Battery diagnosis device and method

Abstract

A battery diagnosis device disclosed in the present document includes: a communication interface for receiving battery data obtained by applying a current signal to a battery pack; and at least one processor for generating preprocess data by removing noise from the battery data, obtaining impedance data by applying fast Fourier transform to the preprocess data, and diagnosing whether the battery pack is abnormal by extracting a feature value in which the influence of a sensing line is corrected from the impedance data.

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. 10-2024-0067308, filed May 23, 2024, and Korean Patent Application No. 10-2024-0152286, filed October 31, 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 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] If these batteries have defects, they can generate heat or fire during charging and discharging, posing a safety risk. Therefore, research is being conducted on methods for diagnosing batteries based on battery data. Specifically, there is a need to inspect battery packs in a short period of time to minimize downtime and ensure performance and safety of battery-powered devices. However, the lack of relevant technology has been a problem.

[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 diagnostic 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 removes noise from the battery data to generate preprocessed data, applies a fast Fourier transform to the preprocessed data to obtain impedance data, and extracts a feature value in which the influence of a sensing line is corrected from the impedance data to diagnose whether the battery pack is abnormal.

[0010] The at least one processor may derive an average voltage signal and an average current signal for a plurality of applied signals included in the battery data, and apply the fast Fourier transform assuming that the average voltage signal and the average current signal are infinitely repeated.

[0011] The at least one processor can obtain the impedance data by dividing the voltage Fourier transform value derived through the fast Fourier transform by the current Fourier transform value.

[0012] The at least one processor can extract the difference between the first real value and the second real value included in the impedance data as the first feature value.

[0013] The at least one processor can derive a ratio of the frequency of the first imaginary value and the frequency of the second imaginary value included in the impedance data, and extract a value obtained by excluding a value obtained by multiplying the second imaginary value by the ratio from the first imaginary value as a second feature value.

[0014] The at least one processor can extract a sum of preset dimensions of the first feature value and the second feature value as a third feature value.

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

[0016] The at least one processor can diagnose whether the battery pack is abnormal 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 exceptional values ​​based on a correlation between the voltage value included in the battery data and the characteristic value, and can diagnose whether the battery pack is abnormal 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, removing noise from the battery data to generate preprocessed data, applying a fast Fourier transform to the preprocessed data to obtain impedance data, and extracting a feature value in which the influence of a sensing line is corrected from the impedance data to diagnose whether the battery pack is abnormal.

[0020] Applying the fast Fourier transform may include deriving an average voltage signal and an average current signal for a plurality of applied signals included in the battery data, and applying the fast Fourier transform while assuming that the average voltage signal and the average current signal are infinitely repeated.

[0021] Obtaining the above impedance data may include obtaining the impedance data by dividing the voltage Fourier transform value derived through the fast Fourier transform by the current Fourier transform value.

[0022] Extracting the above feature value may include extracting the difference between the first real value and the second real value included in the impedance data as the first feature value.

[0023] Extracting the above feature value may include deriving a ratio of the frequency of the first imaginary value and the frequency of the second imaginary value included in the impedance data, and extracting a value obtained by excluding a value obtained by multiplying the second imaginary value by the ratio from the first imaginary value as a second feature value.

[0024] Extracting the above feature value may include extracting the sum of the preset dimensions of the first feature value and the second feature value as a third feature value.

[0025] Diagnosing whether the battery pack is abnormal may include diagnosing whether the battery pack is abnormal 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 whether the battery pack is abnormal may include diagnosing whether the battery pack is abnormal 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 the power lines connected to the battery pack.

[0027] Diagnosing whether the battery pack is abnormal 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 whether the battery pack is abnormal based on the trend line.

[0028] Diagnosing whether the battery pack is abnormal 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, 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 flow chart for diagnosing the status of a battery by a battery diagnostic device according to one embodiment.

[0033] FIG. 4 illustrates impedance data before error correction utilized in a battery diagnostic device according to one embodiment.

[0034] FIG. 5 illustrates feature values ​​before error correction utilized in a battery diagnostic device according to one embodiment.

[0035] FIG. 6 illustrates another feature value before error correction utilized in a battery diagnostic device according to one embodiment.

[0036] Fig. 7 illustrates the structure of a battery pack that is a diagnostic target of a battery diagnostic device according to one embodiment.

[0037] FIG. 8 illustrates feature values ​​included in a pulse graph utilized in a battery diagnostic device according to one embodiment.

[0038] FIG. 9 illustrates an average graph for an authorization signal utilized in a battery diagnostic device according to one embodiment.

