Apparatus for diagnosing battery and operating method thereof

Abstract

A battery diagnostic device according to one embodiment disclosed in this document may include: an interface for acquiring status data including current and voltage values ​​of a battery cell for each diagnostic cycle corresponding to a charging cycle or a discharging cycle; and a controller for calculating a similarity between current data including the current value and reference data, extracting a first current data among the current data in which the similarity exceeds a reference value, and diagnosing whether the battery cell is abnormal based on a first voltage value corresponding to the first current data.

Description

Technology Field

[0001] The embodiments disclosed in this document relate to a battery diagnostic device and a method of operating the same. Background Technology

[0002] Recently, active research and development on secondary batteries has been underway. Here, the term "secondary battery" refers to a rechargeable battery, encompassing conventional Ni / Cd and Ni / MH batteries as well as the more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of significantly higher energy density compared to conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form factor, making them suitable for use as power sources for mobile devices. Recently, their scope of application has expanded to include electric vehicles, drawing attention as a next-generation energy storage medium.

[0003] As the industrial sectors utilizing batteries expand, Battery Management Systems (BMS) for diagnosing battery safety are also evolving. BMS can diagnose battery performance using various diagnostic algorithms and perform appropriate control based on the battery's condition. BMS can diagnose the presence of abnormal battery cells. Here, abnormalities can include all causes that may lead to ignition due to damage or aging of the battery itself. The problem to be solved

[0004] By monitoring the voltage values ​​of battery cells, it is possible to diagnose overvoltage, undervoltage, resistance abnormalities, or voltage balancing between battery cells. However, there is a problem in that monitoring the voltage values ​​of countless battery cells to identify the cause of the abnormality is costly and time-consuming.

[0005] The technical problems of the embodiments disclosed in this document are not limited to those mentioned above, and other unmentioned technical problems will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0006] A battery diagnostic device according to one embodiment disclosed in this document may include: an interface for acquiring status data including current and voltage values ​​of a battery cell for each diagnostic cycle corresponding to a charging cycle or a discharging cycle; and a controller for calculating a similarity between current data including the current value and reference data, extracting a first current data among the current data in which the similarity exceeds a reference value, and diagnosing whether the battery cell is abnormal based on a first voltage value corresponding to the first current data.

[0007] In one embodiment, the controller can calculate the similarity between the current data and the reference data based on a cosine similarity or Pearson similarity calculation technique.

[0008] In one embodiment, the reference value may be any value included in the range of 0.85 or higher and 0.95 or lower.

[0009] In one embodiment, the controller calculates dQ / dV based on the first voltage value to generate voltage value-dQ / dV data, converts the voltage value-dQ / dV data into principal component data based on Principal Component Analysis (PCA), and the principal component value (PC) included in the principal component data corresponding to the nth diagnostic cycle (n: natural number) of the first diagnostic cycle corresponding to the first current data. n The difference value (ΔPC) between ) and the principal component value (PC1) included in the principal component data corresponding to the first diagnostic cycle n : PC nCalculate -PC1) and the above difference value (ΔPC n Based on ), it is possible to diagnose whether there is an abnormality in the battery cell.

[0010] In one embodiment, the controller calculates the average (m) and standard deviation (σ) of the difference values ​​of a plurality of battery cells for each of the first diagnostic cycles, and can diagnose whether the battery cells are abnormal based on the difference values, the average, and the standard deviation.

[0011] In one embodiment, the controller calculates a threshold value based on the mean and the standard deviation, and can diagnose whether the battery cell is abnormal based on the difference value and the threshold value.

[0012] In one embodiment, the controller can diagnose the battery cell as an abnormal cell if the difference value is not included in a threshold range of (m-2.5σ) or more and (m+2.5σ) or less.

[0013] A method of operation of a battery diagnostic device according to an embodiment disclosed in this document may include: acquiring state data including current values ​​and voltage values ​​over time of a battery cell for each diagnostic cycle corresponding to a charging cycle or a discharging cycle; calculating a similarity between current data including current values ​​over time and reference data; extracting a first current data among the current data in which the similarity exceeds a reference value; and diagnosing whether the battery cell is abnormal based on a first voltage value corresponding to the first current data.

[0014] In one embodiment, the operation of calculating the similarity may include the operation of calculating the similarity between the current data and the reference data based on a cosine similarity or Pearson similarity calculation technique.

[0015] In one embodiment, the reference value may be any value included in the range of 0.85 or higher and 0.95 or lower.

[0016] In one embodiment, the diagnosing operation comprises: an operation to generate voltage value-dQ / dV data by calculating dQ / dV based on the first voltage value; an operation to convert the voltage value-dQ / dV data into principal component data based on Principal Component Analysis (PCA); and a principal component value (PC) included in the principal component data corresponding to the nth diagnostic cycle (n: natural number) among the diagnostic cycles corresponding to the first current data. n The difference value (ΔPC) between ) and the principal component value (PC1) included in the principal component data corresponding to the first diagnostic cycle n : PC n The operation of calculating -PC1), and the difference value (ΔPC n It may include an operation to diagnose whether there is an abnormality in the battery cell based on ).

[0017] In one embodiment, the diagnosing operation may include an operation of calculating the average (m) and standard deviation (σ) of the difference values ​​of a plurality of battery cells for each of the first diagnostic cycles, and an operation of diagnosing whether the battery cells are abnormal based on the difference values, the average, and the standard deviation.

