Battery data management device and operation method thereof

Abstract

A battery data management device according to one embodiment disclosed herein may include a communication module that receives battery data from a vehicle, and a controller that classifies the battery data based on an SOC range, generates voltage change information according to a charge / discharge rate (C-rate) for each SOC, and generates OCV change information according to the SOC using the voltage change information according to the charge / discharge rate for each SOC.

Description

[Technical Field]

[0001] This application claims the benefit of priority based on Korean Patent Application No. 10-2023-0029523, filed March 6, 2023, the entire contents of which are incorporated herein by reference. SUMMARY OF THE INVENTION The embodiments disclosed herein relate to a battery data management device and method of operation. [Background technology]

[0002] An electric vehicle receives electricity from an external source to charge its battery cells, and then drives a motor with the voltage charged in the battery cells to generate power. The battery cells of an electric vehicle may generate heat due to chemical reactions that occur during the charging and discharging of electricity, and this heat may damage the performance and lifespan of the battery cells.

[0003] Battery cells age through repeated charging and discharging during use, gradually shortening their lifespan (SOH, State of Health). The lifespan of a battery cell is greatly affected by conditions such as operating temperature, duration of use, charging voltage, and number of discharges, and cannot be accurately diagnosed by simply measuring the remaining capacity of the battery cell. Therefore, in order to estimate the remaining lifespan of a battery, it is necessary to calculate the battery's accurate OCV (Open Circuit Voltage) value.

[0004] However, conventional methods for calculating the OCV of a battery involve directly removing the battery from the vehicle or using a separate charging / discharging device to conduct a charge / discharge experiment on the battery in a specific pattern and measure the battery's voltage behavior, which has the problem of requiring a long time and resources to measure the OCV. Summary of the Invention [Problem to be solved by the invention]

[0005] An object of the embodiments disclosed in this document is to provide a battery data management device and an operating method thereof that can acquire accurate battery OCV data for estimating battery life.

[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 data management device according to one embodiment disclosed herein may include a communication module that receives battery data from a vehicle, and a controller that classifies the battery data based on an SOC range, generates voltage change information according to a charge / discharge rate (C-rate) for each SOC, and generates OCV change information according to the SOC using the voltage change information according to the charge / discharge rate for each SOC.

[0008] In one embodiment, the communication module is capable of receiving the battery data from a data collection device that collects battery data for a vehicle.

[0009] According to an embodiment, the controller may apply interpolation to the battery data to align the battery data at regular intervals based on the SOC.

[0010] According to one embodiment, the controller may divide the battery data based on an SOC range to generate data groups for each SOC, fit each of the data groups for each SOC, and generate voltage change information according to the charge / discharge rate for each SOC.

[0011] According to one embodiment, the controller may extract an OCV, which is a voltage value when the charge / discharge rate is "0", from the voltage change information according to the charge / discharge rate for each SOC, and calculate the OCV for each SOC.

[0012] According to an embodiment, the controller may process the OCV included in the voltage change information according to the charge / discharge rate for each SOC to generate the OCV change information according to the SOC.

[0013] According to an embodiment, the controller may input the OCV for each SOC to a window filter to smooth it, thereby generating information on a change in OCV according to the SOC.

[0014] An operating method of a battery data management device according to one embodiment disclosed in this document may include the steps of receiving battery data from a vehicle, dividing the battery data based on an SOC range to generate voltage change information according to a charge / discharge rate (C-rate) for each SOC, and generating OCV change information according to the SOC using the voltage change information according to the charge / discharge rate for each SOC.

[0015] According to one embodiment, the step of receiving battery data from the vehicle may receive the battery data from a data collection device that collects battery data of the vehicle.

[0016] According to one embodiment, the step of dividing the battery data based on an SOC range and generating voltage change information according to the charge / discharge rate (C-rate) for each SOC may include applying an interpolation method to the battery data to align the battery data at regular intervals based on the SOC.

