Device for managing battery and method for managing battery

Abstract

According to some embodiments, a device for managing a battery comprises: an interface configured to obtain battery data of a battery; and a controller configured to generate environmental factor data and state factor data of the battery on the basis of the battery data, generate a limit charge capacity of the battery corresponding to the environmental factor data and the state factor data on the basis of a limit capacity estimation model, and control charging of the battery on the basis of the limit charge capacity.

Description

Battery management device and battery management method

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority based on Korean Patent Application No. 10-2024-0148698 filed on October 28, 2024, and includes all contents disclosed in the document of said patent application as part of this specification.

[0003] Technology field

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

[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 lithium-based batteries are charged with high currents beyond a specific State of Charge (SOC), lithium may precipitate inside the battery in a metallic form. This precipitated lithium can cause adverse reactions with the electrolyte and other materials, and if this phenomenon becomes severe, it may lead to a risk of internal short circuits. Therefore, technology is required to determine the optimal charging current within a range that does not induce lithium precipitation.

[0007] One of the objectives of the embodiments disclosed in this document is to provide a battery management device and a battery management method capable of calculating a limit charge capacity for charging a battery without lithium precipitation using a machine learning-based model.

[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, the battery management device includes: an interface configured to acquire battery data of a battery; and a controller configured to generate environmental factor data and state factor data of the battery based on the battery data, generate a limit charging capacity of the battery corresponding to the environmental factor data and the state factor data based on a limit capacity estimation model, and control the charging of the battery based on the limit charging capacity.

[0010] According to some embodiments, the environmental factor data includes charge / discharge current data, temperature data, and voltage data, and the state factor data includes capacitance data, resistance data, and degradation data.

[0011] According to some embodiments, the limit capacity estimation model is trained to define the relationship between input experimental data regarding the environmental factor data and the state factor data and output experimental data regarding the limit charge capacity.

[0012] According to some embodiments, the controller is configured to generate a mapping table of the limit charging capacity corresponding to a combination of the environment factor data and the state factor data based on the learned limit capacity estimation model, and to generate the limit charging capacity based on the mapping table.

[0013] According to some embodiments, the output experimental data regarding the limit charge capacity is prepared based on the resistance fluctuation of the battery while a plurality of charge-discharge cycles are applied.

[0014] According to some embodiments, the input experimental data is prepared based on at least one of slow charge / discharge, rapid charge / discharge, high-temperature storage, and internal resistance measurement for sample batteries.

[0015] According to some embodiments, the resistance data according to the internal resistance measurement includes a hysteresis resistance measured based on a charge profile and a discharge profile representing a variation in high-rate charge / discharge voltage according to the battery capacity of the sample batteries.

[0016] According to some embodiments, the hysteresis resistance is calculated by accumulating the difference between the high-rate charging voltage according to the charging profile and the high-rate discharging voltage according to the discharging profile over a target range, and the target range is adjusted based on the components of the sample batteries and the degradation data for the sample batteries.

[0017] According to some embodiments, the limit charging capacity includes a maximum value of charging capacity that prevents lithium from precipitating in the battery, and the controller is configured to diagnose the state of the battery as a lithium precipitation state when the charging capacity of the battery exceeds the limit charging capacity.

[0018] According to some embodiments, the negative electrode of the battery cell of the battery comprises a negative electrode active material, and the negative electrode active material does not comprise a silicon-based active material.

[0019] According to some embodiments, a battery management method comprises: acquiring battery data of a battery; generating environmental factor data and state factor data of the battery based on the battery data; generating a limit charging capacity of the battery corresponding to the environmental factor data and the state factor data based on a limit capacity estimation model; and controlling the charging of the battery based on the limit charging capacity.

[0020] According to the embodiments disclosed in this document, a battery management device and a battery management method capable of calculating a limit charge capacity for charging a battery without lithium precipitation using a machine learning-based model may be provided.

[0021] 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.

