 153, 155), a normalized value of the difference between the long and short moving averages calculated in operation 830 as a moving average diagnostic value.

[0178] In operation 840, the battery diagnostic device (101) can determine whether the diagnostic time has elapsed. The diagnostic time can be pre-set.

[0179] If it is determined that the diagnostic time has not elapsed in operation 840 ('NO'), the battery diagnostic device (101) may recursively repeat operations 820 to 835.

[0180] If it is determined in operation 840 that the diagnostic time has elapsed ('YES'), in operation 845, the battery diagnostic device (101) can diagnose a voltage abnormality of each battery cell (151, 153, 155) based on the moving average diagnostic value calculated in operation 835. For example, the battery diagnostic device (101) can diagnose a voltage abnormality of each battery cell (151, 153, 155) by comparing the moving average diagnostic value with a preset threshold value. As another example, the battery diagnostic device (101) may calculate a filter diagnostic value of each battery cell (151, 153, 155) according to the method described in FIG. 7, and diagnose a voltage abnormality based on the calculated filter diagnostic value.

[0181] Hereinafter, with reference to FIG. 9, the operation of the battery diagnostic device (101) diagnosing the capacity abnormality of each battery cell (151, 153, 155) will be described.

[0182] FIG. 9 is an operation flowchart of a battery diagnostic device according to one embodiment. FIG. 9 can be described using the configurations of FIG. 1.

[0183] The embodiment illustrated in FIG. 9 is merely one example, and the order of steps according to various embodiments of the present invention may differ from that illustrated in FIG. 9, and some steps illustrated in FIG. 9 may be omitted, the order of steps may be changed, or steps may be merged.

[0184] Referring to FIG. 9, in operation 905, the battery diagnostic device (101) can calculate the SOH of each battery cell (151, 153, 155) per unit time based on the time series data obtained in operation 505 of FIG. 5. Here, SOH is a life-related parameter of the battery cell (151, 153, and / or 155), representing the SOH related to the capacity of the battery. c or SOH representing the SOH regarding the resistance growth of the battery rIt may include at least one of the following. In the following, the battery diagnostic device (101) is SOH c An example of calculating is described. However, this is only one example, and the battery diagnostic device (101) is SOH c , SOH r Various SOHs can be calculated, including the final SOH calculated based on these.

[0185] According to one embodiment, the battery diagnostic device (101) can calculate the difference in SOC before and after the charging interval of a plurality of battery cells (151, 153, 155) at each unit time based on the time series data.

[0186] For example, the battery diagnostic device (101) can calculate the SOC difference based on the SOC data included in the time series data, using the SOC value at a first time point before the charging section and the SOC value at a second time point after the charging section.

[0187] As another example, the battery diagnostic device (101) can calculate the Open Circuit Voltage (OCV) of a plurality of battery cells (151, 153, 155) based on at least one of the time series voltage data or time series current data included in the time series data, and calculate the difference in SOC after converting the calculated OCV into SOC using a SOC-OCV table stored in advance in the memory (130).

[0188] According to one embodiment, the battery diagnostic device (101) can calculate the current integration value in the charging section of a plurality of battery cells (151, 153, 155) per unit time based on the time series data. For example, the battery diagnostic device (101) can calculate the current integration value in the charging section based on the current value of the charging section included in the time series data and the time length of the charging section. Here, the unit of the current integration value may be 'Ah (Ampere hour)'.

[0189] According to another embodiment, the battery diagnostic device (101) may calculate the current integration value in the charging section if the calculated SOC difference is greater than or equal to a specified value. For example, if the SOC difference of the battery cell (151, 153, or 155) calculated in the current cycle is less than or equal to a specified value, the battery diagnostic device (101) may not calculate the current integration value of the battery cell (151, 153, or 155) in the current cycle. In this case, since the current integration value of the battery cell (151, 153, or 155) is not calculated, the battery diagnostic device (101) [calculates] the SOH of the corresponding battery cell (151, 153, or 155 c It may also not be calculated. As another example, the battery diagnostic device (101) may calculate the current integrated value of the battery cell (151, 153, or 155) in the current cycle if the difference in SOC of the battery cell (151, 153, or 155) calculated in the current cycle is greater than (or exceeds) a specified value. In this case, the battery diagnostic device (101) uses the current integrated value of the battery cell (151, 153, or 155) to calculate the SOH of the corresponding battery cell (151, 153, or 155). c It can produce.

