CN118043690A

CN118043690A - Apparatus and method for diagnosing battery cells

Info

Publication number: CN118043690A
Application number: CN202380013810.8A
Authority: CN
Inventors: 李淳钟; 金喆泽
Original assignee: LG Energy Solution Ltd
Current assignee: LG Energy Solution Ltd
Priority date: 2022-05-26
Filing date: 2023-05-22
Publication date: 2024-05-14

Abstract

A battery cell diagnosis apparatus according to an embodiment of the present invention includes: a current measurement 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 measurement unit and the voltage sensing unit to an external device, and to receive second information including diagnostic information of the battery cell acquired based on the first information from the external device, and to diagnose an abnormal state of the battery cell based on the first information and the second information.

Description

Apparatus and method for diagnosing battery cells

Technical Field

The present application claims priority from korean patent applications 10-2022-0065020, 10-2022-0065021 and 10-2022-0065022 filed in korea on 5 months 26 of 2022, the disclosures of which are incorporated herein by reference.

The present disclosure relates to a battery cell diagnosis apparatus and method, and more particularly, to a cell diagnosis apparatus and method for diagnosing a state of a battery cell.

Background

Recently, demand for portable electronic products such as laptop computers, video cameras and mobile phones has sharply increased, and with the vigorous development of electric vehicles, energy storage accumulators, robots and satellites, there is a great deal of research on high-performance batteries that can be repeatedly charged.

Currently, commercially available batteries include nickel-cadmium batteries, nickel-hydrogen batteries, nickel-zinc batteries, lithium batteries, and the like, in which the memory effect of the lithium batteries is little or no, and therefore, lithium batteries have advantages of free charge and discharge, extremely low self-discharge rate, and high energy density, as compared with nickel-based batteries, and are receiving increasing attention.

Recently, with the popularity of applications requiring high voltages, such as electric vehicles, energy storage systems, in some cases, batteries used in electric vehicles or Energy Storage Systems (ESS) may fire during use.

Accordingly, there is an increasing need for a diagnostic technique to accurately detect anomalies in a plurality of battery cells connected in a battery pack.

Disclosure of Invention

Technical problem

The present disclosure is directed to solving the problems of the related art, and therefore, the present disclosure is directed to providing an apparatus and method for effectively diagnosing an abnormal state of a battery cell by linking an on-board diagnostic device and an off-board diagnostic device.

These and other objects and advantages of the present disclosure will be understood from the following detailed description, and will become more apparent from the exemplary embodiments of the present disclosure. Moreover, it will be readily understood that the objects and advantages of the present disclosure may be realized by means of the instruments and combinations thereof as set forth in the appended claims.

Technical proposal

A battery cell diagnosis apparatus according to an embodiment of the present disclosure includes: the battery cell diagnosis apparatus includes: a current measurement 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 measurement unit and the voltage sensing unit to an external device, receive second information including diagnostic information of the battery cell acquired based on the first information from the external device, and diagnose an abnormal state of the battery cell based on the first information and the second information.

The diagnosis information of the battery cell may include at least one of lithium analysis diagnosis of the battery cell, abnormality of parallel connection of the battery cell, and internal short circuit of the battery cell.

The first control unit 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 second information.

The first control unit may be configured to: 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; and diagnosing an abnormal state of the battery cell based on at least one of the voltage abnormality, the behavior abnormality, and the second information.

The first control unit may be configured to generate third information indicating whether the battery cell is in the abnormal state based on at least one of the voltage abnormality, the behavior abnormality, and the second information.

The first control unit may be configured to display the third information on a display unit.

The first control unit may be configured to send the third information to a second control unit of a device equipped with the battery cell.

The first control unit may be configured to: generating time-series data representing a history of the cell voltage included in the first information with the lapse of 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; and detecting a voltage abnormality of the battery cell based on a difference between the first average cell voltage and the second average cell voltage.

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

The first control unit may be configured to: determining, for each battery cell of the plurality of battery cells, a long-term and a short-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage; determining an average value of the long-term and short-term average differences for the plurality of battery cells; determining, for each battery cell of the plurality of battery cells, a cell diagnostic bias corresponding to a bias between an average of the long-term and short-term average differences and the long-term and short-term average differences; and detecting the battery cell meeting the condition that the cell diagnosis deviation exceeds a diagnosis threshold as a voltage abnormal cell.

In another aspect of the present disclosure, the battery cell diagnostic device may be configured to diagnose a plurality of battery cells.

The first control unit may be configured to: determining, for each battery cell of the plurality of battery cells, a long-term and a short-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage; determining an average value of the long-term and short-term average differences for the plurality of battery cells; determining, for each battery cell of the plurality of battery cells, a cell diagnostic bias corresponding to a bias between an average of the long-term and short-term average differences and the long-term and short-term average differences; determining a statistical variable threshold that depends on a standard deviation of the cell diagnostic deviations of the plurality of battery cells; filtering the time series data based on the statistical variable threshold to generate filtered time series data; and detecting a voltage anomaly of the battery cell based on a time or an amount of data that the filtered time series data exceeds a diagnostic threshold.

The first control unit may be configured to: determining, for each battery cell of the plurality of battery cells, a long-term and a short-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage; determining a normalized value corresponding to an average of the long-term and short-term average differences of the plurality of 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 statistical variable threshold that depends on a standard deviation of a normalized cell diagnostic deviation of the plurality of battery cells; for each battery cell of the plurality of battery cells, filtering a normalized long-term and short-term average difference for each battery cell based on the statistical variable threshold to generate filtered time series data; and detecting a voltage anomaly of the battery cell based on a time or an amount of data that the filtered time series data exceeds a diagnostic threshold.

The first control unit may be configured to: determining a plurality of sub-voltage curves by applying a moving window of a first time length to a time sequence of the 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 time length; determining a short term average voltage value for each sub-voltage curve using a second average filter of a second time length shorter than the first time length; determining a voltage deviation corresponding to a difference between the long-term average voltage value and the short-term average voltage value for each sub-voltage curve; and comparing each of a plurality of voltage deviations determined for the plurality of sub-voltage curves with at least one of a first threshold deviation and a second threshold deviation to detect a behavioral abnormality of the battery cell.

The first control unit may be configured to detect the behavior abnormality corresponding to two voltage deviations satisfying a first condition, a second condition, and a third condition, respectively, among the plurality of voltage deviations.

The first condition may be satisfied when a first voltage deviation of the two voltage deviations is equal to or greater than the first threshold deviation.

The second condition may be satisfied when a second voltage deviation of the two voltage deviations is equal to or smaller than the second threshold deviation.

The third condition may be satisfied when a time interval between the two voltage deviations is equal to or less than a threshold time.

The second information may indicate whether the cumulative capacity difference variation is greater than or equal to a threshold value, and the cumulative capacity difference variation is a sum of capacity difference variations.

Each of the capacity difference variation amounts may be a difference between a capacity difference of a kth charge-discharge cycle of the battery cell and a capacity difference of a kth-1 charge-discharge cycle of the battery cell, and the k may be a natural number greater than or equal to 2.

The difference in capacity of each charge-discharge cycle may correspond to a difference between a charge capacity of the battery cell during charging of the charge-discharge cycle and a discharge capacity of the battery cell during discharging of the charge-discharge cycle.

Each of the charge capacity and the discharge capacity may be derivable from data acquired from the current measurement unit and included in the first information.

The second information may represent a capacity difference variation amount between successive charge and discharge cycles of the battery cell.

The difference in capacity of each charge-discharge cycle of the battery cell may be a difference between (i) a charge capacity of the battery cell during charging of the charge-discharge cycle of the battery cell and (ii) a discharge capacity of the battery cell during discharging of the charge-discharge cycle of the battery cell.

The second information may represent whether parallel connection of a plurality of unit cells included in the battery cell is abnormal based on a result of the external device monitoring the estimated capacity value over time.

The estimated capacity value may represent a full charge capacity of the battery cell based on charge-discharge data.

The charge and discharge data may include a voltage time series representing the time-varying voltage of the battery cell and a current time series representing the time-varying charge and discharge current of the battery cell.

The second information may indicate whether the battery cell has an internal short circuit based on the first SOC variation of the battery cell and a standard factor.

The standard factor may be determined by applying a statistical algorithm to the first SOC variation of at least two battery cells of the plurality of battery cells.

The first SOC variation may be a difference between a first SOC of a first charging time point and a second SOC of a second charging time point of each battery cell.

The first SOC may be estimated by applying an SOC estimation algorithm to a state parameter of the battery cell at the first charging time point.

The second SOC may be estimated by applying the SOC estimation algorithm to a state parameter of the battery cell at the second charging time point.

The status parameter may be obtained based on the first information.

In another aspect of the present disclosure, there is also provided a battery cell diagnosis system including the battery cell diagnosis apparatus according to an aspect of the present disclosure.

The external device may be configured to derive the second information based on at least a portion of the first information.

In yet another aspect of the present disclosure, there is also provided a battery cell diagnosis method including: acquiring 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 by means of a control unit; transmitting, by means of the control unit, first information of the battery cell including the acquired data to an external device; receiving, by the control unit, second information from the external device, the second information including diagnostic information of the battery cell acquired based on the first information; and diagnosing, by the control unit, an abnormal state of the battery cell based on the first information and the second information.

In yet another aspect of the present disclosure, the battery cell diagnosis method may further include: detecting, by the control unit, at least one of a voltage abnormality and a behavior abnormality of the battery cell based on the first information of the battery cell; and diagnosing, by the control unit, 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.

Advantageous effects

According to at least one embodiment of the present disclosure, by linking the in-vehicle device and the off-vehicle device, the abnormal state of the battery cell can be effectively diagnosed.

According to at least one embodiment of the present disclosure, software resources and time required for diagnosing abnormality of each battery cell may be saved by linking the in-vehicle device and the off-vehicle device, and the possibility of misdiagnosis due to an increase in the number of abnormal battery cells among the plurality of battery cells may be reduced.

According to at least one embodiment of the present disclosure, both long-term and short-term trends of the cell voltage of each battery cell may be considered, so that abnormal changes of the corresponding battery cell may be accurately detected.

Drawings

The accompanying drawings illustrate preferred embodiments of the present disclosure and together with the foregoing disclosure serve to provide a further understanding of the technical features of the present disclosure, and thus the present disclosure is not to be construed as limited to the accompanying drawings.

Fig. 1 is an example diagram illustrating a system including a battery cell diagnostic device according to an embodiment of the present disclosure.

Fig. 2 is a block diagram schematically showing a functional configuration of a battery cell diagnosis apparatus according to an embodiment of the present disclosure.

Fig. 3 is an example diagram conceptually showing a configuration of an electric vehicle according to an embodiment of the present disclosure.

Fig. 4a to 4h are diagrams exemplarily illustrating a process of detecting a voltage abnormality of each of the plurality of battery cells shown in fig. 3 from time-series data representing a cell voltage change of each battery cell.

Fig. 5 is a graph exemplarily showing a voltage curve of battery cells mentioned in various embodiments of the present disclosure corresponding to an original time series of actual voltage values of cell voltages.

Fig. 6 is a graph exemplarily showing a measured voltage curve obtained by integrating measured noise with an original time series corresponding to the voltage curve of fig. 5.

Fig. 7 is a graph exemplarily showing a first moving average curve obtained by applying a first averaging filter to the voltage curve of fig. 6.

Fig. 8 is a graph exemplarily showing a second moving average curve obtained by applying a second averaging filter to the voltage curve of fig. 6.

Fig. 9 is a graph exemplarily showing a voltage deviation curve as a difference between the first moving average curve of fig. 7 and the second moving average curve of fig. 8.

Fig. 10 is a block diagram showing a schematic configuration of an external device according to an embodiment of the present disclosure.

Fig. 11 is a diagram exemplarily showing a schematic configuration of battery cells mentioned in various embodiments of the present disclosure.

Fig. 12 is a diagram for illustrating a first capacity abnormality (incomplete disconnection failure) of the battery cell mentioned in various embodiments of the present disclosure.

Fig. 13 is a diagram for illustrating a second capacity abnormality (full open failure) of the battery cell mentioned in various embodiments of the present disclosure.

Fig. 14 is an exemplary diagram for illustrating a relationship between a capacity abnormality of a battery cell and a full charge capacity mentioned in various embodiments of the present disclosure.

Fig. 15 is a reference diagram for illustrating an exemplary equivalent circuit of a battery cell mentioned in various embodiments of the present disclosure.

Fig. 16 is an exemplary graph for comparing SOC variation of battery cells according to the presence or absence of internal short circuit anomalies mentioned in various embodiments of the present disclosure.

Fig. 17 is another exemplary graph for comparing SOC changes of battery cells according to the presence or absence of internal short circuit anomalies mentioned in various embodiments of the present disclosure.

Fig. 18 is yet another exemplary graph for comparing SOC changes of battery cells according to the presence or absence of internal short circuit anomalies mentioned in various embodiments of the present disclosure.

Fig. 19 is a flowchart of a battery cell diagnosis apparatus according to an embodiment of the present disclosure diagnosing an abnormal state of a battery cell using an external device.

Fig. 20 is a flowchart exemplarily illustrating a voltage abnormality detection method according to an embodiment of the present disclosure.

Fig. 21 is another flowchart exemplarily illustrating a voltage abnormality detection method according to an embodiment of the present disclosure.

Fig. 22 is another flowchart exemplarily illustrating a voltage abnormality detection method according to an embodiment of the present disclosure.

Fig. 23 is a further flowchart exemplarily showing a voltage abnormality detection method according to an embodiment of the present disclosure.

Fig. 24 is a further flowchart exemplarily showing a voltage abnormality detection method according to an embodiment of the present disclosure.

Fig. 25 is a flowchart exemplarily illustrating a behavior anomaly detection method according to an embodiment of the present disclosure.

Fig. 26 is another flowchart schematically illustrating a behavioral abnormality detection method according to an embodiment of the present disclosure.

Fig. 27 is a flowchart exemplarily illustrating a lithium analysis abnormality detection method according to an embodiment of the present disclosure.

Fig. 28 is another flowchart exemplarily illustrating a lithium analysis abnormality detection method according to an embodiment of the present disclosure.

Fig. 29 is a further flowchart exemplarily showing a lithium analysis abnormality detection method according to an embodiment of the present disclosure.

Fig. 30 is another flowchart exemplarily showing a lithium analysis abnormality detection method according to an embodiment of the present disclosure.

Fig. 31 is a graph showing a change in measured data in an experimental example to which a method for an external device to detect whether lithium precipitation occurs according to an embodiment of the present disclosure is applied.

Fig. 32 is a graph showing a change in data measured in another experimental example to which the lithium analysis abnormality detection method according to the embodiment of the present disclosure is applied.

Fig. 33 is a flowchart of a battery cell diagnosis apparatus according to an embodiment of the present disclosure diagnosing an abnormal state of a battery cell using an external device.

Fig. 34 is a flowchart exemplarily illustrating a battery diagnosis method according to an embodiment of the present disclosure.

Fig. 35 is a flowchart of a battery cell diagnosis apparatus according to an embodiment of the present disclosure diagnosing an abnormal state of a battery cell using an external device.

Fig. 36 is a flowchart exemplarily illustrating a battery management method according to an embodiment of the present disclosure.

Fig. 37 is another flowchart exemplarily illustrating a battery management method according to an embodiment of the present disclosure.

Detailed Description

Hereinafter, preferred embodiments of the present disclosure will be described in detail with reference to the accompanying drawings. Before the description, it should be understood that the terms used in the specification and the appended claims should not be construed as limited to general and dictionary meanings, but interpreted based on the meanings and concepts corresponding to technical aspects of the present disclosure on the basis of the principle that the inventor is allowed to define terms appropriately for the best explanation.

Accordingly, the description set forth herein is merely for the purpose of illustration of a preferred embodiment and is not intended to limit the scope of the disclosure, so it should be understood that other equivalents and modifications may be made to the disclosure without departing from the scope of the disclosure.

Fig. 1 is an example diagram showing a battery cell diagnostic system 1 including a battery cell diagnostic apparatus 1000.

Referring to fig. 1, a battery cell diagnosis system 1 may be configured to include a battery cell diagnosis apparatus 1000 and an external device 2000. However, this is merely a preferred embodiment for implementing the present disclosure, and some components may be added or deleted as necessary. It should be noted that the components of the battery cell diagnostic system 1 shown in fig. 1 represent functionally different functional elements, and that a plurality of the components may be implemented to be integrated with each other in an actual physical environment.

In the battery cell diagnosis system 1, the battery cell diagnosis apparatus 1000 is an arithmetic device that diagnoses an abnormal state of a battery cell and provides a diagnosis result to a user. The battery cell diagnosis apparatus 1000 may refer to an on-vehicle operation device included in a BMS (battery management system). For example, the battery cell diagnosis apparatus 1000 may be an arithmetic device included in a BMS provided in an electric vehicle of a user. This is an embodiment, and the present disclosure is not limited thereto, but may include various apparatuses equipped with an arithmetic function and a communication function.

The battery cell diagnosis apparatus 1000 may acquire data including at least one of a charge current and a discharge current of the battery cell and a cell voltage as a voltage across the battery cell. For example, the battery cell diagnosis apparatus 1000 may acquire data on at least one of a charge current and a discharge current of a battery cell provided in an electric vehicle and a cell voltage that is a voltage across the battery cell.

According to embodiments of the present disclosure, the battery cell diagnostic device 1000 may generate first information of the battery cell. The first information may include data related to at least one of a charge current and a discharge current of the battery cell and a cell voltage as a voltage across the battery cell.

The battery cell diagnosis apparatus 1000 may transmit the first information to the external device 2000. The external device 2000 may receive the first information and derive second information about the battery cells based on at least a portion of the received first information. The external device 2000 may transmit the second information to the battery cell diagnosis apparatus 1000. The battery cell diagnosis device 1000 may diagnose an abnormal state of the battery cell based on the first information and the second information.

In the battery cell diagnosis system 1, the external device 2000 may refer to an off-board computing device that provides second information generated using the first information to the battery cell diagnosis apparatus 1000. To this end, the external device 2000 may receive information about 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. Further, the external device 2000 may store information about the voltage, current, or temperature of the battery cells received from various electric vehicles as big data, and store at least one artificial intelligence model capable of diagnosing the battery cells based on the big data. The computing device may be a notebook, a desktop computer, a laptop computer, or the like, but is not limited thereto, and may include any type of device equipped with a computing function and a communication function. However, if the second information is provided to the plurality of devices 1000 for diagnosing battery cells, the external apparatus 2000 may be preferably implemented as a server operation apparatus.

The components of the battery cell diagnostic system 1 may communicate over a network. Here, the network may be implemented as all types of wired/wireless networks, such as a Local Area Network (LAN), a Wide Area Network (WAN), a mobile radio communication network, wibro (wireless broadband internet), and the like.

Thus far, a battery cell diagnostic system 1 according to an embodiment of the present disclosure is described with reference to fig. 1. Hereinafter, a battery cell diagnosis apparatus 1000 according to an embodiment of the present disclosure will be described in detail with reference to fig. 2.

