Battery management device and method of operation thereof

Abstract

According to an embodiment, a battery management device includes a voltage measurement unit that measures a voltage of a battery cell, and a controller that samples data related to the voltage of the battery cell in a specified unit, converts the data into data in a monotonically increasing or decreasing form, smooths the data using optimal smoothing parameters determined by a set algorithm, and converts the data related to the voltage of the battery cell into a differential signal. According to this, in a process of pre-processing the battery voltage data for converting it into a differential signal, an optimal smoothing parameter that enables appropriate fitting with original data while reducing the swing of a smoothed differential signal curve can be determined.

Description

[Technical Field]

[0001] The present invention claims the benefit of priority based on Korean Patent Application No. 10-2021-0186533, filed December 23, 2021, and all contents disclosed in the documents of this Korean patent application are incorporated herein by reference. SUMMARY OF THE INVENTION The embodiments disclosed herein relate to a battery management device and method of operation. [Background technology]

[0002] In recent years, research and development into secondary batteries has been actively pursued. Here, a secondary battery is a rechargeable battery, and includes both conventional Ni / Cd batteries, Ni / MH batteries, and more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of having a much higher energy density than conventional Ni / Cd batteries, Ni / MH batteries, and other batteries. Furthermore, lithium-ion batteries can be manufactured to be compact and lightweight, making them popular as power sources for mobile devices. Their use has also expanded to include power sources for electric vehicles, drawing attention as a next-generation energy storage medium. Furthermore, secondary batteries are typically used as battery packs containing battery modules in which multiple battery cells are connected in series and / or parallel. The status and operation of the battery packs are managed and controlled by a battery management system.

[0003] In the case of such secondary batteries, abnormal voltage drops, suspected to be internal shorts, can be observed during the charging cycle. To detect such phenomena, the differential signal of the battery voltage can be used. In this case, a smoothing process is performed to convert the battery voltage data into a differential signal. To achieve optimal results, it is necessary to determine smoothing parameters that minimize fluctuations in the differential signal curve and enable appropriate fitting with the original data. [Prior art document] [Patent documents] [Patent Document 1] Special Publication No. 2022-545033 [Patent Document 2] U.S. Patent Application Publication No. 2021 / 0165047 Summary of the Invention [Problem to be solved by the invention]

[0004] One objective of the embodiments disclosed herein is to provide a battery management device and an operating method thereof that, in the process of preprocessing battery voltage data to convert it into a differential signal, determines optimal smoothing parameters using a set algorithm and applies the parameters to obtain a differential signal.

[0005] One objective of the embodiments disclosed herein is to provide a battery management device and an operating method thereof that can detect an abnormal voltage drop phenomenon caused by an internal short circuit during battery charging using a differential signal of the battery voltage, and further achieve objectives such as quantifying battery degradation and distinguishing abnormal degradation.

[0006] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the following description. [Means for solving the problem]

[0007] A battery management device according to one embodiment includes a voltage measurement unit that measures the voltage of a battery cell, and a controller that samples data relating to the voltage of the battery cell in a specified unit, converts the data into data in a monotonically increasing or monotonically decreasing form, smooths the data using an optimal smoothing parameter determined by a set algorithm, and converts the data relating to the voltage of the battery cell into a differential signal.

[0008] In one embodiment of the battery management device, the controller may determine the optimal smoothing parameters using an algorithm that minimizes a cost function.

[0009] In one embodiment of the battery management device, the controller obtains a set of differential signals corresponding to each smoothing parameter based on data smoothed using at least one preset smoothing parameter, and calculates the cost function based on the length of the curve of each of the differential signal sets and the difference between the original data and each of the differential signal sets.

[0010] In one embodiment of the battery management device, the controller can multiply a first variable, which is a value obtained by linearly integrating a differential signal corresponding to the smoothing parameter over a specified interval, by a second variable, which is an RMS (root mean square) value of the difference between the original data and the differential signal corresponding to the smoothing parameter, to calculate a cost function value corresponding to the smoothing parameter.

[0011] In one embodiment of the battery management device, the controller can determine, as an optimal smoothing parameter, a smoothing parameter that minimizes the corresponding cost function value among the at least one smoothing parameter.

[0012] In one embodiment of the battery management device, each of the at least one smoothing parameter may be a smoothing parameter that prevents a peak value of the corresponding differential signal from exceeding a specified value.

[0013] In one embodiment of the battery management device, the controller classifies the capacity values ​​of the battery cells having the same voltage magnitude and calculates the average value of the capacity values ​​of the battery cells by the voltage magnitude, thereby performing sampling of the voltage.

