Battery diagnosis device, battery diagnosis method, and battery diagnosis system

Abstract

According to some embodiments disclosed in the present document, a battery diagnosis device comprises an interface configured to acquire first battery measurement values from cells being diagnosed, and a controller configured to: select second battery measurement values satisfying an analysis condition among the first battery measurement values; calculate battery deviation values on the basis of the differences between a representative value of the second battery measurement values and each of the second battery measurement values; calculate analysis index values indicating a variation trend based on the measurement times of the battery deviation values; and diagnose the states of the cells being diagnosed on the basis of the analysis index values.

Description

Battery diagnostic device, battery diagnostic method, and battery diagnostic system

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority to Korean Patent Application No. 10-2024-0131496, filed September 27, 2024, and Korean Patent Application No. 10-2023-0180591, filed December 13, 2023, 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, a battery diagnostic method, and a battery diagnostic system.

[0005] Recently, active research and development has been conducted on secondary batteries. 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 can boast higher energy densities than conventional Ni / Cd and Ni / MH batteries. They can be manufactured in small and lightweight designs, making them highly versatile power sources for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, attracting attention as a next-generation energy storage medium.

[0006] Voltage data from battery cells can be used to diagnose battery condition. For example, diagnosis can be performed by calculating voltage deviations, which are the differences between cell voltages and the average cell voltage, and determining whether these voltage deviations satisfy specific diagnostic conditions. However, this diagnostic method can require extensive computation to determine whether specific diagnostic conditions are met, and the battery condition can be diagnosed based on a single point in time, rather than long-term behavior.

[0007] One purpose of the embodiments disclosed in this document is to provide a battery diagnosis device, a battery diagnosis method, and a battery diagnosis system that can diagnose the state of a battery based on long-term behavior without requiring complex calculations.

[0008] The technical objectives of the embodiments disclosed in this document are not limited to the technical tasks mentioned above, and other technical tasks not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0009] According to some embodiments disclosed in the present document, a battery diagnosis device includes an interface configured to acquire first battery measurement values ​​from cells to be diagnosed; and a controller configured to select second battery measurement values ​​satisfying an analysis condition among the first battery measurement values, calculate battery deviation values ​​based on a representative value of the second battery measurement values ​​and a difference between each of the second battery measurement values, calculate analysis index values ​​representing a variation trend of the battery deviation values ​​over a measurement time, and diagnose a state of the cells to be diagnosed based on the analysis index values.

[0010] In some embodiments, the controller is configured to select open circuit voltage (OCV) measurements from among the first battery measurements, and to select OCV measurements above a threshold voltage value from among the OCV measurements as the second battery measurements.

[0011] In some embodiments, the controller is configured to calculate a moving average indicator representing the long-term behavior of the battery deviation values ​​over the measurement time.

[0012] In some embodiments, the controller is configured to calculate an exponential moving average (EMA) of the battery deviation values ​​to represent the long-term behavior, wherein the long-term behavior comprises a behavior of the battery deviation values ​​over a period of at least twice an analysis period of the exponential moving average (EMA).

[0013] According to some embodiments, the controller is configured to calculate deviation value slopes based on the amount of change in the exponential moving average (EMA) for a period of at least twice the analysis period, and to diagnose the status of the diagnosis target cells based on the deviation value slopes.

[0014] According to some embodiments, the controller is configured to detect a blank period during which the second battery measurement values ​​satisfying the analysis condition do not exist among the acquisition periods for acquiring the first battery measurement values, and to estimate analysis indicator values ​​corresponding to the blank period by performing interpolation based on the analysis indicator values.

[0015] In some embodiments, the diagnostic target cells are included in a diagnostic target battery, and the diagnostic target battery is mounted on a mobility device.

[0016] According to some embodiments disclosed in the present document, a battery diagnosis method includes: obtaining first battery measurement values ​​from cells to be diagnosed; selecting second battery measurement values ​​that satisfy an analysis condition among the first battery measurement values; calculating battery deviation values ​​based on a representative value of the second battery measurement values ​​and a difference between each of the second battery measurement values; calculating analysis index values ​​representing a change trend of the battery deviation values ​​over a measurement time; and diagnosing a state of the cells to be diagnosed based on the analysis index values.

[0017] According to some embodiments, the step of selecting the second battery measurements includes: selecting open circuit voltage (OCV) measurements from among the first battery measurements; and selecting OCV measurements above a threshold voltage value from among the OCV measurements as the second battery measurements.

[0018] In some embodiments, the step of calculating the analysis indicator values ​​includes the step of calculating a moving average indicator representing the long-term behavior of the battery deviation values ​​over the passage of the measurement time.

