KR20240082931A - Apparatus and method for diagnosing battery - Google Patents

Abstract

A battery diagnosis device according to various embodiments includes an information acquisition module configured to acquire measurement data from a target battery, a memory storing a reference table in which a plurality of reference values used for battery diagnosis are defined, and measurement information obtained from the target battery. and a processor configured to diagnose the target battery based on at least one pre-stored reference value, wherein the processor acquires measurement data from a reference battery and processes the measurement data to provide reference values corresponding to a plurality of battery cells. It may be configured to calculate and use a reference value corresponding to one battery cell selected based on the distribution of reference values corresponding to the plurality of battery cells to diagnose the target battery.

Description

Battery diagnostic device and method {APPARATUS AND METHOD FOR DIAGNOSING BATTERY}

The present invention relates to a battery diagnostic device and method, and more specifically, to a battery diagnostic device and method with improved diagnostic accuracy.

Recently, as the demand for portable electronic products such as laptops, video cameras, and portable phones has rapidly increased, and as the development of electric vehicles, energy storage batteries, robots, and satellites has begun, the need for high-performance batteries capable of repeated charging and discharging has increased. Research is actively underway.

Currently commercialized batteries include nickel cadmium batteries, nickel hydrogen batteries, nickel zinc batteries, and lithium batteries. Among them, lithium batteries can be charged and discharged more freely than nickel-based batteries, have a very low self-discharge rate, and have a high energy density. It is in the spotlight for its advantages.

In general, battery performance deteriorates as usage time increases. In other words, the capacity of the battery does not continue to maintain the capacity at the beginning of life (BOL: Beginning Of Life), but decreases as calendar time or cycle time elapses. Here, the calendar time refers to the accumulated time that the battery maintains an unloaded state, and the cycle time refers to the accumulated time that the battery maintains charging or discharging.

The energy charged in these batteries enables driving of electric vehicles or hybrid electric vehicles, so it is very important to diagnose or predict the state of the battery. In particular, since the state of the battery can be used as a measure of the battery replacement cycle, high diagnostic accuracy for the state of the battery is required.

At least one of various embodiments of the present invention seeks to provide a battery diagnosis device and method with improved diagnosis accuracy.

At least one of the various embodiments of the present invention seeks to provide a battery diagnosis device and method for determining a meaningful reference value based on a distribution state among reference values used in battery diagnosis.

A battery diagnosis device according to various embodiments includes an information acquisition module configured to acquire measurement data from a target battery, a memory storing a reference table in which a plurality of reference values used for battery diagnosis are defined, and measurement information obtained from the target battery. and a processor configured to diagnose the target battery based on at least one pre-stored reference value, wherein the processor acquires measurement data from a reference battery and processes the measurement data to provide reference values corresponding to a plurality of battery cells. It may be configured to calculate and use a reference value corresponding to one battery cell selected based on the distribution of reference values corresponding to the plurality of battery cells to diagnose the target battery.

According to various embodiments, the processor calculates standard scores for reference values corresponding to the plurality of battery cells, and defines some reference values having standard scores that satisfy specified conditions as the reference table. .

According to various embodiments, the processor may be configured to calculate the standard score based on at least one of a battery cell unit, a battery module unit, or a battery pack unit, with respect to the reference battery.

According to various embodiments, the processor may be configured to process the measurement data to calculate reference values corresponding to a plurality of types and define them in the reference table, and at least one type of the plurality of types is the measurement data. corresponds to integral processing for, and at least one other type among the plurality of types may correspond to differential processing for the measurement data.

According to various embodiments, the processor may be configured to use a reference value corresponding to one type selected based on a distribution of reference values corresponding to the plurality of types to diagnose the target battery.

According to various embodiments, the processor may provide, for each of the plurality of types, a first group of reference values obtained by processing measurement data corresponding to a first extraction range and a second group of reference values processed by measurement data corresponding to a second extraction range. It may be configured to calculate the reference value of the group, and the first extraction range and the second extraction range may be different from each other.

According to various embodiments, the processor selects one of the reference values of the first group and the reference value of the second group based on the distribution of the reference values of the first group and the second group to the target battery. It can be configured to be used for diagnosis.

According to various embodiments, the plurality of reference values include a plurality of first reference values corresponding to normal judgments and a plurality of second reference values corresponding to abnormal judgments, and the processor distributes the first reference values. and may be configured to select some of the plurality of reference values based on a distribution of the second reference values.

According to various embodiments, the type of measurement information obtained from the target battery and the reference battery may include at least one of voltage, current, temperature, internal resistance, or a change amount thereof.

