Battery diagnostic apparatus and method therefor

Abstract

A battery diagnostic apparatus according to an embodiment of the present document comprises: a memory in which one or more instructions are stored; and a processor executing the one or more instructions, wherein the processor may: identify a first designated range of a state of charge (SOC) of a battery on the basis of obtaining a first profile in a process of charging the battery in a first state; identify a second designated range corresponding to the first designated range on the basis of obtaining a second profile in a process of charging the battery in a second state different from the first state; and identify a degradation degree of the battery on the basis of the first designated range and the second designated range.

Description

Battery diagnostic device and method

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority based on Korean Patent Application No. 10-2024-0158828 filed on November 11, 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 diagnostic device and a method thereof.

[0005] 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.

[0006] Various studies are being conducted to accurately diagnose the condition of batteries. In particular, diverse research is being carried out to accurately diagnose the degree of battery degradation. Furthermore, there is a need to accurately predict and identify available lithium loss associated with battery degradation.

[0007] According to the embodiments disclosed in this document, a battery diagnostic device and a method for identifying the degree of degradation and / or available lithium loss of a battery using the SOC of the battery are to be provided.

[0008] According to the embodiments disclosed in this document, the present invention aims to provide a battery diagnostic device and a method for measuring the degree of degradation and / or available lithium loss of a battery without disassembling the battery by identifying the degree of degradation and / or available lithium loss of the battery using the battery's SOC.

[0009] According to the embodiments disclosed in this document, the present invention aims to provide a battery diagnostic device and a method that reduce the time and cost of measuring the degree of degradation and / or available lithium loss of a battery by identifying the degree of degradation and / or available lithium loss of a battery using the state of charge (SOC) of the battery.

[0010] The technical problems of the present invention are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below.

[0011] A battery diagnostic device according to one embodiment of the present document includes a memory storing one or more instructions and a processor that executes said one or more instructions, wherein the processor identifies a first designated range of the state of charge (SOC) of said battery based on acquiring a first profile during the process of charging a battery in a first state, identifies a second designated range corresponding to said first designated range based on acquiring a second profile during the process of charging said battery in a second state different from said first state, and identifies the degree of degradation of said battery based on said first designated range and said second designated range.

[0012] In one embodiment, the processor can identify the loss of active lithium (LAM) of the battery based on the first designated range and the second designated range, and identify the degree of degradation based on the loss of active lithium.

[0013] In one embodiment, the processor can identify the degree of deterioration based on the difference between the first designated range and the second designated range.

[0014] In one embodiment, the first designated range and the second designated range may include cases where the SOC is greater than or equal to a threshold value.

[0015] In one embodiment, the processor can obtain the first profile and the second profile during the process of charging the battery in the first state and the battery in the second state at a specified charge rate at a specified temperature.

[0016] In one embodiment, the processor can identify the degree of degeneration based on inputting the first designated range and the second designated range into a neural network model.

[0017] In one embodiment, the neural network model may output the degree of degradation based on the difference between the first specified range and the second specified range, the degree to which the resistance of the battery increases during the process of the battery changing from the first state to the second state, or at least one of any combination thereof.

[0018] In one embodiment, the neural network model may include at least one of a machine learning model, a deep learning model, or any combination thereof.

[0019] A battery diagnostic method according to one embodiment of the present document may include: an operation of identifying a first designated range of the state of charge (SOC) of the battery based on acquiring a first profile during the process of charging the battery in a first state by a processor; an operation of identifying a second designated range corresponding to the first designated range based on acquiring a second profile during the process of charging the battery in a second state different from the first state by the processor; and an operation of identifying the degree of degradation of the battery based on the first designated range and the second designated range.

[0020] The battery diagnostic method according to one embodiment may include the operation of identifying the loss of active lithium (LAM) of the battery based on the first designated range and the second designated range by the processor, and the operation of identifying the degree of degradation based on the loss of active lithium by the processor.

[0021] The battery diagnostic method according to one embodiment may include an operation of identifying the degree of degradation based on the difference between the first designated range and the second designated range by the processor.

[0022] In one embodiment, the first designated range and the second designated range may include cases where the SOC is greater than or equal to a threshold value.

[0023] The battery diagnostic method according to one embodiment may include the operation of obtaining the first profile and the second profile during the process of charging the battery in the first state and the battery in the second state at a specified charge rate by the processor at a specified temperature.

[0024] The battery diagnostic method according to one embodiment may include an operation of identifying the degree of deterioration based on inputting the first designated range and the second designated range into a neural network model by the processor.

