Battery diagnosis device and operating method thereof

Abstract

A battery diagnosis device according to an embodiment disclosed in the present document may comprise: an interface for acquiring open circuit voltage (OCV) data on battery cells; and one or more processors for calculating an OCV deviation, indicating the difference between the OCV of a battery cell and an average OCV corresponding to a specific time point, for each of a plurality of battery cells included in a specific battery module, on the basis of the OCV data, obtaining a plurality of OCV moving averages by applying different weighted moving averages to the OCV deviations, and diagnosing abnormalities of the battery cells on the basis of the plurality of OCV moving averages.

Description

Battery diagnostic device and its operating method

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority to Korean Patent Application No. 10-2024-0046861, filed April 5, 2024, the entire contents of which are incorporated herein by reference.

[0003] Technology field

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

[0005] Recently, research and development on secondary batteries has been actively conducted. Here, secondary batteries are rechargeable and include both conventional Ni / Cd and Ni / MH batteries, as well as recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of having a much higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form, making them suitable for use as power sources for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, attracting attention as a next-generation energy storage medium.

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

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

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

[0009] Additionally, the battery management system can manage and control the battery while being separated from the device containing the battery. For example, the battery management system can be implemented as a separate server device. In this case, the battery management system can collect battery data and vehicle data from vehicles and other devices, and utilize the collected data to manage and control the battery.

[0010] If a short circuit or other type of failure occurs within a battery, the risk of damage to devices containing the battery (e.g., EVs, ESS) may increase. Therefore, a method is needed to detect abnormal battery conditions and reduce the risk of damage to devices containing the battery.

[0011] Traditionally, battery cell diagnosis was performed using a calculation method that combined information such as State of Charge (SOC), current, capacity, and Open Circuit Voltage (OCV). Because this diagnostic method required numerous factors, it was difficult to diagnose if certain pieces of information were missing. This could be problematic for battery management systems implemented as server devices that collect data from vehicles, and could result in excessively high memory usage. Consequently, there is a pressing need to streamline the data required for battery diagnosis.

[0012] The embodiments disclosed in this document can provide a battery diagnostic device and an operating method thereof that can diagnose a battery abnormality using only battery OCV data information.

[0013] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0014] A battery diagnostic device according to an embodiment disclosed in the present document may include an interface for obtaining OCV (Open Circuit Voltage) data of a battery cell; and one or more processors for calculating an OCV deviation representing a difference between an average OCV corresponding to a specific point in time and the OCV of a battery cell for each of a plurality of battery cells included in a specific battery module based on the OCV data, obtaining a plurality of OCV moving averages by applying different weighted moving averages to the OCV deviations, and diagnosing an abnormality of the battery cell based on the plurality of OCV moving averages.

[0015] In a battery diagnostic device according to one embodiment disclosed in this document, the one or more processors can obtain the plurality of OCV moving averages by applying different weighted moving averages to a plurality of past OCV moving averages corresponding to past points in time before the specific point in time and the OCV deviation corresponding to the specific point in time.

[0016] In a battery diagnosis device according to one embodiment disclosed in the present document, the one or more processors may obtain a first OCV moving average by applying a first weighted moving average to a first past OCV moving average and the OCV deviation among the plurality of past OCV moving averages, obtain a second OCV moving average by applying a second weighted moving average to a second past OCV moving average and the OCV deviation among the plurality of past OCV moving averages, and diagnose an abnormality of the battery cell based on the first OCV moving average and the second OCV moving average.

[0017] In a battery diagnosis device according to an embodiment disclosed in the present document, the first weighted moving average may be applied to the first past OCV moving average and the OCV deviation, wherein the 1_1 weighted moving average weight applied to the OCV deviation may be greater than the 1_2 weighted moving average weight applied to the first past OCV moving average, and the second weighted moving average may be applied to the second past OCV moving average and the OCV deviation, wherein the 2_1 weighted moving average weight applied to the OCV deviation may be less than the 2_2 weighted moving average weight applied to the second past OCV moving average.

[0018] In a battery diagnosis device according to one embodiment disclosed in this document, the one or more processors can calculate an OCV moving average deviation representing a difference between the plurality of OCV moving averages, apply a preset weight to the OCV moving average deviation to obtain an OCV evaluation value, and diagnose an abnormality of the battery cell based on the OCV evaluation value.

[0019] In a battery diagnosis device according to one embodiment disclosed in this document, the one or more processors can obtain the OCV evaluation value by applying the preset weight to a past OCV evaluation value corresponding to a past point in time before the specific point in time and the OCV moving average deviation corresponding to the specific point in time.

[0020] In a battery diagnostic device according to one embodiment disclosed in this document, among the preset weights, a first weight applied to the OCV moving average deviation may be smaller than a second weight applied to the past OCV evaluation value.

