Battery management device and operating method thereof

Abstract

A battery management apparatus according to an embodiment disclosed in this document may include: a data management unit for calculating a state of health (SOH) of each of a plurality of batteries; and a controller for: identifying a plurality of target batteries based on a first value, the first value being a deviation of a state of health of each of the plurality of batteries from an average value of the states of health of the plurality of batteries; dividing the plurality of target batteries into a plurality of groups based on the state of health; and diagnosing at least one target battery based on a deviation in open circuit voltage (OCV) between a plurality of target batteries included in each of the plurality of groups.

Description

Technical Field

[0001] Cross-references to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2023-0042995, filed with the Korean Intellectual Property Office on March 31, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein relate to battery management devices and methods of operation thereof. Background Technology

[0004] Electric vehicles are powered externally to charge battery cells, which then drive a motor using the voltage generated by the charge within the cells. During production and use, battery cells undergo internal deformation and degradation through various charge / discharge cycles, altering their physical and chemical properties and potentially leading to internal short circuits. When an internal short circuit occurs within a battery cell, an undervoltage defect may result, where the cell voltage drops to a predetermined level or lower, increasing the likelihood of fire and causing immediate problems within the cell. Therefore, a technique is needed to determine whether an internal short circuit has occurred within a battery cell.

[0005] In battery systems with multiple connected cells, traditional battery management devices diagnose internal short circuits by determining the relative voltage deviation based on the cell connection sequence, without considering the differences in the degree of degradation (state of health (SOH)) of each cell. This method has the limitation of failing to detect voltage fluctuations in cells caused by actual internal short circuits because it does not take into account the capacity deviations corresponding to the cumulative use and natural degradation of the cells. Summary of the Invention

[0006] Technical issues

[0007] The embodiments disclosed herein aim to provide a battery management device and its operating method, wherein abnormal battery cells can be accurately diagnosed by reflecting voltage deviations corresponding to differences in the degree of degradation between battery cells.

[0008] The technical problems of the embodiments disclosed herein are not limited to those described above, and those skilled in the art can clearly understand other unmentioned technical problems through the following description.

[0009] Technical solution

[0010] A battery management device according to an embodiment disclosed herein includes: a data management unit configured to calculate the degree of degradation (state of health (SOH)) of each of a plurality of batteries; and a controller configured to: identify a plurality of target batteries based on a first value, the first value being a deviation of the SOH of each of the plurality of batteries from the average SOH of the plurality of batteries; divide the plurality of target batteries into a plurality of groups based on the SOH of the plurality of target batteries; and diagnose at least one target battery based on the deviation of the open-circuit voltage (OCV) among the plurality of target batteries included in each of the plurality of groups.

[0011] According to an implementation, the controller may be further configured to identify a battery among the plurality of batteries that has a first value less than a threshold as one of the plurality of target batteries.

[0012] According to an embodiment, the controller may be further configured to divide the plurality of target batteries into multiple groups in descending order of their SOH.

[0013] According to an embodiment, the controller may be further configured to: calculate the deviation of the OCV of each of the plurality of target batteries included in each of the plurality of groups from the average value of the OCV of the plurality of target batteries, and calculate the amount of change in the deviation of the OCV of each of the plurality of target batteries included in each of the plurality of groups.

[0014] According to an implementation, the controller may be further configured to: calculate a pattern of the change in the deviation of the OCV of each of the plurality of target batteries by calculating the change in the deviation of the OCV of each of the plurality of target batteries in each specific time period, and diagnose at least one target battery by comparing the pattern of the change in the deviation of the OCV of each of the plurality of target batteries with a plurality of diagnostic patterns.

[0015] According to an embodiment, the controller may be further configured to diagnose the at least one target battery when the pattern of the change in the amount of deviation of the OCV of at least one of the plurality of target batteries corresponds to any one of the plurality of diagnostic modes.

[0016] An operation method of a battery management device according to an embodiment disclosed herein includes the following steps: calculating the degree of degradation (state of health (SOH)) of each of a plurality of batteries; calculating a first value, the first value being the deviation of the SOH of each of the plurality of batteries from the average value of the SOH of the plurality of batteries; identifying a plurality of target batteries based on the first value of each of the plurality of batteries; dividing the plurality of target batteries into a plurality of groups based on the SOH of the plurality of target batteries; and diagnosing at least one target battery based on the deviation of the open-circuit voltage (OCV) between the plurality of target batteries included in each of the plurality of groups.

[0017] According to an implementation, the step of identifying a plurality of target batteries based on the first value of each of the plurality of batteries may include: identifying batteries among the plurality of batteries that have the first value less than a threshold as the plurality of target batteries.

[0018] According to an implementation method, the step of dividing the plurality of target batteries into multiple groups based on their State of Health (SOH) may include: dividing the plurality of target batteries into multiple groups in descending order of their SOH values.

[0019] According to an implementation, the step of diagnosing at least one target battery based on the deviation of OCV among the plurality of target batteries included in each of the plurality of groups may include the following steps: calculating the deviation of the OCV of each of the plurality of target batteries included in each of the plurality of groups relative to the average value of the OCV of the plurality of target batteries, and calculating the amount of change in the deviation of the OCV of each of the plurality of target batteries included in each of the plurality of groups.

[0020] According to an implementation, the step of diagnosing at least one target battery based on the deviation of OCV among the plurality of target batteries included in each of the plurality of groups may include the following steps: calculating a pattern of the change in the deviation of OCV of each of the plurality of target batteries by calculating the change in the deviation of OCV of each of the plurality of target batteries in each specific time period, and diagnosing at least one target battery by comparing the pattern of the change in the deviation of OCV of each of the plurality of target batteries with a plurality of diagnostic patterns.

