Battery diagnostic device and battery diagnostic method

Abstract

According to some embodiments disclosed herein, a battery diagnostic device includes a sensor configured to measure an open circuit voltage (OCV) from a battery to be diagnosed and generate first OCV data; and a controller configured to obtain first SOC data related to a state of charge of the battery to be diagnosed based on the first OCV data, derive second SOC data that estimates the state of charge of the battery to be diagnosed based on the first SOC data, obtain second OCV data of the battery to be diagnosed based on the second SOC data, and diagnose a state of the battery to be diagnosed based on the first OCV data and the second OCV data.

Description

[Technical Field]

[0001] This application claims the benefit of priority based on Korean Patent Application No. 10-2022-0120916 filed on September 23, 2022 and Korean Patent Application No. 10-2023-0102473 filed on August 4, 2023, the entire contents of which are incorporated herein by reference. TECHNICAL FIELD The embodiments disclosed herein relate to a battery diagnostic device and a battery diagnostic method. [Background technology]

[0002] In recent years, research and development into secondary batteries has been actively pursued. Here, secondary batteries are batteries that can be charged and discharged, and can be interpreted as encompassing both conventional Ni / Cd batteries, Ni / MH batteries, and more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have a higher energy density than conventional Ni / Cd batteries, Ni / MH batteries, and can be manufactured in a compact and lightweight form, making them highly useful as a power source for mobile devices. In recent years, their range of use has expanded to include power sources for electric vehicles, and they are attracting attention as a next-generation energy storage medium.

[0003] In order to inspect the manufacturing quality of a battery or to diagnose whether or not a defect has occurred, abnormal behavior of the battery can be diagnosed based on the battery voltage. For example, abnormal behavior of the battery voltage can be diagnosed using the deviation of the cell voltage of the battery cells, but such a diagnostic method has a problem of low diagnostic accuracy. Summary of the Invention [Problem to be solved by the invention]

[0004] An object of the embodiments disclosed in this document is to provide a battery diagnostic device and a battery diagnostic method that can diagnose abnormalities in voltage behavior even when the battery voltage fluctuates only slightly.

[0005] 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 following description. [Means for solving the problem]

[0006] According to some embodiments disclosed herein, a battery diagnostic device includes a sensor configured to measure an open circuit voltage (OCV) from a battery to be diagnosed and generate first OCV data; and a controller configured to obtain first SOC data related to a state of charge of the battery to be diagnosed based on the first OCV data, derive second SOC data that estimates the state of charge of the battery to be diagnosed based on the first SOC data, obtain second OCV data of the battery to be diagnosed based on the second SOC data, and diagnose a state of the battery to be diagnosed based on the first OCV data and the second OCV data.

[0007] In some embodiments, the battery to be diagnosed includes a plurality of battery cells, and the first OCV data includes a plurality of OCV values ​​measured at a plurality of time points for each battery cell of the plurality of battery cells.

[0008] In some embodiments, the controller is configured to calculate an average SOC value of multiple SOC values ​​converted from the multiple OCV values ​​for each battery cell, calculate a relative capacity value for each battery cell based on the average SOC value, and calculate multiple estimated SOC values ​​for each battery cell at the multiple points in time based on the relative capacity values.

[0009] In some embodiments, the controller is configured to calculate a pseudoinverse matrix of an average SOC matrix indicating the average SOC value for each battery cell, and multiply the pseudoinverse matrix by an SOC matrix indicating the plurality of SOC values ​​for each battery cell to calculate a relative capacity matrix indicating the relative capacity value.

[0010] In some embodiments, the controller is configured to calculate an estimated SOC matrix indicating the plurality of estimated SOC values ​​for each battery cell by multiplying the average SOC matrix by the relative capacity matrix.

[0011] In some embodiments, the controller is configured to derive OCV deviation data based on a difference between the first OCV data and the second OCV data, and to diagnose a condition of the battery to be diagnosed based on the OCV deviation data.

[0012] In some embodiments, the OCV deviation data includes a plurality of OCV deviation values ​​for each battery cell at the plurality of time points, and the controller is configured to calculate a plurality of OCV deviation change amounts indicating a difference between an OCV deviation value at a current time point and an OCV deviation value at a previous time point based on the plurality of OCV deviation values ​​for each battery cell, and to diagnose a state of each battery cell based on the plurality of OCV deviation change amounts for each battery cell.

[0013] In some embodiments, the controller is configured to diagnose that an abnormality has occurred in a first battery cell among the plurality of battery cells when the plurality of OCV deviation change amounts of the first battery cell are greater than an upper limit value of a normal range or smaller than a lower limit value of the normal range.

[0014] In some embodiments, the controller is configured to convert the first OCV data to the first SOC data based on an OCV-to-SOC mapping table, and to convert the second SOC data to the second OCV data based on the OCV-to-SOC mapping table.

