Battery management device, battery management method, and battery management system

Abstract

According to some embodiments disclosed in this document, a battery management apparatus includes a sensor configured to collect battery data from a battery to be managed, and a controller configured to: extract, from the battery data, a data set relating to charge and discharge rates and voltage for each of a plurality of reference SOC values; generate, by performing a fitting on the data set at each reference SOC value, a first curve of voltage as a function of charge and discharge rate; generate, based on the first curve at each reference SOC value, a second curve of voltage as a function of SOC for each of a plurality of charge and discharge rates; and estimate a state of the battery to be managed based on the plurality of second curves corresponding to the plurality of charge and discharge rates.

Description

Technical Field

[0001] Cross-references to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2023-0160215, filed on November 20, 2023, with the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein relate to battery management devices, battery management methods, and battery management systems. Background Technology

[0004] Recently, research and development of rechargeable batteries have been actively pursued. In this paper, rechargeable batteries, as rechargeable / dischargeable batteries, can be interpreted as including all conventional nickel (Ni) / cadmium (Cd) batteries, Ni / metal hydride (MH) batteries, and more recently, lithium-ion batteries. Among rechargeable batteries, lithium-ion batteries can have higher energy densities than conventional Ni / Cd and Ni / MH batteries, and can be manufactured in smaller and lighter sizes, allowing for high availability in powering mobile devices. Recently, lithium-ion batteries have attracted attention as a next-generation energy storage medium as their applications expand to powering electric vehicles.

[0005] Battery data (such as voltage, current, temperature, state of charge (SOC), etc.) can be analyzed to estimate the state of the battery used in a vehicle. For example, the state of the battery (such as resistance degradation) can be diagnosed, and for this purpose, a process can be performed to separate battery data mixed for multiple charge / discharge rates (C-rate) according to each charge / discharge rate. Meanwhile, during the separation of charge / discharge rates, the performance of the state diagnosis can vary depending on the method used to fit the data. Summary of the Invention

[0006] Technical issues

[0007] The embodiments disclosed herein aim to provide a battery management device, battery management method, and battery management system, wherein a data fitting method can be advanced in the process of separating battery data for each charge / discharge rate to improve the performance of state diagnosis.

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

[0009] Technical solution

[0010] According to some embodiments disclosed herein, a battery management device includes: a sensor configured to collect battery data from a target battery; and a controller configured to: extract a dataset related to charge / discharge rate and voltage from the battery data for each of a plurality of reference state of charge (SOC) values; generate a first curve of voltage versus charge / discharge rate by fitting the dataset at each reference SOC value; generate a second curve of voltage versus SOC for each of the plurality of charge / discharge rates based on the first curve at each reference SOC value; and estimate the state of the target battery based on the plurality of second curves corresponding to the plurality of charge / discharge rates.

[0011] According to some implementations, the controller may also be configured to generate the first curve by performing an optimized polynomial fit on the dataset at each reference SOC value.

[0012] According to some implementations, the controller may also be configured to: determine the polynomial order best suited for the dataset; and generate the first curve by minimizing the difference between the datasets of estimated polynomial expressions based on the polynomial order.

[0013] According to some implementations, the controller may also be configured to: generate a third curve of the resistance of the target battery relative to its state of charge (SOC) based on the plurality of second curves; and estimate the resistance degradation state of the target battery based on the third curve.

[0014] According to some implementations, the controller may also be configured to estimate the resistance degradation state by comparing the third curve with a resistance curve at the manufacturing time of the target battery.

[0015] According to an implementation, the controller may also be configured to: generate correction data by performing current integration and Kalman filter error correction on the battery data; and extract the dataset based on the correction data.

[0016] According to some implementations, the sensor can be configured to collect battery data from the managed target battery, which is being charged or discharged by an electrical device including the managed target battery.

[0017] According to some embodiments disclosed herein, a battery management method includes the following steps: collecting battery data from a target battery; extracting a dataset related to charge / discharge rate and voltage from the battery data for each of a plurality of reference state of charge (SOC) values; generating a first curve of voltage versus charge / discharge rate by fitting the dataset at each reference SOC value; generating a second curve of voltage versus SOC for each of a plurality of charge / discharge rates based on the first curve at each reference SOC value; and estimating the state of the target battery based on the plurality of second curves corresponding to the plurality of charge / discharge rates.