Fig. 2 is a block diagram schematically showing a functional configuration of a battery cell diagnosis apparatus 1000 according to an embodiment of the present disclosure. Referring to fig. 2, the battery cell diagnosis apparatus 1000 may include a current measurement unit 100, a voltage sensing unit 200, a data acquisition unit 300, a first control unit 400, and a display unit 500.

The current measurement unit 100 may measure the current of the battery cell. Here, the current may be at least one of a charge current and a discharge current of the battery cell.

Preferably, the current measurement unit 100 may measure the charging current while the battery cell is charged. In addition, the current measurement unit 100 may measure a discharge current when the battery cell discharges.

The voltage sensing unit 200 may be configured to sense a cell voltage of the battery cell. For example, the voltage sensing unit 200 may sense a voltage signal representing a voltage across the battery cells. This will be described in detail later with reference to fig. 3.

The data acquisition unit 300 may periodically acquire data from the current measurement unit 100 and the voltage sensing unit 200.

In one embodiment, the first control unit 400 may generate the first information of the battery cell based on the data acquired by the data acquisition unit 300. For example, the first information may include current information about at least one of a charge current and a discharge current of the battery cell and voltage information about a cell voltage of the battery cell.

In another embodiment, the first control unit 400 may directly acquire current information about the battery cells from the current measurement unit 100 and voltage information about the battery cells from the voltage sensing unit 200.

The first control unit 400 may be configured to transmit the generated first information to the external device 2000. Further, the first control unit 400 may receive second information including diagnostic information of the battery cells acquired based on the first information from the external device 2000.

Specifically, the second information is diagnostic information of the battery cell generated by the external device 2000 based on the first information. For example, the diagnosis information of the battery cell may include at least one of lithium analysis diagnosis of the battery cell, abnormality of parallel connection of the battery cells, or internal short circuit of the battery cell.

The first control unit 400 may be configured to diagnose an abnormal state of the battery cell based on the first information and the second information.

Specifically, the first control unit 400 may detect at least one of a voltage abnormality of the battery cell and a behavior abnormality of the battery cell based on the first information. Further, the first control unit 400 may be 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. Accordingly, the present disclosure is characterized in that the battery cell diagnosis apparatus 1000 and the external device 2000 diagnose different diagnosis items, not the same diagnosis items. For example, the battery cell diagnosis apparatus 1000 may diagnose at least one of voltage abnormality and behavior abnormality of the battery cell, and the external device 2000 may diagnose lithium precipitation of the battery cell, parallel connection abnormality of the battery cell, and internal short circuit of the battery cell.

The characteristics of the first control unit 400 detecting the voltage abnormality or the behavior abnormality of the battery cells based on the first information will be described later.

The display unit 500 may include at least one display. The display unit 500 may display information about an abnormal state of the battery cell on the included display.

Here, the display unit 500 may be electrically connected to the first control unit 400, and may be included in a load device that receives power from the battery cell group CG. When 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 via an integrated information display of the vehicle.

For example, the first control unit 400 may be configured to display information about an abnormal state of the battery cell on the display unit 500 based on the diagnosis information of the battery cell included in the second information.

As another embodiment, the first control unit 400 may generate third information indicating whether the battery cell is in an abnormal state based on at least one of the voltage abnormality, the behavior abnormality, and the second information. Further, the first control unit 400 may display the third information using a display included in the display unit 500. By displaying the third information by the first control unit 400 using the display included in the display unit 500, the abnormality of the battery cell may be provided to the user in particular.

Furthermore, the first control unit 400 may transmit the third information to the second control unit of the battery cell-equipped device.

Here, the second control unit may be configured to perform a function of controlling the battery cell-equipped device. For example, the battery cell equipped device may be an electric vehicle. In this case, the second control unit may be an ECU (electronic control unit) configured to control the electric vehicle. The first control unit 400 may transmit the third information to a second control unit of the electric vehicle equipped with the battery cell.

Fig. 3 is an example diagram conceptually showing a configuration of an electric vehicle according to an embodiment of the present disclosure. 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.

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

The battery cell group CG may be coupled to the inverter INV by means of a pair of power terminals provided in the battery pack 10. The cell group CG includes a plurality of battery cells BC ₁ to BC _N (where N is a natural number equal to or greater than 2) connected in series. The type of each battery cell BC _i is not particularly limited as long as it can be recharged like a lithium ion battery cell. i is the index of the cell identity. i is a natural number from 1 to N.

The switch S is connected in series to the cell group CG. The switch S is installed on a current path for charging and discharging the cell group CG. The switch S is controlled to be turned on/off in response to a switching signal from the battery cell diagnosis device 1000. Preferably, the operating state of the switch S may be controlled to be an on state or an off state by the first control unit 400.

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

The inverter INV is arranged to convert DC current from the battery cell group CG into AC current in response to a command from the battery cell diagnostic device 1000.

The electric motor M may be, for example, a three-phase AC motor. The electric motor M is driven using AC power from the inverter INV.

The battery cell diagnosis apparatus 1000 is provided to be responsible for overall control related to charge and discharge of the cell group CG.

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

The voltage sensing unit 200 is connected to the positive and negative electrodes of each of the plurality of battery cells BC ₁ to BC _N by means of a plurality of voltage sensing lines. The voltage sensing unit 200 is configured to measure the cell voltage across each battery cell BC _i and generate a voltage signal representative of the measured cell voltage.

The current measurement unit 100 is connected in series to the cell group CG by means of a current path. The current measurement unit 100 is configured to detect a battery current flowing through the cell group CG and generate a current signal (which may also be referred to as "charge-discharge current") representing the detected battery current. Since the plurality of battery cells BC ₁ to BC _N are connected in series, the battery current flowing through any one of the plurality of battery cells BC ₁ to BC _N may be the same as the battery current flowing through the other battery cells. The current measurement unit 100 may be implemented using one or a combination of two or more of known current detection elements such as shunt resistors and hall effect elements.

The temperature sensor T is configured to detect a 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, or may individually measure the temperature of each battery cell BC _i included in the cell group CG.

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 signal refers to a voltage signal, a current signal, and/or a temperature signal that are synchronously detected.

The interface unit I/F may include a communication circuit configured to support wired communication or wireless communication between the first control unit 400 and the second control unit 600. For example, the wired communication may be a CAN (controller area network) and/or CAN-FD (controller area network with flexible data rate) communication, and the wireless communication may be ZigBee or bluetooth communication. Of course, the type of communication protocol is not particularly limited as long as wired/wireless communication between the first control unit 400 and the second control unit 600 is supported.

The interface unit I/F may be coupled with an output device (e.g., display, speaker) that provides information received from the second control unit 600 and/or the first control unit 400 in a form recognizable to a user.

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

The battery cells BC ₁ to BC _N included in the battery pack 10 may be charged or discharged with the switch S turned on when the electric load and/or the charger are operated. In the case where the switch S is turned off when the battery cells BC ₁ to BC _N are charged or discharged, the battery cells BC ₁ to BC _N may be switched to an idle state.

The first control unit 400 may turn on the switch S in response to the on signal. The first control unit 400 may turn off the switch S in response to the off signal. The on signal is a signal requesting a transition from an idle state to charge or discharge. The off signal is a signal requesting a transition from a charge or discharge state to an idle state. Alternatively, the on/off control of the switch S may be performed by the second control unit 600 instead of the first control unit 400.

In fig. 3, the battery cell diagnosis apparatus 1000 is shown as being included in the battery pack 10 of the electric vehicle, but this should be understood as an embodiment. For example, the battery cell diagnostic apparatus 1000 may be included in a test system for selecting behavioural battery cells in the manufacturing process of battery cells BC ₁ to BC _N. As another example, the battery cell diagnostic apparatus 1000 may also be included in an Energy Storage System (ESS) that includes battery cells BC ₁ through BC _N.

The first control unit 400 may detect voltage abnormality and behavior abnormality of the battery cells by using the first information. First, a method of the first control unit 400 detecting the voltage abnormality of the battery cells using the first information will be described in detail with reference to fig. 4.

Fig. 4a to 4h are diagrams exemplarily showing a process of detecting a voltage abnormality of each battery cell from time-series data representing a cell voltage change of each of the plurality of battery cells BC ₁ to BC _N shown in fig. 3.

Fig. 4a shows a voltage curve for each of a plurality of battery cells BC ₁ to BC _N. The number of battery cells shown in fig. 4a is 14. The first control unit 400 collects voltage signals from the voltage sensing unit 200 every unit time and records a voltage value of the cell voltage of each battery cell BC _i in the first information. The unit time may be an integer multiple of the voltage measurement period of the voltage sensing unit 200.

The first control unit 400 may be configured to generate time-series data representing a history of the cell voltage included in the first information with the lapse of time.

Specifically, the first control unit 400 may generate cell voltage time-series data representing a history of the cell voltage of each battery cell with the lapse of time based on the voltage value of the cell voltage included in the first information of each battery cell BC _i. The number of cell voltage time series data is increased by 1 each time the cell voltage is measured.

The plurality of voltage curves shown in fig. 4a are one-to-one correlated with the plurality of battery cells BC ₁ to BC _N. Thus, each voltage curve represents the history of the change in cell voltage of any one of the battery cells BC associated therewith.

The first control unit 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. Here, 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.

Specifically, the first control unit 400 may determine a moving average value of each of the plurality of battery cells BC ₁ to BC _N for each unit time by using one moving window or two moving windows. When two moving windows are used, the time length of one moving window is different from the time length of the other moving window.

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

Hereinafter, for convenience of description, one of the two moving windows associated with a shorter length of time will be referred to as a first moving window, and one of the two moving windows associated with a longer length of time will be referred to as a second moving window.

The first control unit 400 may diagnose the voltage abnormality of each battery cell BC _i using only the first moving window or using both the first moving window and the second moving window.

The first control unit 400 may compare short-term and long-term variation trends of the cell voltage of the i-th battery cell BC _i based on the cell voltage of the i-th battery cell BC _i collected for each unit time.

The first control unit 400 may determine a first average cell voltage for each unit time, which is a moving average of the i-th battery cells BC _i by means of the first moving window, by using the following equation 1 or equation 2. That is, the first control unit 400 may determine the first average cell voltage of each battery cell using the first moving window.

Equation 1 is a moving average calculation formula using an arithmetic average method, and equation 2 is a moving average calculation formula using a weighted average method.

< Equation 1>

/>

< Equation 2>

In equations 1 and 2, k is a time index indicating a current point in time, SMA _i [ k ] is a first average cell voltage of the i-th battery cell BC _i at the current time, S is a time length divided by a first moving window per unit time, and V _i [ k ] is a cell voltage of the i-th battery cell BC _i at the current point in time. For example, if the unit time is 1 second and the time length of the first moving window is 10 seconds, S is 10. When x is a natural number less than or equal to k, V _i [ k-x ] and SMA _i [ k-x ] represent the cell voltage and the first average cell voltage of the ith battery cell BC _i, respectively, when the time index is k-x. For reference, the first control unit 400 may be set to increase the time index by 1 with respect to each unit time.

The first control unit 400 may determine a second average cell voltage for each unit time, which is a moving average of the ith battery cell BC _i by means of the second moving window, by using the following equation 3 or equation 4. That is, the second control unit 400 may determine the second average cell voltage using the second moving window.

Equation 3 is a moving average calculation formula using an arithmetic average method, and equation 4 is a moving average calculation formula using a weighted average method.

< Equation 3>

< Equation 4>

In equations 3 and 4, k is a time index indicating the current point in time, LMA _i [ k ] is the second average cell voltage of the i-th battery cell BC _i at the current time, L is the length of time divided by the second moving window per unit time, and V _i [ k ] is the cell voltage of the i-th battery cell BC _i at the current point in time. For example, if the unit time is 1 second and the time length of the second moving window is 100 seconds, L is 100. When x is a natural number less than or equal to k, LMA _i [ k-x ] represents the second average cell voltage at time index k-x.

In one embodiment, as V _i k of equations 1 to 4, the first control unit 400 may input the difference between the standard cell voltage of the cell group CG at the current point in time and the cell voltage of the battery cell BC _i instead of inputting the cell voltage of each battery cell BC _i at the current point in time.

The standard cell voltage of the cell group CG at the current point in time is an average of the plurality of cell voltages determined from the plurality of battery cells BC ₁ to BC _N at the current point in time. In a variant, the average value of the plurality of cell voltages may be replaced by a median value.

Specifically, the first control unit 400 may set VD _i [ k ] of the following equation 5 to V _i [ k ] of equations 1 to 4.

< Equation 5>

VD_i[k]＝Vav[k]–V_i[k]

In equation 5, vav k is the average of the plurality of cell voltages as the standard cell voltage of the cell group CG at the current point in time.

When the time length of the first moving window is shorter than the time length of the second moving window, the first average cell voltage may be referred to as a "short-term moving average" of the cell voltages, and the second average cell voltage may be referred to as a "long-term moving average" of the cell voltages.

The first control unit may detect voltage abnormality of the battery cell based on a difference between the first average cell voltage and the second average cell voltage. This will be described in detail with reference to fig. 4 b.

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

Fig. 4b shows a short-term movement average line and a long-term movement average line of the cell voltages of the i-th battery cell BC _i determined from the multiple voltage curves shown in fig. 4 a. In fig. 4b, the horizontal axis represents time and the vertical axis represents a moving average of the cell voltages.

Referring to fig. 4b, a plurality of moving average lines Si indicated by dotted lines are one-to-one correlated with a plurality of battery cells BC ₁ to BC _N, and represent a change history of a first average cell voltage (SMA _i [ k ]) of each battery cell BC according to time. Further, a plurality of movement average lines Li indicated by solid lines are associated one-to-one with the plurality of battery cells BC ₁ to BC _N, and represent a change history of the second average cell voltage (LMA _i [ k ]) of each battery cell BC according to time.

The dotted line graph and the solid line graph are obtained using equation 2 and equation 4, respectively. Further, VD _i [ k ] of equation 5 is used as V _i [ k ] of equations 2 and 4, and Vav [ k ] is set as an average value of a plurality of cell voltages. The time length of the first moving window is 10 seconds, and the time length of the second moving window is 100 seconds.

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

Further, for each of the plurality of battery cells, the first control unit 400 may determine a battery cell diagnostic bias corresponding to a bias between an average of long-term and short-term average differences of all battery cells and the long-term and short-term average differences of the battery cells. For example, the first control unit 400 may determine a cell diagnostic bias for each battery cell by calculating a bias between a long-term and short-term average difference and a long-term and short-term average difference for each battery cell.

Fig. 4c shows the change in the long-term and short-term average differences (absolute values) as a function of time corresponding to the difference between the first average cell voltage (SMA _i k) and the second average cell voltage (LMA _i k) for each battery cell shown in fig. 4 b. In fig. 4c, the horizontal axis represents time and the vertical axis represents the long-term and short-term average differences for each battery cell BC _i.

The long-term and short-term average difference for each battery cell BC _i is the difference between the first average cell voltage SMA _i and the second average cell voltage LMA _i for each battery cell BC _i per unit time. As an example, the long-term and short-term average differences of the ith battery cell BC _i may be the same as the value obtained by subtracting one (e.g., the larger one) from the other (e.g., the smaller one) of SMA _i [ k ] and LMA _i [ k ].

The long-term and short-term average differences of the i-th battery cell BC _i depend on the short-term and long-term variations of the cell voltage of the i-th battery cell BC _i.

The temperature or SOH of the i-th battery cell BC _i continuously affects the cell voltage of the i-th battery cell BC _i for a short period as well as for a long period. Thus, if there is no voltage anomaly of the i-th battery cell BC _i, the long-term and short-term average differences of the i-th battery cell BC _i are not significantly different from those of the other battery cells.

On the other hand, the voltage abnormality suddenly generated due to the internal short circuit and/or the external short circuit in the i-th battery cell BCi affects the first average cell voltage (SMA _i k) to a greater extent than the second average cell voltage (LMA _i k). As a result, the long-term and short-term average differences of the i-th battery cell BC _i have a large deviation from the long-term and short-term average differences of the remaining battery cells having no voltage abnormality.

The first control unit 400 may determine a long-term and short-term average difference (|sma _i[k]-LMA_i [ k ] |) of each battery cell BC _i for each unit time. Further, the first control unit 400 may determine an average value of the long-term and short-term average differences (|sma _i[k]-LMA_i [ k ] |). Hereinafter, the average value is denoted as |sma _i[k]-LMA_i [ k ] |av. The first control unit 400 may also determine a deviation of the long-term and short-term average differences (| SMAi [ k ] -LMAi [ k ]) from the average of the long-term and short-term average differences (| SMAi [ k ] -LMAi [ k ] |av) as a cell diagnostic deviation (Ddiag, i [ k ]). Further, the first control unit 400 may detect a voltage abnormality of each battery cell BC _i based on the cell diagnostic bias (Ddiag, i [ k ]).

In an embodiment, for at least one battery cell of the plurality of battery cells, the first control unit 400 may detect a cell satisfying a condition that the cell diagnostic deviation exceeds the diagnostic threshold as a voltage abnormal cell. For example, when the cell diagnosis deviation (Ddiag, i [ k ]) of the i-th battery cell BCi exceeds a preset diagnosis threshold (e.g., 0.015), the first control unit 400 may determine that a voltage abnormality has occurred in the corresponding i-th battery cell BC _i and detect the voltage abnormality of the battery cell.

In another embodiment, the first control unit 400 may determine the statistical variable threshold from the standard deviation of the cell diagnostic deviations of the plurality of battery cells. Furthermore, the first control unit 400 may filter the time series data based on the statistical variable threshold value in order to generate filtered time series data. Finally, the first control unit 400 may be configured to detect, for at least one battery cell of the plurality of battery cells, a voltage abnormality of the battery cell based on the data amount of the time or filtered time series data exceeding a diagnostic threshold. Here, for convenience of explanation, the feature of detecting the voltage abnormality of the battery cell using the statistical variable threshold will be described in detail later by considering the following embodiments of the normalized cell diagnostic deviation.

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

For example, the first control unit 400 may normalize the long-term and short-term average differences (| SMAi [ k ] -LMAi [ k ] |) of each battery cell BC _i using a normalized standard value in order to detect voltage anomalies. Preferably, the normalized standard value is the average of the long-term and short-term average differences (| SMAi [ k ] -LMAi [ k ] |av).

Specifically, the first control unit 400 may set the average value (|sma _i[k]-LMA_i [ k ] |av) of the long-term and short-term average differences of the first to nth battery cells BC _i to BC _N as the normalized standard value. The first control unit 400 may also divide the long-term and short-term average differences (|sma _i[k]-LMA_i [ k ] |) for each battery cell BC _i by a normalized standard value to normalize the long-term and short-term average differences (|sma _i[k]-LMA_i [ k ] |).

Equation 6 below represents an equation that normalizes the long-term and short-term average differences (|sma _i[k]-LMA_i [ k ] |) for each battery cell BC _i. In an embodiment, the value calculated by equation 6 may be referred to as normalized cell diagnostic bias (D x diag, i k).

< Equation 6>

D*diag，i[k]＝(|SMA_i[k]-LMA_i[k]|)÷(|SMA_i[k]-LMA_i[k]|av)

In equation 6, |sma _i[k]-LMA_i [ k ] | is the long-term and short-term average difference of the i-th battery cell BC _i at the current time point, |sma _i[k]-LMA_i [ k ] | av is the average (normalized standard value) of the long-term and short-term average differences of all battery cells, and d×diag, i [ k ] is the normalized cell diagnostic deviation of the i-th battery cell BC _i at the current time point. The symbol "×" indicates that the parameters are normalized.

