Battery diagnosis apparatus and battery diagnosis method

Abstract

According to some embodiments disclosed herein, a battery diagnostic apparatus includes: a sensor configured to measure an open circuit voltage (OCV) of a battery to be diagnosed to generate first OCV data; and a controller configured to: obtain first SOC data related to a state of charge (SOC) of the battery to be diagnosed based on the first OCV data; deriving second SOC data regarding estimating 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.

Description

Technical Field

[0001] Cross-references to related applications

[0002] This application claims priority to and the benefit of Korean Patent Application No. 10-2022-0120916 filed in the Korean Intellectual Property Office on September 23, 2022, and Korean Patent Application No. 10-2023-0102473 filed in the Korean Intellectual Property Office on August 4, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0004] Embodiments disclosed herein relate to a battery diagnosis device and a battery diagnosis method. Background Art

[0005] Recently, research and development of secondary batteries have been actively carried out. In this article, secondary batteries as rechargeable / dischargeable batteries can be interpreted as including all of conventional nickel (Ni) / cadmium (Cd) batteries, Ni / metal hydride (MH) batteries, etc. and recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries can have higher energy density than conventional Ni / Cd batteries, Ni / MH batteries, etc., and can be made small and lightweight, allowing them to have high availability in terms of power sources for mobile devices. In recent years, as the use of lithium-ion batteries has expanded to power sources for electric vehicles, lithium-ion batteries have received attention as the next generation of energy storage media.

[0006] The diagnosis of abnormal behavior can be performed based on the battery voltage to check the manufacturing quality of the battery or diagnose whether a defect has occurred. For example, the abnormal behavior of the battery voltage can be diagnosed by utilizing the deviation of the cell voltage of the battery cell, but this diagnosis method has low diagnostic accuracy. Summary of the invention

[0007] Technical issues

[0008] The embodiments disclosed herein are intended to provide a battery diagnostic device and a battery diagnostic method, by which abnormality in voltage behavior can be diagnosed even when there is a small change in battery voltage.

[0009] Technical problems of the embodiments disclosed herein are not limited to the above-mentioned technical problems, and other unmentioned technical problems will be clearly understood by those of ordinary skill in the art from the following description.

[0010] Technical Solution

[0011] According to some embodiments disclosed herein, a battery diagnostic device includes: a sensor configured to generate first open circuit voltage (OCV) data by measuring an open circuit voltage (OCV) from a diagnostic target battery; and a controller configured to: obtain first SOC data regarding a state of charge (SOC) of a diagnostic target battery based on the first OCV data, derive second SOC data for estimating the SOC of the diagnostic target battery based on the first SOC data, obtain second OCV data of the diagnostic target battery based on the second SOC data, and diagnose a state of the diagnostic target battery based on the first OCV data and the second OCV data.

[0012] According to some embodiments, the diagnosis target battery may include a plurality of battery cells, and the first OCV data may include a plurality of OCV values ​​measured at a plurality of time points for each of the plurality of battery cells.

[0013] According to some embodiments, the controller may also be configured to: calculate an average SOC value of multiple SOC values ​​converted from 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 multiple time points based on the relative capacity values.

[0014] According to some embodiments, the controller may also be configured to: calculate a pseudo-inverse matrix of an average SOC matrix indicating an average SOC value of each battery cell; and calculate a relative capacity matrix indicating relative capacity values ​​for each battery cell by multiplying the pseudo-inverse matrix by a SOC matrix indicating multiple SOC values.

[0015] According to some embodiments, the controller may be further configured to calculate an estimated SOC matrix indicating a plurality of estimated SOC values ​​for each battery cell by multiplying the average SOC matrix by the relative capacity matrix.

[0016] According to some embodiments, the controller may be further configured to: derive OCV deviation data based on a difference between the first OCV data and the second OCV data; and diagnose a state of the diagnosis target battery based on the OCV deviation data.

[0017] According to some embodiments, the OCV deviation data may include multiple OCV deviation values ​​for each battery cell at multiple time points, and the controller may also be configured to: calculate multiple OCV deviation changes indicating the difference between the OCV deviation value at a current time point and the OCV deviation value at a previous time point based on the multiple OCV deviation values ​​of each battery cell, and diagnose the state of each battery cell based on the multiple OCV deviation changes of each battery cell.

[0018] According to some embodiments, the controller may be further configured to diagnose that an abnormality occurs in a first battery cell among the plurality of battery cells when the plurality of OCV deviation changes of the first battery cell are greater than an upper limit value of a normal range or less than a lower limit value of the normal range.

[0019] According to some embodiments, the controller may be further configured to: convert the first OCV data into first SOC data based on the OCV-SOC mapping table; and convert the second SOC data into second OCV data based on the OCV-SOC mapping table.