[0021] According to an implementation, the step of diagnosing at least one target battery based on the deviation of OCV among the plurality of target batteries included in each of the plurality of groups may include the following steps: diagnosing the at least one target battery when the pattern of the change in the amount of OCV deviation of at least one of the plurality of target batteries corresponds to any one of the plurality of diagnostic patterns.

[0022] Beneficial effects

[0023] By using the battery management device and its operating method according to the embodiments disclosed herein, abnormal battery cells can be accurately diagnosed by reflecting voltage deviations that correspond to differences in the degree of degradation between battery cells. Attached Figure Description

[0024] Figure 1 A battery pack according to an embodiment disclosed herein is shown.

[0025] Figure 2 This is a block diagram illustrating the configuration of a battery management device according to an embodiment disclosed herein.

[0026] Figure 3 This is a table showing the degree of degradation of battery cells according to the embodiments disclosed herein.

[0027] Figure 4 This is a table showing the first values ​​of a battery cell according to an embodiment disclosed herein.

[0028] Figure 5 This is a table showing the target battery cells ranked in descending order of their degradation relative to the degree of deterioration according to the embodiments disclosed herein.

[0029] Figure 6 This is a flowchart of a diagnostic method for a target battery cell of a controller according to an embodiment disclosed herein.

[0030] Figure 7 This is a flowchart illustrating an operation method of a battery management device according to an embodiment disclosed herein.

[0031] Figure 8 This is a block diagram illustrating the hardware configuration of a computing system for performing an operation method of a battery management device according to an embodiment disclosed herein. Detailed Implementation

[0032] In the following, some embodiments disclosed in this document will be described in detail with reference to the exemplary accompanying drawings. When adding reference numerals to the components of each drawing, it should be noted that the same components are given the same reference numerals, even if they are represented in different drawings. Furthermore, when describing the embodiments disclosed in this document, detailed descriptions of related known configurations or functions will be omitted if it is determined that such detailed descriptions interfere with the understanding of the embodiments disclosed in this document.

[0033] To describe the components of the embodiments disclosed herein, terms such as first, second, A, B, (a), (B), etc., may be used. These terms are used only to distinguish one component from another and do not limit the components to their nature, order, sequence, etc. The terms used herein, including technical and scientific terms, have the same meaning as commonly understood by those skilled in the art, unless otherwise defined. Generally, terms defined in a general dictionary should be interpreted as having the same meaning as in the context of the relevant art and should not be interpreted as having an ideal or exaggerated meaning unless they are explicitly defined in this document.

[0034] Figure 1 A battery pack according to an embodiment disclosed herein is shown.

[0035] Reference Figure 1 The battery pack 1000 according to the embodiments disclosed herein may include a battery module 100, a battery management device 200, and a relay 300. According to various embodiments, the battery module 100 may be a battery cell, and in this case, the battery pack 1000 may have a cell-to-pack structure.

[0036] Although Figure 1 The diagram shows a single battery module 100, but the battery pack 1000 may include multiple battery modules forming a stacked structure. Battery module 100 may include multiple battery cells 110, 120, 130, and 140. Although in... Figure 1 Multiple battery cells are shown as four, but this disclosure is not limited thereto, and the battery module 100 may include n battery cells (n is a natural number equal to or greater than 2).

[0037] Battery module 100 can supply power to a target device (not shown). For this purpose, battery module 100 can be electrically connected to the target device. In this document, the target device may include, but is not limited to, an electrical, electronic, or mechanical device that operates by receiving power from a battery pack 1000 comprising a plurality of battery cells 110, 120, 130, and 140, and may be, for example, an electric vehicle (EV) or an energy storage system (ESS).

[0038] Each of the multiple battery cells 110, 120, 130, and 140 is a basic unit of a battery that uses electrical energy through charging and discharging. These cells can be lithium-ion (Li-ion) batteries, lithium-ion polymer batteries, nickel-cadmium (Ni-Cd) batteries, nickel-metal hydride (Ni-MH) batteries, and are not limited to these. Meanwhile, although... Figure 1 The image shows one battery module 100, but according to the embodiment, the battery module 100 can be configured as multiple.

[0039] The battery management device (battery management system (BMS)) 200 can manage and / or control the state and / or operation of the battery module 100. For example, the battery management device 200 can manage and / or control the state and / or operation of the plurality of battery cells 110, 120, 130 and 140 included in the battery module 100. The battery management device 200 can manage the charging and / or discharging of the battery module 100.

[0040] The battery management device 200 can control the operation of the relay 300. For example, the battery management device 200 can short-circuit the relay 300 to supply power to the target device. When a charging device is connected to the battery pack 1000, the battery management device 200 can short-circuit the relay 300.

[0041] Furthermore, the battery management device 200 can monitor the voltage, current, temperature, etc. of each of the multiple battery cells 110, 120, 130, and 140 included in the battery module 100. For monitoring to be performed by the battery management device 200, sensors or various measurement modules (not shown) can be additionally installed in the battery module 100, the charging / discharging path, or any location within the battery module 100. The battery management device 200 can calculate parameters indicating the state of the battery module 100 (e.g., state of charge (SOC), state of health (SOH), etc.) based on measurements such as monitored voltage, current, and temperature.

[0042] For the multiple battery cells 110, 120, 130, and 140, as the usage time or number of uses increases, the capacity may decrease, the internal resistance may increase, and various factors of the battery may change. The battery management device 200 can diagnose abnormalities within the multiple battery cells 110, 120, 130, and 140 based on data on various factors that change with battery degradation.

[0043] More specifically, the battery management device 200 can determine the internal abnormal voltage of the multiple battery cells 110, 120, 130 and 140 based on data of various factors that change as the multiple battery cells 110, 120, 130 and 140 deteriorate, in order to determine whether there are abnormal battery cells within the multiple battery packs 110, 120, 130 and 140.

