Battery diagnosis device and operation method thereof

Abstract

A battery diagnosis device according to an embodiment disclosed in the present document may comprise: an interface for obtaining a plurality of charge and discharge data of a plurality of battery units; and one or more processors for obtaining a plurality of voltage-capacity data representing a relationship between a voltage change amount and a capacity change amount of each of the plurality of battery units on the basis of the plurality of charge and discharge data, obtaining a plurality of feature points corresponding to a plurality of charge and discharge cycles for each of the plurality of battery units on the basis of the plurality of voltage-capacity data, and diagnosing an abnormality of a specific battery unit on the basis of an average change amount corresponding to feature points of the plurality of battery units and a specific change amount corresponding to a feature point of the specific battery unit.

Description

Battery diagnostic device and its operating method

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority from Korean Patent Application No. 10-2024-0116701, filed August 29, 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] Previously, assessing battery defects required disassembling some battery samples. For example, lithium plating can occur due to a defective cathode overhang, increasing the risk of battery fire. Therefore, it is crucial to proactively determine whether a battery has an overhang defect and prevent the possibility of fire. This previously required disassembling some battery samples. However, the process of disassembling a battery sample and then analyzing it for actual defects was labor-intensive and time-consuming.

[0012] Accordingly, it is necessary to automatically diagnose whether a battery is defective without disassembling the battery.

[0013] The embodiments disclosed in this document can provide a battery diagnostic device and an operating method thereof capable of diagnosing an abnormality of a battery based on charge / discharge data of the battery.

[0014] 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.

[0015] A battery diagnosis device according to one embodiment disclosed in the present document may include an interface for acquiring a plurality of charge / discharge data of a plurality of battery units; and one or more processors for acquiring a plurality of voltage-capacity data indicating a relationship between a voltage change amount and a capacity change amount of each of the plurality of battery units based on the plurality of charge / discharge data, acquiring a plurality of characteristic points corresponding to a plurality of charge / discharge cycles for each of the plurality of battery units based on the plurality of voltage-capacity data, and diagnosing an abnormality of a specific battery unit based on an average change amount corresponding to the characteristic points of the plurality of battery units and a specific change amount corresponding to the characteristic point of the specific battery unit.

[0016] In a battery diagnosis device according to one embodiment disclosed in this document, the one or more processors can obtain the average change amount corresponding to the characteristic points of the plurality of battery units and the specific change amount corresponding to the characteristic point of the specific battery unit based on a time window, and diagnose an abnormality of the specific battery unit based on the average change amount and the specific change amount.

[0017] In a battery diagnostic device according to one embodiment disclosed in this document, the one or more processors can set a threshold change amount range based on the average change amount, and diagnose an abnormality of the specific battery unit based on the threshold change amount range and the specific change amount.

[0018] In a battery diagnostic device according to one embodiment disclosed in this document, the one or more processors can set the threshold variation range based on the average variation and a standard deviation corresponding to the average variation.

[0019] In a battery diagnosis device according to one embodiment disclosed in the present document, the one or more processors can obtain a first average amount of change to an nth average amount of change corresponding to characteristic points of the plurality of battery units based on a first time window to an nth time window, and a first specific amount of change to an nth specific amount of change corresponding to characteristic points of the specific battery unit, and diagnose an abnormality of the specific battery unit based on the first average amount of change to the nth average amount of change, and the first specific amount of change to the nth specific amount of change.

[0020] In a battery diagnosis device according to one embodiment disclosed in the present document, the one or more processors may set a k-th critical variation range based on a k-th average variation corresponding to a k-th time window among the first time window to the n-th time window, and when the k-th specific variation is outside the k-th critical variation range, the specific battery unit may be diagnosed as a candidate defective battery unit.

[0021] In a battery diagnosis device according to one embodiment disclosed in the present document, the one or more processors may set at least one adjacent threshold variation range based on at least one adjacent average variation corresponding to at least one adjacent time window adjacent to the k-th time window, and when at least one adjacent specific variation in the adjacent time window is outside the adjacent threshold variation range, the candidate defective battery unit may be diagnosed as a defective battery unit.

[0022] In a battery diagnostic device according to one embodiment disclosed in this document, the one or more processors can obtain a plurality of characteristic points corresponding to the plurality of charge / discharge cycles for each of the plurality of battery units based on at least some of the extreme points of the plurality of voltage-capacity data.