The long-term and short-term average differences (|sma _i[k]-LMA_i [ k ] |) for each battery cell BC _i can also be normalized by the logarithmic operation of equation 7 below. In an embodiment, the value calculated by equation 7 may also be referred to as normalized cell diagnostic bias (d×diag, i [ k ]).

< Equation 7>

D*diag,i[k]＝Log|SMA_i[k]-LMA_i[k]|

Fig. 4D shows the normalized cell diagnostic bias (ddiag, ik) of each battery cell BC _i over time. The cell diagnostic bias (d_diag, i [ k ]) is calculated using equation 6. In fig. 4D, the horizontal axis represents time and the vertical axis represents cell diagnostic bias (d_diag, i [ k ]) for each battery cell BC _i.

Referring to FIG. 4d, as the long-term and short-term average differences (|SMA _i[k]-LMA_i [ k ] |) for each battery cell BC _i are normalized, it can be seen that the variation in the long-term and short-term average differences for each battery cell BC _i is amplified based on the average. Therefore, the voltage abnormality of the battery cell can be detected more accurately.

The first control unit 400 may determine the statistical variable threshold from the standard deviation of the normalized cell diagnostic deviations of the plurality of battery cells.

For example, the first control unit 400 may detect voltage anomalies in each battery cell BC _i by comparing the normalized cell diagnostic bias (d×diag, i [ k ]) of each battery cell BC _i with a statistical variable threshold (Dthreshold [ k ]).

For example, the first control unit 400 may determine the statistical variable threshold value (Dthreshold [ k ]) for each unit time using the following equation 8.

< Equation 8>

Dthreshold[k]＝β*Sigma(D*diag,i[k])

In equation 8, sigma is a function of the standard deviation of the normalized cell diagnostic bias (D_diag, i [ k ]) of all battery cells BC at the calculated time index k. Beta is a factor that determines diagnostic sensitivity. Beta may be suitably determined by trial and error such that when the present disclosure is applied to a cell group including battery cells having an actual voltage abnormality, the corresponding battery cells may be detected as voltage abnormality cells. In one embodiment, β may be set to 5 or more, or 6 or more, or 7 or more, or 8 or more, or 9 or more. Since Dthreshold [ k ] generated by equation 8 is plural, they constitute time-series data.

On the other hand, in battery cells with abnormal voltages, the normalized cell diagnostic bias (d_diag, ik) is relatively larger than in normal battery cells. Therefore, when Sigma (dag, ik) at time index k is calculated to improve detection accuracy and reliability, it is desirable to exclude max (dag, ik) corresponding to the maximum value. Here, max is a function of the maximum value of the returned plurality of input variables, and the input variables are normalized cell diagnostic deviations (d×diag, i [ k ]) of all battery cells.

In FIG. 4d, the time series data representing the time variation of the statistical variable threshold (Dthreshold [ k ]) corresponds to the distribution indicated in the darkest color among all distributions.

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

Specifically, the first control unit 400 may generate time-series data of the filtered diagnostic value by filtering the time-series data of the cell diagnostic deviation of each battery cell based on the statistical variable threshold.

For example, the first control unit 400 may determine a statistical variable threshold value (Dthreshold [ k ]) at the time index k, and then determine a filtered diagnostic value (Dfilter, i [ k ]) by filtering the normalized cell diagnostic bias (d×diag, i [ k ]) of each battery cell BC _i using equation 9 below.

< Equation 9>

Dfilter，i[k]＝D*diag，i[k]-Dthreshold[k](IF D*diag，i[k]＞Dthreshold[k])

Dfilter，i[k]＝0(IF D*diag，i[k]≤Dthreshold[k])

Two values may be assigned to the filtered diagnostic value (Dfilter, ik) for each battery cell BCi. That is, if the cell diagnostic bias (Ddiag, ik) is greater than the statistical variable threshold (Dthreshold k), the differential of the cell diagnostic bias (Ddiag, ik) and the statistical variable threshold (Dthreshold k) is assigned a filtered diagnostic value (Dfilter, ik). On the other hand, if the cell diagnostic bias (ddiag, ik) is less than or equal to the statistical variable threshold (Dthreshold k), 0 is assigned to the filtered diagnostic value (Dfilter, ik).

The first control unit 400 may be configured to detect, for at least one battery cell of the plurality of battery cells, a voltage abnormality of the battery cell based on a time or a data amount at which the filtered time series data exceeds a diagnostic threshold.

Specifically, the first control unit 400 may detect the voltage abnormality of the battery cell according to the time when the filtered diagnostic value exceeds the diagnostic threshold or the data amount of the filtered diagnostic value exceeding the diagnostic threshold.

Fig. 4e is a graph showing time-series data of filtered diagnostic values (Dfilter, ik) obtained by filtering the cell diagnostic bias (ddiag, ik) at time index k.

Referring to FIG. 4e, an irregular pattern with positive values of about 3000 seconds is found in the filtered diagnostic value (Dfilter, ik) for a particular battery cell. For reference, a particular battery cell having an irregular pattern is a battery cell having time-series data indicated by a in fig. 4 d.

In one embodiment, the first control unit 400 may integrate a time region in which the filtered diagnostic value (Dfilter, ik) is greater than the diagnostic threshold (e.g., 0) in the time-series data of the filtered diagnostic value (Dfilter, ik) for each battery cell BC _i, and detect a battery cell for which a condition that the integration time is greater than a preset standard time is satisfied as a voltage abnormality cell.

Preferably, the first control unit 400 may integrate time regions continuously satisfying the condition that the filtered diagnostic value (Dfilter, ik) is greater than the diagnostic threshold. The first control unit 400 may calculate the integration time independently for each time interval if there are a plurality of corresponding time intervals.

The first control unit 400 may detect the voltage abnormality of the battery cell according to the time when the filtered diagnostic value exceeds the diagnostic threshold or the data amount of the filtered diagnostic value exceeding the diagnostic threshold.

For example, the first control unit 400 may integrate the number of data included in a time region in which the filtered diagnostic value (Dfilter, ik) is greater than the diagnostic threshold (e.g., 0) in the time-series data of the filtered diagnostic value (Dfilter, ik) for each battery cell BC _i, and detect a battery cell for which a condition that the data integrated value is greater than a preset standard count is satisfied as a voltage abnormality cell.

Preferably, the first control unit 400 may integrate only the amount of data included in a time region continuously satisfying the condition that the filtered diagnostic value (Dfilter, ik) is greater than the diagnostic threshold. The first control unit 400 may independently integrate the data amount of each time zone if the corresponding time zone is a plurality of time zones.

Meanwhile, the first control unit 400 may replace V _i k of equations 1 to 5 with the normalized cell diagnostic deviation (d×diag, i k) of each battery cell BC _i shown in fig. 4D. In addition, at the time index k, the first control unit 400 may calculate an average value of long-term and short-term average differences (|sma _i[k]-LMA_i [ k ]) of the cell diagnosis bias (d×diag, i [ k ]), calculate an average value of long-term and short-term average differences (|sma _i[k]-LMA_i [ k ]) of the cell diagnosis bias (d×diag, i [ k ]), calculate a cell diagnosis bias (Ddiag, i [ k ]) corresponding to a difference of long-term and short-term average differences (|sma _i[k]-LMA_i [ k ]) compared to the average value, calculate a normalized cell diagnosis bias (d×diag, i [ k ])) of long-term and short-term average differences (|sma _i[k]-LMA_i [ k ]) using equation 6, determine a statistical variable threshold value (Dthreshold [ k ]) of the normalized cell diagnosis bias (d×diag, i [ k ]) using equation 8, filter the cell diagnosis bias (d×diag, i [ k ])), filter the cell diagnosis bias (d×diag, i ]) using equation 9, and detect the abnormal voltage sequence using the cell diagnosis sequence (4).

Fig. 4f is a graph showing the long-term and short-term average differences (|sma _i[k]-LMA_i [ k ] |) of time-series data (fig. 4D) of normalized cell diagnostic bias (d|diag, i [ k ]) as a function of time. In equations 2,4 and 5 for calculating the long-term and short-term average differences (|sma _i[k]-LMA_i [ k ] |), V _i [ k ] may be replaced with d×diag i [ k ] and Vav [ k ] may be replaced with the average of d×diag i [ k ].

Fig. 4g is a graph showing time series data of normalized cell diagnostic bias (dag, ik) calculated using equation 6. In FIG. 4g, the time series data of the statistical variable threshold (Dthreshold [ k ]) corresponds to the distribution indicated in the darkest color.

Fig. 4h is a graph showing the distribution of time-series data of filtered diagnostic values (Dfilter, ik) obtained by filtering time-series data of cell diagnostic bias (d×diag, ik) using equation 9.

Preferably, the first control unit 400 may integrate time regions continuously satisfying the condition that the filtered diagnostic value (Dfilter, ik) is greater than the diagnostic threshold. The first control unit 400 may calculate the integration time independently for each time zone if the corresponding time zone is a plurality of time zones.

In another embodiment, the first control unit 400 may integrate the number of data included in a time region in which the filtered diagnostic value (Dfilter, ik) is greater than the diagnostic threshold (e.g., 0) in the time-series data of the filtered diagnostic value (Dfilter, ik) for each battery cell BC _i, and detect a battery cell for which a condition that the data integrated value is greater than a preset standard count is satisfied as a voltage abnormality cell.

Preferably, the first control unit 400 may integrate only the amount of data included in a time region continuously satisfying the filtered diagnostic value (Dfilter, ik) greater than the diagnostic threshold. The first control unit 400 may independently integrate the data amount of each time zone if the corresponding time zone is a plurality of time zones.

The first control unit 400 may additionally repeat the above recursive operation a plurality of times by a standard number. That is, the first control unit 400 may replace the voltage time series data shown in fig. 4a with the time series data (e.g., the data of fig. 4 g) of the normalized cell diagnostic bias (dag, ik). In addition, at the time index k, the first control unit 400 may calculate long-and short-term average differences (|sma _i[k]-LMA_i [ k ] |), calculate an average value of the long-and short-term average differences (|sma _i[k]-LMA_i [ k ] |), calculate a cell diagnosis deviation (Ddiag, i [ k ]) corresponding to a difference of the long-and short-term average differences (|sma _i[k]-LMA_i [ k ] |), calculate a normalized cell diagnosis deviation (d×diag, i [ k ]) of the long-and short-term average differences (|sma _i[k]-LMA_i [ k ] |), calculate a normalized cell diagnosis deviation (d×diag, i [ k ]) of the long-and short-term average differences (|sma _i[k]-LMA_i [ k ] |), determine a statistical variable threshold value (Dthreshold [ k ]) of the cell diagnosis deviation (d×diag, i [ k ]) using equation 8, filter values of the cell diagnosis deviations (d×diag, i [ k ]) using equation 9, and detect abnormal values of the cell diagnosis sequence [ 62 ] using the equation 6.

If the recursive operation process as described above is repeated, the voltage abnormality of the battery cell can be diagnosed more accurately. That is, referring to FIG. 4e, positive distribution patterns are observed only in two time regions in the time series data of the filtered diagnostic values (Dfilter, ik) of the battery cells having voltage anomalies. However, referring to FIG. 4h, in the time series data of the filtered diagnostic values (Dfilter, ik) of the battery cells with voltage anomalies, a positive distribution pattern is observed in more time regions than in FIG. 4 e. Therefore, if the recursive operation process is repeated, the point in time at which the voltage abnormality occurs in the battery cell can be detected more accurately.

Thus far, a detailed method for detecting a voltage abnormality using the first information by the first control unit 400 is described. Next, a method of detecting a behavioral abnormality by the first control unit 400 using the first information will be described in detail with reference to fig. 5 to 9.

Fig. 5 is a graph exemplarily showing a voltage curve of a battery cell corresponding to an original time series of actual voltage values of a cell voltage mentioned in various embodiments of the present disclosure, and fig. 6 is a graph exemplarily showing a measured voltage curve C2 obtained by integrating measured noise with the original time series of voltage curve C1 corresponding to fig. 5. Fig. 7 is a graph exemplarily showing a first moving average curve AC1 obtained by applying a first averaging filter to the voltage curve C2 of fig. 6, and fig. 8 is a graph exemplarily showing a second moving average curve AC2 obtained by applying a second averaging filter to the voltage curve C2 of fig. 6. Fig. 9 is a graph exemplarily showing a voltage deviation curve VC that is a difference between the first moving average curve AC1 of fig. 7 and the second moving average curve AC2 of fig. 8.

First, referring to fig. 5, the voltage curve C1 is an example of an original time series of actual voltage values of the cell voltage of the battery cell BC during charging within the predetermined period t1 to tM. For better understanding, the cell voltage increases linearly, and illustrations of the actual voltage values for the periods before t1 and after tM are omitted.

If the battery cell BC is normal, the cell voltage continues to rise gradually during charging. On the other hand, if the battery cell BC has an abnormal behavior in which any fault condition (e.g., micro short circuit, part of the electrode tab is torn), an abnormal behavior in which the cell voltage temporarily drops or rises even during charging may be irregularly observed.

The voltage curve C1 of fig. 5 relates to an embodiment in which the battery cell BC has a behavior abnormality, in which voltage curve C1, the region X represents a time range in which the cell voltage drops sharply as a behavior abnormality, and the region Y represents a time range in which the cell voltage rises sharply as a behavior abnormality. Fig. 5 shows the cell voltage during charging, but the cell voltage of an behavioural battery cell as behavioural anomaly may change even during discharging or resting. For example, during discharge, the cell voltage of a normal battery cell continuously and slowly decreases, while the cell voltage of an abnormally behaving battery cell may temporarily and rapidly increase or decrease.

Next, referring to fig. 6, a voltage curve C2 represents the result of integrating the 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 the voltage values showing the measured cell voltages are arranged in time series.

When M is a natural number (e.g., 300) indicating a predetermined total number of sampling times and K is a natural number less than or equal to M, tK is a measurement timing (kth measurement timing tK) of a time-series kth voltage value (Vm [ K ]) among the total number M of voltage values included in the voltage curve C2, and a time interval between two adjacent measurement timings is separated by a predetermined sampling time (e.g., 0.1 seconds). The voltage value (Vm [ K ]) is a data point indexed to the measurement timing tK among the total number M of voltage values included in the voltage curve C2.

Due to internal and external factors (e.g., temperature of the voltage measuring device, sampling rate, electromagnetic wave, etc.) of the voltage sensing unit 200, measurement noise may be irregularly generated with the lapse of time. The current curve C3 is a time series including current values of the battery current measured within a predetermined period (t 1 to tM). For ease of explanation, the battery current is illustrated as being constant during a predetermined period (t 1 to tM).

If the voltage curve C2 of fig. 6 is compared with the voltage curve C1 of fig. 5, the abnormal behavior (X, Y) can be easily recognized from the voltage curve C1 of fig. 5 without measurement noise, but there is a problem in that it is difficult to recognize the abnormal behavior (X, Y) from the voltage curve C2 mixed with measurement noise throughout the predetermined period (t 1 to tM).

The inventors of the present disclosure have confirmed that the above-described problem can be solved by applying the first averaging filter and the second averaging filter to a time series of voltage values (measured values) including measurement noise generated in the measurement timing of the cell voltage. The time series of the voltage values with respect to the battery cell BC to be detected acquired in the past predetermined period may be referred to as a "standard voltage curve", and the time series of the current values may be referred to as a "standard current curve". Hereinafter, the voltage curve C2 and the current curve C3 of fig. 6 will be described as being assumed as the standard voltage curve C2 and the standard current curve C3, respectively.

The first control unit 400 may be configured to determine a plurality of sub-voltage curves by applying a moving window of a first time length to a time sequence of the cell voltages included in the first information.

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 standard voltage curve C2. In addition, the first control unit 400 may determine a plurality of sub-current curves one-to-one associated with the plurality of sub-voltage curves by applying a moving window of a first time length to the standard current curve C3.

When K is a natural number less than or equal to M, the total number M of sub-voltage curves (i.e., first to M-th sub-voltage curves) may be determined according to the standard voltage curve C2. The standard voltage curve C2 includes a total number M of voltage values (i.e., first to mth voltage values) measured sequentially for each sampling time W.

The sub-voltage curve SK is a subset of the standard voltage curve C2 and includes (a/w+1) voltage values that are chronologically consecutive. For example, when the sampling time w=0.1 seconds and the first time length a=10 seconds, the sub-voltage curve SK is a time series of 101 voltage values in total, i.e., from the (K-P) th voltage value to the (k+p) th voltage value. P=a/2 w=50.

In fig. 6, RK is a sub-current curve related to the sub-voltage curve SK. Thus, the sub-current curve RK may also comprise (A/W+1) data points (current values) that are consecutive in time series.

Because the battery current fluctuates greatly, the cell voltage also fluctuates greatly. Sudden fluctuations in cell voltage caused by battery current may prevent the identification of abnormal behavior of cell voltage from the standard voltage curve C2.

The first control unit 400 may determine a current variation amount of the sub-current curve RK, which is a difference between a maximum current value and a minimum current value of the sub-current curve RK. The first control unit 400 may detect the abnormal behavior of the battery cell according to the time series of the cell voltage measured when the variation of the battery current is small, such as constant current charging or resting, by using the sub-current curve RK.

The first control unit 400 may determine a long-term average voltage value and a short-term average voltage value of the sub-voltage curve SK related to the sub-current curve RK in a case where the current variation amount is smaller than the threshold variation amount.

The first control unit 400 may perform a calculation process described below with respect to the sub-voltage curve SK in a case where the current variation amount of the sub-current curve RK is equal to or smaller than the threshold variation amount.

The first control unit 400 may determine a long-term average voltage value of each sub-voltage curve SK by using a first average filter of a first time length.

Referring to fig. 7, the first average voltage curve AC1 may be obtained by applying a first average filter of the first time length a to the standard voltage curve C2. The first averaging filter is a low pass filter and may be a centered moving average, the subset size (a/w+1) of which corresponds to the first time length a. For example, the first control unit 400 determines a long-term average voltage value (Vav 1[ K ]) indexed to the measurement timing tK by averaging (a/w+1) voltage values (i.e., the (K-P) th to (K-1) th voltage values, the kth voltage value, and the (k+1) th to (k+p) th voltage values) included in the sub-voltage curve SK. Equation 10 below shows the first averaging filter.

< Equation 10>

In equation 10, vm [ i ] is an i-th voltage value included in the standard voltage curve C2, a is a first time length, W is a sampling time, p=a/2W, and Vav1[ K ] is a long-term average voltage value at the measurement timing tK. The first control unit 400 may determine the first average voltage curve AC1 of fig. 7 by replacing K of equation 10 with 1 to M. The first time length a is predetermined to be an integer multiple of the sampling time W. Thus, the first time length A indicates the size of the subset (A/W+1) for obtaining the long-term average voltage value (Vav 1[ K ]).

Further, the first control unit 400 may determine the short-term average voltage value of the sub-voltage curve by using a second average filter having a second time length shorter than the first time length.

