Appartus and method for estimating the capacity of a lithium ion battery and determining an internal short circuit using the same

Abstract

A battery state estimation device according to the present invention comprises: a memory storing a state estimation program for estimating the capacity and internal short circuit status of a lithium-ion battery; and a processor for executing the state estimation program, wherein the state estimation program receives the current and voltage of the battery, generates voltage change data and State of Charge (SOC) change data for the battery based on the current and voltage of the battery and an equivalent circuit model, sets a cycle section including a discharge section and a charging section after the discharge section in the voltage change data and the state of charge change data, extracts a comparison section in which the average voltage is the same in each of the discharge section and the charging section, and estimates the battery capacity of the cycle section based on the state of charge change rate and average current for the first comparison section of the discharge section and the second comparison section of the charging section.

Description

Technology Field

[0001] The present invention relates to an apparatus and method for estimating the capacity of a lithium-ion battery and determining whether there is an internal short circuit using the estimate. Background Technology

[0003] The content described in this section merely provides background information regarding the present embodiment and does not constitute prior art.

[0004] The demand for lithium-ion batteries is rapidly increasing in industrial sectors such as energy storage systems (ESS) and electric vehicles (EVs). This is due to the advantages of lithium-ion batteries, including long lifespan, low self-discharge rate, and high energy density. Consequently, the need for battery health monitoring is being increasingly emphasized to ensure the safe and efficient use of these batteries. Key indicators for battery health assessment include the degree of aging and the presence of ISC defects.

[0005] Aging is one of the major problems that arise from continuous battery use, and it can lead to various issues by reducing energy efficiency.

[0006] Aging occurs due to repeated charge and discharge cycles of the battery, which alters its electrochemical properties. These changes induce secondary phenomena such as electrolyte decomposition, the formation of solid electrolyte interfaces (SEIs), and the dissolution of active materials, resulting in a decrease in the battery's usable energy or capacity and consequently leading to battery performance degradation. If the accuracy of capacity measurement decreases due to this aging, it may affect the estimation of the battery's operating time or cause ISC defects due to overcharging or overdischarging.

[0007] Precise estimation of battery capacity is very important to improve the accuracy of estimating battery operating time or to prevent overcharging or over-discharging. Methods for estimating battery capacity can be broadly classified into two types: data-based methods and model-based methods.

[0008] Data-driven methods estimate capacity by utilizing voltage and current data collected during charging and discharging. These methods can diagnose battery condition by training an initial model with a sufficient amount of data and then testing it with experimental data. Since building such a model requires a large amount of training data, it is difficult to create a model applicable to multiple battery cells. Furthermore, building a model capable of diagnosing condition across various usage environments requires data corresponding to each specific setting; failure to secure this data can lead to a decrease in diagnostic accuracy. Consequently, using such models results in reduced accuracy in battery condition diagnosis, making it difficult to apply data-driven methods when sufficient data is not available.

[0009] In addition, model-based methods using equivalent circuit models diagnose the state of a battery using the parameters of each battery model. While this allows for the accurate diagnosis of the state of individual batteries, it is difficult to apply it simultaneously to multiple battery cells with different parameters. Since individual battery cells have different characteristics, the magnitudes of the parameters required to implement the equivalent circuit model vary. It is practically difficult to estimate all of these parameters and obtain them, as this requires a very large number of preliminary experiments. Furthermore, there is a problem where the accuracy of battery state diagnosis decreases when a single equivalent circuit model is generalized and applied to multiple battery cells.

[0010] However, the aforementioned method has limitations in that it can only estimate battery capacity due to aging. This limitation can lead to a misidentification of a rapid decrease in State of Charge (SOC) caused by ISC defects as battery aging. Since ISC defects and aging can occur simultaneously in actual battery usage environments and affect performance, it is crucial to analyze the impact of both factors. Therefore, technology capable of simultaneously assessing battery capacity and ISC defects is required. The problem to be solved

[0012] The objective of the present invention is to provide a battery state estimation device and method capable of estimating the capacity of a lithium-ion battery and simultaneously determining an internal short circuit (ISC) fault.

[0013] The objects of the present invention are not limited to those mentioned above, and other unmentioned objects and advantages of the present invention may be understood from the following description and will be more clearly understood by the embodiments of the present invention. Furthermore, it will be readily apparent that the objects and advantages of the present invention can be realized by the means and combinations thereof set forth in the claims. means of solving the problem

[0015] A battery state estimation device according to an embodiment of the present invention, wherein the device for estimating the state of a lithium-ion battery comprises: a memory storing a state estimation program for estimating the capacity and internal short circuit status of the lithium-ion battery; and a processor for executing the state estimation program, wherein the state estimation program receives the current and voltage of the battery, generates voltage change data and State of Charge (SOC) change data for the battery based on the current and voltage of the battery and an equivalent circuit model, sets a cycle section including a discharge section and a charging section after the discharge section in the voltage change data and the state of charge change data, extracts a comparison section in which the average voltage is the same in each of the discharge section and the charging section, and estimates the capacity of the battery in the cycle section based on the state of charge change rate and average current for the first comparison section of the discharge section and the second comparison section of the charging section.

[0016] In addition, the state estimation program can determine the occurrence of an internal short circuit in the battery based on the estimated capacity of the battery in the cycle section and the current and voltage for the first comparison section of the discharge section.

[0017] In addition, the state estimation program can calculate the internal short-circuit resistance based on the estimated capacity of the battery in the cycle section and the average current and average voltage for the first comparison section of the discharge section, and determine whether an internal short circuit has occurred in the battery based on the internal short-circuit resistance.

[0018] In addition, the state estimation program may set the first comparison interval based on a preset charge state range in the discharge interval of the charge state change data, and set the second comparison interval equal to the average voltage of the first comparison interval in the charge interval of the voltage change data.

[0019] In addition, the state estimation program can estimate the charge state for the discharge operation and the charge operation by applying the voltage and current for the discharge operation to the discharge equivalent circuit model and applying the voltage and current for the charge operation to the charge equivalent circuit model.

