Abnormal battery detection device and method

Abstract

An abnormal battery detection device according to an embodiment of the present invention is an abnormal battery detection device for detecting an abnormal battery among a plurality of batteries, and includes at least one processor; a memory for storing at least one instruction executed by the at least one processor; wherein the at least one instruction can include an instruction for collecting status data regarding each of the plurality of batteries; an instruction for performing a correlation analysis between battery statuses based on the collected status data to calculate a correlation coefficient for each of the batteries; and an instruction for detecting an abnormal battery among the plurality of batteries based on the calculated correlation coefficient.

Description

[Technical Field]

[0001] This application claims the benefit of the filing date of Korean Patent Application No. 10-2022-0109748, filed with the Korean Intellectual Property Office on August 31, 2022, and all of the contents disclosed in the documents of that Korean patent application are incorporated herein by reference.

[0002] The present invention relates to an abnormal battery detection device and method, and more particularly to an abnormal battery detection device and method that uses correlation analysis to detect an abnormal battery among a plurality of batteries. [Background technology]

[0003] Secondary batteries are batteries that can be reused by recharging after discharge and can be used as energy sources for small devices such as mobile phones, tablet PCs, and vacuum cleaners, as well as medium- to large-sized devices such as automobiles and smart grid ESS (Energy Storage Systems).

[0004] Secondary batteries are applied to systems in the form of assemblies such as battery modules in which multiple battery cells are connected in series and parallel, or battery packs in which battery modules are connected in series and parallel, depending on the requirements of the system. In the case of medium to large devices such as electric vehicles, high-capacity battery systems in which multiple battery packs are connected in parallel can be applied to meet the required capacity of the device.

[0005] Since the battery cells included in a battery assembly operate under the same conditions, they undergo the same or very similar deterioration processes. Therefore, abnormal battery cells can be detected by collecting status values ​​(e.g., voltage values) related to the battery cells in the battery assembly and comparing the status values ​​of the battery cells.

[0006] In the case of an abnormal battery detection method using such deviations in status values, there is a risk of misdiagnosis due to deviations in status values ​​caused by factors other than battery failure. For example, voltage deviations between battery cells can occur due to measurement errors in open circuit voltage (OCV), internal resistance (IR), and voltage sensors. In such cases, a specific battery may be detected as an abnormal battery even though it is not faulty.

[0007] To solve these problems of the prior art, a suitable technique is needed that can more accurately detect abnormal batteries. Summary of the Invention [Problem to be solved by the invention]

[0008] SUMMARY OF THE INVENTION In order to solve the above problems, an object of the present invention is to provide an abnormal battery detection device with improved detection accuracy.

[0009] Another object of the present invention to solve the above problems is to provide an abnormal battery detection method using such an abnormal battery detection device. [Means for solving the problem]

[0010] To achieve the above object, an abnormal battery detection device according to one embodiment of the present invention is an abnormal battery detection device that detects an abnormal battery among a plurality of batteries, and includes at least one processor; and a memory that stores at least one instruction to be executed through the at least one processor.

[0011] Here, the at least one instruction may include an instruction to collect status data for each of the plurality of batteries; an instruction to perform a correlation analysis between battery statuses based on the collected status data and calculate a correlation coefficient for each of the batteries; and an instruction to detect an abnormal battery among the plurality of batteries based on the calculated correlation coefficient.

[0012] The instructions for calculating a correlation coefficient for each of the batteries may include instructions for calculating a correlation coefficient for each of the batteries using a correlation analysis model defined based on a standard deviation and a covariance of battery state values.

[0013] Here, the command to calculate the correlation coefficient for each of the batteries may include a command to calculate a correlation coefficient from which the influence of sensing noise of the status measurement sensor is removed using a correlation analysis model reflecting a predefined noise constant.

[0014] The noise constant can be defined based on a measurement error value of the condition measurement sensor.

[0015] The command to calculate a correlation coefficient for each of the batteries may include a command to calculate a correlation coefficient for each of the batteries using either a first correlation analysis model defined based on a standard deviation and covariance of battery state values, or a second correlation analysis model in which a noise constant for removing an influence of sensing noise of a state measurement sensor is reflected in the first correlation analysis model.

[0016] The instructions to calculate a correlation coefficient for each of the batteries may include instructions to confirm an operating mode of the battery; and, if in a charge / discharge mode, to calculate a correlation coefficient using the first correlation analysis model, and if in a sleep mode, to calculate a correlation coefficient using the second correlation analysis model.