Referring to fig. 8, a second average voltage curve AC2 is obtained by applying a second average filter of a second time length B shorter than the first time length a to the standard voltage curve C2. The second averaging filter is a low pass filter, which may be a centered moving average, with a subset size (B/w+1) corresponding to the second time length B.

As an example, the first control unit 400 determines a short-term average voltage value (Vav 2[ K ]) indexed to the measurement timing tK by averaging (B/w+1) voltage values (i.e., the (K-Q) th to (K-1) th voltage values, the kth voltage value, and the (k+1) th to (k+q) th voltage values) included in the sub-voltage curve SK. q=b/2W. The short-term average voltage value (Vav 2K) is the average value of the subset UK of the sub-voltage curve SK. The subset UK is located within the time range (tK-P to tk+p) of the sub-voltage curve SK and is a voltage curve of the time range (tK-Q to tk+q) with the same measurement timing tK as the time range (tK-P to tk+p). Equation 11 below shows the second averaging filter.

< Equation 11>

In equation 11, vm [ i ] is an i-th voltage value included in the standard voltage curve C2, B is a second time length, W is a sampling time, q=b/2W, and Vav2[ K ] is a short-term average voltage value at the measurement timing tK. The first control unit 400 may determine the second average voltage curve AC2 of fig. 8 by replacing K of equation 11 with 1 to M one by one. The second time length B is predetermined to be an integer multiple of the sampling time W. Thus, the second time length B indicates the size of the subset (B/W+1) for obtaining the short-term average voltage value (Vav 2[ K ]).

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". For example, a may be ten times that of B.

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

For example, the first control unit 400 may determine the voltage deviation associated with each sub-voltage curve by subtracting one of the long-term average voltage value and the short-term average voltage value of the other sub-voltage curve.

Referring to fig. 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 a time series of the total number M of voltage deviations for the predetermined period (t 1 to tM). The voltage deviation (Δvk) related to the sub-voltage curve SK is a value obtained by subtracting one of the long-term average voltage value (Vav 1K) and the short-term average voltage value (Vav 2K) from the other. For example, Δv [ K ] =vav2 [ K ] -Vav1[ K ].

As described above, the long-term average voltage value (Vav 1[ K ]) is the long-term average cell voltage for the first time length a centered on the measurement timing tK, and the short-term average voltage value (Vav 2[ K ]) is the short-term average cell voltage for the second time length B centered on the measurement timing tK. Thus, by subtracting one of the long-term average voltage value (Vav 1[ K ]) and the short-term average voltage value (Vav 2[ K ]) from the other to obtain the voltage deviation (DeltaV [ K ]), there is an effect that: measurement noise generated in a predetermined period before and after the measurement timing tK is effectively removed.

There is an advantage in that measurement noise generated in a certain period of time before and after the measurement timing tK is offset to a considerable extent by the process of subtracting one of the long-term average voltage value (Vav 1K) and the short-term average voltage value (Vav 2K) from the other.

The first control unit 400 may compare each of a plurality of voltage deviations (Δvk) determined for a plurality of sub-voltage curves with at least one of the first threshold deviation TH1 and the second threshold deviation TH2 in order to detect a behavioural abnormality of the battery cell.

For example, the first control unit 400 may compare the voltage deviation (Δvk) with the first threshold deviation TH1 and the second threshold deviation TH 2. The first threshold deviation TH1 may be a predetermined positive number (e.g., +0.001V), and the second threshold deviation TH2 may be a predetermined negative number (e.g., -0.001V) having the same absolute value as the first threshold deviation TH 1.

The first control unit 400 may detect that the battery cell BC has a malfunction when a predetermined number (e.g., 10) of voltage deviations among all the voltage deviations included in the voltage deviation curve VC are equal to or greater than the first threshold deviation TH1 or equal to or less than the second threshold deviation TH 2.

The first control unit 400 may be configured to detect a malfunction in response to any two voltage deviations among the plurality of voltage deviations satisfying the first condition, the second condition, and the third condition, respectively.

Specifically, the first control unit 400 may be configured to determine that the battery cell has a behavioural abnormality when any two voltage deviations among the plurality of voltage deviations determined for the plurality of sub-voltage curves satisfy the first condition, the second condition, and the third condition.

For example, when any two voltage deviations satisfy the first condition, the second condition, and the third condition among the total number M of voltage deviations included in the voltage deviation curve VC, the first control unit 400 may determine that the battery cell BC has a behavioral abnormality. The first condition is satisfied when one of the two voltage deviations is greater than or equal to the first threshold deviation TH 1. The second condition is satisfied when the other of the two voltage deviations is less than or equal to the second threshold deviation TH 2. The third condition is satisfied when the time interval between the two voltage deviations is equal to or less than the threshold time. The threshold time may be predetermined to be less than the first time length a. Referring to fig. 9, the voltage deviation (Δva) is less than or equal to the second threshold deviation TH2 (the second condition is satisfied), and the voltage deviation (Δvb) is greater than or equal to the first threshold deviation TH1 (the first condition is satisfied). Thus, when the time interval (Δt=tb-ta) between two voltage deviations (Δva, Δvb) is less than or equal to the threshold time, the battery cell BC may be detected as having a malfunction.

Up to this point, a method of the first control unit 400 detecting at least one of a voltage abnormality and a behavior abnormality by using the first information has been described according to various embodiments of the present disclosure with reference to fig. 3 to 9. Hereinafter, the external device 2000 according to an embodiment of the present disclosure will be described in detail with reference to fig. 10.

Fig. 10 is a block diagram showing a schematic configuration of an external device 2000 according to an embodiment of the present disclosure. The external device 2000 may be a dedicated device for diagnosing battery cells. The external device 2000 may include a storage unit 2100 and a third control unit 2200.

The storage unit 2100 may collect charge and discharge data included in the first information and store the collected charge and discharge data.

The type of the memory unit 2100 is not particularly limited as long as it can record and erase data and/or information. As an example, the storage unit 2100 may be a RAM, a ROM, a register, a flash memory, a hard disk, or a magnetic recording medium.

The memory unit 2100 may be electrically connected to the third control unit 2200 via a data bus or the like so as to be accessible by the third control unit 2200.

The storage unit 2100 stores 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, and the like.

First, an embodiment will be described in which the diagnosis information of the battery cell included in the second information is information about lithium analysis diagnosis of the battery cell.

In one embodiment, the second information may indicate whether the accumulated capacity difference variation is greater than or equal to a threshold. Here, the accumulated capacity difference variation amount may be a sum of capacity difference variation amounts. That is, the sum of the plurality of capacity difference variation amounts may be calculated as the cumulative capacity difference variation amount. Here, each of the capacity difference change amounts is a difference between the capacity difference of the kth charge-discharge cycle of the battery cell and the capacity difference of the kth-1 charge-discharge cycle of the battery cell (k is a natural number of 2 or more). Here, the capacity difference of each charge-discharge cycle corresponds to a difference between a charge capacity of the battery cell during charging of the charge-discharge cycle and a discharge capacity of the battery cell during discharging of the charge-discharge cycle. Here, each of the charge capacity and the discharge capacity may be derived from data acquired from the current measurement unit and included in the first information.

The third control unit 2200 may generate the second information including diagnostic information related to the lithium analysis diagnosis of the battery cell by using the first information. The number of charge and discharge cycles required to generate the second information may be set in advance. In one embodiment, the number of charge-discharge cycles required to generate the second information may be 20.

For example, the charge-discharge cycle may include a charge cycle and a discharge cycle. The charging cycle may refer to charging the battery from a lower limit to an upper limit of a preset charging voltage region and stopping the charging while maintaining the battery cell temperature constant. The discharging cycle may refer to stabilizing the battery for a predetermined time after the completion of the charging cycle, then discharging the battery from an upper limit to a lower limit of a preset discharging voltage region and stopping the discharging while maintaining the battery cell temperature to be the same as the charging cycle. The charging voltage region and the discharging voltage region may be the same or different. However, when a plurality of charge-discharge cycles are performed, it is preferable that the charge voltage areas of the charge cycles are the same, and the discharge voltage areas of the discharge cycles are also the same.

In another embodiment, the charging cycle refers to charging the battery from a lower limit to an upper limit of a preset charging voltage region and stopping the charging while keeping the battery cell temperature constant. The discharge cycle starts discharging from the upper limit of the preset discharge voltage region, and the discharge is stopped when the accumulated current value reaches the preset discharge capacity by integrating the discharge current. In performing a plurality of charge-discharge cycles, it is preferable that the charge voltage regions of the charge cycles are the same and the discharge capacities of the discharge cycles are the same.

The third control unit 2200 may derive a charge capacity and a discharge capacity of the battery cell from the data included in the first information. Specifically, the third control unit 2200 may receive the current measurement value included in the first information in the kth (K is a natural number greater than or equal to 2) charge-discharge cycle by using the information on the charge current or the discharge current included in the first information, and calculate the charge capacity (ChgAh [ K ]) and the discharge capacity (DchgAh [ K ]).

For example, the third control unit 2200 may initialize the charge-discharge cycle index k to 1 and respectively change the first capacity difference amount ΔdAH [1] and the first accumulated capacity difference amountInitialized to 0.

The third control unit 2200 may start the first charge-discharge cycle of the battery. In this specification, when the external device 2000 starts a charge-discharge cycle, it means that data corresponding to the charge-discharge cycle is acquired using the first information.

The third control unit 2200 may calculate the charge capacity (ChgAh [1 ]) and the discharge capacity (DchgAh [1 ]) using the current measurement value included in the first information during the first charge-discharge cycle.

The first information may include information about a charge cycle performed in a preset charge voltage region and a discharge cycle performed in a preset discharge voltage region.

The charging voltage region and the discharging voltage region may be the same or different. Preferably, after the end of the charge cycle, the discharge cycle is started after the voltage of the battery cells stabilizes. In addition, 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 controlling the start and end of the charge cycle and the discharge cycle based on the voltage value, the external device 2000 may refer to the voltage measurement value included in the first information. The voltage measurement value included in the first information may be a value measured by the voltage sensing unit 200.

The third control unit 2200 may continue the charge and discharge cycle until the index k for the charge and discharge cycle is equal to n. n is a preset natural number, which is the total number of charge and discharge cycles that can be continued to detect a lithium analysis abnormality. In one embodiment, n may be 20.

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

For example, a 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 storage unit 2100 together with a time stamp. In one embodiment, 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 a capacity difference (dab [1 ]) corresponding to a difference between the charge capacity (ChgAh [1 ]) and the discharge capacity (DchgAh [1 ]), and record the determined capacity difference (dab [1 ]) in the storage unit 2100 together with a time stamp. In one embodiment, 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 kth capacity difference variation (ΔdAH [ K ]) by subtracting the capacity difference (dAH [ K ]) of the kth charge-discharge cycle from the capacity difference (dAH [ K-1 ]) of the kth charge-discharge cycle. That is, the capacity difference change amount (ΔdAb [ K ]) can be calculated as the difference between the capacity difference (dAb [ K ]) of the kth charge-discharge cycle and the capacity difference (dAb [ K-1 ]) of the kth charge-discharge cycle.

For example, the third control unit 2200 may determine the second capacity difference variation 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.

If the K-th capacity difference variation (ΔdAH [ K ]) is greater than the standard value, the third control unit 2200 may update the accumulated capacity difference variation by adding the K-th capacity difference variation (ΔdAH [ K ]) to the accumulated capacity difference variation. Here, the cumulative capacity difference variation may be a sum of a plurality of calculated capacity difference variations.

For example, the third control unit 2200 calculates the second capacity difference variation (ΔdAH [2 ]) and the first cumulative capacity difference variationAdding to update the accumulated capacity difference variation and determining a second accumulated capacity difference variationIs a new value of (c). For reference, the first cumulative capacity difference variation/>Is 0, i.e., the initialization value.

The third control unit 2200 may detect the presence of a lithium analysis abnormality and generate the second information if the updated accumulated capacity difference variation is greater than or equal to the threshold.

The threshold may refer to a value suitable for detecting a lithium-eluting abnormality. For example, the threshold may be 0.1% of the battery capacity. The threshold value may be a value preset in the external device 2000 or a value included in the first information.

The third control unit 2200 may determine that the lithium analysis abnormality occurs inside the battery and generate the second information if the updated accumulated capacity difference variation is greater than or equal to the threshold. That is, the second information may indicate whether the accumulated capacity difference variation amount is greater than or equal to the threshold value.

In another embodiment, the second information may also represent a capacity difference variation amount between successive charge and discharge cycles of the battery cell. Here, the difference in capacity of each charge-discharge cycle of the battery cell may be a difference between (i) a charge capacity of the battery cell during a charging process of the charge-discharge cycle of the battery cell and (ii) a discharge capacity of the battery cell during a discharging process of the charge-discharge cycle of the battery cell. In this case, the first control unit 400 may calculate the cumulative capacity difference variation amount by calculating the sum of the capacity difference variation amounts according to the second information. Further, the first control unit 400 may determine whether the accumulated capacity difference variation amount is greater than or equal to a threshold value.

Next, an embodiment will be described in which the diagnostic information of the battery cells included in the second information is information about abnormality in parallel connection of the battery cells.

The second information may represent a parallel connection abnormality of the plurality of unit cells included in the battery cell based on the result of monitoring the estimated capacity value over time by the external device 2000. Here, the estimated capacity value may represent a full charge capacity of the battery cell based on charge and discharge data. Here, the charge and discharge data may include a voltage time series indicating a change in voltage of the battery cell with time and a current time series indicating a change in charge and discharge current of the battery cell with time.

The storage unit 2100 may collect charge and discharge data including a time series of voltages representing a time-varying voltage across the battery cell and a time series of currents representing a time-varying charge and discharge current flowing through the battery cell by using information about the charge current or the discharge current included in the first information, and store the collected charge and discharge data.

The third control unit 2200 may determine an estimated capacity value representing the full charge capacity of the battery cell based on the charge and discharge data. Further, the third control unit 2200 may detect the parallel connection abnormality by monitoring a change in the determined estimated capacity value with time. Wherein the diagnosis information of the battery cells corresponding to the parallel connection abnormality may be included in the second information.

A method of detecting a parallel connection abnormality by the external device 2000 will be described in detail with reference to fig. 11 to 14.

Fig. 11 is a diagram exemplarily showing a schematic configuration of the battery cell shown in fig. 3, fig. 12 is a diagram for showing that a first capacity abnormality (incomplete disconnection failure) of the battery cell is involved, and fig. 13 is a diagram for showing that a second capacity abnormality (complete disconnection failure) of the battery cell is involved.

Referring to fig. 11, battery B includes an electrode assembly B200, a positive electrode lead B210, a negative electrode lead B220, and a case B230.

The electrode assembly B200 is an embodiment of parallel connection of a plurality of unit cells BUC1 to UCM (M is a natural number of 2 or more). The unit cell BUC 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.

The positive electrode plate B201 has a positive electrode tab B205 as a portion connected to one end of a positive electrode lead B210, and the negative electrode plate B202 has a negative electrode tab B206 as a portion connected to one end of a negative electrode lead B220.

In a state in which the positive electrode tab B205 and the negative electrode tab B206 of the plurality of unit cells UC1 to UCM are coupled to one ends of the positive electrode lead B210 and the negative electrode lead B220, respectively, the electrode assembly B200 is contained in the case B230 together with an electrolyte. The other ends of the positive electrode lead B210 and the negative electrode lead B220 exposed to the outside of the case B230 are provided as a positive electrode terminal and a negative electrode terminal of the battery B, respectively.

Referring to fig. 12, the first capacity abnormality of the electrode assembly B200 may refer to a state in which: the contact resistances R1, R2 between the unit cells UC1, UC2 and the electrode leads B210, B220 change greatly and irregularly due to minor damage and/or incomplete disconnection failure occurring in the electrode tabs B205, B206 of some of the unit cells UC1, UC2 in the plurality of unit cells UC1 to UCM.

The incomplete-disconnection failure may mean that the disconnected portions of the electrode tabs B205, B206 are not maintained in a state of being separated from each other, the disconnected portions are flexibly connected and separated according to the shrinkage and expansion of the battery B, and the contact area during connection is also variable.

When the contact resistance in the unit cells UC1, UC2 is kept small, all the unit cells UC1 to UCM are charged and discharged almost equally, and as the contact resistances R1, R2 are increased, the unit cells UC1, UC2 immediately enter a state of being separated (disconnected) from the remaining unit cells UC3 to UCM, and thus the capacity of the battery B shows an irregular behavior of rapidly increasing or decreasing, which is largely dependent on the contact resistances R1, R2 of the unit cells UC1, UC 2. For example, when a large tensile force acts between the electrode tabs B205, B206 and the electrode leads B210, B220 of the unit cells UC1, UC2 due to the expansion of the battery B, the contact resistances R1, R2 of the unit cells UC1, UC2 increase, whereas the contact resistances R1, R2 of the unit cells UC1, UC2 decrease as the tensile force gradually decreases.

Referring to fig. 13, the second capacity abnormality of electrode assembly B200 is equivalent to a state in which some of the unit cells UC1, UC2 of the plurality of unit cells UC1 to UCM are irreversibly broken, that is: the charge-discharge current path between the unit cells UC1, UC2 and the electrode leads B210, B220 is irreversibly lost due to a complete disconnection failure of the unit cells UC1, UC 2.

The complete disconnection fault is a state in which the electrode tabs B205, B206 or the electrode plates B201, B202 of the unit cells UC1, UC2 are cut into a plurality of parts separated to be incapable of reconnecting, and is distinguished from the above-described incomplete disconnection fault.

The occurrence of the second capacity abnormality caused by the unit cells UC1, UC2 at a specific time during the manufacture or use of the battery B may mean that the unit cells UC1, UC2 are irreversibly removed from the electrode leads B210, B220. Thus, from the specific time when the second capacity abnormality occurs, the unit cells UC1, UC2 do not contribute to the charge and discharge of the battery B at all, and thus the capacity of the battery B depends only on the capacities of the remaining unit cells UC3 to UCM except for the unit cells UC1, UC2.

The external device 2000 may periodically or aperiodically repeat the process for determining an estimated capacity value representing the Full Charge Capacity (FCC) of the battery cell by applying the capacity estimation model to the charge and discharge data. The external device 2000 may monitor the change of the estimated capacity value with time. The capacity estimation model is an algorithm that receives charge and discharge data and provides an estimated capacity value as a corresponding output, which is a combination of several functions.

Specifically, the capacity estimation model may include: (i) A first function that calculates a current integration value of a charge-discharge current of battery B in a certain period or a variable period in the past, from a current time series of battery B; (ii) A second function that calculates OCV (open circuit voltage) of battery B during a certain period or a variable period in the past from the voltage time series and/or the current time series of battery B; (iii) A third function that calculates the SOC (state of charge) of battery B from the OCV of battery B using a given OCV-SOC relationship table; and (iv) a fourth function that calculates an estimation result of the full charge capacity of battery B, that is, an estimated capacity value, from the ratio of the current integrated value and the SOC variation value calculated for the common period, respectively. Equation 12 below is an example of a fourth function.

