Battery data management apparatus and method of operating same

Abstract

A battery data management apparatus according to one embodiment disclosed herein includes: a communication module configured to receive battery data from a vehicle; and a controller configured to generate voltage change information on a state of charge (SOC)-specific charge / discharge rate (C-rate) by dividing the battery data based on an SOC range and generate open circuit voltage (OCV) change information on the SOC by using the voltage change information on the SOC-specific charge / discharge rate.

Description

Technical Field

[0001] Cross-reference to related applications

[0002] This application claims priority to and the benefit of Korean Patent Application No. 10-2023-0029523, filed on March 6, 2023, in the Korean Intellectual Property Office, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein relate to a battery data management device and an operating method thereof. Background Art

[0004] Electric vehicles are supplied with electricity from outside to charge battery cells, and then the motors are driven by the voltage stored in the battery cells to generate power. The battery cells of electric vehicles may generate heat due to chemical reactions that occur during the charging and discharging of electricity, and this heat may damage the performance and life of the battery cells.

[0005] As battery cells are repeatedly used and charged and discharged, they age and their state of health (SOH) gradually decreases. The lifespan of a battery cell is highly affected by conditions such as operating temperature, usage period, charge voltage, and number of discharges, and therefore cannot be accurately diagnosed simply by measuring the remaining capacity of the battery cell. Therefore, in order to estimate the remaining lifespan of a battery, a process is required to calculate the battery's accurate open circuit voltage (OCV).

[0006] However, conventional methods of calculating the OCV of a battery include measuring the voltage behavior of the battery by directly removing the battery mounted on a vehicle or performing charge / discharge tests on the battery in a specific mode using a separate charge / discharge device, and these methods require a long time and resources for OCV measurement. Summary of the Invention

[0007] Technical issues

[0008] The embodiments disclosed herein are intended to provide a battery data management device and an operating method thereof, wherein accurate OCV data of a battery can be obtained to estimate the life of the battery.

[0009] Technical problems of the embodiments disclosed herein are not limited to the above-described technical problems, and other unmentioned technical problems will be clearly understood by those of ordinary skill in the art from the following description.

[0010] Technical Solution

[0011] A battery data management device according to one embodiment disclosed herein includes: a communication module configured to receive battery data from a vehicle; and a controller configured to generate voltage variation information about a state of charge (SOC)-specific charge / discharge rate (C-rate) by dividing the battery data based on a SOC range and to generate open circuit voltage (OCV) variation information about the SOC by using the voltage variation information about the SOC-specific charge / discharge rate.

[0012] According to one embodiment, the communication module may be further configured to receive the battery data from a data collection device that collects battery data of the vehicle.

[0013] According to one embodiment, the controller may be further configured to arrange the battery data in dedicated intervals based on the SOC by applying interpolation to the battery data.

[0014] According to one embodiment, the controller may be further configured to generate SOC-specific data groups by dividing the battery data based on the SOC range and generate voltage variation information according to the SOC-specific charge / discharge rate by fitting each SOC-specific data group.

[0015] According to one embodiment, the controller may be further configured to calculate the SOC-specific OCV by extracting the OCV, which is a voltage value at a charge / discharge rate of “0”, from the voltage variation information according to the SOC-specific charge / discharge rate.

[0016] According to one embodiment, the controller may be further configured to generate OCV variation information about the SOC by processing the OCV included in the voltage variation information according to the SOC-specific charge / discharge rate.

[0017] According to one embodiment, the controller may be further configured to generate OCV variation information about the SOC by inputting the SOC-specific OCV to a window filter to smooth the SOC-specific OCV.

[0018] An operating method of a battery data management device includes receiving battery data from a vehicle, generating voltage variation information regarding a state of charge (SOC)-specific charge / discharge rate (C-rate) by dividing the battery data based on SOC ranges, and generating open circuit voltage (OCV) variation information regarding the SOC by using the voltage variation information regarding the SOC-specific charge / discharge rate.

[0019] According to one embodiment, receiving the battery data from the vehicle may include receiving the battery data from a data collection device that collects battery data of the vehicle.

[0020] According to one embodiment, generating voltage variation information about an SOC-specific charge / discharge rate (C-rate) by dividing battery data based on SOC ranges may include arranging the battery data at dedicated intervals based on SOC by applying interpolation to the battery data.