[0021] In a battery diagnostic device according to one embodiment disclosed in this document, the one or more processors can set a threshold OCV evaluation value corresponding to the specific battery module, and diagnose an abnormality of the battery cell based on the OCV evaluation value and the threshold OCV evaluation value.

[0022] A battery diagnosis method according to an embodiment disclosed in the present document may include: an operation of acquiring OCV (Open Circuit Voltage) data of a battery cell; an operation of calculating an OCV deviation representing a difference between an average OCV corresponding to a specific point in time and the OCV of a battery cell for each of a plurality of battery cells included in a specific battery module based on the OCV data; an operation of acquiring a plurality of OCV moving averages by applying different weighted moving averages to the OCV deviations; and an operation of diagnosing an abnormality of the battery cell based on the plurality of OCV moving averages.

[0023] In a battery diagnosis method according to an embodiment disclosed in this document, the operation of obtaining the plurality of OCV moving averages may include an operation of obtaining the plurality of OCV moving averages by applying different weighted moving averages to the plurality of past OCV moving averages corresponding to past points in time before the specific point in time and the OCV deviation corresponding to the specific point in time.

[0024] In a battery diagnosis method according to an embodiment disclosed in the present document, the operation of obtaining the plurality of OCV moving averages may include an operation of obtaining a first OCV moving average by applying a first weighted moving average to a first past OCV moving average and the OCV deviation among the plurality of past OCV moving averages, and an operation of obtaining a second OCV moving average by applying a second weighted moving average to a second past OCV moving average and the OCV deviation among the plurality of past OCV moving averages, and the operation of diagnosing an abnormality of the battery cell may include an operation of diagnosing an abnormality of the battery cell based on the first OCV moving average and the second OCV moving average.

[0025] In a battery diagnosis method according to an embodiment disclosed in this document, the first_1 weighted moving average weight applied to the OCV deviation may be greater than the first_2 weighted moving average weight applied to the first past OCV moving average, and the second_1 weighted moving average weight applied to the OCV deviation may be less than the second_2 weighted moving average weight applied to the second past OCV moving average.

[0026] In a battery diagnosis method according to an embodiment disclosed in this document, an operation of diagnosing an abnormality of the battery cell may include an operation of calculating an OCV moving average deviation representing a difference between the plurality of OCV moving averages, an operation of obtaining an OCV evaluation value by applying a preset weight to the OCV moving average deviation, and an operation of diagnosing an abnormality of the battery cell based on the OCV evaluation value.

[0027] In a battery diagnosis method according to an embodiment disclosed in this document, the operation of diagnosing an abnormality of the battery cell may include an operation of obtaining the OCV evaluation value by applying the preset weight to a past OCV evaluation value corresponding to a past point in time before the specific point in time and the OCV moving average deviation corresponding to the specific point in time.

[0028] In a battery diagnosis method according to an embodiment disclosed in this document, among the preset weights, a first weight applied to the OCV moving average deviation may be smaller than a second weight applied to the past OCV evaluation value.

[0029] A battery diagnosis method according to an embodiment disclosed in this document may further include an operation of setting a threshold OCV evaluation value corresponding to the specific battery module, and the operation of diagnosing an abnormality of the battery cell may include an operation of diagnosing an abnormality of the battery cell based on the OCV evaluation value and the threshold OCV evaluation value.

[0030] According to the embodiments disclosed in this document, data used for battery abnormality diagnosis can be simplified.

[0031] According to the embodiments disclosed in this document, the accuracy of battery abnormality diagnosis can be improved by diagnosing an abnormality of a battery cell based on a plurality of OCV moving averages obtained by applying different weighted moving averages to the OCV deviation.

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

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

[0034] FIG. 2 is a drawing for explaining an embodiment in which a battery diagnostic device diagnoses an abnormality in a battery cell.

[0035] FIGS. 3A to 3D are drawings for explaining an embodiment in which a battery diagnostic device diagnoses an abnormality in a battery cell.

[0036] Figure 4 is a flowchart illustrating the operation of a battery diagnostic device according to one embodiment.

[0037] FIG. 5 shows a computing system executing a method of operating a battery diagnostic device according to one embodiment.

[0038] Hereinafter, various embodiments of the present invention will be described with reference to the attached drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that the present invention encompasses various modifications, equivalents, and / or alternatives of the embodiments.

[0039] The various embodiments and terminology used in this document are not intended to limit the technical features described in this document to specific embodiments, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiments. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the context clearly indicates otherwise.

[0040] In this document, the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding element from other corresponding elements, and do not limit the corresponding elements in any other respect (e.g., importance or order) unless specifically stated otherwise.