[0025] In one embodiment, the neural network model may output the degree of degradation based on the difference between the first specified range and the second specified range, the degree to which the resistance of the battery increases during the process of the battery changing from the first state to the second state, or at least one of any combination thereof.

[0026] This technology can identify the degree of battery degradation and / or available lithium loss using the battery's SOC.

[0027] In addition, the present technology can measure the degree of degradation and / or available lithium loss of a battery without disassembling the battery by identifying the degree of degradation and / or available lithium loss of the battery using the battery's SOC.

[0028] In addition, the present technology can reduce the time and cost of measuring battery degradation and / or available lithium loss by identifying battery degradation and / or available lithium loss using the battery's SOC.

[0029] In addition, various effects that can be identified directly or indirectly through this document may be provided.

[0030] 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.

[0031] 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.

[0032] FIGS. 3a and 3b illustrate an example of measuring the degree of degradation of a battery by disassembling the battery in one embodiment of the present document.

[0033] FIGS. 4a and FIGS. 4b illustrate examples of a first profile and a second profile in an embodiment of the present document.

[0034] FIG. 5 illustrates an example of factors input to and output to a neural network model in one embodiment of the present document.

[0035] FIG. 6 illustrates an example of comparing output data from a neural network model with actual experimental data in one embodiment of the present document.

[0036] FIG. 7 illustrates an example of a flowchart related to a battery diagnostic method according to one embodiment of the present document.

[0037] FIG. 8 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.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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).

[0042] 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.

[0043] 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, or through a third component.

[0044] According to one embodiment, the method according to the various embodiments disclosed herein may be provided as included in 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 user 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.

[0045] According to various embodiments, each component (e.g., module or program) of the described components may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to various embodiments, one or more of the aforementioned components or operations 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 this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as they were performed by the corresponding component among the multiple components prior to integration. According to various embodiments, operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically; one or more of the operations may be executed in a different order; may be omitted; or one or more other operations may be added.

[0046] Hereinafter, embodiments of the present document will be described in detail with reference to FIGS. 1 to 8.

[0047] 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.

[0048] Referring to FIG. 1, the battery pack (1) may include a battery unit (12), a sensor unit (14), a switching unit (16), and a battery management system (BMS) (20). At this time, the battery pack (1) may be equipped with a plurality of battery units (12), sensor units (14), switching units (16), and battery management systems (20).

[0049] According to one embodiment, the battery unit (12) can supply power to a target device (not shown). To this end, the battery unit (12) may be electrically connected to the target device. Here, the target device may include an electrical, electronic, or mechanical device that operates by receiving power from the battery pack (1). For example, the target device may be an electric vehicle (EV), but is not limited thereto.

[0050] According to one embodiment, the battery unit (12) may include at least one rechargeable battery cell (10). Here, the battery cell (10) may be a basic unit of a battery cell capable of charging and discharging electrical energy. For example, the battery cell (10) may be a lithium-ion (Li-ion) battery, a lithium-ion polymer (Li-ion polymer) battery, a nickel-cadmium (Ni-Cd) battery, a nickel-hydrogen (Ni-MH) battery, etc., but is not limited thereto.

[0051] According to one embodiment, a plurality of battery units (12) may be connected in series or in parallel. For example, a battery unit (12) may be a battery module, a battery bank, or a set of battery cells (cell-to-pack structure).

[0052] According to one embodiment, the sensor unit (14) can obtain information related to the battery unit (12). According to one embodiment, the sensor unit (14) can obtain values ​​(or information) related to the state of each of the battery unit (12) or battery cells (10). In one embodiment, the values ​​related to the state may include one or more values ​​for the voltage, current, resistance, state of charge (SOC), state of health (SOH), or temperature of the battery cell, or a combination thereof.

[0053] According to one embodiment, the sensor unit (14) can provide information of each of the plurality of battery units (12) to the battery management system (20).

[0054] According to one embodiment, the switching unit (16) may include an element for controlling the current flow for charging or discharging the battery unit (12). For example, the switching unit (16) may include at least one relay and / or magnetic contactor, etc., depending on the specifications of the battery pack (1).

