Battery diagnosis apparatus and method thereof

Abstract

A battery diagnosis apparatus according to one embodiment of the present invention comprises: a memory for storing at least one instruction; and one or more processors for executing the at least one instruction, wherein the one or more processors can identify an identifier and the state of a battery unit, identify, on the basis of the state of the battery unit, a utilization grade for reuse or recycling of the battery unit, and generate a certificate on the basis of the identifier of the battery unit, the certificate including at least one of the utilization grade of the battery unit, information about components predicted to be recovered from the battery unit, information about the predicted mass of each of the components predicted to be recovered from the battery unit, information about the predicted price of the battery unit, or any combination thereof.

Description

Battery diagnostic device and method thereof

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority to Korean Patent Application No. 10-2024-0085657, filed June 28, 2024, and Korean Patent Application No. 10-2024-0136291, filed October 8, 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] With the proliferation of various electronic devices due to the Fourth Industrial Revolution, battery usage is rapidly increasing. Batteries are emerging as an essential energy source in various fields, and this is driving the need for technologies to process and recycle batteries.

[0007] Proper battery disposal and recycling are crucial not only for efficient resource use but also for environmental protection. Therefore, battery diagnostic technology is being developed to diagnose battery conditions and distinguish between reusable, recyclable, and disposable batteries.

[0008] According to embodiments disclosed in this document, an object is to provide a battery diagnosis device and method for sharing the results of diagnosing a battery unit.

[0009] According to embodiments disclosed in this document, an object is to provide a battery diagnostic device and method for diagnosing a battery unit and providing a utilization level.

[0010] According to embodiments disclosed in this document, it is an object to provide a battery diagnostic device and method for obtaining mass information on each component of a battery unit without disassembling the battery unit.

[0011] According to embodiments disclosed in this document, an object is to provide a battery diagnosis device and method for generating a certificate based on the results of diagnosing a battery unit.

[0012] According to embodiments disclosed in this document, an object is to provide a battery diagnosis device and method for improving the security strength of a certificate generated and shared based on the results of diagnosing a battery unit.

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

[0014] A battery diagnostic device according to one embodiment of the present document may include a memory storing at least one instruction, and one or more processors executing the at least one instruction.

[0015] According to one embodiment, the one or more processors may identify an identifier and a state of a battery unit, identify a utilization level for reuse or recycling of the battery unit based on the state of the battery unit, and generate a certificate including at least one of the utilization level of the battery unit, information on components expected to be recovered from the battery unit, information on predicted mass of each component expected to be recovered from the battery unit, information on predicted price of the battery unit, or any combination thereof based on the identifier of the battery unit.

[0016] According to one embodiment, the one or more processors may identify at least one of state of health (SOH) information of the battery unit, impedance change information of the battery unit, open circuit voltage (OCV) information of the battery unit, or any combination thereof, based on the state of the battery unit, and identify the utilization level based on at least one of the remaining life information, the impedance change information, the open circuit voltage information, or any combination thereof.

[0017] According to one embodiment, the one or more processors can identify a state of the battery unit including at least one of state of charge (SOC) information of the battery unit, voltage information of the battery unit, temperature information of the battery unit, resistance information of the battery unit, impedance information of the battery unit, charge count information of the battery unit, discharge count information of the battery unit, or any combination thereof.

[0018] According to one embodiment, the certificate may further include at least one of usage information of the battery unit according to the utilization level, model year information of the battery unit, state of health (SOH) information of the battery unit, impedance change information of the battery unit, open circuit voltage (OCV) information of the battery unit, status information of a battery cell included in the battery unit, or any combination thereof.

[0019] According to one embodiment, the one or more processors may convert the certificate into blockchain data or non-fungible token (NFT) data, and store the blockchain data or the NFT data in a plurality of nodes, including an external server as one node.

[0020] According to one embodiment, the one or more processors may identify at least one of remaining life information of the battery unit, impedance change information of the battery unit, open circuit voltage information of the battery unit, component information of a beginning of life (BOL) state of the battery unit corresponding to an identifier of the battery unit, mass information of each component of the BOL state of the battery unit corresponding to the identifier of the battery unit, or any combination thereof, based on the state of the battery unit, and input at least one of the remaining life information, the impedance change information, the open circuit voltage information, component information of the BOL state of the battery unit, mass information of each component of the BOL state of the battery unit corresponding to the identifier of the battery unit, or any combination thereof into a learning model, and identify at least one of the utilization level output from the learning model, component information predicted to be recovered from the battery unit, mass information of each component predicted to be recovered from the battery unit, the price information, or any combination thereof.

[0021] According to one embodiment, the one or more processors input learning data including at least one of remaining life information of a battery unit different from the battery unit, impedance change information of the different battery unit, open circuit voltage information of the different battery unit, or any combination thereof into a learning model, and input target data including at least one of recovery rate information indicating a recovery rate according to a component of the different battery unit identified by comparing mass information of each component of a BOL state of the different battery unit with mass information of each component recovered from the disassembled different battery unit, component information recovered from the different battery unit, mass information of each component recovered from the different battery unit, price information of the different battery unit, or any combination thereof into the learning model, and train the learning model based on a correlation between the learning data and the target data.

[0022] In one embodiment, the certificate may further include at least one of: a first carbon emission amount according to the power consumed by the reused battery unit when the battery unit is reused; a second carbon emission amount according to the power consumed by the recycled and remanufactured battery unit when the battery unit is recycled; information on the predicted recovery rate of the battery unit; or any combination thereof.

[0023] In one embodiment, the one or more processors may generate the certificate including at least one of the second carbon emissions, predicted recovery rate information of the battery unit, predicted component information to be recovered from the battery unit, the mass information, or any combination thereof, based on the utilization grade being a designated grade.

[0024] A battery diagnosis method according to another embodiment of the present document may include an operation of identifying an identifier of a battery unit, an operation of identifying a state of the battery unit, an operation of identifying a utilization level for reuse or recycling of the battery unit based on the state of the battery unit, and an operation of generating a certificate including at least one of the utilization level of the battery unit, information on components expected to be recovered from the battery unit, information on predicted mass of each component expected to be recovered from the battery unit, information on predicted price of the battery unit, or any combination thereof, based on the identifier of the battery unit.

[0025] According to one embodiment, the operation of identifying a utilization level for reuse or recycling of the battery unit based on the state of the battery unit may include an operation of identifying at least one of state of health (SOH) information of the battery unit, impedance change information of the battery unit, open circuit voltage (OCV) information of the battery unit, or any combination thereof, based on the state of the battery unit, and an operation of identifying the utilization level based on at least one of the remaining life information, the impedance change information, the open circuit voltage information, or any combination thereof.

[0026] According to one embodiment, the operation of identifying the state of the battery unit may include an operation of identifying the state of the battery unit including at least one of state of charge (SOC) information of the battery unit, voltage information of the battery unit, temperature information of the battery unit, resistance information of the battery unit, impedance information of the battery unit, charge count information of the battery unit, discharge count information of the battery unit, or any combination thereof.

