Battery diagnosis device and method therefor

Abstract

A battery diagnosis device according to an embodiment of the present document may comprise: a memory in which one or more instructions are stored; and a processor for executing the one or more instructions, wherein the processor, during charging and discharging of a battery, can identify a difference between a charging voltage of the battery and a discharging voltage of the battery and acquire, on the basis of the difference between the charging voltage and the discharging voltage, data related to degradation of the battery.

Description

Battery diagnostic device and method

[0001] Cross-citation with related applications

[0002] The present application claims the benefit of priority based on Korean Patent Application No. 10-2024-0180843 filed December 06, 2024, Korean Patent Application No. 10-2024-0180844 filed December 06, 2024, Korean Patent Application No. 10-2024-0180845 filed December 06, 2024, and Korean Patent Application No. 10-2025-0188534 filed December 02, 2025, and incorporates all contents disclosed in the documents of said patent applications 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 predict the remaining lifespan of batteries. In particular, research is being carried out to predict the remaining lifespan of batteries in real time. Since battery lifespan can vary depending on operating conditions, it is generally difficult to determine the remaining lifespan in advance before operating the battery to its end of life (EoL).

[0007] According to the embodiments disclosed in this document, a battery diagnostic device and a method for predicting the lifespan of a battery using the charging voltage and the discharging voltage of the battery are provided.

[0008] According to the embodiments disclosed in this document, the present invention aims to provide a battery diagnostic device and a method for predicting the State of Charge (SOC) at which a specific substance may be precipitated in a battery using the charging voltage and the discharging voltage of the battery.

[0009] According to the embodiments disclosed in this document, the present invention aims to provide a battery diagnostic device and a method that use the charging voltage and the discharging voltage of a battery to predict the remaining life of the battery and perform a process to increase the remaining life 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, and the processor can identify the difference between the charging voltage of said battery and the discharging voltage of said battery during the process of charging and discharging the battery, and acquire data related to the degradation of said battery based on the difference between said charging voltage and said discharging voltage.

[0012] In one embodiment, the processor can predict the SOC at which a specified substance precipitates in the battery based on data related to the degradation of the battery.

[0013] In one embodiment, the processor can predict the SOC at which a specified material precipitates in the battery based on the resistance of the battery.

[0014] In one embodiment, the processor may perform at least one of the following: adjusting the charging speed of the battery, adjusting the operating temperature of the battery, adjusting the range of the state of charge (SOC) used when operating the battery, or any combination thereof, at the SOC where the specified material is precipitated.

[0015] In one embodiment, the processor can acquire data related to the degradation of the battery using a neural network model.

[0016] In one embodiment, the processor can obtain data related to the degradation of the battery by inputting the current of the battery into the neural network model to obtain the voltage of the battery and identifying the resistance of the battery using the current and the voltage.

[0017] In one embodiment, the processor can train the neural network model based on the difference between the charging voltage and the discharging voltage.

[0018] In one embodiment, the processor can obtain the resistance of the battery based on inputting data related to the degradation of the battery and data related to the temperature at which the battery is driven into the neural network model, and obtain data related to the degradation of the battery based on the resistance of the battery.

[0019] In one embodiment, the processor can identify the difference between the charging voltage and the discharging voltage at a specified state of charge (SOC).

[0020] In one embodiment, the processor can obtain data related to the degradation of the battery based on a state factor including the difference between the charging voltage and the discharging voltage, and a condition factor related to the operating conditions of the battery.

[0021] In one embodiment, the state factor may include at least one of the difference between the charging voltage and the discharging voltage, the maximum temperature during the process of driving the battery, or any combination thereof.

[0022] In one embodiment, the condition factor may include at least one of a C-rate used when charging the battery, the temperature of a chamber containing the battery, a state of charge (SOC) range for driving the battery, or any combination thereof.

[0023] In one embodiment, data related to the degradation of the battery may include the remaining energy of the battery.

[0024] In one embodiment, the remaining energy of the battery may include the difference between the energy stored in the battery at the time of EoL (end of life) and the energy stored in the battery at the current time.

[0025] In one embodiment, the EoL time point may be defined as the time point corresponding to a specified value for the capacity retention rate of the battery.

