Battery life prediction device and battery management method

Abstract

A battery life prediction device disclosed in the present document comprises: a communication unit for receiving battery data of a battery cell; and a control unit for deriving an efficiency index of the battery cell during a preset reference cycle on the basis of the battery data, and deriving a predicted discharge capacity following the reference cycle on the basis of a moving average of the efficiency index and the discharge capacity of the reference cycle.

Description

Battery life prediction device and battery management method

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority from Republic of Korea Patent Application No. 10-2024-0051647, filed April 17, 2024, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a battery life prediction device and a battery life prediction method for predicting battery life.

[0005]

[0006] Recently, active research and development is being conducted on secondary batteries. Here, the term "secondary battery" refers to a rechargeable battery, encompassing both conventional Ni / Cd and Ni / MH batteries, as well as recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of a much higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form, making them suitable for use as power sources for mobile devices. Furthermore, lithium-ion batteries are attracting attention as a next-generation energy storage medium, as their use is expanding to include power sources for electric vehicles.

[0007] Therefore, there is an increasing need to predict the lifespan of battery cells, provide users with information about battery life, and establish battery warranty policies. Various studies are being conducted to predict the lifespan of battery cells.

[0008]

[0009] According to one embodiment disclosed in this document, a battery life prediction device and a battery management method are provided for predicting the discharge capacity of a battery using an efficiency index derived based on a discharge capacity and a charge capacity.

[0010] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0011]

[0012] A battery life prediction device according to one embodiment includes a communication unit that receives battery data of a battery cell, a control unit that derives an efficiency index of the battery cell during a preset reference cycle based on the battery data, and a moving average value of the efficiency index and a discharge capacity of the reference cycle based on a control unit that derives a predicted discharge capacity after the reference cycle.

[0013] The above control unit can derive the predicted discharge capacity by multiplying the discharge capacity of the reference cycle by the moving average value.

[0014] The above control unit can derive the predicted discharge capacity by multiplying the moving average value by the discharge capacity of the reference cycle by the number of cycles corresponding to the predicted discharge capacity.

[0015] The above control unit can derive the efficiency index by dividing the discharge capacity of each cycle by the charge capacity during the above reference cycle.

[0016] The above control unit can input the battery data into a regression model and derive the efficiency index as an output value.

[0017] The above control unit can derive the moving average value based on any one of a simple moving average, an exponential moving average, or a weighted moving average.

[0018] The above control unit can transmit the predicted discharge capacity to an external device through the communication unit.

[0019] A battery life prediction method according to one embodiment includes receiving battery data of a battery cell, deriving an efficiency index of the battery cell for a preset reference cycle based on the battery data, and deriving a predicted discharge capacity after the reference cycle based on a moving average value of the efficiency index and a discharge capacity of the reference cycle.

[0020] Deriving the predicted discharge capacity may include deriving the predicted discharge capacity by multiplying the discharge capacity of the reference cycle by the moving average value.

[0021] Deriving the predicted discharge capacity may include deriving the predicted discharge capacity by multiplying the moving average value by the number of cycles corresponding to the predicted discharge capacity to the discharge capacity of the reference cycle.

[0022] Deriving the above efficiency index may include deriving the efficiency index by dividing the discharge capacity of each cycle by the charge capacity during the above reference cycle.

[0023] Deriving the above efficiency index may include inputting the battery data into a regression model to derive the above efficiency index as an output value.

[0024] A battery life prediction method according to one embodiment may further include deriving the moving average value based on any one of a simple moving average, an exponential moving average, or a weighted moving average.

[0025] A battery life prediction method according to one embodiment may further include transmitting the predicted discharge capacity to an external device through a communication unit.

[0026]

[0027] According to a battery life prediction device according to one embodiment, the discharge capacity can be predicted using an efficiency index derived from the discharge capacity and the charge capacity, and the efficiency index can be derived using a regression model, thereby shortening the time required for evaluating the battery life.

[0028]

[0029] FIG. 1 illustrates a block diagram showing the configuration of a battery life prediction device according to one embodiment.

[0030] FIG. 2 illustrates input and output values ​​of a machine learning model utilized in a battery life prediction device according to one embodiment.