A method of operating a battery diagnosis device according to various embodiments may include obtaining measurement data from a target battery and diagnosing the target battery based on at least some of a plurality of pre-stored reference values and the measurement data. , the operation of diagnosing the target battery includes obtaining measurement data from a reference battery, processing the measurement data to calculate a reference value corresponding to a plurality of battery cells, and calculating a reference value corresponding to the plurality of battery cells. It may include an operation of using a reference value corresponding to one battery cell selected based on the distribution to diagnose the target battery.

According to various embodiments, the method of operating the battery diagnosis device includes calculating a standard score for a reference value corresponding to the plurality of battery cells and storing some reference values having a standard score that satisfies a specified condition. It can be included.

According to various embodiments, the method of operating the battery diagnosis device may include calculating the standard score based on at least one of a battery cell unit, a battery module unit, or a battery pack unit, with respect to the reference battery.

According to various embodiments, a method of operating the battery diagnosis device includes processing the measurement data to calculate and store reference values corresponding to a plurality of types, and at least one type of the plurality of types is the measurement It corresponds to integral processing for data, and at least one other type among the plurality of types may correspond to differential processing for the measurement data.

According to various embodiments, the method of operating the battery diagnosis device may include diagnosing the target battery with a reference value corresponding to one type selected based on a distribution of reference values corresponding to the plurality of types. there is.

According to various embodiments, a method of operating the battery diagnosis device includes, for each of the plurality of types, a first group of reference values obtained by processing measurement data corresponding to a first extraction range and measurement data corresponding to a second extraction range. may include an operation of calculating a reference value of the processed second group, and the first extraction range and the second extraction range may be different from each other.

According to various embodiments, the method of operating the battery diagnosis device is based on the distribution of the reference values of the first group and the second group, one of the reference value of the first group and the reference value of the second group. can be used to diagnose the target battery.

According to various embodiments, the plurality of reference values include a plurality of first reference values corresponding to normal judgments and a plurality of second reference values corresponding to abnormal judgments, and the operation of diagnosing the target battery includes the first reference values corresponding to abnormal judgments. It may include selecting some of the plurality of reference values based on the distribution of the reference value and the distribution of the second reference value.

According to various embodiments, the type of measurement information obtained from the target battery and the reference battery may include at least one of voltage, current, temperature, internal resistance, or a change amount thereof.

The battery diagnosis apparatus and method according to various embodiments disclosed in this document can improve progress accuracy regarding the state of the battery by determining a meaningful reference value based on the distribution state among the reference values used for battery diagnosis.

The effects that can be obtained from this document are not limited to the effects mentioned above.

1 is a diagram illustrating the configuration of a battery advancement device according to various embodiments.
FIGS. 2A, 2B, 2C, and 3 are diagrams for explaining a battery diagnosis operation according to the first embodiment of the present invention.
Figure 4 is a diagram for explaining a battery diagnosis operation according to a second embodiment of the present invention.
5A and 5B are diagrams for explaining a battery diagnosis operation according to a third embodiment of the present invention.
FIG. 6A is a diagram for explaining a battery diagnosis operation according to a fourth embodiment of the present invention.
Figure 6b is a diagram for explaining a battery diagnosis operation according to the fifth embodiment of the present invention.
7 is a flowchart for explaining a battery diagnosis operation according to various embodiments.

Hereinafter, some embodiments of the present invention will be described in detail through illustrative drawings. In adding reference signs to components in each drawing, it should be noted that the same components are given the same reference numerals as much as possible even if they are shown in different drawings. Additionally, when describing embodiments of the present invention, if detailed descriptions of related known configurations or functions are judged to impede understanding of the embodiments of the present invention, the detailed descriptions will be omitted.

In describing the components of embodiments of the present invention, terms such as first, second, A, B, (a), and (b) may be used. These terms are only used to distinguish the component from other components, and the nature, order, or order of the component is not limited by the term. Additionally, unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by a person of ordinary skill in the technical field to which the present invention pertains. Terms as defined in commonly used dictionaries should be interpreted as having meanings consistent with the meanings they have in the context of the related technology, and unless explicitly defined in this document, should not be interpreted in an idealized or overly formal sense. No.