[0044] For example, battery management device 200 can calculate the deviation dV of the OCV of multiple battery cells 110, 120, 130, and 140 using open-circuit voltage (OCV) data of multiple battery cells 110, 120, 130, and 140. Battery management device 200 can diagnose whether at least one of the multiple battery cells 110, 120, 130, and 140 has an internal short circuit by using the average of the deviations of the OCV of the multiple battery cells 110, 120, 130, and 140 and the deviation of the OCV of each of the multiple battery cells 110, 120, 130, and 140. Over time, due to self-discharge, the battery cell where an internal short circuit has occurred may have a voltage deviation compared to a normal battery cell.

[0045] The battery management device 200 can identify multiple target battery cells, excluding those with suspicious noise voltages, by comparing the average state of decay (SOH) of multiple battery cells 110, 120, 130, and 140 included in the battery pack 1000 with the state of decay of each of the multiple battery cells 110, 120, 130, and 140. After identifying the multiple target battery cells, the battery management device 200 can diagnose battery cells with internal short circuits by using the change in the deviation dV of the OCV of the multiple target battery cells included in the battery pack 1000.

[0046] The following operations of the battery management device 200 can also be performed in the battery management device 200 or in various devices (such as servers, cloud, chargers, chargers / dischargers, etc.) connected to the vehicle on which the battery management device 200 is installed.

[0047] Figure 2 This is a block diagram illustrating the configuration of a battery management device according to an embodiment disclosed herein.

[0048] In the following text, reference will be made to Figure 2 The configuration of the battery management device 200 is described in detail.

[0049] Reference Figure 2 The battery management device 200 may include a data management unit 210 and a controller 220.

[0050] Data management unit 210 can calculate the state of decay (SOH) of each of the multiple battery cells 110, 120, 130, and 140. SOH is an indicator of the battery's health or lifespan state in its current state compared to its initial state. The moment when SOH reaches 0% can be defined as the end of life (EOL). Furthermore, the end of battery life can also be the moment when the battery capacity reaches its guaranteed capacity or lower. For example, data management unit 210 can calculate the SOH of the multiple battery cells 110, 120, 130, and 140 based on at least one of the following factors that change with the decay of the multiple battery cells 110, 120, 130, and 140: internal resistance, impedance, conductance, capacity, voltage, self-discharge current, charging performance, and the number of charge and discharge cycles.

[0051] For example, the data management unit 210 can calculate the individual SOH, i.e., SOHC, of ​​each battery cell by using the OCV and current integral values ​​of multiple battery cells 110, 120, 130, and 140. Specifically, the battery management device 200 can calculate the pre-charging OCV OCV_A and post-charging OCV OCV_B of the multiple battery cells 110, 120, 130, and 140.

[0052] The data management unit 210 can calculate SOC_A and SOC_B by converting OCV_A and OCV_B into charge quantities, i.e., SOC, based on the OCV table. The battery management device 200 can calculate the individual SOH of multiple battery cells 110, 120, 130 and 140 based on [Equation 1].

[0053] [Formula 1]

[0054] SOHC=I / ((SOC_B-SOC_A) / 100*X)*100

[0055] In this paper, (SOC_B-SOC_A) can represent the SOC deviation, I can represent the cumulative charging current, and X can represent the capacity of the existing battery cell. The data management unit 210 can calculate the individual SOH, i.e., SOHC, of ​​each of the multiple battery cells 110, 120, 130, and 140 based on [Equation 1].

[0056] Figure 3 This is a table showing the State of Harmony (SOH) of a battery cell according to an embodiment disclosed herein.

[0057] Reference Figure 3 The data management unit 210 can calculate the SOOH (Sodium Oxide Hydrogen) of each of the multiple battery cells.

[0058] According to an embodiment, the battery pack 1000 may include a stacked structure of four battery modules 100, and each of the multiple battery modules 100 may include 10 battery cells connected in series or parallel. That is, the battery pack 1000 may include, for example, 40 battery cells, wherein each of the four battery modules can be assigned a unique battery module number, and each of the 40 battery cells included in the battery modules can be assigned a unique battery cell number. The data management unit 210 can calculate the SOH (State of Health) of each of the 40 battery cells included in the battery pack 1000.

[0059] The controller 220 can diagnose at least one of the multiple battery cells 110, 120, 130 and 140 based on the SOHC of each of them.

[0060] First, the controller 220 can identify multiple target battery cells based on the State of Health (SOH) of multiple battery cells 110, 120, 130 and 140.

[0061] Figure 4 This is a table showing the first values ​​of a battery cell according to an embodiment disclosed herein.

[0062] Reference Figure 4 For example, controller 220 can calculate the average SOH of the 40 battery cells included in battery module 100. For example, controller 220 can calculate the average SOH, that is, the SOHC of the 40 battery cells included in battery module 100, as "98.39%".

[0063] The controller 220 can calculate a first value, which is the deviation of the SOHC of each of the plurality of battery cells from the average value of the SOHC of the plurality of battery cells. For example, when the SOHC of battery cell number 1 is "98.83%", the controller 220 can calculate the first value of the first battery cell as "-0.44%".

[0064] Controller 220 can identify batteries among multiple battery cells 110, 120, 130, and 140 with a first value less than a threshold as multiple target battery cells. Here, the threshold can be defined as a criterion used to determine what is "abnormal" due to extreme output results. That is, the threshold can be defined as a criterion indicating the degree to which data contradicts a particular statistical model. Controller 220 can identify battery cells among multiple battery cells 110, 120, 130, and 140 with a first value exceeding the threshold as noisy battery cells, remove the data from the noisy battery cells, and identify the battery cells from which the noisy battery cells have been removed as target battery cells.