[0017] The instructions to calculate a correlation coefficient for each of the batteries may include instructions to calculate a covariance of battery state values ​​and a covariance of noise component values; and instructions to calculate a correlation coefficient using the first correlation analysis model if the covariance of the battery state values ​​exceeds the covariance of the noise component values, and to calculate a correlation coefficient using the second correlation analysis model if the covariance of the battery state values ​​is equal to or less than the covariance of the noise component values.

[0018] The instruction to detect an abnormal battery from among the plurality of batteries may include an instruction to determine a battery having a correlation coefficient equal to or less than a predefined threshold as an abnormal battery.

[0019] The command to collect the status data may include a command to collect at least one of a voltage value and a current value for each of the batteries per unit time.

[0020] To achieve the above-mentioned other object, an abnormal battery detection method according to one embodiment of the present invention is an abnormal battery detection method using an abnormal battery detection device that detects an abnormal battery among a plurality of batteries, and includes the steps of: collecting status data for each of the plurality of batteries; performing a correlation analysis between battery statuses based on the collected status data to calculate a correlation coefficient for each of the batteries; and detecting an abnormal battery among the plurality of batteries based on the calculated correlation coefficient.

[0021] The step of calculating a correlation coefficient for each of the batteries may include calculating a correlation coefficient for each of the batteries using a correlation analysis model defined based on a standard deviation and a covariance of battery state values.

[0022] The step of calculating the correlation coefficient for each of the batteries may include a step of calculating a correlation coefficient from which the influence of sensing noise of the status measurement sensor is removed using a correlation analysis model reflecting a predefined noise constant.

[0023] The noise constant can be defined based on a measurement error value of the condition measurement sensor.

[0024] The step of calculating a correlation coefficient for each of the batteries may include a step of calculating a correlation coefficient for each of the batteries using either a first correlation analysis model defined based on a standard deviation and covariance of battery state values, or a second correlation analysis model in which a noise constant for removing an influence of sensing noise of a state measurement sensor is reflected in the first correlation analysis model.

[0025] The step of calculating the correlation coefficient for each of the batteries may include the steps of: checking the operating mode of the battery; and, if in a charge / discharge mode, calculating the correlation coefficient using the first correlation analysis model, and, if in a rest mode, calculating the correlation coefficient using the second correlation analysis model.

[0026] The step of calculating the correlation coefficient for each of the batteries may include the steps of: calculating a covariance of battery state values ​​and a covariance of noise component values; and calculating the correlation coefficient using the first correlation analysis model if the covariance of the battery state values ​​exceeds the covariance of the noise component values, and calculating the correlation coefficient using the second correlation analysis model if the covariance of the battery state values ​​is equal to or less than the covariance of the noise component values.

[0027] The step of detecting an abnormal battery from among the plurality of batteries may include the step of determining a battery having a correlation coefficient equal to or less than a predefined threshold as an abnormal battery.

[0028] The step of collecting the status data may include collecting at least one of a voltage value and a current value for each of the batteries per unit time. [Effects of the Invention]

[0029] According to the above-described embodiment of the present invention, abnormal batteries can be detected more accurately by detecting the abnormal batteries based on the correlation coefficients derived using the correlation analysis model. [Brief explanation of the drawings]

[0030] [Figure 1] FIG. 1 is a flow chart of a conventional method for detecting an abnormal battery. [Figure 2] 10 is an example of a misdiagnosis made by a conventional abnormal battery detection method. [Figure 3] 1 is a block diagram showing an abnormal battery detection system according to an embodiment of the present invention; [Figure 4] 1 is a flowchart of an abnormal battery detection method according to an embodiment of the present invention. [Figure 5] FIG. 2 is a flowchart of a correlation coefficient deriving method according to an embodiment of the present invention. [Figure 6] FIG. 10 is a flow diagram of a correlation coefficient deriving method according to another embodiment of the present invention. [Figure 7] 10 shows the results of detecting an abnormal battery using a correlation analysis model according to an embodiment of the present invention. [Figure 8] 10 shows the results of detecting an abnormal battery using a correlation analysis model according to an embodiment of the present invention. [Figure 9] 10 shows the results of detecting an abnormal battery using a correlation analysis model according to an embodiment of the present invention. [Figure 10] 10 shows the results of detecting an abnormal battery using a correlation analysis model according to an embodiment of the present invention. [Figure 11] 1 is a block diagram of an abnormal battery detection device according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0031] Since the present invention can be modified in various ways and can have various embodiments, specific embodiments will be illustrated in the drawings and described in detail in the detailed description. However, it is understood that this is not intended to limit the present invention to the specific embodiments, but rather to include all modifications, equivalents, or alternatives within the spirit and technical scope of the present invention. Like reference numerals are used to refer to like elements throughout the drawings.

