Battery diagnosis device and method therefor

Abstract

A battery diagnosis device according to an embodiment disclosed herein comprises: memory in which one or more instructions are stored; and a processor for executing the one or more instructions, wherein the processor may acquire time-series data related to a target battery for a designated duration, and diagnose the state of the target battery on the basis of the time-series data and a database for diagnosing whether the state of the battery is an abnormal state, and the time-series data may have a format corresponding to the database.

Description

Battery diagnostic device and method thereof

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority from Republic of Korea Patent Application No. 10-2024-0106854, filed August 9, 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 method thereof.

[0005] Recently, research and development on secondary batteries has been actively underway. Here, secondary batteries are defined as rechargeable and dischargeable batteries, encompassing both conventional Ni / Cd and Ni / MH batteries, as well as more recent lithium-ion batteries. Recently, their use has expanded to include power sources for electric vehicles, attracting attention as a next-generation energy storage medium.

[0006] Battery failures, such as lithium deposition, can lead to dangerous situations (e.g., fire). Therefore, it's crucial to quickly and accurately identify battery failures, or abnormal battery conditions. Therefore, various research projects are underway to identify battery abnormalities before a dangerous situation arises.

[0007] According to embodiments disclosed in this document, an object is to provide a battery diagnostic device and method for determining a state of a target battery and / or an abnormality type of the target battery based on time series data obtained from the target battery.

[0008] According to embodiments disclosed in this document, it is intended to provide a battery diagnosis device and method for quickly and accurately identifying the state of a target battery and / or the type of anomaly of a target battery by converting time series data of a target battery into data that is visually represented and determining the state of the target battery and / or the type of anomaly of the target battery based on whether or not it matches a graph included in a database.

[0009] The technical problems of the present invention 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 description below.

[0010] A battery diagnosis device according to one embodiment of the present document includes a memory in which one or more instructions are stored, and a processor for executing the one or more instructions, wherein the processor acquires time series data related to a target battery for a specified period of time, and diagnoses the state of the target battery based on a database for diagnosing whether the state of the battery is abnormal, and the time series data may have a format corresponding to the database.

[0011] In one embodiment, the processor can diagnose the condition of the target battery based on a comparison of the database and the time series data.

[0012] In one embodiment, the processor may generate a graph representing at least one of the voltage, current, temperature, resistance, state of charge (SOC), state of health (SOH), or any combination thereof of the battery included in the time series data, based on identifying at least one of the voltage, current, temperature, resistance, SOC, SOH, or any combination thereof.

[0013] In one embodiment, the processor can diagnose whether the state of the target battery is abnormal based on whether the graph and the database match.

[0014] In one embodiment, the processor can identify that the state of the target battery is abnormal based on a matching between the graph and the database.

[0015] In one embodiment, the processor can identify an abnormality type of the state of the target battery based on a matching between the graph and the database.

[0016] In one embodiment, the processor can identify that the state of the target battery is normal based on a mismatch between the graph and the database.

[0017] In one embodiment, the database may include an abnormal graph representing reference time series data corresponding to an abnormal state of the battery.

[0018] In one embodiment, the processor can identify whether the time series data matches the database based on the feature points of the above graph.

[0019] In one embodiment, the battery may include at least one of a battery cell, a battery pack, a battery module, or any combination thereof.

[0020] A battery diagnosis method according to one embodiment of the present document may include an operation of acquiring, by a processor, time series data related to a target battery for a specified duration, and an operation of diagnosing, by the processor, a state of the target battery based on a database for diagnosing whether the state of the battery is abnormal and the time series data, wherein the time series data may have a format corresponding to the database.

[0021] The battery diagnosis method according to one embodiment may include an operation of diagnosing the state of the target battery based on a comparison of the database and the time series data.

[0022] The battery diagnosis method according to one embodiment may include an operation of generating a graph representing at least one of the voltage, the current, the temperature, the resistance, the SOC, the SOH, or any combination thereof, based on identifying at least one of the voltage, the current, the temperature, the resistance, the SOC, the SOH, or any combination thereof of the battery included in the time series data.

[0023] The battery diagnosis method according to one embodiment may include an operation of diagnosing whether the state of the target battery is abnormal based on whether the graph and the database match.

[0024] The battery diagnosis method according to one embodiment may include an operation of identifying that the state of the target battery is abnormal based on a matching between the graph and the database.

