Battery charging apparatus and method having battery safety diagnosis function

Abstract

Disclosed are a battery charging apparatus and method having a battery safety diagnosis function. According to an aspect of the present embodiment, provided is a battery charging apparatus that supplies power to a battery to charge the battery while simultaneously diagnosing whether each cell in the battery is abnormal, the battery charging apparatus comprising: a communication unit that wirelessly communicates with an apparatus including a battery to be charged and inspected and receives battery state information from the apparatus; a pre-processing unit that pre-processes the received battery state information to remove noise and refine same; a characteristic calculation unit that uses data that has been processed by the pre-processing unit to calculate the maximum capacity of each battery cell in the battery and temperature standard deviation information of each battery module; a risk determination unit that derives a risk information value of each battery cell by using the information calculated by the characteristic calculation unit; and an abnormality diagnosis unit that diagnoses a battery cell determined to have an abnormality by using the risk information value of each battery cell determined by the risk determination unit.

Description

Battery charging device and method equipped with battery safety diagnostic function

[0001] The present embodiment relates to a battery charging device and method equipped with the function of diagnosing the battery status or safety and outputting the diagnosis results to an external party or the vehicle owner.

[0002] The content described in this section merely provides background information regarding the present embodiment and does not constitute prior art.

[0003] Recently, the adoption of electric vehicles (EVs) has been accelerating as an alternative to fossil fuel depletion and environmental pollution. However, battery fire accidents are also continuously increasing in proportion to the expansion of EV adoption. In particular, fires in enclosed spaces, such as underground parking lots, are causing large-scale casualties and property damage, becoming a source of social anxiety.

[0004] Electric vehicle battery fires are caused by various factors such as cell defects, overcharging, or aging; therefore, it is essential to periodically and thoroughly inspect the internal condition of the battery to prevent them. However, conventionally, performing precise battery diagnostics involved spatial and temporal constraints and inconveniences, requiring users to visit an electric vehicle repair shop in person or connect specialized diagnostic equipment to the vehicle separately. Due to these inconveniences, the majority of drivers neglect inspections until signs of abnormality appear, which consequently leads to missing the golden time for fire prevention.

[0005] Meanwhile, electric vehicle users must undergo a charging process to operate the vehicle, and this time is idle time during which the vehicle and the power supply are physically and electrically connected. However, conventional battery chargers were limited to the function of simply supplying power to the battery.

[0006] One objective of the present invention is to provide a battery charging device and method that charges a battery electrically connected to itself, simultaneously diagnoses the condition or safety of the connected battery, and outputs the diagnosis results to an external party or to the vehicle owner.

[0007] According to one aspect of the present embodiment, a battery charging device that supplies power to a battery to charge the battery and simultaneously diagnoses abnormalities in each cell within the battery comprises: a communication unit that receives battery status information from a device including a battery to be charged and inspected; a preprocessing unit that preprocesses the received battery status information to remove noise and refine it; a characteristic calculation unit that calculates the maximum capacity of each battery cell and the temperature standard deviation information of each battery module using the data processed by the preprocessing unit; a risk determination unit that derives a risk information value for each battery cell using the information calculated by the characteristic calculation unit; and an abnormality diagnosis unit that diagnoses a battery cell determined to have an abnormality using the risk information value of each battery cell determined by the risk determination unit.

[0008] According to one aspect of the present embodiment, the battery state information is characterized by including some or all of the current and voltage of the entire battery or the current and voltage of battery packs included in the battery, the voltage and / or current of each battery cell in the battery pack, the temperature of each battery cell or battery modules which are a set of multiple battery cells, the State of Charge (SoC) information of the battery, and insulation resistance information.

[0009] According to one aspect of the present embodiment, the pretreatment unit is characterized by including a Savitzky-Golay filter.

[0010] According to one aspect of the present embodiment, the preprocessing unit is characterized by smoothing peak values ​​and removing noise.

[0011] According to one aspect of the present embodiment, the pretreatment unit is characterized by further including a Kalman filter.

[0012] According to one aspect of the present embodiment, the preprocessing unit is characterized by tracking data regarding the lost portion of data and estimating it as an approximation.

[0013] According to one aspect of the present embodiment, the communication unit is characterized by transmitting to the outside the result of the abnormality diagnosis unit diagnosing whether an abnormality has occurred for each battery cell.

