Battery management device and battery management method

Abstract

A battery management apparatus includes an interface configured to obtain battery data of a management target battery measured at a plurality of degradation points, and a controller. The controller is configured to generate, based on the battery data, a first state of charge - open circuit voltage (SOC - OCV) curve at a beginning of life (BOL) point and voltage - capacity curves of the management target battery at the plurality of degradation points, identify, based on the voltage - capacity curves, a boundary voltage that differentiates an upper end voltage degradation characteristic having an upper end capacity degradation rate and a lower end voltage degradation characteristic having a lower end capacity degradation rate, and estimate, based on the upper end capacity degradation rate, the lower end capacity degradation rate, and the first SOC - OCV curve, a second SOC - OCV curve at a middle of life (MOL) point.

Description

Technical Field

[0001] Cross-references to related applications

[0002] This application claims priority to and is based on Korean Patent Application No. 10-2025-0001447 filed with the Korean Intellectual Property Office on January 6, 2025, and Korean Patent Application No. 10-2024-0048834 filed with the Korean Intellectual Property Office on April 11, 2024, the disclosures of which are incorporated herein by reference in their entirety. Technical Field

[0004] The embodiments disclosed herein relate to a battery management device and a battery management method. Background Technology

[0005] Recently, research and development of rechargeable batteries have been actively underway. Here, rechargeable batteries are batteries that can be recharged and can be interpreted as encompassing both traditional batteries such as Ni / Cd and Ni / MH batteries, and more recently, lithium-ion batteries. Among rechargeable batteries, lithium-ion batteries can have higher energy densities than, for example, traditional Ni / Cd and Ni / MH batteries, and can be manufactured in a small and lightweight manner, providing high versatility as a power source for mobile devices. Recently, lithium-ion batteries have expanded their applications as a power source for electric vehicles and are gaining attention as a next-generation energy storage medium. Summary of the Invention

[0006] Technical issues

[0007] The embodiments disclosed herein provide a battery management device and a battery management method capable of estimating the state-of-charge-open-circuit voltage (SOC-OCV) curve altered by degradation in nickel-cobalt-manganese (NCM) cells with atypical composition ratios.

[0008] Technical solution

[0009] According to embodiments of this disclosure, a battery management device includes: an interface configured to acquire battery data of a target battery being managed, measured at multiple degradation points; and a controller. The controller is configured to: generate a first state-of-charge-open-circuit voltage (SOC-OCV) curve at a beginning-of-life (BOL) point and voltage-capacity curves of the target battery being managed at the multiple degradation points, based on the battery data; identify boundary voltages based on the voltage-capacity curves, distinguishing between an upper-end voltage degradation characteristic with an upper capacity degradation rate and a lower-end voltage degradation characteristic with a lower capacity degradation rate; and estimate a second SOC-OCV curve at a middle-of-life (MOL) point based on the upper capacity degradation rate, the lower capacity degradation rate, and the first SOC-OCV curve.

[0010] The battery data includes low-rate discharge data measured at multiple degradation points by low-rate discharge at approximately 0.1C or lower, and the controller is further configured to generate voltage-capacity curves based on the low-rate discharge data at multiple degradation points.

[0011] The controller is further configured to: generate a dQ / dV curve based on low-rate discharge data, the dQ / dV curve representing the differential capacity value of the target battery relative to voltage at multiple degradation points; and generate a voltage-capacity curve based on the dQ / dV curve at the multiple degradation points.

[0012] The controller is further configured to identify boundary voltages based on a pattern of the dQ / dV curve.

[0013] The controller is further configured to apply the upper capacity degradation rate to the first SOC-OCV curve in the upper interval of the boundary voltage and apply the lower capacity degradation rate to the first SOC-OCV curve in the lower interval of the boundary voltage to generate a corrected SOC-OCV curve at the MOL point.

[0014] The controller is further configured to: perform interpolation on the corrected SOC-OCV curve relative to the SOC unit to generate an interpolated SOC-OCV curve; and apply an open-circuit voltage (OCV) offset to the interpolated SOC-OCV curve to estimate a second SOC-OCV curve.

[0015] The target batteries for management include NCM batteries containing nickel, cobalt, and manganese, and the upper and lower voltage degradation characteristics are due to the constituent elements of the NCM batteries.

[0016] The upper voltage degradation characteristics are determined based on the capacity degradation caused by the redox reaction of nickel and cobalt, while the lower voltage degradation characteristics are determined based on the capacity manifestation caused by the redox reaction of manganese.

[0017] According to embodiments of this disclosure, a battery management method includes: acquiring battery data of a target battery measured at multiple degradation points; generating a first state-of-charge-open-circuit voltage (SOC-OCV) curve at a beginning-of-life (BOL) point and a voltage-capacity curve of the target battery at the multiple degradation points based on the battery data; identifying a boundary voltage based on the voltage-capacity curve, the boundary voltage distinguishing between an upper voltage degradation characteristic with an upper capacity degradation rate and a lower voltage degradation characteristic with a lower capacity degradation rate; and estimating a second SOC-OCV curve at a middle-of-life (MOL) point based on the upper capacity degradation rate, the lower capacity degradation rate, and the first SOC-OCV curve.