< Equation 12>

In the above equation, Δ Aht _1-t2 is the current integration value of the charge-discharge current repeatedly measured in the time range between t1 and t2, Δsoc _t1-t2 is the SOC variation value in the time range between t1 and t2, and FCC _t2 is the estimated capacity value representing the full charge capacity at time t 2. Time t1 may be the last time before time t2 and satisfying that the absolute value of Δah _t1-t2 is greater than or equal to the standard integral value and/or the absolute value of Δsoc _t1-t2 is greater than or equal to the standard variation value. The standard integral value and standard variation value may be predetermined to prevent degradation of accuracy of the FCC _t2 due to the very small absolute value of Δah _t1-t2 and/or Δsoc _t1-t2.

In calculating the current integration value, the charging current may be set to a positive number, and the discharging current may 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 at every first time interval, those skilled in the art will readily understand that time t2 is shifted to the first time interval.

For example, when the current integrated value and the SOC variation value in the past common period are +100Ah [ amp-hour ] and +80%, respectively, the estimated capacity value of the full charge capacity is 125Ah. As another embodiment, if the current integrated value and the SOC variation value during the past common period are-75 Ah [ amp-hour ] and-60%, respectively, the estimated capacity value of the full charge capacity is also 125Ah.

The full charge capacity represents the maximum storage capacity of battery B, i.e., the remaining capacity of battery B at SOC 100%. Normally, the full charge capacity slowly decreases as battery B is degraded. Therefore, when the amount by which the full charge capacity decreases in relation to the short time interval exceeds a certain level, it indicates that the first capacity abnormality or the second capacity abnormality occurs.

Fig. 14 is an exemplary diagram for illustrating a relationship between a capacity abnormality of a battery cell and a full charge capacity.

Referring to fig. 14, curve C21 shows the change in full charge capacity of a normal battery cell over time. For better understanding, curve C21 is simplified to show a linear decrease in full charge capacity of a normal battery cell over time.

Curve C22 illustrates the change in the full charge capacity of battery B over time when the first capacity abnormality and the second capacity abnormality occur in sequence. As shown in fig. 12, a curve C22 represents the full charge capacity of the battery B in which the first capacity abnormality occurs due to the minute damage and/or the incomplete disconnection failure of the unit cells UC1, UC 2. In curve C22, the full charge capacity gradually decreases from time ta (e.g., the release time of the battery cell) to time tb, then rapidly decreases from time tb to time tc, and then rapidly increases from time tc to time td. That is, the amount of decrease in full charge capacity between time tb and time tc is largely recovered between time tc and time td. As described above with reference to fig. 12, this is because the contact resistances R1, R2 of the unit cells UC1, UC2 rapidly increase between time tb and time tc, and then return to the normal level between time tc and time td.

This means that if the first capacity anomaly persists for a long period of time, it may develop (strengthen) into a second capacity anomaly. Referring to curve C22, after time td, from time te to time tf, the full charge capacity decreases rapidly, similar to the previous period from time tb to time tc. However, in contrast to the behavior of the times tc to td, even after the time tf, when the rapid decrease of the full charge capacity is stopped, the curve C22 has a similar slope to the curve C21 in a state where the full charge capacity is not restored to the normal level. As described above with reference to fig. 13, this is because a complete disconnection failure of the unit cells UC1, UC2, i.e., a second capacity abnormality, occurs in the vicinity of the time te.

The external device 2000 determines whether the first capacity abnormality and/or the second capacity abnormality of the parallel connection B200 occurs by monitoring the full charge capacity according to the curve C22, i.e., the estimated capacity value changes with time (time series).

Specifically, the external device 2000 determines whether the first capacity abnormality and/or the second capacity abnormality of the parallel connection B200 occurs by applying the diagnostic logic to two estimated capacity values shifted to a first time and a second time of a first time interval, wherein the second time interval is equal to or greater than the first time interval. The second time is a time after the first time passes the second time interval, and the first time and the second time may be set by the external device 2000 to increase one first time interval for each first time interval. The first time interval may be equal to a collection period of the charge and discharge data (or a 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.

The diagnostic logic may include (i) a first routine that determines that the threshold capacity value at the second time is less than the estimated capacity value at the first time, and (ii) a second routine that compares the estimated capacity value at the second time to the threshold capacity value at the second time.

In the first routine, the threshold capacity value at the second time may be equal to a result of subtracting the standard capacity value from the estimated capacity value at the first time or a result of multiplying the estimated capacity value at the first time by a standard factor smaller than 1. The standard capacity value may be recorded in the memory 120 as a predetermined value in consideration of the total number M of the plurality of unit cells UC1 to UCM included in the battery B and the design capacity (full charge capacity in a new state) of the battery B. The standard factor may be recorded in the memory 120 as a predetermined value (e.g., (M-1)/M, (M-2)/M) in consideration of the total number M of the plurality of unit cells UC1 to UCM included in the battery B. Curve C23 of fig. 14 shows the change with time of the threshold capacity value calculated by applying the first routine to curve C22.

If the estimated capacity value of the second time is smaller than the threshold capacity value of the second time, the external device 2000 may determine that at least one of the first capacity abnormality and the second capacity abnormality has occurred in the parallel connection B200. Further, the external device 2000 may generate second information indicating whether there is a parallel connection abnormality.

Specifically, the external device 2000 may increase the diagnostic count by 1 each time the estimated capacity value at the second time is smaller than the threshold capacity value at 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 capacity value at the second time is greater than or equal to the threshold capacity value at the second time. In response to the estimated capacity value at the second time returning to the threshold capacity value at the second time or higher before the diagnostic count reaches the threshold count, the external device 2000 may determine that the type of abnormality of the parallel connection B200 is the first capacity abnormality. In response to the diagnostic count reaching the threshold count (e.g., 5), the external device 2000 may determine that a second capacity abnormality of the parallel connection B200 has occurred.

In fig. 14, times ta+, tb+, tc+, td+, te+ and tf+ are times shifted from times ta, tb, tc, td, te and tf, respectively, in the positive direction at second time intervals. In the time range between time tx and time ty, curve C22 is located below curve C22. Thus, from time tx to time ty, the diagnostic count is incremented by 1 for each first time interval. The external device 2000 may activate a predetermined protection function related to the second capacity abnormality of the battery B in response to the diagnostic count reaching the threshold count before the time ty.

Finally, such an embodiment will be described: the diagnostic information of the battery cell included in the second information is information about an internal short circuit of the battery cell.

The second information may represent whether the battery cell has an internal short circuit based on the first SOC variation and a standard factor of the battery cell. Here, the standard factor may be determined by applying a statistical algorithm to a first SOC variation of at least two battery cells of the plurality of battery cells. Here, the first SOC variation may be a difference between a first SOC of each battery cell at a first charging time point and a second SOC at a second charging time point. 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 time 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 time point. Here, the state parameter may be acquired based on the first information.

The storage unit 2100 may acquire a state parameter of each of the plurality of battery cells by using information included in the first information. The state parameters may include voltage, current, and/or temperature of the battery cell BC.

The third control unit 2200 may monitor SOC variation of the battery cell BC during the charge period, the discharge period, and/or the idle period of the battery pack 10 by applying an SOC estimation algorithm to the state parameter of the battery cell BC. For example, as the SOC estimation algorithm, an OCV-SOC map or a kalman filter may be used. The OCV-SOC relationship map and the kalman filter are widely used SOC estimation techniques and will not be described in detail.

The third control unit 2200 may determine a first SOC variation of each battery cell BC, which is a difference between a first SOC at a first charging time point and a second SOC at a second charging time point, by applying an SOC estimation algorithm to a state parameter of each of the plurality of battery cells BC ₁ to BC _N acquired during charging. The first charging time point and the second charging time point are not particularly limited as long as they are two different time points within the latest charging period.

The third control unit 2200 may determine the standard factor by applying a statistical algorithm to the first SOC variation of at least two battery cells of the plurality of battery cells. The standard factor may be a representative value of a first SOC variation of at least two battery cells of the plurality of battery cells BC ₁ to BC _N. For example, the standard factor may be an average or median value of the first SOC variation of at least two battery cells of the plurality of battery cells.

The third control unit 2200 may detect an internal short abnormality of each battery cell based on the first SOC variation of each battery cell and the standard factor. In addition, diagnostic information of the battery cell corresponding to whether the battery cell has an internal short circuit may be included in the second information.

Hereinafter, a method of detecting an internal short abnormality by the external device 2000 will be described in detail with reference to fig. 15 to 18.

Fig. 15 is a diagram related to an exemplary equivalent circuit for illustrating a battery cell. In the present specification, the normal battery cell refers to a battery cell having no internal short abnormality among the plurality of battery cells BC ₁ to BC _N, and the abnormal battery cell refers to a battery cell having an internal short abnormality among the plurality of battery cells BC ₁ to BC _N.

Referring to fig. 15, a normal battery cell may be equivalent to a series circuit of a DC voltage source (V _DC), an internal resistance component (R ₀), and an RC pair (R ₁, C). In contrast, an abnormal battery cell may be equivalent to a battery cell in which an additional resistive member (R _ISC) is connected between both ends of a series circuit corresponding to a normal battery cell. The additional resistive component (R _ISC) serves as a path for leakage current (I _ISC).

When an abnormal battery cell is charged, some of the charging power is consumed as leakage current (I _ISC) and is not stored in the abnormal battery cell. Further, when the abnormal battery cell discharges, some of the discharge power is consumed as a leakage current (I _ISC) without being supplied to the electrical load. For reference, when the abnormal battery cell is idle, energy stored in the abnormal battery cell is consumed as a leakage current (I _ISC), similar to discharge. The decrease in the resistance value of the resistor (R _ISC) means that the internal short circuit abnormality increases, and as the internal short circuit abnormality worsens, the amount of power consumed as the leakage current (I _ISC) may increase.

Therefore, during charging, the voltage change of the abnormal battery cell (i.e., the increase in SOC) is smaller than that of the normal battery. Meanwhile, during discharging, the voltage change of the abnormal battery cell (i.e., the decrease amount of the SOC) is greater than that of the normal battery cell.

Fig. 16 to 18 are exemplary graphs for comparing SOC variation of battery cells according to the presence or absence of an internal short circuit abnormality. Fig. 16 to 18 show changes in charge-discharge current, voltage of the battery cell BC, and SOC of the battery cell BC, respectively, within the same period.

Referring to fig. 16, time point t0 and time point t4 represent time points when the idle state is switched to the charge state, time point t1 and time point t5 represent time points when the charge state is switched to the idle state, time point t2 represents time points when the idle state is switched to the discharge state, and time point t3 represents time points when the discharge state is switched to the idle state. That is, in fig. 16, the period from the time point t0 to the time point t1 and the period from the time point t4 to the time point t5 are charging periods, the period from the time point t2 to the time point t3 is a discharging period, and the remaining period is a rest period. For convenience of explanation, in fig. 16, a positive value is assigned to the charge current of each charge period, a negative value is assigned to the discharge current of the discharge period, and the current in each period is shown as constant.

In fig. 17, a curve VC2 represents a voltage curve of a normal battery cell corresponding to the current curve shown in fig. 16, and a curve VC3 represents a voltage curve of an abnormal battery cell corresponding to the current curve shown in fig. 16. The curve VC2 may be regarded as a time series of average voltages of the plurality of battery cells BC ₁ to BC _N. The external device 2000 may periodically or aperiodically acquire a state parameter of each of the plurality of battery cells BC ₁ to BC _N and record a time sequence of the state parameter in the storage unit 2100.

Referring to fig. 17, during charging, the voltages of both the normal battery cell and the abnormal battery cell gradually increase. However, since the abnormal battery cell has a lower charge power capacity than the normal battery cell, the voltage increase of the abnormal battery cell is smaller than that of the normal battery cell.

During discharge, the voltages of both the normal battery cells and the abnormal battery cells gradually decrease. However, in the abnormal battery cell, in addition to the discharge power of the normal battery cell, additional power is consumed due to the leakage current (IISC), and thus the voltage decrease amount of the abnormal battery cell is greater than that of the normal battery cell.

In fig. 18, a curve VC4 represents an SOC curve of a normal battery cell corresponding to the voltage curve VC2 shown in fig. 17, and a curve VC5 represents an SOC curve of an abnormal battery cell corresponding to the voltage curve VC3 shown in fig. 17. The curve VC4 may also be considered as a time series of the average SOCs of the plurality of battery cells BC ₁ to BC _N.

The external device 2000 may monitor the SOC variation of the battery cell BC during the charge period, the discharge period, and/or the idle period of the battery pack 10 by applying an SOC estimation algorithm to the state parameters of the battery cell BC. For example, as for the SOC estimation algorithm, an OCV-SOC relationship map or a kalman filter may be used. The OCV-SOC relationship map and the kalman filter are widely used SOC estimation techniques and will not be described in detail.

Referring to fig. 18, in the charge period, the abnormal battery cells have a smaller SOC increase rate and increase amount than the normal battery cells. In the discharge period, the abnormal battery cell has a higher SOC drop rate and drop amount than the normal battery cell. In addition, during the idle period, the SOC of the normal battery cell is generally constant, while the SOC of the abnormal battery cell gradually decreases even in the case where the charge-discharge current does not flow.

The external device 2000 may perform a diagnostic process for detecting an internal short circuit abnormality of the battery cell BC based on the SOC variation of all of the plurality of battery cells BC ₁ to BC _N in the latest charging period every time the battery pack 10 is charged. For example, when the external device 2000 switches from charging to idle at the time point t1, an internal short-circuit abnormality of the battery cell BC may be detected based on the SOC variation of all the battery cells BC ₁ to BC _N among the plurality of battery cells BC ₁ to BC _N acquired during the charging period (t 0 to t 1).

As another embodiment, when the external device 2000 switches from charging to idle at the time point t5, the internal short circuit abnormality may be detected based on the SOC variation of all of the plurality of battery cells BC ₁ to BC _N acquired in the latest charging period (t 4 to t 5).

Alternatively, the external device 2000 may perform a diagnostic process for detecting an internal short-circuit abnormality of the battery cells BC based on the SOC variation of all of the plurality of battery cells BC ₁ to BC _N in the latest charge period and the SOC variation of all of the plurality of battery cells BC ₁ to BC _N in the latest discharge period each time the battery pack 10 is charged or discharged.

For example, when the external device 2000 is switched from the discharge state to the idle state at the time point t3, the external device 2000 may detect an internal short-circuit abnormality of the battery cell BC based on the SOC variation of all the battery cells BC ₁ to BC _N acquired in the latest charge period (t 0 to t 1) and based on the SOC variation of all the battery cells BC ₁ to BC _N acquired in the latest discharge period (t 2 to t 3).

As another embodiment, when switching from the charged state to the idle state at the time point t5, the external device 2000 may detect an internal short-circuit abnormality of the battery cell BC based on the SOC variation of all of the plurality of battery cells BC ₁ to BC _N acquired in the latest discharging period (t 2 to t 3) and the SOC variation of all of the plurality of battery cells BC ₁ to BC _N acquired in the latest charging period (t 4 to t 5).

In fig. 16 to 18, the idle mode is located between the charge period and the discharge period, but this is just one embodiment. For example, it is possible to switch from the charge state to the discharge state without the idle state, or to switch from the discharge state to the charge state without the idle state.

Hereinafter, a battery cell abnormality state diagnosis method of the present disclosure using the battery cell diagnosis apparatus 1000 and the external device 2000 will be described in detail. The operation of the control circuit 220 will be described in more detail in various embodiments of the battery diagnosis method.

In this specification, the functions performed by the external device 2000 may include functions performed by the third control unit 2200.

Hereinafter, the battery cell diagnosis method abnormal state using the battery cell diagnosis apparatus 1000 and the external device 2000 of the present disclosure described above will be described in detail. The operation of the control circuit 220 will be described in more detail in various embodiments of the battery diagnosis method.

Fig. 19 is a flowchart of a battery cell diagnosis apparatus 1000 according to an embodiment of the present disclosure diagnosing an abnormal state of a cell using an external device 2000.

In step S1000, the battery cell diagnosis device 1000 may acquire data. For example, the battery cell diagnosis apparatus 1000 may acquire data measured by the current measurement unit 100 or the voltage sensing unit 200 using the data acquisition unit 300. Specifically, the battery cell diagnosis apparatus 1000 may acquire data about at least one of the charge current, the discharge current, and the voltage signal measured by the current measurement unit 100 or the voltage sensing unit 200 by using the data acquisition unit 300. Further, the battery cell diagnosis apparatus 1000 may acquire data on the temperature signal detected by the temperature sensor T using the data acquisition unit 300.

In step S2000, the battery cell diagnosis device 1000 may generate first information. For example, the battery cell diagnosis device 1000 may generate first information of the battery cell based on the data acquired in step S1000. The first information may include data on at least one of a charge current, a discharge current, and a voltage signal of the battery cell as an abnormality determination target.

In step S3000, the battery cell diagnosis apparatus 1000 may transmit the generated first information to the external device 2000.

In step S4000, the battery cell diagnosis apparatus 1000 may detect a voltage abnormality. This will be described in detail with reference to fig. 20 to 24.

In step S5000, the battery cell diagnosis apparatus 1000 may detect a behavior abnormality. This will be described in detail with reference to fig. 25 to 26.

In step S7000, the external device 2000 may detect a lithium analysis abnormality. This will be described in detail with reference to fig. 27 to 32.

In step S8000, the external device 2000 may generate second information. The second information may include whether a lithium analysis abnormality is detected.

In step S9000, the external device 2000 may transmit the second information to the battery cell diagnosis apparatus 1000.

In step S6000, the battery cell diagnosis apparatus 1000 may diagnose the battery cell abnormal state. The battery cell diagnosis apparatus 1000 may diagnose an abnormal state of the battery cell based on the voltage abnormality, the behavior abnormality, and the second information.

Fig. 20 to 24 are flowcharts showing in detail a process of detecting a voltage abnormality by the battery cell diagnosis apparatus 1000 according to the embodiment of the present disclosure.

Fig. 20 is a flowchart exemplarily illustrating a voltage abnormality detection method according to an embodiment of the present disclosure. The method of fig. 20 may be periodically performed by the first control unit 400 every unit time.

In step S4310, the first control unit 400 may collect voltage signals representing the cell voltage of each of the plurality of battery cells BC ₁ to BC _N included in the first information and generate time-series data of the cell voltage of each battery cell BC (see fig. 4 a). In the time-series data of the cell voltage, the data amount may be increased by 1 every unit time.

For example, V _i [ k ] or VD _i [ k ] of equation 5 can be used as the cell voltage.

In step S4320, the first control unit 400 may determine a first average cell voltage (S4 MA _i [ k ], see equations 1 and 11) and a second average cell voltage (LMA _i [ k ], see equations 3 and 4) of each battery cell BC _i based on time-series data of the cell voltages of each battery cell BC _i (see fig. 4 b).

The first average cell voltage (S4 MA _i k) may refer to a short-term moving average of the cell voltage of each battery cell BC _i over a first moving window having a first time length. The second average cell voltage (LMA _i [ k ]) may refer to a long-term moving average of the cell voltage of each battery cell BC _i over a second moving window having a second length of time. V _i [ k ] or VD _i [ k ] can be used when calculating the first average cell voltage (S4 MA _i [ k ]) and the second average cell voltage (LMA _i [ k ]).

In step S4330, the first control unit 400 may determine a long-term and short-term average difference (|s4ma _i[k]-LMA_i [ k ] |) of each battery cell BC _i (see fig. 4 c).

