Electronic device and battery soc estimation method thereof

Abstract

An electronic device according to one embodiment of the disclosure includes a memory storing a plurality of estimation algorithms for estimating a state of charge (SOC) of a battery cell, and a processor operatively connected to the memory, wherein the processor is capable of acquiring open circuit voltage (OCV) data of the battery cell, identifying any one estimation algorithm from among the plurality of estimation algorithms stored in the memory based on the acquired OCV data, and estimating the SOC of the battery cell based on the identified estimation algorithm.

Description

Technical Field

[0001] This application is based on and claims priority to Korean Patent Application No. 10-2024-0094928, filed on July 18, 2024, the disclosure of which is incorporated herein by reference in its entirety.

[0002] The embodiments described below relate to an electronic device and a method for estimating the state of charge (SOC) of its battery. Background Technology

[0003] In recent years, research and development of rechargeable batteries have been actively pursued. Rechargeable batteries refer to batteries that can be repeatedly charged and discharged, including not only existing Ni / Cd and Ni / MH batteries, but also the latest lithium-ion batteries. Among rechargeable batteries, lithium-ion batteries have the advantage of significantly higher energy density compared to existing Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form, making them suitable for use as power sources in mobile devices. Recently, with their applications expanding to power sources for electric vehicles, lithium-ion batteries are attracting attention as a next-generation energy storage medium.

[0004] Lithium-ion secondary batteries are typically manufactured by: inserting a separator between a positive electrode comprising a positive electrode active material containing a lithium transition metal oxide and a negative electrode comprising a negative electrode active material capable of storing lithium ions to form an electrode assembly; inserting the electrode assembly into a battery casing; injecting a non-aqueous electrolyte as a medium for transporting lithium ions; and sealing the battery casing. Typically, the non-aqueous electrolyte comprises a lithium salt and an organic solvent capable of dissolving the lithium salt.

[0005] Meanwhile, as the application of secondary batteries expands, the importance of technologies related to management systems for more efficient use and management of these batteries is increasing. For example, management systems need to accurately estimate the state of charge (SOC) of the secondary battery in order to appropriately adjust the charging or discharging output and capacity utilization strategies.

[0006] Typically, to estimate the state of charge (SOC) of a secondary battery, a method is used that estimates the SOC based on the battery's OCV using the SOC-OCV relationship, which represents the relationship between the battery's SOC and open-circuit voltage (OCV). The SOC-OCV relationship is an inherent characteristic determined by the battery's electrode composition, and since in some secondary batteries the SOC-OCV relationship does not change significantly even with battery degradation, an SOC estimation algorithm based on the SOC-OCV relationship at the early life (BOL) of the secondary battery is used.

[0007] Meanwhile, in recent years, to reduce the manufacturing cost of batteries used in electric vehicles, progress has been made in the development of battery cells, including lithium-rich manganese (Mn-rich) oxides as positive electrode active materials (hereinafter referred to as Mn-rich cells). Lithium-rich Mn oxides have the advantages of low cost and excellent stability compared to conventional lithium-nickel-based positive electrode active materials. However, because the SOC-OCV relationship of Mn-rich cells changes with their degradation, it is difficult to use conventional SOC estimation algorithms.

[0008] It is necessary to determine why the SOC-OCV relationship of manganese-rich monomers changes with their degradation, and to apply an SOC estimation algorithm that takes into account the characteristics of manganese-rich monomers. Summary of the Invention

[0009] Technical Purpose

[0010] According to one embodiment of the present disclosure, an electronic device and a method for estimating the SOC of the battery therewith can be provided. The electronic device can identify an estimation algorithm suitable for the current OCV value of the battery cell from a plurality of estimation algorithms based on the OCV data of the battery cell, and estimate the SOC of the battery cell based on the identified estimation algorithm.

[0011] The advantages of the embodiments disclosed herein are not limited to those described above, and other advantages can be inferred from the embodiments described below.

[0012] Technical solution

[0013] An electronic device according to an embodiment of the present disclosure includes: a memory storing a plurality of estimation algorithms for estimating the state of charge (SOC) of a battery cell; and a processor operatively coupled to the memory, wherein the processor acquires open-circuit voltage (OCV) data of the battery cell, identifies any one of the plurality of estimation algorithms stored in the memory based on the acquired OCV data of the battery cell, and estimates the SOC of the battery cell based on the identified estimation algorithm.

[0014] In an electronic device according to an embodiment of the present disclosure, a plurality of estimation algorithms may include a first estimation algorithm and a second estimation algorithm. Based on the acquired OCV data, the processor may identify an OCV range including the OCV values ​​of individual battery cells. When the OCV value of an individual battery cell is included in the first OCV range, the processor may identify the first estimation algorithm from the plurality of estimation algorithms. And when the OCV value of an individual battery cell is included in the second OCV range, the processor may identify the second estimation algorithm from the plurality of estimation algorithms.

[0015] In an electronic device according to one embodiment of the present disclosure, the processor may directly acquire OCV data measured by a sensor or acquire OCV data from an external device via communication.

[0016] In an electronic device according to an embodiment of the present disclosure, when a first estimation algorithm is identified from a plurality of estimation algorithms, the processor can estimate the SOC of a battery cell based on first SOC-OCV relationship information corresponding to a first OCV range, and when a second estimation algorithm is identified from a plurality of estimation algorithms, the processor can estimate the SOC of a battery cell based on second SOC-OCV relationship information corresponding to a second OCV range.

[0017] In an electronic device according to an embodiment of the present disclosure, when a first estimation algorithm is identified from a plurality of estimation algorithms, the processor can estimate a first SOC corresponding to a first OCV range based on first SOC-OCV relationship information, and estimate the SOC of a battery cell corresponding to the entire OCV range including the first OCV range and the second OCV range based on the first SOC. When a second estimation algorithm is identified from a plurality of estimation algorithms, the processor can estimate a second SOC corresponding to a second OCV range based on second SOC-OCV relationship information, and estimate the SOC of a battery cell corresponding to the entire OCV range including the first OCV range and the second OCV range based on the second SOC.

[0018] In an electronic device according to an embodiment of the present disclosure, a first OCV range is a range equal to or greater than a specified OCV value, and a second OCV range is a range less than a specified OCV value.

[0019] In an electronic device according to one embodiment of the present disclosure, the specified OCV value is approximately 3.2 V.

