Diagnosis apparatus and diagnosis method of battery cells

Abstract

A diagnostic device and a diagnostic method for a battery cell may be provided. The diagnostic device calculates multiple OCV (Open circuit voltage) and multiple SOC (State of charge) deviations at each of the multiple time points of the battery cell using multiple OCVs and multiple SOC deviations at each of the multiple time points, extracts first OCV deviations and first SOC deviations during a first period which is part of the diagnostic period, extracts second OCV deviations and second SOC deviations during a second period which is part of the diagnostic period, calculates multiple first differences which are the difference between each of the first OCV deviations during the first period and each of the second OCV deviations during the second period, and multiple second differences which are the difference between each of the first SOC deviations during the first period and each of the second SOC deviations during the second period, and diagnoses the battery cell using the first differences from which outliers have been removed and the second differences after removing outliers from the multiple first differences and the multiple second differences.

Description

Technology Field

[0001] The present disclosure relates to a diagnostic device and a diagnostic method for a battery cell. Background Technology

[0002] Electric vehicles can be driven using electric motors, and they use a battery system, which is a high-voltage power source, to drive the motors.

[0003] The battery system includes multiple battery cells and a Battery Management System (BMS). The BMS monitors the status of the multiple battery cells and diagnoses abnormal cells.

[0004] Among various diagnostic techniques, RdV (relaxation deviation voltage or relaxation delta voltage) diagnostic technology calculates the voltage drop of a battery cell and uses this data to diagnose abnormal cells. However, accurate diagnosis of battery cells is difficult because noise may be included in the voltage drop due to various reasons, such as external environmental factors like weather conditions. The problem to be solved

[0005] Some embodiments of the present disclosure aim to provide a diagnostic device and a diagnostic method for a battery cell that can reduce the effects of noise. means of solving the problem

[0006] According to one embodiment, a method for a diagnostic device to diagnose a battery cell may be provided. A diagnostic method comprises the steps of: calculating multiple Open Circuit Voltages (OCVs) and multiple State of Charges (SOCs) of the battery cell for each of a plurality of time points during a diagnostic period; calculating multiple OCV deviations and multiple SOC deviations for each of a plurality of time points using the multiple OCVs and multiple SOCs at each of a plurality of time points; extracting first OCV deviations and first SOC deviations during a first period which is part of the diagnostic period; extracting second OCV deviations and second SOC deviations during a second period which is part of the diagnostic period and is temporally separated from the first period; calculating multiple first differences which are the difference between each of the first OCV deviations during the first period and each of the second OCV deviations during the second period; calculating multiple second differences which are the difference between each of the first SOC deviations during the first period and each of the second SOC deviations during the second period; and removing outliers from the plurality of first differences and the plurality of second differences. It includes a step of diagnosing the battery cell using the first difference from which the above-mentioned outlier has been removed and the second difference.

[0007] The above removal step may include a step of calculating the mean and standard deviation of the plurality of first differences, a step of calculating the mean and standard deviation of the plurality of second differences, a step of determining the outlier in the plurality of first differences using the mean and standard deviation of the plurality of first differences, and a step of determining the outlier in the plurality of second differences using the mean and standard deviation of the plurality of first differences.

[0008] The step of determining the outlier among the plurality of first differences may include the step of calculating the Z-scores of each of the plurality of first differences using the mean and standard deviation of the plurality of first differences, and the step of determining the first difference among the plurality of first differences that corresponds to a Z-score greater than or equal to a first threshold value as the outlier, and the step of determining the outlier among the plurality of second differences may include the step of calculating the Z-scores of each of the plurality of second differences using the mean and standard deviation of the plurality of second differences, and the step of determining the second difference among the plurality of second differences that corresponds to a Z-score greater than or equal to a second threshold value as the outlier.

[0009] The step of determining the outlier among the plurality of first differences may include determining the first difference corresponding to a standard deviation outside a set first threshold range among the plurality of first differences as the outlier, and the step of determining the outlier among the plurality of second differences may include determining the second difference corresponding to a standard deviation outside a set second threshold range among the plurality of second differences as the outlier.