In addition, the various embodiments of this document and the terms used herein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various changes, equivalents, or replacements of the embodiments. In connection with the description of the drawings, similar reference numbers may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of the items, unless the relevant context clearly indicates otherwise. As used herein, “A or B”, “at least one of A and B”, “at least one of A or B”, “A, B or C”, “at least one of A, B and C”, and “A Each of phrases such as “at least one of , B, or C” may include any one of the items listed together in the corresponding phrase, or any possible combination thereof. Terms such as "first", "second", or "first" or "second" may be used simply to distinguish one element from another, and may be used to distinguish such elements in other respects, such as importance or order) is not limited. A component (e.g., a first component) is “coupled” or “coupled” to another component (e.g., a second component), with or without the terms “functionally” or “communicatively.” When reference is made to “connected,” it means that a component can be connected to another component directly (e.g., wired or wirelessly) or through a third component.

1 is a diagram illustrating the configuration of a battery diagnosis device according to various embodiments. 2A to 3 are diagrams for explaining a battery diagnosis operation according to the first embodiment of the present invention, and FIG. 4 is a diagram for explaining the battery diagnosis operation according to the second embodiment of the present invention. 5A and 5B are diagrams for explaining a battery diagnosis operation according to a third embodiment of the present invention, FIG. 6A is a diagram for explaining a battery diagnosis operation according to a fourth embodiment of the present invention, and FIG. 6B is a diagram for explaining the battery diagnosis operation according to the fourth embodiment of the present invention. This is a diagram for explaining a battery diagnosis operation according to the fifth embodiment of the invention.

Referring to FIG. 1, a battery diagnosis device 100 according to various embodiments may be comprised of an information acquisition module 102, a memory 104, an output module 106, and a processor 108. However, this is only an example, and the various embodiments are not limited thereto. For example, at least one of the components of the battery diagnosis device 100 described above may be omitted, or one or more other components (e.g., an input device, a power management device, etc.) may be added to the configuration of the battery diagnosis device 100. there is. Additionally, at least one of the above-described components may be integrated with other components.

According to various embodiments, the information acquisition module 102 may be configured to acquire measurement data through a battery. Measurement data may be information used to diagnose (or predict) the state of the battery. For example, the information acquisition module 102 may acquire at least one of voltage, current, temperature, internal resistance, or changes thereof for the battery as measurement data. However, this is only an example, and the various embodiments are not limited thereto. For example, various information related to the battery, such as battery charging cycle information, electrode side reaction information, etc., may be obtained as measurement data.

According to one embodiment, a battery may be composed of one or more physically separable cells. In this regard, the information acquisition module 102 may acquire measurement data for at least one battery cell or obtain measurement data for a battery module. Of course, the information acquisition module 102 may also acquire measurement data for the battery pack.

According to one embodiment, the information acquisition module 102 may include at least one sensor (eg, temperature sensor, sense resistor, hall sensor, etc.) configured to acquire measurement data. According to another embodiment, the information acquisition module 102 may include at least one input device (eg, keyboard, mouse, etc.) configured to receive measurement data based on user input. According to another embodiment, the information acquisition module 102 may acquire measurement data from at least one external device (eg, a battery management system, a server device, etc.) connected through wired or wireless communication.

According to various embodiments, the memory 104 may include programs, algorithms, routines, and/or instructions related to the operation (or control) of the battery diagnosis device 100.

For example, the memory 104 may have a flash memory type, a hard disk type, a micro type, and a card type (e.g., a Secure Digital Card (SD Card) or an XD Card ( Memory such as eXtream Digital Card), RAM (Random Access Memory), SRAM (Static RAM), ROM (Read-Only Memory), PROM (Programmable ROM), EEPROM (Electrically Erasable PROM), and magnetic memory It may include at least one type of storage medium among (MRAM, Magnetic RAM), magnetic disk, and optical disk type memory, and various embodiments are not limited thereto. .

According to various embodiments, the output module 106 may provide information related to the operation of the battery diagnosis device 100. For example, at least part of the information output through the output module 106 may include a diagnosis result for the battery.

According to one embodiment, the output module 106 may include a display configured to output visual information. For example, displays include liquid crystal display, thin film transistor-liquid crystal display, organic light-emitting diode, flexible display, and 3D display. display), and may include one or more of transparent displays. Additionally, the display may include a touch sensor configured to detect touch.

However, this is only an example, and the various embodiments are not limited thereto. For example, at least some of the information related to the operation of the battery diagnosis device 100 may be provided as auditory information. In this case, the output module 106 may further include an audio output device (eg, speaker) configured to output an audio signal to the outside of the battery diagnosis device 100.