[0017] According to one embodiment, the step of dividing the battery data based on an SOC range and generating voltage change information according to a charge / discharge rate (C-rate) for each SOC may include dividing the battery data based on an SOC range to generate data groups for each SOC, and fitting each of the data groups for each SOC to generate voltage change information according to the charge / discharge rate for each SOC.

[0018] According to one embodiment, the step of generating information on OCV change according to SOC using information on voltage change according to charge / discharge rate for each SOC may include extracting an OCV, which is a voltage value when the charge / discharge rate is “0”, from the information on voltage change according to charge / discharge rate for each SOC, and calculating the OCV for each SOC.

[0019] According to one embodiment, the step of generating information on OCV change according to SOC using information on voltage change according to charge / discharge rate for each SOC may generate information on OCV change according to SOC by processing OCV included in the information on voltage change according to charge / discharge rate for each SOC.

[0020] According to one embodiment, in the step of generating information on OCV change according to SOC using information on voltage change according to the charge / discharge rate for each SOC, the controller may input the OCV for each SOC to a window filter and smooth it to generate information on OCV change according to the SOC. [Effects of the Invention]

[0021] According to an embodiment of the battery data management device and its operating method disclosed in this document, accurate battery OCV data for estimating the battery life can be obtained. [Brief explanation of the drawings]

[0022] [Figure 1] FIG. 1 illustrates a battery pack according to one embodiment disclosed herein. [Figure 2] 1 is a block diagram showing the configuration of a battery data management device according to an embodiment disclosed in this document. [Figure 3] 1 is a graph showing battery data according to an embodiment described herein. [Figure 4] 1 is a graph illustrating voltage change information according to a charge / discharge rate for each SOC according to an embodiment disclosed herein. [Figure 5] 1 is a graph showing OCV change information according to SOC according to one embodiment disclosed herein; [Figure 6] 10 is a graph showing information on OCV change according to SOC according to another embodiment disclosed herein; [Figure 7] 1 is a flowchart illustrating a method of operating a battery data management device according to an embodiment disclosed herein. [Figure 8] 1 is a block diagram showing the hardware configuration of a computing system that implements an operation method of a battery data management device according to an embodiment disclosed in this document. DETAILED DESCRIPTION OF THE INVENTION

[0023] Some embodiments disclosed herein will be described in detail below with reference to exemplary drawings. When assigning reference numerals to components in each drawing, it should be noted that the same reference numerals are assigned to the same components as long as possible when they appear in other drawings. Furthermore, when describing the embodiments disclosed herein, if a detailed description of related known structures or functions is deemed to hinder understanding of the embodiments disclosed herein, such detailed description will be omitted.

[0024] In describing components of the embodiments disclosed herein, terms such as first, second, A, B, (a), (b), etc. may be used. Such terms are merely used to distinguish the component from other components and do not limit the nature, order, or sequence of the components. Furthermore, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed herein pertain. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the context of the relevant art and should not be interpreted in an idealized or overly formal sense unless expressly defined herein.

[0025] FIG. 1 is a diagram illustrating a battery pack according to one embodiment disclosed herein. 1 , a 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, in which case the battery pack 1000 may have a cell-to-pack structure.

[0026] The battery module 100 may include a plurality of battery cells 110, 120, 130, and 140. Although the number of battery cells is shown as four in FIG. 1, the number of battery cells is not limited to four, and the battery module 100 may include n (n is a natural number equal to or greater than 2) battery cells.

[0027] 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. Here, the target device can include an electrical, electronic, or mechanical device that operates by receiving power from a battery pack 1000 including a plurality of battery cells 110, 120, 130, and 140. For example, the target device can be, but is not limited to, an electric vehicle (EV) or an energy storage system (ESS).

[0028] The plurality of battery cells 110, 120, 130, 140 are basic units of a battery that can be used by charging and discharging electrical energy, and may be, but are not limited to, lithium ion (Li-ion) batteries, lithium ion polymer (Li-ion polymer) batteries, nickel cadmium (Ni-Cd) batteries, nickel metal hydride (Ni-MH) batteries, etc. Meanwhile, although Fig. 1 shows one battery module 100, according to an embodiment, a plurality of battery modules 100 may be included.

