Battery diagnostic device and method of operating the same

Abstract

A battery diagnostic apparatus according to one embodiment disclosed herein can include an acquisition unit to acquire time-series data related to a state of a plurality of battery cells included in a battery module; a voltage diagnostic unit to calculate a long / short-term voltage moving average difference for each battery cell based on the time-series data, and diagnose a voltage abnormality of each battery cell based on the long / short-term voltage moving average difference; a capacity diagnostic unit to calculate a state of health (SOH) deviation for each battery cell based on the time-series data, and diagnose a capacity abnormality of each battery cell based on the SOH deviation; and a detection unit to detect an abnormal battery cell based on a diagnosis result from at least one of the voltage diagnostic unit or the capacity diagnostic unit.

Description

Technical Field

[0001] Cross-references to related applications

[0002] This application claims priority to Korean Patent Application No. 10-2023-0184929, filed on December 18, 2023, with the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein relate to a battery diagnostic device and its operating method. Background Technology

[0004] Recently, research and development of rechargeable batteries have been actively pursued. In this paper, rechargeable batteries, as rechargeable / dischargeable batteries, can include all conventional nickel (Ni) / cadmium (Cd) batteries, Ni / metal hydride (MH) batteries, and more recently, lithium-ion batteries. Among these rechargeable batteries, lithium-ion batteries have a significantly higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured to be small and lightweight, making them suitable for use as power sources in mobile devices. Recently, their application has expanded to electric vehicles, attracting attention as a next-generation energy storage medium.

[0005] In addition, secondary batteries are typically used as battery packs comprising battery modules consisting of multiple battery cells connected in series and / or in parallel. Secondary batteries can also be used as battery racks, which include multiple battery modules and a frame for receiving the battery modules.

[0006] Battery cells, battery modules, battery packs, or battery racks can be used in a variety of devices. For example, batteries can be used not only in mobile devices such as mobile phones, laptops, smartphones, and smartboards, but also in electrically powered vehicles (electric vehicles (EVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs)) and high-capacity energy storage systems (ESS).

[0007] These batteries can be managed and controlled by a battery management system (BMS) based on their state and operation. The battery management system can be included in a device along with the batteries.

[0008] The battery management system can also manage and control the battery in a state separate from the device including 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 the vehicle, etc., and use the collected data to manage and control the battery.

[0009] At the same time, when the battery is defective, the possibility of damage to devices including the battery (e.g., EVs, ESS) may increase. Therefore, there is a need for a solution to reduce the possibility of damage to devices including the battery by detecting abnormal battery conditions. Summary of the Invention

[0010] Technical issues

[0011] Traditionally, attempts have been made to diagnose voltage anomalies by detecting instantaneous changes in the battery cell voltage in order to diagnose battery defects. For example, a battery cell showing an instantaneous voltage change based on a moving average of the cell voltage can be diagnosed as a battery cell with an abnormal voltage.

[0012] However, instantaneous voltage changes in battery cells can originate not only from the separation or contact of the negative or positive terminals, but also from voltage measurement noise. In other words, only in the case of voltage anomaly diagnosis is there a possibility of errors due to noise.

[0013] In this regard, defects caused by the separation or contact of the negative or positive terminals of the battery cell may lead to a decrease in instantaneous capacity or a capacity deviation, and capacity anomaly diagnosis may have a smaller impact on voltage measurement noise compared to voltage anomaly diagnosis.

[0014] The embodiments disclosed herein are intended to address the problems of voltage anomaly diagnosis methods and provide a battery diagnostic device and its operating method, wherein, for battery cells, in addition to voltage anomaly diagnosis, capacity anomaly diagnosis is also performed to reduce the false detection rate caused by voltage measurement noise, thereby detecting abnormal battery cells.

[0015] The technical problems of the embodiments disclosed herein are not limited to those described above, and those skilled in the art will clearly understand from the following description other unmentioned technical problems.

[0016] Technical solution

[0017] A battery diagnostic device according to an embodiment disclosed herein includes: an acquisition unit configured to acquire time-series data relating to the state of a plurality of battery cells included in a battery module; a voltage diagnostic unit configured to calculate a long-short-term voltage moving average difference for each battery cell based on the time-series data, and to diagnose voltage anomalies in each battery cell based on the long-short-term voltage moving average difference; a capacity diagnostic unit configured to calculate a state of health (SOH) deviation for each battery cell based on the time-series data, and to diagnose capacity anomalies in each battery cell based on the SOH deviation; and a detection unit configured to detect abnormal battery cells based on the diagnostic results of at least one of the voltage diagnostic unit and / or the capacity diagnostic unit.

[0018] In the battery diagnostic device according to the embodiments disclosed herein, the voltage diagnostic unit may further be configured to: calculate a short-term voltage moving average for each battery cell based on a first time window; calculate a long-term voltage moving average for each battery cell based on a second time window having a longer time length than the first time window; and for each battery cell, calculate the long-short-term voltage moving average difference corresponding to the difference between the short-term voltage moving average and the long-term voltage moving average.

[0019] In the battery diagnostic device according to the embodiments disclosed herein, the voltage diagnostic unit may also be configured to: for each battery cell, calculate a voltage diagnostic deviation corresponding to the deviation between the average of the long-short-term voltage moving average differences of the plurality of battery cells and the long-short-term voltage moving average differences of each battery cell; and diagnose voltage abnormalities of each battery cell based on the voltage diagnostic deviation.

[0020] In the battery diagnostic device according to the embodiments disclosed herein, the voltage diagnostic unit may further be configured to: determine a statistical variable threshold for the standard deviation of the voltage diagnostic deviation depending on the plurality of battery cells; calculate a filtered diagnostic value for each battery cell by filtering the voltage diagnostic deviation based on the statistical variable threshold; and diagnose voltage anomalies in each battery cell based on the filtered diagnostic value.

[0021] In the battery diagnostic device according to the embodiments disclosed herein, the voltage diagnostic unit may also be configured to: calculate, for each battery cell, a normalized value of the difference between the long-term and short-term voltage moving averages as a normalized voltage diagnostic deviation; and diagnose voltage anomalies in each battery cell based on the normalized voltage diagnostic deviation.

[0022] In the battery diagnostic device according to the embodiments disclosed herein, the voltage diagnostic unit may further be configured to: determine a statistical variable threshold for the standard deviation of the normalized voltage diagnostic deviation depending on the plurality of battery cells; calculate a filtered diagnostic value for each battery cell by filtering the normalized voltage diagnostic deviation based on the statistical variable threshold; and diagnose voltage anomalies for each cell based on the filtered diagnostic value.

[0023] In the battery diagnostic device according to the embodiments disclosed herein, the voltage diagnostic unit may also be configured to calculate a moving average diagnostic value by recursively repeating processes (i) to (iii) at least once for each battery cell, processes (i) to (iii) including: (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) for each battery cell, calculating a long-short-term moving average difference corresponding to the difference between the first moving average and the second moving average; and (iii) for each battery cell, calculating a normalized value of the long-short-term moving average difference as a moving average diagnostic value; and diagnosing a voltage anomaly of each battery cell based on the moving average diagnostic value.

[0024] In the battery diagnostic device according to the embodiments disclosed herein, the capacity diagnostic unit may also be configured to: calculate the state of health (SOH) of each battery cell based on the time series data; and for each battery cell, calculate the SOH deviation corresponding to the deviation between the median or average SOH of the plurality of battery cells and the SOH of each battery cell.

[0025] In the battery diagnostic device according to the embodiments disclosed herein, the capacity diagnostic unit may further be configured to: calculate the state of health (SOH) of each battery cell based on the time series data; calculate the moving average of the SOH of each battery cell based on a third time window; and for each battery cell, calculate the SOH deviation corresponding to the deviation between the average of the moving averages of the multiple battery cells and the moving average of the SOH of each battery cell.

[0026] In the battery diagnostic device according to the embodiments disclosed herein, the capacity diagnostic unit may further be configured to: calculate the state of charge (SOC) difference of each battery cell before and after the charging period based on the time series data; calculate the current integral value of each battery cell during the charging period based on the time series data; for each battery cell, calculate SOHc indicating the SOH with respect to the capacity of each battery cell based on the SOC difference, current integral value and initial capacity of each battery cell; and calculate the SOH deviation of each battery cell based on SOHc.

[0027] In the battery diagnostic device according to the embodiments disclosed herein, the capacity diagnostic unit may also be configured to diagnose a capacity abnormality in at least one battery cell that has been diagnosed as having a voltage abnormality by the voltage diagnostic unit.

[0028] In the battery diagnostic device according to the embodiments disclosed herein, the detection unit may also be configured to detect battery cells diagnosed as having abnormal capacity by the capacity diagnostic unit as abnormal battery cells.

[0029] In the battery diagnostic device according to the embodiments disclosed herein, the detection unit may also be configured to detect a battery cell that is diagnosed by the voltage diagnostic unit as having a voltage abnormality and by the capacity diagnostic unit as having a capacity abnormality as an abnormal battery cell.

[0030] The battery diagnostic method according to the embodiments disclosed herein includes the following steps: acquiring time-series data related to the state of a plurality of battery cells included in a battery module; calculating the long-short-term voltage moving average difference for each battery cell based on the time-series data, and diagnosing voltage anomalies in each battery cell based on the long-short-term voltage moving average difference; calculating the state of health (SOH) deviation for each battery cell based on the time-series data, and diagnosing capacity anomalies in each battery cell based on the SOH deviation; and detecting abnormal battery cells based on the diagnostic results of at least one of a voltage diagnostic unit or a capacity diagnostic unit.