[0025] The present technology can determine the status of a target battery and / or an abnormality type of the target battery based on time series data obtained from the target battery.

[0026] In addition, the present technology can quickly and accurately identify the state of the target battery and / or the abnormality type of the target battery by converting the time series data of the target battery into data that is visually represented and determining the state of the target battery and / or the abnormality type of the target battery based on whether it matches a graph included in a database.

[0027] In addition, various effects may be provided, either directly or indirectly, through this document.

[0028] FIG. 1 illustrates an example of 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.

[0029] FIG. 2a illustrates an example of a block diagram showing the configuration of a battery diagnostic device according to one embodiment of the present document.

[0030] FIG. 2b illustrates an example in which a battery diagnostic device according to one embodiment of the present document establishes a communication link with an electronic device.

[0031] FIG. 3 illustrates an example of a flowchart related to a battery diagnosis method according to one embodiment of the present document.

[0032] FIGS. 4A to 4C illustrate an example of an abnormal graph expressed when the state of a battery is abnormal in one embodiment of the present document.

[0033] FIG. 5 illustrates an example of a flowchart related to a battery diagnosis method according to one embodiment of the present document.

[0034] FIG. 6 illustrates an example of a block diagram showing the hardware configuration of a computing system for performing a battery diagnosis method in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0035] Hereinafter, some embodiments disclosed in this document are described with reference to the accompanying drawings, which illustrate various embodiments of this document. However, this is not intended to limit the present technology to specific embodiments, and it should be understood that various modifications, equivalents, and / or alternatives of the embodiments of this technology are included.

[0036] When assigning reference numerals to components in each drawing, it should be noted that identical components are assigned the same numerals whenever possible, even if they are shown in different drawings. Furthermore, when describing various embodiments disclosed in this document, if a detailed description of a related known configuration or function is deemed to hinder understanding of the embodiments of the present invention, the detailed description will be omitted. The singular form of a noun corresponding to an item may include one or more items, unless the context clearly indicates otherwise.

[0037] 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 only intended to distinguish the components from other components, and the nature, order, or sequence of the components may not be limited by the terms. In addition, unless otherwise defined, all terms used herein, 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 belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning they have in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined in this application.

[0038] In addition, in the present disclosure, expressions such as "more than" or "less than" may be used to determine whether a specific condition is satisfied or fulfilled. However, this is merely a description for expressing an example and does not exclude descriptions such as "more than" or "less than." Conditions described as "more than" may be replaced with "more than," conditions described as "less than," and conditions described as "more than and less than" may be replaced with "more than and less than." In addition, hereinafter, "A" to "B" mean at least one of the elements from A (including A) to B (including B).

[0039] In this document, each of 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" may include any one of the items listed together in that phrase, or all possible combinations thereof.

[0040] In this document, when 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 is referred to as being “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0041] According to one embodiment, the method according to the various embodiments disclosed in the present document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) 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 generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0042] According to various embodiments, each component (e.g., a module or a program) of the 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 integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0043] Hereinafter, embodiments of the present document will be described in detail with reference to FIGS. 1 to 6.

[0044] FIG. 1 illustrates an example of 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.

[0045] Referring to FIG. 1, a 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).

[0046] According to one embodiment, the battery unit (12) can supply power to a target device (not shown). To this end, the battery unit (12) can be electrically connected to the target device. Here, the target device can include an electrical, electronic, or mechanical device that operates by receiving power from the battery pack (1). For example, the target device can be, but is not limited to, an electric vehicle (EV).

[0047] According to one embodiment, the battery unit (12) may include at least one battery cell (10) that is rechargeable and dischargeable. Here, the battery cell (10) may be a basic unit of a battery cell that can charge and discharge electric 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-metal hydride (Ni-MH) battery, etc., but may not be limited thereto.

[0048] According to one embodiment, a plurality of battery units (12) may be connected in series or parallel. For example, the battery unit (12) may be a battery module, a battery bank, or a collection of battery cells (cell-to-pack structure).

[0049] 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 one or more values ​​for voltage, current, resistance, state of charge (SOC), state of health (SOH), or temperature of the battery cell, or a combination thereof.

[0050] According to one embodiment, the sensor unit (14) can provide information on each of the plurality of battery units (12) to the battery management system (20).

[0051] According to one embodiment, the switching unit (16) may include a device 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).