[0015] According to some embodiments disclosed herein, a battery diagnosis method includes the steps of measuring an open circuit voltage (OCV) from a battery to be diagnosed to generate first OCV data, obtaining first SOC data relating to the state of charge of the battery to be diagnosed based on the first OCV data, deriving second SOC data that estimates the state of charge of the battery to be diagnosed based on the first SOC data, obtaining second OCV data of the battery to be diagnosed based on the second SOC data, and diagnosing the state of the battery to be diagnosed based on the first OCV data and the second OCV data.

[0016] In some embodiments, the battery to be diagnosed includes a plurality of battery cells, and the first OCV data includes a plurality of OCV values ​​measured at a plurality of time points for each battery cell of the plurality of battery cells.

[0017] In some embodiments, the step of deriving the second SOC data includes the steps of: calculating an average SOC value of multiple SOC values ​​converted from the multiple OCV values ​​for each battery cell; calculating a relative capacity value of each battery cell based on the average SOC value; and calculating multiple estimated SOC values ​​for each battery cell at the multiple points in time based on the relative capacity values.

[0018] In some embodiments, calculating the relative capacity value includes calculating a pseudo-inverse of an average SOC matrix indicating the average SOC value of each battery cell, and multiplying the pseudo-inverse matrix by an SOC matrix indicating the plurality of SOC values ​​for each battery cell to calculate a relative capacity matrix indicating the relative capacity value.

[0019] In some embodiments, calculating the plurality of estimated SOC values ​​includes multiplying the average SOC matrix by the relative capacity matrix to calculate an estimated SOC matrix indicating the plurality of estimated SOC values ​​for each battery cell.

[0020] In some embodiments, the step of diagnosing the condition of the battery to be diagnosed includes the steps of deriving OCV deviation data based on a difference between the first OCV data and the second OCV data, and diagnosing the condition of the battery to be diagnosed based on the OCV deviation data.

[0021] In some embodiments, the OCV deviation data includes a plurality of OCV deviation values ​​for each battery cell at the plurality of time points, and the step of diagnosing the state of the battery to be diagnosed includes a step of calculating a plurality of OCV deviation change amounts indicating a difference between an OCV deviation value at a current time point and an OCV deviation value at a previous time point based on the plurality of OCV deviation values ​​for each battery cell, and a step of diagnosing the state of each battery cell based on the plurality of OCV deviation change amounts for each battery cell.

[0022] In some embodiments, the step of diagnosing the state of the battery to be diagnosed includes a step of diagnosing that an abnormality has occurred in a first battery cell when the multiple OCV deviation change amounts of a first battery cell among the multiple battery cells are greater than an upper limit value of a normal range or smaller than a lower limit value of the normal range.

[0023] In some embodiments, obtaining the first SOC data includes converting the first OCV data to the first SOC data based on an OCV-SOC mapping table by the controller, and obtaining the second OCV data includes converting the second SOC data to the second OCV data based on the OCV-SOC mapping table by the controller. [Effects of the Invention]

[0024] According to the embodiments disclosed in this document, it is possible to provide a battery diagnostic device and a battery diagnostic method that are capable of diagnosing abnormalities in voltage behavior even when fluctuations in battery voltage are small.

[0025] The technical effects of the embodiments disclosed in this document are not limited to the effects mentioned above, and other effects not mentioned will be apparent to those skilled in the art from the disclosure of this document. [Brief explanation of the drawings]

[0026] [Figure 1] 1 illustrates components of a battery diagnostic system according to some embodiments. [Figure 2] 1 illustrates components constituting a battery diagnostic device according to some embodiments. [Figure 3] 1 illustrates an example process of operation of a battery diagnostic device according to some embodiments. [Figure 4] 1 illustrates a process for measuring open circuit voltage (OCV) from a battery under diagnosis according to some embodiments. [Figure 5] 1 illustrates a process for generating measured OCV data according to some embodiments. [Figure 6] 1 illustrates a process for converting measured OCV data to estimated SOC data according to some embodiments. [Figure 7] 10 illustrates a process for calculating the relative capacity value of each battery cell based on the average SOC value according to some embodiments. [Figure 8] 10 illustrates a process for calculating multiple estimated OCV values ​​based on relative capacity values ​​according to some embodiments. [Figure 9] 1 illustrates a process for generating estimated SOC data and estimated OCV data according to some embodiments. [Figure 10] 10 illustrates a process for converting estimated SOC data to estimated OCV data according to some embodiments. [Figure 11] 1 illustrates a process for deriving OCV deviation data based on the difference between measured OCV data and estimated OCV data according to some embodiments. [Figure 12] 10 illustrates a process for calculating multiple OCV deviation changes based on OCV deviation data according to some embodiments. [Figure 13] 10 illustrates a process of diagnosing the state of each battery cell based on a plurality of OCV deviation change amounts according to some embodiments. [Figure 14] 1 illustrates exemplary steps comprising a battery diagnostic method according to some embodiments. DETAILED DESCRIPTION OF THE INVENTION

[0027] The embodiments described herein will now be described with reference to the accompanying drawings, although it should be understood that this is not intended to limit the disclosure of this document to the particular embodiments, but rather to include various modifications, equivalents, and / or alternatives to the embodiments described herein.