[0018] The battery data includes low-rate discharge data measured at low rates of approximately 0.1C or lower at multiple degradation points, and the voltage-capacity profile is generated based on the low-rate discharge data at multiple degradation points.

[0019] Generating voltage-capacity curves includes: generating dQ / dV curves based on low-rate discharge data, which represent the differential capacity values ​​of the target battery relative to voltage at multiple degradation points; and generating voltage-capacity curves based on the dQ / dV curves at multiple degradation points.

[0020] Identifying boundary voltages includes using patterns based on the dQ / dV curve.

[0021] Estimating the second SOC-OCV curve involves applying the upper capacity degradation rate to the first SOC-OCV curve in the upper interval of the boundary voltage and applying the lower capacity degradation rate to the first SOC-OCV curve in the lower interval of the boundary voltage to generate a corrected SOC-OCV curve at the MOL point.

[0022] Estimating the second SOC-OCV curve includes: interpolating the corrected SOC-OCV curve relative to the SOC unit to generate the interpolated SOC-OCV curve; and applying an open-circuit voltage (OCV) offset to the interpolated SOC-OCV curve to estimate the second SOC-OCV curve.

[0023] The target batteries for management include NCM batteries containing nickel, cobalt, and manganese, and the upper and lower voltage degradation characteristics are due to the constituent elements of the NCM batteries.

[0024] The upper voltage degradation characteristics are determined based on the capacity degradation caused by the redox reaction of nickel and cobalt, while the lower voltage degradation characteristics are determined based on the capacity performance caused by the redox reaction of manganese.

[0025] According to embodiments of this disclosure, a non-transitory computer-readable storage medium stores a program that, when executed, causes a computer to perform a method. The method includes: acquiring battery data of a managed target battery measured at multiple degradation points; generating, based on the battery data, a first state-of-charge-open-circuit voltage (SOC-OCV) curve at a beginning-of-life (BOL) point and voltage-capacity curves of the managed target battery at the multiple degradation points; identifying boundary voltages based on the voltage-capacity curves, the boundary voltages distinguishing between an upper-end voltage degradation characteristic with an upper capacity degradation rate and a lower-end voltage degradation characteristic with a lower capacity degradation rate; and estimating a second SOC-OCV curve at a middle-of-life (MOL) point based on the upper capacity degradation rate, the lower capacity degradation rate, and the first SOC-OCV curve.

[0026] Beneficial effects

[0027] The embodiments disclosed herein can provide a battery management device and a battery management method capable of estimating the SOC-OCV curves altered by degradation in NCM cells with atypical component ratios.

[0028] The technical effects of the embodiments disclosed herein are not limited to those mentioned above, and those skilled in the art will clearly understand other effects not mentioned above based on the disclosure of this document. Attached Figure Description

[0029] The accompanying drawings are merely illustrative of embodiments of this disclosure and serve to further aid in understanding the technical concepts and description herein. Therefore, this disclosure should not be construed as limited to those illustrated in the drawings.

[0030] Figure 1 This is a view illustrating the components of a battery management system according to some embodiments.

[0031] Figure 2 This is a view illustrating the elements of a battery management device according to some embodiments.

[0032] Figure 3 This is a graph illustrating the SOC-OCV curves in a traditional NCM battery cell, showing how degradation occurs or even remains unchanged.

[0033] Figure 4 This is a graph illustrating the capacity degradation that occurs uniformly across the entire voltage range in a traditional NCM battery cell.

[0034] Figure 5 This is a graph illustrating how the SOC-OCV curve of a manganese-rich NCM battery cell changes due to degradation, according to some embodiments.

[0035] Figure 6 This is a graph illustrating the capacity degradation that occurs differently in the low-voltage and high-voltage regions of a manganese-rich NCM battery cell according to some embodiments.

[0036] Figure 7 This is a view illustrating the change in the SOC-OCV curve of a manganese-rich NCM battery cell from the BOL point to the MOL point according to some embodiments.

[0037] Figure 8 This is a view illustrating the change in the difference between the upper and lower voltages based on boundary voltage due to battery degradation, according to some embodiments.

[0038] Figure 9This is a graph illustrating a comparison between an estimated value of the second SOC-OCV curve according to some embodiments and the actual measured value at the MOL point.

[0039] Figure 10 This is a table illustrating the process of generating a second SOC-OCV curve according to some embodiments.

[0040] Figure 11 This is a flowchart illustrating the steps of a battery management method according to some embodiments. Detailed Implementation

[0041] In the following description, embodiments described herein will be illustrated with reference to the accompanying drawings. However, this is not intended to limit the present disclosure to the specific embodiments, but should be understood to include various modifications, equivalents, and / or alternatives to the embodiments described herein.

[0042] The embodiments described herein and the terminology used are not intended to limit the technical features described herein to the specific embodiments, but should be understood to include various modifications, equivalents, or alternatives to the embodiments. Similar reference numerals may be used for similar or related components in relation to the description of the drawings. Unless the relevant context clearly indicates otherwise, the singular form of the noun corresponding to an item may include one or more instances of that item.

