Battery diagnosis device and method

Abstract

A battery diagnosis device disclosed in the present document comprises: a communication interface for receiving battery data acquired by applying a current signal to a battery pack; and at least one processor, which performs error correction in which the influence of a sensing line related to the structure of the battery pack is corrected, so as to generate preprocessed data from the battery data, extracts a feature value on the basis of the preprocessed data, and diagnoses whether the battery pack is abnormal on the basis of the feature value.

Description

Battery diagnostic device and method

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority to Korean Patent Application No. 10-2024-0067305, filed May 23, 2024, and Korean Patent Application No. 10-2025-0047128, filed April 11, 2025, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a battery diagnostic device and a battery diagnostic method.

[0005] 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.

[0006] Research is underway on methods to diagnose battery condition, including deterioration, during battery use. Defective batteries can cause overheating or fire during charging and discharging, posing a safety risk. Therefore, there is a need to diagnose batteries based on battery data to prevent accidents caused by battery defects. In particular, there is a need to inspect battery packs in a short period of time to minimize downtime and ensure performance and safety in battery-powered devices. However, the lack of relevant technology has been a challenge.

[0007] According to one embodiment disclosed in this document, a battery diagnostic device is provided that can precisely analyze voltage behavior by applying a current signal to a battery pack to quickly evaluate the state of the battery.

[0008] 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.

[0009] A battery diagnosis device according to one embodiment includes a communication interface that receives battery data obtained by applying a current signal to a battery pack, and at least one processor that performs error correction to correct the influence of a sensing line related to the structure of the battery pack to generate preprocessed data from the battery data, applies linear transformation to the preprocessed data to obtain linearly transformed data, and diagnoses a state of the battery pack based on a feature value extracted from the linearly transformed data.

[0010] The at least one processor can generate a plurality of sensing line group data by grouping the battery data according to the sensing line shape of the battery pack, and generate the preprocessing data by correcting the difference between the sensing line group data with a correction value.

[0011] The at least one processor can derive the correction value at a point where the difference between the sensing line group data is maximum, and correct the difference between the sensing line group data with the correction value.

[0012] The at least one processor can derive the correction value based on the average value of the correction target data included in the plurality of sensing line group data and the average value of the remaining data.

[0013] The at least one processor may derive a value obtained by subtracting the average value of the average value of the data to be corrected from the average value of the data to be corrected and the average value of the remaining data as the correction value, and may correct the difference with the correction value.

[0014] The at least one processor can diagnose the state of the battery pack by applying a weight determined based on the structure of the battery pack to the feature value.

[0015] The at least one processor can diagnose the state of the battery pack by equally applying the weights to the feature values ​​corresponding to the same battery module among the plurality of battery modules included in the battery pack or corresponding to the same sensing structure.

[0016] The at least one processor can diagnose the state of the battery pack by applying different weights to the feature values ​​based on the relative positions between the plurality of battery modules or the relative positions of the power lines connected to the plurality of battery modules and the battery pack.

[0017] The at least one processor can generate a trend line by removing an exception value based on a correlation between a voltage value included in the battery data and the feature value, and diagnose the state of the battery pack based on the trend line.

[0018] The at least one processor can derive a distance value between each battery cell included in the battery pack and the trend line, and diagnose a battery cell having a distance value greater than a preset reference value as an abnormality.

[0019] A battery diagnosis method according to one embodiment includes receiving battery data obtained by applying a current signal to a battery pack, performing error correction to correct the influence of a sensing line related to the structure of the battery pack to generate preprocessed data from the battery data, applying linear transformation to the preprocessed data to obtain linearly transformed data, and diagnosing the state of the battery pack based on a feature value extracted from the linearly transformed data.

[0020] Generating the above preprocessing data may include grouping the battery data according to the sensing line shape of the battery pack to generate a plurality of sensing line group data, and correcting a difference between the sensing line group data with a correction value to generate the above preprocessing data.

[0021] Correcting the difference between the sensing line group data may include deriving the correction value at a point where the difference between the sensing line group data is maximum, and correcting the difference between the sensing line group data with the correction value.

[0022] Deriving the above correction value may include deriving the correction value based on an average value of correction target data included in the plurality of sensing line group data and an average value of the remaining data.

[0023] Deriving the above correction value may include deriving a value obtained by subtracting the average value of the correction target data and the average value of the remaining data from the average value of the correction target data as the correction value.

[0024] Diagnosing the condition of the battery pack may include diagnosing the condition of the battery pack by applying a weight determined based on the structure of the battery pack to the feature value.

[0025] Diagnosing the state of the battery pack may include diagnosing the state of the battery pack by equally applying the weights to the feature values ​​corresponding to the same battery module or corresponding to the same sensing structure among a plurality of battery modules included in the battery pack.

[0026] Diagnosing the state of the battery pack may include diagnosing the state of the battery pack by applying different weights to the feature values ​​based on the relative positions between the plurality of battery modules or the relative positions of the plurality of battery modules and power lines connected to the battery pack.

[0027] Diagnosing the state of the battery pack may include generating a trend line by removing exceptional values ​​based on a correlation between a voltage value included in the battery data and the characteristic value, and diagnosing the state of the battery pack based on the trend line.

[0028] Diagnosing the condition of the battery pack may include deriving a distance value between each battery cell included in the battery pack and the trend line, and diagnosing a battery cell whose distance value is greater than a preset reference value as abnormal.

[0029] According to one embodiment of the present invention, a battery diagnosis device can quickly diagnose the state of a battery to improve the reliability of a battery system and increase efficiency by reducing the time associated with battery diagnosis.

[0030] FIG. 1 illustrates a block diagram of a typical battery system including a battery diagnostic device according to one embodiment.

[0031] FIG. 2 illustrates a block diagram showing the configuration of a battery diagnostic device according to one embodiment.

[0032] FIG. 3 schematically illustrates a flowchart of a battery diagnostic device according to one embodiment for determining the state of a battery.

[0033] FIG. 4a illustrates a graph of battery voltage over time according to one embodiment.

[0034] FIG. 4b illustrates a linear transformation graph before error correction utilized in a battery diagnostic device according to one embodiment.

[0035] FIG. 5 illustrates feature values ​​before error correction utilized in a battery diagnostic device according to one embodiment.

[0036] FIG. 6 illustrates another feature value before error correction utilized in a battery diagnostic device according to one embodiment.