[0031] FIG. 3 is a schematic flowchart illustrating a battery life prediction device according to one embodiment of the present invention for predicting discharge capacity.

[0032] FIG. 4 illustrates an efficiency index graph utilized in a battery life prediction device according to one embodiment.

[0033] FIG. 5 illustrates an area where an efficiency index becomes constant in an efficiency index graph utilized in a battery life prediction device according to one embodiment.

[0034] FIG. 6 illustrates a control flowchart of a battery life prediction method according to an embodiment.

[0035]

[0036] Hereinafter, various embodiments disclosed in this document will be described in detail with reference to the attached drawings. In this document, identical components in the drawings are designated by the same reference numerals, and redundant descriptions of identical components are omitted.

[0037] With respect to the various embodiments disclosed in this document, specific structural and functional descriptions are merely illustrative for the purpose of explaining the embodiments, and the various embodiments disclosed in this document may be implemented in various forms and should not be construed as being limited to the embodiments described in this document.

[0038] The expressions "first," "second," "first," or "second" used in various embodiments may describe various components, regardless of order and / or importance, and do not limit the components. For example, without departing from the scope of the embodiments disclosed herein, a first component may be renamed a second component, and similarly, a second component may also be renamed a first component.

[0039] The terms used in this document are intended solely to describe specific embodiments and may not be intended to limit the scope of other embodiments. Singular expressions may include plural expressions unless the context clearly indicates otherwise.

[0040] All terms used herein, including technical or scientific terms, have the same meaning as commonly understood by those of ordinary skill in the art of the embodiments disclosed herein. Terms defined in commonly used dictionaries may be interpreted as having the same or similar meaning in the context of the relevant technology, and unless explicitly defined herein, they shall not be interpreted in an idealized or overly formal sense. In some cases, even if a term is defined herein, it cannot be interpreted to exclude the embodiments disclosed herein.

[0041] FIG. 1 illustrates a block diagram showing the configuration of a battery life prediction device according to one embodiment.

[0042] As illustrated in FIG. 1, a battery life prediction device (1) can obtain battery data related to a battery from a sensor unit (14), and the sensor unit (14) can include a current sensor (2), a voltage sensor (3), and a temperature sensor (4), and the sensor unit (14) can be provided at a location adjacent to a battery cell.

[0043] The current sensor (2) can detect the current used in the process of determining the SOC of the battery cell.

[0044] The current sensor (2) may include any configuration that generates a signal corresponding to the size of the charging current, and the current sensor (2) may be installed on a charging / discharging path, which is a path through which the charging / discharging current flows in the battery.

[0045] The current sensor (2) can measure the battery current flowing in the battery, i.e., the charging current and the discharging current, and transmit the measurement results to the battery life prediction device (1). According to one embodiment, the current sensor (2) can measure the battery current at predetermined intervals in a charging cycle for charging the battery with power from an external device or in a discharging cycle for discharging the battery, and transmit the measurement results to the battery life prediction device (1).

[0046] The voltage sensor (3) can be configured to be connected in parallel to the battery, detect the battery voltage, which is the voltage across both terminals of the battery, and generate a voltage signal representing the detected battery voltage.

[0047] The temperature sensor (4) may be configured to measure the battery temperature and generate a temperature signal representing the measured battery temperature. The temperature sensor (4) may be positioned within the case so as to measure a temperature close to the actual temperature of the battery. For example, the temperature sensor (4) may be attached to the surface of at least one battery cell included in the cell group and may detect the surface temperature of the battery cell as the battery temperature.

[0048] The temperature sensor (4) may be configured to measure the external temperature, which is the temperature at a predetermined location away from the battery, and generate a temperature signal representing the measured external temperature. The temperature sensor (4) may be placed at a predetermined location outside the case where heat exchange between the battery and the surroundings occurs. According to an embodiment, the temperature sensor (4) may be implemented as one or a combination of two or more of known temperature detection elements, such as a thermocouple, a thermistor, or a bimetal.

[0049] The battery life prediction device (1) can monitor the voltage, current, temperature, etc. of the battery system and control and manage it to prevent overcharging and overdischarging, etc., and may include, for example, a BMS (Battery Management System).