[0020] In addition, the state estimation program can generate charge state change data by applying the voltage and current for the discharge operation to the discharge equivalent circuit model, calculating the first open-circuit voltage of the discharge equivalent circuit model based on each parameter value of the discharge equivalent circuit model, and estimating the charge state for the discharge operation based on the first open-circuit voltage, the estimated capacity of the battery for the previous cycle interval, and a preset discharge open-circuit voltage-charge state-capacity curve.

[0021] In addition, the state estimation program can generate charge state change data by applying the voltage and current for the charging operation to the charging equivalent circuit model to calculate a second open-circuit voltage, and estimating the charge state for the charging operation based on the second open-circuit voltage, the estimated capacity of the battery in the previous cycle section, and a preset open-circuit voltage-charge state-capacity curve.

[0022] In addition, the state estimation program can update at least one of the parameters of the charging equivalent circuit model and the parameters of the discharge equivalent circuit model based on a parameter curve that is preset for the estimated capacity of the battery in the cycle interval and the parameters of the charging equivalent circuit model and the parameters of the discharge equivalent circuit model.

[0023] In addition, the state estimation program may repeat the operation of estimating the capacity of the battery in the cycle interval using the charge equivalent circuit model and the discharge equivalent circuit model until the estimated capacity of the battery converges, and updating at least one of the parameters of the charge equivalent circuit model and the parameters of the discharge equivalent circuit model based on the estimated capacity.

[0024] A battery state estimation method according to an embodiment of the present invention may include: (a) receiving current and voltage of a battery; (b) generating voltage change data and state of charge (SOC) change data based on the current and voltage of the battery and an equivalent circuit model; (c) setting a cycle section including a discharge section and a charging section after the discharge section in the voltage change data and the state of charge change data; (d) extracting a comparison section in which the average voltage is the same in the discharge section and the charging section; and (e) estimating the capacity of the battery in the cycle section based on the amount of change in state of charge and the average current for the first comparison section of the discharge section and the second comparison section of the charging section.

[0025] In addition, (f) the method may further include the step of updating at least one of the parameters of the charging equivalent circuit model and the plurality of parameters of the discharge equivalent circuit model based on the estimated capacity of the battery in the cycle interval and a preset parameter curve for each parameter of the discharge equivalent circuit model.

[0026] In addition, (g) the method may further include a step of determining whether an internal short circuit has occurred in the battery based on the estimated capacity of the battery in the cycle section and the current and voltage for the first comparison section of the discharge section.

[0027] Additionally, the above step (g) may include: a step of calculating an internal short-circuit resistance based on the estimated capacity and the average current and average voltage for the first comparison interval of the discharge interval; and a step of determining whether an internal short circuit has occurred in the battery based on the internal short-circuit resistance.

[0028] Additionally, the above step (b) may include: a step of estimating the charge state for the discharge operation by applying the voltage and current corresponding to the discharge operation to a discharge equivalent circuit model; a step of estimating the charge state for the charge operation by applying the voltage and current corresponding to the charge operation to a charge equivalent circuit model; and a step of generating charge state change data based on the charge state for the discharge operation and the charge state for the charge operation.

[0029] Additionally, the step of estimating the charge state for the discharge operation may include: a step of calculating a first open-circuit voltage of the discharge equivalent circuit model by applying the voltage and current of the battery for the discharge operation to the discharge equivalent circuit model; and a step of estimating the charge state for the discharge operation based on the first open-circuit voltage, the estimated capacity of the battery in the previous cycle interval, and a preset discharge open-circuit voltage-charge state-capacity curve.

[0030] Additionally, the step of estimating the charge state for the charging operation may include: a step of calculating a second open-circuit voltage of the charging equivalent circuit model by applying the voltage and current of the battery for the charging operation to the charging equivalent circuit model; and a step of estimating the charge state for the charging operation based on the second open-circuit voltage, the estimated capacity of the battery in the previous cycle interval, and a preset open-circuit voltage-charge state-capacity curve.

[0031] Additionally, the above step (d) may include: a step of setting the first comparison interval for the discharge interval in the charge state change data based on a preset charge state range; and a step of setting the second comparison interval for the charge interval in the voltage change data that is equal to the average voltage of the first comparison interval. Effects of the invention

[0033] The battery state estimation device and method of the present invention can improve the accuracy of calculating the estimated capacity of a battery by extracting a section where the average voltage is the same in the discharge section and the charging section.

[0034] In addition, the occurrence of an ISC defect in the battery can be accurately and quantitatively determined through the calculated estimated capacity of the battery and the slope of the state of charge curve.

[0035] In addition to the above, the specific effects of the present invention are described together with the specific details for implementing the invention below. Brief explanation of the drawing

[0037] FIG. 1 is a block diagram schematically illustrating the configuration of a battery state estimation device according to an embodiment of the present invention. FIG. 2 is an example diagram of voltage change data. FIG. 3 is an example diagram of charge state change data. FIG. 4 is an example diagram of a discharge equivalent circuit model. FIG. 5 is an example diagram of a charge equivalent circuit model. FIG. 6 is an example diagram of an open-circuit voltage-charge state curve for a discharge operation. FIG. 7 is an example diagram of an open-circuit voltage-charge state curve for a charge operation. FIG. 8 is an example diagram of a parameter curve for ohmic resistance. FIG. 9 is an example diagram of a parameter curve for polarization resistance. FIG. 10 is an example diagram of a parameter curve for capacitance. FIG. 11 is a flowchart for explaining a battery state estimation method according to an embodiment of the present invention. FIG. 12 is a flowchart for explaining the process of generating charge state change data illustrated in FIG. 11. FIG. 13 is a flowchart for explaining the process of estimating the charge state for a discharge operation illustrated in FIG. 12. FIG. 14 is FIG. 12 is a flowchart for explaining the process of estimating the charge state for a charging operation. FIG. 15 is a flowchart for explaining the process of extracting a comparison section where the average voltage is the same in the discharge section and the charge section shown in FIG. 11. Specific details for implementing the invention

[0038] Terms and words used in this specification and claims shall not be interpreted as being limited to their general or dictionary meanings. In accordance with the principle that an inventor may define the concept of a term or word to best describe their invention, they shall be interpreted in a meaning and concept consistent with the technical spirit of the invention. Furthermore, since the embodiments described in this specification and the configurations illustrated in the drawings are merely one embodiment of the invention and do not represent the entire technical spirit of the invention, it should be understood that various equivalents, modifications, and applicable examples capable of replacing them may exist at the time of filing this application.