[0010] The above-mentioned diagnostic step may include a step of calculating a first average value by accumulating first differences from which the above-mentioned outliers have been removed, a step of calculating a second average value by accumulating second differences from which the above-mentioned outliers have been removed, and a step of diagnosing whether the battery cell is an abnormal cell based on the first average value and the second average value.

[0011] The above second period may include the last point in time of the above diagnosis period.

[0012] The step of calculating multiple OCV deviations at each of the multiple time points may include the step of calculating the OCV deviation at each time point from the difference between the OCV at each time point and the average value of the OCV at each time point, and the average value of the OCV at each time point may be the average value of the SOCs of all battery cells of the battery pack including the battery cell at each time point.

[0013] The step of calculating multiple SOC deviations at each of the plurality of time points may include the step of calculating the SOC deviation at each time point from the difference between the SOC at each time point and the average SOC value at each time point, and the average SOC value at each time point may be the average value of the SOCs of all battery cells of the battery pack including the battery cell at each time point.

[0014] The step of calculating the plurality of OCVs and the plurality of SOCs may include the step of measuring the plurality of OCVs of the battery cell for each of the plurality of time points of the diagnosis period, and the step of estimating the SOC at each of the plurality of time points from the plurality of OCVs for each of the plurality of time points by referring to a table representing the relationship between SOC and OCV.

[0015] According to another embodiment, a diagnostic device for diagnosing a plurality of battery cells by a processor executing one or more instructions may be provided. The diagnostic device comprises: an information collection unit that acquires multiple Open Circuit Voltages (OCVs) at each of multiple time points during a diagnostic period for each battery cell; a State of Charge (SOC) estimation unit that estimates multiple State of Charges (SOCs) at each of the multiple time points using the multiple OCVs at each of the multiple time points for each battery cell; a deviation calculation unit that calculates multiple OCV deviations and multiple SOC deviations at each of the multiple time points using the multiple OCVs and multiple SOCs at each of the multiple time points for each battery cell; a noise removal unit that calculates multiple first differences, which are the difference between each of the first OCV deviations during a first period which is part of the diagnostic period and each of the second OCV deviations during a second period which is temporally separated from the first period; calculates multiple second differences, which are the difference between each of the first SOC deviations during the first period and each of the second SOC deviations during the second period; removes outliers from the multiple first differences and the multiple second differences; and uses the first differences from which outliers have been removed and the second differences. It includes a diagnostic unit for diagnosing the battery cell.

[0016] The noise removal unit can calculate the average and standard deviation of the plurality of first differences and determine the outliers in the plurality of first differences using the average and standard deviation of the plurality of first differences, and can calculate the average and standard deviation of the plurality of second differences and determine the outliers in the plurality of second differences using the average and standard deviation of the plurality of first differences.

[0017] The noise removal unit can determine a first difference corresponding to a standard deviation outside a set first threshold range among the plurality of first differences as the outlier, and determine a second difference corresponding to a standard deviation outside a set second threshold range among the plurality of second differences as the outlier.

[0018] The above diagnostic unit can calculate a first average value by accumulating first differences from which the above-mentioned outliers have been removed, calculate a second average value by accumulating second differences from which the above-mentioned outliers have been removed, and diagnose whether the battery cell is an outlier cell based on the first average value and the second average value. Effects of the invention

[0019] According to at least one of the embodiments, by removing outliers from a data set for determining whether a battery cell is an abnormal cell, an abnormal cell can be diagnosed more accurately. Brief explanation of the drawing

[0020] FIG. 1 is a diagram showing an example of a battery system according to an embodiment. FIG. 2 is a diagram showing a diagnostic device for a battery cell according to one embodiment. Figure 3 is a flowchart illustrating a diagnostic method of the diagnostic device shown in Figure 2. FIG. 4 is a diagram showing an example of voltage data for each of the OCVs of a plurality of battery cells according to one embodiment. FIG. 5 is a diagram showing an example of OCV deviations of each of a plurality of battery cells according to one embodiment. FIG. 6 is a drawing illustrating a diagnostic device according to another embodiment. FIG. 7 is a block diagram of a diagnostic device according to another embodiment. Specific details for implementing the invention

[0021] Embodiments of the present invention are described below with reference to the attached drawings so that those skilled in the art can easily implement them. However, the present invention may be embodied in various different forms and is not limited to the embodiments described herein. Furthermore, in order to clearly explain the present invention in the drawings, parts unrelated to the explanation have been omitted, and similar parts throughout the specification are denoted by similar reference numerals.