[0039] Figure 10 illustrates a process for deriving feature values ​​by a battery diagnostic device according to one embodiment.

[0040] Fig. 11 illustrates feature values ​​derived by a battery diagnostic device according to one embodiment.

[0041] Fig. 12 illustrates a control flowchart of a battery diagnosis method according to one embodiment.

[0042] FIG. 13 continues the control flow diagram of a battery diagnosis method according to one embodiment of FIG. 12.

[0043] 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.

[0044] 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.

[0045] 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.

[0046] 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 dictates otherwise.

[0047] 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.

[0048] FIG. 1 illustrates a block diagram showing the configuration of a typical battery system including a battery diagnostic device according to various embodiments.

[0049] 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.

[0050] 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).

[0051] 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.

[0052] 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.

[0053] The sensor unit (14) may include a current sensor, a voltage sensor, and a temperature sensor.

[0054] The current sensor can detect the current used in the process of determining the SOC of the battery cell (13).

[0055] 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).

[0056] 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.

[0057] 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.

[0058] 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).

[0059] 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).

[0060] 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).

[0061] 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.

[0062] The upper controller (20) can transmit a control signal for controlling the battery module (12) to the battery diagnosis device (1). Accordingly, the operation of the battery diagnosis device (1) can be controlled 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.

[0063] FIG. 2 illustrates a block diagram showing the configuration of a battery diagnostic device according to one embodiment.

[0064] 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.

[0065] 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).

[0066] 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.

[0067] 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.

[0068] 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.

[0069] 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.

[0070] 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.

[0071] 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).

[0072] 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.

[0073] The wireless communication interface (210) may include at least one of a short-range communication module and a long-range communication module.

[0074] 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).

[0075] 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).

[0076] 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.

[0077] 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.

[0078] 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).

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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).

[0083] Specifically, the processor (110) can generate preprocessed data by removing noise from battery data, wherein the process of removing noise may include a process of correcting errors that occur based on the physical structure of the battery pack.

[0084] In addition, the processor (110) can obtain impedance data by applying a fast Fourier transform to the preprocessed data. The fast Fourier transform may refer to a process of converting a time domain signal into a frequency domain, and may refer to an algorithm that quickly calculates the Fourier transform by repeatedly dividing the input signal in half in the Fourier transform that converts a continuous signal into the frequency domain to obtain the amplitude and phase of each frequency component.

[0085] Additionally, the processor (110) can diagnose whether the battery pack is abnormal by extracting a feature value in which the influence of the sensing line is corrected from the impedance data.

[0086] 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.

[0087] The processor (110) can compensate for the influence of the sensing line during the feature value extraction process in order to eliminate the influence of the sensing line during the diagnosis process. Specifically, the processor (110) can compensate for the influence of the sensing line by removing the inductance component based on the ratio of multiple frequencies extracted from the impedance data. This will be described in detail below in FIG. 10.

[0088] In addition, the processor (110) can diagnose whether the battery pack is abnormal 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.

[0089] That is, the processor (110) can apply the same weight to battery data included in the same battery module or having the same sensing structure, and apply different weights to battery data included in different battery modules or having different sensing structures.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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).

[0095] 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).

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] Thereafter, the impedance data generation unit (102) of the control unit (100) can obtain impedance data by applying a fast Fourier transform to the preprocessed data. Specifically, the processor (110) can derive an average voltage signal and an average current signal for a plurality of applied signals included in the battery data, and apply a fast Fourier transform assuming that the average voltage signal and the average current signal are infinitely repeated.

[0103] Thereafter, the processor (110) can obtain impedance data by dividing the voltage Fourier transform value derived through the fast Fourier transform by the current Fourier transform value.

[0104] 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 in which impedance data is expressed, or can extract and correct individual components based on a formula for a chemical reaction.

[0105] 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 correction, or may include a preset time period.

[0106] The processor (110) can extract the difference between the first real value and the second real value included in the impedance data as the first feature value, can extract the value obtained by subtracting the value obtained by multiplying the second imaginary value at another point in time by the frequency ratio from the first imaginary value at a specific point in time as the second feature value, and can extract the sum of the preset orders of the first feature value and the second feature value as the third feature value.

[0107] Afterwards, the battery status diagnosis unit of the processor (110) can diagnose whether the battery cell is abnormal based on the correlation between the extracted feature value and the voltage value.