[0014] According to one aspect of the present embodiment, a method for diagnosing abnormalities in each cell of a battery, wherein a battery charging device supplies power to a battery to charge the battery and simultaneously diagnoses abnormalities in each cell of the battery, is provided, characterized by comprising: a receiving process for receiving battery status information from a device including a battery to be charged and inspected; a preprocessing process for preprocessing the received battery status information; a calculation process for calculating maximum capacity and temperature standard deviation information for each battery cell based on the preprocessed data; a derivation process for deriving risk information values ​​for each battery cell based on the calculated information; and a diagnosis process for diagnosing abnormalities in each battery cell using the derivation risk information values.

[0015] According to one aspect of the present embodiment, the battery state information is characterized by including some or all of the current and voltage of the entire battery or the current and voltage of battery packs included in the battery, the voltage and / or current of each battery cell in the battery pack, the temperature of each battery cell or battery modules which are a set of multiple battery cells, the State of Charge (SoC) information of the battery, and insulation resistance information.

[0016] According to one aspect of the present embodiment, the receiving process is characterized by wirelessly communicating with a device including a battery and receiving directly from said device.

[0017] As described above, according to one aspect of the present embodiment, there is an advantage of charging a battery electrically connected to oneself while simultaneously diagnosing the condition or safety of the connected battery and outputting the diagnosis results to an external party or the vehicle owner.

[0018] FIG. 1 is a diagram illustrating the configuration of a battery charging system according to a first embodiment of the present invention.

[0019] FIG. 2 is a diagram illustrating the configuration of a battery charging device according to a first embodiment of the present invention.

[0020] FIG. 3 is a diagram illustrating data preprocessed by a preprocessing unit according to the first embodiment of the present invention.

[0021] FIG. 4 is a diagram illustrating the process of an abnormality diagnosis unit diagnosing an abnormality according to the first embodiment of the present invention.

[0022] FIG. 5 is a diagram illustrating the result of an abnormality diagnosis unit determining an abnormality according to the first embodiment of the present invention.

[0023] FIG. 6 is a diagram illustrating an example of a diagnostic result output by an output unit according to a first embodiment of the present invention.

[0024] FIG. 7 is a diagram illustrating the configuration of a battery charging system according to a second embodiment of the present invention.

[0025] FIG. 8 is a flowchart illustrating a method for a battery charging device according to an embodiment of the present invention to charge a battery and diagnose an abnormality.

[0026] The present invention is susceptible to various modifications and may have various embodiments, and specific embodiments are illustrated in the drawings and described in detail. However, this is not intended to limit the invention to specific embodiments, and it should be understood that the invention includes all modifications, equivalents, and substitutions that fall within the spirit and scope of the invention. Similar reference numerals have been used for similar components in the description of each drawing.

[0027] Terms such as first, second, A, B, etc., may be used to describe various components, but said components should not be limited by said terms. These terms are used solely for the purpose of distinguishing one component from another. For example, without departing from the scope of the present invention, the first component may be named the second component, and similarly, the second component may be named the first component. The term "and / or" includes a combination of a plurality of related described items or any of a plurality of related described items.

[0028] When it is stated that one component is "connected" or "connected" to another component, it should be understood that while it may be directly connected or connected to that other component, there may also be other components in between. On the other hand, when it is stated that one component is "directly connected" or "directly connected" to another component, it should be understood that there are no other components in between.

[0029] The terms used in this application are used merely to describe specific embodiments and are not intended to limit the invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, terms such as "comprising" or "having" should be understood as not precluding the existence or addition of the features, numbers, steps, actions, components, parts, or combinations thereof described in the specification.

[0030] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which this invention pertains.

[0031] Terms such as those defined in commonly used dictionaries should be interpreted as having meanings consistent with their meanings in the context of the relevant technology, and should not be interpreted in an ideal or overly formal sense unless explicitly defined in this application.

[0032] In addition, each component, process, procedure, or method included in each embodiment of the present invention may be shared within a scope that is not technically contradictory to one another.

[0033] FIG. 1 is a diagram illustrating the configuration of a battery charging system according to a first embodiment of the present invention, and FIG. 6 is a diagram illustrating an example of a diagnostic result output by an output unit according to a first embodiment of the present invention.

[0034] Referring to FIG. 1, a battery charging system (100) according to a first embodiment of the present invention includes a battery charging device (110) and a user terminal (120).

