Battery defect diagnosis apparatus and method

Abstract

A battery defect diagnosis apparatus according to an embodiment of the present invention can calculate a reaction amount of respective batteries stored for a predefined period at a reference temperature, generate a plurality of groups by grouping the batteries on the basis of the reaction amount of the respective batteries, obtain a representative value for each battery group on the basis of a voltage change of each of the batteries within the battery group, and determine, on the basis of the representative value, whether the battery group is defective.

Description

Battery failure diagnosis device and method

[0001] This application claims the benefit of the filing date of Korean Patent Application No. 10-2024-0180044 filed with the Korean Intellectual Property Office on December 6, 2024, and all contents disclosed in the document of said Korean patent application are incorporated into this specification.

[0002] The present invention relates to a battery defect diagnosis device and method, and more specifically, to a battery defect diagnosis device and method that groups batteries based on a reaction amount corresponding to a temperature measurement value of the batteries measured during high-temperature storage, and diagnoses whether the batteries are defective for each battery group.

[0003] As the price of energy sources rises due to the depletion of fossil fuels and concerns about environmental pollution intensify, the demand for secondary batteries as an eco-friendly alternative energy source is rapidly increasing.

[0004] Due to their ability to be repeatedly charged and regenerated, these secondary batteries are being applied in a wide range of sectors—from small devices such as mobile phones and laptops to large-scale industries like automobiles, robots, and energy storage devices—as a response to today's environmental regulations and high oil prices.

[0005] Meanwhile, among secondary batteries, those used in various fields undergo pre-shipment performance testing at the final stage of the manufacturing process to ensure the production of high-quality batteries.

[0006] Accordingly, nowadays, to test the performance of batteries before shipment, the defect status of the battery is determined by storing it at room temperature for a certain period and checking the level of voltage drop over that period.

[0007] The objective of the present invention to solve the above-mentioned problems is to provide a battery defect diagnosis device.

[0008] Another objective of the present invention to solve the above-mentioned problems is to provide a battery defect diagnosis method using a battery defect diagnosis device.

[0009] A battery defect diagnosis device according to an embodiment of the present invention for achieving the above objective comprises at least one processor and a memory storing at least one command performed by said processor, wherein the at least one command includes a command to calculate the reaction amount of each of a plurality of batteries stored for a predetermined period at a reference temperature, a command to group the batteries based on the reaction amount of each of said batteries to create said plurality of groups, and a command to obtain a representative value for each of said battery group based on the voltage change of each of said batteries within said battery group, and to determine whether there is a defect for each of said battery group based on said representative value.

[0010] Here, the command to calculate the reaction amount may include a command to calculate the reaction amount of each of the batteries during the predetermined period.

[0011] Additionally, the command to calculate the reaction amount of each of the batteries during the above-defined period may include a command to calculate the reaction amount of each of the batteries during the above-defined period based on reaction coefficients corresponding to the temperature measurements of each of the batteries.

[0012] Here, a command to calculate the reaction amount of each of the batteries during a predetermined period based on reaction coefficients corresponding to the temperature measurements of each of the batteries may include a command to individually measure the temperature of the batteries at each predetermined period within the predetermined period, a command to individually obtain reaction coefficients corresponding to the temperature measurements of each of the batteries individually measured at each period, a command to calculate the reaction amount of the batteries per period based on the reaction coefficients, and a command to calculate the reaction amount of each of the batteries during the predetermined period based on the reaction amount of each of the batteries per period.

[0013] Meanwhile, according to one embodiment, a command to group the batteries to create the plurality of groups may include a command to group the batteries based on the size of the reaction amount of each of the batteries to create the plurality of groups.

[0014] Additionally, according to another embodiment, the command to group the batteries to create the plurality of groups may include the command to group the batteries to create the plurality of groups based on the probability change of the reaction amount of each of the batteries.

[0015] Meanwhile, a command to determine whether there is a defect for each battery group based on the above representative value may include a command to calculate the voltage drop amounts of each battery within the battery group, a command to calculate the above representative value for each battery group based on the voltage drop amounts, and a command to determine whether there is a defect for each battery group by comparing the above representative value with a predefined threshold value.

[0016] Here, the representative value may include at least one of the average value, mode value, maximum value, and median value of the voltage drop amount for each battery group.

[0017]

[0018] A battery defect diagnosis method according to another embodiment of the present invention for achieving the above objective comprises: a step of calculating the reaction amount of each of a plurality of batteries stored for a predetermined period at a reference temperature; a step of grouping the batteries based on the reaction amount of each of the batteries to generate the plurality of groups; and a step of obtaining a representative value for each battery group based on the voltage change of each of the batteries within the battery group, and determining whether each battery group is defective based on the representative value.

[0019] Here, the step of calculating the reaction amount may include the step of calculating the reaction amount of each of the batteries during the predetermined period.