[0031] In the battery diagnostic method according to the embodiments disclosed herein, the step of diagnosing the capacity abnormality of each battery cell may include diagnosing the capacity abnormality of at least one battery cell diagnosed as having a voltage abnormality, and the step of detecting the abnormal battery cell may include detecting the battery cell diagnosed as having a capacity abnormality as the abnormal battery cell.

[0032] Beneficial effects

[0033] According to the embodiments disclosed herein, the accuracy of battery diagnosis can be improved by reducing errors caused by voltage measurement noise during abnormal battery cell detection.

[0034] In addition, various effects that can be directly or indirectly identified through this document can be provided. Attached Figure Description

[0035] Figure 1 This is a block diagram of a battery diagnostic device according to an embodiment.

[0036] Figure 2 It is a graph showing the voltage time series data of multiple battery cells obtained by a battery diagnostic device according to an embodiment.

[0037] Figures 3a to 3g This is used to describe the battery diagnostic device performed according to the embodiment. Figure 2 A chart illustrating the process of diagnosing voltage anomalies in each battery cell using voltage time series data.

[0038] Figure 4a and Figure 4b It is a diagram used to describe the process of diagnosing capacity abnormalities in each battery cell performed by a battery diagnostic device according to an embodiment.

[0039] Figure 5 This is an operation flowchart of a battery diagnostic device according to an implementation method.

[0040] Figure 6 This is an operation flowchart of a battery diagnostic device according to an implementation method.

[0041] Figure 7 This is an operation flowchart of a battery diagnostic device according to an implementation method.

[0042] Figure 8 This is an operation flowchart of a battery diagnostic device according to an implementation method.

[0043] Figure 9 This is an operation flowchart of a battery diagnostic device according to an implementation method.

[0044] Figure 10 This is an operation flowchart of a battery diagnostic device according to an implementation method. Detailed Implementation

[0045] In the following description, various embodiments of the present disclosure will be described with reference to the accompanying drawings. However, the description is not intended to limit the present disclosure to specific embodiments and should be construed as including various modifications, equivalents, and / or substitutions of embodiments according to the present disclosure.

[0046] It should be understood that the embodiments described in this document and the terminology used therein are not intended to limit the technical features set forth herein to a particular embodiment, and include various changes, equivalents, or substitutions to the corresponding embodiments. Regarding the description of the drawings, similar reference numerals may be used to refer to similar or related elements. It should be understood that unless the relevant context clearly indicates otherwise, the singular form of the noun corresponding to an item may include one or more things.

[0047] As used herein, each of phrases such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B or C,” “at least one of A, B and C,” and “at least one of A, B or C” can include any one or all possible combinations of items enumerated together in the corresponding phrase. Unless otherwise stated, terms such as “first,” “second,” “first,” “second,” “A,” “B,” “(a),” or “(b)” may be used simply to distinguish corresponding parts from each other and do not otherwise limit the parts (e.g., in terms of importance or order).

[0048] In this document, it should be understood that when an element (e.g., a first element) is referred to as “connected,” “linked,” or “attached to” or “connected to” another element (e.g., a second element) with or without the terms “operably” or “communically”, it means that the element can be connected to the other element directly (e.g., wired), wirelessly, or via a third element.

[0049] According to various embodiments of this disclosure, each of the above-described components (e.g., modules or programs) may include a single entity or multiple entities, some of which may be separately disposed on other components. According to various embodiments of this disclosure, one or more of the above-described components or operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (e.g., modules or programs) may be integrated into one component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as the corresponding components in the multiple components prior to integration. According to various embodiments, operations performed by a module, program, or other component may be performed sequentially, in parallel, repeatedly, or heuristically, or one or more operations may be performed in a different order or omitted, or one or more other operations may be added.

[0050] Figure 1 This is a block diagram of a battery diagnostic device according to an embodiment.

[0051] The battery diagnostic device 101 described below can be implemented as a battery management system (BMS) in electronic device 102, and can also be implemented as various external devices such as servers, cloud, chargers, chargers / dischargers.

[0052] Reference Figure 1 The battery diagnostic device 101 can be connected to the electronic device 102 and the user terminal 104 via wired and / or wireless means.

[0053] According to an embodiment, the connection 103 between the battery diagnostic device 101 and the electronic device 102 can be a communication connection via a wired and / or wireless network. In an embodiment, the wired network can be based on local area network (LAN) communication or power line communication. In an embodiment, the wireless network can be based on a short-range communication network (e.g., Bluetooth, Wi-Fi, or Infrared Data Association (IrDA)) or a long-range communication network (e.g., cellular network, fourth-generation (4G) network, fifth-generation (5G) network).

[0054] According to another embodiment, the connection 103 between the battery diagnostic device 101 and the electronic device 102 can be a connection using a device-to-device communication scheme (e.g., bus, general purpose input / output (GPIO), serial peripheral interface (SPI), or mobile industrial processor interface (MIPI)).

[0055] According to the implementation, the electronic device 102 may be a mobile device (e.g., a mobile phone, a laptop computer, a smartphone, a smart board), an electric vehicle (e.g., an electric vehicle (EV), a hybrid electric vehicle (HEV), a plug-in hybrid electric vehicle (PHEV), a fuel cell electric vehicle (FCEV)), an energy storage system (ESS), or a battery swapping system (BSS).

[0056] According to an embodiment, the electronic device 102 may include a plurality of battery cells 151, 153, and 155. Here, each battery cell 151, 153, or 155 may be a single battery cell, or it may be a group of at least two battery cells connected in parallel. According to an 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, and 155. For example, the plurality of battery cells 151, 153, and 155 may be included in a single battery module or a single battery pack.

[0057] According to an embodiment, the connection 105 between the battery diagnostic device 101 and the user terminal 104 can be a communication connection via a wired and / or wireless network.

[0058] According to one embodiment, the user terminal 104 may be a mobile device (e.g., a mobile phone, laptop computer, smartphone, smartboard) or a personal computer (PC). According to another embodiment, the battery diagnostic device 101 may provide the user terminal 104 with information related to the diagnostic results of battery cells 151, 153, or 155.

[0059] According to one embodiment, the battery diagnostic device 101 may include a communication circuit 110, a sensor 120, a memory 130, and a processor 140. According to another embodiment, besides… Figure 1 In addition to the components shown, Figure 1The battery diagnostic device 101 shown may also include at least one component (e.g., a display, input device, or output device), or may omit it. Figure 1 At least one of the components shown (e.g., sensor 120). For example, when the battery diagnostic device 101 is implemented as an external electronic device (such as a server, cloud, etc.) separate from the electronic device 102, the battery diagnostic device 101 can acquire status information of multiple battery cells 151, 153, and 155 by using communication circuit 110. In this case, the battery diagnostic device 101 may not include sensor 120.

[0060] According to the implementation, 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 send data to and receive data from the electronic device 102 and / or the user terminal 104 through the established communication channel.

[0061] According to the implementation, sensor 120 can measure information (e.g., voltage, current, temperature, etc.) related to the state of battery cells 151, 153, and 155 of electronic device 102. For example, when battery diagnostic device 101 is implemented as a BMS in electronic device, battery diagnostic device 101 can directly measure the state of multiple battery cells 151, 153, and 155 by using sensor 120.

[0062] According to an embodiment, the communication circuit 110 and / or sensor 120 can acquire time-series data related to the state of battery cells 151, 153, and 155. In an embodiment, the time-series data related to the state of the plurality of battery cells 151, 153, and 155 may include data indicating the changes in voltage, current, resistance, state of charge (SOC), state of health (SOH), and / or temperature of the plurality of battery cells 151, 153, and 155 over time.

[0063] According to an implementation, memory 130 may include volatile and / or non-volatile memory.

[0064] According to one embodiment, memory 130 may store data used by at least one component of battery diagnostic device 101 (e.g., processor 140). For example, the data may include software (or related instructions), input data, or output data. In one embodiment, the instructions, when executed by processor 140, may cause battery diagnostic device 101 to perform the operations defined by those instructions.

[0065] According to an implementation, the memory 140 may include one or more software components (e.g., an acquisition unit 131, a voltage diagnostic unit 132, a capacity diagnostic unit 133, a detection unit 134, and an anomaly handling unit 135).

[0066] According to an implementation, 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.

[0067] According to an implementation, the processor 140 can execute software (e.g., acquisition unit 131, voltage diagnostic unit 132, capacity diagnostic unit 133, detection unit 134, and anomaly handling unit 135) stored in the memory 130 to control at least one other component (e.g., hardware or software component) of the battery diagnostic device 101 connected to the processor 140 and perform various data processing or operations.

[0068] Below, we will refer to Figure 2 , Figures 3a to 3g , Figure 4a and Figure 4b This describes a method performed by a battery diagnostic device 101 to diagnose anomalies in multiple battery cells 151, 153, and 155 via an acquisition unit 131, a voltage diagnostic unit 132, a capacity diagnostic unit 133, a detection unit 134, and an anomaly handling unit 135. More specifically, reference will be made to... Figures 3a to 3g Describe the voltage anomaly diagnosis process of the battery diagnostic device 101, and refer to Figure 4a and Figure 4b Describe the capacity anomaly diagnosis process of battery diagnostic device 101.