According to various embodiments, the processor 108 may control at least one other component of the battery diagnostic device 100 (e.g., the information acquisition module 102, the memory 104, and the output module 106). , various data processing or calculations may be performed. According to one embodiment, the processor 108 may diagnose (or predict) the state of the battery.

The processor 108 according to various embodiments related to this will be described in detail below.

According to various embodiments, the processor 108 may generate a reference table (reference table 200 in FIG. 2A) in which a plurality of reference values are defined as part of diagnosing the state of the battery. A plurality of reference values may be used to diagnose a target battery. The target battery is a diagnostic target and may include battery cells, battery modules, and/or battery packs.

For example, the processor 108 may process measurement data obtained from a reference battery (or sample battery) to calculate a reference value and create a reference table based on this. The reference battery may be at least one of batteries having the same electrochemical specifications as the target battery. Additionally, the processor 108 may use measurement data obtained through at least one reference battery cell, reference battery module, or reference battery pack to calculate a reference value.

According to one embodiment, as shown in FIG. 2A, the reference table 200 may be composed of a cell identifier 210, cell state information 220, and a reference value (R). For example, the cell identifier 210 is an identifier of a reference battery cell, the cell state information 220 indicates a normal or abnormal state for the reference battery cell, and the reference value (R) is the reference battery cell. Indicates the reference value calculated based on the measurement data of the battery cell. For example, a reference value (e.g., a reference value corresponding to a normal judgment) (or first reference value) calculated from measurement data obtained from a reference battery (e.g., a battery cell, battery module, or battery pack) in a normal state is the target battery. It is used for diagnosing the normal state of the target battery, and the reference value (e.g., the reference value corresponding to the abnormality judgment) (or the second reference value) calculated from the measurement data obtained from the reference battery in the abnormal state is used to diagnose the abnormal state of the target battery. It can be.

Additionally, as shown in FIG. 2A, a plurality of reference values may be calculated in units of at least one reference battery cell. For example, reference values 232, 242, and 252 corresponding to the first reference battery cell, reference values 234, 244, and 254 corresponding to the second reference battery cell, and reference values corresponding to the nth reference battery cell. (236, 246 and 256) can be calculated.

In addition, as shown in Figure 2a, the reference value (R) can be defined by a plurality of types, and each type corresponds to a processing method of the measured data (e.g., integration method, differentiation method, average calculation method, etc.) It can be. For example, reference values (e.g., integral values of measured data) 232, 234, and 236 corresponding to the first type 230 (e.g., integral method), the second type 240 (e.g., differential method) Reference values corresponding to (e.g., differential values of measured data) (242, 244, and 246) and reference values (e.g., average values of measured data) corresponding to the nth type (e.g., average calculation method) (252, 254) and 256) may be created as the reference table 200.

Additionally, as shown in FIG. 2B, the reference values corresponding to each type may be divided into one or more groups. For example, the reference value corresponding to the first type 230 is the reference value of the first group (230-1), the reference value of the second group (230-2), and the reference value of the nth group (230-3). ) can be divided into: For example, the reference values 230-1, 230-2, and 230-3 of each group may be determined by the extraction range of the measurement data. The extraction range of measurement data can be defined by minimum and maximum values.

For example, the reference value 230-1 of the first group is a reference value calculated with measurement data extracted from the first extraction range (e.g., voltage data that is greater than the first minimum value and does not exceed the first maximum value) (e.g. derivative values) may be (230-11, 230-12 and 230-13). In addition, the reference value 230-2 of the second group is measurement data extracted in a second extraction range different from the first extraction range (e.g., voltage data that is greater than the second minimum value and does not exceed the second maximum value). The calculated reference values (e.g. differential values) 230-21, 230-22 and 230-23, and the third group of reference values 230-3 is a third group different from the first extraction range and the second extraction range. Reference values (e.g. derivative values) calculated from measurement data extracted from the extraction range (e.g. voltage data above the third minimum value and not exceeding the third maximum value) (230-31, 230-32 and 230-33) ) can be.

According to various embodiments, the processor 108 may diagnose the target battery based on at least some of the reference values included in the reference table 200.

According to one embodiment, the processor 108 may generate converted data by processing measurement data obtained from a target battery in a predetermined method (eg, integration method, differentiation method, average calculation method, etc.). For example, the conversion data is converted data processed into a first type (e.g., integral value of voltage data), converted data processed into a second type (e.g., differential value of voltage data), and n-th type. It may include processed conversion data (e.g., average value of voltage data), etc.