[0029] According to an embodiment, the battery data management device 200 may be implemented in the form of a battery management system (BMS). Also, according to an embodiment, the battery data management device 200 may be mounted in the battery management system.

[0030] The battery data management device 200 can predict the lifespan, i.e., SOH (State of Health), of the multiple battery cells 110, 120, 130, and 140 based on the temperature and voltage data of the multiple battery cells 110, 120, 130, and 140. The battery data management device 200 can remove noise from the battery data of the multiple battery cells 110, 120, 130, and 140 and predict the SOH (State of Health) of the multiple battery cells 110, 120, 130, and 140 based on the data from which the noise has been removed.

[0031] The battery data management device 200 can manage and / or control the state and / or operation of the battery module 100. For example, the battery data management device 200 can manage and / or control the state and / or operation of the plurality of battery cells 110, 120, 130, 140 included in the battery module 100. The battery data management device 200 can manage the charging and / or discharging of the battery module 100.

[0032] 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 a target device. In addition, the battery data management device 200 can short-circuit the relay 300 when a charging device is connected to the battery pack 1000.

[0033] Furthermore, the battery data management device 200 can monitor the voltage, current, temperature, etc. of the battery module 100 and / or each of the plurality of battery cells 110, 120, 130, 140 included in the battery module 100. For monitoring via the battery data management device 200, sensors and various measurement modules (not shown) can be further provided in the battery module 100, a charge / discharge path, or any position on the battery module 100. The battery data management device 200 can calculate parameters indicating the state of the battery module 100, such as a State of Charge (SOC), based on the measured values ​​of the monitored voltage, current, temperature, etc.

[0034] The battery data management device 200 can estimate the deterioration factors of the positive and negative electrodes of a battery by distinguishing between the states of the positive and negative electrodes of the battery to evaluate the SOH, i.e., the lifespan, of the battery. In this case, the battery data management device 200 can analyze the open circuit voltage (OCV) of the battery to estimate the deterioration factors of the positive and negative electrodes of the battery. Here, OCV refers to the voltage measured when no current flows through the battery. OCV is the voltage between the anode and cathode of a battery when they are not electrically connected. According to Ohm's law, as the resistance of a battery increases infinitely, the current approaches zero, allowing for accurate measurement of the battery's voltage. Therefore, the battery data management device 200 can measure and analyze the OCV of a battery for accurate electrochemical analysis of the battery.

[0035] The battery data management device 200 can differentiate the OCV of a battery and analyze the degradation factors of each of the battery's positive and negative electrodes, such as LLI (Loss of Lithium Inventory), LAMn (Loss of Active Material - negative electrode), or LAMp (Loss of Active Material - positive electrode). Here, LLI is the lithium inventory loss, which refers to the amount of lithium in a battery cell reduced relative to the BOL, LAMn refers to the amount of negative electrode active material reduced relative to the BOL, and LAMp refers to the amount of positive electrode active material reduced relative to the BOL. For example, the battery data management device 200 can extract the battery degradation factors, LLI, LAMn, or LAMp, using an artificial intelligence model that analyzes the battery's OCV or a mathematical modeling technique that calculates the battery degradation factors.

[0036] The battery data management device 200 can calculate a battery OCV graph based on battery data measured during actual vehicle driving. The battery data management device 200 can analyze and process the battery data measured during actual vehicle driving to derive a continuous battery OCV curve.

[0037] FIG. 2 is a block diagram showing the configuration of a battery data management device according to an embodiment disclosed in this document. The configuration of the battery data management device 200 will be specifically described below with reference to FIG.