[0041] In this document, whenever a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or via a third component.

[0042] According to various embodiments, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to various embodiments, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0043] FIG. 1 is a block diagram of a battery diagnostic device according to one embodiment.

[0044] Referring to FIG. 1, a battery pack (110) includes a plurality of battery modules (120, 130, 140), and each of the plurality of battery modules (120, 130, 140) may include a plurality of battery cells (121, 122, 123, 131, 132, 133, 141, 142, 143). According to one embodiment, the battery pack (110) may be a battery mounted inside an electric vehicle to provide power to the electric vehicle.

[0045] According to one embodiment, the battery diagnostic device (150) can diagnose an abnormality of a battery unit based on OCV data obtained from the battery unit. In the present disclosure, the battery unit may mean a battery pack (110), a battery module (120, 130, or 140), or a battery cell (121, 122, 123, 131, 132, 133, 141, 142, or 143).

[0046] According to one embodiment, the battery diagnostic device (150) may be formed integrally with the battery unit. In this case, the battery diagnostic device (150) may be included in the BMS (Battery Management System) of the battery unit.

[0047] According to one embodiment, the battery diagnostic device (150) may be formed separately from the battery unit. In this case, the battery diagnostic device (150) may be implemented as an external server connected to the battery unit via a wireless network.

[0048] In addition, the operation of the battery diagnostic device (150) below can be performed by a BMS (Battery management system) in the vehicle, and can also be performed in various devices such as a server, cloud, charger, or charger / discharger.

[0049] According to one embodiment, the battery diagnostic device (150) may include an interface (151) and one or more processors (152).

[0050] According to one embodiment, the interface (151) can obtain OCV data of battery cells (121, 122, 123, 131, 132, 133, 141, 142, and / or 143). For example, the interface (151) can obtain information about voltage, current, and / or temperature of the battery cells (121, 122, 123, 131, 132, 133, 141, 142, and / or 143) and configure OCV data based on the obtained information. In this case, the interface (151) can include a sensor for obtaining information about voltage, current, and / or temperature and a processor for configuring OCV data based on the obtained information. As another example, the interface (151) can receive OCV data of battery cells (121, 122, 123, 131, 132, 133, 141, 142, and / or 143) acquired by the battery unit. In this case, the interface (151) can include a communication circuit capable of wired and / or wireless network communication.

[0051] According to one embodiment, the processor (152) may calculate a judgment value (e.g., OCV deviation, OCV moving average, OCV moving average deviation, and / or OCV evaluation value) based on the OCV data acquired by the interface (151). According to one embodiment, the processor (152) may extract OCV data of a specified voltage range from the OCV data. The processor (152) may calculate the judgment value based on the extracted OCV data of the specified voltage range. Various embodiments in which the processor (152) calculates the judgment value may be specifically described in FIGS. 2 to 3D to be described below.

[0052] According to one embodiment, the processor (152) can diagnose an abnormality in the battery unit based on the calculated judgment value.

[0053] According to one embodiment, the processor (152) can diagnose an abnormality in the battery unit by comparing the judgment value with a corresponding threshold value. For example, if the judgment value (e.g., OCV evaluation value) is lower than or equal to the threshold value (e.g., -3 mV), the processor (152) can diagnose the battery unit (e.g., battery cells (121, 122, 123, 131, 132, 133, 141, 142, and / or 143)) as an abnormal battery unit.

[0054] The processor (152) described above may be implemented as a single processor or as separate processors. Here, the processor may execute software to control at least one other component (e.g., hardware or software component) of the battery diagnostic device (150) and perform various data processing or calculations.

[0055] According to an embodiment, the battery diagnosis device (150) may transmit the battery diagnosis results to an external device (e.g., a cloud server or a user terminal). Here, the cloud server may provide a service for providing the battery diagnosis results to each of a plurality of users. In addition, the user terminal may include a terminal such as a personal computer (PC) or a smartphone. For example, the battery diagnosis device (150) may provide information on abnormal battery cells to the user terminal through a communication unit (not shown), and may also provide information on abnormal battery cells through a display equipped in a vehicle or a charger.

[0056] Hereinafter, various embodiments in which the processor (152) diagnoses an abnormality of a battery unit will be described with reference to FIGS. 2 to 3D. FIGS. 2 to 3D can be described using the configuration of FIG. 1 (e.g., interface (151), processor (152)).

[0057] Fig. 2 is a diagram illustrating an embodiment in which a battery diagnostic device calculates a judgment value. Figs. 3a to 3d are diagrams illustrating an embodiment in which a battery diagnostic device diagnoses an abnormality in a battery cell.

[0058] Referring to FIG. 2, the processor (152) may include a first processor (210), a second processor (220), and a third processor (230).