[0026] In one embodiment, the processor can obtain data related to the degradation of the battery based on the temperature at which the battery is driven.

[0027] In one embodiment, the processor can identify the difference between the charging voltage and the discharging voltage during the process of charging and discharging the battery using a specified C-rate.

[0028] A battery diagnostic method according to one embodiment of the present document may include, by a processor, an operation of identifying a difference between a charging voltage of the battery and a discharging voltage of the battery during the process of charging and discharging the battery, and by the processor, an operation of acquiring data related to the degradation of the battery based on the difference between the charging voltage and the discharging voltage.

[0029] The battery diagnostic method according to one embodiment may include an operation in which the processor predicts the SOC at which a specified substance precipitates in the battery based on data related to the degradation of the battery.

[0030] The battery diagnostic method according to one embodiment may include an operation in which the processor predicts the SOC at which a specified substance is precipitated in the battery based on the resistance of the battery.

[0031] The battery diagnostic method according to one embodiment may include, by the processor, at least one of the following operations: controlling the charging speed of the battery at the SOC in which the specified material is precipitated; controlling the operating temperature of the battery; controlling the range of the SOC (state of charge) used when operating the battery; or any combination thereof.

[0032] The battery diagnostic method according to one embodiment may include, by the processor, an operation of inputting the current of the battery into the neural network model to obtain the voltage of the battery, and an operation of obtaining data related to the degradation of the battery based on identifying the resistance of the battery using the current and the voltage.

[0033] The battery diagnostic method according to one embodiment may include an operation of training the neural network model based on the difference between the charging voltage and the discharging voltage by the processor.

[0034] This technology can predict the lifespan of a battery by utilizing the battery's charging voltage and discharge voltage.

[0035] In addition, this technology can predict the State of Charge (SOC) at which a specific substance can be precipitated in a battery by utilizing the battery's charging voltage and discharge voltage.

[0036] In addition, the present technology can predict the remaining life of a battery and perform a process to increase the remaining life of a battery by utilizing the charging voltage and the discharging voltage of the battery.

[0037] In addition, various effects that can be identified directly or indirectly through this document may be provided.

[0038] 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.

[0039] 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.

[0040] FIG. 3 illustrates an example of a graph showing the charging voltage and the discharging voltage of a battery in one embodiment of the present document.

[0041] FIG. 4 illustrates an example related to a battery degradation map in one embodiment of the present document.

[0042] FIGS. 5a to 5c illustrate an example of controlling a battery in an embodiment of the present document.

[0043] FIG. 6 illustrates an example of a flowchart related to a battery diagnostic method according to one embodiment of the present document.

[0044] FIG. 7 illustrates an example showing the remaining energy of a battery in one embodiment of the present document.

[0045] FIG. 8 illustrates an example of obtaining the resistance of a battery using data related to battery degradation and the temperature at which the battery is operated, in one embodiment of the present document.

[0046] FIG. 9 is a block diagram showing the hardware configuration of a computing system for performing a battery diagnosis method in a battery diagnosis device and a battery diagnosis method according to one embodiment of the present document.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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).

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] Hereinafter, embodiments of the present document will be described in detail with reference to FIGS. 1 to 7.

[0056] 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.

[0057] 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).

[0058] 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.

[0059] 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.

[0060] 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).

[0061] 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.

[0062] 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).

[0063] 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).

[0064] 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.

[0065] 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.

[0066] 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).

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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.

[0072] 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).

[0073] 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.

[0074] 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).

[0075] For example, the memory (220) may include volatile memory including random-access memory (RAM) and / or non-volatile memory including read-only memory (ROM).

[0076] 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.

[0077] 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.

[0078] 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).

[0079] A processor (210) of a battery diagnostic device (200) according to one embodiment may perform at least some of the operations described below using a neural network model. For example, the neural network model may include at least one of a machine learning model, a deep learning model, or any combination thereof.

[0080] For example, neural network models may include statistical learning algorithms in machine learning and cognitive science that mimic biological neurons. Neural network models can refer to models in general that possess problem-solving capabilities by having artificial neurons (or nodes) forming a network through synaptic connections that change the strength of synaptic connections through learning.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] A processor (210) of a battery diagnostic device (200) according to one embodiment can identify the charging voltage of the battery and the discharging voltage of the battery during the process of charging and discharging the battery. For example, the processor (210) can identify the charging voltage of the battery during the process of charging and discharging the battery. For example, the processor (210) can identify the discharging voltage of the battery during the process of charging and discharging the battery.