[0021] According to one embodiment, generating voltage variation information about an SOC-specific charge / discharge rate (C-rate) by dividing battery data based on an SOC range may include generating SOC-specific data groups by dividing battery data based on an SOC range and generating voltage variation information according to the SOC-specific charge / discharge rate by fitting each SOC-specific data group.

[0022] According to one embodiment, generating voltage variation information about an SOC-specific charge / discharge rate (C-rate) by dividing battery data based on an SOC range may include calculating an SOC-specific OCV by extracting an OCV that is a voltage value at a charge / discharge rate of “0” from the voltage variation information according to the SOC-specific charge / discharge rate.

[0023] According to one embodiment, generating voltage variation information about an SOC-specific charge / discharge rate (C-rate) by dividing battery data based on an SOC range may include generating OCV variation information about the SOC by processing OCV included in the voltage variation information according to the SOC-specific charge / discharge rate.

[0024] According to one embodiment, generating voltage variation information about an SOC-specific charge / discharge rate (C-rate) by dividing battery data based on an SOC range may include generating OCV variation information about the SOC by inputting the SOC-specific OCV to a window filter to smooth the SOC-specific OCV.

[0025] Beneficial effects

[0026] By utilizing a battery data management device and an operating method thereof according to an embodiment disclosed herein, accurate OCV data of a battery can be obtained to estimate the life of the battery. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A battery pack according to one embodiment disclosed herein is illustrated.

[0028] Figure 2 is a block diagram illustrating a configuration of a battery data management device according to one embodiment disclosed herein.

[0029] Figure 3 is a graph illustrating battery data according to one embodiment disclosed herein.

[0030] Figure 4 is a graph showing voltage change information regarding a charge / discharge rate for each SOC according to one embodiment disclosed herein.

[0031] Figure 5 is a graph showing OCV variation information regarding SOC according to one embodiment disclosed herein.

[0032] Figure 6 is a graph illustrating OCV change information regarding SOC according to another embodiment disclosed herein.

[0033] Figure 7 is a flowchart illustrating an operating method of a battery data management device according to one embodiment disclosed herein.

[0034] Figure 8 is a block diagram illustrating a hardware configuration of a computing system for executing an operating method of a battery data management device according to one embodiment disclosed herein. DETAILED DESCRIPTION

[0035] Hereinafter, some embodiments disclosed in this document will be described in detail with reference to the exemplary drawings. When adding reference numerals to the components of each drawing, it should be noted that the same components are given the same reference numerals even if they are indicated in different drawings. In addition, when describing the embodiments disclosed in this document, if it is determined that the detailed description of related known configurations or functions hinders the understanding of the embodiments disclosed in this document, the detailed description will be omitted.

[0036] To describe the components of the embodiments disclosed herein, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are used only to distinguish one component from another and do not limit the components to the essence, sequence, order, etc. of the components. Unless otherwise defined, the terms used herein, including technical and scientific terms, have the same meaning as those commonly understood by those skilled in the art. Generally, terms defined in commonly used dictionaries should be interpreted as having the same meaning as the contextual meaning of the relevant technology and should not be interpreted as having ideal or exaggerated meanings unless they are explicitly defined in this document.

[0037] Figure 1 A battery pack according to one embodiment disclosed herein is illustrated.

[0038] refer to Figure 1A battery pack 1000 according to one embodiment disclosed herein may include a battery module 100, a battery data management device 200, and a relay 300. According to various embodiments, the battery module 100 may be a battery cell, and in this case, the battery pack 1000 may have a cell-to-pack structure.

[0039] The battery module 100 may include a plurality of battery cells 110, 120, 130, and 140. Figure 1 The plurality of battery cells are illustrated as four, but the present disclosure is not limited thereto, and the battery module 100 may include n battery cells (n is a natural number equal to or greater than 2).

[0040] The battery module 100 can supply power to a target device (not shown). To this end, the battery module 100 can be electrically connected to the target device. Herein, the target device may include an electrical, electronic, or mechanical device that operates by receiving power from the battery pack 1000 including the plurality of battery cells 110, 120, 130, and 140. The target device may be, for example, an electric vehicle (EV) or an energy storage system (ESS), but is not limited thereto.