[0022] In the flowchart described with reference to the drawings, the order of operations may be changed, multiple operations may be merged or some operations may be divided, and specific operations may not be performed.

[0023] Throughout the specification and claims, when a part is described as "comprising" a certain component, this means that, unless specifically stated otherwise, it does not exclude other components but may include additional components.

[0024] Additionally, expressions written in the singular form may be interpreted as singular or plural unless explicit expressions such as "one" or "singular" are used.

[0025] Additionally, terms including ordinal numbers, such as first, second, etc., may be used to describe various components, but said components are not limited by said terms. Such terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present disclosure, the first component may be named the second component, and similarly, the second component may be named the first component.

[0026] Furthermore, when it is stated that one component is "connected" to another component, this includes not only cases where they are "directly or physically connected," but also cases where they are "indirectly or non-contactually connected" with another component in between, or where they are "electrically connected." On the other hand, when it is stated that one component is "directly connected" to another component, it should be understood that there is no other component present in between.

[0027] FIG. 1 is a diagram showing an example of a battery system according to an embodiment.

[0028] Referring to FIG. 1, the battery system (10) may have a structure that is connected to an external device (20) through a connection terminal (OT+) and a connection terminal (OT-).

[0029] In some embodiments, when the external device (20) is a load, the battery system (10) may be discharged by operating as a power source that supplies power to the load. When the external device (20) is a charger, the battery system (10) may be charged by receiving external power through the charger. In some embodiments, the external device operating as a load may be, for example, an electronic device, a means of transportation, or an energy storage system (ESS), and the means of transportation may be, for example, a vehicle such as an electric vehicle, a hybrid vehicle, or smart mobility.

[0030] The battery system (10) may include a battery pack (12), a relay (14), and a battery management system (BMS) (16).

[0031] The battery pack (12) includes a plurality of battery cells (121) connected in series. The plurality of battery cells (121) are capable of charging and discharging and may be, for example, lithium-ion cells. The number of battery cells constituting the battery pack (12) and their connection relationships can be designed according to the voltage and capacity required for the battery pack (12).

[0032] The relay (14) can provide a current path during charging and discharging of the battery pack (12). The relay (14) can be connected between the positive terminal (P+) of the battery pack (12) and the connection terminal (OT+) of the battery system (10). When the relay (14) is closed, the positive terminal (P+) of the battery pack (12) can be electrically connected to the connection terminal (OT+) of the battery system (10).

[0033] In some embodiments, a relay may be connected between the negative terminal (P-) of the battery pack (12) and the connection terminal (OT-) of the battery system (10).

[0034] The relay (14) can be turned on and off in response to a control signal from the BMS (16). The relay (14) may be a mechanical contactor that is turned on and off by the magnetic force of a coil, or a semiconductor switch such as a MOSFET (Metal Oxide Semiconductor Field Effect Transistor).

[0035] The BMS (16) may include a sensing circuit (161), an MCU (Main Control Unit) (163), and a communication circuit (165).

[0036] The sensing circuit (161) may be configured to acquire state parameters of a plurality of battery cells (121). The state parameters may include the voltage of the battery cells (121), the voltage and current of the battery pack (12), etc.

[0037] The sensing circuit (161) may include a voltage detection circuit (161_1) and a current sensor (161_2).

[0038] The voltage detection circuit (161_1) is connected to the positive and negative terminals of each of the plurality of battery cells (121) included in the battery pack (12), measures the cell voltage across each end of each battery cell (121), and can transmit the measured cell voltage to the MCU (163).

[0039] According to the embodiment, the cell voltage may include an open circuit voltage (OCV). The OCV may represent the cell voltage measured when the battery cell (121) is in a resting state. The battery cell (121) may be charged during a charging period and discharged during a discharging period. During the charging period, when the voltage of the battery cell (121) reaches the charging voltage, charging is stopped, and during the discharging period, when the voltage of the battery cell (121) reaches the discharge termination voltage, discharging is terminated. When charging or discharging is stopped, the battery cell (121) enters a resting state, at which time the relay (14) is turned off so that the OCV, which is the cell voltage of the battery cell (121), can be measured when the external device (20) is not connected to the battery cell (121).