[0085] For example, the processor (210) can identify the charging voltage during the process of charging the battery using an overcurrent. For example, the processor (210) can identify the discharging voltage during the process of discharging the battery using an overcurrent. Since the battery is charged and discharged using an overcurrent, a difference between the charging voltage and the discharging voltage may occur.

[0086] In one embodiment, the processor (210) can identify the difference between the charging voltage of the battery and the discharging voltage of the battery during the process of charging and discharging the battery. For example, the processor (210) can identify the difference between the charging voltage of the battery and the discharging voltage of the battery based on identifying the charging voltage of the battery and the discharging voltage of the battery during the process of charging and discharging the battery.

[0087] For example, the processor (210) can charge and discharge the battery using a specified C-rate. For example, the processor (210) can obtain data related to the state of the battery (e.g., battery SOC, battery voltage) during the process of charging and discharging the battery using a specified C-rate. For example, the processor (210) can identify the difference between the battery charging voltage and the battery discharging voltage during the process of charging and discharging the battery using a specified C-rate. For example, the processor (210) can obtain data related to battery degradation by using the difference between the battery charging voltage and the battery discharging voltage during the process of charging and discharging the battery using a specified C-rate.

[0088] In one embodiment, the processor (210) can obtain data related to battery degradation based on the difference between the charging voltage of the battery and the discharging voltage of the battery.

[0089] For example, the processor (210) can identify the temperature at which the battery operates. For example, the temperature at which the battery operates may include the temperature of the space in which the battery is placed. For example, the processor (210) can obtain data related to the degradation of the battery based on the temperature at which the battery operates.

[0090] For example, the processor (210) can predict the SOC at which a specified material precipitates from the battery based on data related to the degradation of the battery. For example, the specified material may include lithium. However, the embodiments of this document are not limited to those described above.

[0091] In one embodiment, the processor (210) can obtain data related to battery degradation using a neural network model.

[0092] For example, the processor (210) may input data related to battery degradation and data related to the temperature at which the battery operates into a neural network model. For example, the processor (210) may obtain the resistance of the battery based on the input of data related to battery degradation and data related to the temperature at which the battery operates into the neural network model. For example, the processor (210) may identify the difference between the first resistance of the battery at a first time point and the second resistance of the battery at a second time point based on the input of data related to battery degradation and data related to the temperature at which the battery operates into the neural network model. For example, the second time point may include a time point after the first time point. For example, the processor (210) may identify the ratio of the second resistance to the first resistance. For example, the processor (210) may obtain data related to battery degradation based on the ratio of the second resistance to the first resistance.

[0093] For example, data related to battery degradation may include at least one of the battery's remaining energy, battery's state of health (SOH), battery retention, battery resistance growth rate, battery positive capacity loss, battery negative capacity loss, battery available lithium loss, or any combination thereof. However, the embodiments of this document are not limited to those described above.

[0094] For example, available lithium loss may mean a decrease in the amount of lithium ions available to participate in chemical reactions during the charging and discharging process of the battery.

[0095] For example, the remaining energy of a battery may refer to the difference between the energy stored in the battery at the time of the end of life (EoL) and the energy stored in the battery at the current time. For example, the remaining energy of a battery may include the difference between the energy stored in the battery at the time of the end of life (EoL) and the energy stored in the battery at the current time.

[0096] For example, the End of Life (EoL) point can be defined as the point at which the battery capacity retention rate corresponds to a specified value. For instance, the battery capacity retention rate may refer to the ratio of the currently available capacity to the battery's initial capacity. This can serve as an indicator of how well the battery's performance is maintained while it is in operation.

[0097] In one embodiment, the processor (210) can input the battery current into a neural network model. For example, the processor (210) can obtain a battery voltage corresponding to the battery current based on the input of the battery current into the neural network model. For example, the processor (210) can obtain a battery voltage corresponding to the battery current by inputting the battery current into a neural network model.

[0098] For example, the processor (210) can identify the resistance of the battery using the current and voltage of the battery. For example, the processor (210) can obtain data related to the degradation of the battery based on identifying the resistance of the battery using the current and voltage of the battery.

