Battery management device, battery management method, and computer program

Abstract

According to some embodiments, a battery management device comprises: a processor; and memory configured to store instructions that, when executed by the processor, cause the processor to perform operations. The operations include: calculating the tortuosity of an ion movement path of a battery on the basis of electrode structure data of the battery; and estimating the state of the battery corresponding to the tortuosity on the basis of a state estimation model.

Description

Battery management device, battery management method, and computer program

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority based on Korean Patent Application No. 10-2024-0151813 filed on October 31, 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, a battery management method, and a computer program.

[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] The initial resistance of a battery cell can serve as an indicator of its performance or condition. The electrode structures of the positive and negative electrodes can influence the cell's initial resistance. Since the electrode structure can be determined by various factors, methods for deriving the cell's initial resistance based on these electrode structure factors are currently being discussed. However, a potential issue arises when the number and types of electrode structure factors are numerous, as it becomes difficult to optimize the model for deriving the cell's initial resistance based on them.

[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 computer program capable of estimating the initial cell resistance using electrode curvature as a model input to solve the problem that it is difficult to optimize a model based on electrode structure factors.

[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 processor; and a memory configured to store instructions that, when executed by the processor, cause the processor to perform operations, wherein the operations include: an operation of calculating the tortuosity of an ion movement path of the battery based on electrode structure data of the battery; and an operation of estimating the state of the battery corresponding to the tortuosity based on a state estimation model.

[0010] According to some embodiments, the electrode structure data includes at least one of the positive electrode porosity, negative electrode porosity, positive electrode rolling rate, negative electrode rolling rate, positive electrode thickness, negative electrode thickness, positive electrolyte impregnation time, and negative electrolyte impregnation time of the battery.

[0011] According to some embodiments, the operation of calculating the curvature comprises: an operation of calculating the anode curvature of the battery such that the higher the anode porosity, the higher the anode electrolyte impregnation time, and the lower the anode thickness, the higher the value; and an operation of calculating the cathode curvature of the battery such that the higher the cathode porosity, the higher the cathode electrolyte impregnation time, and the lower the cathode thickness, the higher the value.

[0012] According to some embodiments, the operations further include deriving an optimal range of the positive curvature and an optimal range of the negative curvature based on the state of the battery.

[0013] According to some embodiments, the state estimation model is trained to define the relationship between input experimental data regarding the anode porosity and the cathode porosity and output experimental data regarding the state of the battery.

[0014] According to some embodiments, the state estimation model is trained based on XGBoost regression.

[0015] According to some embodiments, the operation of estimating the state of the battery includes the operation of estimating the initial resistance of the battery corresponding to the curvature based on the state estimation model.

[0016] According to some embodiments, a battery management method comprises: a step of calculating the curvature of an ion movement path of the battery based on electrode structure data of the battery; and a step of estimating the state of the battery corresponding to the curvature based on a state estimation model.

[0017] According to some embodiments, the electrode structure data includes at least one of the positive electrode porosity, negative electrode porosity, positive electrode rolling rate, negative electrode rolling rate, positive electrode thickness, negative electrode thickness, positive electrolyte impregnation time, and negative electrolyte impregnation time of the battery.

[0018] According to some embodiments, the step of calculating the curvature comprises: a step of calculating the anode curvature of the battery such that the higher the anode porosity, the higher the anode electrolyte impregnation time, and the lower the anode thickness, the higher the value; and a step of calculating the cathode curvature of the battery such that the higher the cathode porosity, the higher the cathode electrolyte impregnation time, and the lower the cathode thickness.

[0019] According to some embodiments, the method further includes the step of deriving the optimal range of the positive electrode curvature and the optimal range of the negative electrode curvature based on the state of the battery.

[0020] According to some embodiments, the state estimation model is trained to define the relationship between input experimental data regarding the anode porosity and the cathode porosity and output experimental data regarding the state of the battery.

[0021] According to some embodiments, the state estimation model is trained based on XGBoost regression.

[0022] According to some embodiments, the step of estimating the state of the battery includes the step of estimating the initial resistance of the battery corresponding to the curvature based on the state estimation model.