[0038] Referring to FIG. 2, the battery data management device 200 may include a communication module 210 and a controller 220 . The communication module 210 can receive vehicle battery data from the vehicle. According to one embodiment, the communication module 210 can receive battery data from a data collection device (not shown) that collects vehicle battery data. The data collection device is connected to a vehicle (not shown) equipped with a battery pack and can collect battery data generated during actual vehicle driving. The data collection device can collect battery data generated from various sensors and controllers in the vehicle. Here, the battery data is information about the battery installed in the vehicle and can include the battery's charge / discharge rate (C-rate), voltage (V), current (I), temperature (T), capacity (Q), and SOC. According to an embodiment, the data collection device can include an On-Board Diagnostics (OBD). The OBD is an electronic device that can measure and diagnose the vehicle's status and collect battery data.

[0039] The communication module 210 may be connected to the data collection device via a wired or wireless network. For example, the communication module 210 may be connected to the data collection device via Bluetooth, Wi-Fi, ZigBee, Controller Area Network (CAN) communication, or Ethernet communication.

[0040] According to an embodiment, the communication module 210 may be connected to a plurality of data collection devices. Also, according to an embodiment, the communication module 210 may be connected to a plurality of data collection devices simultaneously, and may simultaneously acquire vehicle battery data collected by each of the plurality of data collection devices.

[0041] FIG. 3 is a graph showing battery data according to one embodiment described herein. 3, the communication module 210 can acquire battery data collected from a data collection device during the entire running period of the vehicle. Here, the battery data can include the voltage (V) and current (I) of the battery collected during the entire running period of the vehicle. According to one embodiment, the communication module 210 can acquire battery data collected during the same period from multiple data collection devices.

[0042] The controller 220 may apply interpolation to the battery data to align the battery data at regular intervals based on the SOC. Here, interpolation is a method of standardizing given data in a form such as a polynomial, and may estimate data that cannot be obtained through observation or experiment. The battery data acquired by the communication module 210 from the data collection device may include voltage data corresponding to different SOC values. That is, the battery data acquired from the data collection device may form a discontinuous data distribution when aligned based on the SOC value. Therefore, the controller 220 may apply interpolation to the battery data to analyze the change trend of the SOC included in the battery data. That is, the controller 220 may correct the battery data to align it at regular intervals based on the SOC using the interpolation, thereby generating continuous battery data.

[0043] According to an embodiment, the controller 220 can generate information on the change in voltage (V) according to the SOC of the battery using the battery data corrected by the interpolation method. That is, the controller 220 can generate an SOC-V profile. According to an embodiment, the controller 220 can generate information on the change in voltage according to the SOC of the battery and plot it on a display (not provided).

[0044] The controller 220 may divide the battery data based on the SOC range to generate data groups for each SOC. Specifically, the controller 220 may group the battery data, which is arranged based on the SOC using an interpolation method, into a plurality of groups.

[0045] According to an embodiment, the controller 220 may divide the battery data based on the SOC range to generate SOC-specific data groups, and then generate a data frame using each SOC-specific data group. Here, a data frame refers to a rectangular data list consisting of rows and columns of data. The controller 220 may process the battery data included in the SOC-specific data groups to generate a SOC-specific data frame. The data frame may include battery voltage, current, and charge / discharge rate (C-rate) data. For example, the controller 220 may extract battery data having an SOC value in the range of 0 to 0.1 from the battery data, and generate a data frame using the battery voltage, current, and charge / discharge rate (C-rate) included in the battery data. The controller 220 can generate voltage change information according to the charge / discharge rate for each SOC.

[0046] FIG. 4 is a graph showing voltage change information according to charge / discharge rates by SOC according to an embodiment disclosed herein. Referring to FIG. 4, the controller 220 can fit each SOC-specific data group. Specifically, the controller 220 can perform polynomial curve fitting on each SOC-specific data group generated by dividing battery data based on SOC ranges. Here, polynomial curve fitting is a method of approximating data with a polynomial curve rather than linearly when calculating the relationship between multiple data that cannot be expressed as a simple proportional relationship. The controller 220 can generate information on the change in voltage (V) depending on the battery's charge / discharge rate (C-rate) by performing polynomial curve fitting on the battery voltage (V) and charge / discharge rate (C-rate) data included in each SOC-specific data group.