[0052] According to one embodiment, a battery management system (BMS) (20) may monitor voltage, current, temperature, etc. of the battery pack (1) to control or manage the battery pack (1) to prevent overcharge, overdischarge, etc. For example, the battery management system (20) may include a plurality of terminals as an interface for receiving values ​​measured from the various parameters described above, and a circuit connected to these terminals to process the input values. In addition, the battery management system (20) may control the sensor unit (14) and / or the 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 ON / OFF of a relay or a contactor, etc.

[0053] According to one embodiment, the operation of the battery management system (20) may be performed by a battery management system (BMS) in the vehicle, as well as by various devices such as a server, cloud, charger, or charger / discharger.

[0054] The upper controller (2) can transmit control signals for multiple battery units (12) to the battery management system (20). Accordingly, the battery management system (20) can be controlled for operation based on signals received from the upper controller (2).

[0055] According to one embodiment, the battery management system (20) may include the battery diagnosis device (200) of FIG. 2. According to another embodiment, the battery management system (20) may be a different system from the battery diagnosis device (200) of FIG. 2. That is, the battery diagnosis device (200) of FIG. 2 may be included in the battery pack (1) or may be configured as another device external to the battery pack (1). For convenience of explanation, the following description will be made on the assumption that the battery diagnosis device (200) is configured as another device external to the battery pack (1). In addition, the operation of the battery diagnosis device (200) below may be performed by an in-vehicle BMS (battery management system), as well as by various devices such as a server, a cloud, a charger, or a charger / discharger.

[0056] FIG. 2a illustrates an example of a block diagram showing the configuration of a battery diagnostic device according to one embodiment of the present document.

[0057] Referring to FIG. 2A, the battery diagnostic device (200) may include a memory (203) and a processor (205). However, the present invention is not limited thereto, and other components may be further included in the battery diagnostic device (200), two or more components may be integrated into one, or one component may be divided into two or more components.

[0058] According to one embodiment, a battery diagnostic device (200) may include a processor (210) and a memory (220). According to an embodiment, the battery diagnostic device (200) may further include a communication circuit (230). The processor (210), the memory (220), or the communication circuit (230) may be electronically and / or operably coupled with each other by an electronic component including a communication bus.

[0059] Hereinafter, the hardwares being operatively coupled may include a direct connection between the hardwares, and / or an indirect connection established by wires and / or wirelessly, such that the second hardware is controlled by the first hardware among the hardwares.

[0060] Although the hardware components included in the battery diagnostic device (200) of FIG. 2A are illustrated in different blocks, the embodiment is not limited thereto. For example, some of the hardware of FIG. 2A may be included in a single integrated circuit including a system on a chip (SoC). The type and / or number of hardware included in the battery diagnostic device (200) is not limited to that illustrated in FIG. 2. For example, the battery diagnostic device (200) may include only some of the hardware illustrated in FIG. 2.

[0061] A battery diagnostic device (200) according to an embodiment may include hardware for processing data based on one or more instructions. The hardware for processing data may include a processor (210). For example, the hardware for processing data may include an arithmetic and logic unit (ALU), a floating point unit (FPU), a field programmable gate array (FPGA), a central processing unit (CPU), and / or an application processor (AP). For example, the processor (210) may have a single-core processor structure, or a multi-core processor structure including dual cores, quad cores, hexa cores, or octa cores.

[0062] The memory (220) of the battery diagnostic device (200) according to one embodiment may include a hardware component for storing data and / or instructions input to and / or output from the processor (210) of the battery diagnostic device (200).

[0063] For example, the memory (210) may include volatile memory including random-access memory (RAM), and / or non-volatile memory including read-only memory (ROM).

[0064] For example, the volatile memory may include at least one of dynamic RAM (DRAM), static RAM (SRAM), cache RAM, pseudo SRAM (PSRAM), or any combination thereof.

[0065] For example, the non-volatile memory may include at least one of a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), a flash memory, a hard disk, a compact disc, a solid state drive (SSD), an embedded multi-media card (eMMC), or any combination thereof.

[0066] For example, within the memory (220) of the battery diagnostic device (200), one or more instructions (or commands) indicating operations and / or actions to be performed on data by the processor (210) of the battery diagnostic device (200) may be stored. A set of one or more instructions may be referred to as a program, firmware, an operating system, a process, a routine, a sub-routine, and / or an application. Hereinafter, the fact that an application is installed within the battery diagnostic device (200) may mean that one or more instructions provided in the form of an application are stored within the memory (220), and that one or more applications are stored in a format executable by the processor (210) of the battery diagnostic device (200) (e.g., a file having an extension designated by the operating system of the battery diagnostic device (200).