[0023] According to some embodiments, instructions of a computer program stored in a computer-readable medium, when executed by a processor, cause the processor to perform: an operation of calculating the curvature of an ion movement path of the battery based on electrode structure data of the battery; and an operation of estimating the state of the battery corresponding to the curvature based on a state estimation model.

[0024] According to the embodiments disclosed in this document, a battery management device, a battery management method, and a computer program capable of estimating the initial cell resistance using electrode curvature as a model input can be provided to solve the problem that it is difficult to optimize a model based on electrode structure factors.

[0025] 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.

[0026] FIG. 1 illustrates elements constituting a battery system according to some embodiments.

[0027] FIG. 2 illustrates elements constituting a battery management device according to some embodiments.

[0028] FIG. 3 illustrates the learning and inference process of a state estimation model according to some embodiments.

[0029] FIG. 4 illustrates the difference between a state estimation model according to some embodiments and a conventional state estimation model.

[0030] FIG. 5 illustrates a process for calculating the curvature of an ion movement path based on electrode structure data according to some embodiments.

[0031] FIG. 6 illustrates the performance difference between a state estimation model according to some embodiments and a conventional state estimation model.

[0032] FIG. 7 illustrates the optimal range of curvature derived based on the state of the battery according to some embodiments.

[0033] FIG. 8 illustrates steps constituting a battery management method according to some embodiments.

[0034] 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.

[0035] 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.

[0036] 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).

[0037] 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.

[0038] 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.

[0039] 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.

[0040] FIG. 1 illustrates elements constituting a battery system according to some embodiments.

[0041] 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).

[0042] 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.

[0043] 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.

[0044] 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.

[0045] The battery management device (130) may include an interface configured to acquire battery data of the battery (120). According to an embodiment, the interface 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).

[0046] 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).

[0047] According to an embodiment, the battery system (100) may be a system for inspecting whether the manufacturing state, performance, etc. of the battery (120) is suitable in a laboratory environment, and in this case, the battery management device (130) may be a computer device such as a desktop PC. The battery management device (130) may inspect or diagnose the battery (120) based on data regarding the structure, manufacturing specifications, physical properties, etc. of the battery (120) instead of data related to the charging and / or discharging of the battery (120). For example, the performance, state, manufacturing quality, etc. of the battery (120) may be inspected or diagnosed based on data regarding the structure, manufacturing specifications, physical properties, etc. of the positive electrode and / or negative electrode of the battery (120).

[0048] FIG. 2 illustrates elements constituting a battery management device according to some embodiments.

[0049] Referring to FIG. 2, the battery management device (130) may include memory (131) and a processor (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).

[0050] The memory (131) may be configured to store various data, instructions, mobile applications, computer programs, etc. The memory (131) may be configured separately from or integrally with the processor (132). For example, the memory (131) 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.

[0051] The processor (132) can process data, instructions, programs, apps, etc. stored in memory (131). The processor (132) can execute instructions, programs, apps, etc. to implement the operations of the battery management device (130). The processor (132) can be implemented as an array of multiple logic gates or a general-purpose microprocessor for processing various operations, and can be composed of a single processor or multiple processors. For example, the processor (132) can be implemented in at least one form of a microprocessor, CPU, GPU, and AP.

[0052] The battery management device (130) may be configured to calculate the tortuosity of the ion movement path of the battery (120) based on electrode structure data of the battery (120). The electrode structure data may be data for determining the positive and negative electrode structures of the battery (120). The electrode structure data may be electrode characteristic data regarding positive and negative electrode characteristics. The tortuosity may be calculated based on the ion movement path in the positive and negative electrodes of the battery (120). The tortuosity of the ion movement path inside the electrode may be determined based on the electrode structure data. For example, a calculation formula, a functional relationship, etc., between the electrode structure data and the tortuosity may be utilized.

[0053] The battery management device (130) may be configured to estimate the state of a battery (120) corresponding to a curvature based on a state estimation model. Training data necessary for training the state estimation model based on sample batteries may be prepared, and training of the state estimation model may be performed using the training data. The training data may include input data regarding the curvature of the sample batteries and output data regarding the state of the sample batteries. The state 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 state estimation model, which has completed training based on the sample batteries, can infer the state of a new battery whose state is unknown.

