Battery management device, battery management method, and battery system

Abstract

According to some embodiments, a battery management device comprises: an interface configured to acquire battery data of a battery; and a controller configured to generate, on the basis of the battery data, a capacity profile indicating a change in the capacity of the battery according to an increase in the number of charging / discharging cycles of the battery, and estimate an anodic side reaction (ASR) of the battery on the basis of a plurality of discharge capacity values of the capacity profile.

Description

Battery management device, battery management method and battery system

[0001] Cross-citation with related applications

[0002] The present application claims the benefit of priority based on Korean Patent Application No. 10-2025-0164290 filed November 4, 2025, Korean Patent Application No. 10-2024-0173441 filed November 28, 2024, Korean Patent Application No. 10-2024-0173442 filed November 28, 2024, and Korean Patent Application No. 10-2024-0173443 filed November 28, 2024, and incorporates all contents disclosed in the documents of said patent applications as part of this specification.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a battery management device, a battery management method, and a battery system.

[0005] Recently, active research and development on secondary batteries has been underway. Here, secondary batteries are rechargeable batteries and can be interpreted to encompass conventional Ni / Cd and Ni / MH batteries, as well as the more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries can possess higher energy density compared to conventional Ni / Cd and Ni / MH batteries, and because they can be manufactured in a compact and lightweight form factor, they offer high utility as power sources for mobile devices. Recently, their scope of application has expanded to include power sources for electric vehicles, drawing attention as a next-generation energy storage medium.

[0006] When a battery undergoes repeated charging and discharging, capacity loss may occur at the positive and negative electrodes. Capacity loss can be caused by various factors, which may include side reactions resulting from electrolyte consumption on the positive or negative electrode surface, structural deformation of the positive or negative electrode material, formation of an SEI layer, and lithium precipitation. If the factors causing capacity loss can be isolated and quantified, it may be possible to diagnose or predict the condition of the battery using this information.

[0007] One of the objectives of the embodiments disclosed in this document is to provide a battery management device, a battery management method, and a battery system that isolate an anterior side reaction (ASR) among the factors of battery capacity loss and use this to diagnose or predict the state of the battery.

[0008] The technical objectives of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below.

[0009] According to some embodiments, a battery management device comprises: a battery configured to store instructions; and a processor configured to execute said instructions, wherein the processor is configured to acquire battery data of the battery, generate a capacity profile indicating a change in the capacity of the battery according to an increase in the number of charge / discharge cycles of the battery based on said battery data, and estimate an anodic side reaction (ASR) of the battery based on a plurality of discharge capacity values ​​of said capacity profile.

[0010] According to some embodiments, the processor is configured to estimate the total amount of negative electrode degradation of the battery based on the plurality of discharge capacity values, and to estimate the ASR based on the total amount of negative electrode degradation.

[0011] According to some embodiments, the total amount of cathode degradation includes the ASR and the amount of cathode material structure degradation of the battery, and the processor is configured to estimate the ASR by subtracting the amount of cathode material structure degradation from the total amount of cathode degradation.

[0012] According to some embodiments, the processor is configured to treat the amount of negative electrode structure degradation as zero in relation to the amount of positive electrode structure degradation of the battery based on the electrode design of the battery, and to estimate the total amount of negative electrode degradation as the ASR based on the amount of negative electrode structure degradation treated as zero.

[0013] According to some embodiments, the electrode design of the battery includes an anode-limiting design having a total cathode capacity greater than the total anode capacity.

[0014] According to some embodiments, the processor is configured to estimate an ASR profile representing a variation in the ASR with increasing charge-discharge cycles of the battery based on the capacity profile, and a plurality of ASR values ​​of the ASR profile correspond to the plurality of discharge capacity values.

[0015] According to some embodiments, the processor is further configured to predict the occurrence of a sudden drop in capacity of the battery based on the ASR.

[0016] According to some embodiments, the processor is configured to estimate an ASR profile representing the variation of the ASR with increasing charge-discharge cycles of the battery based on the battery data, and to predict the occurrence of the abrupt capacity loss based on the ASR profile.

[0017] According to some embodiments, the processor is configured to identify a trend change point based on a variation in the ASR slope according to the ASR profile, and to predict the occurrence of the abrupt capacity loss based on the trend change point.

[0018] According to some embodiments, the processor is configured to calculate a reference ASR slope during a reference period in the early part of the charge-discharge cycles of the battery based on the ASR profile, and to identify the first time point as the trend change time point if the difference between the ASR slope at the first time point and the reference ASR slope exceeds a threshold.

[0019] According to some embodiments, the processor is configured to predict a future point in time as the trend change point when the difference between the ASR slope at the current point in time and the reference ASR slope does not exceed a threshold.

[0020] According to some embodiments, the processor is configured to generate an ASR trend line based on the fluctuation pattern of ASR values ​​up to the current point in time and to predict a future point in time when the difference between the ASR slope along the ASR trend line and the reference ASR slope exceeds a threshold as the point in time when the trend changes.

[0021] According to some embodiments, the processor is configured to generate a capacity profile representing a variation in the capacity of the battery as the number of charge-discharge cycles of the battery increases based on the battery data, and to estimate an ASR profile representing a variation in the ASR as the number of charge-discharge cycles of the battery increases based on the capacity profile.

[0022] According to some embodiments, the processor is further configured to estimate the amount of gas generated by the battery based on the ASR.

[0023] According to some embodiments, the processor is configured to estimate an ASR profile representing the variation of the ASR with increasing charge-discharge cycles of the battery based on the battery data, and to estimate the amount of gas generated based on the ASR profile.

[0024] According to some embodiments, the processor is configured to estimate a gas generation profile representing the variation in the amount of gas generated as the number of charge-discharge cycles of the battery increases by applying a conversion factor to the ASR profile.

[0025] According to some embodiments, the processor is configured to estimate an expected ASR profile for a future point in time based on a current ASR profile up to the current number of charge / discharge cycles, and to estimate an expected gas generation amount for a future point in time based on the expected ASR profile.

[0026] According to some embodiments, the processor is configured to estimate an expected risk point by comparing a profile of the expected gas generation amount for a future point in time with a gas threshold amount.

[0027] According to some embodiments, the processor is configured to estimate the expected ASR profile using a machine learning-based artificial intelligence model trained to estimate ASR values ​​for a future point in time based on the current ASR profile and the state of health (SOH) of the battery.

[0028] According to some embodiments, the processor is configured to generate a capacity profile representing a variation in the capacity of the battery as the number of charge-discharge cycles of the battery increases based on the battery data, and to estimate an ASR profile representing a variation in the ASR as the number of charge-discharge cycles of the battery increases based on the capacity profile.

[0029] According to some embodiments, a battery management method comprises: acquiring battery data of a battery; generating a capacity profile representing a change in the capacity of the battery according to an increase in the number of charge-discharge cycles of the battery based on the battery data; and estimating an anterior cathodic reaction (ASR) of the battery based on a plurality of discharge capacity values ​​of the capacity profile.

[0030] According to some embodiments, the step of estimating the ASR comprises: a step of estimating the total amount of negative electrode degradation of the battery based on the plurality of discharge capacity values; and a step of estimating the ASR based on the total amount of negative electrode degradation.

[0031] According to some embodiments, the total amount of cathode degradation includes the ASR and the amount of cathode material structure degradation of the battery, and the step of estimating the ASR includes the step of estimating the ASR by subtracting the amount of cathode material structure degradation from the total amount of cathode degradation.

[0032] According to some embodiments, the step of estimating the ASR includes: a step of treating the amount of negative electrode structure degradation as zero in relation to the amount of positive electrode structure degradation of the battery based on the electrode design of the battery; and a step of estimating the total amount of negative electrode degradation as the ASR based on the amount of negative electrode structure degradation treated as zero.

