Battery diagnostic device and operating method thereof

Abstract

A battery diagnostic device according to an embodiment disclosed in the present document includes: a data obtaining unit that obtains voltage data and current data of a battery; a state-of-charge (SOC) calculation unit that calculates the SOC of the battery on the basis of the current data; an error calculation that calculates an SOC error of the battery on the basis of the calculated SOC of the battery and a reference SOC of the battery; and an SOH prediction unit that calculates the SOH of the battery on the basis of the SOC and the SOC error of the battery, inputs the calculated SOH of the battery into a linear regression model to derive a linear regression equation of the SOH of the battery, and predicts the SOH of the battery on the basis of the linear regression equation.

Description

Battery diagnostic device and its operating method

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority from Republic of Korea Patent Application No. 10-2024-0021602, filed February 15, 2024, the entire disclosure of which is incorporated herein by reference.

[0003] Technology field

[0004] One embodiment disclosed in this document relates to a battery diagnostic device and an operating method thereof.

[0005] Recently, active research and development has been conducted on secondary batteries. The term "secondary battery" refers to a rechargeable battery, encompassing both conventional Ni / Cd and Ni / MH batteries, as well as more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries boast a significantly higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form, making them a popular power source for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, drawing attention as a next-generation energy storage medium.

[0006] Secondary batteries deteriorate with repeated use. Therefore, efficient management requires continuous monitoring of their State of Charge (SOC) and State of Health (SOH), which indicate the extent of their deterioration. However, because parameters like SOC and SOH, which indicate the extent of secondary battery deterioration, are estimated based on factors such as the battery's voltage and current, the reliability of these parameters needs to be improved.

[0007] One purpose of the embodiments disclosed in this document is to provide a battery diagnostic device and an operating method thereof for predicting the SOH of a battery more accurately.

[0008] 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 to which the present invention pertains from the description below.

[0009] According to an embodiment disclosed in the present document, a battery diagnosis device may include a data acquisition unit that acquires voltage data and current data of a battery; a SOC calculation unit that calculates a State of Charge (SOC) of the battery based on the current data; an error calculation unit that calculates an SOC error of the battery based on the calculated SOC of the battery and a reference SOC of the battery; and an SOH prediction unit that calculates an SOH of the battery based on the SOC and the SOC error of the battery, inputs the calculated SOH of the battery into a linear regression model to derive a linear regression equation of the SOH of the battery, and predicts the SOH of the battery based on the linear regression equation.

[0010] According to one embodiment, the SOC calculation unit calculates the SOC of the battery based on a current integration method that integrates the current data, and the current integration method can be performed based on the following mathematical expression 1.

[0011] [Mathematical Formula 1]

[0012]

[0013] (Capacity above BoL corresponds to the initial capacity of the above battery.)

[0014] According to one embodiment, the error calculation unit can calculate the SOC error by calculating the difference between the SOC of the battery and the reference SOC, which is an SOC converted based on a reference OCV-SOC table.

[0015] According to one embodiment, the reference voltage may include data related to the voltage of a battery maintained in an idle state for a preset threshold time period from the time at which the charging and discharging operation of the battery is terminated.

[0016] According to one embodiment, the error calculation unit can predict the reference voltage based on the voltage data when the length of the time interval between the time at which the charging / discharging operation of the battery is terminated and the time at which the voltage data is acquired is less than the length of the critical time interval.

[0017] According to one embodiment, the error calculation unit can identify an inflection point of a voltage graph according to the passage of time from the time point at which the charging / discharging operation of the battery included in the voltage data ends, and perform exponential function linear modeling based on the identified inflection point to generate a voltage graph up to the length of the critical time section.

[0018] According to one embodiment, the error calculation unit can predict the voltage after the critical time interval has elapsed from the point at which the charging / discharging operation is terminated as the reference voltage based on the generated voltage graph.

[0019] According to one embodiment, the SOH prediction unit can calculate the SOH of the battery based on the following mathematical expression 2.

[0020] [Equation 2]

[0021]

[0022] (Here, SOC error corresponds to the above SOC error.)

[0023] In one embodiment, the linear regression model comprises a weighted linear regression model that assigns different weights to independent variables, wherein the independent variables may correspond to the calculated SOH of the battery.

[0024] According to one embodiment, the SOH prediction unit derives the linear regression equation by assigning different weights to the calculated SOH based on the SOC value, and the value of the weight may increase as the SOC value increases.

[0025] An operating method of a battery diagnosis device according to an embodiment disclosed in the present document may include the steps of: obtaining voltage data and current data of a battery; calculating a State of Charge (SOC) of the battery based on the current data; calculating an SOC error of the battery based on the calculated SOC of the battery and a reference SOC of the battery; and calculating an SOH of the battery based on the SOC and the SOC error of the battery, inputting the calculated SOH of the battery into a linear regression model to derive a linear regression equation of the SOH of the battery, and predicting the SOH of the battery based on the linear regression equation.

[0026] According to one embodiment, the step of calculating the SOC of the battery includes the step of calculating the SOC of the battery based on a current integration method that integrates the current data; and the current integration method can be performed based on the following mathematical expression 1.