[0069] Figure 2 It is a graph showing the voltage time series data of multiple battery cells obtained by a battery diagnostic device according to an embodiment. Figures 3a to 3g This is used to describe the battery diagnostic device performed according to the embodiment. Figure 2 A chart illustrating the process of diagnosing voltage anomalies in each battery cell using voltage time series data. Figure 4a and Figure 4b It is a diagram used to describe the process of diagnosing capacity abnormalities in each battery cell performed by a battery diagnostic device according to an embodiment.

[0070] According to one embodiment, the acquisition unit 131 can acquire time-series data related to the state of battery cells 151, 153, and 155. According to another embodiment, the acquisition unit 131 can acquire the time-series data using communication circuit 110 and / or sensor 120.

[0071] According to the implementation, the acquisition unit 131 can collect signals related to the states of the plurality of battery cells 151, 153, and 155 every unit time by using the communication circuit 110 and / or the sensor 120, and record the state values ​​(e.g., voltage, current, temperature, resistance, etc.) of the plurality of battery cells 151, 153, and 155. Here, the unit time can be an integer multiple of the state signal receiving period of the communication circuit 110 or the state measurement period of the sensor 120.

[0072] According to the implementation, the acquisition unit 131 can generate time-series data indicating the changes in the state values ​​of multiple battery cells 151, 153, and 155 over time based on the state values ​​of multiple battery cells 151, 153, and 155 recorded in the memory 130. In this case, the number of time-series data can be increased by 1 each time the state values ​​of multiple battery cells 151, 153, and 155 are acquired.

[0073] Reference Figure 2 Figure 200 shows voltage time series data indicating voltage changes in multiple battery cells (e.g., 151, 153, and 155), acquired by acquisition unit 131. In Figure 200, the horizontal axis indicates time, and the vertical axis indicates voltage.

[0074] Voltage anomaly diagnosis

[0075] According to the implementation, the voltage diagnostic unit 132 can calculate the long-short term voltage moving average difference for each battery cell 151, 153 or 155 based on the time series data acquired by the acquisition unit 131.

[0076] According to the implementation, the voltage diagnostic unit 132 can calculate the moving average voltage of each of the plurality of battery cells 151, 153, and 155 per unit time by using one or two time windows. When using two time windows, the length of either time window can be different from the length of the other time window.

[0077] Here, the length of each time window can be an integer multiple of a unit of time, the end point of each time window can be the current time point, and the start point of each time window can be a time point that is a predetermined time length earlier than the current time point.

[0078] In the following text, for ease of description, the time window with the shorter duration will be referred to as the first time window, and the time window with the longer duration will be referred to as the second time window.

[0079] According to the implementation, the voltage diagnostic unit 132 can calculate the short-term moving average voltage of each battery cell 151, 153, or 155 per unit time based on a first time window. The voltage diagnostic unit 132 can also calculate the long-term moving average voltage of each battery cell 151, 153, or 155 per unit time based on a second time window. Here, the moving average can represent any one of a simple moving average (SMA), a weighted moving average (WMA), or an exponential moving average (EMA).

[0080] For example, the input factor in calculating the voltage moving average could be the voltage value of each battery cell 151, 153, or 155 within a specific time window (e.g., a first time window or a second time window). In this case, the calculated voltage moving average could indicate a long-short-term moving average of the voltage value of each battery cell 151, 153, or 155.

[0081] In another example, the input factor in the voltage moving average calculation can be the deviation between the reference voltage of multiple battery cells 151, 153, and 155 and the voltage of each battery cell 151, 153, or 155 within a specific time window (e.g., a first time window or a second time window). In this paper, the reference voltage can be determined from the multiple battery cells 151, 153, and 155, and can be determined as the average or median of the voltage values ​​of the multiple battery cells 151, 153, and 155. In this case, the calculated voltage moving average can indicate the long-short-term moving average of the voltage deviation of each battery cell 151, 153, or 155.

[0082] According to the implementation, the voltage diagnostic unit 132 can compare the short-term and long-term trends of cell voltage per unit time based on the long-term moving average and short-term moving average of each battery cell 151, 153 or 155 calculated per unit time.

[0083] Reference Figure 3a Figure 310 shows the output from the voltage diagnostic unit 132. Figure 2 The short-term and long-term moving average voltage lines are calculated from the voltage time series data for multiple battery cells (e.g., 151, 153, and 155). In Figure 310, the horizontal axis indicates time, and the vertical axis indicates the voltage moving average.

[0084] In Figure 310, two moving average lines with the same shape but different thicknesses can be the short-term voltage moving average line and the long-term voltage moving average line for a specific battery cell, respectively, and indicate the change history of the short-term voltage moving average and the long-term voltage moving average of the battery cell. In the moving average calculation of Figure 310, the length of the first time window used can be 10 seconds, and the length of the second time window used can be 100 seconds, but this disclosure is not limited thereto.

[0085] According to the implementation, the voltage diagnostic unit 132 can calculate, per unit time, a long-short 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, or 155. Here, the long-short voltage moving average difference can be a value obtained by subtracting the smaller value from the larger value of the short-term voltage moving average and the long-term voltage moving average.

[0086] Reference Figure 3b Figure 320 shows the results obtained by the voltage diagnostic unit 132 based on... Figure 3a The voltage moving average line calculates the long-short-term voltage moving average difference for multiple battery cells (e.g., 151, 153, and 155). In Figure 320, the horizontal axis indicates time, and the vertical axis indicates the long-short-term voltage moving average difference.

[0087] The difference between the long-term and short-term voltage moving averages of a battery cell can depend on both the short-term and long-term voltage variation history of the battery cell.

[0088] The temperature and state of health (SOH) of a battery cell can continuously affect the cell voltage, both in the short and long term. Therefore, the long-short term voltage moving average difference of a battery cell without voltage anomalies may not be significantly different from that of other battery cells.

[0089] On the other hand, sudden voltage anomalies in battery cells due to internal and / or external short circuits may have a greater impact on the short-term voltage moving average than on the long-term voltage moving average. As a result, the difference between the long-term and short-term voltage moving averages of a battery cell is significantly different from that of other battery cells without voltage anomalies.

[0090] Therefore, the difference between the long-term and short-term voltage moving averages of a battery cell can be an indicator needed for diagnosing voltage anomalies.

[0091] According to the implementation, the voltage diagnostic unit 132 can diagnose voltage abnormalities in each battery cell 151, 153, or 155 based on the difference between the long- and short-term moving average voltage values ​​of each battery cell 151, 153, or 155.

[0092] According to the implementation method, the voltage diagnostic unit 132 can calculate the voltage diagnostic deviation of each battery cell 151, 153 or 155 per unit time based on the difference between the long-term and short-term voltage moving average values.

[0093] According to an embodiment, the voltage diagnostic unit 132 can calculate a voltage diagnostic deviation for each battery cell 151, 153, or 155, corresponding to the average of the long-short-term voltage moving average differences among the plurality of battery cells 151, 153, and 155 and the deviation between the long-short-term voltage moving average differences for each battery cell 151, 153, or 155. In this case, the voltage diagnostic unit 132 can diagnose voltage abnormalities in each battery cell 151, 153, or 155 based on the calculated voltage diagnostic deviation. For example, the voltage diagnostic unit 132 can diagnose battery cells with voltage abnormalities if the calculated voltage diagnostic deviation exceeds a preset threshold (e.g., 0.015).

[0094] According to the implementation method, the voltage diagnostic unit 132 can calculate the normalized difference between the long-term and short-term voltage moving average values ​​per unit time for each battery cell 151, 153 or 155 as the normalized voltage diagnostic deviation.

[0095] For example, voltage diagnostic unit 132 can normalize the long-short-term voltage moving average difference of each battery cell 151, 153, or 155 by averaging the long-short-term voltage moving average differences of multiple battery cells 151, 153, and 155. Alternatively, voltage diagnostic unit 132 can normalize the long-short-term voltage moving average difference by performing a division operation (e.g., DSL / Dav, where DSL indicates the long-short-term voltage moving average difference and Dav indicates the average value) on the long-short-term voltage moving average difference of each battery cell 151, 153, or 155 using the average value.

[0096] In another example, voltage diagnostic unit 132 can perform normalization by taking a logarithmic operation (e.g., Log(DSL), where DSL indicates the long-short-term voltage moving average difference) on the long-short-term voltage moving average difference for each battery cell 151, 153, or 155.

[0097] Reference Figure 3c Figure 330 shows a diagram based on the voltage diagnostic unit 132. Figure 3bThe voltage diagnostic deviation of multiple battery cells (e.g., 151, 153, and 155) is calculated using the difference between the long- and short-term moving average voltage values. In Figure 330, the X-axis can indicate time, and the vertical axis can indicate the voltage diagnostic deviation normalized by performing a division operation on the difference between the long- and short-term moving average voltage values ​​of each battery cell 151, 153, or 155 using the average value.

[0098] As shown in Figure 330, by normalizing the difference between the long-term and short-term voltage moving averages, the variation in the difference between the long-term and short-term voltage moving averages of each battery cell is amplified relative to the average value. Therefore, voltage anomaly diagnosis of battery cells can be performed more accurately.