[0039] The terms first, second, A, B, etc., as used in this specification and claims may be used to describe various components, but said components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component. The term "and / or" includes a combination of a plurality of related described items or any of a plurality of related described items.

[0040] The terms used in this specification and claims are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" should be understood as not precluding the existence or addition of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification.

[0041] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which this invention pertains.

[0042] Terms such as those defined in commonly used dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0043] In addition, each component, process, procedure, or method included in each embodiment of the present invention may be shared within a scope that is not technically contradictory to one another.

[0044] Hereinafter, with reference to FIGS. 1 to 15, a battery state estimation device and method according to embodiments of the present invention will be described in detail.

[0046] FIG. 1 is a block diagram schematically showing the configuration of a battery state estimation device according to an embodiment of the present invention, FIG. 2 is an example diagram of voltage change data, and FIG. 3 is an example diagram of charge state change data. In addition, FIG. 4 is an example diagram of a discharge equivalent circuit model, and FIG. 5 is an example diagram of a charge equivalent circuit model.

[0047] A battery state estimation device is described with reference to FIGS. 1 to 5. The battery state estimation device (100) receives the current and voltage of the battery and generates voltage change data such as FIG. 2 and state of charge (SOC) change data such as FIG. 3.

[0048] Subsequently, based on voltage change data and charge state change data, a cycle section (10) including a discharge section and a charge section after the discharge section is set, and comparison sections (13, 14) in which the average voltage is the same are extracted in each of the discharge section (11) and the charge section (12) of the cycle section (10).

[0049] In addition, the capacity of the battery is estimated based on the average charge state and average current of the two comparison intervals (13, 14), and the internal short circuit of the battery can be determined using the estimated battery capacity. At this time, the battery may be a lithium battery, but is not limited thereto and can be applied to all batteries.

[0050] More specifically, the battery state estimation device (100) receives the current and voltage of the battery and generates voltage change data and charge state change data for the battery based on the current and voltage of the battery and equivalent circuit models (20, 30). Here, the equivalent circuit model may include a discharge equivalent circuit model (20) shown in FIG. 4 and a charge equivalent circuit model (30) shown in FIG. 5, and the discharge equivalent circuit model (20) and the charge equivalent circuit model (30) may include at least one parameter.

[0051] Then, a cycle section (10) is set including a discharge section (11) and a charging section (12) after the discharge section (11) in the voltage change data and charge state change data, and comparison sections (13, 14) in which the average voltage is the same are extracted in each of the discharge section (11) and the charging section (12) of the cycle section (10).

[0052] Afterwards, the battery capacity of the corresponding cycle section (10) is estimated based on the average charge state and average current for the first comparison section (13) of the discharge section (11) and the second comparison section (14) of the charge section (12), and at least one parameter for the discharge equivalent circuit model (20) and the charge equivalent circuit model (30) can be updated based on the estimated capacity for the corresponding cycle section (10) and the preset parameter curve for the discharge equivalent circuit model (20) and the charge equivalent circuit model (30).

[0053] The battery state estimation device (100) can repeat such operations until the estimated capacity of the battery converges. That is, it can calculate the estimated capacity for the next cycle interval using the discharge equivalent circuit model (20) and the charge equivalent circuit model (30), whose parameters are updated based on the estimated capacity for the current cycle interval (10), and repeat the operation of updating at least one parameter for the discharge equivalent circuit model (20) and the charge equivalent circuit model (30) until the estimated capacity of the battery converges.

[0054] Afterwards, the battery state estimation device (100) can determine the occurrence of an internal short circuit in the battery based on the converged estimated capacity and the current and voltage of the first comparison section of the discharge section for the most recent cycle section when the battery state estimation device (100) repeats the above operation and the battery estimation capacity converges.

[0055] To perform such an operation, the battery state estimation device (100) may include a memory (110) and a processor (120).

[0056] Memory (110) may store a state estimation program that extracts a discharge section and a charging section after the discharge section from voltage change data and charge state change data for the battery, and estimates the battery capacity based on the average charge state and average current for a comparison section where the average voltage is the same in each of the discharge section and the charging section. Memory (110) may be interpreted as a general term for non-volatile storage devices that continue to maintain stored information even when power is not supplied, and volatile storage devices that require power to maintain stored information. Additionally, memory (110) may perform the function of temporarily or permanently storing data processed by the processor (120). Memory (110) may include non-volatile storage devices such as magnetic storage media or flash storage media in addition to volatile storage devices that require power to maintain stored information, but the scope of the present invention is not limited thereto.

[0057] The processor (120) can execute a state estimation program stored in memory (110) to extract a comparison section in which the average voltage is the same in each of the discharge section of the battery and any one of the charging sections after the discharge section, and can estimate the battery capacity based on the average charging state and average current of each comparison section. In addition, it can determine an internal short circuit of the battery based on the estimated capacity.

[0058] More specifically, the state estimation program receives the battery's current and voltage, and generates voltage change data and state of charge change data for the battery based on the battery's current and voltage and an equivalent circuit model.

[0059] In addition, the state estimation program can set a cycle interval including a discharge interval and a charging interval following the discharge interval in the voltage change data and charge state change data, and extract a comparison interval in which the average voltage is the same in each of the discharge interval and the charging interval of the set cycle interval.

[0060] Subsequently, the state estimation program can estimate the battery capacity of the cycle section based on the average charge state and average current for the first comparison section of the discharge section and the second comparison section of the charge section.

[0061] In addition, the state estimation program can determine whether an internal short circuit has occurred in the battery based on the estimated capacity of the battery and the current and voltage for the first comparison interval of the discharge interval.

[0062] Figure 6 is an example of an open-circuit voltage-state-charge curve for discharge operation, and Figure 7 is an example of an open-circuit voltage-state-charge curve for charge operation.

[0063] Each operation of the state estimation program will be explained in detail with reference to FIGS. 2 to 7.

[0064] The operation of a state estimation program generating voltage change data is described with reference to the voltage change data illustrated in FIG. 2. The state estimation program can generate voltage change data based on the voltage of the received battery and can determine discharge operation and charging operation based on the sign of the current. If the sign of the current is -, it is determined to be a discharge operation, and if the sign of the current is +, it is determined to be a charging operation. The state estimation program can generate voltage change data in which voltage changes for a discharge section (11) corresponding to a discharge operation and a charging section (12) corresponding to a charging operation are continuously reflected.