[0037] Fig. 7 illustrates the structure of a battery pack that is a diagnostic target of a battery diagnostic device according to one embodiment.

[0038] FIG. 8 illustrates a linear transformation graph after error correction utilized in a battery diagnostic device according to one embodiment.

[0039] FIG. 9 illustrates feature values ​​included in a discharge graph utilized in a battery diagnostic device according to one embodiment.

[0040] FIG. 10 illustrates feature values ​​included in a linear transformation graph utilized in a battery diagnostic device according to one embodiment.

[0041] FIG. 11 illustrates feature values ​​included in an average graph utilized in a battery diagnostic device according to one embodiment.

[0042] Fig. 12 illustrates feature values ​​derived by a battery diagnostic device according to one embodiment.

[0043] FIG. 13 illustrates a trend line generated by a battery diagnostic device according to one embodiment.

[0044] Fig. 14 illustrates a control flowchart of a battery diagnosis method according to one embodiment.

[0045] FIG. 15 continues the control flow diagram of a battery diagnosis method according to one embodiment of FIG. 14.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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.

[0050] 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.

[0051] FIG. 1 illustrates a block diagram showing the configuration of a typical battery system including a battery diagnostic device according to various embodiments.

[0052] Specifically, FIG. 1 schematically illustrates a battery system (10) and an upper controller (20) included in an upper system according to one embodiment disclosed in this document.

[0053] As illustrated in FIG. 1, the battery system (10) may include a plurality of battery modules (12), a sensor unit (14), a switching unit (16), and a battery diagnostic device (1). At this time, the battery system (10) may be equipped with a plurality of battery modules (12), sensor units (14), switching units (16), and battery diagnostic devices (1).

[0054] A plurality of battery modules (12) may include at least one rechargeable battery cell (13). The battery cell (13) may include a cathode, a cathode material, a cathode material, a separator, an electrolyte, a polymer, and a case. In this case, the plurality of battery modules (12) may be connected in series or in parallel.

[0055] Additionally, the battery system (10) may include a battery pack, which may be configured by combining multiple battery modules (12), and may include a battery management system (BMS) to monitor and control the battery status. Furthermore, the battery system (10) may include a battery bank, which may configure an energy storage system by combining multiple battery packs.

[0056] The sensor unit (14) may include a current sensor, a voltage sensor, and a temperature sensor.

[0057] The current sensor can detect the current used in the process of determining the SOC of the battery cell (13).

[0058] The current sensor 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 diagnosis device (1). According to one embodiment, the current sensor can measure the battery current at predetermined intervals during a charging cycle in which the battery is charged with power from an external device (3) or a discharging cycle in which the battery is discharged, and transmit the measurement results to the battery diagnosis device (1).

[0059] The voltage sensor 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.

[0060] The temperature sensor may be configured to measure the battery temperature and generate a temperature signal representing the measured battery temperature. The temperature sensor 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 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.

[0061] The switching unit (16) is connected in series to the (+) terminal side or the (-) terminal side of the battery module (12) to control the charge / discharge current flow of the battery module (12). For example, the switching unit (16) may use at least one relay, magnetic contactor, etc. depending on the specifications of the battery system (10).

[0062] The battery diagnostic device (1) can monitor the voltage, current, temperature, etc. of the battery system (10) and control and manage it to prevent overcharging and overdischarging, etc., and may include, for example, a battery management system (BMS).

[0063] The battery diagnostic device (1) is an interface for receiving various parameters, and may include a plurality of terminals and a circuit connected to these terminals to process the input values. In addition, the battery diagnostic device (1) may control the ON / OFF of a switching unit (16), for example, a relay or a contactor, and may be connected to a battery module (12) to monitor the status of each battery module (12).

[0064] In addition, the battery diagnostic device (1) can receive current data, voltage data, and temperature data from the sensor unit (14) to obtain battery data and diagnose the status of the battery.

[0065] The upper controller (20) can transmit a control signal for controlling the battery module (12) to the battery diagnosis device (1). Accordingly, the battery diagnosis device (1) can be controlled for operation based on the control signal applied from the upper controller (20). In addition, the battery module (12) may be a component included in an ESS (Energy Storage System). In this case, the upper controller (20) may be a controller (BBMS) of a battery bank including a plurality of battery systems (10) or an ESS controller that controls the entire ESS including a plurality of battery banks. However, the battery system (10) is not limited to this purpose.

[0066] FIG. 2 illustrates a block diagram showing the configuration of a battery diagnostic device according to one embodiment.

[0067] Referring to FIG. 2, a battery diagnostic device (1) according to one embodiment includes a control unit (100) including at least one processor (110) and memory (120) and a communication interface (200), and can diagnose a battery by communicating with an external device (3) through the communication interface (200) and transmit the diagnosis result to the outside.

[0068] According to one embodiment, the battery diagnostic device (1) may include a user terminal and / or server device capable of communicating with an external device (3).

[0069] Specifically, when the battery diagnosis device (1) is a user terminal, the control unit (100) of the battery diagnosis device (1) may be configured as a CPU of the user terminal so as to diagnose the battery from the user terminal to an on-device. 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.

[0070] In addition, if the battery diagnostic device (1) 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 the respective server devices.

[0071] A battery diagnostic device (1) according to one embodiment may refer to any electronic device including a processor (110) and a memory (120), and may be mounted on a vehicle and operated. Each component of the battery diagnostic device (1) will be described in detail below.

[0072] The battery data acquisition device (2) illustrated in FIG. 2 may include a data acquisition device as a separate physical component. Here, the data acquisition device may refer to a device that monitors the status and performance of a battery in real time, stores data, and acquires analysis data using sensors and measuring equipment.

[0073] Additionally, the battery data acquisition device (2) may be a configuration that operates within the battery diagnostic device. That is, the battery data acquisition device (2) may operate as a hardware configuration within the battery diagnostic device or may operate in the form of software to acquire battery data.

[0074] Here, the battery data acquisition device (2) may include a constant current power supply for applying a constant current signal and a data acquisition device (DAQ).

[0075] The communication interface (200) may include a wireless communication interface (210) and a wired communication interface (220) for communicating with an external device (3). The communication interface (200) may transmit and receive programs for calculating characteristic values ​​of battery cells, classifying classes, and estimating lifespan, as well as various data, from a separately provided external server.

[0076] The wireless communication interface (210) may include at least one of a short-range communication module and a long-range communication module.