[0043] In this document, 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 one of the items listed together in the corresponding phrase or all possible combinations of these items. Terms such as “first,” “second,” “primary,” “secondary,” “A,” “B,” “(a),” or “(b)” may be used to distinguish a corresponding component from other corresponding components and, unless otherwise expressly stated, do not otherwise limit the components (e.g., in terms of importance or order).

[0044] In this document, when a component (e.g., the first) is described as being “connected,” “coupled,” or “linked” to another component (e.g., the second), whether or not these terms are used, it means that a component can be connected to another component directly (e.g., via wired or wireless) or indirectly (e.g., via a third component).

[0045] The methods according to the various embodiments disclosed herein can be included in and provided as a computer program product. This computer program product can be traded between a seller and a buyer. The computer program product can be distributed in the form of a machine-readable storage medium (e.g., a read-only optical disc (CD-ROM)), or distributed online (e.g., downloaded or uploaded) through an app store, or directly distributed between two drive devices. In the case of online distribution, at least a portion of the computer program product can be temporarily stored or generated in a machine-readable storage medium such as the memory of a manufacturer's server, an app store's server, or a relay server.

[0046] According to the embodiments disclosed herein, each of the above-described components (e.g., modules or programs) may include a single entity or multiple entities, and some of the multiple entities may be placed separately from the other components. According to the embodiments disclosed herein, one or more of the above-described components or operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (e.g., modules or programs) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as performed by the corresponding component among the multiple components prior to integration.

[0047] According to the embodiments disclosed herein, operations performed by modules, programs or other components may be performed sequentially, in parallel, repeatedly or heuristically, and one or more of the operations may be performed in different orders or omitted, or one or more other operations may be added.

[0048] The term “and / or” includes a combination of multiple related listed items or any one of multiple related listed items.

[0049] As used in this specification, the terms “approximately,” “about,” and “substantially” should be understood to refer to a range or approximation of a numerical or degree, taking into account inherent manufacturing and material tolerances.

[0050] NCM cell cells can contain nickel, cobalt, and manganese as cathode materials. For example, the cathode of an NCM cell can include LiNi. x Mn y Co z O2, where x + y + z = 1. Unlike this traditional NCM monomer, manganese-rich (Mn) monomers with higher Mn content and additionally including Li2MnO3 as the cathode material can be used. That is, manganese-rich monomers can include Li2MnO3-LiNi. x' Mn y' Co z'O2, where x' + y' + z' = 1, the ratio of Mn can be high, while the ratio of Co can be extremely low.

[0051] In conventional NCM cells, the mapping between state of charge (SOC) and open-circuit voltage (OCV) may not be significantly affected by cell degradation. Therefore, in the case of conventional NCM cells, the battery management system (BMS) control algorithm can operate normally even when applying the SOC-OCV curve established at the beginning of life (BOL) during the mid-life (MOL). However, in the case of manganese-rich cells, the SOC-OCV mapping may change due to cell degradation, leading to the problem that using the SOC-OCV curve at the BOL point may affect the BMS control algorithm. Considering these issues, this disclosure provides a battery management device and battery management method capable of estimating the SOC-OCV curve that changes due to, for example, cell degradation in manganese-rich NCM battery cells.

[0052] Figure 1 The illustration shows elements of a BMS according to some embodiments.

[0053] refer to Figure 1 System 100 may include power consumption device 110, target management battery 120, and battery management device 130. However, this disclosure is not limited thereto, and some components may be omitted from system 100, or additional general components may be further included in system 100.

[0054] Power-consuming device 110 can be configured to charge or discharge target managed battery 120. Power-consuming device 110 can discharge target managed battery 120 by consuming power and can charge target managed battery 120 by generating power. According to embodiments, power-consuming device 110 may include mobile devices such as electric vehicles (EVs), hybrid electric vehicles (HEVs), or electric bicycles. The mobile device may drive a motor based on power from target managed battery 120 or charge target managed battery 120 using power generated by regenerative braking.

[0055] The target battery 120 may include, for example, a battery pack that has undergone diagnostics in system 100. The battery pack of the target battery 120 may include multiple battery modules, and each battery module may include multiple individual battery cells. According to embodiments, the target battery 120 may be installed in various types of mobile devices.

[0056] Battery management device 130 can perform operations for diagnosing or managing target battery 120. Battery management device 130 can acquire battery data from target battery 120 and diagnose or manage the state of target battery 120 based on the acquired battery data. According to embodiments, battery management device 130 may include an onboard BMS configured to manage target battery 120, and / or an offboard external device located away from target battery 120. External devices may include, for example, a charger at a battery charging station, battery diagnostic equipment, or a cloud computing server.

[0057] System 100 may further include a management server 140. Management server 140 can manage the management results of battery management device 130. Management server 140 can exchange data with battery management device 130 via wired or wireless communication. When a defect is diagnosed in the target battery 120 or the lifespan of the target battery 120 is predicted, the result can be sent to management server 140 and recorded in a database. According to an embodiment, battery management device 130 can perform diagnostic operations by executing battery management software, and management server 140 can provide battery management device 130 with update information for the battery management software.