[0032] Terms such as "first," "second," "A," and "B" may be used to describe various components, but the components should not be limited by these terms. These terms are used only to distinguish one component from another. For example, a first component may be termed a "second component," and similarly, a second component may be termed a "first component," without departing from the scope of the present invention. The term "and / or" includes a combination of multiple associated listed items or any of multiple associated listed items.

[0033] When a component is referred to as being "coupled" or "connected" to another component, it is understood that the component may be directly coupled or connected to the other component, but that there may be other components in between. Conversely, when a component is referred to as being "directly coupled" or "directly connected" to another component, it is understood that there are no other components in between.

[0034] The terms used in this application are merely used to describe specific embodiments and are not intended to limit the present invention. The singular expressions include the plural expressions unless the context clearly indicates otherwise. It should be understood that in this application, the terms "comprise" or "have" are intended to specify the presence of features, numbers, steps, operations, components, parts, or combinations thereof described in the specification, and do not preclude the presence or additional possibility of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0035] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which this invention pertains. Terms as defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning they have in the context of the relevant art, and should not be interpreted as having an ideal or overly formal meaning unless expressly defined in this application.

[0036] Some terms used in this specification are defined as follows:

[0037] A battery cell is the smallest unit that serves to store power, and a battery module refers to an assembly of multiple battery cells that are electrically connected together.

[0038] A battery pack or battery rack refers to the smallest single-structure system that can be monitored and controlled through a BMS by electrically connecting module units set by the battery manufacturer, and can be composed of multiple battery modules and one BPU or protection device.

[0039] A battery bank can refer to a large-scale battery rack system consisting of multiple battery racks connected in parallel. The battery bank BMS can monitor and control the battery rack BMS (RBMS).

[0040] A battery assembly refers to an assembly including a plurality of electrically connected battery cells that is applied to a specific system or device and functions as a power supply source. Here, the battery assembly may refer to a battery module, a battery pack, a battery rack, a battery bank, or the like, but the scope of the present invention is not limited to these.

[0041] FIG. 1 is a flow diagram of a conventional method for detecting an abnormal battery.

[0042] 1, a conventional abnormal battery detection method detects abnormal batteries among a plurality of batteries using deviations in battery status values. More specifically, an abnormal battery detection device collects status data for batteries included in a battery assembly (S110). Then, the abnormal battery detection device derives deviations in status values ​​for each battery based on the collected status data (S120). Then, the abnormal battery detection device detects batteries whose deviations in the derived status values ​​exceed a predefined threshold as abnormal batteries (S130).

[0043] According to such conventional techniques, there is a risk of misdiagnosis occurring due to deviations in the status values ​​caused by factors other than a battery failure.

[0044] FIG. 2 shows an example of a misdiagnosis made by a conventional abnormal battery detection method.

[0045] FIG. 2(A) shows the battery voltage values ​​measured per unit time, and FIG. 2(B) shows the abnormality diagnosis results based on the voltage deviation between battery cells.

[0046] Referring to Figure 2(A), the voltage of a specific battery (Cell 9) differs from the voltage of other batteries due to the influence of the open circuit voltage (OCV) and internal resistance (IR). As shown in Figure 2(B), the high voltage deviation at the relevant point in time can lead to a false diagnosis in which a battery is detected as abnormal even though it is not malfunctioning.

[0047] The present invention has been devised to solve the problems of the prior art, and relates to an abnormal battery detection device and method that can prevent erroneous diagnosis due to factors other than a malfunction and improve the accuracy of detecting an abnormal battery.

[0048] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0049] FIG. 3 is a block diagram showing an abnormal battery detection system according to an embodiment of the present invention.

[0050] Referring to FIG. 3, the abnormal battery detection system includes a battery assembly 100 including a plurality of batteries 10 and an abnormal battery detection device 200.

[0051] A plurality of batteries 10 can be electrically connected to form a battery assembly 100 .