[0065] Next, with reference to FIGS. 4 to 7, the operation of a state estimation program generating charge state change data will be explained.

[0066] A state estimation program can generate charge state change data such as that shown in FIG. 3 by estimating the charge state for each of the discharge operation and the charge operation based on the received battery voltage and current and an equivalent circuit model. At this time, the state estimation program can estimate the charge state for the discharge operation based on the discharge equivalent circuit model shown in FIG. 4 and the open voltage-charge state-capacity curve for the discharge operation shown in FIG. 6, and can estimate the charge state for the charge operation based on the charge equivalent circuit model shown in FIG. 5 and the open voltage-charge state-capacity curve for the charge operation shown in FIG. 7.

[0067] First, with reference to FIGS. 4 and FIGS. 6, the operation for estimating the charge state for the discharge operation of a battery will be explained.

[0068] The state estimation program uses the battery's current (I) and voltage (V t The discharge equivalent circuit model (20) of FIG. 4 is applied to the discharge equivalent circuit model (20), and an Extended Kalman Filter (EKF) is applied to the parameter values ​​of the discharge equivalent circuit model (20), namely the first ohm resistance (R0) (21), polarization resistance (R1) (22), and capacitance (C1) (23), to calculate the first open-circuit voltage (Voc) (24) of the discharge equivalent circuit model (10). Then, based on the calculated first open-circuit voltage (24), the estimated battery capacity of the previous cycle section, and the pre-set discharge open-circuit voltage-charge state-capacity curve as shown in FIG. 6, the charge state for the discharge operation can be estimated.

[0069] At the beginning of the operation of the state estimation program, the capacity of the battery can be set to a nominal capacity, and the charge state of the battery can be set to an arbitrary value, and the first ohm resistance (R0) (21), polarization resistance (R1) (22) and capacitance (C1) (23), which are parameter values ​​of the discharge equivalent circuit model (20), can be set based on the capacity and charge state of the battery.

[0070] The discharge open voltage-charge state-capacity curve shown in FIG. 6 is derived by an experiment that repeats charging and discharging using a discharge equivalent circuit model (20), and the parameter changes according to aging and charge state are calculated by applying a Recursive Least Squares (RLS) algorithm. The discharge open voltage-charge state-capacity curve can be prepared as a lookup table that reflects the first open voltage (24) and charge state changes according to capacity. In the discharge open voltage-charge state-capacity curve, capacity may represent the aging level. That is, the discharge open voltage-charge state-capacity curve may represent the open voltage-charge state curve according to the aging level for the discharge operation.

[0071] Next, with reference to FIGS. 5 and FIGS. 7, an operation for estimating the charge state of a battery charging operation will be described.

[0072] The state estimation program uses the battery's current (I) and voltage (V t The charging equivalent circuit model (30) of FIG. 5 can be applied to the charging equivalent circuit model (30), and an Extended Kalman Filter (EKF) can be applied to the value of the second ohm resistance (R0) (31), which is a parameter value of the discharge equivalent circuit model (30), to calculate the second open-circuit voltage (Voc) (32) of the charging equivalent circuit model (30). Then, based on the calculated second open-circuit voltage (32), the estimated battery capacity of the previous cycle section, and the charging open-circuit voltage-charge state-capacity curve set as in FIG. 7, the charge state for the charging operation can be estimated.

[0073] Likewise, the open-circuit voltage-charge state-capacity curve shown in FIG. 7 is derived from experiments previously performed using the open-circuit equivalent circuit model (30) and can be prepared as a lookup table reflecting the second open-circuit voltage (32) and charge state change according to capacity. In the open-circuit voltage-charge state-capacity curve, capacity may represent the aging level. That is, the open-circuit voltage-charge state-capacity curve may represent the open-circuit voltage-charge state curve according to the aging level for the charging operation.

[0074] Referring again to FIGS. 2 and FIGS. 3, the operation of a state estimation program to set cycle intervals in voltage change data and charge state change data, and to extract comparison intervals where the average voltage is the same in each of the discharge interval and the charge interval of the set cycle interval, is described.

[0075] First, the operation of setting the cycle interval is explained. The state estimation program can set a cycle interval (10) including a discharge interval (11) and a charging interval (12) after the discharge interval (11) in the voltage change data of FIG. 2 and the charge state change data of FIG. 3.

[0076] In FIGS. 2 and 3, the cycle section (10) is composed of a continuous discharge section (11) and a charge section (12), but is not limited thereto, and the cycle section (10) may be configured to include any one of the charge sections (12) after the discharge section (11). That is, at least one of the discharge section and the charge section may be placed between the discharge section (11) and the charge section (12).

[0077] Next, the operation of extracting a comparison section in which the average voltage is the same in each of the discharge section and the charge section of the set cycle section is described. The state estimation program may set a first comparison section (13) based on a preset charge state range in the discharge section (11) of the charge state change data of FIG. 3, and set a second comparison section (14) in the charge section (12) of the voltage change data of FIG. 2 that is the same as the average voltage of the first comparison section (13). At this time, the charge state range may be derived through an experiment in which discharge and charging were repeated for the battery in advance.

[0078] The charging state change data of FIG. 3 represents a case where the preset charging state range is set to be 30% or more and 75% or less. The state estimation program can set the interval between time t1, which is 75%, and time t2, which is 30%, as the first comparison interval (13). Accordingly, the charging interval (12) of the voltage change data of FIG. 2 can set the interval between time t3 and time t4, which is equal to the average voltage of the first comparison interval (13), as the second comparison interval (14).

[0079] As another example, if the charge state of the discharge section (11) in the charge state change data appears as 50% to 20%, the time between the 50% point and the 30% point can be set as the first comparison section (13). Additionally, if there is no section in the charge section (12) of the voltage change data where the average voltage is the same as the first comparison section (13), the cycle section (10) can be re-set to another charge section (12) after the current charge section (12), or the operation of setting the first comparison section (13) and the second comparison section (14) using the next cycle section (10) can be performed. That is, the cycle section (10) can be set using the next discharge section (11).