[0065] For example, controller 220 can identify 37 battery cells out of 40 battery cells as target battery cells, excluding 3 noisy battery cells that have a first value exceeding a threshold of "2%": the first value is the deviation of the battery cell's SOH from the average SOH of the battery cells, which is "98.39%".

[0066] Figure 5 This is a table showing the target battery cells ranked in descending order of their degradation relative to the degree of deterioration according to the embodiments disclosed herein.

[0067] Reference Figure 5 The controller 220 can divide multiple target battery cells into multiple groups based on their State of Health (SOH). Specifically, the controller 220 can list multiple target battery cells based on their SOH and divide them into multiple groups according to the listing order.

[0068] The controller 220 can list multiple target battery cells in descending order of State of Health (SOH). That is, the controller 220 can list multiple target battery cells in descending order of SOH. For example, the controller 220 can list 37 target battery cells (excluding the noise battery cells) out of the 100 battery cells included in the battery pack 1000 in descending order of SOH.

[0069] The controller 220 can list multiple target battery cells in descending order of State of Health (SOH), group battery cells with similar SOH into multiple groups, and perform diagnostics. For example, the controller 220 can list 37 target battery cells based on their SOH and divide them into 4 groups, each containing 8 to 10 battery cells.

[0070] The controller 220 can diagnose at least one target battery cell based on the deviation of OCV between multiple target battery cells included in each of the multiple groups.

[0071] Figure 6 This is a flowchart of a diagnostic method for a target battery cell of a controller according to an embodiment disclosed herein.

[0072] Reference Figure 6 The method for diagnosing at least one target battery cell based on the deviation of OCV between target battery cells, executed by controller 220, will be described in detail.

[0073] In operation S101, the controller 220 can determine whether the OCV of the multiple target battery cells included in each of the multiple groups is in a relaxation state. Here, voltage relaxation can refer to the phenomenon that when the battery enters an idle or no-load state, a potential difference appears between multiple positive electrode materials, and due to this potential difference, working ions move between the positive electrode materials, thereby eliminating the potential difference over time. In operation S101, for example, when the voltage fluctuation of the multiple target battery cells included in each of the multiple groups is 20mV or less for up to 4 hours, the controller 220 can determine that the multiple target battery cells are in a voltage relaxation state.

[0074] In operation S101, controller 220 can determine whether a specific period of time has elapsed since the multiple target battery cells included in each of the multiple groups were in a voltage relaxation state. For example, in operation S101, controller 220 can determine whether 10 days have elapsed since the multiple target battery cells included in each of the multiple groups were in a voltage relaxation state.

[0075] In operation S102, the controller 220 can measure the OCV of each of the multiple target battery cells included in each of the multiple groups after a preset time period has elapsed.

[0076] In operation S102, the controller 220 can calculate the average OCV of multiple target battery cells included in each of the multiple groups. In operation S102, the controller 220 can calculate the average OCV of each of the multiple groups to which multiple target battery cells with similar degradation levels are grouped. In operation S102, for example, the controller 220 can classify 37 target battery cells into a first group G1, a second group G2, a third group G3, and a fourth group G4, and calculate the average OCV Vavg_1 for the first group G1, the average OCV Vavg_2 for the second group G2, the average OCV Vavg_3 for the third group G3, and the average OCV Vavg_4 for the fourth group G4.

[0077] In operation S103, the controller 220 can calculate the deviation dV of the OCV of each of the plurality of target battery cells relative to the average value of the OCV of each of the plurality of groups. For example, in operation S103, the controller 220 can calculate the deviation of the OCV of each of the plurality of target battery cells relative to the average value Vavg_1 of the OCV of the first group G1.

[0078] In operation S103, for example, when the current time is set to “T”, the controller 220 can calculate the deviation dV of the OCV of each of the multiple battery cells in the past times “T-4”, “T-3”, “T-2” and “T-1” and the current time “T”. In operation S103, the controller 220 can calculate: a first OCV deviation dv_“T-4”, that is, the deviation of the OCV of each target battery cell at time “T-4”; a second OCV deviation dv_“T-3”, that is, the deviation of the OCV of each target battery cell at time “T-3”; a third OCV deviation dv_“T-2”, that is, the deviation of the OCV of each target battery cell at time “T-2”; a fourth OCV deviation dv_“T-1”, that is, the deviation of the OCV of each target battery cell at time “T-1”; and a fifth OCV deviation dv_“T”, that is, the deviation of the OCV of each target battery cell at time “T”.

[0079] In operation S104, the controller 220 can calculate the change in the deviation of the OCV of each of the multiple target battery cells included in each of the multiple groups, ΔdV. Specifically, in operation S104, the controller 220 can continuously calculate the OCV of each of the multiple target battery cells in each specific time period to calculate the change in the deviation of the OCV of each of the multiple target battery cells calculated in the current time period relative to the deviation of the OCV of each of the multiple target battery cells calculated in a previous time period, ΔdV.

[0080] In operation S104, the controller 220 can continuously calculate the change in the deviation of the OCV of each of the multiple target battery cells, ΔdV, at each specific time period.

[0081] In operation S104, for example, the controller 220 may calculate a first voltage deviation change ΔdV_“T-3”, which is the change of a second OCV voltage deviation dV_“T-3”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-3”, relative to a first OCV deviation dV_“T-4”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-4”.

[0082] In operation S104, for example, the controller 220 may calculate a second voltage deviation change ΔdV_“T-2”, which is the change of the third OCV voltage deviation dV_“T-2”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-2”, relative to the second OCV deviation dV_“T-3”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-3”.

