Battery diagnostic device and method therefor

Abstract

A battery diagnostic device, according to one embodiment of the present document, comprises: a memory storing at least one instruction; and at least one processor executing the at least one instruction, wherein the at least one processor is configured to: identify a set of data measured from a battery cell while the battery cell is in use, and times at which the data set is acquired; identify a plurality of data regions including at least one of a plurality of first ranges dividing a first state variable of the battery cell by a first interval, a plurality of second ranges dividing a second state variable of the battery cell by a second interval, a plurality of third ranges dividing a third state variable of the battery cell by a third interval, or any combination thereof; and diagnose a state of the battery cell on the basis of battery data regions among the plurality of data regions that include the data set, and retention times, from among the times, corresponding to each of the battery data regions, wherein the data may include at least one of a value of the first state variable, a value of the second state variable, a value of the third state variable, 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 from Republic of Korea Patent Application No. 10-2024-0102815, filed August 2, 2024, the entire disclosure of which is incorporated herein by reference.

[0003] Technology field

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

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

[0006] With the proliferation of various electronic devices driven by the Fourth Industrial Revolution, battery usage is rapidly increasing. Batteries are emerging as an essential energy source in various fields, including electric vehicles, portable electronic devices, and renewable energy storage systems. Consequently, the importance of battery condition diagnostic technology to improve battery performance and reliability is increasing.

[0007] In particular, technologies aimed at improving the accuracy of battery life diagnosis are attracting attention. Improving the accuracy of battery life diagnosis can reduce battery maintenance costs and optimize battery use.

[0008] According to embodiments disclosed in this document, an object is to provide a battery diagnostic device and method for identifying an operation pattern of a battery unit by identifying battery data areas and retention times based on a set of data acquired from a battery unit.

[0009] According to embodiments disclosed in this document, an object of the present invention is to provide a battery diagnosis device and method that improve the accuracy of diagnosis for a battery unit by inputting an operation pattern of a battery unit identified based on a set of data acquired from the battery unit into a learning model.

[0010] According to embodiments disclosed in this document, it is an object to provide a battery diagnostic device and method for performing a diagnosis on a battery unit by identifying the time when the battery unit is not in operation, thereby taking into account degradation during the time when the battery unit is not in operation.

[0011] According to the embodiments disclosed in this document, it is intended to provide a battery diagnosis device and method that contribute to improving the stability of a battery including a battery unit by improving the accuracy of diagnosis for the battery unit.

[0012] 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.

[0013] A battery diagnostic device according to one embodiment of the present document may include a memory storing at least one instruction, and at least one processor executing the at least one instruction.

[0014] According to one embodiment, the at least one processor is configured to identify a set of data measured from a battery cell while the battery cell is in use, and times at which the set of data is acquired, and to diagnose a state of the battery cell based on battery data areas among the plurality of data areas in which the set of data is included, and retention times corresponding to each of the battery data areas among the times, wherein the data may include at least one of a value of the first state variable, a value of the second state variable, a value of the third state variable, or a combination thereof.

[0015] According to one embodiment, the at least one processor may identify a specific data region among the battery data regions based on at least one of a first specific range among the plurality of first ranges including a specific value of the first state variable, a second specific range among the plurality of second ranges including a specific value of the second state variable, a third specific range among the plurality of third ranges including a specific value of the third state variable, or any combination thereof, and may identify a specific time corresponding to the specific data region based on a time during which the first state variable is included in the first specific range, the second state variable is included in the second specific range, and the third state variable is included in the third specific range among the retention times, and may associate the retention times with each of the battery data regions based on the specific data region and the specific time.

[0016] In one embodiment, the at least one processor can identify the set of data of the time when the battery was not operated based on at least one of the first state variable acquired within a second hour after the battery started to operate, the second state variable acquired within the second hour after the battery started to operate, the third state variable acquired within the second hour after the battery started to operate, or any combination thereof, when the battery including the battery cell was not operated for a first hour or more.