[0027] According to one embodiment, the certificate may further include at least one of usage information of the battery unit according to the utilization level, model year information of the battery unit, state of health (SOH) information of the battery unit, impedance change information of the battery unit, open circuit voltage (OCV) information of the battery unit, status information of a battery cell included in the battery unit, or any combination thereof.

[0028] According to one embodiment, the battery diagnosis method may further include an operation of converting the certificate into blockchain data or non-fungible token (NFT) data, and an operation of storing the blockchain data or the NFT data in a plurality of nodes, of which an external server is included as one node.

[0029] According to one embodiment, the operation of generating a certificate including at least one of a utilization level of the battery unit, information on components predicted to be recovered from the battery unit, information on predicted mass of each component predicted to be recovered from the battery unit, information on predicted price of the battery unit, or any combination thereof, based on the identifier of the battery unit, comprises: an operation of identifying at least one of remaining life information of the battery unit, impedance change information of the battery unit, open circuit voltage information of the battery unit, information on the component corresponding to the identifier of the battery unit, information on the mass of each component corresponding to the identifier of the battery unit, or any combination thereof, based on the state of the battery unit; an operation of inputting at least one of the remaining life information, the impedance change information, the open circuit voltage information, information on the component of the BOL state of the battery unit, information on the mass of each component of the BOL state of the battery unit corresponding to the identifier of the battery unit, or any combination thereof, into a learning model; the utilization level, information on the component predicted to be recovered from the battery unit, and the battery The operation may include identifying at least one of mass information of each component expected to be recovered from the unit, the price information, or any combination thereof.

[0030] According to one embodiment, the battery diagnosis method may further include an operation of inputting learning data including at least one of remaining life information of a battery unit different from the battery unit, impedance change information of the different battery unit, open circuit voltage information of the different battery unit, or any combination thereof, into a learning model, an operation of inputting target data including at least one of recovery rate information indicating a recovery rate according to a component of the different battery unit identified by comparing mass information of each component of a BOL state of the different battery unit with mass information of each component recovered from the different battery unit, information on the component recovered from the different battery unit, mass information of each component recovered from the different battery unit, price information of the different battery unit, or any combination thereof, into the learning model, and an operation of training the learning model based on a correlation between the learning data and the target data.

[0031] In one embodiment, the certificate may further include at least one of: a first carbon emission amount according to the power consumed by the reused battery unit when the battery unit is reused; a second carbon emission amount according to the power consumed by the recycled and remanufactured battery unit when the battery unit is recycled; information on the predicted recovery rate of the battery unit; or any combination thereof.

[0032] According to one embodiment, the battery diagnosis method may further include an operation of generating the certificate including at least one of the second carbon emission, predicted recovery rate information of the battery unit, predicted component information to be recovered from the battery unit, the mass information, or any combination thereof, based on the utilization grade being a designated grade.

[0033] This technology can share the results of diagnosing a battery unit.

[0034] Additionally, the present technology can diagnose a battery unit and provide a utilization level.

[0035] Additionally, the present technology can obtain mass information on each component of a battery unit without disassembling the battery unit.

[0036] Additionally, the present technology can generate a certificate based on the results of diagnosing the battery unit.

[0037] Additionally, the present technology can improve the security strength of certificates generated and shared based on the results of diagnosing a battery unit.

[0038] In addition, various effects may be provided, either directly or indirectly, through this document.

[0039] 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.

[0040] FIG. 2 is a block diagram showing the configuration of a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0041] FIG. 3 illustrates an example of a battery diagnostic device that generates a certificate in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0042] FIG. 4 illustrates an example of impedance change information of a battery unit in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0043] FIG. 5 illustrates an example of a certificate of a battery unit whose utilization level is a reuse level in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0044] FIG. 6 illustrates an example of a certificate of a battery unit whose utilization grade is a recycling grade in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0045] FIG. 7 illustrates an example of a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document, wherein a plurality of nodes include an external server as one node.

[0046] FIG. 8 illustrates examples of input data and output data of a learning model in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0047] FIG. 9 illustrates an example of the flow of operations of a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document, wherein a certificate generated based on a learned learning model is stored.

[0048] FIG. 10 illustrates an example of the flow of operations of a battery diagnostic device for generating a certificate in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0049] FIG. 11 is a block diagram showing the hardware configuration of a computing system that performs a battery diagnosis method in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0050] 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.

[0051] 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.

[0052] 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.

[0053] 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).

[0054] 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.

[0055] 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.

[0056] 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.

[0057] 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.

[0058] Hereinafter, embodiments of the present document will be described in detail with reference to FIGS. 1 to 11.

[0059] 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.

[0060] 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).

[0061] 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) or an energy storage system (ESS).

[0062] According to one embodiment, the battery unit (12) may include at least one battery cell (10) that can be charged and discharged. Here, the battery cell (10) may be a basic unit of a battery cell that can charge and discharge electric energy and use it. 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.

[0063] 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).

[0064] 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.

[0065] 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).

[0066] 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).

[0067] 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.

[0068] 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 discharger.

[0069] 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).

[0070] According to one embodiment, the battery management system (20) may include the battery diagnostic device (201) of FIG. 2. According to another embodiment, the battery management system (20) may be a different system from the battery diagnostic device (201) of FIG. 2. That is, the diagnostic device (201) 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 diagnostic device (201) is configured as another device external to the battery pack (1). In addition, the operation of the battery diagnostic device (201) 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 discharger.

[0071] FIG. 2 is a block diagram showing the configuration of a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0072] Referring to FIG. 2, a battery diagnostic device (201) may include a memory (203) and one or more processors (205). The memory (203) may store at least one instruction. The one or more processors (205) may execute at least one instruction.

[0073] According to one embodiment, the state of the battery unit can be divided into a beginning of life (BOL) state, a middle of life (MOL) state, and an end of life (EOL) state.

[0074] The BOL (beginning of life) state may indicate an early life state in which the number of battery charge / discharge cycles is less than a first specified number after the battery unit is manufactured. The MOL state may indicate an intermediate life state in which the number of battery charge / discharge cycles is greater than or equal to the first number and less than a second number. The second number may be greater than the first number. The EOL state may indicate an end of life state in which the number of battery charge / discharge cycles is greater than or equal to the second number. In the EOL state, the battery unit may be reused or recycled depending on the degree of consumption of the battery unit. In order to measure the degree of consumption of the battery unit, one or more processors (205) of the battery diagnostic device (201) may identify the state of the battery unit through a diagnostic sensor.

[0075] The state of the battery unit may include at least one of the state of charge (SOC) information of the battery unit, voltage information of the battery unit, temperature information of the battery unit, resistance information of the battery unit, impedance information of the battery unit, charge count information of the battery unit, discharge count information of the battery unit, or any combination thereof. The state of the battery unit will be described below with reference to FIG. 4.

[0076] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can identify at least one of state of health (SOH) information of the battery unit, impedance change information of the battery unit, open circuit voltage (OCV) information of the battery unit, or any combination thereof, based on the state of the battery unit.

[0077] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can identify a utilization grade for reuse or recycling of the battery unit based on at least one of state of health (SOH) information of the battery unit, impedance change information of the battery unit, open circuit voltage (OCV) information of the battery unit, or any combination thereof.