[0054] According to the embodiment, the electrode structure data may include at least one of the positive electrode porosity, negative electrode porosity, positive electrode rolling rate, negative electrode rolling rate, positive electrode thickness, negative electrode thickness, positive electrolyte impregnation time, and negative electrolyte impregnation time of the battery (120). The porosity of the battery electrode may represent the specific gravity of the volume occupied by the pores inside the electrode relative to the total volume of the electrode; the lower the porosity, the higher the substantial density of the electrode material and the smoother the movement of ions and / or electrons. The rolling rate may represent the level of rolling of the battery electrode plate. The higher the rolling rate, the smaller the thickness of the battery electrode and the higher the density of the electrode material. The electrolyte impregnation time may refer to the time during which the positive and negative electrodes are impregnated with a PC (propylene carbonate) electrolyte during the process of manufacturing the battery cell. Electrode characteristics or battery performance may vary depending on the electrolyte impregnation time.

[0055] According to an embodiment, the battery management device (130) may be configured to calculate the positive curvature of the battery (120) such that the higher the positive porosity, the higher the positive electrolyte impregnation time, and the lower the positive thickness, the higher the value, and to calculate the negative curvature of the battery (120) such that the higher the negative porosity, the higher the negative electrolyte impregnation time, and the lower the negative thickness. The positive curvature and negative curvature of the battery (120) may be calculated based on the porosity, electrode thickness, and / or electrode electrolyte impregnation time, and the relationship between each electrode structural element and the electrode curvature may be determined by considering the influence of the electrode structural elements on the electrode structure, the ion / electron movement path, energy density, etc. For example, the curvature may be higher as the porosity and impregnation time increase, and the curvature may be lower as the electrode thickness increases.

[0056] According to an embodiment, the battery management device (130) may be configured to derive an optimal range of positive curvature and an optimal range of negative curvature based on the state of the battery (120). The state of the battery (120) may be estimated based on the positive curvature and / or negative curvature, and the state of the battery (120) may be classified as a normal state or an abnormal state based on a normal range. For example, the state of the battery (120) may include the battery initial resistance, and the optimal range of positive curvature and the optimal range of negative curvature may be derived based on the normal range of the battery initial resistance. The optimal curvature range may be utilized as specifications or quality standards for battery manufacturing, etc.

[0057] According to an embodiment, the state estimation model can be trained to define the relationship between input experimental data regarding positive and negative porosity and output experimental data regarding the state of the battery (120). For example, the input experimental data and output experimental data can be generated based on sample batteries to provide training data for the state estimation model. For example, the state estimation model can be built 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. Once the training of the state estimation model is complete, state information such as the internal resistance of a new battery corresponding to the newly input positive and / or negative porosity can be estimated.

[0058] According to the embodiment, the state estimation model can be trained based on XGBoost regression. XGBoost regression is one of the machine learning models used for regression modeling for numerical prediction, and can demonstrate high accuracy and high performance speed by utilizing ensemble learning techniques based on tree structures. When a state estimation model is generated based on XGBoost regression, the model performance for estimating the state of the battery (120) based on curvature can be improved based on the root mean square error (RMSE).

[0059] According to an embodiment, the battery management device (130) may be configured to estimate the initial resistance of the battery (120) corresponding to the curvature based on a state estimation model. For example, the state of the battery (120) estimated by the state estimation model may include the initial resistance of the battery (120). For example, the initial resistance may include the internal resistance at the start of the battery cell's life (BOL). This may represent the resistance prior to degradation due to the use of the battery cell and may be referenced, for example, to determine the manufacturing quality of the battery cell.

[0060] FIG. 3 illustrates the learning and inference process of a state estimation model according to some embodiments.

[0061] Referring to FIG. 3, the state 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.

[0062] The state 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).

[0063] When the training of the state estimation model (30) is completed, the state of the battery (120) can be estimated by the state estimation model (30). For example, the state of the battery (120) may include the initial resistance of the battery (120), and the state estimation model (30) can output the initial resistance value most suitable for the bending value of the battery (120) based on the training data.