[0040] The current sensor (161_2) can be connected in series between the negative terminal (P-) and the connection terminal (OT-) of the battery pack (12). Unlike as shown in FIG. 1, the current sensor (161_2) can be connected in series between the positive terminal (P+) and the connection terminal (OT+) of the battery pack (12). The current sensor (161_2) can measure the current flowing through the battery pack (12) and transmit the measured current to the MCU (163).

[0041] The MCU (163) can receive cell voltages from the voltage detection circuit (161_1) and the current of the battery pack (12) from the current sensor (162_2).

[0042] The MCU (163) can be implemented in hardware using at least one of an ASIC (application specific integrated circuit), DSP (digital signal processor), DSPD (digital signal processing device), PLD (programmable logic device), FPGA (field programmable gate array), microprocessor, AP (Application Processor), CPU (Central Processing Unit), GPU (Graphic Processing Unit), and other electrical units for performing functions.

[0043] The MCU (163) can control and manage the overall operation of the battery system (10). The MCU (163) can monitor the overall state of the battery pack (12) and the battery cells (121) included in the battery pack (12) using cell voltages received from the voltage detection circuit (161_1) and the current of the battery pack (12) received from the current sensor (161_2), and can control the charging and discharging of the battery pack (12) and perform cell balancing operations based on the overall state of the battery pack (12) and the battery cells (121) included in the battery pack (12).

[0044] The communication circuit (165) may be configured to support wired or wireless communication between the MCU (163) and an external device. Wired communication may be, for example, CAN (controller area network) communication, and wireless communication may be, for example, Zigbee or Bluetooth communication.

[0045] In some embodiments, the communication circuit (165) can transmit the cell voltage and cell current of each battery cell (121) to the data management server (30).

[0046] The data management server (30) can store and manage data of the battery system (10). The data management server (30) can communicate with the communication circuit (165) of the battery system (10) via a network. Here, the data of the battery system (10) may include the cell voltage and cell current of each battery cell (121). Additionally, the cell voltage of each battery cell (121) may include the OCV. Additionally, the data of the battery system (10) may include the time at which the OCV was measured.

[0047] FIG. 2 is a diagram showing a diagnostic device for a battery cell according to one embodiment, and FIG. 3 is a flowchart showing a diagnostic method of the diagnostic device shown in FIG. 2.

[0048] Referring to FIG. 2, the diagnostic device (200) may include an information collection unit (210), a State of Charge (SOC) estimation unit (220), a deviation calculation unit (230), a noise removal unit (240), a diagnostic unit (250), and a storage unit (260).

[0049] Referring to FIGS. 2 and 3, the information collection unit (210) can acquire voltage data for each of the OCVs of a plurality of battery cells (121) during the diagnosis period (S302). The diagnosis period may be a data collection period for RdV (relaxation deviation voltage or relaxation delta voltage) diagnosis. The voltage data for the OCVs may include a plurality of OCVs for each of a plurality of points in time measured at a predetermined time interval for each battery cell (121) during the diagnosis period. For example, the information collection unit (210) may acquire a plurality of OCVs for each of a plurality of points in time measured at a predetermined time interval for each battery cell (121) during the diagnosis period from a data management server (30 in FIG. 1). As an example, the time interval may be 24 hours, i.e., days, but is not limited thereto.

[0050] In some embodiments, the BMS (16) of the battery system (10) may perform the function of a diagnostic device (200). In this case, the BMS (16) of the battery system (10) may be referred to as a diagnostic device (200), and the information collection unit (210) of the diagnostic device (200) may include a sensing circuit (161).

[0051] The SOC estimation unit (220) can estimate the SOC at each of the multiple time points using multiple OCVs for each battery cell (121) (S304).

[0052] In some embodiments, the SOC estimation unit (220) can estimate the SOC at each of the multiple time points by referring to the SOC-OCV table for each battery cell (121) and using multiple OCVs at multiple time points.

