Battery diagnosis device and method therefor

Abstract

A battery diagnosis device according to one embodiment of the present document may comprise: a memory for storing at least one instruction; and at least one processor for executing the at least one instruction, wherein the at least one processor identifies, at a specific time point, the voltage of any one battery cell included in a battery unit and an individual vector indicating a variation of the voltage according to a predetermined time interval after the specific time point, identifies, on the basis of the individual vector, a designated number of reference vectors at respective past time points before the specific time point, identifies a first distribution characteristic between the voltages of unit battery cells corresponding to the individual vector and included in the battery unit, and second distribution characteristics, corresponding to the respective reference vectors, between the voltages of the unit battery cells, and sets, on the basis of the first distribution characteristic and the second distribution characteristics, a diagnosis criterion for diagnosing the any one battery cell.

Description

Battery diagnostic device and method

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority based on Korean Patent Application No. 10-2024-0192520 filed on December 20, 2024, and includes all contents disclosed in the document of said patent application as part of this specification.

[0003] Technology field

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

[0005] With the proliferation of electric vehicles powered by electrical energy, research and development on new vehicle architectures is actively underway. For example, electric vehicles can be powered by rechargeable batteries; here, rechargeable batteries refer to rechargeable batteries that include conventional Ni / Cd and Ni / MH batteries, as well as recent lithium-ion batteries. Among rechargeable batteries, lithium-ion batteries have the advantage of significantly higher energy density compared to conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight manner, making them suitable for use as power sources for mobile devices. Recently, their scope of application has expanded to include power sources for electric vehicles, drawing attention as a next-generation energy storage medium.

[0006] Furthermore, research is being conducted on technologies for diagnosing battery condition to improve battery stability and efficiency. Technologies are being developed not only to diagnose battery condition based on characteristics measured by sensors, but also to enhance the accuracy and precision of diagnosis by diagnosing the battery condition based on battery data.

[0007] According to the embodiments disclosed in this document, a battery diagnostic device and a method for setting diagnostic criteria for battery cells for each battery unit are provided.

[0008] According to the embodiments disclosed in this document, a battery diagnostic device and a method are provided for setting diagnostic criteria for a battery cell for each battery unit by referring to data of a battery cell from a past point in time.

[0009] The technical problems of this document are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the descriptions below.

[0010] A battery diagnostic device according to one embodiment of the present document may include a memory for storing at least one instruction and at least one processor for executing said at least one instruction.

[0011] According to one embodiment, the at least one processor identifies, at a specific point in time, the voltage of one battery cell included in the battery unit and an individual vector representing the amount of change of the voltage according to a predetermined time interval after the specific point in time, and based on the individual vector, identifies a specified number of reference vectors at each of a plurality of past points in time prior to the specific point in time, identifies a first distribution characteristic between the voltages of the unit battery cells included in the battery unit corresponding to the individual vector, and second distribution characteristics between the voltages of the unit battery cells corresponding to each of the reference vectors, and can set a diagnostic criterion for diagnosing the one battery cell based on the first distribution characteristic and the second distribution characteristics.

[0012] According to one embodiment, the at least one processor identifies a representative value of the first distribution characteristic and the second distribution characteristic, identifies other representative values ​​for a battery cell different from the battery cell among the unit battery cells at a specific point in time corresponding to the representative value, and can diagnose the state of any one battery cell included in the battery unit based on the representative value and the other representative values.

[0013] According to one embodiment, the at least one processor can identify a specified number of reference vectors based on the similarity between the existing vector and the individual vector among existing vectors representing the voltage of any one battery cell at each of the plurality of past points in time and the amount of change of the voltage according to the time interval after each of the plurality of past points in time.

[0014] According to one embodiment, the at least one processor can identify the specified number of reference vectors among the existing vectors in order of proximity to the individual vector.

[0015] According to one embodiment, the at least one processor identifies the average of the representative value and the other representative values, identifies a threshold value serving as a diagnostic criterion for diagnosing whether there is an abnormality in any one battery cell at a specific point in time based on the average, and can diagnose the state of any one battery cell based on the threshold value.

[0016] According to one embodiment, the at least one processor identifies the threshold value based on the average and the median value of the voltage change amounts of each of the unit battery cells at the specific point in time, and can diagnose an abnormality of any one battery cell based on the voltage change amount of any one battery cell falling outside the threshold range identified based on the threshold value.

[0017] According to one embodiment, the at least one processor can identify the individual vector by normalizing a vector including the voltage of any one battery cell at the specific point in time and the amount of change of the voltage according to the time interval after the specific point in time.

[0018] According to one embodiment, the at least one processor can identify the existing vectors by normalizing a vector that is normalized at each of the plurality of past times and stored in the memory, and includes the voltage of any one battery cell at each of the plurality of past times and the amount of change of the voltage according to the time interval after each of the plurality of past times.

[0019] According to one embodiment, the first distribution characteristic may include a first standard deviation between voltages corresponding to the individual vectors of the unit battery cells, and the second distribution characteristic may include a second standard deviation between voltages corresponding to each of the reference vectors of the unit battery cells.

[0020] According to one embodiment, the at least one processor can identify representative values ​​of the first distribution characteristic and the second distribution characteristic based on a value obtained by calculating a first weight on the first standard deviation and values ​​obtained by calculating a second weight on each of the second standard deviations.

[0021] According to one embodiment, the at least one processor can identify the representative value based on the value obtained by calculating a first weight on the first standard deviation and the sum of the values ​​obtained by calculating a second weight on each of the second standard deviations.

[0022] According to one embodiment, the first weight may be smaller than the second weight.