[0055] According to one embodiment, a battery management system (BMS) (20) can monitor the voltage, current, temperature, etc. of a battery pack (1) and control or manage the battery pack (1) to prevent overcharging and over-discharging. For example, the battery management system (20) may include a plurality of terminals as an interface for receiving values ​​of the various parameters described above, and a circuit connected to these terminals to perform processing of the received values. Additionally, the battery management system (20) may control a sensor unit (14) and / or a switching unit (16). For example, the battery management system (20) may be connected to a plurality of battery units (12) to monitor the status of each of the plurality of battery units (12) and control the ON / OFF of relays or contactors.

[0056] According to one embodiment, the operation of the battery management system (20) can be performed by a battery management system (BMS) in the vehicle, as well as by various devices such as a server, cloud, charger, or charger / discharger.

[0057] The upper controller (2) can transmit control signals for a plurality of battery units (12) to the battery management system (20). Accordingly, the operation of the battery management system (20) can be controlled based on the signals applied from the upper controller (2).

[0058] According to one embodiment, the battery management system (20) may include the battery diagnostic device (200) of FIG. 2. According to another embodiment, the battery management system (20) may be a different system from the battery diagnostic device (200) of FIG. 2. That is, the battery diagnostic device (200) of FIG. 2 may be included in the battery pack (1) or may be configured as another device outside the battery pack (1). For convenience of explanation, the following description assumes that the battery diagnostic device (200) is configured as another device outside the battery pack (1). Furthermore, the operation of the battery diagnostic device (200) below may be performed by a battery management system (BMS) within the vehicle, as well as by various devices such as a server, cloud, charger, or charger / discharger.

[0059] 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.

[0060] Referring to FIG. 2, a battery diagnostic device (200) according to one embodiment may include a processor (210) and a memory (220). The processor (210) and the memory (220) may be electrically and / or operably coupled with each other by an electronic device including a communication bus.

[0061] In the following, the hardware being operatively coupled may include direct and / or indirect connections between the hardware being established via wired and / or wireless connections so that the second hardware is controlled by the first hardware among the hardware.

[0062] Although the hardware is illustrated in different blocks, the embodiment is not limited thereto. For example, some of the hardware in FIG. 2 may be included in a single integrated circuit including a system-on-a-chip (SoC). The type and / or number of hardware included in the battery diagnostic device (200) is not limited to that illustrated in FIG. 2. For example, the battery diagnostic device (200) may include only some of the hardware illustrated in FIG. 2.

[0063] A battery diagnostic device (200) according to one embodiment may include hardware for processing data based on one or more instructions. The hardware for processing data may include a processor (210).

[0064] For example, hardware for processing data may include an arithmetic and logic unit (ALU), a floating point unit (FPU), a field programmable gate array (FPGA), a central processing unit (CPU), and / or an application processor (AP). The processor (210) may have the structure of a single-core processor or the structure of a multi-core processor including a dual core, a quad core, a hexa core, or an octa core.

[0065] A memory (220) of a battery diagnostic device (200) according to one embodiment may include a hardware component for storing data and / or instructions that are input and / or output to a processor (210) of the battery diagnostic device (200).

[0066] For example, the memory (220) may include volatile memory including random-access memory (RAM) and / or non-volatile memory including read-only memory (ROM).

[0067] For example, volatile memory may include at least one of DRAM (dynamic RAM), SRAM (static RAM), Cache RAM, PSRAM (pseudo SRAM), or any combination thereof.

[0068] For example, non-volatile memory may include at least one of PROM (programmable ROM), EPROM (erasable PROM), EEPROM (electrically erasable PROM), flash memory, hard disk, compact disk, SSD (solid state drive), eMMC (embedded multi-media card), or any combination thereof.

[0069] For example, within the memory (220) of the battery diagnostic device (200), one or more instructions (or commands) representing operations and / or actions to be performed on data by the processor (210) of the battery diagnostic device (200) may be stored. A set of one or more instructions may be referred to as a program, firmware, operating system, process, routine, sub-routine, and / or application. Hereinafter, the statement that an application is installed within the battery diagnostic device (200) may mean that one or more instructions provided in the form of an application are stored within the memory (220), and that one or more applications are stored in an executable format (e.g., a file having an extension specified by the operating system of the battery diagnostic device (200)) by the processor (210) of the battery diagnostic device (200).

[0070] A processor (210) of a battery diagnostic device (200) according to one embodiment may acquire a first profile during the process of charging a battery in a first state. For example, the processor (210) may acquire a first profile related to the battery during the process of charging a battery in a first state. For example, the first state may include the BOL (beginning of life) state of the battery. For example, the first state may include a state in which the SOH (state of health) of the battery exceeds a first threshold value.