[0018] In one embodiment, the diagnosing operation may include an operation of calculating a threshold value based on the mean and the standard deviation, and an operation of diagnosing whether the battery cell is abnormal based on the difference value and the threshold value.

[0019] In one embodiment, the diagnosing operation may include diagnosing the battery cell as an abnormal cell if the difference value is not included in a threshold range of (m-2.5σ) or more and (m+2.5σ) or less. Effects of the invention

[0020] A battery diagnostic device and a method of operation thereof according to various embodiments disclosed in this document can acquire current data including current values ​​over time for each charging or discharging cycle for each battery cell, and can diagnose whether there is an abnormality in the battery cell by calculating the similarity between the current data and reference data and using the current data in which the similarity is greater than or equal to a specified value. Accordingly, the battery diagnostic device and the method of operation thereof can increase the diagnostic rate and lower the over-detection rate by preventing misdiagnosis caused by differences in voltage values ​​resulting from different current values ​​by extracting charging or discharging cycles in which similar current values ​​flow and diagnosing whether there is an abnormality in the battery.

[0021] A battery diagnostic device and a method of operation thereof according to various embodiments disclosed in this document can distinguish between a normal battery cell and an abnormal battery cell using voltage value-dQ / dV data corresponding to a charge or discharge cycle in which the similarity exceeds a specified value.

[0022] A battery diagnostic device and a method of operation thereof according to various embodiments disclosed in this document can easily detect abnormal battery cells among battery cells by converting voltage value-dQ / dV data for each battery cell into principal component data based on principal components with large variance between data through Principal Component Analysis (PCA).

[0023] The effects of the battery diagnostic device and the method of operation thereof disclosed in this document are not limited to those mentioned above, and other unmentioned effects will be clearly understood by those skilled in the art in accordance with the disclosure of this document. Brief explanation of the drawing

[0024] FIG. 1 is a block diagram of a battery diagnostic system according to one embodiment disclosed in this document. FIG. 2 illustrates a battery pack according to one embodiment disclosed in this document. FIG. 3 illustrates the result of calculating the similarity between voltage data of battery cells and reference data according to one embodiment disclosed in this document. FIG. 4 illustrates a histogram showing the number of battery cells whose similarity exceeds a reference value according to one embodiment disclosed in this document. FIG. 5 illustrates voltage value-dQ / dV data of battery cells according to one embodiment disclosed in this document. FIG. 6 illustrates diagnostic cycle-difference value data of battery cells according to one embodiment disclosed in this document. FIG. 7 is a flowchart illustrating the operation method of a battery diagnostic device according to one embodiment disclosed in this document. FIG. 8 is a flowchart showing detailed operations included in operation 730 disclosed in FIG. 7. FIG. 9 illustrates a computing system for executing operations of a battery diagnostic device according to an embodiment disclosed in this document. In relation to the description of the drawings, the same or similar reference numerals may be used for identical or similar components. Specific details for implementing the invention

[0025] Hereinafter, embodiments of the present invention are described with reference to the accompanying drawings. However, this is not intended to limit the present invention to specific embodiments and should be understood to include various modifications, equivalents, and / or alternatives of the embodiments of the present invention.

[0026] The embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments, and should be understood to include various modifications, equivalents, or substitutions of said embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of a noun corresponding to an item may include one or more of said items unless the relevant context clearly indicates otherwise.

[0027] In this document, each of the following phrases may include any one of the items listed together in the corresponding phrase, or any combination thereof: "A or B," "at least one of A and B," "A, B or C," "at least one of A, B and C," and "at least one of A, B, or C." Terms such as "first," "second," "first," "second," "A," "B," "(a)" or "(b)" may be used simply to distinguish a component from another component and, unless specifically stated otherwise, do not limit the components in any other aspect (e.g., importance or order).

[0028] In this document, where it is stated that any (e.g., 1) component is "connected," "coupled," or "joined" to another (e.g., 2) component, with or without the terms "functionally" or "communicationly," or where it is stated that the component is "coupled" or "connected," it means that the component may be connected to the other component directly (e.g., by wire or wirelessly) or indirectly (e.g., through a 3) component.

[0029] Methods according to the various embodiments disclosed in this document may be provided as part of a computer program product. The computer program product may be traded between a seller and a buyer as a product. The computer program product may be distributed in the form of a device-readable storage medium (e.g., compact disc read-only memory, CD-ROM) or distributed online (e.g., download or upload) through an application store or directly between two user devices. In the case of online distribution, at least a portion of the computer program product may be temporarily stored or temporarily created on a device-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or a relay server.

[0030] According to the embodiments disclosed in this document, each component (e.g., module or program) of the components described above may include a singular or multiple entities, and some of the multiple entities may be separated and placed in other components. According to the embodiments disclosed in this document, one or more of the components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Generally or additionally, multiple components (e.g., module or program) may be integrated into a single component. In such a case, the integrated component may perform one or more functions of each of the components of the multiple components in the same or similar manner as those performed by the corresponding components among the multiple components prior to the integration. According to the embodiments disclosed in this document, operations performed by a module, program, or other component may be executed sequentially, in parallel, iteratively, or heuristically, or one or more of the operations may be executed in a different order, omitted, or one or more other operations may be added.

[0031] FIG. 1 is a block diagram of a battery diagnostic system (1) according to one embodiment disclosed in this document.