[0033] According to some embodiments, the electrode design of the battery includes an anode-limiting design having a total cathode capacity greater than the total anode capacity.

[0034] According to some embodiments, the step of estimating the ASR includes the step of estimating an ASR profile that indicates the variation of the ASR according to an increase in the number of charge and discharge cycles of the battery based on the capacity profile, and a plurality of ASR values ​​of the ASR profile correspond to the plurality of discharge capacity values.

[0035] According to some embodiments, the battery management method further includes the step of predicting the occurrence of a sudden drop in capacity of the battery based on the ASR.

[0036] According to some embodiments, the step of predicting the occurrence of the sudden capacity loss comprises: a step of estimating an ASR profile representing the variation of the ASR according to an increase in the number of charge-discharge cycles of the battery based on the battery data; and a step of predicting the occurrence of the sudden capacity loss based on the ASR profile.

[0037] According to some embodiments, the step of predicting the occurrence of the sudden capacity loss comprises: a step of identifying a point of trend change based on a variation in the ASR slope according to the ASR profile; and a step of predicting the occurrence of the sudden capacity loss based on the point of trend change.

[0038] According to some embodiments, the step of identifying the trend change point comprises: calculating a reference ASR slope during a reference period in the early part of the charge / discharge cycles of the battery based on the ASR profile; and identifying the first time point as the trend change point when the difference between the ASR slope at the first time point and the reference ASR slope exceeds a threshold.

[0039] According to some embodiments, the step of identifying the trend change point includes predicting a future point in time as the trend change point when the difference between the ASR slope at the current point in time and the reference ASR slope does not exceed a threshold.

[0040] According to some embodiments, the step of predicting the trend change point includes: generating an ASR trend line based on the fluctuation pattern of ASR values ​​up to the current point in time; and predicting the future point in time when the difference between the ASR slope according to the ASR trend line and the reference ASR slope exceeds a threshold as the trend change point.

[0041] According to some embodiments, the step of predicting the occurrence of the rapid capacity loss comprises: generating a capacity profile representing a variation in the capacity of the battery according to an increase in the number of charge-discharge cycles of the battery based on the battery data; and estimating an ASR profile representing a variation in the ASR according to an increase in the number of charge-discharge cycles of the battery based on the capacity profile.

[0042] According to some embodiments, the battery management method further includes the step of estimating the amount of gas generated by the battery based on the ASR.

[0043] According to some embodiments, the step of estimating the gas generation amount includes: the step of estimating an ASR profile representing the variation of the ASR according to an increase in the number of charge-discharge cycles of the battery based on the battery data; and the step of estimating the gas generation amount based on the ASR profile.

[0044] According to some embodiments, the step of estimating the gas generation amount includes the step of estimating a gas generation amount profile that represents the variation in the gas generation amount according to an increase in the number of charge and discharge cycles of the battery by applying a conversion factor to the ASR profile.

[0045] According to some embodiments, the step of estimating the gas generation amount includes: a step of estimating an expected ASR profile for a future point in time based on a current ASR profile up to the current number of charge / discharge cycles; and a step of estimating an expected gas generation amount for a future point in time based on the expected ASR profile.

[0046] According to some embodiments, the step of estimating the expected ASR profile includes the step of estimating the expected risk time by comparing the profile of the expected gas generation amount for a future time point with the gas threshold amount.

[0047] According to some embodiments, the step of estimating the expected ASR profile includes estimating the expected ASR profile using a machine learning-based artificial intelligence model trained to estimate ASR values ​​for a future point in time based on the current ASR profile and the state of health (SOH) of the battery.

[0048] According to some embodiments, the step of estimating the expected ASR profile comprises: generating a capacity profile representing a variation in the capacity of the battery according to an increase in the number of charge-discharge cycles of the battery based on the battery data; and estimating an ASR profile representing a variation in the ASR according to an increase in the number of charge-discharge cycles of the battery based on the capacity profile.

[0049] According to some embodiments, the battery system comprises: a battery; a power device configured to perform at least one of charging and discharging of the battery; and the battery management device.

[0050] According to some embodiments, instructions of a computer program stored on a computer-readable medium cause the processor to perform the battery management method when executed by the processor.

[0051] According to the embodiments disclosed in this document, a battery management device, a battery management method, and a battery system can be provided that isolate an anterior side reaction (ASR) among the factors of battery capacity loss and use this to diagnose or predict the state of the battery.

[0052] The technical effects according to the embodiments disclosed in this document are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art in accordance with the disclosure of this document.

[0053] FIG. 1 illustrates elements constituting a battery system according to some embodiments.

[0054] FIG. 2 illustrates elements constituting a first battery management device according to some embodiments.

[0055] FIG. 3 illustrates a capacity profile showing the variation in capacity of a battery as the number of charge and discharge cycles of the battery increases according to some embodiments.

[0056] FIG. 4 illustrates a cathodic side reaction (ASR) profile generated based on a capacity profile according to some embodiments.

[0057] FIG. 5 illustrates steps constituting a first battery management method according to some embodiments.

[0058] FIG. 6 illustrates elements constituting a second battery management device according to some embodiments.

[0059] FIG. 7 illustrates the battery capacity retention rate that fluctuates when a sudden drop in capacity occurs in a battery according to some embodiments.

[0060] FIG. 8 illustrates a variable negative side reaction (ASR) of a battery in the event of a rapid capacity loss of the battery according to some embodiments.

[0061] FIG. 9 illustrates steps constituting a second battery management method according to some embodiments.

[0062] FIG. 10 illustrates elements constituting a third battery management device according to some embodiments.

[0063] FIG. 11 illustrates a method for estimating an expected ASR profile based on a current ASR profile according to some embodiments.

[0064] FIG. 12 illustrates a method for estimating the expected gas generation amount based on an expected ASR profile according to some embodiments.

[0065] FIG. 13 illustrates a method for estimating the expected risk point by comparing the profile of the expected gas generation amount according to some embodiments with the gas threshold amount.

[0066] FIG. 14 illustrates the relationship between the negative electrode side reaction (ASR) and the amount of gas generated of a battery according to some embodiments.

[0067] FIG. 15 illustrates steps constituting a third battery management method according to some embodiments.

[0068] Hereinafter, embodiments described in this document are described with reference to the accompanying drawings. However, this is not intended to limit the disclosure of this document to specific embodiments and should be understood to include various modifications, equivalents, and / or alternatives to the embodiments described in this document.

[0069] The embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments and should be understood to include various modifications, equivalents, or substitutions of said embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of said items unless the relevant context clearly indicates otherwise.

[0070] In this document, each of the phrases such as “A or B,” “at least one of A and B,” “at least one of A or B,” “A, B or C,” “at least one of A, B and C,” and “at least one of A, B, or C” may include any one of the items listed together in the corresponding phrase, or all possible combinations thereof. Terms such as “first,” “second,” “first,” “second,” “A,” “B,” “(a),” or “(b)” may be used simply to distinguish a component from another component and, unless specifically stated otherwise, do not limit the components in any other aspect (e.g., importance or order).

[0071] In this document, where it is stated that any (e.g., 1) component is "connected," "coupled," or "joined" to another (e.g., 2) component, with or without the terms "functionally" or "communicationly," or where it is stated that the component is "coupled" or "connected," it means that the component may be connected to the other component directly (e.g., by wire or wirelessly) or indirectly (e.g., through a 3) component.