[0018] According to some implementations, the step of generating the first curve may include generating the first curve by performing an optimized polynomial fit on the dataset at each reference SOC value.

[0019] According to some implementations, the step of generating the first curve may include the following steps: determining the polynomial order best suited for the dataset; and generating the first curve by minimizing the difference between the datasets of estimated polynomial expressions based on the polynomial order.

[0020] According to some embodiments, the battery management method may further include the following steps: generating a third curve of the resistance of the target battery relative to its state of charge (SOC) based on the plurality of second curves; and estimating the resistance degradation state of the target battery based on the third curve.

[0021] According to some implementations, the step of estimating the resistance degradation state may include estimating the resistance degradation state by comparing the third curve with the resistance curve at the manufacturing time of the target battery.

[0022] According to some implementations, the step of extracting the dataset may include the following steps: generating corrected data by performing current integration and Kalman filter error correction on the battery data; and extracting the dataset based on the corrected data.

[0023] According to some implementations, the step of collecting the battery data may include collecting the battery data from the managed target battery being charged or discharged by an electrical device including the managed target battery.

[0024] According to some embodiments disclosed herein, a battery management system includes: a target battery for management that is charged or discharged by an electrical device, and a battery management device configured to collect battery data from the target battery; extract a dataset related to charge / discharge rate and voltage from the battery data for each of a plurality of reference state of charge (SOC) values; generate a first curve of voltage versus charge / discharge rate by fitting the dataset at each reference SOC value; generate a second curve of voltage versus SOC for each of a plurality of charge / discharge rates based on the first curve at each reference SOC value; and estimate the state of the target battery based on the plurality of second curves corresponding to the plurality of charge / discharge rates.

[0025] Beneficial effects

[0026] According to the embodiments disclosed herein, a battery management device, a battery management method, and a battery management system can be provided, wherein a data fitting method can be advanced during the process of separating battery data for each charge / discharge rate to improve the performance of state diagnosis.

[0027] The technical effects of the embodiments disclosed in this document are not limited to the effects described above, and those skilled in the art will clearly understand other effects not mentioned based on the disclosure of this document. Attached Figure Description

[0028] Figure 1 The components of a battery management system according to some embodiments are shown.

[0029] Figure 2 Components of a battery management device according to some embodiments are shown.

[0030] Figure 3 The process of generating cumulative voltage data relative to SOC by measuring battery data is shown according to some embodiments.

[0031] Figure 4 The SOC and voltage of the battery are shown according to some implementation methods.

[0032] Figures 5 to 7 The dataset for each reference SOC value according to some implementations is shown, along with a first curve obtained by fitting the dataset.

[0033] Figure 8 A second curve for each of a plurality of charge / discharge rates is shown according to some embodiments.

[0034] Figure 9 A third curve showing the battery resistance versus SOC according to some embodiments is shown.

[0035] Figure 10 The operation of a battery management method according to some embodiments is shown. Detailed Implementation

[0036] In the following description, embodiments disclosed herein will be described with reference to the accompanying drawings. However, this description is not intended to limit the disclosure of this document to the specific embodiments, and it should be construed as including various modifications, equivalents, and / or alternatives to the embodiments described herein.

[0037] It should be understood that the embodiments described in this document and the terminology used therein are not intended to limit the technical features set forth herein to specific embodiments, and include various modifications, equivalents, or alternatives to the corresponding embodiments. Regarding the description of the drawings, similar reference numerals may be used to refer to similar or related elements. It should be understood that the singular form of a noun corresponding to an item may include one or more things, unless the relevant context clearly indicates otherwise.

[0038] As used herein, each of 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 or all possible combinations of the items listed together in the corresponding phrase within the phrase. Unless otherwise stated, terms such as “1st,” “2nd,” “first,” “second,” “A,” “B,” “(a),” or “(b)” may be used simply to distinguish one component from another and do not limit the components in other respects (e.g., importance or order).

[0039] In this document, it should be understood that when an element (e.g., a first element) is referred to as being “connected,” “linked,” “coupled,” “attached to,” or “connected to” or “connected to” another element (e.g., a second element) with or without the terms “operably” or “communically”, it means that the element can be connected to the other element directly (e.g., wired or wirelessly) or indirectly (e.g., via a third element).

