Battery diagnostic device and its operating method

Abstract

A battery diagnostic device according to one embodiment disclosed herein may include: an acquisition unit that acquires OCV (Open Circuit Voltage) data of a plurality of battery units; a deviation calculation unit that calculates the OCV change value of the plurality of battery units in a specified time interval based on the OCV data and calculates an OCV deviation value relative to the average, which is the difference between the average OCV change value of the plurality of battery units and the OCV change value of a target battery unit among the plurality of battery units; an EMA (Exponential Moving Average) calculation unit that applies an EMA filter to the OCV deviation value of the target battery unit relative to the average and calculates an EMA deviation value; and a diagnostic unit that diagnoses abnormalities in the target battery unit based on the EMA deviation value.

Description

[Technical Field]

[0001] This invention claims priority under Korean Patent Application No. 10-2023-0119152 dated September 7, 2023, and all content disclosed in the said Korean Patent Application is incorporated herein by reference. The embodiments disclosed herein relate to a battery diagnostic device and a method for operating the same. [Background technology]

[0002] In recent years, research and development on rechargeable batteries has been actively pursued. Here, rechargeable batteries refer to batteries that can be charged and discharged, and include both conventional Ni / Cd batteries, Ni / MH batteries, and the more recent lithium-ion batteries. Among rechargeable batteries, lithium-ion batteries have the advantage of having a much higher energy density than conventional Ni / Cd batteries and Ni / MH batteries. Furthermore, because lithium-ion batteries can be manufactured to be small and lightweight, they are used as power sources for mobile devices, and in recent years, their range of use has expanded to include power sources for electric vehicles, attracting attention as a next-generation energy storage medium.

[0003] Furthermore, secondary batteries can generally be used as battery packs, which include battery modules in which multiple battery cells are connected in series and / or parallel. Secondary batteries can also be used as battery racks, which include multiple battery modules and rack frames that house such battery modules.

[0004] Such 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, laptop computers, smartphones, and smartpads, but also in electric vehicles (EVs, HEVs, PHEVs) and large-capacity energy storage systems (ESS).

[0005] Such batteries can have their state and operation managed and controlled by a battery management system (BMS). The battery management system can be included with the battery in a single device.

[0006] Furthermore, the battery management system can manage and control the battery remotely from the device containing the battery. For example, the battery management system can be implemented on a separate server device. In this case, the battery management system can collect battery data and vehicle data from the vehicle, and use the collected data to manage and control the battery. [Overview of the Initiative] [Problems that the invention aims to solve]

[0007] If a short circuit or other type of failure occurs inside a battery, the likelihood of damage to the device containing the battery (e.g., EV, ESS) increases. Therefore, measures are needed to detect abnormal battery conditions and reduce the likelihood of damage to the device containing the battery.

[0008] Traditionally, battery cell diagnosis was performed using a calculation method that utilized all information, including SOC (State of Charge), current, capacity, and OCV (Open Circuit Voltage). This diagnostic method required the use of many factors, making diagnosis difficult if any specific information was missing. This problem arose in battery management systems implemented on server devices that collect data from vehicles, potentially leading to excessively high memory usage. Therefore, there is a need to simplify the data required for battery diagnosis.

[0009] The embodiments disclosed herein provide a battery diagnostic device and a method for operating the same that can diagnose battery abnormalities using only the battery's OCV data information.

[0010] The technical problems of the embodiments disclosed in this document are not limited to those mentioned above, and other technical problems not mentioned can be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0011] A battery diagnostic device according to one embodiment disclosed herein may include: an acquisition unit that acquires OCV (Open Circuit Voltage) data of a plurality of battery units; a deviation calculation unit that calculates the OCV change value of the plurality of battery units in a specified time interval based on the OCV data and calculates an OCV deviation value relative to the average, which is the difference between the average OCV change value of the plurality of battery units and the OCV change value of a target battery unit among the plurality of battery units; an EMA (Exponential Moving Average) calculation unit that applies an EMA filter to the OCV deviation value of the target battery unit relative to the average and calculates an EMA deviation value; and a diagnostic unit that diagnoses abnormalities in the target battery unit based on the EMA deviation value.

[0012] In a battery diagnostic device according to one embodiment disclosed herein, the designated time interval may be the time interval between a first time point before charging is performed on the battery unit and a second time point after the charging is performed.