[0064] FIG. 4 illustrates the difference between a state estimation model according to some embodiments and a conventional state estimation model.

[0065] Referring to FIG. 4, a first flow (410) showing the process of a conventional state estimation model estimating the first cell initial resistance (413) and a second flow (420) showing the process of a state estimation model estimating the second cell initial resistance (424) in a battery management device (130) may be illustrated.

[0066] In the first flow (410), there may be a variety and large number of types of input data (411) for the first model (412) for battery state estimation. On the other hand, in the second flow (420), only the curvature (422) may be the input data for the second model (423). Therefore, compared to the first flow (410), the structure and complexity of the state estimation model estimating the cell initial resistance can be improved in the second flow (420), and accordingly, the speed and accuracy of state estimation can be improved compared to the conventional method.

[0067] FIG. 5 illustrates a process for calculating the curvature of an ion movement path based on electrode structure data according to some embodiments.

[0068] Referring to FIG. 5, a table (510) showing the type of exemplary electrode structure data, a table (520) showing the curvature of an exemplary ion movement path, and a formula (530) showing the curvature calculation process may be shown.

[0069] As in the table (510), the electrode structure data includes porosity ε and electrolyte impregnation time R for the positive and negative electrodes of the battery (120). Ion ·κ, electrode thickness d, etc. may be included. The curvature τ of the positive or negative electrode of the battery (120) can be calculated as follows.

[0070]

[0071] In mathematical formula 1, A may represent the electrolyte impregnation area of ​​the battery electrode, and for battery cells of the same specification, the values ​​of A may all be the same. As shown in table (520), the positive and negative curvatures for each battery may be calculated. The positive and / or negative curvatures may be used as inputs to a state estimation model, and the initial resistance or other state indicators of each battery may be estimated by the state estimation model.

[0072] FIG. 6 illustrates the performance difference between a state estimation model according to some embodiments and a conventional state estimation model.

[0073] Referring to FIG. 6, a graph (610) representing the performance of a conventional state estimation model and a graph (620) representing the performance of a state estimation model operating in a battery management device (130) may be shown. The horizontal axis of the graph (610) and the graph (620) may represent the actual measured value of the cell initial resistance, and the vertical axis may represent the model predicted value of the cell initial resistance.

[0074] In the case of the graph (610) according to the conventional method, it can be confirmed that the model prediction value for the test data deviates significantly from the actual measurement value. This may be because the model according to the conventional method uses a large amount of electrode structure data as input data. On the other hand, in the case of the graph (620) according to the battery management device (130), it can be confirmed that the model prediction value for the test data does not deviate significantly from the actual measurement value. Based on the root mean square error (RMSE), the RMSE value for the graph (610) is approximately 0.114, whereas the RMSE value for the graph (620) is approximately 0.012, which corresponds to a performance improvement of about 5% based on the actual measurement value.

[0075] FIG. 7 illustrates the optimal range of curvature derived based on the state of the battery according to some embodiments.

[0076] Referring to FIG. 7, a table (700) illustrating an optimal range of curvature derived based on the state of the battery (120) may be shown.

[0077] Table (700) may indicate the optimal range for the positive curvature of the battery positive electrode and the optimal range for the negative curvature of the battery negative electrode. The optimal ranges for the positive and negative electrodes may be determined as different values ​​depending on the battery specifications or battery structure.

[0078] The optimal range of curvature can be determined based on the optimal range for the state of the battery (120). For example, given the optimal range for the initial resistance of the battery (120), the optimal range of curvature can be determined such that the value of the initial resistance estimated by the state estimation model falls within the optimal range. Once the optimal range of curvature is determined, it can be used to determine whether the battery manufacturing has been properly completed.

[0079] FIG. 8 illustrates steps constituting a battery management method according to some embodiments.

[0080] Referring to FIG. 8, the battery management method (800) may include steps (810) to (820). 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 (800) may be executed in a different order than the illustrated order.

[0081] The battery management method (800) 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 (800).

[0082] Steps (810) to (820) of the battery management method (800) can be performed by the memory (131) and processor (132) of the battery management device (130).