[0077] The short-range communication module can communicate with an external device (3) adjacent to the battery diagnostic 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).

[0078] The remote communication module may include a communication module that performs various types of remote communication and may include a mobile communication interface. The mobile communication interface may transmit and receive wireless signals with at least one of a base station, an external terminal, and an external device (3) on a mobile communication network. In addition, the remote communication module may communicate with an external device (3) or an external device (3) such as another electronic device through a surrounding access repeater (AP: Access Point). The access repeater (AP) may connect a local area network (LAN) to which the battery diagnosis device (1) is connected to a wide area network (WAN) to which a communication server is connected. Accordingly, the battery diagnosis device (1) may be connected to the communication server through the wide area network (WAN) and communicate with the external device (3).

[0079] The wired communication interface (220) can connect to a wired communication network and communicate with an external device (3) through the wired communication network. For example, the wired communication interface (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 (3) through the wired communication network.

[0080] A battery diagnostic 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.

[0081] The memory (120) can store various information necessary for operating the battery diagnostic device (1). Specifically, the memory (120) can store an operating system and programs necessary for operating the battery diagnostic device (1), or store data necessary for operating the battery diagnostic device (1). In addition, it can also store a learning model related to machine learning necessary for operating the battery diagnostic device (1).

[0082] Specifically, the memory (120) can store various programs related to calculating characteristic values ​​of battery cells, class classification, and life estimation. In addition, the memory (120) can store various data, such as voltage, current, temperature, and characteristic value data of each battery cell.

[0083] Additionally, the memory (120) can store the diagnosis results of the battery cell diagnosed by the processor (110) and specific information of the battery cell.

[0084] 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.

[0085] The processor (110) outputs control signals to control the overall battery diagnostic 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).

[0086] Specifically, the processor (110) can generate preprocessed data by performing error correction for compensating for the influence of the sensing line on the battery data, and here, the error correction for compensating for the influence of the sensing line can mean a process for compensating for an error that occurs based on the physical structure of the battery pack.

[0087] Specifically, sensing lines are wires placed in accordance with the battery cell, battery module, and battery pack structure during battery pack design. They are installed between the battery cells and the battery management device and serve to transmit battery data. Accordingly, sensing lines can be installed in the battery pack, taking into account wiring length, route, and protective devices to optimize performance for the intended use and purpose of the battery system.

[0088] The processor (110) can generate sensing line group data to remove the influence of the sensing line during the diagnosis process, and can perform error correction to remove the influence of the sensing line by correcting the difference between the sensing line group data.

[0089] At this time, the processor (110) can derive a correction value based on the average value of the correction target data included in the plurality of sensing line group data and the average value of the remaining data at the point where the difference value between the sensing line group data is maximum.

[0090] Specific details regarding error correction for compensating for the influence of the sensing line are described below in Fig. 4.

[0091] In addition, the processor (110) can diagnose the state of the battery, including whether the battery pack is abnormal and the degree of deterioration, by applying a weight determined based on the structure of the battery pack to the feature value, where the weight may mean a value for correcting an error occurring depending on the structure and position of the battery pack.

[0092] That is, the processor (110) can apply the same weight to battery data included in a battery module or having the same sensing structure, and apply different weights to battery data included in a different battery module or having a different sensing structure.

[0093] According to this, the processor (110) can correct errors caused by differences in the position of the battery module or differences in the sensing structure, rather than errors caused by deterioration or chemical properties of the battery.

[0094] In addition, the processor (110) can generate a trend line by removing exceptional values ​​based on the correlation between the voltage value and the characteristic value included in the battery data, and can diagnose whether the battery pack is abnormal based on the distance from the trend line.

[0095] Hereinafter, the correlation between voltage values ​​and characteristic values ​​is described to diagnose whether a battery pack is abnormal, but various other embodiments may be included. For example, the processor (110) may analyze the correlation between battery deterioration and characteristic values, or the correlation between internal resistance and characteristic values.

[0096] As described above, the battery diagnosis device (1) according to one embodiment can correct errors caused by the shape of a sensing line included in a battery pack, and can improve the accuracy of diagnosis by considering the structure of the battery pack.

[0097] FIG. 3 schematically illustrates a flow chart of a battery diagnostic device according to one embodiment for determining the state of a battery. Components 101 to 104 in FIG. 3 are implemented in the form of software blocks, stored in memory (120), and executed by a processor (110).

[0098] Referring to FIG. 3, at least one processor (110) can receive battery data obtained by applying a current signal to a battery pack from a battery data acquisition device (2) provided externally or included within the battery diagnostic device (1).

[0099] Here, the current signal applied to the battery pack may include a discharge current signal after charging, and the current signal may include a constant current signal (CC-signal). Specifically, the current signal applied to the battery pack may be applied as a single constant current signal or as a repeated constant current signal in a situation where charging and discharging are repeated, may be applied as a single constant current signal or as a repeated constant current signal in a situation where charging and resting are repeated, and may be applied as a single constant current signal or as a repeated constant current signal in a situation where discharging and resting are repeated.

[0100] That is, the current signal applied to the battery pack can be a constant current signal suitable for dividing components for ease of analysis based on the fact that voltage change patterns differ depending on the chemical reaction. In this case, the battery data acquisition cycle can be measured with a resolution of 1 mV or less and a measurement cycle of 10 ms or less, and various modifications may be included.

[0101] Afterwards, the preprocessing data generation unit (101) of the control unit (100) can generate preprocessing data by removing noise and the influence of sensing lines from the battery data.

[0102] Specifically, the processor (110) can generate preprocessing data by classifying the charging, discharging, and resting periods in accordance with the synchronization between repeated or multiple applied signals, and accordingly, the precision degradation due to the voltage resolution of the battery data acquisition device (2) can be alleviated and noise can be removed.

[0103] In addition, the processor (110) can generate preprocessed data by removing a preset initial time from one charge or discharge data, and thus, the fast response characteristics of the battery cell in the current change section and noise generated during the battery cell sensing process can be removed.

[0104] In addition, the processor (110) can generate preprocessing data by filtering noise at a certain time and frequency based on various filtering techniques, and according to this, fundamental noise and harmonic noise of mainly 50 Hz and 60 Hz can be removed.