According to one embodiment, the processor 108 selects at least one reference value corresponding to a processing method based on conversion data generation (e.g., a reference value corresponding to a normal judgment and /or a reference value corresponding to an abnormality determination) may be obtained, and the target battery may be diagnosed based on a comparison result between the obtained reference value and the converted data. For example, when conversion data for the target battery similar to the reference value corresponding to the normal judgment is generated, the processor 108 may diagnose the target battery as a normal battery. Additionally, when conversion data for the target battery similar to the reference value corresponding to the abnormality determination is generated, the processor 108 may diagnose the target battery as an abnormal battery.

However, as shown in FIG. 2C, the distribution of reference values 260 corresponding to normal judgments and the distribution 270 of reference values corresponding to abnormal judgments overlap to a certain level or more, resulting in low accuracy of battery diagnosis. There is. In other words, a case may occur where a battery in a normal state is judged to be a battery in an abnormal state based on the reference value 280 corresponding to the abnormal judgment distributed in the overlapping portion.

According to various embodiments, in order to improve diagnostic accuracy, the processor 108 may use reference values corresponding to normal judgments and reference values corresponding to abnormal judgments that do not overlap each other for battery diagnosis.

The processor 108 according to various embodiments related to this will be described in detail through the first to fifth embodiments below.

<First embodiment>

A first embodiment according to various embodiments relates to an embodiment in which a meaningful group of reference values is selected from among a plurality of reference values included in the reference table 200 and used for battery diagnosis. For example, one of the first group reference value 230-1, the second group reference value 230-2, and the nth group reference value 230-3 related to the first type 230. You can select a group's reference value and use it for battery diagnosis.

In this regard, the processor 108 may check the distance from the average of the reference values of each group and use the reference value corresponding to the type with a relatively large distance from the average as a meaningful reference value.

According to one embodiment, as shown in FIG. 3, the processor 108 determines the first group of reference values 230-1 associated with the first type 230 (e.g., 230-11, 230-12, and 230). -13) can be classified into a reference value corresponding to a normal judgment (normal) and a reference value corresponding to an abnormal judgment (abnormal), and the distance 311 between the classified reference values can be checked. Additionally, the processor 108 may check the distances 313 and 315 with respect to the second group reference value 230-2 and the second group reference value 230-3.

Thereafter, the processor 108 may use the reference value 230-2 of the group in which the reference values are relatively far apart as a meaningful reference value. The shaded reference value in FIG. 3 means that it has been selected as a meaningful reference value, and the processor 108 can improve the accuracy of battery diagnosis by excluding reference values that are relatively close in distance from the target battery diagnosis.

In this regard, the processor 108 calculates the variance ratio (e.g., F-value) and significance probability (e.g., P-value) using analysis of variance (e.g., 1-way ANOVA) for the classified reference values, and , Based on this, a group of reference values where the distance between the reference values is relatively far can be determined. For example, the processor 108 may generate a heat map based on the results of variance analysis for each classification and check the distance between reference values based on the brightness of the heat map. However, this is only an example, and the various embodiments are not limited thereto. For example, various known statistical analysis techniques may be used to determine the distance between reference values.

<Second Embodiment>

A second embodiment according to various embodiments relates to an embodiment in which reference values corresponding to one type among reference values corresponding to a plurality of types included in the reference table 200 are used as meaningful reference values. For example, a reference value (e.g., integral value of measured data) corresponding to the first type 230 (e.g., integration method), a reference value (e.g., an integral value of measured data) corresponding to the second type 240 (e.g., differentiation method) : Differential value of measured data) and one of the reference values (e.g., average value of measured data) corresponding to the nth type (e.g., average calculation method) (e.g., reference value corresponding to the first type 230) It can be used for battery diagnosis.

In this regard, the processor 108 may check the distance from the average of the reference values corresponding to each type and use the reference value corresponding to the type with a relatively large distance from the average as a meaningful reference value.

According to one embodiment, as shown in FIG. 4, the processor 108 sets the reference values corresponding to the first type 230 (e.g., 232, 234, and 236 in FIG. 2A) as reference values corresponding to the normal judgment. It is possible to classify (abnormal) into standard values corresponding to (normal) and abnormal judgments, and check the distance (411) between each classified standard value. Additionally, the processor 108 may use reference values corresponding to the second type 240 (e.g., 242, 244, and 246 in FIG. 2A) and reference values corresponding to the third type 250 (e.g., 252, 254, and 256). ), you can also check the distances (413) and (415).