[0022] FIG. 1 illustrates elements constituting a battery system according to some embodiments.

[0023] FIG. 2 illustrates elements constituting a battery management device according to some embodiments.

[0024] FIG. 3 illustrates how a limit capacity estimation model operates according to some embodiments.

[0025] FIG. 4 illustrates the types of input experimental data and output experimental data used for training a limit capacity estimation model according to some embodiments.

[0026] FIG. 5 illustrates a method for calculating hysteresis resistance based on a charging profile and a discharging profile according to some embodiments.

[0027] FIG. 6 illustrates the performance of a trained limit capacity estimation model according to some embodiments.

[0028] FIG. 7 illustrates steps constituting a battery management method according to some embodiments.

[0029] 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.

[0030] 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.

[0031] 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).

[0032] 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.

[0033] 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.

[0034] 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.

[0035] FIG. 1 illustrates elements constituting a battery system according to some embodiments.

[0036] 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).

[0037] 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 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 that charges the battery (120). The mobility device may drive a 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 that charges or discharges the battery (120). The charge / discharger may apply a charge current, a charge voltage, a discharge current, and / or a discharge voltage to the battery (120) based on a given profile or cycle.

[0038] 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.

[0039] 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.

[0040] 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).

[0041] FIG. 2 illustrates elements constituting a battery management device according to some embodiments.

[0042] Referring to FIG. 2, the battery management device (130) may include an interface (131) and a controller (132). However, it is not limited thereto, and some components may be omitted from the battery management device (130), or other general-purpose components may be further included in the battery management device (130).

[0043] The interface (131) can acquire battery data of the battery (120). According to an embodiment, the interface (131) 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 battery management device (130) 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 battery management device (130) 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).

[0044] The controller (132) may have a structure for executing instructions that implement the operations of the battery management device (130). The controller (132) 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.

[0045] The controller (132) 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 (132). The controller (132) 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.

[0046] The interface (131) may be configured to acquire battery data of the battery (120). According to an embodiment, the interface (131) 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 battery management device (130) 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 battery management device (130) 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 (120). 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.

[0047] The controller (132) may be configured to generate environmental factor data and state factor data of the battery (120) based on battery data. The battery data may include measured values ​​such as current, voltage, and temperature, and derived values ​​such as capacity, resistance, and degradation based on the measured values ​​may be derived. Environmental factor data and state factor data may be generated based on the measured values ​​and derived values. Environmental factor data may include data related to the operating environment of the battery (120), and state factor data may include data related to the performance state and / or life state of the battery (120).

[0048] The controller (132) may be configured to generate a limit charge capacity of a battery (120) corresponding to environmental factor data and state factor data based on a limit capacity estimation model. The limit capacity estimation model may be generated based on sample batteries for which the environmental factor data corresponding to the first input, the state factor data corresponding to the second input, and the limit charge capacity corresponding to the output are known. The limit capacity estimation model may include an artificial intelligence neural network model trained through various machine learning techniques, and the AI ​​neural network model may be trained to define the relationship between the input data and the output data. The trained limit capacity estimation model can infer a limit charge capacity corresponding to the environmental factor data and state factor data of a battery (120) for which the limit charge capacity is unknown.

[0049] The controller (132) may be configured to control the charging of the battery (120) based on the limit charging capacity. Once the limit charging capacity of the battery (120) is inferred, the charging capacity of the battery (120) may be controlled to prevent lithium precipitation from occurring in the battery (120) due to charging at a capacity exceeding this limit. For example, when charging the battery (120) at a specific C-rate starting from SOC 0%, if the battery (120) is charged beyond the limit charging capacity, lithium precipitation may occur. The limit charging capacity may vary depending on the value of the charging C-rate. As the value of the charging C-rate increases, the limit charging capacity may decrease.