[0099] In one embodiment, the processor (210) can train a neural network model based on the difference between the charging voltage of the battery and the discharging voltage of the battery. For example, the processor (210) can train a neural network model using the difference between the charging voltage of the battery and the discharging voltage of the battery to predict the SOC at which a specified material is precipitated in the battery.

[0100] For example, the processor (210) can train a neural network model using data related to the SOC in which a specified material is deposited in the battery, which is obtained by the difference between the charging voltage of the battery and the discharging voltage of the battery, and the resistance according to the difference between the charging voltage of the battery and the discharging voltage of the battery.

[0101] For example, if the SOC of the battery exceeds the SOC at which a specified substance is precipitated, the likelihood of the specified substance being precipitated may increase. Accordingly, the battery diagnostic device (200) can charge and / or discharge the battery to a level below the SOC at which the specified substance is precipitated, based on identifying the SOC at which the specified substance is precipitated.

[0102] For example, the processor (210) can identify the difference between the charging voltage of the battery and the discharging voltage of the battery at a specified SOC. Based on identifying the difference between the charging voltage of the battery and the discharging voltage of the battery at a specified SOC, the processor (210) can obtain data related to the degradation of the battery.

[0103] In one embodiment, the processor (210) can predict the state of charge (SOC) at which a specified material is precipitated in the battery based on the resistance of the battery. For example, the specified material may include lithium. For example, the processor (210) may perform at least one of the following: adjusting the charging speed of the battery, adjusting the operating temperature of the battery, adjusting the range of the state of charge (SOC) used when operating the battery, or any combination thereof, at the state of charge (SOC) at which the specified material is precipitated.

[0104] For example, the operating temperature of the battery may include the temperature of the space in which the battery is placed.

[0105] In one embodiment, the processor (210) can identify a state factor including the difference between the charging voltage of the battery and the discharging voltage of the battery. In one embodiment, the processor (210) can identify a condition factor related to the operating conditions of the battery. Based on the state factor including the difference between the charging voltage of the battery and the discharging voltage of the battery, and the condition factor related to the operating conditions of the battery, the processor (210) can obtain data related to the degradation of the battery.

[0106] For example, the state factor may include at least one of the difference between the battery's charging voltage and the battery's discharging voltage, the temperature during the process of operating the battery, or any combination thereof. For example, the state factor may include at least one of the difference between the battery's charging voltage and the battery's discharging voltage, the maximum temperature during the process of operating the battery, or any combination thereof.

[0107] For example, the temperature during the process of operating the battery may include the temperature of the space where the battery is placed while the battery is operating. For example, the maximum temperature during the process of operating the battery may include the maximum temperature of the space where the battery is placed while the battery is operating.

[0108] For example, the condition factor may include at least one of the C-rate used when charging the battery, the temperature of the chamber containing the battery, the SOC range for operating the battery, or any combination thereof.

[0109] For example, a chamber containing a battery may be included in a space where the battery is placed. For example, a SOC range for driving the battery may include a range in which the battery is operated using a specified SOC range. For example, if the SOC range for driving the battery is 0-80, it may mean that the battery is operated in a situation where the battery's SOC is limited to 0 to 80.

[0110] In one embodiment, the processor (210) can train a neural network model using state factors and condition factors. For example, the processor (210) can adjust the weights of each neuron included in the neural network model using state factors and condition factors.

[0111] In one embodiment, the processor (210) may perform a process to reduce the rate of battery degradation based on data related to battery degradation. For example, the processor (210) may perform a process to avoid a section based on identifying a point in time when the rate of battery degradation accelerates or a section in which the rate of battery degradation accelerates.

[0112] As described above, a battery diagnostic device (200) according to one embodiment can acquire data related to battery degradation. By using the data related to battery degradation, the battery diagnostic device (200) can perform various processes to suppress the rate of battery degradation, thereby providing the effect of increasing the battery's usage period.

[0113] FIG. 3 illustrates an example of a graph showing the charging voltage and the discharging voltage of a battery in one embodiment of the present document.