[0019] In some embodiments, the step of calculating the moving average index comprises the step of calculating an exponential moving average (EMA) of the battery deviation values ​​to represent the long-term behavior; wherein the long-term behavior comprises the behavior of the battery deviation values ​​for a period of at least twice the analysis period of the exponential moving average (EMA).

[0020] According to some embodiments, the step of diagnosing the status of the cells to be diagnosed includes: calculating slopes of deviation values ​​based on the amount of change in the exponential moving average (EMA) for a period of at least twice the analysis period; and diagnosing the status of the cells to be diagnosed based on the slopes of the deviation values.

[0021] According to some embodiments, the step of calculating the analysis indicator values ​​includes: detecting a blank period in which the second battery measurement values ​​satisfying the analysis condition do not exist among the acquisition periods for acquiring the first battery measurement values; and performing interpolation based on the analysis indicator values ​​to estimate the analysis indicator values ​​corresponding to the blank period.

[0022] In some embodiments, the diagnostic target cells are included in a diagnostic target battery, and the diagnostic target battery is mounted on a mobility device.

[0023] According to some embodiments disclosed in the present document, a battery diagnosis system includes a battery to be diagnosed, the battery including cells to be diagnosed, and mounted on a power-using device; and a battery diagnosis device configured to obtain first battery measurement values ​​from the cells to be diagnosed, select second battery measurement values ​​satisfying an analysis condition among the first battery measurement values, calculate battery deviation values ​​based on a representative value of the second battery measurement values ​​and a difference between each of the second battery measurement values, calculate analysis index values ​​representing a variation trend of the battery deviation values ​​over a measurement time, and diagnose a state of the cells to be diagnosed based on the analysis index values.

[0024] According to the embodiments disclosed in this document, a battery diagnosis device, a battery diagnosis method, and a battery diagnosis system can be provided that can diagnose the state of a battery based on long-term behavior without requiring complex calculations.

[0025] The technical effects according to the embodiments disclosed in this document are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art according to the disclosure of this document.

[0026] FIG. 1 may illustrate elements constituting a battery diagnostic system according to some embodiments.

[0027] FIG. 2 may illustrate elements constituting a battery diagnostic device according to some embodiments.

[0028] Figure 3 can illustrate a process for diagnosing an abnormality in a battery to be diagnosed according to conventional technology.

[0029] FIG. 4 may illustrate a process for diagnosing an abnormality in a battery to be diagnosed according to some embodiments.

[0030] Figures 5 and 6 can illustrate long-term behavior of a diagnostic target cell according to some embodiments.

[0031] FIG. 7 may illustrate a process for analyzing an exponential moving average (EMA) according to some embodiments.

[0032] FIG. 8 illustrates a method for diagnosing battery cell failure using long-term behavioral slope according to some embodiments.

[0033] FIG. 9 may illustrate a method for artificially inducing a short circuit in a battery cell according to some embodiments.

[0034] FIG. 10 may illustrate the correlation between a 240-day slope and an artificial defective cell according to some embodiments.

[0035] FIG. 11 may illustrate steps of a battery diagnosis method according to some embodiments.

[0036]

[0037]

[0038] Hereinafter, embodiments described in this document are described with reference to the attached drawings. However, this is not intended to limit the disclosure of this document to specific embodiments, and it should be understood that various modifications, equivalents, and / or alternatives of the embodiments described in this document are included.

[0039] The embodiments and terminology used in this document are not intended to limit the technical features described in this document to a specific embodiment, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiment. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the item, unless the context clearly indicates otherwise.

[0040] In this document, the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding component from other corresponding components, and do not limit the corresponding components in any other respect (e.g., importance or order) unless specifically stated otherwise.

[0041] In this document, when a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired or wirelessly), or indirectly (e.g., via a third component).

[0042] The methods according to various embodiments disclosed in this document may be provided as a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory, CD-ROM), or may be distributed online (e.g., downloaded or uploaded) through an application store or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0043] According to the embodiments disclosed in this document, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to the embodiments disclosed in this document, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to the embodiments disclosed in this document, the operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0044] FIG. 1 may illustrate elements constituting a battery diagnostic system according to some embodiments.

[0045] Referring to FIG. 1, the battery diagnosis system (10) may include a power usage device (110), a battery to be diagnosed (120), a battery diagnosis device (130), and a management server (140). However, the present invention is not limited thereto, and some components may be omitted from the battery diagnosis system (100) or other general-purpose components may be further included in the battery diagnosis system (100).

[0046] A battery diagnosis system (100) may refer to a system for diagnosing a battery (120) to be diagnosed. The battery (120) to be diagnosed may be discharged or charged by a power usage device (110), and a battery diagnosis device (130) may diagnose the battery (120) to be diagnosed by analyzing data regarding charging and discharging of the battery (120) to be diagnosed.