[0028] The embodiments and terms 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 include various modifications, equivalents, or alternatives of the embodiment. In connection with the description of the drawings, like reference numerals may be used for like or related components. The singular form of a noun corresponding to an item may include one or more of the said item unless the relevant context clearly dictates otherwise.

[0029] In this document, each phrase such as "A or B," "at least one of A and B," "at least one of A or B," "A, B, or C," "at least one of A, B, and C," and "at least one of A, B, or C" may include any one or all possible combinations of the items listed with that phrase. Terms such as "first," "second," "first," "second," "A," "B," "(a)," or "(b)" may be used simply to distinguish that element from other elements and do not limit that element in other respects (e.g., importance or order) unless specifically stated to the contrary.

[0030] In this document, when a (e.g., first) component is referred to as being "coupled," "coupled," or "connected" to another (e.g., second) component, with or without the terms "functionally" or "communicatively," or when a reference is made to "coupled" or "connected," this means that the component may be coupled to the other component directly (e.g., by wire or wirelessly) or indirectly (e.g., via a third component).

[0031] Methods according to various embodiments disclosed herein may be provided in a computer program product. The computer program product may be traded between a seller and a buyer as a commodity. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disc read-only memory (CD-ROM)) or distributed online (e.g., downloaded or uploaded) via 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 at least temporarily stored or temporarily generated on a machine-readable storage medium such as the memory of a manufacturer's server, an application store server, or an intermediary server.

[0032] According to the embodiments disclosed herein, each of the aforementioned components (e.g., modules or programs) may include one or more entities, and some of the entities may be located separately in other components. According to the embodiments disclosed herein, one or more of the aforementioned components or operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (e.g., modules or programs) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner to those performed by the respective components of the multiple components before the integration. According to the embodiments disclosed herein, operations performed by modules, programs, or other components may be performed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be performed in a different order, omitted, or one or more other operations may be added.

[0033] FIG. 1 illustrates components of a battery diagnostic system according to some embodiments. 1, the battery diagnostic system 100 may include a charger / discharger 110, a battery to be diagnosed 120, a battery diagnostic device 130, and a management server 140. However, the present invention is not limited to this, and some components may be omitted from the battery diagnostic system 100, or other general-purpose components may be further included in the battery diagnostic system 100.

[0034] The battery diagnostic system 100 may refer to a system for diagnosing the state of the battery under diagnosis 120. According to an embodiment, a test voltage may be applied to the battery under diagnosis 120 by the charger / discharger 110, and response data output by the battery under diagnosis 120 in response to the test voltage may be measured by the battery diagnostic device 130.

[0035] The charger / discharger 110 may be configured to charge or discharge the battery under diagnosis 120. According to an embodiment, the charger / discharger 110 may apply a test voltage to the battery under diagnosis 120, and the test voltage may include a plurality of charge / discharge cycle voltages. To this end, the charger / discharger 110 may include a power supply device for applying various types of voltages and currents to the battery under diagnosis 120. According to an embodiment, the charger / discharger 110 may be included in the battery diagnosis device 130 instead of being provided separately from the battery diagnosis device 130.

[0036] The battery under diagnosis 120 can be a target for diagnosis by the battery diagnosis system 100. The battery under diagnosis 120 can include a plurality of battery cells. For example, the battery under diagnosis 120 can include a plurality of battery modules, each of which can include a plurality of battery cells. According to an embodiment, the battery under diagnosis 120 can include m battery cells, and n voltage measurements can be performed on each of the m battery cells, generating m*n voltage measurement values.

[0037] The battery diagnostic device 130 can perform operations to determine whether or not there is an abnormality in the diagnosis target battery 120. The battery diagnostic device 130 performs voltage measurement and data processing, and can diagnose which cell of the diagnosis target battery 120 will have an abnormal voltage behavior.

[0038] The management server 140 may be configured to manage the status of the battery to be diagnosed 120. The management server 140 may be connected to the battery diagnostic device 130 via wired / wireless data communication, and may receive and record data such as the status of the battery to be diagnosed 120, the presence or absence of abnormalities, and the diagnosis results from the battery diagnostic device 130. The management server 140 may control the battery diagnostic device 130 or check the status of the battery to be diagnosed 120 in response to a request from a system administrator or a battery user.

[0039] According to an embodiment, the management server 140 can perform at least a portion of the operations for determining whether the diagnosis target battery 120 is abnormal on behalf of the battery diagnostic device 130. The management server 140 can receive data necessary to diagnose the diagnosis target battery 120 from the battery diagnostic device 130, perform a diagnostic procedure, and transmit the results to the battery diagnostic device 130. According to an embodiment, the management server 140 can install energy management software necessary to diagnose the diagnosis target battery 120 in the battery diagnostic device 130, and can provide update information for the energy management software to the battery diagnostic device 130.