[0067] A communication circuit (230) of a battery diagnostic device (200) according to an embodiment may include hardware components for supporting transmission and / or reception of signals between the battery diagnostic device (200) and an external electronic device. For example, the communication circuit (230) may include at least one of a modem, an antenna, an optical / electronic (O / E) converter, or any combination thereof. For example, the communication circuit (230) may support transmission and / or reception of signals based on various types of protocols including at least one of Ethernet, a local area network (LAN), a wide area network (WAN), wireless fidelity (WiFi), Bluetooth, Bluetooth low energy (BLE), ZigBee, long term evolution (LTE), 5G new radio (NR), a controller area network (CAN), a local interconnect network (LIN), or any combination thereof. However, examples related to the communication circuit (230) are not limited to those described above.

[0068] A battery diagnostic device (200) according to one embodiment may include a memory (220) in which one or more instructions are stored and a processor (210) that executes one or more instructions.

[0069] In one embodiment, the processor (210) may acquire time series data related to the target battery for a specified duration. For example, the time series data may include at least one of the voltage of the target battery, the current of the target battery, the temperature of the target battery, the resistance of the target battery, the state of charge (SOC) of the target battery, the state of health (SOH) of the target battery, or any combination thereof.

[0070] In one embodiment, the processor (210) may identify the voltage of the target battery for a specified period of time. For example, the processor (210) may identify at least one of the voltage, current, temperature, resistance, SOC, SOH, or any combination thereof of the target battery for the specified period of time. Hereinafter, an example of identifying the state of the target battery and / or the abnormality type of the target battery using the voltage of the target battery will be described. However, when the battery diagnosis device (200) identifies the state of the target battery, it may use at least one of the voltage, current, temperature, resistance, SOC, SOH, or any combination thereof of the target battery, including the voltage of the target battery. For convenience of explanation, an example of identifying the state of the target battery and / or the abnormality type of the target battery using the voltage of the target battery will be described, but the embodiments of the present document are not limited thereto. Hereinafter, the target battery may include the battery pack (1), the battery unit (12), and / or the battery cell (10) described in FIG. 1. Additionally, the data described below may be included in time series data.

[0071] For example, the processor (210) may obtain data visually representing the voltage of the target battery based on the identified voltage of the target battery. For example, the processor (210) may convert the data visually representing the voltage of the target battery based on the identified voltage of the target battery. For example, the data visually representing the voltage of the target battery may have a format corresponding to a database. For example, the data visually representing the voltage of the target battery may have a format comparable to the database. For example, the data visually representing the voltage of the target battery may include at least one of a graph, a table, a chart, or any combination thereof. However, examples of data visually representing the voltage of the target battery are not limited to those described above.

[0072] For example, the processor (210) may obtain a graph representing the voltage of the target battery based on the identification of the voltage of the target battery. For example, the graph representing the voltage of the target battery may include a graph representing the voltage of the target battery over time. For example, a first axis (e.g., the x-axis) included in the graph may represent time. For example, a second axis (e.g., the y-axis) included in the graph may represent a value corresponding to the voltage of the target battery.

[0073] In one embodiment, the processor (210) may diagnose the state of the target battery based on a database for diagnosing whether the state of the battery is abnormal and the voltage of the target battery. For example, the database for diagnosing whether the state of the battery is abnormal may be stored in the memory (220) or may be acquired from an electronic device different from the battery diagnosis device (200). For example, when the battery diagnosis device (200) acquires the database from an electronic device different from the battery diagnosis device (200), the database may be acquired through the communication circuit (230). For example, the database may include an abnormality graph in which a voltage corresponding to an abnormal state of the battery is expressed. For example, the abnormality graph may include a graph expressing a voltage pattern that identifies the state of the battery as being abnormal.

[0074] For example, the processor (210) of the battery diagnosis device (200) may compare the voltage of the target battery with a database. For example, the processor (210) may diagnose the state of the battery based on the comparison of the voltage of the target battery with the database. For example, at least one of the target battery, batteries, or any combination thereof may include at least one of a battery cell, a battery pack, a battery module, or any combination thereof. However, examples of at least one of the target battery, batteries, or any combination thereof are not limited to those described above.