[0114] Referring to FIG. 3, the processor (210) of the battery diagnostic device (200) according to one embodiment can identify the charging voltage of the battery during the process of charging and discharging the battery. In one embodiment, the processor (210) can identify the discharging voltage of the battery during the process of charging and discharging the battery. In one embodiment, the processor (210) can identify the difference between the charging voltage of the battery and the discharging voltage of the battery during the process of charging and discharging the battery.

[0115] In the graph of FIG. 3, the horizontal axis may represent the SOC of the battery. For example, the unit of the battery SOC may be %. In the graph of FIG. 3, the vertical axis may represent the voltage of the battery. For example, the unit of the battery voltage may be V (volt).

[0116] The first data (301) may represent the charging voltage of the battery during the process of charging the battery in the BoL (beginning of life) state.

[0117] The second data (302) may represent the discharge voltage of the battery during the process of discharging the battery in a BoL state.

[0118] The third data (303) may represent the charging voltage of the battery during the process of charging the battery in the middle of life (MoL) state.

[0119] The fourth data (304) may represent the discharge voltage of the battery during the process of discharging the battery in the MoL state.

[0120] For example, the BoL state may include a state in which the battery is operated and the battery is operated within a first designated cycle. For example, the MoL state may include a state in which the battery is operated and the battery is operated beyond a first designated cycle and within a second designated cycle. For example, the cycles described above may mean charging and discharging the battery. For example, one cycle may mean charging and discharging the battery once. Accordingly, a designated cycle may mean charging and discharging the battery a specified number of times.

[0121] In one embodiment, the processor (210) can identify a first difference (311) between the charging voltage of the battery in a BoL state and the discharging voltage of the battery. In one embodiment, the processor (210) can identify a second difference (313) between the charging voltage of the battery in a MoL state and the discharging voltage of the battery.

[0122] For example, the processor (210) can obtain data related to battery degradation by using at least one of the first difference (311), the second difference (313), or any combination thereof.

[0123] FIG. 4 illustrates an example related to a battery degradation map in one embodiment of the present document.

[0124] Referring to FIG. 4, the graph illustrated in FIG. 5 may be an example of a change in SOC according to a voltage difference. In the graph of FIG. 5, the horizontal axis may represent the voltage difference. For example, the voltage difference may represent the difference between the charging voltage of the battery and the discharging voltage of the battery. In the graph of FIG. 5, the vertical axis may represent the SOC of the battery.

[0125] The graph of FIG. 5 may include first raw data (501), second raw data (502), third raw data (503), fourth raw data (504), and / or fifth raw data (505). The graph of FIG. 5 may include first predicted data (511), second predicted data (512), third predicted data (513), fourth predicted data (514), and / or fifth predicted data (515).

[0126] For example, the first raw data (501) may include an SOC in which a specified substance is precipitated according to the difference between the charging voltage of the battery and the discharging voltage of the battery during the process of charging and discharging the battery using the first C-rate.

[0127] For example, the second raw data (502) may include an SOC in which a specified substance is precipitated according to the difference between the charging voltage of the battery and the discharging voltage of the battery during the process of charging and discharging the battery using the second C-rate.

[0128] For example, the third raw data (503) may include an SOC in which a specified substance is precipitated according to the difference between the charging voltage of the battery and the discharging voltage of the battery during the process of charging and discharging the battery using the third C-rate.

[0129] For example, the fourth raw data (504) may include an SOC in which a specified substance is precipitated according to the difference between the charging voltage of the battery and the discharging voltage of the battery during the process of charging and discharging the battery using the fourth C-rate.

[0130] For example, the fifth raw data (505) may include an SOC in which a specified substance is precipitated according to the difference between the charging voltage of the battery and the discharging voltage of the battery during the process of charging and discharging the battery using the fifth C-rate.

[0131] For example, the first prediction data (511) may be a prediction of the SOC in which a specified material is precipitated according to the difference between the charging voltage and the discharging voltage of the battery during the process of charging and discharging the battery using the first C-rate.

[0132] For example, the second prediction data (512) may be a prediction of the SOC in which a specified substance is precipitated according to the difference between the charging voltage and the discharging voltage of the battery during the process of charging and discharging the battery using the second C-rate.

[0133] For example, the third prediction data (513) may be a prediction of the SOC in which a specified substance is precipitated according to the difference between the charging voltage and the discharging voltage of the battery during the process of charging and discharging the battery using the third C-rate.