[0032] Referring to FIG. 1, the battery diagnostic system (1) may include a battery diagnostic device (10), a sensing device (12), and battery units (120, 140, 160). Each of the battery units (120, 140, 160) in FIG. 1 may correspond to any one of a battery rack, a battery pack, and a battery module.

[0033] The battery diagnostic device (10) can be connected to the sensing device (12) via wired and / or wireless connections.

[0034] In one embodiment, the connection between the battery diagnostic device (10) and the sensing device (12) may be a communication connection via a wired and / or wireless network. In one embodiment, the wired network may be based on LAN (local area network) communication or power line communication. In one embodiment, the wireless network may be based on a short-range communication network (e.g., Bluetooth, WiFi (wireless fidelity) or IrDA (infrared data association)), or a long-range communication network (cellular network, 4G network, 5G network).

[0035] In one embodiment, the connection between the battery diagnostic device (10) and the sensing device (12) may be a connection via a device-to-device communication method (e.g., bus, GPIO (general purpose input and output), SPI (serial peripheral interface), or MIPI (mobile industry processor interface)).

[0036] The sensing device (12) can obtain values ​​(or information) related to the state of each of the battery units (120, 140, 160). In one embodiment, the values ​​related to the state may include one or more values ​​for the voltage, current, resistance, state of charge (SOC), state of health (SOH), or temperature, or a combination thereof, of each of the battery units (120, 140, 160). Each of the battery units (120, 140, 160) may include one or more battery cells (e.g., 121, 122, 123). For example, the battery cells (121, 122, 123) included in the first battery unit (120) may be electrically connected to each other (series and / or parallel connection). According to an embodiment, battery cells (121, 122, 123) may be included in the first battery unit (120) in an electrically separated state. In FIG. 1, for convenience of explanation, only the first battery cell (121) to the third battery cell (123) included in the first battery unit (120) have been described, but this is not limited thereto, and the second battery unit (140) and the third battery unit (160) may also include one or more battery cells.

[0037] In one embodiment, the sensing device (12) may acquire values ​​(or information) related to the state of each of one or more battery cells (121, 122, 123). In one embodiment, the values ​​related to the state may include one or more values ​​for the voltage, current, resistance, state of charge (SOC), state of health (SOH), or temperature, or a combination thereof, of each of the battery cells (121, 122, 123). Hereinafter, the values ​​related to the state may be referred to as 'state values'.

[0038] The battery diagnostic device (10) can obtain status data including current and voltage values ​​of each of the battery cells (121, 122, 123) from the sensing device (12). The battery diagnostic device (10) can obtain status data for each diagnostic cycle corresponding to a charging cycle or a discharging cycle. Here, the status data may include current values, voltage values, charge status, health status, temperature, or a combination thereof over time.

[0039] A battery diagnostic device (10) can calculate the similarity between a current value over time (hereinafter, current data) and reference data for each of the battery cells (121, 122, 123) for each diagnostic cycle. Here, the technique by which the battery diagnostic device (10) calculates the similarity may include a cosine similarity calculation technique and a Pearson similarity calculation technique, and the current data may include current values ​​over time obtained for each of one or more diagnostic cycles for each of the one or more battery cells (121, 122, 123). In one embodiment, the reference data may be a current value over time obtained in the first charging cycle for the first battery cell (121). In one embodiment, the reference data may be data obtained through a preliminary experiment, and may include current values ​​over time obtained during the process of charging or discharging a normal battery cell.

[0040] The battery diagnostic device (10) can extract first current data in which the similarity exceeds a reference value. The battery diagnostic device (10) can extract data regarding a first diagnostic cycle and a battery cell corresponding to the first current data. Here, the first diagnostic cycle may include one or more charging cycles or discharging cycles for one or more battery cells. For example, if 30 of the 50 charging cycles performed on the first battery cell (121) have a similarity exceeding a reference value, the battery diagnostic device (10) can extract the 30 charging cycles, and the 30 charging cycles may be included in the first diagnostic cycle.

[0041] The battery diagnostic device (10) can calculate dQ / dV based on the voltage values ​​of the battery cells (121, 122, 123) in the extracted first diagnostic cycle. The battery diagnostic device (10) can generate voltage value-dQ / dV data for each of the battery cells (121, 122, 123) and convert the dQ / dV values ​​according to the voltage values ​​into principal component values ​​through Principal Component Analysis (PCA). The battery diagnostic device (10) can diagnose whether each of the battery cells (121, 122, 123) is abnormal based on the extracted principal components.

[0042] In one embodiment, the battery diagnostic device (10) may be included in a BMS capable of diagnosing battery cells included in an electronic device, and operations performed by the battery diagnostic device (10) may be performed in the BMS. In one embodiment, the battery diagnostic device (10) may be included in a server or a charger / discharger capable of diagnosing battery cells outside the electronic device, and operations performed by the battery diagnostic device (10) may be performed in an external server or charger / discharger.

[0043] Hereinafter, for the convenience of explanation, the operation performed by each of the components included in the battery diagnostic device (10) to diagnose whether the first battery cell (121) is abnormal will be described.

[0044] The battery diagnostic device (10) may include an interface (100) and a controller (102). According to an embodiment, the battery diagnostic device (10) illustrated in FIG. 1 may further include at least one component (e.g., a display, an input device, or an output device) in addition to the components illustrated in FIG. 1.