[0041] The plurality of battery cells 110, 120, 130 and 140, each of which is a basic unit of a battery that can be used by charging and discharging electric energy, may be a lithium ion (Li-ion) battery, a Li-ion polymer battery, a nickel-cadmium (Ni-Cd) battery, a nickel-metal hydride (Ni-MH) battery, etc., but are not limited thereto. Figure 1 One battery module 100 is illustrated in FIG. 1 , but according to one embodiment, a plurality of battery modules 100 may be configured.

[0042] According to one embodiment, the battery data management device 200 may be implemented in the form of a battery management system (BMS). Also, according to one embodiment, the battery data management device 200 may be installed on the BMS.

[0043] The battery data management device 200 can predict the lifespan, i.e., the state of health (SOH), of the plurality of battery cells 110, 120, 130, and 140 based on the temperature and voltage data of the plurality of battery cells 110, 120, 130, and 140. The battery data management device 200 can eliminate noise from the battery data of the plurality of battery cells 110, 120, 130, and 140 and predict the SOH of the plurality of battery cells 110, 120, 130, and 140 based on the data from which the noise has been eliminated.

[0044] The battery data management device 200 may manage and / or control the state and / or operation of the battery module 100. For example, the battery data management device 200 may manage and / or control the state and / or operation of the plurality of battery cells 110, 120, 130, and 140 included in the battery module 100. The battery data management device 200 may manage charging and / or discharging of the battery module 100.

[0045] The battery data management device 200 can control the operation of the relay 300. For example, the battery data management device 200 can short-circuit the relay 300 to supply power to the target device. When the charging device is connected to the battery pack 1000, the battery data management device 200 can short-circuit the relay 300.

[0046] In addition, the battery data management device 200 may monitor the voltage, current, temperature, etc. of the battery module 100 and / or each of the plurality of battery cells 110, 120, 130, and 140 included in the battery module 100. Sensors or various measurement modules, not shown, used for monitoring performed by the battery data management device 200 may be additionally installed in the battery module 100, a charge / discharge path, any location of the battery module 100, etc. The battery data management device 200 may calculate parameters indicating the state of the battery module 100, such as the state of charge (SOC), based on the measured values ​​(such as the monitored voltage, current, temperature, etc.).

[0047] The battery data management device 200 can estimate the degradation factor of each of the positive and negative electrodes separately for each battery to evaluate the battery's state of health (SOH), i.e., the battery's lifespan. To do this, the battery data management device 200 can analyze the battery's open circuit voltage (OCV) to estimate the degradation factor of each of the positive and negative electrodes. In this context, OCV refers to the voltage measured when no current is flowing through the battery. OCV is the voltage between the fuel electrode (cathode) and the air electrode (anode) of the battery when they are not electrically connected. According to Ohm's law, as the resistance of the battery increases infinitely, the current approaches "0," thereby accurately measuring the battery's voltage. Therefore, the battery data management device 200 can measure and analyze the battery's OCV for accurate battery electrochemical analysis.

[0048] The battery data management device 200 can analyze degradation factors of each of the positive and negative electrodes by differentiating the battery's OCV, such as loss of lithium inventory (LLI), loss of active material - negative electrode (LAMn), and loss of active material - positive electrode (LAMp). Herein, LLI, which stands for loss of lithium inventory, refers to how much the lithium in a battery cell has decreased relative to its battery-on-load (BOL), LAMn refers to how much the negative electrode active material in the battery has decreased relative to its BOL, and LAMp refers to how much the positive electrode active material in the battery has decreased relative to its BOL. For example, the battery data management device 200 can extract LLI, LAMn, or LAMp as a battery degradation factor by using an artificial intelligence model for analyzing the battery's OCV or a mathematical modeling technique for calculating the battery's degradation factor.

[0049] The battery data management device 200 can calculate the OCV curve graph of the battery based on the battery data measured through actual driving of the vehicle. The battery data management device 200 can derive a continuous OCV curve of the battery by analyzing and processing the battery data measured through actual driving of the vehicle.

[0050] Figure 2 is a block diagram illustrating a configuration of a battery data management device according to one embodiment disclosed herein.

[0051] In the following, reference will be made to Figure 2 The configuration of the battery data management device 200 is described in detail.

[0052] refer to Figure 2 , the battery data management device 200 may include a communication module 210 and a controller 220 .