[0047] That is, the controller 220 can generate a trend line for the battery voltage (V) and charge / discharge rate (C-rate) data included in each of the plurality of data groups for each SOC by polynomial approximation.

[0048] According to one embodiment, the controller 220 may approximate each data group by SOC and calculate a voltage change function according to the charge / discharge rate for each SOC. Specifically, the controller 220 may calculate a voltage change function according to the charge / discharge rate for each SOC using a polynomial regression artificial intelligence model. The controller 220 may analyze a trend line obtained by polynomial approximating data on the voltage (V) and charge / discharge rate (C-rate) of a battery included in each of a plurality of data groups by SOC, and generate a polynomial that indicates the correlation of the data. For example, the controller 220 may analyze a trend line generated by approximating data on the voltage (V) change according to the charge / discharge rate (C-rate) of a battery included in a data group whose SOC value falls within the range of 0 to 0.1, and generate a polynomial that indicates the correlation of the data.

number

[0049] The controller 220 can calculate the OCV for each SOC using a voltage change function according to the charge / discharge rate for each SOC. The controller 220 can use a "0" C-rate interpolation method that searches for a point where the x-coordinate in the voltage change function according to the charge / discharge rate for each SOC becomes "0." That is, the controller 220 can extract the OCV, which is the voltage value when the charge / discharge rate, which is the x-coordinate in the voltage change function according to the charge / discharge rate for each SOC, is "0," and calculate the OCV for each SOC.

[0050] For example, the controller 220 may set the voltage change function "

number

[0051] FIG. 5 is a graph showing information on OCV change according to SOC according to one embodiment disclosed herein. 5, the controller 220 may generate information on the change in OCV according to the SOC by combining the OCVs calculated according to the voltage change function according to the charge / discharge rate for each SOC. That is, the controller 220 may process information on the change in voltage according to the charge / discharge rate for each SOC to generate information on the change in OCV according to the SOC of the battery.

[0052] FIG. 6 is a graph showing information on changes in OCV according to SOC according to another embodiment disclosed herein. 6, the controller 220 may smooth the OCV information for each SOC by inputting it to a window-based filter, thereby removing noise from the OCV change information due to the SOC. That is, the controller 220 may smooth the OCV information for each SOC by inputting it to a window-based filter. The controller 220 may smooth the OCV information for each SOC, thereby deriving a continuous OCV curve from which noise has been removed.

[0053] According to one embodiment, the controller 220 may perform polynomial curve fitting on the information on the change in OCV depending on the SOC. The controller 220 may perform polynomial curve fitting on the information on the change in OCV depending on the SOC to generate a trend line for the data.

[0054] According to one embodiment, the controller 220 may analyze a trend line obtained by polynomial curve approximation of the information on the change in OCV depending on the SOC, and generate a polynomial that indicates the correlation of the data. Specifically, the controller 220 may analyze a trend line of the information on the change in OCV depending on the SOC using a polynomial regression artificial intelligence model, and calculate an OCV change function depending on the SOC.

[0055] As described above, the battery data management device 200 according to one embodiment disclosed in this document can generate an accurate battery OCV graph for estimating battery life using battery data acquired during actual vehicle driving.

[0056] The battery data management device 200 generates a battery OCV graph using battery data acquired while the vehicle is running, eliminating the need to spend time charging and discharging the battery in a specific pattern, and making it possible to analyze a large amount of battery data in a short period of time.

[0057] It takes a short time to calculate the OCV of the battery, and the battery condition can be constantly determined, providing the user with immediate information if an abnormal condition is diagnosed.

[0058] Furthermore, the battery data management device 200 can obtain battery data from which noise has been removed and accurately diagnose the state of the battery, thereby improving the accuracy of battery life prediction.

[0059] FIG. 7 is a flowchart illustrating a method of operating a battery data management device according to an embodiment disclosed herein. The battery data management device 200 is substantially similar to the battery data management device 200 described with reference to FIGS. 1 to 6, and therefore will be described briefly below to avoid duplication of description.