[0108] Specifically, the processor (110) can remove outliers that exhibit unusual behavior with respect to a graph of characteristic values ​​and resting voltage values ​​before signal application, and outliers can mean data points that deviate from an abnormal category compared to other data points based on visual and statistical methods.

[0109] 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.

[0110] 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.

[0111] Additionally, the processor (110) can control the battery based on the diagnostic results. Specifically, if the diagnostic results determine that the battery cell is normal, the processor (110) can maintain the current state. On the other hand, if the diagnostic results determine that the battery cell is abnormal, the processor (110) can protect the battery through control such as stopping charging / discharging, issuing a warning, and activating the cooling system.

[0112] FIG. 4 illustrates an impedance graph before error correction utilized in a battery diagnostic device according to one embodiment.

[0113] Referring to FIG. 4, the processor (110) can obtain impedance data by processing the preprocessed data obtained by preprocessing battery data, and when the impedance data for the preprocessed data is subjected to electrochemical impedance spectroscopy (EIS) to perform electrochemical Nyquist analysis (Nyquist plot), the result can be expressed as shown in FIG. 4.

[0114] Here, the horizontal axis of Fig. 4 may represent the real part of the impedance component, and the vertical axis may represent the imaginary part of the impedance component. Additionally, each shape may represent a different battery cell, and the index on the right side of the graph may represent the battery cell number.

[0115] To this end, the processor (110) can utilize impedance spectroscopy to sequentially apply a sine wave to the sample from a high frequency to a low frequency, measure the change in amplitude and phase according to the response sine wave coming through the sample, and then analyze the impedance. At this time, the processor (110) can derive an electrochemical Nyquist plot, which is a graph expressed as a complex number in which one coordinate is drawn for one frequency, as shown in FIG. 4.

[0116] Specifically, referring to area (a), it can be confirmed that the imaginary values ​​are divided into two groups from -0.0003 ohm to 0.0000 ohm, even though the processor (110) derived impedance data from a battery pack under the same conditions.

[0117] 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.

[0118] Specifically, the effect of the separation phenomenon on battery diagnosis is described together with area (a) of FIG. 4 and FIG. 5, and together with area (b) of FIG. 4 and FIG. 6.

[0119] 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.

[0120] First, referring to area (a) of Fig. 4 and Fig. 5 together, area (a) of Fig. 4 can be displayed such that the impedance values ​​for each battery cell are divided into odd-numbered battery cells (No. 1, No. 3, No. 5, and No. 7) and the rest (No. 2, No. 4, and No. 6) due to the influence of the sensing line. In this way, when feature values ​​are extracted from area (a) of Fig. 4, feature values ​​can be derived in a zigzag shape in the vertical direction, as shown in Fig. 5.

[0121] Continuing with reference to FIG. 5, NM in the index may denote a normal cell, NG may denote an abnormal cell, and m1 to m4 may denote the number 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 value extracted from region (a) of FIG. 4.

[0122] In this way, if the influence of the sensing line is not compensated for, the vertical positions of cells 28, 26, 24, and 22 corresponding to m4_NM (normal) and cell 23 corresponding to m4_NG (abnormal) in module 4 may be similar, which may hinder accurate diagnosis.

[0123] Similarly, referring to area (b) of FIG. 4 and FIG. 6 together, area (b) of FIG. 4 can be displayed with a reduced distinction in impedance values ​​for each battery cell due to a reduced influence of the sensing line compared to area (a). In this way, when feature values ​​are extracted from area (b) of FIG. 4, feature values ​​can be derived in a zigzag shape in the vertical direction in a reduced form compared to FIG. 5, as shown in FIG. 6.

[0124] Continuing with reference to FIG. 6, similarly to FIG. 5, NM in the index may denote a normal cell, NG may denote an abnormal cell, and m1 to m4 may denote the number 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 value extracted from region (b) of FIG. 4.

[0125] When the influence of the sensing line is reduced, the vertical positions of cells 28, 26, 24, and 22 corresponding to m4_NM (normal) and cell 23 corresponding to m4_NG (abnormal) in module 4 can be distinguished, thereby improving the accuracy of diagnosis.

[0126] 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 specifically described in FIG. 7 and below.

[0127] Fig. 7 illustrates the structure of a battery pack that is a diagnostic target of a battery diagnostic device according to one embodiment.

[0128] 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.

[0129] 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).

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] A battery diagnostic device (1) according to one embodiment can eliminate the influence of the sensing line by performing correction using a frequency ratio in the process of extracting a feature value to correct the influence of the sensing line.