[0078] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) may generate a certificate including at least one of: component information expected to be recovered from the battery unit, predicted mass information of each component expected to be recovered from the battery unit, predicted price information of the battery unit, usage information of the battery unit according to the utilization level, model year information of the battery unit, state of health (SOH) information of the battery unit, impedance change information of the battery unit, open circuit voltage (OCV) information of the battery unit, status information of a battery cell included in the battery unit, or any combination thereof, based on a utilization level and an identifier of the battery unit. The certificate will be described below with reference to FIGS. 5 and 6.

[0079] At this time, at least one of the utilization level, component information predicted to be recovered from the battery unit, predicted mass information of each component predicted to be recovered from the battery unit, predicted price information of the battery unit, or any combination thereof can be identified by the learning model. A method of utilizing the learning model is described below in FIG. 8.

[0080] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) may convert a certificate into blockchain data or non-fungible token (NFT) data, and store the blockchain data or NFT data in a plurality of nodes, including an external server as one node.

[0081] The certificate converted into block chain data or NFT data is described below in Fig. 8.

[0082] FIG. 3 illustrates an example of a battery diagnostic device that generates a certificate in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0083] Referring to FIG. 3, the diagnostic sensor (303) can identify the status of the battery unit (305). One or more processors (205) of the battery diagnostic device (201) can obtain the status of the battery unit (305) identified by the diagnostic sensor (303) through the intranet (301).

[0084] For example, a diagnostic sensor (303) (e.g., an IoT (internet of things) sensor) can identify at least one of SOC information of a battery unit (305), voltage information of a battery unit (305), temperature information of a battery unit (305), resistance information of a battery unit (305), impedance information of a battery unit (305), charge count information of a battery unit (305), discharge count information of a battery unit (305), or any combination thereof.

[0085] The server (307) may include one or more processors (205) of the battery diagnostic device (201). The one or more processors (205) of the battery diagnostic device (201) included in the server (307) may identify at least one of remaining life information of the battery unit (305), impedance change information of the battery unit (305), open circuit voltage information of the battery unit (305), or any combination thereof based on the state of the battery unit (305).

[0086] One or more processors (205) of a battery diagnostic device (201) included in a server (307) can identify a utilization level based on at least one of remaining life information of a battery unit (305), impedance change information of a battery unit (305), open circuit voltage information of a battery unit (305), or any combination thereof. One or more processors (205) of a battery diagnostic device (201) included in a server (307) can generate a certificate including a utilization level.

[0087] One or more processors (205) of the battery diagnostic device (201) can convert the generated certificate into blockchain data or NFT data, and then store the certificate converted into blockchain data or NFT data in a plurality of nodes including an external server as one node.

[0088] FIG. 4 illustrates an example of impedance change information of a battery unit in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0089] Referring to FIG. 4, the first graph (401), the second graph (411), and the third graph (421) may represent Nyquist diagrams in which the horizontal axis represents the real part of the impedance and the vertical axis represents the imaginary part of the impedance. The first graph (401), the second graph (411), and the third graph (421) may be created according to electrochemical impedance spectroscopy (EIS).

[0090] According to one embodiment, in the first graph (401), the first line (403) may represent an impedance change according to a frequency change of the AC power of a normal battery unit. The second line (405) may represent an impedance change according to a frequency change of the AC power of a battery unit damaged due to discharge.

[0091] According to one embodiment, in the second graph (411), the third line (413) may represent an impedance change according to a frequency change of the AC power of a normal battery unit. The fourth line (415) may represent an impedance change according to a frequency change of the AC power of a battery unit damaged due to overheating.

[0092] According to one embodiment, in the third graph (421), the fifth line (423) may represent an impedance change according to a frequency change of the AC power of a normal battery unit. The sixth line (425) may represent an impedance change according to a frequency change of the AC power of a battery unit damaged due to a short circuit.

[0093] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can identify whether the battery unit is defective and the cause of the defect in the battery unit based on a change in impedance according to a change in frequency of the AC power of the battery unit.

[0094] FIG. 5 illustrates an example of a certificate of a battery unit whose utilization level is a reuse level in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0095] Referring to FIG. 5, the certificate (501) may include at least one of: remaining life information (503) of the battery unit (305) identified based on the state of the battery unit (305), open circuit voltage information (505) of the battery unit (305), impedance change information (507) of the battery unit (305), first carbon emission information (511) according to power consumed by the reused battery unit when the battery unit is reused, third carbon emission information (513) that is reduced compared to an internal combustion engine vehicle when the battery unit is reused, or any combination thereof.

[0096] The certificate (501) may include a utilization rating (509) of the identified battery unit (305) based on at least one of remaining life information (503), open circuit voltage information (505), impedance change information (507), or any combination thereof.

[0097] Although not shown in FIG. 5, the certificate (501) includes information on the use of the battery unit (305) (510), information on the year of manufacture of the battery unit (305), information on the status of the battery cell included in the battery unit (305), information on the second carbon emission amount according to the power consumed by the battery unit after recycling when the battery unit is recycled (e.g., the second carbon emission amount information (615) of FIG. 6), information on the fourth carbon emission amount saved compared to an internal combustion engine vehicle when the battery unit is recycled and then recycled (e.g., the fourth carbon emission amount information (616) of FIG. 6), information on the predicted total recovery rate of the battery unit (e.g., the total recovery rate information (617) of FIG. 6), information on the recovery rate of each component predicted to be recovered from the battery unit (e.g., recovery rate information (614) of each component predicted to be recovered from the battery unit), information on the component predicted to be recovered from the battery unit (e.g., the component information (611) of FIG. 6), information on each component predicted to be recovered from the battery unit It may include at least one of mass information (e.g., mass information (612) of FIG. 6), predicted price information of the battery unit (e.g., price information (613) of FIG. 6), component acquisition logic (e.g., component acquisition logic (619) of FIG. 6), or any combination thereof.

[0098] In one embodiment, the certificate may include a battery unit identifier (502) (e.g., lot ID (identification number), EOS (eosio) transaction ID (identification number)). The battery unit identifier (502) may be assigned to the battery unit at the time of manufacturing the battery unit.

[0099] For example, the battery unit identifier (502) may include an identifier (e.g., lot ID) assigned to the battery unit when the battery unit is created. For example, the battery unit identifier (502) may include an identifier (e.g., EOS transaction ID) assigned to the battery unit when the certificate is stored in the blockchain network.

[0100] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can identify remaining life information (503) based on at least one of SOC information of the battery unit, voltage information of the battery unit, temperature information of the battery unit, resistance information of the battery unit, impedance information of the battery unit, charge count information of the battery unit, discharge count information of the battery unit, or any combination thereof.

[0101] According to one embodiment, the remaining life information (503) may include the remaining life of the battery unit (305) (e.g., about 81%), and the stage at which the remaining life is included (e.g., poor, average, good).

[0102] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can identify open circuit voltage information (505) of the battery unit (305) based on voltage information of the battery unit (305).

