KR-20260067754-A - APPARATUS FOR DIAGNOSING BATTERY AND METHOD THEREOF

Abstract

A battery diagnostic device according to an embodiment of the present document includes a memory in which one or more instructions are stored, and a processor that executes said one or more instructions. The processor identifies at least one factor related to at least one of an electrode of a battery, an active material constituting the battery, or any combination thereof, and predicts the lifespan of said battery based on inputting said at least one factor into a neural network model, and generates feedback information to provide feedback to a process related to said battery based on the ranking in which said at least one factor affects said lifespan.

Inventors

• 조혁준
• 김기웅
• 정유나

Assignees

• 주식회사 엘지에너지솔루션

Dates

Publication Date

20260513

Application Date

20241106

Claims (15)

1. Memory in which one or more instructions are stored; and It includes a processor that executes one or more of the above instructions, The above processor is, Identifying at least one factor related to at least one of the electrode of the battery, the active material constituting the battery, or any combination thereof, and Predicting the lifespan of the battery based on inputting at least one of the above factors into a neural network model, and A battery diagnostic device configured to generate feedback information for providing feedback to a process related to the battery, based on the ranking of at least one factor affecting the lifespan.
2. In Article 1, The above at least one factor is, A battery diagnostic device comprising at least one of the size of the electrode, the density of the active material charged in the electrode, the type of the active material, a conductive additive, adhesion, thickness after R/P (rate/performance), porosity, sliding, coating width, or any combination thereof.
3. In Article 1, The above processor is, A battery diagnostic device configured to input at least one effect element into a neural network model based on identifying at least one effect element that affects the battery life among the at least one factor.
4. In Article 1, The above neural network model is, Includes the first neural network model, The above processor is, A battery diagnostic device configured to identify the ranking using a second neural network model different from the first neural network model.
5. In Paragraph 4, The above processor is, A battery diagnostic device configured to generate feedback information based on performing recursive feedback on the above ranking.
6. In Paragraph 4, The above first neural network model is, It includes at least one of a machine learning model, a deep learning model, or any combination thereof, and The above second neural network model is, A battery diagnostic device including XAI (explainable AI).
7. In Article 1, The above neural network model is, A battery diagnostic device comprising at least one of linear regression, a decision tree, a random forest, a gradient boosting machine (GBM) algorithm, an XGBoost algorithm, a convolutional neural network model, or any combination thereof.
8. In Article 1, The above processor is, A battery diagnostic device configured to predict the life of the battery at a specific point in time.
9. An operation of identifying at least one factor related to at least one of an electrode of a battery, an active material constituting the battery, or any combination thereof, by a processor; An operation of predicting the life of the battery based on inputting at least one factor into a neural network model by the above processor; and A battery diagnostic method comprising the operation of generating feedback information to provide feedback to a process related to the battery based on the ranking of at least one factor affecting the lifespan by the processor.
10. In Article 9, The above at least one factor is, A battery diagnostic method comprising at least one of the size of the electrode, the density of the active material charged in the electrode, the type of the active material, a conductive additive, adhesion, thickness after R/P (rate/performance), porosity, sliding, coating width, or any combination thereof.
11. In Article 9, The above battery diagnostic method is, A battery diagnostic method comprising the operation of inputting at least one effect element that affects the battery life among at least one factor, based on the identification of at least one effect element by the processor, into the neural network model.
12. In Article 9, The above neural network model is, Includes the first neural network model, The above battery diagnostic method is, A battery diagnostic method comprising the operation of identifying the ranking by using a second neural network model different from the first neural network model by the above processor.
13. In Article 12, The above battery diagnostic method is, A battery diagnostic method comprising an operation to generate feedback information based on performing recursive feedback on the ranking by the above processor.
14. In Article 12, The above first neural network model is, It includes at least one of a machine learning model, a deep learning model, or any combination thereof, and The above second neural network model is, A battery diagnostic method including XAI (explainable AI).
15. In Article 9, The above neural network model is, A battery diagnostic method comprising at least one of linear regression, a decision tree, a random forest, a gradient boosting machine (GBM) algorithm, an XGBoost algorithm, a convolutional neural network model, or any combination thereof.

Description

Battery Diagnostic Apparatus and Method Thereof The embodiments disclosed in this document relate to a battery diagnostic device and a method thereof. Recently, active research and development on secondary batteries has been underway. Here, secondary batteries are rechargeable batteries that can be interpreted to encompass conventional Ni/Cd and Ni/MH batteries, as well as recent lithium-ion batteries. With their scope of application expanding to include power sources for electric vehicles, they are garnering attention as a next-generation energy storage medium. Various studies are being conducted to predict and improve battery lifespan. In particular, diverse research is being carried out on the factors at play in predicting and improving battery lifespan; among these, there is a need to provide feedback for battery-related processes based on the influence of physicochemical properties on battery lifespan. FIG. 1 is a block diagram showing a battery pack in a battery diagnostic device and battery diagnostic method according to one embodiment of the present document. FIG. 2 illustrates an example of a block diagram showing the configuration of a battery diagnostic device according to one embodiment of the present document. FIG. 3 illustrates an example of a flowchart related to a battery diagnostic method according to one embodiment of the present document. FIG. 4 illustrates an example of at least one influencing factor affecting the lifespan of a battery in one embodiment of the present document. FIG. 5 illustrates an example of a flowchart related to a battery diagnostic method according to one embodiment of the present document. FIG. 6 is a block diagram showing the hardware configuration of a computing system for performing a battery diagnosis method in a battery diagnosis device and battery diagnosis method according to one embodiment of the present document. Some embodiments disclosed herein are described below with reference to the various embodiments of the accompanying drawings. However, this is not intended to limit the technology to specific embodiments and should be understood to include various modifications, equivalents, and/or alternatives to embodiments of the technology. It should be noted that when assigning reference numerals to the components of each drawing, the same components are assigned the same reference numeral whenever possible, even if they are shown in different drawings. Furthermore, in describing the various embodiments disclosed in this document, if it is determined that a detailed description of related known configurations or functions would hinder understanding of the embodiments of the present invention, such detailed description is omitted. The singular form of a noun corresponding to an item may include one or more items unless the relevant context clearly indicates otherwise. In describing the components of the embodiments of this document, terms such as first, second, A, B, (a), (b), etc., may be used. These terms are intended merely to distinguish the components from other components and do not limit the essence, order, or sequence of the components. Furthermore, unless otherwise defined, all terms used herein, 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. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application. Additionally, in this disclosure, expressions of "greater than" or "less than" may be used to determine whether a specific condition is satisfied or fulfilled; however, this is merely for the purpose of expressing an example and does not exclude descriptions of "greater than" or "less than." Conditions described as "greater than" may be replaced with "greater than," conditions described as "less than" may be replaced with "less than," and conditions described as "greater than and less than" may be replaced with "greater than and less than." Furthermore, "A" to "B" below refer to at least one of the elements from A (including A) to B (including B). 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. In this document, where any component (e.g., 1) is referred to as being “connected,” “coupled,” or “joined” to another component (e.g., 2), with or without the terms “functionally” or “communicationally,” or where it is referred to as “coupled” or “connected,” it means that the component may be connected to the other component directly (e.g., via a wire), wirelessly,