[0080] Next, the operation of estimating the battery capacity for the current cycle interval using the first comparison interval and the second comparison interval is described.

[0081] The state estimation program can estimate the battery capacity of the cycle section based on the rate of change in charge state per hour and the average current for the first comparison section (13) and the second comparison section (14) of the current cycle section (10). The state estimation program can estimate the battery capacity for the current cycle section (10) based on the following mathematical formula 1.

[0082] [Mathematical Formula 1]

[0083]

[0084] Subsequently, the state estimation program can update one of the multiple parameters of the discharge equivalent circuit model (20) and the parameters of the charge equivalent circuit model (30) based on the estimated capacity for the current cycle interval (10) and the preset parameter curves for each parameter of the discharge equivalent circuit model (20) and the parameters of the charge equivalent circuit model (30).

[0085] FIGS. 8 to 10 are examples of parameter curves for each of the equivalent circuit models. FIG. 8 is an example of a parameter curve for ohmic resistance, FIG. 9 is an example of a parameter curve for polarization resistance, and FIG. 10 is an example of a parameter curve for capacitance.

[0086] The state estimation program can update at least one of the first ohm resistance (21) of the discharge equivalent circuit model (20) and the second ohm resistance (31) of the charge equivalent circuit model (30) according to the change in the charge state based on the estimated capacity for the current cycle interval (10) and the parameter curve for the ohm resistance of FIG. 8.

[0087] Additionally, the state estimation program can update the polarization resistance (22) and capacitance (23) values ​​of the discharge equivalent circuit model (20) according to the change in the charge state based on the estimated capacity for the current cycle interval (10) and the parameter curves of FIG. 9 and FIG. 10.

[0088] The state estimation program can calculate the estimated battery capacity for the next cycle interval using the discharge equivalent circuit model (20) and the charge equivalent circuit model (30) with updated parameters. The state estimation program can repeat this until the estimated capacity of the battery converges.

[0089] That is, the state estimation program can generate charge state change data for a cycle interval using a discharge equivalent circuit model (20) and a charge equivalent circuit model (30) until the battery estimation capacity converges, calculate the battery estimation capacity by extracting a first comparison interval and a second comparison interval in which the average voltage is the same in each of the discharge interval and the charge interval, and repeat the operation of updating at least one of the parameters of the discharge equivalent circuit model and the charge equivalent circuit model based on the estimation capacity.

[0090] Subsequently, when the estimated capacity of the battery converges, the state estimation program calculates the internal short-circuit resistance based on the converged estimated capacity of the battery and the discharge section of the cycle interval, and can determine the occurrence of an internal short circuit in the battery based on the value of the internal short-circuit resistance.

[0091] More specifically, the state estimation program can calculate the internal short-circuit resistance based on the estimated capacity of the converged battery and the change in charge state, average current, and average voltage for the first comparison interval of the cycle interval. The state estimation program can calculate the internal short-circuit resistance based on Equation 2.

[0092] [Mathematical Formula 2]

[0093]

[0094] In mathematical formula 2, α represents the slope of the trend line and may be calculated by applying linear regression to the slope of the first comparison interval.

[0095] The state estimation program determines that the battery cell in which the internal short circuit occurred is not included if the calculated internal short-circuit resistance exceeds a preset threshold, and determines that the internal short-circuit occurred in the specified battery cell if the internal short-circuit resistance is calculated to a predetermined value below the threshold.

[0096] The state estimation program calculates the estimated capacity of the battery by assuming that an internal short circuit has occurred in the battery, and calculates the internal short-circuit resistance based on this; therefore, if there are no battery cells with an internal short circuit, the internal short-circuit resistance value may appear as a very large value, and if there are battery cells with an internal short-circuit failure, it may appear as a predetermined value.

[0097] Additionally, even if the state estimation program determines that the estimated capacity of the battery has converged, it may continue to perform operations such as generating voltage change data and charge state change data, setting cycle intervals, extracting first and second comparison intervals, and calculating the estimated capacity of the battery for the cycle intervals. Furthermore, if the estimated capacity of the battery changes due to aging, the operation to calculate the internal short-circuit resistance of the battery may be stopped until the estimated capacity of the battery converges again.

[0098] Meanwhile, the processor (120) can perform hardware control functions such as file systems, memory allocation, networks, basic libraries, timers, device control (display, media, input devices, 3D, etc.), and other utilities as needed while executing a program. In this embodiment, the processor (120) may be implemented in the form of a microprocessor, a central processing unit (CPU), a processor core, a multiprocessor, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), etc., but the scope of the invention is not limited thereto.

[0099] Additionally, the communication module (130) may include a device comprising hardware and software required to transmit and receive signals, such as control signals or data signals, via wired or wireless connections with other network devices in order to perform data communication regarding signal data with an external device. The database (140) may store various data for the operation of a state estimation program, such as discharge open voltage-charge state-capacity curves, charge open voltage-charge state-capacity curves, and parameter curves of a charge equivalent circuit model.

[0101] FIG. 11 is a flowchart for explaining a battery state estimation method according to an embodiment of the present invention. With reference to FIG. 1 and FIG. 11, a battery state estimation method using a battery state estimation device (100) is described. The battery state estimation method (S100) receives battery voltage and current (step S110), generates voltage change data for the battery based on the current and voltage of the battery (step S120), and generates state of charge (SOC) change data for the battery based on the current and voltage of the battery and an equivalent circuit model (step S130).

[0102] In addition, a cycle section including a discharge section and a charging section after the discharge section can be set in the voltage change data and the charge state change data (step S140), and a comparison section in which the average voltage is the same in each of the discharge section and the charging section of the set cycle section can be extracted (step S150).

[0103] Subsequently, the state estimation program can calculate the estimated battery capacity of the cycle section based on the average charge state and average current for the first comparison section of the discharge section and the second comparison section of the charge section (step S160), and update the parameters of the discharge equivalent circuit model and the charge equivalent circuit model based on the calculated estimated capacity and the parameter curve for the equivalent circuit model (step S170).

[0104] Then, it is determined whether the battery estimated capacity has converged (step S180), and if the battery estimated capacity has not converged, steps S120 to S170 may be repeated.