[0103] According to one embodiment, the open circuit voltage information (505) may include a first open circuit voltage (e.g., OCV1), a second open circuit voltage (e.g., OCV2), a third open circuit voltage (e.g., OCV3), and whether a defect is detected in the battery unit (305). The first open circuit voltage may represent an open circuit voltage during a full charge after a full discharge. The second open circuit voltage may represent an open circuit voltage during a full discharge after a full charge. The third open circuit voltage may represent an open circuit voltage after a predetermined rest period has elapsed after a full charge.

[0104] For example, if the first open circuit voltage is included within the first reference range, the second open circuit voltage is included within the second reference range, and the third open circuit voltage is included within the third reference range, the open circuit voltage information (505) may include the fact that a defect in the battery unit (305) was not detected.

[0105] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can identify impedance change information (507) of the battery unit (305) based on impedance information of the battery unit (305).

[0106] Impedance change information (507) may include a Nyquist diagram, and whether a defect in the battery unit (305) is detected based on the Nyquist diagram.

[0107] According to one embodiment, one or more processors (205) of the battery diagnosis device (201) can identify the degree of consumption of the battery unit (305) based on at least one of remaining life information (503), open circuit voltage information (505), impedance change information (507), or any combination thereof. One or more processors (205) of the battery diagnosis device (201) can identify a utilization level (509) (e.g., reuse level, recycling level) according to an algorithm according to the life cycle of the battery unit (305) based on the degree of consumption of the battery unit (305). For example, when the degree of consumption of the battery unit (305) is less than a reference value, one or more processors (205) of the battery diagnosis device (201) can identify the utilization level (509) as a reuse level for reusing or reprocessing the battery unit. For example, if the degree of consumption of the battery unit (305) is equal to or greater than a reference value, one or more processors (205) of the battery diagnostic device (201) can identify the utilization grade (509) as a recycling grade in which the battery unit is separated into components after disassembling it and then recycled.

[0108] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can identify usage information of the battery unit (e.g., ESS (energy storage system), solar power plant, wind power plant, motorcycle) according to the utilization level (509).

[0109] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) are configured to, based on whether the utilization grade (509) is a designated grade (e.g., a recycling grade), generate second carbon emission information (e.g., the second carbon emission (615) of FIG. 6), predicted total recovery rate information of the battery unit (e.g., the total recovery rate information (617) of FIG. 6), fourth carbon emission information (e.g., the fourth carbon emission information (616) of FIG. 6) that is reduced compared to an internal combustion engine vehicle when the battery unit is recycled and then remanufactured), predicted total recovery rate information of the battery unit (e.g., the total recovery rate information (617) of FIG. 6), recovery rate information of each component predicted to be recovered from the battery unit (e.g., recovery rate information (614) of each component predicted to be recovered from the battery unit), component information predicted to be recovered from the battery unit (e.g., component information (611) of FIG. 6), mass information of each component predicted to be recovered from the battery unit (e.g., mass information of FIG. 6) A certificate (e.g., certificate (601) of FIG. 6) can be generated that includes at least one of information (612)), predicted price information of the battery unit (e.g., price information (613) of FIG. 6), component acquisition logic (e.g., component acquisition logic (619) of FIG. 6), information on whether a profit will be generated when the component is recycled, information on whether a profit will be generated when the battery unit is reused, grade information according to the purity of each component included in the battery unit, the number of battery units that can be generated with each component included in the battery unit, or any combination thereof. This is because, in the case of a recycling grade, since the battery unit (305) will be recovered, there is a need for information on the components predicted to be recovered from the battery unit (305) and the mass information of each component.

[0110] According to another embodiment, one or more processors (205) of the battery diagnostic device (201) may generate a certificate including at least one of component information predicted to be recovered from the battery unit (305) regardless of the utilization level (509) (e.g., component information predicted to be recovered from the battery unit (611) of FIG. 6), mass information of each component predicted to be recovered from the battery unit (e.g., mass information of each component predicted to be recovered from the battery unit (612) of FIG. 6), or any combination thereof. The one or more processors (205) of the battery diagnostic device (201) may include the component information predicted to be recovered from the battery unit (305) and the mass information of each component in the certificate even when the utilization level (509) is a reuse level, thereby preparing for a case where a user uses the battery unit (305) regardless of the utilization level.

[0111] For example, one or more processors (205) of the battery diagnostic device (201) can identify a first carbon emission based on the power consumed by the recycled battery unit (305).

[0112] For example, as the battery unit (305) is used, the state of health (SOH) of the battery unit may decrease. As the state of health (SOH) of the battery unit (305) decreases, the power consumption of the battery unit (305) may increase. As the power consumption of the battery unit (305) increases, the first carbon emission indirectly emitted by the battery unit (305) may increase. This is because the first carbon emission may have a relationship with the power carbon emission coefficient as expressed in Equation 1. The first carbon emission may be included in the first carbon emission information (511).

[0113] [Mathematical Formula 1]

[0114]

[0115] The first carbon emission may include carbon emissions according to the power consumed by the battery unit (305) during a reference period of time. The unit of the first carbon emission may be tC / year (tonnes of carbon per year), but the embodiments of the present document may not be limited thereto. The power usage may include power consumed by the battery unit (305) during a reference period of time. The power usage may be MWh / year (megawatt-hours per year), but the embodiments of the present document may not be limited thereto. The power carbon emission factor (e.g., about 0.125 tC / MWh (tonnes of carbon per megawatt-hours)) represents the carbon emissions emitted during the power generation process according to the power usage (e.g., the amount of carbon included in carbon dioxide during the power generation process), and may vary depending on the power generation method (e.g., thermal power generation, nuclear power generation, hydroelectric power generation). Although carbon emissions are not directly emitted by the use of the battery unit (305), carbon emissions may be emitted when the power discharged from the battery unit (305) is produced. The power usage may represent the power discharged from the battery unit (305). The unit of the power carbon emission factor may be tC / MWh, but the embodiments of this document may not be limited thereto.

[0116] One or more processors (205) of the battery diagnostic device (201) may obtain the total carbon emissions until the battery unit (305) of the reuse grade becomes the recycle grade, based on the power usage of the battery unit (305) in the remaining life information of the battery unit (e.g., about 85%) and the power usage of the battery unit (305) in the remaining life information that serves as the basis for the recycle grade (e.g., about 65%).

[0117] For example, one or more processors (205) of the battery diagnostic device (201) can identify a third carbon emission reduction compared to an internal combustion engine vehicle when the battery unit (305) is reused.

[0118] For example, if the battery unit (305) is reused, the carbon emissions of the battery unit (305) driven over a specific distance may be reduced by a specified percentage (e.g., approximately 19% of the carbon emissions of an internal combustion engine vehicle) compared to the carbon emissions of an internal combustion engine vehicle driven over a specific distance. Therefore, the third carbon emissions may have a relationship with the power consumption of the battery unit (305) as expressed in Equation 2.

[0119] [Equation 2]

[0120]

[0121] The third carbon emission may include the carbon emissions reduced by the reused battery unit (305) compared to an internal combustion engine vehicle over a reference period of time. The unit of the third carbon emission may be tC / year, but the embodiments of the present document may not be limited thereto. The power consumption may include the power consumed by the battery unit (305) over a reference period of time. The power consumption may be MWh / year, but the embodiments of the present document may not be limited thereto. The power carbon emission factor may represent the carbon emissions emitted during the power generation process according to the power consumption. The unit of the power carbon emission factor may be tC / MWh, but the embodiments of the present document may not be limited thereto.