[0017] According to one embodiment, the at least one processor adjusts the holding times so that a sum of the holding times becomes a designated value and a ratio between the holding times is maintained, generates a histogram based on at least one of the plurality of first ranges, the plurality of second ranges, the plurality of third ranges, or any combination thereof and the adjusted holding times, and diagnoses a state of the battery cell based on the histogram.

[0018] In one embodiment, the at least one processor can identify at least one of the first state variable, the second state variable, the third state variable, or any combination thereof, based on at least one of a temperature, a voltage, a current, or a combination thereof of the battery cell.

[0019] According to one embodiment, the at least one processor can obtain a predicted remaining useful life (RUL) of a battery cell from the learning model by inputting input data according to the battery data areas and the maintenance times into the learning model.

[0020] In one embodiment, the at least one processor can obtain the set of data for the specified period of time by correcting the set of data based on temperature changes over a specified period of time longer than the period of time that identified the set of data.

[0021] According to one embodiment, the at least one processor can obtain the predicted remaining useful life (RUL) of the battery cell from the learning model by inputting input data according to the histogram into the learning model.

[0022] According to another embodiment of the present document, a battery diagnosis method includes an operation of identifying a set of data measured from a battery cell while the battery cell is in use, and times at which the set of data is acquired; an operation of identifying a plurality of data areas including at least one of a plurality of first ranges dividing a first state variable of the battery cell into a first size, a plurality of second ranges dividing a second state variable of the battery cell into a second size, a plurality of third ranges dividing a third state variable of the battery cell into a third size, or any combination thereof; and an operation of diagnosing a state of the battery cell based on battery data areas among the plurality of data areas including the set of data, and retention times corresponding to each of the battery data areas among the times, wherein the data may include at least one of a value of the first state variable, a value of the second state variable, a value of the third state variable, or any combination thereof.

[0023] According to one embodiment, the operation of diagnosing the state of the battery cell based on the battery data areas including the set of data among the plurality of data areas, and the retention times corresponding to each of the battery data areas among the times may include: an operation of identifying a specific data area among the battery data areas based on at least one of a first specific range including a specific value of the first state variable among the plurality of first ranges, a second specific range including a specific value of the second state variable among the plurality of second ranges, a third specific range including a specific value of the third state variable among the plurality of third ranges, or any combination thereof; an operation of identifying a specific time corresponding to the specific data area based on a time among the retention times during which the first state variable is included in the first specific range, the second state variable is included in the second specific range, and the third state variable is included in the third specific range; and an operation of corresponding the retention times to each of the battery data areas based on the specific data area and the specific time.

[0024] According to one embodiment, the operation of diagnosing the state of the battery cell based on the battery data areas including the set of data among the plurality of data areas, and the maintenance times corresponding to each of the battery data areas among the times, may include, when a battery including the battery cell has not operated for a first time or more, an operation of identifying the set of data of the time when the battery has not operated based on at least one of the first state variable acquired within a second time after the battery has started to operate, the second state variable acquired within the second time after the battery has started to operate, the third state variable acquired within the second time after the battery has started to operate, or any combination thereof.

[0025] According to one embodiment, the operation of diagnosing the state of the battery cell based on the battery data areas including the set of data among the plurality of data areas, and the holding times corresponding to each of the battery data areas among the times may include the operation of adjusting the holding times so that a sum of the holding times becomes a designated value and a ratio between the holding times is maintained, the operation of generating a histogram based on at least one of the plurality of first ranges, the plurality of second ranges, the plurality of third ranges, or any combination thereof and the adjusted holding times, and the operation of diagnosing the state of the battery cell based on the histogram.

[0026] According to one embodiment, the operation of identifying a plurality of data areas including at least one of a plurality of first ranges dividing a first state variable of the battery cell into a first size, a plurality of second ranges dividing a second state variable of the battery cell into a second size, a plurality of third ranges dividing a third state variable of the battery cell into a third size, or any combination thereof may include an operation of identifying at least one of the first state variable, the second state variable, the third state variable, or any combination thereof based on at least one of a temperature, a voltage, a current, or a combination thereof of the battery cell.