[0045] The interface (100) can acquire status data including current and voltage values ​​of the battery cell for each diagnostic cycle corresponding to a charging or discharging cycle. The interface (100) can acquire status data including current and voltage values ​​of the first battery cell (121) for each diagnostic cycle. The interface (100) can acquire a current value (hereinafter referred to as current data) over time of the first battery cell (121) for each diagnostic cycle, and can acquire a voltage value over time of the first battery cell (121). For convenience of explanation, the following description assumes that the diagnostic cycle is a charging cycle and that the battery diagnostic device (10) diagnoses whether the first battery cell (121) is abnormal.

[0046] The controller (102) can calculate the similarity between current data including current values ​​and reference data. Here, the current data may include one or more time-dependent current values ​​obtained for one or more diagnostic cycles for one or more battery cells. For example, the current data may include time-dependent current values ​​obtained for each of the 50 charging cycles for each of the battery cells (121, 122, 123) when charging is performed for 50 charging cycles for the battery cells (121, 122, 123).

[0047] In one embodiment, the controller (102) can calculate the similarity between current data and reference data based on a cosine similarity or Pearson similarity calculation technique.

[0048] The controller (102) can determine whether the similarity satisfies a specified condition. Here, the specified condition may include a condition regarding whether the similarity exceeds a reference value. The similarity may be calculated as a value between 0 and 1, and a value closer to 1 may indicate that the similarity between data is higher. The reference value may be arbitrarily set by the setter and may be set based on a specified classification item. For example, the specified classification item may include poor (0.000 or less), slight (0.000 to 0.200), fair (0.201 to 0.400), moderate (0.401 to 0.600), substantial (0.601 to 0.800), and almost perfect (0.801 to 1.000), and the reference value may be set to 0.85 or 0.95. The reason the controller (102) extracts first current data that exceeds a reference value is that it extracts only data with similar current flow, and accordingly, can easily detect abnormal battery cells in which the amount of change in voltage value differs even though the current flow is similar.

[0049] The controller (102) can extract first current data in which the similarity among the current data exceeds a reference value. Here, the first current data may include one or more current values ​​over time among one or more current values ​​over time included in the current data in which the similarity exceeds a reference value. Additionally, the first current data may not only refer to the first current data for the first battery cell (121), but may also be a collective term for the first current data for each of the plurality of battery cells (121, 122, 123).

[0050] The controller (102) can generate voltage value-dQ / dV data by calculating dQ / dV based on a first voltage value corresponding to the first current data. The controller (102) can calculate dQ / dV based on a first voltage value obtained in a diagnostic cycle (first diagnostic cycle) for each current value over time included in the first current data. The controller (102) can calculate dQ / dV based on a first voltage value corresponding to the first current data among the voltage values ​​of the first battery cell (121) obtained for each charging cycle, and can generate voltage value-dQ / dV data based on the first voltage value. Here, the dQ / dV value may refer to a value obtained by differentiating the capacity (Q) of the first battery cell (121) with respect to the voltage (V). The first voltage value may refer to one or more voltage values ​​obtained in a diagnostic cycle (hereinafter, the first diagnostic cycle) identical to each of the one or more diagnostic cycles corresponding to the first current data.

[0051] The diagnostic cycle mentioned in Figure 1 below is explained on the premise that it is a diagnostic cycle corresponding to the first current data.

[0052] The controller (102) can convert voltage value-dQ / dV data into principal component data based on Principal Component Analysis (PCA). Here, Principal Component Analysis may be a technique for converting high-dimensional raw data into low-dimensional data based on principal variables, or for extracting principal variables to monitor the characteristics of the raw data. For example, if the raw data is two-dimensional, two coordinate axes corresponding to the variables with the highest variance among the values ​​included in the raw data are extracted, and the raw data is converted based on those axes, the boundaries between the converted values ​​can be made clear. That is, a coordinate axis based on a variable (e.g., a first principal component) that maximizes the variance of values ​​(e.g., dQ / dV values ​​and / or a first voltage value) included in the original data (e.g., voltage value-dQ / dV data) can be extracted, and the two-dimensional original data can be converted into one-dimensional data corresponding to the first principal component based on the new coordinate axis, or the two-dimensional original data can be converted into two-dimensional data based on the first principal component and the second axis corresponding to the second principal component in which the variance of at least one of the dQ / dV values ​​and / or voltage values ​​is maximized among the first axis corresponding to the first principal component and the axes orthogonal to the first axis. Since the controller (102) converts the voltage value-dQ / dV data into principal component data, the boundary between the converted values ​​included in the principal component data can be made clear, so that an abnormal cell among the battery cells can be easily detected. Specifically, since the transformed data (principal component data) may have a greater degree of dispersion than the original data (voltage value-dQ / dV data), it may be easier to identify abnormal trends.

[0053] In one embodiment, the controller (102) can convert voltage value-dQ / dV data into principal component data for each specified voltage range. Here, the unit of the specified voltage range may be 0.1V. For example, the controller (102) can convert the dQ / dV value in the voltage range where the voltage value is 3.7V to 3.8V from the voltage value-dQ / dV data generated in the first charging cycle for the first battery cell (121) into a principal component value. The reason for converting the voltage value-dQ / dV data into principal component data for each specified voltage range is that the principal component with high dispersion of dQ / dV values ​​in the entire voltage range and the principal component with the highest dispersion in each voltage range may be different. Accordingly, the battery diagnostic device (10) can diagnose in detail whether there are any abnormal cells showing a different trend from normal cells in a specific voltage range by obtaining principal component data for each specific voltage range, even if no abnormal cells are detected in the principal component data for the entire voltage range.