[0020] In an electronic device according to an embodiment of the present disclosure, when a first estimation algorithm is identified from a plurality of estimation algorithms, the processor can estimate the first SOC by performing an extended Kalman filter operation based on the first SOC-OCV relationship information and the OCV value of the battery cell, multiply the first SOC by a first reference capacity corresponding to the first OCV range to obtain a charging capacity based on a specified OCV value, and divide the sum of the second reference capacity corresponding to the second OCV range and the charging capacity by the sum of the first reference capacity and the second reference capacity to estimate the SOC of the battery cell.

[0021] In an electronic device according to an embodiment of the present disclosure, a battery cell may include a lithium-rich manganese oxide as a positive electrode active material, and the first SOC-OCV relationship information may represent the relationship between the SOC and OCV of a lithium nickel cobalt manganese oxide, which is part of the lithium-rich manganese oxide, within the first OCV range.

[0022] In an electronic device according to an embodiment of the present disclosure, when a second estimation algorithm is identified from a plurality of estimation algorithms, the processor can estimate the second SOC by performing an extended Kalman filter operation based on the second SOC-OCV relationship information and the OCV value of the battery cell. Based on the second SOC, a first depth of discharge (DoD) corresponding to the second OCV range is calculated. The first DoD is multiplied by a second reference capacity corresponding to the second OCV range to obtain a discharge capacity based on a specified OCV value. The sum of the first reference capacity corresponding to the first OCV range and the discharge capacity is divided by the sum of the first reference capacity and the second reference capacity to obtain a second DoD of the battery cell corresponding to the entire OCV range of the battery cell. Based on the second DoD, the SOC of the battery cell is estimated.

[0023] In an electronic device according to an embodiment of the present disclosure, a battery cell may include a lithium-rich manganese oxide as a positive electrode active material, and the second SOC-OCV relationship information may represent the relationship between the SOC and OCV of a lithium manganese oxide, which is part of the lithium-rich manganese oxide, within the second OCV range.

[0024] A method for estimating the state of charge (SOC) of a battery cell performed by an electronic device according to an embodiment of the present disclosure includes: acquiring OCV data of the battery cell; identifying any one of a plurality of estimation algorithms based on the acquired OCV data; and estimating the SOC of the battery cell based on the identified estimation algorithm.

[0025] In a method for estimating the SOC of a battery cell performed by an electronic device according to an embodiment of the present disclosure, acquiring OCV data may include directly acquiring OCV data measured by a sensor, or acquiring OCV data from an external device via communication.

[0026] In a method for estimating the SOC of a battery cell performed by an electronic device according to an embodiment of the present disclosure, a plurality of estimation algorithms may include a first estimation algorithm and a second estimation algorithm, and identifying any one of the plurality of estimation algorithms may include: identifying an OCV range including the OCV value of the battery cell based on the acquired OCV data; identifying the first estimation algorithm from the plurality of estimation algorithms when the OCV value of the battery cell is included in the first OCV range; and identifying the second estimation algorithm from the plurality of estimation algorithms when the OCV value of the battery cell is included in the second OCV range.

[0027] In a method for estimating the state of charge (SOC) of a battery cell performed by an electronic device according to an embodiment of the present disclosure, estimating the SOC of a battery cell may include: when a first estimation algorithm is identified from a plurality of estimation algorithms, estimating a first SOC corresponding to a first OCV range based on first SOC-OCV relationship information, and estimating the SOC of a battery cell corresponding to the entire OCV range including the first OCV range and the second OCV range based on the first SOC; and when a second estimation algorithm is identified from a plurality of estimation algorithms, estimating a second SOC corresponding to a second OCV range based on second SOC-OCV relationship information, and estimating the SOC of a battery cell corresponding to the entire OCV range including the first OCV range and the second OCV range based on the second SOC.

[0028] In a battery cell SOC estimation method performed by an electronic device according to an embodiment of the present disclosure, a first OCV range may be a range equal to or greater than a specified OCV value, and a second OCV range may be a range less than a specified OCV value.

[0029] In a method for estimating the SOC of a battery cell performed by an electronic device according to an embodiment of the present disclosure, the specified OCV value may be approximately 3.2 V.

[0030] In a method for estimating the SOC of a battery cell performed by an electronic device according to an embodiment of the present disclosure, estimating the SOC of a battery cell may include: when a first estimation algorithm is identified from a plurality of estimation algorithms, estimating the first SOC by performing an extended Kalman filter operation based on first SOC-OCV relationship information and the OCV value of the battery cell; multiplying the first SOC by a first reference capacity corresponding to a first OCV range to obtain a charging capacity based on a specified OCV value; and dividing the sum of a second reference capacity corresponding to a second OCV range and the charging capacity by the sum of the first reference capacity and the second reference capacity to estimate the SOC of the battery cell.

[0031] In a method for estimating the State of Charge (SOC) of a battery cell performed by an electronic device according to an embodiment of the present disclosure, estimating the SOC of a battery cell may include: when a second estimation algorithm is identified from a plurality of estimation algorithms, estimating the second SOC by performing an extended Kalman filter operation based on second SOC-OCV relationship information and the OCV value of the battery cell; calculating a first depth of discharge (DoD) corresponding to the second OCV range based on the second SOC; multiplying the first DoD by a second reference capacity corresponding to the second OCV range to obtain a discharge capacity based on a specified OCV value; dividing the sum of the first reference capacity and the discharge capacity corresponding to the first OCV range by the sum of the first reference capacity and the second reference capacity to obtain a second DoD of the battery cell corresponding to the entire OCV range of the battery cell; and estimating the SOC of the battery cell based on the second DoD.

[0032] Effects of the present invention

[0033] According to the embodiments described herein, a SOC estimation algorithm that takes into account the characteristics of manganese-rich monomers is applied, which improves the accuracy of SOC estimation.

[0034] The beneficial effects of this disclosure are not limited to those described above, and other beneficial effects not described herein can be clearly understood by those skilled in the art based on the descriptions in the claims. Attached Figure Description

[0035] Figure 1 The graph shows the OCV of the manganese-rich monomer relative to the SOC at each of the BOL (early life) and MOL (mid life).

[0036] Figure 2 The graphs show the SOHC of the manganese-rich monomer relative to each OCV range, and the capacity of the manganese-rich monomer at each of the BOL and MOL relative to each OCV range.