[0047] The power usage device (110) can use the battery (120) to be diagnosed as a power source. According to an embodiment, the power usage device (110) can include a mobility device such as an electric vehicle (EV), a hybrid electric vehicle (HEV), or an electric bike. For example, the mobility device can discharge the battery (120) to be diagnosed to drive a motor and charge the battery (120) to be diagnosed through regenerative braking.

[0048] The battery to be diagnosed (120) may include one or more battery packs. Each battery pack of the battery to be diagnosed (120) may include a plurality of battery modules, and each battery module may include a plurality of battery cells. According to an embodiment, the battery to be diagnosed (120) may be mounted on a power usage device (110).

[0049] The battery diagnostic device (130) can perform operations for diagnosing the battery (120) to be diagnosed. The battery diagnostic device (130) can measure battery data from the battery (120) to be diagnosed, and can diagnose cells, modules, packs, etc. of the battery (120) to be diagnosed based on the battery data. According to an embodiment, the battery diagnostic device (130) can diagnose whether a low voltage abnormality or a defect causing a battery fire occurs in the battery (120) to be diagnosed in units of cells, modules, or packs. According to an embodiment, the battery diagnostic device (130) can be a BMS (battery management system) device configured together with the battery (120) to be diagnosed.

[0050] According to an embodiment, the battery diagnostic device (130) may include a battery management system (BMS) configured together with the battery to be diagnosed (120) in an on-board manner, and / or an external device remotely located from the battery to be diagnosed (120) in an off-board manner. The external device may include a charger of a battery charging station, a battery diagnostic device, a cloud computing server, etc.

[0051] The management server (140) can manage the diagnostic process and diagnostic results of the battery diagnostic device (130). For example, the management server (140) can be a cloud computing server. The management server (140) can exchange data with the battery diagnostic device (130) via wired / wireless communication. When battery data is measured or a defect is diagnosed from the battery (120) to be diagnosed, the results can be transmitted to the management server (140) and recorded in a database. According to an embodiment, the management server (140) can receive data for battery diagnosis and perform operations for diagnosing the battery (120) to be diagnosed on behalf of the battery diagnostic device (130). Alternatively, the battery diagnostic device (130) can perform diagnostic operations by executing battery management software, and the management server (140) can provide update information of the battery management software to the battery diagnostic device (130).

[0052] FIG. 2 may illustrate elements constituting a battery diagnostic device according to some embodiments.

[0053] Referring to FIG. 2, the battery diagnostic device (130) may include an interface (131) and a controller (132). However, the present invention is not limited thereto, and some components may be omitted from the battery diagnostic device (130), or other general-purpose components may be further included in the battery diagnostic device (130).

[0054] According to an embodiment, the interface (131) and the controller (132) in the battery diagnostic device (130) may be electrically connected to each other through a device-to-device communication method. The device-to-device communication method may include a bus, a general purpose input and output (GPIO), a serial peripheral interface (SPI), a mobile industry processor interface (MIPI), etc.

[0055] The interface (131) can obtain battery data of the battery (120) to be diagnosed. According to an embodiment, the interface (131) may include a communication unit configured to receive battery data and / or a sensor unit configured to measure battery data. According to an embodiment, when the battery diagnosis device (130) is implemented in an off-board form, the communication unit may receive battery data in a manner such as wired data communication, wireless data communication, etc. Alternatively, when the battery diagnosis device (130) is implemented in an on-board form, the sensor unit may be configured to measure values ​​such as voltage, current, temperature, and resistance from the battery (120) to be diagnosed.

[0056] For example, the sensor unit of the interface (131) may be configured to generate various battery measurement values ​​from the battery to be diagnosed (120). To this end, the sensor unit may include a measuring means such as a voltmeter, an ammeter, and a thermometer.

[0057] The controller (132) may have a structure for executing commands that implement the operations of the battery diagnostic device (130). The controller (132) may be implemented as an array of multiple logic gates for processing various operations or as a general-purpose microprocessor, and may be composed of a single processor or multiple processors. For example, the controller (132) may be implemented in the form of at least one of a microprocessor, a CPU, a GPU, and an AP.

[0058] The controller (132) can operate with a memory configured to store various data, commands, mobile applications, computer programs, etc. The memory can be configured separately from or integrally with the controller (132). The controller (132) can process various operations by executing commands stored in the memory. For example, the memory can be implemented as a non-volatile device such as a ROM, a PROM, an EPROM, an EEPROM, a flash memory, a PRAM, an MRAM, an RRAM, an FRAM, etc., or a volatile device such as a DRAM, an SRAM, an SDRAM, a PRAM, etc., and can be implemented in the form of an HDD, an SSD, an SD, a Micro-SD, etc., or a combination thereof.