[0060] Referring to FIG. 7, the operating method of the battery data management device 200 may include the steps of receiving battery data from a data collection device that collects battery data of a vehicle (S101), dividing the battery data based on an SOC range and generating voltage change information according to a charge / discharge rate (C-rate) for each SOC (S102), and generating OCV change information according to the SOC using the voltage change information according to the charge / discharge rate for each SOC (S103).

[0061] In step 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 is connected to a vehicle (not shown) equipped with a battery pack and may collect battery data generated during actual vehicle driving. Here, the battery data is information about a battery equipped in the vehicle, and may include the battery's charge / discharge rate (C-rate), voltage (V), current (I), temperature (T), capacity (Q), and SOC. According to an embodiment, the data collection device may include an On-Board Diagnostics (OBD). The OBD is an electronic device that can measure and diagnose the vehicle's status and collect battery data.

[0062] In step S101, according to an embodiment, the communication module 210 may be connected to a plurality of data collection devices. Also in step S101, according to an embodiment, the communication module 210 may be connected to a plurality of data collection devices simultaneously, and may simultaneously acquire the vehicle battery data collected by each of the plurality of data collection devices.

[0063] In step S101, the communication module 210 can acquire battery data collected during the entire running period of the vehicle from the data collection device, where the battery data can include the voltage (V) and current (I) of the battery collected during the entire running period of the vehicle.

[0064] In step S101, according to one embodiment, the communication module 210 can obtain battery data collected during the same period from multiple data collection devices.

[0065] In step S102, the controller 220 may apply interpolation to the battery data to align the battery data at regular intervals based on the SOC. Here, the interpolation is a method of standardizing given data in a form such as a polynomial, and may be used to estimate data that cannot be obtained by observation or experiment.

[0066] In step S102, the controller 220 corrects the battery data based on the SOC by interpolation so that the battery data can be aligned at regular intervals, thereby generating continuous battery data.

[0067] In step S102, according to an embodiment, the controller 220 can generate information on voltage (V) changes depending on the SOC of the battery using the battery data corrected by interpolation. In step S102, the controller 220 can generate an SOC-V profile. In step S102, according to an embodiment, the controller 220 can generate information on voltage changes depending on the SOC of the battery and plot it on a display (not provided).

[0068] In operation S102, the controller 220 may divide the battery data based on the SOC range to generate data groups for each SOC. Specifically, the controller 220 may group the battery data, which is arranged based on the SOC using an interpolation method, into a plurality of groups.

[0069] In step S102, according to an embodiment, the controller 220 may divide the battery data based on the SOC range to generate SOC-specific data groups, and then generate a data frame using each SOC-specific data group. Here, a data frame refers to a rectangular data list consisting of rows and columns of data. In step S102, the controller 220 may process the battery data included in the SOC-specific data group to generate a SOC-specific data frame. The data frame may include battery voltage, current, and charge / discharge rate (C-rate) data.

[0070] In step S102, the controller 220 may perform fitting on each of the SOC-specific data groups. Specifically, in step S102, the controller 220 may perform polynomial curve fitting on each of the SOC-specific data groups generated by dividing the battery data based on the SOC range. Here, polynomial curve fitting is a method of approximating data with a polynomial curve rather than a linear relationship when calculating the relationship between multiple pieces of data, since the relationship cannot be expressed as a simple proportional relationship. In step S102, the controller 220 may perform polynomial curve fitting on the battery voltage (V) and charge / discharge rate (C-rate) data included in each of the SOC-specific data groups, thereby generating information on changes in voltage (V) depending on the battery charge / discharge rate (C-rate).

[0071] In step S102, the controller 220 may perform polynomial curve approximation on the battery voltage (V) and charge / discharge rate (C-rate) data included in each of the plurality of SOC-specific data groups to generate a trend line for the data.