[0023] A battery diagnosis method according to an embodiment disclosed in the present document may include an operation of acquiring a plurality of charge / discharge data of a plurality of battery units; an operation of acquiring a plurality of voltage-capacity data indicating a relationship between a voltage change amount and a capacity change amount of each of the plurality of battery units based on the plurality of charge / discharge data; an operation of acquiring a plurality of characteristic points corresponding to a plurality of charge / discharge cycles for each of the plurality of battery units based on the plurality of voltage-capacity data; and an operation of diagnosing an abnormality of a specific battery unit based on an average change amount corresponding to the characteristic points of the plurality of battery units and a specific change amount corresponding to the characteristic point of the specific battery unit.

[0024] In a battery diagnosis method according to an embodiment disclosed in this document, an operation of diagnosing an abnormality of the specific battery unit may include an operation of obtaining the average amount of change corresponding to characteristic points of the plurality of battery units and the specific amount of change corresponding to the characteristic point of the specific battery unit based on a time window, and diagnosing an abnormality of the specific battery unit based on the average amount of change and the specific amount of change.

[0025] In a battery diagnosis method according to an embodiment disclosed in this document, the operation of diagnosing an abnormality of the specific battery unit may include an operation of setting a threshold change amount range based on the average change amount, and diagnosing an abnormality of the specific battery unit based on the threshold change amount range and the specific change amount.

[0026] In a battery diagnosis method according to an embodiment disclosed in this document, the operation of diagnosing an abnormality of the specific battery unit may include an operation of setting the threshold change amount range based on the average change amount and a standard deviation corresponding to the average change amount.

[0027] In a battery diagnosis method according to an embodiment disclosed in this document, an operation of diagnosing an abnormality of the specific battery unit may include an operation of obtaining a first average amount of change to an nth average amount of change corresponding to characteristic points of the plurality of battery units based on a first time window to an nth time window, and a first specific amount of change to an nth specific amount of change corresponding to characteristic points of the specific battery unit, and diagnosing an abnormality of the specific battery unit based on the first average amount of change to the nth average amount of change, and the first specific amount of change to the nth specific amount of change.

[0028] In a battery diagnosis method according to an embodiment disclosed in this document, an operation of diagnosing an abnormality of the specific battery unit may include an operation of setting a k-th critical change amount range based on a k-th average change amount corresponding to a k-th time window among the first time window to the n-th time window, and diagnosing the specific battery unit as a candidate defective battery unit when the k-th specific change amount is outside the k-th critical change amount range.

[0029] In a battery diagnosis method according to an embodiment disclosed in the present document, the operation of diagnosing an abnormality of the specific battery unit may include an operation of setting at least one adjacent critical variation range based on at least one adjacent average variation corresponding to at least one adjacent time window adjacent to the k-th time window, and diagnosing the candidate defective battery unit as a defective battery unit when at least one adjacent specific variation in the adjacent time window is outside the adjacent critical variation range.

[0030] According to the embodiments disclosed in this document, it is possible to diagnose a battery abnormality based on the charge / discharge data of the battery without disassembling the battery.

[0031] According to the embodiments disclosed in this document, manpower and time consumed in diagnosing battery abnormalities can be reduced.

[0032] According to the embodiments disclosed in this document, manpower and time consumed in diagnosing battery abnormalities can be reduced.

[0033] According to the embodiments disclosed in this document, it is possible to prevent a fire in a battery by preventing the shipment or use of a defective battery.

[0034] In addition, various effects may be provided, either directly or indirectly, through this document.

[0035] FIG. 1 is a block diagram of a battery diagnostic device according to one embodiment.

[0036] FIG. 2 is a diagram illustrating voltage-capacity data according to one embodiment.

[0037] Figure 3 is a drawing showing the amount of change corresponding to the characteristic points of multiple battery units.

[0038] Figure 4 is a flowchart illustrating the operation of a battery diagnostic device according to one embodiment.

[0039] FIG. 5 shows a computing system executing a method of operating a battery diagnostic device according to one embodiment.

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] FIG. 1 is a block diagram of a battery diagnostic device according to one embodiment.

[0046] Referring to FIG. 1, the upper battery unit (100) may include a plurality of battery units (110, 120, 130). According to one embodiment, the upper battery unit (100) may be a battery included in an ESS (Energy Storage System) for storing and providing surplus power. According to one embodiment, the upper battery unit (100) may be a battery installed inside an electric vehicle for providing power to the electric vehicle.

[0047] According to one embodiment, the battery diagnostic device (200) can diagnose an abnormality of a specific battery unit based on charge / discharge data obtained from an upper battery unit (100) or a battery unit (110, 120, 130). In the present disclosure, a battery unit (110, 120, 130) may mean a battery pack, a battery module, or a battery cell.