[0122] FIG. 6 illustrates an example of a certificate of a battery unit whose utilization grade is a recycling grade in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0123] Referring to FIG. 6, the certificate (601) includes a battery unit identifier (602) of the battery unit (305), remaining life information (603) of the battery unit (305) identified based on the status of the battery unit (305), open circuit voltage information (605) of the battery unit (305), impedance change information (607) of the battery unit (305), second carbon emission information (615) according to the power consumed by the recycled and remanufactured battery unit (305) in case the battery unit (305) is recycled, fourth carbon emission information (616) that is reduced compared to an internal combustion engine vehicle in case the battery unit (305) is recycled and remanufactured, predicted total recovery rate information (617) of the battery unit (305), recovery rate information (614) of each component predicted to be recovered from the battery unit (305), component information (611) predicted to be recovered from the battery unit (305), The certificate (601) may include at least one of mass information (612) of each component predicted to be recovered, predicted price information (613) of the battery unit (305), component acquisition logic (619), or any combination thereof. The certificate (601) may include a utilization level (609) of the battery unit (305) identified based on at least one of remaining life information (603), open circuit voltage information (605), impedance change information (607), or any combination thereof. The certificate (601) may include at least one of usage information (610) of the battery unit (305), component information (611) predicted to be recovered from the battery unit (305), mass information (612) of each component predicted to be recovered from the battery unit (305), predicted price information (613) of the battery unit, or any combination thereof.

[0124] For example, one or more processors (205) of the battery diagnostic device (201) may identify a second carbon emission based on the power consumed by the recycled and remanufactured battery unit (305). The second carbon emission may be included in the second carbon emission information (615).

[0125] For example, when a battery unit (305) is disassembled and recycled, the mass information of each component of the battery unit (305) before the battery unit (305) is disassembled may be different from the mass information of each component recovered after the battery unit (305) is disassembled.

[0126] One or more processors (205) of the battery diagnostic device (201) can identify recovery rate information indicating a recovery rate according to the components of the battery unit (305) based on the mass information of each component of the battery unit (305) and the mass information of each component recovered after the battery unit (305) is disassembled.

[0127] This is because the break-even point recovery rate may vary depending on the components of the battery unit (305). For example, when the recovery rate of components such as nickel, cobalt, and manganese is about 95% or higher, the cost required to recover nickel, cobalt, or manganese may be less than the price of the recovered nickel, cobalt, or manganese. For example, when the recovery rate of components such as lithium is about 80% to about 85% or higher, the cost required to recover lithium may be less than the price of the recovered lithium.

[0128] The overall recovery rate can be identified based on the recovery rate of each component. Therefore, the power consumption of the remanufactured battery unit (305) after being disassembled and recycled can include the recovery rate and a calculated value of the power consumption of the battery unit before disassembly. The second carbon emission can have a relationship with the power carbon emission factor as shown in Equation 3. The overall recovery rate can be included in the overall recovery rate information (617). The recovery rate of each component can be included in the recovery rate information (614) of each component.

[0129] [Equation 3]

[0130]

[0131] The second carbon emission may include carbon emissions according to the power consumed by the regenerated battery unit (305) during a reference period of time. The unit of the second carbon emission may be tC / year, but the embodiments of the present document may not be limited thereto. The power usage may include the power consumed by the battery unit (305) during a reference period of time before disassembly. The power usage may be MWh / year, but the embodiments of the present document may not be limited thereto. The power carbon emission factor may represent the carbon emissions emitted during the power generation process according to the power usage. The unit of the power carbon emission factor may be tC / MWh, but the embodiments of the present document may not be limited thereto.

[0132] For example, one or more processors (205) of the battery diagnostic device (201) may identify a fourth carbon emission reduction compared to an internal combustion engine vehicle when the battery unit (305) is recycled and then remanufactured. The fourth carbon emission reduction may be included in the fourth carbon emission information (616).

[0133] For example, when a battery unit (305) is disassembled, recycled, and then remanufactured, the carbon emissions of the remanufactured battery unit (305) driven over a specific distance may be less than the carbon emissions of an internal combustion engine vehicle driven over a specific distance by a specified percentage (e.g., about 19% of the carbon emissions of the internal combustion engine vehicle). The carbon emissions of the remanufactured battery unit (305) may be identified by Equation 3. The fourth carbon emissions may have a relationship with the power consumption of the battery unit (305) as shown in Equation 4.

[0134] [Equation 4]

[0135]

[0136] The fourth carbon emission may include the carbon emission reduced by the carbon emission of the remanufactured battery unit (305) compared to the carbon emission of an internal combustion engine vehicle during a reference period. The unit of the fourth carbon emission may be tC / year, but the embodiments of the present document may not be limited thereto. The power consumption may include the power consumed by the battery unit (305) during a reference period before being disassembled. The power consumption may be MWh / year, but the embodiments of the present document may not be limited thereto. The power carbon emission factor may represent the carbon emission emitted during the power generation process according to the power consumption. The unit of the power carbon emission factor may be tC / MWh, but the embodiments of the present document may not be limited thereto.

[0137] A specified ratio (e.g., about 0.19) may represent the ratio of the carbon emissions of an internal combustion engine vehicle driven a certain distance to the carbon emissions of a remanufactured battery unit (305) driven a certain distance.

[0138] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) may identify information on whether recycling a component would be profitable based on the cost of recovering the component and the price of each component. For example, the price of each component may be identified based on the mass of each component included in the battery unit (305), information on the recovery rate of each component, and the price of each component.

[0139] In one embodiment, an external server included as one of the nodes may update the price of each component by linking it to the system.

[0140] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can identify information about whether reusing a component would be profitable based on the cost of refurbishing the battery unit (305) to make it reusable and the price of the reused battery unit (305).

[0141] According to one embodiment, an external server included in one of the nodes may update the price of a reused battery unit (305) by linking it to the system.

[0142] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) may include grade information according to the purity of each component included in the battery unit (305) in the certificate. For example, when the purity of nickel included in the battery unit (305) is about 99.8% or higher, the grade information of nickel may include grade A. For example, when the purity of nickel included in the battery unit (305) is about 99.8% or lower, the grade information of nickel may include grade B.

[0143] According to one embodiment, the battery unit identifier (602) may be described with reference to the description of the battery unit identifier (502) of FIG. 5. According to one embodiment, the certificate (601) may include a battery unit identifier (602) (e.g., lot ID (identification number), EOS (eosio) transaction ID (identification number)). The battery unit identifier (602) may be assigned to the battery unit (305) at the time of manufacturing the battery unit.

[0144] According to one embodiment, the remaining life information (603) may be described with reference to the description of the remaining life information (503) of FIG. 5. According to one embodiment, the remaining life information (603) may include the remaining life of the battery unit (305) (e.g., about 77%), and the stage in which the remaining life is included (e.g., insufficient).

[0145] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can identify open circuit voltage information (605) of the battery unit (305) based on voltage information of the battery unit (305).