[0059] According to one embodiment, the interface (151) may transmit OCV data of a plurality of battery cells included in a plurality of battery modules to the first processor. For example, the interface (151) may transmit OCV data (OCV1, OCV2, OCV3) of a plurality of battery cells (121, 122, 123) of a specific battery module (120) to the first processor (210). For convenience of explanation, one battery module (120) will be described below as an example, but the number of battery modules is not limited thereto.

[0060] According to one embodiment, the first processor (210) calculates an OCV deviation (OCV) representing the difference between the average OCV corresponding to a specific point in time and the OCV (OCV1, OCV2, OCV3) of the battery cells (121, 122, 123) for each of the plurality of battery cells (121, 122, 123) included in a specific battery module (120) based on the OCV data (OCV1, OCV2, OCV3). D1 , OCV D2 , OCV D3 ) can be produced. For example, the first processor (210) may output an OCV deviation (OCV) representing the difference between the average OCV (e.g., (OCV1+OCV2+OCV3) / 3) corresponding to a specific point in time of a plurality of battery cells (121, 122, 123) included in a specific battery module (120) and the OCV (OCV1) corresponding to the specific point in time of the battery cell (121). D1 ) can be produced.

[0061] According to one embodiment, the first processor (210) calculates the OCV deviation (OCV D1 , OCV D2 , OCV D3 ) can be transmitted to the second processor (220).

[0062] According to one embodiment, the second processor (220) is configured to measure the OCV deviation (OCV D1 , OCV D2 , OCV D3 ) by applying different weighted moving averages to multiple OCV moving averages (OCV) for each of multiple battery cells (121, 122, 123). MA1 , OCV MA2 , OCV MA3 ) can be obtained. Here, multiple OCV moving averages (OCV MA1 , OCV MA2 , OCV MA3 ) may each correspond to a plurality of battery cells (121, 122, 123). For example, the second processor (220) may be configured to detect OCV deviation (OCV D1 ) by applying different weighted moving averages to the battery cells (121) to generate multiple OCV moving averages (OCV MA1 ) can be obtained.

[0063] According to one embodiment, the second processor (220) calculates a plurality of past OCV moving averages corresponding to past points in time before a specific point in time and an OCV deviation (OCV) corresponding to a specific point in time. D1 , OCV D2 , OCV D3 ) by applying different weighted moving averages to multiple OCV moving averages (OCV MA1 , OCV MA2 , OCV MA3 ) can be obtained. For example, the second processor (220) can obtain a plurality of past OCV moving averages corresponding to past points in time before a specific point in time and an OCV deviation (OCV) corresponding to a specific point in time. D1) by applying different weighted moving averages to multiple OCV moving averages (OCV MA1 ) can be obtained. Here, the past point in time before a specific point in time may be a point in time immediately before a specific point in time, but this is only an example and is not limited thereto.

[0064] According to one embodiment, the second processor (220) calculates a first past OCV moving average and an OCV deviation (OCV) among a plurality of past OCV moving averages. D1 , OCV D2 and / or OCV D3 ) by applying the first weighted moving average to the first OCV moving average (OCV MA1 , OCV MA2 and / or OCV MA3 ) can be obtained. In addition, the second processor (220) can obtain a second past OCV moving average and OCV deviation (OCV) among a plurality of past OCV moving averages. D1 , OCV D2 and / or OCV D3 ) by applying the second weighted moving average to the second OCV moving average (OCV MA1 , OCV MA2 and / or OCV MA3 ) can be obtained. Here, the method in which the first past OCV moving average and the second past OCV moving average corresponding to a past point in time are obtained is that the first OCV moving average and the second OCV moving average corresponding to a specific point in time (OCV MA1 , OCV MA2 and / or OCV MA3 ) is obtained, so we will omit any redundant explanation.

[0065] For example, the second processor (220) may calculate a first past OCV moving average and an OCV deviation (OCV) among a plurality of past OCV moving averages. D1 ) by applying the first weighted moving average to the first OCV moving average (OCV MA1) can be obtained. In addition, the second processor (220) can obtain a second past OCV moving average and OCV deviation (OCV) among a plurality of past OCV moving averages. D1 ) by applying a second weighted moving average different from the first weighted moving average to the second OCV moving average (OCV MA1 ) can be obtained.

[0066] According to one embodiment, when the second processor (220) applies the first weighted moving average to the first past OCV moving average and the OCV deviation, the first_1 weighted moving average weight applied to the OCV deviation may be greater than the first_2 weighted moving average weight applied to the first past OCV moving average. In addition, when the second processor (220) applies the second weighted moving average to the second past OCV moving average and the OCV deviation, the second_1 weighted moving average weight applied to the OCV deviation may be less than the second_2 weighted moving average weight applied to the second past OCV moving average.