[0058] Figure 2 This is a view illustrating the components of a battery management device 130 according to some embodiments.

[0059] refer to Figure 2 The battery management device 130 may include an interface 131 and a controller 132. However, this disclosure is not limited thereto, and some components may be omitted from the battery management device 130, or other general components may be further included in the battery management device 130.

[0060] Interface 131 can acquire battery data from the target battery 120. According to an embodiment, interface 131 may include a communication unit 131-1 configured to receive battery data and / or a sensor unit 131-2 configured to measure battery data. According to an embodiment, when the battery management device 130 is implemented in a non-airborne form, the communication unit 131-1 can receive battery data via methods such as wired or wireless data communication. Alternatively, when the battery management device 130 is implemented in an airborne form, the sensor unit 131-2 may be configured to measure values ​​such as voltage, current, temperature, and resistance from the target battery 120.

[0061] The controller 132 may have a structure for executing instructions that implement the operation of the battery management device 130. The controller 132 may be implemented as a plurality of logic gate arrays or a general-purpose microprocessor for handling various operations, and may be configured with a single processor or multiple processors. For example, the controller 132 may be implemented in the form of at least one of a microprocessor, a central processing unit (CPU), a graphics processing unit (GPU), and an access point (AP).

[0062] The controller 132 can work in conjunction with a memory configured to store, for example, various data, commands, mobile applications, and computer programs. The memory can store operational data related to the operation of the system 100, required for the operation of the controller 132. Multiple memory cells can be provided if necessary. The memory can be configured separately from or integrated with the controller 132. The controller 132 can execute commands stored in the memory to perform various calculations. For example, the memory can be implemented as a non-volatile device, such as a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), flash memory, phase-change random access memory (PRAM), magnetoresistive random access memory (MRAM), resistive random access memory (RRAM), and ferroelectric random access memory (FRAM), or a volatile device, such as dynamic random access memory (DRAM), static random access memory (SRAM), and synchronous dynamic random access memory (SDRAM). In addition, the memory can be implemented in the form of, for example, hard disk drive (HDD), solid-state drive (SSD), secure digital card (SD), micro SD card, or a combination thereof.

[0063] Interface 131 can be configured to acquire battery data of the managed target battery 120 measured at multiple degradation points. The multiple degradation points may include points with periodic time intervals and / or specific charge-discharge cycles, starting from a BOL point. The multiple degradation points may include MOL points, for example, which may correspond to the time after 100 charge-discharge cycles have been completed. The battery data may include parameters of the managed target battery 120 such as voltage, current, temperature, and resistance. Interface 131 may include a communication unit 131-1 and / or a sensor unit 131-2, wherein the battery data can be received via the communication unit 131-1 or measured via the sensor unit 131-2.

[0064] The controller 132 can be configured to generate a first SOC-OCV curve at the start of life (BOL) point and voltage-capacity curves for the target battery 120 at multiple degradation points, based on battery data. The first SOC-OCV curve at the BOL point can be generated in tabular or linear form based on a mapping between SOC values ​​and their respective unit OCV values. For example, unit OCV values ​​can be formed at 10% intervals, such as 100%, 90%, 80%, etc., and specific values ​​can be changed as needed. The voltage-capacity curves can include, for example, graphs or linear curves showing how capacity (Q) and / or the rate of change of capacity relative to voltage (dQ / dV) changes with changes in cell voltage (V). For details regarding the voltage-capacity curves, please refer to the description below. Figure 6 .

[0065] Controller 132 can be configured to identify boundary voltages based on voltage-capacity curves, distinguishing between upper voltage degradation characteristics with an upper capacity degradation rate and lower voltage degradation characteristics with a lower capacity degradation rate. When voltage-capacity curves at multiple degradation points are displayed together, the degradation trend may differ based on a specific voltage, which can be selected as the boundary voltage. Upper voltage degradation characteristics can be observed in the upper voltage region above the boundary voltage, while lower voltage degradation characteristics can be observed in the lower voltage region below the boundary voltage. As battery degradation progresses from the BOL point to the MOL point, the upper voltage region may degrade at the upper capacity degradation rate, while the lower voltage region may degrade at the lower capacity degradation rate. For details regarding boundary voltages, please refer to the description below. Figure 6 .

[0066] The controller 132 can be configured to estimate a second SOC-OCV curve at the mid-life (MOL) point based on the upper capacity degradation rate, the lower capacity degradation rate, and the first SOC-OCV curve. Because the upper and lower voltage regions exhibit different degradation characteristics, it may be necessary to distinguish between the upper and lower voltage regions and apply the upper and lower capacity degradation rates separately to estimate the second SOC-OCV curve at the MOL point. After applying the upper and lower capacity degradation rates to the first SOC-OCV curve, an additional correction process can be performed to generate the second SOC-OCV curve at the MOL point.