[0059] The interface (131) may be configured to acquire first battery measurements from cells to be diagnosed. The cells to be diagnosed may be included in the battery to be diagnosed (120). The first battery measurements may include measurements regarding cell current and cell voltage at the time of measurement. Each first battery measurement may correspond to each cell to be diagnosed, and the first battery measurements may be continuously collected at each measurement cycle to form time series data.

[0060] According to an embodiment, the first battery measurements may include current and voltage values ​​measured in the diagnostic cells when the diagnostic battery (120) is charged or discharged by the power usage device (110).

[0061] The controller (132) may be configured to select second battery measurement values ​​that satisfy analysis conditions among the first battery measurement values. Analysis conditions may be applied to select values ​​among the first battery measurement values ​​that the battery diagnosis device (130) uses for diagnosis. The analysis conditions may be set based on current values ​​and voltage values. According to an embodiment, OCV data with a current value of 0 and / or data with a voltage value greater than or equal to a specific value may be selected as the second battery measurement values.

[0062] The controller (132) may be configured to calculate battery deviation values ​​based on a representative value of the second battery measurement values ​​and a difference between each of the second battery measurement values. The second battery measurement values ​​may each correspond to a cell to be diagnosed, and a representative value of the second battery measurement values, such as an average value or a median value, may be calculated. By subtracting the representative value from each of the second battery measurement values, battery deviation values ​​corresponding to each of the cells to be diagnosed may be calculated. In an embodiment, a deviation value of an OCV value corresponding to each of the cells to be diagnosed may be calculated.

[0063] The controller (132) may be configured to calculate analysis indicators representing fluctuation trends of battery deviation values ​​over time. Similar to the first battery measurement value described above, the second battery measurement value and the battery deviation value may be time series data having values ​​at regular intervals. For example, each battery deviation value may be implemented in the form of a graph having new values ​​at regular intervals. The analysis indicators representing fluctuation trends of each battery deviation value may be technical analysis indicators such as a moving average (MA) or an exponential moving average (EMA).

[0064] The controller (132) may be configured to diagnose the status of cells to be diagnosed based on analysis indicator values. For example, the status of each cell to be diagnosed may be diagnosed by analyzing the long-term behavior or trend of the analysis indicator values ​​of the corresponding cells. According to an embodiment, the long-term behavior or trend of the analysis indicator values ​​may be expressed in the form of a rate of change or a graph slope for the target period of the indicator values ​​(e.g., 100 days, 240 days, etc.).

[0065] According to an embodiment, the controller (132) may be configured to select open circuit voltage (OCV) measurement values ​​having a battery current of 0 among the first battery measurement values, and to select second battery measurement values ​​having a battery voltage of a threshold voltage or higher among the OCV measurement values. The voltage when the battery current is 0 may correspond to the OCV value measured in the open circuit state. Meanwhile, for a cell with a maximum voltage of 4.3 V, the threshold voltage may be set to 3.9 V or 4.12 V, and only OCV measurement values ​​having a voltage higher than that may be selected as the second battery measurement values. Meanwhile, if there are no measurement values ​​satisfying the current condition and / or voltage condition as described above in a specific measurement cycle, the second battery measurement value may not be selected in the corresponding measurement cycle. To address such data gaps, data interpolation may be utilized, as will be described below.

[0066] In an embodiment, the controller (132) may be configured to calculate a moving average indicator representing the long-term behavior of battery deviation values ​​over the course of measurement time. The moving average (MA) may also be referred to as a rolling average, a running average, etc. The moving average indicator may consider recent data fluctuations and previous data fluctuations together to reflect the long-term behavior. The analysis period of the moving average (MA) may be set to 3 days, 5 days, 10 days, 15 days, 20 days, 30 days, 50 days, 100 days, 120 days, 150 days, 200 days, 240 days, 300 days, or any other value. In an embodiment, the long-term behavior may mean a behavior that considers previous measurement values ​​together instead of only the recent measurement value, which may be distinguished from a method of diagnosing the target battery (120) based on a measurement value at a single measurement point in time.

[0067] According to an embodiment, the controller (132) is configured to calculate an exponential moving average (EMA) of battery deviation values ​​to represent long-term behavior, and the long-term behavior may include a behavior of battery deviation values ​​for a period of at least twice the analysis period of the exponential moving average (EMA). Types of moving average indicators include a simple moving average, a cumulative moving average, a weighted moving average, etc., but the battery diagnostic device (130) may utilize an exponential moving average (EMA). For example, if the analysis period of the exponential moving average (EMA) is 10 days, a new EMA value may be calculated every 10 days. In this case, a rate of change (slope) of the EMA value for a period of 20 days, which is at least twice as long as 10 days, may represent the long-term behavior. Instead of 20 days, 30 days, 50 days, 80 days, 100 days, 120 days, 150 days, 200 days, 240 days, 300 days, or other figures may be used.