Thereafter, the processor 108 may use relatively distant reference values, for example, reference values 232, 234, and 236 corresponding to the first type 230, as meaningful reference values. In FIG. 4 , the shaded reference value means that it has been selected as a meaningful reference value, and the processor 108 can improve the accuracy of battery diagnosis by excluding reference values that are relatively close in distance from the target battery diagnosis.

In this regard, the processor 108 calculates a standard score (e.g., z-score) of reference values corresponding to each type, and distributes the reference values corresponding to normal judgments and corresponding to abnormal judgments based on the calculated scores. You can select a reference value that clearly distinguishes the distribution of reference values.

<Third Embodiment>

A third embodiment according to various embodiments relates to an embodiment in which some of the reference values corresponding to each type are used as meaningful reference values. For example, a reference value (e.g., integral value of measurement data) corresponding to the first type 230 (e.g., integration method) associated with the first battery module, the first type 230 (e.g., integration method) associated with the second battery module One of the reference values corresponding to the first type 230 (e.g., integration method) and the reference value corresponding to the nth battery module (e.g., integration method) can be used for battery diagnosis.

In this regard, the processor 108 may check the distance from the average of the reference values corresponding to each battery module and use the reference value corresponding to the type with a relatively large distance from the average as a meaningful reference value. For example, each battery module may consist of a specified number of battery cells (e.g., 10 battery cells).

According to one embodiment, as shown in FIGS. 5A and 5B, the processor 108 selects a reference value corresponding to the first type 230 (e.g., the first group 230-1 to the nth group 230). Among the reference values of -3), the reference value 501 corresponding to the first battery module 510 is classified (abnormal) into a reference value corresponding to a normal judgment (normal) and a reference value corresponding to an abnormal judgment. You can check the distance 511 between each reference value. In addition, the processor 108 selects a value corresponding to the second battery module among the reference values corresponding to the first type 230 (e.g., reference values of the first group 230-1 to the nth group 230-3). Distances 513 and 515 can also be checked with respect to the reference value 503 and the reference value 505 corresponding to the third battery module. Of course, the processor 108 may also check the distance to the reference value corresponding to the second type and the nth type.

Thereafter, the processor 108 may use relatively distant reference values, for example, the reference value 505 corresponding to the third battery module 530, as a meaningful reference value. The shaded reference value in FIG. 5B means that it has been selected as a meaningful reference value, and the processor 108 can improve the accuracy of battery diagnosis by excluding reference values that are relatively close in distance from the target battery diagnosis.

In this regard, the processor 108 calculates a standard score (e.g., z-score) of reference values corresponding to each battery module, and determines the distribution of reference values corresponding to normal judgments and abnormal judgments based on the calculated scores. You can select a reference value whose distribution of corresponding reference values is clearly distinguished.

<Fourth Embodiment>

A fourth embodiment according to various embodiments relates to an embodiment in which only some of the reference values are generated as the reference table 200 so that the distribution of reference values corresponding to normal judgments and the distribution of reference values corresponding to abnormal judgments do not overlap. do. For example, among a plurality of reference values associated with each battery cell, the reference values other than those having standard scores (e.g., z-score) of the maximum and/or minimum values may be excluded from the reference table 200. there is.

In this regard, the processor 108 calculates a standard score (e.g., z-score) for a plurality of reference values corresponding to each battery cell, and refers to a reference value that satisfies a specified condition (e.g., the maximum value and/or the minimum value). The remaining reference values, excluding the reference value having a standard score of the value, may be excluded from the reference table 200.

According to one embodiment, as shown in FIG. 6A, the processor 108 calculates a plurality of reference values 610 in relation to the first battery cell 212, and calculates a standard score for each calculated reference value. can be calculated. For example, a plurality of reference values may correspond to each operation cycle. For example, the processor 108 may use reference values (e.g., 611-1, 613-1, and 615) corresponding to the first operation cycle 611 to the n-th operation cycle 615 in relation to the first battery cell 212. The standard score for -1) can be calculated. Additionally, the processor 108 calculates reference values 621-1 to 625-1 corresponding to a plurality of operation cycles 621 to 625 in relation to the second battery cell 214 and the third battery cell 216. ), a reference value 635-1 corresponding to a plurality of operation cycles 635 can be calculated.

Thereafter, the processor 108 generates a plurality of reference values 610 associated with the first battery cell 212, a plurality of reference values 620 associated with the second battery cell, and a plurality of reference values 630 associated with the third battery cell. ), the remaining reference values excluding those that satisfy specified conditions (e.g., reference values having standard scores of the maximum and/or minimum values) may be excluded from the reference table 200.