[0040] Methods according to various embodiments disclosed herein can be included and provided in a computer program product. The computer program product can be traded as a product between a seller and a buyer. The computer program product can be distributed in the form of a machine-readable storage medium (e.g., an optical disc read-only memory (CD-ROM)), or distributed online via an app store (e.g., downloaded or uploaded), or distributed directly between two drive devices. In the case of online distribution, at least a portion of the computer program product can be stored at least temporarily in a machine-readable storage medium, such as the memory of a manufacturer's server, the memory of an app store's server, or the memory of a relay server, or can be temporarily generated.

[0041] 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, some of which may be individually disposed on 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 one 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 the corresponding components in the multiple components prior to integration. According to the embodiments disclosed herein, operations performed by modules, programs, or other components may be performed sequentially, in parallel, repeatedly, or heuristically, or one or more operations may be performed in a different order or omitted, or one or more other operations may be added.

[0042] Figure 1 The components of a battery management system according to some embodiments are shown.

[0043] Reference Figure 1 The battery management system 100 may include a power-consuming device 110, a managed target battery 120, a battery management device 130, and a management server 140. However, it is not limited to this; some components may be omitted from the battery management system 100, or other common components may be further included in the battery management system 100.

[0044] The battery management system 100 can refer to a system used to diagnose and manage the state of the target battery 120. When the target battery 120 is charged or discharged by the electrical device 110, the corresponding battery data of the target battery 120 can be measured and analyzed by the battery diagnostic device 130.

[0045] Electrical device 110 can be configured to charge or discharge target battery 120. Electrical device 110 can discharge target battery 120 while consuming power, and charge target battery 120 while generating power. According to embodiments, electrical device 110 may include mobile devices such as electric vehicles, electric bicycles, etc. The mobile device may drive a motor based on power from target battery 120, or charge target battery 120 using power generated through regenerative braking.

[0046] The target battery 120 may include a battery pack or the like, which is the management target of the battery management system 100. The battery pack of the target battery 120 may include multiple battery modules, and each battery module may include multiple battery cells. According to an embodiment, the target battery 120 may be installed in mobile devices such as electric vehicles and electric bicycles.

[0047] The battery management device 130 can perform operations for diagnosing or managing the target battery 120. The battery management device 130 can measure battery data from the target battery 120 and diagnose or manage the state of the target battery 120 based on the battery data.

[0048] The management server 140 can manage the diagnostic results of the battery management device 130. The management server 140 can exchange data with the battery management device 130 via wired / wireless communication. When a fault is diagnosed in the target battery 120 or its lifespan is predicted, the corresponding results can be sent to the management server 140 and recorded in the database.

[0049] According to one embodiment, the management server 140 can perform operations for managing the target battery 120 instead of the battery management device 130. According to another embodiment, the battery management device 130 can perform diagnostic operations by executing battery management software, and the management server 140 can provide the battery management device 130 with update information for the battery management software.

[0050] Figure 2 Components of a battery management device according to some embodiments are shown.

[0051] Reference Figure 2 The battery management device 130 may include a sensor 131 and a controller 132. However, it is not limited thereto; some components may be omitted from the battery management device 130, or other common components may be further included in the battery management device 130.

[0052] According to the implementation, the sensor 131 and controller 132 of the battery management device 130 can be electrically connected to each other in a device-to-device communication manner. The device-to-device communication manner may include general purpose input and output (GPIO), serial peripheral interface (SPI), mobile industrial processor interface (MIPI), etc.

[0053] Sensor 131 can be configured to generate various battery measurements from the target battery 120. For this purpose, sensor 131 may include measuring devices such as voltage sensors, current sensors, temperature sensors, etc.

[0054] 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 using an array of logic gates or a general-purpose microprocessor for handling various operations, and may include a single processor or multiple processors. For example, the controller 132 may be implemented in the form of at least one of a microprocessor, CPU, GPU, and AP.

[0055] The controller 132 can operate in conjunction with a memory configured to store various data, instructions, mobile applications, computer programs, etc. The memory can be configured separately from or integrated with the controller 132. The controller 132 can execute instructions stored in the memory to perform various calculations. For example, the memory can be implemented as a non-volatile memory device such as ROM, PROM, EPROM, EEPROM, flash memory, PRAM, MRAM, RRAM, FRAM, etc., or as a volatile memory device such as DRAM, SRAM, SDRAM, RRAM, etc., or a combination thereof.