[0027] [Mathematical Formula 1]

[0028]

[0029] (Capacity above BoL corresponds to the initial capacity of the above battery.)

[0030] According to one embodiment, the step of calculating the SOC error may include the step of obtaining a reference SOC corresponding to a reference voltage based on a reference OCV-SOC table; and the step of calculating a difference between the calculated SOC and the reference SOC.

[0031] According to one embodiment, the reference voltage may include data related to the voltage of a battery maintained in an idle state for a preset threshold time period from the time at which the charging and discharging operation of the battery is terminated.

[0032] According to one embodiment, the step of obtaining a reference SOC corresponding to the reference voltage may include: identifying a length of a time interval between a time point at which a charge / discharge operation of the battery is terminated and a time point at which the voltage data is obtained; identifying an inflection point of a voltage graph according to the passage of time from a time point at which a charge / discharge operation of the battery is terminated, which is included in the voltage data, when the length of the time interval is less than a length of a preset critical time interval; and performing exponential function linear modeling based on the identified inflection point to generate a voltage graph up to the length of the critical time interval.

[0033] According to one embodiment, the step of predicting the SOH of the battery may be performed based on the following mathematical expression 2.

[0034] [Equation 2]

[0035]

[0036] (Here, SOC error corresponds to the above SOC error, and the above Capacity BoL corresponds to the initial capacity of the above battery.)

[0037] In one embodiment, the linear regression model comprises a weighted linear regression model that assigns different weights to independent variables, wherein the independent variables may correspond to the calculated SOH of the battery.

[0038] According to one embodiment disclosed in this document, the SOH of a battery can be predicted more accurately.

[0039] The effects according to the embodiments disclosed in this document are not limited to the effects mentioned above, and other effects not mentioned can be clearly understood by those skilled in the art according to the disclosure of this document.

[0040] FIG. 1 is a drawing for explaining a battery diagnostic device according to an embodiment disclosed in this document.

[0041] FIG. 2 is a graph for explaining SOC error according to an embodiment disclosed in this document.

[0042] FIG. 3 is a diagram for explaining a process for calculating a reference voltage according to an embodiment disclosed in this document.

[0043] Figure 4 is a drawing for explaining weights assigned according to one embodiment disclosed in this document.

[0044] FIG. 5 is a diagram for explaining a linear regression equation according to an embodiment disclosed in this document.

[0045] FIG. 6 is a drawing for explaining the operation of a battery diagnostic device according to an embodiment disclosed in this document.

[0046] FIG. 7 is a drawing for explaining the operation of a battery diagnostic device according to an embodiment disclosed in this document.

[0047] FIG. 8 is a diagram illustrating a computing system according to an embodiment disclosed in this document.

[0048] Hereinafter, embodiments disclosed in this document will be described in detail with reference to exemplary drawings. When designating components in each drawing, it should be noted that, where possible, identical components are given identical reference numerals, even if they appear in different drawings. Furthermore, when describing embodiments disclosed in this document, detailed descriptions of related known structures or functions will be omitted if they are deemed to hinder understanding of the embodiments disclosed in this document.

[0049] In describing the components of the embodiments disclosed in this document, terms such as first, second, A, B, (a), (b), etc. may be used. These terms are only intended to distinguish the components from other components and do not limit the nature, order, or sequence of the components. In addition, 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 in this document belong. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning they have in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined in this application.

[0050] FIG. 1 is a drawing for explaining a battery diagnostic device (100) according to one embodiment disclosed in this document.

[0051] The target device (10) may include a plurality of battery modules (11, 12, 13, 14). In FIG. 1, the target device (10) is illustrated as including four battery modules, but is not limited to this example, and the target device (10) may be configured to include n battery modules (n is a natural number greater than or equal to 2). In addition, the plurality of battery modules (11, 12, 13) may each include a plurality of battery cells (not shown). Here, the plurality of battery modules (11, 12, 13, 14) may constitute at least one battery pack, and the battery pack may be configured to supply power to the target device (10).

[0052] In addition, a plurality of battery cells (not shown) are basic units of a battery that can be used by charging and discharging electric energy, and may be, but are not limited to, a lithium-ion (Li-ion) battery, a lithium-ion polymer (Li-ion polymer) battery, a nickel-cadmium (Ni-Cd) battery, a nickel-metal hydride (Ni-MH) battery, etc.

[0053] The target device (10) can operate by receiving power from a battery module (11, 12, 13, 14) and / or a battery pack (not shown) including at least one battery module (11, 12, 13, 14). Here, the target device (10) may include an electrical, electronic or mechanical device that operates by receiving power from a battery module (11, 12, 13, 14) and / or a battery pack (not shown) including at least one battery module (11, 12, 13, 14), and for example, the target device (10) may be an electric vehicle (EV), a mobile device (e.g., a mobile phone, a laptop computer, a smartphone, etc.), an energy storage system (ESS), or a battery swapping station (BSS), but is not limited to these examples.

[0054] The battery diagnostic device (100) can be configured to predict the SOH (State of Health) of the battery pack (10).