[0050] In addition, the battery life prediction device (1) can be implemented as an on-device configured as a server or as part of a user terminal, and there is no limitation on its physical form.

[0051] A battery life prediction device (1) according to one embodiment includes a control unit (100) including at least one processor (110) and a memory (120) and a communication unit (200), and can predict the battery life by communicating with an external device (5) through the communication unit (200).

[0052] According to one embodiment, an external device (5) communicating with a battery life prediction device (1) may include a user terminal and a server device that receive the life of the battery predicted by the battery life prediction device (1).

[0053] Specifically, when the external device (5) is a user terminal, the control unit (100) of the battery life prediction device (1) can transmit the predicted discharge capacity and predicted life of the battery to the user terminal so that the user can check them. At this time, the user terminal may include, but is not limited to, a personal computer, a terminal, a portable telephone, a smart phone, a handheld device, a wearable device, etc.

[0054] In addition, when the external device (5) is a server device, the server device may be implemented as various computing devices such as a workstation, a cloud, a data drive, a data station, etc. The server device may be implemented as one or more server devices that are physically or logically separated based on function, detailed configuration of function, or data, etc., and may transmit and receive data and process the transmitted and received data through communication between each server device.

[0055] A battery life prediction device (1) according to one embodiment may refer to any electronic device including a processor (110) and a memory (120), and may be mounted and operated in a vehicle. Each component of the battery life prediction device (1) will be described in detail below.

[0056] The communication unit (200) may include a wireless communication unit (210) and a wired communication unit (220) to communicate with an external device (5). The communication unit (200) may transmit and receive programs for calculating characteristic values ​​of battery cells, class classification, and lifespan estimation, as well as various data, from a separately provided external server.

[0057] The wireless communication unit (210) may include at least one of a short-range communication module and a long-range communication module.

[0058] The short-range communication module can communicate with an external device (5) adjacent to the battery life prediction device (1) using a short-range communication method. Here, the short-range communication module can utilize one of the following communication methods: Bluetooth, Bluetooth low energy, infrared data association (IrDA), Zigbee, Wi-Fi, Wi-Fi direct, Ultra Wideband (UWB), or near field communication (NFC).

[0059] The remote communication module may include a communication module that performs various types of remote communication and may include a mobile communication unit. The mobile communication unit may transmit and receive wireless signals with at least one of a base station and an external terminal on a mobile communication network. In addition, the remote communication module may communicate with an external device (5) or an external device (5) such as another electronic device through a surrounding access point (AP). The access point (AP) may connect a local area network (LAN) to which the battery life prediction device (1) is connected to a wide area network (WAN) to which a communication server is connected. Accordingly, the battery life prediction device (1) may be connected to the communication server through the wide area network (WAN) with the external device (5) so that they may communicate with each other.

[0060] The wired communication unit (220) can connect to a wired communication network and communicate with an external device (5) through the wired communication network. For example, the wired communication unit (220) can connect to a wired communication network through Ethernet (IEEE 802.3 technology standard) or connect to a wired communication network through CAN communication, and transmit and receive data with the external devices (5) through the wired communication network.

[0061] A battery life prediction device (1) according to one embodiment may include an input / output interface (not shown). An interface may be provided that connects an input device (not shown) such as a keyboard, mouse, or touch panel, an output device (not shown) such as a display, and a processor (110) to transmit and receive data.

[0062] The memory (120) can store various information required for operating the battery life prediction device (1). Specifically, the memory (120) can store an operating system and a program required for operating the battery life prediction device (1), or can store data required for operating the battery life prediction device (1).

[0063] Specifically, the memory (120) can store various programs related to calculating characteristic values ​​of battery cells, classifying classes, and estimating lifespan. In addition, the memory (120) can store various data such as voltage, current, and characteristic value data of each battery cell.

[0064] Additionally, the memory (120) can store the discharge capacity and predicted life of the battery cell estimated by the processor (110) and can store a learning model for machine learning.