[0023] A battery diagnostic method according to another embodiment of the present document may include: identifying, at a specific point in time, the voltage of one battery cell included in a battery unit and an individual vector representing the amount of change of said voltage according to a predetermined time interval after said specific point in time; identifying, based on said individual vector, a specified number of reference vectors at each of a plurality of past points in time prior to said specific point in time; identifying a first distribution characteristic between the voltages of unit battery cells included in said battery unit corresponding to said individual vector and a second distribution characteristic between the voltages of said unit battery cells corresponding to each of said reference vectors and a diagnostic criterion for diagnosing said battery cell based on said first distribution characteristic and said second distribution characteristic.

[0024] According to one embodiment, the operation of setting a diagnostic criterion for diagnosing any one battery cell based on the first distribution characteristic and the second distribution characteristic may include the operation of identifying a representative value of the first distribution characteristic and the second distribution characteristic, the operation of identifying other representative values ​​for a battery cell different from the battery cell among the unit battery cells at a specific point in time corresponding to the representative value, and the operation of diagnosing the state of any one battery cell included in the battery unit based on the representative value and the other representative values.

[0025] According to one embodiment, the operation of identifying a specified number of reference vectors at each of a plurality of past points in time prior to the specific point in time based on the individual vector may include identifying a specified number of reference vectors according to the similarity between the existing vector and the individual vector among the existing vectors representing the voltage of any one battery cell at each of the plurality of past points in time and the amount of change of the voltage according to the time interval after each of the plurality of past points in time.

[0026] According to one embodiment, among existing vectors representing the voltage of any one battery cell at each of the plurality of past points in time and the amount of change of the voltage according to the time interval after each of the plurality of past points in time, the operation of identifying a specified number of reference vectors according to the similarity between the existing vector and the individual vector may include the operation of identifying the specified number of reference vectors in order of the shortest distance to the individual vector among the existing vectors.

[0027] According to one embodiment, the operation of diagnosing the state of any one battery cell included in the battery unit based on the representative value and the other representative values ​​may include the operation of identifying the average of the representative value and the other representative values, the operation of identifying a threshold value serving as a diagnostic criterion for diagnosing whether there is an abnormality in any one battery cell at a specific point in time based on the average, and the operation of diagnosing the state of any one battery cell based on the threshold value.

[0028] According to one embodiment, the operation of identifying a threshold value serving as a diagnostic criterion for diagnosing whether any one battery cell is abnormal at a specific point in time based on the average includes the operation of identifying the threshold value based on the average and the median value of the voltage change amounts of each of the unit battery cells at the specific point in time, and the operation of diagnosing the state of any one battery cell based on the threshold value may include the operation of diagnosing an abnormality of any one battery cell based on the voltage change amount of any one battery cell deviating from a threshold range identified based on the threshold value.

[0029] According to one embodiment, the operation of identifying an individual vector representing the voltage of one battery cell included in the battery unit at the specific point in time and the amount of change of the voltage according to a predetermined time interval after the specific point in time may include the operation of identifying the individual vector by normalizing a vector including the voltage of one battery cell at the specific point in time and the amount of change of the voltage according to the time interval after the specific point in time.

[0030] According to one embodiment, among existing vectors representing the voltage of any one battery cell at each of the plurality of past times and the amount of change of the voltage according to the time interval after each of the plurality of past times, the operation of identifying a specified number of reference vectors according to the similarity between the existing vector and the individual vector may include the operation of identifying the existing vectors by normalizing a vector that is normalized at each of the plurality of past times and stored in memory, and includes the voltage of any one battery cell at each of the plurality of past times and the amount of change of the voltage according to the time interval after each of the plurality of past times.

[0031] This technology can set diagnostic criteria for battery cells for each battery unit.

[0032] In addition, this technology can set diagnostic criteria for battery cells for each battery unit by referring to battery cell data from a past point in time.

[0033] In addition, various effects that can be identified directly or indirectly through this document may be provided.

[0034] FIG. 1 is a block diagram showing a battery pack in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0035] FIG. 2 is a block diagram showing the configuration of a battery diagnostic device in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0036] FIG. 3 illustrates examples of individual vectors, existing vectors, and reference vectors in a battery diagnostic device and method according to one embodiment of the present document.

[0037] FIG. 4 illustrates an example of the flow of operation of a battery diagnostic device that stores a vector in a battery diagnostic device and method according to one embodiment of the present document.

[0038] FIG. 5 illustrates the flow of operation of a battery diagnostic device that diagnoses the state of a battery cell based on a threshold value in a battery diagnostic device and method according to one embodiment of the present document.

[0039] FIG. 6 illustrates a graph showing the amount of change in voltage over time in a battery diagnostic device and method according to one embodiment of the present document.

[0040] FIG. 7 illustrates a graph showing the value obtained by subtracting the median value from the amount of change in voltage over time in a battery diagnostic device and method according to one embodiment of the present document.

[0041] FIG. 8 illustrates a graph showing a value normalized by subtracting a median value from a change in voltage in a battery diagnostic device and method according to one embodiment of the present document.

[0042] FIG. 9 illustrates the flow of operation of a battery diagnostic device for setting diagnostic criteria in a battery diagnostic device and method according to one embodiment of the present document.

[0043] FIG. 10 is a block diagram showing the hardware configuration of a computing system for performing a battery diagnostic method according to one embodiment disclosed in this document.

[0044] Some embodiments disclosed herein are described below with reference to the various embodiments of the accompanying drawings. However, this is not intended to limit the technology to specific embodiments and should be understood to include various modifications, equivalents, and / or alternatives to embodiments of the technology.

[0045] It should be noted that when assigning reference numerals to the components of each drawing, the same components are assigned the same reference numeral whenever possible, even if they are shown in different drawings. Furthermore, in describing the various embodiments disclosed in this document, if it is determined that a detailed description of related known configurations or functions would hinder understanding of the embodiments of the present invention, such detailed description is omitted. The singular form of a noun corresponding to an item may include one or more items unless the relevant context clearly indicates otherwise.