[0027] According to one embodiment, the operation of diagnosing the state of the battery cell based on the battery data areas including the set of data among the plurality of data areas, and the maintenance times corresponding to each of the battery data areas among the times, may include an operation of obtaining a predicted remaining useful life (RUL) of the battery cell from the learning model by inputting input data according to the battery data areas and the maintenance times into the learning model.

[0028] According to one embodiment, the operation of diagnosing the state of the battery cell based on the battery data areas including the set of data among the plurality of data areas, and the maintenance times corresponding to each of the battery data areas among the times, may include the operation of obtaining the set of data for the specified period by correcting the set of data based on a temperature change for a specified period longer than the period for which the set of data was identified.

[0029] According to one embodiment, the operation of diagnosing the state of the battery cell based on the histogram may include an operation of obtaining the predicted remaining useful life (RUL) of the battery cell from the learning model by inputting input data according to the histogram into the learning model.

[0030] The present technology can identify the operation pattern of a battery unit by identifying battery data areas and retention times based on a set of data acquired from the battery unit.

[0031] In addition, the present technology can improve the accuracy of diagnosis for a battery unit by inputting the operation pattern of the battery unit identified based on a set of data acquired from the battery unit into a learning model.

[0032] Additionally, the present technology can perform a diagnosis on a battery unit by considering degradation during the time when the battery unit is not in operation by identifying the time when the battery unit is not in operation.

[0033] In addition, the present technology can contribute to improving the stability of a battery including a battery unit by improving the accuracy of diagnosis for the battery unit.

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

[0035] 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.

[0036] 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.

[0037] FIG. 3 illustrates an example of a data area in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0038] FIG. 4 illustrates an example of a set of data of a time when a battery was not operating in a battery diagnostic device and a battery diagnostic method according to an embodiment of the present document.

[0039] FIG. 5 illustrates an example of temperature changes used to correct a set of data in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0040] FIG. 6 illustrates an example of a flow of operations of a battery diagnostic device that diagnoses the state of a battery cell based on battery data areas and retention times in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0041] FIG. 7 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.

[0042] 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.

[0043] 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.

[0044] 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.

[0045] 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).

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] Hereinafter, embodiments of the present document will be described in detail with reference to FIGS. 1 to 7.

[0051] 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.

[0052] 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).

[0053] 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).

[0054] 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.

[0055] 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).

[0056] 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.

[0057] 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).

[0058] 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).

[0059] 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.

[0060] 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.

[0061] 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).

[0062] 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.

[0063] 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.

[0064] FIG. 3 illustrates an example of a data area in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0065] Referring to FIGS. 2 and 3, the battery diagnostic device (201) may include a memory (203) and at least one processor (205). The memory (203) may store at least one instruction. The at least one processor (205) may execute at least one instruction. The first range may be one of a plurality of first ranges that divide a first state variable into a first size (301). The second range may be one of a plurality of second ranges that divide a second state variable into a second size (303). The third range may be one of a plurality of third ranges that divide a third state variable into a third size (305). The first data area, the battery data area (307), the second data area (309), the third data area (311), the fourth data area (313), the fifth data area (315), the sixth data area (317), the seventh data area (319), and the eighth data area (321), may represent multiple data areas that include at least one of a plurality of first ranges, a plurality of second ranges, a plurality of third ranges, or any combination thereof. The battery data area (307) may represent an area among the multiple data areas that includes a set of data.

[0066] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) may acquire data measured from a battery cell while the battery cell is in use. The data may include values ​​of state variables (e.g., voltage, current, temperature) of the battery cell over time. The data may be acquired from an on-board diagnostics (OBD), but the embodiments of the present document may not be limited thereto.