[0053] The communication module 210 may receive battery data of the vehicle from the vehicle. According to one embodiment, the communication module 210 may receive battery data from a data collection device (not shown) that collects battery data of the vehicle. The data collection device may be connected to a vehicle (not shown) having a battery pack installed thereon to collect battery data generated through actual driving of the vehicle. The data collection device may collect battery data generated from various sensors or controllers in the vehicle. Herein, as information about the battery installed in the vehicle, the battery data may include the battery's charge / discharge rate C-rate, voltage V, current I, temperature T, capacity Q, SOC, etc. According to one embodiment, the data collection device may include on-board diagnostics (OBD). An OBD may be an electronic device that can measure and diagnose the status of the vehicle and collect battery data.

[0054] The communication module 210 may be connected to the data collection device via a wired / wireless network, for example, Bluetooth, WiFi, ZigBee, Controller Area Network (CAN) communication, or Ethernet communication.

[0055] According to one embodiment, the communication module 210 may be connected to a plurality of data collection devices. According to one embodiment, the communication module 210 may be connected to the plurality of data collection devices simultaneously to simultaneously obtain the battery data of the vehicle collected by each of the plurality of data collection devices.

[0056] Figure 3 is a graph illustrating battery data according to one embodiment disclosed herein.

[0057] refer to Figure 3 , the communication module 210 may obtain battery data collected during the entire driving period of the vehicle from the data collection device. Herein, the battery data may include the voltage V and current I of the battery collected during the entire driving period of the vehicle. According to one embodiment, the communication module 210 may obtain battery data collected during the same period as the data collection device.

[0058] The controller 220 can apply interpolation to the battery data to arrange the battery data at dedicated intervals based on the SOC. In this article, interpolation, which is a method of normalizing given data in the form of a polynomial, can estimate data that is not obtained through observation or experimentation. The battery data obtained by the communication module 210 from the data collection device may include voltage data corresponding to different SOC values. That is, when arranged based on the SOC value, the battery data obtained from the data collection device may form a discontinuous data distribution. Therefore, the controller 220 can apply interpolation to the battery data to analyze the changing trend of the SOC included in the battery data. That is, the controller 220 can use interpolation to correct the battery data to be arranged at dedicated intervals based on the SOC, thereby generating continuous battery data.

[0059] According to one embodiment, the controller 220 can generate voltage V variation information regarding the battery's SOC by using interpolation-corrected battery data. That is, the controller 220 can generate an SOC-V profile. According to one embodiment, the controller 220 can generate voltage variation information regarding the battery's SOC and plot it on a display (not provided).

[0060] The controller 220 may divide the battery data by SOC range to generate an SOC-specific data group. More specifically, the controller 220 may group the battery data arranged based on SOC using interpolation into a plurality of groups.

[0061] According to one embodiment, the controller 220 may divide the battery data based on the SOC range to generate an SOC-specific data group, and generate a data frame using the SOC-specific data group. In this article, a data frame may mean a rectangular data list including columns and rows of data. The controller 220 may generate an SOC-specific data frame by processing the battery data included in the SOC-specific data group. The data frame may include battery voltage, current, and charge / discharge rate C-rate data. For example, the controller 220 may extract battery data with an SOC value range from 0 to 0.1 from the battery data, and generate a data frame by using the battery voltage, current, and C-rate included in the battery data.

[0062] The controller 220 may generate voltage variation information regarding a specific charge / discharge rate of the SOC.

[0063] Figure 4 is a graph showing voltage change information with respect to a charge / discharge rate for each SOC according to one embodiment disclosed herein.

[0064] refer to Figure 4 , the controller 220 can fit each SOC-specific data set. More specifically, the controller 220 can perform polynomial curve fitting on each SOC-specific data set generated by dividing the battery data based on the SOC range. In this article, polynomial curve fitting is a method of fitting data as a polynomial curve rather than a linear curve, because when the relationship between multiple data is calculated, it may not be expressed as a simple proportional relationship. The controller 220 can perform polynomial curve fitting on the battery voltage V and charge / discharge rate C-rate data included in each SOC-specific data set, thereby generating voltage V change information regarding the battery charge / discharge rate C-rate.

[0065] That is, the controller 220 may perform polynomial curve fitting on the voltage V and charge / discharge rate C-rate data of the battery included in each of the plurality of SOC specific data groups, thereby generating a trend line of the data.