[0014] In one embodiment of the battery management device, the controller can smooth the slope of the voltage of the battery cell so that it satisfies continuity by utilizing a smoothing algorithm (e.g., a smoothing spline) to which a hyper parameter is applied.

[0015] The battery management device according to one embodiment may further include an abnormality diagnosis unit that calculates a statistical value for the differential signal and diagnoses that an abnormality has occurred in the battery cell if the statistical value for the differential signal is equal to or greater than a predetermined reference value. In the battery management device according to one embodiment, the differential signal may be a voltage-capacity differential signal or a voltage-time differential signal of the battery cell.

[0016] An operating method of a battery management device according to one embodiment includes the steps of measuring a voltage of a battery cell, sampling data relating to the voltage of the battery cell in a specified unit and converting the data into data in a monotonically increasing or monotonically decreasing form, smoothing the data using an optimal smoothing parameter determined by a set algorithm, and converting the data relating to the voltage of the battery cell into a differential signal.

[0017] In one embodiment of the method for operating a battery management device, smoothing the data using the optimal smoothing parameters may include determining the optimal smoothing parameters using an algorithm that minimizes a cost function.

[0018] In one embodiment of the method for operating a battery management device, the step of determining the optimal smoothing parameters may include the steps of smoothing the voltage-related data using at least one preset smoothing parameter; obtaining a set of differential signals corresponding to each smoothing parameter based on the smoothed data; and calculating the cost function based on the length of a curve of each of the differential signal sets and a difference between original data and each of the differential signal sets.

[0019] In one embodiment of the method for operating a battery management device, the step of calculating the cost function may include a step of multiplying a first variable, which is a value obtained by linearly integrating a differential signal corresponding to the smoothing parameter over a specified interval, by a second variable, which is an RMS (root mean square) value of a difference between the original data and the differential signal corresponding to the smoothing parameter, to calculate a cost function value corresponding to the smoothing parameter.

[0020] In an operating method of a battery management device according to an embodiment, the step of determining the optimal smoothing parameter may further include a step of determining, among the at least one smoothing parameter, a smoothing parameter that minimizes a corresponding cost function value as the optimal smoothing parameter.

[0021] In the method for operating a battery management device according to an embodiment, each of the at least one smoothing parameter may be a smoothing parameter that prevents a peak value of a corresponding differential signal from exceeding a specified value.

[0022] In one embodiment of the method for operating a battery management device, the step of sampling the data in a specified unit and converting it into data in a monotonically increasing or monotonically decreasing form may include the step of classifying capacitance values ​​of the battery cells having the same voltage magnitude and calculating an average value of the capacitance values ​​of the battery cells for each voltage magnitude, thereby performing sampling for the voltage.

[0023] In an embodiment of the method for operating a battery management device, the step of smoothing the data may include a step of smoothing the slope of the voltage of the battery cell so as to satisfy continuity by utilizing a smoothing algorithm (e.g., a smoothing spline) to which a hyper parameter is applied.

[0024] An operating method of a battery management device according to an embodiment may further include the steps of calculating a statistical value for the differential signal, and diagnosing that an abnormality has occurred in the battery cell if the statistical value for the differential signal is equal to or greater than a predetermined reference value. In the method for operating a battery management device according to an embodiment, the differential signal may be a voltage-capacity differential signal or a voltage-time differential signal of the battery cell. [Effects of the Invention]

[0025] According to one embodiment, in the process of preprocessing data related to the battery voltage to convert it into a differential signal, optimal smoothing parameters can be determined that reduce the fluctuation of the smoothed differential signal curve while enabling appropriate fitting to the original data.

[0026] Furthermore, by using the optimal smoothing parameters, a differential signal (e.g., a voltage-capacity differential signal, a voltage-time differential signal, etc.) for the battery voltage can be obtained, and based on this, abnormal voltage drop phenomena due to internal short circuits in the battery can be accurately and easily detected, thereby achieving goals such as quantifying battery degradation and distinguishing abnormal degradation. In addition, this document may provide a variety of other benefits that may be perceived directly or indirectly. [Brief explanation of the drawings]

[0027] In order to more clearly describe the embodiments disclosed in this document or the technical solutions of the prior art, drawings necessary for describing the embodiments are briefly introduced below. It should be understood that the following drawings are only for describing the embodiments of the present specification and are not intended to limit the same. In addition, for the sake of clarity, the representation of some components in the drawings may be exaggerated or omitted.