[0075] For example, the processor (210) may generate a graph representing the voltage of the target battery based on the identified voltage of the target battery. For example, the processor (210) may compare the graph representing the voltage of the target battery with a database. For example, the processor (210) may diagnose the condition of the target battery based on the comparison of the graph representing the voltage of the target battery with a database.

[0076] For example, the processor (210) can identify whether a graph representing the voltage of the target battery matches a database. For example, the processor (210) can diagnose the condition of the target battery based on whether a graph representing the voltage of the target battery matches a database.

[0077] For example, a match may include a case where, when comparing first data and second data, all or part of the data time interval matches. For example, a match may include a case where, when comparing first data and second data, all or part of the data time interval does not match, but is within a designated error range. For example, a match may include a case where, when comparing first data and second data, at least some of the first representative values ​​included in the first data and the second representative values ​​included in the second data match.

[0078] In one embodiment, the processor (210) can identify a match between a graph representing the voltage of the target battery and a database. For example, the processor (210) can identify that the state of the target battery is abnormal based on a match between a graph representing the voltage of the target battery and a database. For example, the processor (210) can identify an abnormal type of the state of the target battery based on a match between a graph representing the voltage of the target battery and a database.

[0079] For example, matching a graph representing the voltage of a target battery with a database may include matching an anomaly graph contained in the database with a graph representing the voltage of the target battery. For example, matching a graph representing the voltage of a target battery with a database may include matching a pattern represented by an anomaly graph contained in the database with a pattern represented by a graph representing the voltage of the target battery.

[0080] In one embodiment, the processor (210) may identify that the state of the target battery is normal based on a mismatch between a graph representing the voltage of the target battery and a database. For example, a mismatch between a graph representing the voltage of the target battery and a database may include a mismatch between an abnormality graph included in the database and a graph representing the voltage of the first battery. For example, a mismatch between a graph representing the voltage of the target battery and a database may include a mismatch between a pattern represented by an abnormality graph included in the database and a pattern represented by a graph representing the voltage of the target battery.

[0081] For example, the processor (210) can identify whether the voltage of the target battery matches the database based on the characteristic points of the ideal graph included in the database. For example, the processor (210) can identify whether the graph representing the voltage of the target battery matches the database based on the characteristic points of the ideal graph included in the database.

[0082] For example, the processor (210) can identify a first displacement value at a first point where displacement occurs in the abnormal graph, and a second displacement value at a second point different from the first point. For example, the processor (210) can identify whether the state of the target battery is abnormal based on whether a voltage difference exceeding the difference between the first displacement value and the second displacement value is identified in the graph representing the voltage of the target battery. An example of identifying the state of the target battery using the difference between the first displacement value and the second displacement value is one example that can be used when diagnosing the state of the target battery, and is not limited to the above.

[0083] As described above, the battery diagnostic device (200) according to one embodiment can identify the state of the target battery and / or the abnormality type of the target battery based on data visually representing the voltage of the target battery and a database. The battery diagnostic device (200) can quickly and accurately identify the abnormal state of the target battery by identifying the abnormal state of the target battery and / or the abnormality type of the target battery using data visually representing the voltage of the target battery.

[0084] FIG. 2b illustrates an example of a battery diagnostic device according to one embodiment of the present document establishing a communication link with an electronic device.

[0085] Referring to FIG. 2B, a battery diagnostic device (200) according to one embodiment can establish a communication link with an electronic device (240). For example, the battery diagnostic device (200) can establish a communication link with the electronic device (240) through a communication circuit (230). For example, the battery diagnostic device (200) can establish a communication link with the electronic device (240) through a base station (245).

[0086] The battery diagnostic device (200) that has established a communication link with the electronic device (240) can receive a database from the electronic device (240). For example, the battery diagnostic device (200) can diagnose whether the state of the target battery is abnormal based on the database received from the electronic device (240).

[0087] FIG. 3 illustrates an example of a flowchart related to a battery diagnosis method according to one embodiment of the present document.

[0088] In the following, it is assumed that the battery diagnostic device (200) of FIG. 2 performs the process of FIG. 3. In addition, in the description of FIG. 3, the operations described as being performed by the device can be understood as being controlled by the processor (210) of the battery diagnostic device (200).