[0048] According to one embodiment, the battery diagnostic device (200) may be formed integrally with the upper battery unit (100) or the battery unit (110, 120, 130). In this case, the battery diagnostic device (200) may be included in the BMS (Battery Management System) of the upper battery unit (100) or the battery unit (110, 120, 130).

[0049] According to one embodiment, the battery diagnostic device (200) may be formed separately from the upper battery unit (100) or the battery unit (110, 120, 130). In this case, the battery diagnostic device (200) may be implemented as an external server connected to the upper battery unit (100) or the battery unit (110, 120, 130) via a wireless network.

[0050] In addition, the operation of the battery diagnostic device (200) 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.

[0051] According to one embodiment, the battery diagnostic device (200) may include an interface (210) and one or more processors (220).

[0052] According to one embodiment, the interface (210) can obtain charge / discharge data of the battery units (110, 120, 130). For example, the interface (210) can obtain information on voltage, current, capacity, and / or temperature of the battery units (110, 120, 130) and configure charge / discharge data based on the obtained information. In this case, the interface (210) can include a sensor for obtaining information on voltage, current, capacity, and / or temperature and a processor for configuring charge / discharge data based on the obtained information. As another example, the interface (210) can receive charge / discharge data of the battery units (110, 120, 130). In this case, the interface (210) can include a communication circuit capable of wired and / or wireless network communication.

[0053] According to one embodiment, the processor (220) may calculate a judgment value (e.g., voltage-capacity data, characteristic points, average change amount, specific change amount, etc.) based on the charge / discharge data acquired by the interface (210). Various embodiments in which the processor (220) calculates the judgment value may be specifically described in FIGS. 2 and 3, which will be described later.

[0054] According to one embodiment, the processor (220) can diagnose an abnormality of a battery unit based on the calculated judgment value. For example, the processor (220) can diagnose an abnormality of a specific battery unit based on a specific change amount of a specific battery unit and an average change amount of a plurality of battery units.

[0055] The processor (220) 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 (200) and perform various data processing or calculations.

[0056] According to an embodiment, the battery diagnosis device (200) 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 (200) may provide information on an abnormal battery unit to the user terminal through a communication unit (not shown), and may also provide information on an abnormal battery unit through a display equipped in a vehicle or a charger.

[0057] Hereinafter, various embodiments in which a processor (220) diagnoses an abnormality of a battery unit (110, 120, 130) will be described with reference to FIGS. 2 and 3. FIGS. 2 and 3 can be described using the configuration of FIG. 1 (e.g., interface (210), processor (220)).

[0058] First, the processor (220) can obtain a plurality of voltage-capacity data indicating the relationship between the voltage change amount and the capacity change amount of each of the plurality of battery units (110, 120, 130) based on a plurality of charge / discharge data.

[0059] Figure 2 is a diagram illustrating voltage-capacity data. Here, the horizontal axis corresponds to a voltage value (V) in a specific cycle (e.g., the 50th charging cycle) of a battery unit (e.g., 110), and the vertical axis corresponds to a value (dQ / dV) obtained by differentiating the capacity with respect to the voltage in a specific cycle (e.g., the 50th charging cycle) of the battery unit (110).

[0060] Referring to FIG. 2, the relationship between the voltage change amount and the capacity change amount in a specific cycle (e.g., the 50th charging cycle) of the battery unit (110) can be confirmed.

[0061] For reference, in FIG. 2, for convenience of explanation, voltage-capacity data in a single cycle (50th charging cycle) of a single battery unit (110) is exemplarily illustrated, but a similar explanation may be applied to voltage-capacity data in multiple cycles of multiple battery units (110, 120, 130). In addition, in FIG. 2, for convenience of explanation, a charging cycle is exemplarily illustrated, but the present invention is not limited thereto, and a similar explanation may be applied to a discharge cycle.

[0062] For example, the processor (220) may obtain m_1-th voltage-capacity data to n_1-th voltage-capacity data corresponding to the m-th to n-th cycles of the battery unit (110), respectively, based on a plurality of charge / discharge data. In addition, the processor (220) may obtain m_2-th voltage-capacity data to n_2-th voltage-capacity data corresponding to the m-th to n-th cycles of the battery unit (120), respectively. In addition, the processor (220) may obtain m_3-th voltage-capacity data to n_3-th voltage-capacity data corresponding to the m-th to n-th cycles of the battery unit (130), respectively.

[0063] And, the processor (220) can obtain a plurality of characteristic points corresponding to a plurality of charge / discharge cycles for each of a plurality of battery units (110, 120, 130) based on a plurality of voltage-capacity data (e.g., m_1 voltage-capacity data to n_1 voltage-capacity data, m_2 voltage-capacity data to n_2 voltage-capacity data, and m_3 voltage-capacity data to n_3 voltage-capacity data).