[0037] Figure 3a A graph showing the OCV relative to SOC for manganese-rich monomers within the first OCV range is shown.

[0038] Figure 3b A graph showing the OCV relative to SOC for manganese-rich monomers in the second OCV range is shown.

[0039] Figure 4 This is a block diagram of an electronic device according to an embodiment of the present disclosure.

[0040] Figure 5 This is a view illustrating an example of an electronic device according to an embodiment of the present disclosure estimating the SOC of a battery cell based on a first estimation algorithm.

[0041] Figure 6 This is a view illustrating an example of an electronic device according to an embodiment of the present disclosure estimating the SOC of a battery cell based on a second estimation algorithm.

[0042] Figure 7 This is a flowchart of the operation of an electronic device according to an embodiment of the present disclosure.

[0043] Figure 8 This is a flowchart of the operation of an electronic device according to an embodiment of the present disclosure. Detailed Implementation

[0044] In describing embodiments of this disclosure, descriptions of techniques well-known in the art and not directly related to this disclosure will be omitted. This is intended to eliminate unnecessary descriptions, thereby clearly describing this disclosure without obscuring its main points.

[0045] For the same reason, some components are exaggerated, omitted, or shown schematically in the accompanying drawings. Therefore, the dimensions of each component may not accurately reflect its actual size. In each drawing, the same or corresponding components are indicated by the same reference numerals.

[0046] The advantages and features of this disclosure, as well as the methods for implementing them, will be apparent from the embodiments described in detail below with reference to the accompanying drawings. However, this disclosure is not limited to the embodiments, but can be implemented in various forms. The embodiments are provided only to thoroughly describe this disclosure and to fully inform those skilled in the art to which this disclosure pertains, and the disclosure may be defined by the scope set forth in the claims. Throughout the description herein, the same reference numerals refer to the same components.

[0047] It is understood that each block and combination of flowcharts in the accompanying drawings can be executed by computer program instructions. Since computer program instructions can be loaded onto a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, the instructions, which execute via the computer or other programmable data processing equipment, create means for performing the functions described in the flowchart blocks. Since computer program instructions can also be stored in a computer-accessible or computer-readable storage medium that can be assigned to a computer or other programmable data processing equipment to implement the functions in a particular manner, the instructions stored in the computer-accessible or computer-readable storage medium can constitute an article of manufacture including instruction means for performing the functions described in the flowchart blocks. Since computer program instructions can also be loaded onto a computer or other programmable data processing equipment, a series of operational steps can be performed on the computer or other programmable data processing equipment to create a process executed by the computer, such that the instructions, which execute on the computer or other programmable data processing equipment, provide steps for performing the functions described in the flowchart blocks.

[0048] Furthermore, each block can represent a module, segment, or portion of code that includes one or more executable instructions for performing a specified logical function. It should be noted that in some alternative execution examples, the functions described in a block may not be executed in sequence. For example, two consecutively shown blocks can actually be executed simultaneously, and sometimes, they can be executed in reverse order according to their corresponding functions.

[0049] In embodiments of this disclosure, the term "~unit" refers to a software component or a hardware component such as a field-programmable gate array (FPGA) and an application-specific integrated circuit (ASIC) that performs a specific function. However, "~unit" is not limited to software or hardware. A "~unit" can be configured to be located on an addressable storage medium or can be configured to activate one or more processors. Thus, by way of example, "~unit" includes components such as software components, object-oriented software components, class components, and task components, processes, functions, attributes, procedures, subroutines, program code segments, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided in components and "~units" can be combined into fewer components and "~units," or further divided into additional components and "~units." Furthermore, components and "~units" can be implemented to activate one or more CPUs in a device or secure multimedia card.

[0050] Throughout the description, the phrase “at least one of a, b, and c” may encompass “only a,” “only b,” “only c,” “a and b,” “a and c,” “b and c,” or “all of a, b, and c.”

[0051] As used below, "terminal" can be a computer or portable terminal capable of connecting to a server or other terminal via a network. Here, a computer includes, for example, a laptop, desktop, and tablet computer equipped with a web browser, and a portable terminal is, for example, a wireless communication device that ensures portability and mobility, and can include any kind of handheld wireless communication device, such as communication-based terminals, smartphones, and tablet PCs that support International Mobile Telecommunications (IMT), Code Division Multiple Access (CDMA), Wideband Code Division Multiple Access (W-CDMA), and Long Term Evolution (LTE).

[0052] In the following, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings to enable those skilled in the art to practice the invention. However, the present disclosure may be implemented in various different forms and is not limited to the embodiments described below.

[0053] In the following text, reference will be made to Figure 1 , Figure 2 , Figure 3a and Figure 3b Describe the characteristics of manganese-rich monomers, and refer to Figures 4 to 8 The embodiments of this disclosure are described in detail.

[0054] Figure 1 A graph 100 shows the OCV (Optical Value Change) of a manganese-rich cell relative to its State of Charge (SOC) at each of the early life (BOL) and mid-life (MOL) stages. Here, according to an embodiment, the manganese-rich cell may be a battery cell comprising lithium-rich manganese (Mn-rich) oxides as the positive electrode active material. For example, lithium-rich manganese oxides may include lithium manganese oxides (e.g., Li₂MnO₃) and lithium nickel cobalt manganese oxides (e.g., NCM).

[0055] refer to Figure 1 As shown in Figure 100, the SOC-OCV relationship of the manganese-rich monomer differs at BOL and MOL. This difference may be due to the decrease in OCV (OCV decay) of the manganese-rich monomer as the secondary battery is repeatedly charged and discharged. The lithium-rich manganese oxide included in the manganese-rich monomer undergoes a phase transition during charging and discharging, thus generating manganese oxides (e.g., MnO2). The generated manganese oxides participate in redox reactions, thereby increasing the capacity of the manganese-rich monomer. This may be one of the reasons for the OCV decrease phenomenon occurring in the manganese-rich monomer.

[0056] Figure 2 Graph 200 shows the SOHC of the manganese-rich monomer relative to each OCV range and the capacity of the manganese-rich monomer at each of the BOL and MOL relative to each OCV range.

