Battery diagnosis device and method for operating same

Abstract

A battery diagnosis device according to one embodiment disclosed herein may comprise: an acquisition unit for acquiring time-series data related to the states of a plurality of battery cells included in a battery module; a voltage diagnosis unit for calculating a long / short-term voltage moving average difference of each of the battery cells on the basis of the time-series data and diagnosing a voltage abnormality of each of the battery cells on the basis of the long / short-term voltage moving average difference; a capacity diagnosis unit for calculating a state of health (SOH) deviation of each of the battery cells on the basis of the time-series data, and diagnosing a capacity abnormality of each of the battery cells on the basis of the SOH deviation; and a detection unit for detecting an abnormal battery cell on the basis of a diagnosis result from at least one of the voltage diagnosis unit or the capacity diagnosis unit.

Description

Battery diagnostic device and its operating method

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority to Korean Patent Application No. 10-2023-0184929, filed December 18, 2023, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a battery diagnostic device and an operating method thereof.

[0005] Recently, research and development on secondary batteries has been actively conducted. The term "secondary battery" refers to a rechargeable battery, encompassing both conventional Ni / Cd and Ni / MH batteries, as well as more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries boast a significantly higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form, making them ideal power sources for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, drawing attention as a next-generation energy storage medium.

[0006] Additionally, secondary batteries can be utilized as battery packs, which typically include battery modules in which multiple battery cells are connected in series and / or parallel. Furthermore, secondary batteries can be utilized as battery racks, which include multiple battery modules and a rack frame that accommodates these battery modules.

[0007] Battery cells, battery modules, battery packs, or battery racks like these can be utilized in a variety of devices. For example, batteries can be used in mobile devices such as cell phones, laptops, smartphones, and tablets, as well as in electric vehicles (EVs, HEVs, PHEVs) and large-capacity energy storage systems (ESS).

[0008] These batteries can have their status and operation managed and controlled by a battery management system (BMS). The BMS can be included with the batteries in a single device.

[0009] Additionally, the battery management system can manage and control the battery while being 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 vehicles and other devices, and utilize the collected data to manage and control the battery.

[0010] Meanwhile, if a battery is defective, the risk of damage to devices containing the battery (e.g., EVs, ESS) may increase. Therefore, a method is needed to detect abnormal battery conditions and reduce the risk of damage to devices containing the battery.

[0011] Conventional methods for diagnosing battery defects have attempted to detect instantaneous changes in battery cell voltage to diagnose voltage abnormalities. For example, a method has been used to diagnose battery cells exhibiting instantaneous voltage changes as abnormal based on a moving average of cell voltage.

[0012] However, momentary voltage changes in a battery cell can be caused by separation or contact between the cell's negative or positive tabs, but can also be caused by voltage measurement noise. In other words, voltage abnormality diagnosis alone can lead to false positives due to noise.

[0013] In relation to this, in the case of a failure due to separation or contact of the negative tab or positive tab of the battery cell, a momentary capacity decrease or capacity deviation may occur, and in the case of capacity abnormality diagnosis, the influence of voltage measurement noise may be lower than in the case of voltage abnormality diagnosis.

[0014] The embodiments disclosed in this document are intended to supplement the problems of the voltage abnormality diagnosis method, and can provide a battery diagnosis device and an operating method thereof capable of detecting an abnormal battery cell by performing a capacity abnormality diagnosis in addition to a voltage abnormality diagnosis for a battery cell in order to reduce the false detection rate due to voltage measurement noise.

[0015] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0016] A battery diagnosis device according to an embodiment disclosed in the present document may include an acquisition unit that acquires time series data related to the states of a plurality of battery cells included in a battery module, a voltage diagnosis unit that calculates a short-term and long-term voltage moving average difference of each battery cell based on the time series data and diagnoses a voltage abnormality of each battery cell based on the short-term and long-term voltage moving average difference, a capacity diagnosis unit that calculates a State of Health (SOH) deviation of each battery cell based on the time series data and diagnoses a capacity abnormality of each battery cell based on the SOH deviation, and a detection unit that detects an abnormal battery cell based on a diagnosis result of at least one of the voltage diagnosis unit or the capacity diagnosis unit.

[0017] In a battery diagnosis device according to an embodiment disclosed in this document, the voltage diagnosis unit may 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 calculate, for each battery cell, a difference between the short-term and long-term voltage moving averages corresponding to a difference between the short-term voltage moving average and the long-term voltage moving average.

[0018] In a battery diagnosis device according to an embodiment disclosed in this document, the voltage diagnosis unit calculates, for each battery cell, a voltage diagnosis deviation corresponding to a deviation between an average value of short-term and long-term voltage moving average differences of the plurality of battery cells and the short-term and long-term voltage moving average differences of each battery cell, and can diagnose a voltage abnormality of each battery cell based on the voltage diagnosis deviation.

[0019] In a battery diagnosis device according to an embodiment disclosed in this document, the voltage diagnosis unit determines a statistical variable threshold value that depends on a standard deviation of voltage diagnosis deviations of the plurality of battery cells, and for each battery cell, filters the voltage diagnosis deviation based on the statistical variable threshold value to produce a filter diagnosis value, and can diagnose a voltage abnormality of each battery cell based on the filter diagnosis value.

[0020] In a battery diagnostic device according to an embodiment disclosed in this document, the voltage diagnostic unit can calculate a normalized value of a difference between short-term and long-term voltage moving averages for each battery cell as a normalized voltage diagnostic deviation, and diagnose a voltage abnormality of each battery cell based on the normalized voltage diagnostic deviation.

[0021] In a battery diagnosis device according to an embodiment disclosed in this document, the voltage diagnosis unit determines a statistical variable threshold value that depends on a standard deviation of normalized voltage diagnosis deviations of the plurality of battery cells, and for each battery cell, filters the normalized voltage diagnosis deviation based on the statistical variable threshold value to produce a filter diagnosis value, and can diagnose a voltage abnormality of each battery cell based on the filter diagnosis value.

[0022] In a battery diagnosis device according to an embodiment disclosed in the present document, the voltage diagnosis unit calculates a moving average diagnosis value by recursively repeating the following (i) to (iii) at least once for each battery cell, (i) calculating a first moving average corresponding to a short-term moving average of a normalized voltage diagnosis deviation of each battery cell and a second moving average corresponding to a long-term moving average, (ii) calculating a short-term and long-term moving average difference corresponding to a difference between the first moving average and the second moving average for each battery cell, (iii) calculating a normalized value of the short-term and long-term moving average difference as a moving average diagnosis value for each battery cell, and diagnosing a voltage abnormality of each battery cell based on the moving average diagnosis value.

[0023] In a battery diagnosis device according to an embodiment disclosed in this document, the capacity diagnosis unit can calculate the SOH of each battery cell based on the time series data, and, for each battery cell, calculate the SOH deviation corresponding to the deviation of the SOH of each battery cell from the SOH center value or SOH average value of the plurality of battery cells.

[0024] In a battery diagnosis device according to an embodiment disclosed in this document, the capacity diagnosis unit may calculate the SOH of each battery cell based on the time series data, calculate the SOH moving average 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 SOH moving average of each battery cell from the average value of the SOH moving averages of the plurality of battery cells.

[0025] In a battery diagnosis device according to an embodiment disclosed in this document, the capacity diagnosis unit may calculate a difference in SOC (State of Charge) before and after a charging section of each battery cell based on the time series data, calculate a current integration value in the charging section of each battery cell based on the time series data, calculate SOHc representing 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, and calculate the SOH deviation of each battery cell based on the SOHc.

[0026] 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.

[0027] In a battery diagnosis device according to an embodiment disclosed in this document, the detection unit can detect a battery cell diagnosed as having an abnormal capacity by the capacity diagnosis unit as the abnormal battery cell.

[0028] In a battery diagnosis device according to an embodiment disclosed in this document, the detection unit can detect a battery cell diagnosed as having a voltage abnormality by the voltage diagnosis unit and a capacity abnormality by the capacity diagnosis unit as the abnormal battery cell.