[0089] At least one of the operations of FIG. 3 may be performed by the battery diagnostic device (200) of FIG. 2. At least one of the operations of FIG. 3 may be controlled by the processor (210) of FIG. 2. Each of the operations of FIG. 3 may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each of the operations may be changed, and at least two operations may be performed in parallel.

[0090] A battery diagnosis method according to one embodiment may include, in operation S301, an operation of obtaining a voltage of a target battery.

[0091] For example, a battery diagnosis method may include an operation of acquiring data visually representing the voltage of the target battery based on the voltage of the target battery. For example, the data visually representing the voltage of the target battery may include a graph formed by values ​​corresponding to the voltage described in operation S305.

[0092] In operation S303, the battery diagnosis method according to one embodiment may include an operation of acquiring a database.

[0093] For example, a battery diagnostic method may include obtaining a database from an electronic device (240) and / or a memory (220).

[0094] In operation S305, the battery diagnosis method according to one embodiment may include an operation of determining the shape of a graph formed by values ​​corresponding to voltage.

[0095] For example, a battery diagnostic method may include an operation of determining the shape of a graph formed by values ​​corresponding to the voltage of the target battery, based on the identified voltage of the target battery. For example, the graph formed by values ​​corresponding to the voltage of the target battery may be included in the data visually representing the voltage of the target battery, as described in FIG. 1.

[0096] For example, the battery diagnosis method may include an operation of determining whether the shape of a graph formed by values ​​corresponding to the voltage of the target battery is identical to the shape of an ideal graph included in a database.

[0097] A battery diagnosis method according to one embodiment may include an operation of analyzing and determining an abnormal voltage of a target battery. For example, the battery diagnosis method may include an operation of performing a singular value decomposition (SVD). For example, the battery diagnosis method may include an operation of reconstructing a principal component obtained based on the SVD. For example, the battery diagnosis method may include an operation of determining the shape of a voltage signal obtained by reconstructing the principal component. For example, the battery diagnosis method may include an operation of identifying whether a signal corresponding to an abnormal state is detected based on the shape of the voltage signal obtained by reconstructing the principal component. The operation of identifying whether a signal corresponding to an abnormal state is detected will be described below.

[0098] In operation S307, the battery diagnosis method according to one embodiment may include an operation of identifying whether a signal corresponding to an abnormal state is detected.

[0099] If a signal corresponding to an abnormal state is detected (Yes in operation S307), in operation S309, the battery diagnosis method according to one embodiment may include an operation of determining an abnormal state of the target battery and an abnormal type of the target battery.

[0100] A battery diagnosis method according to one embodiment may include an operation of identifying an abnormality type of a target battery based on each graph included in a database. For example, the battery diagnosis method may identify that the abnormality type of the target battery is a first type based on a matching shape between a first graph included in the database and a graph formed by the voltage of the target battery.

[0101] As described above, a battery diagnosis method according to an embodiment may include an operation of identifying an abnormality type matching a graph included in a database, and identifying an abnormality type of a target battery based on the identification of a graph formed by a voltage of the target battery matching the graph included in the database.

[0102] A battery diagnosis method according to an embodiment may include an operation of performing noise filtering on time series data acquired from a target battery to determine an abnormal state and an abnormal type of the target battery. For example, a signal filtered by noise filtering may include a result value of entropy estimation. For example, a signal filtered by noise filtering may include a case where the result of calculating autocorrelation has a very large peak value only when the displacement value of the x-axis is 0, like a Dirac delta function, and is close to 0 for other x-axis displacement values. For example, a signal filtered by noise filtering may include the above-described feature point.

[0103] If a signal corresponding to an abnormal state is not detected (No in operation S307), in operation S311, the battery diagnosis method according to one embodiment may include an operation of determining a normal state of the target battery.

[0104] FIGS. 4A to 4C illustrate an example of an abnormal graph expressed when the state of a battery is abnormal in one embodiment of the present document.

[0105] Referring to FIG. 4A, the first graph (401) may include an abnormal graph that is expressed when the battery is in an abnormal state. For example, the first graph (401) may be included in a database and used when diagnosing the state of a target battery.

[0106] Referring to FIG. 4b, the second graph (402) may include an abnormal graph that is expressed when the battery is in an abnormal state. For example, the second graph (402) may be included in a database and used when diagnosing the state of a target battery.

[0107] Referring to FIG. 4c, the third graph (403) may be included in an abnormal graph that represents an abnormal state of the battery. For example, the third graph (403) may be included in a database and used when diagnosing the state of a target battery.