[0105] When the estimated battery capacity converges, the process may proceed to determining whether an internal short circuit has occurred in the battery based on the estimated battery capacity for the cycle interval and the current and voltage for the first comparison interval of the discharge interval (step S190).

[0106] FIG. 12 is a flowchart for explaining the process of generating charge state change data illustrated in FIG. 11, FIG. 13 is a flowchart for explaining the process of estimating the charge state for a discharge operation illustrated in FIG. 12, FIG. 14 is a flowchart for explaining the process of estimating the charge state for a charge operation illustrated in FIG. 12, and FIG. 15 is a flowchart for explaining the process of extracting a comparison section in which the average voltage is the same in the discharge section and the charge section illustrated in FIG. 11.

[0107] Next, each step of the battery state estimation method (S100) will be explained in detail with reference to FIGS. 12 to 15.

[0108] First, the process of generating voltage change data (step S120) is described. The battery state estimation device (100) can generate voltage change data such as that shown in FIG. 2 based on the received battery voltage, and can determine discharge operation and charging operation according to the sign of the current. If the sign of the current is -, it is determined to be a discharge operation, and if the sign of the current is +, it is determined to be a charging operation. The battery state estimation device (100) can continuously generate voltage changes in the discharge section (11) for the discharge operation and the charging section (12) for the charging operation shown in FIG. 2.

[0109] Next, the process of generating charge state change data (step S120) is described with reference to FIG. 12.

[0110] The battery state estimation device (100) can estimate the charge state for each of the discharge operation and the charge operation based on the received battery voltage and current and an equivalent circuit model, and generate charge state change data such as FIG. 4.

[0111] More specifically, the battery state estimation device (100) estimates the state of charge for a discharge operation based on the discharge equivalent circuit model shown in FIG. 4 and the open voltage-state-capacity curve for a discharge operation shown in FIG. 6 (step S131), and estimates the state of charge for a charge operation based on the charge equivalent circuit model shown in FIG. 5 and the open voltage-state-capacity curve for a charge operation shown in FIG. 7 (step S132). Then, it can generate state of charge change data based on the estimated state of charge for a discharge operation and the estimated state of charge for a charge operation (step S133).

[0112] Referring to FIGS. 4, FIGS. 6 and FIGS. 13, a process (step S131) ​​for estimating the charge state for the discharge operation of a battery is described.

[0113] The battery state estimation device (100) is the current (I) and voltage (V) of the battery. t The discharge equivalent circuit model (20) of FIG. 4 is applied to the discharge equivalent circuit model (20), and an Extended Kalman Filter (EKF) is applied to the parameter values ​​of the discharge equivalent circuit model (20), namely the first ohm resistance (R0) (21), polarization resistance (R1) (22), and capacitance (C1) (23), to calculate the first open-circuit voltage (Voc) (24) of the discharge equivalent circuit model (10) (step S134). Then, based on the calculated first open-circuit voltage (24), the estimated battery capacity of the previous cycle section, and the pre-set discharge open-circuit voltage-charge state-capacity curve as shown in FIG. 6, the charge state for the discharge operation can be estimated (step S135).

[0114] At the beginning of operation of the battery state estimation device (100), the capacity of the battery can be set to a nominal capacity, and the charge state of the battery can be set to an arbitrary value, and the values ​​of the first ohm resistance (R0) (21), polarization resistance (R1) (22) and capacitance (C1) (23), which are parameter values ​​of the discharge equivalent circuit model (20), can be set based on the capacity and charge state of the battery.

[0115] The discharge open voltage-charge state-capacity curve shown in FIG. 6 is derived by an experiment that repeats charging and discharging using a discharge equivalent circuit model (20), and the parameter changes according to aging and charge state are calculated by applying a Recursive Least Squares (RLS) algorithm. The discharge open voltage-charge state-capacity curve can be prepared as a lookup table that reflects the first open voltage (24) and charge state changes according to capacity. In the discharge open voltage-charge state-capacity curve, capacity may represent the aging level. That is, the discharge open voltage-charge state-capacity curve may represent the open voltage-charge state curve according to the aging level for the discharge operation.

[0116] Next, a process (step S132) for estimating the charge state of a battery charging operation is described with reference to FIGS. 5, FIGS. 7, and FIGS. 14.

[0117] The battery state estimation device (100) is the current (I) and voltage (V) of the battery. tThe charging equivalent circuit model (30) of FIG. 5 can be applied to the charging equivalent circuit model (30), and an Extended Kalman Filter (EKF) can be applied to the value of the second ohm resistance (R0) (31), which is a parameter value of the discharge equivalent circuit model (30), to calculate the second open-circuit voltage (Voc) (32) of the charging equivalent circuit model (30) (step S136). Then, based on the calculated second open-circuit voltage (32), the estimated battery capacity of the previous cycle section, and the charging open-circuit voltage-charge state-capacity curve set as in FIG. 7, the charge state for the charging operation can be estimated (step S137).

[0118] Likewise, the open-circuit voltage-charge state-capacity curve shown in FIG. 7 is also derived experimentally using the open-circuit equivalent circuit model (30) and can be prepared as a lookup table reflecting the second open-circuit voltage (32) and charge state change according to capacity. In the open-circuit voltage-charge state-capacity curve, capacity may represent the aging level. That is, the open-circuit voltage-charge state-capacity curve may represent the open-circuit voltage-charge state curve according to the aging level for the charging operation.

[0119] Next, with reference to FIGS. 2 and FIGS. 3, the process of setting a cycle interval in voltage change data and charge state change data (step S140) and the process of extracting a comparison interval in which the average voltage is the same in each of the discharge interval and the charge interval of the set cycle interval (step S150) are described.

[0120] First, the process of setting a cycle interval (step S140) is described. The battery state estimation device (100) can set a cycle interval (10) including a discharge interval (11) and a charging interval (12) after the discharge interval (11) in the voltage change data of FIG. 2 and the charge state change data of FIG. 3. Although the cycle interval (10) in FIG. 2 and FIG. 3 is depicted as including a continuous discharge interval (11) and a charging interval (12), it is not limited thereto, and the cycle interval (10) may also be set by including any one of the charging intervals (12) after the discharge interval (11).

