Battery management device and its operating method

Abstract

A battery management device according to one embodiment disclosed herein may include: an information acquisition unit that acquires the open-circuit voltage of each of a plurality of battery cells; a controller that calculates the average open-circuit voltage of each of the plurality of battery cells, calculates a first linear regression function relating to the open-circuit voltage of the first battery cell based on the open-circuit voltage of the first battery cell and the average open-circuit voltage of the plurality of battery cells, calculates a first error between the predicted value calculated based on the first linear regression function and the open-circuit voltage of the first battery cell, and diagnoses the first battery cell based on the first error.

Description

Technical Field

[0001] The present invention claims the benefit of priority based on Korean Patent Application No. 10-2023-0060037 filed on May 9, 2023, and all the contents disclosed in the literature of the Korean patent application are incorporated herein by reference in their entirety. The embodiments disclosed in this document relate to a battery management device and an operation method thereof.

Background Art

[0002] In recent years, research and development on secondary batteries have been actively carried out. Here, a secondary battery is a rechargeable battery, and includes both conventional Ni / Cd batteries, Ni / MH batteries, etc. and recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of much higher energy density compared to conventional Ni / Cd batteries, Ni / MH batteries, etc. In addition, since lithium-ion batteries can be manufactured in a small and lightweight form, they are used as power sources for mobile devices, and in recent years, their usage range has been extended to power sources for electric vehicles, and they have attracted attention as next-generation energy storage media.

[0003] A method of diagnosing abnormal voltage behavior based on long / short-term moving averages using the deviation of the voltage of a battery cell from its average can diagnose the abnormality of a battery cell only when disconnection of the tab of the battery cell occurs or a sudden change in voltage occurs during the process of reconnecting a disconnected battery cell. In addition, since the method of diagnosing abnormal voltage behavior based on long / short-term moving averages uses real-time voltage, the system resources increase.

Summary of the Invention

Problems to be Solved by the Invention

[0004] One object of the embodiments disclosed in this document is to provide a battery management device and an operation method thereof that can diagnose disconnection of a tab of a battery cell even when the voltage does not change suddenly, based on the OCV voltage of the battery cell after charging or discharging.

[0005] One objective of the embodiments disclosed in this document is to provide a battery management device and its operating method that can diagnose whether or not a tab is broken based on the OCV behavior of a battery cell after charging and discharging, even when no abnormal voltage behavior is occurring.

[0006] The technical problems of the embodiments disclosed in this document are not limited to those mentioned above, and other technical problems not mentioned can 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 disclosed herein may include: an information acquisition unit that acquires the open-circuit voltage of each of a plurality of battery cells; a controller that calculates the average open-circuit voltage of each of the plurality of battery cells, calculates a first linear regression function relating to the open-circuit voltage of the first battery cell based on the open-circuit voltage of the first battery cell and the average open-circuit voltage of the plurality of battery cells, calculates a first error between the predicted value calculated based on the first linear regression function and the open-circuit voltage of the first battery cell, and diagnoses the first battery cell based on the first error.

[0008] In one embodiment, the information acquisition unit can acquire the open-circuit voltage of each of the multiple battery cells after the multiple battery cells have been charged multiple times.

[0009] In one embodiment, the controller can calculate the average open-circuit voltage for each charge based on the open-circuit voltage of each of the plurality of battery cells acquired for each charge, and calculate the first linear regression function based on the average open-circuit voltage calculated for each charge and the open-circuit voltage of the first battery cell acquired for each charge.

[0010] In one embodiment, the controller can calculate the maximum and minimum deviations between the open-circuit voltage of the first battery cell and the predicted value calculated based on the first linear regression function, calculate the error rate between the open-circuit voltage of the first battery cell and the predicted value calculated based on the first linear regression function, and calculate a first error of the open-circuit voltage of the first battery cell based on the difference between the maximum and minimum deviations and the error rate.

[0011] In one embodiment, the controller can calculate a first error in the open-circuit voltage of the first battery cell by multiplying the difference between the maximum deviation and the minimum deviation by the error rate.

[0012] In one embodiment, the error rate can be calculated based on the coefficient of determination (R-squared) error rate. In one embodiment, the controller can diagnose that an abnormality has occurred in the first battery cell if the first error is greater than or equal to a set value.