[0053] The deviation calculation unit (230) can calculate the OCV deviation (dOCV) at each of the multiple time points using multiple OCVs at each of the multiple time points for each battery cell (121) (S306), and can calculate the SOC deviation (dSOC) at each of the multiple time points using multiple SOCs at each of the multiple time points (S308). The deviation calculation unit (230) can calculate the OCV deviation (dOCV) at each of the multiple time points from the difference between each of the multiple OCVs at each of the multiple time points for each battery cell (121) and the average value of the OCVs of the multiple battery cells (121). The deviation calculation unit (230) can calculate the SOC deviation (dSOC) from the difference between each of the multiple SOCs at each of the multiple time points for each battery cell (121) and the average value of the SOCs of the multiple battery cells (121).

[0054] The deviation calculation unit (230) can store multiple OCV deviations (dOCV) and SOC deviations (dSOC) at each of multiple points in time of the diagnosis period for each battery cell (121) in the storage unit (260).

[0055] The noise removal unit (240) can extract the OCV deviation (dOCV) and SOC deviation (dSOC) at each point in time of the first period during the diagnosis period for each battery cell (121) (S310). Additionally, the noise removal unit (240) can extract the OCV deviation (dOCV) and SOC deviation (dSOC) at each point in time of the second period, including the end point during the diagnosis period, for each battery cell (121) (S312).

[0056] According to an embodiment, the second period is a part of the diagnosis period and includes a termination point, and may include a plurality of points prior to the termination point that are chronologically continuous from the termination point. The first period is a part of the diagnosis period and may include a plurality of points prior in time that are chronologically continuous.

[0057] For example, assuming that the diagnosis period is 270 days, the size of the first and second periods is n days, and the interval between the first and second periods is m days, the second period can be set to include days a=[270-(n+1)] to 270, and the first period can be set to days b(=c-(n+1)] to c(=am).

[0058] According to some embodiments, the first period may include the start time of the diagnosis period.

[0059] The noise removal unit (240) can calculate a plurality of first differences (ΔdOCV) for each battery cell (121), which is the difference between each of the OCV deviations (dOCV) at a plurality of points in time of the first period and each of the OCV deviations (dOCV) at a plurality of points in time of the second period (S314). Additionally, the noise removal unit (240) can calculate a plurality of second differences (ΔdSOC), which is the difference between each of the SOC deviations (dSOC) at a plurality of points in time of the first period and each of the OCV deviations (dSOC) at a plurality of points in time of the second period (S316).

[0060] For example, if the number of multiple time points of the first period is 30 and the number of multiple time points of the second period is 30, 900 (=30*30) first differences (ΔdOCV) can be calculated and 900 (=30*30) second differences (ΔdSOC) can be calculated.

[0061] The noise removal unit (240) can detect outliers in a plurality of first differences (ΔdOCV) and a plurality of second differences (ΔdSOC) for each battery cell (121) and remove the outliers (S318). Outliers represent very small or very large values ​​that are far from the range of data.

[0062] The noise removal unit (240) can calculate the mean and variance of a plurality of first differences (ΔdOCV) and calculate a first data distribution for a plurality of first differences (ΔdOCV). The noise removal unit (240) can calculate the mean and variance of a plurality of second differences (ΔdSOC) and calculate a second data distribution for a plurality of second differences (ΔdSOC). The noise removal unit (240) can detect outliers in the first data distribution and detect outliers in the second data distribution.

[0063] According to one embodiment, the noise removal unit (240) can determine that data deviating from a specific threshold or a*σ is an outlier when the data distribution for a plurality of first differences (ΔdOCV) or a plurality of second differences (ΔdSOC) follows a normal distribution.

[0064] The noise removal unit (240) calculates the mean and standard deviation of each of the plurality of first differences (ΔdOCV) and calculates the Z-score of each of the plurality of first differences (ΔdOCV). The Z-score may indicate whether each of the plurality of first differences (ΔdOCV) is within a specific threshold or a*σ range from the mean, and can be calculated as in Equation 1.

[0065]

[0066] In mathematical formula 1, Z represents the Z-score, and x is the data, which can represent the first difference or the second difference. μ represents the mean, and σ represents the standard deviation.