[0083] In operation S104, for example, the controller 220 may calculate a third voltage deviation change ΔdV_“T-1”, which is the change of the fourth OCV voltage deviation dV_“T-1”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-1”, relative to the third OCV deviation dV_“T-2”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-2”.

[0084] In operation S104, for example, the controller 220 can calculate a fourth voltage deviation change ΔdV_“T”, which is the change of the fifth OCV voltage deviation dV_“T”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T”, relative to the fourth OCV deviation dV_“T-1”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-1”.

[0085] In operation S105, the controller 220 can calculate a pattern for the change in the deviation of the OCV of each of the multiple target battery cells. In operation S105, for example, the controller 220 can calculate the change in the deviation of the OCV of each of the multiple target battery cells using a first voltage deviation change ΔdV_“T-3” as the change in the deviation of the OCV of each of the multiple target battery cells calculated at time “T-3”, a second voltage deviation change ΔdV_“T-2” as the change in the deviation of the OCV of each of the multiple target battery cells calculated at time “T-2”, a third voltage deviation change ΔdV_“T-1” as the change in the deviation of the OCV of each of the multiple target battery cells calculated at time “T-1”, and a fourth voltage deviation change ΔdV_“T” as the change in the deviation of the OCV of each of the multiple target battery cells calculated at time “T”.

[0086] In operation S106, the controller 220 can diagnose at least one target battery cell by using a pattern of the variation in the amount of deviation of the OCV of each of the plurality of target battery cells. Specifically, in operation S106, the controller 220 can diagnose at least one target battery cell based on at least one of the pattern of the variation in the amount of deviation of the OCV of each of the plurality of target battery cells, as well as the sum and magnitude of the variation in the OCV deviation.

[0087] In operation S106, according to an embodiment, the controller 220 can diagnose at least one target battery cell by comparing a pattern of changes in the deviation of the OCV of each of the plurality of target battery cells with a plurality of pre-stored diagnostic patterns. Hereinafter, the plurality of pre-stored diagnostic patterns may include a pattern for diagnosing the state of the battery cell by the sum of the changes in the deviation of the OCV of the target battery cell ΔdV, a pattern for diagnosing the state of the battery cell by the magnitude of each change in the deviation of the OCV of the target battery cell ΔdV, a pattern for diagnosing the state of the battery cell by the maximum or minimum magnitude of the change in the deviation of the OCV of the target battery cell ΔdV, and a pattern for diagnosing the state of the battery cell by the increasing or decreasing trend of the change in the deviation of the OCV of the target battery cell ΔdV.

[0088] When the pattern of the variation in the OCV deviation of at least one of the multiple target battery cells corresponds to any of the multiple diagnostic modes, the controller 220 can diagnose an internal short circuit in the corresponding target battery cell.

[0089] As a result of the diagnosis, when the controller 220 determines that an internal short circuit has occurred in a battery cell, the controller 220 can provide information about the battery cell to the user. For example, the controller 220 can provide information about the battery cell with an internal short circuit to the user terminal via a communication unit (not shown), and can also provide information about the battery cell via a display, charger, etc. installed in the vehicle.

[0090] As described above, the battery management device 200 according to the embodiments disclosed herein can accurately diagnose a battery cell that has an internal short circuit by means of a voltage deviation that reflects the difference between the state of charge (SOH) and the state of charge (SOH) of the battery cell.

[0091] Conventional battery management devices may misdiagnose battery cells with voltage deviations relative to the SOH difference by ignoring the SOH difference between battery cells. However, the battery management device 200 according to the embodiments disclosed herein can improve the accuracy of internal short circuit diagnosis by comparing battery cells with similar degrees of degradation.

[0092] In addition, the battery management device 200 can compare the changes in the deviations of multiple OCVs of each of the multiple battery cells to analyze the characteristics of the short-term and long-term voltage behavior of the battery cells.

[0093] The battery management device 200 can diagnose battery cells with internal short circuits at an early stage by using the amount of change in the deviation of the battery cell's OCV (Optical Characteristic Value), thereby ensuring the safety and reliability of battery energy. Furthermore, since it does not require removing the battery cell with the internal short circuit, the battery management device 200 can quickly and conveniently diagnose the battery cell while it is installed in the vehicle.

[0094] Figure 6 This is a flowchart illustrating an operation method of a battery management device according to an embodiment disclosed herein.

[0095] In the following text, reference will be made to Figures 1 to 5 Describe the operation method of the battery management device 200.

[0096] Battery management device 200 can be used with reference Figures 1 to 5 The battery management device 200 described is essentially the same, so it will be described briefly to avoid redundancy.

[0097] Reference Figure 6 The operation method of the battery management device may include the following operations: operation S201, which calculates the SOH of each of a plurality of batteries; operation S202, which calculates a first value, the first value being the deviation of the SOH of each of the plurality of batteries from the average value of the SOH of the plurality of batteries; operation S203, which identifies a plurality of target battery cells based on the first value of each of the plurality of batteries; operation S204, which divides the plurality of target battery cells into a plurality of groups based on the SOH; and operation S205, which diagnoses at least one target battery cell based on the deviation of the OCV between the plurality of target batteries included in each of the plurality of groups.

[0098] Operations S201 to S205 will be described in detail below.

[0099] In operation S201, the data management unit 210 can calculate the state of health (SOH) of each of the plurality of battery cells 110, 120, 130, and 140. SOH is an indicator of the battery's health or lifespan state in its current state compared to its initial state. In operation S201, for example, the data management unit 210 can calculate the SOH of the plurality of battery cells 110, 120, 130, and 140 based on at least any one of the following factors that change with the degradation of the plurality of battery cells 110, 120, 130, and 140: internal resistance, impedance, conductance, capacity, voltage, self-discharge current, charging performance, and number of charge and discharge cycles.