[0108] However, examples of ideal graphs included in the database are not limited to the first graph (401), the second graph (402), and the third graph (403).

[0109] The processor (210) of the battery diagnosis device (200) according to one embodiment can diagnose the state of the target battery based on at least one of the first graph (401), the second graph (402), the third graph (403), or any combination thereof included in the database. For example, the processor can diagnose the state of the target battery based on whether at least one of the first graph (401), the second graph (402), the third graph (403), or any combination thereof matches a graph representing the voltage of the target battery.

[0110] For example, the processor (210) of the battery diagnosis device (200) can diagnose the state of the target battery based on the characteristic points identified in each of the first graph (401), the second graph (402), and the third graph (403).

[0111] FIG. 5 illustrates an example of a flowchart related to a battery diagnosis method according to one embodiment of the present document.

[0112] In the following, it is assumed that the battery diagnostic device (200) of FIG. 2 performs the process of FIG. 5. In addition, in the description of FIG. 5, the operations described as being performed by the device can be understood as being controlled by the processor (210) of the battery diagnostic device (200).

[0113] At least one of the operations of FIG. 5 may be performed by the battery diagnostic device (200) of FIG. 2. At least one of the operations of FIG. 5 may be controlled by the processor (210) of FIG. 2. Each of the operations of FIG. 5 may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each of the operations may be changed, and at least two operations may be performed in parallel.

[0114] In operation S501, a battery diagnosis method according to an embodiment may include an operation of acquiring time series data related to a target battery for a specified period of time.

[0115] For example, time series data related to a target battery may include at least one of the voltage of the target battery, the current of the target battery, the temperature of the target battery, the resistance of the target battery, the SOC of the target battery, the SOH of the target battery, or any combination thereof. For convenience of explanation, an example using the voltage of the target battery will be described below. However, the embodiments of this document are not limited to using the voltage of the target battery.

[0116] For example, a battery diagnostic method may include generating a graph representing the voltage of a target battery based on identifying the voltage of the target battery over a specified period of time.

[0117] In operation S503, a battery diagnosis method according to an embodiment may include an operation of diagnosing a state of a target battery based on a database for diagnosing whether the state of the battery is abnormal and a voltage of the target battery.

[0118] For example, a battery diagnosis method may include an operation of diagnosing a condition of a target battery based on a comparison of a voltage of a database and a target battery.

[0119] For example, the database may include an abnormal graph in which the voltage corresponding to an abnormal condition of the battery is represented.

[0120] For example, a battery diagnosis method may include an operation of comparing a graph representing the voltage of a target battery with a database. For example, the battery diagnosis method may include an operation of identifying whether the target battery is in an abnormal state based on whether the graph representing the voltage of the target battery matches the database.

[0121] For example, a battery diagnosis method may include an operation of identifying that the condition of a target battery is abnormal based on a matching between a graph representing the voltage of the target battery and a database.

[0122] For example, a battery diagnosis method may include an operation of identifying an abnormality type in the condition of a target battery based on a matching between a graph representing the voltage of the target battery and a database.

[0123] For example, a battery diagnosis method may include an operation of identifying that the condition of the target battery is normal based on a mismatch between a graph representing the voltage of the target battery and a database.

[0124] For example, a battery diagnosis method may include an operation of identifying whether a voltage of a target battery matches a database based on feature points of an anomaly graph included in the database.

[0125] FIG. 6 illustrates an example of a block diagram showing the hardware configuration of a computing system for performing a battery diagnosis method in a battery diagnosis device and a battery diagnosis method according to an embodiment of the present document.

[0126] Referring to FIG. 6, a computing system (1100) according to an embodiment disclosed in this document may include an MCU (1110), a memory (1120), an input / output I / F (1130), and a communication I / F (1140).

[0127] The MCU (1110) may be a processor that executes various programs (e.g., 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, etc.) stored in the memory (1120), processes various information including battery cell characteristic data and latent variables through these programs, and performs the functions of the battery diagnosis device (200) shown in the aforementioned FIGS. 1 to 5.

[0128] The memory (1120) 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.

[0129] Such memories (1120) may be provided in multiple numbers as needed. The memories (1120) may be volatile memories or non-volatile memories. As volatile memories (1120), RAM, DRAM, SRAM, etc. may be used. As non-volatile memories (1120), ROM, PROM, EAROM, EPROM, EEPROM, flash memories, etc. may be used. The examples of the memories (1120) listed above are merely examples and are not limited to these examples.