[0055] Referring to FIG. 1, a battery diagnostic device (100) may include a data acquisition unit (110), an SOC calculation unit (120), an error calculation unit (130), an SOH prediction unit (140), and a memory (150).

[0056] The battery diagnostic device (100) may be connected to a target device (10) and configured to diagnose a battery pack (not shown) included in the target device (10) and / or at least one battery module (11, 12, 13, 14) included in the battery pack (not shown). To this end, the battery diagnostic device (100) may be connected to the target device (10) via a wired and / or wireless network, and to this end, the battery diagnostic device (100) may include a communication module, etc., not shown in FIG. 1.

[0057] According to one embodiment, the battery diagnostic device (100) may be connected to the target device (10) via a wired network such as a Local Area Network (LAN) communication, a power line communication, etc. and / or a wireless network such as a Bluetooth connection, a Wireless Fidelity (WIFI), an Infrared Data Association (IrDA), a cellular network, a 4G, or a 5G network connection, etc., but is not limited to these examples.

[0058] According to one embodiment, the battery diagnostic device (100) may be connected to the target device (10) via a device-to-device communication interface. For example, the battery diagnostic device (100) may be connected via a communication interface such as a bus, a GPIO (General Purpose Input and Output), a SPI (Serial Peripheral Interface), or a MIPI (Mobile Industry Processor Interface), but is not limited thereto.

[0059] The battery diagnostic device (100) can directly and / or indirectly obtain voltage data related to voltage, current data related to current, and temperature data related to temperature of each of a battery pack (not shown) included in a target device (10), at least one battery module (11, 12, 13, 14) included in the battery pack (not shown), and battery cells (not shown) included therein. For example, when the battery diagnostic device (100) is implemented in the form of a battery management system (BMS) that manages and controls a battery pack (not shown) and / or at least one battery module (11, 12, 13, 14) included in the battery pack (not shown), or is mounted on a battery management device, the battery diagnostic device (100) can directly measure the voltage, current, and temperature of each of the battery pack (not shown) and / or the battery modules (11, 12, 13, 14). In such cases, various sensors (not shown) not shown in Fig. 1 may be additionally positioned.

[0060] According to another embodiment, the battery diagnosis device (100) can indirectly obtain voltage data related to voltage, current data related to current, and temperature data related to temperature of each of the battery pack (not shown) and / or battery modules (11, 12, 13, 14) and battery cells (not shown) included in the target device (10). That is, when the battery diagnosis device (100) is implemented as a separate device from the battery management device (not shown) described above, the battery diagnosis device (100) can obtain voltage data, current data, and temperature data provided by the battery management device (not shown) included in the target device (10) to a cloud server (not shown), etc. In the following description, it is assumed that the battery diagnosis device (100) is a separate device from the battery management device, but the present invention is not limited to this example.

[0061] The data acquisition unit (110) can acquire voltage data, current data, temperature data, etc. of the battery. Here, the battery may be a unit corresponding to any one of the battery pack (not shown), battery modules (11, 12, 13, 14) and battery cells included therein as described above. According to one embodiment, the data acquisition unit (110) can acquire data provided by a battery management device (not shown) included in the target device (10) to a cloud server, etc. Here, the voltage data and current data may include, but are not limited to, graphs related to changes in the voltage and current of the battery over time.

[0062] The SOC calculation unit (120) can calculate the SOC (State of Charge) of the battery based on the current data acquired by the data acquisition unit (110). According to one embodiment, the SOC calculation unit (120) can calculate the SOC of the battery through the current integration method. Here, the current integration method may be a method of calculating the charge amount by integrating the current during the charge / discharge cycle of the battery with the initial SOC value of the battery.

[0063] According to one embodiment, the SOC calculation unit (120) can calculate the SOC of the battery based on the following mathematical expression 1.

[0064] [Mathematical Formula 1]

[0065]

[0066] (Here, SOC corresponds to the SOC of the battery calculated through the current integration method based on current data, and Capacity BoL corresponds to the initial capacity of the battery.)

[0067] The error calculation unit (130) can calculate the SOC error of the battery based on the calculated SOC of the battery and the reference SOC. Here, the reference SOC may be an SOC corresponding to the reference voltage, which is the voltage of the battery at a point in time when a preset threshold time interval has elapsed from the point in time when the charging and discharging operation of the battery is completed. Details related to the reference voltage will be described later in the description of FIG. 3.

[0068] According to one embodiment, if the length of the time interval between the time at which the charge / discharge operation of the battery is completed and the time at which the voltage data of the battery is acquired is shorter than the length of a preset threshold time interval, the error calculation unit (130) can predict the reference voltage based on the battery data.

[0069] The error calculation unit (130) may calculate the SOC error based on the difference between the calculated SOC of the battery and the reference SOC. According to one embodiment, the error calculation unit (130) may obtain the SOC error by calculating the difference between the calculated SOC of the battery and the reference SOC at the time when the charge / discharge operation is completed.

[0070] The SOH prediction unit (140) can predict the SOH (State of Health) of the battery.

[0071] According to one embodiment, the SOH prediction unit (140) can calculate the SOH of the battery based on the calculated SOC and SOC error of the battery. Here, the SOH prediction unit (140) can calculate the SOH of the battery based on the following mathematical expression 2.