[0028] [Figure 1] FIG. 2 is a block diagram showing the configuration of a battery control system. [Figure 2]1 is a block diagram showing a configuration of a battery management device according to an embodiment; [Figure 3a] 1 is a graph showing raw data of measured voltage of a battery. [Figure 3b] 3b is a graph showing the differential signal of the voltage raw data of FIG. 3a; [Figure 4] FIG. 10 illustrates a method for sampling battery voltage data to remove overlapping signals. [Figure 5a] 10 is a graph showing the results of pre-processing battery voltage data by sampling and smoothing. [Figure 5b] 10 is a graph showing the outline of differentiation of battery voltage data at each pre-processing stage. [Figure 6a] 10 is a graph showing original data of a voltage-capacitance differential signal and changes in signal form depending on a smoothing parameter value according to an embodiment; [Figure 6b] 10 is a graph showing the change in the length (L) of the differential signal curve and the difference (rms) from the original data as a function of the smoothing parameter value. [Figure 7a] 10 is a graph showing the change in the cost function (L*rms) depending on the smoothing parameter value when sampling is performed at 1 second intervals. [Figure 7b] 10 is a graph showing the change in the cost function (L*rms) depending on the smoothing parameter value when sampling at 60 second intervals. [Figure 8] 1 is a flowchart illustrating an operation method of a battery management device according to an embodiment. [Figure 9] 10 is a flowchart illustrating a detailed process of a data smoothing step in an operating method of a battery management device according to an embodiment. [Figure 10] 10 is a flowchart illustrating an operation method of a battery management device according to another embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0029] Hereinafter, the embodiments disclosed herein will be described in detail with reference to the accompanying drawings. When assigning reference numerals to components in each drawing, it should be noted that the same reference numerals are assigned to the same components in other drawings whenever possible. Furthermore, when describing the embodiments disclosed herein, if a detailed description of related known structures or functions is deemed to hinder understanding of the embodiments disclosed herein, such detailed description will be omitted.

[0030] The terms used in this document have been selected as widely used and general as possible while taking into consideration their functions, but these may vary depending on the intentions or practices of engineers in the relevant field or the emergence of new technologies. In addition, in certain cases, the applicant has arbitrarily selected terms, and in such cases, the meanings thereof will be described in the explanation section of the specification. Therefore, it is made clear that the terms used in this document should be interpreted based on the substantive meanings of the terms and the overall content of this document, rather than simply on the names of the terms.

[0031] The terms used in this document are merely used to describe particular embodiments and are not intended to limit the scope of other embodiments. The singular expression may include the plural expression unless otherwise clearly indicated in the context.

[0032] 1 is a block diagram showing the configuration of a battery control system according to one embodiment, and schematically shows a battery control system including a battery pack 1 and a host controller 2 included in a host system. The battery pack 1 may be configured for use in an ESS (Energy Storage System) or a vehicle, but is not limited to such uses.

[0033] Referring to FIG. 1, the battery pack 1 includes a battery module 10 composed of one or more chargeable and dischargeable battery cells, a switching element 14 connected in series to the positive or negative terminal side of the battery module 10 to control the flow of charge and discharge current of the battery module 10, and a battery management device 20 that monitors the voltage, current, temperature, etc. of the battery pack 1 and controls and manages it to prevent overcharging and overdischarging.

[0034] Here, the switching element 14 is a semiconductor switching element for controlling the flow of current for charging or discharging the battery module 10, and may be, for example, at least one MOSFET.

[0035] In addition, the battery management device 20 can measure or calculate the voltage and current of the gate, source, drain, etc. of the semiconductor switching element in order to monitor the voltage, current, temperature, etc. of the battery pack 1, and can measure the current, voltage, temperature, etc. of the battery pack using a sensor 12 provided adjacent to the semiconductor switching element 14.

[0036] The battery management unit 20 is an interface that receives input of measured values ​​of the various parameters described above, and includes a plurality of terminals and circuits that are connected to these terminals and can process the input values. The battery management unit 20 can also control the ON / OFF of the switching elements 14, and is connected to the battery modules 10 to monitor the status of the battery modules 10.

[0037] The upper controller 2 can transmit control signals for the battery modules to the battery management unit 20. As a result, the operation of the battery management unit 20 can be controlled based on the signals applied from the upper controller.

[0038] FIG. 2 is a block diagram showing the configuration of a battery management device according to an embodiment. Referring to FIG. 2, a battery management device 20 according to an embodiment may include a voltage measurement unit 210 and a controller 220, and may optionally further include an abnormality diagnosis unit 230.