[0020] According to some embodiments disclosed herein, a battery diagnosis method includes: generating first open circuit voltage (OCV) data by measuring an open circuit voltage (OCV) from a diagnosis target battery; obtaining first SOC data regarding a state of charge (SOC) of the diagnosis target battery based on the first OCV data; deriving second SOC data for estimating the SOC of the diagnosis target battery based on the first SOC data; obtaining second OCV data of the diagnosis target battery based on the second SOC data; and diagnosing a state of the diagnosis target battery based on the first OCV data and the second OCV data.

[0021] According to some embodiments, the diagnosis target battery may include a plurality of battery cells, and the first OCV data may include a plurality of OCV values ​​measured at a plurality of time points for each of the plurality of battery cells.

[0022] According to some embodiments, the derivation of the second SOC data may include: calculating an average SOC value of multiple SOC values ​​converted from 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 ​​of each battery cell at multiple time points based on the relative capacity values.

[0023] According to some embodiments, calculating the relative capacity value may include: calculating a pseudo-inverse matrix of an average SOC matrix indicating an average SOC value of each battery cell; and calculating a relative capacity matrix indicating the relative capacity value for each battery cell by multiplying the pseudo-inverse matrix by a SOC matrix indicating multiple SOC values.

[0024] According to some embodiments, calculating the plurality of estimated SOC values ​​may include calculating 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.

[0025] According to some embodiments, diagnosing the state of the diagnosis target battery may include: deriving OCV deviation data based on a difference between the first OCV data and the second OCV data; and diagnosing the state of the diagnosis target battery based on the OCV deviation data.

[0026] According to some embodiments, the OCV deviation data may include multiple OCV deviation values ​​for each battery cell at multiple time points, and diagnosing the state of the target battery may include: calculating multiple OCV deviation changes indicating the difference between the OCV deviation value at a current time point and the OCV deviation value at a previous time point based on the multiple OCV deviation values ​​of each battery cell, and diagnosing the state of each battery cell based on the multiple OCV deviation changes of each battery cell.

[0027] According to some embodiments, diagnosing the state of the diagnosing target battery may include diagnosing that an abnormality occurs in a first battery cell among the plurality of battery cells when a plurality of OCV deviation changes of the first battery cell are greater than an upper limit value of a normal range or less than a lower limit value of the normal range.

[0028] According to some embodiments, obtaining the first SOC data may include converting the first OCV data into the first SOC data based on the OCV-SOC mapping table, and obtaining the second OCV data may include converting the second SOC data into the second OCV data based on the OCV-SOC mapping table.

[0029] Beneficial Effects

[0030] According to the embodiments disclosed herein, a battery diagnostic device and a battery diagnostic method can be provided, by which abnormality in voltage behavior can be diagnosed even when there is a small change in battery voltage.

[0031] The technical effects according to the embodiments disclosed in this document are not limited to the above-mentioned effects, and other effects not mentioned will be clearly understood by a person of ordinary skill in the art from the disclosure of this document. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 Components of a battery diagnostic system are shown according to some embodiments.

[0033] Figure 2 Components of a battery diagnostic device according to some embodiments are shown.

[0034] Figure 3 The operation process of the battery diagnostic device according to some embodiments is shown.

[0035] Figure 4 A process of measuring an open circuit voltage (OCV) from a diagnostic target battery according to some embodiments is shown.

[0036] Figure 5 A process for generating actual measured OCV data according to some embodiments is shown.

[0037] Figure 6 A process for converting actual measured OCV data to estimated SOC data is shown according to some embodiments.

[0038] Figure 7 A process of calculating a relative capacity value of each battery cell based on an average SOC value according to some embodiments is shown.

[0039] Figure 8 A process for calculating a plurality of estimated OCV values ​​based on relative capacity values ​​is shown according to some embodiments.

[0040] Fig. 9 A process for generating estimated SOC data and estimated OCV data according to some embodiments is shown.

[0041] Fig.10 A process for converting estimated SOC data to estimated OCV data is shown according to some embodiments.

[0042] Fig.11 A process for deriving OCV deviation data based on a difference between actual measured OCV data and estimated OCV data is shown in accordance with some embodiments.

[0043] Fig.12 A process for calculating a plurality of OCV deviation changes based on OCV deviation data according to some embodiments is shown.

[0044] Fig.13 A process of diagnosing the status of each battery cell based on a plurality of OCV deviation changes according to some embodiments is shown.

[0045] Fig.14 Operation of a battery diagnostic method according to some embodiments is shown. DETAILED DESCRIPTION

[0046] Hereinafter, the embodiments disclosed herein will be described with reference to the accompanying drawings. However, the description is not intended to limit the disclosure of this document to a specific embodiment, and should be construed to include various modifications, equivalents and / or substitutions according to the embodiments described herein.