[0029] A battery diagnosis method according to an embodiment disclosed in the present document may include an operation of acquiring time series data related to states of a plurality of battery cells included in a battery module, an operation of calculating a short-term and long-term voltage moving average difference of each battery cell based on the time series data, and an operation of diagnosing a voltage abnormality of each battery cell based on the short-term and long-term voltage moving average difference, an operation of calculating a State of Health (SOH) deviation of each battery cell based on the time series data, and an operation of diagnosing a capacity abnormality of each battery cell based on the SOH deviation, and an operation of detecting an abnormal battery cell based on a diagnosis result of at least one of the voltage diagnosis unit or the capacity diagnosis unit.

[0030] In a battery diagnosis method according to an embodiment disclosed in this document, the operation of diagnosing a capacity abnormality of each battery cell may include an 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 an operation of detecting a battery cell diagnosed as having a capacity abnormality as the abnormal battery cell.

[0031] According to the embodiments disclosed in this document, battery diagnosis accuracy can be improved by reducing false detection due to voltage measurement noise during an abnormal battery cell detection process.

[0032] In addition, various effects may be provided, either directly or indirectly, through this document.

[0033] FIG. 1 is a block diagram of a battery diagnostic device according to one embodiment.

[0034] FIG. 2 is a graph showing voltage time series data of a plurality of battery cells acquired by a battery diagnostic device according to one embodiment.

[0035] FIGS. 3A to 3G are graphs for explaining a process in which a battery diagnostic device according to one embodiment diagnoses a voltage abnormality of each battery cell from the voltage time series data of FIG. 2.

[0036] FIGS. 4A and 4B are graphs for explaining a process in which a battery diagnostic device according to one embodiment diagnoses an abnormality in the capacity of each battery cell.

[0037] Figure 5 is a flowchart of the operation of a battery diagnostic device according to one embodiment.

[0038] Figure 6 is a flowchart illustrating the operation of a battery diagnostic device according to one embodiment.

[0039] Figure 7 is a flowchart of the operation of a battery diagnostic device according to one embodiment.

[0040] Figure 8 is a flowchart of the operation of a battery diagnostic device according to one embodiment.

[0041] Figure 9 is a flowchart illustrating the operation of a battery diagnostic device according to one embodiment.

[0042] Fig. 10 is a flowchart of the operation of a battery diagnostic device according to one embodiment.

[0043] Hereinafter, various embodiments of the present invention will be described with reference to the attached drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that the present invention encompasses various modifications, equivalents, and / or alternatives of the embodiments.

[0044] The various embodiments and terminology used in this document are not intended to limit the technical features described in this document to specific embodiments, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiments. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise.

[0045] In this document, the phrases "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" can each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding element from other corresponding elements, and do not limit the corresponding elements in any other respect (e.g., importance or order) unless specifically stated otherwise.

[0046] In this document, whenever a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or via a third component.

[0047] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component 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.

[0048] FIG. 1 is a block diagram of a battery diagnostic device according to one embodiment.

[0049] The battery diagnostic device (101) described below can be implemented as a BMS (Battery Management System) in an electronic device (102), but can also be implemented as various external devices such as a server, cloud, charger, or charger / discharger.

[0050] Referring to FIG. 1, a battery diagnostic device (101) can be connected to an electronic device (102) and a user terminal (104) via wires and / or wirelessly.

[0051] 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 a power line communication. In one embodiment, the wireless network may be based on a short-range communication network (e.g., Bluetooth, wireless fidelity (WiFi), or infrared data association (IrDA)) or a wide-range communication network (cellular network, 4G network, 5G network).

[0052] 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., a bus, a general purpose input and output (GPIO), a serial peripheral interface (SPI), or a mobile industry processor interface (MIPI)).

[0053] According to one embodiment, the electronic device (102) may be a mobile device (e.g., a mobile phone, a laptop computer, a smart phone, a smart pad), an electric vehicle (e.g., an electric vehicle (EV), a hybrid EV (HEV), a plug-in HEV (PHEV), a fuel cell EV (FCEV)), an energy storage system (ESS), or a battery swapping system (BSS).

[0054] According to one embodiment, the electronic device (102) may include a plurality of battery cells (151, 153, 155). Here, each of the battery cells (151, 153, 155) may be a single battery cell, or 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.

[0055] According to one embodiment, the connection (105) between the battery diagnostic device (101) and the user terminal (104) may be a communication connection via a wired and / or wireless network.

[0056] According to one embodiment, the user terminal (104) may be a mobile device (e.g., a mobile phone, a laptop computer, a smart phone, a smart pad), or a personal computer (PC). 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).

[0057] 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). Depending on the embodiment, the battery diagnostic device (101) illustrated in FIG. 1 may further include at least one component (e.g., a display, an input device, or an output device) other than the components illustrated in FIG. 1, or may omit at least one component (e.g., a sensor (120)) among the components illustrated in FIG. 1. For example, when 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 the sensor (120).

[0058] According to one embodiment, the communication circuit (110) can establish 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 transmit and receive data with the electronic device (102) and / or the user terminal (104) through the established communication channel.

[0059] According to one embodiment, the sensor (120) can measure information (e.g., voltage, current, temperature, etc.) related to the status of a plurality of battery cells (151, 153, 155) of the electronic device (102). For example, when the battery diagnosis device (101) is implemented as a BMS within the electronic device, the battery diagnosis device (101) can directly measure the status values ​​of the plurality of battery cells (151, 153, 155) using the sensor (120).

[0060] According to one embodiment, the communication circuit (110) and / or the sensor (120) may obtain time series data related to the states of the plurality of battery cells (151, 153, 155). In one embodiment, the time series data related to the states of the plurality of battery cells (151, 153, 155) may be data representing 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.

[0061] According to one embodiment, the memory (130) may include volatile memory and / or non-volatile memory.

[0062] According to one embodiment, the memory (130) may store data used by at least one component (e.g., processor (140)) of the battery diagnosis device (101). For example, the data may include software (or instructions related thereto), input data, or output data. In one embodiment, the instructions, when executed by the processor (140), may cause the battery abnormality diagnosis device (101) to perform operations defined by the instructions.

[0063] According to one embodiment, the memory (140) may include one or more software (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)).

[0064] 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.

[0065] According to one embodiment, the processor (140) may execute software (e.g., acquisition unit (131), voltage diagnosis unit (132), capacity diagnosis unit (133), detection unit (134), and abnormality processing unit (135)) stored in the memory (130) 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 perform various data processing or operations.

[0066] Hereinafter, a method for diagnosing an abnormality of a plurality of battery cells (151, 153, 155) through an acquisition unit (131), a voltage diagnosis unit (132), a capacity diagnosis unit (133), a detection unit (134), and an abnormality processing unit (135) of a battery diagnosis device (101) will be described with reference to FIGS. 2, 3A to 3G, 4A, and 4B. More specifically, a voltage abnormality diagnosis process of the battery diagnosis device (101) will be described with reference to FIGS. 3A to 3G, and a capacity abnormality diagnosis process of the battery diagnosis device (101) will be described with reference to FIGS. 4A and 4B.

[0067] FIG. 2 is a graph showing voltage time series data of a plurality of battery cells acquired by a battery diagnosis device according to an embodiment. FIGS. 3A to 3G are graphs for explaining a process in which a battery diagnosis device according to an embodiment diagnoses a voltage abnormality of each battery cell from the voltage time series data of FIG. 2. FIGS. 4A and 4B are graphs for explaining a process in which a battery diagnosis device according to an embodiment diagnoses a capacity abnormality of each battery cell.

[0068] According to one embodiment, the acquisition unit (131) can acquire time series data related to the states 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).

[0069] According to one embodiment, the acquisition unit (131) may collect signals related to the status of a plurality of battery cells (151, 153, 155) using the communication circuit (110) and / or the sensor (120) for each unit time and record the status values ​​(e.g., voltage values, current values, temperature values, resistance values, 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 status signal reception cycle of the communication circuit (110) or the status measurement cycle of the sensor (120).