[0065] The memory (120) may include volatile memory (120) such as Static Random Access Memory (S-RAM) and Dynamic Random Access Memory (D-RAM) for temporarily storing data. In addition, the memory (120) may include nonvolatile memory (120) such as Read Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM), and Electrically Erasable Programmable Read Only Memory (EEPROM) for long-term storage of data.

[0066] The processor (110) outputs a control signal to control the overall battery life prediction device (1). The processor (110) may include one or more central processing units (CPUs) and graphics processing units (GPUs). In this case, the processor (110) may be implemented as an array of a plurality of logic gates, or may be implemented as a combination of a general-purpose microprocessor (110) and a memory (120) storing a program that can be executed on the microprocessor (110).

[0067] The aforementioned memory (120) and processor (110) can be included in the control unit (100), and the control unit (100) can control the aforementioned components to predict the discharge capacity of the battery cell and evaluate the life of the battery cell.

[0068] Specifically, the control unit (100) can derive an efficiency index of a battery cell during a reference cycle based on the received battery data, and derive a predicted discharge capacity after the reference cycle based on a moving average value of the efficiency index and the discharge capacity of the reference cycle.

[0069] Here, the reference cycle can refer to the point at which the efficiency index becomes constant during the battery cell life evaluation period, and can be experimentally varied. For example, if the efficiency index of a specific battery cell becomes constant at 100 cycles, the reference cycle can be set to 100.

[0070] In addition, the control unit (100) can derive the efficiency index of the battery cell by dividing the discharge capacity of each cycle by the charge capacity during the reference cycle, and the efficiency index can be expressed as in the following mathematical expression 1.

[0071]

[0072] That is, the control unit (100) can determine the value expressed as a percentage by dividing the charge capacity by the discharge capacity as an efficiency index, and the control unit (100) can also input battery data into a regression model to obtain an efficiency index based on machine learning.

[0073] Additionally, the control unit (100) can derive a moving average of an efficiency index to predict discharge capacity. Here, the moving average includes any one of a simple moving average, an exponential moving average, or a weighted moving average, and the moving average can mean a value calculated continuously from the average value over a specific period of time series data.

[0074] The control unit (100) can derive an efficiency index of a battery cell during a reference cycle, derive a moving average value of the efficiency index, and then derive a predicted discharge capacity by multiplying the discharge capacity of the reference cycle by the moving average value based on the following mathematical expression 2.

[0075]

[0076] That is, the control unit (100) can derive the predicted discharge capacity by multiplying the moving average value of the discharge capacity of the reference cycle by the number of cycles corresponding to the predicted discharge capacity.

[0077] Thereafter, the control unit (100) can evaluate the life of the battery cell based on the discharge capacity of the predicted Y cycles, and by comparing the discharge capacity of the Y cycles with that of the reference battery, if the discharge capacity of the Y cycles of the target battery cell is higher than the reference value, the life of the battery can be predicted to be longer than that of the reference battery, and if the discharge capacity of the Y cycles of the target battery cell is lower than the reference value, the life of the battery can be predicted to be shorter than that of the reference battery.

[0078] The control unit (100) can transmit the acquired predicted discharge capacity or predicted battery life to an external device (5) including a user terminal and a server device through a communication unit (200), thereby increasing the convenience of battery diagnosis.

[0079] FIG. 2 illustrates input and output values ​​of a machine learning model utilized in a battery life prediction device according to one embodiment.

[0080] Referring to FIG. 2, the control unit (100) can train an ANN (Artificial Neural Network) model (b) using the number of cycles, discharge capacity, and charge capacity of the battery as training data (a), and can derive the predicted discharge capacity as output data (c).

[0081] Here, the number of cycles, discharge capacity, and charge capacity can be derived by the control unit (100) receiving the current value of each cell through the sensor unit (14) and multiplying the received current data of each cell by the discharge time and the charge time, respectively.

[0082] When the control unit (100) includes an artificial intelligence-only processor (110) (e.g., NPU) for training an ANN (Artificial Neural Network) model (b), the processor (110) can train the artificial neural network by utilizing the weight data stored in the memory (120) as training data for the machine learning model.

[0083] Examples of learning algorithms may include supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning, but the battery life prediction device (1) according to one embodiment may determine whether a battery is defective based on self-supervised learning.