[0071] For example, the processor (210) can obtain a first profile during the process of charging a battery in a first state at a specified charge rate at a specified temperature. For example, the processor (210) can obtain a first profile during the process of charging a battery in a first state using a specified C-rate at a specified temperature.

[0072] In one embodiment, the processor (210) can identify a first designated range of the state of charge (SOC) of the battery based on the acquisition of a first profile during the process of charging the battery in a first state. For example, the first designated range may include a period in which the SOC of the battery is above (or exceeds) a threshold value. For example, the threshold value may include 55%. For convenience of explanation, the threshold value has been described as 55%, but the embodiments of this document are not limited thereto.

[0073] A processor (210) of a battery diagnostic device (200) according to one embodiment may acquire a second profile during the process of charging a battery in a second state different from a first state. For example, the second state may include a middle of life (MOL) state of the battery. For example, the second state may include a state in which the SOH of the battery is below a first threshold value and exceeds a second threshold value.

[0074] For example, the processor (210) can obtain a second profile during the process of charging a battery in a second state at a specified charge rate at a specified temperature. For example, the processor (210) can obtain a second profile during the process of charging a battery in a second state at a specified C-rate using a specified C-rate at a specified temperature.

[0075] In one embodiment, the processor (210) can identify a second designated range corresponding to a first designated range based on the acquisition of a second profile during the process of charging a battery in a second state different from a first state. For example, the second designated range corresponding to the first designated range may include a period in which the battery's SOC is above (or exceeds) a threshold value.

[0076] For example, the first designated range and the second designated range described above may include cases where the battery's SOC is greater than or equal to a threshold value.

[0077] In one embodiment, the processor (210) can identify the degree of degradation of the battery based on a first designated range and a second designated range. For example, the processor (210) can identify the loss of active lithium (LAM) of the battery based on a first designated range and a second designated range. In this document, an example regarding the loss of active lithium of the battery is described, but the embodiments are not limited thereto. For example, the processor (210) of the battery diagnostic device (200) can identify the loss of an element related to the generation of electrical energy contained in the cathode material.

[0078] In one embodiment, the processor (210) can identify the degree of degradation of the battery based on available lithium loss.

[0079] In one embodiment, the processor (210) can identify the difference between a first designated range and a second designated range. For example, the processor (210) can identify the difference between a first designated range identified in a first profile and a second designated range identified in a second profile. For example, the processor (210) can identify a ratio corresponding to the difference between the first designated range and the second designated range based on identifying the difference between the first designated range and the second designated range.

[0080] In one embodiment, the processor (210) can identify the degree of degradation of the battery based on the difference between a first designated range and a second designated range. For example, the processor (210) can identify the degree of degradation of the battery based on a ratio corresponding to the difference between the first designated range and the second designated range.

[0081] A processor (210) of a battery diagnostic device (200) according to one embodiment can input a first designated range and / or a second designated range into a neural network model.

[0082] For example, neural network models may include statistical learning algorithms in machine learning and cognitive science that mimic biological neurons. A neural network model can refer to a model in general in which artificial neurons (or nodes) forming a network through synaptic connections change the strength of synaptic connections through learning to possess problem-solving capabilities.

[0083] For example, the neurons in a neural network model may include a combination of weights and / or biases. A neural network model may include one or more neurons, or one or more layers composed of nodes. A neural network model can infer a result to be predicted from an arbitrary input by changing the weights of the neurons through learning.

[0084] For example, neural network models may include deep neural network models. Neural network models include CNN (Convolutional Neural Network), RNN (Recurrent Neural Network), perceptron, multilayer perceptron, FF (Feed Forward), RBF (Radial Basis Network), DFF (Deep Feed Forward), LSTM (Long Short Term Memory), GRU (Gated Recurrent Unit), AE (Auto Encoder), VAE (Variational Auto Encoder), DAE (Denoising Auto Encoder), SAE (Sparse Auto Encoder), MC (Markov Chain), HN (Hopfield Network), BM (Boltzmann Machine), RBM (Restricted Boltzmann Machine), DBN (Deep Belief Network) model, DCN (Deep Convolutional Network), DN (Deconvolutional Network) model, DCIGN (Deep Convolutional Inverse Graphics Network) model, GAN (Generative Adversarial Network) model, and LSM (Liquid State It may include Machine), ELM (Extreme Learning Machine), ESN (Echo State Network) model, DRN (Deep Residual Network) model, DNC (Differentiable Neural Computer), NTM (Neural Turning Machine), CN (Capsule Network) model, KN (Kohonen Network) model, and / or AN (Attention Network) model.