[0067] For example, the second processor (220) adjusts the OCV deviation (OCV) of the first weighted moving average weight (e.g., 0.95) among the weights (0.95 and 0.05) of the first weighted moving average D1 ) and apply the 1st_2nd weighted moving average weight (e.g. 0.05) to the 1st past OCV moving average, and then add them up to obtain the 1st OCV moving average (OCV MA1 ) can be obtained. In addition, the second processor (220) may obtain the OCV deviation (OCV) by adding the 2nd_1 weighted moving average weight (e.g., 0.05) among the weights (0.05 and 0.95) of the 2nd weighted moving average. D1 ) and apply the 2nd_2 weighted moving average weight (e.g. 0.95) to the 2nd past OCV moving average, and then add them up to obtain the 2nd OCV moving average (OCV MA1 ) can be obtained. Here, 0.95 and 0.05 are only examples of weighted moving average weight values ​​and are not limited thereto.

[0068] Referring to Fig. 3a, a first OCV moving average (310) and a second OCV moving average (310) corresponding to multiple points in time can be confirmed. For reference, Fig. 3a and Figs. 3b to 3c described below illustrate an OCV moving average, an OCV moving average deviation, and an OCV evaluation value obtained based on calculating an OCV deviation at a certain periodic interval based on OCV data acquired at certain periods (e.g., 10 days), and the horizontal axis represents time (days), one division of the horizontal axis corresponds to one month, and the vertical axis represents voltage (mV).

[0069] According to one embodiment, in the case of the first OCV moving average, the weight applied to the OCV deviation (e.g., the 1_1st weighted moving average weight) is greater than the weight applied to the first past OCV moving average (e.g., the 1_2nd weighted moving average weight), so that the value of the first OCV moving average is greatly influenced by the value of the OCV deviation corresponding to the current point in time (a specific point in time). Accordingly, it can be confirmed that the larger the OCV deviation calculated based on the OCV data acquired at a specific point in time, the more rapidly the value of the first OCV moving average (310) changes, as illustrated in FIG. 3a.

[0070] On the other hand, in the case of the second OCV moving average, since the weight applied to the second past OCV moving average (e.g., the 2nd_2nd weighted moving average weight) is greater than the weight applied to the OCV deviation (e.g., the 2nd_1st weighted moving average weight), the value of the second OCV moving average is less affected by the value of the OCV deviation corresponding to the current point in time (a specific point in time). Accordingly, even if the OCV deviation calculated based on the OCV data acquired at a specific point in time is large, it can be confirmed that the value of the second OCV moving average (320) changes relatively gradually, as illustrated in FIG. 3a.

[0071] According to one embodiment, the second processor (220) calculates the first OCV moving average (OCV MA1 , OCVMA2 and / or OCV MA3 ) and the second OCV moving average (OCV MA1 , OCV MA2 and / or OCV MA3 ) can be transmitted to the third processor (230).

[0072] According to one embodiment, the third processor (230) is configured to: MA1 , OCV MA2 and / or OCV MA3 ) and the second OCV moving average (OCV MA1 , OCV MA2 and / or OCV MA3 ), the abnormality of the battery cells (121, 122 and / or 123) can be diagnosed. For example, the third processor (230) may be configured to diagnose the first OCV moving average (OCV MA1 ) and the second OCV moving average (OCV MA1 ), the abnormality of the battery cell (121) can be diagnosed.

[0073] According to one embodiment, the third processor (230) may calculate an OCV moving average deviation representing a difference between a plurality of OCV moving averages. For example, the third processor (230) may calculate a plurality of OCV moving averages (a first OCV moving average (OCV)) corresponding to the battery cell (121). MA1 ) and the second OCV moving average (OCV MA1 )) can be used to calculate the OCV moving average deviation, which represents the difference between the two.

[0074] Referring to FIG. 3b, the OCV moving average deviation (330) representing the difference between the first OCV moving average (310) and the second OCV moving average (320) illustrated in FIG. 3a can be confirmed.

[0075] According to one embodiment, the third processor (230) can calculate an OCV evaluation value by applying a set of evaluation weights to the calculated OCV moving average deviation.

[0076] According to one embodiment, the third processor (230) may obtain an OCV evaluation value by applying a preset weight to a past OCV evaluation value corresponding to a past point in time before a specific point in time and an OCV moving average deviation corresponding to a specific point in time.

[0077] According to one embodiment, among the preset weights, the first weight applied to the OCV moving average deviation may be smaller than the second weight applied to the past OCV evaluation value. For example, the third processor (230) may apply the first weight (0.2) among the preset weights (0.2 and 0.8) to the OCV moving average deviation corresponding to the battery cell (121), apply the second weight (0.8) to the past OCV evaluation value corresponding to the battery cell (121), and then add them up to obtain the OCV evaluation value.