[0067] According to an embodiment, battery data may include low-rate discharge data measured by discharging the target battery 120 at a low rate of approximately 0.1C or lower at multiple degradation points, and controller 132 may be configured to generate a voltage-capacity curve based on the low-rate discharge data at the multiple degradation points. For example, Figure 6The voltage-capacity curve shown can be measured based on low-rate discharge, which can be performed with a discharge current of approximately 0.1C or lower. For example, the low-rate discharge current can be approximately 0.05C or approximately 0.033C. At several degradation points, parameters such as voltage, current, temperature, and resistance during low-rate discharge can be measured, and based on these parameters, parameters such as capacity (Q) and dQ / dV can be calculated.

[0068] According to an embodiment, controller 132 can be configured to generate dQ / dV curves representing differential capacity values ​​(dQ / dV) relative to voltage at multiple degradation points based on low-rate discharge data, and to generate voltage-capacity curves based on the dQ / dV curves at the multiple degradation points. Through low-rate discharge, parameters such as voltage, current, temperature, and resistance at multiple degradation points can be measured, and a calculation algorithm can be used to calculate the capacity (Q) value based on these measurements. The dQ / dV value can be calculated by differentiating the capacity (Q) value relative to voltage, and the dQ / dV curves can be generated by plotting the dQ / dV values ​​relative to voltage (V). The voltage-capacity curves may include these dQ / dV curves corresponding to the multiple degradation points.

[0069] According to an embodiment, the controller 132 can be configured to identify boundary voltages based on the pattern of the dQ / dV curve. For example, regions where the dQ / dV curve shows an increasing pattern for multiple degradation points and regions where the dQ / dV curve shows a decreasing pattern can be distinguished, and voltage values ​​near the locations where this distinction occurs can be set as boundary voltages.

[0070] According to an embodiment, the controller 132 can be configured to apply the upper capacity degradation rate to the first SOC-OCV curve in the upper interval of the boundary voltage and apply the lower capacity degradation rate to the first SOC-OCV curve in the lower interval of the boundary voltage to generate a corrected SOC-OCV curve at the MOL point. When the NCM cell is a manganese-rich cell, applying the SOC-OCV curve at the BOL point to the MOL point may cause distortion. Therefore, correction can be performed by applying the capacity degradation rate to the first SOC-OCV curve at the BOL point, and correction can be performed independently for the upper and lower intervals based on the boundary voltage.

[0071] According to an embodiment, controller 132 can be configured to perform interpolation on the calibrated SOC-OCV curve relative to SOC units to generate an interpolated SOC-OCV curve, and apply an OCV offset to the interpolated SOC-OCV curve to estimate a second SOC-OCV curve. For details regarding interpolation and offset, refer to the description below. Figure 11The interpolation process can resolve mismatches caused by corrections to the reference SOC value at the BOL point. The offset process can collectively adjust the overall values ​​to improve the accuracy of the second SOC-OCV curve.

[0072] According to an embodiment, the target battery 120 may include an NCM battery comprising nickel, cobalt, and manganese, and the upper and lower voltage degradation characteristics may arise due to the constituent elements of the NCM battery. For example, voltage-region-specific degradation characteristics of the NCM battery may be determined based on voltage regions where redox reactions of nickel, cobalt, and manganese primarily occur.

[0073] According to an embodiment, the upper voltage degradation characteristics can be determined based on the capacity degradation caused by the redox reactions of nickel and cobalt, and the lower voltage degradation characteristics can be determined based on the capacity performance caused by the redox reactions of manganese. According to an embodiment, the NCM battery can be a manganese-rich NCM battery, wherein the redox reactions of high manganese content may have a dominant effect on the lower range of the boundary voltage, while the redox reactions of nickel and cobalt may have a dominant effect on the lower range of the boundary voltage.

[0074] Figure 3 The figure shows the SOC-OCV curve in a traditional NCM cell, which may or may not change as degradation progresses.

[0075] refer to Figure 3 As shown in graph 300, despite the degradation that occurs in traditional NCM cells, the SOC-OCV curve remains unchanged.

[0076] In graph 300, the closed-circuit voltage (CCV) at approximately 1C can exhibit different curves at the BOL and MOL points, and the reason for the different curves may be due to the increase in resistance caused by battery degradation. Conversely, in conventional NCM battery cells without high manganese content, the SOC-OCV curve may not even change as battery degradation progresses.

[0077] Figure 4 The diagram illustrates the capacity degradation that occurs uniformly across the entire voltage range in a traditional NCM battery cell.

[0078] refer to Figure 4 The curve 400 illustrates the capacity degradation that occurs uniformly across the entire voltage range in a conventional NCM cell.

[0079] In curve 400, capacity degradation may occur relatively uniformly across the entire voltage range, and therefore the SOC-OCV curve may not show significant differences at the BOL and MOL points.

[0080] Figure 5 The figure shows the SOC-OCV curves of a manganese-rich NCM battery cell that have changed due to degradation, according to some embodiments.

[0081] refer to Figure 5 Figure 500 illustrates the SOC-OCV curve of a manganese-rich NCM battery cell as a result of degradation. Figure 500 can represent a manganese-rich NCM cell.