[0067] According to one embodiment, at least one processor (205) of the battery diagnosis device (201) may obtain the remaining useful life (RUL) of a battery cell from the learning model by inputting data into the learning model. At this time, in order to improve the accuracy of the predicted remaining useful life, at least one processor (205) of the battery diagnosis device (201) may process the data and input it into the learning data.

[0068] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify the data as a set of data by synthesizing the data. In other words, at least one processor (205) of the battery diagnostic device (201) can identify at least one of data about the value of a first state variable (e.g., temperature) of a battery cell over time, data about the value of a second state variable (e.g., voltage) of a battery cell over time, data about the value of a third state variable (e.g., current) of a battery cell over time, or any combination thereof, as a set of data.

[0069] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify a plurality of data areas (e.g., a battery data area (307) as a first data area, a second data area (309), a third data area (311), a fourth data area (313), a fifth data area (315), a sixth data area (317), a seventh data area (319), and an eighth data area (321)) that include at least one of a plurality of first ranges that divide a first state variable of a battery cell into a first size (301), a plurality of second ranges that divide a second state variable of a battery cell into a second size (303), a plurality of third ranges that divide a third state variable of a battery cell into a third size (305), or any combination thereof.

[0070] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify battery data areas (e.g., battery data areas (307)) that include a set of data among a plurality of data areas.

[0071] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify a maintenance time corresponding to the battery data area based on a time at which a first state variable included in a set of data is included in a first range corresponding to the battery data area, a second state variable included in the set of data is included in a second range corresponding to the battery data area, and a third state variable included in the set of data is included in a third range corresponding to the battery data area.

[0072] According to one embodiment, at least one processor (205) of the battery diagnosis device (201) can identify that data is included in the specific data region when a specific value of a first state variable of data acquired from the battery is included in a specific range of the first state variable indicated by a specific data region, a specific value of a second state variable of data acquired from the battery is included in a specific range of the second state variable indicated by a specific data region, and a specific value of a third state variable of data acquired from the battery is included in a specific range of the third state variable indicated by a specific data region. At least one processor (205) of the battery diagnosis device (201) can accumulate and calculate the time during which data acquired from the battery is included in the specific data region. At least one processor (205) of the battery diagnosis device (201) can obtain a retention time, which is a total time during which data is included in the specific data region, by adding up the time from the moment when data acquired from the battery is included in the specific data region to the moment when it leaves it.

[0073] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can diagnose the state of a battery cell (e.g., remaining life of a battery cell) by inputting battery data areas (e.g., battery data area (307)) and maintenance times corresponding to each of the battery areas into a learning model.

[0074] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) may generate a histogram based on battery data areas and retention times corresponding to each of the battery areas, and input the generated histogram into a learning model.

[0075] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can adjust the holding times so that the sum of the holding times becomes a specified value (e.g., about 1) and the ratio between the holding times is maintained.

[0076] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) may adjust the retention time (e.g., about 0.375) based on a ratio of a retention time (e.g., about 3 hours) corresponding to a specific battery data area and 24 hours (e.g., about 0.375) to normalize each of the retention times.

[0077] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) may generate a histogram by representing battery data areas as areas included in a three-dimensional grid and representing the adjusted retention time of each battery data area as a color intensity.

[0078] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can diagnose the state of a battery cell (e.g., remaining useful life (RUL) of the battery cell) based on inputting a histogram into a learning model.

[0079] This is because the degree of battery cell degradation is determined by the operating pattern of the battery cells that power the electronic device.

[0080] The learning model can identify the operating patterns of battery cells through histograms. The operating patterns of the battery cells can be determined based on the operating patterns of the electronic device including the battery cells. For example, when the driver of the electronic device drives the device in a manner that consumes high power, the discharge current of the battery cells may be higher than when the driver drives the device in a manner that consumes low power. If a specific driver primarily drives, at least one processor (205) of the battery diagnostic device (201) can predict the degree of battery cell degradation based on the operating patterns of the battery cells according to the driver.

