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 on December 23, 2021, and all contents disclosed in the documents of the Korean patent application are incorporated as part of this specification. SUMMARY OF THE DISCLOSURE The embodiments disclosed herein relate to a battery management apparatus and method of operation. [Background technology]

[0002] In recent years, research and development of secondary batteries has been actively conducted. Here, a secondary battery is a battery that can be charged and discharged, and includes both conventional Ni / Cd batteries, Ni / MH batteries, and recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have an advantage of having a much higher energy density than conventional Ni / Cd batteries, Ni / MH batteries, and the like. Lithium-ion batteries can be made small and lightweight, so they are used as power sources for mobile devices. The range of use of lithium-ion batteries has also been expanded to include power sources for electric vehicles, and they have attracted attention as a next-generation energy storage medium. Secondary batteries are generally used as battery packs including battery modules in which multiple battery cells are connected in series and / or parallel. The state and operation of the battery pack are managed and controlled by a battery management system.

[0003] In the case of such secondary batteries, an abnormal voltage drop phenomenon suspected to be an internal short may be observed during the charging cycle. To detect such a phenomenon, a 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. In order to obtain optimal results, it is necessary to determine a smoothing parameter that minimizes the swing of the differential signal curve and can perform an appropriate fitting with the original data. 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 pre-processing 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 charging of a battery by using a differential signal with respect to the battery voltage, and further realize 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 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 an optimal smoothing parameter determined by a set algorithm, and converts the data related 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 can obtain a set of differential signals corresponding to each smoothing parameter based on data smoothed using at least one preset smoothing parameter, and calculate the cost function based on the length of the curve of each of the differential signal sets and the difference between 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 a 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 such that a peak value of a corresponding differential signal does not exceed a specified value.

[0013] In one embodiment of the battery management device, the controller can perform sampling of the voltage by classifying the capacity values ​​of the battery cells having the same voltage magnitude and calculating the average capacity values ​​of the battery cells by voltage magnitude.

[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] A 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 greater than or equal to a preset reference value. In the 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.

[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 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 a 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 operating method of a battery management device, the step of determining the optimal smoothing parameters may include the steps of smoothing the data related to the voltage 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 curve length of each of the sets of differential signals and the difference between original data and each of the sets of differential signals.

[0019] In one embodiment of an operating method of 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 a root mean square (RMS) 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 for which a corresponding cost function value is minimized as the optimal smoothing parameter.

[0021] In the operating method of the battery management device according to an embodiment, each of the at least one smoothing parameter may be a smoothing parameter such that a peak value of a corresponding differential signal does not exceed a specified value.

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

[0023] In an operating method of a battery management device according to an embodiment, 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 one embodiment may further include a step of calculating a statistical value for the differential signal, and a step of diagnosing that an abnormality has occurred in the battery cell if the statistical value for the differential signal is greater than or equal to a predetermined reference value. In the operating method of 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. Effect of the Invention

[0025] According to one embodiment, in the process of pre-processing 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 proper fitting with the original data.

[0026] Furthermore, by using the optimal smoothing parameters, a differential signal for the battery voltage (e.g., a voltage-capacity differential signal, a voltage-time differential signal, etc.) can be obtained, and based on that, an abnormal voltage drop phenomenon caused by an internal short circuit 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 description 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 the description of 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 be limiting. In addition, for the sake of clarity, the expressions of some components in the drawings may be exaggerated or omitted.