[0013] In one embodiment, a linear regression function relating to the open-circuit voltage of each of the plurality of battery cells is calculated based on the open-circuit voltage of each of the plurality of battery cells and the average open-circuit voltage. The error between the predicted value calculated based on the linear regression function and the open-circuit voltage of each of the plurality of battery cells is calculated, and each of the plurality of battery cells can be diagnosed based on the error.

[0014] In one embodiment, the information acquisition unit can acquire the open-circuit voltage of each of the multiple battery cells by shifting at reference intervals.

[0015] In one embodiment, the information acquisition unit can acquire the open-circuit voltage of each of the multiple battery cells after each discharge, after the multiple battery cells have been discharged multiple times.

[0016] In one embodiment, the operation method of the battery management device disclosed herein may include: acquiring the open-circuit voltage of each of a plurality of battery cells; calculating the average open-circuit voltage of each of the plurality of battery cells; calculating a first linear regression function relating to the open-circuit voltage of the first battery cell based on the open-circuit voltage of the first battery cell and the average open-circuit voltage of the plurality of battery cells; calculating a first error between a predicted value calculated based on the first linear regression function and the open-circuit voltage of the first battery cell; and diagnosing the first battery cell based on the first error.

[0017] In one embodiment, the operation for calculating a first error between a predicted value calculated based on the first linear regression function and the open-circuit voltage of the first battery cell may include: calculating the maximum and minimum deviations between the open-circuit voltage of the first battery cell and the predicted value calculated based on the first linear regression function; calculating the error rate between the open-circuit voltage of the first battery cell and the predicted value calculated based on the first linear regression function; and calculating a first error of the open-circuit voltage of the first battery cell based on the difference between the maximum and minimum deviations and the error rate.

[0018] In one embodiment, the method may further include: calculating a linear regression function relating to the open-circuit voltage of each of the plurality of battery cells based on the open-circuit voltage of each of the plurality of battery cells and the average open-circuit voltage; calculating the error between the predicted value calculated based on the linear regression function and the open-circuit voltage of each of the plurality of battery cells; and diagnosing each of the plurality of battery cells based on the error. [Effects of the Invention]

[0019] A battery management device and its operating method according to one embodiment disclosed in this document can diagnose whether or not there is a break in the tab of a battery cell based on the OCV behavior after charging and discharging of the battery cell.

[0020] The battery management device and its operation method according to an embodiment disclosed in this document can calculate a prediction model by projecting the OCV voltage of a battery cell onto the average OCV of a plurality of battery cells even when there is no sudden voltage behavior abnormality, and can diagnose disconnection of the tab of the battery cell based on the error between the predicted value and the measured value.

[0021] The battery management device and its operation method according to an embodiment disclosed in this document can calculate a prediction model by projecting the OCV voltage of a battery cell onto the average OCV of a plurality of battery cells by linear regression, and can diagnose the battery cell based on the maximum deviation between the predicted value and the measured value and the determination coefficient (R-squared) error rate. In addition, various effects that can be grasped directly or indirectly are provided by this document.

Brief Description of the Drawings

[0022] [Figure 1] It is a block diagram showing the configuration of a general battery pack. [Figure 2] It is a block diagram showing the battery management device according to an embodiment disclosed in this document. [Figure 3] It is a diagram showing an example in which the battery management device according to an embodiment disclosed in this document calculates a linear regression function for the OCV of each of a plurality of battery cells. [Figure 4] It is a diagram showing an example in which the battery management device according to an embodiment disclosed in this document calculates the error between the predicted value and the measured value of each battery cell. [Figure 5] It is a flowchart showing the operation method of the battery management device according to an embodiment disclosed in this document. [Figure 6] It is a flowchart specifically showing the operation method of the battery management device according to an embodiment disclosed in this document. [Figure 7] It is a flowchart specifically showing the operation method of the battery management device according to an embodiment disclosed in this document. [Figure 8]This is a block diagram showing the hardware configuration of a computing system for performing the operation method of a battery management device according to one embodiment disclosed in this document. [Modes for carrying out the invention]

[0023] The embodiments disclosed in this document will be described in detail below with reference to illustrative drawings. It should be noted that, when assigning reference numerals to components in each drawing, the same reference numerals will be used for the same components whenever possible when they appear in other drawings. Furthermore, in describing the embodiments disclosed in this document, if a detailed description of a related known configuration or function is deemed to hinder understanding of the embodiments disclosed in this document, such detailed description will be omitted.