[0072] [Equation 2]

[0073]

[0074] (Here, SOC can correspond to the calculated SOC of the battery, and SOC error ) can be addressed by SOC error.

[0075] According to one embodiment, the SOH prediction unit (140) can derive a linear regression equation for predicting the SOH of the battery based on the calculated SOH. The SOH prediction unit (140) can input the calculated SOH of the battery into a linear regression model to derive a linear regression equation for predicting the SOH of the battery. Here, linear regression is a technique for modeling a linear correlation between a dependent variable and one or more independent variables, where the dependent variable is the predicted SOH of the battery, and the independent variable may be the SOH calculated based on the SOC and the SOC error.

[0076] According to one embodiment, the SOH prediction unit (140) may collect the SOH of the battery calculated by date and derive a linear regression equation representing the SOH trend of the battery. Here, the SOH prediction unit (140) may derive the linear regression equation by considering the SOC used when calculating the SOH of the battery. The SOH prediction unit (140) may identify the calculated SOH of the battery when the SOC of the battery, i.e., the calculated SOC, is equal to or greater than a threshold value, and derive a linear regression equation based on the identified SOH. For example, the SOH prediction unit (140) may derive a linear regression equation based on the calculated SOH of the battery when the SOC of the battery is equal to or greater than 40%, but is not limited to this example.

[0077] According to one embodiment, the SOH prediction unit (140) may assign different weights to the calculated SOH of the battery based on the SOC used when calculating the SOH of the battery, i.e., the calculated SOC value. Details related to this will be described later in the description of FIG. 4.

[0078] The memory (150) can store various commands, software, etc. for the operation of the battery's SOC, a reference OCV-SOC table, a linear regression model, the calculated SOH of the battery, and / or the battery diagnostic device (100). According to one embodiment, the memory (150) can include a volatile memory device such as a static random access memory (SRAM) or a dynamic random access memory (DRAM), or a non-volatile memory device such as a read only memory (ROM), a programmable ROM (PROM), or a flash memory.

[0079] Referring to FIG. 1, the memory (150) is illustrated as being included in the battery prediction device (100), but is not limited thereto, and the memory (150) may be located outside the battery diagnosis device (100).

[0080] According to one embodiment, the SOC calculation unit (120), the SOH error calculation unit (130), and the SOH prediction unit (140) may be implemented as one processor or as separate processors. Here, the processor may execute software to control at least one other component (e.g., hardware or software) of the battery diagnosis device (100), or perform operations such as processing and / or calculating various data.

[0081] The battery diagnostic device (100) can transmit the predicted SOH of the battery to an external source. According to one embodiment, the battery diagnostic device (100) can provide battery information, for example, the predicted SOH of the battery by date, to the user terminal (1). In addition, the battery diagnostic device (100) can diagnose whether the battery is abnormal based on the predicted SOH of the battery and perform operations such as providing an alarm through the user terminal (1). Here, the user terminal (1) can include a terminal such as a personal computer (PC) or a smartphone.

[0082] FIG. 2 is a graph for explaining SOC error according to an embodiment disclosed in this document.

[0083] Referring to Figure 2, a graph is shown in which the horizontal axis represents time and the vertical axis represents the SOC of the battery.

[0084] Among various indicators indicating the status of a battery, the State of Charge (SOC) may be an indicator indicating the state of charge of the battery, i.e., the remaining capacity of the battery. The SOC of the battery is an indicator that allows the user to predict the charging time and the end time of the battery, and may increase as the charging operation for the battery progresses. Here, the battery may correspond to a unit including at least one of the battery pack (not shown), battery modules (11, 12, 13, 14) and / or battery cells (not shown) included in each of the battery modules (11, 12, 13, 14) described with reference to FIG. 1.

[0085] Referring to FIG. 2, as a charging operation is performed on the battery, the SOC of the battery may increase. At time t1, when charging of the battery is completed, the SOC of the battery may reach its maximum value.

[0086] Referring to FIG. 2, a solid line graph (a) and a dotted line graph (b) are illustrated. According to one embodiment, the solid line graph (a) may be a graph representing the SOC of a battery calculated based on a current integration method that calculates the SOC by integrating the current of the battery. In addition, the dotted line graph (b) may be a graph representing the corrected SOC of the battery, taking into account various errors that may occur when calculating the SOC of the battery based on the current integration method. Here, the errors that may occur when calculating the error of the battery may include, but are not limited to, an error due to a change in the SOH indicating the degree of battery degradation compared to the initial (Beginning of Life) capacity of the battery, a sensing error that may occur in the process of sensing the current of the battery, and an error due to the measured voltage of the battery. That is, when calculating the SOC of a battery according to the current integration method, a difference in capacity compared to the initial capacity of the battery occurs as capacity degradation occurs due to battery use. However, since mathematical expression 1 explained with reference to Fig. 1 calculates based on the initial capacity, it is difficult to reflect the error due to capacity degradation. In addition, since errors may occur due to the accuracy of the sensing current value of the current sensor, etc., there is a need to compensate for this.