[0047] It should be understood that the embodiments of this document and the terms used therein are not intended to limit the technical features set forth herein to specific embodiments, and include various changes, equivalents, or substitutions of the corresponding embodiments. With respect to the description of the drawings, similar reference numerals may be used to refer to similar or related elements. It should be understood that the singular form of a noun corresponding to an item may include one or more things, unless the relevant context clearly indicates otherwise.

[0048] As used herein, each of the phrases 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 or all possible combinations of the items listed together in the corresponding one of the phrases. Unless otherwise specified, phrases such as "the first (1 st )”,"The second one(2 nd )", "first", "second", "A", "B", "(a)" or "(b)" may be used to simply distinguish corresponding components from each other and do not limit the components in other aspects (e.g., importance or order).

[0049] Herein, it should be understood that when an element (e.g., a first element) is referred to as being “connected with,” “coupled with,” or “linked with,” or “coupled to,” or “connected to” another element (e.g., a second element) with or without the term “operably” or “communicatively,” it means that the element can be connected to the other element directly (e.g., wired or wirelessly) or indirectly (e.g., via a third element).

[0050] Methods according to various embodiments disclosed herein may be included and provided in a computer program product. The computer program product may be traded as a product between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disk read-only memory (CD-ROM)), or distributed online (e.g., downloaded or uploaded) via an application store, or distributed directly between two user devices. If distributed online, at least a portion of the computer program product may be temporarily generated or at least temporarily stored in a machine-readable storage medium (such as a memory of a manufacturer's server, a server of an application store, or a relay server).

[0051] According to embodiments disclosed herein, each component (e.g., module or program) in the above-mentioned components may include a single entity or multiple entities, and some of the multiple entities may be separately arranged in different components. According to various embodiments disclosed herein, one or more of the above-mentioned components may be omitted, or one or more other components may be added. Alternatively or additionally, multiple components (e.g., module or program) may be integrated into a single component. In this case, according to various embodiments, the integrated component may still perform one or more functions of each component in the multiple components in the same or similar manner as before the integration by the corresponding components in the multiple components. According to embodiments disclosed herein, the operations performed by a module, program or another component may be performed sequentially, in parallel, repeatedly or heuristically, or one or more of the operations may be performed or omitted in different orders, or one or more other operations may be added.

[0052] Figure 1 Components of a battery diagnostic system are shown according to some embodiments.

[0053] Reference Figure 1 , the battery diagnosis system 100 may include a charger / discharger 110, a diagnosis target battery 120, a battery diagnosis device 130, and a management server 140. However, not limited thereto, some components may be omitted from the battery diagnosis system 100, or other general components may be further included in the battery diagnosis system 100.

[0054] The battery diagnosis system 100 may refer to a system for diagnosing a state of a diagnosis target battery 120. According to an embodiment, a test voltage may be applied to the diagnosis target battery 120 by the charger / discharger 110, and response data outputted by the diagnosis target battery 120 in response to the test voltage may be measured by the battery diagnosis device 130.

[0055] The charger / discharger 110 may be configured to charge or discharge the diagnosis target battery 120. According to an embodiment, the charger / discharger 110 may apply a test voltage to the diagnosis target battery 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 or currents to the diagnosis target battery 120. According to an embodiment, the charger / discharger 110 may be included in the battery diagnosis device 130, rather than being provided separately from the battery diagnosis device 130.

[0056] The diagnostic target battery 120 may be a diagnostic target of the battery diagnostic system 100. The diagnostic target battery 120 may include a plurality of battery cells. For example, the diagnostic target battery 120 may include a plurality of battery modules, and each of the plurality of battery modules may include a plurality of battery cells. According to an embodiment, the diagnostic target battery 120 may include m battery cells, and for each of the m battery cells, voltage measurement may be performed n times, so that m*n voltage measurement values ​​may be generated.

[0057] The battery diagnosis device 130 may perform an operation for determining whether the diagnosis target battery 120 is abnormal. The battery diagnosis device 130 may perform voltage measurement and data processing to diagnose which cell of the diagnosis target battery 120 has abnormal voltage behavior.

[0058] The management server 140 may be configured to manage the state of the diagnosis target battery 120. The management server 140 may be connected to the battery diagnosis device 130 through wired / wireless data communication, and may be provided with data such as the state, abnormality, and diagnosis result of the diagnosis target battery 120 from the battery diagnosis device 130 and record the data. The management server 140 may control the battery diagnosis device 130 or identify the state of the diagnosis target battery 120 at the request of the system manager or the battery user.

[0059] According to an embodiment, the management server 140 may perform at least some of the operations for determining whether the diagnosis target battery 120 is abnormal on behalf of the battery diagnosis device 130. The management server 140 may receive data necessary for diagnosing the diagnosis target battery 120 from the battery diagnosis device 130, perform a diagnosis process, and send the result to the battery diagnosis device 130. According to an embodiment, the management server 140 may install energy management software necessary for diagnosing the diagnosis target battery 120 on the battery diagnosis device 130, and provide update information of the energy management software to the battery diagnosis device 130.