In step S4340, the first control unit 400 may determine a cell diagnostic bias (Ddiag, i [ k ]) for each battery cell BC _i. The cell diagnostic bias (Ddiag, i [ k ]) may refer to the bias of the average of the long-term and short-term average differences (|s4ma _i[k]-LMA_i [ k ] |av) of all battery cells to the long-term and short-term average differences (|s4ma _i[k]-LMA_i [ k ] |) of the ith battery cell BC _i.

In step S4350, the first control unit 400 may determine whether the diagnostic time has elapsed. The diagnostic time may be preset. If the determination of step S4350 is yes, step S4360 is continued, and if the determination of step S4350 is no, steps S4310 to S4340 are repeated again.

In step S4360, the first control unit 400 may generate time-series data on the cell diagnostic bias (Ddiag, i [ k ]) for each battery cell BC _i collected during the diagnostic time.

In step S4370, the first control unit 400 may detect a voltage abnormality of each battery cell BC _i by analyzing time-series data about the cell diagnosis deviation (Ddiag, ik).

In one embodiment, the first control unit 400 may integrate a time region in which the cell diagnosis deviation (Ddiag, ik) is greater than a diagnosis threshold (e.g., 0.015) in time-series data of each battery cell BCi with respect to the cell diagnosis deviation (Ddiag, ik) and detect a cell in which a condition that the integration time is greater than a preset standard time is satisfied as a voltage abnormality cell.

For example, the first control unit 400 may integrate only a time region continuously satisfying the condition that the cell diagnostic deviation (Ddiag, ik) is greater than the diagnostic threshold. The first control unit 400 may calculate the integration time independently for each time zone if the corresponding time zone is a plurality of time zones.

In another embodiment, the first control unit 400 may integrate the number of data of which the cell diagnosis deviation (Ddiag, ik) is greater than the diagnosis threshold (e.g., 0.015) in the time-series data of the cell diagnosis deviation (Ddiag, ik) of each battery cell BC _i, and detect the battery cell for which the condition that the data integration value is greater than the preset standard count is satisfied as the voltage abnormality cell.

The first control unit 400 may integrate only the amount of data included in a time region continuously satisfying the condition that the cell diagnostic deviation (Ddiag, ik) is greater than the diagnostic threshold. The first control unit 400 may independently integrate the data amount of each time zone if the corresponding time zone is a plurality of time zones.

Fig. 21 is another flowchart exemplarily illustrating a voltage abnormality detection method according to an embodiment of the present disclosure. The method of fig. 21 may be periodically performed by the first control unit 400 every unit time.

In the method of detecting voltage abnormality of fig. 21, steps S4310 to S4360 are substantially the same as the embodiment of fig. 20, and thus a description thereof will be omitted. After step S4360, step S4380 continues.

In step S4380, the first control unit 400 may generate time-series data of the statistical variable threshold (Dthreshold [ k ]) using equation 8. The input of the Sigma function of equation 8 is time-series data of the cell diagnostic deviations (Ddiag, ik) of all the battery cells generated in step S4360. Preferably, the maximum value of the cell diagnostic bias (Ddiag, ik) can be excluded from the input value of the Sigma function. Cell diagnostic bias (Ddiag, ik) is the bias from the average of long-term and short-term average differences (|sma _i[k]-LMA_i k|).

In step S4390, the first control unit 400 may generate time-series data of the filtered diagnostic value (Dfilter, i [ k ]) by filtering the cell diagnostic bias (Ddiag, i [ k ]) of each battery cell BC _i using equation 9.

When equation 9 is used, d×diag, i [ k ] may be replaced with Ddiag, i [ k ].

In step S4400, the first control unit 400 may determine that the voltage of each battery cell BC _i is abnormal by analyzing the time-series data of the filtered diagnostic values (Dfilter, ik).

In one embodiment, the first control unit 400 may integrate a time region in which the filtered diagnostic value (Dfilter, i [ k ]) is greater than the diagnostic threshold value (e.g., 0) in the time-series data of the filtered diagnostic value (Dfilter, i [ k ]) of each battery cell BC _i, and determine a cell for which the condition that the integration time is greater than the preset standard time is satisfied as the voltage abnormality cell.

Preferably, the first control unit 400 may integrate only the time regions continuously satisfying the condition that the filtered diagnostic value (Dfilter, ik) is greater than the diagnostic threshold. The first control unit 400 may independently calculate the integration time of each time zone if the corresponding time zone is a plurality of time zones.

In another embodiment, the first control unit 400 may integrate the number of data included in a time region in which the filtered diagnostic value (Dfilter, ik) is greater than the diagnostic threshold (e.g., 0) in the time-series data of the filtered diagnostic value (Dfilter, ik) of each battery cell BC _i, and detect a battery cell, for which a condition that the data integrated value is greater than a preset standard count is satisfied, as the voltage abnormality cell.

Fig. 22 is a further flowchart exemplarily showing a voltage abnormality detection method according to an embodiment of the present disclosure. The method of fig. 22 may be periodically performed by the first control unit 400 every unit time.

The battery diagnosis method according to the third embodiment is substantially the same as the first embodiment, except that steps S4340, S4360, and S4370 are changed to steps S4341, S4361, and S4371, respectively. Therefore, only the configuration having the difference will be described.

In step S4341, the first control unit 400 may determine a normalized cell diagnostic bias (d×diag, i [ k ]) of the long-term and short-term average differences (|sma _i[k]-LMA_i [ k ] |) of each battery cell BC _i using equation 6. The normalized standard value is the average of the long-term and short-term average differences (|sma _i[k]-LMA_i [ k ] |). Equation 6 may be replaced with equation 7.

In step S4361, the first control unit 400 may generate time series data of normalized cell diagnostic deviations (d×diag, i [ k ]) for each battery cell BC _i collected during the diagnostic time (see fig. 4D).

In step S4371, the first control unit 400 may detect the voltage abnormality of each battery cell BC _i by analyzing the time-series data of the normalized cell diagnostic bias (dag, ik).

In one embodiment, the first control unit 400 may integrate a time region in which the cell diagnostic bias (ddiag, ik) is greater than the diagnostic threshold (e.g., 4) in the time-series data of the normalized cell diagnostic bias (ddiag, ik) of each battery cell BC _i, and detect the battery cell for which the condition that the integration time is greater than the preset standard time is satisfied as the voltage abnormality cell.

The first control unit 400 may integrate only the time regions continuously satisfying the condition that the normalized cell diagnostic bias (ddiag, ik) is greater than the diagnostic threshold. The first control unit 400 may independently calculate the integration time of each time zone if the corresponding time zone is a plurality of time zones.

In another embodiment, the first control unit 400 may integrate the number of data having the cell diagnosis deviation greater than the diagnosis threshold (e.g., 4) in the time-series data of the normalized cell diagnosis deviation (d×diag, i [ k ]) of each battery cell BC _i, and detect the battery cell having the condition that the data integration value greater than the preset standard count is satisfied as the voltage abnormality cell.

The first control unit 400 may integrate only the amount of data included in the time region continuously satisfying the normalized cell diagnostic bias (ddiag, ik) greater than the diagnostic threshold. The first control unit 400 may independently integrate the data amount of each time zone if the corresponding time zone is a plurality of time zones.

Fig. 23 is a further flowchart exemplarily showing a voltage abnormality detection method according to an embodiment of the present disclosure. The method of fig. 23 may be periodically performed by the first control unit 400 every unit time.

The battery diagnosis method according to fig. 23 is substantially the same as the battery diagnosis method of fig. 21, except that steps S4340, S4360, S4380, S4390, and S4400 are changed to steps S4341, S4361, S4381, S4391, and S4401, respectively, and the remaining configurations are substantially the same. Therefore, a configuration different from that of fig. 21 will be described only with respect to the embodiment of fig. 23.

In step S4341, the first control unit 400 may determine a normalized cell diagnostic bias (d×diag, i [ k ]) of the long-term and short-term average differences (|sma _i[k]-LMA_i [ k ] |) of each battery cell BC _i using equation 6. The normalized standard value may refer to the average of the long-term and short-term average differences (|sma _i[k]-LMA_i [ k ] |). Equation 6 may be replaced with equation 7.

In step S4381, the first control unit 400 may generate time-series data of the statistical variable threshold (Dthreshold [ k ]) using equation 8. The input of the Sigma function of equation 8 is time-series data of normalized cell diagnostic deviations (d×diag, i [ k ]) of all the battery cells generated in step S4361. According to an embodiment, at each time index, the maximum value of the cell diagnostic bias (d_diag, i [ k ]) may be excluded from the input values of the Sigma function.

In step S4391, the first control unit 400 may generate time-series data of the diagnostic values (Dfilter, i [ k ]) by filtering the cell diagnostic bias (d×diag, i [ k ]) of each battery cell BC _i based on the statistical variable threshold (Dthreshold [ k ]) using equation 9.

In step S4401, the first control unit 400 may detect a voltage abnormality of each battery cell BC _i by analyzing time-series data of the filtered diagnostic values (Dfilter, ik).

In one embodiment, the first control unit 400 may integrate a time region in which the filtered diagnostic value (Dfilter, ik) is greater than the diagnostic threshold (e.g., 0) in the time-series data of the filtered diagnostic value (Dfilter, ik) of each battery cell BC _i, and detect a battery cell for which the condition that the integration time is greater than the preset standard time is satisfied as the voltage abnormality cell.

For example, the first control unit 400 may integrate time regions continuously satisfying the condition that the filtered diagnostic value (Dfilter, i [ k ]) is greater than the diagnostic threshold. The first control unit 400 may independently calculate the integration time of each time zone if the corresponding time zone is a plurality of time zones.

The first control unit 400 may integrate only the amount of data included in a time region continuously satisfying the condition that the filtered diagnostic value (Dfilter, ik) is greater than the diagnostic threshold. The first control unit 400 may independently integrate the data amount of each time zone if the corresponding time zone is a plurality of time zones.

Fig. 24 is a further flowchart illustrating a voltage abnormality detection method according to an embodiment of the present disclosure.

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

In step S4410, the first control unit 400 may generate time-series data of a first moving average (SMA _i [ k ]) and time-series data of a second moving average (LMA _i [ k ]) of the cell diagnostic bias (d×diag, i [ k ]) using time-series data of the normalized cell diagnostic bias (d×diag, i [ k ]) of each battery cell BC _i (see fig. 4 f).

In step S4420, the first control unit 400 may generate time-series data of normalized cell diagnostic deviations (d×diag, i [ k ]) of the time-series data of the first moving average (SMA _i [ k ]) and the time-series data of the second moving average (LMA _i [ k ]) for each battery cell BC _i using equation 6 (see fig. 4 g).

In step S4430, the first control unit 400 may generate time-series data of the statistical variable threshold value (Dthreshold [ k ]) using equation 8 (see fig. 4 g).

In step S4440, the first control unit 400 may generate time-series data of the filtered diagnostic value (Dfilter, i [ k ]) of each battery cell BC _i based on the statistical variable threshold value (Dthreshold [ k ]) using equation 9 (see fig. 4 h).

In step S4450, the first control unit 400 may detect a voltage abnormality of each battery cell BC _i by analyzing time-series data of the filtered diagnostic value (Dfilter, ik) of each battery cell BC _i.

The first control unit 400 may integrate time regions continuously satisfying the condition that the filtered diagnostic value (Dfilter, ik) is greater than the diagnostic threshold. The first control unit 400 may independently calculate the integration time of each time zone if the corresponding time zone is a plurality of time zones.

The first control unit 400 may integrate the number of data included in a time region in which the filtered diagnostic value (Dfilter, ik) is greater than the diagnostic threshold (e.g., 0) among the time-series data of the filtered diagnostic value (Dfilter, ik) of each battery cell BC _i, and detect battery cells in which the data integrated value is greater than a preset standard count as voltage abnormality cells.

In fig. 24, the first control unit 400 may recursively perform steps S4410 and S4420 two or more times. The first control unit 400 may regenerate time-series data of the first moving average (SMA _i [ k ]) and the second moving average (LMA _i [ k ]) of the cell diagnosis bias (d×diag, i [ k ]) in step S4410 by using the time-series data of the normalized cell diagnosis bias (d×diag, i [ k ]) generated in step S4420.

In step S4420, the first control unit 400 may generate time-series data of normalized cell diagnostic bias (d×diag, i [ k ]) based on equation 6 again by using the time-series data of the first moving average (SMA _i [ k ]) and the time-series data of the second moving average (LMA _i [ k ]) of each battery cell BC _i. This recursive algorithm may be repeated a predetermined number of times.

When steps S4410 and S4420 are performed according to 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 by means of the recursive algorithm.

In an embodiment of the present disclosure, the first control unit 400 may detect voltage abnormalities of all battery cells, and then, when voltage abnormalities are detected in a specific battery cell, the first control unit 400 may generate third information including detection result information. Further, the first control unit 400 may record identification information ID of the battery cell diagnosed with the voltage abnormality, a time point at which the voltage abnormality is detected, and a detection flag in a storage unit (not shown).

The third information may include a message indicating that there is a voltage abnormality in the cell group CG. Optionally, the third information may include a warning message indicating that a detailed inspection of the battery cells BC ₁ to BC _N is required.

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

According to another aspect, voltage anomalies of each battery cell can be accurately detected by applying advanced techniques such as normalization and/or statistical variable thresholds when analyzing differences in the trend of variation of the two moving averages of each battery cell.

According to still another aspect, the time region in which the voltage abnormality occurs and/or the voltage abnormality detection count of each battery cell can be accurately detected by analyzing time-series data of the filtered diagnostic value determined based on the statistical variable threshold.

As described above, the method of detecting the voltage abnormality by means of the first control unit 400 included in the battery cell diagnosis apparatus 1000 has been reviewed. Hereinafter, a method of detecting a behavioral abnormality by means of the first control unit 400 included in the battery cell diagnosis apparatus 1000 will be reviewed.

Fig. 25 and 26 are flowcharts showing in detail a process of detecting a behavior abnormality of the battery cell diagnosis device 1000 according to the embodiment of the present disclosure. Fig. 25 is a flowchart exemplarily illustrating a behavior anomaly detection method according to an embodiment of the present disclosure.

In step S5710, the first control unit 400 determines a plurality of sub-voltage curves by applying the moving window of the first time length a to the standard voltage curve C2. The standard voltage curve C2 is a time series of a plurality of voltage values representing the cell voltage of the battery cell BC measured at each sampling time of the predetermined period (t 1to tM).

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

In step S5722, the first control unit 400 may determine a long-term average voltage value (Vav 1[ K ]) of the sub-voltage curve SK by using the first average filter of the first time length a (see equation 10).

In step S5724, the first control unit 400 may determine a short-term average voltage value (Vav 2[ K ]) of the sub-voltage curve SK by using the second average filter of the second time length B (see equation 11).

In step S5726, the first control unit 400 may determine the voltage deviation (Δv [ K ]) by subtracting one of the long-term average voltage value (Vav 1[ K ]) and the short-term average voltage value (Vav 2[ K ]) from the other.

In step S5730, the first control unit 400 may determine whether the battery cell BC has a malfunction by comparing each of the plurality of voltage deviations determined for the plurality of sub-voltage curves with at least one of the first threshold deviation and the second threshold deviation. When the value of step S5730 is yes, the process may proceed to step S5740.

In step S5740, the first control unit 400 may detect an abnormality in the behavior of the battery cell BC.

Fig. 26 is another flowchart exemplarily illustrating a behavior anomaly detection method according to an embodiment of the present disclosure.

In step S5800, the first control unit 400 may determine a plurality of sub-current curves by applying the moving window of the first time length a to the standard current curve C3. The standard current curve C3 is a time series of a plurality of current values representing the battery current of the battery cell BC measured at each sampling time of the predetermined period (t 1 to tM).

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 standard voltage curve C2. Step S5810 is the same as step S5710.

In step S5812, the first control unit 400 may determine a current variation amount of each of the plurality of sub-current curves RK.

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 in which a current variation amount in the plurality of sub-voltage curves is equal to or smaller than a threshold variation amount. Step S5820 may include steps S5722, S5724, and S5726 of fig. 25.

In step S5830, the first control unit 400 may determine whether the battery cell BC has a malfunction by comparing each voltage deviation determined in step S5820 with at least one of the first threshold deviation and the second threshold deviation. If the value of step S5830 is "Yes," the process proceeds to step S5840.

In step S5840, the first control unit 400 may detect an abnormality in the behavior of the battery cell BC.

Fig. 27 to 30 are flowcharts showing in detail a process in which the external device 2000 detects a lithium analysis abnormality while repeating charge and discharge cycles using first information according to an embodiment of the present disclosure.

The external device 2000 may detect the lithium analysis abnormality according to the embodiment of the present disclosure as shown in the flowcharts of fig. 27 to 30 and generate second information including the detection result.

First, the external device 2000 initializes the charge-discharge cycle index k to 1 in step S7010, and initializes the first capacity difference variation amount (Δdah [1 ]) and the first accumulated capacity difference variation amount in step S7020Respectively initialized to 0.

Subsequently, the external device 2000 may start a first charge-discharge cycle of the battery in step S7030. In this specification, when the external device 2000 starts a charge-discharge cycle, it may mean that data corresponding to the charge-discharge cycle is acquired using the first information.

Subsequently, the external device 2000 may calculate a charge capacity (ChgAh [1 ]) and a discharge capacity (DchgAh [1 ]) using the current measurement value included in the first information during the first charge-discharge cycle in step S7040.

The charging voltage region and the discharging voltage region may be the same or different. Preferably, after the charge cycle is completed, the discharge cycle is initiated after the battery cell voltage stabilizes. Further, the discharge cycle may be ended when the battery cell voltage reaches a preset discharge end voltage or when the integrated value of the discharge current reaches a preset discharge capacity. When controlling the start and end of the charge cycle and the discharge cycle based on the voltage value, the external device 2000 may refer to the voltage measurement value included in the first information. The voltage measurement value included in the first information may be a value measured by the voltage sensing unit 200.

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

The external device 2000 may record the determined capacity difference (dAH [1 ]) in the storage unit 2100 together with the time stamp. In one embodiment, the capacity difference (dAH [1 ]) may be determined by subtracting the discharge capacity (DchgAh [1 ]) from the charge capacity (ChgAh [1 ]).

In step S7060, the external device 2000 may determine whether the index k of the charge-discharge cycle is equal to n, which is a preset natural number, which may be the total number of charge-discharge cycles continued for detecting the lithium analysis abnormality. In one embodiment, n may be 20.

If the determination of step S7060 is yes, the external device 2000 may terminate the process for detecting the lithium analysis abnormality. On the other hand, if the determination of step S7060 is no, the external device 2000 may move the process to S7070.

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 those of the first charge-discharge cycle.

Subsequently, the external device 2000 may determine a charge capacity (Chg Ah 2) and a discharge capacity (Dchg Ah [2 ]) of the second charge-discharge cycle of the battery in step S7080, and determine a capacity difference (dab [2 ]) corresponding to a difference between the charge capacity (Chg Ah 2 ]) and the discharge capacity (Dchg Ah [2 ]) in step S7090.

Subsequently, the external device 2000 may determine the second capacity difference change amount (ΔdAh2) 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 in step S7100. After step S7100, step S7110 of fig. 28 may be performed.

