Battery diagnosis device and operation method thereof

Abstract

A battery diagnosis device according to an embodiment disclosed in the present document may comprise: an interface for acquiring state data including a current value and a voltage value of a battery cell for each diagnosis cycle corresponding to a charging cycle or a discharging cycle; and a controller for calculating a similarity between reference data and current data including the current value, extracting, from the current data, first current data having the similarity exceeding a reference value, and diagnosing whether the battery cell is abnormal, on the basis of a first voltage value corresponding to the first current data.

Description

Battery diagnostic device and method of operation thereof

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority from Korean Patent Application No. 10-2024-0084732, filed June 27, 2024, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] Embodiments disclosed in this document relate to a battery diagnostic device and an operating method thereof.

[0005] Recently, active research and development has been conducted on secondary batteries. The term "secondary battery" refers to a rechargeable battery, encompassing both conventional Ni / Cd and Ni / MH batteries, as well as more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries boast a significantly higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight form, making them a popular power source for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, drawing attention as a next-generation energy storage medium.

[0006] As the industrial sector utilizing batteries expands, battery management systems (BMSs), which diagnose battery safety, are also evolving. BMSs utilize various diagnostic algorithms to assess battery performance and implement appropriate control based on battery condition. BMSs can detect the presence of abnormal battery cells. These abnormalities can include any cause that could lead to fire, such as damage or aging of the battery itself.

[0007] Monitoring the voltage values ​​of battery cells can diagnose issues such as overvoltage, undervoltage, resistance abnormalities, or voltage imbalances between battery cells. However, monitoring the voltage values ​​of countless battery cells to identify the cause of anomalies is costly and time-consuming.

[0008] The technical problems of the embodiments disclosed in this document are not limited to the technical problems mentioned above, and other technical problems not mentioned will be clearly understood by those skilled in the art from the descriptions below.

[0009] A battery diagnostic device according to an embodiment disclosed in this document may include an interface for obtaining status data including current values ​​and voltage values ​​of a battery cell for each diagnostic cycle corresponding to a charge cycle or a discharge cycle; and a controller for calculating a similarity between current data including the current value and reference data, extracting first current data whose similarity exceeds the reference value from among the current data, and diagnosing whether the battery cell is abnormal based on a first voltage value corresponding to the first current data.

[0010] In one embodiment, the controller may calculate the similarity between the current data and the reference data based on a cosine similarity or Pearson similarity calculation technique.

[0011] In one embodiment, the reference value may be any value within a range of 0.85 or more and 0.95 or less.

[0012] In one embodiment, the controller generates voltage value-dQ / dV data by calculating dQ / dV based on the first voltage value, converts the voltage value-dQ / dV data into principal component data based on principal component analysis (PCA), and converts the 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 ) and the difference value (ΔPC) between the principal component values ​​(PC1) included in the principal component data corresponding to the first diagnostic cycle n : PC n -PC1) is calculated, and the difference value (ΔPC n ) can be used to diagnose whether the battery cell is abnormal.

[0013] In one embodiment, the controller can calculate an average (m) and a standard deviation (σ) of difference values ​​of a plurality of battery cells for each of the first diagnostic cycles, and diagnose whether the battery cell is abnormal based on the difference value, the average, and the standard deviation.

[0014] In one embodiment, the controller calculates a threshold value based on the average and the standard deviation, and can diagnose whether the battery cell is abnormal based on the difference value and the threshold value.

[0015] In one embodiment, the controller may diagnose the battery cell as an abnormal cell if the difference value is not within a threshold range of (m-2.5σ) or more and (m+2.5σ) or less.

[0016] An operating method of a battery diagnosis device according to an embodiment disclosed in this document may include an operation of acquiring status data including current values ​​and voltage values ​​of a battery cell over time for each diagnosis cycle corresponding to a charge cycle or a discharge cycle; an operation of calculating a similarity between current data including the current values ​​over time and reference data; an operation of extracting first current data among the current data, the similarity of which exceeds the reference value; and an operation of diagnosing whether the battery cell is abnormal based on a first voltage value corresponding to the first current data.

[0017] In one embodiment, the operation of calculating the similarity may include an operation of calculating the similarity between the current data and the reference data based on a cosine similarity or Pearson similarity calculation technique.