[0028] [Figure 1] FIG. 2 is a block diagram showing a configuration of a battery control system. [Diagram 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 battery voltage. [Figure 3b] 3b is a graph showing a differential signal of the voltage raw data of FIG. 3a; [Figure 4] FIG. 1 illustrates a method for sampling battery voltage data to remove duplicate signals. [Figure 5a] 11 is a graph showing the results of pre-processing battery voltage data by sampling and smoothing. [Figure 5b] 11 is a graph showing an outline of differentiation of battery voltage data at each pre-processing stage; [Figure 6a] 1 is a graph showing original data of a voltage-capacity differential signal and changes in signal form depending on a smoothing parameter value according to an embodiment; [Figure 6b] 1 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] 13 is a graph showing the change in the cost function (L*rms) according to the smoothing parameter value when sampling at 1 second intervals. [Figure 7b] 13 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] 4 is a flowchart illustrating an operation method of a battery management device according to an embodiment. [Figure 9] 10 is a flowchart showing a detailed process of a data smoothing step in a method of operating a battery management device according to an embodiment. [Figure 10] 10 is a flowchart illustrating a method of operating a battery management device according to another embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0029] Hereinafter, the embodiments disclosed in this document will be described in detail with reference to the exemplary drawings. When referring to components in each drawing, it should be noted that the same components are referred to by the same reference numerals as much as possible when they are displayed in other drawings. In addition, when describing the embodiments disclosed in this document, if a detailed description of related publicly known configurations or functions is deemed to hinder understanding of the embodiments disclosed in this document, the detailed description will be omitted.

[0030] The terms used in this document are currently selected as widely used as possible while taking into consideration the functions, but this may vary depending on the intention or practice of the technical personnel in the field, or the emergence of new technology. In addition, in certain cases, the applicant may arbitrarily select some terms, and in such cases, the meanings of these terms will be described in the description of the specification. Therefore, it is clear that the terms used in this document must be interpreted based on the substantial meanings of the terms and the overall content of this document, rather than simply the names of the terms.

[0031] The terms used in this document are merely used to describe a particular embodiment and are not intended to limit the scope of other embodiments. A singular expression may include a 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 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, a 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 + terminal side or the - terminal side of the battery module 10 for controlling the flow of charge / 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, over-discharging, etc.

[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 device 20 is an interface that receives input of measured values ​​of the various parameters described above, and can include a plurality of terminals, a circuit connected to these terminals and capable of processing the input values, etc. The battery management device 20 can also control the ON / OFF of the switching element 14, and is connected to the battery module 10 to monitor the state of the battery module 10.

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

[0038] FIG. 2 is a block diagram showing a 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 further include an abnormality diagnosis unit 230 as an option.

[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 may pre-process the voltage data of the battery cell (e.g., a voltage signal over time) and convert the voltage of the battery cell so that it can be differentiated in a certain section. As illustrated in the graph of FIG. 3a, typically measured battery voltage data may not be able to be differentially analyzed due to overlapping signals and discontinuous sections. Therefore, the controller 220 may pre-process the voltage data of the battery cell before converting the differential signal and convert the voltage of the battery cell so that it can be differentiated in a preset section.

[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 in a certain interval) or a monotonically decreasing (i.e., decreasing without increasing in 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 voltage data of the battery cell into a gentle form by performing a smoothing process on the sampled data. This may satisfy the continuity between adjacent data in a certain section and make the data differentiable. In this case, the gentleness 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, and the smaller the parameter value, the closer the curve is to the original data, but the greater the swing of the curve. Therefore, when determining an optimal smoothing parameter, it is necessary to appropriately consider variables in such a trade-off relationship. This will be described later with reference to FIGS. 6a to 6b.

[0043] The controller 220 can convert the voltage data of the battery cells that have been pre-processed (sampled and smoothed) into differential signals. In this case, the controller 220 can calculate differential signals (e.g., dQ / dV, dV / dQ, dV / dt, etc.) with respect to the capacity and voltage of the battery cells. According to an embodiment, in order to determine the optimal smoothing parameters, the controller 220 can obtain a set of differential signals corresponding to at least one preset smoothing parameter, and determine the optimal smoothing parameters for the set of differential signals by considering two or more variables (e.g., the gentleness 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 for determining abnormal behavior of the battery in a sliding window (or moving window) manner. 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 a statistical value for 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 for the statistical value of the differential signal of the battery voltage. In this way, 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. At this time, the warning alarm may be provided in the form of a message on a display unit (not shown) connected to the battery control system, or may be provided 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 an embodiment, the controller 220 may sample data related 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] Fig. 3a is a graph showing raw data of the measured voltage of a battery, and Fig. 3b is a graph showing a differential signal of the raw voltage data of Fig. 3a. In this case, the horizontal axis of Fig. 3a shows the battery capacity (Ah), and the vertical axis shows the measured voltage (V) of the battery. Also, the horizontal axis of Fig. 3b shows the battery voltage (V), and the vertical axis shows the differential signal (dQ / dV) with respect to the battery capacity and voltage.

