KR-20260063763-A - BATTERY MANAGEMENT APPARATUS, BATTERY MANAGEMENT METHOD AND COMPUTER PROGRAM

KR20260063763AKR 20260063763 AKR20260063763 AKR 20260063763AKR-20260063763-A

Abstract

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.

Inventors

오혜령
최동인

Assignees

주식회사 엘지에너지솔루션

Dates

Publication Date: 20260507
Application Date: 20241031

Claims (15)

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.
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.
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.
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.
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.
In paragraph 5, The above state estimation model is a battery management device trained based on XGBoost regression.
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.
A step of calculating the curvature of the ion movement 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.
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.
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.
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.
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.
In Paragraph 12, The above state estimation model is a battery management method learned based on XGBoost regression.
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.
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.

Description

Battery Management Apparatus, Battery Management Method and Computer Program The embodiments disclosed in this document relate to a battery management device, a battery management method, and a computer program. 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. 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. FIG. 1 illustrates elements constituting a battery system according to some embodiments. FIG. 2 illustrates elements constituting a battery management device according to some embodiments. FIG. 3 illustrates the learning and inference process of a state estimation model according to some embodiments. FIG. 4 illustrates the difference between a state estimation model according to some embodiments and a conventional state estimation model. FIG. 5 illustrates a process for calculating the curvature of an ion movement path based on electrode structure data according to some embodiments. FIG. 6 illustrates the performance difference between a state estimation model according to some embodiments and a conventional state estimation model. FIG. 7 illustrates the optimal range of curvature derived based on the state of the battery according to some embodiments. FIG. 8 illustrates steps constituting a battery management method according to some embodiments. 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. 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. 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). 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. 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