Battery cell diagnostic device and method

Abstract

A battery cell diagnostic device according to an embodiment of the present invention includes a current measuring unit configured to measure a current of a battery cell, a voltage sensing unit configured to sense a cell voltage of the battery cell, and a first control unit configured to transmit first information of the battery cell including data acquired from the current measuring unit and the voltage sensing unit to an external device, receive second information from the external device including diagnostic information of the battery cell acquired based on the first information, and diagnose an abnormal state of the battery cell based on the first information and the second information.

Description

[Technical field]

[0001] This application claims priority to Korean Patent Application Nos. 10-2022-0065020, 10-2022-0065021, and 10-2022-0065022 filed on May 26, 2022, the entire contents of which are incorporated herein by reference in their entirety in the specification and drawings.

[0002] The present invention relates to a battery cell diagnostic device and method, and more particularly, to a battery cell diagnostic device and method capable of diagnosing the state of a battery cell. [Background technology]

[0003] In recent years, as the demand for portable electronic products such as laptops, video cameras, and mobile phones has grown rapidly and the development of electric vehicles, energy storage batteries, robots, and artificial satellites has accelerated, active research is being conducted on high-performance batteries that can be repeatedly charged and discharged.

[0004] Currently commercially available batteries include nickel-cadmium batteries, nickel-metal hydride batteries, nickel-zinc batteries, and lithium batteries. Among them, lithium batteries are attracting attention due to their advantages of being able to be freely charged and discharged since they have almost no memory effect compared to nickel-based batteries, as well as their extremely low self-discharge rate and high energy density.

[0005] Furthermore, in recent years, with the spread of applications requiring high voltages (e.g., electric vehicles and energy storage systems (ESS)), there have been incidents of batteries used in electric vehicles and energy storage systems catching fire during use.

[0006] Therefore, there is an increasing need for a diagnostic technique that can accurately detect abnormalities in multiple battery cells connected in a battery pack. Summary of the Invention [Problem to be solved by the invention]

[0007] The present invention has been made to solve the above problems, and aims to provide an apparatus and method capable of efficiently diagnosing an abnormal state of a battery cell by linking an on-board diagnostic device and an off-board diagnostic device.

[0008] Other objects and advantages of the present invention can be understood from the following description and will become more apparent from the embodiments of the present invention. Also, the objects and advantages of the present invention can be realized by the means and combinations thereof as set forth in the claims. [Means for solving the problem]

[0009] According to one aspect of the present invention, a battery cell diagnostic device includes: The battery cell includes at least one sensor; and a first control unit configured to acquire data including at least one of a voltage, a current, and a temperature of a battery cell from the at least one sensor, acquire first diagnostic information based on the data, transmit the data to an external device, receive second diagnostic information of the battery cell based on the data from the external device, and diagnose an abnormal state of the battery cell based on the first diagnostic information and the second diagnostic information.

[0010] The above Second The diagnostic information may include at least one of a lithium deposition diagnosis of the battery cell, an abnormality in a parallel connection of the battery cell, and an internal short circuit of the battery cell.

[0011] The first control unit Second diagnostic information The diagnostic information of the battery cell may be configured to display information about an abnormal state of the battery cell on a display unit based on the diagnostic information of the battery cell included in the diagnostic information of the battery cell.

[0012] The first control unit data Based on the above, at least one of a voltage abnormality of the battery cell and a behavior abnormality of the battery cell is detected. Information Detects obtaining the first diagnostic information including at least one detected piece of information; The voltage abnormality, the behavior abnormality, and Second diagnostic information The abnormal condition of the battery cell may be diagnosed based on at least one of the following:

[0013] The first control unit detects the voltage abnormality, the behavior abnormality, and the Second diagnostic information and indicating whether the abnormal state of the battery cell is present based on at least one of the following: Third diagnostic information The signal processing unit may be configured to generate

[0014] The first control unit Third diagnostic information may be configured to be displayed on the display unit.

[0015] The first control unit Third diagnostic information to a second controller of a device in which the battery cell is provided.

[0016] The first control unit data and determining a first average cell voltage and a second average cell voltage of each battery cell based on the time series data, where the first average cell voltage is a short-term moving average and the second average cell voltage is a long-term moving average, and detecting a voltage anomaly of the battery cell based on a difference between the first average cell voltage and the second average cell voltage.

[0017] The battery cell diagnostic device may be configured to diagnose a plurality of battery cells.

[0018] The first control unit may be configured to determine, for each of the plurality of battery cells, a long-term and short-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage of the battery cell, determine an average value of the determined long-term and short-term average differences for the plurality of battery cells, determine, for each of the plurality of battery cells, a cell diagnostic deviation corresponding to a deviation between the average value of the long-term and short-term average differences and the long-term and short-term average differences of the battery cell, and detect, for at least one of the plurality of battery cells, a battery cell that satisfies a condition that the cell diagnostic deviation exceeds a diagnostic critical value as an abnormal voltage cell.

[0019] A battery cell diagnostic device according to another aspect of the present invention may be configured to diagnose a plurality of battery cells.

[0020] The first control unit may be configured to determine, for each of the plurality of battery cells, a long-term and short-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage of the battery cell, determine an average value of the determined long-term and short-term average differences for the plurality of battery cells, determine, for each of the plurality of battery cells, a cell diagnostic deviation corresponding to a deviation between an average value of the long-term and short-term average differences and the long-term and short-term average difference of the battery cell, determine a statistical variable critical value depending on a standard deviation for the cell diagnostic deviation of the plurality of battery cells, filter the time series data based on the statistical variable critical value to generate filtered time series data, and detect a voltage abnormality of at least one of the plurality of battery cells based on a time or a number of data of the filtered time series data exceeding the diagnostic critical value.

[0021] The first control unit may be configured to: determine, for each of the plurality of battery cells, a long-term and short-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage of the battery cell; determine a normalization value corresponding to an average value of the determined long-term and short-term average differences for the plurality of battery cells; normalize the long-term and short-term average difference according to the normalization value for each of the plurality of battery cells; determine a statistical variable critical value depending on a standard deviation for a normalized cell diagnosis deviation of the plurality of battery cells; filter, for each of the plurality of battery cells, the normalized long-term and short-term average difference of each battery cell based on the statistical variable critical value to generate filtered time series data; and detect a voltage abnormality of at least one of the plurality of battery cells based on a time or a number of data of the filtered time series data exceeding the diagnosis critical value.

[0022] The first control unit datadetermining a plurality of sub-voltage curves by applying a moving window of a first time length to the time series of cell voltages included in the first voltage curve; determining a long-term average voltage value of each sub-voltage curve using a first averaging filter of the first time length; determining a short-term average voltage value of each sub-voltage curve using a second averaging filter of a second time length shorter than the first time length; and determining a voltage deviation corresponding to a difference between the long-term average voltage value and the short-term average voltage value of each sub-voltage curve; The plurality of The method may be configured to compare each of the determined voltage deviations for the sub-voltage curves with at least one of a first critical deviation and a second critical deviation to detect an abnormal behavior of the battery cell.

[0023] The first control unit may be configured to detect the behavior abnormality in response to two voltage deviations that respectively satisfy a first condition, a second condition, and a third condition, among the plurality of voltage deviations.

[0024] The first condition may be configured to be met when a first voltage deviation of the two voltage deviations is greater than or equal to the first critical deviation.

[0025] The second condition may be configured to be met when a second voltage deviation of the two voltage deviations is less than or equal to the second critical deviation.

[0026] The third condition may be configured to be met when the time interval between two of the voltage deviations is less than or equal to a critical time.

[0027] The above Second diagnostic information indicates whether the cumulative capacitance difference change is equal to or greater than a threshold value, and the cumulative capacitance difference change may be a sum of capacitance difference changes.

[0028] The capacity difference change amount is a difference between a capacity difference in the k-th charge / discharge cycle of the battery cell and a capacity difference in the k-1-th charge / discharge cycle of the battery cell, where k is a natural number of 2 or more.

[0029] The capacity difference of each of the charge / discharge cycles may correspond to a difference between a charge capacity of the battery cell in a charging portion of the charge / discharge cycle and a discharge capacity of the battery cell in a discharging portion of the charge / discharge cycle.

[0030] the data includes current measurements obtained from a current measurement unit configured to measure a current of the battery cell; The charge capacity and the discharge capacity are each Said data Included in Current Measurement can be derived from

[0031] The above Second diagnostic information may indicate the amount of capacity differential change between successive charge / discharge cycles of the battery cell.

[0032] The capacity difference for each of the charge / discharge cycles of the battery cell may be the difference between (i) the charge capacity of the battery cell during the charge process of the charge / discharge cycle of the battery cell and (ii) the discharge capacity of the battery cell during the discharge process of the charge / discharge cycle of the battery cell.

[0033] The above Second diagnostic information The external device may indicate whether or not there is an abnormality in a parallel connection of a plurality of unit cells included in the battery cell based on a result of monitoring a change over time in the estimated capacity value.

[0034] The estimated capacity value may indicate a full charge capacity of the battery cell based on charge and discharge data.

[0035] The charge / discharge data may include a voltage time series indicating a change in voltage of the battery cell over time, and a current time series indicating a change in charge / discharge current of the battery cell over time.

[0036] The above Second diagnostic information may indicate whether or not there is an internal short circuit in the battery cell based on a first SOC change in the battery cell and a reference factor.

[0037] The criterion factor may be determined by applying a statistical algorithm to a first SOC change of at least two battery cells of the plurality of battery cells.

[0038] The first SOC change may be a difference between a first SOC at a first charging point and a second SOC at a second charging point of the respective battery cell.

[0039] The first SOC may be estimated by applying a SOC estimation algorithm to state parameters of the battery cell at the first charging point.

[0040] The second SOC may be estimated by applying the SOC estimation algorithm to state parameters of the battery cell at the second charging point.

[0041] The state parameter is data The signal intensity can be obtained based on the following:

[0042] A battery cell diagnostic system according to another aspect of the present invention includes the battery cell diagnostic device according to the aspect of the present invention and the external device.

[0043] The external device is data Based at least in part on Second diagnostic information The method may be configured to derive

[0044] According to another aspect of the present invention, a battery cell diagnostic method includes: The method includes the steps of: acquiring first diagnostic information based on data including at least one of a voltage, a current, and a temperature of a battery cell; transmitting the data to an external device and receiving second diagnostic information of the battery cell based on the data from the external device; and diagnosing an abnormal state of the battery cell based on the first diagnostic information and the second diagnostic information.

[0045] According to another aspect of the present invention, a battery cell diagnostic method includes: The above The battery cell data At least one of a voltage abnormality and a behavior abnormality of the battery cell based on Information Detect and obtaining the first diagnostic information including at least one piece of detected information. and The above Abnormal voltage of the battery cell, abnormal behavior of the battery cell, and Second diagnostic informationand diagnosing an abnormal state of the battery cell based on at least one of the above. Effect of the Invention

[0046] According to one aspect of the present invention, an abnormal state of a battery cell can be efficiently diagnosed by linking an on-board device and an off-board device.

[0047] According to one aspect of the present invention, by linking the on-board device and the off-board device, it is possible to save software resources and time required for diagnosing abnormalities in each battery cell, and to reduce the possibility of misdiagnosis due to an increase in the number of abnormal battery cells among multiple battery cells.

[0048] According to one aspect of the present invention, both long-term and short-term trends of the cell voltage of each battery cell can be taken into consideration, and abnormal changes in the corresponding battery cell can be accurately detected.

[0049] The following drawings attached to this specification illustrate preferred embodiments of the present invention and, together with the detailed description of the invention, serve to further understand the technical ideas of the present invention. Therefore, the present invention should not be interpreted as being limited to only the matters described in the drawings. [Brief description of the drawings]

[0050] [Figure 1] 1 is a diagram illustrating an example of a system including a battery cell diagnostic device according to an embodiment of the present invention. [Diagram 2] 1 is a block diagram illustrating a functional configuration of a battery cell diagnosis device according to an embodiment of the present invention; [Diagram 3] 1 is a diagram conceptually illustrating a configuration of an electric vehicle according to an embodiment of the present invention; [Figure 4a] 4 is a graph showing an example of a process of detecting an abnormal voltage of each battery cell from time-series data showing a change over time in cell voltages of the battery cells shown in FIG. 3; [Figure 4b] 4 is a graph showing an example of a process of detecting an abnormal voltage of each battery cell from time-series data showing a change over time in cell voltages of the battery cells shown in FIG. 3; [Figure 4c] 4 is a graph showing an example of a process of detecting an abnormal voltage of each battery cell from time-series data showing a change over time in cell voltages of the battery cells shown in FIG. 3; [Figure 4d] 4 is a graph showing an example of a process of detecting an abnormal voltage of each battery cell from time-series data showing a change over time in cell voltages of the battery cells shown in FIG. 3; [Figure 4e] 4 is a graph showing an example of a process of detecting an abnormal voltage of each battery cell from time-series data showing a change over time in cell voltages of the battery cells shown in FIG. 3; [Figure 4f] 4 is a graph showing an example of a process of detecting an abnormal voltage of each battery cell from time-series data showing a change over time in cell voltages of the battery cells shown in FIG. 3; [Figure 4g] 4 is a graph showing an example of a process of detecting an abnormal voltage of each battery cell from time-series data showing a change over time in cell voltages of the battery cells shown in FIG. 3; [Figure 4h] 4 is a graph showing an example of a process of detecting an abnormal voltage of each battery cell from time-series data showing a change over time in cell voltages of the battery cells shown in FIG. 3; [Diagram 5] 4 is a graph showing an example of a voltage curve corresponding to a raw time series of actual voltage values ​​of cell voltages of a battery cell referred to in various embodiments of the present invention; [Figure 6] 6 is a graph showing an example of a measured voltage curve obtained by combining a measurement noise with an original time series corresponding to the voltage curve of FIG. 5. [Figure 7] 7 is a graph showing an example of a first moving average curve obtained by applying a first averaging filter to the voltage curve of FIG. 6; [Figure 8]7 is a graph showing an example of a second moving average curve obtained by applying a second average filter to the voltage curve of FIG. 6. [Figure 9] 9 is a graph showing an example of a voltage deviation curve that is a difference between the first moving average curve of FIG. 7 and the second moving average curve of FIG. 8. [Figure 10] 1 is a block diagram showing a schematic configuration of an external device according to an embodiment of the present invention; [Figure 11] FIG. 2 is a diagram illustrating a schematic configuration of a battery cell referred to in various embodiments of the present invention; [Figure 12] 1 is a diagram illustrating a first capacity abnormality (partial open circuit failure) of a battery cell referred to in various embodiments of the present invention. [Figure 13] 11A and 11B are diagrams illustrating a second capacity abnormality (complete disconnection failure) of a battery cell referred to in various embodiments of the present invention. [Figure 14] 4 is an exemplary diagram illustrating a relationship between a capacity abnormality and a full charge capacity of a battery cell referred to in various embodiments of the present invention; FIG. [Figure 15] FIG. 2 is a diagram referred to in describing an exemplary equivalent circuit of a battery cell referred to in various embodiments of the present invention. [Figure 16] 1 is an exemplary graph comparing changes in SOC of a battery cell depending on the presence or absence of an internal short circuit abnormality, which is referred to in various embodiments of the present invention; [Figure 17] 11 is another exemplary graph comparing a change in SOC of a battery cell depending on the presence or absence of an internal short circuit abnormality referred to in various embodiments of the present invention. [Figure 18] 11 is yet another exemplary graph comparing a change in SOC of a battery cell depending on the presence or absence of an internal short circuit abnormality referred to in various embodiments of the present invention. [Figure 19] 4 is a flow chart illustrating a battery cell diagnostic device according to an embodiment of the present invention diagnosing an abnormal state of a battery cell using an external device. [Figure 20] 1 is a flow chart illustrating an example of a method for detecting a voltage abnormality according to an embodiment of the present invention. [Figure 21]4 is another flow chart illustrating an example of a method for detecting a voltage abnormality according to an embodiment of the present invention. [Figure 22] 11 is yet another flow chart illustrating an example of a method for detecting a voltage abnormality according to an embodiment of the present invention. [Diagram 23] 11 is yet another flow chart illustrating an example of a method for detecting a voltage abnormality according to an embodiment of the present invention. [Figure 24] 11 is yet another flow chart illustrating an example of a method for detecting a voltage abnormality according to an embodiment of the present invention. [Diagram 25] 1 is a flow diagram illustrating an exemplary behavioral anomaly detection method according to an embodiment of the present invention. [Figure 26] 4 is another exemplary flow diagram illustrating a behavioral anomaly detection method according to an embodiment of the present invention. [Figure 27] FIG. 2 is a flow chart illustrating an example of a method for detecting a lithium deposition anomaly according to an embodiment of the present invention. [Figure 28] FIG. 4 is another flow chart illustrating an example of a method for detecting an abnormality in lithium deposition according to an embodiment of the present invention. [Figure 29] FIG. 11 is yet another flow chart illustrating an example of a method for detecting a lithium deposition abnormality according to an embodiment of the present invention. [Diagram 30] FIG. 11 is yet another flow chart illustrating an example of a method for detecting a lithium deposition abnormality according to an embodiment of the present invention. [Diagram 31] 1 is a graph showing changes in data measured in an experimental example in which a method for detecting lithium deposition using an external device according to an embodiment of the present invention is applied; [Diagram 32] 11 is a graph showing changes in data measured in another experimental example to which a method for detecting lithium precipitation anomaly according to an embodiment of the present invention is applied. [Diagram 33] 4 is a flow chart illustrating a battery cell diagnostic device according to an embodiment of the present invention diagnosing an abnormal state of a battery cell using an external device. [Diagram 34] FIG. 2 is a flow chart illustrating an example of a battery diagnosis method according to an embodiment of the present invention. [Diagram 35]4 is a flow chart illustrating a battery cell diagnostic device according to an embodiment of the present invention diagnosing an abnormal state of a battery cell using an external device. [Diagram 36] FIG. 2 is a flow chart illustrating an exemplary battery management method according to an embodiment of the present invention. [Figure 37] FIG. 4 is another flow chart illustrating an example of a battery management method according to an embodiment of the present invention. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0051] Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. Prior to this, the terms and words used in the present specification and claims are not to be construed as being limited to their ordinary and dictionary meanings, but are to be construed as having meanings and concepts corresponding to the technical ideas of the present invention, in accordance with the principle that the inventors themselves can appropriately define the concepts of terms in order to best describe the invention.

[0052] Therefore, it should be understood that the embodiment described in this specification and the configurations shown in the drawings are merely the most preferred embodiment of the present invention and do not represent the entire technical idea of ​​the present invention, and therefore there may be various equivalents and modifications that can be substituted therefor at the time of this application.

[0053] FIG. 1 is a diagram illustrating an example of a battery cell diagnostic system 1 including a battery cell diagnostic device 1000.

[0054] Referring to Fig. 1, a battery cell diagnostic system 1 may include a battery cell diagnostic device 1000 and an external device 2000. However, this is only a preferred embodiment for achieving the present invention, and it is of course possible to add or remove some components as necessary. It should be noted that each component of the battery cell diagnostic system 1 shown in Fig. 1 is a functional component that is functionally divided, and multiple components can be realized in an integrated form in an actual physical environment.

[0055] In the battery cell diagnostic system 1, the battery cell diagnostic device 1000 is a computing device that diagnoses an abnormal state of a battery cell and provides the diagnosis result to a user. The battery cell diagnostic device 1000 may mean an on-board computing device included in a Battery Management System (BMS). For example, the battery cell diagnostic device 1000 may be a computing device included in a BMS provided in a user's electric vehicle. However, this is merely an example, and any type of device having a computing function and a communication function may be included without limitation.

[0056] The battery cell diagnostic device 1000 may obtain data including at least one of a charging current and a discharging current of a battery cell, and a cell voltage, which is a voltage across a battery cell. For example, the battery cell diagnostic device 1000 may obtain data regarding at least one of a charging current, a discharging current, and a cell voltage, which is a voltage across a battery cell, of a battery cell provided in an electric vehicle.

[0057] According to an embodiment of the present invention, the battery cell diagnostic device 1000 data may be generated. data may include data regarding at least one of a charging current and a discharging current of the battery cell, and a cell voltage, which is the voltage across the battery cell. Additionally, the data may include data related to the temperature of the battery cells.

[0058] The battery cell diagnostic device 1000 includes: data The external device 2000 may transmit the data Received and received data Regarding the battery cell based at least in part on Second diagnostic information The external device 2000 may derive Second diagnostic information may be transmitted to the battery cell diagnostic device 1000. The battery cell diagnostic device 1000 may First diagnostic information and Second diagnostic information An abnormal state of the battery cell can be diagnosed based on the above.

[0059] In the battery cell diagnostic system 1, the external device 2000 includes: data Generated using Second diagnostic information The external device 2000 may refer to an off-board computing device that provides the second diagnostic information to the battery cell diagnosis device 1000. For this purpose, the external device 2000 may receive information on the voltage, current, or temperature of the battery cell from various electric vehicles, and store at least one algorithm capable of diagnosing the battery cell based on the received information. In addition, the external device 2000 may store information on the voltage, current, or temperature of the battery cell received from various electric vehicles as big data, and store at least one artificial intelligence model capable of diagnosing the battery cell based on the big data. The computing device may be, but is not limited to, a notebook computer, a desktop, a laptop, etc., and may include any type of device equipped with a computing function and a communication function. However, when providing the second diagnostic information to a plurality of battery cell diagnosis devices 1000, it may be preferable that the external device 2000 is realized as a server computing device.