[0039] The voltage measurement unit 210 measures the voltage of the battery cell to obtain voltage data. For example, the voltage measurement unit 210 may be configured to measure the voltage of one or more battery cells included in the battery module 10 at regular time intervals via the sensor 12 provided in the battery pack 1 of FIG.

[0040] The controller 220 can preprocess the battery cell voltage data (e.g., a voltage signal over time) and convert the battery cell voltage so that it can be differentiated over a certain interval. As illustrated in the graph of FIG. 3a, typically measured battery voltage data may not be suitable for differential analysis due to overlapping signals and discontinuous intervals. Therefore, the controller 220 can preprocess the battery cell voltage data before converting it into a differential signal and convert the battery cell voltage so that it can be differentiated over a preset interval.

[0041] Specifically, the controller 220 can convert the voltage of the battery cell into data in the form of a monotonically increasing (i.e., increasing without decreasing within a certain interval) or a monotonically decreasing (i.e., decreasing without increasing within a certain interval) form by sampling the voltage data. For example, the controller 220 can perform sampling of the voltage by classifying the capacitance values ​​(Q) of battery cells having the same voltage magnitude (V) and calculating the average capacitance values ​​of the battery cells by voltage magnitude. This will be described later with reference to FIG. 4.

[0042] In addition, the controller 220 may convert the curve of the battery cell voltage data into a gentler slope by performing a smoothing process on the sampled data. This may ensure continuity between adjacent data within a certain section and make the data differentiable. The gentleness of the slope of the curve may vary depending on the smoothing parameter value applied. For example, in the case of a smoothing technique according to an embodiment, the larger the parameter value, the gentler the curve, but the greater the difference from the original data. Conversely, the smaller the parameter value, the closer the curve is to the original data, but the greater the fluctuation of the curve. Therefore, when determining the optimal smoothing parameters, it is necessary to appropriately consider variables in a trade-off relationship, as will be described below with reference to FIGS. 6a and 6b.

[0043] The controller 220 can convert the pre-processed (sampled and smoothed) battery cell voltage data into differential signals. In this case, the controller 220 can calculate differential signals (e.g., dQ / dV, dV / dQ, dV / dt, etc.) related to the capacity and voltage of the battery cell. According to one embodiment, to determine the optimal smoothing parameters, the controller 220 acquires a set of differential signals corresponding to at least one preset smoothing parameter, and determines the optimal smoothing parameters for the set of differential signals by taking into account two or more variables (e.g., the smoothness of the differential signal curve, the difference from the original data, etc.).

[0044] The abnormality diagnosis unit 230 may calculate statistics for the converted differential signal. In this case, the statistical values ​​of the differential signal calculated by the controller 220 are used to determine abnormal behavior of the battery using a sliding window (or moving window) method. For example, the statistical values ​​for the differential signal may include a standard deviation.

[0045] The abnormality diagnosis unit 230 can diagnose an abnormality in the battery cell based on the differential signal converted by the controller 220. Specifically, the abnormality diagnosis unit 230 can diagnose that an abnormal voltage drop has occurred in the battery cell when the statistical value of the differential signal of the battery voltage is equal to or greater than a preset reference value.

[0046] In addition, the abnormality diagnosis unit 230 may diagnose abnormalities in the battery cells using a sliding window method with respect to the statistical value of the differential signal of the battery voltage. When the abnormality diagnosis unit 230 diagnoses abnormalities in the battery cells using the sliding window method, the size of the window may be arbitrarily set by the user. In this case, the statistical value of the differential signal of the battery voltage may include a standard deviation.

[0047] Although not shown, the battery management device 20 according to an embodiment may further include a memory unit and an alarm unit. In this case, the memory unit may store the voltage of the battery cell measured by the voltage measurement unit 210 and a differential signal of the voltage calculated by the controller 220. In addition, the alarm unit may generate a warning alarm when the abnormality diagnosis unit 230 determines that an abnormality has occurred in the battery cell. In this case, the warning alarm may be displayed in the form of a message on a display unit (not shown) connected to the battery control system, or may be displayed as a light or sound signal.

[0048] Although not shown, in addition to the abnormality diagnosis unit 230, an additional device or system capable of performing functions such as quantifying battery deterioration and determining abnormal deterioration may be included.

[0049] Hereinafter, the pre-processing process for converting voltage data into a differentiable signal will be described in detail with reference to the drawings. According to one embodiment, the controller 220 may sample data relating to the voltage of the battery cell in a specified unit and convert the data into data in a monotonically increasing or decreasing form.