The shaded reference values in FIG. 6A are selected as meaningful reference values, and the reference values that are not excluded in the reference table 200 clearly show the distribution of reference values corresponding to normal judgments and the distribution of reference values corresponding to abnormal judgments. If they are separated and used for battery diagnosis, you can expect diagnostic results that guarantee a certain level of accuracy.

<Embodiment 5>

In the fifth embodiment according to various embodiments, only some of the reference values corresponding to the battery module among the reference values are included in the reference table 200 so that the distribution of reference values corresponding to the normal judgment and the distribution of the reference values corresponding to the abnormal judgment do not overlap. It is related to an embodiment of generating.

In this regard, the processor 108 calculates a standard score (e.g., z-score) for a plurality of reference values corresponding to each battery cell, and a reference value (e.g., maximum value) that satisfies the conditions specified on a battery module basis. and/or reference values having the minimum standard score) may be used as the reference table 200 and the remaining reference values may be excluded from the reference table 200.

According to one embodiment, as shown in FIG. 6B, the processor 108 may calculate a reference value corresponding to the battery cell 212 and calculate a standard score for each calculated reference value.

Thereafter, the processor 108 selects a reference value ( For example, reference values having a standard score of the maximum and/or minimum value) may be excluded from the reference table 200.

The reference values shaded in Figure 6b are selected as meaningful reference values. Compared to Figure 6a, the distribution of reference values corresponding to normal judgments and the distribution of reference values corresponding to abnormal judgments are more clearly distinguished, thereby improving the diagnosis. Diagnosis results with improved accuracy can be expected.

Hereinafter, a method of operating the battery diagnosis device 100 according to various embodiments will be described in detail with reference to FIG. 7.

7 is a flowchart for explaining a battery diagnosis operation according to various embodiments. Each operation in the following embodiments may be performed sequentially, but is not necessarily performed sequentially. For example, the order of each operation may be changed, and at least two operations may be performed in parallel. Additionally, at least one of the following operations may be omitted depending on the embodiment.

Referring to FIG. 7 , the battery diagnosis device 100 (eg, processor 108) according to various embodiments may acquire measurement data from a reference battery in operation 710. According to one embodiment, the battery diagnosis device 100 may obtain at least one of voltage, current, temperature, internal resistance, or change amount thereof for a reference battery as measurement data.

According to various embodiments, the battery diagnosis device 100 (eg, processor 108) may calculate reference values for a plurality of types based on measurement data in operation 720. The reference value may be a reference value used to diagnose a target battery. According to one embodiment, the battery diagnosis apparatus 100 may calculate at least one of an integral value of the measurement data, a differential value of the measurement data, or an average value of the measurement data as a reference value.

According to various embodiments, the battery diagnosis apparatus 100 (e.g., processor 108) may extract a reference value with a large distribution difference based on the distribution of the calculated reference values in operation 730. According to one embodiment, the battery diagnosis apparatus 100 may extract reference values corresponding to normal judgments and reference values corresponding to abnormal judgments that do not overlap with each other. In this regard, the battery diagnosis device 100 may extract a reference value based on at least one of the first to fifth embodiments described above.

According to various embodiments, the battery diagnosis device 100 (eg, processor 108) may diagnose a target battery based on the extracted reference value in operation 740. According to one embodiment, the battery diagnosis device 100 may generate converted data by processing measurement data obtained from a target battery in a predetermined method (eg, integration method, differentiation method, average calculation method, etc.). Thereafter, the battery diagnosis apparatus 100 may diagnose the target battery based on a comparison result between the conversion data and the extracted reference value.

As described above, various embodiments can improve the accuracy of battery diagnosis by using, among a plurality of reference values, reference values corresponding to normal judgments and reference values corresponding to abnormal judgments that do not overlap with each other for battery diagnosis.

In this regard, the above-described first to fifth embodiments may each be implemented as independent embodiments, but depending on the embodiment, at least one embodiment of the first to fifth embodiments may be implemented as an independent embodiment. It may also be implemented by merging with the embodiment. For example, according to the second embodiment, some reference values may be selected firstly, and some of the reference values first selected according to the first embodiment may be selected secondarily.

The above description is merely an illustrative explanation of the technical idea of the present invention, and various modifications and variations will be possible to those skilled in the art without departing from the essential characteristics of the present invention.

Accordingly, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but rather to explain it, and the scope of the technical idea of the present invention is not limited by these embodiments. The scope of protection of the present invention should be interpreted in accordance with the claims below, and all technical ideas within the equivalent scope should be construed as being included in the scope of rights of the present invention.