[0024] In describing the components of the embodiments disclosed herein, terms such as First, Second, A, B, (a), (b), etc., may be used. Such terms are merely for distinguishing a component from other components and do not limit the nature, order, or sequence of the component. Furthermore, unless otherwise specifically defined, all terms used herein, including technical or scientific terms, have the same meaning as those generally understood by a person of ordinary skill in the art to which the embodiments disclosed herein belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and not as an ideal or overly formal meaning unless explicitly defined in this application.

[0025] Figure 1 is a block diagram showing the configuration of a typical battery pack. Referring to Figure 1, a battery control system including a battery pack 1 according to one embodiment of the present invention and a higher-level controller 2 included in a higher-level system is schematically shown.

[0026] As shown in Figure 1, the battery pack 1 consists of one or more battery cells and includes a plurality of rechargeable battery cells 10, a switching unit 14 connected in series to the (+) terminal side or (-) terminal side of the plurality of battery cells 10 for controlling the flow of charge and discharge current to the plurality of battery cells 10, and a battery management system 20 that monitors the voltage, current, temperature, etc. of the battery pack 1 and controls and manages to prevent overcharging and over-discharging. In this case, the battery pack 1 can be provided with a plurality of battery cells 10, sensors 12, switching units 14, and battery management systems 20.

[0027] Here, the switching unit 14 is an element for controlling the flow of current for charging or discharging multiple battery cells 10, and for example, at least one relay, electromagnetic contactor, etc. can be used depending on the specifications of the battery pack 1.

[0028] The battery management system 20 is an interface that receives input values ​​of the various parameters described above, and may include multiple terminals and circuits connected to these terminals that process the input values. The battery management system 20 can also control the ON / OFF state of a switching unit 14, such as a relay or contactor, and can be connected to multiple battery cells 10 to monitor the state of each of the multiple battery cells 10. According to one embodiment, the battery management system 20 may include the battery management device 100 shown in Figure 2. According to another embodiment, the battery management system 20 may be a different system from the battery management device 100 shown in Figure 2. That is, the battery management device 100 shown in Figure 2 may be included in the battery pack 1, or it may be configured as another device outside the battery pack 1. Furthermore, the operation of the battery management device 100 described below may be performed by a BMS (Battery management system) in the vehicle, as well as by various devices such as a server, cloud, charger, or charger / discharger.

[0029] The higher-level controller 2 can transmit control signals to the battery management system 20 for multiple battery cells 10. This allows the battery management system 20 to be controlled based on the signals applied from the higher-level controller 2.

[0030] Figure 2 is a block diagram showing a battery management device according to one embodiment disclosed in this document. Referring to Figure 2, the battery management device 100 according to one embodiment disclosed herein may include an information acquisition unit 110 and a controller 120. According to the embodiment, the battery management device 100 may be included in the battery management system 20 of Figure 1, or it may be a different device from the battery management system 20 of Figure 1. According to the embodiment, the battery management device 100 may be included in various devices for managing or testing battery cells.

[0031] The information acquisition unit 110 can acquire the open-circuit voltage (OCV) of each of the multiple battery cells. For example, the information acquisition unit 110 can acquire the open-circuit voltage of each of the multiple battery cells after charging and / or discharging has been performed. According to the embodiment, the information acquisition unit 110 can acquire the open-circuit voltage of each of the multiple battery cells after each charge and / or discharge, after the multiple battery cells have been charged and / or discharged multiple times.

[0032] According to this embodiment, the information acquisition unit 110 can acquire the open-circuit voltage of each of the multiple battery cells by shifting at reference intervals. For example, because there may be a limit to the memory, if the reference interval is 10 times, the information acquisition unit 110 can delete the open-circuit voltage of each of the multiple battery cells acquired for the 11th time. In this case, the controller 120, which will be described later, can calculate a linear regression function for each of the multiple battery cells based on the open-circuit voltages of the multiple battery cells acquired for the reference interval, and diagnose each of the multiple battery cells.

[0033] The controller 120 can calculate the average open-circuit voltage of each of the multiple battery cells. For example, the controller 120 can calculate the average open-circuit voltage of each of the multiple battery cells after each of the multiple battery cells has been charged. As another example, the controller 120 can calculate the average open-circuit voltage of each of the multiple battery cells after each of the multiple battery cells has been discharged. According to the embodiment, if multiple charging or discharging cycles are performed, the controller 120 can calculate the average open-circuit voltage of each of the multiple battery cells multiple times after each charging or discharging cycle.