[0060] Figure 2 Components of a battery diagnostic device according to some embodiments are shown.

[0061] Reference Figure 2 , the battery diagnosis device 130 may include a sensor 131 and a controller 132. However, not limited thereto, some components may be omitted from the battery diagnosis device 130, or other general components may be further included in the battery diagnosis device 130.

[0062] According to an embodiment, in the battery diagnostic device 130, the sensor 131 and the controller 132 may be electrically connected to each other through communication between devices such as a bus, a general purpose input output (GPIO), a serial peripheral interface (SPI), a mobile industry processor interface (MIPI), etc.

[0063] The sensor 131 of the battery diagnosis device 130 may be configured to measure a voltage from the diagnosis target battery 120. When the test cycle voltage is applied to the diagnosis target battery 120 by the charger / discharger 110, an output voltage may be generated in the diagnosis target battery 120 in response thereto, and the output voltage may be measured by the sensor 131. To this end, the sensor 131 may include a measurement device such as a voltmeter, an ammeter, a thermometer, etc.

[0064] The controller 132 may have a structure for executing instructions for implementing the operation of the battery diagnostic device 130. The controller 132 may be implemented with an array of multiple logic gates or a general-purpose microprocessor for processing various operations, and may include 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.

[0065] The controller 132 may be configured separately or integrally with a memory (not shown) configured to store instructions, and may process various operations by executing instructions stored in the memory. The memory may store various data, instructions, mobile applications, computer programs, etc. For example, the memory may be implemented in the form of a non-volatile memory such as ROM, PROM, EPROM, EEPROM, flash memory, PRAM, MRAM, FRAM, etc., or a volatile memory such as DRAM, SRAM, SDRAM, RRAM, HDD, SSD, SD, micro SD, etc., or may be implemented in the form of a combination thereof.

[0066] The sensor 131 of the battery diagnostic device 130 may be configured to measure the OCV from the diagnostic target battery 120 and generate the first OCV data OCVs. The first OCV data OCVs may mean an actually measured OCV value. When the charge / discharge cycle voltage is applied to the diagnostic target battery 120, the sensor 131 may measure the voltage from the diagnostic target battery 120, and derive the OCV of the diagnostic target battery 120 based on the measured voltage. According to an embodiment, the OCV value may be measured n times for each of the m battery cells of the diagnostic target battery 120, and the first OCV data may include m*n OCV values.

[0067] The controller 132 of the battery diagnosis device 130 may be configured to obtain first state of charge data SOCs about the state of charge (SOC) of the diagnosis target battery 120 based on the first OCV data OCV. 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.

[0068] The controller 132 of the battery diagnosis device 130 may be configured to derive second SOC data SOCests for estimating the SOC of the diagnosis target battery 120 based on the first SOC data SOCs. The first SOC data SOCs may mean a value converted from the first OCV data OCVs, and the second SOC data SOCests may be a value obtained by estimating the SOC of the diagnosis target battery 120 using 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.

[0069] The controller 132 of the battery diagnostic device 130 may be configured to obtain second OCV data OCVests of the diagnostic target battery 120 based on the second SOC data SOCests. For example, the second SOC data SOCests may include m*n estimated SOC values ​​that 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 to the second OCV data OCVests may be performed based on an OCV-SOC mapping table, which may be the same as a table for converting the first OCV data OCVs into the first SOC data SOCs.

[0070] The controller 132 of the battery diagnostic device 130 may be configured to diagnose the state of the diagnostic 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 the 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 actually measured OCV values, the second OCV data OCVests may include m*n estimated OCV values, and the difference between the corresponding actually measured OCV values ​​and the estimated OCV values ​​may generate m*n OCV deviation values. The OCV deviation data OCVdevs may include m*n OCV deviation values.

[0071] The controller 132 of the battery diagnosis device 130 may be configured to diagnose the state of the diagnosis target battery 120 based on the OCV deviation data OCVdevs. According to an embodiment, the controller 132 may diagnose whether an abnormality occurs in the voltage behavior of each battery cell of the diagnosis target battery 120 by comparing the OCV deviation data OCVdevs or a variation thereof with a normal range. In this way, even when there is no sudden voltage change due to cell disconnection, short circuit, etc., the voltage behavior abnormality may be detected.

[0072] Figure 3 The operation process of the battery diagnostic device according to some embodiments is shown.

[0073] Reference Figure 3 The operation process 300 of the battery diagnosis 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 the process described later. Fig.14 Operations 1410 to 1460.