[0105] In addition, the processor (110) can generate preprocessing data by dividing battery data into structurally identical groups according to the appearance caused by the shape of the sensing line, defining the difference in voltage behavior between the groups, and quantifying and compensating the difference between the groups.

[0106] Thereafter, the linear transformation data generation unit (102) of the control unit (100) can generate linear transformation data by linearly transforming the cell voltage data. Here, the linear transformation may include operations performed on the cell voltage, such as calculating the amount of change in the cell voltage or calculating a differential value for the cell voltage. Hereinafter, the values ​​included in the linear transformation data may be referred to as linear transformation values.

[0107] Linear transformation data may include linear transformation values ​​calculated based on cell voltage. For example, linear transformation values ​​may include a change in cell voltage, a derivative obtained by differentiating cell voltage based on a change in cell voltage over time, etc. Specifically, the processor (110) may generate linear transformation data based on the following mathematical expressions 1 or 2 for cell voltage data included in battery data.

[0108] [Mathematical Formula 1]

[0109] (V nt -V t ) / Time change

[0110] [Equation 2]

[0111] (V t2 -V t1 ) / Time change

[0112] Here, the time change may be a time interval corresponding to the difference between two time points (e.g., time n (nt) - time 1 (t)), and may include a time-related interval corresponding to the difference between two values ​​related to time (e.g., log(nt) - log(t)).

[0113] The processor (110) can generate linear transformation data based on the V value change rate of the first time interval (t to nt) (the slope value obtained by first-order linear fitting of the V value of the first time interval) and the V value change rate of the second time interval (t1 to t2) (the slope value obtained by first-order linear fitting of the V value of the second time interval). The reason for generating the linear transformation data may be to clearly confirm the change in the voltage values ​​included in the battery data (e.g., voltage over time), and by generating the linear transformation data, the trend of the data of the battery cells can be more easily determined. Here, the mathematical formula and algorithm for generating the linear transformation data are exemplary, and a modified embodiment for generating the linear transformation data may be included. In addition, the processor (110) can perform normalization on the derivative value derived based on the above mathematical formula 1 or 2, and derive a feature value from the normalized derivative value.

[0114] The feature value extraction unit (103) of the control unit (100) can extract and correct feature values ​​by selecting a specific point in time from a graph obtained by log differentiation, or can extract and correct individual components based on a formula for a chemical reaction.

[0115] At this time, the processor (110) can extract the feature value of the time period in which the influence of the sensing line is the smallest, and the time period in which the influence of the sensing line is the smallest may be obtained during the process in which the processor (110) performs error correction, or may include a preset time period.

[0116] Examples of feature values ​​extracted by the processor (110) may include a maximum value obtained from a linear transformation graph, a point in time at the maximum value, a linear transformation value at a specific point in time, and various modifications may be included.

[0117] Thereafter, the battery status diagnosis unit (104) of the processor (110) can diagnose the status of the battery cell based on the correlation between the extracted feature value and the voltage value.

[0118] Specifically, the processor (110) can remove outliers that exhibit unusual behavior in graphs of feature values ​​and voltage values, and outliers can refer to data points that deviate from an abnormal category compared to other data points based on visual and statistical methods.

[0119] The processor (110) can generate a trend line for a group from which exception values ​​have been removed, calculate a distance value from the trend line for each battery cell, and diagnose a battery cell whose distance value from the trend line is greater than a reference value as an abnormal battery cell.

[0120] Thereafter, the processor (110) can transmit information about the battery cell diagnosed as abnormal or normal to an external device (3) through a communication interface (200), thereby allowing a user related to the battery to remotely check the diagnosis results of the battery cell.

[0121] FIG. 4A illustrates a graph of a battery's voltage over time according to one embodiment. FIG. 4B illustrates a linear transformation graph before error correction utilized in a battery diagnostic device according to one embodiment.

[0122] Referring to FIG. 4A, the voltage graph may include voltage values ​​of battery cells over time during the discharge process. The voltage graph of FIG. 4A may be included in battery data. Referring to FIGS. 4A and 4B, the processor (110) may perform preprocessing on battery data (e.g., the voltage graph of FIG. 4A) to obtain a linear transformation graph such as FIG. 4B.

[0123] Referring to the linear transformation graph of Fig. 4b, in area (a), it is difficult to determine the trend of the data of the battery cells, but in area (b), it can be confirmed that the data is divided into the first group (#1, #3, #5, #7) and the second group (#2, #4, #6).

[0124] The linear transformation graph may include a derivative of the battery voltage. Here, the derivative may be a derivative of the voltage based on the amount of change over time. The processor (110) may preprocess battery data to obtain preprocessed data, and obtain a linear transformation value by performing a linear transformation including noise removal and differentiation on the preprocessed data. If the linear transformation value for the preprocessed data is generated as a graph, it may be expressed as in FIG. 4b.

[0125] Here, the horizontal axis of Fig. 4b may represent time, and the vertical axis may represent the voltage differential value. Additionally, each line may represent a different battery cell, and the index on the right side of the graph may represent the battery cell number.

[0126] Referring to area (a), even though the processor (110) derived the differential value from the battery pack under the same conditions, t O from t B You can see that the differential data is divided into two groups. Here, t O Wow t B can correspond to the voltage measurement point (unit: sec or msec, etc.).

[0127] Such a separation phenomenon may be caused by the influence of the sensing line that senses the battery data, or may be caused by a mutual coupling phenomenon, for example, in which two or more electrical elements interact with each other's electromagnetic fields and influence each other in a direction opposite to the expected effect.

[0128] Specifically, the effect of the separation phenomenon on battery diagnosis is described together with area (b) of FIG. 4 and FIG. 5, and together with area (c) of FIG. 4b and FIG. 6.

[0129] FIG. 5 illustrates feature values ​​before error correction used in a battery diagnosis device according to one embodiment, and FIG. 6 illustrates other feature values ​​before error correction used in a battery diagnosis device according to one embodiment.

[0130] First, referring to area (b) of Fig. 4b and Fig. 5 together, area (b) of Fig. 4b can be displayed as a differential graph for each battery cell divided into 4, 6, and the rest (1, 2, 3, 5, 7) due to the influence of the sensing line. In this way, if feature values ​​are extracted from area (b) of Fig. 4b, feature values ​​can be derived in a zigzag shape in the vertical direction, as shown in Fig. 5.