[0013] In a battery diagnostic device according to one embodiment disclosed in this document, the EMA filter can calculate the EMA deviation value by inputting the EMA deviation value of a previous period and the OCV deviation value of the target battery unit relative to the average into a specified formula.

[0014] In a battery diagnostic device according to one embodiment disclosed herein, the designated formula may be the following formula 1.

[0015] [Formula 1] EMA t =α·dOCV t +(1-α)·EMA t-1

[0016] (In formula 1, EMA t is the EMA deviation score, α is the weight, dOCV t This is the OCV deviation value of the target battery unit relative to the average, EMA t-1 (This is the EMA deviation value for the previous period.)

[0017] In a battery diagnostic device according to one embodiment disclosed in this document, the acquisition unit acquires time-series voltage data and time-series current data of the plurality of battery units, and can acquire OCV data of a specified current value and a specified voltage range based on the time-series voltage data and the time-series current data.

[0018] In a battery diagnostic device according to one embodiment disclosed herein, the battery unit may include at least one of a battery cell, a battery module, a battery pack, or a battery rack.

[0019] A battery diagnostic device according to one embodiment disclosed herein further includes an abnormality processing unit that performs an abnormality processing function based on the abnormality diagnosis result of each of the plurality of battery units, and the abnormality processing function may include a notification function or a short-circuit function.

[0020] A battery diagnostic method according to one embodiment disclosed herein may include: acquiring OCV (Open Circuit Voltage) data of a plurality of battery units; calculating the OCV change value of the plurality of battery units in a specified time interval based on the OCV data; calculating an OCV deviation value relative to the average, which represents the difference between the average OCV change value of the plurality of battery units and the OCV change value of a target battery unit among the plurality of battery units; applying an EMA (Exponential Moving Average) filter to the OCV deviation value of the target battery unit relative to the average to calculate an EMA deviation value; and diagnosing an abnormality of the target battery unit based on the EMA deviation value.

[0021] In the battery diagnosis method according to an embodiment disclosed in this document, the specified time period may be a time period between a first time point before charging is performed on the battery unit and a second time point after the charging is performed.

[0022] In the battery diagnosis method according to an embodiment disclosed in this document, the EMA filter can calculate the EMA deviation value by inputting the EMA deviation value of the previous cycle and the OCV deviation value with respect to the average of the target battery unit into a specified formula. In the battery diagnosis method according to an embodiment disclosed in this document, the specified formula may be the above formula 1.

[0023] The battery diagnosis method according to an embodiment disclosed in this document further includes an operation of acquiring time-series voltage data and time-series current data of the plurality of battery units, and the operation of acquiring the OCV data may include an operation of acquiring the OCV data within a specified current value and a specified voltage range based on the time-series voltage data and the time-series current data.

[0024] In the battery diagnosis method according to an embodiment disclosed in this document, the battery unit may include at least one of a battery cell, a battery module, a battery pack, or a battery rack.

[0025] The battery diagnosis method according to an embodiment disclosed in this document further includes an operation of performing an abnormality processing function based on the abnormality diagnosis results of each of the plurality of battery units, and the abnormality processing function may include a notification function or a short-circuit function.

Advantages of the Invention

[0026] According to the embodiment disclosed in this document, the data used for battery abnormality diagnosis can be simplified. According to the embodiment disclosed in this document, the accuracy of low-capacity abnormality diagnosis of the battery can be improved. In addition, this document can provide various effects that can be understood directly or indirectly. [Brief explanation of the drawing]

[0027] [Figure 1] This is a block diagram of a battery diagnostic device according to one embodiment. [Figure 2] This graph shows the OCV deviation value relative to the average battery calculated by a battery diagnostic device according to one embodiment. [Figure 3] This graph shows the EMA deviation value of a battery calculated by a battery diagnostic device according to one embodiment. [Figure 4] This is an operation flowchart of a battery diagnostic device according to one embodiment. [Modes for carrying out the invention]

[0028] Various embodiments of the present invention are described below with reference to the accompanying drawings. However, this should be understood not as limiting the present invention to any particular embodiment, but rather as including various modifications, equivalents, and / or alternatives to the embodiments of the present invention.