[0060] The components of the battery cell diagnosis system 1 may communicate with each other via a network. Here, the network may be realized by any kind of wired or wireless network such as a local area network (LAN), a wide area network (WAN), a mobile radio communication network, or a wireless broadband internet (WiBro).

[0061] The battery cell diagnostic system 1 according to an embodiment of the present invention has been described above with reference to Fig. 1. Hereinafter, the battery cell diagnostic device 1000 according to an embodiment of the present invention will be described in detail with reference to Fig. 2.

[0062] 2 is a block diagram showing a schematic functional configuration of the battery cell diagnostic device 1000. Referring to FIG. 2, the battery cell diagnostic device 1000 may include a current measuring unit 100, a voltage sensing unit 200, a data acquiring unit 300, a first control unit 400, and a display unit 500. The battery cell diagnostic device 1000 may include at least one sensor that measures data including at least one of the voltage, current, and temperature of the battery cell.

[0063] The current measuring unit 100 may measure a current of the battery cell, where the current may be at least one of a charging current and a discharging current of the battery cell.

[0064] Preferably, the current measuring unit 100 may measure a charging current during a charging process of the battery cell, and may measure a discharging current during a discharging process of the battery cell.

[0065] The voltage sensing unit 200 may be configured to sense a cell voltage of a battery cell. For example, the voltage sensing unit 200 may sense a voltage signal indicating a cell voltage, which is a voltage across the battery cell. This will be described in detail below with reference to FIG.

[0066] The data acquiring unit 300 may acquire data from the current measuring unit 100 and the voltage sensing unit 200 .

[0067] For example, the first control unit 400 may determine the temperature of the battery cell based on the data acquired by the data acquisition unit 300. data For example, data current information about at least one of a charging current and a discharging current of the battery cell; , B Voltage information about the cell voltage of the battery cell and at least one of temperature information about the temperature of the battery cell. may include.

[0068] As another example, the first control unit 400 may directly obtain current information about the battery cell from the current measuring unit 100 and directly obtain voltage information about the battery cell from the voltage sensing unit 200.

[0069] The first control unit 400 The first control unit 400 may obtain first diagnostic information based on the obtained data.did data The first control unit 400 may be configured to transmit the data Contains battery cell diagnostic information obtained based on Second diagnostic information may be received from the external device 2000.

[0070] in particular, Second diagnostic information The external device 2000 data For example, the battery cell diagnostic information may include a lithium deposition diagnosis for the battery cell, an abnormality in the parallel connection of the battery cell, etc. diagnosis , Battery cell tab disconnection diagnosis, and an internal short circuit in the battery cell diagnosis At least one of About It may include information such as:

[0071] The first control unit 400 First diagnostic information and Second diagnostic information The battery cell may be configured to diagnose an abnormal condition based on the

[0072] Specifically, the first control unit 400 data Based on this, at least one of a voltage abnormality of the battery cell and a behavior abnormality of the battery cell can be detected. That is, the first control unit 400 can obtain the first diagnosis information (information about at least one of a voltage abnormality and a behavior abnormality) based on the data. The first control unit 400 detects voltage abnormality, behavior abnormality, and Second diagnostic information The battery cell diagnostic device 1000 may be configured to diagnose an abnormal state of the battery cell based on at least one of the above. As described above, the present invention is characterized in that the battery cell diagnostic device 1000 and the external device 2000 do not diagnose the same diagnostic item, but diagnose different diagnostic items. As an example, the battery cell diagnostic device 1000 may diagnose at least one of a voltage abnormality of the battery cell and a behavior abnormality of the battery cell, and the external device 2000 may diagnose at least one of a lithium deposition diagnosis of the battery cell, an abnormality of a parallel connection of the battery cell, and an internal short circuit of the battery cell.

[0073] The first control unit 400 data The specific details of detecting a voltage abnormality or behavior abnormality of a battery cell based on the above will be described later.

[0074] The display unit 500 may include at least one display, and may display information about the abnormal state of the battery cell on the display.

[0075] Here, the display unit 500 may be included in a load device electrically connected to the first control unit 400 and receiving power from the cell group CG. If the load device is an electric vehicle, a hybrid vehicle, a plug-in hybrid vehicle, or the like, the diagnosis result information may be output through an integrated information display of the vehicle.

[0076] For example, the first control unit 400 Second diagnostic information The display unit 500 may be configured to display information about an abnormal state of the battery cell based on the diagnostic information of the battery cell included in the battery cell diagnostic information. The information about the abnormal state of the battery cell displayed on the display unit 500 may include at least one of a diagnosis result related to the first diagnosis information and a diagnosis result related to the second diagnosis information.

[0077] As another example, the first control unit 400 may detect a voltage abnormality, a behavior abnormality, and Second diagnostic information Indicating whether the battery cell is in an abnormal state based on at least one of Third diagnostic information Then, the first control unit 400 generates Third diagnostic information The first control unit 400 may display the image using a display included in the display unit 500. Third diagnostic information By displaying this, it is possible to provide the user with specific information regarding any abnormalities in the battery cells.

[0078] Then, the first control unit 400 Third diagnostic information to a second control unit of a device in which the battery cell is provided.

[0079] Here, the second control unit may be configured to perform a function of controlling an apparatus equipped with the battery cell. For example, the apparatus equipped with the battery cell may be an electric vehicle. In this case, the second control unit may be an electronic control unit (ECU) configured to control the electric vehicle. The first control unit 400 is Third diagnostic information to a second controller of an electric vehicle equipped with the battery cell.

[0080] 3 is a diagram conceptually illustrating the configuration of an electric vehicle according to an embodiment of the present invention. Referring to FIG. 3, the electric vehicle includes a battery pack 10, an inverter INV, an electric motor M, and a second control unit 600.

[0081] The battery pack 10 may include a cell group CG, a switch S, and a battery cell diagnostic device 1000.

[0082] The cell group CG may be coupled to the inverter INV through a pair of power supply terminals provided on the battery pack 10. The cell group CG may be formed of a plurality of battery cells BC connected in series. 1 ~BC N (N is a natural number equal to or greater than 2). Each battery cell BC i The type of the battery is not particularly limited as long as it is rechargeable, such as a lithium ion battery cell, etc. i is an index for identifying the cell, and is a natural number from 1 to N.

[0083] The switch S is connected in series to the cell group CG. The switch S is provided in a current path for charging and discharging the cell group CG. The switch S is controlled to be turned on and off according to a switching signal from the battery cell diagnosis device 1000. Preferably, the operation state of the switch S may be controlled by the first control unit 400 to be in a turned-on state or a turned-off state.

[0084] For example, the switch S may be a mechanical relay that is turned on and off by the magnetic force of a coil. As another example, the switch S may be a semiconductor switch such as a FET (Field Effect Transistor) or a MOSFET (Metal Oxide Semiconductor Field Effect Transistor).

[0085] An inverter INV is provided to convert the direct current from the cell group CG into an alternating current according to instructions from the battery cell diagnostic device 1000 .

[0086] The electric motor M may be, for example, a three-phase AC motor. The electric motor M may be driven using AC power from an inverter INV.

[0087] The battery cell diagnostic device 1000 is provided to take charge of overall control related to charging and discharging of the cell group CG.

[0088] The battery cell diagnostic device 1000 may further include at least one of a temperature sensor T and an interface unit I / F.

[0089] The voltage sensing unit 200 senses a plurality of battery cells BC via a plurality of voltage sensing lines. 1 ~BC N The voltage sensing unit 200 is connected to the positive and negative electrodes of each battery cell BC. i and generating a voltage signal indicative of the measured cell voltage.

[0090] The current measuring unit 100 is connected in series to the cell group CG via a current path. The current measuring unit 100 is configured to detect a battery current flowing through the cell group CG and generate a current signal indicative of the detected battery current (also referred to as a "charge / discharge current"). 1 ~BC Nare connected in series, so multiple battery cells BC 1 ~BC N The battery current flowing through any one of the battery cells may be the same as the battery current flowing through the other battery cells. The current measuring unit 100 may be realized by one or a combination of two or more of known current detection elements such as a shunt resistor, a Hall effect element, etc.

[0091] The temperature sensor T is configured to detect the temperature of the cell group CG and generate a temperature signal indicative of the detected temperature. For example, the temperature sensor T may measure the temperature of the cell group CG, and may generate a temperature signal indicative of the temperature of each battery cell BC included in the cell group CG. i The temperatures may be measured individually.

[0092] The first control unit 400 may be operatively coupled to the voltage sensing unit 200, the temperature sensor T, the current measuring unit 100, the interface unit I / F, and / or the switch S. The first control unit 400 may collect sensing signals from the voltage sensing unit 200, the current measuring unit 100, and the temperature sensor T. The sensing signals refer to synchronously detected voltage signals, current signals, and / or temperature signals.

[0093] The interface unit I / F may include a communication circuit configured to support wired or wireless communication between the first control unit 400 and the second control unit 600. For example, the wired communication may be CAN (Controller Area Network) and / or CAN-FD (CAN with flexible data rate) communication, and the wireless communication may be ZigBee (registered trademark) or Bluetooth (registered trademark) communication. Of course, the type of communication protocol is not particularly limited as long as it supports wired or wireless communication between the first control unit 400 and the second control unit 600.

[0094] The interface unit I / F may be coupled to an output device (eg, a display, a speaker) that provides information received from the second control unit 600 and / or the first control unit 400 in a form that can be recognized by a user.

[0095] The second control unit 600 may control the inverter INV based on battery information (eg, voltage, current, temperature, SOC) collected through communication with the battery cell diagnosis device 1000.

[0096] When the switch S is turned on during operation of the electrical load and / or the charger, the battery cell BC 1 ~BC N The battery cell BC can be charged or discharged. 1 ~BC N When the switch S is turned off during charging or discharging of the battery cell BC 1 ~BC N can be switched to a hibernation state.

[0097] The first control unit 400 may turn on the switch S in response to a key-on signal. The first control unit 400 may turn off the switch S in response to a key-off signal. The key-on signal is a signal requesting switching from a dormant state to a charging state or a discharging state. The key-off signal is a signal requesting switching from a charging state or a discharging state to a dormant state. Alternatively, the on / off control of the switch S may be handled by the second control unit 600 instead of the first control unit 400.

[0098] 3 illustrates the battery cell diagnostic device 1000 being included in a battery pack 10 for an electric vehicle, but this is merely an example. For example, the battery cell diagnostic device 1000 may include a battery cell BC 1 ~BC N As another example, the battery cell diagnostic device 1000 may be included in a test system used to screen abnormally behaving battery cells during the manufacturing process of the battery cell BC. 1 ~BC N may be included in an energy storage system (ESS) including

[0099] The first control unit 400 data First, the first control unit 400 detects voltage abnormalities and behavior abnormalities of the battery cell using the above. data Fig. 4 shows how to detect voltage anomalies in battery cells using a to Figure 4h For a more detailed explanation, refer to.

[0100] 4a to 4h show a plurality of battery cells BC shown in FIG. 1 ~BC N 11 is a graph showing an example of a process of detecting an abnormal voltage of each battery cell from time-series data showing a change in cell voltage over time.

[0101] FIG. 4a shows a plurality of battery cells BC 1 ~BC N The number of battery cells shown in FIG. 4a is 14. The first control unit 400 collects the voltage signals from the voltage sensing unit 200 every unit time and calculates the voltage curves of each battery cell BC. i The voltage value of the cell voltage data The unit time may be an integer multiple of the voltage measurement period of the voltage sensing unit 200.

[0102] The first control unit 400 data The cell voltages included in the signal processing circuit may be configured to generate time series data indicative of a history of the cell voltages over time.

[0103] Specifically, the first control unit 400 data Each battery cell BC i Based on the voltage values ​​of the cell voltages, cell voltage time series data indicating a history of the cell voltage of each battery cell over time may be generated. Each time a cell voltage is measured, the number of cell voltage time series data is increased by one.

[0104] The voltage curves shown in FIG. 4a are for multiple battery cells BC 1 ~BC N Therefore, each voltage curve indicates the change history of the cell voltage of the corresponding battery cell BC.

[0105] The first controller 400 may be configured to determine a first average cell voltage and a second average cell voltage of each battery cell based on the time series data, where the first average cell voltage may be a short-term moving average and the second average cell voltage may be a long-term moving average.

[0106] Specifically, the first control unit 400 uses one moving window or two moving windows to calculate the temperature of the battery cells BC. 1 ~BC N may be determined for each unit of time. When two moving windows are used, the time length for one moving window is different from the time length for the other moving window.

[0107] Here, the time length of each moving window is an integer multiple of the unit time, the end point of each moving window is the current time, and the start point of each moving window is a time a predetermined time length before the current time.

[0108] Hereinafter, for ease of explanation, of the two moving windows, the one associated with the shorter time length will be referred to as a first moving window, and the one associated with the longer time length will be referred to as a second moving window.

[0109] The first control unit 400 uses only the first moving window, or uses both the first moving window and the second moving window, to calculate the current density of each battery cell BC i It is possible to diagnose voltage abnormalities.

[0110] The first control unit 400 receives the battery voltage from the i-th battery cell BC i Based on the cell voltage of the i-th battery cell BC i The short-term and long-term trends of the cell voltages can be compared for each unit time.

[0111] The first control unit 400 calculates the current of the i-th battery cell BC according to the first moving window by using the following Equation 1 or 2: i That is, the first control unit 400 may determine the first average cell voltage of each battery cell using a first moving window.

[0112] Equation 1 is a moving average formula based on the arithmetic average method, and Equation 2 is a moving average formula based on the weighted average method.

number

number

[0113] The first control unit 400 calculates the current of the i-th battery cell BC according to the second moving window by using the following Equation 3 or 4: i That is, the first control unit 400 may determine the second average cell voltage by using a second moving window.

[0114] Equation 3 is a moving average formula based on the arithmetic average method, and Equation 4 is a moving average formula based on the weighted average method.

number

number

[0115] In one embodiment, the first control unit 400 calculates V i [k] is the current value of each battery cell BC i Instead of the cell voltage of the current cell group CG, the reference cell voltage of the current cell group CG and the battery cell BC i The difference between the cell voltage of the

[0116] The current reference cell voltage of cell group CG is the voltage of multiple battery cells BC 1 ~BC N In a variant, the average of the multiple cell voltages can be replaced by a median.

[0117] Specifically, the first control unit 400 calculates VD i [k] is V in Equations 1 to 4 i [k].

number

[0118] If the time length of the first moving window is less than the time length of the second moving window, the first average cell voltage may be referred to as the "short-term moving average" of the cell voltage, and the second average cell voltage may be referred to as the "long-term moving average" of the cell voltage.

[0119] The first controller may detect a voltage abnormality of the battery cell based on a difference between the first average cell voltage and the second average cell voltage, which will be described in detail with reference to FIG.

[0120] The first controller may determine, for each of the plurality of battery cells, a long-term and short-term average difference corresponding to a difference between a first average cell voltage and a second average cell voltage of the battery cell.

[0121] FIG. 4b illustrates the voltage curves of the i-th battery cell BC i 4b shows the short-term moving average line and the long-term moving average line for the cell voltage of . In FIG. 4b, the horizontal axis shows time, and the vertical axis shows the moving average value of the cell voltage.

[0122] Referring to FIG. 4b, a plurality of moving average lines S i shown by dotted lines represent the battery cells BC 1 ~BC N , and the first average cell voltage (SMA i [k]) over time. In addition, the multiple moving average lines Li shown by solid lines represent the time series of multiple battery cells BC 1 ~BC N , and the second average cell voltage (LMA i [k]) over time.

[0123] The dotted line graph and the solid line graph are obtained by using Equation 2 and Equation 4, respectively. i [k] is the VD of Equation 5 i [k] is used, V av [k] was set to the average of multiple cell voltages. The time length of the first moving window was 10 seconds, and the time length of the second moving window was 100 seconds.

[0124] The first control unit 400 may determine an average value of the long-term and short-term average differences determined for the plurality of battery cells.

[0125] The first control unit 400 may determine a cell diagnosis deviation for each of the battery cells, the cell diagnosis deviation corresponding to the deviation between the average value of the long-term and short-term average differences and the long-term and short-term average differences of the battery cells. For example, the first control unit 400 may determine a cell diagnosis deviation for each of the battery cells by calculating the deviation between the average value of the long-term and short-term average differences and the long-term and short-term average differences of each of the battery cells.

[0126] FIG. 4c shows the first average cell voltage (SMA) of each battery cell shown in FIG. 4b. i [k]) and the second average cell voltage (LMA i In Fig. 4c, the horizontal axis indicates time, and the vertical axis indicates each battery cell BC i This shows the long-term and short-term average differences.

[0127] Each battery cell BC i The long-term and short-term average difference of each battery cell BC i First average cell voltage SMA i and the second average cell voltage LMA i As an example, the i-th battery cell BC i The long-term and short-term average difference is SMA i [k] and LMA i It may be equal to one of the [k] (e.g., a larger one) minus another (e.g., a smaller one).

[0128] i-th battery cell BC i The long-term and short-term average difference of the i-th battery cell BC i The change in the cell voltage depends on the short-term and long-term history of the cell voltage.

[0129] i-th battery cell BC i The temperature and SOH of the i-th battery cell BC will continue to be constant not only in the short term but also in the long term. iTherefore, the voltage of the i-th battery cell BC i If there is no voltage abnormality, the i-th battery cell BC i The long-term and short-term average differences of are not significantly different from the long-term and short-term average differences of other battery cells.

[0130] On the other hand, the i-th battery cell BC i In the event of a sudden voltage abnormality caused by an internal short circuit and / or an external short circuit, the second average cell voltage (LMA i [k]) than the first average cell voltage (SMA i As a result, the i-th battery cell BC i The long-term and short-term average difference of the battery cells has a large deviation from the long-term and short-term average differences of the other battery cells without voltage abnormality.

[0131] The first control unit 400 controls each battery cell BC i Long-term and short-term average difference (|SMA i [k]-LMA i [k]|. The first control unit 400 may also determine the long-term and short-term average difference (|SMA i [k]-LMA i [k]|) can be determined. Hereafter, the average value is referred to as |SMA i [k]-LMA i [k]|av”. Furthermore, the first control unit 400 calculates the average value of the long-term and short-term average differences (|SMA i [k]-LMA i [k]|av) and the long-term and short-term average difference (|SMA i [k]-LMA i The first control unit 400 may determine the deviation of each battery cell BC from the cell diagnosis deviation (Ddiag,i[k]|) as the cell diagnosis deviation (Ddiag,i[k]). i It is possible to detect voltage abnormalities.

[0132] In an embodiment, the first control unit 400 may detect a battery cell that satisfies a condition that the cell diagnosis deviation exceeds a diagnosis critical value as an abnormal voltage cell for at least one of the plurality of battery cells. i If the cell diagnostic deviation (Ddiag,i[k]) for the i-th battery cell BC exceeds a preset diagnostic critical value (e.g., 0.015), i It is possible to determine that a voltage abnormality has occurred in a battery cell and detect a voltage abnormality in the battery cell.

[0133] In another embodiment, the first control unit 400 may determine a statistical variable threshold value depending on a standard deviation for the cell diagnosis deviation of the plurality of battery cells. The first control unit 400 may then filter the time series data based on the statistical variable threshold value to generate filtered time series data. Finally, the first control unit 400 may be configured to detect a voltage abnormality of the battery cell based on a time or a number of data of the filtered time series data exceeding the diagnosis threshold value for at least one battery cell of the plurality of battery cells. Here, for convenience of explanation, a specific content of detecting a voltage abnormality of the battery cell using the statistical variable threshold value will be described through the following embodiment considering a normalized cell diagnosis deviation.

[0134] Meanwhile, the first control unit 400 may determine a normalization value corresponding to an average value of the long-term and short-term average differences determined for the plurality of battery cells, and may normalize the long-term and short-term average differences for each of the plurality of battery cells according to the normalization value.

[0135] For example, the first control unit 400 may detect a voltage abnormality by i Long-term and short-term average difference (|SMA i [k]-LMA i [k]|) may be normalized using a normalization criterion. Preferably, the normalization criterion is the average of the long-term and short-term mean differences (|SMA i [k]-LMA i [k]|av).

[0136] Specifically, the first control unit 400 controls the first battery cell BC 1 ~Nth battery cell BC N The average difference between the long-term and short-term averages (|SMA i [k]-LMA i [k]|av) as a normalization reference value. i Long-term and short-term average difference (|SMA i [k]-LMA i [k]|) by the normalized standard value to calculate the long-term and short-term average difference (|SMA i [k]-LMA i [k]|) can be normalized.