[0070] According to one embodiment, the acquisition unit (131) may generate time series data representing changes in the state values ​​of the plurality of battery cells (151, 153, 155) over time based on the state values ​​of the plurality of battery cells (151, 153, 155) recorded in the memory (130). At this time, the number of time series data may increase by 1 each time the state values ​​of the plurality of battery cells (151, 153, 155) are acquired.

[0071] Referring to FIG. 2, a graph (200) can be seen that exemplarily shows voltage time series data representing voltage changes 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 can represent time, and the vertical axis can represent voltage.

[0072] Voltage abnormality diagnosis

[0073] According to one embodiment, the voltage diagnostic unit (132) can calculate the short-term and long-term voltage moving average difference of each battery cell (151, 153, 155) based on the time series data acquired by the acquisition unit (131).

[0074] According to one embodiment, the voltage diagnostic unit (132) may use one or two time windows to calculate the voltage moving average of each of the plurality of battery cells (151, 153, 155) per unit time. 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.

[0075] 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 time point, and the start point of each time window can be a time point that is a given time length in advance from the current time point.

[0076] Hereinafter, for convenience of explanation, the time window having a shorter time length among the two time windows is referred to as the first time window, and the time window having a longer time length is referred to as the second time window.

[0077] According to one embodiment, the voltage diagnostic unit (132) can calculate a short-term voltage moving average of each battery cell (151, 153, 155) per unit time based on a first time window. In addition, the voltage diagnostic unit (132) can calculate a 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 mean any one of a simple moving average (SMA), a weighted moving average (WMA), or an exponential moving average (EMA).

[0078] For example, the input factors when 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 a short-term and long-term moving average of the voltage values ​​of each battery cell (151, 153, 155).

[0079] As another example, the input factor when calculating the voltage moving average may be the deviation between the reference voltage of the plurality of 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 plurality of battery cells (151, 153, 155), and may be determined as an average value or a central value of the voltage values ​​of the plurality of battery cells (151, 153, 155). In this case, the calculated voltage moving average may represent a short-term and long-term moving average of the voltage deviations of each battery cell (151, 153, 155).

[0080] 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 for 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 for each unit time.

[0081] Referring to FIG. 3a, a graph (310) can be seen, which exemplarily shows a short-term voltage moving average line and a 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 can represent time, and the vertical axis can represent the voltage moving average value.

[0082] In the graph (310), two moving average lines of the same shape but with different thicknesses are each a short-term voltage moving average line and a long-term voltage moving average line of a specific battery cell, and can represent the history of changes in the short-term voltage moving average and the long-term voltage moving average of the battery cell over time. In this case, the time length of the first time window used in 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.

[0083] According to one embodiment, the voltage diagnostic unit (132) can calculate the short-term and long-term voltage moving average differences corresponding to the difference between the short-term voltage moving average and the long-term voltage moving average for each battery cell (151, 153, 155) per unit time. Here, the short-term and long-term voltage moving average differences can be a value obtained by subtracting the smaller one from the larger one among the short-term voltage moving average and the long-term voltage moving average.

[0084] Referring to FIG. 3b, a graph (320) can be seen that exemplarily shows the short-term and long-term voltage moving average differences 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 can represent time, and the vertical axis can represent the short-term and long-term voltage moving average difference values.

[0085] The difference between the short-term and long-term moving average voltages of a battery cell depends on the short-term and long-term history of changes in cell voltage.

[0086] Battery cell temperature and State of Health (SOH) consistently affect cell voltage, both in the short term and over the long term. Therefore, the short-term and long-term moving average voltage differences of battery cells without voltage abnormalities are not significantly different from those of the remaining battery cells.

[0087] Conversely, sudden voltage abnormalities in battery cells, such as those caused by internal and / or external short circuits, have a greater impact on the short-term moving average of the voltage than on the long-term moving average. Consequently, the difference between the short-term and long-term moving averages of a battery cell is significantly different from the differences between the short-term and long-term moving averages of the remaining battery cells without voltage abnormalities.

[0088] Therefore, the difference between the short-term and long-term moving average voltages of battery cells can be an indicator necessary for diagnosing voltage abnormalities.

[0089] According to one embodiment, the voltage diagnostic 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).

[0090] According to one embodiment, the voltage diagnostic unit (132) can calculate the voltage diagnostic deviation of each battery cell (151, 153, 155) per unit time based on the difference between the short-term and long-term voltage moving averages.

[0091] According to one embodiment, the voltage diagnosis unit (132) may calculate, for each battery cell (151, 153, 155), a voltage diagnosis deviation corresponding to a deviation between an average value of short-term and long-term voltage moving average differences of a plurality of battery cells (151, 153, 155) and the short-term and long-term voltage moving average differences of each battery cell (151, 153, 155). In this case, the voltage diagnosis unit (132) may diagnose a voltage abnormality of each battery cell (151, 153, 155) based on the calculated voltage diagnosis deviation. For example, the voltage diagnosis unit (132) may diagnose that a battery cell in which the calculated voltage diagnosis deviation exceeds a preset threshold value (e.g., 0.015) has a voltage abnormality.

[0092] According to one embodiment, the voltage diagnostic unit (132) can calculate a normalized value of the difference between the short-term and long-term voltage moving averages per unit time for each battery cell (151, 153, 155) as a normalized voltage diagnostic deviation.

[0093] For example, the voltage diagnostic unit (132) can normalize the short-term and long-term voltage moving average differences of each battery cell (151, 153, 155) by using the average value of the short-term and long-term voltage moving average differences of the plurality of battery cells (151, 153, 155). For example, the voltage diagnostic unit (132) can normalize the short-term and long-term voltage moving average differences of each battery cell (151, 153, 155) by dividing the short-term and long-term voltage moving average differences by the average value (e.g., DSL / Dav, where DSL is the short-term and long-term voltage moving average difference and Dav is the average value).

[0094] As another example, the voltage diagnostic unit (132) may also normalize the short-term and long-term voltage moving average differences of each battery cell (151, 153, 155) through a logarithmic operation (e.g., Log(DSL), where DSL is the short-term and long-term voltage moving average difference).

[0095] Referring to FIG. 3c, a graph (330) can be seen that exemplarily shows voltage diagnosis deviations of a plurality of battery cells (e.g., 151, 153, 155) calculated by the voltage diagnosis unit (132) based on the short-term and long-term voltage moving average differences of FIG. 3b. In the graph (330), the X-axis represents time, and the vertical axis represents a voltage diagnosis deviation normalized by dividing the short-term and long-term voltage moving average differences of each battery cell (151, 153, 155) by the above average value.

[0096] According to graph (330), the change in the short-term and long-term moving average differences in voltage is amplified based on the average value of each battery cell by normalizing the short-term and long-term moving average differences. This allows for more accurate diagnosis of battery cell voltage abnormalities.

[0097] According to one embodiment, the voltage diagnostic unit (132) can diagnose a voltage abnormality of each battery cell (151, 153, 155) by comparing the calculated voltage diagnostic deviation (or normalized voltage diagnostic deviation) with a statistical variable threshold value.

[0098] According to one embodiment, the voltage diagnostic unit (132) may 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 the plurality of battery cells (151, 153, 155). For example, the voltage diagnostic unit (132) may determine the statistical variable threshold value based on the following Equation 1.

[0099] [Formula 1]

[0100] D threshold =β*Sig(D diag )

[0101] In the above formula 1, D threshold is the statistical variable threshold value, β is a predetermined constant, and Sig is a function that calculates the standard deviation for voltage diagnosis deviations of a plurality of battery cells (151, 153, 155). β is a factor that determines the diagnosis sensitivity and can be experimentally determined. β can be appropriately determined by trial and error so that when the embodiments disclosed in the present specification are performed on a cell group including a battery cell having 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 or more, at least 6 or more, at least 7 or more, at least 8 or more, or at least 9 or more.