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

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

[0053] In this case, the battery voltage data can be sampled by classifying the battery capacity values ​​based on a specific voltage level for the battery capacity and voltage data and calculating the average value. For example, as shown in FIG. 4, the average value of the capacity values ​​corresponding to each voltage can be calculated based on the overlapping battery voltages of 3.23V and 3.24V. For example, when the voltage is 3.23V, the capacity value can be set to 44.3Ah, which is the average value of the battery capacities of 43Ah, 44Ah, and 46Ah, and when the voltage is 3.24V, the capacity value can be set to 46Ah, which is the average value of the 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] According to one embodiment, the controller 220 may perform smoothing so that the sampled data is differentiable within a preset interval.

[0056] Fig. 5a is a graph showing the results of preprocessing the battery voltage data by sampling and smoothing, and Fig. 5b is a graph showing the outline of the differentiation of the battery voltage data at each preprocessing stage. In this case, the horizontal axis of Fig. 5a indicates the battery capacity (Ah), and the vertical axis indicates the measured battery voltage (V). Also, the horizontal axis of Fig. 5b indicates the battery voltage (V), and the vertical axis indicates 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 are generated, but in the case of voltage data sampled according to FIG. 4, it can be seen that it shows a monotonically increasing form.

[0058] On the other hand, even if the raw voltage data is sampled, there may be a section where differentiation is impossible due to the difference in slope between adjacent data. In this regard, referring to FIG 5b, it can be seen that a complete differential signal value is not obtained when only the raw voltage data is sampled.

[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 an embodiment, a Smoothing Spline (SS) technique can be applied, and the calculation formula of the Smoothing Spline can be expressed as follows:

[0060]

number

[0061] The smoothing spline formula can prevent the slope of the sampled voltage data from changing abruptly and convert it into a continuous curve. In this case, the larger the λ value of the smoothing parameter, 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 use any other data smoothing algorithm in which hyperparameters (variable values ​​input by the user during modeling) are applied other than the smoothing spline technique. The optimal smoothing parameters to be applied to each smoothing technique can be determined according to the embodiment described below.

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

[0064] As described above, the smoothness of the differential signal curve varies depending on the smoothing parameter (λ) value, and the larger the parameter value, the smoother the curve but the greater the difference from the original data, while the smaller the parameter value, the closer the curve is to the original data but the greater the swing of the curve. 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 pre-defined 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 such that a peak value of the corresponding derivative signal does not exceed a specified value.

[0066] 6a is a graph showing a change in signal form according to the original data (Vsample) and the smoothing parameter value (λ1, λ2, ..., λ5) of the voltage-capacity 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, but the difference from the original data becomes larger.

[0067] The cost function may be calculated for each of the differential signal sets by simultaneously considering the gentleness of the curve and the difference from the original data. According to an embodiment, the gentleness of the curve may be represented by a first variable, which is a value obtained by integrating the differential signal over a certain section (for example, a section in which the voltage magnitude is 3.4V to 4.2V), and the difference from the original data may be represented by a second variable, which is a 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 (as 1-λ decreases), the differential signal curve becomes gentler and the first variable value indicating the length of the curve becomes smaller. On the other hand, as the smoothing parameter value increases (as 1-λ decreases), the second variable value indicating the difference from the original data increases.

[0068] 6b is a graph showing the change in the length (L) of the differential signal curve and the difference (rms) from the original data according to the smoothing parameter (λ) value. Referring to FIG. 6b, it can be seen that as the smoothing parameter (λ) value increases (i.e., as 1-λ decreases to 10-6, 10-7, ..., 10-10), the length (L) of the curve decreases and the difference (rms) from the original data increases.