[0137] The following formula 6 is for each battery cell BC i Long-term and short-term average difference (|SMA i [k]-LMA i 6 is a formula for normalizing the cell diagnostic deviation (D*diag,i[k]|). In an embodiment, the value calculated by Equation 6 may be referred to as a normalized cell diagnostic deviation (D*diag,i[k]).

number

[0138] Each battery cell BC i Long-term and short-term average difference (|SMA i [k]-LMA iThe cell diagnostic deviation (D*diag,i[k]|) can also be normalized by a logarithmic operation according to the following Equation 7. In an embodiment, the value calculated by Equation 7 may also be referred to as a normalized cell diagnostic deviation (D*diag,i[k]).

number

[0139] Referring to FIG. 4d, each battery cell BC i Long-term and short-term average difference (|SMA i [k]-LMA i [k]|) is normalized to each battery cell BC i It can be seen that the change in the long-term and short-term average difference is amplified based on the average value. This allows for more accurate detection of voltage anomalies in battery cells.

[0140] The first control unit 400 may determine a statistically variable critical value that depends on a standard deviation for the normalized cell diagnosis deviation of the plurality of battery cells.

[0141] For example, the first control unit 400 controls each battery cell BC i The normalized cell diagnostic deviation (D*diag,i[k]) of each battery cell BC is compared with the statistical variable threshold (Dthreshold[k]). i It is possible to detect voltage abnormalities.

[0142] For example, the first controller 400 may determine a statistical variable threshold value (Dthreshold[k]) for each unit time using Equation 8 below.

number

[0143] Meanwhile, the normalized cell diagnostic deviation (D*diag,i[k]) of a battery cell having an abnormal voltage is relatively larger than that of a normal battery cell. Therefore, in order to improve the accuracy and reliability of detection, it is preferable to exclude max(D*diag,i[k]) corresponding to the maximum value when calculating Sigma(D*diag,i[k]) at time index k. Here, max is a function that extracts the maximum value of multiple input variables, and the input variables are the normalized cell diagnostic deviation (D*diag,i[k]) of all battery cells.

[0144] In FIG. 4d, the time series data showing the change over time of the statistical variable threshold value (Dthreshold[k]) corresponds to the profile shown in the darkest color among all the profiles.

[0145] The first control unit 400 may be configured to filter the normalized long-term and short-term average differences of each battery cell based on the statistical variable threshold value to generate filtered time series data for each of the plurality of battery cells.

[0146] Specifically, the first control unit 400 may filter the time series data regarding the cell diagnosis deviation of each battery cell based on a statistical variable threshold value to generate time series data of a filtered diagnosis value.

[0147] For example, the first control unit 400 determines a statistical variable threshold value (Dthreshold[k]) at a time index k, and then calculates the statistical variable threshold value (Dthreshold[k]) for each battery cell BC using Equation 9 below: i The normalized cell diagnostic deviation (D*diag,i[k]) may be filtered to determine a filtered diagnostic value (Dfilter,i[k]).

number

[0148] The first controller 400 may be configured to detect a voltage abnormality of at least one of the plurality of battery cells based on a time or a number of data of the filtered time series data exceeding a diagnostic threshold.

[0149] Specifically, the first control unit 400 may detect the voltage abnormality of the battery cell from the time during which the filter diagnostic value exceeds the diagnostic critical value or the number of data of the filter diagnostic value exceeding the diagnostic critical value.

[0150] FIG. 4e shows time series data of the filtered diagnostic value (Dfilter,i[k]) obtained through filtering the cell diagnostic deviation (D*diag,i[k]) at time index k.

[0151] Referring to Fig. 4e, an irregular pattern having positive values ​​around 3000 seconds is confirmed for the filter diagnosis value (Dfilter,i[k]) of a specific battery cell. For reference, the specific battery cell having an irregular pattern is the battery cell having the time series data indicated by A in Fig. 4d.

[0152] As an example, the first control unit 400 controls each battery cell BC i In the time series data of the filter diagnostic value (Dfilter, i[k]) for i, the time period during which the filter diagnostic value (Dfilter, i[k]) is greater than a diagnostic critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the accumulated time is greater than a preset reference time is satisfied can be detected as a voltage abnormal cell.

[0153] Preferably, the first controller 400 may integrate the time intervals during which the condition that the filter diagnosis value (Dfilter,i[k]) is greater than the diagnosis threshold value is continuously satisfied. If there are a plurality of corresponding time intervals, the first controller 400 may independently calculate the integration time for each time interval.

[0154] The first controller 400 may detect a voltage abnormality of the battery cell from the time during which the filter diagnostic value exceeds the diagnostic critical value or the number of data of the filter diagnostic value exceeding the diagnostic critical value.

[0155] For example, the first control unit 400 controls each battery cell BC i In the time series data of the filter diagnostic value (Dfilter, i[k]) for i, the number of data included in the time interval in which the filter diagnostic value (Dfilter, i[k]) is greater than a diagnostic critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the data accumulation value is greater than a preset reference count is satisfied can be detected as a voltage abnormal cell.

[0156] Preferably, the first controller 400 may accumulate only the number of data included in a time period in which the condition that the filter diagnosis value (Dfilter,i[k]) is greater than the diagnosis threshold value is continuously satisfied. If there are a plurality of corresponding time periods, the first controller 400 may accumulate the number of data in each time period independently.

[0157] Meanwhile, the first control unit 400 is V i [k] represents each battery cell BC i In addition, the first control unit 400 may use the normalized cell diagnosis deviation (D*diag,i[k]) of the long-term and short-term average difference (|SMA i [k]-LMA i [k]|), calculation of the long-term and short-term average difference (|SMA i [k]-LMA i [k]|), calculation of the cell diagnostic deviation (Ddiag,i[k]) corresponding to the difference compared to the average of the long-term and short-term average difference (|SMAi[k]-LMAi[k]|), calculation of the long-term and short-term average difference (|SMA i [k]-LMA i [k]|), determining a statistical variable threshold value (Dthreshold[k]) for the normalized cell diagnostic deviation (D*diag,i[k]) using Equation 8, determining a filtered diagnostic value (Dfilter,i[k]) by filtering the cell diagnostic deviation (D*diag,i[k]) using Equation 9, and detecting a voltage anomaly of a battery cell using the time series data of the filtered diagnostic value (Dfilter,i[k]).

[0158] Figure 4f shows the long-term and short-term average difference (|SMA i [k]-LMA i This is a graph showing the time change of the long-term and short-term average difference (|SMA [k]|). i [k]-LMA iIn Equation 2, Equation 4, and Equation 5 used to calculate V i [k] can be replaced by D*diag,i[k], and V av [k] can be replaced by the average value of D*diag,i[k].

[0159] Figure 4g is a graph showing the time series data of the normalized cell diagnostic deviation (D*diag,i[k]) calculated using Equation 6. In Figure 4g, the time series data of the statistical variable threshold value (Dthreshold[k]) corresponds to the profile shown in the darkest color.

[0160] FIG. 4h is a profile showing the time series data of the filtered diagnosis value (Dfilter,i[k]) obtained by filtering the time series data of the cell diagnosis deviation (D*diag,i[k]) using Equation 9.

[0161] As an example, the first control unit 400 controls each battery cell BC i In the time series data of the filter diagnostic value (Dfilter, i[k]) for i, the time period during which the filter diagnostic value (Dfilter, i[k]) is greater than a diagnostic critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the accumulated time is greater than a preset reference time is satisfied can be detected as a voltage abnormal cell.

[0162] Preferably, the first controller 400 may integrate the time intervals during which the condition that the filter diagnosis value (Dfilter,i[k]) is greater than the diagnosis threshold value is continuously satisfied. If there are a plurality of corresponding time intervals, the first controller 400 may independently calculate the integration time for each time interval.

[0163] As another example, the first control unit 400 may iIn the time series data of the filter diagnostic value (Dfilter, i[k]) for i, the number of data included in the time interval in which the filter diagnostic value (Dfilter, i[k]) is greater than a diagnostic critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the data accumulation value is greater than a preset reference count is satisfied can be detected as a voltage abnormal cell.

[0164] Preferably, the first controller 400 may accumulate only the number of data included in a time period in which the condition that the filter diagnosis value (Dfilter,i[k]) is greater than the diagnosis threshold value is continuously satisfied. If there are a plurality of corresponding time periods, the first controller 400 may accumulate the number of data in each time period independently.

[0165] The first control unit 400 may further repeat the above-mentioned recursive calculation process a reference number of times. That is, the first control unit 400 may replace the voltage time series data shown in FIG. 4a with the time series data of the normalized cell diagnosis deviation (D*diag,i[k]) (for example, the data in FIG. 4g). In addition, the first control unit 400 may calculate the long-term and short-term average difference (|SMA i [k]-LMA i [k]|) calculation, Long-term and Short-term Average Difference (|SMA i [k]-LMA i Calculation of the average value of |SMA i [k]-LMA i Calculate the cell diagnostic deviation (Ddiag,i[k]) corresponding to the difference compared to the average value of |SMA [k]| using Equation 6. i [k]-LMA i [k]|), determining a statistical variable threshold value (Dthreshold[k]) for the cell diagnostic deviation (D*diag,i[k]) using Equation 8, determining a filtered diagnostic value (Dfilter,i[k]) by filtering the cell diagnostic deviation (D*diag,i[k]) using Equation 9, and detecting a voltage anomaly of a battery cell using the time series data of the filtered diagnostic value (Dfilter,i[k]).

[0166] By repeating the above recursive calculation process, the voltage abnormality diagnosis of the battery cell can be performed more accurately. That is, referring to FIG. 4e, a positive profile pattern is observed in only two time intervals in the time series data of the filter diagnosis value (Dfilter,i[k]) of the battery cell in which the voltage abnormality occurs. However, referring to FIG. 4h, a positive profile pattern is observed in more time intervals than FIG. 4e in the time series data of the filter diagnosis value (Dfilter,i[k]) of the battery cell in which the voltage abnormality occurs. Therefore, by repeating the recursive calculation process, the time point when the voltage abnormality occurs in the battery cell can be detected more accurately.

[0167] As described above, the first control unit 400 data Next, referring to FIG. 5 to FIG. 9, the first control unit 400 data We will now explain in detail how to detect behavioral anomalies using this technique.

[0168] Fig. 5 is a graph showing an example of a voltage curve C1 corresponding to an original time series of actual voltage values ​​of cell voltages of a battery cell referred to in various embodiments of the present invention, and Fig. 6 is a graph showing an example of a measured voltage curve C2 obtained by combining measurement noise with an original time series corresponding to the voltage curve C1 of Fig. 5. Fig. 7 is a graph showing an example of a first moving average curve AC1 obtained by applying a first average filter to the voltage curve C2 of Fig. 6, and Fig. 8 is a graph showing an example of a second moving average curve AC2 obtained by applying a second average filter to the voltage curve C2 of Fig. 6. Fig. 9 is a graph showing an example of a voltage deviation curve VC which is a difference between the first moving average curve AC1 of Fig. 7 and the second moving average curve AC2 of Fig. 8.

[0169] First, referring to FIG. 5, a voltage curve C1 is 1 ~t M This is an example of a primitive time series that contains the actual cell voltage values ​​of a battery cell BC when it is charged over a period of t. For ease of understanding, the cell voltage increases linearly and 1 Previous period and tM The actual voltage values ​​for the subsequent periods are not shown.

[0170] When the battery cell BC is normal, the cell voltage increases gradually and continuously during charging. On the other hand, when the battery cell BC is abnormal in behavior due to some internal fault condition (e.g., micro-short circuit, partial breakage of electrode tab), the cell voltage may irregularly drop or rise suddenly during charging.

[0171] The voltage curve C1 in Fig. 5 relates to an embodiment in which the battery cell BC is abnormal, and region X indicates the time range in which the cell voltage drops abnormally, and region Y indicates the time range in which the cell voltage rises abnormally. Although Fig. 5 illustrates the cell voltage during charging, the cell voltage of the abnormal battery cell may also change abnormally during discharging or resting. For example, during discharging, the cell voltage of a normal battery cell may drop gradually over a continuous period, while the cell voltage of the abnormal battery cell may rise or fall suddenly for a short period of time.

[0172] Next, referring to Fig. 6, a voltage curve C2 shows the result of combining measurement noise with the actual cell voltage of the voltage curve C1 of Fig. 5. That is, the voltage curve C2 of Fig. 6 shows a time series in which voltage values ​​indicating the measured cell voltages are arranged in chronological order.

[0173] Let M be a natural number indicating a predetermined total number of sampling times (e.g., 300), and let K be a natural number less than or equal to M. K is the K-th voltage value (V m [K]) measurement timing (Kth measurement timing t K ), and the time interval between two adjacent measurement timings is a predetermined sampling time (e.g., 0.1 seconds). m [K]) is the voltage value at measurement timing t K are data points indexed by

[0174] The measurement noise may occur irregularly over time due to internal and external factors of the voltage sensing unit 200 (e.g., temperature of the voltage measuring device, sampling rate, electromagnetic waves, etc.). 1 ~t M t is a time series containing current values ​​of the battery current measured over a given period t 1 ~t M The example given shows that it is constant.

[0175] Comparing the voltage curve C2 in FIG. 6 with the voltage curve C1 in FIG. 5, it is easy to identify abnormal behavior (X, Y) from the voltage curve C1 without measurement noise in FIG. 5, whereas it is easy to identify abnormal behavior (X, Y) from the voltage curve C1 without measurement noise in FIG. 1 ~t M There is an issue in that it is difficult to identify abnormal behavior (X, Y) from voltage curve C2, which is mixed with measurement noise throughout.

[0176] The inventors have confirmed that the above-mentioned problem can be solved by applying a first averaging filter and a second averaging filter to a time series of voltage values ​​(measured values) including measurement noise generated at the timing of measuring the cell voltage. The time series of voltage values ​​acquired over a predetermined period of time in the past for the battery cell BC to be detected may be referred to as a "reference voltage curve" and the time series of current values ​​may be referred to as a "reference current curve". In the following, the voltage curve C2 and the current curve C3 in FIG. 6 are assumed to be the reference voltage curve C2 and the reference current curve C3, respectively.

[0177] The first control unit 400 data may be configured to apply a moving window of a first time length to the time series of cell voltages included in the time series to determine a plurality of sub-voltage curves.

[0178] Specifically, the first control unit 400 may determine a plurality of sub-voltage curves by applying a moving window of a first time length to the reference voltage curve C2, and may further determine a plurality of sub-current curves in one-to-one correspondence with the plurality of sub-voltage curves by applying a moving window of a first time length to the reference current curve C3.

[0179] When K is a natural number equal to or smaller than M, a total of M sub-voltage curves (i.e., the first to Mth sub-voltage curves) can be determined from the reference voltage curve C2. The reference voltage curve C2 includes a total of M voltage values ​​(i.e., the first to Mth voltage values) measured sequentially for each sampling time W.

[0180] Sub-voltage curve S K is a subset of the reference voltage curve C2 and includes (A / W+1) voltage values ​​that are consecutive in time order. For example, if the sampling time W is 0.1 seconds and the first time length A is 10 seconds, the sub-voltage curve SK is a time series of a total of 101 voltage values, that is, the (KP)th voltage value to the (K+P)th voltage value. P=A / 2W=50.

[0181] In Figure 6, R K is the sub-voltage curve S K Therefore, the subcurrent curve R K may also include (A / W+1) data points (current values) that are consecutive in time order.

[0182] The greater the fluctuation in the battery current, the greater the fluctuation in the cell voltage. The sudden fluctuation in the cell voltage due to the battery current can be an obstacle to identifying abnormal behavior of the cell voltage from the reference voltage curve C2.

[0183] The first control unit 400 controls the sub current curve R K The first control unit 400 may determine a current change amount of the sub-current curve RK, which is a difference between the maximum current value and the minimum current value of the sub-current curve RK. The first control unit 400 may use the sub-current curve RK to detect abnormal behavior of the battery cell from a time series of cell voltages measured during a period in which the battery current fluctuation is small, such as during constant current charging or rest.

[0184] The first control unit 400 controls the sub-current curve R on the condition that the current change amount is less than the critical change amount. K Sub-voltage curve S related to K A long-term average voltage value and a short-term average voltage value of may be determined.

[0185] The first control unit 400 controls the sub current curve R K The current change in the sub-voltage curve S is below the critical change. K The calculation process described below can be performed on

[0186] The first control unit 400 uses a first averaging filter of a first duration to calculate each sub-voltage curve S K A long term average voltage value of may be determined.

[0187] 7, the first average voltage curve AC1 may be obtained by applying a first averaging filter of a first time length A to the reference voltage curve C2. The first averaging filter may be a type of low-pass filter, and may be a centered moving average having a subset size (A / W+1) corresponding to the first time length A. As an example, the first control unit 400 may apply a sub-voltage curve S K The (A / W+1) voltage values ​​included in the (A / W+1) are averaged, that is, the (KP) voltage value to the (K-1) voltage value, the K voltage value, and the (K+1) voltage value to the (K+P) voltage value. K Long-term average voltage value (V av1 [K]) is determined. The following Equation 10 shows the first averaging filter.

number

[0188] The first control unit 400 may then determine the short-term average voltage value of the sub-voltage curve using a second averaging filter having a second time length that is shorter than the first time length.

[0189] 8, the second average voltage curve AC2 is obtained by applying a second averaging filter of a second time length B, which is shorter than the first time length A, to the reference voltage curve C2. The second averaging filter is a type of low-pass filter, and may be a centered moving average having a subset size (B / W+1) corresponding to the second time length B.

[0190] As an example, the first control unit 400 may select a sub-voltage curve S K The (B / W+1) voltage values ​​included in the (B / W+1) are averaged, that is, the (KQ)th voltage value to the (K-1)th voltage value, the Kth voltage value, and the (K+1)th voltage value to the (K+Q)th voltage value. K The short-term average voltage value (V av2 [K]) Q = B / 2W. Short-term average voltage value (V av2 [K]) is the sub-voltage curve S K The subset UK is the average of the sub-voltage curve S K Time range (t K-P ~t K+P ) and within the time range (t K-P ~t K+P ) and measurement timing t K The same time range (t K-Q ~t K+Q ) is the voltage curve. The following Equation 11 shows the second averaging filter.

number

[0191] When the first time length A>the second time length B, each data point (i.e., long-term average voltage value) of the first average voltage curve AC1 may be referred to as a “long-term average value”, and each data point (i.e., short-term average voltage value) of the second average voltage curve AC2 may be referred to as a “short-term average value”. As an example, A may be 10 times B.

[0192] The first control unit 400 may determine a voltage deviation corresponding to the difference between the long-term average voltage value and the short-term average voltage value of the sub-voltage curve.

[0193] For example, the first control unit 400 may subtract one of the long-term average voltage value and the short-term average voltage value of each sub-voltage curve from the other to determine a voltage deviation associated with the sub-voltage curve.

[0194] 9, the voltage deviation curve VC is a result of subtracting one of the first average voltage curve AC1 and the second average voltage curve AC2 from the other. That is, the voltage deviation curve VC is 1 ~t M The time series of the total M voltage deviations for the sub-voltage curve S K The voltage deviation (ΔV [K]) associated with the long-term average voltage value (V av1 [K]) and short-term average voltage value (V av2 [K]) from the other. For example, ΔV[K]=V av2 [K]-V av1 [K].

[0195] As mentioned above, the long-term average voltage value (V av1 [K]) is the measurement timing t Kis the average cell voltage for the long term of the first time length A centered on av2 [K]) is the measurement timing t K is the average cell voltage for a short period of time of the second length B centered on the cell voltage V. Therefore, the long-term average voltage value (V av1 [K]) and short-term average voltage value (V av2 By subtracting one of the voltages tK (tK=1) from the other to obtain the voltage deviation (ΔV[K]), the measurement noise that occurs over a certain period of time before and after the measurement timing tK can be effectively removed.

[0196] Long-term average voltage value (V av1 [K]) and short-term average voltage value (V av2 [K]) from the other, the measurement noise generated over a certain period before and after the measurement timing tK is canceled out to a large extent.

[0197] The first control unit 400 calculates a first critical deviation TH based on a plurality of voltage deviations (ΔV[K]) determined for a plurality of sub-voltage curves in order to detect abnormal behavior of the battery cell. 1 and the second critical deviation TH 2 The method may be configured to compare the signal strength of the signal with at least one of the following:

[0198] For example, the first control unit 400 may calculate the voltage deviation (ΔV [K]) based on a first critical deviation TH 1 and the second critical deviation TH 2 The first critical deviation TH 1 is a predetermined positive number (e.g., +0.001V), and the second critical deviation TH 2 is the first critical deviation TH 1 The absolute value of the negative voltage Vcc may be a predetermined negative number (e.g., −0.001 V) having an absolute value equal to that of the positive voltage Vcc.

[0199] The first control unit 400 determines whether or not a predetermined number (for example, 10) of voltage deviations among all voltage deviations included in the voltage deviation curve VC are a first critical deviation TH 1 or greater than the second critical deviation TH 2 If the above is true, it can be detected that the battery cell BC has an abnormal behavior.

[0200] The first control unit 400 may be configured to detect a behavior abnormality corresponding to two voltage deviations that respectively satisfy a first condition, a second condition, and a third condition among the multiple voltage deviations.

[0201] Specifically, the first control unit 400 may be configured to determine that the battery cell has a behavior abnormality when two of the multiple voltage deviations determined for the multiple sub-voltage curves satisfy a first condition, a second condition, and a third condition.