[0040] FIG. 2 illustrates components of a battery diagnostic device according to some embodiments. 2, the battery diagnostic device 130 may include a sensor 131 and a controller 132. However, without being limited thereto, 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.

[0041] According to an embodiment, the sensor 131 and the controller 132 in the battery diagnostic device 130 may be electrically connected to each other by an inter-device communication method such as a bus, a general purpose input and output (GPIO), a serial peripheral interface (SPI), or a mobile industry processor interface (MIPI).

[0042] The sensor 131 of the battery diagnostic device 130 may be configured to measure the voltage from the battery under diagnosis 120. When the charger / discharger 110 applies a test cycle voltage to the battery under diagnosis 120, an output voltage may be generated in the battery under diagnosis 120 in response, which may be measured by the sensor 131. For this purpose, the sensor 131 may include measuring means such as a voltmeter, an ammeter, a thermometer, or the like.

[0043] The controller 132 may have a structure for executing commands that implement the operation of the battery diagnostic device 130. The controller 132 may be implemented as an array of logic gates for processing various operations or a general-purpose microprocessor, and may be configured with 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.

[0044] The controller 132 may be configured separately from or integral with a memory (not shown) configured to store instructions, and may execute the instructions stored in the memory to perform various operations. The memory may store various data, instructions, mobile applications, computer programs, etc. For example, the memory may be implemented as a non-volatile memory such as a ROM, PROM, EPROM, EEPROM, flash memory, PRAM, MRAM, or FRAM (registered trademark), or a volatile memory such as a DRAM, SRAM, SDRAM, or RRAM (registered trademark), and may be implemented in the form of an HDD, SSD, SD, Micro-SD, or a combination thereof.

[0045] The sensor 131 of the battery diagnostic device 130 may be configured to measure the open circuit voltage (OCV) from the battery under diagnosis 120 to generate first OCV data (OCVs). The first OCV data (OCVs) may refer to an actually measured OCV value. When a charge / discharge cycle voltage is applied to the battery under diagnosis 120, the sensor 131 may measure a voltage from the battery under diagnosis 120, and the open circuit voltage (OCV) of the battery under diagnosis 120 may be derived based on the voltage. According to an embodiment, n OCV values ​​may be measured for each of m battery cells of the battery under diagnosis 120, and the first OCV data may include m*n OCV values.

[0046] The controller 132 of the battery diagnostic device 130 may be configured to obtain first state of charge data (SOCs) related to the state of charge (SOC) of the diagnosis target battery 120 based on the first OCV data (OCVs). For example, m*n OCV values ​​of the first OCV data (OCVs) may be converted into m*n SOC values, and the first SOC data (SOCs) may include m*n SOC values. According to an embodiment, the conversion of the first OCV data (OCVs) to the first SOC data (SOCs) may be performed based on an OCV-SOC mapping table.

[0047] The controller 132 of the battery diagnostic device 130 may be configured to derive second SOC data (SOCests) that estimates the state of charge of the diagnosis target battery 120 based on the first SOC data (SOCs). The first SOC data (SOCs) may refer to a value converted from the first OCV data (OCVs), and the second SOC data (SOCests) may refer to a value obtained by estimating the SOC of the diagnosis target battery 120 through an estimation process. According to an embodiment, the second SOC data (SOCests) may be estimated based on an average SOC value and a relative capacity value of each battery cell.

[0048] The controller 132 of the battery diagnostic device 130 may be configured to obtain second OCV data (OCVests) of the diagnosis target battery 120 based on the second SOC data (SOCests). For example, the second SOC data (SOCests) may include m*n estimated SOC values, and the m*n estimated SOC values ​​may be converted into m*n estimated OCV values. The second OCV data (OCVests) may include m*n estimated OCV values. According to an embodiment, the conversion of the second SOC data (SOCests) into the second OCV data (OCVests) may be performed based on an OCV-SOC mapping table, which may be similar to a table for converting the first OCV data (OCVs) into the first SOC data (SOCs).

[0049] The controller 132 of the battery diagnostic device 130 may be configured to diagnose the condition of the diagnosis target battery 120 based on the first OCV data (OCVs) and the second OCV data (OCVests). According to an embodiment, the controller 132 of the battery diagnostic device 130 may be configured to derive OCV deviation data (OCVdevs) based on the difference between the first OCV data (OCVs) and the second OCV data (OCVests). For example, the first OCV data (OCVs) may include m*n measured OCV values, and the second OCV data (OCVests) may include m*n estimated OCV values, and the differences between the corresponding measured OCV values ​​and estimated OCV values ​​may generate m*n OCV deviation values. The OCV deviation data (OCVdevs) may include m*n OCV deviation values.