[0067] The noise removal unit (240) can determine a first difference among a plurality of first differences (ΔdOCV) in which the Z-score is greater than or equal to a specific threshold value as an outlier, and remove the first difference determined to be an outlier.

[0068] Likewise, the noise removal unit (240) calculates the mean and standard deviation of each of the plurality of second differences (ΔdSOC) and calculates the Z-score of each of the plurality of second differences (ΔdSOC). The noise removal unit (240) determines that a second difference among the plurality of second differences (ΔdSOC) has a Z-score greater than or equal to a specific threshold value is an outlier, and can remove the second difference determined to be an outlier.

[0069] According to one embodiment, the noise removal unit (240) calculates the mean and standard deviation of each of the plurality of first differences (ΔdOCV), determines a first difference having a standard deviation outside the a*σ range among the plurality of first differences (ΔdOCV) as an outlier, and can remove the first difference determined as an outlier. For example, when a is 6, data outside the range of ±3 standard deviations can be determined as an outlier.

[0070] Likewise, the noise removal unit (240) can calculate the average and standard deviation of each of the plurality of second differences (ΔdSOC), determine the second difference that is outside the a*σ range among the plurality of second differences (ΔdSOC) as an outlier, and remove the second difference determined as an outlier.

[0071] The diagnostic unit (250) can diagnose whether each battery cell (121) is an abnormal cell by using the first difference (ΔdOCV) and the second difference (ΔdSOC) from which the abnormal value has been removed by the noise removal unit (240) for each battery cell (121) (S320).

[0072] In some embodiments, the diagnostic unit (250) calculates a first average value by accumulating first differences (ΔdOCV) and calculates a second average value by accumulating second differences (ΔdSOC), and can diagnose an abnormal cell based on the first average value and the second average value. For example, the diagnostic unit (250) can diagnose the battery cell as an abnormal cell according to RdV diagnosis if the average value of the first differences (ΔdOCV) is greater than or equal to a set first threshold value or the average value of the second differences (ΔdSOC) is greater than or equal to a set second threshold value.

[0073] FIG. 4 is a diagram showing an example of voltage data for the OCV of each of a plurality of battery cells according to one embodiment, and FIG. 5 is a diagram showing an example of OCV deviations of each of a plurality of battery cells according to one embodiment.

[0074] As illustrated in FIG. 4, the information collection unit (210) can obtain voltage data (410–450) for the OCV of each of the plurality of battery cells (121) during the diagnosis period. In FIG. 4, for convenience, voltage data (410–450) of five battery cells are shown.

[0075] In this case, the second period during the diagnosis period is from time point c to the last time point, and time point c may be a point n days prior to the last time point. The first period is from time point b to time point a, where time point a is a point m days prior to time point c, and time point b may be a point n days prior to time point a.

[0076] Next, as illustrated in FIG. 5, the deviation calculation unit (230) can calculate the OCV deviations (510–550) at each of a plurality of time points for each battery cell (121) using voltage data (410–450) for each OCV of the battery cell (121) illustrated in FIG. 4.

[0077] Next, for each battery cell (121), a plurality of first differences (ΔdOCV) can be calculated from the difference between each of the OCV deviations of the first period and each of the OCV deviations of the second period.

[0078] In addition, the deviation calculation unit (230) can estimate the SOCs at each of the multiple time points using voltage data (410~450) for the OCV of each battery cell (121) shown in FIG. 4, and can calculate the SOC deviations at each of the multiple time points for each battery cell (121) using the SOCs at each of the multiple time points.

[0079] Next, for each battery cell (121), a plurality of second differences (ΔdSOC) can be calculated from the difference between each of the SOC deviations of the first period and each of the SOC deviations of the second period.

[0080] FIG. 6 is a drawing illustrating a diagnostic device according to another embodiment.

[0081] Referring to FIG. 6, the diagnostic device (620) can be connected to an external device (e.g., an electric vehicle) (610) equipped with a battery system (611) via a network (630).

[0082] In some embodiments, the network (630) may be a wireless network or a wired network. The diagnostic device (620) may diagnose a plurality of battery cells of the battery system (611) based on voltage data for the OCV provided by the BMS of the battery system (611) mounted on the vehicle (610).

[0083] In some embodiments, the diagnostic device (620) may be a computing device.