[0066] According to one embodiment, the controller 220 may fit each SOC-specific data group to calculate a voltage variation function with respect to an SOC-specific charge / discharge rate. More specifically, the controller 220 may calculate a voltage variation function with respect to an SOC-specific charge / discharge rate by using a polynomial regression artificial intelligence model. The controller 220 may analyze a trend line obtained by performing polynomial curve fitting on the voltage V and charge / discharge rate C-rate data of the battery included in each of the plurality of SOC-specific data groups, thereby generating a polynomial indicating the correlation between the data. For example, the controller 220 may analyze a trend line generated by fitting the voltage V variation data with respect to the charge / discharge rate C-rate of the battery included in the data group corresponding to the SOC value range from 0 to 0.1, thereby calculating a “ ” as a function of voltage variation with respect to the charge / discharge rate.

[0067] Controller 220 can calculate the SOC-specific OCV using a voltage variation function for an SOC-specific charge / discharge rate. Controller 220 can use a "0" C-rate interpolation to search for a point with an X-coordinate of "0" in the voltage variation function for an SOC-specific charge / discharge rate. In other words, controller 220 can calculate the SOC-specific OCV by extracting the OCV from the voltage variation function for an SOC-specific charge / discharge rate, which is the voltage value when the charge / discharge rate, as the X-coordinate, is "0."

[0068] For example, the controller 220 may calculate the voltage variation function “ ”, and then the OCV value is calculated as “3.45”, which is a voltage value when the charge / discharge rate C-rate as the X coordinate in the voltage change function is “0”.

[0069] The controller 220 may generate OCV variation information regarding the SOC by using a voltage variation function regarding a specific charge / discharge rate of the SOC.

[0070] Figure 5 is a graph showing OCV variation information regarding SOC according to one embodiment disclosed herein.

[0071] refer to Figure 5 , the controller 220 can generate OCV variation information about the SOC by connecting the SOC-specific OCV calculated by the voltage variation function with respect to the SOC-specific charge / discharge rate. That is, the controller 220 can generate OCV variation information about the SOC of the battery by processing the voltage variation information with respect to the SOC-specific charge / discharge rate.

[0072] Figure 6 is a graph illustrating OCV change information regarding SOC according to another embodiment disclosed herein.

[0073] refer to Figure 6 , the controller 220 may input the SOC-specific OCV information into a window-based filter to smooth the SOC-specific OCV information, thereby removing noise from the OCV variation information about the SOC. That is, the controller 220 may smooth the SOC-specific OCV information by inputting the SOC-specific OCV information into the window-based filter. The controller 220 may smooth the continuous OCV curve from which noise is removed by smoothing the SOC-specific OCV information.

[0074] According to one embodiment, the controller 220 may perform polynomial curve fitting on the OCV variation information regarding the SOC. The controller 220 may perform polynomial curve fitting on the OCV variation information regarding the SOC to generate a trend line of the data.

[0075] According to one embodiment, the controller 220 may perform polynomial curve fitting on the OCV variation information about the SOC to generate a polynomial indicating the correlation. More specifically, the controller 220 may analyze the trend line of the OCV variation information about the SOC using a polynomial regression artificial intelligence model to calculate the OCV variation function about the SOC.

[0076] As described above, with the battery data management apparatus 200 according to one embodiment disclosed herein, it is possible to generate an accurate OCV graph of a battery for life estimation of the battery by using battery data obtained through actual driving of a vehicle.

[0077] By generating an OCV graph of a battery using battery data obtained by driving a vehicle, the battery data management device 200 may not require time for charging and discharging the battery in a specific pattern and may analyze a large amount of battery data in a short time.

[0078] Calculation of the OCV of a battery requires a short time, and when the battery is diagnosed as being in an abnormal state by constantly determining its state, information can be immediately provided to the user.

[0079] Also, the battery data management device 200 can accurately diagnose the state of the battery and improve the accuracy of battery life prediction by obtaining battery data from which noise is eliminated.

[0080] Figure 7 is a flowchart illustrating an operating method of a battery data management device according to one embodiment disclosed herein.

[0081] The battery data management device 200 can be used with reference Figures 1 to 6 The described battery data management device 200 is substantially the same and thus will be described briefly to avoid redundant description.