[0068] In an embodiment, when the analysis cycle is 10 days, a new EMA value may be calculated by reflecting the new battery deviation value (Deviation_new) at a 5% rate and the previous EMA value (EMA_Deviation_old) at a 95% rate. Reference may be made to FIG. 7, which will be described later, for this purpose. In an embodiment, instead of the 5% rate, other values ​​such as 1%, 2%, 3%, 7%, 8%, 10%, 12%, and 15% may be utilized.

[0069] According to an embodiment, the controller (132) may be configured to calculate slopes of deviation values ​​based on the amount of change in an exponential moving average (EMA) for a period of at least twice the analysis cycle, and to diagnose the status of cells to be diagnosed based on the slopes of the deviation values. For example, if the analysis cycle is 10 days, the amount of change (slope) of the EMA value may be calculated for a period of at least 20 days, for example, 100 days or 240 days, and the status of cells to be diagnosed may be diagnosed by comparing the slope values ​​with a reference range. For example, if the absolute value of the slope in some cells exceeds a threshold value, the cells may be diagnosed as defective.

[0070] According to an embodiment, the controller (132) may be configured to detect a blank period in which no second battery measurement values ​​satisfying an analysis condition exist among acquisition periods for acquiring first battery measurement values, and to estimate analysis index values ​​corresponding to the blank period by performing interpolation based on analysis index values. The acquisition periods for acquiring the first battery measurement values ​​may be measurement periods for collecting the first battery measurement values. For example, if there is no value in the first measurement period in which the current value is 0 and the voltage value is 3.9 V or higher among the measurement values, the first measurement period may be a blank period in which no second battery measurement values ​​satisfying the analysis condition exist. Since the second battery measurement value, the battery deviation value, the EMA value, etc. do not exist in the blank period, an interpolation method may be used to estimate them. According to an embodiment, linear interpolation may be utilized to estimate the value in the blank period.

[0071] According to an embodiment, the cells to be diagnosed are included in a battery to be diagnosed (120), the battery to be diagnosed (120) is mounted on a power-using device (110), and the power-using device (110) may include a mobility device that is driven by the battery to be diagnosed (120). For example, the mobility device may include an electric vehicle (EV), a hybrid electric vehicle (HEV), an electric bike, etc. The mobility device may discharge the battery to be diagnosed (120) to drive a motor and charge the battery to be diagnosed (120) through regenerative braking. In the process, first battery measurement values ​​of the battery to be diagnosed (120) may be periodically collected.

[0072] Figure 3 can illustrate a process for diagnosing an abnormality in a battery to be diagnosed according to conventional technology.

[0073] Referring to FIG. 3, a conventional technique (300) for diagnosing an abnormality in a battery (120) to be diagnosed may be exemplarily illustrated. In the conventional technique (300), an abnormality in a battery (120) to be diagnosed may be diagnosed through steps (310) to (340).

[0074] In step (310), battery data regarding time, voltage, current, balancing time, and SOHC may be collected. In this case, information requiring additional processing, such as balancing time or SOHC, may be required for diagnosis. In step (320), OCV values ​​greater than 3.9 V may be obtained. To extract the OCV value, it may be determined whether the current value is 0.

[0075] In step (330), an OCV deviation value based on an OCV average value may be calculated. In step (340), an enable condition for diagnosing an abnormality of a battery (120) to be diagnosed may be determined based on the OCV deviation value, etc. In order to confirm whether the enable condition is satisfied, in addition to the calculation for the OCV deviation value, a process of determining whether the difference between the maximum and minimum values ​​of the cell SOHC falls within a specific range may be performed. Therefore, in the case of the prior art (300), a somewhat complicated process may be involved in battery diagnosis.

[0076] FIG. 4 may illustrate a process for diagnosing an abnormality in a battery to be diagnosed according to some embodiments.

[0077] Referring to FIG. 4, the process of diagnosing an abnormality in a battery (120) to be diagnosed may include, for example, steps (410) to (460).

[0078] In step (410), data such as time, voltage, and current can be collected. Compared to step (310) of the prior art (300), balancing time or SOHC, which require additional processing steps, may not be required. In step (420), OCV values ​​exceeding 3.9 V can be obtained, and for this purpose, voltage values ​​having a current value of 0 can be compared with 3.9 V. In step (430), an OCV deviation value using an OCV average value can be calculated.