[0046] In describing the components of the embodiments of this document, terms such as first, second, A, B, (a), (b), etc., may be used. These terms are intended merely to distinguish the components from other components and do not limit the essence, order, or sequence of the components. Furthermore, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments disclosed in this document pertain. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0047] Additionally, in this disclosure, expressions of "greater than" or "less than" may be used to determine whether a specific condition is satisfied or fulfilled; however, this is merely for the purpose of expressing an example and does not exclude descriptions of "greater than" or "less than." Conditions described as "greater than" may be replaced with "greater than," conditions described as "less than" may be replaced with "less than," and conditions described as "greater than and less than" may be replaced with "greater than and less than." Furthermore, "A" to "B" below refer to at least one of the elements from A (including A) to B (including B).

[0048] In this document, each of the phrases such as "A or B", "at least one of A and B", "at least one of A or B", "A, B or C", "at least one of A, B and C", and "at least one of A, B, or C" may include any one of the items listed together in the corresponding phrase, or all possible combinations thereof.

[0049] In this document, where any component (e.g., 1) is referred to as being “connected,” “coupled,” or “joined” to another component (e.g., 2), with or without the terms “functionally” or “communicationally,” or where it is referred to as “coupled” or “connected,” it means that the component may be connected to the other component directly (e.g., via a wire), wirelessly, or through a third component.

[0050] According to one embodiment, the method according to the various embodiments disclosed herein may be provided as included in a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or distributed online (e.g., download or upload) through an application store or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0051] According to various embodiments, each component (e.g., module or program) of the described components may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to various embodiments, one or more of the aforementioned components or operations may be omitted, or one or more other components or operations may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as they were performed by the corresponding component among the multiple components prior to integration. According to various embodiments, operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically; one or more of the operations may be executed in a different order; may be omitted; or one or more other operations may be added.

[0052] Hereinafter, embodiments of the present document will be described in detail with reference to FIGS. 1 to 10.

[0053] FIG. 1 is a block diagram showing a battery pack in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0054] Referring to FIG. 1, the battery pack (1) may include a battery unit (12), a sensor unit (14), a switching unit (16), and a battery management system (BMS) (20). At this time, the battery pack (1) may be equipped with a plurality of battery units (12), sensor units (14), switching units (16), and battery management systems (20).

[0055] According to one embodiment, the battery unit (12) can supply power to a target device (not shown). To this end, the battery unit (12) may be electrically connected to the target device. Here, the target device may include an electrical, electronic, or mechanical device that operates by receiving power from the battery pack (1). For example, the target device may be an electric vehicle (EV) or an energy storage system (ESS), but is not limited thereto.

[0056] According to one embodiment, the battery unit (12) may include at least one battery cell (10) capable of charging and discharging. Here, the battery cell (10) may be a basic unit of a battery cell capable of charging and discharging electrical energy. For example, the battery cell (10) may be a lithium-ion (Li-ion) battery, a lithium-ion polymer (Li-ion polymer) battery, a nickel-cadmium (Ni-Cd) battery, a nickel-hydrogen (Ni-MH) battery, etc., but is not limited thereto.

[0057] According to one embodiment, a plurality of battery units (12) may be connected in series or in parallel. For example, a battery unit (12) may be a battery module, a battery bank, or a set of battery cells (cell-to-pack structure).

[0058] According to one embodiment, the sensor unit (14) can obtain information related to the battery unit (12). According to one embodiment, the sensor unit (14) can obtain values ​​(or information) related to the state of each of the battery unit (12) or battery cells (10). In one embodiment, the values ​​related to the state may include at least one value for the voltage, current, resistance, state of charge (SOC), state of health (SOH), or temperature of the battery cell, or a combination thereof.

[0059] According to one embodiment, the sensor unit (14) can provide information of each of the plurality of battery units (12) to the battery management system (20).

[0060] According to one embodiment, the switching unit (16) may include an element for controlling the current flow for charging or discharging the battery unit (12). For example, the switching unit (16) may include at least one relay and / or magnetic contactor, etc., depending on the specifications of the battery pack (1).

[0061] According to one embodiment, a battery management system (BMS) (20) can control or manage a battery pack (1) by monitoring the voltage, current, temperature, etc. of the battery pack (1). For example, the battery management system (20) may include a plurality of terminals as an interface for receiving values ​​of the various parameters described above, and a circuit connected to these terminals to perform processing of the received values. Additionally, the battery management system (20) can control a sensor unit (14) and / or a switching unit (16). For example, the battery management system (20) may be connected to a plurality of battery units (12) to monitor the status of each of the plurality of battery units (12) and control the ON / OFF of relays or contactors.

[0062] According to one embodiment, the operation of the battery management system (20) can be performed by a battery management system (BMS) in the vehicle, as well as by various devices such as a server, cloud, charger, or discharger.

[0063] The upper controller (2) can transmit control signals for a plurality of battery units (12) to the battery management system (20). Accordingly, the operation of the battery management system (20) can be controlled based on the signals applied from the upper controller (2).

[0064] According to one embodiment, the battery management system (20) may include the battery diagnostic device (201) of FIG. 2. According to another embodiment, the battery management system (20) may be a different system from the battery diagnostic device (201) of FIG. 2. That is, the battery diagnostic device (201) of FIG. 2 may be included in the battery pack (1) or may be configured as another device outside the battery pack (1). For convenience of explanation, the following description assumes that the battery diagnostic device (201) is configured as another device outside the battery pack (1). Furthermore, the operation of the battery diagnostic device (201) below may be performed by a battery management system (BMS) within the vehicle, as well as by various devices such as a server, cloud, charger, or discharger.