[0138] However, the battery structure of FIG. 7 is merely exemplary, and various structural variations 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 variations in the method for compensating for the influence of the sensing line, and the compensation may be performed in a manner equivalent to that of the battery diagnostic device (1) according to one embodiment.

[0139] Below, an embodiment of deriving a feature value utilized in a battery diagnosis device (1) according to one embodiment is described.

[0140] FIG. 8 illustrates feature values ​​included in a pulse graph utilized in a battery diagnostic device according to one embodiment.

[0141] Specifically, the processor (110) can select any point in time that can be used for analysis along with the feature values ​​extracted from the impedance data among the pulse graphs and use the values ​​at that point in time as the feature values.

[0142] 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.

[0143] At this time, the horizontal axis of Fig. 8 may represent time [ms], and the vertical axis may represent cell voltage. In addition, each line (a) may represent a change in voltage obtained according to a pulse applied to the battery cell.

[0144] Specifically, the processor (110) can derive the cell voltage value of the idle period before pulse application as feature 0 (f_0). Specifically, the processor (110) can extract the cell voltage value of the idle period before pulse application at a standard interval for a standard number of times and determine the average value of the extracted cell voltage values ​​as feature 0 (f_0).

[0145] For example, the processor (110) can extract 100 cell voltage values ​​at 10 ms intervals during a pause period before pulse application, and determine the average value of the 100 extracted cell voltage values ​​as feature 0 (f_0).

[0146] In particular, the processor (110) can generate a diagnostic index through correlation analysis of feature 0 (f_0), which is a cell voltage value of a rest period before pulse application, and other feature values, and can diagnose the battery by utilizing the generated diagnostic index.

[0147] FIG. 9 illustrates an average graph for an authorization signal utilized in a battery diagnostic device according to one embodiment.

[0148] Referring to FIG. 9, the processor (110) can obtain voltage data and current data by applying multiple signals to the battery pack. At this time, the obtained voltage data and current data include voltage data and current data obtained by the processor (110) directly applying the signals multiple times or voltage data and current data obtained from a separate device.

[0149] Thereafter, the processor (110) can perform N measurements under the same conditions to collect M samples in each measurement, and align and normalize the measured voltage and current data based on the same criteria.

[0150] The processor (110) can derive one average voltage signal as in (a) of FIG. 9 by averaging N measurement data at each sample point, and can derive one average current signal as in (b) of FIG. 9.

[0151] Thereafter, the processor (110) can perform a fast Fourier transform assuming that the average voltage signal and average current signal derived from (a) and (b) of FIG. 9 are infinitely repeated. Accordingly, the effect of the Fourier transform can be maximized by processing the signal as a periodic signal in which the beginning and end are naturally connected.

[0152] The processor (110) can obtain impedance data according to the following mathematical expression 1 by dividing the voltage Fourier transform value obtained by performing a fast Fourier transform by the current Fourier transform value.

[0153] [Mathematical Formula 1]

[0154] Impedance data (impedance) = voltage Fourier transform value (voltage_fft) / current Fourier transform value (current_fft)

[0155] Figure 10 illustrates a process for deriving feature values ​​by a battery diagnostic device according to one embodiment.

[0156] Referring to FIG. 10, the processor (110) can derive a characteristic value for determining whether the battery is abnormal from the electrochemical Nyquist plot of the impedance data obtained in FIG. 9.

[0157] Specifically, the processor (110) can extract the difference between the first real value and the second real value included in the impedance data as the first feature value. That is, in FIG. 10, the processor (110) can extract the difference between the real value [1.5 Hz] at point (a-1) and the real value [0.5 Hz] at point (b-1) as the first feature value.

[0158] At this time, since not all paths to the battery cells are the same due to the structure of the battery pack, there is a difference in the absolute resistance value, so the processor (110) can derive the relative difference in the real value as feature value 1 to compensate for this difference.

[0159] In addition, the processor (110) can derive a ratio of the frequency of the first imaginary value and the frequency of the second imaginary value included in the impedance data, and extract a value obtained by subtracting the product of the frequency ratio derived from the second imaginary value from the first imaginary value as a second feature value.

[0160] In FIG. 10, the processor (110) can derive a second characteristic value based on the difference between the imaginary value at point (a-2) and the imaginary value at point (b-2). In the case of the imaginary value, mutual coupling may be applied to the sensing line, so that an inductance component (jwL) may be added. Accordingly, in order to compensate for the inductance component, the imaginary value may be multiplied by the ratio of the high frequency to the low frequency, thereby deriving a difference value.