[0018] In one embodiment, the reference value may be any value within a range of 0.85 or more and 0.95 or less.

[0019] In one embodiment, the diagnosing operation includes 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 an operation of converting the principal component value (PC) included in the principal component data corresponding to the nth diagnosis cycle (n: natural number) among the diagnosis cycles corresponding to the first current data. n ) and the difference value (ΔPC) between the principal component values ​​(PC1) included in the principal component data corresponding to the first diagnostic cycle n : PC n -PC1) and the operation of producing the difference value (ΔPC n ) may include an operation for diagnosing whether the battery cell is abnormal.

[0020] In one embodiment, the diagnosing operation may include an operation of calculating an average (m) and a standard deviation (σ) of difference values ​​of a plurality of battery cells for each of the first diagnosis cycles, and an operation of diagnosing whether the battery cell is abnormal based on the difference value, the average, and the standard deviation.

[0021] In one embodiment, the diagnosing operation may include an operation of calculating a threshold value based on the average and the standard deviation, and an operation of diagnosing whether the battery cell is abnormal based on the difference value and the threshold value.

[0022] In one embodiment, the diagnosing operation may include diagnosing the battery cell as an abnormal cell if the difference value is not within a threshold range of (m-2.5σ) or more and (m+2.5σ) or less.

[0023] A battery diagnosis device and an operating method thereof according to various embodiments disclosed in this document can acquire current data including a current value over time for each charge or discharge cycle for each battery cell, calculate a similarity between the current data and reference data, and diagnose whether the battery cell is abnormal using current data having a similarity greater than a specified value. Accordingly, the battery diagnosis device and the operating method thereof can diagnose whether the battery is abnormal by extracting charge or discharge cycles in which similar current values ​​flow, thereby preventing misdiagnosis due to differences in voltage values ​​caused by different current values, thereby increasing the diagnosis rate and reducing the over-inspection rate.

[0024] The battery diagnostic device and its operating method according to various embodiments disclosed in this document can distinguish between normal battery cells and abnormal battery cells using voltage value-dQ / dV data corresponding to a charge or discharge cycle in which the similarity exceeds a specified value.

[0025] The battery diagnostic device and its operating method 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 data dispersion through principal component analysis (PCA).

[0026] The effects of the battery diagnostic device and the operating method thereof according to the disclosure of this document are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art according to the disclosure of this document.

[0027] FIG. 1 is a block diagram of a battery diagnostic system according to one embodiment disclosed in this document.

[0028] FIG. 2 illustrates a battery pack according to one embodiment disclosed in this document.

[0029] FIGS. 3A to 3E illustrate the results of calculating the similarity between voltage data of battery cells and reference data according to one embodiment disclosed in the present document.

[0030] 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.

[0031] FIG. 5 illustrates voltage value-dQ / dV data of battery cells according to one embodiment disclosed in the present document.

[0032] FIG. 6 illustrates diagnostic cycle-difference value data of battery cells according to one embodiment disclosed in the present document.

[0033] FIG. 7 is a flowchart illustrating an operation method of a battery diagnostic device according to an embodiment disclosed in this document.

[0034] FIG. 8 is a flowchart showing detailed operations included in operation 730 disclosed in FIG. 7.

[0035] FIG. 9 illustrates a computing system for executing operations of a battery diagnostic device according to an embodiment disclosed in this document.

[0036] In connection with the description of the drawings, the same or similar reference numerals may be used for identical or similar components.

[0037] Hereinafter, embodiments of the present invention will be described with reference to the attached drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood that the present invention encompasses various modifications, equivalents, and / or alternatives of the embodiments.

[0038] The embodiments and terminology used in this document are not intended to limit the technical features described in this document to a specific embodiment, but should be understood to encompass various modifications, equivalents, or alternatives of the embodiment. In connection with the description of the drawings, similar reference numerals may be used to refer to similar or related components. The singular form of a noun corresponding to an item may include one or more of the item, unless the relevant context clearly indicates otherwise.