[0052] Although the abnormal battery detection system according to an embodiment of the present invention may be embodied in an electric vehicle, the scope of the present invention is not limited to such an electric vehicle. That is, the abnormal battery detection system according to the present invention may be applied to a device equipped with multiple batteries and operate to detect an abnormal battery by performing the abnormal battery detection method described below.

[0053] In an embodiment, the abnormal battery detection device 200 may be embodied and included in a BMS located inside the battery system.

[0054] The abnormal battery detection device 200 can collect status data for each of the plurality of batteries. Here, the status data can correspond to data relating to one or more status values ​​of the battery voltage and current.

[0055] The abnormal battery detection device 200 may receive status data from a status measurement sensor that measures a status value related to each battery. For example, the abnormal battery detection device 200 may receive status data from a voltage measurement sensor that senses a voltage value for each battery or a current measurement sensor that senses a current value flowing through each battery.

[0056] The abnormal battery detection device 200 can collect status data every predefined unit time. For example, the abnormal battery detection device 200 can be configured to receive status data regarding each of the batteries every second.

[0057] The abnormal battery detection device 200 can perform a correlation analysis between battery states based on the collected status data and calculate a correlation coefficient for each battery.The abnormal battery detection device 200 can then detect an abnormal battery from among multiple batteries based on the calculated correlation coefficient.Here, the abnormal battery detection device 200 can determine a battery having a correlation coefficient equal to or less than a predefined threshold as an abnormal battery.

[0058] The abnormal battery detection device according to the present invention can detect an abnormal battery based on the state values ​​of batteries operating under the same conditions. Hereinafter, a method for detecting an abnormal battery based on the voltage values ​​of batteries connected in parallel within a battery assembly will be described as an example, but the scope of the present invention is not limited thereto.

[0059] FIG. 4 is a flow diagram of an abnormal battery detection method according to an embodiment of the present invention.

[0060] The abnormal battery detection device 200 can collect status data for each of the plurality of batteries (S410). Here, the abnormal battery detection device 200 can collect status data for each battery per unit time, the status data being related to at least one of the voltage value and the current value.

[0061] The abnormal battery detection device 200 can perform a correlation analysis between battery states based on the collected state data and calculate a correlation coefficient (r) for each battery (S420).

[0062] The abnormal battery detection device 200 can calculate a correlation coefficient for each battery using a predefined correlation analysis model. Here, the correlation analysis model can be defined so that when status data for a plurality of batteries is input, a correlation coefficient for each battery is output.

[0063] A correlation analysis model according to a first embodiment of the present invention (hereinafter referred to as a first correlation analysis model) may be defined based on the standard deviation and covariance of the battery state values. For example, the first correlation analysis model may be defined based on the following Equation 1:

number

[0064] where r X,Y is the correlation coefficient between battery X and battery Y, COV X,Y is the covariance of the state values ​​of battery X and battery Y, σ X is the standard deviation of the state value of battery X, σ Y means the standard deviation of the state value of battery Y. Also, x is the state value of battery X, μ x is the average value for the state value of battery X during a predefined period, y is the state value of battery Y, μ y denotes the average value for the state value of battery Y during a predefined period.

[0065] When deriving a correlation coefficient for a specific battery (e.g., Cell_1) based on Equation 1, status data for the corresponding battery (Cell_1) may be input as x, and status data for other batteries may be input as y, into the correlation analysis model. Here, y may correspond to the average or median of the status values ​​of the other batteries. In an embodiment, to accurately calculate the correlation coefficient, the abnormal battery detection device 200 may correct the median of the status values ​​of the other batteries using a predefined process, and input the corrected median into the correlation analysis model as y.

[0066] When a correlation coefficient is calculated using the first correlation analysis model according to an embodiment of the present invention and an abnormal battery is detected based on the calculated correlation coefficient, erroneous diagnosis due to the influence of open circuit voltage (OCV) or internal resistance (IR) can be prevented.

[0067] More specifically, when the fluctuation of the voltage value (αX+β) due to the influence of the open circuit voltage (OCV) or the internal resistance (IR) is reflected in the above Equation 1, the average value of the voltage value also fluctuates similarly, so the correlation coefficient can be calculated as the same value as the correlation coefficient according to Equation 1, as shown in the following Equation 2.

number

[0068] The abnormal battery detection device 200 calculates a correlation coefficient using a first correlation analysis model defined based on Equation 1, and detects an abnormal battery using the calculated correlation coefficient, thereby preventing erroneous diagnosis due to the influence of open circuit voltage (OCV) or internal resistance (IR).