[0161] That is, the processor (110) can calculate the frequency ratio of the imaginary value [1.5 Hz] at point (a-2) and the imaginary value [0.5 Hz] at point (b-2) as 1.5 Hz / 0.5 Hz and derive it as 3. The processor (110) can extract the difference value of 3*imaginary value [0.5 Hz], which is obtained by multiplying the frequency ratio of the imaginary value [1.5 Hz] at point (a-2) and the imaginary value [0.5 Hz] at point (b-2), as the second feature value.

[0162] In addition, the processor (110) may extract the sum of the squares of each of the first and second feature values ​​as the third feature value in order to comprehensively reflect the information of the first and second feature values. At this time, the processor (110) is not limited to the sum of squares, and may determine a sum of a preset order, such as a sum of cubes or a sum of fourths, as the third feature value.

[0163] Below, the process of diagnosing a battery using feature values ​​derived by the processor (110) is described.

[0164] Fig. 11 illustrates feature values ​​derived by a battery diagnostic device according to one embodiment.

[0165] Referring to FIG. 11, the horizontal axis may mean the feature 0 (f_0) value, which may mean the cell voltage in the resting state before signal application, and the vertical axis may mean the third feature value (f_3) among the feature values ​​selected by the processor (110).

[0166] Additionally, in the index at the upper right of the graph, NM may indicate a normal cell, NG may indicate an abnormal cell, and module_1 to module_4 may indicate the number of each battery module.

[0167] Since the graph shown in Fig. 11 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_NM (normal) in module 4, which was difficult to accurately diagnose in Fig. 5, and cell 23 corresponding to NG (abnormal) can be clearly distinguished.

[0168] A battery diagnostic device (1) according to one embodiment can diagnose whether a battery is abnormal even in the state of FIG. 11, but can improve the accuracy of the diagnosis by removing exceptional values ​​of peculiar behavior based on battery characteristics.

[0169] Specifically, the processor (110) can generate a trend line excluding exceptional values ​​and derive a normal area based on the distance from the trend line. The processor (110) can determine a battery cell included within the normal area as a normal battery cell and determine a battery cell detected outside the normal area as an abnormal battery cell.

[0170] 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).

[0171] 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.

[0172] FIG. 12 illustrates a control flowchart of a battery diagnosis method according to one embodiment, and FIG. 13 continues the control flowchart of the battery diagnosis method according to one embodiment from FIG. 12.

[0173] Referring to FIG. 12, the processor (110) can receive battery data obtained by applying a current signal to a battery pack (1200). 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.

[0174] Thereafter, the processor (110) can generate preprocessed data by removing noise from the battery data (1210). The processor (110) can apply a fast Fourier transform to the average voltage signal and the average current signal for the plurality of applied signals included in the battery data (1220).

[0175] Thereafter, the processor (110) can obtain impedance data by dividing the voltage Fourier transform value derived through the fast Fourier transform by the current Fourier transform value (1230), and can extract a plurality of feature values ​​based on the impedance data.

[0176] Specifically, the processor (110) can extract the difference between the first real value and the second real value included in the impedance data as a first feature value (1240), extract the second feature value based on the frequency ratio of the first imaginary value and the second imaginary value included in the impedance data (1250), and extract the sum of the squares of the first feature value and the second feature value as a third feature value (1260).

[0177] Next, referring to FIG. 13, the processor (110) can determine whether battery data corresponding to the same battery module exists among a plurality of battery modules. That is, the processor (110) can determine whether battery data measured from a battery cell included in the same battery module exists (1300).

[0178] Thereafter, if battery data corresponding to the same battery module among the multiple battery modules does not exist (No in 1300), the processor (110) can determine whether battery data corresponding to the same sensing structure exists (1310). That is, the processor (110) can determine whether battery data measured in a structure equally affected by the sensing line and the power line exists.

[0179] If the processor (110) determines that battery data corresponding to the same battery module exists among a plurality of battery modules (example of 1300) or that battery data corresponding to the same sensing structure exists (example of 1310), the processor (110) may apply the same weight to each of the corresponding battery data (1320).

[0180] 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.

[0181] Thereafter, the processor (110) can generate a trend line excluding exceptional values ​​based on the correlation between the voltage value included in the battery data and the third characteristic value (1330), and the processor (110) can derive a distance value between the battery cell and the trend line (1340).

[0182] Here, the distance value between the battery cell and the trend line may mean the distance value between the position of the feature value measured from the battery cell and the trend line, and the processor (110) may utilize a distance measurement algorithm such as the Euclidean distance or Dijkstra's algorithm to derive the distance value.