[0074] The OCV voltage of the diagnosis target battery 120 may be measured in a first process 310 , first OCV data OCVs may be converted into first SOC data SOCs in a second process 320 , and estimated SOC data SOCests may be calculated based on a relative capacity value compared to an average SOC value in a third process 330 .

[0075] In the fourth process 340, the second SOC data SOCests can be converted into second OCV data OCVests, in the fifth process 350, the deviation between the first OCV data OCVs and the second OCV data OCVests can be calculated, and in the sixth process 360, the OCV deviation change can be calculated based on the OCV deviation data OCVdevs, so that by comparing the OCV deviation change with the threshold, the voltage behavior abnormality of each cell of the diagnostic target battery 120 can be diagnosed.

[0076] Figure 4 A process of measuring OCV from a diagnostic target battery according to some embodiments is shown.

[0077] Reference Figure 4 , a charge / discharge profile 410 and an OCV graph 420 of the diagnosis target battery 120 are shown.

[0078] The charge / discharge profile 410 may indicate a voltage measured from any one battery cell of the diagnosis target battery 120 when a charge / discharge cycle voltage is applied to the battery cell. According to an embodiment, a peak value 411 in a measurement cycle of the charge / discharge profile 410 may be an actually measured OCV value 421. For example, the OCV graph 420 may include n actually measured OCV values ​​421 for one battery cell.

[0079] Figure 5 A process of generating first OCV data according to some embodiments is shown.

[0080] Reference Figure 5 , an OCV graph 420 and first OCV data OCVs 510 of the diagnosis target battery 120 are shown.

[0081] The OCV graph 420 may include n actually measured OCV values ​​421 of the i-th battery cell, and the first OCV data OCVs 510 of the m battery cells of the diagnosis target battery 120 may be m*n actually measured OCV values. According to an embodiment, the first OCV data OCVs 510 may be represented in the form of a matrix of size m*n.

[0082] According to an embodiment, the diagnosis target battery 120 may include a plurality of battery cells, and the first OCV data OCVs510 may include a plurality of OCV values ​​measured at a plurality of time points for each of the plurality of battery cells.

[0083] Figure 6 A process of converting first OCV data to second SOC data according to some embodiments is shown.

[0084] Reference Figure 6 , first OCV data OCVs 510 , an OCV-SOC mapping table 610 , and first SOC data SOCs 620 are shown.

[0085] 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 OSC data SOCs 620 based on the OCV-SOC mapping table 610. The OCV-SOC mapping table 610 may mean a table that maps the OCV value of the vertical axis to the SOC value of the horizontal axis and records the mapping relationship therebetween.

[0086] Figure 7 A process of calculating a relative capacity value of each battery cell based on an average SOC value according to some embodiments is shown, and Figure 8 A process for calculating a plurality of estimated OCV values ​​based on relative capacity values ​​is shown according to some embodiments.

[0087] Reference Figure 7 and Figure 8 , showing the first SOC data SOCs 620 of the diagnostic target battery 120, the average SOC value SOCavg 710 of each of the multiple time points 1, ..., n, the relative capacity values ​​A720 and 730, and the multiple estimated SOC values ​​SOCest of the i-th battery cell i 810 and second SOC data SOCests 820 .

[0088] For each of the plurality of time points 1, . . . , n, an average value of the first SOC data SOCs 620 may be calculated, and as a result, an average SOC value SOCavg 710 may be calculated. A relative capacity value A 720 may be calculated based on the average SOC value SOCavg 710 and the first SOC data SOCs 620.

[0089] When calculating the relative capacity value A from the first battery cell to the mth battery cell i In the manner of 730, the relative capacity value A 720 may be calculated. In this process, a pseudo-inverse matrix (PINV) operation of the average SOCavg 710 may be performed. The pseudo-inverse matrix may refer to a Moore-Penrose inverse matrix. The relative capacity value A of the i-th battery cell i 730 may include a slope component A slopei and the offset component A offseti .

[0090] The average SOC value SOCavg 710 and the relative capacity value A of the i-th battery cell may be used to calculate the relative capacity of the battery cell. i 730 is used to calculate a plurality of estimated SOC values ​​SOCest of the i-th battery cell. i 810. Based on the slope component A slopei , offset component A offseti and the average SOC value SOCavg 710 to calculate a plurality of estimated SOC values ​​SOCest of the i-th battery cell. i 810.

[0091] By calculating a plurality of estimated SOC values ​​SOCest of the i-th battery cell from the first battery cell to the m-th battery cell i 810 , a second SOC data SOCests 820 of the diagnosis target battery 120 may be calculated.

[0092] According to an embodiment, the controller 132 of the battery diagnosis device 130 may be configured to calculate an average SOC value SOCavg 710 of a plurality of SOC values ​​converted from a plurality of OCV values ​​of each battery cell, and calculate a relative capacity value A of each battery cell based on the average SOC value SOCavg 710. i , and based on the relative capacity value A i Calculate multiple estimated SOC values ​​SOCest for each battery cell at multiple time points i .