[0050] The controller 132 of the battery diagnostic device 130 can be configured to diagnose the condition of the diagnosis target battery 120 based on the OCV deviation data (OCVdevs). According to an embodiment, it is possible to diagnose whether an abnormality has occurred in the voltage behavior of each battery cell of the diagnosis target battery 120 by comparing the OCV deviation data (OCVdevs) or its change amount with a normal range. In this manner, it is possible to detect an abnormality in voltage behavior even when there is no sudden voltage fluctuation due to a cell break or short circuit.

[0051] FIG. 3 illustrates the process by which the battery diagnostic device according to some embodiments operates. 3, the operation process 300 of the battery diagnostic device 130 may include a first process 310 to a sixth process 360. The first process 310 to the sixth process 360 may correspond to steps 1410 to 1460 in FIG. 14, which will be described later.

[0052] In a first step 310, the OCV voltage of the battery 120 to be diagnosed can be measured, in a second step 320, the first OCV data (OCVs) can be converted into first SOC data (SOCs), and in a third step 330, estimated SOC data (SOCests) can be calculated based on the relative capacity value to the average SOC value.

[0053] In a fourth process 340, the second SOC data (SOCests) can be converted into second OCV data (OCVests). In a fifth process 350, the deviation between the first OCV data (OCVs) and the second OCV data (OCVests) can be calculated to derive OCV deviation data (OCVdevs). In a sixth process 360, the OCV deviation change amount is calculated based on the OCV deviation data (OCVdevs), and the calculated amount is compared with a threshold value to diagnose abnormalities in the voltage behavior of each cell of the battery 120 to be diagnosed.

[0054] FIG. 4 illustrates a process for measuring the open circuit voltage (OCV) from a battery under diagnosis according to some embodiments. Referring to FIG. 4, a charge / discharge profile 410 and an OCV graph 420 of the battery 120 under diagnosis are shown.

[0055] The charge / discharge profile 410 may indicate the voltage measured from any one battery cell of the battery under diagnosis 120 when a charge / discharge cycle voltage is applied to the battery cell. According to an embodiment, a cusp value 411 in a measurement cycle of the charge / discharge profile 410 may be a measured OCV value 421. For example, for one battery cell, the OCV graph 420 may include n measured OCV values ​​421.

[0056] FIG. 5 illustrates a process for generating first OCV data according to some embodiments. Referring to FIG. 5, the OCV graph 420 and first OCV data (OCVs) 510 of the battery under diagnosis 120 are shown.

[0057] The OCV graph 420 may include n measured OCV values ​​421 for the i-th battery cell, and the first OCV data (OCVs) 510 may include m*n measured OCV values ​​for m battery cells of the diagnosis target battery 120. According to an embodiment, the first OCV data (OCVs) 510 may be expressed in the form of a matrix having a size of m*n.

[0058] According to an embodiment, the battery under diagnosis 120 includes a plurality of battery cells, and the first OCV data (OCVs) 510 may include a plurality of OCV values ​​measured at a plurality of time points for each of the plurality of battery cells.

[0059] FIG. 6 illustrates a process for converting first OCV data to second SOC data according to some embodiments. Referring to FIG. 6, primary OCV data (OCVs) 510, an OCV-SOC mapping table 610, and primary SOC data (SOCs) 620 are shown.

[0060] According to an embodiment, the controller 132 of the battery diagnostic device 130 may be configured to convert the first OCV data (OCVs) 510 into the first SOC data (SOCs) 620 based on an OCV-SOC mapping table 610. The OCV-SOC mapping table 610 may refer to a table that matches the OCV value on the vertical axis with the SOC value on the horizontal axis and records the correspondence between them.

[0061] FIG. 7 illustrates a process for calculating a relative capacity value for each battery cell based on an average SOC value, according to some embodiments, and FIG. 8 illustrates a process for calculating multiple estimated OCV values ​​based on the relative capacity values, according to some embodiments.

[0062] 7 and 8, first SOC data (SOCs) 620, average SOC values ​​(SOCavg) 710 for each of a plurality of time points (1, . . . , n), relative capacity values ​​(A) 720, 730, and a plurality of estimated SOC values ​​(SOCest) for the i-th battery cell are shown. i) 810 and second SOC data (SOCests) 820 of the battery 120 under diagnosis are shown.

[0063] For each of the multiple time points (1,...,n), an average of the first SOC data (SOCs) 620 can be calculated, resulting in an average SOC value (SOCavg) 710. A relative capacity value (A) 720 can be calculated based on the average SOC value (SOCavg) 710 and the first SOC data (SOCs) 620.

[0064] From the first battery cell to the mth battery cell, the relative capacity value (A i The relative capacity value (A) 720 can be calculated by calculating the average SOC value (SOCavg) 710. In this process, a pseudoinverse matrix (PINV) of the average SOC value (SOCavg) 710 can be calculated. The pseudoinverse matrix can refer to the Moore-Penrose inverse matrix. The relative capacity value (A) of the i-th battery cell can be calculated by i ) 730 is the gradient component (A slopei ) and the offset component (A offseti ).