[0102] Meanwhile, battery cells with abnormal voltage may have a relatively larger voltage diagnosis deviation than normal battery cells. Therefore, to improve the accuracy and reliability of diagnosis, Sig(D) is calculated per unit time. diag ) can be excluded from the maximum value of the voltage diagnosis deviation.

[0103] According to one embodiment, the voltage diagnostic unit (132) may filter the voltage diagnostic deviation (or normalized voltage diagnostic deviation) for each battery cell (151, 153, 155) based on a statistical variable threshold value to calculate a filter diagnostic value. For example, the voltage diagnostic unit (132) may calculate one of two values ​​as the filter diagnostic value based on the following equation 2.

[0104] [Formula 2]

[0105] D filter =D diag -D threshold (IF D diag >D threshold )

[0106] D filter =0 (IF D diag ≤D threshold )

[0107] 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 as the filter diagnosis value if the voltage diagnosis deviation (or normalized voltage diagnosis deviation) is greater than the statistical variable threshold. 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.

[0108] Referring to FIG. 3d, a graph (340) can be seen that exemplarily shows filter diagnosis values ​​of a plurality of battery cells (e.g., 151, 153, 155) calculated by the voltage diagnosis unit (132) based on the voltage diagnosis deviation and statistical variable threshold value of FIG. 3c. In the graph (330), the X-axis can represent time, and the vertical axis can represent the filter diagnosis value.

[0109] According to one embodiment, the voltage diagnostic unit (132) can diagnose voltage abnormalities of each battery cell (151, 153, 155) based on the calculated filter diagnostic values.

[0110] For example, the voltage diagnostic unit (132) can integrate a time section in which a filter diagnostic value is greater than (or greater than or equal to) a diagnostic threshold value (e.g., 0) for each battery cell (151, 153, 155), and diagnose a battery cell in which a condition in which the integrated time is greater than (or greater than or equal to) a preset reference time is satisfied as a battery cell with a voltage abnormality. The voltage diagnostic unit (132) can integrate a time section in which a condition in which the filter diagnostic value is greater than (or greater than or equal to) the diagnostic threshold value is continuously satisfied. If there are multiple corresponding time sections, the voltage diagnostic unit (132) can independently calculate an integrated time for each time section.

[0111] As another example, the voltage diagnostic unit (132) may accumulate the number of data included in a time section in which the filter diagnostic value is greater than (or greater than or equal to) a diagnostic threshold value (e.g., 0) in the time series data of the filter diagnostic value, and diagnose a battery cell in which a condition in which the data accumulation value is greater than (or greater than or equal to) a preset reference count is satisfied as a battery cell with a voltage abnormality. The voltage diagnostic unit (132) may accumulate only the number of data included in a time section in which a condition in which the filter diagnostic value is greater than (or greater than or equal to) the diagnostic threshold value is continuously satisfied. If there are multiple corresponding time sections, the voltage diagnostic unit (132) may independently accumulate the number of data in each time section.

[0112] According to one embodiment, the voltage diagnosis unit (132) can calculate a moving average diagnosis value of each battery cell (151, 153, 155) based on the voltage diagnosis deviation (or normalized voltage diagnosis deviation). The voltage diagnosis unit (132) can recursively execute the process of (i) calculating a first moving average corresponding to a short-term moving average of the voltage diagnosis deviation (or normalized voltage diagnosis deviation) of each battery cell (151, 153, 155) and a second moving average corresponding to a long-term moving average, (ii) calculating, for each battery cell (151, 153, 155), a short-term and long-term moving average difference corresponding to a difference between the first moving average and the second moving average, and (iii) calculating, for each battery cell (151, 153, 155), a normalized value of the short-term and long-term moving average difference as a moving average diagnosis value. 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 can be based on the second time window when calculating the second moving average.

[0113] The voltage diagnostic unit (132) can use the calculated moving average diagnostic value to calculate the filter diagnostic value of each battery cell (151, 153, 155) according to the method described above, and can diagnose a voltage abnormality based on the calculated filter diagnostic value.

[0114] Referring to FIG. 3e, a graph (350) can be seen exemplarily showing the short-term and long-term moving average differences of a plurality of battery cells (151, 153, 155) calculated by the voltage diagnostic unit (132) through the above processes (i) and (ii). In the graph (350), the horizontal axis can represent time, and the vertical axis can represent the moving average difference.

[0115] Referring to FIG. 3f, a graph (360) can be seen that exemplarily shows the moving average diagnostic values ​​of a plurality of battery cells (151, 153, 155) calculated by the voltage diagnostic unit (132) through the above process (iii). In the graph (360), the horizontal axis can represent time, and the vertical axis can represent the moving average diagnostic values.

[0116] Referring to FIG. 3g, a graph (370) can be seen that exemplarily shows filter diagnosis values ​​of a plurality of battery cells (151, 153, 155) calculated by the voltage diagnosis unit (132) from the moving average diagnosis value of FIG. 3f. In the graph (370), the horizontal axis can represent time, and the vertical axis can represent the filter diagnosis value.

[0117] According to one embodiment, the voltage diagnostic unit (132) can recursively repeat the above processes (i) to (iii) at least once. That is, the voltage diagnostic unit (132) can replace the voltage time series data of FIG. 2 with the moving average diagnostic value of FIG. 3f.

[0118] If the above recursive operation process is repeated, the voltage abnormality diagnosis of the battery cell can be performed more precisely. Referring to Fig. 3d, a positive profile pattern is confirmed in only two time intervals in the time series data of the filter diagnosis value of the battery cell with a voltage abnormality. However, referring to Fig. 3g, a positive profile pattern is confirmed in more time intervals in the time series data of the filter diagnosis value of the battery cell with a voltage abnormality than in Fig. 3d. Therefore, if the recursive operation process is repeated, the time point at which the voltage abnormality of the battery cell occurs can be detected more accurately.

[0119] Diagnosis of overcapacity

[0120] 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 the time series data acquired by the acquisition unit (131). Here, the SOH is a life-related parameter of the battery cells (151, 153, and / or 155), and can include at least one of SOHc indicating SOH related to the capacity of the battery or SOHr indicating SOH related to the resistance growth of the battery. Hereinafter, an embodiment in which the capacity diagnosis unit (133) calculates SOHc will be described. However, this is only one embodiment, and the capacity diagnosis unit (133) can calculate various SOHs, such as SOHc, SOHr, and a final SOH calculated based on these.

[0121] According to one embodiment, the capacity diagnosis unit (133) can calculate the SOC difference before and after the charging period of a plurality of battery cells (151, 153, 155) per unit time based on the time series data.

[0122] For example, the capacity diagnosis unit (133) can calculate the SOC difference using the SOC value at the first point in time before the charging section and the SOC value at the second point in time after the charging section based on the SOC data included in the time series data.

[0123] As another example, the capacity diagnosis unit (133) may calculate the OCV (Open Circuit Voltage) of a plurality of battery cells (151, 153, 155) based on at least one of the time series voltage data or the time series current data included in the time series data, and may convert the calculated OCV into SOC using the SOC-OCV table stored in advance in the memory (130) and then calculate the SOC difference.

[0124] According to one embodiment, the capacity diagnosis unit (133) may calculate the current integration value in the charging section of the plurality of battery cells (151, 153, 155) per unit time based on the time series data. For example, the capacity diagnosis unit (133) may calculate the current integration value in the charging section based on the current value of the charging section and the time length of the charging section included in the time series data. Here, the unit of the current integration value may be 'Ah (Ampere hour)'.

[0125] According to another embodiment, the capacity diagnosis unit (133) may calculate the current integration value in the charging section if the calculated SOC difference is equal to or greater than 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 below) a specified value, the capacity diagnosis 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 diagnosis unit (133) may also not calculate the SOHc of the corresponding battery cell (151, 153, or 155). As another example, the capacity diagnosis unit (133) can calculate the current integration 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 equal to or greater than a specified value. In this case, the capacity diagnosis unit (133) can calculate the SOHc of the corresponding battery cell (151, 153, or 155) using the current integration value of the battery cell (151, 153, or 155).