[0093] According to an embodiment, the controller 132 of the battery diagnosis device 130 may calculate the SOC matrix (SOC matrix) representing a plurality of SOC values ​​SOCs of each battery cell. i1 SOC i2 SOC i3 …SOC in ) is multiplied by the pseudo inverse matrix (PINV(SOCavg)) to calculate the average SOC matrix representing the average SOC value SOCavg 710 of each battery cell PINV matrix Calculation of relative capacity value A i The relative capacity matrix A.

[0094] According to an embodiment, the controller 132 of the battery diagnosis device 130 may convert an average SOC matrix representing average SOC values ​​of battery cells into an average SOC matrix. Multiply by the relative capacity matrix A to calculate a plurality of estimated SOC values ​​SOCest indicating each battery cell i The estimated SOC matrix SOCests of 810 .

[0095] Fig. 9 A process of generating second SOC data and second OCV data according to some embodiments is shown.

[0096] Reference Fig. 9 , shows a process of converting first OCV data OCVs 910 measured for four battery cells into second OSC data and second OCV data at five time points (n=5).

[0097] The first OCV data OCVs 910 may be converted into the first SOC data SOCs 920 based on the OCV-SOC mapping table 610. The average SOC value SOCavg 930 may be calculated based on the first SOC data SOCs 920.

[0098] Based on the average SOC value SOCavg 930, for the first battery cell (i=1) among the four battery cells, the relative capacity value A1 940 of the first battery cell can be obtained by converting the average SOC matrix The pseudo-inverse matrix It is calculated by multiplying by the average SOC value SOCavg 930. Thereafter, the average SOC matrix The pseudo-inverse matrix Multiplied by the relative capacity value A1 940 , an estimated SOC value SOCest1 950 of the first battery cell may be calculated, and the OCV-SOC mapping table 610 may be applied thereto to calculate an estimated OCV value OCVest1 960 of the first battery cell.

[0099] Fig.10 A process of converting the second SOC data into the second OCV data according to some embodiments is shown.

[0100] Reference Fig.10 , shows a process of converting the second SOC data SOCests 820 of the diagnosis target battery 120 into the second OCV data OCVests 1010 based on the OCV-SOC mapping table 610 .

[0101] According to an embodiment, the controller 132 of the battery diagnostic device 130 may be configured to convert the second OCV 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 for converting the first OCV data OCVs 510 into the first SOC data SOCs 620.

[0102] Fig.11 shows a process for deriving OCV deviation data based on a difference between first OCV data and second OCV data according to some embodiments, and Fig.12 A process for calculating a plurality of OCV deviation changes based on OCV deviation data according to some embodiments is shown.

[0103] Reference Fig.11 , shows a process of deriving OCV deviation data OCVdevs 1110 based on the difference between the first OCV data OCVs 510 and the second OCV data OCVests 1010, and refers to Fig.12 , shows a process of calculating a plurality of OCV deviation change amounts 1210 and 1220 based on a difference between a value of the OCV deviation data OCVdevs1110 at a current time point and a value at a previous time point.

[0104] According to an embodiment, the OCV deviation data OCVdevs 1110 may include a plurality of OCV deviation values ​​OCVdev of each battery cell at a plurality of time points 1 to n. i1 、OCVdev i2 、…、OCVdev in According to an embodiment, the controller 132 of the battery diagnosis device 130 may be configured to determine the OCV deviation value OCVdev based on the plurality of OCV deviation values ​​OCVdev of each battery cell. i1 、OCVdev i2 、…、OCVdev in A plurality of OCV deviation variation values ​​OCVdiff indicating the difference 1220 between the OCV deviation value at the current time point and the OCV deviation value at the previous time point are calculated. i 1210, and based on a plurality of OCV deviation variation amounts OCVdiff of each battery cell i 1210 to diagnose the status of each battery cell.

[0105] Fig.13 A process of diagnosing the status of each battery cell based on a plurality of OCV deviation changes according to some embodiments is shown.

[0106] Reference Fig.13 In the diagnosis process of the target battery 120, when the first OCV data OCVs 420 is input, a plurality of OCV deviation variation amounts OCVdiff for each battery cell may be derived. i The OCV deviation variation OCVdiffs 1300 of the diagnosis target battery 1210 is obtained, and abnormal voltage behavior of each battery cell of the diagnosis target battery 120 may be detected based on the OCV deviation variation OCVdiffs 1300 .