[0054] The controller (102) can extract a first diagnostic cycle corresponding to the first current data, and a principal component value (PC) corresponding to the nth diagnostic cycle (n: natural number) included in the first diagnostic cycle. n The difference value (ΔPC) between ) and the principal component value (PC1) corresponding to the first diagnostic cycle n : PC n -PC1) can be calculated. For example, the controller (102) can convert voltage value-dQ / dV data generated in the 5th diagnostic cycle included in the 1st diagnostic cycle for the 1st battery cell (121) into principal component data, and can calculate the difference value (ΔPC5) between the principal component value (PC5) included in the principal component data in the 5th charging cycle and the principal component value (PC1) in the first diagnostic cycle included in the 1st diagnostic cycle.

[0055] In one embodiment, the controller (102) has a principal component value (PC) corresponding to the nth diagnostic cycle for each specified voltage interval. n A difference value between the principal component value (PC1) corresponding to the first diagnostic cycle and the controller (102) can be calculated. For example, the controller (102) can convert the dQ / dV value corresponding to the voltage range where the voltage value is 3.7V to 3.8V into principal component data, and can calculate a difference value (ΔPC5) between the principal component value (PC5) in the 5th charging cycle included in the principal component data and the principal component value (PC1) in the first charging cycle. Similarly, the controller (102) can calculate a difference value according to the above method even in other voltage ranges (e.g., 3.8V to 3.9V) where the voltage value is.

[0056] The controller (102) is the difference value (ΔPC) above. n The first battery cell (121) can be diagnosed for abnormality based on the difference value. Here, the abnormality may include overvoltage, undervoltage, resistance abnormality, or a combination thereof. In one embodiment, the controller (102) can diagnose the first battery cell (121) for abnormality based on whether the difference value falls within a threshold range. Here, the threshold range is the difference value (ΔPC) for each of the battery cells (121, 122, 123). nIt can be calculated based on the mean (m) and standard deviation (σ) of ). For example, the critical range may include a range greater than the critical minimum value (m-2.5σ) and less than or equal to the critical maximum value (m+2.5σ). Although the above description assumes that the critical range is a range greater than (m-2.5σ) and less than or equal to (m+2.5σ), this is merely for convenience of explanation and the critical minimum and critical maximum values ​​of the critical range are not limited thereto. In another embodiment, the controller (102) can diagnose whether the first battery cell (121) is abnormal based on the relationship between the difference value and the critical value. Here, the critical value is the difference value (ΔPC) of each of the battery cells (121, 122, 123). n It can be calculated based on the mean (m) and standard deviation (σ) of ).

[0057] In one embodiment, the controller (102) can diagnose whether the first battery cell (121) is abnormal for each designated voltage range. The controller (102) can calculate the average and standard deviation of the difference values ​​for each designated voltage range. For example, the controller (102) can convert voltage value-dQ / dV data for each of the battery cells (121, 122, 123) into principal component data based on a voltage range in which the voltage value in the nth charging cycle is 3.7V to 3.8V, and based on the principal component values ​​included in the principal component data, the difference value (ΔPC) for each of the battery cells (121, 122, 123) n It can calculate the average and standard deviation accordingly. The controller (102) can diagnose whether the first battery cell (121) is abnormal based on the average and standard deviation calculated for each specified voltage range.

[0058] FIG. 2 illustrates a battery pack according to one embodiment disclosed in this document.

[0059] Referring to FIG. 2, the battery pack (2) may be included in an electronic device. Here, the electronic device may be a mobile device (e.g., mobile phone, laptop computer, smartphone, smart pad), an electric vehicle (e.g., EV (electric vehicle), HEV (hybrid EV), PHEV (plug-in HEV), FCEV (fuel cell EV)), an energy storage system (ESS), or a battery swapping system (BSS).

[0060] The battery pack (2) may include a BMS (20) and battery units (120, 140, 160). Each of the battery units (120, 140, 160) in FIG. 2 may correspond to a battery module. The BMS (20) may diagnose the condition of the battery units (120, 140, 160) and the battery cells included therein. The BMS (20) may include a battery diagnostic device (10) and a sensing device (12) to diagnose the condition of the battery units (120, 140, 160) and the battery cells included therein.

[0061] The sensing device (12) can obtain the status value of each of the battery units (120, 140, 160) and / or the battery cells included therein included in the battery pack (2).

[0062] The battery diagnostic device (10) can diagnose whether each of the battery units (120, 140, 160) and / or the battery cells included therein is abnormal based on a state value obtained by the sensing device (12). In one embodiment, the battery diagnostic device (10) may be a processor (not shown) of the BMS (20) and may be a device included in the processor (not shown). In one embodiment, the operations performed by the battery diagnostic device (10) may be executed by the processor (not shown) of the BMS (20) as a diagnostic algorithm.

[0063] FIG. 3 illustrates the result of calculating the similarity between voltage data of battery cells and reference data according to one embodiment disclosed in this document. FIG. 4 illustrates a histogram showing the number of battery cells whose similarity exceeds a reference value according to one embodiment disclosed in this document. Hereinafter, with reference to FIG. 3 and FIG. 4, a method for calculating the similarity between current data and reference data and extracting a first diagnostic cycle in which the similarity exceeds a specified value will be described.