[0087] According to one embodiment, the SOC error disclosed in the present document may be the difference between the SOC of the battery (SOC) calculated through the current integration method at a time point (t1) when a charge / discharge operation for the battery is completed and a reference SOC corrected by reflecting an error due to capacity degradation of the battery after charging and a current sensing error. Here, the reference SOC may be an SOC obtained based on a reference voltage of the battery maintained in an idle state for a preset threshold time (tref) period from the time point (t1) when the charge operation for the battery is completed, and the preset threshold time period interval may be a minimum time interval for measuring an open circuit voltage (OCV) of the battery.

[0088] FIG. 3 is a diagram for explaining a process for calculating a reference voltage according to an embodiment disclosed in this document.

[0089] As described above in the description of FIG. 2, the error calculation unit (130, see FIG. 1) can calculate the SOC error, which is the difference between the SOC calculated by integrating the current of the battery at the time point (t1) when the battery is fully charged and the reference SOC. Here, the reference SOC may be a value converted into SOC based on the voltage of the battery maintained in an idle state for a preset threshold time period (tref) from the time point (t1) when the battery is fully charged, based on the reference OCV-SOC table. The reference OCV-SOC table may be a table indicating the SOC of the battery corresponding to each open circuit voltage (OCV) of the battery maintained in an idle state for a preset threshold time period (tref).

[0090] The error calculation unit (130) can obtain a reference voltage for obtaining a reference SOC by comparing the time interval between the time at which the battery's charge / discharge operation ends and the time at which the battery's voltage data is obtained with a preset critical time interval (tref).

[0091] According to one embodiment, if the length of the time interval between the time point (t0) at which the charge / discharge operation of the battery is terminated and the time point (ta) at which the voltage data of the battery is acquired is longer than the length of the preset critical time interval (tref) (a), that is, if the voltage data includes the voltage value of the battery after the preset critical time interval has elapsed from the time point at which the charge / discharge operation of the battery is terminated, the error calculation unit (130) can acquire the voltage of the battery after the preset critical time interval (tref) has elapsed from the time point (t0) at which the charge / discharge operation of the battery is terminated as the reference voltage.

[0092] According to one embodiment, if the length of the time interval between the time point (t0) at which the charging / discharging operation of the battery is terminated and the time point (tb) at which the voltage data of the battery is acquired is less than the length of a preset critical time interval (tref) (b), the error calculation unit (130) can predict the reference voltage based on the battery voltage data acquired up to the time point tb.

[0093] In one embodiment, the error calculation unit (130) may identify an inflection point based on the battery voltage data (e.g., voltage profile) acquired up to time point tb. Specifically, the error calculation unit (130) may identify, but is not limited to, a point where the sign of a value obtained by doubly differentiating a graph related to changes in the battery voltage over time included in the battery voltage data changes as an inflection point.

[0094] According to one embodiment, the error calculation unit (130) may perform linear modeling of a graph related to a change in the voltage of the battery based on the identified inflection point. Here, the graph related to the change in voltage after the charge / discharge operation of the battery is completed may be in the form of an exponential function, and the error calculation unit (130) may predict a linear graph after the inflection point by linearly modeling the exponential function. Based on the predicted graph, the error calculation unit (130) may predict the voltage at a point in time when a preset critical time interval (tref) has passed from the point in time (t0) when the charge / discharge operation of the battery is completed, as a reference voltage.

[0095] The error calculation unit (130) can obtain a reference SOC corresponding to the reference voltage based on the reference OCV-SOC table.

[0096] Figure 4 is a drawing for explaining weights assigned according to one embodiment disclosed in this document.

[0097] The SOH prediction unit (140, see FIG. 1) can calculate the SOH of a battery based on the SOC and SOC error of the battery calculated based on the current integration method. According to one embodiment, the SOH prediction unit (140) can calculate the SOH of the battery based on the mathematical expression 2 described with reference to FIG. 1.

[0098] According to one embodiment, the SOH prediction unit (140) can derive a linear regression equation for predicting the SOH of a battery based on the calculated SOH. The SOH prediction unit (140) can input the calculated SOH of the battery into a linear regression model and derive a linear regression equation for predicting the SOH of the battery.

[0099] According to one embodiment, the SOH prediction unit (140) may derive a linear regression equation by considering the SOC value used when calculating the SOH of the battery among the calculated SOH of the battery. The SOH prediction unit (140) may identify the calculated SOH of the battery when the SOC of the battery, i.e., the calculated SOC, is equal to or greater than a threshold value, and derive a linear regression equation based on the identified SOH data. For example, the SOH prediction unit (140) may derive a linear regression equation based on the calculated SOH of the battery when the SoC of the battery is equal to or greater than 40%, but is not limited to this example.

[0100] For example, if the SOC input into Equation 2 to calculate SOH is 35%, the SOH may not be treated as valid data for deriving a linear regression equation. As another example, if the SOC input into Equation 2 to calculate SOH is 65%, the SOH may be treated as valid data for deriving a linear regression equation. However, this is merely exemplary, and the embodiments disclosed in this document are not limited to these examples.