[0069] In step S420, the abnormal battery detection device 200 can calculate a correlation coefficient for each battery using a correlation analysis model according to a second embodiment of the present invention (hereinafter referred to as the second correlation analysis model). Here, the second correlation analysis model can be defined based on the standard deviation, covariance, and noise constant of the battery state values. For example, the correlation analysis model can be defined based on the following Equation 3.

number

[0070] Here, N represents a noise constant and can be defined as Equation 4 below.

number

[0071] Here, RMS (Noise) is the RMS (Root Mean Square) value of the measurement error value of the state measurement sensor, and a is a weighting coefficient.

[0072] Referring to Equation 3, the second correlation analysis model is a model in which a noise constant (N) for removing the influence of sensing noise of the state measurement sensor is reflected in the first correlation analysis model.

[0073] Generally, a state value measured by a state measurement sensor includes a noise component. When the noise component is reflected in Equation 1, Equation 1 can be expressed as Equation 5 below.

number

[0074] Here, N denotes the noise component of x, and M denotes the noise component of y.

[0075] Depending on the relationship between the noise component and the eigenstate value (the measured value from which the noise component has been removed), the following two cases can be assumed. (case 1) COV X,Y > COV N,M (case 2) COV X,Y ≦ COV N,M

[0076] Case 1 is a case where the amount of change in the intrinsic state value is greater than the noise component, which may correspond to, for example, a charging / discharging period of a battery. In Case 1, since the amount of change in the intrinsic state value is greater than the noise component, the influence of sensing noise of the state measurement sensor is small, and even if the correlation coefficient is calculated using the first correlation analysis model, the detection accuracy does not decrease significantly.

[0077] Case 2 is a case where the change in the intrinsic state value is equal to or smaller than the noise component, which may correspond to, for example, a rest period of the battery. In Case 2, since the noise component is larger than the change in the intrinsic state value, when the correlation coefficient is calculated using the first correlation analysis model, an erroneous diagnosis may occur due to the influence of the sensing noise.

[0078] In case 2, COV X,Y = COV N,M In this case, the noise component and the eigenstate value have the same level of variation, so Equation 5 can be simplified as Equation 6 below.

number

[0079] Here, if the noise component is white noise, the average value of the noise component (μ N ,μ M ) is 0, and if the battery state values ​​are measured by the same sensor, N = M, so Equation 6 can be expressed as Equation 7 below.

number

[0080] In other words, in case 2, we add an appropriate noise constant N to the numerator and denominator of Equation 1 to obtain the correlation coefficient (r X+N,Y+M ) yields the same results, so a second correlation analysis model defined by Equation 3 can be used.

[0081] The noise constant N can be defined as a value obtained by multiplying RMS (Noise) by a weighting coefficient a according to Equation 4. For example, the noise constant N can be defined as a value obtained by multiplying the RMS value of 5 mV, which is the measurement error value of the voltage sensor, by a weighting coefficient of 0.8.

[0082] The weighting coefficient a is a value for adjusting the detection accuracy, and may be defined as a specific value between 0.2 and 1.1, preferably between 0.8 and 1.0.

[0083] The abnormal battery detection device 200 can calculate the correlation coefficient using the second correlation analysis model defined based on Equation 3 in case 2 (e.g., sleep mode), but can also be configured to calculate the correlation coefficient using the second correlation analysis model in case 1 (e.g., charge / discharge mode) to provide higher detection accuracy.

[0084] The abnormal battery detection device 200 can compare the correlation coefficient (r) for each battery calculated in step S420 with a predefined threshold value (r_th) (S430). The correlation coefficient (r) according to an embodiment of the present invention indicates the degree of correlation between the state of a specific battery and the state of other batteries, and can be calculated as a specific value between -1 and 1. A battery with a correlation coefficient (r) close to 1 can be considered to have a high correlation with other batteries, and a battery with a correlation coefficient (r) close to 0 can be considered to have a low correlation with other batteries.

[0085] The abnormal battery detection device 200 can determine a battery having a correlation coefficient equal to or less than a threshold value (r_th) as an abnormal battery (S440). For example, if the threshold value (r_th) is defined as 0.5, the abnormal battery detection device 200 can determine a battery having a correlation coefficient equal to or less than 0.5 among a plurality of batteries as an abnormal battery. Meanwhile, the threshold value (r_th) can be determined as an appropriate value as needed, and can be defined as a specific value between 0.4 and 0.8.