[0084] An artificial neural network included in a machine learning model may be composed of multiple neural network layers. Each of the multiple neural network layers has multiple weight values ​​and performs neural network operations through operations between the calculation results of the previous layer and the multiple weights. The multiple weights of the multiple neural network layers can be optimized based on the learning results of an artificial intelligence model. For example, during the learning process, the multiple weights may be updated so that the loss or cost values ​​obtained from the artificial intelligence model are reduced or minimized.

[0085] The artificial neural network may include a deep neural network (DNN), for example, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), a restricted boltzmann machine (RBM), a deep belief network (DBN), a bidirectional recurrent deep neural network (BRDNN), or deep Q-networks, but is not limited to the examples described above.

[0086] The control unit (100) can learn the correlation between the number of cycles, discharge capacity, and charge capacity, which are training data (a), and the predicted discharge capacity, which is output data (c), based on the selected artificial intelligence model.

[0087] FIG. 3 illustrates a schematic flowchart of a battery life prediction device according to one embodiment predicting discharge capacity. The components 101 to 103 in FIG. 3 are implemented in the form of software blocks, stored in a memory (120), and executed by a processor (110).

[0088] Referring to FIG. 3, the control unit (100) can obtain the number of cycles, discharge capacity, and charge capacity included in the battery data from the sensor unit (14), and the control unit (100) can store each received sensor data in the sensor data DB (121).

[0089] Thereafter, the efficiency index derivation unit (101) of the control unit (100) can derive an efficiency index based on the discharge capacity and charge capacity of the battery stored in the sensor data DB (121). That is, the control unit (100) can derive an efficiency index expressed as a ratio of the discharge capacity and charge capacity, and use the efficiency index as label data to train a machine learning model.

[0090] Specifically, the machine learning model learning unit (102) of the control unit (100) can learn a model based on preset hyperparameters and selected features. Specifically, the control unit (100) can learn model parameters by utilizing the selected features to predict discharge capacity.

[0091] That is, the control unit (100) can train a regression model to predict the efficiency index and discharge capacity by applying the regression model to the battery data.

[0092] Here, the regression model can be applied to various models to predict the efficiency index from battery data, and for example, the regression model can include any one of Linear Regression, Multiple Linear Regression, Polynomial Regression, Logistic Regression, Ridge Regression, Lasso Regression, and ElasticNet Regression.

[0093] The control unit (100) can determine the efficiency index obtained through the above process as the target output value of the machine learning model, and the control unit (100) can train the machine learning model by changing the hyper parameters of the machine learning learning model or adding data, and the control unit (100) can store the trained machine learning model in the machine learning learning model DB (122).

[0094] Thereafter, the predicted discharge capacity derivation unit (103) of the control unit (100) derives an efficiency index, which is an output value of the learned machine learning model, and multiplies the moving average value of the efficiency index by the number of cycles corresponding to the predicted discharge capacity to derive the predicted discharge capacity.

[0095] In addition, the control unit (100) can evaluate the life of the battery cell based on the predicted discharge capacity, and the control unit (100) can transmit the derived efficiency index, predicted discharge capacity, and / or predicted battery life to an external device (5), so that a user or manager of the battery can easily check the life information of the battery remotely.

[0096] FIG. 4 illustrates an efficiency index graph utilized in a battery life prediction device according to one embodiment, and FIG. 5 illustrates an area in which an efficiency index becomes constant in an efficiency index graph utilized in a battery life prediction device according to one embodiment.

[0097] Referring first to FIG. 4, the control unit (100) can obtain a graph of efficiency indices based on a regression model, as shown in FIG. 4. Here, the horizontal axis may represent battery cycles, and the vertical axis may represent efficiency indices obtained by the regression model. Furthermore, graphs (a), (b), and (c) may each represent different battery cells.

[0098] The efficiency index obtained by the control unit (100) may exhibit different aspects depending on the battery cell, and in particular, may have relatively uneven values ​​in the early cycle compared to the later cycle.

[0099] Based on these characteristics, the battery life prediction device (1) according to one embodiment can predict the discharge capacity after a section where the efficiency index becomes constant after a certain point in the cell life evaluation period.