[0020] Additionally, the step of calculating the reaction amount of each of the batteries during the above-defined period may include the step of calculating the reaction amount of each of the batteries during the above-defined period based on reaction coefficients corresponding to the temperature measurements of each of the batteries.

[0021] Here, the step of calculating the reaction amount of each of the batteries during the predetermined period based on reaction coefficients corresponding to the temperature measurements of each of the batteries may include: the step of individually measuring the temperature of the batteries for each predetermined period within the predetermined period; the step of individually obtaining reaction coefficients corresponding to the temperature measurements of each of the batteries individually measured for each period; the step of calculating the reaction amount of each of the batteries per period based on the reaction coefficients; and the step of calculating the reaction amount of each of the batteries during the predetermined period based on the reaction amount of each of the batteries per period.

[0022] Meanwhile, according to one embodiment, the step of grouping the batteries to create the plurality of groups may include the step of grouping the batteries based on the size of the reaction amount of each of the batteries to create the plurality of groups.

[0023] Additionally, according to another embodiment, the step of grouping the batteries to generate the plurality of groups may include the step of grouping the batteries based on the probability change of the reaction amount of each of the batteries to generate the plurality of groups.

[0024] Meanwhile, the step of determining whether there is a defect for each battery group based on the above representative value may include the step of calculating the voltage drop amounts of each battery within the battery group, the step of calculating the above representative value for each battery group based on the voltage drop amounts, and the step of determining whether there is a defect for each battery group by comparing the above representative value with a predefined threshold value.

[0025] Here, the representative value may include at least one of the average value, mode value, maximum value, and median value of the voltage drop amount for each battery group.

[0026] The battery defect diagnosis device and method according to an embodiment of the present invention can improve reliability when determining low voltage defects for each battery group by calculating the reaction amount of each of a plurality of batteries and grouping the batteries based on this.

[0027] FIG. 1 is a block diagram of a battery failure diagnosis device according to an embodiment of the present invention.

[0028] FIG. 2 is a flowchart for explaining a battery failure diagnosis method operated by a processor in a battery failure diagnosis device according to an embodiment of the present invention.

[0029] FIG. 3 is a flowchart illustrating the step of generating a reaction amount for each of the batteries in a battery defect diagnosis method according to an embodiment of the present invention.

[0030] FIG. 4 is a flowchart illustrating the step of generating a plurality of battery groups based on the probability change of the reaction amount in a battery defect diagnosis method according to an embodiment of the present invention.

[0031] FIG. 5 is a flowchart illustrating the step of determining whether there is a defect for each battery group among the battery defect diagnosis method according to an embodiment of the present invention.

[0032] FIG. 6 is a probability distribution graph for explaining the step of generating a plurality of battery groups based on the probability change of the reaction amount according to an experimental example of the present invention.

[0033] FIGS. 7 to 10 are images showing group information according to the voltage drop amount of each battery and the reaction amount of FIG. 6 for each of the first to fourth trays, according to an experimental example of the present invention.

[0034] Figure 11 is a performance comparison table of voltage drop amounts by battery group according to the comparative example compared to the experimental example of the present invention.

[0035] FIGS. 12 to 15 are graphs of the first performance comparison results according to the experimental examples of the present invention.

[0036] FIGS. 16 to 19 are graphs of the first performance comparison results according to the comparative example of the present invention.

[0037] FIGS. 20 to 23 are graphs of the second performance comparison results according to the experimental examples of the present invention.

[0038] FIGS. 24 to 27 are graphs of the second performance comparison results according to the comparative example of the present invention.

[0039] 100: Battery failure diagnostic device 110: Memory

[0040] 120: Processor 130: Transmitter / Receiver

[0041] 140: Input interface device 150: Output interface device

[0042] 160: Storage device 170: Bus

[0043] The present invention is susceptible to various modifications and may have various embodiments; specific embodiments are illustrated in the drawings and described in detail in the detailed description. However, this is not intended to limit the invention to specific embodiments, and it should be understood that the invention includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the invention. Similar reference numerals have been used for similar components in the description of each drawing.

[0044] Terms such as first, second, A, B, etc., may be used to describe various components, but said components shall not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component. The term "and / or" includes a combination of a plurality of related described items or any of a plurality of related described items.

[0045] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. On the other hand, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

[0046] The terms used in this application are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" are intended to specify the presence of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification, and should be understood as not precluding the existence or addition of one or more other features, numbers, steps, actions, components, parts, or combinations thereof.

[0047] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the present invention pertains. Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0048]

[0049] FIG. 1 is a block diagram of a battery failure diagnosis device according to an embodiment of the present invention.

[0050] Referring to FIG. 1, the battery defect diagnosis device (100) according to an embodiment of the present invention is a device for inspecting whether there is a defect before shipping the battery, and can pre-diagnose whether a low voltage defect occurs in the battery.