[0029] The various embodiments and terminology used in this document are not intended to limit the technical features described herein to any particular embodiment, but should be understood to include various modifications, equivalents, or substitutes of such embodiments. In relation to the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more such items unless the context clearly indicates otherwise.

[0030] In this document, each phrase such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B, or C,” “at least one of A, B, and C,” and “at least one of A, B, or C” may include any one of the items listed together with the applicable phrase, or any possible combination thereof. Terms such as “first,” “second,” “first,” “second,” “A,” “B,” “(a),” or “(b)” may be used simply to distinguish one component from other components and, unless otherwise stated, do not limit the component in any other respect (e.g., importance or order).

[0031] Wherever a component (e.g., the first) is referred to as being "coupled," "joined," or "connected" to another component (e.g., the second) with or without such terms, it means that the first component may be connected to the other component directly (e.g., by wire), wirelessly, or via the third component.

[0032] According to various embodiments, each of the aforementioned components (e.g., a module or a program) may include one or more individuals, and some of the individuals may be separated and arranged in other components. According to various embodiments, one or more of the aforementioned components or operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (e.g., a module or a program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as those performed by the components of the multiple components before the integration. According to various embodiments, operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0033] Figure 1 is a block diagram of a battery diagnostic device according to one embodiment. The battery diagnostic device 101, described later, may be implemented in the BMS (Battery Management System) within the electronic device 102, or it may be implemented in various external devices such as a server, cloud, charger, or charger / discharger.

[0034] Referring to Figure 1, the battery diagnostic device 101 can be connected to the electronic device 102 and the user terminal 104 by wire and / or wirelessly. According to one embodiment, the connection 103 between the battery diagnostic device 101 and the electronic device 102 may be a communication connection via a wired and / or wireless network. In one embodiment, the wired network may be based on LAN (local area network) communication or power line communication. In one embodiment, the wireless network may be based on a short-range communication network (e.g., Bluetooth®, WiFi (wireless fidelity), or IrDA (infrared data association)) or a long-range communication network (cellular network, 4G network, 5G network).

[0035] In other embodiments, the connection 103 between the battery diagnostic device 101 and the electronic device 102 may be a connection via a communication method between devices (for example, a bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0036] According to one embodiment, the electronic device 102 may be a mobile device (e.g., a mobile phone, laptop computer, smartphone, smartpad), an electric vehicle (e.g., an EV (electric vehicle), an HEV (hybrid EV), a PHEV (plug-in HEV), an FCEV (fuel cell EV)), an energy storage system (ESS), or a battery swapping system (BSS).

[0037] According to one embodiment, the electronic device 102 may include a plurality of battery units 151, 153, 155. According to one embodiment, the plurality of battery units 151, 153, 155 may include at least one of a battery cell, a battery module, a battery pack, or a battery rack.

[0038] According to one embodiment, the connection 105 between the battery diagnostic device 101 and the user terminal 104 may be a communication connection via a wired and / or wireless network.

[0039] According to one embodiment, the user terminal 104 may be a mobile device (e.g., a mobile phone, laptop computer, smartphone, smartpad) or a PC (personal computer). According to one embodiment, the battery diagnostic device 101 can provide the user terminal 104 with information related to the diagnostic results of the battery units 151, 153, or 155.

[0040] 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 the embodiment, the battery diagnostic device 101 shown in Figure 1 may further include at least one component other than the components shown in Figure 1 (e.g., a display, an input device, or an output device), and at least one component of the components shown in Figure 1 (e.g., the sensor 120) may be omitted. For example, if the battery diagnostic device 101 is implemented on an external electronic device separate from the electronic device 102, such as a server or cloud, the battery diagnostic device 101 can acquire status information of a plurality of battery units 151, 153, and 155 using the communication circuit 110. In this case, the battery diagnostic device 101 may not include the sensor 120.

[0041] According to one embodiment, the communication circuit 110 establishes a wired communication channel and / or wireless communication channel between the battery diagnostic device 101 and the electronic device 102 and / or the user terminal 104, and can send and receive data with the electronic device 102 and / or the user terminal 104 via the established communication channel.

[0042] According to one embodiment, the sensor 120 can measure information (e.g., voltage, current, temperature, etc.) related to the state of multiple battery units 151, 153, and 155 of the electronic device 102. For example, if the battery diagnostic device 101 is implemented in the BMS within the electronic device 102, the battery diagnostic device 101 can directly measure the state values ​​of the multiple battery units 151, 153, and 155 using the sensor 120.