[0100] Next, referring to FIG. 5, the control unit (100) can derive a moving average value for the discharge capacity during a period in which the efficiency index becomes constant after a certain point in time.

[0101] That is, the control unit (100) can derive a section in which the trend of the efficiency index becomes constant, as in line (d), and can derive an efficiency index of the section in which the trend of the efficiency index becomes constant, as in line (e).

[0102] In this way, since the efficiency index has a characteristic in which the trend becomes constant after a certain number of cycles have passed, the control unit (100) can derive the efficiency index of the battery cell during the reference cycle based on the regression model, and derive the predicted discharge capacity after the reference cycle by multiplying the moving average value of the efficiency index by the discharge capacity at the time of the reference cycle.

[0103] At this time, since the moving average value of a future point in time after the reference cycle changes by the number of cycles of the future point in time, the predicted discharge capacity can be derived by multiplying the moving average value by the number of cycles of the future point in time to the discharge capacity of the reference cycle.

[0104] Thereafter, the control unit (100) can determine that the battery has relatively much life remaining if the predicted discharge capacity of the battery is relatively large compared to the reference battery based on the characteristic that the discharge capacity gradually decreases as the battery is repeatedly used, and can determine that the battery has relatively little life remaining if the predicted discharge capacity of the battery is relatively small compared to the reference battery.

[0105] That is, according to the battery life prediction device (1) according to one embodiment, an efficiency index can be obtained based on a machine learning model, and accordingly, the life of the battery can be predicted, so there is an effect of being able to take the predicted life into consideration in battery use and design.

[0106] FIG. 6 illustrates a control flowchart of a battery life prediction method according to an embodiment.

[0107] Referring to FIG. 6, the control unit (100) can receive battery data of a battery cell (600) and derive an efficiency index of the battery cell based on regression analysis (610).

[0108] At this time, the control unit (100) can determine an efficiency index by dividing the discharge capacity by the charge capacity, and can also predict an efficiency index by inputting the discharge capacity and charge capacity into a regression model, respectively.

[0109] The control unit (100) can determine whether a preset reference cycle has elapsed (620), and if it is determined that the preset reference cycle has elapsed (620), it can derive a moving average value for the efficiency index during the corresponding reference cycle (630).

[0110] Thereafter, the control unit (100) can derive the predicted discharge capacity by multiplying the moving average value by the number of cycles corresponding to the predicted discharge capacity in the reference cycle (640). That is, the control unit (100) can derive the predicted discharge capacity by squaring the moving average value by the number of cycles to be predicted and then multiplying it by the discharge capacity in the reference cycle.

[0111] The control unit (100) can evaluate the life of the battery cell based on the predicted discharge capacity (650), and the control unit (100) can transmit the evaluated life of the battery cell to an external device (5) through the communication unit (200).

[0112] In this way, the battery life prediction device (1) according to one embodiment derives an efficiency index based on machine learning to predict the life of a battery and can predict the discharge capacity at a future point in time, so it has the effect of predicting the life of a battery more accurately and utilizing it for maintenance and warranty.

[0113] Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium storing computer-executable instructions. The instructions may be stored in the form of program code, and when executed by a processor, may generate program modules to perform the operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

[0114] Computer-readable storage media include all types of storage media that store instructions that can be deciphered by a computer. Examples include read-only memory (ROM), random access memory (RAM), magnetic tape, magnetic disks, flash memory, and optical data storage devices.

[0115] Additionally, a computer-readable recording medium may be provided in the form of a non-transitory storage medium. Here, the term "non-transitory storage medium" simply means a tangible device that does not contain signals (e.g., electromagnetic waves). This term does not distinguish between cases where data is permanently stored in the storage medium and cases where data is temporarily stored. For example, a "non-transitory storage medium" may include a buffer in which data is temporarily stored.

[0116] According to one embodiment, the method according to various embodiments disclosed in the present document may be provided as included in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable recording medium (e.g., compact disc read only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) through an application store (e.g., Play Store™) or directly between two user devices (e.g., smartphones). In the case of online distribution, at least a portion of the computer program product (e.g., a downloadable app) may be temporarily stored or temporarily generated on a machine-readable recording medium, such as a memo on a manufacturer's server, an application store's server, or an intermediary server.