[0069] According to an 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. Also, among the smoothing parameters (e.g., λ1, λ2, ..., λ5), the smoothing parameter that minimizes the corresponding cost function value may be determined as the optimal smoothing parameter. The optimal smoothing parameter determined in consideration of the cost function refers to a parameter value that reduces the swing of the curve of the generated data (e.g., differential signals such as dQ / dV, dV / dQ, dV / dt) while allowing proper fitting with the original data.

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

[0071] 8 is a flow chart showing a method of operating a battery management device according to an embodiment of the present invention, the structure and components of which are as described above with reference to FIG.

[0072] 8, in an operation method of a battery management device according to an embodiment, first, a step (S810) of measuring the voltage of a battery cell 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 obtain voltage data (see FIGS. 1 and 2).

[0073] Next, a step of sampling the voltage data in a designated unit and converting it into data of a monotonically increasing or decreasing form (S820) is performed. The controller 220 of the battery management device 20 samples the voltage data of the battery cell measured by the voltage measurement unit 210 and can convert it into data of a form that increases without decreasing for a certain period (monotonically increasing) or decreases without increasing for a certain period (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 performing sampling on the voltage by calculating an average value of the capacitance values ​​of the battery cells according to the voltage magnitude (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 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 for which the pre-processing steps (S820 to S830) have been completed 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 showing a detailed process of determining an optimal smoothing parameter using a set algorithm and smoothing data using the optimal smoothing 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. According to one embodiment, each of the at least one smoothing parameter may be a smoothing parameter that does not cause a peak value of a corresponding differential signal to exceed a specified value.

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

[0080] Next, a step (S833) of calculating a cost function based on the length of the curve of each of the differential signal sets and the difference between the original differential signals obtained by converting the original data and each of the differential signal sets is performed. 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 linearly integrating a differential signal corresponding to the smoothing parameter over a specified interval, by a second variable, which is a root mean square (RMS) 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.

[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 this manner in consideration of the cost function means a parameter value that reduces the swing of the generated data (e.g., dQ / dV data) curve while allowing proper fitting with the original data.

[0083] In this manner, any smoothing algorithm (e.g., a smoothing spline) to which a hyper parameter is applied can be utilized so that the slope of the voltage of the battery cell satisfies 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 similar to steps (S810 to S840) in Fig. 8, so duplicated explanations will be omitted.

[0085] Following the step of converting the voltage of the battery cell into a differential signal (S1040), a step of calculating statistics for the differential signal (S1050) is performed. At this time, the statistics for the differential signal are calculated in a sliding window (or moving window) manner to determine abnormal behavior of the battery. For example, the statistics for the differential signal may include a standard deviation.

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

[0087] Although not shown, the method may further include a step of storing the measured battery cell voltage and differential signal in a memory unit, and / or a step of 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 provided in the form of a message on a display unit (not shown) or as a light or sound signal.

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

[0089] According to the embodiment described above, in the process of pre-processing the 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 the fluctuation of the smoothed differential signal curve. Furthermore, a differential signal (e.g., a voltage-capacity differential signal) for the battery voltage is obtained using the optimal smoothing parameter, and based on the signal, an abnormal voltage drop phenomenon caused by an internal short circuit of the battery can be accurately and easily detected, thereby achieving the purpose of quantifying battery degradation, determining abnormal degradation, etc.

[0090] Although it has been described above that all components constituting the embodiments are combined or operate in combination, the present invention is not necessarily limited to such an embodiment, and all components may be selectively combined and operate in one or more combinations within the scope of the purpose. Furthermore, the terms "include," "constitute," "have," and the like described above mean that the relevant component can be contained within the description, unless otherwise specified, and therefore should not be interpreted as 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 explain, not to 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 below, and all technical ideas within the equivalent scope 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 a battery cell; 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 decreasing form, smooths the data using optimal smoothing parameters determined by a set algorithm, and converts the data relating to the voltage of the battery cell into a differential signal; A battery management device comprising:

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

3. The controller obtains a set of differential signals corresponding to each smoothing parameter based on the data smoothed using at least one preset smoothing parameter; The battery management device of claim 2 , wherein the cost function is calculated based on a length of a curve of each of the sets of differential signals and a difference between original data and each of the sets of differential signals.