[0051] More specifically, the battery defect diagnosis device (100) can calculate the reaction amount of each of the multiple batteries stored at a reference temperature for a predetermined period and, based on this, create multiple battery groups. Subsequently, the battery defect diagnosis device (100) measures the voltage change of each of the batteries included in each battery group and, based on this, obtains a representative value for each battery group. Accordingly, the battery defect diagnosis device (100) can determine whether each battery group is defective based on the obtained representative value.

[0052] To describe the battery fault diagnosis device (100) in more detail according to its configuration, the battery fault diagnosis device (100) may include a memory (110), a processor (120), a transmission / reception device (130), an input interface device (140), an output interface device (150), and a storage device (160).

[0053] According to the embodiment, each component (110, 120, 130, 140, 150, 160) included in the battery failure diagnosis device (100) can communicate with each other by being connected by a bus (170).

[0054] Among the above configurations (110, 120, 130, 140, 150, 160), the memory (110) and the storage device (160) may be configured with at least one of a volatile / transitory storage medium and a non-volatile / non-transitory storage medium. For example, the memory (110) and the storage device (160) may be configured with at least one of a read-only memory (ROM) and a random access memory (RAM), and may include an EEPROM (Electrically Erasable Programmable Read-only Memory).

[0055] Among these, the memory (110) may include at least one instruction executed by the processor (120). According to an embodiment, the at least one instruction includes: a instruction to calculate the reaction amount of each of a plurality of batteries stored for a predetermined period at a reference temperature; a instruction to group the batteries based on the reaction amount of each of the batteries to create the plurality of groups; and a instruction to obtain a representative value for each battery group based on the voltage change of each of the batteries within the battery group, and to determine whether each battery group is defective based on the representative value.

[0056] Here, the command to calculate the reaction amount may include a command to calculate the reaction amount of each of the batteries during the predetermined period.

[0057] Additionally, the command to calculate the reaction amount of each of the batteries during the above-defined period may include a command to calculate the reaction amount of each of the batteries during the above-defined period based on reaction coefficients corresponding to the temperature measurements of each of the batteries.

[0058] Here, a command to calculate the reaction amount of each of the batteries during a predetermined period based on reaction coefficients corresponding to the temperature measurements of each of the batteries may include a command to individually measure the temperature of the batteries at each predetermined period within the predetermined period, a command to individually obtain reaction coefficients corresponding to the temperature measurements of each of the batteries individually measured at each period, a command to calculate the reaction amount of the batteries per period based on the reaction coefficients, and a command to calculate the reaction amount of each of the batteries during the predetermined period based on the reaction amount of each of the batteries per period.

[0059] Meanwhile, according to one embodiment, a command to group the batteries to create the plurality of groups may include a command to group the batteries based on the size of the reaction amount of each of the batteries to create the plurality of groups.

[0060] Additionally, according to another embodiment, the command to group the batteries to create the plurality of groups may include the command to group the batteries to create the plurality of groups based on the probability change of the reaction amount of each of the batteries.

[0061] Meanwhile, a command to determine whether there is a defect for each battery group based on the above representative value may include a command to calculate the voltage drop amounts of each battery within the battery group, a command to calculate the above representative value for each battery group based on the voltage drop amounts, and a command to determine whether there is a defect for each battery group by comparing the above representative value with a predefined threshold value.

[0062] Here, the representative value may include at least one of the average value, mode value, maximum value, and median value of the voltage drop amount for each battery group.

[0063] Meanwhile, the processor (120) may mean a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which methods according to embodiments of the present invention are performed.

[0064] As previously described, the processor (120) can execute at least one program command stored in memory (110).

[0065] Hereinafter, a battery defect diagnosis method operated by a processor in a battery defect diagnosis device according to an embodiment of the present invention will be described.

[0066]

[0067] FIG. 2 is a flowchart for explaining a battery failure diagnosis method operated by a processor in a battery failure diagnosis device according to an embodiment of the present invention.

[0068] Referring to FIG. 2, a processor (120) in a battery defect diagnosis device (100) according to an embodiment of the present invention can calculate a reaction amount for each of a plurality of batteries stored for a predetermined period at a reference temperature (S210). Here, the reference temperature may be 45℃, and the predetermined period may be, for example, 1.5 days, the time when the high-temperature aging process within the activation process is completed.

[0069] To explain more specifically according to an embodiment, the processor (120) can calculate the reaction amount of each of the batteries during the predetermined period based on reaction coefficients corresponding to the temperature measurements of each of the batteries.

[0070] Subsequently, the processor (120) can group the batteries based on the reaction amount of each of the batteries. In other words, the processor (120) can create multiple battery groups based on the reaction amount of each of the batteries (S220).