[0099] According to the implementation, the voltage diagnostic unit 132 can diagnose voltage anomalies in each battery cell 151, 153, or 155 by comparing the calculated voltage diagnostic deviation (or normalized voltage diagnostic deviation) with a statistical variable threshold.

[0100] According to the implementation, the voltage diagnostic unit 132 can determine a statistical variable threshold per unit time the standard deviation of the voltage diagnostic deviation (or normalized voltage diagnostic deviation) depending on the multiple battery cells 151, 153, and 155. For example, the voltage diagnostic unit 132 can determine the statistical variable threshold based on Equation 1.

[0101] [Equation 1]

[0102] D threshold = β Sig(D diag )

[0103] In equation 1, D threshold The statistical variable threshold can be indicated, β can indicate a predetermined constant, and Sig can indicate a function of the standard deviation of the voltage diagnostic deviation for operating the plurality of battery cells 151, 153, and 155. β can be a factor used to determine diagnostic sensitivity and can be determined experimentally. When the embodiments disclosed herein are performed on a cell group including battery cells in which voltage abnormalities occur, β can be appropriately determined through experimentation and error to detect the corresponding battery cell as a voltage abnormality unit. For example, β can be set to at least 5, at least 6, at least 7, at least 8, or at least 9.

[0104] Meanwhile, battery cells with voltage anomalies may have a larger voltage diagnostic deviation than normal battery cells. Therefore, Sig(D) is calculated per unit time. diag This can eliminate the maximum value of voltage diagnostic deviation, thereby improving the accuracy and reliability of the diagnosis.

[0105] According to the implementation, the voltage diagnostic unit 132 can calculate a filtered diagnostic value by filtering the voltage diagnostic deviation (or normalized voltage diagnostic deviation) of each battery cell 151, 153, or 155 based on a statistical variable threshold. For example, the voltage diagnostic unit 132 can calculate one of two values ​​as the filtered diagnostic value based on Equation 2.

[0106] [Equation 2]

[0107] D filter = D diag - D threshold (If D) diag > D threshold )

[0108] D filter = 0 (if D diag ≤ D threshold )

[0109] In other words, when the voltage diagnostic deviation (or normalized voltage diagnostic deviation) is greater than the statistical variable threshold, the voltage diagnostic unit 132 can calculate the difference between the voltage diagnostic deviation and the statistical variable threshold as the filtered diagnostic value. On the other hand, when the voltage diagnostic deviation (or normalized voltage diagnostic deviation) is less than or equal to the statistical variable threshold, the voltage diagnostic unit 132 can calculate the filtered diagnostic value as 0.

[0110] Reference Figure 3d Figure 340 shows a diagram based on the voltage diagnostic unit 132. Figure 3c The voltage diagnostic deviation and statistical variable thresholds are used to calculate the filtered diagnostic values ​​for multiple battery cells (e.g., 151, 153, and 155). In Figure 330, the X-axis indicates time, and the vertical axis indicates the filtered diagnostic values.

[0111] According to the implementation, the voltage diagnostic unit 132 can diagnose voltage abnormalities in each battery cell 151, 153, or 155 based on the calculated filtered diagnostic values.

[0112] For example, the voltage diagnostic unit 132 can accumulate time periods for which the filtered diagnostic value of each battery cell 151, 153, or 155 is greater than (or greater than or equal to) a diagnostic threshold (e.g., 0), and diagnose battery cells that meet the condition that the accumulated time is greater than (or greater than or equal to) a preset reference time as battery cells with voltage abnormalities. The voltage diagnostic unit 132 can accumulate time periods for which the filtered diagnostic value continuously meets the condition that the value is greater than (or greater than or equal to) the diagnostic threshold. When multiple time periods exist, the voltage diagnostic unit 132 can calculate the accumulated time independently for each time period.

[0113] In another example, voltage diagnostic unit 132 can accumulate the number of data points included in time periods where the filtered diagnostic value is greater than (or greater than or equal to) a diagnostic threshold (e.g., 0) from the time series data of filtered diagnostic values, and diagnose battery cells that meet the condition that the accumulated data value is greater than (or greater than or equal to) a preset reference count as battery cells with voltage abnormalities. Voltage diagnostic unit 132 can accumulate the number of data points included in time periods where the filtered diagnostic value is continuously greater than (or greater than or equal to) the diagnostic threshold. When multiple time periods exist, voltage diagnostic unit 132 can independently accumulate the number of data points for each time period.

[0114] According to an implementation, the voltage diagnostic unit 132 can calculate a moving average diagnostic value for each battery cell 151, 153, or 155 based on a voltage diagnostic deviation (or normalized voltage diagnostic deviation). The voltage diagnostic unit 132 can recursively perform the following processes: (i) calculating a first moving average corresponding to a short-term moving average of the voltage diagnostic deviation (or normalized voltage diagnostic deviation) for each battery cell 151, 153, or 155 and a second moving average corresponding to a long-term moving average; (ii) calculating a long-short-term moving average difference corresponding to the difference between the first and second moving averages for each battery cell 151, 153, or 155; and (iii) calculating a normalized value of the long-short-term moving average difference as the moving average diagnostic value for each battery cell 151, 153, or 155. That is, the voltage diagnostic unit 132 can use... Figure 3c Normalized voltage diagnostic deviation replacement Figure 2 The voltage time series data. That is, the voltage diagnostic unit 132 can be based on a first time window for calculating a first moving average and a second time window for calculating a second moving average.

[0115] The voltage diagnostic unit 132 can calculate the filtered diagnostic value for each battery cell 151, 153 or 155 by using the calculated moving average diagnostic value in the manner described above, and diagnose voltage abnormalities based on the calculated filtered diagnostic value.

[0116] Reference Figure 3e Figure 350 shows the long-short term voltage moving average difference for multiple battery cells (e.g., 151, 153, and 155) calculated by voltage diagnostic unit 132 through processes (i) and (ii). In Figure 350, the horizontal axis indicates time, and the vertical axis indicates the moving average difference.

[0117] Reference Figure 3fFigure 360 ​​shows the moving average diagnostic values ​​for multiple battery cells (e.g., 151, 153, and 155) calculated via process (iii). In Figure 360, the horizontal axis indicates time, and the vertical axis indicates the moving average diagnostic values.

[0118] Reference Figure 3g Figure 370 shows a diagram based on the voltage diagnostic unit 132. Figure 3f The moving average diagnostic value is used to calculate the filtered diagnostic values ​​for multiple battery cells (e.g., 151, 153, and 155). In Figure 370, the horizontal axis indicates time, and the vertical axis indicates the filtered diagnostic value.

[0119] According to the implementation, the voltage diagnostic unit 132 can recursively repeat processes (i) to (iii) at least once. That is, the voltage diagnostic unit 132 can use Figure 3f Moving average diagnostic value replacement Figure 2 Voltage time series data.

[0120] By repeating the above recursive process, the diagnosis of abnormal battery cell voltages can be performed more accurately. (See reference...) Figure 3d A positive profile pattern was observed in two time periods from the time series data of filtered diagnostic values ​​of battery cells with voltage anomalies. However, referring to... Figure 3g In the time series data of filtered diagnostic values ​​for battery cells with abnormal voltage, from the ratio Figure 3d The positive profile pattern is visible over a longer period of time. Therefore, during repeated recursive operations, the timing of voltage anomalies in battery cells can be detected more accurately.

[0121] Diagnosis of volume abnormalities

[0122] According to an embodiment, the capacity diagnostic unit 133 can calculate the state of harmonics (SOH) of multiple battery cells 151, 153, and 155 based on time-series data acquired by the acquisition unit 131. Hereinafter, SOH can indicate life-related parameters of battery cells 151, 153, and / or 155, and may include at least one of SOHc, which indicates battery capacity, or SOHr, which indicates battery resistance growth. An embodiment of the capacity diagnostic unit 133 calculating SOHc will be described below. However, this is merely an embodiment, and the capacity diagnostic unit 133 can calculate various SOHs (such as SOHc, SOHr, and the final SOH calculated based thereon).

[0123] According to the implementation, the capacity diagnostic unit 133 can calculate the SOC difference before and after the charging period of the plurality of battery cells 151, 153 and 155 based on time series data.

[0124] For example, the capacity diagnostic unit 133 can calculate the SOC difference based on the SOC data included in the time series data by using the SOC value at a first time point before the charging period and the SOC value at a second time point after the charging period.

[0125] In another example, the capacity diagnostic unit 133 may calculate the open-circuit voltage (OCV) of a plurality of battery cells 151, 153 and 155 based on at least one of time-series voltage data or time-series current data included in the time-series data, convert the calculated OCV to SOC by using an SOC-OCV table pre-stored in memory 130, and calculate the SOC difference.

[0126] According to an implementation, the capacity diagnostic unit 133 can calculate the integral current values ​​of multiple battery cells 151, 153, and 155 during a charging period based on time-series data. For example, the capacity diagnostic unit 133 can calculate the current value and the integral current value during the charging period, which are included in the time-series data, based on the duration of the charging period. Here, the unit of the integral current value can be 'ampere-hours (Ah)'.