[0043] According to one embodiment, the communication circuit 110 and / or sensor 120 can acquire time-series data related to the state of a plurality of battery units 151, 153, and 155. In one embodiment, the time-series data related to the state of a plurality of battery units 151, 153, and 155 may be data showing the voltage, current, resistance, SOC (state of charge), SOH (state of health), and / or temperature of the plurality of battery units 151, 153, and 155 against time.

[0044] According to one embodiment, the memory 130 may include volatile memory and / or non-volatile memory. According to one embodiment, the memory 130 can store data used by at least one component of the battery diagnostic device 101 (e.g., the processor 140). For example, the data may include software (or associated instructions), input data, or output data. In one embodiment, the instructions can cause the battery diagnostic device 101 to perform the operation defined by the instructions when executed by the processor 140.

[0045] According to one embodiment, the memory 130 may include one or more software components (for example, an acquisition unit 131, a deviation calculation unit 132, an EMA (Exponential Moving Average) calculation unit 133, a diagnostic unit 134, and an anomaly processing unit 135).

[0046] According to one embodiment, the processor 140 may include a central processing unit, an application processor, a graphics processing unit, an NPU (neural processing unit), an image signal processor, a sensor hub processor, or a communication processor.

[0047] According to one embodiment, the processor 140 can execute software stored in the memory 130 (for example, an acquisition unit 131, a deviation calculation unit 132, an EMA calculation unit 133, a diagnostic unit 134, and an anomaly processing unit 135), control at least one other component (for example, a hardware or software component) of the battery diagnostic device 101 connected to the processor 140, and perform various data processing or calculations.

[0048] The following describes how the battery diagnostic device 101 diagnoses abnormalities in multiple battery units 151, 153, and 155 via the acquisition unit 131, deviation calculation unit 132, EMA calculation unit 133, diagnosis unit 134, and abnormality processing unit 135, with reference to Figures 2 and 3.

[0049] Figure 2 is a graph showing the OCV deviation value relative to the average battery calculated by a battery diagnostic device according to one embodiment. Figure 3 is a graph showing the EMA deviation value of a battery calculated by a battery diagnostic device according to one embodiment.

[0050] According to one embodiment, the acquisition unit 131 can acquire OCV (Open Circuit Voltage) data from multiple battery units 151, 153, and 155. According to one embodiment, the acquisition unit 131 can acquire the OCV data using a communication circuit 110 and / or a sensor 120.

[0051] According to one embodiment, the acquisition unit 131 can acquire time-series voltage data and time-series current data from a plurality of battery units 151, 153, and 155. In this case, the acquisition unit 131 can acquire the OCV data based on the time-series voltage data and the time-series current data. For example, the acquisition unit 131 can acquire only the data that satisfies a specified current value (e.g., 0A) and a specified voltage range (e.g., a voltage range of 3.5V or more and less than 4.3V) from the time-series voltage data and the time-series current data as the OCV data.

[0052] According to one embodiment, the deviation calculation unit 132 can calculate the OCV deviation value relative to the average of the target battery units 151, 153, or 155 based on the OCV data acquired by the acquisition unit 131.

[0053] According to one embodiment, the deviation calculation unit 132 can extract OCV data within a specified voltage range (for example, 0.4V or higher) from the OCV data. Based on the extracted OCV data within the specified voltage range, the deviation calculation unit 132 can calculate an OCV deviation value relative to the average.

[0054] According to one embodiment, the deviation calculation unit 132 can calculate the OCV change value of a plurality of battery units 151, 153, and 155 in a specified time interval based on OCV data. According to one embodiment, the specified time interval can be set to the time interval between a first time point in time before charging is performed on the battery units 151, 153, or 155 and a second time point in time after the charging is performed. For example, the deviation calculation unit 132 can calculate the OCV change value, which is the difference between the OCV value at the first time point and the OCV value at the second time point, based on the OCV data of the battery units 151, 153, or 155.