[0117] Although all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination as one, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purpose of the embodiments disclosed in this document, all of the components may be selectively combined and operated one or more times.

[0118] Furthermore, terms such as "include," "comprise," or "have" described above, unless specifically stated otherwise, imply that the corresponding component may be present, and therefore should be interpreted to include other components rather than excluding other components. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as terms defined in dictionaries, should be interpreted to be consistent with the contextual meaning of the relevant technology, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

[0119] The above description is merely an illustrative description of the technical idea disclosed in this document, and those skilled in the art to which the embodiments disclosed in this document pertain may make various modifications and variations without departing from the essential characteristics of the embodiments disclosed in this document. Therefore, the embodiments disclosed in this document are not intended to limit the technical idea of ​​the embodiments disclosed in this document, but to explain it, and the scope of the technical idea disclosed in this document is not limited by these embodiments. The scope of protection of the technical idea disclosed in this document should be interpreted by the claims below, and all technical ideas within a scope equivalent thereto should be interpreted as being included in the scope of rights of this document.

[0120] [Explanation of symbols]

[0121] 1: Battery life prediction device

[0122] 2: Voltage sensor

[0123] 3: Current sensor

[0124] 4: Temperature sensor

[0125] 5: External devices

[0126] 14: Sensor section

[0127] 100: Control Unit

[0128] 110: Processor

[0129] 120: Memory

[0130] 200: Communications Department

[0131] 210: Wireless Communications Department

[0132] 220: Wired Communications Department

Claims

1. A communication unit that receives battery data of a battery cell; and A battery life prediction device comprising a control unit that derives an efficiency index of the battery cell during a preset reference cycle based on the battery data, and derives a predicted discharge capacity after the reference cycle based on a moving average value of the efficiency index and a discharge capacity of the reference cycle.

2. In claim 1, The above control unit, A battery life prediction device that derives the predicted discharge capacity by multiplying the discharge capacity of the reference cycle by the moving average value.

3. In claim 2, The above control unit, A battery life prediction device that derives the predicted discharge capacity by multiplying the moving average value by the number of cycles corresponding to the predicted discharge capacity to the discharge capacity of the reference cycle.

4. In claim 1, The above control unit, A battery life prediction device that derives the efficiency index by dividing the discharge capacity of each cycle by the charge capacity during the above reference cycle.

5. In claim 1, The above control unit, A battery life prediction device that inputs the above battery data into a regression model and derives the above efficiency index as an output value.

6. In claim 1, The above control unit, A battery life prediction device that derives the moving average value based on any one of a simple moving average, an exponential moving average, or a weighted moving average.

7. In claim 1, The above control unit, A battery life prediction device that transmits the predicted discharge capacity to an external device through the communication unit.

8. Receive battery data from the battery cell; Based on the above battery data, an efficiency index of the battery cell is derived for a preset reference cycle; A battery life prediction method comprising: deriving a predicted discharge capacity after the reference cycle based on the moving average value of the efficiency index and the discharge capacity of the reference cycle.

9. In claim 8, Deriving the above predicted discharge capacity is: A battery life prediction method comprising: multiplying the discharge capacity of the reference cycle by the moving average value to derive the predicted discharge capacity.

10. In claim 9, Deriving the above predicted discharge capacity is: A battery life prediction method comprising: multiplying the discharge capacity of the reference cycle by the moving average value by the number of cycles corresponding to the predicted discharge capacity to derive the predicted discharge capacity.

11. In claim 8, Deriving the above efficiency indicators is as follows: A battery life prediction method comprising: deriving the efficiency index by dividing the discharge capacity of each cycle by the charge capacity during the above reference cycle.

12. In claim 8, Deriving the above efficiency indicators is as follows: A battery life prediction method comprising: inputting the above battery data into a regression model to derive the above efficiency index as an output value.

13. In claim 8, A battery life prediction method further comprising: deriving the moving average value based on any one of a simple moving average, an exponential moving average, or a weighted moving average.

14. In claim 8, A battery life prediction method further comprising: transmitting the predicted discharge capacity to an external device through a communication unit.

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