[0072] Methods according to the various embodiments disclosed in this document may be provided as part of a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory, CD-ROM) or distributed online (e.g., download or upload) through an application store or directly between two driver devices. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0073] According to the embodiments disclosed in this document, each component (e.g., module or program) of the components described above may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to the embodiments disclosed in this document, one or more of the components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the components of the multiple components in the same or similar manner as those performed by the corresponding components among the multiple components prior to the integration. According to the embodiments disclosed in this document, 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.

[0074] FIG. 1 illustrates elements constituting a battery system according to some embodiments.

[0075] Referring to FIG. 1, the battery system (100) may include a power device (110), a battery (120), and a battery management device (130). However, it is not limited thereto, and some components may be omitted from the battery system (100) or other general-purpose components may be further included in the battery system (100).

[0076] A power device (110) may be configured to charge or discharge a battery (120). The power device (110) may include a power consuming device and / or a power supply device. The power device (110) may discharge the battery (120) while consuming power, or charge the battery (120) while generating power. According to an embodiment, the power consuming device may include a motor for a mobility device such as an electric vehicle (EV), a hybrid electric vehicle (HEV), or an electric bike. The mobility device may also operate as a power supply device for charging the battery (120). The mobility device may drive the motor using the power of the battery (120) or charge the battery (120) with power generated through regenerative braking. According to an embodiment, the power supply device may include a charge / discharger for charging or discharging the battery (120). The charge / discharger may apply a charging current, a charging voltage, a discharging current, and / or a discharging voltage to the battery (120) based on a given profile or cycle.

[0077] The battery (120) may include one or more battery packs. The battery pack of the battery (120) may include a plurality of battery modules, and each battery module may include a plurality of battery cells. According to an embodiment, the battery (120) may be mounted on various types of mobility devices.

[0078] The battery management device (130) can perform operations for diagnosing, managing, and / or controlling the battery (120). The battery management device (130) can acquire battery data of the battery (120), diagnose or manage the state of the battery (120) based thereon, and control the output or charging and discharging of the battery (120). According to an embodiment, the battery management device (130) may include a battery management system (BMS) configured with the battery (120) in an on-board manner, and / or an external device remotely positioned with the battery (120) in an off-board manner. The external device may include a charger at a battery charging station, a battery diagnostic device, a cloud computing server, etc.

[0079] The battery system (100) may further include a management server. The management server can manage the management results of the battery management device (130). The management server can exchange data with the battery management device (130) via wired or wireless communication. When a defect in the battery (120) is diagnosed or its lifespan is predicted, the results can be transmitted to the management server and recorded in a database. According to an embodiment, the battery management device (130) can perform diagnostic operations by running battery management software, and the management server can provide update information of the battery management software to the battery diagnostic device (130).

[0080] FIG. 2 illustrates elements constituting a first battery management device according to some embodiments.

[0081] Referring to FIG. 2, the first battery management device (130a) may include an interface (131a) and a controller (132a). However, it is not limited thereto, and some components may be omitted from the first battery management device (130a), or other general-purpose components may be further included in the first battery management device (130a). The first battery management device (130a) may be an example of the battery management device (130) of the battery system (100) shown in FIG. 1.

[0082] The interface (131a) may be configured to acquire battery data of the battery (120). According to an embodiment, the interface (131a) may include a communication unit configured to receive battery data and / or a sensor unit configured to measure battery data. According to an embodiment, if the first battery management device (130a) is implemented in an off-board form, the communication unit may receive battery data in a manner such as wired data communication or wireless data communication. Alternatively, if the first battery management device (130a) is implemented in an on-board form, the sensor unit may be configured to measure values ​​such as voltage, current, temperature, and resistance from the battery (120). According to an embodiment, the battery data may include charging data related to the charging of the battery (120) and / or discharge data related to the discharging of the battery. For example, the charging data and / or discharge data may include current data, voltage data, temperature data, capacity data, resistance data, and / or degradation data.

[0083] The controller (132a) may have a structure for executing instructions that implement the operations of the first battery management device (130a). The controller (132a) may be implemented as an array of logic gates or a general-purpose microprocessor for processing various operations, and may be composed of a single processor or multiple processors. For example, the controller (132) may be implemented in at least one form of a microprocessor, CPU, GPU, and AP.

[0084] The controller (132a) can operate with memory configured to store various data, instructions, mobile applications, computer programs, etc. The memory may be configured separately from or integrally with the controller (132a). The controller (132a) can process various operations by executing instructions stored in the memory. For example, the memory may be implemented as a non-volatile device such as ROM, PROM, EPROM, EEPROM, flash memory, PRAM, MRAM, RRAM, FRAM, etc., or as a volatile device such as DRAM, SRAM, SDRAM, PRAM, etc., and may be implemented in the form of an HDD, SSD, SD, Micro-SD, etc., or a combination thereof.

[0085] The controller (132a) may be configured to generate a capacity profile representing the variation in capacity of the battery (120) as the number of charge-discharge cycles of the battery (120) increases, based on battery data. The battery data may include charge-discharge data of the battery (120), and the capacity of the battery (120) may be calculated based on the charge-discharge current and charge-discharge time. A capacity profile may be generated by plotting the variation in capacity as the charge-discharge cycle progresses. The capacity profile may include a plurality of discharge capacity values ​​corresponding to a fully discharged state of the battery (120) and a plurality of charge capacity values ​​corresponding to a fully charged state of the battery (120).

[0086] The controller (132a) may be configured to estimate the anodic side reaction (ASR) of the battery (120) based on multiple discharge capacity values ​​of the capacity profile. The multiple discharge capacity values ​​may increase as the number of charge and discharge cycles of the battery (120) increases. Compared to the early lifespan of the battery (120), the increase in the multiple discharge capacity values ​​may correspond to the amount of degradation occurring at the negative electrode of the battery (120). The anodic side reaction (ASR) of the battery (120) may be estimated based on the amount of degradation occurring at the negative electrode. Once the ASR is estimated, the performance and / or condition of the battery (120) may be diagnosed using it. Once the performance / condition of the battery (120) is diagnosed, the first battery management device (130a) may perform various additional actions based on this. For example, circuit opening, grounding, output limiting, performance limiting, usage time limiting, etc. of the battery (120) may be performed, and warnings or notifications to the user of the battery (120) and / or power device (110) may be performed through the user's registered terminal device.

[0087] According to an embodiment, the controller (132a) may be configured to estimate the total amount of negative electrode degradation of the battery (120) based on a plurality of discharge capacity values, and to estimate the ASR based on the total amount of negative electrode degradation. The total amount of negative electrode degradation may include the amount of negative electrode side reaction (ASR) of the battery (120), and the ASR may be estimated based on the relationship with the remaining amount of degradation.

[0088] According to the embodiment, the total amount of cathode degradation may include the ASR and the amount of cathode material structure degradation of the battery (120), and the controller (132a) may be configured to estimate the ASR by subtracting the amount of cathode material structure degradation from the total amount of cathode degradation. The total amount of cathode degradation may include the ASR and the amount of cathode material structure degradation. The amount of cathode material structure degradation of the battery (120) may include the loss of active material (LAM-). The loss of active material (LAM-) may refer to the amount of reduced delivery of active lithium due to structural degradation occurring within the cathode material itself, which may occur due to the formation of a solid electrolyte interphase (SEI) layer, lithium precipitation, etc. Therefore, the ASR of the battery (120) may be estimated by subtracting the loss of active material (LAM-) from the total amount of cathode degradation.