[0085] For example, the neural network model may be stored in memory (220) or received from an external electronic device through a communication circuit (not shown). For example, the processor (210) may use the neural network model stored in memory (220) through the communication circuit. For example, when using the neural network model through the communication circuit, the processor (210) may transmit input values ​​to be input to the neural network model to an external electronic device including the neural network model, and receive output values ​​output from the external electronic device including the neural network model. For example, the external electronic device may include at least one of a vehicle, a cloud server, or any combination thereof. However, examples of external electronic devices are not limited to those described above.

[0086] In one embodiment, the processor (210) can identify the degree of degradation of the battery based on inputting the first designated range and the second designated range into a neural network model.

[0087] In one embodiment, the processor (210) can train a neural network model using at least one of a first designated range, a second designated range, a battery degradation level, or any combination thereof. For example, the processor (210) can train a neural network model using a first designated range, a second designated range, and a battery degradation level.

[0088] For example, the neural network model can output the degree of battery degradation based on the difference between a first specified range and a second specified range, the degree to which the resistance of the battery increases during the process of the battery changing from a first state to a second state, or at least one of any combination thereof.

[0089] As described above, a battery diagnostic device (200) according to one embodiment can identify available lithium loss and / or the degree of degradation of the battery by using the SOC of the battery. By identifying available lithium loss and / or the degree of degradation of the battery by using the SOC of the battery, the battery diagnostic device (200) can provide the effect of reducing the cost and time required when measuring available lithium loss and / or the degree of degradation of the battery.

[0090] FIGS. 3a and 3b illustrate an example of measuring the degree of degradation of a battery by disassembling the battery in one embodiment of the present document.

[0091] FIGS. 3a and 3b may relate to an example of disassembling a battery to measure the degree of degradation of the battery. For example, FIG. 3a illustrates an example of disassembling a battery in a first state to produce a coin cell and checking the available lithium loss and / or the degree of degradation of the battery.

[0092] FIG. 3a illustrates an example of a graph related to the capacity and voltage of a first coin cell produced through a battery in a first state. FIG. 3b illustrates an example of a graph related to the capacity and voltage of a second coin cell produced through a battery in a second state.

[0093] In the graphs shown in Figs. 3a and 3b, the horizontal axis represents the capacity of the coin cell, and the vertical axis represents the voltage.

[0094] In FIG. 3a, the first data (301) may represent the cathode voltage of the first coin cell according to the capacity of the first coin cell. In FIG. 3a, the second data (302) may represent the anode voltage of the first coin cell according to the capacity of the first coin cell. In FIG. 3a, the third data (303) may represent the total voltage of the first coin cell.

[0095] In FIG. 3b, the fourth data (331) may represent the cathode voltage of the second coin cell according to the capacity of the second coin cell. In FIG. 3b, the fifth data (332) may represent the anode voltage of the second coin cell according to the capacity of the second coin cell. In FIG. 3b, the sixth data (333) may represent the total voltage of the second coin cell.

[0096] The first section (321) and the second section (341) of FIGS. 3a and 3b may represent the discharge capacity of the anode. The total amount of available lithium loss can be identified by the difference between the second section (341) and the first section (321).

[0097] As mentioned above, available lithium loss can generally be verified through additional processes after disassembling the battery. Since verifying available lithium loss in this manner is costly and time-consuming, this document proposes a method to identify available lithium loss without disassembling the battery.

[0098] FIGS. 4a and FIGS. 4b illustrate examples of a first profile and a second profile in an embodiment of the present document.

[0099] FIG. 4a may represent a first profile obtained during the process of charging a battery in a first state. FIG. 4b may represent a second profile obtained during the process of charging a battery in a second state.

[0100] In the graphs shown in FIGS. 4a and 4b, the horizontal axis represents the capacity of the battery, and the vertical axis represents the voltage.

[0101] The data (401) in FIG. 4a may represent a voltage according to the capacity of the battery in the first state. The data (431) in FIG. 4b may represent a voltage according to the capacity of the battery in the second state.

[0102] Referring to FIG. 4a, the processor (210) of the battery diagnostic device (200) according to one embodiment can identify a first partial section (411) and a second partial section (412) in a first profile. For example, at least one of the first partial section (411) and the second partial section (412) may be included in the first designated range described in FIG. 2.