[0190] According to one embodiment, the battery diagnostic device (101) determines the SOH of the battery cells (151, 153, and / or 155) based on the SOC difference, current integration value, and initial capacity of the battery cells (151, 153, and / or 155). c It can be calculated. Here, the unit of the initial capacity of the battery cells (151, 153, and / or 155) may be 'Ah', which is the same as the unit of the current integration value.

[0191] For example, the battery diagnostic device (101) [describes] the SOH of the battery cells (151, 153, and / or 155) based on the above mathematical formula 3. c It can produce.

[0192] In operation 910, the battery diagnostic device (101) can calculate the SOH deviation of each battery cell (151, 153, 155). For example, the SOH deviation may be the deviation of the SOH of each battery cell (151, 153, 155) from the median SOH value of the plurality of battery cells (151, 153, 155), the deviation of the SOH of each battery cell (151, 153, 155) from the average SOH value of the plurality of battery cells (151, 153, 155), or the deviation of the SOH of each battery cell (151, 153, 155) from the average value of the moving averages of the SOH of the plurality of battery cells (151, 153, 155).

[0193] According to one embodiment, the battery diagnostic device (101) can calculate the moving average of the SOH of each battery cell (151, 153, 155) at each unit time based on a third time window. Here, the time length of the third time window is an integer multiple of the unit time, the end point of the third time window is the current time, and the starting point may be a time point preceding the current time by a predetermined time length. Additionally, the moving average may refer to any one of a simple moving average, a weighted moving average, or an exponential moving average.

[0194] Hereinafter, an example is described in which a battery diagnostic device (101) calculates a SOH index moving average. However, this is merely one example, and the battery diagnostic device (101) can calculate various moving average values, such as a simple SOH moving average, a SOH weighted moving average, or a SOH index moving average.

[0195] According to one embodiment, the battery diagnostic device (101) can calculate the SOH exponential moving average at the current time by inputting the SOH of each battery cell (151, 153, 155) into the above mathematical formula 4.

[0196] According to one embodiment, the battery diagnostic device (101) can calculate, for each battery cell (151, 153, 155), the average value of multiple SOH moving averages of multiple battery cells (151, 153, 155) and the SOH deviation corresponding to the deviation of the SOH moving average of each battery cell (151, 153, 155) at each unit time.

[0197] In operation 915, the battery diagnostic device (101) can diagnose a capacity abnormality of each battery cell (151, 153, 155) based on the SOH deviation. For example, the battery diagnostic device (101) can diagnose whether there is a defect due to a decrease in capacity of each battery cell (151, 153, 155).

[0198] According to one embodiment, the battery diagnostic device (101) can diagnose an excess capacity of each battery cell (151, 153, 155) by comparing the SOH deviation with a preset threshold value. For example, the battery diagnostic device (101) can diagnose a battery cell with an excess capacity as a battery cell in which the SOH deviation exceeds (or is greater than) the threshold value.

[0199] For example, a battery diagnostic device (101) can accumulate time intervals in which the SOH deviation is greater than (or greater than or equal to) the threshold value for each battery cell (151, 153, 155), and diagnose a battery cell as having a capacity greater than that of a battery cell in which the condition is met where the accumulated time is greater than (or greater than or equal to) a preset reference time. The battery diagnostic device (101) can accumulate time intervals in which the condition that the SOH deviation is greater than (or greater than or equal to) the threshold value is continuously satisfied. If there are multiple such time intervals, the battery diagnostic device (101) can independently calculate the accumulated time for each time interval.