[0202] For example, the first control unit 400 may determine that the battery cell BC has a behavior abnormality when two voltage deviations among a total of M voltage deviations included in the voltage deviation curve VC satisfy a first condition, a second condition, and a third condition. The first condition is that one of the two voltage deviations is equal to or larger than a first critical deviation TH 1 The second condition is satisfied when the other of the two voltage deviations is equal to or greater than the second critical deviation TH 2 The third condition is met when the time interval between two voltage deviations is less than or equal to a critical time. The critical time may be predetermined to be less than the first time length A. Referring to FIG. 9, the voltage deviation (ΔV[a]) is less than or equal to the second critical deviation TH 2 (satisfying the second condition), and the voltage deviation (ΔV[b]) is equal to or smaller than the first critical deviation TH 1 or more (satisfies the first condition). Therefore, the time interval (Δt = t b -t a ) is less than or equal to the critical time, the battery cell BC may be detected as having an abnormal behavior.

[0203] As described above, the first control unit 400 according to various embodiments of the present invention has been described with reference to FIGS. data The method for detecting at least one of a voltage abnormality and a behavior abnormality using the above-described method has been described. Next, the external device 2000 according to an embodiment of the present invention will be described in detail with reference to FIG.

[0204] 10 is a block diagram showing a schematic configuration of an external device 2000 according to an embodiment of the present invention. The external device 2000 may be a dedicated device for diagnosing a battery cell. The external device 2000 may include a recording unit 2100 and a third control unit 2200.

[0205] The recording unit 2100 includes: data and collect charging and discharging data included in the charging and discharging data and store the collected charging and discharging data.

[0206] There is no particular limitation on the type of the recording unit 2100, so long as it is capable of recording and erasing data and / or information. As an example, the recording unit 2100 may be a RAM, a ROM, a register, a flash (registered trademark) memory, a hard disk, or a magnetic recording medium.

[0207] The recording unit 2100 can be electrically connected to the third control unit 2200 via a data bus or the like so that the recording unit 2100 can be accessed by the third control unit 2200.

[0208] The recording unit 2100 records and / or updates and / or erases and / or transmits programs including various control logics executed by the third control unit 2200, and / or data generated when the control logics are executed, and / or preset data, parameters, lookup information / tables, etc.

[0209] first, Second diagnostic information An embodiment will be described in which the battery cell diagnostic information included in is information about lithium precipitation diagnosis of the battery cell.

[0210] In one embodiment, Second diagnostic informationmay indicate whether the cumulative capacity difference change is equal to or greater than a critical value. Here, the cumulative capacity difference change may be a sum of the capacity difference changes. That is, a sum of a plurality of capacity difference changes may be calculated as the cumulative capacity difference change. Here, each capacity difference change is the difference between the capacity difference in the kth (k is a natural number equal to or greater than 2) charge / discharge cycle of the battery cell and the capacity difference in the k-1th charge / discharge cycle of the battery cell. Here, the capacity difference in each charge / discharge cycle corresponds to the difference between the charge capacity of the battery cell in the charge process of the charge / discharge cycle and the discharge capacity of the battery cell in the discharge process of the charge / discharge cycle. Here, The data includes a current measurement value obtained from a current measurement unit; The charge capacity and discharge capacity are respectively: data Included in Current Measurement It can be derived from

[0211] The third control unit 2200 is data using the above to include battery cell diagnostic information regarding lithium precipitation diagnosis Second diagnostic information may be generated. Second diagnostic information The number of charge / discharge cycles required to generate may be preset. Second diagnostic information The number of charge-discharge cycles required to generate 20 may be 20.

[0212] For example, the charge / discharge cycle may include a charge cycle and a discharge cycle. The charge cycle may mean charging the battery from a lower limit to an upper limit of a preset charge voltage section while maintaining a constant temperature of the battery cell, and then interrupting the charge. The discharge cycle may mean stabilizing the battery for a predetermined time after the charge cycle is completed, and then discharging the battery from an upper limit to a lower limit of a preset discharge voltage section while maintaining the same temperature of the battery cell as in the charge cycle, and then interrupting the discharge. The charge voltage section and the discharge voltage section may be the same or different. However, when a plurality of charge / discharge cycles are performed, it is preferable that the charge voltage section between the charge cycles is the same, and the discharge voltage section between the discharge cycles is also the same.

[0213] As another example, a charge cycle refers to charging a battery from a lower limit to an upper limit of a preset charge voltage range while maintaining a constant temperature of the battery cell, and then interrupting the charge. A discharge cycle refers to starting discharge from an upper limit of a preset discharge voltage range, accumulating a discharge current, and interrupting the discharge when the accumulated current value reaches a preset discharge capacity. When a plurality of charge and discharge cycles are performed, it is preferable that the charge voltage ranges between the charge cycles are the same, and the discharge capacity between the discharge cycles is the same.

[0214] The third control unit 2200 is data Included in Current Measurement The third control unit 2200 may derive the charge capacity and discharge capacity of the battery cell from the above. data Using the information on the charge current or discharge current included in data The charge capacity (ChgAh[k]) and discharge capacity (DchgAh[k]) can be calculated based on the current measurement value included in the

[0215] For example, the third control unit 2200 initializes the charge / discharge cycle index k to 1, and calculates the first capacity difference change amount (ΔdAh[1]) and the first cumulative capacity difference change amount (

number

[0216] The third control unit 2200 may start a first charge / discharge cycle for the battery. In this specification, the external device 2000 starting a charge / discharge cycle means that data may mean obtaining data corresponding to a charge / discharge cycle using

[0217] The third control unit 2200 performs the following during the first charge / discharge cycle: data The current measurements contained in can be used to calculate the charge capacity (ChgAh[1]) and discharge capacity (DchgAh[1]).

[0218] data may include information about charge cycles performed in a preset charge voltage range and discharge cycles performed in a preset discharge voltage range.

[0219] The charge voltage section and the discharge voltage section may be the same or different. Preferably, the discharge cycle is started after the voltage of the battery cell is stabilized after the charge cycle is completed. Also, the discharge cycle may be ended when the voltage of the battery cell reaches a preset discharge end voltage or when the integrated value of the discharge current reaches a preset discharge capacity. When the start and end of the charge cycle and the discharge cycle are controlled based on the voltage value, the external device 2000: data , the voltage measurements contained in the data The voltage measurement value included in may be a value measured through the voltage sensing unit 200.

[0220] The third control unit 2200 may repeat the charge / discharge cycle until the index k for the charge / discharge cycle becomes equal to n, where n is the total number of charge / discharge cycles performed to detect the lithium precipitation abnormality and is a preset natural number. For example, n may be 20.

[0221] The third control unit 2200 may determine a capacity difference (dAh[k]) corresponding to the difference between the charge capacity (ChgAh[k]) and the discharge capacity (DchgAh[k]). That is, the capacity difference (dAh[k]) may be calculated as the difference between the charge capacity (ChgAh[k]) and the discharge capacity (DchgAh[k]).

[0222] For example, the first charge / discharge cycle (e.g., k=1) will be described. The third control unit 2200 may record the determined capacity difference (dAh[1]) in the recording unit 2100 together with a time stamp. As an example, the capacity difference (dAh[1]) may be determined by subtracting the discharge capacity (DchgAh[1]) from the charge capacity (ChgAh[1]). The third control unit 2200 may determine the capacity difference (dAh[1]) corresponding to the difference between the charge capacity (ChgAh[1]) and the discharge capacity (DchgAh[1]), and record the determined capacity difference (dAh[1]) in the recording unit 2100 together with a time stamp. As an example, the capacity difference (dAh[1]) may be determined by subtracting the discharge capacity (DchgAh[1]) from the charge capacity (ChgAh[1]).

[0223] The third control unit 2200 may determine the k-th capacity difference change amount (ΔdAh[k]) by subtracting the capacity difference (dAh[k]) in the k-th charge / discharge cycle from the capacity difference (dAh[k-1]) in the k-1-th charge / discharge cycle. That is, the capacity difference change amount (ΔdAh[k]) may be calculated as the difference between the capacity difference (dAh[k]) in the k-th charge / discharge cycle of the battery cell and the capacity difference (dAh[k-1]) in the k-1-th charge / discharge cycle.

[0224] For example, the third control unit 2200 may determine the second capacity difference change amount (ΔdAh[2]) by subtracting the capacity difference in the second charge / discharge cycle (dAh[2]) from the capacity difference in the first charge / discharge cycle (dAh[1]).

[0225] If the k-th capacitance difference change amount (ΔdAh[k]) is greater than a reference value, the third control unit 2200 may update the accumulated capacitance difference change amount by adding the k-th capacitance difference change amount (ΔdAh[k]) to the accumulated capacitance difference change amount. Here, the accumulated capacitance difference change amount may be the sum of the calculated multiple capacitance difference changes.

[0226] For example, the third control unit 2200 determines the first cumulative capacitance difference change amount (

number

number

number

[0227] The third control unit 2200 detects that there is a lithium deposition abnormality if the updated cumulative capacity difference change amount is equal to or greater than a critical value, Second diagnostic information may be generated.

[0228] The critical value may be a value suitable for detecting an abnormality in lithium precipitation. For example, the critical value may be 0.1% of the battery capacity. The critical value may be a value preset in the external device 2000. data may be a value included in

[0229] The third control unit 2200 determines that a lithium deposition abnormality has occurred inside the battery if the updated cumulative capacity difference change amount is equal to or greater than a critical value, Second diagnostic information That is, Second diagnostic information may indicate whether the cumulative capacitance difference change is greater than or equal to a critical value.

[0230] As another example, Second diagnostic information may indicate a capacity difference change amount between successive charge / discharge cycles of the battery cell. Here, the capacity difference for each charge / discharge cycle of the battery cell may be a difference between (i) the charge capacity of the battery cell in a charging process of the charge / discharge cycle of the battery cell and (ii) the discharge capacity of the battery cell in a discharging process of the charge / discharge cycle of the battery cell. In this case, the first control unit 400 may Second diagnostic information The first control unit 400 may calculate a cumulative capacitance difference change amount by summing the capacitance difference change amounts from the first capacitance difference change amount and the first capacitance difference change amount. Then, the first control unit 400 may determine whether the cumulative capacitance difference change amount is equal to or greater than a threshold value.

[0231] below, Second diagnostic information In the following, an embodiment will be described in which the diagnostic information of the battery cell included in is information regarding an abnormality in a parallel connection of battery cells.

[0232] Second diagnostic information may indicate whether there is an abnormality in a parallel connection of a plurality of unit cells included in a battery cell based on a result of monitoring a change in the estimated capacity value over time by the external device 2000. Here, the estimated capacity value may indicate a full charge capacity of the battery cell based on the charge / discharge data. In addition, the charge / discharge data may include a voltage time series indicating a change in the voltage of the battery cell over time and a current time series indicating a change in the charge / discharge current of the battery cell over time.

[0233] The recording unit 2100 includes: data By using the information about the charging current or discharging current included in the battery cell, charging and discharging data including a voltage time series showing a change over time in the voltage across the battery cell and a current time series showing a change over time in the charging and discharging current flowing through the battery cell may be collected, and the collected charging and discharging data may be stored.

[0234] The third control unit 2200 may determine an estimated capacity value indicating a full charge capacity of the battery cell based on the charge / discharge data. The third control unit 2200 may then monitor a change over time in the determined estimated capacity value to detect an abnormality in the parallel connection. The diagnosis information of the battery cell corresponding to whether or not the parallel connection is abnormal is Second diagnostic information may be included in

[0235] A method in which the external device 2000 detects an abnormality in a parallel connection will be described in detail with reference to FIGS.

[0236] FIG. 11 is an exemplary diagram showing a schematic configuration of the battery cell shown in FIG. 3, FIG. 12 is a diagram for reference in explaining a first capacity abnormality (partial open circuit failure) of the battery cell, and FIG. 13 is a diagram for reference in explaining a second capacity abnormality (complete open circuit failure) of the battery cell.

[0237] Referring to FIG. 11, battery B includes an electrode assembly B200, a positive electrode lead B210, a negative electrode lead B220, and a housing material B230.

[0238] The electrode assembly B200 includes a plurality of unit cells UC 1 ~UC M (M is a natural number of 2 or more) is connected in parallel. The unit cell UC includes a separator B203, a positive electrode plate B201, and a negative electrode plate B202 insulated from the positive electrode plate B201 by the separator B203.

[0239] The positive electrode plate B201 has a positive electrode tab B205 which is connected to one end of the positive electrode lead B210, and the negative electrode plate B202 has a negative electrode tab B206 which is connected to one end of the negative electrode lead B220.

[0240] Multiple unit cells UC 1 ~UC M With the positive electrode tab B205 and the negative electrode tab B206 connected to one end of the positive electrode lead B210 and the negative electrode lead B220, respectively, the electrode assembly B200 is housed in an exterior material B230 together with an electrolyte. The other end of the positive electrode lead B210 and the negative electrode lead B220 exposed to the outside of the exterior material B230 are provided as the positive electrode terminal and the negative electrode terminal of the battery B.

[0241] Referring to FIG. 12, the first capacitance of the electrode assembly B200 is determined by dividing the unit cells UC 1 ~UC M Some unit cells UC 1 , U.C. 2 Micro-damage and / or partial disconnection failure occurred in the electrode tabs B205 and B206 of the unit cell UC 1 , U.C. 2 and the contact resistance R between the electrode leads B210 and B220 1 , R 2 It can mean a state in which the temperature changes significantly and irregularly.

[0242] An incomplete open circuit failure may mean that the cut portions of the electrode tabs B205 and B206 cannot maintain a separation between each other, and connection and separation occur fluidly between the cut portions due to the contraction and expansion of battery B, and the contact area when connected is also variable.

[0243] Unit cell UC 1 , U.C. 2 While the contact resistance at the UC 1 ~UC M are charged and discharged almost evenly, but the contact resistance R 1 , R 2 As increases, other unit cells UC 3 ~UC M From unit cell UC 1 , U.C. 2 This reduces the capacity of the unit cell UC 1 , U.C. 2 Contact resistance R 1 , R 2 For example, the unit cell UC 1 , U.C. 2 A large tensile force acts between the electrode tabs B205 and B206 and the electrode leads B210 and B220 of the unit cell UC 1 , U.C. 2 Contact resistance R 1 , R 2 As the tensile force gradually decreases, the unit cell UC 1 , U.C. 2 Contact resistance R 1 , R 2 decreases.

[0244] Referring to FIG. 13, the second capacitance of the electrode assembly B200 is determined by dividing the unit cells UC 1 ~UC M Some unit cells UC 1 , U.C. 2 is irretrievably broken, i.e., the unit cell UC 1 , U.C. 2 A complete break in the unit cell UC1 , U.C. 2 and the electrode leads B210, B220 may be irreversibly lost.

[0245] A complete open circuit fault occurs in the unit cell UC 1 , U.C. 2 This is distinguishable from the partial open circuit failure described above in that the electrode tabs B205, B206 or the electrode plates B201, B202 are cut into multiple parts that are so far apart that they cannot be reconnected.

[0246] At some time during the manufacture or use of battery B, a unit cell UC 1 , U.C. 2 The second capacitance anomaly occurred due to the unit cell UC 1 , U.C. 2 This may mean that the electrode leads B210 and B220 have fallen off from the electrode leads B210 and B220 in an irretrievable manner. Therefore, from the specific time when the second capacitance abnormality occurs, the unit cell UC 1 , U.C. 2 cannot contribute to the charging and discharging of battery B at all, so the capacity of battery B is 1 , U.C. 2 Other unit cells except UC 3 ~UC M It depends only on the capacity of

[0247] The external device 2000 may periodically or aperiodically repeat the process of applying a capacity estimation model to the charge / discharge data to determine an estimated capacity value indicating a full charge capacity (FCC) of the battery cell. The external device 2000 may monitor changes in the estimated capacity value over time. The capacity estimation model is a type of algorithm that receives charge / discharge data as an input and provides an estimated capacity value as a corresponding output, and is a combination of many functions.

[0248] Specifically, the capacity estimation model may include (i) a first function that calculates an integrated current value of the charge / discharge current of battery B over a fixed or variable period in the past from a current time series of battery B, (ii) a second function that calculates an open circuit voltage (OCV) of battery B over a fixed or variable period in the past from a voltage time series and / or a current time series of battery B, (iii) a third function that calculates an SOC of battery B from the OCV of battery B using a predefined OCV-SOC (State Of Charge) relationship table, and (iv) a fourth function that calculates an estimated result of the full charge capacity of battery B, i.e., an estimated capacity value, from the ratio of the integrated current value and the SOC change value calculated for a common period. The following Equation 12 is an example of the fourth function.

number

[0249] When calculating the current integration value, the charging current can be set to a positive number and the discharging current can be set to a negative number. Time t2 is the calculation timing of the full charge capacity. If the full charge capacity is repeatedly calculated every first time interval, it will be easily understood by those skilled in the art that time t2 is shifted to the first time interval.

[0250] As an example, if the current integration value and SOC change value over the past common period are +100Ah and +80%, respectively, the estimated capacity value of the full charge capacity is 125Ah. As another example, if the current integration value and SOC change value over the past common period are -75Ah and -60%, respectively, the estimated capacity value of the full charge capacity is also 125Ah.

[0251] The full charge capacity indicates the maximum capacity that can be stored in battery B, i.e., the remaining capacity of battery B at SOC 100%. It is normal for the full charge capacity to decrease gradually as battery B deteriorates. Therefore, the decrease in the full charge capacity over a short time interval exceeding a certain level indicates the occurrence of a first capacity abnormality or a second capacity abnormality.

[0252] FIG. 14 is an exemplary graph referred to in explaining the relationship between the capacity abnormality and the full charge capacity of a battery cell.

[0253] 14, curve C21 illustrates the change over time of the full charge capacity of a normal battery cell. For ease of understanding, curve C21 simplifies the linear decrease in the full charge capacity of a normal battery cell over time.

[0254] Curve C22 illustrates the change over time in the full charge capacity of battery B when the first and second capacity anomalies occur in sequence. 1 , U.C. 2 The full charge capacity of the battery B in which the first capacity abnormality occurs due to micro-damage and / or partial open circuit failure is shown in the curve C22. a (e.g., the time when the battery cell is delivered) to time t b Then, it gradually decreases until time t b From time t c After a rapid decrease around time t c From time t d The number of cases has increased sharply since 。 That is, at time t b From time t cThe decrease in the full charge capacity at time t c From time t d As described above with reference to FIG. 1 , U.C. 2 Contact resistance R 1 , R 2 is time t b From time t c Then, the rate increases sharply from time t c From time t d This resulted in a recovery to normal levels around the same time.

[0255] If the first volume abnormality continues for a long period of time, it may develop (deepen) into a second volume abnormality. d After that, at time t e From time t f At the previous time t b From time t c As in the previous example, a sudden drop in the full charge capacity occurs. However, at time t c ~t d In contrast to the behavior at t f The full charge capacity cannot recover to a normal level even after time t e Near the unit cell UC 1 , U.C. 2 This was the result of a complete open circuit failure, i.e., a second capacity anomaly.

[0256] The external device 2000 monitors the full charge capacity according to the curve C22, that is, the change over time (time series) of the estimated capacity value, and determines whether the first capacity abnormality and / or the second capacity abnormality has occurred in the parallel connection unit B200.

[0257] Specifically, the external device 2000 may apply a diagnostic logic to two estimated capacity values ​​at a first time and a second time, which are shifted to the first time interval with a second time interval that is equal to or greater than the first time interval, to determine the occurrence of a first capacity abnormality and / or a second capacity abnormality in the parallel connection body B200. The second time is a time delayed by about the second time interval from the first time, and the first time and the second time may be set by the external device 2000 so as to increase by the first time interval for each first time interval. The first time interval may be the same as the collection period of charge / discharge data (or the calculation period of the estimated capacity value), and the second time interval may be an integer multiple (e.g., 10 times) of the first time interval.

[0258] The diagnostic logic may include (i) a first routine that determines a critical capacitance value for a second time that is less than the estimated capacitance value at the first time, and (ii) a second routine that compares the estimated capacitance value at the second time to the critical capacitance value for the second time.

[0259] In the first routine, the critical capacity value for the second time may be equal to a result of subtracting a reference capacity value from a result of the estimated capacity value at the first time, or a result of multiplying the estimated capacity value at the first time by a reference factor less than 1. The reference capacity value is a value obtained by multiplying the estimated capacity value at the first time by a reference factor less than 1. 1 ~UC M The reference factor is a value determined in advance in consideration of the total number (M) of unit cells U C included in the battery B and the design capacity (full charge capacity in a new state) of the battery B, and may be stored in the memory 120. 1 ~UC M The value may be a predetermined value (e.g., (M-1) / M, (M-2) / M) taking into consideration the total number (M) of the capacitance elements, and may be recorded in the memory 120. A curve C23 in Fig. 14 illustrates a change over time in the critical capacitance value calculated by applying the first routine to the curve C22.