[0065] The relative capacity value (A i ) 730 and the average SOC value (SOCavg) 710, multiple estimated SOC values ​​(SOCest) of the i-th battery cell are calculated. i ) 810 can be calculated. i ) 810 is the gradient component (A slopei ), offset component (A offseti ), and the average SOC value (SOCavg) 710.

[0066] Multiple estimated SOC values ​​(SOCest i ) 810 from the first battery cell to the m-th battery cell, second SOC data (SOCests) 820 of the diagnosis target battery 120 can be calculated.

[0067] According to the embodiment, the controller 132 of the battery diagnostic device 130 calculates an average SOC value (SOCavg) 710 of a plurality of SOC values ​​converted from a plurality of OCV values ​​for each battery cell, and calculates a relative capacity value (A) of each battery cell based on the average SOC value (SOCavg) 710. i ) and calculate the relative capacitance value (A i ) based on multiple estimated SOC values ​​(SOCest) for each battery cell at multiple points in time. i ) can be configured to calculate

[0068] According to the embodiment, the controller 132 of the battery diagnostic device 130 calculates an average SOC matrix (SOCavg) 710 indicating the average SOC value (SOCavg) of each battery cell.

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[0069] According to an embodiment, the controller 132 of the battery diagnostic device 130 calculates an average SOC matrix indicating the average SOC value of the battery cells.

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[0070] FIG. 9 illustrates a process for generating second SOC data and second OCV data according to some embodiments. Referring to FIG. 9, a process is shown in which first OCV data (OCVs) 910 measured for four battery cells at five time points (n=5) are converted into second SOC data and second OCV data.

[0071] The first OCV data (OCVs) 910 can be converted into first SOC data (SOCs) 920 based on the OCV-SOC mapping table 610. An average SOC value (SOCavg) 930 can be calculated based on the first SOC data (SOCs) 920.

[0072] Based on the average SOC value (SOCavg) 930, for the first battery cell (i=1) among the four battery cells, the average SOC matrix

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[0073] FIG. 10 illustrates a process for converting second SOC data to second OCV data according to some embodiments. 10, a process is shown in which the second SOC data (SOCests) 820 of the battery to be diagnosed 120 is converted into second OCV data (OCVests) 1010 based on the OCV-SOC mapping table 610.

[0074] According to an embodiment, the controller 132 of the battery diagnostic device 130 may be configured to convert the second SOC data (SOCests) 820 into the second OCV data (OCVests) 1010 based on the OCV-SOC mapping table 610. Here, the OCV-SOC mapping table 610 may be the same as the table used when converting the first OCV data (OCVs) 510 into the first SOC data (SOCs) 620.

[0075] FIG. 11 illustrates a process for deriving OCV deviation data based on the difference between first OCV data and second OCV data according to some embodiments, and FIG. 12 illustrates a process for calculating multiple OCV deviation changes based on the OCV deviation data according to some embodiments.

[0076] FIG. 11 shows a process of deriving OCV deviation data (OCVdevs) 1110 based on the difference between first OCV data (OCVs) 510 and second OCV data (OCVests) 1010, and FIG. 12 shows a process of calculating multiple OCV deviation changes 1210, 1220 based on the difference between the current value and the previous value of the OCV deviation data (OCVdevs) 1110.

[0077] According to an embodiment, the OCV deviation data (OCVdevs) 1110 includes multiple OCV deviation values ​​(OCVdevs) at multiple time points (1, . . . , n) for each battery cell. i1 , OCVdev i2 , ..., OCVdev in According to an embodiment, the controller 132 of the battery diagnostic device 130 may include a plurality of OCV deviation values ​​(OCVdev i1 , OCVdev i2 , ..., OCVdev in), a plurality of OCV deviation changes (OCVdiffi) 1210 indicating the difference 1220 between the OCV deviation value at the current time and the OCV deviation value at the previous time are calculated based on the OCV deviation changes (OCVdiffi) 1210, and the state of each battery cell is diagnosed based on the plurality of OCV deviation changes (OCVdiffi) 1210 of each battery cell.

[0078] FIG. 13 illustrates a process of diagnosing the state of each battery cell based on a plurality of OCV deviation changes according to some embodiments. Referring to FIG. 13, when the first OCV data (OCVs) 420 is input during the diagnosis of the diagnosis target battery 120, a plurality of OCV deviation changes (OCVdiff i An OCV deviation change amount (OCVdiffs) 1300 including the OCV deviation change amount (OCVdiffs) 1210 can be derived, and an abnormality in the voltage behavior of each battery cell of the diagnosis target battery 120 can be detected based on the OCV deviation change amount (OCVdiffs) 1300.