[0050] According to the embodiment, environmental factor data may include charge / discharge current data, temperature data, and voltage data, and state factor data may include capacity data, resistance data, and degradation data. Environmental factor data may represent current, voltage, temperature, etc., regarding the operating environment of the battery (120), and state factor data may represent capacity, resistance, degradation, etc., regarding the state of the performance and / or lifespan of the battery (120). Environmental factor data and state factor data may influence the estimation of the limit charge capacity of the battery (120) from different perspectives. According to the embodiment, environmental factor data and state factor data may be considered with different weights during the process of training the limit capacity estimation model. For example, the weights for environmental factor data and state factor data may be determined based on the constituent components and / or form factor of the battery (120). For example, if the component ratio of the NCM battery changes, the weights may change, or even if the constituent components are the same, the weights may change depending on whether it is a cylindrical battery or a pouch-type battery. The weights for environmental factor data and state factor data may be optimized through the limit capacity estimation model or a separate model.

[0051] According to an embodiment, the limit capacity estimation model can be trained to define the relationship between input experimental data regarding environmental factor data and state factor data, and output experimental data regarding the limit charge capacity. For example, the input experimental data and output experimental data can be generated based on sample batteries to provide training data for the limit capacity estimation model. For example, the limit capacity estimation model can be constructed based on various forms of neural network models, and model parameters such as weights and activations for the layers and nodes of the neural network model can be adjusted to define the relationship between the input experimental data and the output experimental data. According to an embodiment, the environmental factor data and state factor data in the input experimental data can define the relationship regarding the limit charge capacity independently of each other.

[0052] According to an embodiment, the controller (132) may be configured to generate a mapping table of limit charging capacities corresponding to a combination of environmental factor data and state factor data based on a learned limit capacity estimation model, and to generate limit charging capacities based on the mapping table. The mapping table may be generated based on sample batteries, and the limit charging capacities of the battery (120) may be derived using the mapping table.

[0053] According to an embodiment, output experimental data regarding the limit charge capacity may be prepared based on the resistance fluctuation of the battery (120) while a plurality of charge-discharge cycles are applied. For example, the amount of resistance change and / or the resistance fluctuation rate in the current cycle may be calculated relative to the BOL resistance value calculated in the first cycle among the plurality of charge-discharge cycles, and the limit charge capacity may be determined based thereon. Output experimental data regarding the limit charge capacity may be prepared based on the amount of resistance change and / or the resistance fluctuation rate of the sample batteries.

[0054] According to the embodiment, input experimental data may be prepared based on at least one of slow charging / discharging, rapid charging / discharging, high-temperature storage, and internal resistance measurement for sample batteries. If input experimental data with a wide data range is not secured, the accuracy of estimating the limit charge capacity of the battery (120) may be reduced. In order to secure the widest possible data range for environmental factor data and state factor data corresponding to the input experimental data, charging / discharging experiments and / or degradation experiments may be performed on the sample batteries in various forms. Through slow charging / discharging, rapid charging / discharging, high-temperature storage, etc., input data for model training, such as voltage, current, temperature, capacity, resistance, and degradation of the sample batteries, can be secured evenly without data bias or skewness.

[0055] According to an embodiment, resistance data resulting from internal resistance measurement may include hysteresis resistance measured based on charge profiles and discharge profiles representing variations in high-rate charge / discharge voltages according to the battery capacity of the sample batteries. In the process of preparing input experimental data, the internal resistance values ​​of the sample batteries may be calculated in the form of hysteresis resistance. For example, hysteresis resistance may be calculated by accumulating the voltage difference between the charge profile and the discharge profile within a specific range of battery capacity.

[0056] According to the embodiment, hysteresis resistance can be calculated by accumulating the difference between the high-rate charging voltage according to the charging profile and the high-rate discharge voltage according to the discharging profile over a target range, and the target range can be adjusted based on the components of the sample batteries and degradation data for the sample batteries. By accumulating the difference between the high-rate charging voltage and the high-rate discharge voltage, the graph area between the charging profile and the discharging profile can be calculated, which corresponds to the hysteresis resistance. The target range serving as the basis for calculating the graph area can be changed based on the characteristics of the sample batteries. For example, the target range of battery capacity can be determined based on the N / C / M composition ratio, State of Health (SOH) values, etc. of the sample batteries. For example, the target range can be set to SOC 10% to 90%, SOC 20% to 70%, or other appropriate ranges.