The external device 2000 may determine in step S7110 whether the second capacity difference change amount (Δdah [2 ]) is greater than a standard value. The standard value may be 0.

If the determination of step S7110 is YES, the external device 2000 may determine in step S7120 that the second capacity difference variation (DeltadAb [2 ]) is equal to the first accumulated capacity difference variationAdditively updating the cumulative capacity difference change amount, and determining an updated value as a second cumulative capacity difference change amount/>First cumulative capacity difference variationMay be 0 as an initialization value.

On the other hand, if the judgment of step S7110 is no, an initial value of 0 may be assigned to the second accumulated capacity difference variation amountWithout the second capacity difference variation (DeltadAb 2) being compared with the first cumulative capacity difference variationAnd (5) adding.

The external device 2000 may determine the second accumulated capacity difference variation amount in step S7140Whether greater than or equal to a threshold. The threshold may refer to a value suitable for detecting a lithium-eluting abnormality. For example, the threshold may be 0.1% of the battery capacity. The threshold value may be a value preset in the external device 2000 or a value included in the first information.

If the determination of step S7140 is yes, the external device 2000 may detect a lithium analysis abnormality in step S7150.

If the judgment of step S7140 is no, i.e., if the second accumulated capacity difference variationLess than the threshold (or 0), the external device 2000 may determine whether the index k of the charge-discharge cycle is equal to n in step S7160. Here, n is the total number of charge-discharge cycles that can be performed to detect a lithium-precipitation abnormality.

If the determination of step S7160 is yes, the external device 2000 may finally determine that no lithium precipitation abnormality has occurred in the battery, and terminate the process due to completion of the charge-discharge cycle for detecting lithium precipitation.

The external device 2000 may output the second information that no lithium precipitation abnormality is detected. For example, the second information may include a message indicating that no lithium analysis abnormality has occurred.

On the other hand, if the determination of step S7160 is negative, the external device 2000 may further continue the charge-discharge cycle to detect a lithium analysis abnormality. After step S7160, step S7180 of fig. 29 is performed.

Fig. 29 is a further flowchart exemplarily showing a lithium analysis abnormality detection method according to an embodiment of the present invention.

In step S7180, the external device 2000 starts the 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.

The external device 2000 may determine the charge capacity (Chg Ah 3) and the discharge capacity (Dchg Ah 3) of the battery during the third charge-discharge cycle in step S7190, and determine a capacity difference (dab 3) corresponding to a difference between the charge capacity (Chg Ah 3) and the discharge capacity (Dchg Ah 3) in step S7200.

The external device 2000 may determine the third capacity difference change amount (ΔdAb [3 ]) by subtracting the capacity difference (dAb [3 ]) of the third charge-discharge cycle from the capacity difference (dAb [2 ]) of the second charge-discharge cycle in step S7210.

The external device 2000 may determine in step S7220 whether the third capacity difference change amount (Δdab [3 ]) is greater than a standard value. For example, the standard value may be 0.

If the determination of step S7220 is YES, the external device 2000 may determine in step S7230 that the third capacity difference variation (ΔdAb [3 ]) is different from the second cumulative capacity difference variationAdditively updating the cumulative capacity difference change amount, and determining an updated value as a third cumulative capacity difference change amount/>

On the other hand, if the determination of step S7220 is negative, in step S7240, the external device 2000 may assign an initial value of 0 to the third cumulative capacity difference change amountWithout combining the third capacity difference variation (DeltadAb <3 >) with the second cumulative capacity difference variation/>And (5) adding.

After step S7230 and step S7240, step S7250 may be performed.

In step S7250, the external device 2000 may determine a third accumulated capacity difference variation amountWhether greater than or equal to a threshold.

If the determination of step S7250 is yes, the external device 2000 may detect a lithium analysis abnormality inside the battery in step S7260.

The external device 2000 may terminate the process after detecting the lithium analysis abnormality in step S7260.

If the judgment of step S7250 is NO, i.e., if the third accumulated capacity difference variationLess than the threshold (or 0), the external device 2000 may determine whether the index k of the charge-discharge cycle is equal to n in step S7270. Here, n is the total number of charge and discharge cycles that can be performed to detect whether lithium precipitation has occurred inside the battery.

If the judgment of step S7270 is YES, the charge-discharge cycle for detecting the lithium analysis abnormality has been completed, so it is judged that the lithium analysis abnormality has not occurred in the battery, and the process may be terminated.

The external device 2000 may generate the second information after the process is terminated. The external device 2000 may generate a warning message indicating that lithium precipitation has been detected in the second information. Alternatively, the second information may include a message indicating that no lithium analysis abnormality is detected.

On the other hand, if the determination of step S7270 is negative, the external device 2000 may further continue the charge-discharge cycle to detect a lithium analysis abnormality.

The detection logic of the lithium analysis abnormality continued by the external device 2000 in the fourth charge/discharge cycle and the subsequent charge/discharge cycles is basically the same as that described above.

Fig. 30 is another flowchart exemplarily showing a lithium analysis abnormality detection method according to an embodiment of the present disclosure. Hereinafter, the process performed by the external device 2000 in the fourth to n-th charge-discharge cycles will be summarized and described with reference to fig. 30.

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

Subsequently, the external device 2000 determines the charge capacity (ChgAh [ K ]) and the discharge capacity (DchgAh [ K ]) of the battery during the kth charge-discharge cycle in step S7290, and determines the capacity difference (dab [ K ]) corresponding to the difference between the charge capacity (ChgAh [ K ]) and the discharge capacity (DchgAh [ K ]).

Subsequently, the external device 2000 determines a kth capacity difference variation (ΔdAH [ K ]) by subtracting the capacity difference (dAH [ K ]) of the kth charge-discharge cycle from the capacity difference (dAH [ K ]) of the kth-1 charge-discharge cycle in step S7310.

Subsequently, the external device 2000 determines in step S7320 whether the K-th capacity difference change amount (Δdab [ K ]) is larger than a standard value. Preferably, the standard value is 0.

If the determination of step S7320 is YES, the external device 2000 may determine in step S7330 that the Kth capacity difference variation amount (DeltadAb [ K ]) is equal to the Kth-1 cumulative capacity difference variation amountAdding up to update the cumulative capacity difference change amount, and determining the updated value as the kth cumulative capacity difference change amount/>

On the other hand, if the determination of step S7320 is no, in step S7340, the external device 2000 may assign an initial value of 0 to the kth cumulative capacity difference change amountWithout combining the K-th capacity difference variation (. DELTA.dAb [ K ]) with the K-1 th cumulative capacity difference variation/>And (5) adding.

Step S7350 is continued after step S7330 and step S7340.

In step S7350, the external device 2000 determines a kth cumulative capacity difference change amountWhether greater than or equal to a threshold.

If the determination in step S7350 is yes, the external device 2000 may determine that a lithium analysis abnormality is detected in the battery and terminate the process in step S7360.

If the judgment of step S7350 is NO, i.e., if the K-th accumulated capacity difference variationLess than the threshold (or, 0), the external device 2000 may determine whether the index k of the charge-discharge cycle is equal to n in step S7370. Here, n is the total number of charge and discharge cycles that can be performed to detect whether lithium precipitation has occurred inside the battery.

If the judgment of step S7370 is yes, the charge-discharge cycle for detecting lithium analysis is completed, so that it is finally judged that no abnormality of lithium analysis has occurred in the battery, and the process may be terminated.

On the other hand, if the determination of step S7370 is negative, the external device 2000 returns the process to step S7280 to further continue the charge-discharge cycle to detect a lithium analysis abnormality. Accordingly, steps S7280 to S7370 may be periodically repeated until the index k of the charge-discharge cycle becomes n.

According to an embodiment of the present disclosure, if the capacity difference variation calculated in the current charge-discharge cycle is equal to or smaller than the standard value, the cumulative capacity difference variation calculated up to the previous cycle may be initialized to 0. Further, if the capacity difference variation calculated in the current charge-discharge cycle is larger than the standard value, the current capacity difference variation may be added to the previous accumulated capacity difference variation. Thus, the cumulative capacity difference variation increases. The previous cumulative capacity difference change amount has a value of 0 or a positive value. If it has a positive value, the capacity difference variation calculated in successive charge and discharge cycles exceeding the standard value may be integrated.

Further, when the capacity difference variation is integrated and the capacity difference variation is reduced to a standard value or less in a specific charge-discharge cycle, the cumulative capacity difference variation may be initialized to 0. By applying this logic, the cumulative capacity difference change amount corresponds to a quantitative indicator for measuring one kind of lithium analysis abnormality. That is, if the capacity difference variation is larger than the standard value, it may mean that there is a possibility of lithium precipitation.

Further, if the condition that the capacity difference variation exceeds the criterion value is satisfied successively in a plurality of charge-discharge cycles that are continuous in time series, the cumulative capacity difference variation increases to the threshold value or more, it may mean that the possibility of lithium precipitation is high. The technical meaning of the present disclosure is to quantify the possibility of lithium analysis using a factor of the cumulative capacity difference variation.

Fig. 31 is a graph showing a change in data measured in an experimental example in which a method for detecting whether lithium precipitation occurs by the external device 2000 according to an embodiment of the present disclosure is applied.

In this experimental example, a pouch-type lithium polymer battery was used. The lithium polymer battery selected for the experiment was degraded and thus in a state in which lithium precipitation on the anode had started. The current capacity of the lithium polymer battery reflecting the degree of degradation is about 50Ah. The charging condition of the charging cycle is CC-CV charging. When the CC charge target voltage is reached, the CC charge is terminated and converted to CV charge, and when the CV charge current reaches the target current, the charge is terminated. The discharge condition of the discharge cycle is CC discharge, and the discharge is terminated when discharge up to a given discharge capacity is performed. The temperature conditions for the charge cycle and the discharge cycle were 45 ℃. A standard value for determining whether to integrate the capacity difference variation is set to 0, and a threshold value for diagnosing the lithium analysis abnormality is set to 0.06Ah.

The graph ① is a graph showing measurement results of the charge capacity (ChgAh [ k ]) and the discharge capacity (DchgAh [ k ]) for each charge-discharge cycle. The charge capacity (ChgAh [ k ]) and discharge capacity (DchgAh [ k ]) are calculated by integrating the current value measured via the sense resistor. The discharge capacity is greater than the charge capacity from the fourth discharge cycle due to an error in the discharge current measurement.

Graph ② is a graph showing the capacity difference (dAH [ k ]) per charge-discharge cycle. Referring to the graph, since the discharge capacity is larger than the charge capacity from the fourth charge-discharge cycle, the capacity difference (dAH [ k ]) becomes negative from the fourth cycle.

The graph ③ is a graph showing the capacity difference change amount (ΔdAH [ k ]) per charge-discharge cycle. The index of the positive charge-discharge cycle of the capacity difference variation (ΔdAH [ k ]) is 2 to 13, 17, 18 and 20. The index of the charge-discharge cycle in which the capacity difference variation (ΔdAH [ k ]) is negative is 14 to 16 and 19.

FIG. ④ is a graph showing the cumulative capacity difference change per charge-discharge cycleIs a graph of (2). The index of the positive charge-discharge cycle of the capacity difference variation (DeltadAH [ k ]) is 2 to 13. Therefore, as the capacity difference change amount (ΔdAH [ k ]) of the second to 13 th charge-discharge cycles is integrated, the accumulated capacity difference change amount/>And (3) increasing. Further, when the capacity difference variation amount of the 13 th charge-discharge cycle is accumulated, the accumulated capacity difference variation amount/>Exceeding the threshold value of 0.06Ah. Accordingly, the external device 2000 continues to the 13 th charge-discharge cycle, then determines that the lithium analysis abnormality has occurred inside the battery, outputs a lithium analysis abnormality detection result via the second information, and terminates the detection process. Since lithium is precipitated from the negative electrode of the lithium polymer battery used in the present experiment, it can be seen that the detection accuracy of the present disclosure is high.

In fig. 32, a chart ①' is the same as the chart ① of the above experimental example. The graph ①' is a graph showing measurement results of the charge capacity (ChgAh [ k ]) and the discharge capacity (DchgAh [ k ]) when a current measurement device having a current measurement error different from the above-described experimental example is used. In this experimental example, the error of the discharge current measurement value is larger than that in the above experimental example. Therefore, the discharge capacity (DchgAh k) chart is shifted upward as compared with the above experimental example.

Graphs ② and ② ' are graphs showing the capacity difference (dAH [ k ]) per charge-discharge cycle, graphs ③ and ③ ' are graphs showing the capacity difference variation (ΔdAH [ k ]) per charge-discharge cycle, and graphs ④ and ④ ' are graphs showing the cumulative capacity difference variation per charge-discharge cycleIs a graph of (2).

Graphs ②、③ and ④ are calculated using the data of graph ①, and graphs ②'、③ ' and ④ ' are calculated using the data of graph ① '.

As shown in fig. 32, charts ②、③ and ④ and charts ②'、③ 'and ④' are substantially identical. Therefore, even if the discharge current value includes a measurement error, the external device 2000 proceeds to the 13 th charge-discharge cycle regardless of the magnitude of the error, then judges that a lithium analysis abnormality occurs inside the battery, outputs a lithium analysis abnormality detection result via the second information, and terminates the detection process. From experimental results, it can be seen that the present disclosure can reliably detect lithium precipitation anomalies regardless of errors in current measurement values.

Fig. 33 is a flowchart of a battery cell diagnosis apparatus 1000 diagnosing an abnormal state of a battery cell using an external device 2000 according to an embodiment of the present disclosure. The same features as in the previous embodiment will not be described in detail.

In step S7000 of fig. 33, the external device 2000 may detect whether there is a parallel connection abnormality. This will be described in detail with reference to fig. 34.

Fig. 34 is a flowchart exemplarily illustrating a battery diagnosis method according to an embodiment of the present disclosure. The method of fig. 34 may be repeated at first time intervals.

Referring to fig. 34, in step S7610, the external device 2000 may collect charge and discharge data of the battery B included in the first information.

In step S7620, the external device 2000 may determine an estimated capacity value representing the full charge capacity of the battery B. Step S7620 may include steps S7622 and S7624. In step S7622, external device 2000 may determine the current integrated value and the SOC variation value of battery B by inputting charge and discharge data to the capacity estimation model. In step S7624, the external device 2000 may determine an estimated capacity value representing the full charge capacity of the battery B according to a ratio between the current integrated value and the SOC variation value of the battery B. The external device 2000 may store a time series of estimated capacity values.

In step S7630, the external device 2000 may detect an abnormality in the parallel connection B200 by monitoring a change in the estimated capacity value with time. Step S7630 may include steps S7632, S7634, S7636, S7638, and S7639. In step S7632, the external device 2000 may determine that the threshold capacity value at the second time is smaller than the estimated capacity value at the first time. For example, the second time may be the timing of calculating the current estimated capacity value, and the first time may be the timing of calculating the estimated capacity value 10 times before. In step S7634, the external device 2000 may compare the estimated capacity value of the second time with the threshold capacity value of the second time. If the estimated capacity value at the second time is less than the threshold capacity value at the second time, this indicates that at least one of the first capacity anomaly and the second capacity anomaly occurred for parallel connection B200. If the value of step S7634 is "Yes", the process proceeds to step S7636. Otherwise, the process may 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 reaches the threshold count. If the value of step S7639 is yes, this indicates that at least one of the unit cells UC of the parallel connection B200 is detected to have a second capacity abnormality, which is a complete disconnection fault.

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 having a complete turn-off fault among the plurality of unit cells UC1 to UCM according to two estimated capacity values of the past two times (e.g., te, tf, at which the maximum decrease value of the estimated capacity value occurs) having the second time interval or less.

The external device 2000 may determine the number of unit cells having a complete-open failure among the plurality of unit cells UC1 to UCM. The number of abnormal cell cells may be determined to be equal to a maximum integer of no more than Δ Ahmax/(Ahp/M). Ahp is an estimated capacity value for the earlier of the two times (e.g., te, tf). Δ Ahmax is the maximum decrease in full charge capacity within two times (e.g., te, tf) before the timing at which an abnormality of parallel connection 200 is detected, and is the result obtained by subtracting the estimated capacity value at the later time (tf) from the estimated capacity value at the previous time (te). For example, when ahp=122 Ah, Δ Ahmax =27 Ah, and m=10, since 2.ltoreq.27 Ah/(122 Ah/10) < 3, the number of abnormal cell cells can be determined to be 2.

Fig. 35 is a flowchart of a battery cell diagnosis apparatus 1000 according to an embodiment of the present disclosure diagnosing an abnormal state of a cell using an external device 2000. The same features as in the previous embodiment will not be described in detail.

In step S7000 of fig. 35, the external device 2000 may detect whether an internal short circuit abnormality exists. This will be described in detail with reference to fig. 36 and 37.

Fig. 36 and 37 are flowcharts specifically showing a process of detecting an internal short abnormality when the external device 2000 repeatedly performs charge and discharge cycles using first information according to an embodiment of the present disclosure.

According to the flowcharts shown in fig. 36 and 37, the external device 2000 may detect an internal short abnormality according to an embodiment of the present disclosure and generate second information including the detection result.

Fig. 36 is a flowchart exemplarily illustrating a battery management method according to an embodiment of the present disclosure. The method of fig. 36 is used to detect internal short-circuit anomalies of battery cell BC based on SOC trends of all of the plurality of battery cells BC ₁ to BC _N monitored during the most recent charging period. For convenience of explanation, it is assumed that the latest charging period is from time point t4 to time point t5.

Referring to fig. 36, in step S7610, the external device 2000 may determine a first SOC variation, which is a difference between a first SOC at a first charging time point and a second SOC at a second charging time point, by applying an SOC estimation algorithm to each state parameter of each of the plurality of battery cells BC ₁ to BC _N acquired using the first information for each battery cell BC during charging of the battery pack 10. The first charging time point and the second charging time point are not particularly limited as long as they are two different time points within the latest charging period.

For example, the first charging time point may be a start time point t4 of the latest charging period, and the second charging time point may be an end time point t5 of the latest charging period. Since the method of fig. 36 involves charging, the first SOC variation represents an increase in SOC from the first charging time point to the second charging time point. For example, referring to fig. 18, the first SOC variation of the abnormal battery cell is a difference between the first SOC VC54 and the second SOC VC 55.

In step S7620, the external device 2000 determines the standard factor by applying a statistical algorithm to the first SOC variation of at least two battery cells of the plurality of battery cells BC ₁ to BC _N. The standard factor may be equal to an average or median of the first SOC variation of at least two battery cells of the plurality of battery cells BC ₁ to BC _N. For example, referring to fig. 18, when the curve VC4 is the average value of the first SOC variation, the standard factor is the difference between the SOC VC44 and the SOC VC 45.

In step S7630, the external device 2000 detects an internal short abnormality by comparing the first SOC variation with the standard factor of each 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.

[ Condition #1: the first SOC variation must be less than the standard factor by a threshold value TH1 or more

[ Condition #2: the ratio of the first SOC variation to the standard factor must be equal to or less than a standard value TH2, wherein TH2 is 0 to 1

[ Condition #3: the ratio of the first SOC variation to the standard factor must be smaller than the previous ratio by the threshold TH3 or more ]

In condition #3, the previous ratio is the ratio of the first SOC variation to the standard factor in the charging period preceding the latest charging period (t 0 to t1 in fig. 17).