[0121] Next, a process (step S150) for extracting a comparison section in which the average voltage is the same in each of the discharge section and the charge section of the cycle section set with reference to FIG. 15 is described. The battery state estimation device (100) may set a first comparison section (13) based on a preset charge state range in the discharge section (11) of the charge state change data of FIG. 3 (step S151), and set a second comparison section (14) in the charge section (12) of the voltage change data of FIG. 2 that is the same as the average voltage of the first comparison section (13) (step S152). At this time, the charge state range may be derived through an experiment in which charging and discharging are repeated, which has been performed on the battery in advance.

[0122] The charging state change data of FIG. 3 represents a case where the preset charging state range is set to be 30% or more and 75% or less. The state estimation program can set the interval between time t1, which is 75%, and time t2, which is 30%, as the first comparison interval (13). Accordingly, the charging interval (12) of the voltage change data of FIG. 2 can set the interval between time t3 and time t4, which is equal to the average voltage of the first comparison interval (13), as the second comparison interval (14).

[0123] Additionally, if the charging state of the discharge section (11) in the charging state change data appears as 50% to 20%, the 50% point and the 30% point can be set as the first comparison section (13). Furthermore, if there is no section in the charging section (12) of the voltage change data where the average voltage is the same as the first comparison section (13), the cycle section (10) can be set again to another charging section (12) after the current charging section (12), or the operation of setting the first comparison section (13) and the second comparison section (14) using the next cycle section (10) can be performed.

[0124] Next, the process of calculating the estimated capacity of the battery for the current cycle interval using the first comparison interval and the second comparison interval (step S160) is described.

[0125] The battery state estimation device (100) can estimate the battery capacity of the cycle section based on the rate of change in charge state per hour and the average current for the first comparison section (13) and the second comparison section (14) of the current cycle section (10). The battery state estimation device (100) can estimate the battery capacity for the current cycle section (10) based on the following mathematical formula 1.

[0126] [Mathematical Formula 1]

[0127]

[0128] Next, with reference to FIGS. 8 to 10, the process of updating the discharge equivalent circuit model and the parameters of the discharge equivalent circuit model (step S170) is described.

[0129] The battery state estimation device (100) can update one of the multiple parameters of the discharge equivalent circuit model (20) and the parameters of the charge equivalent circuit model (30) based on the estimated capacity for the current cycle interval (10) and the preset parameter curves for each parameter of the discharge equivalent circuit model (20) and the parameters of the charge equivalent circuit model (30).

[0130] FIG. 8 is an example of a parameter curve for ohmic resistance, FIG. 9 is an example of a parameter curve for polarization resistance, and FIG. 10 is an example of a parameter curve for capacitance.

[0131] The battery state estimation device (100) can update at least one of the first ohm resistance (21) of the discharge equivalent circuit model (20) and the second ohm resistance (31) of the charge equivalent circuit model (30) according to the change in charge state based on the estimated capacity of the battery for the current cycle interval (10) and the parameter curve for the ohm resistance of FIG. 8.

[0132] Additionally, the battery state estimation device (100) can update at least one of the polarization resistance (22) and capacitance (23) values ​​of the discharge equivalent circuit model (20) according to the change in charge state based on the estimated capacity of the battery calculated in the current cycle interval (10) and the parameter curves of FIG. 9 and FIG. 10.

[0133] Afterwards, it is determined whether the estimated capacity of the battery calculated in the current cycle interval (10) has converged (step S180), and if it is determined that the estimated capacity has not converged, steps S120 and S170 can be repeated until the estimated capacity of the battery converges.

[0134] That is, if it is determined that the calculated estimated capacity has not converged, the battery state estimation device (100) can calculate the battery estimated capacity for the next cycle interval (10) using the discharge equivalent circuit model (20) and the charge equivalent circuit model (30) with updated parameters, and repeat steps S120 and S170, which are processes of updating the parameters of the discharge equivalent circuit model (20) and the charge equivalent circuit model (30) based on the calculated estimated capacity, until the estimated capacity of the battery converges.

[0135] On the other hand, if the calculated estimated capacity is determined to have converged, the internal short-circuit resistance is calculated based on the converged estimated capacity of the battery and the discharge section of the cycle section, and the occurrence of an internal short circuit in the battery can be determined based on the value of the internal short-circuit resistance.

[0136] More specifically, the battery state estimation device (100) can calculate the internal short-circuit resistance based on the estimated capacity of the converged battery and the amount of change in charge state, average current, and average voltage for the first comparison interval of the cycle interval. The battery state estimation device (100) can calculate the internal short-circuit resistance based on Equation 2.

[0137] [Mathematical Formula 2]

[0138]

[0139] In mathematical formula 2, α represents the slope of the trend line and may be calculated by applying linear regression to the slope of the first comparison interval.

[0140] The battery state estimation device (100) can determine that the battery cell in which the internal short circuit occurred is not included if the calculated internal short circuit resistance exceeds a preset threshold, and can determine that the internal short circuit occurred in the battery cell in which the internal short circuit resistance is calculated to a predetermined value less than the threshold.

[0141] The battery state estimation device (100) calculates the estimated capacity of the battery by assuming that an internal short circuit has occurred in the battery, and calculates the internal short circuit resistance based on this. Therefore, if there are no battery cells with an internal short circuit, the internal short circuit resistance value may appear as a very large value, and if there are battery cells with an internal short circuit, it may appear as a predetermined value.

[0142] Additionally, the battery state estimation device (100) may continue to perform steps S110 to S170 even if it is determined that the estimated capacity of the battery has converged in step S180, and if the estimated capacity of the battery changes due to aging, it may stop the process of determining an internal short circuit of the battery (step S190) until the estimated capacity of the battery converges again.