[0082] In the manganese-rich NCM monomer represented by graph 500, the closed-circuit voltage (CCV) at the MOL point may degrade compared to the BOL point by approximately 0.33C. Furthermore, the OCV may also degrade at the MOL point compared to the BOL point. Therefore, in the case of manganese-rich NCM monomers, it may be difficult to directly use the SOC-OCV curve generated at the BOL point at the MOL point.

[0083] Figure 6 The illustration shows the capacity degradation that occurs differently in the low-voltage and high-voltage regions of a manganese-rich NCM battery cell according to some embodiments.

[0084] refer to Figure 6 The curve 600 illustrates the capacity degradation that occurs differently in the low-voltage and high-voltage regions of manganese-rich NCM battery cells.

[0085] In curve 600, approximately 3V can be chosen as the boundary voltage, which distinguishes the lower and upper voltage regions. In the lower voltage region, the dQ / dV value may decrease as degradation progresses, while in the upper voltage region, the dQ / dV value may increase with degradation. In the lower voltage region, due to the high manganese content, the redox reaction of manganese may have a dominant effect, while in the upper voltage region, the redox reactions of nickel and cobalt may have a dominant effect.

[0086] Figure 7 This is a view illustrating how the SOC-OCV curve changes as a manganese-rich NCM battery cell degrades from the BOL point to the MOL point according to some embodiments.

[0087] refer to Figure 7 The mapping relationship between OCV and SOC at BOL point 710 and MOL point 720 can be illustrated. At BOL point 710 and MOL point 720, OCV can be divided into an upper voltage region and a lower voltage region based on a 3V boundary voltage. For example, MOL point 720 can correspond to the point after 100 charge-discharge cycles have been performed.

[0088] First, at the BOL point 710, the total capacity of the NCM cell may be 38.68 Ah, while at the MOL point 720, the degraded total capacity of the NCM cell may be 34.55 Ah. Second, at the BOL point 710, a 3V boundary voltage corresponds to 5.05 Ah and 13% SOC, while at the MOL point 720, a 3V boundary voltage corresponds to 5.5 Ah and 16% SOC. Third, at the BOL point 710, the upper voltage range corresponds to 33.63 Ah, while at the MOL point 720, the upper voltage range corresponds to 29.05 Ah.

[0089] The total capacity of 34.55 Ah at point MOL 720 can be 89.3% of the total capacity of 38.68 Ah at point BOL 710. The capacity of 29.05 Ah at the upper voltage region of point MOL 720 can be 86.4% of the capacity of 33.63 Ah at the upper voltage region of point BOL 710. The capacity of 5.5 Ah at the lower voltage region of point MOL 720 can be 108.9% of the capacity of 5.05 Ah at the lower voltage region of point BOL 710.

[0090] In this case, the upper capacity degradation rate in the upper voltage region can be 89.3 / 86.4 = 1.034, and the lower capacity degradation rate in the lower voltage region can be 89.3 / 108.9 = 0.819. Therefore, the OCV value corresponding to 90% SOC (= DOD (depth of discharge) 10%) at point BOL 710 can correspond to 10% DOD at point MOL 720. 1.034 = DOD 10.34% (= SOC 89.66%). In this way, the SOC-OCV curve at MOL point 720 can be reconstructed based on the SOC-OCV curve at BOL point 710.

[0091] Figure 8 The illustration shows how the difference between the upper and lower voltages based on the boundary voltage changes due to battery degradation, according to some embodiments.

[0092] refer to Figure 8 The curve 800 can be illustrated to show the first SOC-OCV curve at the BOL point and the second SOC-OCV curve at the MOL point.

[0093] The curve 800 can indicate how the SOC-OCV curve may change as degradation progresses from the BOL point to the MOL point. The upper and lower voltage regions can be distinguished based on a 3V boundary voltage. The ratio of the upper and lower voltage regions may change due to degradation from the BOL point to the MOL point.

[0094] Figure 9 The figure shows a comparison between the estimated value of the second SOC-OCV curve according to some embodiments and the actual measured value at the MOL point.

[0095] refer to Figure 9 The curve graph 900 shows the comparison between the estimated value of the second SOC-OCV curve and the actual measured value at the MOL point.

[0096] In graph 900, BOL_EXP can represent the first SOC-OCV curve measured at the BOL point, and 100Cycle_EXP can represent the SOC-OCV curve measured at the MOL point. As illustrated, a difference can be observed between the two curves in the manganese-rich NCM monomer. However, because generating the SOC-OCV curve at the MOL point through actual measurement requires significant time and cost, a technique for estimating the curve may be needed as an alternative to actual measurement.

[0097] By applying the upper and lower capacity degradation rates to the first SOC-OCV curve (BOL_EXP), a corrected SOC-OCV curve (BOL_Applied Correction DOD) can be generated. A second SOC-OCV curve (BOL_Second Correction) can be generated through a second correction, which applies the offset to the already generated corrected SOC-OCV curve. As illustrated, no significant difference can be observed between the second SOC-OCV curve (BOL_Second Correction) and the actually measured SOC-OCV curve (100Cycle_EXP).

[0098] Figure 10 The illustration shows the process of generating a second SOC-OCV curve according to some embodiments.