[0055] According to one embodiment, the deviation calculation unit 132 can calculate an OCV deviation value relative to the average of the target battery unit 151, 153, or 155 based on the OCV change values ​​of the multiple battery units 151, 153, and 155. According to one embodiment, the deviation calculation unit 132 can calculate the difference between the average OCV change value of the multiple battery units 151, 153, and 155 and the OCV change value of the target battery unit 151, 153, or 155 (for example, difference value = average OCV change value - OCV change value of the target battery unit).

[0056] Referring to Figure 2, we can see Graph 200, which shows the OCV deviation value relative to the average over time for the battery units being diagnosed. On Graph 200, the X axis represents time, and the Y axis represents the OCV deviation relative to the average (the difference between the average OCV change value of the battery units being diagnosed and the OCV change value of each battery unit).

[0057] According to one embodiment, the deviation calculation unit 132 can calculate the OCV deviation value relative to the average of the battery unit to be diagnosed at specified intervals. The battery diagnostic device 101 can store the OCV deviation values ​​relative to the average calculated by the deviation calculation unit 132 cumulatively in the memory 130 over a specified period of time, and manage OCV deviation data as shown in graph 200.

[0058] Referring again to Figure 1, the EMA calculation unit 133 can calculate the EMA deviation value by applying an EMA filter to the OCV deviation value relative to the average of the target battery units 151, 153, or 155 calculated by the deviation calculation unit 132.

[0059] According to one embodiment, the EMA filter can calculate the EMA deviation value for the current period by inputting the EMA deviation values ​​for previous periods and the OCV deviation values ​​relative to the mean of the current period of the target battery units 151, 153, or 155 into a specified formula. For example, the specified formula may be formula 1 below.

[0060] [Formula 1] EMA t = α · dOCV t + (1 - α) · EMA t-1

[0061] In the above formula 1, EMA t is the EMA deviation value of the current cycle of the target battery units 151, 153, or 155, α is the weight, and dOCV t is the OCV deviation value with respect to the average of the current cycle of the target battery units 151, 153, or 155, and EMA t-1 is the EMA deviation value of the previous cycle of the target battery units 151, 153, or 155. Here, the weight can be variously set according to the specifications of the plurality of battery units 151, 153, 155. For example, the weight can be set to 0.05, but is not limited thereto.

[0062] Referring to FIG. 3, a graph 300 showing the EMA deviation value of the current cycle of the battery unit to be diagnosed can be confirmed. On the graph 300, the X-axis indicates the battery unit number, and the Y-axis can indicate the EMA deviation value.

[0063] Referring to FIG. 1 again, the diagnosis unit 134 can diagnose an abnormality of the target battery units 151, 153, or 155 based on the EMA deviation value calculated by the EMA calculation unit 133. For example, the diagnosis unit 134 can diagnose whether the target battery units 151, 153, or 155 have a low capacity.

[0064] According to one embodiment, the diagnosis unit 134 can compare the EMA deviation value with a threshold value pre-stored in the memory 130 and diagnose an abnormality of the target battery units 151, 153, or 155.

[0065] According to one embodiment, the diagnosis unit 134 can diagnose an abnormality of the plurality of battery units 151, 153, 155 based on the EMA deviation values of the plurality of battery units 151, 153, 155 respectively.

[0066] According to one embodiment, the abnormality processing unit 135 can perform abnormality processing functions based on the abnormality diagnosis results of the multiple battery units 151, 153, and 155. Here, the abnormality processing function may include a notification function or a short-circuit function.

[0067] According to one embodiment, the abnormality processing unit 135 can transmit the abnormality diagnosis results of the plurality of battery units 151, 153, and 155 to a user terminal 104 connected via a wired and / or wireless network.

[0068] According to one embodiment, the abnormality processing unit 135 can isolate an abnormal battery unit from the electronic device 102 based on the abnormality diagnosis results of the multiple battery units 151, 153, and 155. Here, isolation may include electrical and / or mechanical isolation.

[0069] Figure 4 is an operation flowchart of a battery diagnostic device according to one embodiment. Figure 4 will be explained using the configuration shown in Figure 1. The embodiment shown in Figure 4 is only one embodiment, and the order of steps in various embodiments of the present invention may differ from that shown in Figure 4. Some of the steps shown in Figure 4 may be omitted, the order of the steps may be changed, or steps may be merged.