[0260] If the estimated capacitance value at the second time is less than the critical capacitance value for the second time, the external device 2000 may determine that at least one of the first capacitance anomaly and the second capacitance anomaly has occurred in the parallel connection B200. Then, the external device 2000 may display a display indicating whether or not the parallel connection is abnormal. Second diagnostic information may be generated.

[0261] Specifically, the external device 2000 may increase the diagnostic count by 1 each time the estimated capacitance value at the second time is less than the critical capacitance value for the second time. The calculation circuit 130 may reset the diagnostic count to an initial value (e.g., 0) or decrease the diagnostic count by 1 each time the estimated capacitance value at the second time is equal to or greater than the critical capacitance value for the second time. In response to the estimated capacitance value at the second time before the diagnostic count reaches the critical count recovering to equal to or greater than the critical capacitance value for the second time, the external device 2000 may determine that the abnormality type of the parallel connection unit B200 is a first capacitance abnormality. In response to the diagnostic count reaching the critical count (e.g., 5), the external device 2000 may determine that a second capacitance abnormality has occurred in the parallel connection unit B200.

[0262] In FIG. 14, at time t a + , t b + , t c + , t d + , t e + and t f + are at time t a , t b , t c , t d , t e and t f The time t is shifted in the positive direction by the second time interval from t x and time t y The curve C22 is located below the curve C23 over the time range from t x From time t y The diagnostic count is incremented by one every first time interval until time t yIn response to the diagnostic count previously reaching a critical count, a predetermined protective function associated with a second capacity anomaly of battery B may be activated.

[0263] lastly, Second diagnostic information In the embodiment, the battery cell diagnostic information included in the battery cell diagnostic information is information about an internal short circuit in the battery cell.

[0264] Second diagnostic information may indicate whether or not there is an internal short circuit in the battery cell based on a first SOC change of the battery cell and a reference factor. Here, the reference factor may be determined by applying a statistical algorithm to a first SOC change of at least two battery cells among the plurality of battery cells. Here, the first SOC change may be a difference between a first SOC at a first charging point and a second SOC at a second charging point of each battery cell. Here, the first SOC may be estimated by applying an SOC estimation algorithm to a state parameter of the battery cell at the first charging point. Here, the second SOC may be estimated by applying an SOC estimation algorithm to a state parameter of the battery cell at the second charging point. Here, the state parameter may be data The signal intensity can be obtained based on the following:

[0265] The recording unit 2100 includes: data may obtain state parameters of each of the plurality of battery cells using the information contained in the battery cell BC. The state parameters may include the voltage, current and / or temperature of the battery cell BC.

[0266] The third control unit 2200 may apply an SOC estimation algorithm to the state parameters of the battery cell BC to monitor a change in SOC of the battery cell BC during a charging period, a discharging period, and / or a resting period of the battery pack 10. As an example, an OCV-SOC relationship map or a Kalman filter may be used as the SOC estimation algorithm. The OCV-SOC relationship map and the Kalman filter are widely used methods for estimating the SOC, and therefore detailed description thereof will be omitted.

[0267] The third control unit 2200 detects the temperature of the battery cells BC acquired during charging. 1 ~BC N A SOC estimation algorithm may be applied to each of the state parameters to determine a first SOC change for each battery cell BC, the first SOC change being the difference between a first SOC at a first charging point and a second SOC at a second charging point. The first charging point and the second charging point are not particularly limited as long as they are two different points in time within a recent charging period.

[0268] The third controller 2200 may apply a statistical algorithm to the first SOC changes of at least two battery cells among the plurality of battery cells to determine a reference factor. 1 ~BC N For example, the reference factor may be an average or median value of the first SOC changes of the at least two battery cells of the plurality of battery cells.

[0269] The third control unit 2200 may detect an internal short circuit abnormality of each battery cell based on the first SOC change and the reference factor of each battery cell. And, the diagnosis information of the battery cell corresponding to whether the battery cell has an internal short circuit is: Second diagnostic information may be included in

[0270] Hereinafter, a method in which the external device 2000 detects an internal short-circuit abnormality will be described in detail with reference to FIGS.

[0271] FIG. 15 is a diagram referred to for explaining an exemplary equivalent circuit of a battery cell. In this specification, a normal battery cell refers to a plurality of battery cells BC 1 ~BC N Among them, those without internal short circuit abnormality are referred to as abnormal battery cells. 1 ~BC N This refers to those that have an internal short circuit abnormality.

[0272] Referring to FIG. 15, a normal battery cell is connected to a DC voltage source (V DC ), internal resistance component (R 0 ) and RC pair (R 1 , C) in series. In comparison, the abnormal battery cell can be equivalent to an additional resistance component (R ISC ) can be equivalent to the additional resistance component (R ISC ) is the leakage current (I ISC ) pathway.

[0273] When charging an abnormal battery cell, some of the charging power cannot be stored in the abnormal battery cell and becomes a leakage current (I ISC In addition, when an abnormal battery cell is discharged, a part of the discharged power cannot be supplied to the electrical load and becomes a leakage current (I ISC For reference, when the abnormal battery cell is at rest, the energy stored in the abnormal battery cell is consumed as a leakage current (I ISC ) is wasted as resistance (R ISC ) indicates the deepening of the internal short circuit abnormality, and the more serious the internal short circuit abnormality becomes, the greater the leakage current (I ISC ) may increase the amount of power consumed.

[0274] As a result, the voltage change (i.e., the increase in SOC) of the abnormal battery cell is smaller than that of the normal battery cell during charging, whereas the voltage change (i.e., the decrease in SOC) of the abnormal battery cell is larger than that of the normal battery cell during discharging.

[0275] 16 to 18 are exemplary graphs used to compare changes in SOC of a battery cell depending on the presence or absence of an internal short circuit anomaly, and each of the graphs illustrates changes in charge / discharge current, voltage of the battery cell BC, and SOC of the battery cell BC during the same period.

[0276] Referring to Fig. 16, time t0 and time t4 indicate the time when the rest state is switched to the charging state, time t1 and time t5 indicate the time when the rest state is switched to the charging state, time t2 indicates the time when the rest state is switched to the discharging state, and time t3 indicates the time when the discharging state is switched to the rest state. That is, in Fig. 16, the period from time t0 to time t1 and the period from time t4 to time t5 are charging periods, the period from time t2 to time t3 is discharging periods, and the rest are rest periods. For convenience of explanation, in Fig. 16, the charging current in each charging period is assigned a positive value, the discharging current in the discharging period is assigned a negative value, and the current in each period is illustrated as being constant.

[0277] In Fig. 17, a curve VC2 indicates a voltage curve of a normal battery cell corresponding to the current curve shown in Fig. 16, and a curve VC3 indicates a voltage curve of an abnormal battery cell corresponding to the current curve shown in Fig. 16. The curve VC2 is a curve obtained by dividing a plurality of battery cells BC 1 ~BC N The external device 2000 may be treated as a time series of average voltages of the plurality of battery cells BC 1 ~BC N The state parameters of the above may be acquired periodically or non-periodically, and the time series of the state parameters may be stored in the recording unit 2100.

[0278] 17, during charging, the voltages of both the normal and abnormal battery cells gradually increase, but the abnormal battery cells have a lower charging power capacity than the normal battery cells, so the voltage increase of the abnormal battery cells is smaller than that of the normal battery cells.

[0279] During the discharge period, the voltages of both the normal and abnormal battery cells gradually drop. However, in the abnormal battery cell, in addition to the discharge power of the normal battery cell, the leakage current (I ISC ) causes additional power consumption, so the voltage drop of the abnormal battery cell is larger than the voltage drop of the normal battery cell.

[0280] In Fig. 18, a curve VC4 indicates the SOC curve of a normal battery cell corresponding to the voltage curve VC2 shown in Fig. 17, and a curve VC5 indicates the SOC curve of an abnormal battery cell corresponding to the voltage curve VC3 shown in Fig. 17. The curve VC4 is a curve of a plurality of battery cells BC 1 ~BC N It may be treated as a time series of the average SOC of

[0281] The external device 2000 may apply an SOC estimation algorithm to the state parameters of the battery cell BC to monitor a change in SOC of the battery cell BC during a charging period, a discharging period, and / or a resting period of the battery pack 10. As an example, an OCV-SOC relationship map or a Kalman filter may be used as the SOC estimation algorithm. The OCV-SOC relationship map and the Kalman filter are widely used methods for estimating the SOC, and therefore detailed description thereof will be omitted.

[0282] 18, during the charging period, the abnormal battery cell has a lower rate and amount of increase in SOC than the normal battery cell. During the discharging period, the abnormal battery cell has a higher rate and amount of decrease in SOC than the normal battery cell. Furthermore, during the resting period, the SOC of the normal battery cell is approximately constant, while the SOC of the abnormal battery cell gradually decreases even when no charging or discharging current is flowing.

[0283] The external device 2000 measures the current state of the battery cells BC during the most recent charging period every time the battery pack 10 is charged. 1 ~BC N Based on all the SOC changes, a diagnosis process may be performed to detect an internal short circuit abnormality of the battery cells BC. As an example, when the external device 2000 switches from charging to resting at time t1, the external device 2000 may perform a diagnosis process to detect an internal short circuit abnormality of the battery cells BC during the charging period (t0 to t1). 1 ~BC N Based on all the SOC changes, an internal short circuit abnormality in the battery cell BC can be detected.

[0284] As another example, when the external device 2000 switches from charging to resting at time t5, the external device 2000 outputs the battery charge data of the plurality of battery cells BC acquired during the most recent charging period (t4 to t5). 1 ~BC N Based on all the SOC changes, an internal short circuit abnormality in the battery cell BC can be detected.

[0285] Alternatively, the external device 2000 may measure the current of a number of battery cells BC during a recent charging period each time the battery pack 10 is charged or discharged. 1 ~BC N Multiple battery cells BC with all SOC changes and recent discharge period 1 ~BC N Based on all the SOC changes, a diagnostic process may be performed to detect an internal short circuit abnormality in the battery cell BC.

[0286] As an example, when the external device 2000 switches from the discharging state to the hibernation state at time t3, the external device 2000 receives the charge currents of the plurality of battery cells BC acquired during the most recent charging period (t0 to t1). 1 ~BC N All SOC changes and multiple battery cells BC acquired during the recent discharge period (t2~t3) 1 ~BC N Based on all the SOC changes, an internal short circuit abnormality in the battery cell BC can be detected.

[0287] As another example, when the external device 2000 switches from the charging state to the resting state at time t5, the external device 2000 outputs the charge currents (Pp) and the charge currents (Pb) of the plurality of battery cells BC acquired during the most recent discharging period (t2 to t3). 1 ~BC N All SOC changes and multiple battery cells BC acquired during the recent charging period (t4~t5) 1 ~BC N Based on all the SOC changes, an internal short circuit abnormality in the battery cell BC can be detected.

[0288] 16 to 18, the pause mode is located between the charging period and the discharging period, but this is merely one example. For example, the charging state can be switched to the discharging state without the pause state, or the discharging state can be switched to the charging state without the pause state.

[0289] Hereinafter, a method for diagnosing an abnormal state of a battery cell using the battery cell diagnosis device 1000 and the external device 2000 according to an embodiment of the present invention will be described in detail. Battery cell diagnostic device 1000 and external device 2000 The operation of will be described in more detail below in various embodiments of the battery diagnosis method.

[0290] In this specification, “performed by the external device 2000 ” may include performance by the third control unit 2200 .

[0291] Hereinafter, a method for diagnosing an abnormal state of a battery cell using the battery cell diagnosis device 1000 and the external device 2000 according to an embodiment of the present invention will be described in detail. Battery cell diagnostic device 1000 and external device 2000 The operation of will be described in more detail below in various embodiments of the battery diagnosis method.

[0292] 19 is a flow diagram of the battery cell diagnosis device 1000 according to an embodiment of the present invention diagnosing an abnormal state of a battery cell using an external device 2000. Parts that overlap with those described above will be briefly described or omitted.

[0293] In step S1000, the battery cell diagnostic device 1000 may acquire data. For example, the battery cell diagnostic device 1000 may acquire data measured by the current measuring unit 100 or the voltage sensing unit 200 using the data acquiring unit 300. Specifically, the battery cell diagnostic device 1000 may acquire data related to at least one of a charging current, a discharging current, and a voltage signal measured by the current measuring unit 100 or the voltage sensing unit 200 using the data acquiring unit 300. In addition, the battery cell diagnostic device 1000 may also acquire data related to a temperature signal detected by the temperature sensor T using the data acquiring unit 300.

[0294] In step S2000, the battery cell diagnostic device 1000 data For example, the battery cell diagnostic device 1000 may generate a battery cell diagnostic result based on the data acquired in step S1000. data may be generated. data may include data regarding at least one of a charging current, a discharging current, and a voltage signal of the battery cell that is the subject of an abnormality determination.

[0295] In step S3000, the battery cell diagnostic device 1000 generates data may be transmitted to the external device 2000.

[0296] In step S4000, the battery cell diagnostic device 1000 may detect a voltage abnormality, which will be described in detail below with reference to FIGS.

[0297] In step S5000, the battery cell diagnostic device 1000 may detect a behavior abnormality, which will be described in detail below with reference to FIGS.

[0298] In step S7000, the external device 2000 can detect a lithium deposition abnormality, which will be described in detail later with reference to FIGS.

[0299] In step S8000, the external device 2000 Second diagnostic information may be generated. Second diagnostic information may include whether or not lithium precipitation anomalies are detected.

[0300] In step S9000, the external device 2000 Second diagnostic information may be transmitted to the battery cell diagnostic device 1000.

[0301] In step S6000, the battery cell diagnostic device 1000 may diagnose the abnormal state of the battery cell. Second diagnostic information An abnormal state of the battery cell can be diagnosed based on the above.

[0302] 20 to 24 are flow charts showing in detail a process in which the battery cell diagnostic device 1000 detects a voltage abnormality according to an embodiment of the present invention.

[0303] 20 is a flow diagram illustrating an example of a method for detecting an abnormal voltage according to an embodiment of the present invention, which may be periodically performed by the first control unit 400 at unit time intervals.

[0304] In step S4310, the first control unit 400 data Multiple battery cells BC contained in 1 ~BC N The voltage signals indicating the cell voltages of the battery cells BC may be collected to generate time series data of the cell voltages of the battery cells BC (see FIG. 4a). The number of data items in the time series data of the cell voltages may increase by one every time a unit time elapses.

[0305] For example, the cell voltage is V i [k] or VD of Equation 5 i [k] may be used.

[0306] In step S4320, the first control unit 400 controls each battery cell BC i Based on the time series data of the cell voltage of each battery cell BC i First average cell voltage (SMA i [k], see Equations 1 and 11) and the second average cell voltage (LMA i [k], see Equations 3 and 4) can be determined (see FIG. 4b).

[0307] First average cell voltage (SMA i [k]) for each battery cell BC over a first moving window having a first time length i The second average cell voltage (LMA i [k]) for each battery cell BC over a second moving window having a second time length i The first average cell voltage (SMA i[k]) and second average cell voltage (LMA i When calculating V i [k] or VD i [k] may be used.

[0308] In step S4330, the first control unit 400 controls each battery cell BC i Long-term and short-term average difference (|SMAi[k]-LMA i [k]|) can be determined (see FIG. 4c).

[0309] In step S4340, the first control unit 400 controls each battery cell BC i The cell diagnostic deviation (Ddiag,i[k]) can be determined by calculating the average of the long-term and short-term average differences (|SMAi[k]-LMA i [k]|av) and the i-th battery cell BC i Long-term and short-term average difference (|SMAi[k]-LMA i [k]|)

[0310] In step S4350, the first controller 400 may determine whether a diagnosis time has elapsed. The diagnosis time may be preset. If the determination in step S4350 is "yes," the process proceeds to step S4360. If the determination in step S4350 is "no," steps S4310 to S4340 are repeated.

[0311] In step S4360, the first control unit 400 receives the battery voltage Vcc of each battery cell BC collected during the diagnosis time. i The time series data for the cell diagnostic deviation (Ddiag,i[k]) of each cell may be generated.

[0312] In step S4370, the first control unit 400 analyzes the time series data for the cell diagnostic deviation (Ddiag,i[k]) and calculates the cell diagnostic deviation (Ddiag,i[k]) for each battery cell BC i It is possible to detect voltage abnormalities.

[0313] As an example, the first control unit 400 controls each battery cell BC iIn the time series data for the cell diagnostic deviation (Ddiag,i[k]), the time period during which the cell diagnostic deviation (Ddiag,i[k]) is greater than a diagnostic critical value (e.g., 0.015) is accumulated, and a battery cell for which the accumulated time is greater than a preset reference time is satisfied can be detected as a cell with abnormal voltage.

[0314] For example, the first controller 400 may accumulate only the time intervals in which the condition that the cell diagnostic deviation (Ddiag,i[k]) is greater than the diagnostic threshold is continuously satisfied. If there are a plurality of corresponding time intervals, the first controller 400 may independently calculate the accumulated time for each time interval.

[0315] As another example, the first control unit 400 may i In the time series data for the cell diagnostic deviation (Ddiag,i[k]), the number of data in which the cell diagnostic deviation (Ddiag,i[k]) is greater than a diagnostic critical value (e.g., 0.015) is accumulated, and a battery cell for which the condition that the accumulated data value is greater than a preset reference count is satisfied can be detected as a cell with abnormal voltage.

[0316] The first controller 400 may accumulate only the number of data included in the time interval in which the condition that the cell diagnosis deviation (Ddiag,i[k]) is greater than the diagnosis threshold is continuously satisfied. If there are multiple corresponding time intervals, the first controller 400 may accumulate the number of data in each time interval independently.

[0317] 21 is a flow chart showing an example of a method for detecting an abnormal voltage according to an embodiment of the present invention, which may be periodically performed by the first control unit 400 at unit time intervals.

[0318] In the method for detecting an abnormal voltage in Fig. 21, steps S4310 to S4360 are substantially the same as those in the embodiment of Fig. 20, and therefore the description thereof will be omitted. After step S4360, the method proceeds to step S4380.

[0319] In step S4380, the first control unit 400 may generate time series data of the statistical variable threshold value (Dthreshold[k]) using Equation 8. The input of the Sigma function of Equation 8 is the time series data for the cell diagnostic deviation (Ddiag,i[k]) of all the battery cells generated in step S4360. Preferably, the maximum value of the cell diagnostic deviation (Ddiag,i[k]) may be excluded from the input value of the Sigma function. The cell diagnostic deviation (Ddiag,i[k]) may be calculated by multiplying the long-term and short-term average difference (|SMA i [k]-LMA i [k]|) is the deviation from the mean value.

[0320] In step S4390, the first control unit 400 calculates the current density of each battery cell BC using Equation 9. i The cell diagnostic deviation (Ddiag,i[k]) may be filtered to generate time series data of a filtered diagnostic value (Dfilter,i[k]).

[0321] In Equation 9, D*diag,i[k] can be replaced with Ddiag,i[k].

[0322] In step S4400, the first control unit 400 analyzes the time series data of the filter diagnosis value (Dfilter,i[k]) to determine the filter diagnosis value (Dfilter,i[k]) of each battery cell BC i It is possible to determine voltage abnormalities.

[0323] As an example, the first control unit 400 may accumulate the time period during which the filter diagnostic value (Dfilter, i[k]) for each battery cell BCi is greater than a diagnostic critical value (e.g., 0) in time series data of the filter diagnostic value (Dfilter, i[k]), and may determine that a battery cell for which the accumulated time is greater than a preset reference time is an abnormal voltage cell.

[0324] Preferably, the first controller 400 may integrate only the time intervals in which the condition that the filter diagnosis value (Dfilter,i[k]) is greater than the diagnosis threshold value is continuously satisfied. If there are a plurality of corresponding time intervals, the first controller 400 may independently calculate the integration time for each time interval.

[0325] As another example, the first control unit 400 may i In the time series data of the filter diagnostic value (Dfilter, i[k]) for i, the number of data included in the time interval in which the filter diagnostic value (Dfilter, i[k]) is greater than a diagnostic critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the data accumulation value is greater than a preset reference count is satisfied can be detected as a voltage abnormal cell.

[0326] Preferably, the first controller 400 may accumulate only the number of data included in a time period in which the condition that the filter diagnosis value (Dfilter,i[k]) is greater than the diagnosis threshold value is continuously satisfied. If there are a plurality of corresponding time periods, the first controller 400 may accumulate the number of data in each time period independently.

[0327] 22 is a flow chart showing an example of a method for detecting an abnormal voltage according to an embodiment of the present invention, which may be periodically performed by the first control unit 400 every unit time.

[0328] The battery diagnosis method according to the present embodiment is substantially the same as the embodiment of Fig. 20 except that steps S4340, S4360, and S4370 are changed to steps S4341, S4361, and S4371, respectively, and therefore only the different steps will be described.

[0329] In step S4341, the first control unit 400 calculates the current density of each battery cell BC using Equation 6. i Long-term and short-term average difference (|SMA i [k]-LMA i The normalized cell diagnostic deviation (D*diag,i[k]) for the long-term and short-term average difference (|SMA i [k]-LMA i [k]|) Equation 6 can be replaced by Equation 7.