[0082] refer to Figure 7 The operating method of the battery data management apparatus 200 may include an operation S101 of receiving battery data from a data collection device that collects battery data of a vehicle, an operation S102 of generating voltage variation information about an SOC-specific charge / discharge rate C-rate by dividing the battery data based on an SOC range, and an operation S103 of generating OCV variation information about the SOC by using the voltage variation information about the SOC-specific charge / discharge rate.

[0083] In operation S101, the communication module 210 may receive battery data from a data collection device (not shown) that collects battery data of a vehicle. The data collection device may be connected to a vehicle (not shown) having a battery pack installed thereon to collect battery data generated through actual driving of the vehicle. Herein, battery data, as information regarding the battery installed in the vehicle, may include the battery's charge / discharge rate C-rate, voltage V, current I, temperature T, capacity Q, state of charge (SOC), etc. According to one embodiment, the data collection device may include an on-board diagnostics (OBD). An OBD is an electronic device that can measure and diagnose the vehicle's status and collect battery data.

[0084] In operation S101, the communication module 210 may be connected to a plurality of data collection devices. In operation S101, the communication module 210 may be simultaneously connected to the plurality of data collection devices to simultaneously obtain battery data of the vehicle collected by each of the plurality of data collection devices.

[0085] In operation S101, the communication module 210 may obtain battery data collected during the entire driving period of the vehicle from a data collection device. Herein, the battery data may include a voltage V and a current I of the battery collected during the entire driving period of the vehicle.

[0086] In operation S101 , according to one embodiment, the communication module 210 may obtain battery data collected during the same period as a data collection device.

[0087] In operation S102 , the controller 220 may apply interpolation to the battery data to arrange the battery data at dedicated intervals based on SOC. Herein, interpolation, which is a method of normalizing given data in the form of a polynomial, may estimate data not obtained through observation or experimentation.

[0088] In operation S102 , the controller 220 may correct the battery data to be arranged at dedicated intervals based on the SOC using interpolation, thereby generating continuous battery data.

[0089] In operation S102, according to one embodiment, the controller 220 may generate voltage V variation information regarding the battery's SOC by using interpolation-corrected battery data. In operation S102, the controller 220 may generate an SOC-V profile. In operation S102, according to one embodiment, the controller 220 may generate voltage variation information regarding the battery's SOC and plot it on a display (not provided).

[0090] In operation S102, the controller 220 may divide the battery data according to the SOC range to generate an SOC-specific data group. More specifically, the controller 220 may group the battery data arranged based on the SOC using interpolation into a plurality of groups.

[0091] In operation S102, according to one embodiment, the controller 220 may divide the battery data based on the SOC range to generate an SOC-specific data group, and generate a data frame using the SOC-specific data group. Herein, a data frame may refer to a rectangular data list including columns and rows of data. In operation S102, the controller 220 may generate an SOC-specific data frame by processing the battery data included in the SOC-specific data group. The data frame may include battery voltage, current, and charge / discharge rate (C-rate) data.

[0092] In operation S102, the controller 220 may fit each SOC-specific data set. In operation S102, more specifically, the controller 220 may perform polynomial curve fitting on each SOC-specific data set generated by dividing the battery data based on the SOC range. In this article, polynomial curve fitting is a method of fitting data as a polynomial curve rather than a linear curve because when the relationship between multiple data is calculated, it may not be expressed as a simple proportional relationship. In operation S102, the controller 220 may perform polynomial curve fitting on the battery voltage V and charge / discharge rate C-rate data included in each SOC-specific data set, thereby generating voltage V change information regarding the battery charge / discharge rate C-rate.

[0093] In operation S102 , the controller 220 may perform polynomial curve fitting on the voltage V and charge / discharge rate C-rate data of the battery included in each of the plurality of SOC specific data groups, thereby generating a trend line of the data.

[0094] In operation S102, according to one embodiment, the controller 220 may fit each SOC-specific data set to calculate a voltage variation function for an SOC-specific charge / discharge rate. More specifically, in operation S102, the controller 220 may calculate the voltage variation function for an SOC-specific charge / discharge rate using a polynomial regression artificial intelligence model. In operation S102, the controller 220 may analyze a trend line obtained by performing polynomial curve fitting on the battery voltage V and charge / discharge rate C-rate data included in each of the multiple SOC-specific data sets, thereby generating a polynomial indicating the correlation between the data.