[0071] Here, the reaction amount can be defined as the magnitude of the chemical reaction occurring between the positive and negative electrodes within the battery due to heat. More specifically, the reaction amount is the integral of the temperature of each battery over time, which can represent the thermal energy of each battery during the high-temperature aging process. In other words, a high reaction amount during the high-temperature aging process implies that the chemical reaction of the battery at high temperatures is significant.

[0072] According to one embodiment, the processor (120) can group the batteries based on the size of the reaction amounts of each of the batteries to create the plurality of groups. For example, the processor (120) can sort the reaction amounts of each of the batteries in order of size and group the top 0 to 10% range into a first battery group, the top 10 to 40% range into a second battery group, the top 40 to 70% range into a third battery group, and the top 70% or lower range into a fourth battery group.

[0073] According to another embodiment, the processor (120) can group the batteries based on the probability change of the reaction amount of each of the batteries to generate the plurality of groups.

[0074] Subsequently, the processor (120) can obtain a representative value for each battery group based on the voltage change of each battery in the battery group (S230). Subsequently, the processor (120) can determine whether there is a defect for each battery group based on the calculated representative value (S230). Here, the voltage change may be a voltage drop amount, and the voltage at this time may be an open circuit voltage (OCV).

[0075]

[0076] FIG. 3 is a flowchart illustrating the step of generating a reaction amount for each of the batteries in a battery defect diagnosis method according to an embodiment of the present invention.

[0077] Referring to FIG. 3, the processor (120) can calculate the reaction amount of each of the plurality of batteries as in step S210.

[0078] According to an embodiment, the processor (120) can individually measure the temperature of a plurality of batteries at each defined cycle within a defined period (S310). More specifically, the processor (120) can measure the surface temperature of each of the plurality of batteries. Here, the defined cycle may be 1 minute (min). In other words, the processor (120) can obtain the temperature measurement value of each of the batteries at a 1-minute interval during the period in which high-temperature aging is performed within the activation process.

[0079] Afterwards, the processor (120) can individually obtain corresponding reaction coefficients based on the temperature measurements of each of the batteries, which are individually measured for each defined period (S320).

[0080] According to the embodiment, the reaction coefficient may have different values ​​as a predetermined value according to the temperature range, as shown in [Table 1] below. Additionally, for example, information on the reaction coefficient according to the temperature range may be pre-stored in memory (110) or storage device (160).

[0081] According to one embodiment, the processor (120) can obtain a reaction coefficient of 1.6 for a specific battery based on information regarding a reaction coefficient according to a pre-stored temperature range when the surface temperature of the specific battery is 47°C.

[0082]

[0083] Temperature Range Reaction Coefficient 20~40℃ 140~45℃ 1.245~50℃ 1.650~55℃ 1.955~60℃ 2.460~65℃ 365~70℃ 3.770~75℃ 4.775~80℃ 5.8

[0084] The processor (120) can calculate the reaction amount per cycle of each of the batteries based on the reaction coefficients (S330). Here, the reaction amount per cycle may be the reaction amount at the time of measurement of each of the batteries measured at each predetermined cycle.

[0085] To explain more specifically according to the embodiment, the processor (120) can calculate the reaction amount (TR) of each battery per cycle by multiplying the temperature measurement value (T) of each battery measured at each predetermined cycle according to the following [Equation 1] and the corresponding reaction coefficient (R).

[0086]

[0087]

[0088]

[0089] Here, T R ≡ is the reaction amount per cycle of each battery, T is the temperature measurement of each battery, and R may be the reaction coefficient of each battery.

[0090] Afterwards, the processor (120) can calculate the reaction amount of each of the batteries for a predetermined period based on the reaction amount of each of the batteries per cycle (S340).

[0091] To explain more specifically according to the embodiment, the processor (120) can calculate the reaction amount by adding a plurality of cycle-by-cycle reaction amounts obtained during a predetermined period for each of the batteries. For example, the processor (120) can calculate the reaction amount by adding 2,160 cycle-by-cycle reaction amount information obtained at 1-minute intervals during a predetermined period of 1.5 days.

[0092] According to one embodiment, the processor (120) has a cycle-by-cycle response amount (T) of each of the batteries, which is temporarily stored in memory (110) or storage device (160) for a predetermined period. R The reaction amount for a predetermined period can be calculated by adding the ) at once. Subsequently, the processor (120) calculates the periodic reaction amount (T) of each of the batteries temporarily stored in the memory (110) or storage device (160) at the predetermined period. R ) can be deleted.

[0093] According to another embodiment, the processor (120) has a periodic response amount (T) of each of the batteries that is periodically calculated over a predetermined period. R The reaction amount for a predefined period can be calculated by accumulating and summing ) without separate storage.

[0094]

[0095] FIG. 4 is a flowchart illustrating the step of generating a plurality of battery groups based on the probability change of the reaction amount in a battery defect diagnosis method according to an embodiment of the present invention.