Claims (18)

In the battery diagnostic device,
an information acquisition module configured to acquire measurement data from a target battery;
a memory that stores a reference table in which a plurality of reference values used for battery diagnosis are defined; and
A processor configured to diagnose the target battery based on measurement information obtained from the target battery and at least one pre-stored reference value,
The processor,
Obtain measurement data from a reference battery,
Processing the measurement data to calculate reference values corresponding to a plurality of battery cells,
A battery diagnosis device configured to use a reference value corresponding to one battery cell selected based on a distribution of reference values corresponding to the plurality of battery cells to diagnose the target battery.
According to claim 1,
The processor,
Calculate a standard score for a reference value corresponding to the plurality of battery cells,
A battery diagnostic device configured to define, with the reference table, some reference values having standard scores that satisfy specified conditions.
According to clause 2,
The processor,
A battery diagnosis device configured to calculate the standard score based on at least one of a battery cell unit, a battery module unit, and a battery pack unit, with respect to the reference battery.
According to claim 1,
The processor,
It is configured to process the measurement data to calculate reference values corresponding to a plurality of types and define them in the reference table,
At least one type among the plurality of types corresponds to integration processing for the measurement data,
A battery diagnosis device wherein at least one other type among the plurality of types corresponds to differential processing for the measurement data.
According to clause 4,
The processor,
A battery diagnosis device configured to use a reference value corresponding to one type selected based on a distribution of reference values corresponding to the plurality of types to diagnose the target battery.
According to claim 1,
The processor,
For each of the plurality of types, it is configured to calculate a first group reference value obtained by processing measurement data corresponding to a first extraction range and a second group reference value obtained by processing measurement data corresponding to a second extraction range. ,
The first extraction range and the second extraction range are different from each other.
According to clause 6,
The processor,
A battery diagnosis device configured to use one of the reference values of the first group and the reference value of the second group to diagnose the target battery, based on the distribution of the reference values of the first group and the second group.
According to claim 1,
The plurality of reference values include a plurality of first reference values corresponding to normal judgments and a plurality of second reference values corresponding to abnormal judgments,
The processor,
A battery diagnosis device configured to select some of the plurality of reference values based on a distribution for the first reference value and a distribution for the second reference value.
According to claim 1,
The type of measurement information obtained from the target battery and the reference battery includes at least one of voltage, current, temperature, internal resistance, or change amount thereof.
In a method of operating a battery diagnostic device,
Obtaining measurement data from a target battery; and
Diagnosing the target battery based on at least some of a plurality of pre-stored reference values and the measurement data,
The operation of diagnosing the target battery is,
Obtaining measurement data from a reference battery;
Processing the measurement data to calculate reference values corresponding to a plurality of battery cells; and
A method comprising using a reference value corresponding to one battery cell selected based on a distribution of reference values corresponding to the plurality of battery cells to diagnose the target battery.
According to claim 10,
An operation of calculating a standard score for a reference value corresponding to the plurality of battery cells; and
A method that includes the act of storing some criterion value whose standard score satisfies a specified condition.
According to claim 11,
A method comprising calculating the standard score based on at least one of a battery cell unit, a battery module unit, and a battery pack unit, with respect to the reference battery.
According to claim 10,
Processing the measurement data to calculate and store reference values corresponding to a plurality of types,
At least one type among the plurality of types corresponds to integration processing for the measurement data,
A method wherein at least one other type among the plurality of types corresponds to differential processing for the measurement data.
According to claim 13,
A method comprising diagnosing the target battery using a reference value corresponding to one type selected based on a distribution of reference values corresponding to the plurality of types.
According to claim 10,
For each of the plurality of types, an operation of calculating a reference value of a first group obtained by processing measurement data corresponding to a first extraction range and a reference value of a second group obtained by processing measurement data corresponding to a second extraction range. Includes,
The first extraction range and the second extraction range are different from each other.
According to claim 15,
A method of using one of the reference values of the first group and the second group to diagnose the target battery, based on the distribution of the reference values of the first group and the second group.
According to claim 10,
The plurality of reference values include a plurality of first reference values corresponding to normal judgments and a plurality of second reference values corresponding to abnormal judgments,
The operation of diagnosing the target battery is,
A method comprising selecting some of the plurality of reference values based on a distribution for the first reference value and a distribution for the second reference value.
According to claim 10,
The type of measurement information obtained from the target battery and the reference battery includes at least one of voltage, current, temperature, internal resistance, or change amount thereof.

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