[0057] exist Figure 2 In graph 200, the MOL segment capacity and BOL segment capacity represent the capacity observed in the MOL and BOL of the manganese-rich monomer within the corresponding OCV ranges (e.g., the ranges of 3 V to 3.1 V and 3.1 V to 3.2 V, respectively). The segment-based SOHC represents the state of health (SOH) of the manganese-rich monomer relative to capacity within the corresponding OCV range, and is obtained by dividing the MOL segment capacity by the BOL segment capacity within the corresponding OCV range.

[0058] Referring to graph 200, it can be seen that, based on the OCV of 3.2 V for manganese-rich monomers, in the OCV range before this point (e.g., the range from 3 V to 3.1 V and from 3.1 V to 3.2 V), the capacity of the MOL segment is greater than that of the BOL segment, while in the OCV range after this point (e.g., the range from 3.2 V to 3.3 V and from 3.3 V to 3.4 V), the capacity of the BOL segment is greater than that of the MOL segment. Therefore, it can be seen that the SOHC of manganese-rich monomers is higher than 1 in the OCV range equal to or less than 3.2 V, but lower than 1 in the OCV range equal to or greater than 3.2 V. For example, it can be understood that the capacity of manganese-rich monomers increases at OCVs where SOHC is higher than 1 at OCVs equal to or less than 3.2 V, but decreases at OCVs where SOHC is lower than 1 at OCVs equal to or greater than 3.2 V.

[0059] Based on the above data, it can be assumed that the SOC-OCV relationship of manganese-rich monomers can vary based on a specific OCV value (e.g., 3.2V).

[0060] Figure 3a A graph 310 is shown, representing the OCV of the manganese-rich monomer relative to the SOC within a first OCV range (e.g., 3.2 V to 4.35 V). Figure 3b A graph 320 is shown, representing the OCV of the manganese-rich monomer relative to its state of charge (SOC) within a second OCV range (e.g., 2.5 V to 3.2 V). In graph 310, the X-axis represents the SOC of the manganese-rich monomer within a first OCV range, scaled from 0% to 100%. In graph 320, the X-axis represents the SOC of the manganese-rich monomer within a second OCV range, scaled from 0% to 100%.

[0061] refer to Figure 3a The curves 310 and Figure 3bAs shown in graph 320, based on the OCV value of 3.2 V, the SOC-OCV relationship is consistent at BOL and MOL for each OCV range when the first OCV range is equal to or greater than 3.2 V and the second OCV range is equal to or less than 3.2 V. For example, it can be understood that the SOC-OCV relationship of manganese-rich monomers changes based on the OCV of 3.2 V.

[0062] Therefore, when estimating the SOC of manganese-rich monomers, the SOC-OCV relationship corresponding to the first OCV range can be used to estimate the SOC in the first OCV range that is equal to or greater than a specific OCV (e.g., 3.2 V), and the SOC-OCV relationship corresponding to the second OCV range can be used to estimate the SOC in the second OCV range that is equal to or less than a specific OCV (e.g., 3.2 V), thereby enabling accurate SOC estimation.

[0063] Although an example of an OCV value of approximately 3.2 V at which the SOC-OCV relationship of a manganese-rich monomer changes has been described, this disclosure is not limited thereto, and the OCV value at which the SOC-OCV relationship of a manganese-rich monomer changes can vary, for example, depending on the composition of the manganese-rich monomer.

[0064] Figure 4 This is a block diagram of an electronic device 400 according to an embodiment of the present disclosure.

[0065] According to one embodiment, the electronic device 400 may include a communication circuit 410, a sensor 420, a memory 430, and / or a processor 440. According to the embodiment, Figure 4 The electronic device 400 shown may also include, in addition to Figure 4 At least one component other than those shown (e.g., a display, input device, or output device).

[0066] According to one embodiment, the electronic device 400 can be implemented as a battery management system disposed in a battery pack to manage and control the state of the individual battery cells included in the battery pack. According to another embodiment, the electronic device 400 can be implemented to receive data from individual battery cells from an external electronic device to estimate at least one of a laptop, desktop, laptop computer, and server computing device of a System-on-Chip (SOC).

[0067] According to one embodiment, the communication circuit 410 can establish a wired communication channel and / or a wireless communication channel between the electronic device 400 and an external electronic device and / or an external server, including a battery cell, and send data to and receive data from the external electronic device and / or the external server through the established communication channel. According to one embodiment, the communication circuit 410 can receive OCV data of the battery cell from the external electronic device and / or the external server.

[0068] Here, communication (i.e., data transmission and reception) can be performed in a wired or wireless manner. For this purpose, the communication circuit 310 may include a wired communication module connected to the Internet via a local area network (LAN), a mobile communication module connected to a mobile communication network via a mobile communication base station to transmit and receive data, a short-range communication module utilizing a wireless local area network (WLAN) type communication method such as Wi-Fi or a wireless personal area network (WPAN) type communication method such as Bluetooth or Zigbee, a satellite communication module utilizing a global navigation satellite system (GNSS) such as Global Positioning System (GPS), or a combination thereof.

[0069] According to one embodiment, sensor 420 can measure values ​​related to the state of a single battery cell. According to one embodiment, the state-related values ​​may include at least one of the battery cell's voltage, current, and temperature.

[0070] According to one embodiment, either the communication circuit 410 or the sensor 420 in the electronic device 400 can be omitted. For example, when the electronic device 400 is implemented as a BMS, since the electronic device 400 can acquire OCV data by calculating the OCV based on the value related to the state of the battery cell measured by the sensor 420, the communication circuit 410 can be omitted. As another example, when the electronic device 400 is implemented as a computing device (e.g., a server) that receives data from the battery cells from an external electronic device, the sensor 420 can be omitted since the electronic device 400 can acquire the OCV data of the battery cells through the communication circuit 410.

[0071] According to one embodiment, memory 430 may include volatile memory and / or non-volatile memory.

[0072] According to one embodiment, memory 430 may store data used by at least one component of electronic device 400 (e.g., processor 440). For example, the data may include software (or its associated instructions), input data, or output data. In one embodiment, instructions may, when executed by processor 440, cause electronic device 400 to perform operations defined by the instructions.

[0073] According to one embodiment, memory 430 may store multiple estimation algorithms for estimating the SOC of a single battery cell.