[0057] According to the embodiment, the limit charging capacity may include a maximum value of charging capacity that prevents lithium from precipitating in the battery (120), and the controller (132) may be configured to diagnose the state of the battery (120) as a lithium precipitation state when the charging capacity of the battery (120) exceeds the limit charging capacity. For example, when charging the battery (120) from SOC 0% to 1.0 C-rate, the limit charging capacity may be approximately 58 Ah, and if the battery (120) is charged beyond 58 Ah, the possibility of lithium precipitation may increase. When it is confirmed that the battery (120) has been charged beyond the limit charging capacity, the battery management device (130) may diagnose the battery (120) as a lithium precipitation state.

[0058] According to the embodiment, the negative electrode of the battery cell of the battery (120) may include a negative electrode active material, and the negative electrode active material may not include a silicon-based active material. For example, the negative electrode active material of the negative electrode of the battery cell of the battery (120) may include a carbon-based active material, a graphite-based active material, etc. For example, if the negative electrode active material of the sample batteries includes a silicon-based active material, the process of training a limit capacity estimation model based on the data of the sample batteries may not be smooth, and accordingly, the performance of estimating the limit charge capacity of the battery (120) using the model may be degraded.

[0059] FIG. 3 illustrates how a limit capacity estimation model operates according to some embodiments.

[0060] Referring to FIG. 3, the limit capacity estimation model (30) can be trained to output output experimental data (320) based on input experimental data (310). The input experimental data (310) and output experimental data (320) can be prepared based on previously known sample batteries.

[0061] The limit capacity estimation model (30) may include an artificial intelligence neural network model learned through various machine learning techniques. There are no specific restrictions on the types of machine learning techniques and neural network models, and the AI ​​neural network model may be learned to define the relationship between input experimental data (310) and output experimental data (320).

[0062] Once the training of the limit capacity estimation model (30) is complete, the limit charge capacity of the battery (120) can be estimated by the limit capacity estimation model (30). For example, the battery (120) may be a new battery whose limit charge capacity is not known in advance. The limit capacity estimation model (30) can accurately estimate the limit charge capacity of the battery (120) based solely on the input experimental data (310).

[0063] FIG. 4 illustrates the types of input experimental data and output experimental data used for training a limit capacity estimation model according to some embodiments.

[0064] Referring to FIG. 4, a table (410) indicating the type of input experimental data and a table (420) indicating the type of output experimental data may be shown.

[0065] The input experimental data of the table (410) can be divided into environmental factor data and state factor data. Environmental factor data may include the charging current, discharging current, temperature, charging voltage, discharging voltage, etc. of the battery (120). State factor data may include capacity information, internal resistance information, degradation information, etc. of the battery (120). For example, capacity information may include SOC, and degradation information may include SOH.

[0066] The output experimental data of the table (420) may include a lithium deposition limit depth. The lithium deposition limit depth may correspond to the limit charge capacity of the battery (120). When the battery (120) is charged with a charge capacity lower than the lithium deposition limit depth, lithium deposition in the battery (120) may not occur. According to an embodiment, the lithium deposition limit depth may be determined based on how the resistance of the battery (120) fluctuates while a plurality of charge-discharge cycles are applied. According to an embodiment, the resistance of the battery (120) may include hysteresis resistance.

[0067] FIG. 5 illustrates a method for calculating hysteresis resistance based on a charging profile and a discharging profile according to some embodiments.

[0068] Referring to FIG. 5, a graph (500) illustrating a method for calculating hysteresis resistance based on a charging profile and a discharging profile may be shown. The horizontal axis of the graph (500) may represent the charging capacity, and the vertical axis may represent the charging voltage or the discharging voltage.