[0126] 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 can be produced. Here, the unit of the initial capacity of the battery cell (151, 153, and / or 155) may be 'Ah', which is the same as the unit of the current accumulation value.

[0127] For example, the capacity diagnostic unit (133) determines the SOH of the battery cells (151, 153, and / or 155) based on the following equation 3. c can be produced.

[0128] [Formula 3]

[0129] SOH c =100*(IΔT) / (C*ΔSOC / 100)

[0130] In the above equation 3, IΔT is the current integration value of the battery cell (151, 153, and / or 155), ΔSOC is the SOC difference of the battery cell (151, 153, and / or 155), and C is the initial capacity of the battery cell (151, 153, and / or 155).

[0131] Referring to Fig. 4a, the SOH of multiple battery cells (e.g., 151, 153, 155) calculated by the capacity diagnosis unit (133) c You can check the graph (410) representing . In the graph (410), the horizontal axis represents time and the vertical axis represents SOH c can represent.

[0132] According to one embodiment, the capacity diagnostic unit (133) determines the SOH of the battery cells to be diagnosed per unit time. c can be produced. The battery diagnostic device (101) can produce the SOH produced by the capacity diagnostic unit (133). c SOH such as graph (410) is accumulated and stored in memory (130) for a specified period of time c You can manage your data.

[0133] According to one embodiment, the capacity diagnostic unit (133) determines the SOH (e.g., calculated SOH) of each battery cell (151, 153, 155). c ) can be used to calculate the SOH deviation. For example, the SOH deviation may be a deviation between the SOH median value of the plurality of battery cells (151, 153, 155) and the SOH of each battery cell (151, 153, 155), a deviation between the SOH average value of the plurality of battery cells (151, 153, 155) and the SOH of each battery cell (151, 153, 155), or a deviation between the average value of the SOH moving averages of the plurality of battery cells (151, 153, 155) and the SOH moving average of each battery cell (151, 153, 155).

[0134] According to one embodiment, the capacity diagnostic unit (133) may calculate the SOH moving average of each battery cell (151, 153, 155) per unit time based on the 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 may be the current time point, and the start point may be a time point ahead of the current time point by a predetermined time length. In addition, the moving average may mean any one of a simple moving average, a weighted moving average, or an exponential moving average.

[0135] Below, an embodiment in which the capacity diagnosis unit (133) calculates the SOH index moving average is described. However, this is only one embodiment, and the capacity diagnosis unit (133) can calculate various moving average values, such as the SOH simple moving average, the SOH weighted moving average, or the SOH index moving average.

[0136] According to one embodiment, the capacity diagnostic unit (133) can input the SOH of each battery cell (151, 153, 155) into the following equation 4 to calculate the moving average of the SOH index at the current point in time.

[0137] [Formula 4]

[0138] EMA t =α*SOH+(1-α)*EMA t-1

[0139] In Equation 4, EMA t is the moving average of the SOH index of the current point in time of the battery cell (151, 153, or 155), α is the weight, SOH is the SOH of the current point in time of the battery cell (151, 153, or 155), EMA t-1 is a moving average of the SOH index of the previous point in time of the battery cell (151, 153, or 155). Here, the weight may be set in various ways depending on the specifications of the plurality of battery cells (151, 153, 155). For example, the weight may be set to 0.05, but is not limited thereto.

[0140] Referring to FIG. 4b, a graph (420) representing a moving average of the SOH index over time of a plurality of battery cells (151, 153, 155) calculated by the capacity diagnosis unit (133) can be confirmed. In the graph (420), the horizontal axis can represent time, and the vertical axis can represent the moving average of the SOH index.

[0141] According to one embodiment, the capacity diagnosis unit (133) can calculate the SOH moving average of the battery cells to be diagnosed per unit time. The battery diagnosis device (101) can cumulatively store the SOH moving average calculated by the capacity diagnosis unit (133) in the memory (130) for a specified time and manage the moving average data such as the graph (420). According to one embodiment, the capacity diagnosis unit (133) can also obtain the moving average data of the battery cells to be diagnosed by applying the SOH data (e.g., the graph (410) of FIG. 4A) stored in the memory (130) to a moving average filter.

[0142] According to one embodiment, the capacity diagnostic unit (133) can calculate, for each battery cell (151, 153, 155), an SOH deviation corresponding to the deviation of the average value of the plurality of SOH moving averages of the plurality of battery cells (151, 153, 155) and the SOH moving average of each battery cell (151, 153, 155) per unit time.

[0143] According to one embodiment, the capacity diagnosis unit (133) can diagnose an abnormality in the capacity 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).

[0144] According to one embodiment, the capacity diagnosis unit (133) can diagnose an abnormal capacity 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 whose SOH deviation exceeds (or is higher than) the threshold value as an abnormal capacity battery cell.

[0145] For example, the capacity diagnosis unit (133) can integrate a time period 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 in which the integration time is greater than (or greater than or equal to) a preset reference time as a battery cell with an abnormal capacity. The battery diagnosis device (101) can integrate a time period in which the condition in which the SOH deviation is greater than (or greater than or equal to) the threshold value is continuously satisfied. If there are multiple corresponding time periods, the battery diagnosis device (101) can independently calculate the integration time for each time period.

[0146] As another example, the battery diagnosis device (101) can accumulate the number of data included in a time section in which 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 in which a condition in which the data accumulation value is greater than (or greater than or equal to) a preset reference count is satisfied as a battery cell with an abnormal capacity. The battery diagnosis device (101) can accumulate only the number of data included in a time section in which the condition in which the SOH deviation is greater than (or greater than or equal to) the threshold value is continuously satisfied. If there are multiple corresponding time sections, the battery diagnosis device (101) can independently accumulate the number of data in each time section.

[0147] 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).

[0148] For example, the detection unit (134) can detect a battery cell diagnosed as having a voltage abnormality by the voltage diagnosis unit (132) and a capacity abnormality by the capacity diagnosis unit (133) as an abnormal battery cell.

[0149] As 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), 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 the capacity abnormality as an abnormal battery cell.

[0150] According to one embodiment, the abnormality processing unit (135) may 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.

[0151] 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.

[0152] According to one embodiment, the abnormality processing unit (135) may isolate abnormal battery cells from the electronic device (102) based on the abnormality diagnosis results of the plurality of battery cells (151, 153, 155). Here, the isolation may include electrical and / or mechanical isolation.

[0153] Fig. 5 is a flowchart illustrating the operation of a battery diagnostic device according to one embodiment. Fig. 5 can be explained using the configurations of Fig. 1.

[0154] The embodiment illustrated in FIG. 5 is only one embodiment, and the order of steps according to various embodiments of the present invention may be different from that illustrated in FIG. 5, and some of the steps illustrated in FIG. 5 may be omitted, the order between steps may be changed, or steps may be merged.

[0155] Referring to FIG. 5, in operation 505, the battery diagnostic device (101) may obtain time series data related to the states of a plurality of battery cells (151, 153, 155). According to one embodiment, the battery diagnostic device (101) may obtain the time series data using a communication circuit (110) and / or a sensor (120).

[0156] In operation 510, the battery diagnosis device (101) can diagnose a voltage abnormality of each battery cell (151, 153, 155) based on the time series data acquired in operation 505. More specifically, the battery diagnosis device (101) can diagnose a voltage abnormality of each battery cell (151, 153, 155) based on voltage time series data included in the time series data.

[0157] According to one embodiment, the battery diagnostic device (101) can calculate the short-term and long-term voltage moving average differences of each battery cell (151, 153, 155) based on time series data, and diagnose voltage abnormalities of each battery cell (151, 153, 155) based on the calculated short-term and long-term voltage moving average differences.

[0158] The operation 510 of the battery diagnostic device (101) diagnosing an abnormal voltage of each battery cell (151, 153, 155) can be specifically described through FIGS. 6 to 8, which will be described later.