[0146] According to one embodiment, the open circuit voltage information (605) may include a first open circuit voltage (e.g., OCV1), a second open circuit voltage (e.g., OCV2), a third open circuit voltage (e.g., OCV3), and whether a defect in the battery unit (305) is detected.

[0147] According to one embodiment, the first to third open circuit voltages may be described with reference to the description of the first to third open circuit voltages of FIG. 5. For example, if the first open circuit voltage is included in the first reference range, the second open circuit voltage is not included in the second reference range, and the third open circuit voltage is included in the third reference range, the open circuit voltage information (605) may include the fact that a defect in the battery unit (305) is detected based on the second open circuit voltage.

[0148] According to one embodiment, the impedance change information (607) may be described with reference to the description of the impedance change information (507) of FIG. 5. For example, the impedance change information (607) may include a Nyquist diagram indicating an impedance change due to discharge, and the fact that a defect in the battery unit (305) has been detected based on the Nyquist diagram.

[0149] According to one embodiment, the utilization level (609) may be described with reference to the description of the utilization level (509) of FIG. 5.

[0150] One or more processors (205) of the battery diagnostic device (201) can identify a utilization grade (609) (e.g., a recycling grade) based on the degree of consumption of the battery unit (305). For example, when the degree of consumption of the battery unit (305) is equal to or greater than a reference value, the one or more processors (205) of the battery diagnostic device (201) can identify the utilization grade (609) as a recycling grade in which the battery unit is separated into components after disassembling it and then recycled.

[0151] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) may not identify the usage information (e.g., ESS (energy storage system), solar power plant, wind power plant, motorcycle) of the battery unit (305) when the utilization grade (609) is a recycling grade.

[0152] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) may generate a certificate (601) including at least one of component information (611) expected to be recovered from the battery unit, mass information (612) of each component expected to be recovered from the battery unit, or any combination thereof, based on whether the utilization grade (609) is a designated grade (e.g., a recyclable grade). This is because, in the case of a recyclable grade, the component information expected to be recovered from the battery unit (305) and the mass information of each component are required.

[0153] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can identify at least one of component information (611) predicted to be recovered from the battery unit (305), mass information (612) of each component predicted to be recovered from the battery unit (305), predicted price information (613) indicating the price of the battery unit (305), or any combination thereof, through a learning model (e.g., learning model (803) of FIG. 8).

[0154] The component acquisition logic (619) may include a method for identifying at least one of component information (611) predicted to be recovered from the battery unit (305) through a learning model, mass information (612) of each component predicted to be recovered from the battery unit (305), price information (613) indicating a predicted price of the battery unit (305), or any combination thereof.

[0155] For example, the component acquisition logic (619) may represent a method of acquiring mass information (612) of each component predicted to be recovered from the battery unit (305) by calculating the recovery rate information (614), the mass information of each component in the BOL state of the battery unit (305), the recovery rate of each component included in the recovery rate information (614), and the mass information of each component in the BOL state. As another example, the component acquisition logic (619) may include a type of learning model.

[0156] The component acquisition logic (619) is described below in Fig. 8.

[0157] FIG. 7 illustrates an example of a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document, wherein a plurality of nodes include an external server as one node.

[0158] Referring to FIG. 7, one or more processors (205) of the battery diagnostic device (201) can convert a certificate into blockchain data or non-fungible token (NFT) data.

[0159] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) may store blockchain data or NFT data in a plurality of nodes including a first node (701), a second node (703), a third node (705), a fourth node (707), and a fifth node (709).

[0160] According to one embodiment, the first node (701), the second node (703), the third node (705), the fourth node (707), and the fifth node (709) may represent external servers.

[0161] In one embodiment, the external server may include a server of a seller of battery units, or a server of a buyer.

[0162] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) may store blockchain data or NFT data via a smart contract when diagnosing a battery unit or selling or purchasing a battery unit. If there is a buyer of the battery unit, one or more processors (205) of the battery diagnostic device (201) may also store information about the battery unit buyer along with the converted certificate.

[0163] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can check the nodes and determine their integrity whenever information about the battery unit and the converted certificate are stored.

[0164] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can retrieve a battery unit identifier and utilization level converted into blockchain data via a QR (quick response) code when a battery unit is purchased or sold, or retrieve a certificate converted into NFT data.

[0165] FIG. 8 illustrates examples of input data and output data of a learning model in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0166] Referring to FIG. 8, the learning model (803) can output output data (805) based on input data (801). The input data (801) can include at least one of remaining life information of the battery unit (305), impedance change information of the battery unit (305), open circuit voltage information of the battery unit (305), component information of the BOL state of the battery unit (305), mass information of each component of the BOL state of the battery unit (305), or a combination thereof. The output data (805) can include at least one of utilization level of the battery unit (305), component information predicted to be recovered from the battery unit (305), mass information of each component predicted to be recovered from the battery unit (305), predicted price information of the battery unit, or a combination thereof.

[0167] According to one embodiment, the learning model (803) can identify recovery rate information indicating the recovery rate of each component based on at least one of remaining life information, impedance change information, open circuit voltage information, or any combination thereof. The recovery rate can indicate the mass information of each component recovered after the battery unit (305) is disassembled with respect to the mass information of each component of the battery unit (305).

[0168] The learning model (803) can identify component information predicted to be recovered from the battery unit (305), mass information of each component predicted to be recovered from the battery unit (305), or price information based on at least one of a recovery rate, component information predicted to be recovered from the battery unit (305), mass information predicted to be recovered from the battery unit (305), or any combination thereof.

[0169] This is because the degree of degradation of the battery unit (305) can be known through remaining life information, impedance change information, and open circuit voltage information, and the greater the degree of degradation of the battery unit (305), the lower the recovery rate.

[0170] For example, the greater the remaining life information, the greater the degree of degradation of the battery unit (305).

[0171] For example, the greater the difference between the Nyquist diagram of the battery unit included in the impedance change information and the Nyquist diagram of the normal battery unit, the greater the degree of degradation of the battery unit (305).

[0172] For example, if the first open circuit voltage, the second open circuit voltage, or the third open circuit voltage included in the open circuit voltage information is outside the reference range, the degree of degradation of the battery unit (305) may be greater than the reference degree of degradation.

[0173] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) may input learning data including at least one of remaining life information of a battery unit different from the battery unit (305), impedance change information of a different battery unit, open circuit voltage information of a different battery unit, or any combination thereof, into the learning model (803) for learning of the learning model (803).

[0174] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) may input target data including at least one of recovery rate information indicating a recovery rate identified by comparing mass information of each component in a BOL state of different battery units with mass information of each component recovered from different disassembled battery units, component information recovered from different battery units, mass information of each component recovered from different battery units, price information of different battery units, or any combination thereof, into the learning model (803).

[0175] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can train a learning model (803) based on a correlation between learning data and target data.

[0176] According to one embodiment, if one or more processors (205) of the battery diagnostic device (201) include an artificial intelligence processor (e.g., NPU) for training a learning model (803), the artificial intelligence processor may train an artificial neural network by utilizing weight data stored in memory as training data for a machine learning model. The learning model (803) may include an artificial neural network (ANN) model.