[0069] In the graph (500), the fully charged state and fully discharged state of the battery (120) can form a hysteresis loop. The battery (120) can be charged along a charging profile and discharged along a discharging profile. A gap can be formed between the charging profile and the discharging profile, and a hysteresis resistance can be calculated by accumulating this between the lower bound and the upper bound. The hysteresis resistance can correspond to the area between the charging profile and the discharging profile.

[0070] The target range between the lower bound and upper bound can be set to a range of 10% to 90% of the maximum SOC. SOC and capacity can be correlated. The lower bound and upper bound of the target range can be changed based on the degradation level or composition of the sample batteries to generate input experimental data.

[0071] FIG. 6 illustrates the performance of a trained limit capacity estimation model according to some embodiments.

[0072] Referring to FIG. 6, a graph (600) illustrating the performance of a trained limit capacity estimation model may be shown. The horizontal axis of the graph (600) may represent the limit capacity measured through actual experiments, and the vertical axis may represent the limit capacity estimated through the limit capacity estimation model.

[0073] As shown in graph (600), in all cases where the charging current is 0.75 C-rate, 1.0 C-rate, 1.5 C-rate, and 2.0 C-rate, it can be confirmed that the limit charging capacity measured through actual experiments and the limit charging capacity estimated through the limit charging capacity estimation model are similar. For performance evaluation conditions, the evaluation temperature may be 25℃ and the test start SOC may be 0%. The Root Mean Squared Error (RMSE) to verify model performance may be 0.74 Ah, and this value is very small, which indicates that the model performance is high.

[0074] FIG. 7 illustrates steps constituting a battery management method according to some embodiments.

[0075] Referring to FIG. 7, the battery management method (700) may include steps (710) through (740). However, it is not limited thereto, some steps may be omitted or other general steps may be added, and the steps of the battery management method (700) may be executed in a different order than the illustrated order.

[0076] The battery management method (700) may consist of steps processed sequentially in the battery management device (130). Therefore, even if the details are omitted below, the description of the battery management device (130) above may be equally applicable to the battery management method (700).

[0077] Steps (710) to (740) of the battery management method (700) can be performed by the interface (131) and controller (132) of the battery management device (130).

[0078] In step (710), the battery management device (130) can perform the step of acquiring battery data of the battery.

[0079] In step (720), the battery management device (130) may perform the step of generating environmental factor data and state factor data of the battery based on battery data.

[0080] In step (730), the battery management device (130) may perform the step of generating a limit charge capacity of the battery corresponding to environmental factor data and state factor data based on a limit capacity estimation model.

[0081] In step (740), the battery management device (130) can perform the step of controlling the charging of the battery based on the limit charging capacity.

[0082] According to an embodiment, the battery management method (700) 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 battery management method (700), and the instructions of the program may be stored on a computer-readable storage medium. The computer program may include a mobile application.

[0083] 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.

[0084] 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.

[0085] 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. An interface configured to acquire battery data of a battery; and Based on the above battery data, environmental factor data and state factor data of the battery are generated, and Based on a limit capacity estimation model, a limit charge capacity of the battery corresponding to the environmental factor data and the state factor data is generated, and A battery management device comprising a controller configured to control the charging of the battery based on the above-mentioned limit charging capacity.

2. In Paragraph 1, The above environmental factor data includes charge / discharge current data, temperature data, and voltage data, and A battery management device in which the above state factor data includes capacity data, resistance data, and degradation data.

3. In Paragraph 2, A battery management device in which the above limit capacity estimation model is learned to define the relationship between input experimental data regarding the above environmental factor data and the above state factor data and output experimental data regarding the above limit charge capacity.

4. In Paragraph 3, The above controller generates a mapping table of the limit charging capacity corresponding to a combination of the environment factor data and the state factor data based on the learned limit capacity estimation model, and A battery management device configured to generate the limit charge capacity based on the above mapping table.