[0127] According to another embodiment, when the calculated SOC difference is greater than or equal to (exceeds) a specified value, the capacity diagnostic unit 133 can calculate the current integral value during the charging period. For example, when the SOC difference of battery cells 151, 153, or 155 calculated in the current period is less than (or less than or equal to) a specified value, the capacity diagnostic unit 133 may not calculate the current integral value of battery cells 151, 153, or 155 in the current period. In this case, since the current integral value of battery cells 151, 153, or 155 is not calculated, the capacity diagnostic unit 133 may not calculate the SOHc of battery cells 151, 153, or 155. In another example, when the SOC difference of battery cells 151, 153, or 155 calculated in the current period is greater than or equal to (or exceeds) a specified value, the capacity diagnostic unit 133 can calculate the current integral value of battery cells 151, 153, or 155 in the current period. In this case, the capacity diagnostic unit 133 can calculate the SOHc of battery cell 151, 153 or 155 by using the current integral value of battery cell 151, 153 or 155.

[0128] According to the implementation, the capacity diagnostic unit 133 can calculate the SOH of battery cells 151, 153, and / or 155 based on the SOC difference, current integral value, and initial capacity of battery cells 151, 153, and / or 155. cHere, the units of the initial capacity of battery cells 151, 153 and / or 155 can be the same as the units of the current integral value 'Ah'.

[0129] For example, the capacity diagnostic unit 133 can calculate the SOH of battery cells 151, 153 and / or 155 based on Equation 3. c .

[0130] [Equation 3]

[0131] SOH c = 100 (IΔT) / (C ΔSOC / 100)

[0132] In Equation 3, IΔT indicates the integral value of the current of battery cells 151, 153 and / or 155, ΔSOC indicates the SOC difference of battery cells 151, 153 and / or 155, and C indicates the initial value of battery cells 151, 153 and / or 155.

[0133] Reference Figure 4a Figure 410 shows the SOH of multiple battery cells (e.g., 151, 153, and 155) calculated by the capacity diagnostic unit 133. c In Figure 410, the horizontal axis indicates time, and the vertical axis indicates SOH. c .

[0134] According to the implementation method, the capacity diagnostic unit 133 can calculate the SOH of the target battery cell per unit time. c The battery diagnostic device 101 can measure the SOH (State of Health) calculated by the capacity diagnostic unit 133. c The SOH is cumulatively stored in memory 130 over a specified period of time to manage the SOH as shown in Figure 410. c data.

[0135] According to the implementation, the capacity diagnostic unit 133 can be based on the SOH (e.g., the calculated SOH) of each battery cell 151, 153, or 155. c The SOH deviation can be calculated using the following methods: (e.g., the deviation between the median SOH of multiple battery cells 151, 153, and 155 and the SOH of each individual battery cell 151, 153, or 155); (e.g., the deviation between the average SOH of multiple battery cells 151, 153, and 155 and the SOH of each individual battery cell 151, 153, or 155); or (e.g., the deviation between the moving average SOH of multiple battery cells 151, 153, and 155 and the moving average SOH of each individual battery cell 151, 153, or 155).

[0136] According to the implementation method, the capacity diagnostic unit 133 can calculate the SOH moving average value of each battery cell 151, 153, or 155 per unit time based on a third time window. The length of the third time window can be an integer multiple of a unit time, the end point of the third time window can be the current time point, and the start point of the third time window can be a time point predetermined before the current time point. The moving average value can represent any one of a simple moving average, a weighted moving average, or an exponential moving average.

[0137] The following describes an implementation of how the capacity diagnostic unit 133 calculates the SOH exponential moving average. However, this is merely an example, and the capacity diagnostic unit 133 can calculate various moving averages, such as the simple SOH moving average, the SOH weighted moving average, the SOH exponential moving average, etc.

[0138] According to the implementation method, the capacity diagnostic unit 133 can input the SOH of each battery cell 151, 153 or 155 into Equation 4 to calculate the exponential moving average of SOH at the current time point.

[0139] [Equation 4]

[0140] EMA t = α SOH + (1-α) EMA t-1

[0141] In Equation 4, EMAt indicates the exponentially moving average of the SOH of battery cells 151, 153, or 155 at the current time point, α indicates the weight value, and SOH indicates the SOH of battery cells 151, 153, or 155 at the current time point. t-1 This indicates the moving average of the State of Health (SOH) index of battery cells 151, 153, or 155 at a previous time point. The weight values ​​can be set differently depending on the specifications of the various battery cells 151, 153, and 155. For example, the weight value can be set to, but is not limited to, 0.05.

[0142] Reference Figure 4b Figure 420 shows the exponential moving average of SOH for multiple battery cells (e.g., 151, 153, and 155) calculated by the capacity diagnostic unit 133. In Figure 420, the horizontal axis indicates time, and the vertical axis indicates the exponential moving average of SOH.

[0143] According to an embodiment, the capacity diagnostic unit 133 can calculate the moving average of the state of equilibrium (SOH) of the target battery cell per unit time. The battery diagnostic device 101 can cumulatively store the moving average of SOH calculated by the capacity diagnostic unit 133 in the memory 130 over a specified period of time to manage the moving average data as shown in Figure 420. According to an embodiment, the capacity diagnostic unit 133 can obtain the moving average data of the target battery cell by applying the SOH data stored in the memory 130 (e.g., Figure 410 of FIG. 4) to moving average filtering.

[0144] According to the implementation, the capacity diagnostic unit 133 can calculate, per unit time, the SOH deviation corresponding to the deviation between the average of multiple moving averages of SOH of multiple battery cells 151, 153 and 155 and the moving average of SOH of each battery cell 151, 153 or 155.

[0145] According to the implementation, the capacity diagnostic unit 133 can diagnose capacity abnormalities in each battery cell 151, 153, or 155 based on the SOH deviation. For example, the capacity diagnostic unit 133 can diagnose whether there is a defect due to a reduction in the capacity of each battery cell 151, 153, or 155.

[0146] According to the implementation method, the capacity diagnostic unit 133 can compare the SOH deviation with a preset threshold to diagnose capacity abnormalities in each battery cell 151, 153, or 155. For example, the capacity diagnostic unit 133 can diagnose battery cells with SOH deviations exceeding (or greater than or equal to) the threshold as battery cells with abnormal capacity.

[0147] For example, the capacity diagnostic unit 133 can accumulate time periods for each battery cell 151, 153, or 155 where the SOH deviation is greater than (or greater than or equal to) a threshold, and diagnose battery cells that meet the condition that the accumulated time is greater than (or greater than or equal to) a preset reference time as battery cells with abnormal capacity. The battery diagnostic device 101 can accumulate time periods where the SOH deviation continuously meets the condition that is greater than (or greater than or equal to) a threshold. When multiple time periods exist, the battery diagnostic device 101 can independently calculate the accumulated time for each time period.

[0148] In another example, battery diagnostic device 101 can accumulate the number of data points included in time periods where the SOH deviation from the time series data is greater than (or greater than or equal to) a threshold, and diagnose battery cells that meet the condition that the accumulated data value is greater than (or greater than or equal to) a preset reference count as battery cells with abnormal capacity. Battery diagnostic device 101 can accumulate the number of data points included in time periods where the SOH deviation is consistently greater than (or greater than or equal to) the threshold. When multiple time periods exist, battery diagnostic device 101 can independently accumulate the number of data points for each time period.

[0149] According to the implementation, the detection unit 134 can detect abnormal battery cells 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.

[0150] For example, the detection unit 134 can detect a battery cell that is diagnosed by the voltage diagnosis unit 132 as having a voltage abnormality and by the capacity diagnosis unit 133 as having a capacity abnormality as an abnormal battery cell.

[0151] In another example, capacity diagnostic unit 133 can diagnose a capacity abnormality in at least one battery cell diagnosed as having a voltage abnormality by voltage diagnostic unit 132. In this case, detection unit 134 can detect the battery cell diagnosed as having a capacity abnormality by capacity diagnostic unit 133 as a faulty battery cell. That is, in the corresponding example, battery diagnostic device 101 can primarily use voltage diagnostic unit 132 to diagnose voltage abnormalities in each battery cell 151, 153, or 155, secondarily use capacity diagnostic unit 133 to diagnose capacity abnormalities for at least one battery cell with a voltage abnormality, and finally detect the battery cell with a capacity abnormality as a faulty battery cell.

[0152] According to an embodiment, the anomaly handling unit 135 can perform anomaly handling functions based on the anomaly diagnosis results of multiple battery cells 151, 153, and 155. Here, the anomaly handling functions may include notification functions or short-circuit functions.

[0153] According to the implementation, the anomaly processing unit 135 can send the anomaly diagnosis results for the plurality of battery cells 151, 153 and 155 to the user terminal 104 connected via a wired and / or wireless network.

[0154] According to an embodiment, the anomaly handling unit 135 can isolate the abnormal battery cell from the electronic device 102 based on the anomaly diagnosis results for the plurality of battery cells 151, 153, and 155. Here, isolation may include electrical and / or mechanical isolation.

[0155] Figure 5 This is an operation flowchart of a battery diagnostic device according to an implementation method. It will be used... Figure 1 To describe the components Figure 5 .

[0156] Figure 5 The illustrated embodiment is merely one embodiment, and the order of operation according to the various embodiments of this disclosure can be related to... Figure 5 The differences shown can be omitted. Figure 5 Some of the operations shown can either change the order of operations or combine operations.