[0034] The controller 120 can calculate a first linear regression function relating to the open-circuit voltage of the first battery cell based on the open-circuit voltage and the average open-circuit voltage of the first battery cell among the multiple battery cells. For example, the first battery cell may be any one of the multiple battery cells.

[0035] According to the embodiment, the controller 120 can calculate a linear regression function relating to the open-circuit voltage of each of the multiple battery cells based on the open-circuit voltage and the average open-circuit voltage of each of the multiple battery cells.

[0036] According to the embodiment, the controller 120 can calculate an average open-circuit voltage for each charge based on the open-circuit voltage of each of the multiple battery cells acquired for each charge, and can calculate a first linear regression function based on the average open-circuit voltage calculated for each charge and the open-circuit voltage of the first battery cell acquired for each charge.

[0037] According to one embodiment, the controller 120 can calculate the first linear regression function using [Equation 1].

[0038] [Formula 1] y = ax + b x is the average open-circuit voltage of multiple battery cells, and y is the predicted open-circuit voltage of the first battery cell.

[0039] For example, the controller 120 can calculate values ​​a and b such that the error between the predicted and measured values ​​of the open-circuit voltage of the first battery cell is minimized. According to the embodiment, the error between the predicted and measured values ​​of the open-circuit voltage of the first battery cell may include any one of the following: absolute error, relative error, percentage error, root mean square error, or coefficient of determination (R-squared) error. According to this embodiment, the controller 120 can calculate a linear regression function for each of the multiple battery cells using [Equation 1].

[0040] Figure 3 shows an example in which a battery management device according to one embodiment disclosed in this document calculates a linear regression function for the OCV of each of several battery cells. Referring to Figure 3, the controller 120 can calculate a linear regression function 320 for each of the multiple battery cells based on the measured open-circuit voltage 310 of each of the multiple battery cells and the average open-circuit voltage of the multiple battery cells. For example, the controller 120 can project the measured open-circuit voltage 310 of each of the multiple battery cells onto the average open-circuit voltage of the multiple battery cells and calculate the respective linear regression function 320.

[0041] According to the embodiment, the controller 120 can calculate a predicted open-circuit voltage for each of the multiple battery cells corresponding to the average open-circuit voltage, based on the linear regression function of each of the multiple battery cells.

[0042] According to the embodiment, the controller 120 can calculate the error rate between the measured open-circuit voltage of each of the multiple battery cells and the predicted value calculated based on the linear regression function of each of the multiple battery cells, based on the coefficient of determination (R-squared) error rate. For example, the controller 120 can calculate the error rate for each of the multiple battery cells by subtracting the coefficient of determination error rate from 1. In this case, the coefficient of determination error rate has a value closer to 1 as the error is smaller, so a smaller calculated error rate (1 - coefficient of determination error rate) means that the error between the measured value and the predicted value is small, and a larger calculated error rate (1 - coefficient of determination error rate) may mean that the error between the measured value and the predicted value is large.

[0043] Referring again to Figure 2, the controller 120 can calculate a first error between the predicted value calculated based on the first linear regression function and the open-circuit voltage of the first battery cell. For example, the controller 120 can calculate the maximum and minimum deviations between the open-circuit voltage of the first battery cell and the predicted value calculated based on the first linear regression function, calculate the error rate between the open-circuit voltage of the first battery cell and the predicted value calculated based on the first linear regression function, and calculate the first error of the open-circuit voltage of the first battery cell based on the difference between the maximum and minimum deviations and the error rate. According to the embodiment, the controller 120 can calculate the first error of the open-circuit voltage of the first battery cell by multiplying the difference between the maximum deviation and the minimum deviation by the error rate.

[0044] According to one embodiment, the error rate can be calculated based on the coefficient of determination (R-squared) error rate. For example, the error rate may be the value obtained by subtracting the coefficient of determination error rate from 1.