[0070] Referring to Figure 4, in operation 405, the battery diagnostic device 101 can acquire OCV data from multiple battery units 151, 153, and 155. According to one embodiment, the battery diagnostic device 101 can acquire the OCV data using a communication circuit 110 and / or a sensor 120.

[0071] According to one embodiment, the battery diagnostic device 101 can acquire time-series voltage data and time-series current data of a plurality of battery units 151, 153, and 155. In this case, the battery diagnostic device 101 can acquire the OCV data based on the time-series voltage data and the time-series current data. For example, the battery diagnostic device 101 can acquire as OCV data only the data that satisfies a specified current value (e.g., 0A) and a specified voltage range (e.g., a voltage range of 3.5V or more and less than 4.3V) from the time-series voltage data and the time-series current data.

[0072] According to one embodiment, the battery diagnostic device 101 can calculate the OCV change value of a plurality of battery units 151, 153, and 155 in a specified time interval based on the OCV data acquired in operation 405. According to one embodiment, the specified time interval can be set to the time interval between a first time point in time before charging is performed on the battery units 151, 153, or 155 and a second time point in time after the charging is performed. For example, the deviation calculation unit 132 can calculate the OCV change value, which is the difference between the OCV value at the first time point and the OCV value at the second time point, based on the OCV data of the battery units 151, 153, or 155.

[0073] According to one embodiment, the battery diagnostic device 101 can extract OCV data within a specified voltage range (for example, 0.4V or higher) from the OCV data. Based on the extracted OCV data within the specified voltage range, the battery diagnostic device 101 can calculate the OCV change value.

[0074] In operation 410, the battery diagnostic device 101 can calculate an OCV deviation value relative to the average of the target battery unit 151, 153, or 155 based on the OCV change values ​​of the multiple battery units 151, 153, and 155.

[0075] According to one embodiment, the battery diagnostic device 101 can calculate the difference between the average OCV change value of a plurality of battery units 151, 153, and 155 and the OCV change value of a target battery unit 151, 153, or 155 (for example, difference value = average OCV change value - OCV change value of the target battery unit) as the OCV deviation value relative to the average.

[0076] In operation 415, the battery diagnostic device 101 can calculate the EMA deviation value of the target battery unit 151, 153, or 155. According to one embodiment, the battery diagnostic device 101 can calculate the EMA deviation value by applying an EMA filter to the OCV deviation value relative to the average calculated in operation 410.

[0077] According to one embodiment, the EMA filter can calculate the EMA deviation value for the current period by inputting the EMA deviation values ​​for previous periods and the OCV deviation values ​​relative to the mean of the current period of the target battery units 151, 153, or 155 into a specified formula. For example, the specified formula may be formula 1 above.

[0078] In operation 420, the battery diagnostic device 101 can diagnose an abnormality in the target battery unit 151, 153, or 155 based on the EMA deviation value calculated in operation 415. For example, the battery diagnostic device 101 can diagnose whether the target battery unit 151, 153, or 155 is low capacity.

[0079] According to one embodiment, the battery diagnostic device 101 can diagnose an abnormality in the target battery unit 151, 153, or 155 by comparing the EMA deviation value with a pre-stored threshold.

[0080] According to one embodiment, the battery diagnostic device 101 can diagnose abnormalities in multiple battery units 151, 153, and 155 based on the EMA deviation values ​​of each of the multiple battery units 151, 153, and 155.

[0081] According to one embodiment, the battery diagnostic device 101 can perform an abnormality processing function based on the abnormality diagnosis results of a plurality of battery units 151, 153, and 155. Here, the abnormality processing function may include a notification function or a short-circuit function.

[0082] According to one embodiment, the battery diagnostic device 101 can transmit abnormality diagnosis results of the plurality of battery units 151, 153, and 155 to a user terminal 104 connected via a wired and / or wireless network.

[0083] According to one embodiment, the battery diagnostic device 101 can isolate abnormal battery units from the electronic device 102 based on the abnormality diagnosis results of a plurality of battery units 151, 153, and 155. Here, isolation may include electrical and / or mechanical isolation.

[0084] The terms “contain,” “constitute,” or “have,” as used above, mean “may contain,” and should not be interpreted as meaning that they may contain, unless otherwise specified, other components, rather than excluding them. All terms, including technical or scientific terms, should have the same meaning as that generally understood by a person of ordinary skill in the art to which the embodiments disclosed herein belong, unless otherwise specified. Commonly used terms, such as those defined in dictionaries, should be interpreted to be consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined herein.