[0081] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify an operating pattern of an electronic device including a battery cell through a histogram.

[0082] However, the embodiments of this document may not be limited thereto. For example, at least one processor (205) of the battery diagnostic device (201) may input data according to battery data areas and retention times corresponding to each of the battery areas into the learning model without generating a histogram.

[0083] FIG. 4 illustrates an example of a set of data of a time when a battery was not operating in a battery diagnostic device and a battery diagnostic method according to an embodiment of the present document.

[0084] Referring to FIG. 4, a first session (400) may represent a first period on a specific date during which a battery including a battery cell did not operate. A second session (401) may represent a first period during which a battery including a battery cell operated on a specific date. A third session (402) may represent a second period during which a battery including a battery cell did not operate on a specific date. A fourth session (403) may represent a second period during which a battery including a battery cell operated on a specific date. A fifth session (404) may represent a third period during which a battery including a battery cell did not operate on a specific date. A sixth session (405) may represent a third period during which a battery including a battery cell operated on a specific date. A seventh session (406) may represent a last period during which a battery including a battery cell did not operate on a specific date.

[0085] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify a set of data of a time when the battery including the battery cell has not been operated for a first time (e.g., about 300 seconds) or more, based on at least one of a first state variable acquired within a second time (e.g., about 0.5 seconds) after the battery starts to operate, a second state variable acquired within the second time after the battery starts to operate, a third state variable acquired within the second time after the battery starts to operate, or any combination thereof.

[0086] For example, at least one processor (205) of the battery diagnostic device (201) can identify a set of data in the first period (407), the second period (409), and the third period (411) based on a battery including a battery cell not operating for a first hour or more in the first period (407), the second period (409), and the third period (411) included in the first session (400), based on at least one of a first state variable acquired within a second hour after the battery starts operating, a second state variable acquired within a second hour after the battery starts operating, a third state variable acquired within a second hour after the battery starts operating, or any combination thereof in the fourth period (413) included in the second session (403).

[0087] For example, at least one processor (205) of the battery diagnostic device (201) can identify a set of data in the fifth period (415), the sixth period (417) based on a battery including a battery cell not operating for more than a first hour in the fifth period (415), the sixth period (417) included in the third session (402), based on at least one of a first state variable acquired within a second hour after the battery starts operating, a second state variable acquired within a second hour after the battery starts operating, a third state variable acquired within a second hour after the battery starts operating, or any combination thereof in the seventh period (419) included in the fourth session (403).

[0088] For example, at least one processor (205) of the battery diagnostic device (201) can identify a set of data in the intervals included in the fifth session (404) based on at least one of a first state variable acquired within a second hour after the battery starts to operate, a second state variable acquired within a second hour after the battery starts to operate, a third state variable acquired within a second hour after the battery starts to operate, or any combination thereof, in the intervals included in the sixth session (405) based on a battery including a battery cell not operating for a first hour or more in the interval included in the fifth session (404).

[0089] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify a set of data of a time when the battery including the battery cell has not been operated for a first time or more, based on at least one of a first state variable acquired within a second time immediately before the battery stops operating, a second state variable acquired within a second time immediately before the battery stops operating, a third state variable acquired within a second time immediately before the battery stops operating, or any combination thereof.

[0090] For example, at least one processor (205) of the battery diagnostic device (201) may identify a set of data in the intervals included in the seventh session (406) based on at least one of a first state variable acquired within a second hour immediately before the battery terminates operation in the sixth session (405), a second state variable acquired within a second hour immediately before the battery terminates operation in the sixth session (405), a third state variable acquired within a second hour immediately before the battery terminates operation in the sixth session (405), or any combination thereof, based on the battery not having operated for a first hour or more in the seventh session (406).