[0056] Sensor 131 can be configured to collect battery data from the managed target battery 120. The battery data may include voltage, current, temperature, etc., of the managed target battery 120 measured at specific intervals. According to embodiments, the SOC of the managed target battery 120 can be derived based on the voltage, current, temperature, etc., of the battery data, and the state of charge / discharge (C-rate (CR)) of the managed target battery 120 can be derived based on the current, etc., of the battery data. When the state of charge / discharge is 0, the managed target battery 120 may be in an open-circuit state, and in this case, the voltage may be the open-circuit voltage (OCV). The sign (+ / -) of the state of charge / discharge can indicate whether the managed target battery 120 is in a charging state or a discharging state.

[0057] The controller 132 can be configured to extract a dataset Si related to charge / discharge rate (C rate (CR)) and voltage from battery data for each of a plurality of reference SOC values ​​SOC1 to SOCn. In the following text, references will be made to... Figures 3 to 7Battery data may include multiple measurements generated per measurement cycle, and the measurements at each time point may include voltage, current, and temperature values, and the SOC value at each time point can be further derived. According to an implementation, a graph 330 can be generated plotting data indicating the SOC and voltage values ​​at each time point, and a dataset Si can be extracted based on the graph 330. For example, SOC-voltage data corresponding to reference SOC values ​​(SOCx, SOCy, SOCz) can be extracted to generate datasets (Sx, Sy, Sz).

[0058] The controller 132 can be configured to generate a first curve P1i of voltage versus charge / discharge rate CR by performing a fitting operation on a dataset Si at each reference SOC value SOCi. For example, a dataset Sx can be generated by extracting measurements of a specific SOC value SOCx from accumulated data of SOC-voltage measured at multiple time points (as shown in Figure 330). The dataset Sx can have the charge / discharge rate CR and voltage V as shown in Figure 500, and a first curve P1x that best fits the pattern of the dataset Sx can be derived. For example, the first curve P1x can be represented as a first-order polynomial expression.

[0059] The controller 132 can be configured to generate a second voltage curve P2j relative to the SOC for each of a plurality of charge / discharge rates CR1 to CRm, based on a first curve P1i at each reference SOC value SOCi. See below for further details. Figure 8 To describe the second curve P2j. For example, the method of transferring the seven points of the reference SOC value Sz in graph 700 to graph 800 for multiple reference SOC values ​​S1 to S1n can be repeated to generate the second curves P21 to P2 for multiple charge / discharge rates CR1 to CRm. m .

[0060] Controller 132 can be configured to be based on multiple second curves P21 to P2 corresponding to multiple charge / discharge rates CR1 to CRm. m To estimate the state of the target battery 120. This can be based on multiple second curves P21 to P2 m This allows for the diagnosis of the resistance degradation SOHR of the target battery 120, and the lifespan of the target battery 120 can be estimated based on the value of the resistance degradation SOHR. For example, for a specific SOC value SOCz in Figure 800, multiple charge / discharge rates CR1 to CRm and multiple voltage values ​​can be provided, and therefore, based on them, the resistance value of the target battery 120 for a specific SOC value SOCz can be estimated.

[0061] According to the implementation, controller 132 can be configured to generate a first curve P1i by performing optimized polynomial fitting on dataset Si at each reference SOC value SOCi. For example, dataset Sx of chart 500 can be fitted with a first-order polynomial expression P1x, while dataset Sy of chart 600 can be fitted with a second-order polynomial expression P1y. Since fitting can be performed using a polynomial expression of the pattern that best fits dataset Si, the accuracy of data fitting and the performance of state diagnosis can be improved compared to uniformly using linear or quadratic equations for fitting.

[0062] According to the implementation, the controller 132 can be configured to determine the most suitable polynomial order for the dataset Si and minimize the differences between the datasets Si of the estimated polynomial expression based on the polynomial order, thereby generating a first curve P1i. For this purpose, various curve fitting models can be used, and after determining the polynomial order, the coefficients of the estimated polynomial expression can be adjusted using methods such as least squares.

[0063] According to an implementation, the controller 132 can also be configured to be based on a plurality of second curves P21 to P2 m A third curve P3 is generated to represent the resistance of the target battery 120 relative to its state of charge (SOC), and the resistance degradation state of the target battery 120 is estimated based on the third curve P3. For example, the resistance degradation SOHRi can be estimated using multiple charge / discharge rates CR1 to CRm and multiple voltage values ​​at each reference SOC value SOCi, and this process can be repeated for reference SOC values ​​SOC1 to SOCn to generate the third curve P3 of SOHR relative to SOC.