[0089] According to an embodiment, the controller (132a) may be configured to treat the amount of negative electrode structure degradation as zero in relation to the amount of positive electrode structure degradation of the battery (120) based on the electrode design of the battery (120), and to estimate the total amount of negative electrode degradation as ASR based on the amount of negative electrode structure degradation treated as zero. Depending on the electrode design method of the battery (120), the amount of negative electrode structure degradation may be close to zero relative to the amount of positive electrode structure degradation. In this case, the negative electrode active material loss (LAM-) may be treated as zero, and the total amount of negative electrode degradation may be treated as equal to ASR. For example, the electrode design of the battery (120) may be determined based on the positive electrode capacity and the negative electrode capacity.

[0090] According to an embodiment, the electrode design of the battery (120) may include a positive limiting design having a total negative capacity greater than the total positive capacity. In the battery (120) according to the positive limiting design, the total negative capacity may be greater than the total positive capacity. For example, the N / P ratio, which represents the ratio of the total negative capacity to the total positive capacity, may be greater than 1. In this case, most of the electrode degradation of the battery (120) may occur at the positive electrode, and almost no electrode degradation may occur at the negative electrode. Therefore, in the positive limiting design, most of the electrode degradation of the battery (120) may correspond to the loss of positive active material (LAM+), and the loss of negative active material (LAM-) may be close to 0. For example, the N / P ratio of the positive limiting design may be about 1.1, or other appropriate values ​​may be used depending on the battery specifications.

[0091] According to an embodiment, the controller (120) may be configured to estimate an ASR profile that indicates the variation of ASR according to an increase in the number of charge / discharge cycles of the battery (120) based on a capacity profile, and a plurality of ASR values ​​of the ASR profile may correspond to a plurality of discharge capacity values. For example, when a charge / discharge cycle is applied to the battery (120), one charge capacity value and one discharge capacity value may be recorded for each cycle, and an ASR profile for n charge / discharge cycles may include n charge capacity values ​​and n discharge capacity values. Alternatively, when the battery (120) is used by a mobility device, etc., the capacity before charging and the capacity after charging may be recorded whenever charging of the battery (120) is performed, and an ASR profile for n charges may include n charge capacity values ​​and n discharge capacity values.

[0092] According to an embodiment, the controller (132a) may be further configured to predict at least one of the occurrence of sudden capacity loss of the battery (120) and the occurrence of by-product gas based on the ASR. Sudden capacity loss may refer to a phenomenon in which the total capacity of the battery (120) decreases rapidly, and the occurrence of by-product gas may cause swelling of the battery (120). Considering that sudden capacity loss and by-product gas are closely related to the ASR of the battery (120), the time at which sudden capacity loss is expected to start and / or the time at which the amount of by-product gas is expected to exceed a threshold may be estimated based on the ASR.

[0093] FIG. 3 illustrates a capacity profile showing the variation in capacity of a battery as the number of charge and discharge cycles of the battery increases according to some embodiments.

[0094] Referring to FIG. 3, a graph (300) illustrating a capacity profile showing the change in capacity of the battery (120) as the number of charge / discharge cycles of the battery (120) increases may be shown. In the graph (300), the horizontal axis may represent the number of charge / discharge cycles, and the vertical axis may represent the battery capacity.

[0095] At the bottom of the capacity profile of the graph (300), multiple discharge capacity values ​​may increase as the number of charge-discharge cycles increases. The amount of increase in the discharge capacity values ​​may correspond to the total amount of cathode degradation, and the total amount of cathode degradation may include ASR and LAM-. At the top of the capacity profile of the graph (300), multiple charge capacity values ​​may decrease as the number of charge-discharge cycles increases. The amount of decrease in the charge capacity values ​​may correspond to the total amount of anode degradation, and the total amount of anode degradation may include cathode side reaction (CSR) and LAM+.

[0096] In the total amount of negative electrode degradation, if the electrode design of the battery (120) corresponds to a positive limit design and the N / P ratio is greater than 1, most of the electrode degradation of the battery (120) may correspond to LAM+ and LAM- may be close to 0. Accordingly, the profile of the total amount of negative electrode degradation formed by multiple discharge capacity values ​​of the capacity profile can be treated as a profile of ASR.

[0097] FIG. 4 illustrates a cathodic side reaction (ASR) profile generated based on a capacity profile according to some embodiments.

[0098] Referring to FIG. 4, a graph (400) illustrating a cathodic side reaction (ASR) profile generated based on a capacity profile may be shown. In the graph (400), the horizontal axis may represent the number of charge / discharge cycles, and the vertical axis may represent the ASR.

[0099] The ASR profile of the graph (400) can be formed by discharge capacity values. Once the ASR profile is estimated, the performance and / or condition of the battery (120) can be diagnosed or predicted using it. For example, considering that rapid capacity loss and side reaction gases of the battery (120) are closely related to ASR, the risk of rapid capacity loss and the amount of side reaction gases generated at the current time or at the current number of charge / discharge cycles can be estimated. Alternatively, an ASR profile for a future time point can be estimated based on the ASR profile up to the current time point or the current number of charge / discharge cycles, and based on this, rapid capacity loss and side reaction gases for the future time point can be predicted.

[0100] FIG. 5 illustrates steps constituting a first battery management method according to some embodiments.

[0101] Referring to FIG. 5, the first battery management method (500) may include steps (510) through (530). However, it is not limited thereto, some steps may be omitted or other general steps may be added, and the steps of the first battery management method (500) may be executed in a different order than the illustrated order.

[0102] The first battery management method (500) may consist of steps processed sequentially in the first battery management device (130a). Therefore, even if the details are omitted below, the description of the first battery management device (130a) above may be equally applicable to the first battery management method (500).

[0103] Steps (510) to (530) of the first battery management method (500) can be performed by the interface (131a) and controller (132a) of the first battery management device (130a).

[0104] In step (510), the first battery management device (130a) can perform the step of acquiring battery data of the battery.

[0105] In step (520), the first battery management device (130a) may perform the step of generating a capacity profile that indicates the change in capacity of the battery as the number of charge and discharge cycles of the battery increases based on battery data.

[0106] In step (530), the first battery management device (130a) may perform the step of estimating the negative side reaction (ASR) of the battery based on a plurality of discharge capacity values ​​of the capacity profile.

[0107] According to an embodiment, the first battery management method (500) may be implemented in the form of a computer program stored on a computer-readable storage medium. That is, the computer program may include instructions for implementing the first battery management method (500), and the instructions of the program may be stored on a computer-readable storage medium. The computer program may include a mobile application.

[0108] According to an embodiment, a computer-readable storage medium may include magnetic media such as a hard disk, a floppy disk, and a magnetic tape, optical media such as a CD-ROM and a DVD, magneto-optical media such as a floptical disk, and a hardware device specifically configured to store and execute computer program instructions such as ROM, RAM, and flash memory. Computer program instructions may include machine code generated by a compiler and high-level language code that can be executed by a computer using an interpreter, etc.

[0109] FIG. 6 illustrates elements constituting a second battery management device according to some embodiments.

[0110] Referring to FIG. 6, the second battery management device (130b) may include an interface (131b) and a controller (132b). However, it is not limited thereto, and some components may be omitted from the second battery management device (130b), or other general-purpose components may be further included in the second battery management device (130b). The second battery management device (130b) may be an example of the battery management device (130) of the battery system (100) shown in FIG. 1.

[0111] The interface (131b) of the second battery management device (130b) may be substantially the same as the interface (131a) of the first battery management device (130a). The structure of the controller (132b) of the second battery management device (130b) may be substantially the same as the structure of the controller (132a) of the first battery management device (130a).

[0112] The controller (132b) may be configured to estimate the negative side reaction (ASR) of the battery (120) based on battery data. For example, the ASR profile of the battery (120) may be estimated based on a capacity profile that indicates a change in capacity with increasing charge-discharge cycles.