[0035] The battery charging system (100) charges a battery of an electric vehicle, etc., and simultaneously analyzes the state or safety of the battery being charged and provides the analysis results to an external device or to a user's terminal (120). The battery charging system (100) proceeds with charging without the user needing to separately inspect the state of the battery, and simultaneously analyzes the state or safety of the battery being charged. Accordingly, the user can simultaneously inspect the state or safety of the battery simply by using the battery charging system (100) to charge, without needing to separately inspect the state or safety of the battery installed in the electric vehicle, etc. used by the user. For convenience, the following description limits the device including the battery to which charging and state or safety analysis are to be performed to an electric vehicle, but any device that operates using a battery that performs charging and discharging may be applicable.

[0036] The battery charging device (110) supplies power to the battery inside the electric vehicle that is electrically connected to it to charge the battery, and at the same time receives battery status information and diagnoses whether there is an abnormality in the entire battery or in each cell within the battery. The battery charging device (110) is electrically connected to the electric vehicle containing the battery and supplies power to the battery within the device. Separately, the battery charging device (110) receives battery status information from a battery management system (BMS) that manages the battery within the device. Here, the battery status information includes some or all of the current and voltage of the entire battery or the current and voltage of the battery packs contained within the battery, the voltage and / or current of each battery cell within the battery pack, the temperature of each battery cell or battery modules which are a set of multiple battery cells, the State of Charge (SoC) information of the battery, and insulation resistance information. Based on the received information, the battery charging device (110) diagnoses whether an abnormality has occurred in each battery cell within the battery. The battery charging device (110) may output the diagnostic results to the outside of itself, or transmit the results to a terminal (120) owned by the user so that the user can recognize the status or safety analysis results of the battery placed inside their vehicle.

[0037] The user terminal (120) receives and outputs the battery status or safety analysis results from the battery charging device (110). For example, as illustrated in FIG. 6, the user can recognize the status of the battery in the vehicle they are using based on the battery status or safety analysis results output by the user terminal (120). Referring to FIG. 6, the user can check some or all of the abnormal diagnosis results for each battery cell (described later with reference to FIG. 2, etc.), the location of the cell where the abnormality occurred, whether the number of battery cells analyzed as having abnormalities have certain risk information values, and the accuracy information of the diagnosis results based on the information output by the user terminal (120).

[0038] Meanwhile, the user terminal (120) is not necessarily limited to the user of the electric vehicle and may be a separate management server or a third party's terminal. For example, if the battery charging device (110) or battery charging system (100) is implemented in an apartment or a specific building, the user terminal (120) may be a terminal possessed by the manager of the apartment or building. Alternatively, the user terminal (120) may be a terminal possessed by an insurance company or a battery manufacturer, etc.

[0039] FIG. 2 is a diagram illustrating the configuration of a battery charging device according to a first embodiment of the present invention.

[0040] Referring to FIG. 2, a battery charging device (110) according to a first embodiment of the present invention includes a communication unit (210), a preprocessing unit (220), a characteristic calculation unit (230), a risk determination unit (240), and an abnormality diagnosis unit (250). Furthermore, the battery charging device (110) may further include an output unit (260).

[0041] The communication unit (210) communicates with a vehicle including a battery that is to be charged and inspected to receive battery status information from the vehicle, and wirelessly communicates with a user terminal (120) to transmit the diagnosis result of the abnormality diagnosis unit (250) to the user terminal (120).

[0042] The communication unit (210) communicates with a vehicle containing a battery that is subject to charging and inspection, and receives battery status information from the vehicle. The communication unit (210) may perform wireless communication with the vehicle or wired communication. A separate port (e.g., an OBD port) exists within the vehicle for transmitting and receiving battery status information to and from the outside. The communication unit (210) communicates wirelessly with a device (not shown) mounted within the port and can receive battery status information obtained by the device (not shown) through the port. Alternatively, the communication unit (210) may be connected to the charging port of the vehicle to perform wired communication with the vehicle, such as a PLC (Power Line Communication), and can receive battery status information directly from the vehicle. The communication unit (210) receives battery status information including the aforementioned information from the device (not shown) or the vehicle.

[0043] The communication unit (210) communicates wirelessly with the user terminal (120), and the abnormality diagnosis unit (250) transmits the result of diagnosing whether an abnormality has occurred in the entire battery or each battery cell to the user terminal (120) according to the operation of each component within the battery charging device (110). Accordingly, the user can check the result regarding whether an abnormality has occurred in the battery being charged from a distance without having to stay within a certain radius of the battery charging device (110).