[0045] Figure 4 shows an example of a battery management device according to one embodiment disclosed in this document that calculates the error between the predicted value and the measured value of each battery cell. Referring to Figure 4, the controller 120 can calculate the deviation by comparing the open-circuit voltage measurement value of the first battery cell with the first linear regression function. For example, the controller 120 can calculate the open-circuit voltage measurement value 410 with the maximum deviation from the first linear regression function and the open-circuit voltage measurement value 420 with the minimum deviation from the first linear regression function. In this case, the controller 120 can calculate the deviation between the open-circuit voltage measurement value 410 with the maximum deviation and the first linear regression function, calculate the deviation between the open-circuit voltage measurement value 420 with the minimum deviation and the first linear regression function, and then calculate the difference between the maximum and minimum deviations.

[0046] Figure 4 shows an example of calculating the maximum and minimum deviations by comparing the measured open-circuit voltage with a linear regression function for a single battery cell. However, the controller 120 is not limited to this example; it can also compare the open-circuit voltages of multiple battery cells with the linear regression functions of multiple battery cells for each of the multiple battery cells and calculate the maximum and minimum deviations for each of the multiple battery cells.

[0047] Referring again to Figure 2, the controller 120 can diagnose the first battery cell based on the first error. For example, if the first error is greater than or equal to a set value, the controller 120 can diagnose that an abnormality has occurred in the first battery cell.

[0048] According to the embodiment, the first error is calculated by multiplying the difference between the maximum and minimum deviations of the first battery cell by the error rate (1 - coefficient of determination error rate). Therefore, the controller 120 can diagnose whether or not an abnormality has occurred in the first battery cell by simultaneously considering the maximum and minimum deviations and the overall error rate.

[0049] According to the embodiment, the controller 120 can calculate a linear regression function relating to the open-circuit voltage of each of the multiple battery cells based on the open-circuit voltage and the average open-circuit voltage of each of the multiple battery cells, calculate the error between the predicted value calculated based on the linear regression function and the open-circuit voltage of each of the multiple battery cells, and diagnose each of the multiple battery cells based on the error.

[0050] According to the embodiment, the battery management device 100 disclosed in this document can diagnose each of the multiple battery cells by projecting the open-circuit voltages of the multiple battery cells onto the average open-circuit voltage after each of the multiple battery cells has been charged or discharged under the same conditions, and by comparing the predicted value calculated based on the linear regression function with the measured value.

[0051] A battery management device 100 according to one embodiment disclosed in this document can diagnose whether or not there is a break in the tab of a battery cell based on the OCV behavior after charging and discharging of the battery cell.

[0052] The battery management device 100 according to one embodiment disclosed herein can calculate a prediction model by projecting the OCV voltage of a battery cell onto the average OCV of multiple battery cells, even when no sudden voltage behavior abnormality has occurred, and can diagnose a break in the tab of a battery cell based on the error between the predicted value and the measured value.

[0053] A battery management device 100 according to one embodiment disclosed in this document can calculate a prediction model by projecting the OCV voltage of a battery cell onto the average OCV of a plurality of battery cells using linear regression, and can diagnose a battery cell based on the maximum deviation between the predicted value and the measured value and the coefficient of determination (R-squared) error rate.

[0054] Furthermore, the battery management device 100 according to one embodiment disclosed in this document acquires the open-circuit voltage of each of the multiple battery cells by shifting it every reference number of cycles, recalculates the linear regression function every reference number of cycles to calculate the error, and diagnoses each battery cell, thereby enabling accurate diagnosis of each battery cell even in environments with insufficient memory.

[0055] Furthermore, the battery management device 100 according to one embodiment disclosed in this document can, in an environment with sufficient memory, continuously store the open-circuit voltage of each of the multiple battery cells, calculate a more accurate linear regression function based on the continuously stored open-circuit voltages, calculate the error, and diagnose each battery cell, thereby enabling accurate diagnosis of each battery cell.

[0056] Figure 5 is a flowchart showing the operation method of a battery management device according to one embodiment disclosed in this document. According to the embodiment, the operation shown in Figure 5 can be performed via the battery management device 100 of Figure 2.

[0057] Referring to Figure 5, in operation S110, the information acquisition unit 110 can acquire the open-circuit voltage of each of the multiple battery cells. In operation S120, the controller 120 can calculate the average open-circuit voltage of each of the multiple battery cells.

[0058] In operation S130, the controller 120 can calculate a first linear regression function relating to the open-circuit voltage of the first battery cell based on the open-circuit voltage and the average open-circuit voltage of the first battery cell among the multiple battery cells.