[0113] The controller (132b) may be configured to predict the occurrence of a sudden drop in capacity of the battery (120) based on the ASR. A sudden drop in capacity may refer to a phenomenon in which the battery capacity decreases rapidly due to the depletion of the electrolyte, and may be referred to as a sudden drop or capacity fading. Chemical side reactions that consume the electrolyte and active lithium may occur on the surface of the battery electrode, which may lead to a sudden drop in capacity of the battery (120). The ASR of the battery (120) may be used as a quantitative indicator to diagnose the sudden drop in capacity. When the occurrence of a sudden drop in capacity is diagnosed or predicted, the second battery management device (130b) may perform various additional actions based on this. For example, circuit opening, grounding, output limiting, performance limiting, usage time limiting, etc. of the battery (120) may be performed, and warnings or notifications to the user of the battery (120) and / or power device (110) may be performed through the user's registered terminal device.

[0114] According to an embodiment, the controller (132b) may be configured to estimate an ASR profile representing the variation of ASR as the number of charge / discharge cycles of the battery (120) increases based on battery data, and to predict the occurrence of rapid capacity loss based on the ASR profile. For example, the probability of rapid capacity loss of the battery (120) occurring may be predicted based on the ASR profile. Alternatively, the extent to which rapid capacity loss of the battery (120) progresses may be predicted based on the ASR profile. The ASR profile and rapid capacity loss may have a positive correlation.

[0115] According to an embodiment, the controller (132b) may be configured to identify a trend change point based on the variation of the ASR slope according to the ASR profile and to predict the occurrence of a rapid capacity loss based on the trend change point. The trend change point may be a point in time when the variation trend of the ASR of the battery (120) changes. For example, if the amount of variation or the rate of variation of the ASR slope during a reference period exceeds a threshold, it may be determined that a change in the variation trend has occurred. The reference period may be calculated based on the charging and discharging of the battery (120). The trend change point of the ASR slope may be treated as the point in time when the rapid capacity loss of the battery (120) begins.

[0116] According to an embodiment, the controller (132b) may be configured to calculate a reference ASR slope during a reference period in the early part of the charge / discharge cycles of the battery (120) based on an ASR profile, and to identify the first point in time as a trend change point if the difference between the ASR slope at the first point in time and the reference ASR slope exceeds a threshold. The reference period in the early part of the ASR profile may correspond to the period during which normal degradation of the battery (120) occurs, and this may be set differently depending on the specifications or operating environment of the battery (120). The reference ASR slope may be calculated based on the average rate of change during the reference period, and if the difference from the reference ASR slope exceeds a threshold, it may be determined that a trend change point has occurred. The slope difference may be calculated based on the amount and / or ratio of slope fluctuation.

[0117] According to an embodiment, the controller (132b) may be configured to predict a future point in time as the point of trend change if the difference between the current point in time ASR slope and the reference point ASR slope does not exceed a threshold. If the difference in slope from the reference point ASR slope at the current point in time exceeds a threshold, it may be determined that the point of trend change has already occurred and that a rapid capacity loss is in progress at the current point in time. On the other hand, if the difference in slope from the reference point ASR slope at the current point in time does not yet exceed a threshold, it may be determined that the point of trend change has not yet occurred and that a future point in time when the point of trend change is expected to occur may be estimated.

[0118] According to an embodiment, the controller (132b) may be configured to generate an ASR trend line based on the fluctuation pattern of ASR values ​​up to the current point in time and to predict a future point in time when the difference between the ASR slope according to the ASR trend line and the reference ASR slope exceeds a threshold as the point of trend change. For example, the ASR trend line may be generated by a model based on various machine learning techniques such as regression analysis. When a future point in time when the difference between the reference ASR slope and the predicted slope according to the trend line exceeds a threshold is identified, additional actions may be performed to prevent the occurrence of a sudden loss of capacity of the battery (120), such as limiting the output or performance of the battery (120) or notifying the user of the status of the battery (120).

[0119] According to an embodiment, the controller (132b) may be configured to generate a capacity profile representing a change in the capacity of the battery (120) as the number of charge / discharge cycles of the battery (120) increases based on battery data, and to estimate an ASR profile representing a change in the ASR as the number of charge / discharge cycles of the battery (120) increases based on the capacity profile. For a specific process for estimating the ASR of the battery (120), the details described above regarding the first battery management device (130a) may be referenced.

[0120] FIG. 7 illustrates the battery capacity retention rate that fluctuates when a sudden drop in capacity occurs in a battery according to some embodiments.

[0121] Referring to FIG. 7, a graph (700) illustrating a battery capacity retention rate (710) that fluctuates when a sudden drop in battery capacity occurs may be shown. In the graph (700), the horizontal axis may represent the number of charge / discharge cycles, and the vertical axis may represent the capacity retention rate (%).

[0122] In the graph (700), the capacity retention rate (710) may decrease to a normal degradation level during the initial stage of the charge / discharge cycle. During this period, a reference period (720) may be set, and a reference slope (730) may be calculated based on the reference period (720). A point in time (750) at which the difference (740) from the reference slope (730) exceeds a threshold may be identified, and from that point in time (750), a sudden drop in capacity loss may begin, in which the capacity retention rate (710) decreases rapidly.

[0123] The occurrence of rapid capacity loss can be predicted based on ASR. As ASR increases, the capacity retention rate (710) may decrease, and as ASR increases, the probability of occurrence and / or progression level of rapid capacity loss may increase. Using ASR, the point at which rapid capacity loss begins (750) can be identified, and based on this, measures regarding the occurrence of rapid capacity loss or actions to prevent rapid capacity loss may be taken.

[0124] FIG. 8 illustrates a variable negative side reaction (ASR) of a battery in the event of a rapid capacity loss of the battery according to some embodiments.

[0125] Referring to FIG. 8, a graph (800) illustrating the ASR (810) of a battery that fluctuates when a rapid loss of capacity occurs in the battery may be shown. In the graph (800), the horizontal axis may represent the number of charge / discharge cycles, and the vertical axis may represent the ASR.

[0126] Similar to the graph (700), a reference period may be set during the initial phase of normal battery degradation, and a reference ASR slope (820) may be calculated based on the reference period. When a trend change point (830) is identified where the difference in slope from the reference ASR slope (820) exceeds a threshold, a rapid capacity loss may be predicted based on this. According to the embodiment, the trend change point (830) may correspond to the point (750) at which the rapid capacity loss of the graph (700) begins.

[0127] If the point of trend change (830) is not identified up to the current point in time or the current number of cycles, an ASR trend line can be generated using the ASR (810) up to the current point in time or the current number of cycles. The ASR trend line can be estimated based on regression analysis, etc. A future point in time where the difference between the slope according to the ASR trend line and the reference ASR slope (820) exceeds a threshold can be identified, and based on this, an action to prevent the occurrence of sudden capacity loss can be performed.

[0128] FIG. 9 illustrates steps constituting a second battery management method according to some embodiments.

[0129] Referring to FIG. 9, the second battery management method (900) may include steps (910) through (930). However, it is not limited thereto, some steps may be omitted or other general steps may be added, and the steps of the second battery management method (900) may be executed in a different order than the illustrated order.

[0130] The second battery management method (900) may consist of steps processed sequentially in the second battery management device (130b). Therefore, even if the details are omitted below, the description of the second battery management device (130b) above may be equally applicable to the second battery management method (900).

[0131] Steps (910) to (930) of the second battery management method (900) can be performed by the interface (131b) and controller (132b) of the second battery management device (130b).

[0132] In step (910), the second battery management device (130b) can perform the step of acquiring battery data of the battery.

[0133] In step (920), the second battery management device (130b) can perform the step of estimating the negative side reaction (ASR) of the battery based on battery data.