[0078] Referring to FIG. 3c, the OCV evaluation value (340) obtained based on the OCV moving average deviation (330) illustrated in FIG. 3b can be confirmed.

[0079] Specifically, since the preset weight (e.g., the second weight) applied to the past OCV evaluation value corresponding to a past point in time is greater than the preset weight (e.g., the first weight) applied to the OCV moving average deviation (330) corresponding to a specific point in time, it can be confirmed that the OCV evaluation value changes relatively gradually even if the OCV moving average deviation (330) changes rapidly at a specific point in time. Accordingly, it is possible to reduce the over-inspection rate in diagnosing an abnormality of the battery cells (121, 122, 123, 131, 132, 133, 141, 142, and / or 143) based on a comparison with the critical OCV evaluation value described later.

[0080] According to one embodiment, the third processor (230) can diagnose an abnormality in a battery cell based on the acquired OCV evaluation value.

[0081] According to one embodiment, the third processor (230) may set a threshold OCV evaluation value corresponding to a specific battery module (120) and diagnose an abnormality of a battery cell (121, 122, and / or 123) based on the OCV evaluation value and the threshold OCV evaluation value.

[0082] For example, the third processor (230) can set a threshold OCV evaluation value (e.g., -3 mV) corresponding to a specific battery module (120) and diagnose an abnormality in the battery cell (121) based on the OCV evaluation value of the battery cell (121) and the threshold OCV evaluation value (e.g., -3 mV). For example, the third processor (230) can diagnose that there is an abnormality in the battery cell (121) when the OCV evaluation value of the battery cell (121) is lower than or equal to the threshold OCV evaluation value.

[0083] Figure 3d shows the OCV evaluation values ​​for each of the multiple battery cells.

[0084] Referring to FIG. 3D, it can be confirmed that the OCV evaluation value (350) corresponding to a specific point in time of the kth battery cell among the plurality of battery cells is -24.12 mV, which is lower than the critical OCV evaluation value of -3 mV. In this case, the third processor (230) can diagnose that there is a problem with the kth battery cell.

[0085] The first processor (210) to the third processor described above may be implemented as a single processor or as separate processors. Here, the processor may execute software to control at least one other component (e.g., hardware or software component) of the battery diagnostic device (150) and perform various data processing or calculations.

[0086] According to an embodiment, the battery diagnostic device (150) may transmit battery diagnostic results to an external device (e.g., a cloud server or a user terminal). Here, the cloud server may provide a service for providing battery diagnostic results to each of multiple users. Furthermore, the user terminal may include a terminal such as a personal computer (PC) or a smartphone.

[0087] Fig. 4 is a flowchart illustrating the operation of a battery diagnostic device according to an embodiment. Fig. 4 may be an explanation of the operation of the battery diagnostic device (150) of Fig. 1, and may be explained using the configuration of Fig. 1.

[0088] The embodiment illustrated in FIG. 4 is only one embodiment, and the order of steps according to various embodiments of the present invention may be different from that illustrated in FIG. 4, and some of the steps illustrated in FIG. 4 may be omitted, the order between steps may be changed, or steps may be merged.

[0089] Referring to FIG. 4, in operation 405, the battery diagnostic device (150) can obtain OCV data of battery cells (121, 122, 123, 131, 132, 133, 141, 142 and / or 143). For example, the battery diagnostic device (150) can configure the OCV data based on voltage, current, and / or temperature measurement information of the battery cells (121, 122, 123, 131, 132, 133, 141, 142 and / or 143). As another example, the battery diagnostic device (150) can receive OCV data of a battery cell (121, 122, 123, 131, 132, 133, 141, 142 and / or 143) acquired by a battery module (120, 130, 140) or a battery cell (121, 122, 123, 131, 132, 133, 141, 142 and / or 143).

[0090] In operation 410, the battery diagnostic device (150) may calculate an OCV deviation, which represents the difference between the average OCV corresponding to a specific point in time and the OCV of the battery cell for each of a plurality of battery cells included in a specific battery module, based on the OCV data acquired in operation 405. According to one embodiment, the battery diagnostic device (150) may extract OCV data of a specified voltage range (e.g., a voltage range of 3.9 V or higher) from among the OCV data. The battery diagnostic device (150) may calculate the OCV deviation based on the extracted OCV data of the specified voltage range.