[0330] In step S4361, the first control unit 400 receives the battery voltage Vcc of each battery cell BC collected during the diagnosis time. i Then, time series data for the normalized cell diagnostic deviation (D*diag,i[k]) of may be generated (see FIG. 4d).

[0331] In step S4371, the first control unit 400 analyzes the time series data for the normalized cell diagnosis deviation (D*diag,i[k]) to determine the normalized cell diagnosis deviation (D*diag,i[k]) for each battery cell BC i It is possible to detect voltage abnormalities.

[0332] As an example, the first control unit 400 controls each battery cell BC i In the time series data for the normalized cell diagnostic deviation (D*diag,i[k]), the time period during which the cell diagnostic deviation (D*diag,i[k]) is greater than a diagnostic critical value (e.g., 4) is accumulated, and a battery cell for which the accumulated time is greater than a preset reference time is satisfied can be detected as a voltage abnormal cell.

[0333] The first controller 400 may accumulate only the time intervals in which the condition that the normalized cell diagnosis deviation (D*diag,i[k]) is greater than the diagnosis threshold is continuously satisfied. If there are a plurality of corresponding time intervals, the first controller 400 may independently calculate the accumulated time for each time interval.

[0334] As another example, the first control unit 400 may i In the time series data for the normalized cell diagnostic deviation (D*diag,i[k]), the number of data in which the cell diagnostic deviation is greater than a diagnostic critical value (e.g., 4) is accumulated, and a battery cell for which the condition that the accumulated data value is greater than a preset reference count is satisfied can be detected as a voltage abnormal cell.

[0335] The first controller 400 may accumulate only the number of data included in the time interval in which the condition that the normalized cell diagnosis deviation (D*diag,i[k]) is greater than the diagnosis threshold is continuously satisfied. If there are multiple time intervals, the first controller 400 may accumulate the number of data in each time interval independently.

[0336] 23 is a flow chart showing an example of a method for detecting an abnormal voltage according to an embodiment of the present invention, which may be periodically performed by the first control unit 400 every unit time.

[0337] The battery diagnosis method of Fig. 23 is substantially the same as that of Fig. 21 except that S4340, S4360, S4380, S4390 and S4400 are changed to steps S4341, S4361, S4381, S4391 and S4401, respectively. Therefore, only the configuration different from Fig. 21 will be described for the embodiment of Fig. 23.

[0338] In step S4341, the first control unit 400 calculates the current density of each battery cell BC using Equation 6. i Long-term and short-term average difference (|SMA i [k]-LMA i The normalized cell diagnostic deviation (D*diag,i[k]) for the long-term and short-term average difference (|SMA i [k]-LMA i [k]|) Equation 6 can be replaced by Equation 7.

[0339] In step S4361, the first control unit 400 receives the battery voltage Vcc of each battery cell BC collected during the diagnosis time. i Then, time series data for the normalized cell diagnostic deviation (D*diag,i[k]) of may be generated (see FIG. 4d).

[0340] In step S4381, the first control unit 400 may generate time series data of the statistical variable threshold value (DthreShold[k]) using Equation 8. An input of the Sigma function in Equation 8 is the time series data for the normalized cell diagnostic deviation (D*diag,i[k]) of all the battery cells generated in step S4361. Depending on the embodiment, at each time index, the maximum value of the cell diagnostic deviation (D*diag,i[k]) may be excluded from the input value of the Sigma function.

[0341] In step S4391, the first control unit 400 calculates the k-threshold value of each battery cell BC based on the statistical variable threshold value (Dthreshold[k]) using Equation 9. i The cell diagnostic deviation (D*diag,i[k]) may be filtered to generate time series data of the filtered diagnostic value (Dfilter,i[k]).

[0342] In step S4401, the first control unit 400 analyzes the time series data of the filter diagnosis value (Dfilter,i[k]) to determine the filter diagnosis value (Dfilter,i[k]) of each battery cell BC i It is possible to detect voltage abnormalities.

[0343] As an example, the first control unit 400 controls each battery cell BC i In the time series data of the filter diagnostic value (Dfilter, i[k]) for i, the time period during which the filter diagnostic value (Dfilter, i[k]) is greater than a diagnostic critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the accumulated time is greater than a preset reference time is satisfied can be detected as a voltage abnormal cell.

[0344] For example, the first controller 400 may accumulate the time intervals during which the condition that the filter diagnosis value (Dfilter,i[k]) is greater than the diagnosis threshold value is continuously satisfied. If there are a plurality of corresponding time intervals, the first controller 400 may independently calculate the accumulated time for each time interval.

[0345] As another example, the first control unit 400 may i In the time series data of the filter diagnostic value (Dfilter, i[k]) for i, the number of data included in the time interval in which the filter diagnostic value (Dfilter, i[k]) is greater than a diagnostic critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the data accumulation value is greater than a preset reference count is satisfied can be detected as a voltage abnormal cell.

[0346] The first controller 400 may accumulate only the number of data included in a time period in which the condition that the filter diagnosis value (Dfilter,i[k]) is greater than the diagnosis threshold value is continuously satisfied. If there are a plurality of time periods, the first controller 400 may accumulate the number of data in each time period independently.

[0347] FIG. 24 is a flowchart illustrating a method for detecting an abnormal voltage according to an embodiment of the present invention.

[0348] In Figure 24, steps S4310 to S4361 are substantially the same as those in Figure 23. Therefore, only the configuration different from that in Figure 23 will be described.

[0349] In step S4410, the first control unit 400 controls each battery cell BC i Using the time series data of the normalized cell diagnostic deviation (D*diag,i[k]), the first moving average (SMA i [k]) time series data and second moving average (LMA i [k]) of time series data (see Figure 4f).

[0350] In step S4420, the first control unit 400 calculates the current density of each battery cell BC using Equation 6. i The first moving average (SMA i [k]) time series data and second moving average (LMA i In this manner, a time series of normalized cell diagnostic deviation (D*diag,i[k]) using the time series of cell diagnostic deviation (D*diag,i[k]) may be generated (see FIG. 4g).

[0351] In operation S4430, the first control unit 400 may generate time series data of the statistical variable threshold value (Dthreshold[k]) using Equation 8 (see FIG. 4g).

[0352] In step S4440, the first control unit 400 calculates the current density of each battery cell BC based on the statistical variable threshold value (Dthreshold[k]) using Equation 9. iThen, a time series of data for the filter diagnosis value (Dfilter,i[k]) may be generated (see FIG. 4h).

[0353] In step S4450, the first control unit 400 controls each battery cell BC i The time series data of the filter diagnosis value (Dfilter,i[k]) of each battery cell BC i It is possible to detect voltage abnormalities.

[0354] As an example, the first control unit 400 controls each battery cell BC i In the time series data of the filter diagnostic value (Dfilter, i[k]) for i, the time period during which the filter diagnostic value (Dfilter, i[k]) is greater than a diagnostic critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the accumulated time is greater than a preset reference time is satisfied can be detected as a voltage abnormal cell.

[0355] The first controller 400 may accumulate the time intervals during which the condition that the filter diagnosis value (Dfilter,i[k]) is greater than the diagnosis threshold value is continuously satisfied. If there are a plurality of corresponding time intervals, the first controller 400 may independently calculate the accumulated time for each time interval.

[0356] The first control unit 400 controls each battery cell BC i In the time series data of the filter diagnostic value (Dfilter, i[k]) for i, the number of data included in the time interval in which the filter diagnostic value (Dfilter, i[k]) is greater than a diagnostic critical value (e.g., 0) is accumulated, and a battery cell for which the condition that the data accumulation value is greater than a preset reference count is satisfied can be detected as a voltage abnormal cell.

[0357] The first controller 400 may accumulate only the number of data included in a time period in which the condition that the filter diagnosis value (Dfilter,i[k]) is greater than the diagnosis threshold value is continuously satisfied. If there are a plurality of time periods, the first controller 400 may accumulate the number of data in each time period independently.

[0358] 24, the first control unit 400 may perform steps S4410 and S4420 recursively two or more times. The first control unit 400 calculates a first moving average (SMA) for the cell diagnosis deviation (D*diag,i[k]) again in step S4410 using the time series data of the normalized cell diagnosis deviation (D*diag,i[k]) generated in step S4420. i [k]) time series data and second moving average (LMA i [k]) of time series data may be generated.

[0359] The first control unit 400 again controls each battery cell BC i The first moving average (SMA i [k]) time series data and second moving average (LMA i Using the time series data of the cell diagnosis deviation (D*diag,i[k]), the time series data of the normalized cell diagnosis deviation (D*diag,i[k]) can be generated by Equation 6. Such a recursive algorithm can be repeated a predetermined number of times.

[0360] When steps S4410 and S4420 are performed by the recursive algorithm, steps S4430 to S4450 may be performed using time series data of the cell diagnosis deviation (D*diag,i[k]) finally calculated through the recursive algorithm.

[0361] In an embodiment of the present invention, the first control unit 400 performs voltage abnormality detection for all battery cells, and if a voltage abnormality is detected in a specific battery cell (or the like), outputs a detection result information including the Third diagnostic information In addition, the first control unit 400 may record identification information (ID) of the battery cell diagnosed as having a voltage abnormality, the time when the voltage abnormality was detected, and a detection flag in a memory unit (not shown).

[0362] Third diagnostic information may include a message indicating that a cell in the cell group CG has a voltage abnormality. Third diagnostic information is the battery cell BC 1 ~BC NThe notification may include a warning message that a closer inspection of the device is required.

[0363] According to the above-described embodiment, two moving averages of the cell voltage of each battery cell for two different time lengths are determined for each unit time, and a voltage abnormality of each battery cell can be efficiently and accurately detected based on the difference between the two moving averages of each of the multiple battery cells.

[0364] According to another aspect, by applying advanced techniques such as normalization and / or statistical variable thresholds when analyzing the difference in the change trends of the two moving averages for each battery cell, voltage anomalies for each battery cell can be accurately detected.

[0365] According to yet another aspect, by analyzing time series data of a filter diagnosis value determined based on a statistical variable critical value, it is possible to precisely detect a time period during which a voltage abnormality occurred in each battery cell and / or a voltage abnormality detection count.

[0366] The above has been a description of the method for detecting voltage abnormality by the first control unit 400 included in the battery cell diagnosis device 1000. Next, a method for detecting behavior abnormality by the first control unit 400 included in the battery cell diagnosis device 1000 will be described.

[0367] 25 and 26 are flow charts illustrating in detail a process of detecting a behavior abnormality by the battery cell diagnosis device 1000 according to an embodiment of the present invention. FIG 25 is a flow chart illustrating an example of a method for detecting a behavior abnormality according to an embodiment of the present invention.

[0368] In step S5710, the first control unit 400 applies a moving window of a first time length A to the reference voltage curve C2 to determine a plurality of sub-voltage curves. 1 ~t M 1 is a time series of a plurality of voltage values ​​indicating the cell voltage of a battery cell BC measured at each sampling time over a period of time.

[0369] In step S5720, the first control unit 400 determines a voltage deviation (ΔV [K]) associated with each sub-voltage curve SK of the plurality of sub-voltage curves. Step S5720 may include steps S5722, S5724, and S5726 as sub-steps.

[0370] In step S5722, the first control unit 400 calculates a long-term average voltage value (V av1 [K]) may be determined (see Equation 10).

[0371] In step S5724, the first control unit 400 calculates the short-term average voltage value (V av2 [K]) may be determined (see Equation 11).

[0372] In step S5726, the first control unit 400 calculates the long-term average voltage value (V av1 [K]) and short-term average voltage value (V av2 By subtracting one of the voltages (V [K]) from the other, the voltage deviation (ΔV [K]) can be determined.

[0373] In step S5730, the first control unit 400 may compare the voltage deviations determined for the sub-voltage curves with at least one of the first critical deviation and the second critical deviation to determine whether the battery cell BC is behaving abnormally. If the value of step S5730 is 'Yes', the process may proceed to step S5740.

[0374] In step S5740, the first control unit 400 may detect an abnormal behavior of the battery cell BC.

[0375] FIG. 26 is another flow chart illustrating an example of a battery diagnosis method according to an embodiment of the present invention.

[0376] In step S5800, the first control unit 400 may determine a plurality of sub-current curves by applying a moving window of a first time length A to the reference current curve C3. The reference current curve C3 is1 ~t M 1 is a time series of a plurality of current values ​​showing the battery current of a battery cell BC measured at each sampling time over a period of time.

[0377] In step S5810, the first control unit 400 may determine a plurality of sub-voltage curves by applying a moving window of a first time length A to the reference voltage curve C2. Step S5810 is the same as step S5710.

[0378] In operation S5812, the first control unit 400 may determine a current change amount of each sub-current curve RK of the plurality of sub-current curves.

[0379] In step S5820, the first control unit 400 may determine a voltage deviation (ΔV [K]) of each sub-voltage curve SK associated with each sub-current curve RK having a current change amount equal to or less than a critical change amount among the plurality of sub-voltage curves. Step S5820 may include steps S5722, S5724, and S5726 of FIG.

[0380] In step S5830, the first control unit 400 may compare each voltage deviation determined in step S5820 with at least one of the first critical deviation and the second critical deviation to determine whether the battery cell BC is behaving abnormally. If the value of step S5830 is 'YES', the process proceeds to step S5840.

[0381] In step S5840, the first control unit 400 may detect an abnormal behavior of the battery cell BC.

[0382] 27 to 30 show an embodiment of the present invention in which an external device 2000 data FIG. 1 is a flow chart showing in detail a process for detecting an abnormality in lithium precipitation while repeatedly performing charge / discharge cycles using the battery.

[0383] The external device 2000 detects the lithium deposition abnormality according to the embodiment of the present invention in accordance with the flow charts shown in FIGS. 27 to 30, and includes the detected results. Second diagnostic information may be generated.

[0384] FIG. 27 is a flow diagram illustrating an example of a method for detecting lithium precipitation anomaly according to an embodiment of the present invention.

[0385] First, the external device 2000 initializes a charge / discharge cycle index k to 1 in step S7010, and calculates a first capacity difference change amount (ΔdAh[1]) and a first cumulative capacity difference change amount (

number

[0386] The external device 2000 may then start a first charge / discharge cycle for the battery in step S7030. data may mean obtaining data corresponding to a charge / discharge cycle using

[0387] Next, in step S7040, the external device 2000 performs the following during the first charge / discharge cycle: data The current measurements contained in can be used to calculate the charge capacity (ChgAh[1]) and discharge capacity (DchgAh[1]).

[0388] data may include information about charge cycles performed in a preset charge voltage range and discharge cycles performed in a preset discharge voltage range.

[0389] The charge voltage section and the discharge voltage section may be the same or different. Preferably, the discharge cycle is started after the voltage of the battery cell is stabilized after the charge cycle is completed. Also, the discharge cycle may be ended when the voltage of the battery cell reaches a preset discharge end voltage or when the integrated value of the discharge current reaches a preset discharge capacity. When the start and end of the charge cycle and the discharge cycle are controlled based on the voltage value, the external device 2000: data , the voltage measurements contained in the dataThe voltage measurement value included in may be a value measured through the voltage sensing unit 200.

[0390] In step S7050, the external device 2000 may determine a capacity difference (dAh[1]) corresponding to the difference between the charge capacity (ChgAh[1]) and the discharge capacity (DchgAh[1]).

[0391] The external device 2000 may record the determined capacity difference (dAh[1]) together with a timestamp in the recording unit 2100. As an example, the capacity difference (dAh[1]) may be determined by subtracting the discharge capacity (DchgAh[1]) from the charge capacity (ChgAh[1]).

[0392] In step S7060, the external device 2000 may determine whether an index k for the charge / discharge cycle is equal to n, where n is a total number of charge / discharge cycles performed to detect the lithium precipitation anomaly and is a preset natural number. For example, n may be 20.

[0393] If the determination in step S7060 is "Yes", the external device 2000 may end the process for detecting a lithium deposition anomaly. On the other hand, if the determination in step S7060 is "No", the external device 2000 may transition the process to S7070.

[0394] In step S7070, the external device 2000 may start a second charge / discharge cycle. The conditions of the second charge / discharge cycle may be substantially the same as the first charge / discharge cycle.

[0395] Next, the external device 2000 may calculate the charge capacity (ChgAh[2]) and discharge capacity (DchgAh[2]) of the second charge / discharge cycle for the battery in step S7080, and determine a capacity difference (dAh[2]) corresponding to the difference between the charge capacity (ChgAh[2]) and the discharge capacity (DchgAh[2]) in step S7090.

[0396] Then, in step S7100, the external device 2000 may determine a second capacity difference change amount (ΔdAh[2]) by subtracting the capacity difference (dAh[2]) of the second charge / discharge cycle from the capacity difference (dAh[1]) of the first charge / discharge cycle. Step S7110 of FIG. 28 may be performed after step S100.

[0397] FIG. 28 is another flow diagram illustrating a method for detecting lithium precipitation anomaly according to an embodiment of the present invention.

[0398] In step S7110, the external device 2000 may determine whether the second capacitance difference change amount (ΔdAh[2]) is greater than a reference value. The reference value may be zero.

[0399] If the determination in step S7110 is 'YES', the external device 2000 calculates the first cumulative capacitance difference change amount (

number

number

number

[0400] On the other hand, if the determination in step S7110 is "No", the first cumulative capacitance difference change amount (

number

number

[0401] In step S7140, the external device 2000 detects the second cumulative capacitance difference change amount (

number

[0402] If the determination in step S7140 is "YES", the external device 2000 may detect a lithium deposition abnormality in step S7150.

[0403] If the determination in step S7140 is "No", that is, the second cumulative capacitance difference change amount (

number

[0404] If the determination at step S7160 is 'Yes', the external device 2000 may make a final determination that no lithium deposition abnormality has occurred inside the battery since all charge / discharge cycles for detecting lithium deposition have been performed, and may terminate the process.

[0405] The external device 2000 is Second diagnostic information It is possible to output that no lithium deposition abnormality was detected through the Second diagnostic information may include a message indicating that no lithium deposition anomalies occurred.

[0406] On the other hand, if the determination in step S7160 is "NO", the external device 2000 may perform further charge / discharge cycles to detect the lithium deposition abnormality. After step S7160, step S7180 in FIG.

[0407] FIG. 29 is a flow chart illustrating a method for detecting a lithium deposition anomaly according to an embodiment of the present invention.

[0408] In step S7180, the external device 2000 starts a third charge / discharge cycle. The conditions of the third charge / discharge cycle may be substantially the same as those of the first charge / discharge cycle.

[0409] In step S7190, the external device 2000 may determine the charge capacity (ChgAh[3]) and the discharge capacity (DchgAh[3]) during the progress of the third charge / discharge cycle of the battery, and in step S7200, may determine a capacity difference (dAh[3]) corresponding to the difference between the charge capacity (ChgAh[3]) and the discharge capacity (DchgAh[3]).

[0410] In step S7210, the external device 2000 may determine a third capacity difference change amount (ΔdAh[3]) by subtracting the capacity difference (dAh[3]) of the third charge / discharge cycle from the capacity difference (dAh[2]) of the second charge / discharge cycle.

[0411] The external device 2000 may determine whether the third capacitance difference change amount (ΔdAh[3]) is greater than a reference value in operation S7220. For example, the reference value may be zero.

[0412] If the determination in step S7220 is 'YES', the external device 2000 calculates the second cumulative capacitance difference change amount (

number

number

[0413] On the other hand, if the determination in step S7220 is "No", the external device 2000 calculates the second cumulative capacitance difference change amount (

number

number

[0414] Step S7250 may be performed after steps S7230 and S7240.

[0415] In step S7250, the external device 2000 detects a third cumulative capacitance difference change amount (

number

[0416] If the determination in step S7250 is 'YES', the external device 2000 may detect an abnormality in lithium deposition within the battery in step S7260.

[0417] The external device 2000 may end the process after detecting the lithium deposition anomaly in step S7260.

[0418] If the determination in step S7250 is "no", that is, the third cumulative capacitance difference change amount (

number

[0419] If the determination at step S7270 is 'Yes', it may be determined that no lithium deposition anomaly has occurred within the battery since all charge / discharge cycles for detecting the lithium deposition anomaly have been performed, and the process may be terminated.

[0420] After the process is completed, the external device 2000 Second diagnostic information The external device 2000 may generate Second diagnostic information or Second diagnostic information may include a message indicating that no lithium deposition anomalies were detected.

[0421] On the other hand, if the determination at step S7270 is "No", the external device 2000 may perform further charge / discharge cycles to detect lithium precipitation abnormality.

[0422] The detection logic for detecting the lithium deposition abnormality performed by the external device 2000 in the fourth charge / discharge cycle and the subsequent charge / discharge cycles is substantially as described above.

[0423] 30 is a flow chart illustrating a method for detecting a lithium precipitation abnormality according to an embodiment of the present invention. Hereinafter, a process performed by the external device 2000 in the fourth to nth charge / discharge cycles will be generally described with reference to FIG.

[0424] In step S7280, the external device 2000 starts the k-th (k is a natural number from 4 to n) charge / discharge cycle. The conditions of the k-th charge / discharge cycle are substantially the same as those of the first charge / discharge cycle.