[0084] FIG. 7 is a block diagram of a diagnostic device according to another embodiment.

[0085] Referring to FIG. 7, the diagnostic device (700) includes a processor (710), memory (720), storage device (730), communication interface (740), and bus (750). The diagnostic device (700) may further include other general-purpose components of a computing device.

[0086] The processor (710) can control the overall operation of each component of the diagnostic device. The processor (710) can be implemented as at least one of various processing units such as a CPU (central processing unit), MPU (microprocessor unit), MCU (micro controller unit), and GPU (graphic processing unit), and can also be implemented as a parallel processing unit. Additionally, the processor (710) can perform operations on a program for executing the diagnostic method described above.

[0087] The memory (720) can store various data, instructions and / or information. The memory (720) can load a computer program from the storage device (730) to execute the diagnostic method described above. The storage device (730) can store the program non-temporarily. The storage device (730) can be implemented as non-volatile memory.

[0088] The communication interface (740) can support wired and wireless internet communication of the diagnostic device. Additionally, the communication interface (740) may support various communication methods other than internet communication. The processor (710) of the diagnostic device can receive data of the battery pack from the vehicle through the communication interface (740).

[0089] The bus (750) can provide communication functions between components of the diagnostic device. The bus (750) can be implemented as various types of buses, such as an address bus, a data bus, and a control bus.

[0090] The computer program may include instructions that cause the processor (710) to perform a diagnostic method when loaded into memory (720). That is, the processor (710) can perform an operation for the diagnostic method by executing the instructions.

[0091] In some embodiments, the diagnostic method may be implemented as a computer program on a computer-readable storage medium. In some embodiments, the computer-readable recording medium may be a portable recording medium or a fixed recording medium. In some embodiments, the computer program recorded on the computer-readable recording medium may be transmitted to another computing device via a network, such as the Internet, and installed and executed on the other computing device.

[0092] Although embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements by those skilled in the art using the basic concept of the present invention as defined in the following claims also fall within the scope of the present invention.

[0093] Although embodiments of the present invention have been described in detail above, the scope of the present invention is not limited thereto, and various modifications and improvements by those skilled in the art using the basic concept of the present invention as defined in the following claims also fall within the scope of the present invention.