[0064] According to an embodiment, the controller 132 can be configured to estimate the resistance degradation state by comparing the third curve P3 with the resistance curve during the manufacturing of the target battery 120. For example, as described below Figure 9 In Figure 900, as the target battery 120 deteriorates, the resistance of the target battery 120 can increase, and therefore can be compared with a reference time point, making it possible to estimate the resistance deterioration state.

[0065] According to one implementation, controller 132 can be configured to generate corrected data by performing current integration and Kalman filter error correction on battery data, and to extract dataset Si based on the corrected data. Current integration may include a method of processing battery data by integrating current values ​​to generate SOC-voltage data. Therefore, performing error correction via a Kalman filter can generate a data graph such as chart 330.

[0066] According to an embodiment, sensor 131 can be configured to collect battery data from a managed target battery 120 that is being charged or discharged by an electrical device including a managed target battery 120. For example, the electrical device may include an electric vehicle, and during the charging or discharging of the managed target battery 120 by the motor of the electric vehicle, voltage, current, temperature, etc., can be measured at specific intervals at each time point.

[0067] Figure 3 The process of generating cumulative voltage data relative to SOC by measuring battery data is shown according to some embodiments.

[0068] Reference Figure 3 The process of generating a graph 330 of cumulative voltage data relative to the State of Charge (SOC) by measuring battery data 310 from the target battery 120 can be illustrated. The graph 330 of cumulative data may include multiple SOC-voltage matching values.

[0069] According to the implementation, SOC data 320 can be derived from battery data 310 by current integration or other suitable methods. Here, error correction using a Kalman filter or the like can be performed as needed. Battery data 310 and SOC data 320 can include multiple data points measured at multiple time points. The SOC value and voltage value in SOC data 320 can be set as a point, and multiple points can be plotted in two dimensions to generate a graph 330 of accumulated data.

[0070] Figure 4 The SOC and voltage of the battery are shown according to some implementation methods.

[0071] Reference Figure 4 Chart 330 can be shown, in which data points of SOC and voltage derived from battery data are cumulatively indicated. Chart 330 can show SOC-voltage matching values ​​measured for multiple charge / discharge rates CR1 to CRm without distinction.

[0072] Since graph 330 can indiscriminately indicate SOC-voltage matching values ​​for multiple charge / discharge rates CR1 to CRm, a differentiation process may be necessary. For example, data points of reference SOC values ​​(Sx, Sy, Sz, etc.) can be extracted, and the extracted points can be processed from different perspectives to make the data points distinguishable from each other for multiple charge / discharge rates CR1 to CRm. Upon completion of the differentiation, the multiple data points of graph 330 can be divided into multiple second curves P21 to P2 of graph 900. m .

[0073] Figures 5 to 7The dataset for each reference SOC value according to some implementations is shown, along with a first curve obtained by fitting the dataset.

[0074] Reference Figures 5 to 7 Figures 500, 600, and 700 can be shown to illustrate the optimized polynomial fits used to describe several datasets (Sx, Sy, Sz). Each dataset Si can include matched values ​​for charge / discharge rates CR and voltages, and the amount of data can vary depending on the vehicle's driving mode, battery usage time, etc.

[0075] Based on the data distribution pattern of each dataset Si, each dataset Si can be fitted with a polynomial expression of the most suitable order. For example, the dataset Sx in Figure 500 can be fitted with a first-order polynomial expression, while the dataset Sy in Figure 600 can be fitted with a second-order polynomial expression. In this way, since the fitting order for various patterns may not be fixed, the performance of data fitting and the accuracy of state diagnosis can be improved. When data fitting is completed through optimized polynomial fitting in each dataset Si, even for the charging / discharging rate CR value where there is no measurement point, the voltage value at that value can be estimated.

[0076] Figure 8 A second curve for each of a plurality of charge / discharge rates is shown according to some embodiments.

[0077] Reference Figure 8 A graph 800 showing a second curve P2j for each of a plurality of charge / discharge rates CR1 to CRm can be displayed. Graph 800 can individually indicate data points of graph 330 relative to the plurality of charge / discharge rates CR1 to CRm.

[0078] A second curve P2j can be generated by collecting voltage values ​​at each SOC value for a specific charge / discharge rate CRj. For example, the voltage values ​​at some SOC values ​​SOCz in graph 700 can be indicated in graph 800, and this method can be repeated for other SOC values ​​to form the second curve P2j.