[0200] As another example, the battery diagnostic device (101) can accumulate the number of data included in time intervals where the SOH deviation is greater than (or greater than or equal to) the threshold value in the time series data of the SOH deviation, and diagnose a battery cell as having a capacity greater than that of a battery cell in which the condition is satisfied where the accumulated data value is greater than (or greater than or equal to) a preset reference count. The battery diagnostic device (101) can accumulate only the number of data included in time intervals where the condition that the SOH deviation is greater than (or greater than or equal to) the threshold value is continuously satisfied. If there are multiple such time intervals, the battery diagnostic device (101) can independently accumulate the number of data for each time interval.

[0201] FIG. 10 is an operation flowchart of a battery diagnostic device according to one embodiment. FIG. 10 can be described using the configurations of FIG. 1.

[0202] The embodiment illustrated in FIG. 10 is merely one example, and the order of steps according to various embodiments of the present invention may differ from that illustrated in FIG. 10, and some steps illustrated in FIG. 10 may be omitted, the order of steps may be changed, or steps may be merged.

[0203] Operation 1005 of Fig. 10 is the same as operation 505 of Fig. 5, so the explanation will be omitted.

[0204] Referring to FIG. 10, in operation 1010, the battery diagnostic device (101) can diagnose whether there is a voltage abnormality in the battery cell (151, 153, or 155). The voltage abnormality diagnosis operation of operation 1010 may refer to the operation 510 of FIG. 5 and the operations of FIG. 6 through FIG. 8 described above.

[0205] If, in operation 1010, it is diagnosed that there is a voltage abnormality in the battery cell (151, 153, or 155), in operation 1015, the battery diagnostic device (101) can diagnose whether there is a capacity abnormality in the battery cell (151, 153, or 155). The capacity abnormality diagnosis operation of operation 1015 may refer to the operations 515 of FIG. 5 and FIG. 9 described above.

[0206] If, in operation 1015, a battery cell (151, 153, or 155) is diagnosed as having an abnormal capacity, in operation 1020, the battery diagnostic device (101) can detect the battery cell (151, 153, or 155) as an abnormal battery cell.

[0207] If, in operation 1010, the battery cell (151, 153, or 155) is diagnosed as having no voltage abnormality, or in operation 1015, the battery cell (151, 153, or 155) is diagnosed as having no capacity abnormality, then in operation 1025, the battery diagnostic device (101) can detect the battery cell (151, 153, or 155) as a normal battery cell.

[0208] According to one embodiment, the battery diagnostic device (101) can perform an abnormality processing function based on the abnormality diagnosis results of a plurality of battery cells (151, 153, 155). Here, the abnormality processing function may include a notification function or a short-circuit function.

[0209] According to one embodiment, the battery diagnostic device (101) can transmit the abnormality diagnosis results of the plurality of battery cells (151, 153, 155) to a user terminal (104) connected via a wired and / or wireless network.

[0210] According to one embodiment, the battery diagnostic device (101) can isolate an abnormal battery cell from the electronic device (102) based on the abnormality diagnosis results of a plurality of battery cells (151, 153, 155). Here, the isolation may include electrical and / or mechanical isolation.

[0211] Terms such as "include," "compose," or "have" as used above, unless specifically stated otherwise, mean that the relevant component may be inherent; therefore, they should be interpreted as allowing for the inclusion of additional components rather than excluding them. All terms, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as those defined in advance, should be interpreted in accordance with their contextual meanings in the relevant technology and, unless explicitly defined in this document, should not be interpreted in an ideal or overly formal sense.