[0096] Referring to FIG. 4, the processor (120) can calculate probability values ​​of reaction amounts by comparing the reaction amounts of each of the plurality of batteries (S410). Here, the reaction amount is a value that predicts the degree of reaction of each of the batteries according to heat, and the probability values ​​of the reaction amounts are calculated to relatively compare the degree of reaction between the batteries, thereby obtaining a probability pattern (S420).

[0097] Subsequently, the processor (120) can group the batteries based on the point where an inflection occurs, based on the probability pattern of the batteries. In other words, the processor (120) can group the batteries based on the inflection point on the probability pattern obtained from the reaction amounts of the batteries to create a plurality of battery groups (S430).

[0098] A defect diagnosis method according to an embodiment of the present invention can group batteries having common reaction characteristics by grouping batteries with similar reaction amounts based on inflection points.

[0099]

[0100] FIG. 5 is a flowchart illustrating the step of determining whether there is a defect for each battery group among the battery defect diagnosis method according to an embodiment of the present invention.

[0101] Referring to FIG. 5, the processor (120) can calculate the voltage drop amounts of each of the batteries in a plurality of battery groups (S510).

[0102] Afterwards, the processor (120) can calculate a representative value of the voltage drop for each battery group based on the voltage drop amounts of each of the batteries (S520).

[0103] According to one embodiment, the processor (120) can calculate an average value based on the individual voltage drop amounts of a plurality of batteries included in each of the battery groups. Subsequently, the processor (120) can set the average value as a representative value of the battery group.

[0104] According to another embodiment, the processor (120) can calculate a mode value based on the individual voltage drop amounts of a plurality of batteries included in each of the battery groups. Subsequently, the processor (120) can set the median value as the representative value of the battery group.

[0105] However, not limited to what is disclosed, the processor (120) may calculate at least one of the maximum value, minimum value, and median value of the voltage drop of each of the plurality of batteries in the battery group and set it as the representative value.

[0106] Afterwards, the processor (120) can determine whether there is a defect for each battery group by comparing the representative value with a predefined threshold value (S530).

[0107] To explain in more detail according to the embodiment, the processor (120) can compare a representative value for each battery group with a predefined threshold value. Subsequently, if the representative value is greater than or equal to the threshold value, the processor (120) can determine that a low voltage defect has occurred in a plurality of batteries within the battery group.

[0108] Below, a battery defect diagnosis method according to an experimental example compared to a comparative example of the present invention will be described.

[0109]

[0110] Comparison of battery defect diagnosis performance according to experimental example compared to comparative example of the present invention

[0111] To compare the defect diagnosis method according to the experimental example with the comparative example of the present invention, a total of 576 batteries were prepared in 4 trays aged at a high temperature (45℃) for 1.5 days.

[0112] Subsequently, according to the defect diagnosis method according to the experimental example of the present invention, the reaction amount based on the temperature measurement value of each of the 576 batteries was calculated, and based on this, the batteries were grouped to individually calculate the first representative value of the first to fourth battery groups.

[0113] In addition, according to the defect diagnosis method according to the comparative example of the present invention, the second representative value of the first to fourth trays, each accommodating 144 batteries, was individually calculated.

[0114]

[0115] FIG. 6 is a probability distribution graph for explaining the step of generating a plurality of battery groups based on the probability change of the reaction amount according to an experimental example of the present invention.

[0116] Referring to FIG. 6, for a total of 576 batteries contained in the first to fourth trays, the reaction amount of each was calculated and a probability pattern graph was obtained.

[0117] According to the example, the reaction amounts of a total of 576 batteries were individually displayed on a probability distribution graph in which the x-axis is defined as the magnitude of the reaction amount of the battery that underwent high-temperature aging and the y-axis is defined as the cumulative percentage of a normal distribution, and the probability pattern graph thereof was obtained.

[0118] Subsequently, based on the reaction amounts at P1 to P3 where inflection points appeared on the probability pattern graph, the 576 batteries were each grouped to belong to one of the first to fourth battery groups.

[0119] As a result, the first battery group contained 99 batteries, the second battery group contained 187 batteries, the third battery group contained 154 batteries, and the fourth battery group contained 136 batteries.

[0120]

[0121] FIGS. 7 to 10 are images showing group information according to the voltage drop amount of each battery and the reaction amount of FIG. 6, according to an experimental example of the present invention, displayed by the first to fourth trays (Tray #1 - Tray #4).

[0122] Referring to FIGS. 7 to 10, it can be seen that different groups of batteries are located in the same tray. In other words, even if they are located in the same tray, it can be seen that differences in the amount of reaction according to the temperature measurements between the batteries may occur differently.

[0123]

[0124] Figure 11 is a performance comparison table of voltage drop amounts by battery group according to the comparative example compared to the experimental example of the present invention.