[0103] Referring to FIG. 4b, the processor (210) of the battery diagnostic device (200) according to one embodiment can identify a third partial section (441) and a fourth partial section (442) in the second profile. For example, at least one of the third partial section (441) and the fourth partial section (442) may be included in the second designated range described in FIG. 2.

[0104] In this document, for convenience of explanation, the second subsection (412) is referred to as the first designated range and the fourth subsection (442) is referred to as the second designated range, but the embodiments are not limited thereto. For example, the processor (210) can identify the degree of degradation of the battery based on the difference between the second subsection (412) included in the first designated range and the fourth subsection (442) included in the second designated range. Specifically, the processor (210) can identify available lithium loss based on the difference between the second subsection (412) included in the first designated range and the fourth subsection (442) included in the second designated range. According to an embodiment, the processor (210) can identify the degree of degradation of the battery based on the difference between the first subsection (411) and the third subsection (441).

[0105] For example, the processor (210) can identify the degree of reduction by comparing the second portion section (412) and the fourth portion section (442). The processor (210) can identify available lithium loss based on the degree of reduction. However, the embodiments are not limited to those described above. For example, the processor (210) can identify the degree of increase by comparing the first portion section (411) and the third portion section (441). The processor (210) can identify available lithium loss based on the degree of increase.

[0106] As described above, the processor (210) of the battery diagnostic device (200) according to one embodiment can provide the effect of reducing cost and time by identifying the available lithium loss of the battery and / or the degree of degradation of the battery without disassembling the battery.

[0107] FIG. 5 illustrates an example of factors input to and output to a neural network model in one embodiment of the present document.

[0108] Referring to FIG. 5, the processor (210) of the battery diagnostic device (200) according to one embodiment can identify the degree of degradation of the battery using a neural network model.

[0109] For example, in FIG. 5, the first X factor and the second X factor may represent data to be input to a neural network model. For example, in FIG. 5, the Y factor may represent data to be output from a neural network model.

[0110] For example, the first X factor may represent the difference between specified ranges. For example, the processor (210) may input the difference between specified ranges into the neural network model. However, the embodiment is not limited thereto, and the processor (210) may input the first specified range and the second specified range into the neural network model.

[0111] For example, the second X factor may represent the degree of resistance increase. For example, the degree of resistance increase may represent the difference between the first resistance of the battery in the first state and the second resistance of the battery in the second state.

[0112] For example, the processor (210) may use, as a second X factor, at least one of the ratio of the current voltage (CV) charge amount to the total charge amount, the DC-IR (direct current internal resistance), the voltage drop during the idle period immediately after charging, or any combination thereof.

[0113] The reason for using the degree of resistance increase as the second X factor is that resistance affects the first specified range and the second specified range, so this may be used to exclude the influence of resistance.

[0114] A processor (210) of a battery diagnostic device (200) according to one embodiment may input a first X factor and a second X factor into a neural network model and obtain a Y factor from the neural network model. The Y factor may represent the actual positive capacity degradation of the battery.

[0115] In one embodiment, the processor (210) can obtain available lithium loss by subtracting the value output as the Y factor from the degree of total capacity degradation of the battery.

[0116] FIG. 6 illustrates an example of comparing output data from a neural network model with actual experimental data in one embodiment of the present document.

[0117] In the graph of Fig. 6, the horizontal axis may represent actual experimental data. In the graph of Fig. 6, the vertical axis may represent output data obtained from a neural network model.

[0118] In the graph of FIG. 6, the points (601) may represent available lithium loss. As can be seen in the graph of FIG. 6, the experimental data and the output data are similar. Specifically, the average RMSE (root means squared error) of the experimental data and the output data is 0.34, which confirms that the experimental data and the output data are similar.

[0119] FIG. 7 illustrates an example of a flowchart related to a battery diagnostic method according to one embodiment of the present document.

[0120] In the following, it is assumed that the battery diagnostic device (200) of FIG. 2 performs the process of FIG. 7. Also, in the description of FIG. 7, the operation described as being performed by the device can be understood as being controlled by the processor (210) of the battery diagnostic device (200).

[0121] At least one of the operations of FIG. 7 can be performed by the battery diagnostic device (200) of FIG. 2. At least one of the operations of FIG. 7 can be controlled by the processor (210) of FIG. 2. Each of the operations of FIG. 7 can be performed sequentially, but is not necessarily performed sequentially. For example, the order of each of the operations can be changed, and at least two operations can be performed in parallel.