[0044] The preprocessing unit (220) preprocesses the received battery status information to remove noise and refine it into a form suitable for analysis. The preprocessing process of the preprocessing unit (220) is exemplified in FIG. 3.

[0045] FIG. 3 is a diagram illustrating data preprocessed by a preprocessing unit according to the first embodiment of the present invention.

[0046] Referring to FIG. 3, it can be seen that the battery status information value just received from the communication unit (210) has a step shape or an irregular peak shape rather than a smooth curve shape. This corresponds to data containing noise caused by the resolution limit of the sensor or precision issues during the data transmission process. If such data is applied directly to analysis, inaccurate results may be obtained.

[0047] Recognizing these problems, the preprocessing unit (220) preprocesses the received battery status information to remove noise and refine it into a form suitable for analysis. The preprocessing unit (220) may include a Savitzky-Golay filter and may additionally include a Kalman filter. By including the Savitzky-Golay filter, the preprocessing unit (220) removes noise while smoothing parts where values ​​change rapidly (such as peak values). Meanwhile, the preprocessing unit (220) may additionally include a Kalman filter and tracks data regarding parts where data is lost to estimate an approximation. By including the aforementioned filters or filters, the preprocessing unit (220) refines the data into a smooth linear form.

[0048] That is, the preprocessing unit (220) removes noise (peak values, etc.) contained in the data and refines it into a smooth form that is easy to analyze.

[0049] Referring again to FIG. 2, the characteristic calculation unit (230) calculates the maximum capacity of each battery cell in the battery and the temperature standard deviation information of each battery module from the data that has passed through the preprocessing unit (220).

[0050] The characteristic calculation unit (230) calculates the maximum capacity for each battery cell as follows using the data processed by the preprocessing unit (220).

[0051]

[0052] Here, cell qmax represents the maximum capacity of the battery cell, Q represents the charge, and OCV represents the open-circuit voltage of the battery cell.

[0053] Maximum capacity of a battery cell (cell qmax ) indicates the change in SoC relative to the change in charge amount. The charge amount can be calculated by integrating the current, and the change in SoC can be derived from the change in the open-circuit voltage value. Since there is an SoC-OCV curve matching SoC and OCV for each battery, the characteristic calculation unit (230) can easily derive the SoC value from the OCV. Accordingly, the characteristic calculation unit (230) calculates the change in charge amount by integrating the current value for a certain period for each battery cell (among the data passed through the preprocessing unit (220), and uses the (open) voltage of each battery cell at the moment charging starts (initial time point) and the (open) voltage of each battery cell at a specific time point to calculate the SoC (SoC) at each moment. final , SoC initial The characteristic calculation unit (230) calculates the change value. The calculated change value of the charge amount is divided by the calculated change value of the SoC to calculate the maximum capacity for each battery cell.

[0054] Meanwhile, the characteristic calculation unit (230) calculates the temperature standard deviation for each battery module from the temperature information of each battery module.

[0055]

[0056] Here, T STDEV is the temperature standard deviation for each battery module, T i is the temperature value of each battery module, T avg represents the average temperature value of each battery module, and n represents the total number of battery modules. The characteristic calculation unit (230) calculates the temperature standard deviation for each battery module using the temperature of each battery module among the data passed through the preprocessing unit (220).

[0057] The risk determination unit (240) derives a risk information value for each battery cell using information calculated by the characteristic calculation unit (230). The risk determination unit (240) derives a risk information value for each battery cell using the maximum capacity information for each battery cell and the temperature standard deviation information between each battery module calculated by the characteristic calculation unit (230).

[0058]

[0059] The risk determination unit (240) derives a risk information value for each battery cell by assigning appropriate weights (K1, K2) to the maximum capacity information for each battery cell and the temperature standard deviation information between each battery module. The weights (K1, K2) may be appropriately assigned according to the resolution of the sensor or the characteristics of each vehicle. For the temperature standard deviation information between each battery module, the risk determination unit (240) may derive a risk information value using the module information containing the corresponding battery cell.

[0060] The abnormality diagnosis unit (250) diagnoses whether an abnormality has occurred in each battery cell using the risk level information value of each battery cell (determined by the risk level judgment unit (240)).