[0091] In one embodiment, since a battery can degrade even while the battery cell is not in operation, at least one processor (205) of the battery diagnostic device (201) can refer to a set of data before and after the battery starts operating to calculate a retention time corresponding to the battery data area while the battery cell is not in operation.

[0092] FIG. 5 illustrates an example of temperature changes used to correct a set of data in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0093] Referring to FIG. 5, table (501) may represent monthly temperature changes used to calibrate a set of data.

[0094] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can obtain a set of data for a specified period of time by correcting the set of data (e.g., a set of data identified for about 30 days) based on temperature changes over a specified period of time (e.g., about 1 year) longer than the period of time (e.g., about 30 days) for which the set of data was identified.

[0095] For example, at least one processor (205) of the battery diagnostic device (201) can obtain a set of data in a specific period (e.g., July). For example, if temperature is included among the state variables included in the set of data, at least one processor (205) of the battery diagnostic device (201) can move the temperature included in the set of data with reference to the table (501), thereby obtaining a set of data in another period (e.g., January, February, March, April, May, June, August, September, October, November, December) within the specified period.

[0096] For example, at least one processor (205) of the battery diagnostic device (201) moves the temperature included in the set of data by referring to table (501) if the temperature is included among the state variables included in the set of data (e.g., about -2.56 By moving (as much as) we can obtain data sets from other intervals (e.g. June).

[0097] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can obtain the state of the battery cell (e.g., the predicted remaining life of the battery cell) from the learning model by inputting input data according to a set of data for a specified period (e.g., about one year) into the learning model.

[0098] In one embodiment, the accuracy of a diagnosis performed by inputting a set of data for a specified period (e.g., approximately one year) into a learning model may be higher than the accuracy of a diagnosis performed by inputting a set of data into a learning model. This is because the set of data for a specified period includes information about temperature changes due to seasonal changes.

[0099] According to one embodiment, at least one processor (205) of the battery diagnostic device (201) can identify a set of data for a determined period of time.

[0100] According to one embodiment, if a data set is identified only for a period shorter than a certain period, the accuracy of the diagnosis may decrease, and if a data set is identified for a period longer than a certain period, the driver's driving habits of the electronic device may change. The period for identifying the data set may be determined to be a period that allows the battery cell's operating pattern to be representative without significantly changing. The period for identifying the data set may be determined using the Kullback-Leibler (KL) divergence method, which measures the difference between two probability distributions, or the Jensen-Shannon Divergence (JSD) method, which measures the similarity between two probability distributions.

[0101] FIG. 6 illustrates an example of a flow of operations of a battery diagnostic device that diagnoses the state of a battery cell based on battery data areas and retention times in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0102] Referring to FIG. 6, in a first operation (601), at least one processor (205) of a battery diagnostic device (201) according to one embodiment can identify a set of data measured from a battery cell while the battery cell is in use, and times at which the set of data is acquired.

[0103] In a second operation (603), at least one processor (205) of a battery diagnostic device (201) according to an embodiment may identify a plurality of data areas including at least one of a plurality of first ranges that divide a first state variable of a battery cell into a first size, a plurality of second ranges that divide a second state variable of a battery cell into a second size, a plurality of third ranges that divide a third state variable of a battery cell into a third size, or any combination thereof.

[0104] In a third operation (605), at least one processor (205) of a battery diagnostic device (201) according to an embodiment can diagnose the state of a battery cell based on battery data areas including a set of data among a plurality of data areas, and maintenance times corresponding to each of the battery data areas among times.

[0105] FIG. 7 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.

[0106] Referring to FIG. 7, a computing system (700) according to an embodiment disclosed in this document may include an MCU (710), a memory (720), an input / output I / F (730), and a communication I / F (740).

[0107] The MCU (710) may be at least one processor that executes various programs stored in the memory (720) (e.g., a battery cell data collection program, a graph generation program, a data analysis program, a data decomposition algorithm, a normalization program, a battery cell diagnosis program, etc.), processes various information including battery cell characteristic data and latent variables through these programs, and performs the functions of the battery diagnosis device (201) shown in the above-described FIGS. 2 to 5.