[0099] refer to Figure 10 Table 1000 illustrates the process of generating the second SOC-OCV curve.

[0100] The first and second columns of Table 1000 may represent the first SOC-OCV curve at the BOL point. The first SOC-OCV curve is generated in units of 10% SOC, but may be generated in other units as needed. The third column of Table 1000 may represent the SOC-OCV curve actually measured at the MOL point, and generating an estimated curve similar to it may be one of the purposes of this disclosure.

[0101] The fifth column of Table 1000 can represent the corrected SOC-OCV curve generated by applying the upper and lower capacity degradation rates to the first SOC-OCV curve. To generate the corrected SOC-OCV curve in the fifth column, the upper and lower capacity degradation rates from the fourth column can be used.

[0102] Column 6 of Table 1000 can represent the interpolated SOC-OCV curve generated by interpolating the calibrated SOC-OCV curve. Column 7 of Table 1000 can represent the second SOC-OCV curve generated by commonly shifting the interpolated SOC-OCV curves by a specific offset value. As illustrated, it can be observed that the difference between the second SOC-OCV curve in column 7 and the actually measured SOC-OCV curve in column 3 is not significant.

[0103] Figure 11 The illustration shows the steps of a battery management method 1100 according to some embodiments.

[0104] refer to Figure 11 The battery management method 1100 may include steps 1110 to 1140. However, without limitation, some steps may be omitted or other general steps may be added, and the steps of the battery management method 1100 may be performed in a different order than that shown in the figures.

[0105] Battery management method 1100 may include steps processed sequentially by battery management device 130. Therefore, even if the following description is omitted, the above description of battery management device 130 can be equally applied to battery management method 1100.

[0106] Steps 1110 to 1140 of the battery management method 1100 can be performed by the sensor 131 and controller 132 of the battery management device 130.

[0107] At step 1110, the battery management device 130 may perform the step of acquiring battery data of the management target battery 120 measured at multiple degradation points.

[0108] At step 1120, the battery management device 130 may perform the steps of generating a first SOC-OCV curve at the beginning of life (BOL) point and a voltage-capacity curve of the target battery 120 at multiple degradation points based on battery data.

[0109] At step 1130, the battery management device 130 may perform a step of identifying a boundary voltage based on a voltage-capacity curve, which distinguishes between an upper voltage degradation characteristic with an upper capacity degradation rate and a lower voltage degradation characteristic with a lower capacity degradation rate.

[0110] At step 1140, the battery management device 130 may perform the step of estimating the second SOC-OCV curve at the mid-life (MOL) point based on the upper capacity degradation rate, the lower capacity degradation rate, and the first SOC-OCV curve.

[0111] According to an embodiment, the battery management method 1100 can be implemented as a computer program stored in a computer-readable storage medium. That is, the computer program may include instructions for implementing the battery management method 1100, and these instructions may be stored in a computer-readable storage medium. The computer program may include a mobile application.

[0112] According to embodiments, computer-readable storage media may include magnetic media such as hard disks, floppy disks, or magnetic tapes; optical media such as CD-ROMs or DVDs; magneto-optical media such as flexible optical discs; and hardware devices such as ROMs, RAMs, or flash memory specifically configured to store and execute computer program instructions. Computer program instructions may include machine language code created by a compiler and high-level language code that can be executed by a computer using, for example, an interpreter.

[0113] Terms such as “comprising,” “including,” or “having” as used above indicate that the corresponding component may be inherent, unless otherwise expressly stated. Therefore, these terms should not be construed as excluding other components, but rather as capable of further including other components. Unless otherwise defined, all terms, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments disclosed herein pertain. Common terms, such as those defined in dictionaries, should be interpreted as consistent with the context of the relevant art and should not be interpreted in an idealized or overly formal sense unless otherwise expressly defined herein.

[0114] The foregoing description is merely an illustrative example of the technical concepts disclosed herein. Those skilled in the art to which the embodiments disclosed herein pertain will be able to make various modifications and variations without departing from the essential characteristics of the disclosed embodiments. Therefore, the embodiments disclosed herein are not intended to limit the technical concepts of the disclosed embodiments, but rather to illustrate them, and the scope of the technical concepts disclosed herein is not limited to these embodiments. The scope of protection of the technical concepts disclosed herein should be interpreted in accordance with the following claims, and all technical concepts within the equivalent scope should be interpreted as being included within the scope of the rights of this disclosure. Therefore, the technical scope of the various embodiments of this disclosure should not be limited to the detailed description provided in the specification, but should be defined by the claims.

Claims

1. A battery management device, comprising: An interface configured to acquire battery data of a management target battery measured at multiple degradation points; as well as Controller The controller is configured as follows: Based on the battery data, a first SOC-OCV curve at the start of life and a voltage-capacity curve of the target battery at the plurality of degradation points are generated; Based on the voltage-capacity curve, boundary voltages are identified, distinguishing between upper-end voltage degradation characteristics with an upper capacity degradation rate and lower-end voltage degradation characteristics with a lower capacity degradation rate; and Based on the upper capacity degradation rate, the lower capacity degradation rate, and the first SOC-OCV curve, the second SOC-OCV curve at the mid-life point is estimated.