[0072] In step S102, according to one embodiment, the controller 220 may approximate each of the data groups by SOC to calculate a voltage change function according to the charge / discharge rate for each SOC. Specifically, in step S102, the controller 220 may calculate a voltage change function according to the charge / discharge rate for each SOC using a polynomial regression artificial intelligence model. In step S102, the controller 220 may analyze a trend line obtained by polynomial curve approximating the battery voltage (V) and charge / discharge rate (C-rate) data included in each of the plurality of data groups by SOC, and generate a polynomial that indicates the correlation of the data.

[0073] In step S103, the controller 220 can calculate the OCV for each SOC using a voltage change function according to the charge / discharge rate for each SOC. In step S103, the controller 220 can use a "0" C-rate interpolation method to find a point where the x-coordinate in the voltage change function according to the charge / discharge rate for each SOC becomes "0." In step S103, the controller 220 can extract the OCV, which is the voltage value when the charge / discharge rate, which is the x-coordinate in the voltage change function according to the charge / discharge rate for each SOC, is "0," and calculate the OCV for each SOC.

[0074] In step S103, the controller 220 may generate information on OCV change according to SOC using a voltage change function according to the charge / discharge rate for each SOC. In step S103, the controller 220 may combine the OCVs for each SOC to generate information on OCV change according to SOC. In step S103, the controller 220 may process information on voltage change according to the charge / discharge rate for each SOC to generate information on OCV change according to the SOC of the battery.

[0075] In step S103, the controller 220 may smooth the OCV information for each SOC by inputting it into a window-based filter, thereby removing noise from the OCV change information due to the SOC. In step S103, the controller 220 may smooth the OCV information for each SOC by inputting it into a window-based filter. In step S103, the controller 220 may smooth the OCV information for each SOC, thereby deriving a continuous OCV curve from which noise has been removed.

[0076] In step S103, according to one embodiment, the controller 220 can perform polynomial curve approximation on the information on the change in OCV depending on the SOC. In step S103, the controller 220 can perform polynomial curve approximation on the information on the change in OCV depending on the SOC to generate a trend line for the data.

[0077] In step S103, according to one embodiment, the controller 220 may analyze a trend line obtained by polynomial curve approximation of the OCV change information according to the SOC, and generate a polynomial that indicates the correlation of the data. Specifically, in step S103, the controller 220 may analyze the trend line of the OCV change information according to the SOC using a polynomial regression artificial intelligence model, and calculate an OCV change function according to the SOC.

[0078] FIG. 8 is a block diagram showing the hardware configuration of a computing system that implements the method of operating a battery data management device according to an embodiment disclosed herein.

[0079] Referring to FIG. 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.

[0080] The MCU 2100 may be a processor that executes various programs (e.g., OCV calculation algorithms) stored in the memory 2200, processes various data through such programs, and performs the functions of the battery data management device 200 of FIG. 1 described above.

[0081] The memory 2200 can store various programs related to the operation of the battery data management device 200. The memory 2200 can also store operation data of the battery data management device 200.

[0082] A plurality of such memories 2200 may be provided as necessary. The memories 2200 may be volatile memories or nonvolatile memories. The volatile memories 2200 may be RAM, DRAM, SRAM, etc. The nonvolatile memories 2200 may be ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. The examples of the memories 2200 listed above are merely illustrative and are not limited to these examples.

[0083] The input / output I / F 2300 can provide an interface that connects input devices (not shown) such as a keyboard, mouse, or touch panel, and output devices such as a display (not shown), to the MCU 2100, enabling data to be sent and received.

[0084] The communication I / F 2400 is configured to be able to send and receive various data to and from a server, and may be any device that supports wired or wireless communication. For example, programs for measuring resistance and diagnosing abnormalities and various data can be sent and received from a separately provided external server via the communication I / F 2400.

[0085] In this way, the computer program of one embodiment disclosed in this document may be recorded in memory 2200 and processed by MCU 2100, thereby realizing, for example, a module that performs each function of battery data management device 200 described with reference to Figures 1 and 2.