[0131] Continuing with reference to FIG. 5, the indices m1 to m4 may represent the numbers of each battery module. Furthermore, the horizontal axis may represent the cell voltage in the resting state before signal application, and the vertical axis may represent the feature values ​​extracted from area (b) of FIG. 4.

[0132] If the influence of the sensing line is not compensated for, accurate diagnosis may be hindered due to the location of characteristic values ​​of cells included in a specific module to be diagnosed. For example, if there are abnormal cells with characteristic values ​​similar to cells 28, 26, 24, and 22 corresponding to module 4 illustrated in FIG. 5, accurate diagnosis may be hindered.

[0133] Similarly, referring to area (c) of FIG. 4b together with FIG. 5, area (c) of FIG. 4 can be displayed with a reduced division of linear transformation graphs for each battery cell due to a reduced influence of sensing lines compared to area (b). In this way, when feature values ​​are extracted from area (c) of FIG. 4b, the difference between feature values ​​can be derived in a zigzag shape in the vertical direction, as shown in FIG. 6, in a form in which the difference is reduced compared to FIG. 5.

[0134] Continuing with reference to FIG. 6, similarly to FIG. 5, the indices m1 to m4 may denote the numbers of each battery module. Furthermore, the horizontal axis may denote the cell voltage in the resting state before signal application, and the vertical axis may denote the feature values ​​extracted from area (c) of FIG. 4b.

[0135] When the influence of the sensing line is reduced, the characteristic values ​​of the cells can be distinguished, so the accuracy of the diagnosis can be improved.

[0136] Based on these facts, the battery diagnostic device (1) according to one embodiment can compensate for the influence of the sensing line, and the specific compensation principle and result are described with reference to FIGS. 7 and 8.

[0137] FIG. 7 illustrates the structure of a battery pack that is a diagnostic target of a battery diagnostic device according to one embodiment, and FIG. 8 illustrates a linear transformation graph after error correction utilized in a battery diagnostic device according to one embodiment.

[0138] Referring to FIG. 7, an example of the structure of a battery pack (P), battery modules (M-1, M-2, M-3, M-4) and battery cells (#1 to #14) for performing a diagnosis in a battery diagnosis device (1) according to one embodiment is illustrated.

[0139] Specifically, each battery cell (#1 to #14) constitutes a battery module (M-1, M-2), and multiple battery modules (M-1, M-2, M-3, M-4) can constitute a battery pack (P), each sensing line (a-1, a-2, b-1, b-2) monitors the status of each cell, and the power lines (c-1, c-2) can transmit power to the battery pack (P).

[0140] For battery cells (#1 to #7) included in a battery module (M-1), sensing can be performed by different sensing lines for odd-numbered battery cells (#1, #3, #5, #7) and even-numbered battery cells (#2, #4, #6, #8).

[0141] In addition, since the battery cells (#1 to #7) included in the battery module (M-1) are affected by different power lines (c-1, c-2) due to differences in physical location and structure, differences may occur in the battery data obtained even under the same conditions.

[0142] In addition, each battery module (M-1, M-2, M-3, M-4) may have different heat distribution depending on its location, and the pressure applied may vary depending on the stacking method and structure. Accordingly, the voltage, internal resistance, capacity, etc. of each battery module (M-1, M-2, M-3, M-4) may vary, and the processor (110) may apply weights to each battery data to compensate for differences depending on the location of the battery module.

[0143] Specifically, the processor (110) can equally apply weights to feature values ​​corresponding to the same battery module or corresponding to the same sensing structure, and the processor (110) can differently apply weights to feature values ​​based on the relative positions between the plurality of battery modules or the relative positions of power lines connected to the plurality of battery modules and the battery pack.

[0144] That is, the processor (110) can compensate for this by assigning the same weight to feature values ​​extracted from the same battery module since they exhibit similar behaviors, and by assigning different weights to feature values ​​extracted from different battery modules since they exhibit different behaviors depending on the location or structure of each battery module.

[0145] In addition, the processor (110) can set weights when designing a battery system to compensate for electromagnetic influences on multiple sensing lines or power lines, as shown in FIG. 7, and can utilize an algorithm or machine learning model to derive optimal weights.

[0146] A battery diagnostic device (1) according to one embodiment can eliminate the influence of the sensing line through equivalent analysis to compensate for the influence of the sensing line.

[0147] Specifically, when the direction or path of the sensing loop between the even and odd battery cells is different, as in the exemplary structure of FIG. 7, the mutual coupling pattern may occur in reverse, showing opposite tendencies for a certain period of time immediately after the current change.

[0148] In one embodiment, as one of the methods for compensating for the influence of the sensing line, the processor (110) may perform the compensation using, in the case of odd battery cells, i) a linear transformation value measured in an existing odd battery cell, ii) an average of linear transformation values ​​in the odd battery cell (a first average), iii) an average of linear transformation values ​​in the even battery cell (a second average), and iv) a value based on an average of linear transformation values ​​in the odd battery cell (a first average) and an average of linear transformation values ​​in the even battery cell (a second average) (e.g., an arithmetic average of the first average and the second average).

[0149] Here, the reason why the first average and the second average are calculated separately may be because the number of odd battery cells may be different from the number of even battery cells. If the average of the linear transformation values ​​in all battery cells is calculated without distinguishing between odd and even battery cells, the proportion related to the linear transformation values ​​in the odd battery cells may be reflected more significantly. For example, if the number of odd battery cells is 4 and the linear transformation values ​​are {1, 3, 8, 12}, and the number of even battery cells is 3 and the linear transformation values ​​are {2, 4, 6}, the first average may be calculated as 6 (a value corresponding to ii described above) and the second average may be calculated as 4 (a value corresponding to iii described above), and the average value of the first average and the second average may be calculated as 5 (a value corresponding to iv described above). On the other hand, if the average of the entire linear transformation values ​​is calculated without distinguishing between odd and even battery cells, the value can be calculated as 5.14, which is closer to the first average. In summary, since calculating the average of the linear transformation values ​​of all battery cells can reflect the linear transformation values ​​of odd battery cells more in the average value, calculating the first and second averages separately can more precisely compensate for the influence of the sensing line.

[0150] Specifically, the processor (110) can derive the value obtained by subtracting iv) from ii) as the correction value v), and obtain the linear transformation value of the corrected odd battery cells by excluding the correction value v) from i). The linear transformation value described above can include a value obtained by differentiating the voltage value of each battery cell based on the amount of change in logarithmic time (e.g., log(t)).