[0107] In the OCV deviation variation OCVdiffs 1300, an upper limit value 1310 and a lower limit value 1320 of a normal range may be set. According to an embodiment, when the OCV deviation variation of a specific battery cell exceeds the upper limit value 1310 or does not reach the lower limit value 1320 at a specific time point, it may be diagnosed that the corresponding battery cell has abnormal voltage behavior at the excess / shortage time point. For example, when the OCV deviation variation falls outside the normal range at the shortage time points 1330 and 1340 and the excess time point 1350, the battery cell having the OCV deviation variation OCVdiffs 1300 may be diagnosed as an abnormal cell.

[0108] According to an embodiment, the upper limit value 1310 and the lower limit value 1320 of the normal range may be changed according to the battery performance requirement. When a high battery performance requirement is required, the normal range may be narrowed, and in the opposite case, the normal range may be widened. According to an embodiment, the abnormality of the battery cell may be diagnosed based on the number and / or frequency of the battery performance requirement falling outside the normal range. For example, the number and / or frequency of the battery performance requirement falling outside the normal range may be compared with a threshold value.

[0109] Fig.14 Operation of a battery diagnostic method according to some embodiments is shown.

[0110] Reference Fig.14 , the battery diagnosis method 1400 may include operations 1410 to 1460. However, not limited thereto, some operations may be omitted and other general operations may be added, and the operations of the battery diagnosis method 1400 may be performed in an order different from that shown.

[0111] The battery diagnosis method 1400 may include operations processed in a time series by the battery diagnosis device 130. Therefore, the matters described above with respect to the battery diagnosis device 130 may also be applied to the battery diagnosis method 1400 even if omitted below.

[0112] Operations 1410 through 1460 of the battery diagnostic method 1400 may be performed by the sensor 131 and the controller 132 of the battery diagnostic apparatus 130 .

[0113] In operation 1410 , the battery diagnosis apparatus 130 may generate first OCV data by measuring OCV from a diagnosis target battery.

[0114] In operation 1420 , the battery diagnosis apparatus 130 may obtain first SOC data regarding the SOC of the diagnosis target battery based on the first OCV data.

[0115] In operation 1430 , the battery diagnosis apparatus 130 may derive second SOC data for estimating the SOC of the diagnosis target battery based on the first SOC data.

[0116] In operation 1440 , the battery diagnosis apparatus 130 may acquire second OCV data of the diagnosis target battery based on the second SOC data.

[0117] In operation 1450 , the battery diagnosis apparatus 130 may diagnose a state of a diagnosis target battery based on the first OCV data and the second OCV data.

[0118] In addition, the battery diagnostic method 1400 can be implemented in the form of a computer program stored in a computer-readable storage medium. That is, the computer program can include instructions for implementing the battery diagnostic method 1400, and the instructions of the program can be stored in a computer-readable storage medium. The computer program can include a mobile application.

[0119] For example, computer-readable storage media may include magnetic media such as hard disks, floppy disks, and magnetic tapes, optical media such as compact disk read-only memories (CD-ROMs) and digital versatile disks (DVDs), magneto-optical media such as floppy disks, and hardware devices specifically configured to store and execute program instructions (such as read-only memories (ROMs), random access memories (RAMs), and flash memories, etc.). Computer program instructions may include machine language codes created by a compiler and high-level language codes that can be executed by a computer using an interpreter.

[0120] Unless otherwise specified, the above terms such as "including", "consisting of" or "having" may mean that the corresponding components may be inherent and should therefore be interpreted as also including other components rather than excluding other components. Unless otherwise defined, all terms including technical or scientific terms have the same meaning as those generally understood by ordinary technicians in the field to which the embodiments disclosed herein belong. General terms such as terms defined in dictionaries should be interpreted as having the same meaning as the contextual meaning of the relevant technology, and should not be interpreted as having ideal or overly formal meanings unless they are clearly defined in this document.

[0121] The above description only illustrates the technical concept of the present disclosure, and various modifications and changes will be feasible for those of ordinary skill in the art to which the embodiments disclosed herein belong without departing from the basic features of the embodiments disclosed herein. Therefore, the embodiments disclosed herein are intended to describe rather than limit the technical spirit of the embodiments disclosed herein, and the scope of the technical spirit of the present disclosure is not limited by these embodiments disclosed herein. The scope of protection of the technical spirit disclosed herein should be interpreted by the attached claims, and all technical spirits within the same scope should be understood to be included within the scope of the present disclosure.

[0122] [Description of Reference Signs]

[0123] 100: Battery diagnostic system 110: Charger / discharger

[0124] 120: Diagnostic target battery 130: Battery diagnostic equipment

[0125] 131: Sensor 132: Controller

[0126] 140: Management Server

Claims

1. A battery diagnostic device, comprising: a sensor configured to generate first OCV data by measuring an open circuit voltage OCV from a diagnosis target battery; as well as A controller, the controller being configured to: obtaining first SOC data on a state of charge SOC of the diagnosis target battery based on the first OCV data; deriving second SOC data for estimating a state of charge of the diagnosis target battery based on the first SOC data; obtaining second OCV data of the diagnosis target battery based on the second SOC data; and A state of the diagnosis target battery is diagnosed based on the first OCV data and the second OCV data.