[0039] In this document, the phrases "A or B", "at least one of A and B", "at least one of A or B", "A, B, or C", "at least one of A, B, and C", and "at least one of A, B, or C" can each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used merely to distinguish the corresponding component from other corresponding components, and do not limit the corresponding components in any other respect (e.g., importance or order) unless specifically stated otherwise.

[0040] In this document, when a component (e.g., a first component) is referred to as being “connected,” “coupled,” or “connected,” with or without the terms “functionally” or “communicatively,” or “coupled” or “connected,” it means that the component can be connected to the other component directly (e.g., wired or wirelessly), or indirectly (e.g., via a third component).

[0041] The methods according to various embodiments disclosed in this document may be provided as included in a computer program product. The computer program product may be traded as a commodity between a seller and a buyer. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., compact disc read-only memory, CD-ROM), or may be distributed online (e.g., downloaded or uploaded) 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 generated in a machine-readable storage medium, such as the memory of a manufacturer's server, an application store's server, or an intermediary server.

[0042] According to the embodiments disclosed in this document, each component (e.g., a module or a program) of the above-described components may include one or more entities, and some of the entities may be separated and placed in other components. According to the embodiments disclosed in this document, one or more components or operations of the aforementioned components may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., a module or a program) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the plurality of components identically or similarly to those performed by the corresponding component among the plurality of components prior to the integration. According to the embodiments disclosed in this document, the 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.

[0043] FIG. 1 is a block diagram of a battery diagnosis system (1) according to one embodiment disclosed in this document.

[0044] Referring to FIG. 1, a battery diagnosis system (1) may include a battery diagnosis 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.

[0045] The battery diagnostic device (10) can be connected to the sensing device (12) wired and / or wirelessly.

[0046] 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 a local area network (LAN) communication or a power line communication. In one embodiment, the wireless network may be based on a short-range communication network (e.g., Bluetooth, wireless fidelity (WiFi), or infrared data association (IrDA)) or a wide-range communication network (cellular network, 4G network, 5G network).

[0047] 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., a bus, a general purpose input and output (GPIO), a serial peripheral interface (SPI), or a mobile industry processor interface (MIPI)).

[0048] The sensing device (12) can obtain values ​​(or information) related to the status of each of the battery units (120, 140, 160). In one embodiment, the values ​​related to the status may include one or more values ​​for voltage, current, resistance, state of charge (SOC), state of health (SOH), 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 connected). According to an embodiment, the 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) are described, but the present invention is not limited thereto, and the second battery unit (140) and the third battery unit (160) may also include one or more battery cells.

[0049] In one embodiment, the sensing device (12) may obtain 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 voltage, current, resistance, state of charge (SOC), state of health (SOH), 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'.

[0050] The battery diagnostic device (10) can obtain status data including current values ​​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 charge cycle or a discharge cycle. Here, the status data can include current values, voltage values, charge status, health status, temperature, or a combination thereof over time.

[0051] The battery diagnosis device (10) can calculate the similarity between the current value (hereinafter, current data) over time for each diagnosis cycle for each of the battery cells (121, 122, 123) and the reference data. Here, the technique by which the battery diagnosis 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 diagnosis cycles for each of 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 include current values ​​over time obtained in the process of charging or discharging a normal battery cell as data obtained through a preliminary experiment.

[0052] The battery diagnosis device (10) can extract first current data whose similarity exceeds a reference value. The battery diagnosis device (10) can extract data regarding a first diagnosis cycle and a battery cell corresponding to the first current data. Here, the first diagnosis cycle may include one or more charging cycles or discharging cycles for one or more battery cells. For example, if there are 30 charging cycles whose similarity exceeds a reference value among 50 charging cycles performed on the first battery cell (121), the battery diagnosis device (10) can extract the 30 charging cycles, and the 30 charging cycles can be included in the first diagnosis cycle.

[0053] The battery diagnosis device (10) can calculate dQ / dV based on the voltage values ​​of the battery cells (121, 122, 123) in the extracted first diagnosis cycle. The battery diagnosis 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 diagnosis device (10) can diagnose whether each of the battery cells (121, 122, 123) is abnormal based on the extracted principal components.

[0054] 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 in 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 charger / discharger capable of diagnosing battery cells outside of the electronic device, and operations performed in the battery diagnostic device (10) may be performed in an external server or charger / discharger.