[0125] Referring to FIG. 11, in order to compare the performance of battery defect diagnosis methods according to experimental and comparative examples of the present invention, the average voltage drop of each battery individually included in battery groups (first to fourth battery groups) and battery groups (Tray #1 to #4) according to the reaction amount, the standard deviation based thereon, the CPK1 value which is the Process Capability Index, and the CPK2 value which is the Process Capability Index considering the average voltage drop of each battery were calculated.

[0126] When comparing the average value of the standard deviations calculated based on the voltage drop amounts of each battery individually included in the first battery group grouped according to the reaction amount and the battery group grouped by tray (Tray #4), it can be confirmed that the difference in the average value is 0.008.

[0127] In addition, when comparing the average values ​​of the standard deviations for the voltage drop amounts of each battery individually included in the second battery group grouped according to the reaction amount and the battery group grouped by tray (Tray #3), it can be confirmed that the difference in the average values ​​is 0.001.

[0128] In addition, when comparing the average values ​​of the standard deviations for the voltage drop amounts of each battery individually included in the third battery group grouped according to the reaction amount and the battery group grouped by tray (Tray #2), it can be confirmed that the difference in the average values ​​is 0.010.

[0129] In addition, when comparing the average values ​​of the standard deviations for the voltage drop amounts of each battery individually included in the 4th battery group grouped according to the reaction amount and the battery group (Tray #1) grouped by tray, it can be confirmed that the difference in the average values ​​is 0.002.

[0130] In other words, it can be confirmed that the standard deviation of the voltage drop of each of the first to fourth battery groups grouped according to the reaction amount according to the experimental example of the present invention is lower than the standard deviation of the voltage drop of each battery group grouped by tray according to the comparative example of the present invention. More specifically, it can be confirmed that the standard deviation of the voltage drop of each of the first to fourth battery groups grouped according to the reaction amount is improved by an average of 7.57% compared to the standard deviation of the voltage drop of each battery group grouped by tray (see [Equation 2] below). Therefore, the precision of multiple battery groups grouped according to the battery defect diagnosis method according to the experimental example of the present invention can be higher than that of the conventional method.

[0131]

[0132]

[0133] BG n : Number of batteries in the nth battery group

[0134] BGSD n : Standard deviation of the voltage drop of each battery in the nth battery group

[0135] TG n : Tray #n Number of my batteries

[0136] TGSD n : Standard deviation of the voltage drop of each battery in Tray #n

[0137]

[0138] FIGS. 12 to 15 are graphs of the first performance comparison results according to the experimental example of the present invention, and FIGS. 16 to 19 are graphs of the first performance comparison results according to the comparative example of the present invention.

[0139] Referring to FIGS. 11, 12, and 16, for the performance comparison of voltage drop amounts according to the experimental example versus the comparative example of the present invention, when comparing the first performance value (CPK1), which is a process performance index for the voltage drop amounts of each battery individually included in the first battery group grouped according to the reaction amount and the battery group (Tray #4) grouped by tray, it can be confirmed that the difference value of the first performance value (CPK1) is 0.63. Here, the first performance value (CPK1) may be a process performance index that considers the dispersion and deviation of voltage drop amounts for process control.

[0140] In addition, referring to FIGS. 11, 13, and 17, when comparing the first performance value (CPK1) for the voltage drop amounts of each of the batteries individually included in the second battery group grouped according to the reaction amount and the battery group grouped by tray (Tray #3), it can be confirmed that the difference value of the first performance value (CPK1) is 0.06.

[0141] Additionally, referring to FIGS. 11, 14, and 18, when comparing the first performance value (CPK1) for the voltage drop amounts of each of the batteries individually included in the third battery group grouped according to the reaction amount and the battery group grouped by tray (Tray #2), it can be confirmed that the difference value of the first performance value (CPK1) is 0.56.

[0142] In addition, referring to FIGS. 11, 15, and 19, when comparing the first performance value (CPK1) for the voltage drop amounts of each of the batteries individually included in the fourth battery group grouped according to the reaction amount and the battery group grouped by tray (Tray #1), it can be confirmed that the difference value of the first performance value (CPK1) is 0.

[0143] In other words, it can be confirmed that the first performance value (CPK1) for the voltage drop amounts of each of the first to fourth battery groups grouped according to the reaction amount according to the experimental example of the present invention is higher than the first performance value (CPK1) for the voltage drop amounts of the batteries grouped by tray according to the comparative example of the present invention, and it can be confirmed that the average of the first performance value (CPK1) has improved by 9.48% (see [Equation 3] below). Therefore, it can be confirmed that the process capability of the battery groups grouped according to the battery defect diagnosis method according to the experimental example of the present invention is further improved compared to the comparative example.