2. The battery diagnostic device according to claim 1, wherein: The diagnosis target battery 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 of the plurality of battery cells.

3. The battery diagnostic device according to claim 2, wherein: The controller is also configured to: calculating, for each battery cell, an average SOC value of a plurality of SOC values ​​converted from the plurality of OCV values; Calculating a relative capacity value of each battery cell based on the average SOC value; as well as A plurality of estimated SOC values ​​of each battery cell at the plurality of time points are calculated based on the relative capacity value.

4. The battery diagnostic device according to claim 3, wherein: The controller is also configured to: calculating a pseudo-inverse matrix of an average SOC matrix indicating the average SOC value of each battery cell; as well as A relative capacity matrix indicating the relative capacity values ​​is calculated for each battery cell by multiplying the pseudo inverse matrix by an SOC matrix indicating the plurality of SOC values.

5. The battery diagnostic device according to claim 4, wherein: The controller is further 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 battery diagnostic device according to claim 1, wherein: The controller is also configured to: deriving OCV deviation data based on a difference between the first OCV data and the second OCV data; and The state of the diagnosis target battery is diagnosed based on the OCV deviation data.

7. The battery diagnostic device according to claim 6, wherein: The OCV deviation data includes a plurality of OCV deviation values ​​of each battery cell at a plurality of time points, and The controller is also configured to: calculating a plurality of OCV deviation change amounts indicating differences 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 ​​of each battery cell; and The state of each battery cell is diagnosed based on the plurality of OCV deviation change amounts of each battery cell.

8. The battery diagnostic device according to claim 7, wherein: The controller is further configured to diagnose that an abnormality occurs 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 less than a lower limit value of the normal range.

9. The battery diagnostic device according to claim 1, wherein: The controller is also configured to: Based on the OCV-SOC mapping table, converting the first OCV data into the first SOC data; as well as Based on the OCV-SOC mapping table, the second SOC data is converted into the second OCV data.

10. A battery diagnosis method, the battery diagnosis method comprising the following steps: generating first OCV data by measuring an open circuit voltage OCV from a diagnosis target battery; obtaining first SOC data on a state of charge SOC of the diagnosis target battery based on the first OCV data; deriving second SOC data for estimating a state of charge of the diagnosis target battery based on the first SOC data; obtaining second OCV data of the diagnosis target battery based on the second SOC data; and A state of the diagnosis target battery is diagnosed based on the first OCV data and the second OCV data.

11. The battery diagnosis method according to claim 10, wherein: The diagnosis target battery 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 of the plurality of battery cells.

12. The battery diagnosis method according to claim 11, wherein: The step of deriving the second SOC data comprises the following steps: calculating, for each battery cell, an average SOC value of a plurality of SOC values ​​converted from the plurality of OCV values; Calculating a relative capacity value of each battery cell based on the average SOC value; and A plurality of estimated SOC values ​​of each battery cell at the plurality of time points are calculated based on the relative capacity value.

13. The battery diagnosis method according to claim 12, wherein: The step of calculating the relative capacity value comprises the following steps: calculating a pseudo-inverse matrix of an average SOC matrix indicating the average SOC value of each battery cell; and A relative capacity matrix indicating the relative capacity values ​​is calculated for each battery cell by multiplying the pseudo inverse matrix by an SOC matrix indicating the plurality of SOC values.

14. The battery diagnosis method according to claim 13, wherein: The step of calculating the plurality of estimated SOC values ​​includes the step of calculating 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.

15. The battery diagnosis method according to claim 10, wherein: The step of diagnosing the state of the diagnosis target battery includes the following steps: deriving OCV deviation data based on a difference between the first OCV data and the second OCV data; and The state of the diagnosis target battery is diagnosed based on the OCV deviation data.

16. The battery diagnosis method according to claim 15, wherein: The OCV deviation data includes a plurality of OCV deviation values ​​of each battery cell at a plurality of time points, and The step of diagnosing the state of the diagnosis target battery includes the following steps: calculating a plurality of OCV deviation change amounts indicating differences 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 ​​of each battery cell; and The state of each battery cell is diagnosed based on the plurality of OCV deviation change amounts of each battery cell.

17. The battery diagnosis method according to claim 16, wherein: The step of diagnosing the state of the diagnosis target battery includes the following steps: when the multiple OCV deviation changes of a first battery cell among the multiple battery cells are greater than an upper limit value of a normal range or less than a lower limit value of the normal range, diagnosing that an abnormality occurs in the first battery cell.

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

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