[0122] Referring to FIG. 7, in operation S701, a battery diagnostic method according to one embodiment may include an operation of identifying a first designated range of the battery's SOC based on obtaining a first profile during the process of charging a battery in a first state.

[0123] For example, a battery diagnostic method may include the operation of obtaining a first profile during the process of charging a battery in a first state at a specified charge rate at a specified temperature.

[0124] In operation S703, a battery diagnostic method according to one embodiment may include an operation of identifying a second designated range corresponding to a first designated range based on obtaining a second profile during the process of charging a battery in a second state different from a first state.

[0125] For example, the battery diagnostic method may include the operation of obtaining a second profile during the process of charging a battery in a second state at a specified charge rate at a specified temperature.

[0126] In operation S705, the battery diagnostic method according to one embodiment may include an operation of identifying the degree of degradation of the battery based on a first designated range and a second designated range.

[0127] For example, the first designated range and the second designated range may include cases where the battery's SOC is greater than or equal to a threshold value.

[0128] For example, the battery diagnostic method may include an operation of identifying available lithium loss of the battery based on a first specified range and a second specified range. For example, the battery diagnostic method may include an operation of identifying the degree of degradation of the battery based on available lithium loss.

[0129] For example, the battery diagnostic method may include an operation of identifying the degree of degradation of the battery based on the difference between a first specified range and a second specified range. For example, the battery diagnostic method may include an operation of identifying available lithium loss based on the difference between a first specified range and a second specified range.

[0130] For example, the battery diagnostic method may include an operation to identify the degree of degradation of the battery based on inputting a first specified range and a second specified range into a neural network model. For example, the battery diagnostic method may include an operation to identify available lithium loss based on inputting a first specified range and a second specified range into a neural network model.

[0131] A battery diagnostic method according to one embodiment may include an operation to identify the degree of degradation of a battery based on at least one of the difference between a first specified range and a second specified range, the degree of increase in the resistance of the battery during the process of the battery changing from a first state to a second state, or any combination thereof. For example, a neural network model may output the degree of degradation of the battery based on receiving the difference between the first specified range and the second specified range, and the degree of increase in the resistance of the battery during the process of the battery changing from a first state to a second state. As another example, a neural network model may output available lithium loss based on receiving the difference between the first specified range and the second specified range, and the degree of increase in the resistance of the battery during the process of the battery changing from a first state to a second state.

[0132] For example, a neural network model may include at least one of a machine learning model, a deep learning model, or any combination thereof.

[0133] As described above, a battery diagnostic method according to one embodiment may include an operation of identifying available lithium loss and / or the degree of degradation of the battery based on the battery's SOC. By identifying available lithium loss and / or the degree of degradation of the battery based on the battery's SOC, the battery diagnostic method can provide the effect of reducing cost and time.

[0134] FIG. 8 is a block diagram showing the hardware configuration of a computing system for performing a battery diagnostic method in a battery diagnostic device and a battery diagnostic method according to one embodiment of the present document.

[0135] Referring to FIG. 8, a computing system (1100) according to one embodiment disclosed in this document may include an MCU (1110), memory (1120), input / output I / F (1130) and communication I / F (1140).

[0136] The MCU (1110) may be a processor that executes various programs stored in memory (1120) (e.g., battery cell data collection program, graph calculation program, data analysis program, data decomposition algorithm, normalization program, battery cell diagnosis program, etc.), processes various information including characteristic data and potential variables of the battery cell through these programs, and performs the functions of the battery diagnosis device (200) shown in FIGS. 1 to 7.

[0137] The memory (1120) can store various programs such as a battery cell data collection program, a graph generation program, a data analysis program, a data decomposition algorithm, a normalization program, and a battery cell diagnosis program.

[0138] These memories (1120) may be provided in multiple quantities as needed. The memories (1120) may be volatile memories or non-volatile memories. As volatile memories, the memory (1120) may use RAM, DRAM, SRAM, etc. As non-volatile memories, the memory (1120) may use ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. The examples of the listed memories (1120) are merely examples and are not limited to these examples.

[0139] The input / output I / F (1130) can provide an interface that enables data transmission and reception between an input device (not shown), such as a keyboard, mouse, or touch panel, an output device (not shown), and an MCU (1110).

[0140] The communication I / F (1140) is configured to transmit and receive various data with a server and may be various devices capable of supporting wired or wireless communication. For example, the battery diagnostic device (200) can transmit and receive various information, including the shape model of a battery cell, from a separately provided external server via the communication I / F (1140).