[0108] The memory (720) 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.

[0109] Such memories (720) may be provided in multiples as needed. The memories (720) may be volatile memories or non-volatile memories. As volatile memories (720), RAM, DRAM, SRAM, etc. may be used. As non-volatile memories (720), ROM, PROM, EAROM, EPROM, EEPROM, flash memories, etc. may be used. The examples of the memories (720) listed above are merely examples and are not limited to these examples.

[0110] The input / output I / F (730) 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 (710).

[0111] The communication I / F (740) 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 (740).

[0112] In this way, a computer program according to an embodiment disclosed in this document may be implemented as a module that performs each function illustrated in FIG. 2, for example, by being recorded in a memory (720) and processed by an MCU (710).

[0113] 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.

[0114] 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.

[0115] 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 at least one processor executing at least one instruction; At least one processor, Identifying a set of data measured from a battery cell while the battery cell is in use, and the times at which the set of data is acquired; Identifying a plurality of data areas including at least one of a plurality of first ranges dividing a first state variable of the battery cell into a first size, a plurality of second ranges dividing a second state variable of the battery cell into a second size, a plurality of third ranges dividing a third state variable of the battery cell into a third size, or any combination thereof; It is configured to diagnose the state of the battery cell based on battery data areas including the set of data among the plurality of data areas, and maintenance times corresponding to each of the battery data areas among the times, The above data is, At least one of the values ​​of the first state variable, the second state variable, the third state variable, or any combination thereof, Battery diagnostic device.

2. In claim 1, At least one processor, Identifying a specific data region among the battery data regions based on at least one of a first specific range including a specific value of the first state variable among the plurality of first ranges, a second specific range including a specific value of the second state variable among the plurality of second ranges, a third specific range including a specific value of the third state variable among the plurality of third ranges, or any combination thereof; Identifying a specific time corresponding to the specific data area based on a time during which the first state variable is included in the first specific range, the second state variable is included in the second specific range, and the third state variable is included in the third specific range among the above maintenance times, Based on the specific data area and the specific time, the holding times are configured to correspond to each of the battery data areas. Battery diagnostic device.

3. In claim 1, At least one processor, When the battery including the battery cell has not been operated for a first hour or more, the battery is configured to identify the set of data of the time when the battery has not been operated based on at least one of the first state variable acquired within a second hour after the battery starts to operate, the second state variable acquired within a second hour after the battery starts to operate, the third state variable acquired within a second hour after the battery starts to operate, or any combination thereof. Battery diagnostic device.

4. In claim 1, At least one processor, The sum of the above holding times becomes a specified value, and the holding times are adjusted so that the ratio between the holding times is maintained. Generating a histogram based on at least one of the plurality of first ranges, the plurality of second ranges, the plurality of third ranges, or any combination thereof and the adjusted holding times, Based on the above histogram, configured to diagnose the state of the battery cell, Battery diagnostic device.

5. In claim 1, At least one processor, configured to identify at least one of the first state variable, the second state variable, the third state variable, or any combination thereof based on at least one of the temperature, voltage, current, or a combination thereof of the battery cell; Battery diagnostic device.

6. In claim 1, At least one processor, By inputting the input data according to the above battery data areas and the above maintenance times into the learning model, the predicted remaining useful life (RUL) of the battery cell is obtained from the learning model. Battery diagnostic device.

7. In claim 1, At least one processor, configured to obtain a set of data for the specified period of time by correcting the set of data based on temperature changes over a specified period of time longer than the period of time for which the set of data was identified; Battery diagnostic device.

8. In claim 4, At least one processor, By inputting input data according to the above histogram into a learning model, the predicted remaining useful life (RUL) of the battery cell is obtained from the learning model. Battery diagnostic device.