[0159] In operation 515, the battery diagnostic device (101) can diagnose an abnormality in the capacity of each battery cell (151, 153, 155) based on the time series data acquired in operation 505.

[0160] According to one embodiment, the battery diagnostic device (101) can calculate the SOH deviation of each battery cell (151, 153, 155) based on time series data, and diagnose a capacity abnormality of each battery cell (151, 153, 155) based on the calculated SOH deviation.

[0161] The operation 515 in which the battery diagnostic device (101) diagnoses an abnormality in the capacity of each battery cell (151, 153, 155) can be specifically described through FIG. 9, which will be described later.

[0162] 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.

[0163] For example, the battery diagnostic device (101) can detect a battery cell diagnosed as having an abnormal voltage in operation 510 and an abnormal capacity in operation 515 as an abnormal battery cell.

[0164] Hereinafter, with reference to FIGS. 6 to 8, the operation of the battery diagnostic device (101) to diagnose voltage abnormalities of each battery cell (151, 153, 155) will be described.

[0165] Fig. 6 is a flowchart illustrating the operation of a battery diagnostic device according to one embodiment. Fig. 6 can be explained using the configurations of Fig. 1.

[0166] The embodiment illustrated in FIG. 6 is only one embodiment, and the order of steps according to various embodiments of the present invention may be different from that illustrated in FIG. 6, and some of the steps illustrated in FIG. 6 may be omitted, the order between steps may be changed, or steps may be merged.

[0167] Referring to FIG. 6, in operation 605, the battery diagnostic device (101) can calculate the short-term and long-term voltage moving average difference of each battery cell (151, 153, 155) based on the time series data acquired in operation 505 of FIG. 5.

[0168] According to one embodiment, the battery diagnostic device (101) can calculate a short-term voltage moving average of each battery cell (151, 153, 155) per unit time based on a first time window. In addition, the battery diagnostic device (101) can calculate a long-term voltage moving average of each battery cell (151, 153, 155) per unit time based on a second time window.

[0169] For example, the input factors when 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 a short-term and long-term moving average of the voltage values ​​of each battery cell (151, 153, 155).

[0170] As another example, the input factor when calculating the voltage moving average may be the deviation between the reference voltage of the plurality of 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 plurality of battery cells (151, 153, 155), and may be determined as an average value or a central value of the voltage values ​​of the plurality of battery cells (151, 153, 155). In this case, the calculated voltage moving average may represent a short-term and long-term moving average of the voltage deviations of each battery cell (151, 153, 155).

[0171] In operation 610, the battery diagnostic device (101) can calculate a short-term and long-term voltage moving average difference corresponding to the difference between the short-term voltage moving average and the long-term voltage moving average for each battery cell (151, 153, 155) per unit time. Here, the short-term and long-term voltage moving average difference may be a value obtained by subtracting the smaller one from the larger one among the short-term voltage moving average and the long-term voltage moving average calculated in operation 605.

[0172] According to one embodiment, the battery diagnostic device (101) may also 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).

[0173] 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.

[0174] According to one embodiment, the battery diagnostic device (101) can calculate, for each battery cell (151, 153, 155), a voltage diagnostic deviation corresponding to an average value of the short-term and long-term voltage moving average differences of a plurality of battery cells (151, 153, 155) and a deviation of the short-term and long-term voltage moving average differences of each battery cell (151, 153, 155).

[0175] According to one embodiment, the battery diagnostic device (101) can calculate a normalized value of the difference between the short-term and long-term voltage moving averages per unit time for each battery cell (151, 153, 155) as a normalized voltage diagnostic deviation.

[0176] For example, the battery diagnosis device (101) can normalize the short-term and long-term voltage moving average differences of each battery cell (151, 153, 155) by using the average value of the short-term and long-term voltage moving average differences of the plurality of battery cells (151, 153, 155). For example, the battery diagnosis device (101) can normalize the short-term and long-term voltage moving average differences of each battery cell (151, 153, 155) by dividing the short-term and long-term voltage moving average differences by the average value (e.g., DSL / Dav, where DSL is the short-term and long-term voltage moving average difference and Dav is the average value).

[0177] As another example, the battery diagnostic device (101) may also normalize the short-term and long-term voltage moving average differences of each battery cell (151, 153, 155) through a logarithmic operation (e.g., Log(DSL), where DSL is the short-term and long-term voltage moving average difference).

[0178] 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 that there is a voltage abnormality in a battery cell in which the calculated voltage diagnostic deviation exceeds a preset threshold value (e.g., 0.015).

[0179] According to one embodiment, the battery diagnosis device (101) can diagnose a voltage abnormality of each battery cell (151, 153, 155) by comparing the voltage diagnosis deviation (or normalized voltage diagnosis deviation) calculated in operation 615 with a statistical variable threshold value. The operation of the battery diagnosis device (101) diagnosing a voltage abnormality by comparing the voltage diagnosis deviation with the statistical variable threshold value can be specifically described through FIG. 7, which will be described later.

[0180] Fig. 7 is a flowchart illustrating the operation of a battery diagnostic device according to one embodiment. Fig. 7 can be explained using the configurations of Fig. 1.

[0181] The embodiment illustrated in FIG. 7 is only one embodiment, and the order of steps according to various embodiments of the present invention may be different from that illustrated in FIG. 7, and some of the steps illustrated in FIG. 7 may be omitted, the order between steps may be changed, or steps may be merged.

[0182] Referring to FIG. 7, in operation 705, the battery diagnostic device (101) may 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 the plurality of battery cells (151, 153, 155). For example, the voltage diagnostic unit (132) may determine the statistical variable threshold value based on the above-described Equation 1.

[0183] In operation 710, the battery diagnostic device (101) may filter the voltage diagnostic deviation (or normalized voltage diagnostic deviation) for each battery cell (151, 153, 155) based on a statistical variable threshold value to produce a filter diagnostic value. For example, the battery diagnostic device (101) may produce one of two values ​​as the filter diagnostic value based on the above-described Equation 2.

[0184] 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.

[0185] For example, the battery diagnostic device (101) can integrate a time section in which a filter diagnostic value is greater than (or greater than or equal to) a diagnostic threshold (e.g., 0) for each battery cell (151, 153, 155), and diagnose a battery cell in which a condition in which the integrated time is greater than (or greater than or equal to) a preset reference time is satisfied as a battery cell with an abnormal voltage. The battery diagnostic device (101) can integrate a time section in which a condition in which the filter diagnostic value is greater than (or greater than or equal to) the diagnostic threshold is continuously satisfied. If there are multiple corresponding time sections, the battery diagnostic device (101) can independently calculate an integrated time for each time section.

[0186] As another example, the battery diagnosis device (101) may accumulate the number of data included in a time section in which the filter diagnosis value is greater than (or greater than or equal to) a diagnosis threshold (e.g., 0) in the time series data of the filter diagnosis value, and diagnose a battery cell in which a condition in which the data accumulation value is greater than (or greater than or equal to) a preset reference count is satisfied as a battery cell with an abnormal voltage. The battery diagnosis device (101) may accumulate only the number of data included in a time section in which a condition in which the filter diagnosis value is greater than (or greater than or equal to) the diagnosis threshold is continuously satisfied. If there are multiple corresponding time sections, the battery diagnosis device (101) may independently accumulate the number of data in each time section.

[0187] Fig. 8 is a flowchart illustrating the operation of a battery diagnostic device according to one embodiment. Fig. 8 can be explained using the configurations of Fig. 1.

[0188] The embodiment illustrated in FIG. 8 is only one embodiment, and the order of steps according to various embodiments of the present invention may be different from that illustrated in FIG. 8, and some of the steps illustrated in FIG. 8 may be omitted, the order between steps may be changed, or steps may be merged.

[0189] Since operations 805 to 815 of FIG. 8 are the same as operations 605 to 615 of FIG. 6, their description will be omitted.

[0190] Referring to FIG. 8, in operation 820, the battery diagnostic device (101) may 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 calculate the first moving average based on a first time window.