[0079] In step (440), an exponential moving average (EMA) filter may be applied to the OCV deviation value. For example, an EMA value may be calculated with an analysis period of 10 days, and a graph may be generated that plots the EMA values ​​updated every 10 days for each cell. In an embodiment, an EMA filter may be applied that reflects the new EMA value at 5% and the existing EMA value at 95%.

[0080] In step (450), interpolation may be performed on the EMA value of the battery deviation value. For example, when calculating the slope in a graph of deviation EMA values, if the deviation EMA value at a certain point in time is missing, interpolation may be performed to compensate for this. Missing deviation EMA values ​​may occur in step (420) if there are no measured values ​​where the current value is 0 and the voltage value is greater than 3.9 V.

[0081] In step (460), a slope may be calculated from a graph of deviation EMA values. For example, the slope may be calculated based on the amount of change in deviation EMA values ​​over a period of 240 days, and a battery failure may be diagnosed by comparing the slope with a diagnostic threshold.

[0082] Figures 5 and 6 can illustrate long-term behavior of a diagnostic target cell according to some embodiments.

[0083] Referring to FIGS. 5 and 6, a graph representing the long-term behavior of a normal cell and a graph representing the long-term behavior of an abnormal cell can be illustrated.

[0084] Fig. 5 may illustrate 14 battery deviation values ​​of 14 cells in a battery module including the first to fourteenth cells. The battery deviation values ​​may be OCV deviation values. The battery module of Fig. 5 includes 14 normal cells, and the battery deviation values ​​(510) of the normal cells may all be close to 0. For example, when the OCV deviation value of a specific cell is calculated in units of 10 days, relatively low values ​​such as 0.5 mV, 1.0 mV, and 0.9 mV may be observed.

[0085] Fig. 6 may illustrate 14 battery deviation values ​​of 14 cells in a battery module including the 57th to 70th cells. It may be confirmed that the battery deviation value (620) of an abnormal cell among the 14 cells is far from the remaining values. For example, when the OCV deviation value of a specific cell is calculated in units of 10 days, relatively high values ​​such as 8.3 mV, 12.5 mV, and 11.2 mV may be observed. On the other hand, it may be confirmed that the battery deviation values ​​(610) of 13 normal cells are maintained close to 0.

[0086] FIG. 7 may illustrate a process for analyzing an exponential moving average (EMA) according to some embodiments.

[0087] Referring to Figure 7, pseudocode illustrating a process for analyzing an exponential moving average (EMA) may be illustrated. The seven processes of the pseudocode may correspond to steps (410) to (460) of Figure 4.

[0088] In process 1, OCV values ​​with a current value of 0 can be selected from all measured values, and values ​​exceeding 4.12 V can be additionally selected from among the OCV values. Meanwhile, the reference value of 4.12 V can be changed to another value, such as 3.9 V. In process 2, the last values ​​of each date can be designated as the representative value for that date. In processes 3 and 4, a deviation value based on the average value can be calculated.

[0089] In Process 5, an exponential moving average (EMA) can be applied to the deviation value. For example, with a 10-day analysis period, the current EMA value can be calculated using the current deviation value and the EMA value from 10 days ago. In an embodiment, the percentage reflecting the current deviation value may be 5%, but this can vary depending on the battery diagnostic design.

[0090] In process 6, data interpolation can be performed to compensate for missing data that did not meet the conditions of the first process. This interpolation can be linear. In process 7, the slope of the EMA values ​​over 240 days can be calculated, which can be used to diagnose the presence of defective cells.

[0091] FIG. 8 illustrates a method for diagnosing battery cell failure using long-term behavioral slope according to some embodiments.

[0092] Referring to FIG. 8, a graph showing the results of observing EMA values ​​of battery cells over a long period of time can be depicted.

[0093] FIG. 8 can illustrate the long-term behavior of EMA values ​​for eight or more battery cells, and among them, for cell number 61 (devVcell_61), it can be confirmed that the magnitude of the EMA slope can be larger than that of the remaining normal cells when considering the long-term trend.

[0094] For example, unlike other normal cells, cell 61 (devVcell_61) may experience a fluctuation in EMA value close to 8 mV over approximately 240 days from January 2022 to May 2023. The magnitude of the fluctuation of 8 mV may exceed the threshold for failure diagnosis. For example, the threshold for failure diagnosis may be set to 3 mV, 4 mV, 5 mV, 6 mV, or any other appropriate value over 240 days.

[0095] FIG. 9 may illustrate a method for artificially inducing a short circuit in a battery cell according to some embodiments.

[0096] Referring to FIG. 9, an electrode plate (910) and a separator (920) may be illustrated to explain a method of artificially inducing a short circuit in a battery cell. An electrode tab (911) may be formed on the electrode plate (910).