[0055] Below, for convenience of explanation, the operations performed by each of the components included in the battery diagnostic device (10) to diagnose whether the first battery cell (121) is abnormal are described.

[0056] 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) other than the components illustrated in FIG. 1.

[0057] The interface (100) can obtain status data including current values ​​and voltage values ​​of a battery cell for each diagnosis cycle corresponding to a charge or discharge cycle. The interface (100) can obtain status data including current values ​​and voltage values ​​of a first battery cell (121) for each diagnosis cycle. The interface (100) can obtain current values ​​(hereinafter, current data) of the first battery cell (121) over time for each diagnosis cycle, and can obtain voltage values ​​of the first battery cell (121) over time. Hereinafter, for convenience of explanation, it is assumed that the diagnosis cycle is a charge cycle and the battery diagnosis device (10) is explained on the assumption that it diagnoses whether the first battery cell (121) is abnormal.

[0058] The controller (102) can calculate a similarity between current data including current values ​​and reference data. Here, the current data may include one or more time-dependent current values ​​acquired for one or more diagnostic cycles for one or more battery cells. For example, when charging is performed for 50 charging cycles for battery cells (121, 122, 123), the current data may include time-dependent current values ​​acquired for each of the 50 charging cycles for each of the battery cells (121, 122, 123).

[0059] In one embodiment, the controller (102) can calculate the similarity between the current data and the reference data based on a cosine similarity or Pearson similarity calculation technique.

[0060] 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 greater than or equal to 0 and less than or equal to 1, and a similarity value closer to 1 may indicate a higher similarity between data. The reference value may be arbitrarily set by a 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 why the controller (102) extracts the first current data exceeding the reference value is that it extracts only data with similar current flows, and thus it can easily detect abnormal battery cells with different voltage value changes even though the current flows are similar.

[0061] The controller (102) may extract first current data among the current data whose similarity exceeds a reference value. Here, the first current data may include one or more current values ​​according to time among one or more current values ​​according to time included in the current data whose similarity exceeds a reference value. In addition, the first current data may not only refer to the first current data for the first battery cell (121), but may also be a term that collectively refers to the first current data for each of the plurality of battery cells (121, 122, 123).

[0062] The controller (102) can generate voltage value-dQ / dV data by calculating dQ / dV based on the first voltage value corresponding to the first current data. The controller (102) can calculate dQ / dV based on the first voltage value acquired in the diagnostic cycle (the first diagnostic cycle) for each current value according to time included in the first current data. The controller (102) can calculate dQ / dV based on the first voltage value corresponding to the first current data among the voltage values ​​of the first battery cell (121) acquired for each charging cycle, and can generate voltage value-dQ / dV data based on the first voltage value. Here, the dQ / dV value can refer to a value obtained by differentiating the capacity (Q) of the first battery cell (121) by the voltage (V). The first voltage value can refer to one or more voltage values ​​acquired in the same diagnostic cycle (hereinafter, the first diagnostic cycle) as each of one or more diagnostic cycles corresponding to the first current data.

[0063] The diagnostic cycle mentioned in the following Figure 1 is explained on the assumption that it is a diagnostic cycle corresponding to the first current data.

[0064] The controller (102) can convert voltage value-dQ / dV data into principal component data based on principal component analysis (PCA). Here, PCA can be a technique for converting high-dimensional raw data into low-dimensional data based on major variables or extracting major variables for monitoring the characteristics of the raw data. For example, if the raw data is two-dimensional, if two coordinate axes corresponding to variables with the highest variance among values ​​included in the raw data are extracted and the raw data is converted based on the axes, the boundaries between the converted values ​​can become clear. That is, on the raw data (e.g., voltage value-dQ / dV data), a coordinate axis is extracted based on a variable (e.g., a first principal component) that maximizes the dispersion of values ​​(e.g., dQ / dV values ​​and / or first voltage values) included in the raw data, and based on the new coordinate axis, the two-dimensional raw data can be converted into one-dimensional data corresponding to the first principal component, or based on the first axis corresponding to the first principal component and the second axis corresponding to the second principal component in which at least one of the dQ / dV values ​​and / or voltage values ​​among the axes orthogonal to the first axis is dispersed, the two-dimensional raw data can be converted into two-dimensional data based on the first principal component and the second principal component. 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, it may be easier to identify abnormal trends because the transformed data (principal component data) may have a greater degree of data dispersion than the original data (voltage value-dQ / dV data).