[0134] In step (930), the second battery management device (130b) can perform the step of predicting the occurrence of a rapid capacity loss of the battery based on ASR.

[0135] According to an embodiment, the second battery management method (900) may be implemented in the form of a computer program stored on a computer-readable storage medium. That is, the computer program may include instructions for implementing the second battery management method (900), and the instructions of the program may be stored on a computer-readable storage medium. The computer program may include a mobile application.

[0136] According to an embodiment, a computer-readable storage medium may include magnetic media such as a hard disk, a floppy disk, and a magnetic tape, optical media such as a CD-ROM and a DVD, magneto-optical media such as a floptical disk, and a hardware device specifically configured to store and execute computer program instructions such as ROM, RAM, and flash memory. Computer program instructions may include machine code generated by a compiler and high-level language code that can be executed by a computer using an interpreter, etc.

[0137] FIG. 10 illustrates elements constituting a third battery management device according to some embodiments.

[0138] Referring to FIG. 10, the third battery management device (130c) may include an interface (131c) and a controller (132c). However, it is not limited thereto, and some components may be omitted from the third battery management device (130c), or other general-purpose components may be further included in the second battery management device (130c). The third battery management device (130c) may be an example of the battery management device (130) of the battery system (100) shown in FIG. 1.

[0139] The interface (131c) of the third battery management device (130c) may be substantially the same as the interface (131a) of the first battery management device (130a) and / or the interface (131b) of the second battery management device (130b). The structure of the controller (132c) of the third battery management device (130c) may be substantially the same as the structure of the controller (132a) of the first battery management device (130a) and / or the controller (132b) of the second battery management device (130b).

[0140] The controller (132c) may be configured to estimate the negative side reaction (ASR) of the battery (120) based on battery data. For example, the ASR profile of the battery (120) may be estimated based on a capacity profile that indicates a change in capacity with increasing charge-discharge cycles.

[0141] The controller (132c) may be configured to estimate the amount of gas generated by the battery (120) based on the ASR. When a chemical side reaction consuming the electrolyte and active lithium occurs on the surface of the battery electrode, a side reaction gas may be generated along with it. The ASR of the battery (120) may be used as a quantitative indicator to diagnose the generation of the side reaction gas. When the amount of gas generated is diagnosed or predicted, the third battery management device (130c) may perform various additional actions based on this. For example, the circuit of the battery (120), grounding, output limiting, performance limiting, usage time limiting, etc. may be performed, and warnings or notifications to the user of the battery (120) and / or power device (110) may be performed through the user's registered terminal device. While conventional methods used to predict gas generation involved visually observing battery swelling or installing pressure sensors inside the battery, quantitative non-destructive testing of gas generation amounts can be performed because side reaction gases can be estimated based on ASR.

[0142] According to an embodiment, the controller (132c) may be configured to estimate an ASR profile representing the variation of ASR according to the increase in the number of charge / discharge cycles of the battery (120) based on battery data, and to estimate the amount of gas generated based on the ASR profile. The ASR profile and the amount of gas generated by the battery (120) may have a positive correlation, and the amount of gas generated may be predicted based on the ASR profile.

[0143] According to an embodiment, the controller (132c) may be configured to estimate a gas generation amount profile that indicates the variation in the amount of gas generated as the number of charge-discharge cycles of the battery (120) increases by applying a conversion factor to the ASR profile. The amount of gas generated by the battery (120) and the ASR profile may have a positive correlation, and a conversion factor may be applied for conversion between the two. For example, the conversion factor may be a linear coefficient that has a constant value regardless of the number of charge-discharge cycles, or a non-linear coefficient that changes according to the number of charge-discharge cycles. The amount of gas generated at a desired point in time can be identified using the gas generation amount profile.

[0144] According to an embodiment, the controller (132c) may be configured to estimate an expected ASR profile for a future point in time based on the current ASR profile up to the current number of charge / discharge cycles, and to estimate an expected gas generation amount for a future point in time based on the expected ASR profile. The ASR for a future point in time may be estimated based on the fluctuation pattern of the ASR up to the present. An expected gas generation amount may be estimated by applying a conversion factor to the expected ASR profile.

[0145] According to an embodiment, the controller (132c) may be configured to estimate an expected risk point by comparing a profile of the expected gas generation amount for a future point in time with a gas threshold. The point in time when the expected gas generation amount exceeds the gas threshold may be estimated as the expected risk point. To prevent the expected gas generation amount from exceeding the gas threshold, the output and / or performance of the battery (120) may be limited, and a warning message regarding the expected risk point may be provided to the user.

[0146] According to an embodiment, the controller (132c) may be configured to estimate an expected ASR profile using a machine learning-based artificial intelligence model that is trained to estimate ASR values ​​for a future point in time based on the current ASR profile and the state of health (SOH) of the battery (120). For example, regression models or various trend analysis models may be trained to estimate ASR values ​​for a future point in time based on the current ASR profile. The state of health (SOH) of the battery (120) may be referenced to estimate the ASR for a future point in time. The higher the SOH, the lower the ASR may be, and the lower the SOH, the higher the ASR may be. In the process of estimating ASR values ​​for a future point in time through the artificial intelligence model, the trend of the SOH may be considered.

[0147] According to an embodiment, the controller (132c) may be configured to generate a capacity profile representing a change in the capacity of the battery (120) as the number of charge / discharge cycles of the battery (120) increases based on battery data, and to estimate an ASR profile representing a change in the ASR as the number of charge / discharge cycles of the battery (120) increases based on the capacity profile. For a specific process for estimating the ASR of the battery (120), the details described above regarding the first battery management device (130a) may be referenced.

[0148] FIG. 11 illustrates a method for estimating an expected ASR profile based on a current ASR profile according to some embodiments.

[0149] Referring to FIG. 11, a graph (1100) illustrating a method for estimating an expected ASR profile (1150) based on a current ASR profile (1110) may be shown. In the graph (1100), the horizontal axis may represent the number of charge / discharge cycles, and the vertical axis may represent ASR.

[0150] In the graph (1100), a current ASR profile (1110) can be estimated based on the ASR of the battery (120) for the actual measurement period (1130) prior to the current time point (1120). For the future prediction period (1140) after the current time point (1120), an expected ASR profile (1150) can be estimated. The expected ASR profile (1150) can be estimated using a machine learning-based artificial intelligence model. For example, the expected ASR profile (1150) can be estimated using a regression analysis-based trend line, and the SOH of the battery (120) can be considered for the estimation.

[0151] FIG. 12 illustrates a method for estimating the expected gas generation amount based on an expected ASR profile according to some embodiments.

[0152] Referring to FIG. 12, a graph (1200) illustrating a method for estimating the expected gas generation amount (1250) based on the expected ASR profile may be shown. In the graph (1200), the horizontal axis may represent the number of charge / discharge cycles, and the vertical axis may represent the amount of by-product gas generated.

[0153] In the graph (1200), a current gas generation profile (1210) can be estimated based on the current ASR profile (1110) for the actual measurement section (1230) prior to the current time point (1220). For the future prediction section (1240) after the current time point (1220), an expected gas generation profile (1250) can be estimated. The expected gas generation profile (1250) can be estimated based on the expected ASR profile (1150). For example, the expected gas generation profile (1250) can be estimated by applying a conversion factor to the expected ASR profile (1150), and the conversion factor may be a linear coefficient having a constant value regardless of the time point or the number of charge / discharge cycles, or a non-linear coefficient that changes according to the time point or the number of charge / discharge cycles. The value of the linear coefficient and / or the conversion formula of the non-linear coefficient may be determined based on the results of experiments and / or simulations regarding the gas generation amount.