Claims

Claim 1 A battery diagnostic device comprising: a memory configured to store instructions; and a processor configured to execute said instructions, wherein the processor is configured to acquire time series data related to the state of a plurality of battery cells included in a battery module, calculate a long-term and short-term voltage moving average difference of each battery cell based on said time series data, diagnose a voltage abnormality of each battery cell based on said long-term and short-term voltage moving average difference, calculate a State of Health (SOH) of each battery cell based on said time series data, calculate a SOH deviation of each battery cell based on a plurality of SOHs of said battery cells, diagnose a capacity abnormality of each battery cell based on said SOH deviation, and detect an abnormal battery cell based on at least one of said voltage abnormality diagnosis result or said capacity abnormality diagnosis result. Claim 2 A battery diagnostic device according to claim 1, wherein the processor is configured to calculate a short-term voltage moving average of each battery cell based on a first time window, calculate a long-term voltage moving average of each battery cell based on a second time window having a longer time length than the first time window, and, for each battery cell, calculate the difference between the short-term and long-term voltage moving averages corresponding to the difference between the short-term voltage moving average and the long-term voltage moving average. Claim 3 A battery diagnostic device according to claim 1, wherein the processor calculates, for each battery cell, a voltage diagnostic deviation corresponding to the average value of the long-term and short-term voltage moving average differences of the plurality of battery cells and the deviation of the long-term and short-term voltage moving average difference of each battery cell, and is configured to diagnose a voltage abnormality of each battery cell based on the voltage diagnostic deviation. Claim 4 A battery diagnostic device according to claim 3, wherein the processor determines a statistical variable threshold value based on a standard deviation of voltage diagnostic deviations of the plurality of battery cells, for each battery cell, filters the voltage diagnostic deviation based on the statistical variable threshold value to calculate a filter diagnostic value, and is configured to diagnose a voltage abnormality of each battery cell based on the filter diagnostic value. Claim 5 A battery diagnostic device according to claim 1, wherein the processor calculates, for each battery cell, a normalized value of the difference between the long and short-term voltage moving averages as a normalized voltage diagnostic deviation, and is configured to diagnose a voltage abnormality of each battery cell based on the normalized voltage diagnostic deviation. Claim 6 A battery diagnostic device according to claim 5, wherein the processor determines a statistical variable threshold value based on a standard deviation of normalized voltage diagnostic deviations of the plurality of battery cells, calculates a filter diagnostic value for each battery cell by filtering the normalized voltage diagnostic deviation based on the statistical variable threshold value, and diagnoses a voltage abnormality of each battery cell based on the filter diagnostic value. Claim 7 A battery diagnostic device according to claim 5, wherein the processor calculates a moving average diagnostic value by recursively repeating the following (i) to (iii) at least once for each battery cell, (i) calculates a first moving average corresponding to a short-term moving average of the normalized voltage diagnostic deviation of each battery cell and a second moving average corresponding to a long-term moving average, (ii) calculates a difference between the long-term and short-term moving averages corresponding to the difference between the first moving average and the second moving average for each battery cell, (iii) calculates a normalized value of the difference between the long-term and short-term moving averages for each battery cell as a moving average diagnostic value, and is configured to diagnose a voltage abnormality of each battery cell based on the moving average diagnostic value. Claim 8 A battery diagnostic device according to claim 1, wherein the processor is configured to calculate, for each battery cell, the SOH deviation corresponding to the deviation of the SOH of each battery cell from the median SOH value or average SOH value of the plurality of battery cells. Claim 9 A battery diagnostic device according to claim 1, wherein the processor is configured to calculate the SOH of each battery cell based on the time series data, calculate the moving average of the SOH of each battery cell based on a third time window, and, for each battery cell, calculate the SOH deviation corresponding to the average value of the moving averages of the SOH of the plurality of battery cells and the deviation of the moving average of the SOH of each battery cell. Claim 10 A battery diagnostic device according to claim 1, wherein the processor is configured to calculate the difference in State of Charge (SOC) before and after a charging interval of each battery cell based on the time series data, calculate the current integration value in the charging interval of each battery cell based on the time series data, calculate SOHc representing the SOH related to the capacity of each battery cell based on the SOC difference, the current integration value, and the initial capacity of each battery cell for each battery cell, and calculate the SOH deviation of each battery cell based on the SOHc. Claim 11 A battery diagnostic device according to claim 1, wherein the processor is configured to diagnose the capacity abnormality only for at least one battery cell diagnosed as having a voltage abnormality according to the