[0050] Figure 3a is a graph showing raw data of the measured voltage of a battery, and Figure 3b is a graph showing a differential signal of the raw voltage data of Figure 3a. In Figure 3a, the horizontal axis represents the battery capacity (Ah) and the vertical axis represents the measured voltage (V) of the battery. In Figure 3b, the horizontal axis represents the battery voltage (V) and the vertical axis represents the differential signal (dQ / dV) of the battery capacity and voltage.

[0051] Referring to Figure 3a, the measured voltage data of the battery may contain noise due to errors in the voltage sensor itself, overlapping voltage signals, etc. Therefore, as shown in Figure 3b, it may be difficult to analyze the differential signal for the voltage or current data.

[0052] 4 is a diagram illustrating a sampling method for removing overlapping signals in battery voltage data. Referring to FIG. 4, measurement data of battery capacity and voltage for each time period is shown. Here, the voltage is 3.23V in the sections where the battery capacity is 43Ah, 44Ah, and 46Ah, and the voltage is 3.24V in the sections where the battery capacity is 45Ah and 47Ah. Therefore, as shown in FIGS. 3a and 3b, overlapping signals in the voltage data may occur, making differential analysis impossible.

[0053] In this case, battery voltage data can be sampled by classifying battery capacity values ​​based on specific voltage levels and calculating the average value. For example, as shown in Figure 4, the average capacity value corresponding to each voltage can be calculated using the overlapping battery voltages of 3.23V and 3.24V as the reference. For example, if the voltage is 3.23V, the capacity value can be set to 44.3Ah, which is the average of battery capacities of 43Ah, 44Ah, and 46Ah. If the voltage is 3.24V, the capacity value can be set to 46Ah, which is the average of battery capacities of 45Ah and 47Ah.

[0054] In this manner, the controller 220 according to an embodiment can sample the voltage data in a specified unit and convert the voltage data into a monotonically increasing or decreasing form based on the measured voltage.

[0055] In one embodiment, the controller 220 may perform smoothing so that the sampled data is differentiable within a preset interval.

[0056] Figure 5a is a graph showing the results of preprocessing battery voltage data by sampling and smoothing, and Figure 5b is a graph showing the outline of the differentiation of the battery voltage data at each preprocessing stage. In Figure 5a, the horizontal axis represents the battery capacity (Ah), and the vertical axis represents the measured battery voltage (V). In Figure 5b, the horizontal axis represents the battery voltage (V), and the vertical axis represents the differential signal (Ah / V) for the battery capacity and voltage.

[0057] As shown in FIG. 5a, in the case of raw voltage data, overlapping signals and noise occur, but in the case of voltage data sampled as shown in FIG. 4, it can be seen that it shows a monotonically increasing pattern.

[0058] On the other hand, even if the raw voltage data is sampled, there may be sections where differentiation is not possible due to the difference in slope between adjacent data. In this regard, referring to Figure 5b, it can be seen that simply sampling the raw voltage data does not result in a complete differential signal value.

[0059] Therefore, the controller 220 can convert the slope of the battery voltage data to satisfy continuity by smoothing the sampled voltage data. According to one embodiment, a smoothing spline (SS) technique can be applied, and the calculation formula for the smoothing spline can be expressed as follows:

[0060]

number

[0061] The smoothing spline formula prevents abrupt changes in the slope of the sampled voltage data and converts it into a continuous curve. The larger the value of the smoothing parameter λ, the more gentle the curve becomes. For example, the λ values ​​may be 0.001 (V) and 0.01 (Q), respectively.

[0062] Meanwhile, the data preprocessing method according to the embodiment disclosed herein can be used with any data smoothing algorithm that uses hyperparameters (variable values ​​input by the user during modeling) other than the smoothing spline technique. The optimal smoothing parameters to be applied to each smoothing technique can be determined according to the embodiments described below.

[0063] Referring again to Figure 5a, when a smoothing spline (SS) is applied to sampled voltage data, the voltage data is converted into a differentiable form while maintaining continuity of the slope. Also, as shown in Figure 5b, when a differential signal is sampled by further applying a smoothing spline, the graph is shown smoothly without noise.

[0064] As described above, the smoothness of the differential signal curve varies depending on the value of the smoothing parameter (λ). As the parameter value increases, the curve becomes smoother, but the difference from the original data increases. As the parameter value decreases, the curve approaches the original data, but the fluctuation of the curve increases. Therefore, the disclosed embodiment provides an algorithm for determining the optimal smoothing parameter using a cost function that takes into account two factors that are in a trade-off relationship.