[0101] According to one embodiment, the SOH prediction unit (140) may use a weighted linear regression model that derives a linear regression equation by assigning different weights to dependent variables based on the value of the SOC input to calculate the SOH. Here, the weight may increase as the value of the SOC input to the weighted linear regression model increases. For example, assuming that the SOC standard of valid data for deriving the linear regression equation is 40%, the SOH prediction unit (140) may assign a weight of a to the calculated SOH of the battery when the input SOC is between 40% and 70%, and may assign a weight of b to the calculated SOH of the battery when the input SOC is between 70% and 100%, but is not limited to these examples. In this case, the value of the weight b may be greater than the value of the weight a. That is, the higher the input SOC value when calculating the SOH of the battery, the more influential the calculated SOH may be.

[0102] FIG. 5 is a diagram for explaining a linear regression equation according to an embodiment disclosed in this document.

[0103] Referring to Fig. 5, the SOH of the produced battery and the linear regression equation derived based on the same are shown.

[0104] According to one embodiment, the horizontal axis of the graph illustrated in FIG. 5 may represent time, and the vertical axis may represent the SOH of the battery.

[0105] The data depicted on the coordinate plane illustrated in Fig. 5 may represent the SOH of the battery calculated based on mathematical equation 2 described with reference to Fig. 1. Here, the data indicated by O may be the SOH calculated when the SOC input into mathematical equation 2 is less than the threshold value (SOCref), and the data indicated by X may be the SOH calculated when the SOC input into mathematical equation 2 is greater than the threshold value (SOCref).

[0106] According to one embodiment, if the SOC input into Equation 2 is less than the threshold value (SOCref), the calculated SOH may not be treated as valid data. That is, the SOH prediction unit (140, see FIG. 1) may derive a linear regression equation based on the data indicated by X. The SOH prediction unit (140) may input the data indicated by X into the linear regression model to derive a linear regression equation representing the trend between the SOHs of the batteries.

[0107] In one embodiment, the linear regression model may be a weighted linear regression model in which weights are assigned to increase or decrease the influence of the independent variables.

[0108] According to one embodiment, the SOH prediction unit (140) can predict the SOH of the battery by date based on the derived linear regression equation.

[0109] FIGS. 6 and 7 are drawings for explaining the operation of a battery diagnostic device according to an embodiment disclosed in this document.

[0110] In step S101, a battery diagnostic device (100, see FIG. 1) can obtain voltage data and current data of a battery. Here, the battery may be a unit corresponding to any one of a battery pack, a battery module (11, 12, 13, 14, see FIG. 1), and at least one battery cell included in the battery module.

[0111] In one embodiment, the voltage data and current data may include, but are not limited to, graphs (e.g., voltage profiles and current profiles) relating to changes in voltage and current of the battery over time, respectively.

[0112] In step S102, the battery diagnostic device (100) can calculate the SOC (State of Charge) of the battery based on current data. According to one embodiment, the battery diagnostic device (100) can calculate the SOC of the battery through a current integration method based on the current data of the battery.

[0113] In one embodiment, the current integration method may be a method of calculating the amount of charge by integrating the current during the charge / discharge cycle of the battery to the initial SOC value of the battery.

[0114] According to one embodiment, the battery diagnostic device (100) can calculate the SOC of the battery based on the mathematical expression 1 above.

[0115] At step S103, the battery diagnostic device (100) can calculate the error of SOC based on the calculated SOC and the reference SOC.

[0116] According to one embodiment, the battery diagnostic device (100) can calculate an SOC error based on the difference between the calculated SOC and the reference SOC. Here, the reference SOC may be an SOC corresponding to a reference voltage, which is the voltage of the battery at a point in time when a preset threshold time interval has elapsed from the point in time when the charging and discharging operation of the battery is completed.

[0117] According to one embodiment, the battery diagnostic device (100) can obtain a reference SOC corresponding to the reference voltage by converting the reference voltage based on a previously stored reference OCV-SOC table.

[0118] According to one embodiment, the battery diagnostic device (100) can obtain a reference voltage for obtaining a reference SOC by comparing the time interval between the time at which the charging / discharging operation of the battery is terminated and the time at which the voltage data of the battery is obtained with a preset threshold time interval.

[0119] According to one embodiment, if the length of the time interval between the time at which the charge / discharge operation of the battery is terminated and the time at which the voltage data of the battery is acquired is longer than the length of the preset critical time interval, that is, if the voltage data includes the voltage value of the battery after the preset critical time interval has elapsed from the time at which the charge / discharge operation of the battery is terminated, the battery diagnosis device (100) can acquire the voltage of the battery after the preset critical time interval has elapsed from the time at which the charge / discharge operation of the battery is terminated as the reference voltage.

[0120] According to one embodiment, if the length of the time interval between the time at which the charge / discharge operation of the battery is terminated and the time at which the voltage data of the battery is acquired is less than the length of a preset threshold time interval, the battery diagnostic device (100) can predict the reference voltage based on the acquired battery voltage data.