[0059] In operation S140, the controller 120 can calculate a first error between the predicted value calculated based on the first linear regression function and the open-circuit voltage of the first battery cell. In operation S150, the controller 120 can diagnose the first battery cell based on the first error.

[0060] Figures 6 and 7 are flowcharts specifically illustrating the operation method of a battery management device according to one embodiment disclosed in this document. According to the embodiment, the operations shown in Figures 6 and 7 can be performed via the battery management device 100 of Figure 2.

[0061] Referring to Figure 6, in operation S210, the controller 120 can calculate the maximum and minimum deviations between the open-circuit voltage of the first battery cell and the predicted value calculated based on the first linear regression function.

[0062] In operation S220, the controller 120 can calculate the error rate between the open-circuit voltage of the first battery cell and the predicted value calculated based on the first linear regression function.

[0063] In operation S230, the controller 120 can calculate the first error of the open-circuit voltage of the first battery cell based on the difference between the maximum and minimum deviations and the error rate. According to this embodiment, operations S210 to S230 can be performed as part of operation S140 in Figure 5.

[0064] Referring to Figure 7, in operation S310, the controller 120 can calculate a linear regression function relating to the open-circuit voltage of each of the multiple battery cells based on the open-circuit voltage and the average open-circuit voltage of each of the multiple battery cells.

[0065] In operation S320, the controller 120 can calculate the error between the predicted value calculated based on the linear regression function and the open-circuit voltage of each of the multiple battery cells. In operation S330, the controller 120 can diagnose each of the multiple battery cells based on the error.

[0066] Figure 8 is a block diagram showing the hardware configuration of a computing system for performing the operation method of a battery management device according to one embodiment disclosed in this document.

[0067] Referring to Figure 8, the computing system 1000 according to one embodiment disclosed in this document may include an MCU 1010, a memory 1020, an input / output interface 1030, and a communication interface 1040.

[0068] The MCU1010 may be a processor that executes various programs stored in memory 1020 (for example, a program for acquiring the open-circuit voltage of the battery cell, a program for calculating the linear regression function, an error calculation program, etc.), processes various information including the open-circuit voltage of the battery cell, the linear regression function, and the error through such programs, and performs the functions of the controller included in the battery management device shown in Figure 2 above.

[0069] Memory 1020 can store various programs, such as a program for acquiring the open-circuit voltage of the battery cell, a program for calculating the linear regression function, a program for calculating the error, and a program for diagnosing the battery cell. Memory 1020 can also store various information, including the open-circuit voltage of the battery cell, the linear regression function, and the error.

[0070] Multiple such memory 1020s may be provided as needed. Memory 1020 may be volatile memory or non-volatile memory. As volatile memory, RAM, DRAM, SRAM, etc., can be used for memory 1020. As non-volatile memory, ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc., can be used for memory 1020. The examples of memory 1020 listed above are merely illustrative and are not limiting.

[0071] The input / output interface 1030 can provide an interface that connects input devices (not shown), such as keyboards, mice, and touch panels, with output devices (not shown), such as displays, and the MCU 1010, enabling data transmission and reception.

[0072] The communication interface 1040 is configured to send and receive various data with a server and may be various devices that support wired or wireless communication. For example, the battery management device can send and receive various information, including the open-circuit voltage of the battery cells, linear regression function, and error, from a separately provided external server via the communication interface 1040.

[0073] Thus, the computer program according to one embodiment disclosed in this document may be recorded in memory 1020 and processed by MCU 1010 to be implemented as a module that performs, for example, the functions shown in Figure 2.

[0074] The above description is merely illustrative of the technical concept disclosed in this document, and any person with ordinary skill in the art to which the embodiments disclosed in this document belong can make various modifications and variations without departing from the essential characteristics of the embodiments disclosed in this document.

[0075] Therefore, the embodiments disclosed herein are for illustrative purposes only, not to limit, the technical ideas disclosed herein, and such embodiments do not limit the scope of the technical ideas disclosed herein. The scope of protection for the technical ideas disclosed herein must be interpreted according to the claims described below, and all technical ideas within an equivalent scope should be interpreted as being included in the scope of rights of this document.