[0151] Similarly, the processor (110) may perform correction based on a value (e.g., an average of the first average and the second average) based on i) a linear transformation value measured in an existing even battery cell, ii) an average of linear transformation values ​​in an even battery cell, and iii) an average of linear transformation values ​​in an even battery cell (a second average) and an average of linear transformation values ​​in an odd battery cell (a first average) in order to compensate for the influence of the sensing line in the case of an even battery cell.

[0152] Specifically, the processor (110) can derive the value obtained by subtracting iii) from ii) as the correction value iv), and obtain the linear transformation value of the corrected even battery cell by excluding the correction value iv) from i).

[0153] However, the battery structure of FIG. 7 is exemplary, and various structural modifications may exist depending on the shape of the battery (e.g., square, cylindrical, pouch-shaped, etc.) and the product to which the battery is applied (e.g., electric vehicles, ESS, robots, etc.). Accordingly, there may be various modifications (e.g., a method using an electromagnetic model, a method using scale adjustment, etc.) for correcting the influence of the sensing line, and correction may be performed in a method equivalent to the battery diagnosis device (1) according to one embodiment through equivalent analysis.

[0154] In this way, the processor (110) can obtain a logarithmic differential graph as in FIG. 8 by removing the influence of the sensing line.

[0155] Referring to FIG. 8, the processor (110) can remove noise from battery data, perform error correction to obtain preprocessed data, linearly transform the preprocessed data to obtain a linear transformation value, and generate a graph of the linear transformation value for the preprocessed data with the influence of the sensing line corrected, which can be expressed as in FIG. 8.

[0156] Here, the horizontal axis of Fig. 8 may represent time, and the vertical axis may represent linear transformation values. Additionally, each line may represent a different battery cell, and the index on the right side of the graph may represent the battery cell number.

[0157] That is, in Fig. 4b, since the influence of the sensing line is not compensated, the graph corresponding to each battery cell may be expressed by dividing it into two groups, and if feature values ​​are extracted from data divided into two groups, an inaccurate battery diagnosis may be made.

[0158] Accordingly, the battery diagnostic device (1) according to one embodiment can perform error correction to remove the influence of the sensing line to alleviate this, and when the processor (110) performs error correction as shown in FIG. 8, the linear transformation values ​​for each battery cell form one group, so that uniform feature values ​​can be derived according to the height of the linear transformation values.

[0159] Below, an embodiment of deriving a feature value utilized in a battery diagnosis device (1) according to one embodiment is described.

[0160] Specifically, the processor (110) can select a meaningful point in a linear transformation graph, extract the value at that point, and utilize it as a feature value. In addition, the chemical reaction of the battery can be analyzed based on a formula through linear transformation, and extracted into individual components.

[0161] In this process, the processor (110) can select the characteristic value of the time period with the least influence of the sensing line, and the process of selecting the characteristic value of the time period with the least influence of the sensing line can be performed in parallel with the task of correcting the influence of the sensing line.

[0162] Fig. 9 illustrates feature values ​​included in a discharge graph utilized in a battery diagnostic device according to one embodiment. Here, the horizontal axis of Fig. 9 may represent time, and the vertical axis may represent cell voltage.

[0163] Additionally, the indices on the right may each mean a different battery cell (#1 to #7), and each line may mean a discharge graph of each battery cell (#1 to #7).

[0164] Specifically, the processor (110) can extract multiple different feature values ​​by cropping a specific section of the discharge graph. That is, the processor (110) can determine the resistance value at a specific point in time (e.g., 10 ms point in time) as feature 1 (f_1) based on the following mathematical expression 3. Here, V represents voltage at each point in time, and I represents current.

[0165] [Equation 3]

[0166]

[0167] Additionally, the processor (110) can determine the slope after the first linear fitting of a specific section (e.g., a 900 to 1000 ms section) as feature 2 (f_2).

[0168] To this end, the processor (110) can perform a first-order linear fitting based on techniques such as the least squares method, maximum likelihood estimation, and least absolute deviations regression, and the processor (110) can find a straight line in the form of y=mx+b for time t and voltage V.

[0169] Here, y represents the dependent variable voltage V, x represents the independent variable time t, m represents the slope corresponding to feature 2 (f_2), and b represents the y-intercept.

[0170] In addition, the processor (110) can determine the resistance value at a specific point in time (e.g., 1000 ms point in time) as feature 3 (f_3) based on the mathematical expression 4 below. Here, V represents voltage at each point in time, and I represents current.

[0171] [Equation 4]

[0172]

[0173] In particular, the processor (110) can generate a diagnostic index through correlation analysis with other feature values ​​for feature 1 (f_1), feature 2 (f_2), and feature 3 (f_3), as well as feature 0 (f_0), and can diagnose the battery by utilizing the generated diagnostic index.

[0174] Fig. 10 illustrates feature values ​​included in a linear transformation graph utilized in a battery diagnostic device according to one embodiment. Here, the horizontal axis of Fig. 11 may represent time, and the vertical axis may represent linear transformation values.

[0175] Additionally, the indices on the right may each mean a different battery cell (#1 to #7), and each line may mean a linear transformation graph of each battery cell (#1 to #7).

[0176] The processor (110) can extract multiple different feature values ​​from the graph after removing the initial section (e.g., initial 3 ms) of the linear transformation graph and applying a filter with a cutoff frequency of 60 Hz using a low-pass filter (LPF).

[0177] Specifically, the processor (110) can determine the linear transformation value having the maximum value as feature 4 (f_4), and at any point in time (e.g., t D The linear transformation value of (100ms) can be determined as feature 5 (f_5), and the final point of the battery cell (#7) whose height of the highest point has the minimum value can be determined as feature 6 (f_6).

[0178] Additionally, the processor (110) may be configured to execute the following command at any time (e.g., t EThe linear transformation value of (490ms) can be determined as feature 7 (f_7), and the point in time of the linear transformation value with the maximum value can be determined as feature 8 (f_8).

[0179] Fig. 11 illustrates feature values ​​included in an average graph utilized in a battery diagnostic device according to one embodiment. Here, the horizontal axis of Fig. 11 may represent time, and the vertical axis may represent a linear transformation value.

[0180] The processor (110) can obtain linear transformation values ​​from all battery cells included in the battery pack, and extract multiple feature values ​​by cropping a graph obtained by averaging the linear transformation values ​​for the entire battery pack.