[0065] FIG. 2 is a block diagram showing the configuration of a battery diagnostic device in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0066] Referring to FIG. 2, the battery diagnostic device (201) may include a memory (203) for storing at least one instruction and at least one processor (205) for executing at least one instruction. A battery unit (e.g., battery pack, battery module, battery cell) may include at least one battery cell and additional elements associated with at least one battery cell.

[0067] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify whether there is an abnormality in the battery cell based on data of the battery cell (e.g., voltage data, current data, temperature data).

[0068] According to one embodiment, the magnitude of noise when diagnosing an abnormality in a battery cell based on data from at least one battery cell included in a battery unit (e.g., a slave) that includes some battery cells of the entire battery may be smaller than the magnitude of noise when diagnosing an abnormality in a battery cell based on data from at least one battery cell included in the entire battery. This is because a relatively short electrical path is formed between the battery cells included in the battery unit, thereby reducing noise or interference of electrical signals. The slave is an electrical unit that includes at least one battery cell of the entire battery and can perform control and monitoring.

[0069] However, the amount of data used for the abnormality diagnosis criteria when the diagnosis is performed based on data of at least one battery cell included in the entire battery may be greater than the amount of data used for the abnormality diagnosis criteria when the diagnosis is performed based on data of at least one battery cell included in the battery unit.

[0070] The accuracy of diagnosing abnormalities in battery cells can be improved as the magnitude of noise is reduced and the amount of data used to set diagnostic criteria is increased.

[0071] At least one processor (205) of a battery diagnostic device (201) according to one embodiment can set diagnostic criteria for diagnosing a battery cell based on data included in a battery unit at a past point in time in order to reduce noise and increase the amount of data used to set diagnostic criteria for abnormalities.

[0072] Specifically, at least one processor (205) of the battery diagnostic device (201) can identify the voltage of any one battery cell included in the battery unit at a specific point in time and the amount of change in voltage according to a predetermined time interval after the specific point in time.

[0073] The amount of change in voltage according to a predetermined time interval may include the difference between the voltage value at a first time point in which voltage data was acquired and the voltage value at a second time point immediately after the first time point, which is after the time interval determined from the first time point. However, the embodiments of this document may not be limited thereto. For example, the amount of change in voltage according to a predetermined time interval may include the difference between the voltage value at a third time point and the voltage value at a fourth time point, which is after the time interval determined from the third time point.

[0074] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify individual vectors by normalizing a vector including the voltage of any one battery cell included in the battery unit and the amount of change in voltage.

[0075] Vector normalization can refer to adjusting the size of each component in a vector so that the vector's size becomes a specified value (e.g., 1) and the ratio between the elements in the vector remains the same. For example, vector normalization may involve an operation that divides each component in the vector by the vector's size.

[0076] According to one embodiment, at least one processor (205) of a battery diagnostic device (201) can identify existing vectors stored in memory at each of a plurality of past points in time from memory after a vector including the voltage of one battery cell at each of a plurality of past points in time and the amount of change in voltage according to the time interval after each of a plurality of past points in time is normalized. The memory may be referred to as a vector storage or a vector store, but the embodiments of this document may not be limited thereto.

[0077] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify a specified number of reference vectors among existing vectors based on the similarity between an existing vector and an individual vector.

[0078] The similarity between an existing vector and an individual vector may be identified based on at least one of the distance between the existing vector and the individual vector, the angle between the existing vector and the individual vector, the Gaussian similarity between the existing vector and the individual vector, the Manhattan distance between the existing vector and the individual vector, the Mahalanobis distance between the existing vector and the individual vector, or any combination thereof, but the embodiments of this document may not be limited thereto.

[0079] For example, at least one processor (205) of the battery diagnostic device (201) can identify a specified number of reference vectors among existing vectors in order of proximity to individual vectors. However, the embodiments of this document may not be limited thereto.

[0080] As another example, at least one processor (205) of the battery diagnostic device (201) can identify a specified number of reference vectors among existing vectors in order of the smallest angle between the existing vector and the individual vector. However, the embodiments of this document may not be limited thereto.

[0081] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify a first distribution characteristic among the voltages of unit battery cells at a specific point in time corresponding to an individual vector. The unit battery cells may represent at least one battery cell included in a battery unit. The first distribution characteristic may include a standard deviation or variance, but the embodiments of this document may not be limited thereto.

[0082] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify second distribution characteristics between the voltages of unit battery cells at a past point in time corresponding to each of the reference vectors. The second distribution characteristics may include a standard deviation or variance, but the embodiments of this document are not limited thereto.

[0083] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify representative values ​​of a first distribution characteristic (e.g., a first standard deviation) and a second distribution characteristic (e.g., a second standard deviation).

[0084] For example, at least one processor (205) of the battery diagnostic device (201) can identify a representative value of the first distribution characteristic and the second distribution characteristic based on the sum of the values ​​obtained by calculating a first weight on the first distribution characteristic and the values ​​obtained by calculating a second weight on each of the second distribution characteristics. However, the embodiments of this document may not be limited thereto.

[0085] Here, the first weight may be smaller than the second weight. This is because battery cells from past time points are assumed to be normal battery cells. This is because if there had been any abnormal battery cells among the battery cells from past time points, the abnormality would have been detected.

[0086] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify different representative values ​​for a battery cell and other battery cells among the unit battery cells at a specific point in time. The other representative values ​​may be expressions indicating that they are representative values ​​for other battery cells, rather than being different in magnitude from the representative value.

[0087] In other words, at least one processor (205) of the battery diagnostic device (201) can identify representative values ​​for all unit battery cells at a specific point in time.

[0088] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify the average of representative values ​​for all unit battery cells. The representative values ​​for all unit battery cells may represent a representative value and other representative values.

[0089] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify a critical deviation based on the average of representative values ​​for all unit battery cells. At least one processor (205) of the battery diagnostic device (201) can identify a critical median value based on the median value of the voltage change amounts of each unit battery cell at a specific point in time.