[0061] The abnormality diagnosis unit (250) diagnoses a battery cell that is determined to have an abnormality based on the risk level information value of each battery cell determined by the risk level determination unit (240) using a preset algorithm.

[0062] The anomaly diagnosis unit (250) utilizes the first to third algorithms, which are unsupervised algorithms. The anomaly diagnosis unit (250) utilizes the first to third algorithms to separate information values ​​that differ from the majority for all risk information values ​​of each battery cell determined by the risk determination unit (240). Typically, battery cells in the normal range without anomalies have similar risk information values, whereas the risk information values ​​of battery cells that have anomalies have different risk information values ​​from those of battery cells in the normal range. The anomaly diagnosis unit (250) utilizes all of the first to third algorithms or selectively utilizes one of them to precisely diagnose and separate the risk information values ​​of battery cells that have anomalies.

[0063] The first algorithm randomly separates and isolates data regarding all risk information of each battery cell. By randomly separating data that is separated from one another, the first algorithm isolates data that is not clustered. As previously mentioned, battery cells in the normal range are clustered because they have similar risk information values. Accordingly, the risk information values ​​of battery cells that have occurred are isolated according to the first algorithm. The first algorithm quantifies the isolation level for all risk information of each battery cell, and if the isolation level is greater than or equal to a preset first threshold, it can determine that the battery cell has occurred. For example, the first algorithm can be implemented as an Isolation Forest algorithm that performs the aforementioned operation.

[0064] The second algorithm forms boundaries for information values ​​that are clustered together. By forming boundaries for each clustered information value, the second algorithm distinguishes risk information values ​​that are not clustered and are located separately. Through this process, the second algorithm similarly separates the risk information values ​​of battery cells that have experienced abnormalities. For example, the second algorithm can be implemented as a One-Class SVM algorithm.

[0065] The third algorithm analyzes the density for each of the risk information values. By analyzing the density for each of the risk information values, the third algorithm separates those with a density below a preset threshold and determines them as the risk information values ​​of the battery cell where the abnormality occurred. For example, the third algorithm can be implemented as a Local Outlier Factor (LOF) algorithm.

[0066] The second algorithm and the third algorithm quantify the clustering or density for all risk information of each battery cell, and if the clustering or density is below a preset second threshold, it can be determined that the battery cell is abnormal.

[0067] In this way, the abnormality diagnosis unit (250) separates the battery cells where abnormalities have occurred by using the first to third algorithms, which are unsupervised algorithms. Since the abnormality diagnosis unit (250) uses unsupervised algorithms, it does not require separate learning or the construction of data for learning. Accordingly, the abnormality diagnosis unit (250) can be simply constructed and operated. In addition, the abnormality diagnosis unit (250) does not determine abnormalities simply by whether the risk information value exceeds a preset threshold, but determines whether an abnormality has occurred by using unsupervised algorithms. If abnormalities are determined solely by whether the risk information value exceeds a preset threshold, there is a high possibility of misdiagnosis in situations where the battery is aged and the risk information value of most of the cells within the battery rises together. Recognizing this problem, the abnormality diagnosis unit (250) can perform an accurate diagnosis regardless of the battery's age by conducting a relative evaluation with other things using unsupervised algorithms. The abnormality diagnosis unit (250) can diagnose abnormalities as exemplified in FIGS. 4 and 5.

[0068] FIG. 4 is a diagram illustrating the process of an abnormality diagnosis unit diagnosing an abnormality according to a first embodiment of the present invention, and FIG. 5 is a diagram illustrating the result of an abnormality diagnosis unit determining an abnormality according to a first embodiment of the present invention.

[0069] The abnormality diagnosis unit (250) uses an unsupervised algorithm based on the risk information of each battery cell (determined by the risk judgment unit (240)) to distinguish risk information values ​​that are isolated or do not form clusters, and diagnoses that an abnormality has occurred for them.

[0070] The abnormality diagnosis unit (250) can intuitively distinguish which cells have abnormalities among the battery cells that have abnormalities, as shown in FIG. 4a.

[0071] The abnormality diagnosis unit (250) can determine how many battery cells are determined to be normal and what the risk information values ​​of the battery cells are, as illustrated in FIG. 4b. The x-axis indicates the risk information value of each battery cell, and the y-axis indicates the number of battery cells having the same risk information value. For each battery cell, the abnormality diagnosis unit (250) can determine how many battery cells have been analyzed as having abnormalities by using the risk information value and the number of battery cells having each risk information value.