[0097] In order to cause a short circuit between two electrode plates separated by a separator (920) and thereby cause a battery cell to fail, an artificial damage (921) may be formed in the separator (920). For example, the damage (921) may be formed with a size of 2 mm*2 mm at a distance of 70 mm from the bottom of the separator (920) and 70 mm from the right end. By using a battery cell in which an artificial damage (921) is formed, the performance of a diagnostic method utilizing the long-term behavior of a battery diagnostic device (130) may be verified.

[0098] FIG. 10 may illustrate the correlation between a 240-day slope and an artificial defective cell according to some embodiments.

[0099] Referring to FIG. 10, a table can be illustrated for comparing the performance of various failure diagnosis methods by labeling battery cells that have been artificially caused to fail, as in FIG. 9.

[0100] The table in Figure 10 can represent correlations between various types of diagnostic criteria and artificially induced defects in battery cells ("Labels"). In an embodiment, the correlations in the table can be expressed in the form of Pearson correlation coefficients. The closer the correlation value is to 0, the lower the correlation between the two variables. The closer the correlation value is to ±1, the higher the correlation between the two variables.

[0101] Among the diagnostic criteria of FIG. 10, the slope criteria (1010) may be related to battery diagnosis using the slope of the EMA value for the battery deviation value. Among the slope criteria (1010), the first criterion (1011) with an analysis cycle of 100 days and the second criterion (1012) with an analysis cycle of 240 days may be related to a battery diagnosis method based on long-term behavior.

[0102] For the first criterion (1011), the method of diagnosing cell failure using the EMA slope for 100 days may have a correlation of -0.47 with the actual defective cell ("Label"). Similarly, for the second criterion (1012), the method of diagnosing cell failure using the EMA slope for 240 days may have a correlation of -0.54 with the actual defective cell ("Label"). Therefore, it can be confirmed that among the methods of diagnosing battery failure based on long-term behavior, the method using the 240-day slope shows higher performance than the method using the 240-day slope.

[0103] FIG. 11 may illustrate steps of a battery diagnosis method according to some embodiments.

[0104] Referring to FIG. 11, the battery diagnosis method may include steps (1110) to (1150). However, the present invention is not limited thereto, and some steps may be omitted or other general steps may be added, and the steps of the battery diagnosis method may be executed in a different order than the illustrated order.

[0105] The battery diagnosis method may be composed of steps that are processed in a time-series manner in the battery diagnosis device (130). Therefore, even if the details are omitted below, the details described above for the battery diagnosis device (130) may be equally applied to the battery diagnosis method.

[0106] Steps (1110) to (1150) of the battery diagnosis method can be performed by the interface (131) and controller (132) of the battery diagnosis device (130).

[0107] In step (1110), the battery diagnostic device (130) can obtain first battery measurement values ​​from cells to be diagnosed. In step (1120), the battery diagnostic device (130) can select second battery measurement values ​​that satisfy analysis conditions among the first battery measurement values.

[0108] In step (1130), the battery diagnosis device (130) can calculate battery deviation values ​​based on the representative value of the second battery measurement values ​​and the difference between each second battery measurement value. In step (1140), the battery diagnosis device (130) can calculate analysis index values ​​representing the change trend of the battery deviation values ​​according to the measurement time. In step (1150), the battery diagnosis device (130) can diagnose the status of the diagnosis target cells based on the analysis index values.

[0109] According to an embodiment, the battery diagnosis method may be implemented in the form of a computer program stored on a computer-readable storage medium. That is, the computer program may include instructions for implementing the battery diagnosis method, and the program instructions may be stored on the computer-readable storage medium. The computer program may include a mobile application.

[0110] According to an embodiment, the computer-readable storage medium may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs, DVDs, magneto-optical media such as floptical disks, and hardware devices specially configured to store and execute computer program instructions such as ROMs, RAMs, flash memories, and the like. The computer program instructions may include machine language codes generated by a compiler and high-level language codes that can be executed by a computer using an interpreter, etc.

[0111] The terms "include," "comprise," or "have" used herein, unless otherwise specifically stated, imply that the corresponding component may be included, and therefore should be interpreted to include other components rather than to exclude 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 their contextual meaning in the relevant art, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

[0112] 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 protection scope 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.