[0074] According to one embodiment, processor 440 may be implemented as a computer or a computer-like device, depending on hardware, software, or a combination thereof. For hardware, processor 440 may be implemented as an electronic circuit that processes electrical signals to perform control functions, and for software, processor 440 may be implemented as a program that drives hardware processor 440. In one embodiment, processor 440 may include a central processing unit, an application processor, a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor.

[0075] Furthermore, unless otherwise specified in the description below, the operation of electronic device 400 can be interpreted as being performed under the control of processor 440.

[0076] The method for estimating the state of charge (SOC) of a single battery cell by an electronic device 400 will be described below. In this context, the battery cell may be a manganese-rich cell.

[0077] According to one embodiment, electronic device 400 can acquire OCV data of individual battery cells. For example, the OCV data can be time-series data representing past and current OCV values ​​of the individual battery cells. As described above, for example, when electronic device 400 is implemented as a BMS, electronic device 400 can acquire OCV data by calculating the OCV based on values ​​related to the state of the individual battery cells measured by sensor 420. Alternatively, when electronic device 400 is implemented as a computing device (e.g., a server) that receives data from individual battery cells from external electronic devices, electronic device 400 can acquire the OCV data of the individual battery cells via communication circuitry 410.

[0078] According to one embodiment, electronic device 400 can identify an estimation algorithm from a plurality of estimation algorithms stored in memory 430 based on the OCV data of a battery cell. According to one embodiment, based on the OCV data of the battery cell, electronic device 400 can identify an estimation algorithm from a plurality of estimation algorithms to be used to estimate the SOC of the battery cell. For example, electronic device 400 can identify an estimation algorithm corresponding to an OCV range that includes the OCV values ​​of the battery cell.

[0079] Here, the multiple estimation algorithms may include a first estimation algorithm and a second estimation algorithm. The first estimation algorithm and the second estimation algorithm may correspond to a first OCV range and a second OCV range, respectively.

[0080] According to one embodiment, electronic device 400 can identify an OCV range including the OCV values ​​of individual battery cells based on OCV data. Here, the entire OCV range of a battery cell can include a first OCV range and a second OCV range. For example, the first OCV range can be a range equal to or greater than a specified OCV value (e.g., 3.2 V), and the second OCV range can be a range less than a specified OCV value (e.g., 3.2 V). Electronic device 400 can identify the OCV range including the OCV values ​​of individual battery cells from the first OCV range and the second OCV range.

[0081] According to one embodiment, the electronic device 400 can identify an estimation algorithm from a plurality of estimation algorithms that corresponds to an OCV range including the OCV value of a battery cell. For example, when the OCV value of a battery cell is included in a first OCV range (e.g., a range equal to or greater than 3.2 V), the electronic device 400 can identify a first estimation algorithm from a plurality of estimation algorithms. As another example, when the OCV value of a battery cell is included in a second OCV range (e.g., a range less than 3.2 V), the electronic device 400 can identify a second estimation algorithm from a plurality of estimation algorithms.

[0082] According to one embodiment, electronic device 400 can estimate the SOC of a single battery cell based on an estimation algorithm identified from a plurality of estimation algorithms.

[0083] In the following text, see references Figure 5 and Figure 6 This paper describes a method for estimating the State of Charge (SOC) of a battery cell using an electronic device 400 based on a first estimation algorithm and a second estimation algorithm. The following section details... Figure 5 The description involves the first estimation algorithm, and the following... Figure 6 The description involves the second estimation algorithm.

[0084] Figure 5 This is a view illustrating an example of an electronic device 400 according to an embodiment of the present disclosure estimating the SOC of a battery cell based on a first estimation algorithm.

[0085] refer to Figure 5 The electronic device 400 can estimate a first SOC 510 corresponding to a first OCV range, and based on the estimated first SOC 510, a first reference capacity 520, a charging capacity 530, and a second reference capacity 540, estimate an SOC 550 corresponding to the entire OCV range including the first OCV range and the second OCV range.

[0086] According to one embodiment, the electronic device 400 can estimate the SOC 550 corresponding to the entire OCV range of a single battery cell based on first SOC-OCV relationship information corresponding to a first OCV range. Here, the first SOC-OCV relationship information can represent the SOC-OCV relationship of lithium nickel cobalt manganese oxide, which is part of lithium-rich manganese oxide, within the first OCV range. For example, the first SOC-OCV relationship information can be a lookup table representing the SOC associated with OCV values ​​within the first OCV range.

[0087] According to one embodiment, electronic device 400 can estimate a first SOC 510 corresponding to a first OCV range based on first SOC-OCV relationship information obtained from a lookup table. Here, the first SOC 510 can be within a scale range of 0% to 100% of the first OCV range. For example, the first SOC 510 can be understood as representing the SOC within the first OCV range, rather than the entire SOC of the battery cell. For example, electronic device 400 can estimate the first SOC 510 by performing an extended Kalman filter operation based on the first SOC-OCV relationship information obtained from the lookup table and the OCV value of the battery cell corresponding to a specific OCV range.

[0088] According to one embodiment, the electronic device 400 can multiply a first SOC 510 by a first reference capacity 520 to obtain a charging capacity 530 based on a specified OCV value (e.g., 3.2 V). Here, the first reference capacity 520 may correspond to a first OCV range. For example, the first reference capacity 520 may be a capacity (e.g., 80 Ah) corresponding to the total capacity of the battery cell (e.g., 100 Ah) minus the capacity at the specified OCV value (e.g., 3.2 V) (e.g., 20 Ah). The charging capacity 530 may be the capacity required to charge from the specified OCV value to the current OCV value of the battery cell.

[0089] According to one embodiment, the electronic device 400 can calculate the charging capacity 530 based on the following Equation 1.

[0090] [Equation 1]

[0091] According to one embodiment, the electronic device 400 can estimate the SOC 550 of a battery cell by dividing the sum of a second reference capacity 540 and a charging capacity 530 by the sum of a first reference capacity 520 and a second reference capacity 540. Here, the second reference capacity 540 may correspond to a second OCV range. For example, the second reference capacity 540 may be the capacity of the battery cell at a specified OCV value (e.g., 3.2 V) (e.g., 20 Ah).

[0092] According to one embodiment, the electronic device 400 can calculate the SOC 550 corresponding to the entire OCV range of the battery cell based on the following Equation 2.