[0064] According to one embodiment, the processor (220) may obtain a plurality of characteristic points corresponding to a plurality of charge / discharge cycles for each of a plurality of battery units (110, 120, 130) based on at least some of the extreme points of a plurality of voltage-capacity data.

[0065] For example, referring to FIG. 2, the processor (220) may obtain a 50th feature point corresponding to the 50th charging cycle of the battery unit (110) based on the coordinates of the pole (10) having the minimum value among a plurality of poles of voltage-capacity data in a specific cycle (the 50th charging cycle) of the battery unit (110). For example, the value of the 50th feature point corresponding to the 50th charging cycle of the battery unit (110) may be the x-coordinate value (x1) of the pole (10). However, the value of the 50th feature point is not limited to the x-coordinate value (x1) of the pole (10), and may also be the y-coordinate value (y1) of the pole (10).

[0066] As another example, referring to FIG. 2, the processor (220) may obtain a 50th feature point corresponding to the 50th charging cycle of the battery unit (110) based on the coordinates of the pole (20) having the maximum value among multiple poles of voltage-capacity data in a specific cycle (the 50th charging cycle) of the battery unit (110). For example, the value of the 50th feature point corresponding to the 50th charging cycle of the battery unit (110) may be the x-coordinate value (x2) of the pole (20). However, the value of the feature point is not limited to the x-coordinate value (x2) of the pole (20), and may also be the y-coordinate value (y1) of the pole (20).

[0067] For reference, for convenience of explanation, FIG. 2 exemplarily describes a process for acquiring characteristic points in a single cycle (the 50th charging cycle) of a single battery unit (110), and a similar description may be applied to a process for acquiring characteristic points in multiple cycles (charging cycles or discharging cycles) of multiple battery units (110, 120, 130).

[0068] For example, the processor (220) may obtain the m_1-th feature point to the n_1-th feature point corresponding to the m-th cycle to the n-th cycle of the battery unit (110), respectively, based on a plurality of voltage-capacity data (the m_1-th voltage-capacity data to the n_1-th voltage-capacity data). In addition, the processor (220) may obtain the m_2-th feature point to the n_2-th feature point corresponding to the m-th cycle to the n-th cycle of the battery unit (120), respectively, based on a plurality of voltage-capacity data (the m_2-th voltage-capacity data to the n_2-th voltage-capacity data). In addition, the processor (220) may obtain the m_3-th feature point to the n_3-th feature point corresponding to the m-th cycle to the n-th cycle of the battery unit (130), respectively, based on a plurality of voltage-capacity data (the m_3-th voltage-capacity data to the n_3-th voltage-capacity data).

[0069] In addition, the processor (220) can diagnose an abnormality of a specific battery unit based on an average amount of change corresponding to the characteristic points of a plurality of battery units (110, 120, 130) and a specific amount of change corresponding to the characteristic point of a specific battery unit.

[0070] For reference, as a phenomenon of aging due to the use of a battery unit, the values ​​of characteristic points of the battery unit (e.g., voltage value (V) or differential value (dQ / dV)) may increase as the number of cycles increases. Accordingly, abnormalities in the battery unit can be diagnosed based on such characteristics.

[0071] In one embodiment, the amount of change corresponding to a feature point of a battery unit may be a slope of the feature point in a specific time period (e.g., a specific time window).

[0072] For example, when the k-th charging cycle to the (k+10)-th charging cycle are referred to as a specific time period (specific time window), the slope (i.e., specific change amount) of the characteristic point of a specific battery unit (110) in the specific time window may be a value obtained by subtracting the value of the characteristic point of a specific battery unit (110) in the k-th charging cycle from the value of the characteristic point of a specific battery unit (110) in the (k+10)-th charging cycle and dividing the value by the length of the time period (e.g., 10).

[0073] In addition, when the k-th charging cycle to the (k+10)-th charging cycle are referred to as a specific time period (specific time window), the slope (i.e., average change) of the feature points of the plurality of battery units (110, 120, 130) in the specific time window may be a value obtained by subtracting the sum of the feature points of the plurality of battery units (110, 120, 130) in the k-th charging cycle from the sum of the feature points of the plurality of battery units (110, 120, 130) in the (k+10)-th charging cycle, divided by the product of the number of battery units (3) and the length of the time period (e.g., 10).