[0083] In step (810), the battery management device (130) can perform the step of calculating the curvature of the ion movement path of the battery based on the electrode structure data of the battery.

[0084] In step (810), the battery management device (130) can perform the step of estimating the state of the battery corresponding to the curvature based on a state estimation model.

[0085] According to an embodiment, the battery management method (800) 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 (800), and the instructions of the program may be stored on a computer-readable storage medium. The computer program may include a mobile application.

[0086] 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.

[0087] 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.

[0088] 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. Processor; and It includes a memory configured to store instructions that cause the processor to perform operations when executed by the processor, and The above operations include the operation of calculating the tortuosity of the ion migration path of the battery based on electrode structure data of the battery; and A battery management device comprising an operation to estimate the state of the battery corresponding to the curvature based on a state estimation model.

2. In Paragraph 1, A battery management device comprising at least one of the above electrode structure data, a positive electrode porosity, a negative electrode porosity, a positive electrode rolling rate, a negative electrode rolling rate, a positive electrode thickness, a negative electrode thickness, a positive electrolyte impregnation time, and a negative electrode electrolyte impregnation time of the battery.

3. In Paragraph 2, The operation of calculating the above curvature is, The operation of calculating the anode curvature of the battery such that the higher the anode porosity, the higher the anode electrolyte impregnation time, and the lower the anode thickness, the higher the value; and A battery management device comprising the operation of calculating the negative electrode curvature of the battery such that the higher the negative electrode porosity, the higher the negative electrode electrolyte impregnation time, and the lower the negative electrode thickness, the higher the value.

4. In Paragraph 3, The above operations are, A battery management device further comprising the operation of deriving the optimal range of the positive curvature and the optimal range of the negative curvature based on the state of the battery.

5. In Paragraph 3, A battery management device, wherein the above state estimation model is trained to define the relationship between input experimental data regarding the anode porosity and the cathode porosity and output experimental data regarding the state of the battery.

6. In Paragraph 5, The above state estimation model is a battery management device trained based on XGBoost regression.

7. In Paragraph 1, The operation of estimating the state of the above battery is, A battery management device comprising an operation to estimate the initial resistance of the battery corresponding to the curvature based on the above state estimation model.

8. A step of calculating the curvature of the ion migration path of the battery based on electrode structure data of the battery; and A battery management method comprising the step of estimating the state of the battery corresponding to the curvature based on a state estimation model.

9. In Paragraph 8, A battery management method comprising at least one of the above electrode structure data, a positive electrode porosity, a negative electrode porosity, a positive electrode rolling rate, a negative electrode rolling rate, a positive electrode thickness, a negative electrode thickness, a positive electrode electrolyte impregnation time, and a negative electrode electrolyte impregnation time of the battery.

10. In Paragraph 9, The step of calculating the above curvature is, A step of calculating the anode curvature of the battery such that the higher the anode porosity, the higher the anode electrolyte impregnation time, and the lower the anode thickness, the higher the value; and A battery management method comprising the step of calculating the negative electrode curvature of the battery such that the higher the negative electrode porosity, the higher the negative electrode electrolyte impregnation time, and the lower the negative electrode thickness, the higher the value.

11. In Paragraph 10, A battery management method further comprising the step of deriving the optimal range of the positive curvature and the optimal range of the negative curvature based on the state of the battery.

12. In Paragraph 10, A battery management method in which the above state estimation model is trained to define the relationship between input experimental data regarding the anode porosity and the cathode porosity and output experimental data regarding the state of the battery.

13. In Paragraph 12, The above state estimation model is a battery management method learned based on XGBoost regression.

14. In Paragraph 8, The step of estimating the state of the battery above is, A battery management method comprising the step of estimating the initial resistance of the battery corresponding to the curvature based on the above state estimation model.

15. In a computer program stored on a computer-readable medium, When the instructions of the above computer program are executed by a processor, the processor: The operation of calculating the curvature of the ion movement path of the battery based on electrode structure data of the battery; and A computer program stored on a computer-readable medium that performs the operation of estimating the state of the battery corresponding to the curvature based on a state estimation model.

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