[0100] In operation S201, for example, the data management unit 210 can calculate a single SOH, i.e., the SOHC of each battery cell, by using the OCV and current integral values ​​of multiple battery cells 110, 120, 130 and 140.

[0101] In operation S201, specifically, the battery management device 200 can calculate the pre-charging OCV OCV_A and post-charging OCV OCV_B of multiple battery cells 110, 120, 130, and 140. In operation S201, the data management unit 210 can calculate SOC_A and SOC_B by converting OCV_A and OCV_B into charge quantity, i.e., SOC, based on the OCV table. The battery management device 200 can calculate the individual SOH of the multiple battery cells 110, 120, 130, and 140 based on [Equation 1].

[0102] [Formula 1]

[0103] SOHC=I / ((SOC_B-SOC_A) / 100*X)*100

[0104] Here, (SOC_B-SOC_A) can represent the SOC deviation, I can represent the cumulative charging current, and X can represent the capacity of the existing battery cell. In operation S201, the data management unit 210 can calculate the individual SOH, i.e., SOHC, of ​​each of the multiple battery cells 110, 120, 130, and 140 based on [Equation 1].

[0105] In operation S201, the data management unit 210 can calculate the SOOH (Sodium Oxide Hydrogen) of each of the multiple battery cells.

[0106] In operation S201, according to the embodiment, the battery pack 1000 may include a stacked structure of four battery modules 100, and each of the multiple battery modules 100 may include 10 battery cells connected in series or parallel. That is, the battery pack 1000 may include, for example, 40 battery cells, wherein each of the four battery modules can be assigned a unique battery module number, and each of the 40 battery cells included in the battery modules can be assigned a unique battery cell number. In operation S201, the data management unit 210 can calculate the SOH (Solar OH) of each of the 40 battery cells included in the battery pack 1000.

[0107] In operation S202, the controller 220 can calculate the average SOH of the 40 battery cells included in the battery module 100.

[0108] In operation S202, the controller 220 can calculate a first value, which is the deviation of the SOH of each of the plurality of battery cells from the average SOH of the plurality of battery cells.

[0109] In operation S203, the controller 220 can identify a battery among the plurality of battery cells 110, 120, 130 and 140 that has a first value less than a threshold as a plurality of target battery cells.

[0110] In operation S203, the controller 220 can identify a battery cell among the plurality of battery cells 110, 120, 130 and 140 that has a first value exceeding a threshold as a noisy battery cell, remove the data of the noisy battery cell, and identify the battery cell with the noisy battery cell removed as the target battery cell.

[0111] In operation S203, for example, controller 220 can identify 37 of the 40 battery cells as target battery cells, excluding 3 noisy battery cells that have a first value exceeding a threshold of "2%": the first value is the deviation of the SOH of the battery cell from the average SOH of the battery cells, which is "98.39%".

[0112] In operation S204, the controller 220 can divide multiple target battery cells into multiple groups based on their State of Health (SOH). Specifically, in operation S204, the controller 220 can list multiple target battery cells based on their SOH values ​​and divide them into multiple groups according to the listing order. In operation S204, according to an embodiment, the controller 220 can list the multiple target battery cells in descending order of SOH. That is, the controller 220 can list the multiple target battery cells in descending order of SOH.

[0113] In operation S204, controller 220 can list multiple target battery cells based on SOH, divide battery cells with similar SOH into multiple groups, and perform diagnostics. For example, in operation S204, controller 220 can list 37 target battery cells based on their SOH and divide them into 4 groups, each group containing 8 to 10 battery cells.

[0114] In operation S205, controller 220 can diagnose at least one target battery cell based on the deviation of OCV between multiple target battery cells included in each of the multiple groups.

[0115] In operation S205, the controller 220 can determine whether the OCV of the multiple target battery cells included in each of the multiple groups is in a relaxed state. Here, voltage relaxation can refer to the phenomenon that when the battery enters an idle or no-load state, a potential difference appears between multiple positive electrode materials, and the working ions move between the positive electrode materials due to this potential difference, thereby eliminating the potential difference over time.

[0116] In operation S205, controller 220 can determine whether a specific period of time has elapsed after the multiple target battery cells included in each of the multiple groups have been in a voltage relaxation state. For example, in operation S205, controller 220 can determine whether 10 days have elapsed after the multiple target battery cells included in each of the multiple groups have been in a voltage relaxation state.

[0117] In operation S205, the controller 220 can measure the OCV of each of the multiple target battery cells included in each of the multiple groups after a preset time period has elapsed.

[0118] In operation S205, the controller 220 can calculate the average OCV of multiple target battery cells included in each of the multiple groups. That is, the controller 220 can calculate the average OCV of each of the multiple groups to which multiple target battery cells with similar degradation levels are grouped. In operation S205, for example, the controller 220 can classify 37 target battery cells into a first group G1, a second group G2, a third group G3, and a fourth group G4, and calculate the average OCV Vavg_1 of the first group G1, the average OCV Vavg_2 of the second group G2, the average OCV Vavg_3 of the third group G3, and the average OCV Vavg_4 of the fourth group G4.

[0119] In operation S205, the controller 220 can calculate the deviation dV of the OCV of each of the plurality of target battery cells relative to the average value of the OCV of each of the plurality of groups. For example, in operation S205, the controller 220 can calculate the deviation of the OCV of each of the plurality of target battery cells relative to the average value Vavg_1 of the OCV of the first group G1.