[0121] According to one embodiment, the battery diagnostic device (100) may identify an inflection point based on acquired voltage data of the battery (e.g., a voltage profile). Specifically, the battery diagnostic device (100) may identify, but is not limited to, a point at which the sign of a value obtained by doubly differentiating a graph related to changes in the voltage of the battery over time included in the voltage data of the battery changes as an inflection point.

[0122] According to one embodiment, the battery diagnosis device (100) may perform linear modeling of a graph related to a change in the voltage of the battery based on the identified inflection point. Here, the graph related to the change in voltage after the charge / discharge operation of the battery is completed may be in the form of an exponential function, and the battery diagnosis device (100) may predict the linear graph after the inflection point by linearly modeling the exponential function. Based on the predicted graph, the battery diagnosis device (100) may predict the voltage at a point in time when a preset critical time interval has passed from the point in time when the charge / discharge operation of the battery is completed as a reference voltage.

[0123] At step S104, the battery diagnostic device (100) can predict the SOH of the battery based on the SOC and SOC error.

[0124] Referring to FIG. 7, a specific process of step S104 described with reference to FIG. 6 is illustrated.

[0125] In step S201, the battery diagnostic device (100) can calculate the SOH (State of Health) of the battery based on the calculated SOC and SOC error. According to one embodiment, the battery diagnostic device (100) can calculate the SOH of the battery based on the mathematical expression 2 above.

[0126] In step S202, the battery diagnostic device (100) can input the calculated SOH into a linear regression model to derive a linear regression equation for predicting the SOH of the battery.

[0127] In step S203, the battery diagnostic device (100) can predict the SOH of the battery based on the derived linear regression equation. According to one embodiment, the battery diagnostic device (100) can predict the SOH of the battery by date based on the derived linear regression equation.

[0128] According to one embodiment, the battery diagnostic device (100) may use a weighted linear regression model that derives a linear regression equation by assigning different weights to dependent variables based on the SOC (SOC) value of the battery input to calculate the SOH. Here, the weight may increase as the SOC value input to the weighted linear regression model increases.

[0129] In one embodiment, the linear regression model may be a weighted linear regression model in which weights are assigned to increase or decrease the influence of the independent variables.

[0130] According to one embodiment, the battery diagnostic device (100) can predict the date-specific SOH of the battery based on the derived linear regression equation.

[0131] FIG. 8 is a diagram illustrating a computing system according to an embodiment disclosed in this document.

[0132] Referring to FIG. 8, the computing system (800) may include an MCU (810), a memory (820), an input / output I / F (830), and a communication I / F (840).

[0133] The MCU (810) may be a processor that executes various programs (e.g., a battery SOH prediction program, etc.) stored in the memory (820), processes various data including multiple battery SOCs and SOHs through these programs, and performs the functions of the battery diagnosis device (100) described with reference to the above-described FIG. 1.

[0134] The memory (820) can store various programs for predicting the SOH of a battery. In addition, the memory (820) can store various data generated and / or processed during the process of predicting the SOH of a target battery.

[0135] Such memories (820) may be provided in multiples as needed. The memories (820) may be volatile memories or non-volatile memories. As volatile memories, RAM, DRAM, SRAM, etc. may be used. As non-volatile memories, ROM, PROM, EAROM, EPROM, EEPROM, flash memories, etc. may be used. The examples of the memories (820) listed above are merely examples and are not limited to these examples.

[0136] The input / output I / F (830) can provide an interface that enables data transmission and reception between an input device (not shown) such as a keyboard, mouse, or touch panel, and an output device (not shown) such as a display and the MCU (810).

[0137] The communication I / F (840) is a component capable of transmitting and receiving various data with a server, and may be any device capable of supporting wired or wireless communication. For example, a program for predicting the SOH of a battery or various data may be transmitted and received from a separately provided external server via the communication I / F (840).

[0138] In this way, the battery SOH prediction method according to one embodiment disclosed in this document can be recorded in the memory (820) and executed by the MCU (810).

[0139] In the above, all components constituting the embodiments have been described as being combined or operating in combination as one. However, this is not necessarily limited to such embodiments, and within the scope of the purpose, all components may be selectively combined and operated in one or more combinations. Furthermore, terms such as "include," "comprise," or "have" described above, unless specifically stated to the contrary, imply that the corresponding component may be inherent, and therefore should be interpreted to include other components rather than excluding other components.

[0140] The above description is merely an example of the technical idea disclosed in this document, and those skilled in the art to which the embodiments disclosed in this document pertain may make various modifications and variations without departing from the essential characteristics of the embodiments disclosed in this document.

[0141] Accordingly, the embodiments disclosed in this document are intended to illustrate, rather than limit, the technical concepts disclosed in this document, and the scope of the technical concepts disclosed in this document is not limited by these embodiments. The scope of protection of the technical concepts disclosed in this document should be interpreted by the claims below, and all technical concepts within the equivalent scope should be interpreted as being included within the scope of the rights of this document.