[0157] Reference Figure 5 In operation 505, battery diagnostic device 101 can acquire time-series data related to the state of multiple battery cells 151, 153, and 155. According to an embodiment, battery diagnostic device 101 can acquire time-series data using communication circuitry 110 and / or sensor 120.

[0158] In operation 510, battery diagnostic device 101 can diagnose voltage anomalies in each battery cell 151, 153, or 155 based on the time-series data acquired in operation 505. More specifically, battery diagnostic device 101 can diagnose voltage anomalies in each battery cell 151, 153, or 155 based on voltage time-series data included in the time-series data.

[0159] According to the implementation, the battery diagnostic device 101 can calculate the long-short term voltage moving average difference for each battery cell 151, 153 or 155 based on time series data, and diagnose voltage abnormalities for each battery cell 151, 153 or 155 based on the calculated long-short term voltage moving average difference.

[0160] The following will refer to Figures 6 to 8 The operation 510 performed by the battery diagnostic device 101 to diagnose voltage abnormalities in each battery cell 151, 153, or 155 is described in detail.

[0161] In operation 515, battery diagnostic device 101 can diagnose capacity anomalies in each battery cell 151, 153, or 155 based on time-series data acquired in operation 505.

[0162] According to the implementation, the battery diagnostic device 101 can calculate the SOH difference of each battery cell 151, 153 or 155 based on time series data, and diagnose the capacity abnormality of each battery cell 151, 153 or 155 based on the calculated SOH deviation.

[0163] The following will refer to Figure 9The operation 515 performed by the battery diagnostic device 101 to diagnose capacity abnormalities in each battery cell 151, 153, or 155 is described in detail.

[0164] In operation 520, battery diagnostic device 101 can detect abnormal battery cells among a plurality of battery cells 151, 153, and 155. According to an embodiment, battery diagnostic device 101 can detect abnormal battery cells based on at least one of the voltage abnormality diagnostic result of operation 510 or the capacity abnormality diagnostic result of operation 515.

[0165] For example, battery diagnostic device 101 can detect a battery cell that is diagnosed as having a voltage abnormality in operation 510 and a capacity abnormality in operation 515 as an abnormal battery cell.

[0166] Below, we will refer to Figures 6 to 8 This describes the operation performed by the battery diagnostic device 101 to diagnose voltage abnormalities in each battery cell 151, 153, or 155.

[0167] Figure 6 This is an operation flowchart of a battery diagnostic device according to an implementation method. It will be used... Figure 1 To describe the components Figure 6 .

[0168] Figure 6 The embodiments shown are merely embodiments, and the order of operation according to the various embodiments of this disclosure may vary. Figure 6 The differences shown can be omitted. Figure 6 Some of the operations shown can either change the order of operations or combine operations.

[0169] Reference Figure 6 In operation 605, the battery diagnostic device 101 can be based on... Figure 5 The time series data obtained in operation 505 is used to calculate the long-short term voltage moving average difference for each battery cell 151, 153, or 155.

[0170] According to an embodiment, the battery diagnostic device 101 can calculate the short-term moving average voltage of each battery cell 151, 153, or 155 per unit time based on a first time window. The battery diagnostic device 101 can also calculate the long-term moving average voltage of each battery cell 151, 153, or 155 per unit time based on a second time window.

[0171] For example, the input factor in calculating the voltage moving average could be the voltage value of each battery cell 151, 153, or 155 within a specific time window (e.g., a first time window or a second time window). In this case, the calculated voltage moving average could indicate a long-short-term moving average of the voltage value of each battery cell 151, 153, or 155.

[0172] In another example, the input factor in the voltage moving average calculation can be the deviation between a reference voltage of multiple battery cells 151, 153, and 155 and the voltage of each battery cell 151, 153, or 155 within a specific time window (e.g., a first time window or a second time window). Here, the reference voltage can be determined based on the multiple battery cells 151, 153, and 155, and can be determined as the average or median of the voltage values ​​of the multiple battery cells 151, 153, and 155. In this case, the calculated voltage moving average can indicate a long-short-term moving average of the voltage deviation of each battery cell 151, 153, or 155.

[0173] In operation 610, the battery diagnostic device 101 can calculate, per unit time, a long-short 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, or 155. Here, the long-short voltage moving average difference can be a value obtained by subtracting the smaller value from the larger of the short-term and long-term voltage moving averages calculated in operation 605.

[0174] According to the implementation, the battery diagnostic device 101 can diagnose voltage abnormalities in each battery cell 151, 153, or 155 based on the difference between the long- and short-term moving average voltage values ​​of each battery cell 151, 153, or 155.

[0175] In operation 615, the battery diagnostic device 101 can calculate the voltage diagnostic deviation of each battery cell 151, 153, or 155 per unit time based on the long-short-term voltage moving average difference calculated in operation 610.

[0176] According to the implementation, the battery diagnostic device 101 can calculate for each battery cell 151, 153, or 155 a voltage diagnostic deviation corresponding to the average of the long-short-term voltage moving average difference of the plurality of battery cells 151, 153, and 155 and the deviation between the long-short-term voltage moving average difference of each battery cell 151, 153, or 155.

[0177] According to the implementation method, the battery diagnostic device 101 can calculate a normalized value of the long-short-term voltage moving average difference for each battery cell 151, 153 or 155 as the normalized voltage diagnostic deviation per unit time.

[0178] For example, battery diagnostic device 101 can normalize the long-short-term voltage moving average difference of each battery cell 151, 153, or 155 by averaging the long-short-term voltage moving average differences of multiple battery cells 151, 153, and 155. Alternatively, battery diagnostic device 101 can normalize the long-short-term voltage moving average difference by performing a division operation (e.g., DSL / Dav, where DSL indicates the long-short-term voltage moving average difference and Dav indicates the average) on the long-short-term voltage moving average difference of each battery cell 151, 153, or 155 using the average value.

[0179] In another example, the battery diagnostic device 101 can perform normalization by taking a logarithmic operation (e.g., Log(DSL), where DSL indicates the long-short-term voltage moving average difference) on the long-short-term voltage moving average difference for each battery cell 151, 153, or 155.

[0180] In operation 620, battery diagnostic device 101 can diagnose voltage anomalies in each battery cell 151, 153, or 155 based on the voltage diagnostic deviation obtained in operation 615. For example, battery diagnostic device 101 can diagnose battery cells with voltage anomalies whose calculated voltage diagnostic deviation exceeds a preset threshold (e.g., 0.015).

[0181] According to an embodiment, the battery diagnostic device 101 can diagnose voltage anomalies in each battery cell 151, 153, or 155 by comparing the voltage diagnostic deviation (or normalized voltage diagnostic deviation) calculated in operation 615 with a statistical variable threshold. The following will refer to... Figure 7 The operation performed by the battery diagnostic device 101 to diagnose voltage abnormalities by comparing voltage diagnostic deviations with statistical variable thresholds is described in detail.

[0182] Figure 7 This is an operation flowchart of a battery diagnostic device according to an implementation method. It will be used... Figure 1 To describe the components Figure 7 .

[0183] Figure 7 The embodiments shown are merely embodiments, and the order of operation according to the various embodiments of this disclosure may vary. Figure 7 The differences shown can be omitted. Figure 7Some of the operations shown can either change the order of operations or combine operations.

[0184] Reference Figure 7 In operation 705, battery diagnostic device 101 can determine, per unit time, a statistical variable threshold for the standard deviation of voltage diagnostic deviation (or normalized voltage diagnostic deviation) depending on multiple battery cells 151, 153, and 155. For example, voltage diagnostic unit 132 can determine the statistical variable threshold based on Equation 1.

[0185] In operation 710, battery diagnostic device 101 can calculate a filtered diagnostic value by filtering the voltage diagnostic deviation (or normalized voltage diagnostic deviation) of each battery cell 151, 153, or 155 based on a statistical variable threshold. For example, battery diagnostic device 101 can calculate one of two values ​​as the filtered diagnostic value based on Equation 2.

[0186] In operation 715, battery diagnostic device 101 can diagnose voltage anomalies in each battery cell 151, 153, or 155 based on the filtered diagnostic values ​​calculated in operation 710.

[0187] For example, battery diagnostic device 101 can accumulate time periods for each battery cell 151, 153, or 155 where the filtered diagnostic value is greater than (or greater than or equal to) a diagnostic threshold (e.g., 0), and diagnose battery cells that meet the condition that the accumulated time is greater than (or greater than or equal to) a preset reference time as battery cells with abnormal voltage. Battery diagnostic device 101 can accumulate time periods that continuously meet the condition that the filtered diagnostic value is greater than (or greater than or equal to) the diagnostic threshold. When multiple time periods exist, battery diagnostic device 101 can calculate the accumulated time independently for each time period.

[0188] In another example, battery diagnostic device 101 can accumulate the number of data points included in time periods where the filtered diagnostic values ​​from the time series data of filtered diagnostic values ​​are greater than (or greater than or equal to) a diagnostic threshold (e.g., 0), and diagnose battery cells that meet the condition that the accumulated data value is greater than (or greater than or equal to) a preset reference count as battery cells with voltage abnormalities. Battery diagnostic device 101 can accumulate the number of data points included in time periods where the filtered diagnostic values ​​are continuously greater than (or greater than or equal to) the diagnostic threshold. When multiple time periods exist, battery diagnostic device 101 can independently accumulate the number of data points for each time period.