[0120] In operation S205, the controller 220 can calculate the change in the deviation of the OCV of each of the multiple target battery cells included in each of the multiple groups, ΔdV. In operation S205, the controller 220 can continuously calculate the OCV of each of the multiple target battery cells in each specific time period to calculate the change in the deviation of the OCV of each of the multiple target battery cells calculated in the current time period relative to the deviation of the OCV of each of the multiple target battery cells calculated in a previous time period, ΔdV. In operation S205, the controller 220 can continuously calculate the change in the deviation of the OCV of each of the multiple target battery cells in each specific time period, ΔdV.

[0121] In operation S205, for example, when the current time is set to “T”, the controller 220 can calculate the deviation dV of the OCV of each of the multiple battery cells in the past times “T-4”, “T-3”, “T-2” and “T-1” and the current time “T”.

[0122] In operation S205, the controller 220 can calculate: a first OCV deviation dv_“T-4”, that is, the deviation of the OCV of each target battery cell at time “T-4”; a second OCV deviation dv_“T-3”, that is, the deviation of the OCV of each target battery cell at time “T-3”; a third OCV deviation dv_“T-2”, that is, the deviation of the OCV of each target battery cell at time “T-2”; a fourth OCV deviation dv_“T-1”, that is, the deviation of the OCV of each target battery cell at time “T-1”; and a fifth OCV deviation dv_“T”, that is, the deviation of the OCV of each target battery cell at time “T”.

[0123] In operation S205, the controller 220 can calculate a first voltage deviation change ΔdV_“T-3”, which is the change of a second OCV voltage deviation dV_“T-3”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-3”, relative to a first OCV deviation dV_“T-4”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-4”.

[0124] In operation S205, the controller 220 can calculate the second voltage deviation change ΔdV_“T-2”, which is the change of the third OCV voltage deviation dV_“T-2”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-2”, relative to the second OCV deviation dV_“T-3”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-3”.

[0125] In operation S205, the controller 220 can calculate a third voltage deviation change ΔdV_“T-1”, which is the change of the fourth OCV voltage deviation dV_“T-1”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-1”, relative to the third OCV deviation dV_“T-2”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-2”.

[0126] In operation S205, the controller 220 can calculate the fourth voltage deviation change ΔdV_“T”, which is the change of the fifth OCV voltage deviation dV_“T”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T”, relative to the fourth OCV deviation dV_“T-1”, which is the deviation of the OCV of each of the plurality of target battery cells calculated at time “T-1”.

[0127] In operation S205, the controller 220 can calculate a pattern for the change in the deviation of the OCV of each of the multiple target battery cells. For example, the controller 220 can calculate the change in the deviation of the OCV of each of the multiple target battery cells using a first voltage deviation change ΔdV_“T-3” as the change in the deviation of the OCV of each of the multiple target battery cells calculated at time “T-3”, a second voltage deviation change ΔdV_“T-2” as the change in the deviation of the OCV of each of the multiple target battery cells calculated at time “T-2”, a third voltage deviation change ΔdV_“T-1” as the change in the deviation of the OCV of each of the multiple target battery cells calculated at time “T-1”, and a fourth voltage deviation change ΔdV_“T” as the change in the deviation of the OCV of each of the multiple target battery cells calculated at time “T”.

[0128] In operation S205, controller 220 can diagnose at least one target battery cell based on at least one of the patterns of the amount of change in the OCV deviation of each of the plurality of target battery cells, as well as the sum and magnitude of the amount of change in the OCV deviation.

[0129] In operation S205, according to the embodiment, the controller 220 can diagnose at least one target battery cell by comparing a pattern of changes in the deviation of the OCV of each of the plurality of target battery cells with a plurality of pre-stored diagnostic patterns. Hereinafter, the plurality of pre-stored diagnostic patterns may include a pattern for diagnosing the state of the battery cell by the sum of the changes in the deviation of the OCV of the target battery cell ΔdV, a pattern for diagnosing the state of the battery cell by the magnitude of each change in the deviation of the OCV of the target battery cell ΔdV, a pattern for diagnosing the state of the battery cell by the maximum or minimum magnitude of the change in the deviation of the OCV of the target battery cell ΔdV, and a pattern for diagnosing the state of the battery cell by the increasing or decreasing trend of the change in the deviation of the OCV of the target battery cell ΔdV.

[0130] In operation S205, when the pattern of the change in the OCV deviation of at least one of the multiple target battery cells corresponds to any one of the multiple diagnostic modes, the controller 220 can diagnose that an internal short circuit has occurred in the corresponding target battery cell.

[0131] In operation S205, as a result of the diagnosis, when the controller 220 determines that an internal short circuit has occurred in the battery cell, the controller 220 can provide information about the battery cell to the user. In operation S205, for example, the controller 220 can provide information about the battery cell with the internal short circuit to the user terminal via a communication unit (not shown), and also provide information about the battery cell via a display, charger, etc., installed in the vehicle.

[0132] Figure 7 This is a block diagram illustrating the hardware configuration of a computing system for performing an operation method of a battery management device according to an embodiment disclosed herein.

[0133] Reference Figure 7 The computing system 2000 according to the embodiments disclosed herein may include an MCU 2100, a memory 2200, an input / output I / F 2300, and a communication I / F 2400.

[0134] The MCU 2100 can be a processor that executes various programs (e.g., battery voltage change analysis programs) stored in the memory 2200, processes various data through these programs, and performs other tasks. Figure 1 The battery management device 200 shown above performs the aforementioned functions.

[0135] The memory 2200 can store various programs related to the operation of the battery management device 200. Furthermore, the memory 2200 can store operational data of the battery management device 200.

[0136] Multiple memory units 2200 can be configured as needed. Memory units 2200 can be volatile or non-volatile. For memory units 2200 used as volatile memory, random access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), etc., can be used. For memory units 2200 used as non-volatile memory, read-only memory (ROM), programmable ROM (PROM), electrically variable ROM (EAROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), flash memory, etc., can be used. The examples of memory units 2200 described above are merely examples and are not limited to these.