[0142] [Explanation of symbols]

[0143] 1: User terminal 10: Target device

[0144] 11, 12, 13, 14: Battery module 100: Battery diagnostic device

[0145] 110: Data acquisition unit 120: SOC operation unit

[0146] 130: Error calculation unit 140: SOH prediction unit

[0147] 150: Memory

Claims

1. A data acquisition unit that acquires voltage data and current data of the battery; A SOC calculation unit that calculates the SOC (State of Charge) of the battery based on the current data; An error calculation unit that calculates the SOC error of the battery based on the calculated SOC of the battery and the reference SOC of the battery; and A battery diagnostic device comprising an SOH prediction unit that calculates the SOH of the battery based on the SOC and SOC error of the battery, inputs the calculated SOH of the battery into a linear regression model to derive a linear regression equation of the SOH of the battery, and predicts the SOH of the battery based on the linear regression equation.

2. In the first paragraph, the SOC operation unit, A battery diagnostic device that calculates the SOC of the battery based on a current integration method that integrates the above current data, and wherein the current integration method is performed based on the following mathematical expression 1. [Mathematical Formula 1] (Capacity above BoL corresponds to the initial capacity of the above battery.) 3. In the first paragraph, the error calculation unit, A battery diagnostic device that calculates the SOC error by calculating the difference between the SOC of the battery and the reference voltage, which is a reference SOC converted based on a reference OCV-SOC table.

4. In the third paragraph, the reference voltage is A battery diagnostic device including data related to the voltage of a battery maintained in an idle state for a preset critical time period from the time when the charging and discharging operation of the battery is terminated.

5. In the fourth paragraph, the error calculation unit, A battery diagnostic device that predicts a reference voltage based on the voltage data when the length of the time interval between the time at which the charge / discharge operation of the battery ends and the time at which the voltage data is acquired is less than the length of the critical time interval.

6. In the fifth paragraph, the error calculation unit, A battery diagnostic device that identifies an inflection point of a voltage graph according to the passage of time from the time point at which the charge / discharge operation of the battery included in the voltage data ends, and performs exponential function linear modeling based on the identified inflection point to generate a voltage graph up to the length of the critical time interval.

7. In the 6th paragraph, the error calculation unit, A battery diagnostic device that predicts the voltage after the critical time interval elapses from the point at which the charging / discharging operation ends based on the generated voltage graph as the reference voltage.

8. In paragraph 1, The above SOH prediction unit is a battery diagnostic device that calculates the SOH of the battery based on the following mathematical expression 2. [Equation 2] (Here, SOC error corresponds to the above SOC error.) 9. In paragraph 8, The above linear regression model includes a weighted linear regression model that assigns different weights to independent variables, The above independent variable is a battery diagnostic device corresponding to the calculated SOH of the battery.

10. In the 9th paragraph, the SOH prediction unit, A battery diagnostic device that derives the linear regression equation by assigning different weights to the calculated SOH based on the SOC value, wherein the value of the weight increases as the SOC value increases.

11. Step of acquiring voltage data and current data of the battery; A step of calculating the SOC (State of Charge) of the battery based on the current data; A step of calculating the SOC error of the battery based on the calculated SOC of the battery and the reference SOC of the battery; and A method for operating a battery diagnosis device, comprising: calculating the SOH of the battery based on the SOC and SOC error of the battery, inputting the calculated SOH of the battery into a linear regression model to derive a linear regression equation for the SOH of the battery, and predicting the SOH of the battery based on the linear regression equation.

12. In the 11th paragraph, the step of calculating the SOC of the battery is: A method of operating a battery diagnostic device, comprising: calculating the SOC of the battery based on a current integration method that integrates the current data; wherein the current integration method is performed based on the following mathematical expression 1. [Mathematical Formula 1] (Capacity above BoL corresponds to the initial capacity of the above battery.) 13. In the 11th paragraph, the step of calculating the SOC error is: A step of obtaining a reference SOC corresponding to a reference voltage based on a reference OCV-SOC table; and A method for operating a battery diagnostic device, comprising: a step of calculating the difference between the calculated SOC and the reference SOC.

14. In the 13th paragraph, the reference voltage is An operating method of a battery diagnostic device including data related to the voltage of a battery maintained in an idle state for a preset critical time period from the time when the charging and discharging operation of the battery is terminated.

15. In the 13th paragraph, the step of obtaining the reference SOC corresponding to the reference voltage is: A step of identifying the length of the time interval between the point in time when the charging / discharging operation of the battery is terminated and the point in time when the voltage data is acquired; If the length of the above time interval is less than the length of the preset critical time interval, a step of identifying an inflection point of the voltage graph according to the passage of time from the time at which the charge / discharge operation of the battery included in the voltage data ends; and An operating method of a battery diagnostic device, comprising the step of generating a voltage graph up to the length of the critical time interval by performing exponential function linear modeling based on the identified inflection point.

16. In the 11th paragraph, the step of predicting the SOH of the battery comprises: An operating method of a battery diagnosis device performed based on the following mathematical expression 2. [Equation 2] (Here, SOC error corresponds to the above SOC error, and SOC corresponds to the SOC calculated based on the above current data, and the Capacity BoL corresponds to the initial capacity of the above battery.) 17. In paragraph 16, The above linear regression model includes a weighted linear regression model that assigns different weights to independent variables, The above independent variable is an operating method of a battery diagnostic device corresponding to the calculated SOH of the battery.

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