Battery diagnostic device and method thereof

Abstract

A battery diagnostic device, according to an embodiment of the present document, comprises: an interface that acquires a first profile related to a state of charge (SOC) of a battery; and a processor, wherein the processor acquires a second profile that corrects the first profile on the basis of applying a designated algorithm to the first profile, identifies, on the basis of having identified at least one feature point in the second profile, at least one second feature point in the first profile using the at least one first feature point, and acquires an operating pattern of the battery on the basis of the at least one second feature point.

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. 10-2024-0120896, filed September 5, 2024, the entire disclosure of which is 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 performance deteriorates over time, and this can be attributed to chemical and physical changes in the battery. Therefore, technologies that accurately predict battery life are crucial for ensuring battery reliability and safety, determining appropriate replacement times, and reducing maintenance costs. Therefore, technologies that accurately predict battery life are becoming increasingly important across various industrial sectors, and ongoing research and development may be required.

[0007] According to embodiments disclosed in this document, a battery diagnostic device and method for accurately predicting the life of a battery by obtaining an operating pattern of the battery and predicting the life of the battery are provided.

[0008] According to embodiments disclosed in this document, there is provided a battery diagnostic device and method for accurately predicting the lifespan of a battery without actually charging and discharging the battery.

[0009] According to embodiments disclosed in this document, there is provided a battery diagnostic device and method for accurately predicting the life of a battery even when the battery is operated in multiple operating modes by predicting the life of the battery using the operating pattern of the battery.

[0010] 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.

[0011] According to one embodiment of the present document, a battery diagnostic device includes an interface for obtaining a first profile related to a state of charge (SOC) of a battery, and a processor, wherein the processor obtains a second profile by correcting the first profile based on applying a designated algorithm to the first profile, identifies at least one first feature point in the second profile, and identifies at least one second feature point in the first profile using the at least one first feature point, and obtains an operating pattern of the battery based on the at least one second feature point.

[0012] In one embodiment, the processor can predict the life of the battery based on the operating pattern of the battery.

[0013] In one embodiment, the processor can identify at least one second feature point that exists among a plurality of feature points including at least one first feature point.

[0014] In one embodiment, the processor may obtain the first profile related to the SOC of the battery identified for a specified duration.

[0015] In one embodiment, the specified algorithm may include a local regression analysis algorithm.

[0016] In one embodiment, the local regression analysis algorithm may include locally estimated scatterplot smoothing (LOESS).

[0017] In one embodiment, the at least one first feature point may include an extremum point, and the at least one second feature point may include a local extremum point.

[0018] In one embodiment, the operating pattern may include at least one of a cycle mode, a FR mode, or any combination thereof.

[0019] A battery diagnosis method according to one embodiment of the present document may include: an operation of obtaining a second profile by correcting a first profile based on applying a designated algorithm to a first profile related to an SOC of a battery by a processor; an operation of identifying at least one first feature in the second profile by the processor, using the at least one first feature, at least one second feature in the first profile; and an operation of obtaining an operating pattern of the battery based on the at least one second feature by the processor.

[0020] The battery diagnosis method according to one embodiment may include an operation of predicting the life of the battery based on an operating pattern of the battery.

[0021] The battery diagnosis method according to one embodiment may include an operation of identifying at least one second characteristic point existing among a plurality of characteristic points including at least one first characteristic point.

[0022] The battery diagnosis method according to one embodiment may include an operation of obtaining the first profile related to the SOC of the battery identified for a specified duration.

[0023] In one embodiment, the specified algorithm may include a local regression analysis algorithm.

[0024] In one embodiment, the local regression analysis algorithm may include locally estimated scatterplot smoothing (LOESS).

[0025] In one embodiment, the at least one first feature point may include an extremum point, and the at least one second feature point may include a local extremum point.

[0026] This technology can accurately predict the life of a battery by acquiring the operating pattern of the battery and predicting the life of the battery.

[0027] Additionally, the present technology can accurately predict the lifespan of a battery without actually charging and discharging the battery.

[0028] In addition, the present technology can accurately predict the life of a battery even when the battery is operated in multiple operating modes by predicting the life of the battery using the battery's operating pattern.

[0029] In addition, various effects may be provided, either directly or indirectly, through this document.

[0030] FIG. 1 is a block diagram showing a battery pack in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0031] FIG. 2 illustrates an example of a block diagram showing the configuration of a battery diagnostic device according to one embodiment of the present document.

[0032] FIGS. 3A to 3E illustrate examples related to the first profile and / or the second profile in one embodiment of the present document.

[0033] FIG. 4 illustrates an example of a flowchart related to a battery diagnosis method according to one embodiment of the present document.