[0095] In operation S103, the controller 220 may calculate the SOC-specific OCV by using a voltage variation function with respect to an SOC-specific charge / discharge rate. In operation S103, the controller 220 may use a "0" C-rate interpolation to search for a point with an X coordinate of "0" in the voltage variation function with respect to an SOC-specific charge / discharge rate. In operation S103, the controller 220 may calculate the SOC-specific OCV by extracting the OCV, which is a voltage value when the charge / discharge rate, as the X coordinate, is "0," from the voltage variation function with respect to an SOC-specific charge / discharge rate.

[0096] In operation S103, the controller 220 may generate OCV variation information about the SOC by using a voltage variation function with respect to an SOC-specific charge / discharge rate. In operation S103, the controller 220 may generate OCV variation information about the SOC by connecting the SOC-specific OCV. In operation S103, that is, the controller 220 may generate OCV variation information about the SOC of the battery by processing the voltage variation information with respect to an SOC-specific charge / discharge rate.

[0097] In operation S103, the controller 220 may input the SOC-specific OCV information to a window-based filter to smooth the SOC-specific OCV information, thereby eliminating noise in the OCV variation information about the SOC. In operation S103, the controller 220 may smooth the SOC-specific OCV information by inputting the SOC-specific OCV information to the window-based filter. In operation S103, the controller 220 may smooth the continuous OCV curve from which noise has been eliminated by smoothing the SOC-specific OCV information.

[0098] In operation S103, according to one embodiment, the controller 220 may perform polynomial curve fitting on the OCV variation information regarding the SOC. In operation S103, the controller 220 may perform polynomial curve fitting on the OCV variation information regarding the SOC to generate a trend line of the data.

[0099] In operation S103, according to one embodiment, the controller 220 may perform polynomial curve fitting on the OCV variation information about the SOC to generate a polynomial indicating a correlation. More specifically, in operation S103, the controller 220 may analyze a trend line of the OCV variation information about the SOC using a polynomial regression artificial intelligence model to calculate an OCV variation function about the SOC.

[0100] Figure 8 is a block diagram illustrating a hardware configuration of a computing system for executing an operating method of a battery data management device according to one embodiment disclosed herein.

[0101] refer to Figure 8 , a computing system 2000 according to one embodiment disclosed herein may include an MCU 2100 , a memory 2200 , an input / output I / F 2300 , and a communication I / F 2400 .

[0102] The MCU 2100 may be a processor that executes various programs (eg, OCV calculation algorithm, etc.) stored in the memory 2200, processes various data through these programs, and performs Figure 1 The above-described functions of the battery data management device 200 are shown.

[0103] The memory 2200 may store various programs related to the operation of the battery data management device 200. Also, the memory 2200 may store operation data of the battery data management device 200.

[0104] Depending on needs, multiple memories 2200 may be provided. Memory 2200 may be volatile memory or non-volatile memory. For volatile memory 2200, random access memory (RAM), dynamic RAM (DRAM), static RAM (SRAM), etc. may be used. For non-volatile memory 2200, read-only memory (ROM), programmable ROM (PROM), electrically alterable ROM (EAROM), erasable programmable memory (EPROM), electrically erasable programmable memory (EEPROM), flash memory, etc. may be used. The examples of memory 2200 listed above are merely examples and are not limiting.

[0105] The input / output I / F 2300 may provide an interface for transmitting and receiving data by connecting an input device (not shown) such as a keyboard, a mouse, a touch panel, etc. and an output device (not shown) such as a display, etc. to the MCU 2100 .

[0106] The communication I / F 2400, which is a component capable of transmitting and receiving various data to and from a server, may be various devices capable of supporting wired or wireless communication. For example, a program or various data for measuring resistance and diagnosing abnormalities of battery cells may be transmitted to and received from a separately provided external server via the communication I / F 2400.

[0107] Thus, a computer program according to an embodiment disclosed herein may be recorded in the memory 2200 and processed by the MCU 2100, thereby being implemented as an execution Figure 1 and 2 Modules of the battery data management device 200 are shown with their respective functions.