[0137] The Input / Output I / F 2300 provides an interface for sending and receiving data by connecting input devices (not shown) such as a keyboard, mouse, or touchscreen and output devices such as a display (not shown) to the MCU 2100.

[0138] The Communication I / F 2400 is a component capable of sending and receiving various types of data to and from a server. It can be any device capable of supporting wired or wireless communication. For example, programs for measuring the resistance of battery cells and diagnosing malfunctions, or various data, can be sent to and received from a separately configured external server via the Communication I / F 2400.

[0139] The above description merely illustrates the technical concept of this disclosure, and those skilled in the art to which this disclosure pertains can make various modifications and changes without departing from the essential characteristics of this disclosure.

[0140] Therefore, the embodiments disclosed herein are intended to describe, and not limit, the technical spirit of this disclosure, and the scope of the technical spirit of this disclosure is not limited by these embodiments. The scope of protection of this disclosure should be interpreted by the appended claims, and all technical spirit within the same scope should be understood to be included within the scope of this disclosure.

[0141] [Symbol Explanation]

[0142] 1000: Battery pack

[0143] 100: Battery Module

[0144] 110: First battery cell

[0145] 120: Second battery cell

[0146] 130: Third battery cell

[0147] 140: Fourth battery cell

[0148] 200: Battery Management Device

[0149] 210: Data Management Unit

[0150] 220: Controller

[0151] 300: Relay

[0152] 2000: Computing Systems

[0153] 2100: MCU

[0154] 2200: Memory

[0155] 2300: Input / Output I / F

[0156] 2400: Communication I / F

Claims

1. A battery management device, the battery management device comprising: A data management unit configured to calculate the degree of degradation (state of health (SOH)) of each of a plurality of batteries; as well as The controller is configured to: Multiple target batteries are identified based on a first value, which is the deviation of the SOH of each of the multiple batteries from the average SOH of the multiple batteries; Based on the SOH of the multiple target batteries, the multiple target batteries are divided into multiple groups; as well as At least one target cell is diagnosed based on the deviation of the open-circuit voltage (OCV) among the multiple target cells included in each of the multiple groups.

2. The battery management device according to claim 1, wherein, The controller is further configured to identify a battery among the plurality of batteries that has a first value less than a threshold as one of the plurality of target batteries.

3. The battery management device according to claim 2, wherein, The controller is further configured to divide the plurality of target batteries into multiple groups in descending order of their SOH.

4. The battery management device according to claim 3, wherein, The controller is further configured to: calculate the deviation of the OCV of each of the plurality of target batteries included in each of the plurality of groups from the average value of the OCV of the plurality of target batteries, and calculate the amount of change in the deviation of the OCV of each of the plurality of target batteries included in each of the plurality of groups.

5. The battery management device according to claim 4, wherein, The controller is further configured to: calculate a pattern of the change in the deviation of the OCV of each of the plurality of target batteries by calculating the change in the deviation of the OCV of each of the plurality of target batteries in each specific time period, and diagnose at least one target battery by comparing the pattern of the change in the deviation of the OCV of each of the plurality of target batteries with a plurality of diagnostic patterns.

6. The battery management device according to claim 5, wherein, The controller is further configured to diagnose the at least one target battery when the pattern of the change in the amount of deviation of the OCV of at least one of the plurality of target batteries corresponds to any of the plurality of diagnostic modes.

7. A method for operating a battery management device, the method comprising the following steps: Calculate the degree of degradation (state of health (SOH)) of each of the multiple batteries; Calculate a first value, which is the deviation of the SOH of each of the plurality of batteries from the average SOH of the plurality of batteries; Multiple target batteries are identified based on the first value of each of the plurality of batteries; Based on the SOH of the multiple target batteries, the multiple target batteries are divided into multiple groups; as well as At least one target cell is diagnosed based on the deviation of the open-circuit voltage (OCV) among the multiple target cells included in each of the multiple groups.

8. The operating method according to claim 7, wherein, The step of identifying a plurality of target batteries based on the first value of each of the plurality of batteries includes the following steps: identifying batteries among the plurality of batteries that have a first value less than a threshold as the plurality of target batteries.

9. The operating method according to claim 8, wherein, The step of dividing the multiple target batteries into multiple groups based on their State of Health (SOH) includes the following steps: dividing the multiple target batteries into multiple groups in descending order of their SOH values.

10. The operating method according to claim 9, wherein, The step of diagnosing at least one target battery based on the deviation of OCV among the multiple target batteries included in each of the multiple groups includes the following steps: calculating the deviation of the OCV of each of the multiple target batteries included in each of the multiple groups relative to the average value of the OCV of the multiple target batteries, and calculating the amount of change in the deviation of the OCV of each of the multiple target batteries included in each of the multiple groups.

11. The operating method according to claim 10, wherein, The step of diagnosing at least one target battery based on the deviation of OCV among the multiple target batteries included in each of the multiple groups includes the following steps: calculating a pattern of the change in the deviation of OCV of each of the multiple target batteries by calculating the change in the deviation of OCV of each of the multiple target batteries in each specific time period, and diagnosing at least one target battery by comparing the pattern of the change in the deviation of OCV of each of the multiple target batteries with multiple diagnostic patterns.

12. The operating method according to claim 11, wherein, The step of diagnosing at least one target battery based on the deviation of OCV among the plurality of target batteries included in each of the plurality of groups includes the following steps: diagnosing the at least one target battery when the pattern of the change in the amount of OCV deviation of at least one of the plurality of target batteries corresponds to any one of the plurality of diagnostic patterns.

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