[0144]

[0145]

[0146] BG n : Number of batteries in the nth battery group

[0147] BGCPK1 n : First performance value for the voltage drop of each battery existing in the n-th battery group

[0148] TG n : Tray #n Number of my batteries

[0149] TGCPK1 n : First performance value for the voltage drop of each battery present in Tray #n

[0150]

[0151] FIGS. 20 to 23 are graphs of the second performance comparison results according to the experimental example of the present invention, and FIGS. 24 to 27 are graphs of the second performance comparison results according to the comparative example of the present invention.

[0152] Referring to FIGS. 11, 20, and 24, when comparing the performance of voltage drop amounts according to the experimental example versus the comparative example of the present invention, the second performance value (CPK2), which is a process performance index considering the average value of the voltage drop amounts of individual batteries included in the first battery group grouped according to the reaction amount and the battery group (Tray #4) grouped by tray, is compared, it can be confirmed that the difference value of the second performance value (CPK2) is 0.54. Here, the second performance value (CPK2) may be a process performance index considering the dispersion, deviation, and average of the voltage drop amount for process control.

[0153] In addition, referring to FIGS. 11, 21, and 25, when comparing the second performance value (CPK2) considering the average value of the voltage drop of individual batteries within the second battery group grouped according to the reaction amount and the battery group grouped by tray (Tray #3), it can be confirmed that the difference value of the second performance value (CPK2) is 0.06.

[0154] In addition, referring to FIGS. 11, 22, and 26, when comparing the second performance value (CPK2) considering the average value of the voltage drop of individual batteries within the third battery group grouped according to the reaction amount and the battery group grouped by tray (Tray #2), it can be confirmed that the difference value of the second performance value (CPK2) is 0.56.

[0155] In addition, referring to FIGS. 11, 23, and 27, when comparing the second performance value (CPK2) considering the average value of the voltage drop amounts of the individual batteries included in the fourth battery group grouped according to the reaction amount and the battery group grouped by tray (Tray #1), it can be confirmed that the difference value of the second performance value (CPK2) is 0.01.

[0156] In other words, it can be confirmed that the second performance value (CPK2), which takes into account the average value of the voltage drop amounts of each of the first to fourth battery groups grouped according to the reaction amount according to the experimental example of the present invention, is higher than the second performance value (CPK2), which takes into account the average value of the voltage drop amounts of each battery group grouped by tray according to the comparative example of the present invention, and that it is improved by an average of 10.5% (see [Equation 4] below). Therefore, it can be confirmed that the process capability of the battery groups grouped according to the battery defect diagnosis method according to the experimental example of the present invention is further improved compared to the comparative example.

[0157]

[0158]

[0159] BG n : Number of batteries in the nth battery group

[0160] BGCPK2 n : A second performance value, which is the average value of the voltage drops of the batteries within the n-th battery group

[0161] TG n : Tray #n Number of my batteries

[0162] TGCPK2 n : Tray #n The second performance value, which is the average value of the voltage drops of the batteries in Tray #n

[0163]

[0164] The battery defect diagnosis device and method according to the embodiments of the present invention have been described above.

[0165] A battery defect diagnosis device and method according to an embodiment of the present invention can determine reliability defects compared to a conventional diagnosis method that diagnoses battery defects by calculating a representative value of voltage drop based on a tray, by grouping the batteries based on the reaction amount of each of the batteries to create multiple battery groups, and calculating a representative value based on the voltage drop amount for each of the multiple battery groups to determine whether the battery group is defective.

[0166]

[0167] The operation of the method according to an embodiment of the present invention may be implemented as a computer-readable program or code on a computer-readable recording medium. The computer-readable recording medium may include any type of recording device in which data that can be read by a computer system is stored. The computer-readable recording medium may also be distributed across networked computer systems, so that the computer-readable program or code can be stored and executed in a distributed manner. The operation of the method according to an embodiment of the present invention may be implemented in various forms related to the program, such as the computer program or code itself or a computer program product.

[0168] Additionally, computer-readable recording media may include one or more of volatile / transitory recording media and non-volatile / non-transitory recording media.

[0169] Computer-readable recording media may include hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, and flash memory, and may include, for example, various types of servers located on a network. Program instructions may include machine code, such as that generated by a compiler, as well as high-level language code that can be executed by a computer using an interpreter, etc.

[0170] Some aspects of the invention have been described in the context of a device, but may also be described according to a corresponding method, wherein a block or device corresponds to a method step or a feature of a method step. Similarly, aspects described in the context of a method may also be described according to a corresponding block or item or a feature of a corresponding device. Some or all of the method steps may be performed by (or using) a hardware device, such as, for example, a microprocessor, a programmable computer, or an electronic circuit. In some embodiments, one or more of the most important method steps may be performed by such a device.

[0171] Although the present invention has been described with reference to preferred embodiments, those skilled in the art will understand that various modifications and changes can be made to the invention without departing from the spirit and scope of the invention as described in the following claims.