[0079] Chart 800 may include second curves P21 to P27 for 0 CR, ±1 CR, ±2 CR, and ±3 CR. Meanwhile, when no direct measurement point exists for 0.5 CR or 1.5 CR, the corresponding value can be estimated from the polynomial expression of the first curve P1i in Charts 500 to 700.

[0080] Figure 9 A third curve showing the battery resistance versus SOC according to some embodiments is shown.

[0081] Reference Figure 9 Figure 900 shows the third curve P3 indicating the battery resistance relative to the state of charge (SOC).

[0082] In Figure 900, a third curve P3 can be generated by collecting the resistance values ​​at each SOC value. For example, multiple charge / discharge rates CR and voltage values ​​can be provided at the corresponding SOC values ​​in Figure 800, and the charge / discharge rate CR can correspond to a current value, making it possible to estimate the resistance value at each SOC value based on a pattern of these values.

[0083] The resistance degradation state of the target battery 120 can be estimated by comparing the third curve P3 with the reference resistance curve 910. According to an embodiment, the reference resistance curve 910 can represent the resistance of each state of charge (SOC) measured at the beginning of life (BOL) state of the target battery 120. It can be determined that the larger the resistance difference ΔR between the third curve P3 and the reference resistance curve 910, the more severe the resistance degradation of the target battery 120.

[0084] Figure 10 The operation of a battery management method according to some embodiments is shown.

[0085] Reference Figure 10 The battery management method 1000 may include operations 1010 to 1050. However, it is not limited to this; some operations may be omitted and other general operations may be added, and the operations of the battery management method 1000 may be performed in a different order than that shown.

[0086] Battery management method 1000 may include operations processed sequentially by battery management device 130. Therefore, the matters described above for battery management device 130 (even those omitted below) can be equivalently applied to battery management method 1000.

[0087] Operations 1010 to 1050 of the battery management method 1000 can be performed by the sensor 131 and controller 132 of the battery management device 130.

[0088] In operation 1010, the battery management device 130 can collect battery data from the target battery being managed.

[0089] In operation 1020, the battery management device 130 can extract a dataset Si related to the charge / discharge rate (C rate) CR and voltage from the battery data for each of a plurality of reference SOC values ​​SOC1 to SOCn.

[0090] In operation 1030, the battery management device 130 can generate a first curve P1i of voltage versus charge / discharge rate CR by performing fitting on the dataset Si at each reference SOC value SOCi.

[0091] In operation 1040, the battery management device 130 can generate a second voltage curve P2j relative to the SOC for each of a plurality of charge / discharge rates CR1 to CRm, based on a first curve P1i at each reference SOC value SOCi.

[0092] In operation 1050, the battery management device 130 can be based on multiple second curves P21 to P2 corresponding to multiple charge / discharge rates CR1 to CRm. m To estimate the state of the target battery.

[0093] According to an embodiment, the battery management method 1000 can be implemented as a computer program stored on a computer-readable storage medium. That is, the computer program may include instructions for implementing the battery management method 1000, and the instructions may be stored on a computer-readable storage medium. The computer program may include a mobile application.

[0094] According to embodiments, computer-readable storage media may include magnetic media (such as hard disks, floppy disks, and magnetic tapes), optical media (such as optical disc read-only memories (CD-ROMs) and digital versatile discs (DVDs)), magneto-optical media (such as floppy disks), and hardware devices specifically configured to store and execute program instructions (such as ROMs, RAMs, and flash memory). 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 an interpreter.

[0095] Unless otherwise stated, terms such as “comprising,” “constituting,” or “having” described above may mean that the corresponding component may be inherent and should therefore be interpreted as further including rather than excluding 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. General terms, such as those defined in dictionaries, should be interpreted as having the same meaning as in the context of the relevant art and should not be interpreted as having an ideal or overly formal meaning unless they are expressly defined in this document.

[0096] The above description is merely an illustration of the technical concept disclosed herein, and various modifications and variations will be possible for those skilled in the art without departing from the essential characteristics of the disclosed embodiments. Therefore, the embodiments disclosed herein are intended to describe, and not limit, the technical spirit of the disclosed embodiments, and the scope of the technical spirit of this disclosure is not limited by these disclosed embodiments. The scope of protection of the technical spirit disclosed herein should be interpreted by the appended claims, and all technical spirit within the same scope should be understood to be included within the scope of this document.