Claims

1. An information acquisition unit that acquires the open-circuit voltage of each of the multiple battery cells, The average open-circuit voltage of each of the aforementioned multiple battery cells is calculated. Based on the open-circuit voltage of the first battery cell and the average open-circuit voltage among the plurality of battery cells, a first linear regression function relating to the open-circuit voltage of the first battery cell is calculated. A first error is calculated between the predicted value calculated based on the first linear regression function and the open-circuit voltage of the first battery cell. A controller that diagnoses the first battery cell based on the first error, A battery management device, including a battery management device.

2. The aforementioned information acquisition unit, The battery management device according to claim 1, wherein after the plurality of battery cells have been charged multiple times, the open-circuit voltage of each of the plurality of battery cells is obtained after each charge.

3. The aforementioned controller, Based on the open-circuit voltage of each of the multiple battery cells obtained for each charge, the average open-circuit voltage is calculated for each charge. The battery management device according to claim 2, wherein the first linear regression function is calculated based on the average open-circuit voltage calculated for each charge and the open-circuit voltage of the first battery cell obtained for each charge.

4. The aforementioned controller, The maximum and minimum deviations between the open-circuit voltage of the first battery cell and the predicted value calculated based on the first linear regression function are calculated. The error rate between the open-circuit voltage of the first battery cell and the predicted value calculated based on the first linear regression function is calculated. The battery management device according to claim 1, which calculates a first error of the open-circuit voltage of the first battery cell based on the difference between the maximum deviation and the minimum deviation and the error rate.

5. The aforementioned controller, The battery management device according to claim 4, wherein the first error of the open-circuit voltage of the first battery cell is calculated by multiplying the difference between the maximum deviation and the minimum deviation by the error rate.

6. The battery management device according to claim 4, wherein the error rate is calculated based on the coefficient of determination error rate.

7. The aforementioned controller, A battery management device according to any one of claims 1 to 6, which diagnoses that an abnormality has occurred in the first battery cell if the first error is greater than or equal to a set value.

8. The aforementioned controller, Based on the open-circuit voltage of each of the plurality of battery cells and the average open-circuit voltage, a linear regression function relating to the open-circuit voltage of each of the plurality of battery cells is calculated. The error between the predicted value calculated based on the linear regression function and the open-circuit voltage of each of the multiple battery cells is calculated. A battery management device according to any one of claims 1 to 6, which diagnoses each of the plurality of battery cells based on the aforementioned error.

9. The aforementioned information acquisition unit, A battery management device according to any one of claims 1 to 6, which shifts at reference cycles to acquire the open-circuit voltage of each of the multiple battery cells.

10. The aforementioned information acquisition unit, A battery management device according to any one of claims 1 to 6, wherein after the plurality of battery cells have been discharged multiple times, the open-circuit voltage of each of the plurality of battery cells is obtained after each discharge.

11. The operation of obtaining the open-circuit voltage of each of the multiple battery cells, The operation of calculating the average open-circuit voltage of each of the aforementioned multiple battery cells, The operation involves calculating a first linear regression function relating to the open-circuit voltage of the first battery cell based on the open-circuit voltage of the first battery cell and the average open-circuit voltage of the plurality of battery cells, An operation to calculate a first error between the predicted value calculated based on the first linear regression function and the open-circuit voltage of the first battery cell, An operation to diagnose the first battery cell based on the first error, A method for operating a battery management device, including the operation of the battery management device.

12. The operation to calculate the first error between the predicted value calculated based on the first linear regression function and the open-circuit voltage of the first battery cell is as follows: The operation involves calculating the maximum and minimum deviations between the open-circuit voltage of the first battery cell and the predicted value calculated based on the first linear regression function, An operation to calculate the error rate between the open-circuit voltage of the first battery cell and the predicted value calculated based on the first linear regression function, A method for operating a battery management device according to claim 11, comprising the operation of calculating a first error of the open-circuit voltage of the first battery cell based on the difference between the maximum deviation and the minimum deviation and the error rate.

13. The operation of calculating a linear regression function relating to the open-circuit voltage of each of the multiple battery cells based on the open-circuit voltage of each of the multiple battery cells and the average open-circuit voltage, The operation involves calculating the error between the predicted value calculated based on the linear regression function and the open-circuit voltage of each of the multiple battery cells. A method for operating a battery management device according to claim 11 or 12, further comprising the operation of diagnosing each of the plurality of battery cells based on the aforementioned error.

Images