[0079] An upper limit 1310 and a lower limit 1320 of the normal range can be set for the OCV deviation change (OCVdiffs) 1300. According to an embodiment, if the OCV deviation change of a specific battery cell exceeds the upper limit 1310 or does not reach the lower limit 1320 at a specific time point, it can be diagnosed that an abnormality in voltage behavior has occurred in the battery cell at the time point of overshoot / undershoot. For example, if it is detected that the OCV deviation change is outside the normal range at the undershoot times 1330, 1340 and the overshoot time point 1350, the battery cell having the OCV deviation change (OCVdiffs) 1300 can be diagnosed as an abnormal cell.

[0080] According to an embodiment, the upper limit 1310 and the lower limit 1320 of the normal range may be changed according to the required performance value of the battery. When a high required performance value is required, the normal range may be narrowed, and vice versa. According to an embodiment, whether a battery cell is abnormal may be diagnosed based on the number and / or frequency of deviations from the normal range. For example, the number and / or frequency of deviations from the normal range may be compared with a threshold value.

[0081] FIG. 14 illustrates steps comprising a battery diagnostic method according to some embodiments. 14, a battery diagnosis method 1400 may include steps 1410 to 1460. However, the present invention is not limited to this, and some steps may be omitted or other general steps may be added, and the steps of the battery diagnosis method 1400 may be performed in an order different from that shown.

[0082] Battery diagnostic method 1400 can be configured with steps that are processed in time series in battery diagnostic device 130. Therefore, even if the content is omitted below, the content described above regarding battery diagnostic device 130 can be similarly applied to battery diagnostic method 1400.

[0083] Steps 1410 to 1460 of the battery diagnostic method 1400 can be performed by the sensor 131 and the controller 132 of the battery diagnostic device .

[0084] In step 1410, the battery diagnostic device 130 can measure the open circuit voltage (OCV) from the battery under diagnosis to generate first OCV data. In step 1420, the battery diagnostic device 130 can obtain first SOC data relating to the state of charge of the battery under diagnosis based on the first OCV data.

[0085] In step 1430, the battery diagnostic device 130 can derive second SOC data for estimating the state of charge of the battery under diagnosis based on the first SOC data.

[0086] In step 1440, the battery diagnostic device 130 can acquire second OCV data of the battery under diagnosis based on the second SOC data. In step 1450, the battery diagnostic device 130 can diagnose the state of the battery to be diagnosed based on the first OCV data and the second OCV data.

[0087] Meanwhile, the battery diagnosis method 1400 may be implemented in the form of a computer program stored in a computer-readable storage medium. That is, the computer program may include instructions for implementing the battery diagnosis method 1400, and the program instructions may be stored in a computer-readable storage medium. The computer program may include a mobile application.

[0088] For example, computer-readable storage media may include hardware devices specially configured to store and execute computer program instructions, such as magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as CD-ROMs and DVDs, magneto-optical media such as floptical disks, and ROMs, RAMs, flash memories, etc. Computer program instructions may include machine language code produced by a compiler and high-level language code that can be executed by a computer using an interpreter, etc.

[0089] As used above, terms such as "comprise," "comprise," or "have" mean that the relevant element can be contained within the term, unless otherwise specified, and should be interpreted as meaning that other elements may be included, rather than excluding other elements. 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 herein belong, unless otherwise defined. Commonly used terms, such as dictionary-defined terms, should be interpreted to be consistent with the contextual meaning of the relevant art, and should not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0090] The above description merely exemplifies the technical concepts disclosed herein, and those skilled in the art to which the embodiments disclosed herein pertain may make various modifications and variations without departing from the essential characteristics of the embodiments disclosed herein. Therefore, the embodiments disclosed herein are intended to illustrate, rather than limit, the technical concepts of the embodiments disclosed herein, and such embodiments do not limit the scope of the technical concepts disclosed herein. The scope of protection of the technical concepts disclosed herein should be interpreted by the scope of the claims below, and all technical concepts within the scope equivalent thereto should be interpreted as being within the scope of the rights of this document. [Explanation of symbols]

[0091] 100: Battery diagnostic system 110: Charger / discharger 120: Battery to be diagnosed 130: Battery diagnostic device 131: Sensor 132: Controller 140: Management server

Claims

1. a sensor configured to measure an open circuit voltage (OCV) from the battery under diagnosis to generate first OCV data; acquiring first SOC data relating to a state of charge of the diagnosis target battery based on the first OCV data; deriving second SOC data for estimating a state of charge of the battery to be diagnosed based on the first SOC data; acquiring second OCV data of the battery to be diagnosed based on the second SOC data; a controller configured to diagnose a state of the diagnosis target battery based on the first OCV data and the second OCV data; A battery diagnostic device comprising:

2. the battery to be diagnosed includes a plurality of battery cells, The battery diagnostic device according to claim 1 , wherein the first OCV data includes a plurality of OCV values ​​measured at a plurality of points in time for each of the plurality of battery cells.

3. The controller Calculating an average SOC value of a plurality of SOC values ​​converted from the plurality of OCV values ​​for each battery cell; calculating a relative capacity value of each battery cell based on the average SOC value; The battery diagnostic device according to claim 2 , configured to calculate a plurality of estimated SOC values ​​of each battery cell at the plurality of points in time based on the relative capacity values.