[0189] Figure 8 This is an operation flowchart of a battery diagnostic device according to an implementation method. It will be used... Figure 1 To describe the components Figure 8 .

[0190] Figure 8 The embodiments shown are merely embodiments, and the order of operation according to the various embodiments of this disclosure may differ. Figure 8 The order shown can be omitted. Figure 8 Some of the operations shown can either change the order of operations or combine operations.

[0191] Figure 8 Operations 805 to 815 and Figure 6 Operations 605 to 615 are the same and therefore will not be described again.

[0192] Reference Figure 8 In operation 820, battery diagnostic device 101 can calculate a first moving average corresponding to a short-term moving average of the voltage diagnostic deviation (or normalized voltage diagnostic deviation) calculated in operation 815. Battery diagnostic device 101 can calculate the first moving average based on a first time window.

[0193] In operation 825, battery diagnostic device 101 can calculate a second moving average corresponding to the long-term moving average of the voltage diagnostic deviation (or normalized voltage diagnostic deviation) calculated in operation 815. Battery diagnostic device 101 can calculate the second moving average based on a second time window.

[0194] In operation 830, the battery diagnostic device 101 can calculate, for each battery cell 151, 153, or 155, a long-short-term moving average difference corresponding to the difference between the first moving average calculated in operation 820 and the second moving average calculated in operation 825.

[0195] In operation 835, battery diagnostic device 101 can calculate a moving average diagnostic value for each battery cell 151, 153, or 155. According to an embodiment, battery diagnostic device 101 can calculate a normalized value of the long-short term voltage moving average difference calculated in operation 830 as a moving average diagnostic value for each battery cell 151, 153, or 155.

[0196] In operation 804, the battery diagnostic device 101 can determine whether the diagnostic time has elapsed. The diagnostic time can be preset.

[0197] If it is determined in operation 840 that the diagnostic time has not yet passed ("No"), the battery diagnostic device 101 may recursively repeat operations 820 to 835.

[0198] When it is determined in operation 840 that the diagnostic time has elapsed (“Yes”), the battery diagnostic device 101 can diagnose voltage anomalies in each battery cell 151, 153, or 155 based on a moving average diagnostic value calculated in operation 835. For example, the battery diagnostic device 101 can diagnose voltage anomalies in each battery cell 151, 153, or 155 by comparing the moving average diagnostic value with a preset threshold. In another example, the battery diagnostic device 101 can diagnose voltage anomalies based on a reference... Figure 7 The described method calculates a filter diagnostic value for each battery cell 151, 153, or 155, and diagnoses voltage anomalies based on the calculated filter diagnostic values.

[0199] Below, we will refer to Figure 9 This describes the operation performed by the battery diagnostic device 101 to diagnose capacity abnormalities in each battery cell 151, 153, or 155.

[0200] Figure 9 This is an operation flowchart of a battery diagnostic device according to an implementation method. It will be used... Figure 1 To describe the components Figure 9 .

[0201] Figure 9 The illustrated embodiment is merely one embodiment, and the order of operation according to the various embodiments of this disclosure can be related to... Figure 9 The differences shown can be omitted. Figure 9 Some of the operations shown can either change the order of operations or combine operations.

[0202] Reference Figure 9 In operation 905, the battery diagnostic device 101 can be based on... Figure 5 The time-series data acquired in operation 505 is used to calculate the SOH per unit time for each battery cell 151, 153, or 155. In this document, SOH can indicate a lifespan-related parameter of battery cells 151, 153, and / or 155, and may include at least one of SOHc, which indicates SOH of battery capacity, or SOHr, which indicates SOH of battery resistance growth. An implementation of how the battery diagnostic device 101 calculates SOHc will be described below. However, this is merely one implementation, and the battery diagnostic device 101 can calculate various SOHs (such as SOHc, SOHr, and the final SOH calculated based thereon).

[0203] According to the implementation method, the battery diagnostic device 101 can calculate the SOC difference of multiple battery cells 151, 153 and 155 before and after the charging period based on time series data.

[0204] For example, battery diagnostic device 101 can calculate the SOC difference based on SOC data included in time series data by using the SOC value at a first time point before the charging period and the SOC value at a second time point after the charging period.

[0205] In another example, battery diagnostic device 101 can calculate the open-circuit voltage (OCV) of a plurality of battery cells 151, 153 and 155 based on at least one of time-series voltage data or time-series current data included in the time-series data, convert the calculated OCV to SOC by using an SOC-OCV table pre-stored in memory 130, and calculate the SOC difference.

[0206] According to an embodiment, the battery diagnostic device 101 can calculate the integral current values ​​of multiple battery cells 151, 153, and 155 during a charging period based on time-series data. For example, the battery diagnostic device 101 can calculate the current value and the integral current value during the charging period included in the time-series data based on the duration of the charging period. Here, the unit of the integral current value can be 'ampere-hours (Ah)'.

[0207] According to another embodiment, when the calculated SOC difference is greater than or equal to (exceeds) a specified value, the battery diagnostic device 101 can calculate the current integral value during the charging period. For example, when the SOC difference of battery cells 151, 153, or 155 calculated in the current period is less than (or less than or equal to) a specified value, the battery diagnostic device 101 may not calculate the current integral value of battery cells 151, 153, or 155 in the current period. In this case, since the current integral value of battery cells 151, 153, or 155 is not calculated, the battery diagnostic device 101 may not calculate the SOHc of battery cells 151, 153, or 155. In another example, when the SOC difference of battery cells 151, 153, or 155 calculated in the current period is greater than or equal to (or exceeds) a specified value, the battery diagnostic device 101 can calculate the current integral value of battery cells 151, 153, or 155 in the current period. In this case, the battery diagnostic device 101 can calculate the SOHc of battery cell 151, 153 or 155 by using the current integral value of battery cell 151, 153 or 155.

[0208] According to an embodiment, the battery diagnostic device 101 can calculate the SOHc of battery cells 151, 153, and / or 155 based on the SOC difference, current integral value, and initial capacity of battery cells 151, 153, and / or 155. Here, the unit of the initial capacity of battery cells 151, 153, and / or 155 can be the same as the unit 'Ah' of the current integral value.

[0209] For example, battery diagnostic device 101 can calculate the SOHc of battery cells 151, 153 and / or 155 based on Equation 3.

[0210] In operation 910, the battery diagnostic device 101 can calculate the SOH deviation for each battery cell 151, 153, or 155. For example, the SOH deviation can be the deviation between the median SOH of multiple battery cells 151, 153, and 155 and the SOH of each battery cell 151, 153, or 155; the deviation between the average SOH of multiple battery cells 151, 153, and 155 and the SOH of each battery cell 151, 153, or 155; or the deviation between the moving average SOH of multiple battery cells 151, 153, and 155 and the moving average SOH of each battery cell 151, 153, or 155.

[0211] According to the implementation method, the battery diagnostic device 101 can calculate the SOH moving average per unit time for each battery cell 151, 153, or 155 based on a third time window. Here, the length of the third time window can be an integer multiple of a unit time, the end point of the third time window can be the current time point, and the start point of the third time window can be a time point that is a predetermined time length earlier than the current time point. The moving average can be any one of a simple moving average, a weighted moving average, or an exponential moving average.

[0212] The following describes an implementation of how the battery diagnostic device 101 calculates the SOH exponential moving average. However, this is merely an example, and the battery diagnostic device 101 can calculate various moving averages, such as the simple SOH moving average, the SOH weighted moving average, the SOH exponential moving average, etc.

[0213] According to the implementation method, the battery diagnostic device 101 can input the SOH of each battery cell 151, 153 or 155 into Equation 4 to calculate the exponential moving average of SOH at the current time point.

[0214] According to an embodiment, the battery diagnostic device 101 can calculate for each battery cell 151, 153, or 155 a deviation corresponding to the average of multiple moving averages of SOH for multiple battery cells 151, 153, and 155 and the deviation between the moving average of SOH for each battery cell 151, 153, or 155.

[0215] In operation 915, battery diagnostic device 101 can diagnose capacity abnormalities in each battery cell 151, 153, or 155 based on SOH deviation. For example, battery diagnostic device 101 can diagnose whether there is a defect due to a reduction in capacity of each battery cell 151, 153, or 155.

[0216] According to the implementation method, the battery diagnostic device 101 can compare the SOH deviation with a preset threshold to diagnose capacity abnormalities in each battery cell 151, 153, or 155. For example, the battery diagnostic device 101 can diagnose battery cells with SOH deviations exceeding (or greater than or equal to) the threshold as capacity-abnormal battery cells.

[0217] For example, battery diagnostic device 101 can accumulate time periods for which the State of Health (SOH) deviation of each battery cell 151, 153, or 155 is greater than (or greater than or equal to) a threshold, and diagnose battery cells that meet the condition that the accumulated time is greater than (or greater than or equal to) a preset reference time as battery cells with capacity abnormalities. Battery diagnostic device 101 can accumulate time periods for which the SOH deviation continuously meets the condition that is greater than (or greater than or equal to) the threshold. When multiple time periods exist, battery diagnostic device 101 can calculate the accumulated time independently for each time period.