[0065] In one embodiment, the controller (102) can convert voltage value-dQ / dV data into principal component data for each specified voltage section. Here, the unit of the specified voltage section may be 0.1 V. For example, the controller (102) can convert the dQ / dV value in the voltage section where the voltage value is 3.7 V to 3.8 V 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 section is that the principal component with a high dispersion of the dQ / dV value in the entire voltage section and the principal component with the highest dispersion in each voltage section may be different. Accordingly, even if no abnormal cell is detected in the principal component data for the entire voltage section, the battery diagnosis device (10) can diagnose in detail whether there is an abnormal cell that shows a different tendency from a normal cell in a specific voltage section by securing the principal component data for each specific voltage section.

[0066] The controller (102) can extract the first diagnostic cycle corresponding to the first current data, and the principal component value (PC) corresponding to the nth diagnostic cycle (n: natural number) included in the first diagnostic cycle n ) and the difference value (ΔPC) between 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 the voltage value -dQ / dV data generated in the fifth diagnostic cycle included in the first diagnostic cycle for the first 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 fifth charging cycle and the principal component value (PC1) in the first diagnostic cycle included in the first diagnostic cycle.

[0067] In one embodiment, the controller (102) determines the principal component value (PC) corresponding to the nth diagnostic cycle for each specified voltage interval. n ) and the difference value between the principal component value (PC1) corresponding to the first diagnosis cycle can be calculated. For example, the controller (102) can convert the dQ / dV value corresponding to the voltage section in which the voltage value is 3.7 V to 3.8 V into principal component data, and can calculate the difference value (ΔPC5) between the principal component value (PC5) in the fifth 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 the difference value in the voltage section in which the voltage value is other than 3.8 V (e.g., 3.8 V to 3.9 V) according to the above-described method.

[0068] The controller (102) determines the difference value (ΔPC) n ) can be used to diagnose whether the first battery cell (121) is abnormal. Here, the abnormality may include overvoltage, undervoltage, resistance abnormality, or a combination thereof. In one embodiment, the controller (102) can diagnose whether the first battery cell (121) is abnormal 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) can be calculated based on the mean (m) and standard deviation (σ). For example, the critical range may include a range greater than or equal to the critical minimum value (m-2.5σ) and less than or equal to the critical maximum value (m+2.5σ). In the above, it is assumed that the critical range is a range greater than or equal to (m-2.5σ) and less than or equal to (m+2.5σ), but this is 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 threshold value. Here, the threshold value is the difference value (ΔPC) of each of the battery cells (121, 122, 123). n ) can be calculated based on the mean (m) and standard deviation (σ).

[0069] In one embodiment, the controller (102) can diagnose whether the first battery cell (121) is abnormal for each specified voltage section. The controller (102) can calculate the average and standard deviation of the difference values ​​for each specified voltage section. For example, the controller (102) can convert the voltage value-dQ / dV data for each of the battery cells (121, 122, 123) into principal component data based on the voltage section in which the voltage value is 3.7 V to 3.8 V in the nth charging cycle, and can calculate the difference value (ΔPC) for each of the battery cells (121, 122, 123) based on the principal component values ​​included in the principal component data. n ) can be calculated, and the average and standard deviation can be calculated 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 section.

[0070] FIG. 2 illustrates a battery pack according to one embodiment disclosed in this document.

[0071] Referring to FIG. 2, a battery pack (2) may be included in an electronic device. Here, the electronic device may be a mobile device (e.g., a mobile phone, a laptop computer, a smart phone, a smart pad), an electric vehicle (e.g., an electric vehicle (EV), a hybrid EV (HEV), a plug-in HEV (PHEV), a fuel cell EV (FCEV)), an energy storage system (ESS), or a battery swapping system (BSS).

[0072] 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 status of the battery units (120, 140, 160) and the battery cells included therein. The BMS (20) may include a battery diagnosis device (10) and a sensing device (12) to diagnose the status of the battery units (120, 140, 160) and the battery cells included therein.