4. The controller calculating a pseudoinverse matrix of an average SOC matrix indicating the average SOC value of each battery cell; The battery diagnostic device according to claim 3 , configured to calculate a relative capacity matrix indicating the relative capacity value by multiplying the pseudo-inverse matrix by an SOC matrix indicating the plurality of SOC values ​​for each battery cell.

5. The battery diagnostic device according to claim 4 , wherein the controller is configured to calculate an estimated SOC matrix indicating the plurality of estimated SOC values ​​of each battery cell by multiplying the average SOC matrix by the relative capacity matrix.

6. The controller Deriving OCV deviation data based on a difference between the first OCV data and the second OCV data; The battery diagnostic device according to claim 2 , configured to diagnose the state of the battery to be diagnosed based on the OCV deviation data.

7. the OCV deviation data includes a plurality of OCV deviation values ​​of each battery cell at the plurality of time points; The controller calculating a plurality of OCV deviation change amounts indicating differences between the OCV deviation value at a current time point and the OCV deviation value at a previous time point based on the plurality of OCV deviation values ​​of each battery cell; The battery diagnostic device according to claim 6 , configured to diagnose the state of each battery cell based on the plurality of OCV deviation change amounts of each battery cell.

8. 8. The battery diagnostic device according to claim 7, wherein the controller is configured to diagnose that an abnormality has occurred in a first battery cell among the plurality of battery cells when the plurality of OCV deviation change amounts of the first battery cell are greater than an upper limit value of a normal range or smaller than a lower limit value of the normal range.

9. The controller converting the first OCV data into the first SOC data based on an OCV-SOC mapping table; 9. The battery diagnostic device according to claim 1, wherein the battery diagnostic device is configured to convert the second SOC data into the second OCV data based on the OCV-SOC mapping table.

10. measuring an open circuit voltage (OCV) from the battery to be diagnosed and generating first OCV data; acquiring first SOC data relating to a state of charge of the diagnosis target battery based on the first OCV data; deriving second SOC data for estimating a state of charge of the battery to be diagnosed based on the first SOC data; acquiring second OCV data of the battery to be diagnosed based on the second SOC data; diagnosing the state of the diagnosis target battery based on the first OCV data and the second OCV data; A battery diagnostic method comprising:

11. the battery to be diagnosed includes a plurality of battery cells, The battery diagnosis method according to claim 10 , wherein the first OCV data includes a plurality of OCV values ​​measured at a plurality of time points for each of the plurality of battery cells.

12. The step of deriving the second SOC data includes: calculating an average SOC value of a plurality of SOC values ​​converted from the plurality of OCV values ​​for each battery cell; calculating a relative capacity value of each battery cell based on the average SOC value; and calculating a plurality of estimated SOC values ​​for each battery cell at the plurality of times based on the relative capacity values.

13. The step of calculating the relative capacitance value comprises: calculating a pseudo-inverse of an average SOC matrix indicating the average SOC value of each battery cell; and calculating a relative capacity matrix indicating the relative capacity values ​​by multiplying the pseudo-inverse matrix by an SOC matrix indicating the plurality of SOC values ​​for each battery cell.

14. The step of calculating the plurality of estimated SOC values ​​includes: The battery diagnosis method according to claim 13 , further comprising the step of multiplying the average SOC matrix by the relative capacity matrix to calculate an estimated SOC matrix indicating the plurality of estimated SOC values ​​of each battery cell.

15. The step of diagnosing the state of the diagnosis target battery includes: Deriving OCV deviation data based on a difference between the first OCV data and the second OCV data; The battery diagnostic method according to claim 11, further comprising the step of diagnosing the state of the battery to be diagnosed based on the OCV deviation data.

16. the OCV deviation data includes a plurality of OCV deviation values ​​of each battery cell at the plurality of time points; The step of diagnosing the state of the diagnosis target battery includes: calculating a plurality of OCV deviation change amounts indicating differences between the current OCV deviation value and the previous OCV deviation value based on the plurality of OCV deviation values ​​of each battery cell; and diagnosing a state of each battery cell based on the plurality of OCV deviation change amounts of each battery cell.

17. The step of diagnosing the state of the diagnosis target battery includes:

17. The battery diagnostic method according to claim 16, further comprising the step of diagnosing that an abnormality has occurred in a first battery cell among the plurality of battery cells when the plurality of OCV deviation change amounts of the first battery cell are greater than an upper limit value of a normal range or smaller than a lower limit value of the normal range.

18. the step of acquiring the first SOC data includes converting the first OCV data into the first SOC data based on an OCV-SOC mapping table; 18. The battery diagnosis method according to claim 10, wherein the step of acquiring the second OCV data includes the step of converting the second SOC data into the second OCV data based on the OCV-SOC mapping table.

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