5. In Paragraph 3, A battery management device, wherein the output experimental data regarding the above-mentioned limit charge capacity is prepared based on the resistance fluctuation of the battery while a plurality of charge-discharge cycles are applied.

6. In Paragraph 3, A battery management device in which the above input experimental data is prepared based on at least one of slow charge / discharge, rapid charge / discharge, high temperature storage, and internal resistance measurement for sample batteries.

7. In Paragraph 6, A battery management device comprising a hysteresis resistance measured based on a charging profile and a discharging profile, wherein the resistance data according to the internal resistance measurement above includes a variation in high-rate charging and discharging voltage according to the battery capacity of the sample batteries.

8. In Paragraph 7, The hysteresis resistance is calculated by accumulating the difference between the high-rate charging voltage according to the charging profile and the high-rate discharge voltage according to the discharge profile over a target range, and A battery management device that adjusts the above target range based on the components of the sample batteries and the degradation data for the sample batteries.

9. In Paragraph 1, The above limit charging capacity includes a maximum charging capacity that prevents lithium from precipitating in the battery, and A battery management device configured such that the controller is configured to diagnose the state of the battery as a lithium precipitation state when the charging capacity of the battery exceeds the limit charging capacity.

10. In Paragraph 1, A battery management device in which the negative electrode of the battery cell of the above battery includes a negative electrode active material, and the negative electrode active material does not include a silicon-based active material.

11. Step of acquiring battery data of the battery; A step of generating environmental factor data and state factor data of the battery based on the battery data above; A step of generating a limit charge capacity of the battery corresponding to the environmental factor data and the state factor data based on a limit capacity estimation model; and A battery management method comprising the step of controlling the charging of the battery based on the above-mentioned limit charging capacity.

12. In Paragraph 11, The above environmental factor data includes charge / discharge current data, temperature data, and voltage data, and A battery management method in which the above state factor data includes capacity data, resistance data, and degradation data.

13. In Paragraph 12, A battery management method in which the above limit capacity estimation model is trained to define the relationship between input experimental data regarding the environment factor data and the state factor data and output experimental data regarding the limit charge capacity.

14. In Paragraph 13, The step of generating the above limit charging capacity is, A step of generating a mapping table of the limit charging capacity corresponding to the combination of the environmental factor data and the state factor data based on the limit capacity estimation model that has been trained; and A battery management method comprising the step of generating the limit charge capacity based on the above mapping table.

15. In Paragraph 13, A battery management method in which the output experimental data regarding the above-mentioned limit charge capacity is prepared based on the resistance fluctuation of the battery while a plurality of charge-discharge cycles are applied.

16. In Paragraph 13, A battery management method in which the above input experimental data is prepared based on at least one of slow charging / discharging, rapid charging / discharging, high-temperature storage, and internal resistance measurement for sample batteries.

17. In Paragraph 16, A battery management method comprising a resistance data according to the internal resistance measurement above, wherein the resistance data includes a hysteresis resistance measured based on a charging profile and a discharging profile representing a variation in high-rate charging and discharging voltage according to the battery capacity of the sample batteries.

18. In Paragraph 17, The hysteresis resistance is calculated by accumulating the difference between the high-rate charging voltage according to the charging profile and the high-rate discharge voltage according to the discharge profile over a target range, and A battery management method, wherein the above target range is adjusted based on the components of the sample batteries and the degradation data for the sample batteries.

19. In Paragraph 11, The above limit charging capacity includes a maximum charging capacity that prevents lithium from precipitating in the battery, and The battery management method described above further comprises the step of diagnosing the state of the battery as a lithium precipitation state when the charging capacity of the battery exceeds the limit charging capacity.

20. In Paragraph 11, A battery management method in which the negative electrode of a battery cell of the above battery includes a negative electrode active material, and the negative electrode active material does not include a silicon-based active material.

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