[0093] [Equation 2]

[0094] Figure 6 This is a view illustrating an example of an electronic device 400 according to an embodiment of the present disclosure estimating the SOC of a battery cell based on a second estimation algorithm.

[0095] refer to Figure 6 The electronic device 400 can estimate a second SOC 610 corresponding to the second OCV range, and based on the estimated second SOC 610, the first reference capacity 520, the second reference capacity 540, and the discharge capacity 620, estimate an SOC 630 corresponding to the entire OCV range including the first OCV range and the second OCV range.

[0096] According to one embodiment, the electronic device 400 can estimate the SOC 630 corresponding to the entire OCV range of the battery cell based on second SOC-OCV relationship information corresponding to the second OCV range. Here, the second SOC-OCV relationship information can represent the SOC-OCV relationship of lithium manganese oxide, which is part of lithium-rich manganese oxide, within the second OCV range. For example, the second SOC-OCV relationship information can be a lookup table representing the SOC associated with the OCV value within the second OCV range.

[0097] According to one embodiment, electronic device 400 can estimate a second SOC 610 corresponding to a second OCV range based on second SOC-OCV relationship information obtained from a lookup table. Here, the second SOC 610 can be within a scale range of 0% to 100% of the second OCV range. For example, the second SOC 610 can be understood as representing the SOC within the second OCV range, rather than the entire SOC of the battery cell. For example, electronic device 400 can estimate the second SOC 610 by performing an extended Kalman filter operation based on the second SOC-OCV relationship information obtained from the lookup table and the OCV value of the battery cell corresponding to a specific OCV range.

[0098] According to one embodiment, the electronic device 400 can calculate a first depth of discharge (DoD) corresponding to a second OCV range based on a second SOC 610. For example, the electronic device 400 can calculate the first DoD based on a specified operation (e.g., 100 - second SOC).

[0099] According to one embodiment, electronic device 400 can multiply a first DoD by a second reference capacity 540 to obtain a discharge capacity 620 based on a specified OCV value (e.g., 3.2 V). Here, the discharge capacity 620 can be the capacity of the battery cell discharged from the specified OCV value (e.g., 3.2 V) to the current OCV value of the battery cell.

[0100] According to one embodiment, the electronic device 400 can calculate the discharge capacity 620 based on the following equation 3.

[0101] [Equation 3]

[0102] According to one embodiment, the electronic device 400 can calculate the second DoD of a battery cell by dividing the sum of the first reference capacity 520 and the discharge capacity 620 by the sum of the first reference capacity 520 and the second reference capacity 540. Here, the second DoD can correspond to the entire OCV range of the battery cell.

[0103] According to one embodiment, the electronic device 400 can calculate the second DoD of a single battery cell based on the following Equation 4.

[0104] [Equation 4]

[0105] According to one embodiment, the electronic device 400 can calculate the SOC 630 of a single battery cell based on a second DoD. For example, the electronic device 400 can calculate the SOC 630 corresponding to the entire OCV range, including the first OCV range and the second OCV range, based on a specified operation (e.g., 100 - second DoD).

[0106] Figure 7 This is a flowchart illustrating the operation of an electronic device according to an embodiment of the present disclosure. Because... Figure 7 The operation method can be provided by Figure 4 The electronic device 400 executes the description, therefore its description may be omitted if it repeats the above description, or it may use... Figure 4 It is done using components.

[0107] Figure 7 The embodiments shown are merely examples, and the order of operation according to the various embodiments of this disclosure may vary. Figure 7 The order of operations shown is different. Figure 7 Some of the operations shown can be omitted, the order of operations can be changed, or operations can be combined.

[0108] refer to Figure 7At operation 710, electronic device 400 can acquire OCV data of individual battery cells. For example, the OCV data can be time-series data representing past and current OCV values ​​of the individual battery cells. As described above, for example, when electronic device 400 is implemented as a BMS, electronic device 400 can acquire OCV data by calculating the OCV based on values ​​related to the state of the individual battery cells measured by sensor 420. Alternatively, when electronic device 400 is implemented as a computing device (e.g., a server) that receives data from individual battery cells from external electronic devices, electronic device 400 can acquire the OCV data of the individual battery cells via communication circuitry 410.

[0109] At operation 720, electronic device 400 can identify an estimation algorithm from multiple estimation algorithms stored in memory 430 that corresponds to the OCV range including the OCV values ​​of the battery cells, based on the OCV data of the battery cells acquired at operation 710.

[0110] At operation 730, electronic device 400 can estimate the SOC corresponding to the entire OCV range of the battery cell based on the estimation algorithm identified at operation 720.

[0111] refer to Figure 8 The following will describe operations 720 and 730 in more detail: In operation 720, electronic device 400 identifies an estimation algorithm from multiple estimation algorithms, and in operation 730, electronic device 400 estimates the SOC corresponding to the entire OCV range of the battery cell based on the identified estimation algorithm.

[0112] Figure 8 This is a flowchart illustrating the operation of an electronic device according to an embodiment of the present disclosure. Because... Figure 8 The operation method can be provided by Figure 4 The electronic device 400 executes the description, therefore its description may be omitted if it repeats the above description, or it may use... Figure 4 It is done using components.

[0113] Figure 8 The embodiments shown are merely examples, and the order of operation according to the various embodiments of this disclosure may vary. Figure 8 The order of operations shown is different. Figure 8 Some of the operations shown can be omitted, the order of operations can be changed, or operations can be combined.

[0114] refer to Figure 8 At operation 810, electronic device 400 can be based on... Figure 7The electronic device 400 uses the OCV data acquired at operation 710 to identify the OCV range including the OCV values ​​of individual battery cells. Here, the entire OCV range of a single battery cell can include a first OCV range and a second OCV range. For example, the first OCV range can be a range equal to or greater than a specified OCV value (e.g., 3.2 V), and the second OCV range can be a range less than a specified OCV value. The electronic device 400 can identify the OCV range including the OCV values ​​of individual battery cells from the first OCV range and the second OCV range.

[0115] When it is identified at operation 810 that the OCV value of a single battery cell is included in the first OCV range, the operation proceeds to operation 820 (operation 810 - first OCV range), so that the electronic device 400 can identify the first estimation algorithm from a plurality of estimation algorithms stored in memory 430.