[0090] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify an upper limit value of the threshold range based on a value obtained by adding a threshold deviation to a threshold median value, and can identify a lower limit value of the threshold range based on a value obtained by subtracting a threshold deviation from a threshold median value.

[0091] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can diagnose an abnormality in one of the battery cells based on the voltage change amount of one of the battery cells included in the battery unit exceeding a critical range.

[0092] FIG. 3 illustrates examples of individual vectors, existing vectors, and reference vectors in a battery diagnostic device and method according to one embodiment of the present document.

[0093] Referring to FIG. 3, the battery unit (12) may include a first battery cell (8), a second battery cell (9), a third battery cell (10), and a fourth battery cell (11).

[0094] According to one embodiment, at least one processor (e.g., at least one processor (205) of FIG. 2) of a battery diagnostic device (e.g., battery diagnostic device (201) of FIG. 2) can identify individual vectors (e.g., first individual vector (301), second individual vector (311), third individual vector (321), fourth individual vector (331)) corresponding to a battery cell based on data (e.g., voltage, voltage change amount) of a battery cell (e.g., first individual vector (301), second individual vector (311), third individual vector (321), fourth individual vector (331)).

[0095] Specifically, at least one processor of the battery diagnostic device (201) (e.g., at least one processor (205) of FIG. 2) can identify individual vectors (e.g., first individual vector (301), second individual vector (311), third individual vector (321), fourth individual vector (331)) based on the voltage of any one battery cell (e.g., first battery cell (8), second battery cell (9), third battery cell (10), fourth battery cell (11)) included in the battery unit at a specific point in time and the amount of change in voltage according to a predetermined time interval after a specific point in time.

[0096] According to one embodiment, at least one processor (205) of a battery diagnostic device (201) can identify existing vectors (e.g., first existing vector (303), second existing vector (305), third existing vector (307), fourth existing vector (313), fifth existing vector (315), sixth existing vector (317), seventh existing vector (323), eighth existing vector (325), ninth existing vector (327), tenth existing vector (333), eleventh existing vector (335), twelveth existing vector (337)) at each of a plurality of past points in time prior to a specific point in time that correspond to the individual vectors, based on individual vectors (e.g., first existing vector (303), second existing vector (305), third existing vector (307), fourth existing vector (313), fifth existing vector (315), sixth existing vector (317), seventh existing vector (323), eighth existing vector (325), ninth existing vector (327), tenth existing vector (333), eleventh existing vector (335), twelveth existing vector (337)) corresponding to the individual vectors.

[0097] Specifically, at least one processor (205) of the battery diagnostic device (201) can identify existing vectors stored in memory at each of the multiple past points in memory after a vector including the voltage of the battery cell at each of the multiple past points in time and the amount of change in voltage according to the time interval after each of the multiple past points in time is normalized.

[0098] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify a specified number of reference vectors among existing vectors corresponding to individual vectors.

[0099] For example, at least one processor (205) of the battery diagnostic device (201) can identify the first existing vector (303) and the second existing vector (305) as reference vectors among the existing vectors corresponding to the first battery cell (8), identify the fifth existing vector (315) and the sixth existing vector (317) as reference vectors among the existing vectors corresponding to the second battery cell (9), identify the eighth existing vector (325) and the ninth existing vector (327) as reference vectors among the existing vectors corresponding to the third battery cell (10), and identify the tenth existing vector (333) and the eleventh existing vector (335) as reference vectors among the existing vectors corresponding to the fourth battery cell (11). The time at which the existing vectors are acquired may be independent of each other among the existing vectors.

[0100] For example, when identifying reference vectors for a first battery cell (8), at least one processor (205) of the battery diagnostic device (201) may identify a specified number of reference vectors (e.g., first existing vector (303), second existing vector (305)) among existing vectors (e.g., first existing vector (303), second existing vector (305), third existing vector (307)) in order of proximity to an individual vector (e.g., first individual vector (301)). However, the embodiments of this document may not be limited thereto.

[0101] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify a first distribution characteristic between the voltages of unit battery cells at a specific point in time corresponding to an individual vector, and a second distribution characteristic between the voltages of unit battery cells at a past point in time corresponding to each of the reference vectors.

[0102] For example, when identifying a first distribution characteristic and a second distribution characteristic for a first battery cell (8), at least one processor (205) of the battery diagnostic device (201) can identify a first distribution characteristic between the voltages of unit battery cells (e.g., first battery cell (8), second battery cell (9), third battery cell (10), fourth battery cell (11)) at a specific point in time corresponding to an individual vector (e.g., first individual vector (301)), and a second distribution characteristic between the voltages of unit battery cells at a past point in time corresponding to each of reference vectors (e.g., first existing vector (303), second existing vector (305)).

[0103] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify representative values ​​of a first distribution characteristic (e.g., a first standard deviation) and a second distribution characteristic (e.g., a second standard deviation).

[0104] For example, when identifying a representative value for the first battery cell (8), at least one processor (205) of the battery diagnostic device (201) can identify the average value of the first distribution characteristic and the second distribution characteristic.

[0105] For example, at least one processor (205) of the battery diagnostic device (201) can identify representative values ​​corresponding to a battery cell (e.g., first battery cell (8)) and a different battery cell (e.g., second battery cell (9), third battery cell (10), fourth battery cell (11)) among the unit battery cells (e.g., first battery cell (8), second battery cell (9), third battery cell (10), fourth battery cell (11)) at a specific point in time.

[0106] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify the average of representative values ​​for all unit battery cells.

[0107] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify a critical deviation based on the average of representative values ​​for all unit battery cells. At least one processor (205) of the battery diagnostic device (201) can identify a critical median value based on the median value of the voltage change amounts of each unit battery cell at a specific point in time.