[0134] For example, the fourth prediction data (514) may be a prediction of the SOC in which a specified substance is precipitated according to the difference between the charging voltage and the discharging voltage of the battery during the process of charging and discharging the battery using the fourth C-rate.

[0135] For example, the fifth prediction data (515) may be a prediction of the SOC in which a specified substance is precipitated according to the difference between the charging voltage and the discharging voltage of the battery during the process of charging and discharging the battery using the fifth C-rate.

[0136] In the graph of Figure 5, it can be seen that the trends of the raw data and the predicted data are similar during the process of charging and discharging the battery using a specific C-rate.

[0137] Accordingly, the battery diagnostic device (200) can predict the SOC at which a specified substance is precipitated in the battery by using the difference between the charging voltage of the battery and the discharging voltage of the battery.

[0138] FIGS. 5a to 5c illustrate an example of controlling a battery in an embodiment of the present document.

[0139] Referring to FIG. 5a, the processor (210) of the battery diagnostic device (200) according to one embodiment can control the charging speed of the battery. For example, the processor (210) can reduce the charging speed of the battery. The processor (210) can reduce the charging speed of the battery to suppress the degradation of the battery in the first direction (520).

[0140] Referring to FIG. 5b, the processor (210) of the battery diagnostic device (200) according to one embodiment can control the temperature at which the battery is driven. For example, the processor (210) can reduce the temperature at which the battery is driven. The processor (210) can reduce the temperature at which the battery is driven to suppress the degradation of the battery in a second direction (530).

[0141] Referring to FIG. 3b, the processor (210) of the battery diagnostic device (200) according to one embodiment can adjust the range of SOC used when driving the battery. For example, the processor (210) can reduce the maximum SOC in the range of SOC used when driving the battery. For example, the processor (210) can increase the minimum SOC in the range of SOC used when driving the battery. The processor (210) can reduce the range of SOC used when driving the battery to suppress battery degradation in the third direction (540).

[0142] FIG. 6 illustrates an example of a flowchart related to a battery diagnostic method according to one embodiment of the present document.

[0143] In the following, it is assumed that the battery diagnostic device (200) of FIG. 2 performs the process of FIG. 6. Also, in the description of FIG. 6, 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).

[0144] At least one of the operations of FIG. 6 can be performed by the battery diagnostic device (200) of FIG. 2. At least one of the operations of FIG. 6 can be controlled by the processor (210) of FIG. 2. Each of the operations of FIG. 6 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.

[0145] Referring to FIG. 6, a battery diagnostic method according to one embodiment may include, in operation S601, an operation of identifying the difference between the charging voltage of the battery and the discharging voltage of the battery during the process of charging and discharging the battery.

[0146] For example, a battery diagnostic method may include an operation to identify the difference between the charging voltage of the battery and the discharging voltage of the battery at a specified SOC.

[0147] In operation S603, the battery diagnostic method according to one embodiment may include the operation of obtaining data related to battery degradation (401) based on the difference between the charging voltage and the discharging voltage.

[0148] For example, a battery diagnostic method may include an operation to acquire data related to battery degradation using a neural network model.

[0149] For example, a battery diagnostic method may include an operation of inputting the battery current into a neural network model to obtain the battery voltage corresponding to the battery current.

[0150] For example, a battery diagnostic method may include an operation of identifying the resistance of a battery using the current and voltage of the battery. For example, a battery diagnostic method may include an operation of obtaining data related to battery degradation based on identifying the resistance of the battery using the current and voltage of the battery.

[0151] For example, a battery diagnostic method may include an operation of training a neural network model based on the difference between the battery's charging voltage and the battery's discharging voltage.

[0152] For example, a battery diagnostic method may include an operation to predict the SOC at which a specified substance precipitates in the battery based on data related to battery degradation.

[0153] For example, a battery diagnostic method may include an operation to predict the SOC at which a specified material precipitates in the battery based on the resistance of the battery.

[0154] For example, the battery diagnostic method may include an operation of controlling the charging speed of the battery at the SOC where a specified material precipitates in the battery, controlling the operating temperature of the battery, controlling the range of SOC used when operating the battery, or at least one of any combination thereof.