[0154] FIG. 13 illustrates a method for estimating the expected risk point by comparing the profile of the expected gas generation amount according to some embodiments with the gas threshold amount.

[0155] Referring to FIG. 13, a graph (1300) illustrating a method of estimating an expected risk point (1340) by comparing a profile (1320) of the expected gas generation amount with a gas critical amount (1330) may be shown. In the graph (1300), the horizontal axis may represent the number of charge / discharge cycles, and the vertical axis may represent the amount of gas generated.

[0156] In the graph (1300), since the amount of gas generated does not exceed the gas threshold (1330) based on the current time point (1310), it may be necessary to predict the amount of gas generated for a future time point. Based on the expected ASR profile, etc., the profile (1320) of the expected amount of gas generated can be estimated, and based on the profile (1320), an expected risk time point (1340) at which the expected amount of gas generated is expected to exceed the gas threshold (1330) can be predicted. When the expected risk time point (1340) is predicted, the performance and / or output of the battery (120) may be limited based on this, and a warning message regarding the expected risk time point (1340) may be provided to the user.

[0157] FIG. 14 illustrates the relationship between the negative electrode side reaction (ASR) and the amount of gas generated of a battery according to some embodiments.

[0158] Referring to FIG. 14, a graph (1400) illustrating the relationship between the negative electrode side reaction (ASR) and the amount of gas generated of a battery (1400) may be shown. In the graph (1400), the horizontal axis may represent the ASR, and the vertical axis may represent the amount of gas generated.

[0159] It can be confirmed from the graph (1400) that the profiles of the ASR and the amount of gas generated are similar, and the coefficient of determination of the R2 for the ASR and the amount of gas generated may be 0.9954. These results can support the theory that when a negative side reaction (ASR) occurs in the battery (120), a side reaction gas is generated. Based on results such as the graph (1400), it may be possible to estimate the profile of the amount of gas generated by applying a conversion factor to the ASR profile.

[0160] FIG. 15 illustrates steps constituting a third battery management method according to some embodiments.

[0161] Referring to FIG. 15, the third battery management method (1500) may include steps (1510) through (1530). However, it is not limited thereto, some steps may be omitted or other general steps may be added, and the steps of the third battery management method (1500) may be executed in a different order than the illustrated order.

[0162] The third battery management method (1500) may consist of steps processed sequentially in the third battery management device (130c). Therefore, even if the details are omitted below, the description of the third battery management device (130c) above may be equally applicable to the third battery management method (1500).

[0163] Steps (1510) to (1530) of the third battery management method (1500) can be performed by the interface (131c) and controller (132c) of the third battery management device (130c).

[0164] In step (1510), the third battery management device (130c) can perform the step of acquiring battery data of the battery.

[0165] In step (1520), the third battery management device (130c) can perform the step of estimating the negative side reaction (ASR) of the battery based on battery data.

[0166] In step (1530), the third battery management device (130c) can perform the step of estimating the amount of gas generated by the battery based on the ASR.

[0167] According to an embodiment, the third battery management method (1500) may be implemented in the form of a computer program stored on a computer-readable storage medium. That is, the computer program may include instructions for implementing the third battery management method (1500), and the instructions of the program may be stored on a computer-readable storage medium. The computer program may include a mobile application.

[0168] According to an embodiment, a computer-readable storage medium may include magnetic media such as a hard disk, a floppy disk, and a magnetic tape, optical media such as a CD-ROM and a DVD, magneto-optical media such as a floptical disk, and a hardware device specifically configured to store and execute computer program instructions such as ROM, RAM, and flash memory. Computer program instructions may include machine code generated by a compiler and high-level language code that can be executed by a computer using an interpreter, etc.

[0169] Terms such as "include," "compose," or "have" as used above, unless specifically stated otherwise, mean that the relevant component may be inherent; therefore, they should be interpreted as allowing for the inclusion of additional components rather than excluding them. All terms, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as those defined in advance, should be interpreted in accordance with their meaning in the context of the relevant technology and, unless explicitly defined in this document, should not be interpreted in an ideal or overly formal sense.

[0170] The foregoing description is merely an illustrative explanation of the technical concept disclosed in this document, and a person skilled in the art to which the embodiments disclosed in this document pertain can make various modifications and variations within the scope of the essential characteristics of the embodiments disclosed in this document. Accordingly, the embodiments disclosed in this document are intended to explain, not limit, the technical concept of the embodiments disclosed in this document, and the scope of the technical concept disclosed in this document is not limited by these embodiments. The scope of protection of the technical concept disclosed in this document shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of this document.

Claims

1. A battery configured to store commands; and A battery management device comprising a processor configured to execute the above instructions, The above processor acquires battery data of the battery, and Based on the above battery data, a capacity profile is generated that indicates the change in capacity of the battery according to an increase in the number of charge and discharge cycles of the battery, and A battery management device configured to estimate the anodic side reaction (ASR) of the battery based on a plurality of discharge capacity values ​​of the above capacity profile.

2. In Paragraph 1, The processor estimates the total amount of negative electrode degradation of the battery based on the plurality of discharge capacity values, and A battery management device configured to estimate the ASR based on the total amount of cathodic degradation.

3. In Paragraph 2, The total amount of cathode degradation mentioned above includes the amount of degradation of the cathode material structure of the ASR and the battery, and A battery management device configured such that the processor estimates the ASR by subtracting the amount of cathode material structure degradation from the total amount of cathode degradation.

4. In Paragraph 3, The processor treats the amount of degradation of the negative electrode structure as zero in relation to the amount of degradation of the positive electrode structure of the battery based on the electrode design of the battery, and A battery management device configured to estimate the total amount of cathode degradation as the ASR based on the amount of cathode material structure degradation treated as zero.

5. In Paragraph 4, A battery management device in which the electrode design of the above battery includes a positive limiting design having a total negative electrode capacity greater than the total positive electrode capacity.

6. In Paragraph 1, The processor is configured to estimate an ASR profile representing the variation of the ASR according to an increase in the number of charge and discharge cycles of the battery based on the capacity profile, and A battery management device in which a plurality of ASR values ​​of the above ASR profile correspond to the plurality of discharge capacity values.

7. In Paragraph 1, A battery management device, wherein the processor is further configured to predict the occurrence of a sudden drop in capacity of the battery based on the ASR.

8. In Paragraph 7, The processor estimates an ASR profile representing the variation of the ASR according to an increase in the number of charge and discharge cycles of the battery based on the battery data, and A battery management device configured to predict the occurrence of the above-mentioned rapid capacity loss based on the above-mentioned ASR profile.

9. In Paragraph 8, The processor identifies a trend change point based on the variation of the ASR slope according to the ASR profile, and A battery management device configured to predict the occurrence of the above-mentioned rapid capacity loss based on the point of change in the above-mentioned trend.

10. In Paragraph 9, The processor calculates a reference ASR slope during a reference period in the early part of the charge-discharge cycles of the battery based on the ASR profile, and A battery management device configured to identify the first point in time as the trend change point when the difference between the ASR slope of the first point in time and the reference ASR slope exceeds a threshold.

11. In Paragraph 10, A battery management device configured such that the processor predicts a future point in time as the trend change point when the difference between the ASR slope at the current point in time and the reference ASR slope does not exceed a threshold.

12. In Paragraph 11, The processor generates an ASR trend line based on the fluctuation pattern of ASR values ​​up to the current point in time, and A battery management device configured to predict a future point in time when the difference between the ASR slope according to the above ASR trend line and the above reference ASR slope exceeds a threshold as the above trend change point.