[0116] At operation 830, the electronic device 400 can estimate the first SOC corresponding to the first OCV range based on the first SOC-OCV relationship information corresponding to the first OCV range. Here, the first SOC can be in the range of 0% to 100%. For example, it can be understood that the first SOC represents the SOC within the scale range of the first OCV range, rather than the entire SOC of the battery cell. For example, the electronic device 400 can estimate the first SOC within the scale range of the first OCV range by performing an extended Kalman filter operation based on the first SOC-OCV relationship information and the OCV value of the battery cell.

[0117] At operation 840, the electronic device 400 can estimate the SOC of a single battery cell corresponding to the entire OCV range based on the first SOC estimated at operation 830.

[0118] According to one embodiment, electronic device 400 can multiply a first SOC by a first reference capacity to obtain a charging capacity based on a specified OCV value (e.g., 3.2 V). Here, the first reference capacity may correspond to a first OCV range. For example, the first reference capacity may be the capacity corresponding to the total capacity of the battery cell (e.g., 100 Ah) minus the capacity at the specified OCV value (e.g., 3.2 V) (e.g., 20 Ah) (e.g., 80 Ah). The charging capacity may be the capacity required to charge from the specified OCV value to the current OCV value of the battery cell.

[0119] According to one embodiment, the electronic device 400 can estimate the state of charge (SOC) of a battery cell by dividing the sum of a second reference capacity and the charging capacity by the sum of a first reference capacity and the second reference capacity. Here, the second reference capacity may correspond to a second OCV range. For example, the second reference capacity may be the capacity of the battery cell at a specified OCV value (e.g., 20 Ah).

[0120] When it is identified at operation 810 that the OCV value of a battery cell is included in the second OCV range, the operation proceeds to operation 850 (operation 810 - second OCV range), so that the electronic device 400 can identify the second estimation algorithm from the multiple estimation algorithms stored in the memory 430.

[0121] At operation 860, the electronic device 400 can estimate the second SOC corresponding to the second OCV range based on the second SOC-OCV relationship information corresponding to the second OCV range. Here, the second SOC can be in the range of 0% to 100%. For example, it can be understood that the second SOC represents the SOC within the scale range of the second OCV range, rather than the entire SOC of the battery cell. For example, the electronic device 400 can estimate the second SOC within the scale range of the second OCV range by performing an extended Kalman filter operation based on the second SOC-OCV relationship information and the OCV value of the battery cell.

[0122] At operation 870, the electronic device 400 can estimate the SOC of a single battery cell corresponding to the entire OCV range based on the second SOC estimated at operation 860.

[0123] According to one embodiment, the electronic device 400 can calculate a first DoD corresponding to the second OCV range based on a second SOC.

[0124] According to one embodiment, the electronic device 400 can multiply a first DoD by a second reference capacity to obtain a discharge capacity based on a specified OCV value (e.g., 3.2 V). Here, the discharge capacity can be the capacity to discharge from the specified OCV value to the current OCV value of the battery cell.

[0125] According to one embodiment, the electronic device 400 can calculate the second DoD of a battery cell by dividing the sum of a first reference capacity and a discharge capacity by the sum of the first reference capacity and a second reference capacity. Here, the second DoD can correspond to the entire OCV range of the battery cell.

[0126] According to one embodiment, the electronic device 400 can calculate the state of charge (SOC) of a single battery cell based on a second DoD. For example, the electronic device 400 can calculate the SOC based on a specified operation (e.g., 100 - second DoD).

[0127] The battery management system according to the foregoing embodiments may include, for example, a processor, a memory for storing and executing program data, permanent storage such as a disk drive, a communication port for communicating with external devices, and a user interface device such as a touch panel, buttons, or icons. Methods implemented by software modules or algorithms can be stored on a computer-readable recording medium as computer-readable code or program instructions executable on a processor. Here, computer-readable recording media include magnetic storage media (e.g., read-only memory (ROM), random access memory (RAM), floppy disks, and hard disks) and optically readable media (e.g., CD-ROMs and DVDs (Digital Versatile Discs)). The computer-readable recording medium can be distributed across computer systems connected via a network, allowing computer-readable code to be stored and executed in a distributed manner. The medium can be read by a computer, stored in memory, and executed by a processor.

[0128] Various embodiments of this disclosure can be represented by functional block components and various processing steps. Functional blocks can be implemented by various hardware and / or software components that perform specific functions. For example, embodiments can employ direct circuit components such as memory, processing, logic, and lookup tables, which can perform various functions under the control of one or more microprocessors or other control devices. Similar to components that can be programmed or executed by software components, embodiments of this disclosure can be implemented in programming or scripting languages ​​(such as C, C++, Java, or assembler), including various algorithms implemented by combinations of data structures, processes, routines, or other programming components. Functional aspects can be implemented by algorithms that execute on one or more processors. Furthermore, embodiments of this disclosure can employ conventional techniques for electronic environment setup, signal processing, and / or data processing. Terms such as “mechanism,” “component,” “device,” and “configuration” can be used broadly and are not limited to mechanical and physical components. These terms can include the meaning of a series of routines executed by software combined with a processor, etc.

[0129] The foregoing embodiments are merely examples, and other embodiments may be implemented within the scope of the appended claims.

Claims

1. An electronic device, comprising: A memory that stores multiple estimation algorithms for estimating the SOC of a single battery cell; as well as A processor, operatively coupled to the memory, Wherein, the processor: Obtain the OCV data of the battery cell. Based on the acquired OCV data of the battery cell, any one of the multiple estimation algorithms stored in the memory is identified, and Based on the identified estimation algorithm, the SOC of the battery cell is estimated.

2. The electronic device according to claim 1, wherein, The plurality of estimation algorithms includes a first estimation algorithm and a second estimation algorithm. Based on the acquired OCV data, the processor identifies the OCV range including the OCV values ​​of the individual battery cells. When the OCV value of the battery cell is included within the first OCV range, the processor identifies the first estimation algorithm from the plurality of estimation algorithms, and When the OCV value of the battery cell is included in the second OCV range, the processor identifies the second estimation algorithm from the plurality of estimation algorithms.

3. The electronic device according to claim 2, wherein, When the first estimation algorithm is identified from the plurality of estimation algorithms, the processor estimates the SOC of the battery cell based on the first SOC-OCV relationship information corresponding to the first OCV range, and When the second estimation algorithm is identified from the plurality of estimation algorithms, the processor estimates the SOC of the battery cell based on the second SOC-OCV relationship information corresponding to the second OCV range.