Claims

Claim 1 A method for a diagnostic device to diagnose a battery cell comprises: a step of calculating a plurality of Open Circuit Voltages (OCVs) and a plurality of State of Charges (SOCs) of the battery cell for each of a plurality of time points of a diagnostic period; a step of calculating a plurality of OCV deviations and a plurality of SOC deviations at each of a plurality of time points using the plurality of OCVs at each of a plurality of time points and the plurality of SOCs at each of a plurality of time points; a step of extracting first OCV deviations and first SOC deviations during a first period which is part of the diagnostic period; a step of extracting second OCV deviations and second SOC deviations during a second period which is part of the diagnostic period and is temporally separated from the first period; a step of calculating a plurality of first differences which are the difference between each of the first OCV deviations during the first period and each of the second OCV deviations during the second period; a step of calculating a plurality of second differences which are the difference between each of the first SOC deviations during the first period and each of the second SOC deviations during the second period; and from the plurality of first differences and the plurality of second differences A diagnostic method comprising the steps of removing outliers, and diagnosing the battery cell using the first differences from which the outliers have been removed and the second differences. Claim 2 A diagnostic method according to claim 1, wherein the removing step comprises: a step of calculating the mean and standard deviation of the plurality of first differences; a step of calculating the mean and standard deviation of the plurality of second differences; a step of determining the outlier in the plurality of first differences using the mean and standard deviation of the plurality of first differences; and a step of determining the outlier in the plurality of second differences using the mean and standard deviation of the plurality of first differences. Claim 3 A diagnostic method according to claim 2, wherein the step of determining the outlier in the plurality of first differences comprises: a step of calculating Z-scores for each of the plurality of first differences using the mean and standard deviation of the plurality of first differences, and a step of determining as the outlier a first difference corresponding to a Z-score greater than or equal to a first threshold among the plurality of first differences, and the step of determining the outlier in the plurality of second differences comprises: a step of calculating Z-scores for each of the plurality of second differences using the mean and standard deviation of the plurality of second differences, and a step of determining as the outlier a second difference corresponding to a Z-score greater than or equal to a second threshold among the plurality of second differences. Claim 4 A diagnostic method according to claim 2, wherein the step of determining the outlier among the plurality of first differences includes the step of determining as the outlier a first difference corresponding to a standard deviation outside a set first threshold range among the plurality of first differences, and the step of determining the outlier among the plurality of second differences includes the step of determining as the outlier a second difference corresponding to a standard deviation outside a set second threshold range among the plurality of second differences. Claim 5 A diagnostic method according to claim 1, wherein the diagnosing step comprises: a step of calculating a first average value by accumulating first differences from which the outliers have been removed; a step of calculating a second average value by accumulating second differences from which the outliers have been removed; and a step of diagnosing whether the battery cell is an abnormal cell based on the first average value and the second average value. Claim 6 A diagnostic method according to claim 1, wherein the second period includes the last point in time of the diagnostic period. Claim 7 A diagnostic method according to claim 1, wherein the step of calculating a plurality of OCV deviations at each of the plurality of time points includes the step of calculating the OCV deviation at each time point from the difference between the OCV at each time point and the average value of the OCV at each time point, and the average value of the OCV at each time point is the average value of the SOCs of all battery cells of a battery pack including the battery cell at each time point. Claim 8 A diagnostic method according to claim 1, wherein the step of calculating a plurality of SOC deviations at each of the plurality of time points includes the step of calculating the SOC deviation at each time point from the difference between the SOC at each time point and the average SOC value at each time point, and the average SOC value at each time point is the average value of the SOCs of all battery cells of a battery pack including the battery cell at each time point. Claim 9 A diagnostic method according to claim 1, wherein the step of calculating the plurality of OCVs and the plurality of SOCs comprises the step of measuring the plurality of OCVs of the battery cell for each of the plurality of time points of the diagnostic period, and the step of estimating the SOC at each of the plurality of time points from the plurality of OCVs for each of the plurality of time points by referring to a table representing the relationship between SOC and OCV. Claim 10 A diagnostic device for diagnosing a plurality of battery cells by a processor executing one or more instructions, comprising: an information collection unit for each battery cell that acquires a plurality of Open Circuit Voltages (OCVs) at each of a plurality of time points during a diagnostic period; a State of Charge (SOC) estimation unit for each battery cell that estimates a plurality of State of Charges (SOCs) at each of a plurality of time points using the plurality of OCVs at each of a plurality of time points; a deviation calculation unit for each battery cell that calculates a plurality of OCV deviations and a plurality of SOC deviations at each of a plurality of time points using the plurality of OCVs and a plurality of SOCs at each of a plurality of time points; a noise removal unit that calculates a plurality of first differences which are the difference between each of the first OCV deviations during a first period which is part of the diagnostic period and each of the second OCV deviations during a second period which is temporally separated from the first period, calculates a plurality of second differences which are the difference between each of the first SOC deviations during the first period and each of the second SOC deviations during the second period, and removes outliers from the plurality of first differences and the plurality of second differences. A diagnostic device comprising a diagnostic unit that diagnoses the battery cell using the first differences from which the above-mentioned outliers have been removed and the second differences. Claim 11 A diagnostic device according to claim 10, wherein the noise removal unit calculates the average and standard deviation of the plurality of first differences, determines the outliers in the plurality of first differences using the average and standard deviation of the plurality of first differences, calculates the average and standard deviation of the plurality of second differences, and determines the outliers in the plurality of second differences using the average and standard deviation of the plurality of first differences. Claim 12 A diagnostic device according to claim 11, wherein the noise removal unit determines a first difference corresponding to a standard deviation outside a set first threshold range among the plurality of first differences as the outlier, and determines a second difference corresponding to a standard deviation outside a set second threshold range among the plurality of second differences as the outlier. Claim 13 A diagnostic device according to claim 10, wherein the diagnostic unit calculates a first average value by accumulating first differences from which the above-mentioned outliers have been removed, calculates a second average value by accumulating second differences from which the above-mentioned outliers have been removed, and diagnoses whether the battery cell is an outlier cell based on the first average value and the second average value.

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