[0181] The processor (110) derives the maximum and minimum values ​​of the linear transformation values ​​from a graph that averages the linear transformation values ​​for the entire battery pack, and calculates the time at each of the maximum and minimum values ​​as T. max and T min can be decided by

[0182] Thereafter, the processor (110) can utilize the following mathematical equations 5 to 7 to derive feature 9 (f_9) and feature 10 (f_10).

[0183] [Equation 5]

[0184]

[0185] [Equation 6]

[0186]

[0187] [Equation 7]

[0188]

[0189] The processor (110) is the above T max From T min The point of division into 1 / 4 of the interval can be determined as f_10s, and T max From T minThe point of 3 / 4 division among the sections can be determined as f_10e. At this time, if f_10s is a value 20ms ago, f_10s can be fixed to 20ms.

[0190] Afterwards, the processor (110) T mid The linear transformation value of the point can be determined as feature 9 (f_9), and the slope after the first linear fitting in the section from f_10s to f_10e can be determined as f_10.

[0191] In this way, the battery diagnosis device (1) according to one embodiment can select feature values ​​from various battery data and linear transformation graphs, and diagnose the battery according to the correlation of the feature values, so that the battery can be quickly inspected by targeting various environments.

[0192] Fig. 12 illustrates feature values ​​derived by a battery diagnostic device according to one embodiment. Fig. 13 illustrates a trend line generated by a battery diagnostic device according to one embodiment.

[0193] Referring to Fig. 12, the horizontal axis may represent the feature 0 (f_0) value, which may represent the cell voltage in the resting state before signal application, and the vertical axis may represent any feature value A among the feature values ​​selected by the processor (110). In the index at the upper right of the graph, m1 to m4 may represent the number of each battery module.

[0194] Since the graph shown in Fig. 12 is a result in which the influence of the sensing line has been corrected, the vertical positions of cells 28, 26, 24, and 22 corresponding to module 4, which were difficult to accurately diagnose in Fig. 5, can be clearly distinguished.

[0195] A battery diagnostic device (1) according to one embodiment can diagnose whether a battery is abnormal even in the state of FIG. 12, but can improve the accuracy of the diagnosis by controlling the exception value of the special behavior based on the battery characteristics as in FIG. 13.

[0196] Referring to FIG. 13, the horizontal axis may represent the feature 0 (f_0) value, which may represent the cell voltage in the resting state before signal application, and the vertical axis may represent any feature value B among the feature values ​​selected by the processor (110).

[0197] In addition, line (d) may represent a trend line of normal battery cells, and area (e) may represent an area indicating a preset distance from the trend line. Symbols (a) to (c) may be illustrated by dividing battery cells into a certain state (e.g., normal or abnormal). For example, symbol (a) may represent an outlier removed by the processor (110) during the trend line generation process, symbol (b) may represent an actual abnormal battery cell, and symbol (c) may represent a predicted abnormal battery cell predicted by the processor (110). However, this is merely an example, and other embodiments indicated by symbols (a) to (c) may exist.

[0198] The processor (110) can diagnose the status of a battery cell based on line (d) and area (e). For example, the processor (110) can determine a symbol (a) not included in area (e) as an exception value and exclude it from the target for diagnosing the status of the cell.

[0199] Here, the method for removing exceptional values ​​by the processor (110) may utilize statistical techniques such as the mean, standard deviation, and interquartile range, or machine learning techniques such as the isolation forest, local outlier factor (LOF), and one-class support vector machine (One-Class SVM).

[0200] In this way, the battery diagnostic device (1) according to one embodiment can identify a trend within a normal range in a group in which exceptional values ​​of unusual behavior are removed from all battery cells, thereby improving the precision of the analysis.

[0201] FIG. 14 illustrates a control flowchart of a battery diagnosis method according to one embodiment, and FIG. 15 continues the control flowchart of the battery diagnosis method according to one embodiment from FIG. 14.

[0202] Referring to FIG. 14, the processor (110) can receive battery data obtained by applying a current signal to a battery pack (1400). Here, the battery data obtained by applying a current signal may include battery data obtained by the processor (110) applying a current signal to the battery pack, battery data obtained by an external device (3), and battery data received by a server device.

[0203] Thereafter, the processor (110) can generate a plurality of sensing line group data according to the sensing line shape of the battery pack (1410), and the processor (110) can determine whether a difference between the sensing line group data exceeds a preset value (1420).

[0204] That is, if the difference between the sensing line group data does not exceed a preset value (No of 1420), the processor (110) may not perform error correction to remove the influence of the sensing line, as the influence of the sensing line is canceled out and does not affect feature value extraction.

[0205] On the other hand, if the processor (110) determines that the difference between the sensing line group data exceeds a preset value (Yes in 1420), it can derive a correction value based on the average value of the data to be corrected and the average value of the remaining data (1430).

[0206] Thereafter, the processor (110) can compensate for the difference between the sensing line group data with a compensation value (1440), and here, the processor (110) can compensate for the sensing line influence by eliminating the difference between each sensing line group data through equivalent interpretation.

[0207] Continuing with reference to FIG. 15, the processor (110) can determine whether battery data corresponding to the same battery module exists among a plurality of battery modules (1500). That is, the processor (110) can determine whether battery data measured from a battery cell included in the same battery module exists.

[0208] Thereafter, if there is no battery data corresponding to the same battery module among the multiple battery modules (No of 1500), the processor (110) can determine whether there is battery data corresponding to the same sensing structure (1510). That is, the processor (110) can determine whether there is battery data measured in a structure equally affected by the sensing line and the power line.

[0209] If the processor (110) determines that battery data corresponding to the same battery module exists among a plurality of battery modules (Yes in 1500) or that battery data corresponding to the same sensing structure exists (Yes in 1510), the processor (110) may apply the same weight to each piece of battery data (1520).

[0210] According to this, a uniform battery diagnosis environment can be created by compensating for variations in battery data due to physical characteristics such as the location and structure of the battery module and variations in battery data due to the sensing structure.

[0211] Thereafter, the processor (110) can diagnose the status of the battery cell based on the correlation between the voltage value and the characteristic value included in the battery data (1430).

[0212] According to an embodiment, the processor (110) may generate a trend line excluding exceptional values, and the processor (110) may derive a distance value between a feature value for each battery cell and the trend line. The distance value between the battery cell and the trend line may mean a distance value between a position of a feature value measured from the battery cell and the trend line, and the processor (110) may utilize a distance measurement algorithm, such as a Euclidean distance or Dijkstra's algorithm, to derive the distance value.