[0108] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify an upper limit value of the threshold range based on a value obtained by adding a threshold deviation to a threshold median value, and can identify a lower limit value of the threshold range based on a value obtained by subtracting a threshold deviation from a threshold median value.

[0109] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can diagnose an abnormality in one of the battery cells based on the voltage change amount of one of the battery cells included in the battery unit exceeding a critical range.

[0110] FIG. 4 illustrates an example of the flow of operation of a battery diagnostic device that stores a vector in a battery diagnostic device and method according to one embodiment of the present document.

[0111] In the following, it is assumed that at least one processor (205) of the battery diagnostic device (201) of FIG. 2 performs the process of FIG. 4. Also, in the description of FIG. 4, the operation described as being performed by the battery diagnostic device (201) can be understood as being controlled by at least one processor (205) of the battery diagnostic device (201).

[0112] Referring to FIG. 4, in the first operation (401), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can obtain existing data from a data pool.

[0113] The data pool can store existing data corresponding to the time when the battery cell data is acquired. The existing data may include battery cell data (e.g., voltage data, current data, temperature data) at each of multiple past time points.

[0114] In the second operation (403), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can identify an existing vector through preprocessing based on acquired existing data. The preprocessing process may include a process of identifying an existing vector by changing the existing data into a vector that includes the voltage of one battery cell at each of a plurality of past time points and the amount of change in voltage according to the time interval after each of the plurality of past time points, and then performing normalization of the vector.

[0115] In the third operation (405), at least one processor (205) of the battery diagnostic device (201) according to one embodiment may store an existing vector in a vector storage. The vector storage may be referred to as memory, but the embodiments of this document may not be limited thereto.

[0116] In the fourth operation (407), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can identify whether there is data remaining in the data pool. If there is data remaining in the data pool, at least one processor (205) of the battery diagnostic device (201) can perform the first operation (401). If there is no data remaining in the data pool, the operation of the battery diagnostic device storing the existing vector can be terminated.

[0117] According to one embodiment, the battery diagnostic device (201) can store an existing vector in a vector storage by performing a first operation (401) to a fourth operation (407).

[0118] FIG. 5 illustrates the flow of operation of a battery diagnostic device that diagnoses the state of a battery cell based on a threshold value in a battery diagnostic device and method according to one embodiment of the present document.

[0119] In the following, it is assumed that at least one processor (205) of the battery diagnostic device (201) of FIG. 2 performs the process of FIG. 5. Additionally, in the description of FIG. 5, the operation described as being performed by the battery diagnostic device (201) can be understood as being controlled by at least one processor (205) of the battery diagnostic device (201).

[0120] In the first operation (501), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can acquire data of any one battery cell.

[0121] In the second operation (503), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can identify individual vectors through preprocessing based on acquired data.

[0122] In the third operation (505), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can perform search and retrieval of reference vectors from a vector storage based on individual vectors.

[0123] In the fourth operation (507), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can identify a representative value based on the acquired data, reference vector, and individual vector.

[0124] In the fifth operation (509), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can identify other representative values ​​for the unit battery cells.

[0125] In the sixth operation (511), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can identify a threshold value based on a representative value and other representative values.

[0126] In the seventh operation (513), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can diagnose the state of any one battery cell based on a threshold value.

[0127] FIG. 6 illustrates a graph showing the amount of change in voltage over time in a battery diagnostic device and method according to one embodiment of the present document.

[0128] FIG. 7 illustrates a graph showing the value obtained by subtracting the median value from the amount of change in voltage over time in a battery diagnostic device and method according to one embodiment of the present document.

[0129] FIG. 8 illustrates a graph showing a value normalized by subtracting a median value from a change in voltage in a battery diagnostic device and method according to one embodiment of the present document.

[0130] Referring to FIGS. 6 through 8, the first graph (600) may include first lines (609) representing the voltage change amount of a normal battery cell, a second line (607) representing the voltage change amount of an abnormal battery cell, a fourth line (601) representing the upper limit value of a threshold range, a fifth line (603) representing the lower limit value of a threshold range, and a third line (605) representing a threshold center value identified based on the median value of the voltage change amounts of each unit battery cell at a specific point in time.

[0131] The second graph (700) may include: a sixth line (709) representing a value obtained by subtracting the critical median value of the first graph (600) from the voltage change amount of the normal battery cell of the first graph (600); a seventh line (707) representing a value obtained by subtracting the critical median value of the first graph (600) from the voltage change amount of the abnormal battery cell of the first graph (600); a ninth line (701) representing a value obtained by subtracting the critical median value of the first graph (600) from the upper limit value of the critical range of the first graph (600); a tenth line (703) representing a value obtained by subtracting the critical median value of the first graph (600) from the lower limit value of the critical range of the first graph (600); and an eighth line (705) representing a value obtained by subtracting the critical median value from the critical median value of the first graph (600).

[0132] The third graph (800) may include a 11th line (809) normalized to the size of the threshold range by the 6th line (709) of the second graph (700), which is the value obtained by subtracting the threshold median value from the voltage change amount of a normal battery cell; a 12th line (807) normalized to the size of the threshold range by the 7th line (707), which is the value obtained by subtracting the threshold median value from the voltage change amount of an abnormal battery cell; a 14th line (801) normalized to the size of the threshold range by the 9th line (701), which is the value obtained by subtracting the threshold median value from the upper limit value of the threshold range; a 15th line (803) normalized to the size of the threshold range by the 10th line (703), which is the value obtained by subtracting the threshold median value from the lower limit value of the threshold range; and a 13th line (705) normalized to the size of the threshold range by the 8th line (705), which is the value obtained by subtracting the median value from the threshold median value.

[0133] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can diagnose an abnormality in the battery cell corresponding to the 12th line (807) based on identifying that at least a portion of the 12th line (807) is outside the threshold range between the 14th line (801) and the 15th line (803).