[0064] Referring to FIG. 3, the battery diagnostic device (10) can acquire current data (30) including current values ​​according to time for each diagnostic cycle. The current data (30) may include current values ​​according to time acquired for each diagnostic cycle for each of the battery cells (121, 122, 123). The graphs included in the current data (30) may each correspond to the nth battery cell in the nth diagnostic cycle (n: natural number).

[0065] The battery diagnostic device (10) can calculate the similarity between the current value over time included in the current data (30) and the reference data. The battery diagnostic device (10) can extract first current data in which the similarity exceeds the reference value. Here, the first current data may correspond to the first data (300) to the fourth data (312) according to the similarity calculation method and / or the reference value.

[0066] The first data (300) may correspond to first current data with a reference value exceeding 0.85 based on a cosine similarity calculation technique. The second data (302) may correspond to first current data with a reference value exceeding 0.95 based on a cosine similarity calculation technique. The third data (310) may correspond to first current data with a reference value exceeding 0.85 based on a Pearson similarity calculation technique. The fourth data (312) may correspond to first current data with a reference value exceeding 0.95 based on a Pearson similarity calculation technique.

[0067] Current data (30) may include current values ​​over time for 577 battery cells. The first data (300) may include current data for 570 battery cells among the 577 battery cells where the cosine similarity exceeds 0.85. The second data (302) may include current data for 337 battery cells among the 577 battery cells where the cosine similarity exceeds 0.95. The third data (310) may include current data for 569 battery cells among the 577 battery cells where the Pearson similarity exceeds 0.85. The fourth data (312) may include 337 battery cells among the 577 battery cells where the Pearson similarity exceeds 0.95. By referring to the first data (300) to the fourth data (312), it can be confirmed that the first current data extracted according to the similarity calculation technique and / or reference value is different.

[0068] Referring to FIG. 4, the histogram (40) is a graph corresponding to the first data (300) and the second data (302) of FIG. 3, and through the histogram (40), the number of battery cells (570) with a cosine similarity exceeding 0.85 and the number of battery cells (337) with a cosine similarity exceeding 0.95 can be identified.

[0069] FIG. 5 illustrates voltage value-dQ / dV data of battery cells according to one embodiment disclosed in this document. FIG. 6 illustrates diagnostic cycle-difference value data of battery cells according to one embodiment disclosed in this document. Hereinafter, the effects of principal component analysis will be explained with reference to FIG. 5 and FIG. 6.

[0070] In the following description, for convenience of explanation, the battery diagnostic device (10) is described on the premise that it diagnoses the first battery cell (121) based on the first current data extracted through FIG. 3 and FIG. 4, the voltage value-dQ / dV data (50) of the battery cells is described as the fifth data (50), and the diagnostic cycle-difference value data (60) of the battery cells is referred to as the sixth data (60).

[0071] Referring to FIG. 5, the fifth data (50) may include graphs corresponding to the voltage values ​​of each of the battery cells (121, 122, 123) included in the first battery unit (120) and the corresponding dQ / dV values.

[0072] Referring to FIG. 6, the sixth data (60) may include principal component data for each of the battery cells (121, 122, 123) included in the first battery unit (120). The sixth data (60) may include graphs corresponding to difference values ​​according to the diagnostic cycle for each of the battery cells (121, 122, 123) included in the first battery unit (120). For example, if the final diagnostic cycle performed is 150 times, the sixth data (60) may include, for each of the battery cells (121, 122, 123), a difference value (ΔPC1=0) corresponding to the principal component value (PC1) in the first diagnostic cycle to the principal component value (PC1) in the 150th diagnostic cycle. 150 The difference value (ΔPC) between ) and the principal component value (PC1) in the first diagnostic cycle 150 It may include ).

[0073] In one embodiment, the sixth data (60) may include graphs corresponding to difference values ​​according to a diagnostic cycle for each of the battery cells (121, 122, 123) in a designated voltage range (3.7V to 3.8V). Hereinafter, the sixth data (60) is described under the premise that it is data containing difference values ​​according to a diagnostic cycle for each of the battery cells (121, 122, 123) in a designated voltage range (3.7V to 3.8V).

[0074] The fifth data (50) of FIG. 5 may correspond to the original data before principal component analysis, and the sixth data (60) of FIG. 6 may correspond to the principal component data converted from the original data. In the fifth data (50), the boundaries of the dQ / dV values ​​of each of the battery cells (121, 122, 123) are unclear, whereas in the sixth data (60), the boundaries of the data between the battery cells (121, 122, 123) can be seen to become clear as the fifth data (50) is converted by extracting principal components with large dispersion between data in a specified voltage range.

[0075] Based on the above description, the battery diagnostic device (10) can diagnose whether there is an abnormality in the first battery cell (121) based on the sixth data (60). For example, by referring to the graph (600) for the first battery cell (121), the battery diagnostic device (10) can diagnose that the difference value of the first battery cell (121) is not included in the threshold range.

[0076] FIG. 7 is a flowchart illustrating the operation method of a battery diagnostic device according to one embodiment disclosed in this document.

[0077] Referring to FIG. 7, in operation 700, the battery diagnostic device (10) can acquire status data including current and voltage values ​​of the battery cell for each diagnostic cycle corresponding to a charging or discharging cycle. The battery diagnostic device (10) can acquire status data including current and voltage values ​​of the first battery cell (121) for each diagnostic cycle. The battery diagnostic device (10) can acquire a current value (current data) over time of the first battery cell (121) for each diagnostic cycle, and can acquire a voltage value over time of the first battery cell (121).