[0074] However, the slope of the feature point is only an example to explain the degree of change of the feature point, and the change amount of the feature point disclosed in this document is not limited to the slope of the feature point. For example, the change amount of the feature point may be the difference value between the start feature point and the end feature point in a specific time period (specific time window). For example, the change amount of the feature point corresponding to a specific time period of the battery unit may be the value obtained by subtracting the k-th feature point corresponding to the end point of the specific time period (e.g., the (k+10)th charging cycle) from the (k+10)th feature point corresponding to the end point of the specific time period (e.g., the k-th charging cycle).

[0075] In addition, the processor (220) can diagnose an abnormality of a specific battery unit based on the average change amount of a plurality of battery units (110, 120, 130) and the specific change amount of a specific battery unit.

[0076] For example, the processor (220) can diagnose an abnormality in a specific battery unit (120) based on whether a specific change amount of a specific battery unit (e.g., 120) is similar to an average change amount of a plurality of battery units (110, 120, 130).

[0077] According to one embodiment, the processor (220) can set a threshold variation range based on an average variation of a plurality of battery units (110, 120, 130).

[0078] According to one embodiment, the processor (220) may additionally use a standard deviation corresponding to the average change amount of the plurality of battery units (110, 120, 130) to set a threshold change amount range.

[0079] For example, the processor (220) can set a critical change range according to Equation 1 below. For reference, α (scale factor) in Equations 1 and 2 below is a hyperparameter, and an appropriate value can be set according to charge / discharge data.

[0080] [Formula 1]

[0081] Critical change >= (average change - standard deviation * α)

[0082]

[0083] And, the processor (220) can diagnose an abnormality of a specific battery unit (120) based on the critical change amount range and the specific change amount. For example, when the critical change amount range is set to 0.2 or more based on the value (0.2) obtained by subtracting the product of the standard deviation and the scale factor (e.g., 0.1) from the average change amount (e.g., 0.3) of multiple battery units (110, 120, 130), and the specific change amount of a specific battery unit (120) is 0.15, the processor (220) can diagnose that an abnormality has occurred in the specific battery unit (120).

[0084]

[0085] As another example, the processor (220) can set the threshold variation range according to Equation 2 below.

[0086] [Formula 2]

[0087] (average change - standard deviation * α) <= critical change < (average change + standard deviation * α)

[0088]

[0089] And, the processor (220) can diagnose an abnormality of a specific battery unit (120) based on the critical change amount range and the specific change amount. For example, when the critical change amount range is set to be 0.3 or more and less than 0.5 based on a value (0.3) obtained by subtracting the product of the standard deviation and the scale factor (e.g., 0.1) from the average change amount (e.g., 0.4) of a plurality of battery units (110, 120, 130) and a value (0.5) obtained by adding the product of the standard deviation and the scale factor (0.1) to the average change amount (0.4), and the specific change amount of a specific battery unit (120) is 0.35, the processor (220) can diagnose that the specific battery unit (120) is normal.

[0090]

[0091] Although the above description describes a case where the processor (220) diagnoses an abnormality in the battery unit based on the amount of change (or slope) in a single time period (a single time window), the present invention is not limited thereto. For example, the processor (220) may diagnose an abnormality in the battery unit based on the amount of change in a plurality of adjacent time periods (a plurality of time windows).

[0092] For example, if (i) the number of time windows in which a specific battery unit (e.g., 130) is diagnosed as having a problem is greater than or equal to a preset number of consecutive times, or (ii) the number of time windows in which a specific battery unit (130) is diagnosed as having a problem is greater than or equal to a preset number, or (iii) the ratio of the total number of time windows to the number of time windows in which a specific battery unit (130) is diagnosed as having a problem is greater than or equal to a preset ratio, the processor (220) may diagnose that a problem has actually occurred in the specific battery unit (130).

[0093] According to one embodiment, the processor (220) may obtain a first average amount of change to an nth average amount of change corresponding to characteristic points of a plurality of battery units (110, 120, 130) based on a first time window to an nth time window, and a first specific amount of change to an nth specific amount of change corresponding to characteristic points of a specific battery unit (e.g., 130). In this case, n is an integer greater than or equal to 2.

[0094] For example, the processor (220) can obtain a first average change amount corresponding to a feature point in a first time window of a plurality of battery units (110, 120, 130), a second average change amount corresponding to a feature point in a second time window, a third average change amount corresponding to a feature point in a third time window, and a fourth average change amount corresponding to a feature point in a fourth time window. In addition, the processor (220) can obtain a first specific change amount corresponding to a feature point in a first time window of a specific battery unit (e.g., 130), a second specific change amount corresponding to a feature point in a second time window, a third specific change amount corresponding to a feature point in a third time window, and a fourth specific change amount corresponding to a feature point in a fourth time window.