[0086] FIG. 5 is a flow diagram of a correlation coefficient deriving method according to an embodiment of the present invention.

[0087] The abnormal battery detection device 200 can check the operation mode of the battery (S510). Here, the operation mode can include a charge / discharge mode and a sleep mode.

[0088] The abnormal battery detection device 200 can determine the operation mode based on the output or input current value for the battery. For example, if the output or input current value of the battery is 0, the sleep mode can be determined, and if not, the charge / discharge mode can be determined.

[0089] The abnormal battery detection device 200 can determine the operation mode based on a battery operation state signal received from an external device. For example, it can receive a battery operation state signal from a battery charge / discharge control device and determine whether the battery is in a charging / discharging state or a resting state.

[0090] If the battery operation mode is the charge / discharge mode (Y in S520), the abnormal battery detection device 200 can determine a first correlation analysis model defined based on Equation 1 as a model for calculating the correlation coefficient (S530).

[0091] If the operating mode of the battery is the sleep mode (N in S520), the abnormal battery detection device 200 can determine the second correlation analysis model defined based on Equation 3 as the model for calculating the correlation coefficient (S540).

[0092] The abnormal battery detection device 200 can derive a correlation coefficient (r) for each of the batteries using the correlation analysis model determined by the operating mode of the battery (S550).

[0093] FIG. 6 is a flow diagram of a correlation coefficient deriving method according to another embodiment of the present invention.

[0094] The abnormal battery detection device 200 detects the COV based on the battery status data. X,Y and COV N,M can be calculated (S610).

[0095] where COV X,Y and COV N,M can be calculated based on the following formula:

number

number

[0096] The abnormal battery detection device 200 X,Y and COV N,M can be compared (S620).

[0097] COV X,Y COV N,M If it exceeds (Y in S620), the abnormal battery detection apparatus 200 may determine the first correlation analysis model defined based on Equation 1 as a model for calculating the correlation coefficient (S630).

[0098] COV X,Y COV N,M In the following case (N in S620), the abnormal battery detection device 200 can determine the second correlation analysis model defined based on Equation 3 as the model for calculating the correlation coefficient (S640).

[0099] The abnormal battery detection device 200 can derive a correlation coefficient (r) for each of the batteries using the correlation analysis model determined by the operating mode of the battery (S650).

[0100] 7 to 10 show the results of detecting an abnormal battery using a correlation analysis model according to an embodiment of the present invention.

[0101] Figure 7 shows the voltage measurements for each of the eight parallel-connected cells (Cells 1 to 8). Referring to Figure 7, it can be seen that abnormal voltages occur in Cells 1 and 2 at approximately 1375 seconds.

[0102] 8 to 10 show correlation coefficients for each battery cell calculated by applying a correlation analysis model according to an embodiment of the present invention. Fig. 8 is a graph showing the results when a first correlation analysis model is applied, Fig. 9 is a graph showing the results when a second correlation analysis model is applied in which the weighting coefficient a is defined as an RMS value of 0.1 and the RMS (Noise) is defined as an RMS value of 0.5 mV, and Fig. 10 is a graph showing the results when a second correlation analysis model is applied in which the weighting coefficient a is defined as an RMS value of 1.0 and the RMS (Noise) is defined as an RMS value of 0.5 mV. The threshold value (r_th) for detecting an abnormal battery was set to 0.5.

[0103] Referring to FIG. 8, when the first correlation analysis model was used, batteries with correlation coefficients below the threshold value (0.5) were detected in the sections other than the time when the abnormal voltage phenomenon occurred, and Cells 3 to 8 were erroneously detected as abnormal batteries.

[0104] Referring to FIG. 9, when the second correlation analysis model in which the weighting coefficient a is defined as 0.1 is used, batteries having a correlation coefficient below the threshold value (0.5) are detected in the section other than the time when the abnormal voltage phenomenon occurred, and Cells 3 to 8 are erroneously detected as abnormal batteries, as in the case of FIG. 8.

[0105] Referring to FIG. 10, when the second correlation analysis model in which the weighting coefficient a is defined as 1.0 is used, Cell 1 and Cell 2 are detected as batteries having a correlation coefficient below the threshold value (0.5).

[0106] Referring to Figures 8 to 10, it can be seen that when the second correlation analysis model is applied with a weighting coefficient within the optimal range (0.2 to 1.1), an abnormal battery can be detected with high accuracy.