[0086] The above description merely exemplifies the technical concept of the present disclosure, and various modifications and variations are possible by a person having ordinary knowledge in the technical field to which the present disclosure pertains, without departing from the essential characteristics of the present disclosure.

[0087] Therefore, the embodiments disclosed in this disclosure are intended to illustrate, not limit, the technical idea of ​​the disclosure, and the scope of the technical idea of ​​the disclosure is not limited by such embodiments. The scope of protection of the disclosure should be interpreted by the claims below, and all technical ideas within the equivalent range should be interpreted as being included in the scope of rights of the disclosure. [Explanation of symbols]

[0088] 1000: Battery pack 100: Battery module 200: Battery data management device 210: Communication module 220: Controller 300: Relay 2000: Computing Systems 2100:MCU 2200:Memory 2300: Input / output interface 2400:Communication I / F

Claims

1. a communication module for receiving battery data from the vehicle; a controller that classifies the battery data based on an SOC range to generate voltage change information according to a charge / discharge rate for each SOC, and generates OCV change information according to the SOC using the voltage change information according to the charge / discharge rate for each SOC; A battery data management device comprising:

2. The battery data management device according to claim 1 , wherein the communication module receives the battery data from a data collection device that collects battery data of a vehicle.

3. The battery data management device according to claim 2 , wherein the controller applies an interpolation method to the battery data and aligns the battery data at regular intervals based on the SOC.

4. 4. The battery data management device according to claim 3, wherein the controller divides the battery data based on an SOC range to generate data groups for each SOC, approximates each of the data groups for each SOC, and generates voltage change information according to the charge / discharge rate for each SOC.

5. 5. The battery data management device according to claim 4, wherein the controller extracts an OCV, which is a voltage value when the charge / discharge rate is "0", from the voltage change information according to the charge / discharge rate by SOC, and calculates the OCV by SOC.

6. The battery data management device according to claim 5 , wherein the controller processes an OCV included in the information on voltage changes according to the charge / discharge rate by SOC to generate information on OCV changes according to the SOC.

7. The battery data management device according to claim 6 , wherein the controller inputs the OCV for each SOC to a window filter to smooth the OCV, and generates information on a change in OCV according to the SOC.

8. receiving battery data from the vehicle; classifying the battery data based on an SOC range to generate voltage change information according to a charge / discharge rate for each SOC; generating OCV change information according to the SOC using the voltage change information according to the charge / discharge rate for each SOC; A method of operating a battery data management device, comprising:

9. The method for operating a battery data management device according to claim 8 , wherein the step of receiving battery data from the vehicle receives the battery data from a data collection device that collects battery data of the vehicle.

10. 10. The method of claim 9, wherein the step of dividing the battery data based on an SOC range and generating voltage change information according to a charge / discharge rate for each SOC comprises applying an interpolation method to the battery data and arranging the battery data at regular intervals based on the SOC.

11. 11. The method of claim 10, wherein the step of dividing the battery data based on an SOC range and generating voltage change information according to a charge / discharge rate for each SOC comprises dividing the battery data based on an SOC range to generate data groups for each SOC, approximating each of the data groups for each SOC, and generating the voltage change information according to a charge / discharge rate for each SOC.

12. 12. The method for operating a battery data management device according to claim 11, wherein the step of generating the OCV change information according to the SOC using the voltage change information according to the charge / discharge rate by SOC comprises extracting an OCV, which is a voltage value when the charge / discharge rate is “0”, from the voltage change information according to the charge / discharge rate by SOC, and calculating the OCV according to the SOC.

13. 13. The method of claim 12, wherein the step of generating the OCV change information according to the SOC using the voltage change information according to the charge / discharge rate for each SOC comprises processing an OCV included in the voltage change information according to the charge / discharge rate for each SOC to generate the OCV change information according to the SOC.

14. 14. The method of claim 13, wherein the step of generating the OCV change information according to the SOC using the voltage change information according to the charge / discharge rate by the SOC comprises: a controller inputting the OCV by the SOC to a window filter to smooth the OCV, thereby generating the OCV change information according to the SOC.

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