[0065] According to one embodiment, the controller 220 may apply at least one preset smoothing parameter (e.g., λ1, λ2, ..., λ5) to smooth the data and obtain a set of derivative signals corresponding to each smoothing parameter, where each of the at least one smoothing parameter may be a smoothing parameter that prevents a peak value of the corresponding derivative signal from exceeding a specified value.

[0066] 6A is a graph illustrating a change in signal shape according to the original data (Vsample) and the smoothing parameter values ​​(λ1, λ2, ..., λ5) of a voltage-capacitance differential signal (dQ / dV) according to an embodiment. Referring to FIG. 6A, it can be seen that as the smoothing parameter value (λ) increases (i.e., as 1-λ decreases to 10-6, 10-7, ..., 10-10), the curve becomes gentler and the difference from the original data becomes larger.

[0067] The cost function can be calculated for each set of differential signals by simultaneously considering the gentleness of the curve and the difference from the original data. According to one embodiment, the gentleness of the curve is represented by a first variable, which is a value obtained by integrating the differential signal over a certain interval (e.g., an interval where the voltage magnitude is 3.4 V to 4.2 V), and the difference from the original data can be represented by a second variable, which is the root mean square (RMS) value of the difference between the original data and the differential signal. In other words, as the smoothing parameter value increases (i.e., as 1-λ decreases), the differential signal curve becomes gentler and the value of the first variable, which indicates the length of the curve, decreases. Conversely, as the smoothing parameter value increases (i.e., as 1-λ decreases), the value of the second variable, which indicates the difference from the original data, increases.

[0068] Figure 6b is a graph showing the change in the length (L) of the differential signal curve and the difference (rms) from the original data depending on the smoothing parameter (λ). As the smoothing parameter (λ) value increases (i.e., as 1-λ decreases from 10-6 to 10-7, ..., 10-10), the length (L) of the curve decreases and the difference (rms) from the original data increases.

[0069] According to one embodiment, the controller 220 may multiply the first variable (length of the differential signal curve) by the second variable (rms difference from the original data) to calculate a cost function value corresponding to each smoothing parameter. Furthermore, the controller 220 may determine the smoothing parameter (e.g., λ1, λ2, ..., λ5) that minimizes the corresponding cost function value as the optimal smoothing parameter. The optimal smoothing parameter determined in consideration of the cost function refers to a parameter value that minimizes the fluctuation of the curve of the generated data (e.g., differential signals such as dQ / dV, dV / dQ, and dV / dt) while ensuring appropriate fitting to the original data.

[0070] Figure 7a is a graph showing the change in the cost function (L*rms) depending on the smoothing parameter value when sampling at 1-second intervals, and Figure 7b is a graph showing the change in the cost function (L*rms) depending on the smoothing parameter value when sampling at 60-second intervals. It can be seen that the smoothing parameter value that minimizes the cost function varies depending on the sampling difference. In other words, different smoothing parameters are applied depending on the measurement environment, but this does not mean that the optimal smoothing parameters can be determined by applying a consistent criterion rather than empirical implementation.

[0071] 8 is a flowchart showing a method of operating a battery management device according to an embodiment. The structure and components of the battery management device are as described above with reference to FIG.

[0072] 8, in an operating method of a battery management device according to an embodiment, first, a step of measuring the voltage of a battery cell (S810) is performed. For example, the voltage measurement unit 210 of the battery management device 20 can measure the voltage of the battery cell via the provided sensor 12 and acquire voltage data (see FIGS. 1 and 2).

[0073] Next, the voltage data is sampled in a designated unit and converted into data in a monotonically increasing or monotonically decreasing form (S820). The controller 220 of the battery management unit 20 samples the voltage data of the battery cell measured by the voltage measurement unit 210 and can convert it into data in a form that increases without decreasing within a certain interval (monotonically increasing) or decreases without increasing within a certain interval (monotonically decreasing) (see FIG. 5a).

[0074] According to a specific embodiment, the step (S820) may include a step of classifying the capacitance values ​​of the battery cells having the same voltage magnitude, and calculating an average value of the capacitance values ​​of the battery cells according to the voltage magnitude, thereby sampling the voltage (see FIG. 4).

[0075] Next, the data is smoothed using optimal smoothing parameters (S830). The controller 220 performs a smoothing process to convert the slope of the sampled data into a gentle curve, thereby ensuring continuity between adjacent data. The details of step 830 will be described later with reference to FIG. 9.

[0076] Next, a step (S840) of converting the data related to the voltage of the battery cell into a differential signal is performed. The controller 220 can convert the voltage data of the battery cell after the pre-processing steps (S820 to S830) into a differential signal (e.g., a voltage-capacity differential signal dQ / dV). The voltage-capacity differential signal can be used to detect an abnormal voltage drop phenomenon of the battery cell.