4. 4. The battery management device according to claim 3, 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 specified interval, by a second variable, which is a root mean square (RMS) 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.

5. The battery management device according to claim 4 , wherein the controller determines, as an optimal smoothing parameter, a smoothing parameter that minimizes a corresponding cost function value among the at least one smoothing parameter.

6. The battery management device of claim 5 , wherein each of the at least one smoothing parameter is a smoothing parameter such that a peak value of a corresponding differential signal does not exceed a specified value.

7. 7. The battery management device according to claim 1, wherein the controller performs sampling of the voltage by classifying capacity values ​​of the battery cells having the same voltage magnitude and calculating an average value of the capacity values ​​of the battery cells by voltage magnitude.

8. The controller utilizes a smoothing algorithm to which hyper-parameters are applied; The battery management device according to claim 1 , wherein the voltage gradient of the battery cell is smoothed so as to satisfy continuity.

9. 7. The battery management device according to claim 1, further comprising 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 preset reference value.

10. 7. The battery management device according to claim 1, wherein the differential signal is a voltage-capacity differential signal or a voltage-time differential signal of the battery cell.

11. measuring a voltage of a battery cell; Sampling data on the voltage of the battery cell in a specified unit and converting the data into data in a monotonically increasing or decreasing form; smoothing the data using optimal smoothing parameters determined by a set algorithm; converting data relating to the voltage of the battery cell into a differential signal; A method of operating a battery management device, comprising:

12. 12. The method of claim 11, wherein smoothing the data comprises determining the optimal smoothing parameters using an algorithm that minimizes a cost function.

13. The step of determining the optimal smoothing parameters comprises: smoothing the voltage related data using at least one predefined smoothing parameter; obtaining a set of differential signals corresponding to respective smoothing parameters based on the smoothed data; 13. The method of claim 12, further comprising the step of: calculating the cost function based on a length of a curve of each of the set of differential signals and a difference between original data and each of the set of differential signals.

14. 14. The method for operating a battery management device according to claim 13, wherein the step of calculating the cost function includes a step of multiplying a first variable, which is a value obtained by linearly integrating a differentiated 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 differentiated signal corresponding to the smoothing parameter, to calculate a cost function value corresponding to the smoothing parameter.

15. The method for operating a battery management device according to claim 14 , wherein the step of determining the optimal smoothing parameter further comprises the step of determining, among the at least one smoothing parameter, a smoothing parameter for which a corresponding cost function value is minimized as the optimal smoothing parameter.

16. 16. The method of claim 15, wherein each of the at least one smoothing parameter is a smoothing parameter such that a peak value of a corresponding differential signal does not exceed a specified value.

17. The step of converting the data into monotonically increasing or decreasing data by sampling the data in a specified unit includes:

17. The method for operating a battery management device according to claim 11, further comprising a step of classifying the capacity values ​​of the battery cells having the same voltage magnitude, and sampling the voltage by calculating an average value of the capacity values ​​of the battery cells according to the voltage magnitude.

18. smoothing the data utilizes a smoothing algorithm adapted to hyper-parameters; The method for operating a battery management device according to claim 11 , further comprising the step of smoothing a slope of the voltage of the battery cell so as to satisfy continuity.

19. calculating statistics for the differential signal; The method for operating a battery management device according to claim 11 , further comprising the step of diagnosing that an abnormality has occurred in the battery cell when a statistical value for the differential signal is equal to or greater than a preset reference value.

20. The method for operating a battery management device according to any one of claims 11 to 16, wherein the differential signal is a voltage-capacity differential signal or a voltage-time differential signal of the battery cell.

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