[0144] The above description is merely an illustrative explanation of the technical concept of the present embodiment, and a person skilled in the art to which the present embodiment belongs would be able to make various modifications and variations within the scope of the essential characteristics of the present embodiment. Accordingly, the present embodiments are intended to explain, not limit, the technical concept of the present embodiment, and the scope of the technical concept of the present embodiment is not limited by these embodiments. The scope of protection of the present embodiment shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of the present embodiment. Explanation of the symbols

[0146] 100: Battery status estimation device 110: Memory 120: Processor

Claims

Claim 1 A battery state estimation device for estimating the state of a lithium-ion battery, comprising: a memory storing a state estimation program for estimating the capacity and internal short circuit status of the lithium-ion battery; and a processor for executing the state estimation program, wherein the state estimation program receives the current and voltage of the battery, generates voltage change data and State of Charge (SOC) change data for the battery based on the current and voltage of the battery and an equivalent circuit model, sets a cycle section including a discharge section and a charging section after the discharge section in the voltage change data and the state of charge change data, extracts a first comparison section in the discharge section and extracts a second comparison section in the charging section for a section where the average voltage of the discharge section and the charging section are the same, and estimates the capacity of the battery in the cycle section based on the rate of change of state of charge and the average current for the first comparison section and the second comparison section. Claim 2 A battery state estimation device according to claim 1, wherein the state estimation program determines the occurrence of an internal short circuit of the battery based on the estimated capacity of the battery in the cycle section and the current and voltage for the first comparison section of the discharge section. Claim 3 In paragraph 2, the battery state estimation device, wherein the state estimation program calculates an internal short-circuit resistance based on the estimated capacity of the battery in the cycle section and the average current and average voltage for the first comparison section of the discharge section, and determines whether an internal short circuit has occurred in the battery based on the internal short-circuit resistance. Claim 4 A battery state estimation device according to claim 1, wherein the state estimation program sets the first comparison interval based on a preset charge state range in the discharge interval of the charge state change data, and sets the second comparison interval equal to the average voltage of the first comparison interval in the charge interval of the voltage change data. Claim 5 A battery state estimation device according to claim 1, wherein the state estimation program applies the voltage and current for a discharge operation to a discharge equivalent circuit model and applies the voltage and current for a charge operation to a charge equivalent circuit model to estimate the charge state for the discharge operation and the charge operation. Claim 6 A battery state estimation device according to claim 5, wherein the state estimation program applies the voltage and current for the discharge operation to the discharge equivalent circuit model, calculates a first open-circuit voltage of the discharge equivalent circuit model based on each parameter value of the discharge equivalent circuit model, and generates charge state change data by estimating the charge state for the discharge operation based on the first open-circuit voltage, the estimated capacity of the battery in the previous cycle section, and a preset discharge open-circuit voltage-charge state-capacity curve. Claim 7 A battery state estimation device according to claim 5, wherein the state estimation program calculates a second open-circuit voltage by applying the voltage and current for the charging operation to the charging equivalent circuit model, and generates the charging state change data by estimating the charging state for the charging operation based on the second open-circuit voltage, the estimated capacity of the battery in the previous cycle interval, and a preset charging open-circuit voltage-charge state-capacity curve. Claim 8 A battery state estimation device according to claim 5, wherein the state estimation program updates at least one of the parameters of the charging equivalent circuit model and the parameters of the discharge equivalent circuit model based on a parameter curve that is preset for the battery estimation capacity for the cycle interval and the parameters of the charging equivalent circuit model and the parameters of the discharge equivalent circuit model. Claim 9 A battery state estimation device according to claim 8, wherein the state estimation program estimates the capacity for the cycle interval using the charge equivalent circuit model and the discharge equivalent circuit model until the estimated capacity of the battery converges, and repeats the operation of updating at least one of the parameters of the charge equivalent circuit model and the parameters of the discharge equivalent circuit model based on the estimated capacity. Claim 10 A method for estimating the state of a lithium-ion battery using a battery state estimation device, comprising: (a) receiving current and voltage of the battery; (b) generating voltage change data and state of charge (SOC) change data based on the current and voltage of the battery and an equivalent circuit model; (c) setting a cycle section including a discharge section and a charge section after the discharge section in the voltage change data and the state of charge change data; (d) extracting a first comparison section in the discharge section and extracting a second comparison section in the charge section for a section where the average voltage of the discharge section and the charge section are the same; and (e) estimating the capacity of the battery in the cycle section based on the amount of change in state of charge and the average current for the first comparison section and the second comparison section. Claim 11 A battery state estimation method according to claim 10, further comprising the step of updating at least one of the parameters of the charging equivalent circuit model and the plurality of parameters of the discharge equivalent circuit model based on a preset parameter curve for each parameter of the estimated capacity of the battery and the discharge equivalent circuit model in the cycle interval. Claim 12 A battery state estimation method according to claim 10, further comprising the step of determining whether an internal short circuit has occurred in the battery based on the estimated capacity of the battery in the cycle section and the current and voltage for the first comparison section of the discharge section. Claim 13 A battery state estimation method according to claim 12, wherein step (g) comprises: a step of calculating an internal short-circuit resistance based on the estimated capacity and the average current and average voltage for a first comparison interval of the discharge interval; and a step of determining whether an internal short circuit has occurred in the battery based on the internal short-circuit resistance. Claim 14 A battery state estimation method according to claim 10, wherein step (b) comprises: a step of estimating a charge state for a discharge operation by applying the voltage and current corresponding to the discharge operation to a discharge equivalent circuit model; a step of estimating a charge state for a charge operation by applying the voltage and current corresponding to the charge operation to a charge equivalent circuit model; and a step of generating charge state change data based on the charge state for the discharge operation and the charge state for the charge operation. Claim 15 A battery state estimation method according to claim 14, wherein the step of estimating the charge state for the discharge operation comprises: a step of applying the voltage and current of the battery for the discharge operation to the discharge equivalent circuit model to calculate a first open-circuit voltage of the discharge equivalent circuit model; and a step of estimating the charge state for the discharge operation based on the first open-circuit voltage, the estimated capacity of the battery in the previous cycle interval, and a preset discharge open-circuit voltage-charge state-capacity curve. Claim 16 A battery state estimation method according to claim 14, wherein the step of estimating the charge state for the charging operation comprises: a step of applying the voltage and current of the battery for the charging operation to the charging equivalent circuit model to calculate a second open-circuit voltage of the charging equivalent circuit model; and a step of estimating the charge state for the charging operation based on the second open-circuit voltage, the estimated capacity of the battery in the previous cycle interval, and a preset open-circuit voltage-charge state-capacity curve. Claim 17 A battery state estimation method according to claim 10, wherein step (d) comprises: a step of setting a first comparison interval for the discharge interval in the charge state change data based on a preset charge state range; and a step of setting a second comparison interval equal to the average voltage of the first comparison interval for the charge interval in the voltage change data.

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