[0077] FIG. 9 is a flowchart illustrating a detailed process of determining an optimal smoothing parameter using a set algorithm and smoothing data using the parameter in an operating method of a battery management device according to an embodiment.

[0078] 9, first, a step of smoothing voltage data using at least one preset smoothing parameter (S831) is performed, where each of the at least one smoothing parameter may be a smoothing parameter that prevents a peak value of a corresponding differential signal from exceeding a specified value.

[0079] Next, a step (S832) is performed in which a set of differential signals corresponding to each smoothing parameter is obtained based on the smoothed voltage data. At this time, the degree of smoothness of each differential signal curve varies depending on the smoothing parameter value. As the parameter value increases, the curve becomes smoother but the difference from the original data increases. As the parameter value decreases, the curve approaches the original data but the fluctuation of the curve increases.

[0080] Next, a step (S833) is performed in which a cost function is calculated based on the length of the curve of each of the differential signal sets and the difference between the original differential signals converted from the original data and each of the differential signal sets. The controller 220 can calculate the cost function by simultaneously considering the gentleness of the differential signal curve and the difference from the original data.

[0081] According to one embodiment, the step of calculating the cost function (S833) may include a step of multiplying a first variable, which is a value obtained by integrating a derivative signal corresponding to the smoothing parameter over a specified interval, by a second variable, which is an RMS (root mean square) value of the difference between the original data and the derivative signal corresponding to the smoothing parameter, to calculate a cost function value corresponding to the smoothing parameter.

[0082] Next, a step (S834) is performed in which the smoothing parameter that minimizes the corresponding cost function value is determined as the optimal smoothing parameter from among at least one smoothing parameter. The optimal smoothing parameter determined in consideration of the cost function in this way means a parameter value that reduces the fluctuation of the generated data (e.g., dQ / dV data) curve while ensuring appropriate fitting with the original data.

[0083] In this way, any smoothing algorithm (for example, a smoothing spline) to which a hyper parameter is applied can be utilized to make the slope of the voltage of the battery cell satisfy continuity.

[0084] Fig. 10 is a flowchart showing an operation method of a battery management device according to another embodiment. Steps (S1010 to S1040) in Fig. 10 are substantially the same as steps (S810 to S840) in Fig. 8, and therefore redundant explanations will be omitted.

[0085] Following the step of converting the battery cell voltage into a differential signal (S1040), a step of calculating statistics for the differential signal (S1050) is performed. In this case, the statistical values ​​of the differential signal are calculated to identify abnormal behavior of the battery using a sliding window (or moving window) method. For example, the statistical values ​​for the differential signal may include a standard deviation.

[0086] Next, if the statistical value of the differential signal is equal to or greater than a predetermined reference value, the abnormality diagnosis unit 230 performs a step of diagnosing that an abnormality has occurred in the battery cell (S1060). Specifically, if the statistical value of the differential signal of the battery voltage is equal to or greater than a predetermined reference value, the abnormality diagnosis unit 230 can diagnose that an abnormal voltage drop has occurred in the battery cell.

[0087] Although not shown, the method may further include storing the measured battery cell voltage and differential signal in a memory unit, and / or generating a warning alarm when it is determined that an abnormality has occurred in the battery cell. In this case, the warning alarm may be displayed in the form of a message on a display unit (not shown) or as a light or sound signal.

[0088] The method for operating the battery management device according to the above-described embodiment may be implemented as an application or as a program instruction executable by various computer components and recorded on a computer-readable recording medium, which may include program instructions, data files, data structures, and the like, singly or in combination.

[0089] According to the above-described embodiment, in the process of preprocessing battery voltage data to convert it into a differential signal, it is possible to determine an optimal smoothing parameter determined by a set algorithm, i.e., a smoothing parameter that enables appropriate fitting with the original data while reducing fluctuations in the smoothed differential signal curve. Furthermore, a differential signal (e.g., a voltage-capacity differential signal) with respect to the battery voltage is obtained using the optimal smoothing parameter, and based on the obtained signal, an abnormal voltage drop phenomenon due to an internal short circuit in the battery can be accurately and easily detected, thereby achieving the objectives of quantifying battery degradation, identifying abnormal degradation, etc.

[0090] Although all components constituting the embodiments have been described above as being combined or operating in combination, this does not necessarily mean that the embodiments are limited to such embodiments, and all components may be selectively combined and operate in one or more combinations within the intended scope. Furthermore, unless otherwise specified, the terms "include," "comprise," "have," and the like used above mean that the component in question can be contained within them, and therefore should be interpreted as not excluding other components but as including other components.