Claims

1. An acquisition unit that acquires OCV data from multiple battery units, A deviation calculation unit calculates the OCV change value of the plurality of battery units in a specified time interval based on the OCV data, and calculates an OCV deviation value relative to the average, which is the difference between the average OCV change value of the plurality of battery units and the OCV change value of the target battery unit among the plurality of battery units. An EMA calculation unit calculates an EMA deviation value by applying an EMA filter to the OCV deviation value of the target battery unit relative to the average, A diagnostic unit that diagnoses abnormalities in the target battery unit based on the EMA deviation value, Battery diagnostic device, including

2. The battery diagnostic device according to claim 1, wherein the specified time interval is the time interval between a first time point before charging is performed on the battery unit and a second time point after the charging is performed.

3. The battery diagnostic device according to claim 1, wherein the EMA filter calculates the EMA deviation value by inputting the EMA deviation value of a previous period and the OCV deviation value of the target battery unit relative to the average into a specified formula.

4. The battery diagnostic device according to claim 3, wherein the specified formula is formula 1 below. [Formula 1] 57! ｔ α・dOCV ｔ +(1-α)･E7A ｔ－１ (In formula 1, EMA ｔ is the EMA deviation score, α is the weight, dOCV ｔ This is the OCV deviation value of the target battery unit relative to the average, EMA ｔ－１ (This is the EMA deviation value for the previous period.)

5. The acquisition unit is, Time-series voltage data and time-series current data are acquired from the plurality of battery units. A battery diagnostic device according to any one of claims 1 to 4, which acquires OCV data of a specified current value and a specified voltage range based on the time-series voltage data and the time-series current data.

6. The battery diagnostic device according to any one of claims 1 to 4, wherein the plurality of battery units include at least one of a battery cell, a battery module, a battery pack, or a battery rack.

7. The system further includes an abnormality processing unit that performs an abnormality processing function based on the abnormality diagnosis result of each of the aforementioned multiple battery units. The battery diagnostic device according to any one of claims 1 to 4, wherein the abnormality processing function includes a notification function or a short-circuit function.

8. The operation of acquiring OCV data from multiple battery units, Based on the OCV data, the operation of calculating the OCV change value of the plurality of battery units in a specified time interval, The operation involves calculating an OCV deviation value relative to the average, which represents the difference between the average OCV change value of the plurality of battery units and the OCV change value of the target battery unit among the plurality of units. The operation of applying an EMA filter to the OCV deviation value of the target battery unit relative to the average to calculate the EMA deviation value, Based on the EMA deviation value, the operation diagnoses an abnormality in the target battery unit, Battery diagnostic methods, including those mentioned above.

9. The battery diagnostic method according to claim 8, wherein the specified time interval is the time interval between a first time point before charging is performed on the battery unit and a second time point after the charging is performed.

10. The battery diagnostic method according to claim 8, wherein the EMA filter calculates the EMA deviation value by inputting the EMA deviation value of a previous period and the OCV deviation value of the target battery unit relative to the average into a specified formula.

11. The battery diagnostic method according to claim 10, wherein the specified formula is formula 1 below. [Formula 1] 57! ｔ α・dOCV ｔ +(1-α)･E7A ｔ－１ (In formula 1, EMA ｔ is the EMA deviation score, α is the weight, dOCV ｔ This is the OCV deviation value of the target battery unit relative to the average, EMA ｔ－１ (This is the EMA deviation value for the previous period.)

12. The operation further includes acquiring time-series voltage data and time-series current data of the plurality of battery units, The battery diagnostic method according to any one of claims 8 to 11, wherein the operation to acquire the OCV data includes the operation to acquire the OCV data of a specified current value and a specified voltage range based on the time-series voltage data and the time-series current data.

13. The battery diagnostic method according to any one of claims 8 to 11, wherein the plurality of battery units include at least one of a battery cell, a battery module, a battery pack, or a battery rack.

14. The operation further includes performing an abnormality processing function based on the abnormality diagnosis result of each of the aforementioned multiple battery units, The battery diagnostic method according to any one of claims 8 to 11, wherein the abnormality processing function includes a notification function or a short-circuit function.

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