13. In Paragraph 7, The processor generates a capacity profile representing a change in the capacity of the battery according to an increase in the number of charge and discharge cycles of the battery based on the battery data, and A battery management device configured to estimate an ASR profile representing the variation of the ASR according to an increase in the number of charge and discharge cycles of the battery based on the above capacity profile.

14. In Paragraph 1, The above processor is further configured to estimate the amount of gas generated by the battery based on the above ASR, in a battery management device.

15. In Paragraph 14, The processor estimates an ASR profile representing the variation of the ASR according to an increase in the number of charge and discharge cycles of the battery based on the battery data, and A battery management device configured to estimate the amount of gas generated based on the above ASR profile.

16. In Paragraph 15, A battery management device configured such that the processor applies a conversion factor to the ASR profile to estimate a gas generation amount profile representing the variation in the gas generation amount according to the increase in the number of charge and discharge cycles of the battery.

17. In Paragraph 15, The above processor estimates an expected ASR profile for a future point in time based on the current ASR profile up to the current number of charge / discharge cycles, and A battery management device configured to estimate the expected gas generation amount for a future point in time based on the above-mentioned expected ASR profile.

18. In Paragraph 17, A battery management device configured such that the processor estimates an expected risk point in time by comparing the profile of the expected gas generation amount for a future point in time with a gas threshold amount.

19. In Paragraph 17, A battery management device configured such that the processor estimates the expected ASR profile using a machine learning-based artificial intelligence model trained to estimate ASR values ​​for a future point in time based on the current ASR profile and the state of health (SOH) of the battery.

20. In Paragraph 14, The processor generates a capacity profile representing a change in the capacity of the battery according to an increase in the number of charge and discharge cycles of the battery based on the battery data, and A battery management device configured to estimate an ASR profile representing the variation of the ASR according to an increase in the number of charge and discharge cycles of the battery based on the above capacity profile.

21. Step of acquiring battery data of the battery; A step of generating a capacity profile indicating a change in the capacity of the battery according to an increase in the number of charge and discharge cycles of the battery based on the battery data; and A battery management method comprising the step of estimating the negative side reaction (ASR) of the battery based on a plurality of discharge capacity values ​​of the above capacity profile.

22. In Paragraph 21, The step of estimating the above ASR is, A step of estimating the total amount of negative electrode degradation of the battery based on the plurality of discharge capacity values ​​above; and A battery management method comprising the step of estimating the ASR based on the total amount of cathodic degradation.

23. In Paragraph 22, The total amount of cathode degradation mentioned above includes the amount of degradation of the cathode material structure of the ASR and the battery, and The step of estimating the above ASR is, A battery management method comprising the step of estimating the ASR by subtracting the amount of cathode material structure degradation from the total amount of cathode degradation.

24. In Paragraph 23, The step of estimating the above ASR is, A step of treating the amount of structural degradation of the negative electrode as zero in relation to the amount of structural degradation of the positive electrode of the battery based on the electrode design of the battery; and A battery management method comprising the step of estimating the total amount of cathode degradation as the ASR based on the amount of cathode material structure degradation treated as zero.

25. In Paragraph 24, A battery management method in which the electrode design of the above battery includes a positive limiting design having a total negative electrode capacity greater than the total positive electrode capacity.

26. In Paragraph 21, The step of estimating the above ASR is, The method includes the step of estimating an ASR profile that indicates the variation of the ASR according to an increase in the number of charge and discharge cycles of the battery based on the above capacity profile, A battery management method in which a plurality of ASR values ​​of the above ASR profile correspond to the plurality of discharge capacity values.

27. In Paragraph 21, A battery management method further comprising the step of predicting the occurrence of a sudden drop in capacity of the battery based on the above ASR.

28. In Paragraph 27, The step of predicting the occurrence of the above-mentioned sudden capacity loss is, A step of estimating an ASR profile representing the variation of the ASR according to an increase in the number of charge and discharge cycles of the battery based on the battery data; and A battery management method comprising the step of predicting the occurrence of the rapid capacity loss based on the above ASR profile.

29. In Paragraph 28, The step of predicting the occurrence of the above-mentioned sudden capacity loss is, A step of identifying a trend change point based on the variation of the ASR slope according to the above ASR profile; and A battery management method comprising the step of predicting the occurrence of the above-mentioned rapid capacity loss based on the above-mentioned trend change point.

30. In Paragraph 29, The step of identifying the point of change in the above trend is, A step of calculating a reference ASR slope during a reference period in the early part of the charge / discharge cycles of the battery based on the above ASR profile; and A battery management method comprising the step of identifying the first time point as the trend change time point when the difference between the ASR slope at the first time point and the reference ASR slope exceeds a threshold.

31. In Paragraph 30, The step of identifying the point of change in the above trend is, A battery management method comprising the step of predicting a future point in time as the trend change point when the difference between the current point in time ASR slope and the reference ASR slope does not exceed a threshold.

32. In Paragraph 31, The step of predicting the point of change in the above trend is, A step of generating an ASR trend line based on the fluctuation pattern of ASR values ​​up to the current point in time; and A battery management method comprising the step of predicting a future point in time when the difference between the ASR slope according to the above ASR trend line and the above reference ASR slope exceeds a threshold as the above trend change point.

33. In Paragraph 27, The step of predicting the occurrence of the above-mentioned sudden capacity loss is, A step of generating a capacity profile indicating a change in the capacity of the battery according to an increase in the number of charge and discharge cycles of the battery based on the battery data; and A battery management method comprising the step of estimating an ASR profile representing the variation of the ASR according to an increase in the number of charge and discharge cycles of the battery based on the above capacity profile.

34. In Paragraph 21, A battery management method further comprising the step of estimating the amount of gas generated by the battery based on the above ASR.

35. In Paragraph 34, The step of estimating the above gas generation amount is, A step of estimating an ASR profile representing the variation of the ASR according to an increase in the number of charge and discharge cycles of the battery based on the battery data; and A battery management method comprising the step of estimating the amount of gas generated based on the above ASR profile.

36. In Paragraph 35, The step of estimating the above gas generation amount is, A battery management method comprising the step of estimating a gas generation amount profile representing the variation in the gas generation amount according to an increase in the number of charge and discharge cycles of the battery by applying a conversion factor to the above ASR profile.

37. In Paragraph 35, The step of estimating the above gas generation amount is, A step of estimating an expected ASR profile for a future point in time based on a current ASR profile up to the current number of charge / discharge cycles; and A battery management method comprising the step of estimating the expected gas generation amount for a future point in time based on the above-mentioned expected ASR profile.

38. In Paragraph 37, The step of estimating the above-mentioned expected ASR profile is, A battery management method comprising the step of estimating an expected risk point by comparing the profile of the expected gas generation amount for a future point in time with a gas threshold amount.

39. In Paragraph 37, The step of estimating the above-mentioned expected ASR profile is, A battery management method comprising the step of estimating the expected ASR profile using a machine learning-based artificial intelligence model trained to estimate ASR values ​​for a future point in time based on the current ASR profile and the state of health (SOH) of the battery.

40. In Paragraph 34, The step of estimating the above-mentioned expected ASR profile is, A step of generating a capacity profile indicating a change in the capacity of the battery according to an increase in the number of charge and discharge cycles of the battery based on the battery data; and A battery management method comprising the step of estimating an ASR profile representing the variation of the ASR according to an increase in the number of charge and discharge cycles of the battery based on the above capacity profile.

41. Battery; A power device configured to perform at least one of charging and discharging of the above battery; and A battery system comprising a battery management device according to any one of claims 1 to 20.

42. A computer program stored on a computer-readable medium, wherein the instructions of the computer program cause the processor to perform the battery management method of any one of claims 21 to 40 when executed by the processor.

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