[0095] And, the processor (220) can diagnose an abnormality of a specific battery unit (e.g., 130) based on the first average change amount to the n-th average change amount and the first specific change amount to the n-th specific change amount.

[0096] At this time, the processor (220) can set the first critical change amount range to the n-th critical change amount range based on the first average change amount to the n-th average change amount corresponding to the first time window to the n-th time window, respectively.

[0097] For example, the processor (220) can set the 100th critical variation range based on the 100th average variation corresponding to the 100th time window among the 1st time window to the nth time window.

[0098] And, the processor (220) can diagnose a specific battery unit (e.g., 130) as a candidate defective battery unit if a specific change amount of the specific battery unit (e.g., 130) in each of the first time window to the n-th time window is outside the corresponding threshold change amount range.

[0099] For example, the processor (220) may diagnose the specific battery unit (130) as a candidate defective battery unit if the 100th specific change amount of the specific battery unit (e.g., 130) corresponding to the 100th time window is outside the 100th critical change amount range.

[0100] And, the processor (220) can perform a similar process to the k-th time window for a time window adjacent to the k-th time window (for example, at least one of the (k-2)-th time window, the (k-1)-th time window, the (k+1)-th time window, and the (k+2)-th time window as an adjacent time window). If a specific battery unit (for example, 130) diagnosed as a candidate defective battery unit has a specific variation amount outside the threshold variation amount range even for an adjacent time window, the processor (220) can diagnose the specific battery unit (130) as a defective battery unit.

[0101] For reference, the width (size) and period (moving range) of the time window are hyperparameters, and appropriate values ​​can be set based on the charge / discharge data. For example, the width of the time window can be 10 cycles, and the period can be 5 cycles. In this case, the starting point of the first time window can be the first cycle, and the ending point can be the 11th cycle, and the starting point of the second time window adjacent to the first time window can be the 6th cycle, and the ending point can be the 16th cycle.

[0102] For example, the processor (220) may diagnose that a problem has occurred in a specific battery unit if a specific change amount of a specific battery unit is outside a corresponding threshold change amount range in four consecutive time windows.

[0103] For example, the processor (220) may diagnose a specific battery unit as a defective battery unit if each of the specific change amounts (e.g., the 101st specific change amount to the 103rd specific change amount) of a specific battery unit corresponding to three consecutive time windows (e.g., the 101st time window to the 103rd time window) adjacent to the 100th time window is outside the corresponding critical change amount range (e.g., the 101st critical change amount range to the 103rd critical change amount range).

[0104] FIG. 3 is a diagram illustrating the results of diagnosing an abnormality of a battery unit based on the amount of change corresponding to a characteristic point in a plurality of time windows of a first battery unit (OK) and a second battery unit (NG) by a processor (220). At this time, the horizontal axis corresponds to the number of cycles, and the vertical axis corresponds to the value of the characteristic point in each cycle (e.g., the voltage value as the value of the x-coordinate of the extreme point). As described above, it can be confirmed that as the cycle value of the horizontal axis increases, the characteristic point value of the battery unit also tends to increase.

[0105] Referring to FIG. 3, it can be confirmed that the first battery unit (OK) is diagnosed as normal when a specific change amount (e.g., 0.25) of the first battery unit (OK) satisfies a threshold change amount range (e.g., 0.2 or more) set based on an average change amount (e.g., 0.3) of multiple battery units.

[0106] For example, since it is determined that the k-th specific change amount in the k-th time window (30) of the first battery unit (OK) is within the k-th critical change amount range set based on the k-th average change amount in the k-th time window (30) of the plurality of battery units (110, 120, 130), and the (k+1)-th specific change amount in the (k+1)-th time window (40) of the first battery unit (OK) is within the (k+1)-th critical change amount range set based on the (k+1)-th average change amount in the (k+1)-th time window (40) of the plurality of battery units (110, 120, 130), the processor (220) can diagnose that the first battery unit (OK) is normal.

[0107] On the other hand, if a specific variation amount (e.g., 0.1) of the second battery unit (NG) does not satisfy a threshold variation amount range (e.g., 0.2 or more) set based on the average variation amount (e.g., 0.3) of multiple battery units, it can be confirmed that the second battery unit (NG) is diagnosed as defective.

[0108] For example, since it is determined that the k-th specific change amount in the k-th time window (30) of the second battery unit (NG) is outside the k-th critical change amount range set based on the k-th average change amount in the k-th time window (30) of the plurality of battery units (110, 120, 130), and the (k+1)-th specific change amount in the (k+1)-th time window (40) of the second battery unit (NG) is outside the (k+1)-th critical change amount range set based on the (k+1)-th average change amount in the (k+1)-th time window (40) of the plurality of battery units (110, 120, 130), the processor (220) can diagnose that the second battery unit (NG) is defective.