[0155] As described above, the battery diagnostic method according to one embodiment can provide the effect of efficiently driving the battery and suppressing the degradation rate of the battery by controlling the battery.

[0156] FIG. 7 illustrates an example showing the remaining energy of a battery in one embodiment of the present document.

[0157] FIG. 7 may include an example of a graph expressing the prediction of remaining energy using the difference between the charging voltage and the discharging voltage of the battery.

[0158] In the graph of Fig. 7, the horizontal axis may represent the voltage difference, and the vertical axis may represent the residual energy.

[0159] The graph of FIG. 7 may include an example of comparing the first raw data (701) and the first predicted data (711) under the first condition. For example, the first raw data (701) may refer to data obtained through actual experiments under the first condition. For example, the first predicted data (711) may refer to data predicting the remaining energy of the battery using the difference between the charging voltage and the discharging voltage of the battery under the first condition.

[0160] The graph of FIG. 7 may include an example of comparing the second raw data (702) and the second predicted data (712) under the second condition. For example, the second raw data (702) may refer to data obtained through actual experiments under the second condition. For example, the second predicted data (712) may refer to data that predicts the remaining energy of the battery using the difference between the charging voltage and the discharging voltage of the battery under the second condition.

[0161] FIG. 8 illustrates an example of obtaining the resistance of a battery using data related to battery degradation and the temperature at which the battery is operated, in one embodiment of the present document.

[0162] Referring to FIG. 8, the processor (210) of the battery diagnostic device (200) according to one embodiment can obtain data related to battery degradation (801). In one embodiment, the processor (210) can obtain data related to the temperature (802) at which the battery operates.

[0163] In one embodiment, the processor (210) can identify a correlation between the degradation (801) of the battery and the temperature (802) at which the battery is driven. For example, the processor (210) can identify a resistance (810) according to the degradation (801) of the battery. For example, the processor (210) can identify a correlation between the resistance (810) according to the temperature (802) at which the battery is driven. For example, the processor (210) can identify a correlation between the resistance (810) according to the degradation (801) of the battery and the resistance (810) according to the temperature (802) at which the battery is driven.

[0164] For example, the processor (210) can train a neural network model using the battery degradation (801) and the temperature (802) operating the battery. For example, the processor (210) can train a neural network model using the battery degradation (801) and the temperature (802) operating the battery to obtain the battery resistance (810). For example, the processor (210) can obtain data related to the battery degradation based on obtaining the battery resistance (810) using the trained neural network model.

[0165] In one embodiment, the processor (210) may obtain an integrated factor (820) including temperature and degradation based on identifying the relationship between the resistance (810) due to battery degradation (801) and the resistance (810) due to the temperature (802) driving the battery. For example, the processor (210) may obtain data related to battery degradation based on obtaining the integrated factor (820) including temperature and degradation.

[0166] In one embodiment, the processor (210) may obtain data related to the degradation of each of the same type of batteries. However, the embodiment is not limited thereto, and the processor (210) may train a neural network model using data related to the degradation of the first battery among the different types of batteries and obtain data related to the degradation of the second battery that is different from the first battery.

[0167] FIG. 9 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.

[0168] Referring to FIG. 9, a computing system (1100) according to one embodiment disclosed in this document may include an MCU (1110), memory (1120), an input / output I / F (1130), and a communication I / F (1140).

[0169] The MCU (1110) may be a processor that executes various programs stored in memory (1120) (e.g., battery cell data collection program, graph generation 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 6.

[0170] 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.

[0171] 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.

[0172] 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).

[0173] 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).

[0174] 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).

[0175] 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.

[0176] 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.

[0177] 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 disclosure 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 and discharging the battery, the difference between the charging voltage of the battery and the discharging voltage of the battery is identified, and A battery management device configured to acquire data related to the degradation of the battery based on the difference between the charging voltage and the discharging voltage.

2. In Paragraph 1, The above processor is, A battery diagnostic device configured to predict the SOC at which a specified substance precipitates in the battery based on data related to the degradation of the battery.

3. In Paragraph 1, The above processor is, A battery diagnostic device configured to predict the SOC at which a specified substance precipitates in the battery based on the resistance of the battery.