4. The electronic device according to claim 3, wherein, When the first estimation algorithm is identified from the plurality of estimation algorithms, the processor estimates the first SOC corresponding to the first OCV range based on the first SOC-OCV relationship information, and Based on the first SOC, the SOC of the battery cell corresponding to the entire OCV range including the first OCV range and the second OCV range is estimated, and When the second estimation algorithm is identified from the plurality of estimation algorithms, the processor estimates the second SOC corresponding to the second OCV range based on the second SOC-OCV relationship information, and Based on the second SOC, the SOC of the battery cell corresponding to the entire OCV range including the first OCV range and the second OCV range is estimated.

5. The electronic device according to claim 4, wherein, The first OCV range is a range equal to or greater than the specified OCV value, and The second OCV range is a range smaller than the specified OCV value.

6. The electronic device according to claim 5, wherein, When the first estimation algorithm is identified from the plurality of estimation algorithms, the processor estimates the first SOC by performing an extended Kalman filter operation based on the first SOC-OCV relationship information and the OCV value of the battery cell. The first SOC is multiplied by a first reference capacity corresponding to the first OCV range to obtain the charging capacity based on the specified OCV value, and The sum of the second reference capacity corresponding to the second OCV range and the charging capacity is divided by the sum of the first reference capacity and the second reference capacity to estimate the SOC of the battery cell.

7. The electronic device according to claim 6, wherein, The battery cell includes a lithium-rich manganese oxide as the positive electrode active material, and The first SOC-OCV relationship information represents the relationship between the SOC and OCV of lithium nickel cobalt manganese oxide, which is part of the lithium-rich manganese oxide, within the first OCV range.

8. The electronic device according to claim 5, wherein, When the second estimation algorithm is identified from the plurality of estimation algorithms, the processor estimates the second SOC by performing an extended Kalman filter operation based on the second SOC-OCV relationship information and the OCV value of the battery cell. Based on the second SOC, calculate the first DoD corresponding to the second OCV range. Multiply the first DoD by the second reference capacity corresponding to the second OCV range to obtain the discharge capacity based on the specified OCV value. Dividing the sum of the first reference capacity and the discharge capacity corresponding to the first OCV range by the sum of the first reference capacity and the second reference capacity yields the second DoD of the battery cell corresponding to the entire OCV range of the battery cell. Based on the second DoD, the SOC of the battery cell is estimated.

9. The electronic device according to claim 8, wherein, The battery cell includes a lithium-rich manganese oxide as the positive electrode active material, and The second SOC-OCV relationship information represents the relationship between the SOC and OCV of lithium manganese oxide, which is part of the lithium-rich manganese oxide, within the second OCV range.

10. A method for estimating the state of charge (SOC) of a single battery cell, performed by an electronic device, the SOC estimation method comprising: Obtain the OCV data of the battery cell; Based on the acquired OCV data, identify any one of the multiple estimation algorithms; as well as Based on the identified estimation algorithm, the SOC of the battery cell is estimated.

11. The SOC estimation method according to claim 10, wherein, The plurality of estimation algorithms includes a first estimation algorithm and a second estimation algorithm, and Identifying any one of the plurality of estimation algorithms includes: Based on the acquired OCV data, the OCV range including the OCV values ​​of the individual battery cells is identified. When the OCV value of the battery cell is included in the first OCV range, the first estimation algorithm is identified from the plurality of estimation algorithms, and When the OCV value of the battery cell is included in the second OCV range, the second estimation algorithm is identified from the plurality of estimation algorithms.

12. The SOC estimation method according to claim 11, wherein, Estimating the SOC of the battery cell includes: When the first estimation algorithm is identified from the plurality of estimation algorithms Based on the first SOC-OCV relationship information, the first SOC corresponding to the first OCV range is estimated, and Based on the first SOC, estimate the SOC of the battery cell corresponding to the entire OCV range including the first OCV range and the second OCV range, and When the second estimation algorithm is identified from the plurality of estimation algorithms The second SOC corresponding to the second OCV range is estimated based on the second SOC-OCV relationship information, and Based on the second SOC, the SOC of the battery cell corresponding to the entire OCV range including the first OCV range and the second OCV range is estimated.

13. The SOC estimation method according to claim 12, wherein, The first OCV range is a range equal to or greater than the specified OCV value, and The second OCV range is a range smaller than the specified OCV value.

14. The SOC estimation method according to claim 13, wherein, Estimating the SOC of the battery cell includes: When the first estimation algorithm is identified from the plurality of estimation algorithms, the first SOC is estimated by performing an extended Kalman filter operation based on the first SOC-OCV relationship information and the OCV value of the battery cell. The first SOC is multiplied by a first reference capacity corresponding to the first OCV range to obtain the charging capacity based on the specified OCV value, and The sum of the second reference capacity corresponding to the second OCV range and the charging capacity is divided by the sum of the first reference capacity and the second reference capacity to estimate the SOC of the battery cell.

15. The SOC estimation method according to claim 13, wherein, Estimating the SOC of the battery cell includes: When the second estimation algorithm is identified from the plurality of estimation algorithms, the second SOC is estimated by performing an extended Kalman filter operation based on the second SOC-OCV relationship information and the OCV value of the battery cell. Based on the second SOC, calculate the first DoD corresponding to the second OCV range. Multiply the first DoD by the second reference capacity corresponding to the second OCV range to obtain the discharge capacity based on the specified OCV value. Dividing the sum of the first reference capacity and the discharge capacity corresponding to the first OCV range by the sum of the first reference capacity and the second reference capacity yields the second DoD of the battery cell corresponding to the entire OCV range of the battery cell. Based on the second DoD, the SOC of the battery cell is estimated.

16. The electronic device according to claim 1, wherein, The processor directly acquires the OCV data measured by the sensor, or acquires the OCV data from an external device via communication.

17. The electronic device according to claim 5, wherein, The specified OCV value is approximately 3.2 V.

18. The SOC estimation method according to claim 10, wherein, Acquiring the OCV data includes directly acquiring the OCV data measured by the sensor, or acquiring the OCV data from an external device via communication.

19. The SOC estimation method according to claim 13, wherein, The specified OCV value is approximately 3.2 V.

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