[0034] FIG. 5 illustrates an example of predicting the life of a battery using a battery diagnosis device and / or a battery diagnosis method according to one embodiment of the present document.

[0035] FIG. 6 is 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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).

[0040] 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.

[0041] 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.

[0042] 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.

[0043] 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.

[0044] Hereinafter, embodiments of the present document will be described in detail with reference to FIGS. 1 to 6.

[0045] FIG. 1 is a block diagram showing a battery pack in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0046] 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).

[0047] 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).

[0048] 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.

[0049] 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).

[0050] 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.

[0051] 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).

[0052] 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).

[0053] 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.

[0054] 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.

[0055] 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).

[0056] 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.

[0057] FIG. 2 illustrates an example of a block diagram showing the configuration of a battery diagnostic device according to one embodiment of the present document.

[0058] Referring to FIG. 2, a battery diagnostic device (200) according to one embodiment may include a processor (210) and a memory (220). The processor (210) and the memory (220) may be electronically and / or operably coupled with each other by an electronic device 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 is illustrated in different blocks, the embodiment is not limited thereto. For example, some of the hardware of FIG. 2 may be included in a single integrated circuit including a system-on-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 one embodiment may include hardware for processing data based on one or more instructions. The hardware for processing data may include a processor (210).

[0062] For example, 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). 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.

[0063] An interface (220) of a battery diagnostic device (200) according to one embodiment may be configured to generate battery measurement values ​​from a battery (e.g., one or more battery cells (11) of FIG. 1). To this end, the interface (220) may include a measuring means such as a voltmeter, an ammeter, a thermometer, and / or a communication module for establishing a connection with an external device.

[0064] For example, the interface (220) may include at least one of a hardware interface, a software interface, or any combination thereof.

[0065] For example, the interface (220) may obtain a first profile related to the state of charge (SOC) of the battery. For example, the first profile may be obtained based on the charging and discharging of the battery. For example, the first profile may be related to the SOC of the battery obtained over a specified period of time. For example, the processor (210) may obtain a first profile related to the SOC of the battery identified over a specified period of time.

[0066] According to an embodiment, a processor (210) of a battery diagnostic device (200) may obtain a first profile related to the SOC of a battery. For example, the processor (210) may apply a designated algorithm to the first profile related to the SOC of the battery. For example, the processor (210) may correct the first profile based on the application of the designated algorithm to the first profile. For example, the processor (210) may obtain a second profile that is a correction of the first profile based on the application of the designated algorithm to the first profile. For example, the first profile and / or the second profile may be expressed as a graph.

[0067] For example, the specified algorithm may include a local regression analysis algorithm. For example, the specified algorithm may include at least one of LOESS (locally estimated scatterplot smoothing), LOWESS (locally weighted scatterplot smoothing), LWR (locally weighted regression), or any combination thereof. For example, the specified algorithm may include an algorithm for smoothing a profile and / or graph.

[0068] In one embodiment, the processor (210) may identify at least one first feature point in the second profile. For example, the at least one first feature point may include an extreme point.

[0069] For example, the processor (210) may identify at least one second feature point in the first profile using the at least one first feature point based on the identification of at least one first feature point in the second profile. For example, the at least one second feature point may include a local extremum.

[0070] For example, the processor (210) can identify at least one second feature point using at least one first feature point by overlapping the first profile and the second profile.

[0071] For example, the processor (210) may identify at least one second feature point that exists between a plurality of feature points including at least one feature point. For example, the processor may identify at least one second feature point that exists between a first partial feature point and a second partial feature point among the plurality of feature points.

[0072] For example, the processor (210) may acquire an operating pattern of the battery based on the identification of at least one second characteristic point. For example, the processor (210) may determine the operating pattern of the battery by connecting the at least one second characteristic point. For example, the operating pattern may include at least one of a cycle mode, an FR mode, or any combination thereof.

[0073] In one embodiment, the processor (210) may acquire an operating pattern of the battery based on at least one second characteristic point. For example, the processor (210) may acquire an operating pattern of the battery based on a plurality of characteristic points including at least one second characteristic point. For example, the processor (210) may distinguish an operating pattern of the battery based on a trend line connecting a plurality of characteristic points including at least one second characteristic point.

[0074] In one embodiment, the processor (210) can predict the life of the battery based on the operating pattern of the battery.

[0075] As described above, the battery diagnostic device (200) according to one embodiment can obtain an operating pattern using the battery by using a first profile related to the SOC of the battery and a second profile that corrects the first profile. The battery diagnostic device (200) can predict the lifespan of the battery based on the operating pattern using the battery. The battery diagnostic device (200) can provide the effect of accurately predicting the lifespan of the battery by predicting the lifespan of the battery based on the operating pattern using the battery.