9. An operation for identifying a set of data measured from a battery cell while the battery cell is in use, and the times at which the set of data is acquired; An operation of identifying a plurality of data areas including at least one of a plurality of first ranges that divide a first state variable of the battery cell into a first size, a plurality of second ranges that divide a second state variable of the battery cell into a second size, a plurality of third ranges that divide a third state variable of the battery cell into a third size, or any combination thereof; and An operation of diagnosing a state of the battery cell based on battery data areas including the set of data among the plurality of data areas, and maintenance times corresponding to each of the battery data areas among the times, The above data is, At least one of the values ​​of the first state variable, the second state variable, the third state variable, or any combination thereof, How to diagnose a battery.

10. In claim 9, An operation of diagnosing the state of the battery cell based on the battery data areas including the set of data among the plurality of data areas, and the maintenance times corresponding to each of the battery data areas among the times, An operation of identifying a specific data region among the battery data regions based on at least one of a first specific range including a specific value of the first state variable among the plurality of first ranges, a second specific range including a specific value of the second state variable among the plurality of second ranges, a third specific range including a specific value of the third state variable among the plurality of third ranges, or any combination thereof; An operation of identifying a specific time corresponding to the specific data area based on a time during which the first state variable is included in the first specific range, the second state variable is included in the second specific range, and the third state variable is included in the third specific range among the above maintenance times; and An operation of corresponding the retention times to each of the battery data areas based on the specific data area and the specific time, How to diagnose a battery.

11. In claim 9, An operation of diagnosing the state of the battery cell based on the battery data areas including the set of data among the plurality of data areas, and the maintenance times corresponding to each of the battery data areas among the times, An operation of identifying a set of data of a time when the battery including the battery cell has not been operated for a first hour or more, based on at least one of the first state variable acquired within a second hour after the battery starts to operate, the second state variable acquired within a second hour after the battery starts to operate, the third state variable acquired within a second hour after the battery starts to operate, or any combination thereof. How to diagnose a battery.

12. In claim 9, An operation of diagnosing the state of the battery cell based on the battery data areas including the set of data among the plurality of data areas, and the maintenance times corresponding to each of the battery data areas among the times, An operation of adjusting the holding times so that the sum of the holding times becomes a specified value and the ratio between the holding times is maintained; An operation of generating a histogram based on at least one of the plurality of first ranges, the plurality of second ranges, the plurality of third ranges, or any combination thereof and the adjusted holding times; and Based on the histogram, including an operation of diagnosing the state of the battery cell, How to diagnose a battery.

13. In claim 9, An operation of identifying a plurality of data areas including at least one of a plurality of first ranges dividing a first state variable of the battery cell into a first size, a plurality of second ranges dividing a second state variable of the battery cell into a second size, a plurality of third ranges dividing a third state variable of the battery cell into a third size, or any combination thereof, An operation of identifying at least one of the first state variable, the second state variable, the third state variable, or any combination thereof, based on at least one of the temperature, voltage, current, or combinations thereof of the battery cell, How to diagnose a battery.

14. In claim 9, An operation of diagnosing the state of the battery cell based on the battery data areas including the set of data among the plurality of data areas, and the maintenance times corresponding to each of the battery data areas among the times, An operation of obtaining a predicted remaining useful life (RUL) of a battery cell from a learning model by inputting input data according to the battery data areas and the maintenance times into a learning model, How to diagnose a battery.

15. In claim 9, An operation of diagnosing the state of the battery cell based on the battery data areas including the set of data among the plurality of data areas, and the maintenance times corresponding to each of the battery data areas among the times, An operation of obtaining a set of data for a specified period of time by correcting the set of data based on temperature changes over a specified period of time longer than the period of time for which the set of data was identified, How to diagnose a battery.

16. In claim 12, Based on the above histogram, the operation of diagnosing the status of the battery cell is as follows: By inputting input data according to the histogram to the learning model, an operation of obtaining the predicted remaining useful life (RUL) of the battery cell from the learning model is included. How to diagnose a battery.

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