diagnosis result of the voltage abnormality. Claim 12 A battery diagnostic device according to claim 11, wherein the processor is configured to detect a battery cell diagnosed as having an abnormal capacity according to a diagnosis result of having an abnormal capacity as the abnormal battery cell. Claim 13 A battery diagnostic device according to claim 1, wherein the processor detects a battery cell as an abnormal battery cell, which is diagnosed as having an abnormal voltage according to a diagnosis result of the voltage being higher than the voltage and diagnosed as having an abnormal capacity according to a diagnosis result of the capacity being higher than the capacity. Claim 14 A battery diagnostic method performed by a processor executing instructions stored in memory, comprising: a step of acquiring time series data related to the state of a plurality of battery cells included in a battery module; a step of calculating a long-term and short-term moving average voltage difference of each battery cell based on the time series data and diagnosing a voltage abnormality of each battery cell based on the long-term and short-term moving average voltage difference; a step of calculating a State of Health (SOH) of each battery cell based on the time series data, calculating a deviation of the SOH of each battery cell based on the plurality of SOHs of the plurality of battery cells, and diagnosing a capacity abnormality of each battery cell based on the deviation of the SOH; and a step of detecting an abnormal battery cell based on at least one of the diagnosis result of the voltage abnormality or the diagnosis result of the capacity abnormality. Claim 15 A battery diagnosis method according to claim 14, wherein the step of diagnosing a capacity abnormality of each battery cell comprises diagnosing a capacity abnormality only for at least one battery cell diagnosed as having a voltage abnormality according to the diagnosis result of the voltage abnormality, and the step of detecting the abnormal battery cell comprises detecting the battery cell diagnosed as having a capacity abnormality according to the diagnosis result of the capacity abnormality as the abnormal battery cell. Claim 16 A battery diagnostic device comprising: a memory configured to store instructions; and a processor configured to execute said instructions, wherein the processor is configured to acquire data related to the state of a plurality of battery cells included in a battery module, acquire information regarding the State of Health (SOH) of the plurality of battery cells based on said data, calculate an average value or a median value of the SOH of the plurality of battery cells based on the SOH of each battery cell included in the information regarding the SOH, and identify a battery cell in an abnormal state among the plurality of battery cells by comparing the SOH of each battery cell with said average value or median value of the SOH. Claim 17 A battery diagnostic device according to claim 16, wherein the processor is configured to obtain a plurality of SOH deviation values ​​of the plurality of battery cells based on the difference between the average SOH value or the median SOH value of the plurality of battery cells and the SOH of each battery cell, and to identify the battery cell in an abnormal state based on the plurality of SOH deviation values. Claim 18 A battery diagnostic device according to claim 16, wherein the processor is configured to apply a moving average filter to the SOH of each battery cell to obtain a plurality of moving average values ​​corresponding to a plurality of SOHs of the plurality of battery cells, obtain a plurality of moving average deviation values ​​based on the average value of the plurality of moving average values ​​and the difference between each moving average value, and identify the battery cell in an abnormal state based on the plurality of moving average deviation values. Claim 19 A battery diagnostic method performed by a processor executing instructions stored in memory, comprising: a step of obtaining data related to the state of a plurality of battery cells included in a battery module; a step of obtaining information regarding the state of health of the plurality of battery cells based on the data; a step of calculating an average value or a median value of the state of health of the plurality of battery cells based on the state of health of each battery cell included in the information regarding the state of health of the plurality of battery cells; and a step of identifying a battery cell in an abnormal state among the plurality of battery cells by comparing the state of health of each battery cell with the average value or the median value of the state of health of the plurality of battery cells. Claim 20 A battery diagnostic method according to claim 19, wherein the step of identifying the battery cell in an abnormal state comprises: a step of obtaining a plurality of SOH deviation values ​​of the plurality of battery cells based on the difference between the average SOH value or the median SOH value of the plurality of battery cells and the SOH of each battery cell; and a step of identifying the battery cell in an abnormal state based on the plurality of SOH deviation values. Claim 21 A battery diagnostic method according to claim 19, wherein the step of identifying a battery cell in an abnormal state comprises: a step of obtaining a plurality of moving average values ​​corresponding to a plurality of SOHs of a plurality of battery cells by applying a moving average filter to the SOH of each battery cell; a step of obtaining a plurality of moving average deviation values ​​based on the average value of the plurality of moving average values ​​and the difference between each moving average value; and a step of identifying a battery cell in an abnormal state based on the plurality of moving average deviation values.

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