[0218] In another example, battery diagnostic device 101 can accumulate the number of data points included in time periods where the SOH deviation from the time series data is greater than (or greater than or equal to) a threshold (e.g., 0), and diagnose battery cells that meet the condition that the accumulated data value is greater than (or greater than or equal to) a preset reference count as battery cells with capacity abnormalities. Battery diagnostic device 101 can accumulate the number of data points included in time periods where the SOH deviation is consistently greater than (or greater than or equal to) the threshold. When multiple time periods exist, battery diagnostic device 101 can independently accumulate the number of data points for each time period.

[0219] Figure 10 This is an operation flowchart of a battery diagnostic device according to an implementation method. It will be used... Figure 1 To describe the components Figure 10 .

[0220] Figure 10 The embodiments shown are merely embodiments, and the order of operation according to the various embodiments of this disclosure may differ. Figure 10 The order shown can be omitted. Figure 10 Some of the operations shown can either change the order of operations or combine operations.

[0221] Figure 10 Operation 1005 and Figure 5 The operation is the same as 505, and therefore will not be described again.

[0222] Reference Figure 10In operation 1010, the battery diagnostic device 101 can diagnose whether each battery cell 151, 153, or 155 has a voltage abnormality. The voltage abnormality diagnostic operation of operation 1010 can refer to... Figure 5 Operation 510 and the above Figures 6 to 8 The operation.

[0223] When a voltage abnormality is diagnosed in battery cell 151, 153, or 155 during operation 1010, battery diagnostic device 101 can diagnose whether battery cell 151, 153, or 155 has a capacity abnormality during operation 1015. The capacity abnormality diagnosis operation in operation 1015 can refer to... Figure 5 Operation 510 and the above Figure 9 The operation.

[0224] When battery cells 151, 153, or 155 are diagnosed to have abnormal capacity in operation 1015, battery diagnostic device 101 can detect battery cells 151, 153, or 155 as abnormal battery cells in operation 1020.

[0225] When battery cells 151, 153, or 155 are diagnosed as having no voltage abnormalities in operation 1010 or no capacity abnormalities in operation 1015, the battery diagnostic device 101 can detect the corresponding battery cells 151, 153, or 155 as normal battery cells in operation 1025.

[0226] According to an embodiment, the battery diagnostic device 101 can perform anomaly handling functions based on the anomaly diagnostic results for a plurality of battery cells 151, 153, and 155. Here, the anomaly handling functions may include notification functions or short-circuit functions.

[0227] According to an embodiment, the battery diagnostic device 101 can send the abnormal diagnostic results of multiple battery cells 151, 153 and 155 to a user terminal 104 connected via a wired and / or wireless network.

[0228] According to an embodiment, the battery diagnostic device 101 can isolate abnormal battery cells from the electronic device 102 based on abnormal diagnostic results for a plurality of battery cells 151, 153, and 155. Here, isolation may include electrical and / or mechanical isolation.

[0229] Unless otherwise stated, terms such as “comprising,” “constituting,” or “having” above may mean that the corresponding component may be inherent and should therefore be interpreted as further including rather than excluding other components. Unless otherwise defined, all terms including technical or scientific terms have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments disclosed herein pertain. Terms generally used with respect to terms as defined in dictionaries should be interpreted as having the same meaning as in the context of the relevant art and should not be interpreted as having an ideal or overly formal meaning unless they are clearly defined in this document.

Claims

1. A battery diagnostic device, the battery diagnostic device comprising: An acquisition unit is configured to acquire time-series data related to the state of multiple battery cells included in the battery module; A voltage diagnostic unit is configured to calculate the long-short-term voltage moving average difference for each battery cell based on the time series data, and to diagnose voltage anomalies in each battery cell based on the long-short-term voltage moving average difference. A capacity diagnostic unit is configured to calculate the state of health (SOH) deviation of each battery cell based on the time series data, and to diagnose capacity abnormalities of each battery cell based on the SOH deviation. as well as A detection unit configured to detect abnormal battery cells based on the diagnostic results of at least one of the voltage diagnostic unit or the capacity diagnostic unit.

2. The battery diagnostic device according to claim 1, wherein, The voltage diagnostic unit is also configured to: The short-term voltage moving average of each battery cell is calculated based on the first time window; The long-term voltage moving average of each battery cell is calculated based on a second time window with a longer duration than the first time window. as well as For each battery cell, calculate the long-short voltage moving average difference, which corresponds to the difference between the short-term voltage moving average and the long-term voltage moving average.

3. The battery diagnostic device according to claim 1, wherein, The voltage diagnostic unit is also configured to: For each battery cell, calculate the voltage diagnostic deviation corresponding to the average of the long-short-term voltage moving average differences of the plurality of battery cells and the deviation between the long-short-term voltage moving average differences of each battery cell; and The voltage abnormality of each battery cell is diagnosed based on the voltage diagnostic deviation.

4. The battery diagnostic device according to claim 3, wherein, The voltage diagnostic unit is also configured to: Determine the threshold of a statistical variable for the standard deviation of the voltage diagnostic deviation that depends on the plurality of battery cells; For each battery cell, a filtered diagnostic value is calculated by filtering the voltage diagnostic deviation based on a statistical variable threshold; and The voltage anomalies of each battery cell are diagnosed based on the filtered diagnostic values.

5. The battery diagnostic device according to claim 1, wherein, The voltage diagnostic unit is also configured to: For each battery cell, the normalized value of the difference between the long-term and short-term voltage moving averages is calculated as the normalized voltage diagnostic bias; and The voltage anomaly of each battery cell is diagnosed based on the normalized voltage diagnostic deviation.

6. The battery diagnostic device according to claim 5, wherein, The voltage diagnostic unit is also configured to: Determine the threshold of a statistical variable for the standard deviation of the normalized voltage diagnostic deviation that depends on the plurality of battery cells; For each battery cell, a filtered diagnostic value is calculated by filtering the normalized voltage diagnostic deviation based on the threshold of the statistical variable; and The voltage anomalies of each cell are diagnosed based on the filtered diagnostic values.

7. The battery diagnostic device according to claim 5, wherein, The voltage diagnostic unit is also configured to: A moving average diagnostic value is calculated by recursively repeating processes (i) to (iii) at least once for each battery cell, said processes (i) to (iii) including: (i) Calculate a first moving average corresponding to the short-term moving average of the normalized voltage diagnostic deviation for each battery cell and a second moving average corresponding to the long-term moving average; (ii) for each battery cell, calculate the long-short-term moving average difference corresponding to the difference between the first and second moving averages; and (iii) for each battery cell, calculate the normalized value of the long-short-term moving average difference as the moving average diagnostic value. The voltage anomalies of each battery cell are diagnosed based on the moving average diagnostic value.

8. The battery diagnostic device according to claim 1, wherein, The capacity diagnostic unit is also configured to: The State of Health (SOH) of each battery cell is calculated based on the time series data; and For each battery cell, calculate the SOH deviation corresponding to the deviation between the median or average SOH of the plurality of battery cells and the SOH of each battery cell.

9. The battery diagnostic device according to claim 1, wherein, The capacity diagnostic unit is also configured to: The State of Health (SOH) of each battery cell is calculated based on the time series data. The moving average SOH of each battery cell is calculated based on the third time window; as well as For each battery cell, calculate the SOH deviation corresponding to the deviation between the average of the moving average of the SOH of the plurality of battery cells and the moving average of the SOH of each battery cell.

10. The battery diagnostic device according to claim 1, wherein, The capacity diagnostic unit is also configured to: The SOC difference of each battery cell before and after the charging period is calculated based on the time series data. Calculate the integral value of the current for each battery cell during the charging period based on the time series data; For each battery cell, SOHc, which indicates the SOH of the capacity of each battery cell, is calculated based on the SOC difference, current integral value, and initial capacity of each battery cell. as well as The SOH deviation of each battery cell is calculated based on SOHc.

11. The battery diagnostic device according to claim 1, wherein, The capacity diagnostic unit is also configured to diagnose a capacity abnormality in at least one battery cell that has been diagnosed as having a voltage abnormality by the voltage diagnostic unit.

12. The battery diagnostic device according to claim 11, wherein, The detection unit is also configured to detect battery cells that are diagnosed as having abnormal capacity by the capacity diagnostic unit as abnormal battery cells.

13. The battery diagnostic device according to claim 1, wherein, The detection unit is also configured to detect battery cells that are diagnosed by the voltage diagnostic unit as having voltage abnormalities and by the capacity diagnostic unit as having capacity abnormalities as abnormal battery cells.

14. A battery diagnostic method, the battery diagnostic method comprising the following steps: Acquire time-series data related to the state of multiple battery cells included in the battery module; The long-short term voltage moving average difference for each battery cell is calculated based on the time series data, and voltage anomalies in each battery cell are diagnosed based on the long-short term voltage moving average difference. The state of health (SOH) deviation of each battery cell is calculated based on the time series data, and the capacity abnormality of each battery cell is diagnosed based on the SOH deviation. as well as Abnormal battery cells are detected based on the diagnostic results of at least one of the voltage diagnostic unit or the capacity diagnostic unit.

15. The battery diagnostic method according to claim 13, wherein, The steps for diagnosing capacity abnormalities in each battery cell include the following: performing capacity abnormality diagnosis only on at least one battery cell diagnosed as having a voltage abnormality, and The steps for detecting the abnormal battery cells include the following: detecting battery cells diagnosed as having capacity abnormalities as the abnormal battery cells.

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