[0072] As illustrated in FIG. 4c, the abnormality diagnosis unit (250) can also analyze the diagnostic accuracy regarding whether an abnormality has occurred in the battery cell. In the data of FIG. 4c, the vertical axis represents whether an abnormality has occurred in the actual battery cell, and the horizontal axis represents the result diagnosed by the abnormality diagnosis unit (250). For both axes, a small value indicates that it is within the normal range, and a large value indicates that an abnormality has occurred. In FIG. 4c, the upper left indicates the number of data points where the actual battery cell is operating normally and the abnormality diagnosis unit (250) also diagnoses that no abnormality has occurred; the lower right indicates the number of data points where an abnormality has occurred in the actual battery cell and the abnormality diagnosis unit (250) also diagnoses that an abnormality has occurred; the upper right indicates the number of data points where the actual battery cell is operating normally but the abnormality diagnosis unit (250) diagnoses that an abnormality has occurred; and the lower left indicates the number of data points where an abnormality has occurred in the actual battery cell but the abnormality diagnosis unit (250) diagnoses that no abnormality has occurred. The abnormality diagnosis unit (250) thus enables the accuracy of the diagnosis result to be known.

[0073] In addition, the abnormality diagnosis unit (250) diagnoses abnormalities using an unsupervised algorithm as shown in FIG. 5, so it can be confirmed that it accurately detects that an abnormality has occurred in a specific cell.

[0074] Referring again to FIG. 2, the abnormality diagnosis unit (250) can control the communication unit (210) to transmit some or all of the diagnosis results, the location of the cell where the abnormality occurred, whether the number of battery cells having certain risk information values ​​were analyzed as having an abnormality, and the accuracy information of the diagnosis results to the user terminal (120).

[0075] Furthermore, the battery charging device (110) may further include an output unit (260). The output unit (260) directly outputs the aforementioned results diagnosed or analyzed by the abnormality diagnosis unit (250), thereby allowing a user, etc., to check them without using the user terminal (120).

[0076] FIG. 7 is a diagram illustrating the configuration of a battery charging system according to a second embodiment of the present invention.

[0077] Referring to FIG. 7, a battery charging system (700) according to a first embodiment of the present invention includes a battery charging device (710), a user terminal (120), and a diagnostic server (730).

[0078] The battery charging device (710) performs an operation similar to that of the communication unit (210) within the battery charging device (110). It communicates with a vehicle containing a battery to be charged and inspected, receives battery status information from the vehicle, and transmits it to the diagnostic server (730). Additionally, the battery charging device (710) wirelessly communicates with a user terminal (120) to transmit the diagnostic results of the diagnostic server (730) to the user terminal (120).

[0079] The operation of the preprocessing unit (220), characteristic calculation unit (230), risk determination unit (240), and abnormality diagnosis unit (250) within the battery charging device (110) can be performed by the diagnosis server (730). The diagnosis server (730) receives battery status information of a vehicle from the battery charging device (710) and diagnoses whether there are abnormalities in each cell within the battery. The diagnosis server (730) transmits the diagnosis result to the battery charging device (710), thereby enabling the user to check the diagnosis result on their terminal (120) via the battery charging device (710).

[0080] In this way, the battery charging system (700) includes a diagnostic server (730), thereby minimizing structural changes to the existing battery charging device while also providing the user with a diagnostic result regarding whether there is an abnormality in the battery being charged.

[0081] FIG. 8 is a flowchart illustrating a method for a battery charging device according to an embodiment of the present invention to charge a battery and diagnose an abnormality.

[0082] The communication unit (210) receives battery status information from the electric vehicle (S810).

[0083] The preprocessing unit (220) or the diagnostic server (730) preprocesses the received battery status information (S820).

[0084] The characteristic calculation unit (230) or the diagnostic server (730) calculates the maximum capacity and temperature standard deviation information for each cell based on the preprocessed data (S830).

[0085] The risk determination unit (240) or the diagnostic server (730) derives risk information values ​​for each battery cell based on the calculated information (S840).

[0086] The abnormality diagnosis unit (250) or diagnosis server (730) diagnoses whether each battery cell is abnormal using the derived risk information value (S850).