[0134] FIG. 9 illustrates the flow of operation of a battery diagnostic device for setting diagnostic criteria in a battery diagnostic device and method according to one embodiment of the present document.

[0135] In the following, it is assumed that at least one processor (205) of the battery diagnostic device (201) of FIG. 2 performs the process of FIG. 9. Additionally, in the description of FIG. 9, the operation described as being performed by the battery diagnostic device (201) can be understood as being controlled by at least one processor (205) of the battery diagnostic device (201).

[0136] Referring to FIG. 9, in the first operation (901), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can identify individual vectors at a specific point in time.

[0137] In the second operation (903), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can identify a specified number of reference vectors at each of a plurality of past points in time prior to a specific point in time based on individual vectors.

[0138] In the third operation (905), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can identify a first distribution characteristic between the voltages of unit battery cells that are different from one battery cell and correspond to an individual vector, and a second distribution characteristic between the voltages of unit battery cells that correspond to each of the reference vectors.

[0139] In the fourth operation (907), at least one processor (205) of the battery diagnostic device (201) according to one embodiment can set a diagnostic criterion for diagnosing any one battery cell based on the first distribution characteristic and the second distribution characteristic.

[0140] FIG. 10 is a block diagram showing the hardware configuration of a computing system for performing a battery diagnostic method according to one embodiment disclosed in this document.

[0141] Referring to FIG. 10, a computing system (1000) according to one embodiment disclosed in this document may include an MCU (1010), a memory (1020), an input / output I / F (1030), and a communication I / F (1040).

[0142] The MCU (1010) may be at least one processor that executes various programs stored in memory (1020) (e.g., battery cell data collection program, graph generation program, data analysis program, data decomposition algorithm, normalization program, battery cell diagnosis program, etc.), processes various information including characteristic data of the battery cell, potential variables, etc. through these programs, and performs the functions of the battery diagnosis device (201) shown in FIGS. 1 to 9.

[0143] The memory (1020) can store various programs such as a battery cell data collection program, a graph generation program, a data analysis program, a data decomposition algorithm, a normalization program, and a battery cell diagnosis program.

[0144] Multiple such memories (1020) may be provided as needed. The memories (1020) may be volatile memories or non-volatile memories. As volatile memories, the memory (1020) may use RAM, DRAM, SRAM, etc. As non-volatile memories, the memory (1020) may use ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. The examples of the listed memories (1020) are merely examples and are not limited to these examples.

[0145] The input / output I / F (1030) can provide an interface that enables data transmission and reception between an input device (not shown), such as a keyboard, mouse, or touch panel, an output device (not shown), and an MCU (1010).

[0146] The communication I / F (1040) is configured to transmit and receive various data with a server and may be various devices capable of supporting wired or wireless communication. For example, the battery diagnostic device (201) can transmit and receive various information, including the shape model of a battery cell, from a separately provided external server via the communication I / F (1040).

[0147] In this way, a computer program according to one embodiment disclosed in this document may be implemented as a function module that performs, for example, the functions illustrated in FIG. 1, by being written to memory (1020) and processed by an MCU (1010).

[0148] As described above, even though all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purposes of the embodiments disclosed in this document, all components may be selectively combined in one or more ways to operate.

[0149] Furthermore, terms such as "include," "compose," or "have" as described above, unless specifically stated otherwise, mean that the relevant component may be inherent; thus, they should be interpreted as allowing for the inclusion of additional components rather than excluding them. All terms, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as those defined in advance, should be interpreted in accordance with their contextual meanings in the relevant technology and, unless explicitly defined in this document, should not be interpreted in an ideal or overly formal sense.

[0150] The foregoing disclosure outlines the features of several embodiments to enable those skilled in the art to better understand the aspects of the present disclosure. Those skilled in the art will understand that the present disclosure can be readily used as a basis for designing or modifying other structures to perform the same purpose or achieve the same advantages as the embodiments introduced herein. Furthermore, those skilled in the art will recognize that such equivalent configurations do not depart from the scope of the present disclosure and that various changes, substitutions, and modifications may be made in the present specification without departing from the scope of the present disclosure.