[0191] In operation 825, the battery diagnostic device (101) can calculate a second moving average corresponding to a long-term moving average of the voltage diagnostic deviation (or normalized voltage diagnostic deviation) calculated in operation 815. The battery diagnostic device (101) can calculate the second moving average based on a second time window.

[0192] In operation 830, the battery diagnostic device (101) can calculate a short-term and long-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).

[0193] In operation 835, the battery diagnostic device (101) may calculate a moving average diagnostic value of each battery cell (151, 153, 155). According to one embodiment, the battery diagnostic device (101) may calculate a normalized value of the difference between the short-term and long-term moving averages calculated in operation 830 as a moving average diagnostic value for each battery cell (151, 153, 155).

[0194] In operation 840, the battery diagnostic device (101) can determine whether the diagnostic time has elapsed. The diagnostic time can be set in advance.

[0195] If it is determined that the diagnosis time has not elapsed in operation 840 ('NO'), the battery diagnosis device (101) can recursively repeat operations 820 to 835.

[0196] If it is determined that the diagnosis time has elapsed in operation 840 ('YES'), in operation 845, the battery diagnosis device (101) can diagnose a voltage abnormality of each battery cell (151, 153, 155) based on the moving average diagnosis value calculated in operation 835. For example, the battery diagnosis device (101) can diagnose a voltage abnormality of each battery cell (151, 153, 155) by comparing the moving average diagnosis value with a preset threshold value. As another example, the battery diagnosis device (101) can calculate a filter diagnosis 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 diagnosis value.

[0197] Hereinafter, with reference to FIG. 9, the operation of the battery diagnostic device (101) to diagnose an abnormality in the capacity of each battery cell (151, 153, 155) will be described.

[0198] Fig. 9 is a flowchart illustrating the operation of a battery diagnostic device according to one embodiment. Fig. 9 can be explained using the configurations of Fig. 1.

[0199] The embodiment illustrated in FIG. 9 is only one embodiment, and the order of steps according to various embodiments of the present invention may be different from that illustrated in FIG. 9, and some of the steps illustrated in FIG. 9 may be omitted, the order between steps may be changed, or steps may be merged.

[0200] Referring to FIG. 9, in operation 905, the battery diagnostic device (101) may calculate the SOH of each battery cell (151, 153, 155) per unit time based on the time series data acquired in operation 505 of FIG. 5. Here, the SOH is a life-related parameter of the battery cells (151, 153, and / or 155), and may include at least one of SOHc indicating SOH related to the capacity of the battery or SOHr indicating SOH related to the resistance growth of the battery. Hereinafter, an embodiment in which the battery diagnostic device (101) calculates SOHc will be described. However, this is only one embodiment, and the battery diagnostic device (101) may calculate various SOHs, such as SOHc, SOHr, and a final SOH calculated based on these.

[0201] According to one embodiment, the battery diagnostic device (101) can calculate the SOC difference before and after the charging period of a plurality of battery cells (151, 153, 155) per unit time based on the time series data.

[0202] For example, the battery diagnostic device (101) can calculate the SOC difference 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 based on the SOC data included in the time series data.

[0203] As another example, the battery diagnostic device (101) may calculate the OCV (Open Circuit Voltage) of a plurality of battery cells (151, 153, 155) based on at least one of the time series voltage data or the time series current data included in the time series data, and may convert the calculated OCV into SOC using the SOC-OCV table stored in advance in the memory (130) and then calculate the SOC difference.

[0204] According to one embodiment, the battery diagnosis device (101) can calculate the current integration value in the charging section of the plurality of battery cells (151, 153, 155) per unit time based on the time series data. For example, the battery diagnosis device (101) can calculate the current integration value in the charging section based on the current value of the charging section and the time length of the charging section included in the time series data. Here, the unit of the current integration value can be 'Ah (Ampere hour)'.

[0205] According to another embodiment, the battery diagnosis device (101) may calculate the current integration value in the charging section if the calculated SOC difference is equal to or greater than 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 below) a specified value, the battery diagnosis 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 diagnosis device (101) may also not calculate the SOHc of the corresponding battery cell (151, 153, or 155). As another example, the battery diagnostic device (101) can calculate the current integration 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 equal to or greater than a specified value. In this case, the battery diagnostic device (101) can calculate the SOHc of the corresponding battery cell (151, 153, or 155) using the current integration value of the battery cell (151, 153, or 155).

[0206] According to one embodiment, the battery diagnostic device (101) can calculate the SOHc of the battery cells (151, 153, and / or 155) based on the SOC difference, the current integration value, and the initial capacity of the battery cells (151, 153, and / or 155). Here, the unit of the initial capacity of the battery cells (151, 153, and / or 155) can be 'Ah', which is the same as the unit of the current integration value.

[0207] For example, the battery diagnostic device (101) can calculate the SOHc of the battery cells (151, 153, and / or 155) based on the above equation 3.

[0208] 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 a deviation between the SOH median value of the plurality of battery cells (151, 153, 155) and the SOH of each battery cell (151, 153, 155), a deviation between the SOH average value of the plurality of battery cells (151, 153, 155) and the SOH of each battery cell (151, 153, 155), or a deviation between the average value of the SOH moving averages of the plurality of battery cells (151, 153, 155) and the SOH moving average of each battery cell (151, 153, 155).

[0209] According to one embodiment, the battery diagnostic device (101) may calculate the SOH moving average of each battery cell (151, 153, 155) per 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 may be the current time point, and the start point may be a time point ahead of the current time point by a predetermined time length. In addition, the moving average may mean any one of a simple moving average, a weighted moving average, or an exponential moving average.

[0210] Below, an embodiment in which the battery diagnostic device (101) calculates an SOH index moving average is described. However, this is only one embodiment, and the battery diagnostic device (101) can calculate various moving average values, such as an SOH simple moving average, an SOH weighted moving average, or an SOH index moving average.

[0211] According to one embodiment, the battery diagnostic device (101) can input the SOH of each battery cell (151, 153, 155) into the above equation 4 to calculate the moving average of the SOH index at the current point in time.

[0212] According to one embodiment, the battery diagnostic device (101) can calculate, for each battery cell (151, 153, 155), an SOH deviation corresponding to a deviation of the average value of a plurality of SOH moving averages of a plurality of battery cells (151, 153, 155) and the SOH moving average of each battery cell (151, 153, 155) per unit time.

[0213] In operation 915, the battery diagnostic device (101) can diagnose an abnormality in the capacity 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 the capacity of each battery cell (151, 153, 155).

[0214] According to one embodiment, the battery diagnostic device (101) can diagnose an abnormal 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 whose SOH deviation exceeds (or is above) the threshold value as an abnormal capacity battery cell.

[0215] For example, the battery diagnosis device (101) can integrate a time section 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 in which the integration time is greater than (or greater than or equal to) a preset reference time as a battery cell with an abnormal capacity. The battery diagnosis device (101) can integrate a time section in which the condition in which the SOH deviation is greater than (or greater than or equal to) the threshold value is continuously satisfied. If there are multiple corresponding time sections, the battery diagnosis device (101) can independently calculate the integration time for each time section.

[0216] As another example, the battery diagnosis device (101) can accumulate the number of data included in a time section in which 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 in which a condition in which the data accumulation value is greater than (or greater than or equal to) a preset reference count is satisfied as a battery cell with an abnormal capacity. The battery diagnosis device (101) can accumulate only the number of data included in a time section in which the condition in which the SOH deviation is greater than (or greater than or equal to) the threshold value is continuously satisfied. If there are multiple corresponding time sections, the battery diagnosis device (101) can independently accumulate the number of data in each time section.

[0217] Fig. 10 is a flowchart illustrating the operation of a battery diagnostic device according to one embodiment. Fig. 10 can be explained using the configurations of Fig. 1.

[0218] The embodiment illustrated in FIG. 10 is only one embodiment, and the order of steps according to various embodiments of the present invention may be different from that illustrated in FIG. 10, and some of the steps illustrated in FIG. 10 may be omitted, the order between steps may be changed, or steps may be merged.