[0141] In this way, a computer program according to one embodiment disclosed in this document may be implemented as a module that performs, for example, the functions illustrated in FIG. 2, by being recorded in memory (1120) and processed by an MCU (1110).

[0142] As described above, even though all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purposes of the embodiments disclosed in this document, all components may be selectively combined in one or more ways to operate.

[0143] Furthermore, terms such as "include," "compose," or "have" as described above, unless specifically stated otherwise, mean that the relevant component may be inherent; thus, 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 contextual meanings in the relevant technology and, unless explicitly defined in this document, should not be interpreted in an ideal or overly formal sense.

[0144] The foregoing disclosure outlines the features of several embodiments to enable those skilled in the art to better understand the aspects of the present disclosure. Those skilled in the art will understand that the present disclosure can be readily used as a basis for designing or modifying other structures to perform the same purpose or achieve the same advantages as the embodiments introduced herein. Furthermore, those skilled in the art will recognize that such equivalent configurations do not depart from the scope of the present disclosure and that various changes, substitutions, and modifications may be made in the present specification without departing from the scope of the present disclosure.

Claims

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, In the process of charging a battery in a first state, based on obtaining a first profile, a first designated range of the state of charge (SOC) of the battery is identified, and In the process of charging the battery in a second state different from the first state, based on the acquisition of a second profile, a second designated range corresponding to the first designated range is identified, and A battery diagnostic device configured to identify the degree of degradation of the battery based on the first designated range and the second designated range.

2. In Paragraph 1, The above processor is, Based on the first designated range and the second designated range, the available lithium loss (loss of active lithium, LAM) of the battery is identified, and A battery diagnostic device configured to identify the degree of degradation based on the above available lithium loss.

3. In Paragraph 1, The above processor is, A battery diagnostic device configured to identify the degree of degradation based on the difference between the first specified range and the second specified range.

4. In Paragraph 1, The above-mentioned first designated range and the above-mentioned second designated range are, A battery diagnostic device including the case where the above SOC is greater than or equal to a threshold value.

5. In Paragraph 1, The above processor is, A battery diagnostic device configured to obtain the first profile and the second profile during the process of charging the battery in the first state and the battery in the second state at a specified charge rate at a specified temperature.

6. In Paragraph 1, The above processor is, A battery diagnostic device configured to identify the degree of degeneration based on inputting the first designated range and the second designated range into a neural network model.

7. In Paragraph 6, The above neural network model is, A battery diagnostic device configured to output the degree of degradation based on at least one of the difference between the first designated range and the second designated range, the degree of increase in the resistance of the battery during the process of changing the battery from the first state to the second state, or any combination thereof.

8. In Paragraph 6, The above neural network model is, A battery diagnostic device comprising at least one of a machine learning model, a deep learning model, or any combination thereof.

9. An operation of identifying a first designated range of the state of charge (SOC) of the battery based on the acquisition of a first profile during the process of charging a battery in a first state by a processor; An operation of identifying a second designated range corresponding to the first designated range based on the acquisition of a second profile during the process of charging the battery in a second state different from the first state by the above processor; and A battery diagnostic method comprising an operation to identify the degree of degradation of the battery based on the first designated range and the second designated range.

10. In Paragraph 9, The above battery diagnostic method is, An operation to identify the loss of active lithium (LAM) of the battery based on the first designated range and the second designated range by the processor above; A battery diagnostic method comprising the operation of identifying the degree of degradation based on the available lithium loss by the above processor.

11. In Paragraph 9, The above battery diagnostic method is, A battery diagnostic method comprising an operation to identify the degree of degradation based on the difference between the first designated range and the second designated range by the processor.

12. In Paragraph 9, The above-mentioned first designated range and the above-mentioned second designated range are, A battery diagnostic method including the case where the above SOC is greater than or equal to a threshold value.

13. In Paragraph 9, The above battery diagnostic method is, A battery diagnostic method comprising the operation of obtaining the first profile and the second profile in the process of charging the battery in the first state and the battery in the second state at a specified charge rate by the above processor at a specified temperature.

14. In Paragraph 9, The above battery diagnostic method is, A battery diagnostic method comprising an operation to identify the degree of deterioration based on inputting the first designated range and the second designated range into a neural network model by the above processor.

15. In Paragraph 14, The above neural network model is, A battery diagnostic method configured to output the degree of degradation based on at least one of the difference between the first designated range and the second designated range, the degree of increase in the resistance of the battery during the process of changing the battery from the first state to the second state, or any combination thereof.

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