[0183] Thereafter, the processor (110) can determine whether the distance value between the derived battery cell and the trend line is greater than a preset reference value (1350).

[0184] If the processor (110) determines that the distance value between the battery cell and the trend line is less than a preset reference value (No of 1350), the processor can diagnose the corresponding battery cell as normal (1370), and if the distance value between the battery cell and the trend line is determined to exceed a preset reference value (Yes of 1350), the processor can diagnose the corresponding battery cell as abnormal (1360).

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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 diagnostic device comprising at least one processor for generating preprocessed data by removing noise from the battery data, obtaining impedance data by applying a fast Fourier transform to the preprocessed data, and extracting a feature value in which the influence of a sensing line is corrected from the impedance data to diagnose whether the battery pack is abnormal.

2. In claim 1, At least one processor, A battery diagnostic device that derives an average voltage signal and an average current signal for a plurality of applied signals included in the above battery data, and applies the fast Fourier transform assuming that the average voltage signal and the average current signal are infinitely repeated.

3. In claim 2, At least one processor, A battery diagnostic device that obtains the impedance data by dividing the voltage Fourier transform value derived through the above fast Fourier transform by the current Fourier transform value.

4. In claim 1, At least one processor, A battery diagnostic device that extracts the difference between the first real value and the second real value included in the above impedance data as the first feature value.

5. In claim 4, At least one processor, A battery diagnostic device that derives a ratio of the frequency of a first imaginary value and a frequency of a second imaginary value included in the impedance data, and extracts a value obtained by excluding a value obtained by multiplying the second imaginary value by the ratio from the first imaginary value as a second feature value.

6. In claim 5, At least one processor, A battery diagnostic device that extracts the sum of the preset orders of the first feature value and the second feature value as a third feature value.

7. In claim 1, At least one processor, A battery diagnostic device that diagnoses whether the battery pack is abnormal by equally applying weights to the feature values ​​corresponding to the same battery module or the same sensing structure among a plurality of battery modules included in the battery pack.

8. In claim 7, At least one processor, A battery diagnostic device that diagnoses whether the battery pack is abnormal 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.

9. In claim 1, 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 whether the battery pack is abnormal based on the trend line.

10. In claim 9, At least one processor, A battery diagnostic device that derives a distance value between 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.

11. Receive battery data obtained by applying a current signal to the battery pack; Generate preprocessed data by removing noise from the above battery data; Impedance data is obtained by applying a fast Fourier transform to the above preprocessed data; A battery diagnosis method comprising: extracting a feature value in which the influence of a sensing line is corrected from the above impedance data to diagnose whether the battery pack is abnormal; 12. In claim 11, Applying the above fast Fourier transform, A battery diagnosis method comprising: deriving an average voltage signal and an average current signal for a plurality of applied signals included in the battery data, and applying the fast Fourier transform assuming that the average voltage signal and the average current signal are infinitely repeated.

13. In claim 12, Obtaining the above impedance data is as follows: A battery diagnosis method comprising: obtaining the impedance data by dividing the voltage Fourier transform value derived through the fast Fourier transform by the current Fourier transform value.

14. In claim 13, Extracting the above feature values ​​is as follows: A battery diagnosis method comprising: extracting the difference between a first real value and a second real value included in the above impedance data as a first feature value.

15. In claim 14, Extracting the above feature values ​​is as follows: A battery diagnosis method comprising: deriving a ratio of the frequency of a first imaginary value and a frequency of a second imaginary value included in the impedance data, and extracting a value obtained by excluding a value obtained by multiplying the second imaginary value by the ratio from the first imaginary value as a second feature value.

16. In claim 15, Extracting the above feature values ​​is as follows: A battery diagnosis method comprising: extracting a sum of preset dimensions of the first feature value and the second feature value as a third feature value.

17. In claim 16, Diagnosing whether the above battery pack is abnormal is as follows: A battery diagnosis method comprising: diagnosing whether the battery pack is abnormal by applying an equal weight 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.

18. In claim 17, Diagnosing whether the above battery pack is abnormal is as follows: A battery diagnosis method comprising: diagnosing whether the battery pack is abnormal 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.

19. In claim 11, Diagnosing whether the above battery pack is abnormal is as follows: A battery diagnosis method comprising: 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 whether the battery pack is abnormal based on the trend line.

20. In claim 19, Diagnosing whether the above battery pack is abnormal is as follows: A battery diagnosis method comprising: deriving a distance value between 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