[0113] [Explanation of symbols]

[0114] 100: Battery diagnostic system 110: Power usage device

[0115] 120: Battery to be diagnosed 130: Battery diagnostic device

[0116] 131: Interface 132: Controller

[0117] 140: Management Server

Claims

1. An interface configured to obtain first battery measurement values ​​from cells to be diagnosed; and Select the second battery measurement values ​​that satisfy the analysis conditions among the first battery measurement values, Calculate battery deviation values ​​based on the representative values ​​of the second battery measurement values ​​and the difference between each of the second battery measurement values, Calculate analysis indicator values ​​that represent the change trend of the above battery deviation values ​​according to the measurement time, A battery diagnostic device, comprising: a controller configured to diagnose the status of the diagnosis target cells based on the analysis indicator values; 2. In paragraph 1, The above controller selects open circuit voltage (OCV) measurements from among the first battery measurements, A battery diagnostic device configured to select OCV measurement values ​​higher than a threshold voltage value among the above OCV measurement values ​​as the second battery measurement values.

3. In paragraph 1, A battery diagnostic device, wherein the controller is configured to calculate a moving average index representing the long-term behavior of the battery deviation values ​​over the elapse of the measurement time.

4. In paragraph 3, The controller is configured to calculate an exponential moving average (EMA) of the battery deviation values ​​to represent the long-term behavior, A battery diagnostic device, wherein the long-term behavior includes the behavior of the battery deviation values ​​for a period of at least twice the analysis period of the exponential moving average (EMA).

5. In paragraph 4, The above controller calculates slopes of deviation values ​​based on the amount of change in the exponential moving average (EMA) for a period of at least twice the analysis period, A battery diagnostic device configured to diagnose the status of the diagnosis target cells based on the slopes of the above deviation values.

6. In paragraph 1, The controller detects a blank period in which the second battery measurement values ​​satisfying the analysis condition do not exist among the acquisition periods for acquiring the first battery measurement values, A battery diagnostic device configured to estimate analysis index values ​​corresponding to the blank period by performing interpolation based on the above analysis index values.

7. In paragraph 1, A battery diagnostic device, wherein the above diagnostic target cells are included in a diagnostic target battery, and the above diagnostic target battery is mounted on a mobility device.

8. A step of obtaining first battery measurement values ​​from cells to be diagnosed; A step of selecting second battery measurement values ​​that satisfy analysis conditions among the first battery measurement values; A step of calculating battery deviation values ​​based on a representative value of the second battery measurement values ​​and a difference between each of the second battery measurement values; A step of calculating analysis indicator values ​​representing the change trend of the above battery deviation values ​​according to the measurement time; and A battery diagnosis method, comprising: a step of diagnosing the status of the diagnosis target cells based on the analysis indicator values; 9. In paragraph 8, The step of selecting the above second battery measurement values ​​is: A step of selecting open circuit voltage (OCV) measurement values ​​among the above first battery measurement values; and A battery diagnosis method, comprising: a step of selecting OCV measurement values ​​higher than a threshold voltage value among the above OCV measurement values ​​as the second battery measurement values; 10. In paragraph 8, The steps for calculating the above analysis indicator values ​​are: A battery diagnosis method, comprising: a step of calculating a moving average index representing the long-term behavior of the battery deviation values ​​over the elapse of the measurement time; 11. In paragraph 10, The steps for calculating the above moving average indicator are: A step of calculating an exponential moving average (EMA) of the battery deviation values ​​to represent the long-term behavior; comprising; A battery diagnosis method, wherein the long-term behavior includes the behavior of the battery deviation values ​​for a period of at least twice the analysis period of the exponential moving average (EMA).

12. In paragraph 11, The step of diagnosing the status of the above diagnostic target cells is: A step of calculating slopes of deviation values ​​based on the amount of change in the exponential moving average (EMA) for a period of at least twice the above analysis period; and A battery diagnosis method, comprising: a step of diagnosing the status of the diagnosis target cells based on the slopes of the deviation values; 13. In paragraph 8, The steps for calculating the above analysis indicator values ​​are: A step of detecting a blank period in which the second battery measurement values ​​satisfying the analysis condition do not exist among the acquisition period for acquiring the first battery measurement values; and A battery diagnosis method, comprising: a step of estimating analysis indicator values ​​corresponding to the blank period by performing interpolation based on the analysis indicator values; 14. In paragraph 8, A battery diagnosis method, wherein the above diagnosis target cells are included in a diagnosis target battery, and the diagnosis target battery is mounted in a mobility device.

15. A diagnostic battery including diagnostic target cells and mounted on a power-using device; and A battery diagnosis system comprising: a battery diagnosis device configured to obtain first battery measurement values ​​from the diagnosis target cells, select second battery measurement values ​​that satisfy analysis conditions among the first battery measurement values, calculate battery deviation values ​​based on a difference between a representative value of the second battery measurement values ​​and each of the second battery measurement values, calculate analysis index values ​​representing a change trend of the battery deviation values ​​according to a measurement time, and diagnose the status of the diagnosis target cells based on the analysis index values;

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