[0091] The above description is merely an illustrative example of the technical ideas disclosed in this document, and various modifications and variations may be made by a person having ordinary skill in the art to which the embodiments disclosed in this document pertain without departing from the essential characteristics of the embodiments disclosed in this document.

[0092] Therefore, the embodiments disclosed in this document are intended to illustrate, not limit, the technical ideas disclosed in this document, and such embodiments do not limit the scope of the technical ideas disclosed in this document. The scope of protection of the technical ideas disclosed in this document should be interpreted according to the claims set forth below, and all technical ideas within the scope equivalent thereto should be interpreted as being included in the scope of rights of this document.

Claims

1. a voltage measuring unit that measures the voltage of the battery cell; a controller that converts the data related to the voltages of the battery cells into data that monotonically increases or decreases with respect to the capacity values ​​of the battery cells by classifying the capacity values ​​of the battery cells that have the same voltage magnitude for data related to the voltages of the battery cells and calculating an average value of the capacity values ​​of the battery cells for each voltage magnitude, and smooths the data using optimal smoothing parameters determined by a set algorithm, and converts the data related to the voltages of the battery cells into a differential signal that is a voltage-capacity differential signal or a voltage-time differential signal of the battery cells; Including, The controller obtains a set of differential signals corresponding to each smoothing parameter based on data smoothed using at least one predetermined smoothing parameter; calculating a cost function based on the length of the curve of each of the differential signal sets and the difference between the original data and each of the differential signal sets; The battery management device determines, as the optimum smoothing parameter, the smoothing parameter that minimizes the corresponding cost function value among the at least one smoothing parameter.

2. The battery management device of claim 1 , wherein the controller determines the optimal smoothing parameters using an algorithm that minimizes the cost function.

3. 3. The battery management device according to claim 1, wherein the controller multiplies a first variable, which is a value obtained by linearly integrating a differential signal corresponding to the smoothing parameter over a predetermined interval, by a second variable, which is an RMS (root mean square) value of a difference between the original data and the differential signal corresponding to the smoothing parameter, to calculate a cost function value corresponding to the smoothing parameter.

4. the controller applies a smoothing spline technique to data relating to the voltages of the battery cells; The battery management device according to claim 1 or 2, wherein the slope of the voltage of the battery cell is smoothed so as to satisfy continuity.

5. A battery management device as described in claim 1 or 2, further comprising an abnormality diagnosis unit that diagnoses that an abnormality has occurred in the battery cell based on a statistical value calculated for the differential signal.

6. measuring the voltage of a battery cell; a step of converting the data on the voltages of the battery cells into data in a monotonically increasing or monotonically decreasing form with respect to the capacity values ​​of the battery cells by classifying the capacity values ​​of the battery cells having the same voltage magnitude, calculating an average value of the capacity values ​​of the battery cells according to the voltage magnitude, and sampling the voltages; smoothing the data using optimal smoothing parameters determined by a set algorithm; converting the data relating to the voltage of the battery cell into a differential signal, which is a voltage-capacity differential signal or a voltage-time differential signal of the battery cell; Including, The step of determining the optimal smoothing parameters comprises: smoothing the voltage-related data using at least one preset smoothing parameter; obtaining a set of differential signals corresponding to each smoothing parameter based on the smoothed data; calculating a cost function based on the length of the curve of each of the differential signal sets and the difference between the original data and each of the differential signal sets; determining, as the optimal smoothing parameter, a smoothing parameter that minimizes a corresponding cost function value among the at least one smoothing parameter; A method of operating a battery management device, comprising:

7. 7. The method of claim 6, wherein smoothing the data comprises determining the optimal smoothing parameters using an algorithm that minimizes the cost function.

8. 8. The method for operating a battery management device according to claim 6, wherein the step of calculating the cost function includes a step of multiplying a first variable, which is a value obtained by integrating a derivative signal corresponding to the smoothing parameter over a specified interval, by a second variable, which is an RMS value of a difference between the original data and the derivative signal corresponding to the smoothing parameter, to calculate a cost function value corresponding to the smoothing parameter.

9. smoothing the data includes applying a smoothing spline technique to the data relating to the voltages of the battery cells; 8. The method for operating a battery management device according to claim 6, further comprising the step of smoothing the slope of the voltage of the battery cell so that the slope satisfies continuity.

10. A method for operating a battery management device as described in claim 6 or 7, further comprising a step of diagnosing that an abnormality has occurred in the battery cell based on a statistical value calculated for the differential signal.

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