[0078] In operation 710, the battery diagnostic device (10) can calculate the similarity between current data and reference data. The battery diagnostic device (10) can determine whether the similarity between current data and reference data satisfies a specified condition. Here, the specified condition may include a condition regarding whether the similarity exceeds a reference value.

[0079] The battery diagnostic device (10) can return to operation 700 and obtain state data of the battery cell based on a new diagnostic cycle if the similarity between the current data and the reference data does not satisfy the specified condition (NO).

[0080] The battery diagnostic device (10) can perform operation 720 using current data (first current data) that satisfies the specified condition when the similarity between current data and reference data satisfies the specified condition (YES).

[0081] In operation 720, the battery diagnostic device (10) can extract first current data in which the similarity exceeds a reference value.

[0082] In operation 730, the battery diagnostic device (10) can diagnose whether there is an abnormality in the battery cell based on a first voltage value corresponding to the first current data. Detailed operations of operation 730 are described in detail in FIG. 8 below.

[0083] FIG. 8 is a flowchart showing detailed operations included in operation 730 disclosed in FIG. 7.

[0084] Referring to FIG. 8, operations 732 to 738 may be included in operation 730 of FIG. 7. The diagnostic cycle mentioned in FIG. 8 below is described on the premise that it is a diagnostic cycle corresponding to the first current data extracted by operation 710 of FIG. 7.

[0085] In operation 732, the battery diagnostic device (10) can generate voltage value-dQ / dV data by calculating dQ / dV based on a first voltage value corresponding to the first current data. The controller (102) can calculate dQ / dV based on a first voltage value corresponding to the first current data among the voltage values ​​of the first battery cell (121) obtained for each charging cycle, and can generate voltage value-dQ / dV data based on the first voltage value.

[0086] In operation 734, the battery diagnostic device (10) can convert voltage value-dQ / dV data into principal component data based on Principal Component Analysis (PCA).

[0087] In one embodiment, the battery diagnostic device (10) can convert voltage value-dQ / dV data into principal component data for each specified voltage range.

[0088] In operation 736, the battery diagnostic device (10) obtains a principal component value (PC) corresponding to the nth diagnostic cycle (n: natural number). n The difference value (ΔPC) between ) and the principal component value (PC1) corresponding to the first diagnostic cycle n : PC n-PC1) can be calculated.

[0089] In one embodiment, the battery diagnostic device (10) has a principal component value (PC) corresponding to the nth diagnostic cycle for each designated voltage interval. n The difference value between the principal component value (PC1) corresponding to the first diagnostic cycle and ) can be calculated.

[0090] In operation 738, the battery diagnostic device (10) determines the difference value (ΔPC). n The abnormality of the first battery cell (121) can be diagnosed based on ). In one embodiment, the battery diagnostic device (10) can diagnose the abnormality of the first battery cell (121) based on whether the difference value is included in a threshold range. Here, the threshold range is the difference value (ΔPC) for each of the battery cells (121, 122, 123). n It can be calculated based on the mean (m) and standard deviation (σ) of ). For example, the threshold range may include a range greater than the threshold minimum value (m-2.5σ) and less than or equal to the threshold maximum value (m+2.5σ). In another embodiment, the battery diagnostic device (10) may diagnose whether the first battery cell (121) is abnormal based on the relationship between the difference value and the threshold value. Here, the threshold value is the difference value (ΔPC) of each of the battery cells (121, 122, 123). n It can be calculated based on the mean (m) and standard deviation (σ) of ).

[0091] In one embodiment, the battery diagnostic device (10) can diagnose whether the first battery cell (121) is abnormal for each designated voltage range. The battery diagnostic device (10) can calculate the average and standard deviation of the difference values ​​for each designated voltage range. The battery diagnostic device (10) can diagnose whether the first battery cell (121) is abnormal based on the average and standard deviation calculated for each designated voltage range.

[0092] FIG. 9 illustrates a computing system that performs operations of a battery diagnostic device according to an embodiment disclosed in this document.

[0093] Referring to FIG. 9, a computing system (90) according to one embodiment disclosed in this document may include an MCU (900), memory (910), an input / output I / F (920), and a communication I / F (930).

[0094] The MCU (900) may be a processor that executes various programs (e.g., battery diagnostic programs) stored in memory (910), processes various data from these programs, and performs the functions of the battery diagnostic device (10) shown in FIGS. 1 to 8.

[0095] The memory (910) can store various programs regarding the operation of the battery diagnostic device (10). In addition, the memory (910) can store operation data of the battery diagnostic device (10).

[0096] These memories (910) may be provided in multiple quantities as needed. The memories (910) may be volatile memories or non-volatile memories. As volatile memories, the memory (910) may use RAM, DRAM, SRAM, etc. As non-volatile memories, the memory (910) may use ROM, PROM, EAROM, EPROM, EEPROM, flash memory, etc. The memories (910) listed above are merely examples and are not limited to these examples.

[0097] The input / output I / F (920) can provide an interface that enables data transmission and reception between an input device (not shown), such as a keyboard, mouse, or touch panel, and an output device (not shown), such as a display, and the MCU (600).

[0098] The communication I / F (930) is configured to transmit and receive various data to and from a server and may be various devices capable of supporting wired or wireless communication. For example, through the communication I / F (930), a program for diagnosing abnormalities or various data (e.g., status values) can be transmitted and received from a separately provided external server.