[0109] Figures 4 and 5 are flowcharts illustrating the operation of a battery diagnostic device according to one embodiment. Figure 4 may be an explanation of the operation of the battery diagnostic device (200) of Figure 1, and may be explained using the configuration of Figure 1.

[0110] 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.

[0111] Referring to FIG. 4, in operation 405, the battery diagnostic device (200) can obtain multiple charge / discharge data of multiple battery units (110, 120 and / or 130).

[0112] In operation 410, the battery diagnostic device (200) can obtain a plurality of voltage-capacity data indicating the relationship between the voltage change amount and the capacity change amount of each of the plurality of battery units (110, 120, 130) based on the plurality of charge-discharge data obtained in operation 405.

[0113] In operation 415, the battery diagnostic device (200) can obtain a plurality of characteristic points corresponding to a plurality of charge / discharge cycles for each of a plurality of battery units (110, 120, 130) based on a plurality of voltage-capacity data.

[0114]

[0115] *In operation 420 of step 107, the battery diagnosis device (200) can diagnose an abnormality of a specific battery unit based on an average amount of change corresponding to feature points of a plurality of battery units (110, 120, 130) and a specific amount of change corresponding to a feature point of a specific battery unit. According to an embodiment, the battery diagnosis device (200) can obtain an average amount of change corresponding to feature points of a plurality of battery units (110, 120, 130) and a specific amount of change corresponding to a feature point of a specific battery unit based on a time window, and can diagnose an abnormality of a specific battery unit based on the average amount of change and the specific amount of change. According to an embodiment, the battery diagnosis device (200) can set a critical amount of change range based on the average amount of change, and can diagnose an abnormality of a specific battery unit based on the critical amount of change range and the specific amount of change. According to an embodiment, the battery diagnosis device (200) can set a critical amount of change range based on a standard deviation and an average amount of change corresponding to the average amount of change. According to one embodiment, the battery diagnosis device (200) may acquire first to n-th average variations corresponding to characteristic points of a plurality of battery units (110, 120, 130) based on a first to n-th time windows, and first specific variations to n-th specific variations corresponding to characteristic points of a specific battery unit, and may diagnose an abnormality of a specific battery unit based on the first to n-th average variations and the first specific variations to n-th specific variations. According to one embodiment, the battery diagnosis device (200) may set a k-th critical variation range based on a k-th average variation corresponding to a k-th time window among the first to n-th time windows, and when the k-th specific variation is outside the k-th critical variation range, the specific battery unit may be diagnosed as a candidate defective battery unit.According to one embodiment, the battery diagnosis device (200) sets at least one adjacent critical variation range based on at least one adjacent average variation corresponding to at least one adjacent time window adjacent to the k-th time window, and when at least one adjacent specific variation in the adjacent time window is outside the adjacent critical variation range, the candidate defective battery unit can be diagnosed as a defective battery unit. According to one embodiment, the battery diagnosis device (200) can obtain a plurality of characteristic points corresponding to a plurality of charge / discharge cycles for each of the plurality of battery units (110, 120, 130) based on at least some of the extreme points of the plurality of voltage-capacity data.

[0116] FIG. 5 shows a computing system executing a method of operating a battery diagnostic device according to one embodiment disclosed in this document.

[0117] 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).

[0118] 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 (200) described with reference to FIGS. 1 to 3 described above, or a processor that executes the operating method of the battery diagnosis device described with reference to FIG. 4.

[0119] The memory (2200) can store various programs related to calculating the voltage, current, and / or capacity of a battery unit, a battery connection failure judgment program, a battery data transmission program, a battery diagnosis program, etc. In addition, the memory (2200) can store various data such as sensing values ​​and temperatures of each battery unit.

[0120] 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.

[0121] 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).

[0122] 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 determining battery cell abnormalities, 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).

[0123] 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).

[0124] 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 a dictionary, should be interpreted to be consistent with the contextual meaning 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 acquiring multiple charge / discharge data of multiple battery units; and A battery diagnostic device comprising at least one processor that obtains a plurality of voltage-capacity data representing a relationship between a voltage change amount and a capacity change amount of each of the plurality of battery units based on the plurality of charge-discharge data, obtains a plurality of characteristic points corresponding to a plurality of charge-discharge cycles for each of the plurality of battery units based on the plurality of voltage-capacity data, and diagnoses an abnormality of a specific battery unit based on an average change amount corresponding to the characteristic points of the plurality of battery units and a specific change amount corresponding to the characteristic point of the specific battery unit.