[0087] Although FIG. 8 describes each process as being executed sequentially, this is merely an illustrative explanation of the technical concept of one embodiment of the present invention. In other words, a person skilled in the art to which one embodiment of the present invention belongs can modify and adapt it in various ways, such as changing the order described in each figure or executing one or more of the processes in parallel, without departing from the essential characteristics of one embodiment of the present invention; therefore, FIG. 8 is not limited to a chronological order.

[0088] Meanwhile, the processes illustrated in FIG. 8 can be implemented as computer-readable code on a computer-readable recording medium. A computer-readable recording medium includes all types of recording devices in which data that can be read by a computer system is stored. That is, a computer-readable recording medium includes storage media such as magnetic storage media (e.g., ROM, floppy disk, hard disk, etc.) and optical reading media (e.g., CD-ROM, DVD, etc.). Additionally, computer-readable recording media can be distributed across networked computer systems, allowing computer-readable code to be stored and executed in a distributed manner.

[0089]

[0090] CROSS-REFERENCE TO RELATED APPLICATION

[0091] If this patent application claims priority under Section 119(a) of the U.S. Patent Act (35 USC § 119(a)) for Korean patent applications filed on December 16, 2024, Nos. 10-2024-0186872 and 10-2024-0186874 and Korean patent application filed on December 16, 2025, No. 10-2025-0200283, the entire contents thereof shall be incorporated into this patent application by reference. Furthermore, if this patent application claims priority in countries other than the United States for the same reasons as above, the entire contents thereof shall be incorporated into this patent application by reference.

Claims

1. A battery charging device that supplies power to a battery to charge the battery and simultaneously diagnoses whether each cell within the battery is abnormal, A communication unit that receives battery status information from a device including a battery to be charged and inspected; A preprocessing unit that preprocesses received battery status information to remove noise and refine it; A characteristic calculation unit that calculates the maximum capacity for each battery cell and the temperature standard deviation information for each battery module using data processed by the above preprocessing unit; A risk determination unit that derives a risk information value for each battery cell using information calculated by the above characteristic calculation unit; and An anomaly diagnosis unit that diagnoses battery cells determined to have an anomaly using the risk level information value of each battery cell determined by the above-mentioned risk level determination unit A battery charging device characterized by including 2. In Paragraph 1, The above battery status information is, A battery charging device characterized by including some or all of the following: the current and voltage of the entire battery or the current and voltage of battery packs contained within the battery, the voltage and / or current of each battery cell within the battery pack, the temperature of each battery cell or battery modules which are a set of multiple battery cells, the State of Charge (SoC) information of the battery, and insulation resistance information.

3. In Paragraph 1, The above preprocessing unit is, A battery charging device characterized by including a Savitzky-Golay filter.

4. In Paragraph 3, The above preprocessing unit is, A battery charging device characterized by smoothing peak values ​​and removing noise.

5. In Paragraph 3, The above preprocessing unit is, A battery charging device characterized by further including a Kalman filter.

6. In Paragraph 5, The above preprocessing unit is, A battery charging device characterized by tracking data for lost data portions and estimating them as an approximation.

7. In Paragraph 1, The above communication unit is, A battery charging device characterized by the above-mentioned abnormality diagnosis unit transmitting the result of diagnosing whether an abnormality has occurred for each battery cell to the outside.

8. A method for a battery charging device to supply power to a battery to charge the battery, and simultaneously diagnose whether each cell within the battery is abnormal, A receiving process for receiving battery status information from a device including a battery to be charged and inspected; A preprocessing process for preprocessing received battery status information; A calculation process for calculating maximum capacity and temperature standard deviation information for each battery cell based on preprocessed data; A derivation process for deriving risk information values ​​of each battery cell based on calculated information; and A diagnostic process that diagnoses abnormalities in each battery cell using derived risk information values. An abnormal diagnosis method characterized by including 9. In Paragraph 8, The above battery status information is, An abnormality diagnosis method characterized by including some or all of the current and voltage of the entire battery or the current and voltage of battery packs contained within the battery, the voltage and / or current of each battery cell within the battery pack, the temperature of each battery cell or battery modules which are a set of multiple battery cells, the State of Charge (SoC) information of the battery, and insulation resistance information.

10. In Paragraph 8, The above receiving process is, An abnormality diagnosis method characterized by wirelessly communicating with a device including a battery and receiving directly from the said device.

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