[0107] FIG. 11 is a block diagram of an abnormal battery detection device according to an embodiment of the present invention.

[0108] The abnormal battery detection device 200 according to an embodiment of the present invention may include at least one processor 210, a memory 220 for storing at least one instruction to be executed by the processor, and a transceiver 230 connected to a network for communication.

[0109] The at least one instruction may include an instruction to collect status data for each of a plurality of batteries; an instruction to perform a correlation analysis between battery statuses based on the collected status data and calculate a correlation coefficient for each of the batteries; and an instruction to detect an abnormal battery among the plurality of batteries based on the calculated correlation coefficient.

[0110] The instructions for calculating a correlation coefficient for each of the batteries may include instructions for calculating a correlation coefficient for each of the batteries using a correlation analysis model defined based on a standard deviation and a covariance of battery state values.

[0111] Here, the command to calculate the correlation coefficient for each of the batteries may include a command to calculate a correlation coefficient from which the influence of sensing noise of the status measurement sensor is removed using a correlation analysis model reflecting a predefined noise constant.

[0112] The noise constant can be defined based on a measurement error value of the condition measurement sensor.

[0113] The command to calculate a correlation coefficient for each of the batteries may include a command to calculate a correlation coefficient for each of the batteries using either a first correlation analysis model defined based on a standard deviation and covariance of battery state values, or a second correlation analysis model in which a noise constant for removing an influence of sensing noise of a state measurement sensor is reflected in the first correlation analysis model.

[0114] The instructions to calculate a correlation coefficient for each of the batteries may include instructions to confirm an operating mode of the battery; and, if in a charge / discharge mode, to calculate a correlation coefficient using the first correlation analysis model, and if in a sleep mode, to calculate a correlation coefficient using the second correlation analysis model.

[0115] The instructions to calculate a correlation coefficient for each of the batteries may include instructions to calculate a covariance of battery state values ​​and a covariance of noise component values; and instructions to calculate a correlation coefficient using the first correlation analysis model if the covariance of the battery state values ​​exceeds the covariance of the noise component values, and to calculate a correlation coefficient using the second correlation analysis model if the covariance of the battery state values ​​is equal to or less than the covariance of the noise component values.

[0116] The instruction to detect an abnormal battery from among the plurality of batteries may include an instruction to determine a battery having a correlation coefficient equal to or less than a predefined threshold as an abnormal battery.

[0117] The command to collect the status data may include a command to collect at least one of a voltage value and a current value for each of the batteries per unit time.

[0118] The abnormal battery detection device 200 may further include an input interface device 240, an output interface device 250, a storage device 260, etc. The components included in the abnormal battery detection device 200 are connected by a bus 270 to communicate with each other.

[0119] Here, the processor 210 may refer to a central processing unit (CPU), a graphics processing unit (GPU), or a dedicated processor on which the method according to the embodiment of the present invention is performed. The memory (or storage device) may be composed of at least one of a volatile storage medium and a non-volatile storage medium. For example, the memory may be composed of at least one of a read-only memory (ROM) and a random access memory (RAM).

[0120] The operations of the methods according to the embodiments of the present invention can be embodied as a computer-readable program or code on a computer-readable recording medium. The computer-readable recording medium includes all kinds of storage devices in which data that can be read by a computer system is stored. In addition, the computer-readable recording medium can be distributed among computer systems connected via a network, so that the computer-readable program or code can be stored and executed in a distributed manner.

[0121] Some aspects of the invention have been described in the context of an apparatus, but they may also be described in terms of a corresponding method, where a block or apparatus corresponds to a method step or feature of a method step. Similarly, aspects described in the context of a method may be described in terms of a corresponding block or item or feature of a corresponding apparatus. Some or all of the method steps may be performed by (or using) a hardware apparatus, such as 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 an apparatus.