2. In claim 1, One or more of the above processors, A battery diagnosis device that obtains the average change amount corresponding to the characteristic points of the plurality of battery units and the specific change amount corresponding to the characteristic point of the specific battery unit based on a time window, and diagnoses an abnormality of the specific battery unit based on the average change amount and the specific change amount.

3. In claim 2, One or more of the above processors, A battery diagnostic device that sets a critical change amount range based on the average change amount, and diagnoses an abnormality of the specific battery unit based on the critical change amount range and the specific change amount.

4. In claim 3, One or more of the above processors, A battery diagnostic device that sets the critical change amount range based on the standard deviation corresponding to the average change amount and the average change amount.

5. In claim 1, One or more of the above processors, A battery diagnosis device that acquires a first average amount of change to an nth average amount of change corresponding to characteristic points of the plurality of battery units based on a first time window to an nth time window, and a first specific amount of change to an nth specific amount of change corresponding to characteristic points of the specific battery unit, and diagnoses an abnormality of the specific battery unit based on the first average amount of change to the nth average amount of change, and the first specific amount of change to the nth specific amount of change.

6. In claim 5, One or more of the above processors, A battery diagnostic device that sets a k-th critical change amount range based on a k-th average change amount corresponding to a k-th time window among the first to n-th time windows, and diagnoses the specific battery unit as a candidate defective battery unit when the k-th specific change amount is outside the k-th critical change amount range.

7. In claim 6, One or more of the above processors, A battery diagnosis device that sets at least one adjacent critical change amount range based on at least one adjacent average change amount corresponding to at least one adjacent time window adjacent to the k-th time window, and diagnoses the candidate defective battery unit as a defective battery unit when at least one adjacent specific change amount in the adjacent time window is outside the adjacent critical change amount range.

8. In claim 1, One or more of the above processors, A battery diagnostic device that acquires a plurality of characteristic points corresponding to the plurality of charge / discharge cycles for each of the plurality of battery units based on at least some of the extreme points of the plurality of voltage-capacity data.

9. An operation of acquiring multiple charge / discharge data of multiple battery units; An operation of obtaining a plurality of voltage-capacity data indicating a relationship between a voltage change amount and a capacity change amount of each of the plurality of battery units based on the plurality of charge-discharge data; An operation of obtaining a plurality of characteristic points corresponding to a plurality of charge / discharge cycles for each of the plurality of battery units based on the plurality of voltage-capacity data; and A battery diagnosis method including an operation of diagnosing an abnormality of a specific battery unit based on an average amount of change corresponding to a characteristic point of the plurality of battery units and a specific amount of change corresponding to a characteristic point of the specific battery unit.

10. In claim 9, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the specific battery unit includes an operation of obtaining the average change amount corresponding to the characteristic points of the plurality of battery units and the specific change amount corresponding to the characteristic point of the specific battery unit based on a time window, and diagnosing an abnormality of the specific battery unit based on the average change amount and the specific change amount.

11. In claim 10, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the specific battery unit includes an operation of setting a threshold change amount range based on the average change amount, and diagnosing an abnormality of the specific battery unit based on the threshold change amount range and the specific change amount.

12. In claim 11, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the specific battery unit includes an operation of setting the critical change amount range based on the standard deviation corresponding to the average change amount and the average change amount.

13. In claim 9, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the specific battery unit comprises an operation of obtaining a first average amount of change to an nth average amount of change corresponding to characteristic points of the plurality of battery units based on a first time window to an nth time window, and a first specific amount of change to an nth specific amount of change corresponding to characteristic points of the specific battery unit, and diagnosing an abnormality of the specific battery unit based on the first average amount of change to the nth average amount of change, and the first specific amount of change to the nth specific amount of change.

14. In claim 13, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the specific battery unit comprises an operation of setting a k-th critical change amount range based on a k-th average change amount corresponding to a k-th time window among the first time window to the n-th time window, and diagnosing the specific battery unit as a candidate defective battery unit when the k-th specific change amount is outside the k-th critical change amount range.

15. In claim 14, A battery diagnosis method, wherein the operation of diagnosing an abnormality of the specific battery unit comprises an operation of setting at least one adjacent critical variation range based on at least one adjacent average variation corresponding to at least one adjacent time window adjacent to the k-th time window, and diagnosing the candidate defective battery unit as a defective battery unit when at least one adjacent specific variation in the adjacent time window is outside the adjacent critical variation range.

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