[0108] The above description merely illustrates the technical idea of ​​the present disclosure, and for those skilled in the art to which the present disclosure pertains, various modifications and changes will be possible without departing from the basic features of the present disclosure.

[0109] Therefore, the embodiments disclosed in this disclosure are intended to describe rather than limit the technical spirit of this disclosure, and the scope of the technical spirit of this disclosure is not limited by these embodiments. The scope of protection of this disclosure should be interpreted by the following claims, and all technical spirits within the same scope should be understood to be included in the scope of this disclosure.

[0110] Explanation of symbols

[0111] 1000: Battery Pack

[0112] 100: Battery module

[0113] 200: Battery data management equipment

[0114] 210: Communication module

[0115] 220: Controller

[0116] 300: Relay

[0117] 2000: Computing Systems

[0118] 2100: MCU

[0119] 2200: Memory

[0120] 2300: Input / Output I / F

[0121] 2400: Communication I / F

Claims

1. A battery data management device, comprising: a communication module configured to receive battery data from a vehicle; and A controller is configured to generate voltage variation information about an SOC-specific charge / discharge rate (C-rate) by dividing the battery data based on an SOC range and to generate OCV variation information about the SOC by using the voltage variation information about the SOC-specific charge / discharge rate.

2. The battery data management device according to claim 1, wherein: The communication module is further configured to receive the battery data from a data collection device that collects the battery data of the vehicle.

3. The battery data management device according to claim 2, wherein: The controller is further configured to arrange the battery data at dedicated intervals based on the SOC by applying interpolation to the battery data.

4. The battery data management device according to claim 3, wherein: The controller is further configured to generate SOC-specific data groups by dividing the battery data based on SOC ranges and generate voltage variation information according to the SOC-specific charge / discharge rate by fitting each SOC-specific data group.

5. The battery data management device according to claim 4, wherein: The controller is further configured to calculate the SOC-specific OCV by extracting an OCV that is a voltage value at a charge / discharge rate of 0 from the voltage change information according to the SOC-specific charge / discharge rate. The battery data management device according to claim 5 , wherein: The controller is further configured to generate OCV variation information about the SOC by processing the OCV included in the voltage variation information according to the SOC-specific charge / discharge rate.

7. The battery data management device according to claim 6, wherein: The controller is further configured to generate OCV variation information about the SOC by inputting the SOC-specific OCV into a window filter to smooth the SOC-specific OCV.

8. A method for operating a battery data management device, the method comprising: receiving battery data from the vehicle; generating voltage variation information regarding an SOC-specific charge / discharge rate (C-rate) by dividing the battery data based on an SOC range; and OCV variation information about the SOC is generated by using the voltage variation information about the SOC-specific charge / discharge rate.

9. The operating method according to claim 8, wherein: Receiving the battery data from the vehicle includes receiving the battery data from a data collection device that collects the battery data of the vehicle.

10. The operating method according to claim 9, wherein: Generating the voltage variation information about the SOC-specific charge / discharge rate (C-rate) by dividing the battery data based on the SOC range includes arranging the battery data at dedicated intervals based on the SOC by applying interpolation to the battery data.

11. The operating method according to claim 10, wherein: Generating the voltage variation information about the SOC-specific charge / discharge rate (C-rate) by dividing the battery data based on the SOC range includes generating SOC-specific data groups by dividing the battery data based on the SOC range and generating the voltage variation information according to the SOC-specific charge / discharge rate by fitting each SOC-specific data group.

12. The operating method according to claim 11, wherein: Generating the voltage variation information about the SOC-specific charge / discharge rate (C-rate) by dividing the battery data based on the SOC range includes calculating the SOC-specific OCV by extracting OCV, which is a voltage value at a charge / discharge rate of 0, from the voltage variation information according to the SOC-specific charge / discharge rate.

13. The operating method according to claim 12, wherein: Generating the voltage variation information about the SOC-specific charge / discharge rate (C-rate) by dividing the battery data based on the SOC range includes generating OCV variation information about the SOC by processing OCV included in the voltage variation information according to the SOC-specific charge / discharge rate.

14. The operating method according to claim 13, wherein: Generating the voltage variation information about the SOC-specific charge / discharge rate (C-rate) by dividing the battery data based on the SOC range includes generating OCV variation information about the SOC by inputting the SOC-specific OCV to a window filter to smooth the SOC-specific OCV.

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