[0213] Thereafter, the processor (110) can determine whether the distance value between the characteristic value and the trend line for each of the derived battery cells is greater than or equal to a preset reference value. If the processor (110) determines that the distance value between the characteristic value and the trend line for the battery cell is less than the preset reference value, the processor (110) can diagnose the corresponding battery cell as normal, and if the distance value between the characteristic value and the trend line for the battery cell is determined to exceed the preset reference value, the processor (110) can diagnose the corresponding battery cell as abnormal.

[0214] In this way, the battery diagnostic device (1) according to one embodiment can eliminate the influence of the structure of the battery pack when diagnosing an abnormality such as lithium deposition in a battery cell, and can accurately and quickly diagnose the battery according to various characteristic values, thereby reducing the time required for battery diagnosis and reducing costs.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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) via 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 the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0219] 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.

[0220] 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.

[0221] 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.

Claims

1. A communication interface that receives battery data obtained by applying a current signal to the battery pack; and A battery diagnosis device comprising at least one processor that generates preprocessing data from the battery data, extracts features based on the preprocessing data, and diagnoses the state of the battery pack based on the features.

2. In claim 1, At least one processor, A battery diagnostic device that obtains linear transformation data by applying linear transformation to the above preprocessed data and extracts the feature value from the linear transformation data.

3. In claim 1, At least one processor, A battery diagnostic device that generates the preprocessing data by performing error correction that compensates for the influence of the sensing line related to the structure of the battery pack.

4. In claim 3, At least one processor, A battery diagnostic device that groups the battery data according to the sensing line shape of the battery pack to generate a plurality of sensing line group data, and corrects the difference between the sensing line group data with a correction value to generate the preprocessing data.

5. In claim 4, At least one processor, A battery diagnostic device that derives the correction value at a point where the difference between the sensing line group data is maximum, and corrects the difference between the sensing line group data with the correction value.

6. In claim 5, At least one processor, A battery diagnostic device that derives the correction value based on the average value of the correction target data included in the plurality of sensing line group data and the average value of the remaining data.

7. In claim 6, At least one processor, A battery diagnostic device that derives the correction value by excluding the average value of the correction target data and the average value of the remaining data from the average value of the correction target data, and corrects the difference with the correction value.

8. In claim 3, At least one processor, A battery diagnosis device that diagnoses the state of the battery pack by applying a weight determined based on the structure of the battery pack to the feature value.

9. In claim 8, At least one processor, A battery diagnostic device that diagnoses the state of the battery pack by equally applying the weights to the feature values ​​corresponding to the same battery module or corresponding to the same sensing structure among a plurality of battery modules included in the battery pack.

10. In claim 9, At least one processor, A battery diagnostic device that diagnoses the state of the battery pack by applying different weights to the feature values ​​based on the relative positions between the plurality of battery modules or the relative positions of the power lines connected to the plurality of battery modules and the battery pack.

11. In claim 3, At least one processor above A battery diagnostic device that generates a trend line by removing exceptional values ​​based on the correlation between the voltage value included in the battery data and the characteristic value, and diagnoses the state of the battery pack based on the trend line.

12. In claim 11, At least one processor, A battery diagnostic device that derives a distance value between a characteristic value for each battery cell included in the battery pack and the trend line, and diagnoses a battery cell whose distance value is greater than a preset reference value as an abnormality.

13. Receive battery data obtained by applying a current signal to the battery pack; Generate preprocessing data from the above battery data; Extract features based on the above preprocessing data; A battery diagnosis method comprising: diagnosing the state of the battery pack based on the above characteristic values.

14. In claim 13, Extracting the above feature values ​​is as follows: A battery diagnosis method comprising obtaining linear transformation data by applying linear transformation to the above preprocessed data and extracting the feature value from the linear transformation data.

15. In claim 13, Generating the above preprocessing data is as follows: A battery diagnosis method further comprising: generating the preprocessing data by performing error correction that compensates for the influence of the sensing line related to the structure of the battery pack.

16. In claim 15, Generating the above preprocessing data is as follows: A battery diagnosis method comprising: grouping the battery data according to the sensing line shape of the battery pack to generate a plurality of sensing line group data, and correcting the difference between the sensing line group data with a correction value to generate the preprocessing data.

17. In claim 16, Compensating for the difference between the above sensing line group data, A battery diagnosis method comprising: deriving the correction value at a point where the difference between the sensing line group data is maximum, and correcting the difference between the sensing line group data with the correction value.

18. In claim 17, Deriving the above correction value is as follows: A battery diagnosis method comprising: deriving the correction value based on the average value of the correction target data included in the plurality of sensing line group data and the average value of the remaining data.

19. In claim 18, Deriving the above correction value is as follows: A battery diagnosis method comprising: deriving a value obtained by excluding the average value of the data to be corrected and the average value of the remaining data from the average value of the data to be corrected as the correction value.

20. In claim 15, Diagnosing the condition of the above battery pack is as follows: A battery diagnosis method comprising: diagnosing whether the battery pack is abnormal by applying a weight determined based on the structure of the battery pack to the feature value.

21. In claim 20, Diagnosing the condition of the above battery pack is as follows: A battery diagnosis method comprising: diagnosing the state of the battery pack by equally applying the weights to the feature values ​​corresponding to the same battery module or corresponding to the same sensing structure among a plurality of battery modules included in the battery pack.

22. In claim 21, Diagnosing the condition of the above battery pack is as follows: A battery diagnosis method comprising: diagnosing the state of the battery pack by applying different weights to the feature values ​​based on the relative positions between the plurality of battery modules or the relative positions of the power lines connected to the plurality of battery modules and the battery pack.

23. In claim 15, Diagnosing the condition of the above battery pack is as follows: A battery diagnosis method comprising: generating a trend line by removing exceptional values ​​based on the correlation between the voltage value included in the battery data and the characteristic value, and diagnosing the state of the battery pack based on the trend line.

24. In claim 23, Diagnosing the condition of the above battery pack is as follows: A battery diagnosis method comprising: deriving a distance value between a characteristic value for each battery cell included in the battery pack and the trend line, and diagnosing a battery cell having a distance value greater than a preset reference value as an abnormality.

Images