[0099] Terms such as "include," "compose," or "have" as used above, unless specifically stated otherwise, mean that the relevant component may be inherent; therefore, they should be interpreted as allowing for the inclusion of additional components rather than excluding them. All terms, including technical or scientific terms, have the same meaning as generally understood by those skilled in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as those defined in advance, should be interpreted in accordance with their meaning in the context of the relevant technology and, unless explicitly defined in this document, should not be interpreted in an ideal or overly formal sense.

[0100] The foregoing description is merely an illustrative explanation of the technical concept disclosed in this document, and a person skilled in the art to which the embodiments disclosed in this document pertain can make various modifications and variations within the scope of the essential characteristics of the embodiments disclosed in this document. Accordingly, the embodiments disclosed in this document are intended to explain, not limit, the technical concept of the embodiments disclosed in this document, and the scope of the technical concept disclosed in this document is not limited by these embodiments. The scope of protection of the technical concept disclosed in this document shall be interpreted by the claims below, and all technical concepts within an equivalent scope shall be interpreted as being included within the scope of rights of this document.

Claims

Claim 1 A battery diagnostic device comprising: an interface for acquiring status data including current and voltage values ​​of a battery cell for each diagnostic cycle corresponding to a charging cycle or a discharging cycle; and a controller for calculating a similarity between current data including the current value and reference data, extracting a first current data among the current data in which the similarity exceeds a reference value, and diagnosing whether the battery cell is abnormal based on a first voltage value corresponding to the first current data. Claim 2 A battery diagnostic device according to claim 1, wherein the controller calculates the similarity between the current data and the reference data based on a cosine similarity or Pearson similarity calculation technique. Claim 3 A battery diagnostic device according to claim 1, wherein the reference value is any value included in the range of 0.85 or more and 0.95 or less. Claim 4 In claim 1, the controller calculates dQ / dV based on the first voltage value to generate voltage value-dQ / dV data, converts the voltage value-dQ / dV data into principal component data based on Principal Component Analysis (PCA), and the principal component value (PC) included in the principal component data corresponding to the nth diagnostic cycle (n: natural number) of the first diagnostic cycle corresponding to the first current data. n The difference value (ΔPC) between ) and the principal component value (PC1) included in the principal component data corresponding to the first diagnostic cycle n : PC n Calculate -PC1) and the above difference value (ΔPC n A battery diagnostic device that diagnoses whether there is an abnormality in the battery cell based on ). Claim 5 A battery diagnostic device according to claim 4, wherein the controller calculates the average (m) and standard deviation (σ) of the difference values ​​of a plurality of battery cells for each of the first diagnostic cycles, and diagnoses whether the battery cells are abnormal based on the difference values, the average, and the standard deviation. Claim 6 A battery diagnostic device according to claim 5, wherein the controller calculates a threshold value based on the mean and the standard deviation, and diagnoses whether the battery cell is abnormal based on the difference value and the threshold value. Claim 7 A battery diagnostic device according to claim 5, wherein the controller diagnoses the battery cell as an abnormal cell when the difference value is not included in a threshold range of (m-2.5σ) or more and (m+2.5σ) or less. Claim 8 A method of operating a battery diagnostic device comprising: acquiring state data including current values ​​and voltage values ​​over time of a battery cell for each diagnostic cycle corresponding to a charging cycle or a discharging cycle; calculating a similarity between current data including current values ​​over time and reference data; extracting a first current data among the current data in which the similarity exceeds a reference value; and diagnosing whether the battery cell is abnormal based on a first voltage value corresponding to the first current data. Claim 9 A method of operation of a battery diagnostic device according to claim 8, wherein the operation of calculating the similarity includes the operation of calculating the similarity between the current data and the reference data based on a cosine similarity or Pearson similarity calculation technique. Claim 10 A method of operation of a battery diagnostic device according to claim 8, wherein the reference value is any value included in the range of 0.85 or more and 0.95 or less. Claim 11 In claim 8, the diagnosing operation comprises: an operation of generating voltage value-dQ / dV data by calculating dQ / dV based on the first voltage value; an operation of converting the voltage value-dQ / dV data into principal component data based on Principal Component Analysis (PCA); and a principal component value (PC) included in the principal component data corresponding to the nth diagnostic cycle (n: natural number) among the first diagnostic cycles corresponding to the first current data. n The difference value (ΔPC) between ) and the principal component value (PC1) included in the principal component data corresponding to the first diagnostic cycle n : PC n The operation of calculating -PC1), and the difference value (ΔPC n A method of operating a battery diagnostic device comprising an operation of diagnosing whether there is an abnormality in the battery cell based on ). Claim 12 A method of operation of a battery diagnostic device according to claim 11, wherein the diagnosing operation comprises: an operation of calculating the average (m) and standard deviation (σ) of the difference values ​​of a plurality of battery cells for each of the first diagnostic cycles; and an operation of diagnosing whether the battery cells are abnormal based on the difference values, the average, and the standard deviation. Claim 13 A method of operation of a battery diagnostic device according to claim 12, wherein the diagnosing operation comprises: an operation of calculating a threshold value based on the mean and the standard deviation; and an operation of diagnosing whether the battery cell is abnormal based on the difference value and the threshold value. Claim 14 A method of operation of a battery diagnostic device according to claim 12, wherein the diagnosing operation includes diagnosing the battery cell as an abnormal cell when the difference value is not included in a threshold range of (m-2.5σ) or more and (m+2.5σ) or less.

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