[0130] The input / output I / F (1130) 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 (1110).

[0131] The communication I / F (1140) is a component capable of transmitting and receiving various data with the server, and may be any device capable of supporting wired or wireless communication. For example, the battery diagnostic device (200) can transmit and receive various types of information, including battery cell shape models, from a separately provided external server via the communication I / F (1140).

[0132] In this way, a computer program according to an embodiment disclosed in this document may be implemented as a module that is recorded in a memory (1120) and processed by an MCU (1110) to perform each function illustrated in FIG. 2, for example.

[0133] In the above, although all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination as one, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purpose of the embodiments disclosed in this document, all of the components may be selectively combined and operated one or more times.

[0134] In addition, terms such as "include," "comprise," or "have" described above, unless specifically stated to the contrary, should be interpreted to imply the inclusion of the corresponding component, and thus should not be interpreted to exclude other components, but rather to include 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 belong, 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.

[0135] The foregoing disclosure outlines 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 readily appreciate that the present disclosure can be readily used as a basis for designing or modifying other structures to achieve the same purposes or advantages of the embodiments introduced herein. Furthermore, those skilled in the art will recognize that such equivalent structures do not depart from the scope of the present disclosure, and that various changes, substitutions, and modifications can be made herein without departing from the scope of the present disclosure.

Claims

Memory in which one or more instructions are stored; and A processor comprising one or more of the above instructions, The above processor, Obtain time series data associated with the target battery for a specified period of time, A database for diagnosing whether the battery is in an abnormal state and a state of the target battery based on the time series data are configured to diagnose the state of the target battery. The above time series data is, A battery diagnostic device having a format corresponding to the above database. In the first paragraph, The above processor, A battery diagnostic device configured to diagnose the state of the target battery based on a comparison of the database and the time series data. . In the first paragraph, The above processor, A battery diagnostic device configured to generate a graph representing at least one of the voltage, the current, the temperature, the resistance, the SOC, the SOH, or any combination thereof, based on identifying at least one of the voltage, the current, the temperature, the resistance, the SOC, the SOH, or any combination thereof of the battery included in the time series data. In the third paragraph, The above processor, A battery diagnostic device configured to diagnose whether the state of the target battery is abnormal based on whether the graph and the database match. In the fourth paragraph, The above processor, A battery diagnostic device configured to identify that the state of the target battery is abnormal based on a matching between the graph and the database. In the fourth paragraph, The above processor, A battery diagnostic device configured to identify an abnormality type in the state of the target battery based on a matching between the above graph and the above database. In the fourth paragraph, The above processor, A battery diagnostic device configured to identify that the state of the target battery is normal based on the mismatch between the graph and the database. In the first paragraph, The above database is, A battery diagnostic device, comprising an abnormality graph in which reference time series data corresponding to an abnormal state of the battery is expressed. In paragraph 8, The above processor, A battery diagnostic device configured to identify whether the time series data matches the database based on the characteristic points of the above-described graph. In the first paragraph, The above battery, A battery diagnostic device comprising at least one of a battery cell, a battery pack, a battery module, or any combination thereof. An operation of acquiring time series data related to a target battery for a specified duration by a processor; and By the above processor, an operation for diagnosing the state of the target battery based on a database for diagnosing whether the state of the battery is abnormal and the time series data is included, The above time series data is, A battery diagnosis method having a format corresponding to the above database. In Article 11, The above battery diagnosis method is, A battery diagnosis method, comprising an operation of diagnosing the state of the target battery based on a comparison of the database and the time series data. In Article 11, The above battery diagnosis method is, A battery diagnosis method, comprising an operation of generating a graph representing at least one of the voltage, the current, the temperature, the resistance, the SOC, the SOH, or any combination thereof, based on identifying at least one of the voltage, the current, the temperature, the resistance, the SOC, the SOH, or any combination thereof of the battery included in the time series data. In the 13th paragraph, The above battery diagnosis method is, A battery diagnosis method, comprising an operation of diagnosing whether the state of the target battery is abnormal based on whether the graph and the database match. In Article 14, The above battery diagnosis method is, A battery diagnosis method, comprising an operation of identifying that the state of the target battery is abnormal based on a matching between the graph and the database.

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