[0122] Although the present invention has been described above with reference to preferred embodiments, those skilled in the art will understand that various modifications and variations of the present invention can be made without departing from the spirit and scope of the present invention as set forth in the following claims. [Explanation of symbols]

[0123] 10:Battery 100: Battery assembly 200: Abnormal battery detection device

Claims

1. An abnormal battery detection device for detecting an abnormal battery among a plurality of batteries, at least one processor; and a memory for storing at least one instruction to be executed by said at least one processor; The at least one instruction: instructions for collecting status data regarding each of the plurality of batteries; an instruction to perform a correlation analysis between a battery state value of a first battery among the plurality of batteries and a value based on battery state values ​​of two or more batteries different from the first battery based on the collected state data, and calculate a correlation coefficient for each of the plurality of batteries; and a command to detect an abnormal battery among the plurality of batteries based on the calculated correlation coefficient; The instruction to calculate a correlation coefficient for each of the plurality of batteries includes: An abnormal battery detection device including instructions for calculating a correlation coefficient for each of the plurality of batteries using a first correlation analysis model defined based on the standard deviation and covariance of battery state values, and a second correlation analysis model in which a noise constant for removing the influence of sensing noise of a state measurement sensor is reflected in the first correlation analysis model.

2. The noise constant is The abnormal battery detection device according to claim 1 , wherein the abnormal battery detection device is defined based on a measurement error value of the state measurement sensor.

3. The instruction to calculate a correlation coefficient for each of the plurality of batteries includes: instructions to ascertain an operating mode of the plurality of batteries; and 2. The abnormal battery detection device according to claim 1, further comprising instructions to calculate a correlation coefficient using the first correlation analysis model in a charge / discharge mode, and to calculate a correlation coefficient using the second correlation analysis model in a sleep mode.

4. The instruction to calculate a correlation coefficient for each of the plurality of batteries includes: instructions for calculating the covariance of the battery state values ​​and the covariance of the noise component values; and 2. The abnormal battery detection device of claim 1, further comprising instructions to calculate a correlation coefficient using the first correlation analysis model if the covariance of the battery state values ​​exceeds the covariance of the noise component values, and to calculate a correlation coefficient using the second correlation analysis model if the covariance of the battery state values ​​is equal to or less than the covariance of the noise component values.

5. The instruction to detect an abnormal battery among the plurality of batteries includes:

2. The abnormal battery detection device according to claim 1, further comprising instructions for determining a battery having a correlation coefficient equal to or less than a predefined threshold as an abnormal battery.

6. The instructions to collect status data include: The abnormal battery detection device according to claim 1 , further comprising a command to collect at least one of a voltage value and a current value for each of the plurality of batteries per unit time.

7. An abnormal battery detection method using an abnormal battery detection device that detects an abnormal battery among a plurality of batteries, comprising: collecting status data for each of the plurality of batteries; Based on the collected status data, performing a correlation analysis between a battery status value of a first battery among the plurality of batteries and a value based on battery status values ​​of two or more batteries different from the first battery, and calculating a correlation coefficient for each of the plurality of batteries; and detecting an abnormal battery from among the plurality of batteries based on the calculated correlation coefficient; The step of calculating a correlation coefficient for each of the plurality of batteries includes: A method for detecting an abnormal battery, comprising the steps of: calculating a correlation coefficient for each of the plurality of batteries using a first correlation analysis model defined based on the standard deviation and covariance of battery state values; and a second correlation analysis model in which a noise constant for removing the influence of sensing noise of a state measurement sensor is reflected in the first correlation analysis model.

8. The noise constant is The abnormal battery detection method according to claim 7 , wherein the abnormal battery detection method is defined based on a measurement error value of the state measurement sensor.

9. The step of calculating a correlation coefficient for each of the plurality of batteries includes: determining an operating mode of the plurality of batteries; and 8. The abnormal battery detection method according to claim 7, further comprising the steps of: calculating a correlation coefficient using the first correlation analysis model in a charge / discharge mode; and calculating a correlation coefficient using the second correlation analysis model in a sleep mode.

10. The step of calculating a correlation coefficient for each of the plurality of batteries includes: calculating the covariance of the battery state values ​​and the covariance of the noise component values; and 8. The abnormal battery detection method of claim 7, further comprising the steps of: calculating a correlation coefficient using the first correlation analysis model if the covariance of the battery state values ​​exceeds the covariance of the noise component values; and calculating a correlation coefficient using the second correlation analysis model if the covariance of the battery state values ​​is equal to or less than the covariance of the noise component values.

11. The step of detecting an abnormal battery from among the plurality of batteries includes:

8. The method for detecting an abnormal battery according to claim 7, further comprising the step of determining a battery having a correlation coefficient equal to or less than a predefined threshold as an abnormal battery.

12. The step of collecting status data includes:

8. The abnormal battery detection method according to claim 7, further comprising the step of collecting at least one of a voltage value and a current value for each of the plurality of batteries per unit time.

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