[0076] FIGS. 3A to 3E illustrate examples related to the first profile and / or the second profile in one embodiment of the present document.

[0077] Referring to FIG. 3A, the processor (210) of the battery diagnostic device (200) according to one embodiment may obtain a first graph (320) related to a first profile. For example, in the first graph (310), a line (311) may represent the SOC of the battery.

[0078] For example, the horizontal axis of the first graph (310) may be time. For example, the vertical axis of the first graph (310) may be the SOC of the battery.

[0079] Referring to FIG. 3B, the processor (210) of the battery diagnostic device (200) according to one embodiment may obtain a second graph (320) by performing a specified algorithm on the first graph (310). For example, the line (321) illustrated in the second graph (320) may represent the SOC of the battery. For example, the line (323) illustrated in the second graph (320) may include result values ​​obtained by performing the specified algorithm. For example, the second graph (320) may include graphs obtained by overlapping the first profile and the second profile.

[0080] For example, the horizontal axis of the second graph (320) may be time. For example, the vertical axis of the second graph (320) may be the SOC of the battery.

[0081] Referring to FIGS. 3c and 3d, the processor (210) of the battery diagnostic device (200) according to one embodiment can obtain a local pole.

[0082] Referring to FIG. 3C, the processor (210) of the battery diagnostic device (200) according to one embodiment may identify a first partial pole (331) in the third graph (330). For example, the processor (210) may identify a second partial pole (333) in the third graph (330). For example, the processor (210) may identify a first partial pole (331) and a second partial pole (333) in the third graph (330). For example, the third graph (330) may include at least a portion of the second graph (320). For example, the processor (210) may identify a first local pole (335) existing between the first partial pole (331) and the second partial pole (333).

[0083] Referring to FIG. 3D, the processor (210) of the battery diagnostic device (200) according to one embodiment can identify a third partial pole (341) in the fourth graph (340). For example, the processor (210) can identify a fourth partial pole (343) in the fourth graph (340). For example, the processor (210) can identify a third partial pole (341) and a fourth partial pole (343) in the fourth graph (340). For example, the fourth graph (340) can include at least a portion of the second graph (320). For example, the processor (210) can identify a second local pole (345) existing between the third partial pole (341) and the fourth partial pole (343).

[0084] For example, the first local pole (335) and / or the second local pole (345) may include local poles existing in the first profile. For example, the first partial pole (331), the second partial pole (333), the third partial pole (341), and / or the fourth partial pole (343) may include poles existing in the second profile.

[0085] Referring to FIG. 3e, the processor (210) of the battery diagnostic device (200) according to one embodiment may obtain a fifth graph (350). For example, the processor (210) may obtain the fifth graph (350) based on the processes described in FIGS. 3a to 3d. For example, in the fifth graph (350), line (351) may include the SOC of the battery. For example, in the fifth graph (350), line (353) may include result values ​​obtained by performing data processing on the SOC of the battery.

[0086] According to one embodiment, the processor (210) of the battery diagnostic device (200) can identify the operating pattern of the battery based on the fifth graph (350). For example, the processor (210) of the battery diagnostic device (200) can predict the lifespan of the battery based on the identified operating pattern of the battery.

[0087] FIG. 4 illustrates an example of a flowchart related to a battery diagnosis method according to one embodiment of the present document.

[0088] Hereinafter, it is assumed that the battery diagnostic device (200) of FIG. 2 performs the process of FIG. 4. In addition, in the description of FIG. 4, 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. 4 may be performed by the battery diagnostic device (200) of FIG. 2. At least one of the operations of FIG. 4 may be controlled by the processor (210) of FIG. 2. Each of the operations of FIG. 4 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] Referring to FIG. 4, in operation S401, a battery diagnosis method according to an embodiment may include an operation of obtaining a second profile that corrects the first profile based on applying a specified algorithm to the first profile related to the SOC of the battery.

[0091] For example, a battery diagnosis method may include an operation of obtaining a first profile based on the SOC of a battery identified over a specified period of time. For example, a battery diagnosis method may include an operation of obtaining a first profile related to the SOC of a battery identified over a specified period of time.

[0092] For example, the specified algorithm may include a local regression analysis algorithm. For example, the local regression analysis algorithm may include LOESS.

[0093] In operation S403, the battery diagnosis method according to one embodiment may include an operation of identifying at least one second feature point in the first profile using at least one first feature point based on identifying at least one first feature point in the second profile.

[0094] For example, a battery diagnosis method may include an operation of identifying at least one second feature point that exists among a plurality of feature points including at least one first feature point.