[0097] [Explanation of reference numerals in the attached diagram]

[0098] 100: Battery Management System 110: Electrical Equipment

[0099] 120: Target battery for management; 130: Battery management device.

[0100] 131: Sensor 132: Controller

[0101] 140: Management Server

Claims

1. A battery management device, the battery management device comprising: Sensors configured to collect battery data from the target battery being managed; as well as The controller is configured to: Extract datasets related to charge / discharge rate and voltage from each of the multiple reference state of charge (SOC) values ​​from the battery data; A first curve of voltage versus charge / discharge rate is generated by fitting the dataset at each reference SOC value; Based on the first curve at each reference SOC value, a second curve of voltage versus SOC is generated for each of a plurality of charge / discharge rates; as well as The state of the target battery is estimated based on multiple second curves corresponding to the multiple charge / discharge rates.

2. The battery management device according to claim 1, wherein, The controller is also configured to generate the first curve by performing an optimized polynomial fit on the dataset at each reference SOC value.

3. The battery management device according to claim 2, wherein, The controller is also configured to: Determine the polynomial order that best suits the dataset; as well as The first curve is generated by minimizing the difference between the datasets of estimated polynomial expressions based on the polynomial order.

4. The battery management device according to claim 1, wherein, The controller is also configured to: A third curve representing the resistance of the target battery relative to its state of charge (SOC) is generated based on the plurality of second curves; and The resistance degradation state of the target battery is estimated based on the third curve.

5. The battery management device according to claim 4, wherein, The controller is also configured to estimate the resistance degradation state by comparing the third curve with the resistance curve at the manufacturing time of the target battery.

6. The battery management device according to claim 1, wherein, The controller is also configured to: Corrected data is generated by performing current integration and Kalman filter error correction on the battery data; and The dataset is extracted based on the corrected data.

7. The battery management device according to claim 1, wherein, The sensor is configured to collect battery data from the managed target battery, which is being charged or discharged by an electrical device including the managed target battery.

8. A battery management method, the battery management method comprising the following steps: Collect battery data from the target battery for management; Extract datasets related to charge / discharge rate and voltage from each of the multiple reference state of charge (SOC) values ​​from the battery data; A first curve of voltage versus charge / discharge rate is generated by fitting the dataset at each reference SOC value; Based on the first curve at each reference SOC value, a second curve of voltage versus SOC is generated for each of a plurality of charge / discharge rates; as well as The state of the target battery is estimated based on multiple second curves corresponding to the multiple charge / discharge rates.

9. The battery management method according to claim 8, wherein, The step of generating the first curve includes generating the first curve by performing an optimized polynomial fit on the dataset at each reference SOC value.

10. The battery management method according to claim 9, wherein, The steps for generating the first curve include the following: Determine the polynomial order best suited for the dataset; and The first curve is generated by minimizing the difference between the datasets of estimated polynomial expressions based on the polynomial order.

11. The battery management method according to claim 8, further comprising the following steps: A third curve representing the resistance of the target battery relative to its state of charge (SOC) is generated based on the plurality of second curves. as well as The resistance degradation state of the target battery is estimated based on the third curve.

12. The battery management method according to claim 11, wherein, The step of estimating the resistance degradation state includes estimating the resistance degradation state by comparing the third curve with the resistance curve at the manufacturing time of the management target battery.

13. The battery management method according to claim 8, wherein, The steps to extract the dataset include the following: Corrected data is generated by performing current integration and Kalman filter error correction on the battery data; and The dataset is extracted based on the corrected data.

14. The battery management method according to claim 8, wherein, The step of collecting the battery data includes collecting the battery data from the managed target battery being charged or discharged by an electrical device including the managed target battery.

15. A battery management system, the battery management system comprising: A target battery is managed, which is charged or discharged by an electrical device; as well as A battery management device configured to collect battery data from a target battery, extract a dataset related to charge / discharge rate and voltage from the battery data for each of a plurality of reference state of charge (SOC) values, and generate a first curve of voltage versus charge / discharge rate by fitting the dataset at each reference SOC value; Based on the first curve at each reference SOC value, a second curve of voltage versus SOC is generated for each of a plurality of charge / discharge rates; And the state of the target battery is estimated based on a plurality of second curves corresponding to the plurality of charge / discharge rates.

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