Claims

1. At least one processor; and It includes a memory that stores at least one instruction executed through the above processor, and The above at least one command is, A command to calculate the reaction amount of each of multiple batteries stored for a predetermined period at a reference temperature, A command to group the batteries based on the reaction amount of each of the batteries to create the plurality of groups, and A battery defect diagnosis device comprising a command to obtain a representative value for each battery group based on the voltage change of each battery within the battery group, and to determine whether each battery group is defective based on the representative value.

2. In Claim 1, The command to calculate the above reaction amount is, A battery failure diagnosis device comprising a command to calculate the reaction amount of each of the batteries during the above-defined period.

3. In Claim 2, The command to calculate the reaction amount of each of the batteries during the above-defined period is, A battery failure diagnosis device comprising a command to calculate the reaction amount of each of the batteries during a predetermined period based on reaction coefficients corresponding to the temperature measurements of each of the batteries.

4. In Claim 3, An instruction to calculate the reaction amount of each of the batteries during the predetermined period based on reaction coefficients corresponding to the temperature measurements of each of the batteries is, A command to measure the temperature of the batteries individually at each defined cycle within the above-defined period, A command to individually obtain reaction coefficients corresponding to the temperature measurements of each of the batteries, individually measured for each of the above cycles, A command to calculate the reaction amount of the batteries per cycle based on the above reaction coefficients, and A battery failure diagnosis device comprising a command to calculate the reaction amount of each of the batteries during a predetermined period based on the reaction amount of each of the batteries per cycle.

5. In Claim 1, A command to group the above batteries to create the above plurality of groups is, A battery failure diagnosis device comprising a command to group the batteries based on the magnitude of the reaction amount of each of the batteries and to generate the plurality of groups.

6. In Claim 1, A command to group the above batteries to create the above plurality of groups is, A battery defect diagnosis device comprising a command to group the batteries based on the probability change of the reaction amount of each of the batteries and to generate the plurality of groups.

7. In Claim 1, A command to determine whether there is a defect for each battery group based on the above representative value is, A command to calculate the voltage drop amount of each battery in the above battery group, A command to calculate the representative value for each battery group based on the above voltage drop amounts, and A battery defect diagnosis device comprising a command to determine whether there is a defect for each battery group by comparing the above representative value with a predefined threshold value.

8. In Claim 7, The above representative value is, A battery failure diagnosis device comprising at least one of the average value, mode value, maximum value, and median value of the voltage drop amount for each battery group.

9. A step of calculating the reaction amount of each of a plurality of batteries stored for a predetermined period at a reference temperature; A step of grouping the batteries based on the reaction amount of each of the batteries to generate the plurality of groups; and A battery defect diagnosis method comprising the step of obtaining a representative value for each battery group based on the voltage change of each battery within the battery group, and determining whether each battery group is defective based on the representative value.

10. In Claim 9, The step of calculating the above reaction amount is, A battery defect diagnosis method comprising the step of calculating the reaction amount of each of the batteries during the aforementioned predetermined period.

11. In Claim 10, The step of calculating the reaction amount of each of the batteries during the aforementioned predetermined period is, A battery defect diagnosis method comprising the step of calculating the reaction amount of each of the batteries during a predetermined period based on reaction coefficients corresponding to the temperature measurements of each of the batteries.

12. In Claim 11, The step of calculating the reaction amount of each of the batteries during the predetermined period based on reaction coefficients corresponding to the temperature measurements of each of the batteries, A step of individually measuring the temperature of the batteries at each predetermined cycle within the aforementioned predetermined period; A step of individually obtaining reaction coefficients corresponding to temperature measurements of each of the batteries, individually measured for each of the above cycles; A step of calculating the reaction amount of the batteries per cycle based on the above reaction coefficients; and A battery defect diagnosis method comprising the step of calculating the reaction amount of each of the batteries during a predetermined period based on the reaction amount of each of the batteries per cycle.

13. In Claim 9, The step of grouping the batteries to create the plurality of groups is A battery defect diagnosis method comprising the step of grouping the batteries based on the magnitude of the reaction amount of each of the batteries to generate the plurality of groups.

14. In Claim 9, The step of grouping the batteries to create the plurality of groups is A battery defect diagnosis method comprising the step of grouping the batteries based on the probability change of the reaction amount of each of the batteries, and generating the plurality of groups.

15. In Claim 9, The step of determining whether there is a defect for each battery group based on the above representative value is, A step of calculating the voltage drop amount of each of the batteries in the above battery group; A step of calculating the representative value for each battery group based on the above voltage drop amounts; and A battery defect diagnosis method comprising the step of comparing the above representative value with a predefined threshold value to determine whether there is a defect for each battery group.

16. In Claim 15, The above representative value is, A battery failure diagnosis method comprising at least one of the average value, mode value, maximum value, and median value of the voltage drop amount for each battery group.

17. A computer-readable medium storing a program for executing a battery defect diagnosis method of any one of claims 9 to 16 on a computer.

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