[0091] In operation 415, the battery diagnosis device (150) may obtain a plurality of OCV moving averages by applying different weighted moving averages to the OCV deviations. Here, the plurality of OCV moving averages may correspond to battery cells. According to one embodiment, the battery diagnosis device (150) may obtain a plurality of OCV moving averages by applying a plurality of weighted moving averages to a plurality of past OCV moving averages and OCV deviations corresponding to past points in time before a specific point in time. According to one embodiment, the battery diagnosis device (150) may obtain a first OCV moving average by applying a first weighted moving average to a first past OCV moving average and OCV deviation among the plurality of past OCV moving averages, and may obtain a second OCV moving average by applying a second weighted moving average to a second past OCV moving average and OCV deviation among the plurality of past OCV moving averages. According to one embodiment, the first_1 weighted moving average weight applied to the OCV deviation may be greater than the first_2 weighted moving average weight applied to the first past OCV moving average, and the second_1 weighted moving average weight applied to the OCV deviation may be less than the second_2 weighted moving average weight applied to the second past OCV moving average.

[0092] In operation 420, the battery diagnosis device (150) can diagnose an abnormality of a battery cell based on a plurality of OCV moving averages. According to one embodiment, the battery diagnosis device (150) can diagnose an abnormality of a battery cell based on a first OCV moving average and a second OCV moving average. According to one embodiment, the battery diagnosis device (150) can calculate an OCV moving average deviation representing a difference between a plurality of OCV moving averages, apply a preset weight to the OCV moving average deviation to obtain an OCV evaluation value, and diagnose an abnormality of a battery cell based on the OCV evaluation value. According to one embodiment, the battery diagnosis device (150) can obtain an OCV evaluation value by applying a preset weight to a past OCV evaluation value and an OCV moving average deviation corresponding to a past point in time before a specific point in time. According to one embodiment, among the preset weights, the first weight applied to the OCV moving average deviation may be smaller than the second weight applied to the past OCV evaluation value. According to one embodiment, the battery diagnosis device (150) may set a threshold OCV evaluation value corresponding to a specific battery module, and may diagnose an abnormality of the battery cell based on the OCV evaluation value and the threshold OCV evaluation value.

[0093] FIG. 5 shows a computing system executing a method of operating a battery diagnostic device according to one embodiment disclosed in this document.

[0094] Referring to FIG. 5, a computing system (2000) according to an embodiment disclosed in the present document may include an MCU (2100), a memory (2200), a communication I / F (2300), and an input / output I / F (2400).

[0095] The MCU (2100) may be a processor that executes various programs (e.g., a battery diagnosis program) stored in the memory (2200) and performs the functions of the battery diagnosis device (150) described with reference to FIGS. 1 and 2 described above, or a processor that executes the operating method of the battery diagnosis device described with reference to FIG. 4.

[0096] The memory (2200) can store various programs related to calculating the SOH of a battery cell and determining whether cell balancing is to be performed, a battery connection failure determination program, a battery data transmission program, a battery diagnosis program, etc. In addition, the memory (2200) can store various data such as the SOC, SOH data, sensing values, and temperature of each battery cell.

[0097] A plurality of such memories (2200) may be provided as needed. The memories (2200) may be volatile memories or non-volatile memories. As volatile memories (2200), RAM, DRAM, SRAM, etc. may be used. As non-volatile memories (22100), ROM, PROM, EAROM, EPROM, EEPROM, flash memories, etc. may be used. The examples of the memories (2200) listed above are merely examples and are not limited to these examples.

[0098] The input / output I / F (2400) can provide an interface that enables data transmission and reception between an input device (not shown) such as a keyboard, mouse, or touch panel, and an output device (not shown) such as a display and the MCU (2100).

[0099] The communication I / F (2300) is a component capable of transmitting and receiving various data with a server, and may be any device capable of supporting wired or wireless communication. For example, programs for calculating the SOH of battery cells, determining balancing targets, various data, battery data transmission programs, battery diagnostic programs, etc. can be transmitted and received from a separately provided external server via the communication I / F (2300).

[0100] In this way, the operating method of the battery management device according to one embodiment disclosed in this document can be recorded in the memory (2200) and executed by the MCU (2100).

[0101] The terms "include," "comprise," or "have" used herein, unless otherwise specifically stated, imply that the corresponding component may be included, and therefore should be interpreted to include other components rather than to exclude other components. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as terms defined in dictionaries, should be interpreted to be consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

Claims

1. An interface for obtaining OCV (Open Circuit Voltage) data of a battery cell; and A battery diagnostic device including at least one processor that calculates an OCV deviation representing the difference between the average OCV corresponding to a specific point in time and the OCV of a battery cell for each of a plurality of battery cells included in a specific battery module based on the OCV data, obtains a plurality of OCV moving averages by applying different weighted moving averages to the OCV deviations, and diagnoses an abnormality of the battery cell based on the plurality of OCV moving averages.