[0095] For example, at least one first feature point may include a pole. For example, at least one second feature point may include a local pole.

[0096] In operation S405, the battery diagnosis method according to one embodiment may include an operation of obtaining an operating pattern of the battery based on at least one second characteristic point.

[0097] For example, the operating pattern may include at least one of a cycle mode, a FR mode, or any combination thereof. However, examples of the operating pattern are not limited to those described above.

[0098] For example, a battery diagnostic method may include an action to predict the life of a battery based on the operating pattern of the battery.

[0099] As described above, a battery diagnosis method according to one embodiment may include an operation of identifying an operating pattern of a battery using a first profile and a second profile. The battery diagnosis method may further include an operation of predicting the lifespan of a battery based on the operating pattern of the battery. By predicting the lifespan of a battery based on the operating pattern of the battery, the battery diagnosis method can provide the effect of accurately predicting the lifespan of a battery.

[0100] FIG. 5 illustrates an example of predicting the life of a battery using a battery diagnosis device and / or a battery diagnosis method according to one embodiment of the present document.

[0101] Referring to FIG. 5, the graph (500) may be a prediction of the lifespan of a battery. For example, the horizontal axis of the graph (500) may represent time. For example, the vertical axis of the graph (500) may represent the lifespan of the battery.

[0102] The line (501) shown in the graph (500) may be a prediction of the life of the battery without applying the present technology.

[0103] The line (503) shown in the graph (500) may be a prediction of the life of a battery by applying the present technology.

[0104] The point (505) shown in the graph (500) may be the actual life of the battery.

[0105] As shown in the graph (500) of Fig. 5, it can be confirmed that by applying the present technology, the life of the battery can be accurately predicted.

[0106] FIG. 6 is 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.

[0107] 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).

[0108] The MCU (1110) may be a processor that executes various programs stored in the memory (1120) (e.g., a battery cell data collection program, a graph generation program, a data analysis program, a data decomposition algorithm, a normalization program, a battery cell diagnosis program, etc.), 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 4.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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

1. An interface for obtaining a first profile related to the SOC (state of charge) of the battery; and Contains a processor, The above processor, Based on applying the designated algorithm to the first profile, a second profile is obtained by correcting the first profile, Based on identifying at least one first feature point in the second profile, identifying at least one second feature point in the first profile using the at least one first feature point, A battery diagnostic device configured to obtain an operating pattern of the battery based on at least one second characteristic point.

2. In paragraph 1, The above processor, A battery diagnostic device configured to predict the life of the battery based on the operating pattern of the battery.

3. In paragraph 1, The above processor, A battery diagnostic device configured to identify at least one second feature point existing among a plurality of feature points including at least one first feature point.

4. In paragraph 1, The above processor, A battery diagnostic device configured to obtain the first profile related to the SOC of the battery identified for a specified duration.

5. In paragraph 1, The above specified algorithm is, A battery diagnostic device comprising a local regression analysis algorithm.

6. In paragraph 5, The above local regression analysis algorithm, A battery diagnostic device including LOESS (locally estimated scatterplot smoothing).

7. In paragraph 1, At least one first feature point is, Including extremum points, At least one second feature point is, A battery diagnostic device comprising a local pole.

8. In paragraph 1, The above operating pattern is, A battery diagnostic device comprising at least one of a cycle mode, a FR mode, or any combination thereof.

9. An operation of obtaining a second profile that is a correction of the first profile based on applying a designated algorithm to the first profile related to the SOC of the battery by the processor; An operation of identifying at least one second feature in the first profile using the at least one first feature based on the identification of at least one first feature in the second profile by the processor; and A battery diagnosis method, comprising an operation of obtaining an operating pattern of the battery based on the at least one second characteristic point by the processor.

10. In paragraph 9, The above battery diagnosis method is, A battery diagnosis method, comprising an operation of predicting the life of the battery based on the operating pattern of the battery.

11. In paragraph 9, The above battery diagnosis method is, A battery diagnosis method comprising an operation of identifying at least one second feature point existing among a plurality of feature points including at least one first feature point.

12. In paragraph 9, The above battery diagnosis method is, A battery diagnostic method comprising an operation of obtaining the first profile related to the SOC of the battery identified for a specified duration.

13. In paragraph 9, The above specified algorithm is, A battery diagnosis method comprising a local regression analysis algorithm.

14. In paragraph 13, The above local regression analysis algorithm, A battery diagnosis method including LOESS (locally estimated scatterplot smoothing).

15. In paragraph 9, At least one first feature point is, Including extremum points, At least one second feature point is, A battery diagnosis method including local poles.

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