[0177] In one embodiment, examples of learning algorithms may include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0178] According to one embodiment, the artificial neural network included in the learning model (803) may be composed of a plurality of neural network layers. Each of the plurality of neural network layers has a plurality of weight values, and can perform neural network operations through operations between the operation results of the previous layer and the plurality of weights. The plurality of weights of the plurality of neural network layers may be optimized based on the learning results of the artificial intelligence model. For example, the plurality of weights may be updated so that the loss value or cost value acquired from the artificial intelligence model is reduced or minimized during the learning process.

[0179] According to one embodiment, the artificial neural network may include a deep neural network (DNN), such as a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted Boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or deep Q-networks, but may not be limited to the examples described above.

[0180] According to one embodiment, the processor (205) of the battery diagnosis device (201) may output at least one of the remaining life information of the battery unit (305), impedance change information of the battery unit (305), open circuit voltage information of the battery unit (305), component information of the BOL state of the battery unit (305), mass information of each component of the BOL state of the battery unit (305), or a combination thereof, which is input data (801), based on the selected learning model (803), and at least one of the utilization level of the battery unit (305), component information predicted to be recovered from the battery unit (305), mass information of each component predicted to be recovered from the battery unit (305), predicted price information of the battery unit, or a combination thereof, which is output data (805) having a causal relationship with at least one of the following:

[0181] FIG. 9 illustrates an example of the flow of operations of a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document, wherein a certificate generated based on a learned learning model is stored.

[0182] Referring to FIG. 9, in the first operation (901), one or more processors (205) of the battery diagnostic device (201) can obtain at least one of component information of the BOL state of the battery unit, mass information of each component of the BOL state of the battery unit, a battery unit identifier, or any combination thereof.

[0183] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can obtain mass information of each component of the BOL state of the disassembled battery unit based on the battery unit identifier.

[0184] In a second operation (903), one or more processors (205) of a battery diagnostic device (201) according to an embodiment may obtain at least one of remaining life information of a battery unit, impedance change information of a battery unit, open circuit voltage information of a battery unit, or any combination thereof.

[0185] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can identify a state of a battery unit through a diagnostic sensor, including at least one of SOC information of the battery unit, voltage information of the battery unit, temperature information of the battery unit, resistance information of the battery unit, impedance information of the battery unit, charge count information of the battery unit, discharge count information of the battery unit, or any combination thereof.

[0186] According to one embodiment, one or more processors (205) of the battery diagnostic device (201) can identify at least one of remaining life information, impedance change information of the battery unit, open circuit voltage information of the battery unit, or a combination thereof, based on the state of the battery unit.

[0187] In a third operation (905), one or more processors (205) of a battery diagnostic device (201) according to an embodiment may obtain at least one of recovery rate information of a battery unit, component information recovered from a battery unit, mass information of each component recovered from a battery unit, price information, or any combination thereof.

[0188] There is a need to disassemble the battery unit to recover the components. The method for disassembling the battery unit may include physically disassembling the battery unit, or extracting the components using an aqueous solution and crystallizing the extracted components to obtain them (e.g., a method for recovering components such as nickel, manganese, and cobalt).

[0189] For example, copper, plastic, aluminum, iron phosphate, and lithium can be recovered by physically separating the battery units, such as by crushing or pulverizing them after the battery units are discharged.

[0190] For example, black mass can be obtained by physically disintegrating a battery unit after it is discharged. Furthermore, cobalt, nickel, manganese, graphite, and carbon can be recovered by adding an organic acid, such as sulfuric acid, to the black mass, followed by extraction and crystallization using an aqueous solution.

[0191] In one embodiment, pricing information may be identified based on the mass information of each component recovered from the battery unit. The pricing information may include the cost incurred during the recycling process or the price of the recovered component.

[0192] In the fourth operation (907), one or more processors (205) of the battery diagnostic device (201) according to one embodiment can learn a learning model based on learning data and target data.

[0193] In other words, one or more processors (205) of the battery diagnostic device (201) according to one embodiment may teach a learning model the correlation between learning data and target data. The learning model may include a neural network trained through supervised learning (e.g., linear regression), but the embodiments of this document may not be limited thereto.

[0194] In the fifth operation (909), one or more processors (205) of the battery diagnostic device (201) according to one embodiment may input input data into the learning model to obtain output data.

[0195] In the sixth operation (911), one or more processors (205) of the battery diagnostic device (201) according to one embodiment may generate a certificate.

[0196] In the seventh operation (913), one or more processors (205) of the battery diagnostic device (201) according to one embodiment may store a certificate.

[0197] FIG. 10 illustrates an example of the flow of operations of a battery diagnostic device for generating a certificate in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0198] Referring to FIG. 10, in a first operation (1001), one or more processors (205) of a battery diagnostic device (201) according to one embodiment can identify an identifier of a battery unit.

[0199] In a second operation (1003), one or more processors (205) of a battery diagnostic device (201) according to an embodiment can identify the status of a battery unit through a diagnostic sensor.

[0200] In a third operation (1005), one or more processors (205) of a battery diagnostic device (201) according to an embodiment may identify a utilization grade for reuse or recycling of the battery unit based on the state of the battery unit.

[0201] In a fourth operation (1007), one or more processors (205) of a battery diagnostic device (201) according to an embodiment may generate a certificate including at least one of a utilization level of the battery unit, information on components expected to be recovered from the battery unit, information on the mass of each component expected to be recovered from the battery unit, price information indicating an expected price of the battery unit, or any combination thereof, based on an identifier of the battery unit.

[0202] FIG. 11 is a block diagram showing the hardware configuration of a computing system that performs a battery diagnosis method in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0203] Referring to FIG. 11, a computing system (1100) according to an embodiment disclosed in the present document may include an MCU (1110), a memory (1120), an input / output I / F (1130), and a communication I / F (1140).

[0204] The MCU (1110) may be one or more processors that execute 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, a battery cell diagnosis program, etc.) stored in the memory (1120), process various information including battery cell characteristic data, latent variables, etc. through these programs, and perform the functions of the battery diagnosis device (201) shown in the above-described FIGS. 2 to 10.

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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 diagnostic device (201) 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).

[0209] 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.

[0210] 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.

[0211] 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.

[0212] 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. Memory that stores at least one instruction; and comprising one or more processors executing at least one instruction; One or more of the above processors, Identify the identifier and status of the battery unit, Based on the condition of the above battery unit, identify the utilization grade for reuse or recycling of the above battery unit, Based on the identifier of the battery unit, a certificate is configured to generate at least one of the utilization level of the battery unit, information on components expected to be recovered from the battery unit, information on the expected mass of each component expected to be recovered from the battery unit, information on the expected price of the battery unit, or any combination thereof. Battery diagnostic device.

2. In claim 1, One or more of the above processors, Based on the state of the battery unit, at least one of state of health (SOH) information of the battery unit, impedance change information of the battery unit, open circuit voltage (OCV) information of the battery unit, or any combination thereof is identified, configured to identify the utilization level based on at least one of the remaining life information, the impedance change information, the open circuit voltage information, or any combination thereof; Battery diagnostic device.