[0425] Next, in step S7290, the external device 2000 determines the charge capacity (ChgAh[k]) and discharge capacity (DchgAh[k]) during the kth charge / discharge cycle of the battery, and in step S7300, determines a capacity difference (dAh[k]) corresponding to the difference between the charge capacity (ChgAh[k]) and the discharge capacity (DchgAh[k]).

[0426] Next, the external device 2000 determines the kth capacity difference change amount (ΔdAh[k]) by subtracting the capacity difference (dAh[k]) of the kth charge / discharge cycle from the capacity difference (dAh[k−1]) of the k−1th charge / discharge cycle in step S7310.

[0427] Next, the external device 2000 determines whether the k-th capacitance difference change amount (ΔdAh[k]) is greater than a reference value in step S7320.

[0428] If the determination in step S7320 is 'YES', the external device 2000 calculates the k-1th cumulative capacitance difference change amount (

number

number

[0429] On the other hand, if the determination in step S7320 is "No", the external device 2000 calculates the k-1th cumulative capacitance difference change amount (

number

number

[0430] Step S7350 is performed after steps S7330 and S7340.

[0431] In step S7350, the external device 2000 calculates the k-th cumulative capacitance difference change amount (

number

[0432] If the determination in step S7350 is 'YES', the external device 2000 may terminate the process after determining in step S7360 that an abnormality in lithium deposition within the battery has been detected.

[0433] If the determination in step S7350 is "no", that is, the kth cumulative capacitance difference change amount (

number

[0434] If the determination at step S7370 is 'Yes', all charge / discharge cycles for detecting lithium deposition have been performed, so it may be determined that no lithium deposition abnormality has occurred within the battery, and the process may be terminated.

[0435] On the other hand, if the determination in step S7370 is "NO", the external device 2000 returns the process to S7280 to perform another charge / discharge cycle for detecting the lithium precipitation abnormality. Thus, steps S7280 to S7370 may be periodically repeated until the charge / discharge cycle index k becomes n.

[0436] According to an embodiment of the present invention, if the capacity difference change amount calculated in the current charge / discharge cycle is equal to or less than a reference value, the cumulative capacity difference change amount calculated up to the previous time may be initialized to 0. Also, if the capacity difference change amount calculated in the current charge / discharge cycle is greater than a reference value, the current capacity difference change amount may be added to the previous cumulative capacity difference change amount. As a result, the cumulative capacity difference change amount increases. The previous cumulative capacity difference change amount may be 0 or a positive value, but if it has a positive value, the capacity difference change amount exceeding the reference value calculated in successive charge / discharge cycles may be accumulated.

[0437] In addition, while the capacity difference change amount is accumulated, if the capacity difference change amount decreases to a reference value or less in a specific charge / discharge cycle, the accumulated capacity difference change amount can be initialized to 0. By applying such logic, the accumulated capacity difference change amount corresponds to a kind of quantitative index for estimating lithium deposition abnormality. In other words, if the capacity difference change amount is larger than the reference value, it may mean that there is a possibility of lithium deposition.

[0438] In addition, if the condition that the capacity difference change exceeds the reference value is continuously satisfied in a plurality of chronologically consecutive charge / discharge cycles and the cumulative capacity difference change increases to or exceeds the critical value, it may mean that there is a high possibility of lithium deposition. The present invention is technically significant in that it quantifies the possibility of lithium deposition using a factor called the cumulative capacity difference change.

[0439] FIG. 31 is a graph showing changes in data measured in an experimental example in which the method of detecting lithium deposition by the external device 2000 according to an embodiment of the present invention is applied.

[0440] In this experimental example, a pouch-type lithium polymer battery was used. The lithium polymer battery selected for the experiment was degraded and lithium deposition had begun on the negative electrode. The current capacity of the lithium polymer battery reflecting the degree of degradation was approximately 50Ah. The charging conditions for the charge cycle were CC (Constant Current)-CV (Constant Voltage) charging, and when the CC charging target voltage was reached, the CC charging was terminated and switched to CV charging, and charging was terminated when the CV charging current reached the target current. The discharging conditions for the discharge cycle were CC discharging, and discharging was terminated when a predetermined discharge capacity was discharged. The temperature conditions for the charge cycle and discharge cycle were 45°C. The reference value, which is the criterion for determining whether or not to accumulate the capacity difference change amount, was 0, and the critical value, which is the diagnostic criterion for lithium deposition abnormality, was set to 0.06Ah.

[0441] Graph 1 shows the measurement results of the charge capacity (ChgAh[k]) and discharge capacity (DChgAh[k]) for each charge / discharge cycle. The charge capacity (ChgAh[k]) and discharge capacity (DChgAh[k]) were calculated by integrating the current value measured through the sense resistor. Due to an error in the discharge current measurement, the discharge capacity is larger than the charge capacity from the fourth discharge cycle.

[0442] Graph 2 shows the capacity difference (dAh [k]) for each charge / discharge cycle. Referring to graph 1, the capacity difference (dAh [k]) becomes negative from the fourth charge / discharge cycle because the discharge capacity is greater than the charge capacity.

[0443] Graph 3 shows the change in capacity difference (ΔdAh[k]) for each charge / discharge cycle. The indices of charge / discharge cycles where the change in capacity difference (ΔdAh[k]) is positive are 2 to 13, 17, 18, and 20. The indices of charge / discharge cycles where the change in capacity difference (ΔdAh[k]) is negative are 14 to 16, and 19.

[0444] Graph 4 shows the cumulative capacity difference change (

number

number

number

[0445] FIG. 32 is a graph showing changes in data measured in another experimental example to which the method for detecting lithium precipitation anomaly according to an embodiment of the present invention is applied.

[0446] In FIG. 32, graph circle 1 is the same as graph circle 1 of the above-mentioned experimental example. Graph circle 1' is a graph showing the measurement results of the charge capacity (ChgAh[k]) and discharge capacity (DchgAh[k]) when using a current measurement means with a different current measurement error than in the above-mentioned experimental example. In this experimental example, the error in the discharge current measurement value is larger than in the above-mentioned experimental example. Therefore, the discharge capacity (DchgAh[k]) graph is shifted upward compared to the above-mentioned experimental example.

[0447] Graphs 2 and 2' are graphs showing the capacity difference (dAh [k]) for each charge / discharge cycle, graphs 3 and 3' are graphs showing the capacity difference change (ΔdAh [k]) for each charge / discharge cycle, and graphs 4 and 4' are graphs showing the cumulative capacity difference change (

number

[0448] Graphs circles 2, 3, and 4 were calculated using the data in graph circle 1, and graphs circles 2', 3', and 4' were calculated using the data in graph circle 1'.

[0449] 32, the graphs 2, 3, and 4 are substantially the same as the graphs 2', 3', and 4'. Therefore, even if the discharge current value includes a measurement error, the external device 2000 determines that a lithium deposition abnormality has occurred inside the battery after the 13th charge / discharge cycle, regardless of the magnitude of the error, and reports the result of the lithium deposition abnormality detection as Second diagnostic information From these experimental results, it can be seen that the present invention can reliably detect lithium deposition anomalies regardless of the error of the current measurement.

[0450] 33 is a flow diagram of the battery cell diagnosis device 1000 according to an embodiment of the present invention diagnosing an abnormal state of a battery cell using an external device 2000. Parts that overlap with those described above will be briefly described or omitted.

[0451] In step S7000 of Fig. 33, the external device 2000 may detect an abnormality in the parallel connection, which will be described in detail below with reference to Fig. 34.

[0452] 34 is a flow diagram illustrating an exemplary battery diagnostic method according to one embodiment of the present invention. The method of FIG. 34 may be repeatedly performed at a first time interval.

[0453] Referring to FIG. 34, in step S7610, the external device 2000 data The charging and discharging data of battery B included in the battery B can be collected.

[0454] In step S7620, the external device 2000 may determine an estimated capacity value indicating a full charge capacity of battery B. Step S7620 may include steps S7622 and S7624. In step S7622, ​​the external device 2000 may input the charge / discharge data into a capacity estimation model to determine a current integration value and an SOC change value of battery B. In step S7624, the external device 2000 may determine an estimated capacity value indicating a full charge capacity of battery B from a ratio of the current integration value and the SOC change value of battery B. The external device 2000 may store a time series of the estimated capacity values.

[0455] In step S7630, the external device 2000 may detect an abnormality in the parallel connection B200 by monitoring a change over time in the estimated capacitance value. Step S7630 may include steps S7632, S7634, S7636, S7638, and S7639. In step S7632, the external device 2000 may determine a critical capacitance value for a second time that is smaller than the estimated capacitance value at the first time. For example, the second time may be a timing at which the current estimated capacitance value is calculated, and the first time may be a timing at which the estimated capacitance value ten or earlier is calculated. In step S7634, the external device 2000 may compare the estimated capacitance value at the second time with the critical capacitance value for the second time. The estimated capacitance value at the second time being smaller than the critical capacitance value for the second time indicates that at least one of a first capacitance abnormality and a second capacitance abnormality has occurred in the parallel connection B200. If the value of step S7634 is "yes", proceed to step S7636. Otherwise, proceed to step S7638. In step S7636, the external device 2000 may increment the diagnostic count by 1. In step S7638, the external device 2000 may reset the diagnostic count. In step S7639, the external device 2000 may determine whether the diagnostic count has reached a critical count. The value of step S7639 being "yes" indicates that a second capacitance anomaly, which is a complete open circuit fault, has been detected in at least one unit cell UC of the parallel connection B200.

[0456] In step S7640, the external device 2000 may detect the parallel connection abnormality. The external device 2000 may determine the number of unit cells in which the parallel connection abnormality is detected. The external device 2000 may determine the number of unit cells in which the parallel connection abnormality is detected from two estimated capacitance values ​​at two past times (e.g., te, tf) that are spaced apart by a time interval equal to or less than a second time interval at which the estimated capacitance maximum decrease occurs. 1 ~UC M It is possible to determine the number of abnormal (completely open) unit cells among the unit cells.

[0457] The external device 2000 includes a plurality of unit cells UC 1 ~UC MThe number of abnormal (completely open circuit faulted) unit cells among the two times (e.g., te, tf) may be determined. The number of abnormal unit cells may be determined to be equal to the maximum integer not greater than ΔAhmax / (Ahp / M). Ahp is the estimated capacity value at the preceding time (te) among the two times (e.g., te, tf). ΔAhmax is the maximum decrease in full charge capacity over two times (e.g., te, tf) before the timing when the abnormality of the parallel connection body 200 is detected, and is the result of subtracting the estimated capacity value at the following time (tf) from the estimated capacity value at the preceding time (te). For example, when Ahp=122Ah, ΔAhmax=27Ah, and M=10, 2≦27Ah / (122Ah / 10)<3, the number of abnormal unit cells may be determined to be 2.

[0458] 35 is a flow diagram of the battery cell diagnosis device 1000 according to an embodiment of the present invention diagnosing an abnormal state of a battery cell using an external device 2000. Parts that overlap with those described above will be briefly described or omitted.

[0459] In step S7000 of Fig. 35, the external device 2000 may detect an internal short circuit anomaly, which will be described in detail later with reference to Figs.

[0460] 36 and 37 show an embodiment of the present invention in which an external device 2000 data 1 is a detailed flow chart showing a process for detecting an internal short circuit anomaly while repeating charge / discharge cycles using the battery.

[0461] The external device 2000 detects an internal short circuit abnormality according to an embodiment of the present invention in accordance with the flow charts shown in FIGS. 36 and 37, and includes the detected result. Second diagnostic information may be generated.

[0462] 36 is a flow diagram illustrating an example of a battery management method according to an embodiment of the present invention. The method of FIG. 36 includes: 1 ~BC NThis is to detect an internal short circuit abnormality of the battery cell BC based on all SOC transitions. For convenience of explanation, it is assumed that the most recent charging period is from time t4 to time t5.

[0463] Referring to FIG. 36, in step S7610, the external device 2000 data The charging period of the battery pack 10 obtained using 1 ~BC N A SOC estimation algorithm may be applied to each state parameter to determine a first SOC change for each battery cell BC, the first SOC change being the difference between a first SOC at a first charging point and a second SOC at a second charging point, where the first charging point and the second charging point are not particularly limited as long as they are two different points within a recent charging period.

[0464] As an example, the first charging point may be a start point t4 of the most recent charging interval, and the second charging point may be an end point t5 of the most recent charging interval. Since the method of FIG. 36 is related to charging, the first SOC change indicates the increase in SOC from the first charging point to the second charging point. As an example, referring to FIG. 18, the first SOC change of the abnormal battery cell is the difference between the first SOC (VC54) and the second SOC (VC55).

[0465] In step S7620, the external device 2000 includes a plurality of battery cells BC 1 ~BC N A statistical algorithm is applied to the first SOC changes of at least two of the battery cells BC to determine a reference factor. The reference factor is 1 ~BC N The reference factor may be equal to the average or median of the first SOC changes of at least two battery cells among the battery cells 100 and 102. As an example, referring to FIG. 18, if the curve VC4 is the average of the first SOC changes, the reference factor is the difference between SOC(VC44) and SOC(VC45).

[0466] In step S7630, the external device 2000 detects an internal short circuit abnormality by comparing the first SOC change with a reference factor for each battery cell BC. In detecting the internal short circuit abnormality, one or a combination of two or more of the following detection conditions may be utilized.

[0467] [Condition #1: The first SOC change is smaller than the reference factor by a critical value (TH1) or more] [Condition #2: The ratio of the first SOC change to the reference factor is less than or equal to a critical value (TH2), where TH2 is between 0 and 1] [Condition #3: The ratio of the first SOC change to the reference factor is less than the previous ratio by a critical value (TH3) or more] In condition #3, the previous ratio is the ratio of the first SOC change to the reference factor in the charging period preceding the most recent charging period (t0 to t1 in FIG. 17).

[0468] The critical values ​​(TH1, TH2, TH3) may be predetermined fixed values. Alternatively, the external device 2000 may determine at least one of the critical values ​​(TH1, TH2, TH3) based on an integrated value of the charging current measured during a period from the first charging time point to the second charging time point.

[0469] At least one of the critical values ​​(TH1, TH2, TH3) may be updated each time the charging mode of the battery pack 10 is resumed. As an example, the external device 2000 may obtain a target value (e.g., 60%) of the SOC change by dividing the integrated value of the charging current (e.g., 3 Ah [ampere-hours]) by the design capacity (e.g., 5 Ah) of the battery cell BC, and may determine at least one of the critical values ​​(TH1, TH2, TH3) by multiplying the ratio of the reference factor to the target value by a predetermined scaling constant (positive value). The scaling constant used to determine any one of the critical values ​​(TH1, TH2, TH3) may be different from the scaling constant used to determine the other one of the critical values ​​(TH1, TH2, TH3). The target value may be determined during the progress of at least one of steps S7610, S7620, and S7630. At least one of the threshold values ​​(TH1, TH2, TH3) may be determined during the progress of at least one of S7620 and S7630.

[0470] Multiple battery cells BC 1 ~BC N When all of the battery cells BC are normal, the target value and the reference factor may be approximately the same. 1 ~BC N As the number of battery cells with an internal short circuit increases, the reference factor is significantly decreased from the target value. Therefore, by determining at least one of the critical values ​​(TH1, TH2, TH3) according to the above-mentioned method, the accuracy of detecting an internal short circuit can be improved.

[0471] Meanwhile, the target value is determined before step S7620, and then, in step S7620, the target value is determined by the plurality of battery cells BC 1 ~BC N Among all the first SOC changes of the battery cells BC, only the first SOC changes that are equal to or smaller than the target value may be used to determine the reference factor. In this case, in determining the reference factor, 1 ~BC NSince all of the first SOC changes that exceed the target value are excluded, the battery cell BC having a relatively serious internal short circuit abnormality is classified into a plurality of battery cells BC 1 ~BC N It can be preferentially detected from

[0472] 37 is a flow diagram showing another exemplary battery management method according to an embodiment of the present invention. The method of FIG. 37 includes: 1 ~BC N This is to detect an internal short circuit abnormality in the battery cell BC based on all SOC transitions. For convenience of explanation, it is assumed that the most recent charging period is from time t4 to time t5, and the most recent discharging period is from time t6 to time t7.

[0473] Referring to FIG. 37, in step S7710, the external device 2000 data A plurality of battery cells BC acquired during the charging period of the battery pack 10 using 1 ~BC N A SOC estimation algorithm may be applied to each state parameter to determine a first SOC change for each battery cell BC, the first SOC change being the difference between a first SOC at a first charging time point and a second SOC at a second charging time point. The first charging time point and the second charging time point may be two different time points within a recent charging period, but are not limited thereto. As an example, the first charging time point may be a start time point t4 of the recent charging period, and the second charging time point may be an end time point t5 of the recent charging period.

[0474] In step S7720, the external device 2000 detects a plurality of battery cells BC acquired during the discharging period of the battery pack 10. 1 ~BC NA SOC estimation algorithm may be applied to each state parameter to determine a second SOC change for each battery cell BC, the second SOC change being a difference between a third SOC at a first discharge time point and a fourth SOC at a second discharge time point. The first discharge time point and the second discharge time point may be two different time points within a recent discharge period, but are not limited thereto. As an example, the first discharge time point may be a start time point t6 of a recent charge period, and the second discharge time point may be an end time point t7 of a recent charge period.

[0475] Referring to FIG. 18, in an abnormal battery cell, the first SOC change is the difference between the first SOC (VC54) and the second SOC (VC55), and the second SOC change is the difference between the third SOC (VC56) and the fourth SOC (VC57).

[0476] 37, step S7710 precedes step S7720, but this should be understood as an example. For example, if the most recent charging period precedes the most recent discharging period, step S7720 may precede step S7710. As another example, steps S7710 and S7720 may be performed simultaneously after both the most recent charging period and the most recent discharging period have ended.

[0477] In step S7730, the external device 2000 may determine an abnormality factor for each battery cell BC by dividing the first SOC change by the second SOC change. That is, the abnormality factor may be determined by the formula "abnormality factor = (first SOC change) ÷ (second SOC change)".

[0478] 18, the abnormality factor of an abnormal battery cell may be determined by the formula "{SOC(VC55)-SOC(VC54)}÷{SOC(VC56)-SOC(VC57)}". The abnormality factor may also be referred to as coulombic efficiency.

[0479] In step S7740, the external device 2000 includes a plurality of battery cells BC 1 ~BC NA statistical algorithm may be applied to the abnormality factors of at least two of the battery cells to determine a reference factor.

[0480] The reference factor is a multiple battery cell BC 1 ~BC N As an example, referring to FIG. 18, a curve VC4 may be the same as the average or median of the abnormality factors of at least two battery cells BC 1 ~BC N If the average SOC is VC45, then the criterion factor can be determined by the formula: {SOC(VC45)-SOC(VC44)}÷{SOC(VC46)-SOC(VC47)}.

[0481] In step S7750, the external device 2000 may compare the abnormality factor with the reference factor for each battery cell BC to detect an internal short circuit abnormality of the battery cell BC. In detecting an internal short circuit abnormality, one or a combination of two or more of the following detection conditions may be utilized.

[0482] [Condition #1: The abnormal factor is smaller than the reference factor by a critical value (TH11) or more] [Condition #2: The relative coulombic efficiency is less than or equal to a critical value (TH12), where TH12 is between 0 and 1] [Condition #3: The ratio of the abnormal factor to the reference factor must be smaller than the previous ratio by a critical value (TH13) or more] In condition #2, the relative coulombic efficiency may be the ratio of the abnormal factor to the reference factor, ie, the abnormal factor divided by the reference factor.

[0483] In condition #3, the previous ratio is the ratio of the abnormal factor to the reference factor based on the first SOC during the charging period (t4 to t5 in FIG. 17) preceding the most recent discharging period (t6 to t7) and the second SOC during the discharging period (t2 to t3 in FIG. 17).

[0484] The threshold values ​​(TH11, TH12, TH13) may be predetermined values. For example, the threshold values ​​(TH11, TH12, TH13) may be the same as the predetermined threshold values ​​(TH1, TH2, TH3) described above with reference to FIG.

[0485] The external device 2000 may determine at least one of the critical values ​​(TH11, TH12, TH13) based on an integrated value of the charging current measured over a period from a first charging point to a second charging point and an integrated value of the discharging current measured over a period from a first discharging point to a second discharging point.

[0486] At least one of the critical values ​​(TH11, TH12, TH13) may be newly updated each time the charging mode or the discharging mode of the battery pack 10 is resumed. As an example, the external device 2000 may obtain a target value by dividing an integrated value of the charging current by an integrated value of the discharging current. The external device 2000 may determine at least one of the critical values ​​(TH11, TH12, TH13) by multiplying a ratio of a reference factor to the target value by a predetermined scaling constant (positive value). The scaling constant used in determining any one of the critical values ​​(TH11, TH12, TH13) may be different from the scaling constant used in determining the other one of the critical values ​​(TH11, TH12, TH13). The target value may be determined during the progress of at least one of steps S7710, S7720, S7730, and S7740. At least one of the threshold values ​​(TH1, TH2, TH3) may be determined during the progress of at least one of S7730 and S7740.