The thresholds TH1, TH2, TH3 may be predetermined fixed values. Alternatively, the external device 2000 may determine at least one of the thresholds TH1, TH2, TH3 based on an integrated value of the charging current measured in a period from the first charging time point to the second charging time point.

At least one of the thresholds TH1, TH2, TH3 may be updated again each time the charging mode of the battery pack 10 is restarted. For example, the external device 2000 may obtain a target value of the SOC variation (e.g., 60%) by dividing an integrated value of the charging current (e.g., 3Ah [ ampere hours ]) by a design capacity of the battery cell BC (e.g., 5 Ah), and determine at least one of the thresholds TH1, TH2, TH3 by multiplying a ratio of the standard factor to the target value by a predetermined scaling constant (which is a positive value). The scaling constant used to determine any one of the thresholds TH1, TH2, TH3 may be different from the scaling constant used to determine another one of the thresholds TH1, TH2, TH 3. The target value may be determined during at least one of steps S7610, S7620 and S7630. At least one of the thresholds TH1, TH2, TH3 may be determined during at least one of steps S7620 and S7630.

When all of the plurality of battery cells BC ₁ to BC _N are normal, the target value and the standard factor may be substantially equal to each other. Meanwhile, as the number of battery cells having an internal short abnormality among the plurality of battery cells BC ₁ to BC _N increases, the standard factor is greatly reduced from the target value. Therefore, by determining at least one of the thresholds TH1, TH2, TH3 according to the above-described method, the accuracy of detecting an internal short circuit abnormality can be improved.

Meanwhile, after the target value is determined before step S7620, in step S7620, the standard factor may be determined using only the first SOC variation less than or equal to the target value among all the first SOC variations of the plurality of battery cells BC ₁ to BC _N. In this case, in determining the standard factor, among all the first SOC variations of the plurality of battery cells BC ₁ to BC _N, the first SOC variation exceeding the target value is excluded, so that the battery cell BC having the relatively serious internal short circuit abnormality can be preferentially detected from the plurality of battery cells BC ₁ to BC _N.

Fig. 37 is another flowchart exemplarily illustrating a battery management method according to an embodiment of the present disclosure. The method of fig. 37 is used to detect internal short-circuit anomalies of the battery cells BC based on the SOC trend of all the plurality of battery cells BC ₁ to BC _N monitored in the last discharge period and the last charge period, respectively. For convenience of explanation, it is assumed that the latest charge period is from time point t4 to time point t5, and the latest discharge period is from time point t6 to time point t7.

Referring to fig. 37, in step S7710, the external device 2000 may determine a first SOC variation, which is a difference between a first SOC at a first charging time point and a second SOC at a second charging time point, by applying an SOC estimation algorithm to each state parameter of each of the plurality of battery cells BC ₁ to BC _N acquired during charging of the battery pack 10 using the first information for each battery cell BC. The first charging time point and the second charging time point are not particularly limited as long as they are two different time points within the latest charging period. For example, the first charging time point may be a start time point t4 of the latest charging period, and the second charging time point may be an end time point t5 of the latest charging period.

In step S7720, the external device 2000 may determine a second SOC variation, which is a difference between the third SOC at the first discharge time point and the fourth SOC at the second discharge time point, by applying an SOC estimation algorithm to the state parameter of each of the plurality of battery cells BC ₁ to BC _N acquired during the discharge of the battery pack 10 for each battery cell BC. The first discharge time point and the second discharge time point are not particularly limited as long as they are two different time points within the latest discharge period. For example, the first discharge time point may be a start time point t6 of the latest charge period, and the second discharge time point may be an end time point t7 of the latest charge period.

Referring to fig. 18, in the abnormal battery cell, the first SOC variation is a difference between the first SOC VC54 and the second SOC VC55, and the second SOC variation is a difference between the third SOC VC56 and the fourth SOC VC 57.

In fig. 37, step S7710 precedes step S7720, but this should be understood as an embodiment. For example, if the last charge period is before the last discharge period, step S7720 may precede step S7710. As another example, after both the latest charge period and the latest discharge period are ended, step S7710 and step S7720 may be performed simultaneously.

In step S7730, the external device 2000 may determine the abnormality factor by dividing the first SOC variation by the second SOC variation for each battery cell BC. That is, the abnormality factor may be determined according to the formula of "abnormality factor= (first SOC variation)/(second SOC variation)".

For example, referring to fig. 18, the abnormality factor of the abnormal battery cell may be determined according to the formula "{ SOC (VC 55) -SOC (VC 54) } and { SOC (VC 56) -SOC (VC 57) }. The anomaly factor may also be referred to as coulombic efficiency.

In step S7740, the external device 2000 may determine the standard factor by applying a statistical algorithm to the abnormal factors of at least two battery cells of the plurality of battery cells BC ₁ to BC _N.

The standard factor may be equal to an average or median of the anomaly factors of at least two battery cells in the plurality of battery cells BC ₁ to BC _N. For example, referring to fig. 18, when the curve VC4 is the average SOC of the plurality of battery cells BC ₁ to BC _N, the standard factor may be determined according to the formula "{ SOC (VC 45) -SOC (VC 44) } ζ SOC (VC 46) -SOC (VC 47) }".

In step S7750, the external device 2000 may detect an internal short abnormality of the battery cell BC by comparing the abnormality factor with the standard factor for each 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.

[ Condition #1: the abnormality factor must be smaller than the standard factor by a threshold value TH11 or more

[ Condition #2: the relative coulombic efficiency must be equal to or less than a threshold value TH12, where TH12 is 0 to 1

[ Condition #3: the ratio of the abnormality factor to the standard factor must be smaller than the previous ratio by the threshold TH13 or more ]

In condition #2, the relative coulombic efficiency may be the ratio of the anomaly factor to the standard factor, i.e. "anomaly factor ≡standard factor".

In condition #3, the previous ratio is a ratio of an abnormality factor to a standard factor based on the first SOC in the charging period (t 4 to t5 in fig. 17) and the second SOC in the discharging period (t 2 to t3 in fig. 17) preceding the latest discharging period (t 6 to t 7).

The threshold values TH11, TH12, TH13 may be predetermined values. As an 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. 36.

The external device 2000 may determine at least one of the thresholds TH11, TH12, TH13 based on an integrated value of the charging current measured in a period from the first charging time point to the second charging time point and an integrated value of the discharging current measured in a period from the first discharging time point to the second discharging time point.

At least one of the thresholds TH11, TH12, TH13 may be updated again each time the charge mode or the discharge mode of the battery pack 10 is restarted. For example, the external device 2000 may obtain the target value by dividing the integrated value of the charging current by the integrated value of the discharging current. The external device 2000 may determine at least one of the thresholds TH11, TH12, TH13 by multiplying a ratio of the standard factor to the target value by a predetermined scaling constant (which is a positive value). The scaling constant used to determine any one of the thresholds TH11, TH12, TH13 may be different from the scaling constant used to determine the other one of the thresholds TH11, TH12, TH 13. The target value may be determined during at least one of steps S7710, S7720, S7730, and S7740. At least one of the thresholds TH1, TH2, TH3 may be determined during at least one of steps S7730 and S7740.

When all of the plurality of battery cells BC ₁ to BC _N are normal, the target value and the standard factor may be substantially equal to each other. Meanwhile, as the number of battery cells having an internal short abnormality among the plurality of battery cells BC ₁ to BC _N increases, the standard factor is greatly reduced from the target value. Therefore, by determining at least one of the thresholds TH11, TH12, TH13 according to the above-described method, the accuracy of detecting an internal short circuit abnormality can be improved.

Meanwhile, after the target value is determined before step S7740, in step S7740, only the abnormality factors less than or equal to the target value among all the abnormality factors of the plurality of battery cells BC ₁ to BC _N may be used to determine the criterion factor. In this case, in determining the standard factor, since the abnormality factor exceeding the target value is excluded from all of the abnormality factors of the plurality of battery cells BC ₁ to BC _N, it is possible to preferentially detect the battery cell BC having a relatively serious internal short-circuit abnormality from the plurality of battery cells BC ₁ to BC _N.

In each embodiment, when an internal short circuit abnormality is detected in a predetermined number or more of the plurality of battery cells BC ₁ to BC _N, the external device 2000 may generate second information indicating that the internal short circuit abnormality is detected.

In each embodiment, the external device 2000 may reduce the allowable range of the charge and discharge current when an internal short abnormality is detected in a predetermined number or more of the plurality of battery cells BC ₁ to BC _N. For example, the upper limit (positive value) of the allowable range may be decreased, or the lower limit (negative value) of the allowable range may be increased in proportion to the number of abnormal battery cells.

For example, the external device 2000 according to the embodiment of the present disclosure may be included in a diagnostic system for diagnosing an abnormality of a battery cell. The diagnostic system may be operated in an electric vehicle repair shop, battery manufacturer, or battery maintenance company. For example, the diagnostic system may be used to diagnose anomalies in battery cells onboard an electric vehicle or an energy storage system, or may be used to diagnose anomalies in newly developed models of batteries produced by battery manufacturers. In particular, in the latter case, before commercializing a newly developed model of battery, the state of the battery may be checked by using the external device 2000.

As another example, the external device 2000 may be included in a control element of a battery-equipped system.

In one embodiment, 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 the battery cell during the charge and discharge of the battery installed in the electric vehicle, diagnose the state of the battery cell using the collected data, and output the diagnosis result to the integrated control display of the electric vehicle.

In another embodiment, 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 cell during charge and discharge of the energy storage system, diagnose the state of the battery cell using the collected data, and output the diagnosis result via a display of the integrated management computer accessible to the operator.

When the diagnosis result regarding the lithium analysis abnormality is output via the display, a user of the electric vehicle or an operator of the energy storage system may take appropriate safety measures. In one embodiment, a user of an electric vehicle may visit a repair shop and receive inspection. In another embodiment, an operator of the energy storage system may replace the corresponding battery with a new battery.

In this disclosure, the external device 2000 may optionally include a processor, an Application Specific Integrated Circuit (ASIC), other chipset, logic circuit, register, communication modem, data processing device, etc. as known in the art to perform the various control logic described above. In addition, when the control logic is implemented in software, the external device 2000 may be implemented as a set of program modules. In this case, the program modules may be stored in the memory and executed by the processor. The memory may be provided within the processor or external to the processor and may be connected to the processor by means of various well-known computer components. Further, a memory may be included in the storage unit 2100 of the present disclosure. Further, the memory refers to a device in which information is stored regardless of the type of device, and does not refer to a specific storage device.

At least one or more of the various control logics of the external device 2000 may be combined, and the combined control logics may be written in a computer-readable code scheme and recorded in a computer-readable recording medium. The type of recording medium is not particularly limited as long as it can be accessed by a processor included in a computer. As an embodiment, the recording medium includes at least one selected from the group consisting of ROM, RAM, registers, CD-ROM, magnetic tape, hard disk, floppy disk, and optical data recording device. Furthermore, the code schemes may be distributed, stored, and executed on networked computers. Furthermore, functional programs, codes, and code segments for implementing the combined control logic can be easily inferred by programmers in the art to which the present disclosure pertains.

The present disclosure has been described in detail. However, it should be understood that the detailed description and specific examples, while indicating preferred embodiments of the disclosure, are given by way of illustration only, since various changes and modifications within the scope of the disclosure will become apparent to those skilled in the art from this detailed description.

(Description of the reference numerals)

1: Battery cell diagnostic system

1000: Battery cell diagnosis device

2000: External device

100: Current measuring unit

200: Voltage sensing unit

300: Data acquisition unit

400: First control unit

500: Display unit

600: Second control unit

10: Battery pack

Claims

1. A battery cell diagnostic apparatus, the battery cell diagnostic apparatus comprising:

a current measurement 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 measurement unit and the voltage sensing unit to an external device, receive second information including diagnostic information of the battery cell acquired based on the first information from the external device, and diagnose an abnormal state of the battery cell based on the first information and the second information.

2. The battery cell diagnosis apparatus according to claim 1,

The diagnosis information of the battery cell comprises at least one of lithium precipitation diagnosis of the battery cell, abnormal parallel connection of the battery cell and internal short circuit of the battery cell.

3. The battery cell diagnosis apparatus according to claim 1,

Wherein the first control unit is 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 second information.

4. The battery cell diagnosis apparatus according to claim 1,

Wherein the first control unit is configured to:

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; and

Diagnosing an abnormal state of the battery cell based on at least one of the voltage abnormality, the behavioral abnormality, and the second information.

5. The battery cell diagnosis apparatus according to claim 4,

Wherein the first control unit is configured to generate third information indicating whether the battery cell is in the abnormal state based on at least one of the voltage abnormality, the behavior abnormality, and the second information.

6. The battery cell diagnosis apparatus according to claim 5,

Wherein the first control unit is configured to display the third information on a display unit.

7. The battery cell diagnosis apparatus according to claim 5,

Wherein the first control unit is configured to send the third information to a second control unit of a device equipped with the battery cell.

8. The battery cell diagnosis apparatus according to claim 1,

Wherein the first control unit is configured to:

generating time-series data representing a history of the cell voltage included in the first information with the lapse of 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; and

Detecting 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 diagnosis apparatus according to claim 8,

Wherein the battery cell diagnostic apparatus is configured to diagnose a plurality of battery cells, and

Wherein the first control unit is configured to:

determining, for each battery cell of the plurality of battery cells, a long-term and a short-term average difference corresponding to a difference between the first average cell voltage and the second average cell voltage;

determining an average value of the long-term and short-term average differences for the plurality of battery cells;

Determining, for each battery cell of the plurality of battery cells, a cell diagnostic bias corresponding to a bias between an average of the long-term and short-term average differences and the long-term and short-term average differences; and

And detecting the battery cell meeting the condition that the cell diagnosis deviation exceeds a diagnosis threshold as a voltage abnormal cell.

10. The battery cell diagnosis apparatus according to claim 8,

Wherein the first control unit is configured to:

determining, for each battery cell of the plurality of battery cells, a cell diagnostic bias corresponding to a bias between an average of the long-term and short-term average differences and the long-term and short-term average differences;

determining a statistical variable threshold that depends on a standard deviation of the cell diagnostic deviations of the plurality of battery cells;

filtering the time series data based on the statistical variable threshold to generate filtered time series data; and

Detecting a voltage anomaly of the battery cell based on a time or an amount of data that the filtered time series data exceeds a diagnostic threshold.

11. The battery cell diagnosis apparatus according to claim 8,

The first control unit is configured to:

Determining a normalized value corresponding to an average of the long-term and short-term average differences of the plurality of 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 statistical variable threshold that depends on a standard deviation of a normalized cell diagnostic deviation of the plurality of battery cells;

For each battery cell of the plurality of battery cells, filtering a normalized long-term and short-term average difference for each battery cell based on the statistical variable threshold to generate filtered time series data; and

12. The battery cell diagnosis apparatus according to claim 1,

Wherein the first control unit is configured to:

Determining a plurality of sub-voltage curves by applying a moving window of a first time length to a time sequence of the 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 time length;

determining a short term average voltage value for each sub-voltage curve using a second average filter of a second time length shorter than the first time length;

determining a voltage deviation corresponding to a difference between the long-term average voltage value and the short-term average voltage value for each sub-voltage curve; and

Each of a plurality of voltage deviations determined for the plurality of sub-voltage curves is compared to at least one of a first threshold deviation and a second threshold deviation to detect a behavioral abnormality of the battery cell.

13. The battery cell diagnosis apparatus according to claim 12,

Wherein the first control unit is configured to detect the behavior abnormality corresponding to two voltage deviations satisfying a first condition, a second condition, and a third condition, respectively, among the plurality of voltage deviations,

Wherein the first condition is satisfied when a first voltage deviation of the two voltage deviations is equal to or greater than the first threshold deviation,

Wherein the second condition is satisfied when a second voltage deviation of the two voltage deviations is equal to or smaller than the second threshold deviation, and

Wherein the third condition is satisfied when a time interval between the two voltage deviations is equal to or less than a threshold time.

14. The battery cell diagnosis apparatus according to claim 1,

Wherein the second information indicates whether the accumulated capacity difference variation amount is greater than or equal to a threshold value,

The cumulative capacity difference variation is the sum of capacity difference variation,

Each of the capacity difference variation amounts is a difference between a capacity difference of a kth charge-discharge cycle of the battery cell and a capacity difference of a kth-1 charge-discharge cycle of the battery cell,

The k is a natural number greater than or equal to 2,

The capacity difference of each charge-discharge cycle corresponds to the difference between the charge capacity of the battery cell during the charge of the charge-discharge cycle and the discharge capacity of the battery cell during the discharge of the charge-discharge cycle, and

Each of the charge capacity and the discharge capacity can be derived from data obtained from the current measurement unit and included in the first information.

15. The battery cell diagnosis apparatus according to claim 1,

Wherein the second information indicates a capacity difference variation amount between successive charge and discharge cycles of the battery cell, and

The difference in capacity of each charge-discharge cycle of the battery cell is a difference between (i) a charge capacity of the battery cell during charging of the charge-discharge cycle of the battery cell and (ii) a discharge capacity of the battery cell during discharging of the charge-discharge cycle of the battery cell.

16. The battery cell diagnosis apparatus according to claim 1,

Wherein the second information indicates whether parallel connection of a plurality of unit cells included in the battery cell is abnormal based on a result of monitoring the estimated capacity value with time by the external device,

The estimated capacity value represents the full charge capacity of the battery cell based on charge and discharge data, an

The charge-discharge data includes a time series of voltages representing the time-dependent voltage of the battery cells and a time series of currents representing the time-dependent charge-discharge current of the battery cells.

17. The battery cell diagnosis apparatus according to claim 1,

Wherein the second information indicates whether the battery cell has an internal short circuit based on a first SOC variation of the battery cell and a standard factor,

Determining the standard factor by applying a statistical algorithm to the first SOC variation of at least two battery cells of the plurality of battery cells,

The first SOC variation is a difference between a first SOC of a first charging time point and a second SOC of a second charging time point of each battery cell,

Estimating the first SOC by applying an SOC estimation algorithm to a state parameter of the battery cell at the first charging time point,

Estimating the second SOC by applying the SOC estimation algorithm to a state parameter of the battery cell at the second charging time point, and

And acquiring the state parameter based on the first information.

18. A battery cell diagnosis system comprising an external device and the battery cell diagnosis apparatus according to claim 1,

Wherein the external device is configured to derive the second information based on at least a portion of the first information.

19. A battery cell diagnostic method, the battery cell diagnostic method comprising the steps of:

acquiring 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 by means of a control unit;

Transmitting, by means of the control unit, first information of the battery cell including the acquired data to an external device;

Receiving, by the control unit, second information from the external device, the second information including diagnostic information of the battery cell acquired based on the first information; and

And diagnosing an abnormal state of the battery cell based on the first information and the second information by means of the control unit.

20. The battery cell diagnosis method according to claim 19, further comprising the steps of:

detecting, by the control unit, at least one of a voltage abnormality and a behavior abnormality of the battery cell based on the first information of the battery cell; and

Diagnosing, by the control unit, 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.