2. In claim 1, One or more of the above processors, A battery diagnostic device that obtains the plurality of OCV moving averages by applying different weighted moving averages to the plurality of past OCV moving averages corresponding to past points in time before the specific point in time and the OCV deviation corresponding to the specific point in time.

3. In claim 2, One or more of the above processors, A battery diagnosis device that obtains a first OCV moving average by applying a first weighted moving average to a first past OCV moving average and the OCV deviation among the plurality of past OCV moving averages, obtains a second OCV moving average by applying a second weighted moving average to a second past OCV moving average and the OCV deviation among the plurality of past OCV moving averages, and diagnoses an abnormality of the battery cell based on the first OCV moving average and the second OCV moving average.

4. In claim 3, One or more of the above processors, The first weighted moving average is applied to the first past OCV moving average and the OCV deviation, and the first_1 weighted moving average weight applied to the OCV deviation is greater than the first_2 weighted moving average weight applied to the first past OCV moving average. A battery diagnostic device, wherein the second weighted moving average is applied to the second past OCV moving average and the OCV deviation, and the second_1 weighted moving average weight applied to the OCV deviation is smaller than the second_2 weighted moving average weight applied to the second past OCV moving average.

5. In claim 1, One or more of the above processors, A battery diagnostic device that calculates an OCV moving average deviation representing a difference between the plurality of OCV moving averages, applies a preset weight to the OCV moving average deviation to obtain an OCV evaluation value, and diagnoses an abnormality in the battery cell based on the OCV evaluation value.

6. In claim 5, One or more of the above processors, A battery diagnostic device that obtains the OCV evaluation value by applying the preset weight to the past OCV evaluation value corresponding to a past point in time before the specific point in time and the OCV moving average deviation corresponding to the specific point in time.

7. In claim 6, A battery diagnostic device, wherein among the above preset weights, the first weight applied to the OCV moving average deviation is smaller than the second weight applied to the past OCV evaluation value.

8. In claim 5, One or more of the above processors, A battery diagnostic device that sets a threshold OCV evaluation value corresponding to the specific battery module and diagnoses an abnormality in the battery cell based on the OCV evaluation value and the threshold OCV evaluation value.

9. Operation to acquire OCV (Open Circuit Voltage) data of battery cells; An operation of calculating an OCV deviation, which represents the difference between the average OCV corresponding to a specific point in time and the OCV of the battery cell for each of a plurality of battery cells included in a specific battery module, based on the above OCV data; An operation of obtaining multiple OCV moving averages by applying different weighted moving averages to the above OCV deviation; and A battery diagnosis method, comprising an operation of diagnosing an abnormality of the battery cell based on the plurality of OCV moving averages.

10. In claim 9, A battery diagnosis method, wherein the operation of obtaining the plurality of OCV moving averages includes an operation of obtaining the plurality of OCV moving averages by applying different weighted moving averages to the plurality of past OCV moving averages corresponding to past points in time before the specific point in time and the OCV deviation corresponding to the specific point in time.

11. In claim 10, The operation of obtaining the plurality of OCV moving averages includes an operation of obtaining a first OCV moving average by applying a first weighted moving average to a first past OCV moving average and the OCV deviation among the plurality of past OCV moving averages, and an operation of obtaining a second OCV moving average by applying a second weighted moving average to a second past OCV moving average and the OCV deviation among the plurality of past OCV moving averages, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the battery cell includes an operation of diagnosing an abnormality of the battery cell based on the first OCV moving average and the second OCV moving average.

12. In claim 11, The first_1 weighted moving average weight applied to the above OCV deviation is greater than the first_2 weighted moving average weight applied to the first past OCV moving average, A battery diagnosis method, wherein the second_1 weighted moving average weight applied to the above OCV deviation is smaller than the second_2 weighted moving average weight applied to the second past OCV moving average.

13. In claim 9, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the battery cell includes an operation of calculating an OCV moving average deviation representing a difference between the plurality of OCV moving averages, an operation of obtaining an OCV evaluation value by applying a preset weight to the OCV moving average deviation, and an operation of diagnosing an abnormality of the battery cell based on the OCV evaluation value.

14. In claim 13, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the battery cell includes an operation of obtaining the OCV evaluation value by applying the preset weight to a past OCV evaluation value corresponding to a past point in time before the specific point in time and the OCV moving average deviation corresponding to the specific point in time.

15. In claim 14, A battery diagnosis method, wherein among the above preset weights, the first weight applied to the OCV moving average deviation is smaller than the second weight applied to the past OCV evaluation value.

16. In claim 13, Further comprising an operation of setting a threshold OCV evaluation value corresponding to the specific battery module, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the battery cell includes an operation of diagnosing an abnormality of the battery cell based on the OCV evaluation value and the threshold OCV evaluation value.

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