Claims

Memory that stores at least one instruction; It includes at least one processor that executes the above at least one instruction, and The above at least one processor is, Identifying the voltage of a single battery cell included in a battery unit at a specific point in time and an individual vector representing the amount of change of the voltage according to a predetermined time interval after the specific point in time, and Based on the individual vectors above, a specified number of reference vectors at each of the multiple past points prior to the specific point in time are identified, and Identifying a first distribution characteristic between the voltages of unit battery cells included in the battery unit corresponding to the individual vectors above, and second distribution characteristics between the voltages of the unit battery cells corresponding to each of the reference vectors above, A configuration for establishing a diagnostic criterion for diagnosing any one of the battery cells based on the first distribution characteristics and the second distribution characteristics. Battery diagnostic device. In claim 1, The above at least one processor is, Identify representative values ​​of the first distribution characteristics and the second distribution characteristics, and Identifying other representative values ​​for battery cells other than the battery cell among the unit battery cells at a specific point in time that correspond to the above representative value, and Based on the above representative value and the other representative values, configured to diagnose the state of any one of the battery cells included in the battery unit, Battery diagnostic device. In claim 1, The above at least one processor is, Among existing vectors representing the voltage of any one battery cell at each of the plurality of past points in time and the amount of change of the voltage according to the time interval after each of the plurality of past points in time, configured to identify a specified number of reference vectors according to the similarity between the existing vector and the individual vector. Battery diagnostic device. In claim 3, The above at least one processor is, Among the above existing vectors, configured to identify the specified number of reference vectors in order of shortest distance to the individual vectors, Battery diagnostic device. In claim 2, The above at least one processor is, Identify the average of the above representative value and the other representative values, Based on the above average, identify a threshold value serving as a diagnostic criterion for diagnosing whether any one of the battery cells is abnormal at the above specific point in time, and Configured to diagnose the state of any one of the battery cells based on the above threshold value, Battery diagnostic device. In claim 5, The above at least one processor is, Based on the above average and the median value of the voltage change amounts of each of the unit battery cells at the above specific point in time, the threshold value is identified, and A configuration for diagnosing an abnormality in any one of the battery cells based on the voltage change amount of any one of the battery cells exceeding a threshold range identified based on the threshold value. Battery diagnostic device. In claim 1, The above at least one processor is, A configuration for identifying the individual vector by normalizing a vector including the voltage of any one battery cell at the aforementioned specific point in time and the amount of change in the voltage according to the time interval after the aforementioned specific point in time, Battery diagnostic device. In claim 3, The above at least one processor is, A vector that is normalized at each of the plurality of past points in time and stored in the memory, and is configured to identify the existing vectors by normalizing a vector that includes the voltage of any one battery cell at each of the plurality of past points in time and the amount of change in the voltage according to the time interval after each of the plurality of past points in time. Battery diagnostic device. In claim 1, The above first distribution characteristic is, It includes a first standard deviation between voltages corresponding to the individual vectors of the above unit battery cells, and The above second distribution characteristic is, including a second standard deviation between voltages corresponding to each of the reference vectors of the unit battery cells, Battery diagnostic device. In claim 9, The above at least one processor is, A method configured to identify representative values ​​of the first distribution characteristics and the second distribution characteristics based on the value obtained by applying a first weight to the first standard deviation and the values ​​obtained by applying a second weight to each of the second standard deviations. Battery diagnostic device. In claim 10, The above at least one processor is, A representative value configured to be identified based on the sum of the values ​​obtained by applying a first weight to the first standard deviation and the values ​​obtained by applying a second weight to each of the second standard deviations. Battery diagnostic device. In claim 10, The above first weight is, Smaller than the second weight mentioned above, Battery diagnostic device. An operation of identifying, at a specific point in time, the voltage of any one battery cell included in the battery unit and an individual vector representing the amount of change of the voltage according to a predetermined time interval after the specific point in time; An operation of identifying a specified number of reference vectors at each of a plurality of past points prior to the specific point in time based on the individual vectors above; An operation to identify a first distribution characteristic between the voltages of unit battery cells included in the battery unit corresponding to the individual vectors above, and second distribution characteristics between the voltages of the unit battery cells corresponding to each of the reference vectors above; and The operation of setting a diagnostic criterion for diagnosing any one of the battery cells based on the first distribution characteristics and the second distribution characteristics, Battery diagnostic method. In claim 13, The operation of setting a diagnostic criterion for diagnosing any one of the battery cells based on the first distribution characteristics and the second distribution characteristics is, An operation to identify representative values ​​of the first distribution characteristic and the second distribution characteristic; An operation to identify other representative values ​​for a battery cell different from the battery cell among the unit battery cells at a specific point in time, corresponding to the above representative value; and Based on the above representative value and the other representative values, the operation of diagnosing the state of any one of the battery cells included in the battery unit, Battery diagnostic method. In claim 13, Based on the individual vectors above, the operation of identifying a specified number of reference vectors at each of a plurality of past points in time prior to the specific point in time is, The operation includes identifying a specified number of reference vectors based on the similarity between the existing vector and the individual vector among existing vectors representing the voltage of any one battery cell at each of the plurality of past points in time and the amount of change of the voltage according to the time interval after each of the plurality of past points in time, Battery diagnostic method. In claim 15, The operation of identifying a specified number of reference vectors according to the similarity between an existing vector and an individual vector among existing vectors representing the voltage of any one battery cell at each of the plurality of past points in time and the amount of change of the voltage according to the time interval after each of the plurality of past points in time, The operation of identifying the specified number of reference vectors among the existing vectors in order of proximity to the individual vectors, Battery diagnostic method. In claim 14, Based on the above representative value and the other representative values, the operation of diagnosing the state of any one of the battery cells included in the battery unit is, An operation to identify the average of the above representative value and the other representative values; An operation to identify a threshold value serving as a diagnostic criterion for diagnosing whether any one of the battery cells is abnormal at a specific point in time based on the above average; and Based on the above threshold value, the operation of diagnosing the state of any one of the battery cells, Battery diagnostic method. In claim 17, The operation of identifying a threshold value serving as a diagnostic criterion for diagnosing whether any one of the battery cells is abnormal at a specific point in time based on the above average is, The operation of identifying the threshold value based on the above average and the median value of the voltage change amounts at the specific point in time of each of the unit battery cells, and Based on the above threshold value, the operation of diagnosing the state of any one of the battery cells is, The method includes an operation to diagnose an abnormality in any one of the battery cells based on the voltage change amount of any one of the battery cells deviating from a threshold range identified based on the threshold value. Battery diagnostic method. In claim 13, The operation of identifying the voltage of any one battery cell included in the battery unit at the aforementioned specific point in time and an individual vector representing the amount of change of the voltage according to a predetermined time interval after the aforementioned specific point in time is, The operation of identifying the individual vector by normalizing a vector including the voltage of any one battery cell at the aforementioned specific point in time and the amount of change in the voltage according to the time interval after the aforementioned specific point in time, Battery diagnostic method. In claim 15, The operation of identifying a specified number of reference vectors according to the similarity between an existing vector and an individual vector among existing vectors representing the voltage of any one battery cell at each of the plurality of past points in time and the amount of change of the voltage according to the time interval after each of the plurality of past points in time, The method includes an operation of identifying existing vectors by normalizing a vector that is stored in memory after being normalized at each of the plurality of past points in time, and includes the voltage of any one battery cell at each of the plurality of past points in time and the amount of change in the voltage according to the time interval after each of the plurality of past points in time. Battery diagnostic method.

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