[0219] Since operation 1005 of Fig. 10 is the same operation as operation 505 of Fig. 5, its description will be omitted.

[0220] Referring to FIG. 10, in operation 1010, the battery diagnostic device (101) can diagnose whether there is a voltage abnormality in a battery cell (151, 153, or 155). The voltage abnormality diagnostic operation of operation 1010 may refer to operation 510 of FIG. 5 described above and operations of FIGS. 6 to 8.

[0221] If a voltage abnormality is diagnosed in a battery cell (151, 153, or 155) in operation 1010, the battery diagnostic device (101) can diagnose whether a capacity abnormality exists in the battery cell (151, 153, or 155) in operation 1015. The capacity abnormality diagnosis operation of operation 1015 may refer to operation 515 of FIG. 5 and operations of FIG. 9 described above.

[0222] If a battery cell (151, 153, or 155) is diagnosed to have an abnormal capacity in operation 1015, in operation 1020, the battery diagnostic device (101) can detect the corresponding battery cell (151, 153, or 155) as an abnormal battery cell.

[0223] If it is diagnosed that there is no voltage abnormality in the battery cell (151, 153, or 155) in operation 1010, or if it is diagnosed that there is no capacity abnormality in the battery cell (151, 153, or 155) in operation 1015, then in operation 1025, the battery diagnostic device (101) can detect the corresponding battery cell (151, 153, or 155) as a normal battery cell.

[0224] According to one embodiment, the battery diagnostic device (101) may perform an abnormality processing function based on 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.

[0225] According to one embodiment, the battery diagnostic device (101) can transmit abnormal diagnosis results of the plurality of battery cells (151, 153, 155) to a user terminal (104) connected via a wired and / or wireless network.

[0226] According to one embodiment, the battery diagnostic device (101) can isolate abnormal battery cells from the electronic device (102) based on abnormal diagnosis results of a plurality of battery cells (151, 153, 155). Here, the isolation may include electrical and / or mechanical isolation.

[0227] The terms "include," "comprise," or "have" used herein, unless otherwise specifically stated, imply that the corresponding component may be included, and therefore should be interpreted to include other components rather than to exclude other components. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as terms defined in dictionaries, should be interpreted to be consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

Claims

1. An acquisition unit that acquires time series data related to the status of multiple battery cells included in a battery module; A voltage diagnosis unit that calculates the short-term and long-term voltage moving average difference of each battery cell based on the above time series data, and diagnoses a voltage abnormality of each battery cell based on the short-term and long-term voltage moving average difference; A capacity diagnosis unit that calculates the SOH (State of Health) deviation of each battery cell based on the above time series data and diagnoses a capacity abnormality of each battery cell based on the SOH deviation; and A battery diagnostic device, comprising a detection unit that detects an abnormal battery cell based on a diagnosis result of at least one of the voltage diagnostic unit and the capacity diagnostic unit.

2. In claim 1, The above voltage diagnostic unit, Based on the first time window, the short-term voltage moving average of each battery cell is calculated, A long-term voltage moving average of each battery cell is calculated based on a second time window having a longer time length than the first time window, A battery diagnostic device that calculates, for each battery cell, the short-term and long-term voltage moving average difference corresponding to the difference between the short-term voltage moving average and the long-term voltage moving average.

3. In claim 1, The above voltage diagnostic unit, For each battery cell, a voltage diagnostic deviation corresponding to the deviation between the average value of the short-term and long-term voltage moving average differences of the plurality of battery cells and the short-term and long-term voltage moving average differences of each battery cell is calculated, A battery diagnostic device that diagnoses a voltage abnormality of each battery cell based on the above voltage diagnostic deviation.

4. In claim 3, The above voltage diagnostic unit, Determine a statistical variable threshold value that depends on the standard deviation of the voltage diagnostic deviations of the plurality of battery cells, For each battery cell, the voltage diagnosis deviation is filtered based on the statistical variable threshold value to produce a filter diagnosis value. A battery diagnostic device that diagnoses an abnormal voltage of each battery cell based on the above filter diagnostic value.

5. In claim 1, The above voltage diagnostic unit, For each battery cell, the normalized value of the difference between the short-term and long-term voltage moving averages is calculated as the normalized voltage diagnostic deviation, A battery diagnostic device that diagnoses a voltage abnormality of each battery cell based on the above normalized voltage diagnostic deviation.

6. In claim 5, The above voltage diagnostic unit, Determine a statistical variable threshold that depends on the standard deviation of the normalized voltage diagnostic deviations of the plurality of battery cells, For each battery cell, the normalized voltage diagnostic deviation is filtered based on the statistical variable threshold to produce a filter diagnostic value, A battery diagnostic device that diagnoses an abnormal voltage of each battery cell based on the above filter diagnostic value.

7. In claim 5, The above voltage diagnostic unit, For each battery cell, the following (i) to (iii) are repeated recursively at least once to produce a moving average diagnostic value, (i) calculating 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) calculating, for each battery cell, a short-term and long-term moving average difference corresponding to the difference between the first moving average and the second moving average, (iii) calculating, for each battery cell, a normalized value of the short-term and long-term moving average difference as a moving average diagnostic value. A battery diagnostic device that diagnoses a voltage abnormality of each battery cell based on the above moving average diagnostic value.

8. In claim 1, The above capacity diagnostic section, Based on the above time series data, the SOH of each battery cell is calculated, A battery diagnostic device, which calculates, for each battery cell, the SOH deviation corresponding to the deviation of the SOH of each battery cell from the SOH center value or SOH average value of the plurality of battery cells.

9. In claim 1, The above capacity diagnostic section, Based on the above time series data, the SOH of each battery cell is calculated, Based on the third time window, the moving average of SOH of each battery cell is calculated, A battery diagnostic device that calculates, for each battery cell, the SOH deviation corresponding to the deviation of the SOH moving average of each battery cell from the average value of the SOH moving averages of the plurality of battery cells.

10. In claim 1, The above capacity diagnostic section, Based on the above time series data, the difference in SOC (State of Charge) before and after the charging period of each battery cell is calculated, Based on the above time series data, the current accumulation value in the charging section of each battery cell is calculated, For each battery cell, SOHc, which represents the SOH for the capacity of each battery cell, is calculated based on the SOC difference, the current integration value, and the initial capacity of each battery cell, A battery diagnostic device that calculates the SOH deviation of each battery cell based on the SOHc.

11. In claim 1, A battery diagnostic device, wherein the capacity diagnostic unit diagnoses a capacity abnormality only for at least one battery cell diagnosed as having a voltage abnormality by the voltage diagnostic unit.

12. In claim 11, The above detection unit is a battery diagnosis device that detects a battery cell diagnosed as having an abnormal capacity by the capacity diagnosis unit as the abnormal battery cell.

13. In claim 1, A battery diagnostic device, wherein the detection unit detects a battery cell diagnosed as having an abnormal voltage by the voltage diagnostic unit and as having an abnormal capacity by the capacity diagnostic unit as the abnormal battery cell.

14. An operation of acquiring time series data related to the status of multiple battery cells included in a battery module; An operation of calculating the short-term and long-term voltage moving average difference of each battery cell based on the above time series data, and diagnosing a voltage abnormality of each battery cell based on the short-term and long-term voltage moving average difference; An operation of calculating the SOH (State of Health) deviation of each battery cell based on the above time series data and diagnosing a capacity abnormality of each battery cell based on the SOH deviation; and A battery diagnosis method, comprising an operation of detecting an abnormal battery cell based on a diagnosis result of at least one of the voltage diagnosis unit and the capacity diagnosis unit.

15. In claim 13, The operation of diagnosing the capacity abnormality of each battery cell above includes the operation of diagnosing the capacity abnormality only for at least one battery cell diagnosed as having a voltage abnormality, A battery diagnosis method, wherein the operation of detecting the above-mentioned abnormal battery cell includes an operation of detecting a battery cell diagnosed as having an abnormal capacity as the above-mentioned abnormal battery cell.

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