4. In Paragraph 3, The above processor is, At the SOC where the above-specified substance is precipitated: A battery management device configured to perform at least one of the following: controlling the charging speed of the battery, controlling the operating temperature of the battery, controlling the range of the state of charge (SOC) used when operating the battery, or any combination thereof.

5. In Paragraph 1, The above processor is, A battery diagnostic device configured to acquire data related to the degradation of the battery using a neural network model.

6. In Paragraph 5, The above processor is, In the above neural network model, the current of the battery is input to obtain the voltage of the battery, and A battery diagnostic device configured to obtain data related to battery degradation based on identifying the resistance of the battery using the above current and the above voltage.

7. In Paragraph 5, The above processor is, A battery diagnostic device configured to train a neural network model based on the difference between the charging voltage and the discharging voltage.

8. In Paragraph 5, The above processor is, Based on inputting data related to the degradation of the battery and data related to the operating temperature of the battery into the above neural network model, the resistance of the battery is obtained, and A battery diagnostic device configured to acquire data related to the degradation of the battery based on the resistance of the battery.

9. In Paragraph 1, The above processor is, A battery management device configured to identify the difference between the charging voltage and the discharging voltage at a specified state of charge (SOC).

10. In Paragraph 1, The above processor is, A battery diagnostic device configured to acquire data related to battery degradation based on a state factor including the difference between the charging voltage and the discharging voltage, and a condition factor related to the operating conditions of the battery.

11. In Paragraph 10, The above state factor is, A battery diagnostic device comprising at least one of the difference between the charging voltage and the discharging voltage, the maximum temperature during the process of driving the battery, or any combination thereof.

12. In Paragraph 10, The above condition factor is, A battery diagnostic device comprising at least one of a C-rate used when charging the battery, the temperature of a chamber containing the battery, a state of charge (SOC) range for driving the battery, or any combination thereof.

13. In Paragraph 1, The data related to the degradation of the above battery is, A battery diagnostic device including the remaining energy of the above battery.

14. In Paragraph 13, The remaining energy of the above battery is, A battery diagnostic device comprising the difference between the energy stored in the battery at the time of EoL (end of life) and the energy stored in the battery at the current time.

15. In Paragraph 14, The above EoL point in time is, A battery diagnostic device in which the capacity retention rate of the above battery is defined as a point in time corresponding to a specified value.

16. In Paragraph 1, The above processor is, A battery diagnostic device configured to acquire data related to the degradation of the battery based on the operating temperature of the battery.

17. In Paragraph 1, The above processor is, A battery diagnostic device configured to identify the difference between the charging voltage and the discharging voltage during the process of charging and discharging the battery using a specified C-rate.

18. An operation of identifying the difference between the charging voltage of the battery and the discharging voltage of the battery by the processor during the process of charging and discharging the battery; and A battery diagnostic method comprising the operation of acquiring data related to the degradation of the battery based on the difference between the charging voltage and the discharging voltage by the above processor.

19. In Paragraph 18, The above battery diagnostic method is, A battery diagnostic method comprising the operation of predicting the SOC at which a specified substance precipitates in the battery based on data related to the degradation of the battery by the above processor.

20. In Paragraph 18, The above battery diagnostic method is, A battery diagnostic method comprising the operation of predicting the SOC at which a specified material precipitates in the battery based on the resistance of the battery by the above processor.

21. In Paragraph 20, The above battery diagnostic method is, In the SOC where the specified material is precipitated by the above processor: A battery diagnostic method comprising: a charge speed control operation of the battery; a drive temperature control operation of the battery; a state of charge (SOC) range control operation used when driving the battery; or at least one of any combination thereof.

22. In Paragraph 18, The above battery diagnostic method is, A battery diagnosis method comprising the operation of acquiring data related to the degradation of the battery using a neural network model by the above processor.

23. In Paragraph 22, The above battery diagnostic method is, An operation to obtain the voltage of the battery by inputting the current of the battery into the neural network model by the above processor; A battery diagnostic method comprising the operation of obtaining data related to the degradation of the battery based on identifying the resistance of the battery using the current and the voltage.

24. In Paragraph 22, The above battery diagnostic method is, A battery diagnostic method comprising the operation of training the neural network model based on the difference between the charging voltage and the discharging voltage by the above processor.

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