3. In claim 1, One or more of the above processors, The battery unit is configured to identify a state of the battery unit, including at least one of SOC (state of charge; SOC) information of the battery unit, voltage information of the battery unit, temperature information of the battery unit, resistance information of the battery unit, impedance information of the battery unit, charge count information of the battery unit, discharge count information of the battery unit, or any combination thereof. Battery diagnostic device.

4. In claim 1, The above certificate, It is configured to further include at least one of usage information of the battery unit according to the utilization grade, model year information of the battery unit, state of health (SOH) information of the battery unit, impedance change information of the battery unit, open circuit voltage (OCV) information of the battery unit, status information of the battery cell included in the battery unit, or any combination thereof. Battery diagnostic device.

5. In claim 1, One or more of the above processors, Convert the above certificate into blockchain data or NFT (non-fungible token; NFT) data, An external server is configured to store the blockchain data or the NFT data in multiple nodes including one node. Battery diagnostic device.

6. In claim 1, One or more of the above processors, Based on the state of the battery unit, at least one of remaining life information of the battery unit, impedance change information of the battery unit, open circuit voltage information of the battery unit, component information of the BOL (beginning of life; BOL) state of the battery unit corresponding to the identifier of the battery unit, mass information of each component of the BOL state of the battery unit corresponding to the identifier of the battery unit, or any combination thereof is identified, At least one of the remaining life information, the impedance change information, the open circuit voltage information, the component information of the BOL state of the battery unit, the mass information of each component of the BOL state of the battery unit corresponding to the identifier of the battery unit, or any combination thereof is input into the learning model, The utilization level output from the learning model, the component information predicted to be recovered from the battery unit, the mass information of each component predicted to be recovered from the battery unit, the price information, or at least one of any combination thereof, is configured to be identified. Battery diagnostic device.

7. In claim 6, One or more of the above processors, Input learning data including at least one of remaining life information of a battery unit different from the above battery unit, impedance change information of the different battery unit, open circuit voltage information of the different battery unit, or any combination thereof into a learning model, Target data including at least one of recovery rate information indicating a recovery rate according to a component of the different battery unit identified by comparing the mass information of each component of the BOL state of the different battery unit with the mass information of each component recovered from the disassembled different battery unit, component information recovered from the different battery unit, mass information of each component recovered from the different battery unit, price information of the different battery unit, or any combination thereof is input into the learning model, configured to train the learning model based on the correlation between the learning data and the target data, Battery diagnostic device.

8. In claim 1, The above certificate, When the battery unit is reused, it is configured to further include at least one of a first carbon emission amount according to the power consumed by the reused battery unit, when the battery unit is recycled, a second carbon emission amount according to the power consumed by the recycled and remanufactured battery unit, information on the predicted recovery rate of the battery unit, or any combination thereof. Battery diagnostic device.

9. In claim 8, One or more of the above processors, Based on the above utilization rating being a designated rating, configured to generate the certificate including at least one of the second carbon emission amount, the predicted recovery rate information of the battery unit, the component information predicted to be recovered from the battery unit, the mass information, or any combination thereof. Battery diagnostic device.

10. Action to identify the identifier of the battery unit; An operation for identifying the status of the above battery unit; An operation of identifying a utilization grade for reuse or recycling of the battery unit based on the status of the battery unit; and An operation of generating a certificate including at least one of a utilization level of the battery unit, component information expected to be recovered from the battery unit, predicted mass information of each component expected to be recovered from the battery unit, predicted price information of the battery unit, or any combination thereof, based on an identifier of the battery unit. How to diagnose a battery.

11. In claim 10, An operation of identifying a utilization grade for reuse or recycling of the battery unit based on the status of the battery unit is as follows: An operation of identifying at least one of state of health (SOH) information of the battery unit, impedance change information of the battery unit, open circuit voltage (OCV) information of the battery unit, or any combination thereof, based on the state of the battery unit; An operation of identifying the utilization level based on at least one of the remaining life information, the impedance change information, the open circuit voltage information, or any combination thereof. How to diagnose a battery.

12. In claim 10, The operation of identifying the status of the above battery unit is as follows: An operation for identifying a state of the battery unit, including at least one of SOC (state of charge; SOC) information of the battery unit, voltage information of the battery unit, temperature information of the battery unit, resistance information of the battery unit, impedance information of the battery unit, charge count information of the battery unit, discharge count information of the battery unit, or any combination thereof. How to diagnose a battery.

13. In claim 10, The above certificate, It is configured to further include at least one of usage information of the battery unit according to the utilization grade, model year information of the battery unit, state of health (SOH) information of the battery unit, impedance change information of the battery unit, open circuit voltage (OCV) information of the battery unit, status information of the battery cell included in the battery unit, or any combination thereof. How to diagnose a battery.

14. In claim 10, An operation of converting the above certificate into blockchain data or NFT (non-fungible token; NFT) data; and Further comprising an operation of storing the blockchain data or the NFT data in a plurality of nodes including an external server as one node. How to diagnose a battery.

15. In claim 10, An operation of generating a certificate including at least one of a utilization level of the battery unit, information on components expected to be recovered from the battery unit, information on predicted mass of each component expected to be recovered from the battery unit, information on predicted price of the battery unit, or any combination thereof, based on an identifier of the battery unit, An operation of identifying at least one of remaining life information of the battery unit, impedance change information of the battery unit, open circuit voltage information of the battery unit, component information corresponding to the identifier of the battery unit, mass information of each component corresponding to the identifier of the battery unit, or any combination thereof, based on the status of the battery unit; An operation of inputting at least one of the remaining life information, the impedance change information, the open circuit voltage information, the component information of the BOL state of the battery unit, the mass information of each component of the BOL state of the battery unit corresponding to the identifier of the battery unit, or any combination thereof into a learning model; An operation including identifying at least one of the utilization grade output from the learning model, component information predicted to be recovered from the battery unit, mass information of each component predicted to be recovered from the battery unit, the price information, or any combination thereof. How to diagnose a battery.

16. In claim 15, An operation of inputting learning data including at least one of remaining life information of a battery unit different from the above battery unit, impedance change information of the different battery unit, open circuit voltage information of the different battery unit, or any combination thereof, into a learning model; An operation of inputting target data including at least one of recovery rate information indicating a recovery rate according to a component of the different battery unit identified by comparing the mass information of each component of the BOL state of the different battery unit with the mass information of each component recovered from the different battery unit, component information recovered from the different battery unit, mass information of each component recovered from the different battery unit, price information of the different battery unit, or any combination thereof, into the learning model; and Further comprising an operation of training the learning model based on the correlation between the learning data and the target data. How to diagnose a battery.

17. In claim 10, The above certificate, When the battery unit is reused, it is configured to further include at least one of a first carbon emission amount according to the power consumed by the reused battery unit, when the battery unit is recycled, a second carbon emission amount according to the power consumed by the recycled and remanufactured battery unit, information on the predicted recovery rate of the battery unit, or any combination thereof. How to diagnose a battery.

18. In claim 17, Based on the above utilization rating being a designated rating, Further comprising an operation of generating the certificate including at least one of the second carbon emission amount, predicted recovery rate information of the battery unit, predicted component information to be recovered from the battery unit, the mass information, or any combination thereof. How to diagnose a battery.

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