2. The battery management device according to claim 1, wherein, The battery data includes low-rate discharge data measured at the plurality of degradation points using a low-rate discharge of 0.1C or lower. The controller is further configured to generate the voltage-capacity curve based on the low-rate discharge data at the plurality of degradation points.

3. The battery management device according to claim 2, wherein, The controller is further configured to: Based on the low-rate discharge data, a dQ / dV curve is generated, which represents the differential capacity value of the target battery relative to voltage at the plurality of degradation points; and The voltage-capacity curve is generated based on the dQ / dV curves at the plurality of degradation points.

4. The battery management device according to claim 3, wherein, The controller is further configured to: The boundary voltage is identified based on the pattern of the dQ / dV curve.

5. The battery management device according to claim 1, wherein, The controller is further configured to: The upper capacity degradation rate is applied to the first SOC-OCV curve at the upper interval of the boundary voltage, and the lower capacity degradation rate is applied to the first SOC-OCV curve at the lower interval of the boundary voltage to generate a corrected SOC-OCV curve at the MOL point.

6. The battery management device according to claim 5, wherein, The controller is further configured to: Interpolation is performed on the corrected SOC-OCV curve relative to the SOC unit to generate an interpolated SOC-OCV curve; and The open-circuit voltage offset is applied to the interpolated SOC-OCV curve to estimate the second SOC-OCV curve.

7. The battery management device according to claim 1, wherein, The target battery for management includes NCM batteries containing nickel, cobalt, and manganese, and The upper voltage degradation characteristics and the lower voltage degradation characteristics are due to the constituent elements of the NCM battery.

8. The battery management device according to claim 7, wherein, The upper voltage degradation characteristics are determined based on the capacity degradation caused by the redox reaction of nickel and cobalt, and the lower voltage degradation characteristics are determined based on the capacity performance caused by the redox reaction of manganese.

9. A battery management method, comprising: Acquire battery data for the target battery at multiple degradation points; Based on the battery data, a first SOC-OCV curve at the start of life and a voltage-capacity curve of the target battery at the plurality of degradation points are generated; Based on the voltage-capacity curve, boundary voltages are identified, distinguishing between upper-end voltage degradation characteristics with an upper capacity degradation rate and lower-end voltage degradation characteristics with a lower capacity degradation rate; and Based on the upper capacity degradation rate, the lower capacity degradation rate, and the first SOC-OCV curve, the second SOC-OCV curve at the mid-life point is estimated.

10. The battery management method according to claim 9, wherein, The battery data includes low-rate discharge data measured at the plurality of degradation points using a low-rate discharge of 0.1C or lower. Generating the voltage-capacity curve includes: The voltage-capacity curve is generated based on the low-rate discharge data at the multiple degradation points.

11. The battery management method according to claim 10, wherein, Generating the voltage-capacity curve includes: Based on the low-rate discharge data, a dQ / dV curve is generated, which represents the differential capacity value of the managed target battery relative to voltage at the plurality of degradation points; and The voltage-capacity curve is generated based on the dQ / dV curves at the plurality of degradation points.

12. The battery management method according to claim 11, wherein, Identifying the boundary voltage includes: The boundary voltage is identified based on the pattern of the dQ / dV curve.

13. The battery management method according to claim 9, wherein, Estimating the second SOC-OCV curve includes: The upper capacity degradation rate is applied to the first SOC-OCV curve at the upper interval of the boundary voltage, and the lower capacity degradation rate is applied to the first SOC-OCV curve at the lower interval of the boundary voltage to generate a corrected SOC-OCV curve at the MOL point.

14. The battery management method according to claim 13, wherein, Estimating the second SOC-OCV curve includes: Interpolate the corrected SOC-OCV curve relative to the SOC unit to generate an interpolated SOC-OCV curve; and The open-circuit voltage offset is applied to the interpolated SOC-OCV curve to estimate the second SOC-OCV curve.

15. The battery management method according to claim 9, wherein, The target battery for management includes NCM batteries containing nickel, cobalt, and manganese, and The upper voltage degradation characteristics and the lower voltage degradation characteristics are due to the constituent elements of the NCM battery.

16. The battery management method according to claim 15, wherein, The voltage degradation characteristics at the upper end are determined based on the capacity degradation caused by the redox reaction of nickel and cobalt, and The lower voltage degradation characteristic is determined based on the capacity performance caused by the redox reaction of manganese.

17. A non-transitory computer-readable storage medium that stores a program, which, when executed, causes a computer to perform methods comprising: Acquire battery data for the target battery at multiple degradation points; Based on the battery data, a first SOC-OCV curve at the start of life and a voltage-capacity curve of the target battery at the plurality of degradation points are generated; Based on the voltage-capacity curve, boundary voltages are identified, distinguishing between upper-end voltage degradation characteristics with an upper capacity degradation rate and lower-end voltage degradation characteristics with a lower capacity degradation rate; and Based on the upper capacity degradation rate, the lower capacity degradation rate, and the first SOC-OCV curve, the second SOC-OCV curve at the mid-life point is estimated.

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