[0487] Multiple battery cells BC 1 ~BC N When all of the battery cells BC are normal, the target value and the reference factor may be approximately the same. 1 ~BC NAs the number of battery cells with an internal short circuit increases, the reference factor is significantly decreased from the target value. Therefore, by determining at least one of the critical values ​​(TH11, TH12, TH13) according to the above-mentioned method, the accuracy of detecting an internal short circuit can be improved.

[0488] Meanwhile, the target value is determined before step S7740, and then, in step S7740, the target value is determined by the plurality of battery cells BC 1 ~BC N Among all the abnormality factors, only the abnormality factors that are equal to or less than the target value can be used to determine the reference factor. In this case, in determining the reference factor, 1 ~BC N Since all abnormality factors exceeding the target value are excluded, the battery cell BC having a relatively serious internal short circuit abnormality is divided into multiple battery cells BC 1 ~BC N It can be preferentially detected from

[0489] In each embodiment, the external device 2000 includes a plurality of battery cells BC 1 ~BC N If an internal short circuit abnormality is detected in a predetermined number or more of battery cells, an internal short circuit abnormality is detected. Second diagnostic information may be generated.

[0490] In each embodiment, the external device 2000 includes a plurality of battery cells BC 1 ~BC N If an internal short circuit abnormality is detected in a predetermined number or more of the battery cells, the allowable range of the charge / discharge current may be reduced. For example, the upper limit (positive value) of the allowable range may be reduced or the lower limit (negative value) of the allowable range may be increased in proportion to the number of abnormal battery cells (etc.).

[0491] For example, the external device 2000 according to the embodiment of the present invention may be included in a diagnostic system for diagnosing an abnormality in a battery cell. The diagnostic system may be operated in an electric vehicle maintenance shop, a battery manufacturer, or a battery maintenance company. For example, the diagnostic system may be used to diagnose an abnormality in a battery cell mounted on an electric vehicle or an energy storage system, or may be used to diagnose an abnormality in a battery of a newly developed model manufactured by a battery manufacturer. In particular, in the latter case, the external device 2000 may be used to diagnose the state of the battery prior to commercialization of the battery of the newly developed model.

[0492] Additionally, the external device 2000 may be included as a control element in a battery-equipped system.

[0493] For example, the external device 2000 may be included in a control system of an electric vehicle. In this case, the external device 2000 may collect data on the charge capacity and discharge capacity of a battery cell during a charging process and a discharging process of a battery mounted on the electric vehicle, diagnose the state of the battery cell using the collected data, and output the diagnosis result to an integrated control display of the electric vehicle.

[0494] As another example, the external device 2000 may be included in a control system of an energy storage system. In this case, the external device 2000 may collect data on the charge capacity and discharge capacity of the battery cells during the charging and discharging processes of the energy storage system, diagnose the state of the battery cells using the collected data, and output the diagnosis result through a display of an integrated management computer accessible to an operator.

[0495] When the diagnosis result regarding the lithium deposition abnormality is output through the display, the user of the electric vehicle or the operator of the energy storage system may take appropriate safety measures. For example, the user of the electric vehicle may visit a maintenance shop to have the battery inspected. For another example, the operator of the energy storage system may replace the battery with a new battery.

[0496] In an embodiment of the present invention, the external device 2000 may selectively include a processor, an ASIC (Application-Specific Integrated Circuit), other chipsets, logic circuits, registers, a communication modem, a data processing device, and the like known in the art to execute the various control logics described above. When the control logic is embodied as software, the external device 2000 may be embodied as a collection of program modules. In this case, the program modules may be stored in a memory and executed by a processor. The memory may be provided inside or outside the processor and may be connected to the processor via various well-known computer components. The memory may be included in the recording unit 2100 of the present invention. The memory is a general term for devices in which information is stored, regardless of the type of device, and does not refer to a specific memory device.

[0497] At least one of the various control logics of the external device 2000 may be combined, and the combined control logic may be created as a computer-readable code system and recorded in a computer-readable recording medium. There is no particular limitation on the type of the recording medium as long as it can be accessed by a processor included in a computer. As an example, the recording medium may include at least one selected from the group including a ROM, a RAM, a register, a CD-ROM, a magnetic disk, a hard disk, a floppy disk, and an optical data recording device. Also, the code system may be distributed and recorded in and executed by computers connected via a network. Also, functional programs, codes, and code segments for realizing the combined control logic may be easily inferred by a programmer skilled in the art to which the present invention pertains.

[0498] As described above, the present invention has been described using limited embodiments and drawings. However, the present invention is not limited thereto, and it goes without saying that various modifications and variations can be made by a person having ordinary knowledge in the technical field to which the present invention belongs within the technical spirit of the present invention and the equivalent scope of the claims. [Item 1] A current measurement unit configured to measure a current of the battery cell; a voltage sensing unit configured to sense a cell voltage of the battery cell; a first control unit configured to transmit first information of the battery cell including data acquired from the current measuring unit and the voltage sensing unit to an external device, receive second information from the external device including diagnostic information of the battery cell acquired based on the first information, and diagnose an abnormal state of the battery cell based on the first information and the second information. [Item 2] The diagnostic information of the battery cell is 2. The battery cell diagnostic device according to item 1, comprising at least one of information on lithium precipitation diagnosis of the battery cell, an abnormality in a parallel connection of the battery cell, and an internal short circuit of the battery cell. [Item 3] The first control unit is 2. The battery cell diagnostic device according to item 1, configured to display information about an abnormal state of the battery cell based on the diagnostic information of the battery cell included in the second information on a display unit. [Item 4] The first control unit is Detecting at least one of a voltage abnormality of the battery cell and a behavior abnormality of the battery cell based on the first information; 2. The battery cell diagnostic device of item 1, configured to diagnose an abnormal state of the battery cell based on at least one of the voltage abnormality, the behavior abnormality, and the second information. [Item 5] The first control unit is 5. The battery cell diagnostic device of item 4, configured to generate third information indicating whether the battery cell is in an abnormal state or not based on at least one of the voltage abnormality, the behavior abnormality, and the second information. [Item 6] The first control unit is 6. The battery cell diagnostic device according to item 5, configured to display the third information on a display unit. [Item 7] The first control unit is 6. The battery cell diagnostic device according to item 5, configured to transmit the third information to a second control unit of an apparatus in which the battery cell is provided. [Item 8] The first control unit is generating time series data indicating a time history of the cell voltages included in the first information; determining a first average cell voltage and a second average cell voltage for each battery cell based on the time series data, the first average cell voltage being a short-term moving average and the second average cell voltage being a long-term moving average; 2. The battery cell diagnostic device of item 1, configured to detect a voltage abnormality of the battery cell based on a difference between the first average cell voltage and the second average cell voltage. [Item 9] The battery cell diagnostic device is configured to diagnose a plurality of battery cells; The first control unit is determining a long-term and short-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage for each of the plurality of battery cells; determining an average of long-term and short-term average differences for a plurality of said battery cells; determining a cell diagnostic deviation corresponding to a deviation between the average value of the long-term and short-term average differences and the long-term and short-term average differences for each of the plurality of battery cells; 9. The battery cell diagnostic device according to item 8, wherein the battery cell that satisfies the condition that the cell diagnostic deviation exceeds a diagnostic critical value is detected as an abnormal voltage cell. [Item 10] The battery cell diagnostic device is configured to diagnose a plurality of battery cells; The first control unit is determining a long-term and short-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage for each of the plurality of battery cells; determining an average of long-term and short-term average differences for a plurality of said battery cells; determining a cell diagnostic deviation corresponding to a deviation between the average value of the long-term and short-term average differences and the long-term and short-term average differences for each of the plurality of battery cells; determining a statistically variable critical value dependent on a standard deviation for cell diagnostic deviations of the plurality of battery cells; filtering the time series data based on the statistical variable critical value to generate filtered time series data; 9. The battery cell diagnostic device according to item 8, configured to detect a voltage abnormality of the battery cell based on the time or number of data of the filtered time series data exceeding a diagnostic critical value. [Item 11] The battery cell diagnostic device is configured to diagnose a plurality of battery cells; The first control unit is determining a long-term and short-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage for each of the plurality of battery cells; determining a normalized value corresponding to an average of long-term and short-term average differences of a plurality of the battery cells; normalizing the long-term and short-term average differences according to the normalization value for each of the plurality of battery cells; determining a statistically variable critical value dependent on a standard deviation for normalized cell diagnostic deviations of the plurality of battery cells; For each of the plurality of battery cells, filtering the normalized long-term and short-term average differences for each battery cell based on the statistical variable critical value to generate filtered time series data; 9. The battery cell diagnostic device according to item 8, configured to detect a voltage abnormality of the battery cell based on the time or number of data of the filtered time series data exceeding a diagnostic critical value. [Item 12] The first control unit is determining a plurality of sub-voltage curves by applying a moving window of a first time length to the time series of cell voltages included in the first information; determining a long-term average voltage value for each sub-voltage curve using a first averaging filter of the first length of time; determining a short-term average voltage value for each sub-voltage curve using a second averaging filter having a second time duration shorter than the first time duration; determining a voltage deviation corresponding to a difference between the long-term average voltage value and the short-term average voltage value of each sub-voltage curve; 2. The battery cell diagnostic device of item 1, configured to compare a plurality of voltage deviations determined for a plurality of the sub-voltage curves with at least one of a first critical deviation and a second critical deviation to detect abnormal behavior of the battery cell. [Item 13] The first control unit is the behavior abnormality is detected in response to two voltage deviations that respectively satisfy a first condition, a second condition, and a third condition, among the plurality of voltage deviations; the first condition is satisfied when a first voltage deviation of the two voltage deviations is equal to or greater than the first critical deviation; the second condition is satisfied when a second voltage deviation of the two voltage deviations is less than or equal to the second critical deviation; Item 13. The battery cell diagnostic device according to item 12, wherein the third condition is configured to be satisfied when a time interval between two of the voltage deviations is less than or equal to a critical time. [Item 14] the second information indicates whether a cumulative capacitance difference change amount is equal to or greater than a threshold value; The cumulative capacitance difference change amount is a sum of capacitance difference changes amount, The capacity difference change amount is a difference between a capacity difference in the k-th charge / discharge cycle of the battery cell and a capacity difference in the k-1-th charge / discharge cycle of the battery cell, The k is a natural number of 2 or more, A capacity difference of each of the charge / discharge cycles corresponds to a difference between a charge capacity of the battery cell in a charging process of the charge / discharge cycle and a discharge capacity of the battery cell in a discharging process of the charge / discharge cycle; 2. The battery cell diagnostic device according to item 1, wherein the charge capacity and the discharge capacity are each derivable from data acquired from the current measurement unit and included in the first information. [Item 15] The second information indicates a capacity difference change amount between successive charge / discharge cycles of the battery cell, 2. The battery cell diagnostic device according to item 1, wherein the capacity difference for each of the charge / discharge cycles of the battery cell is the difference between (i) the charge capacity of the battery cell during the charge process of the charge / discharge cycle of the battery cell and (ii) the discharge capacity of the battery cell during the discharge process of the charge / discharge cycle of the battery cell. [Item 16] The second information indicates whether or not there is an abnormality in a parallel connection of a plurality of unit cells included in the battery cell based on a result of monitoring a change over time of an estimated capacity value by the external device, the estimated capacity value indicates a full charge capacity of the battery cell based on charge / discharge data; 2. The battery cell diagnostic device of item 1, wherein the charge / discharge data includes a voltage time series indicating a change in voltage of the battery cell over time, and a current time series indicating a change in charge / discharge current of the battery cell over time. [Item 17] The second information indicates whether or not an internal short circuit occurs in the battery cell based on a first SOC change and a reference factor of the battery cell, The criterion factor is determined by applying a statistical algorithm to a first SOC change of at least two battery cells of the plurality of battery cells; the first SOC change being a difference between a first SOC at a first charging point and a second SOC at a second charging point of each battery cell; The first SOC is estimated by applying a SOC estimation algorithm to state parameters of the battery cell at the first charging point; The second SOC is estimated by applying the SOC estimation algorithm to state parameters of the battery cell at the second charging point; 2. The battery cell diagnostic device of item 1, wherein the state parameter is obtained based on the first information. [Item 18] 18. A battery cell diagnostic device according to any one of claims 1 to 17, and an external device, The external device is configured to derive the second information based on at least a portion of the first information. [Item 19] A controller obtains data including at least one of a charging current and a discharging current of a battery cell and a cell voltage of the battery cell; The controller transmits first information of the battery cell including the acquired data to an external device; receiving, from the external device, second information including diagnostic information of the battery cell obtained based on the first information by the controller; and the controller diagnosing an abnormal state of the battery cell based on the first information and the second information. [Item 20] detecting at least one of a voltage abnormality and a behavior abnormality of the battery cell based on the first information of the battery cell; 20. The battery cell diagnosis method of item 19, further comprising: a step of the controller diagnosing an abnormal state of the battery cell based on at least one of a voltage abnormality of the battery cell, a behavior abnormality of the battery cell, and the second information. [Explanation of symbols]

[0499] 1: Battery cell diagnostic system 1000: Battery cell diagnostic device 2000: External device 100: Current measurement section 200: Voltage sensing section 300: Data acquisition section 400: First control section 500: Display section 600: Second control section 10: Battery pack

Claims

1. A current measurement unit configured to measure a current of the battery cell; a voltage sensing unit configured to sense a cell voltage of the battery cell; a first control unit configured to transmit first information of the battery cell including data acquired from the current measuring unit and the voltage sensing unit to an external device, receive second information from the external device including diagnostic information of the battery cell acquired based on the first information, and diagnose an abnormal state of the battery cell based on the first information and the second information.

2. The diagnostic information of the battery cell is The battery cell diagnostic device according to claim 1 , comprising at least one of information on lithium precipitation diagnosis of the battery cell, an abnormality in a parallel connection of the battery cell, and an internal short circuit of the battery cell.

3. The first control unit is The battery cell diagnostic device according to claim 1 , further configured to display information about an abnormal state of the battery cell based on the diagnostic information of the battery cell included in the second information on a display unit.

4. The first control unit is Detecting at least one of a voltage abnormality of the battery cell and a behavior abnormality of the battery cell based on the first information; The battery cell diagnostic device according to claim 1 , configured to diagnose an abnormal state of the battery cell based on at least one of the voltage abnormality, the behavior abnormality, and the second information.

5. The first control unit is The battery cell diagnostic device according to claim 4 , configured to generate third information indicating whether the battery cell is in an abnormal state or not based on at least one of the voltage abnormality, the behavior abnormality, and the second information.

6. The first control unit is The battery cell diagnostic device according to claim 5 , configured to display the third information on a display unit.

7. The first control unit is The battery cell diagnostic device according to claim 5 , configured to transmit the third information to a second control unit of an apparatus in which the battery cell is provided.

8. The first control unit is generating time series data indicating a history of the cell voltages included in the first information over time; determining a first average cell voltage and a second average cell voltage for each battery cell based on the time series data, the first average cell voltage being a short-term moving average and the second average cell voltage being a long-term moving average; The battery cell diagnostic device according to claim 1 , configured to detect a voltage abnormality of the battery cell based on a difference between the first average cell voltage and the second average cell voltage.

9. The battery cell diagnostic device is configured to diagnose a plurality of battery cells; The first control unit is determining a long-term and short-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage for each of the plurality of battery cells; determining an average of long-term and short-term average differences for a plurality of said battery cells; determining a cell diagnostic deviation corresponding to a deviation between the average value of the long-term and short-term average differences and the long-term and short-term average differences for each of the plurality of battery cells; The battery cell diagnostic device according to claim 8 , further comprising: detecting a battery cell that satisfies a condition that the cell diagnostic deviation exceeds a diagnostic critical value as an abnormal voltage cell.

10. The battery cell diagnostic device is configured to diagnose a plurality of battery cells; The first control unit is determining a long-term and short-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage for each of the plurality of battery cells; determining an average of long-term and short-term average differences for a plurality of said battery cells; determining a cell diagnostic deviation corresponding to a deviation between the average value of the long-term and short-term average differences and the long-term and short-term average differences for each of the plurality of battery cells; determining a statistically variable critical value dependent on a standard deviation for cell diagnostic deviations of the plurality of battery cells; filtering the time series data based on the statistical variable critical value to generate filtered time series data; The battery cell diagnostic device according to claim 8 , configured to detect a voltage abnormality of the battery cell based on a time or a number of data of the filtered time series data exceeding a diagnostic critical value.

11. The battery cell diagnostic device is configured to diagnose a plurality of battery cells; The first control unit is determining a long-term and short-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage for each of the plurality of battery cells; determining a normalized value corresponding to an average of long-term and short-term average differences of a plurality of the battery cells; normalizing the long-term and short-term average differences according to the normalization value for each of the plurality of battery cells; determining a statistically variable critical value dependent on a standard deviation for normalized cell diagnostic deviations of the plurality of battery cells; For each of the plurality of battery cells, filtering the normalized long-term and short-term average differences for each battery cell based on the statistical variable critical value to generate filtered time series data; The battery cell diagnostic device according to claim 8 , configured to detect a voltage abnormality of the battery cell based on a time or a number of data of the filtered time series data exceeding a diagnostic critical value.

12. The first control unit is determining a plurality of sub-voltage curves by applying a moving window of a first time length to the time series of cell voltages included in the first information; determining a long-term average voltage value for each sub-voltage curve using a first averaging filter of the first length of time; determining a short-term average voltage value for each sub-voltage curve using a second averaging filter having a second time duration shorter than the first time duration; determining a voltage deviation corresponding to a difference between the long-term average voltage value and the short-term average voltage value of each sub-voltage curve; 2. The battery cell diagnostic device of claim 1, further comprising: a first critical deviation and a second critical deviation determining means for determining whether or not the battery cell is in a defective state;

13. The first control unit is the behavior abnormality is detected in response to two voltage deviations that respectively satisfy a first condition, a second condition, and a third condition, among the plurality of voltage deviations; the first condition is satisfied when a first voltage deviation of the two voltage deviations is equal to or greater than the first critical deviation; the second condition is satisfied when a second voltage deviation of the two voltage deviations is less than or equal to the second critical deviation; The battery cell diagnostic device according to claim 12 , wherein the third condition is configured to be satisfied when a time interval between two of the voltage deviations is equal to or less than a critical time.

14. the second information indicates whether a cumulative capacitance difference change amount is equal to or greater than a threshold value; The cumulative capacitance difference change amount is a sum of capacitance difference changes amount, The capacity difference change amount is a difference between a capacity difference in the kth charge / discharge cycle of the battery cell and a capacity difference in the k-1th charge / discharge cycle of the battery cell, The k is a natural number of 2 or more, A capacity difference of each of the charge / discharge cycles corresponds to a difference between a charge capacity of the battery cell in a charging process of the charge / discharge cycle and a discharge capacity of the battery cell in a discharging process of the charge / discharge cycle; The battery cell diagnostic device of claim 1 , wherein the charge capacity and the discharge capacity are each derivable from data acquired from the current measurement unit and included in the first information.

15. The second information indicates a capacity difference change amount between successive charge / discharge cycles of the battery cell, 2. The battery cell diagnostic device of claim 1, wherein the capacity difference for each of the charge and discharge cycles of the battery cell is a difference between (i) a charge capacity of the battery cell during a charge process of the charge and discharge cycle of the battery cell and (ii) a discharge capacity of the battery cell during a discharge process of the charge and discharge cycle of the battery cell.

16. The second information indicates whether or not a parallel connection of a plurality of unit cells included in the battery cell is abnormal based on a result of monitoring a change over time of an estimated capacity value by the external device, the estimated capacity value indicates a full charge capacity of the battery cell based on charge / discharge data; The battery cell diagnostic device according to claim 1 , wherein the charge / discharge data includes a voltage time series indicating a change over time in a voltage of the battery cell, and a current time series indicating a change over time in a charge / discharge current of the battery cell.

17. The second information indicates whether or not an internal short circuit occurs in the battery cell based on a first SOC change and a reference factor of the battery cell; The criterion factor is determined by applying a statistical algorithm to a first SOC change of at least two battery cells of the plurality of battery cells; the first SOC change being a difference between a first SOC at a first charging point and a second SOC at a second charging point of each battery cell; The first SOC is estimated by applying an SOC estimation algorithm to state parameters of the battery cell at the first charging point; The second SOC is estimated by applying the SOC estimation algorithm to state parameters of the battery cell at the second charging point; The battery cell diagnostic device according to claim 1 , wherein the state parameter is obtained based on the first information.

18. The battery cell diagnostic device according to any one of claims 1 to 17 and the external device, The external device is configured to derive the second information based at least in part on the first information.

19. A controller obtains data including at least one of a charging current and a discharging current of a battery cell and a cell voltage of the battery cell; The controller transmits first information of the battery cell including the acquired data to an external device; receiving second information from the external device, the second information including diagnostic information of the battery cell obtained based on the first information; the controller diagnosing an abnormal state of the battery cell based on the first information and the second information.

20. The controller detects at least one of a voltage abnormality and a behavior abnormality of the battery cell based on the first information of the battery cell; The battery cell diagnosis method according to claim 19 , further comprising: diagnosing an abnormal state of the battery cell based on at least one of a voltage abnormality of the battery cell, a behavior abnormality of the battery cell, and the second information by the controller.

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