[0073] The sensing device (12) can obtain the status values ​​of each of the battery units (120, 140, 160) included in the battery pack (2) and / or the battery cells included therein.

[0074] The battery diagnostic device (10) can diagnose whether there is an abnormality in each of the battery units (120, 140, 160) and / or the battery cells included therein based on the status values ​​acquired by the sensing device (12). In one embodiment, the battery diagnostic device (10) may be a processor (not shown) of the BMS (20) or a device included in the processor (not shown). In one embodiment, operations performed in the battery diagnostic device (10) may be executed by the processor (not shown) of the BMS (20) as a single diagnostic algorithm.

[0075] FIGS. 3A to 3E illustrate the results of calculating the similarity between voltage data of battery cells and reference data according to an embodiment disclosed in the present document. FIG. 4 illustrates a histogram showing the number of battery cells whose similarity exceeds a reference value according to an embodiment disclosed in the present document. Hereinafter, with reference to FIGS. 3A to 3E and FIG. 4, a method for calculating the similarity between current data and reference data and extracting a first diagnostic cycle whose similarity exceeds a specified value will be described.

[0076] Referring to FIGS. 3A to 3E, the battery diagnostic device (10) can obtain current data (30) including current values ​​according to time for each diagnostic cycle. The current data (30) can include current values ​​according to time obtained for each diagnostic cycle for each of the battery cells (121, 122, 123). Each graph included in the current data (30) can correspond to the nth battery cell in the nth diagnostic cycle (n: natural number).

[0077] 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 the first current data whose similarity exceeds the reference value. Here, the first current data can correspond to the first data (300) to the fourth data (312) depending on the similarity calculation method and / or the reference value.

[0078] The first data (300) may correspond to the first current data having a reference value exceeding 0.85 based on the cosine similarity calculation technique. The second data (302) may correspond to the first current data having a reference value exceeding 0.95 based on the cosine similarity calculation technique. The third data (310) may correspond to the first current data having a reference value exceeding 0.85 based on the Pearson similarity calculation technique. The fourth data (312) may correspond to the first current data having a reference value exceeding 0.95 based on the Pearson similarity calculation technique.

[0079] Current data (30) may include current values ​​over time for 577 battery cells. First data (300) may include current data for 570 battery cells among the 577 battery cells having a cosine similarity exceeding 0.85. Second data (302) may include current data for 337 battery cells among the 577 battery cells having a cosine similarity exceeding 0.95. Third data (310) may include current data for 569 battery cells among the 577 battery cells having a Pearson similarity exceeding 0.85. Fourth data (312) may include 337 battery cells among the 577 battery cells having a Pearson similarity exceeding 0.95. 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 the reference value are different.

[0080] Referring to FIG. 4, the histogram (40) is a diagram corresponding to the first data (300) of FIG. 3b and the second data (302) of FIG. 3d, and through the histogram (40), the number of battery cells (570) having a cosine similarity exceeding 0.85 and the number of battery cells (337) having a cosine similarity exceeding 0.95 can be confirmed.

[0081] Figure 5 illustrates voltage value-dQ / dV data of battery cells according to one embodiment disclosed in this document. Figure 6 illustrates diagnostic cycle-difference value data of battery cells according to one embodiment disclosed in this document. Below, the effect according to principal component analysis will be explained with reference to Figures 5 and 6.

[0082] Hereinafter, for convenience of explanation, it is assumed that the battery diagnosis device (10) diagnoses the first battery cell (121) based on the first current data extracted through FIGS. 3A to 3E and FIG. 4, and the voltage value-dQ / dV data (50) of the battery cells is described as the fifth data (50), and the diagnosis cycle-difference value data (60) of the battery cells is referred to as the sixth data (60).

[0083] 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.

[0084] 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 diagnostic cycles for each of the battery cells (121, 122, 123) included in the first battery unit (120). For example, when the number of diagnostic cycles finally performed is 150, the sixth data (60) may include a difference value (ΔPC1=0) corresponding to the principal component value (PC1) in the first diagnostic cycle for each of the battery cells (121, 122, 123) to the principal component value (PC) in the 150th diagnostic cycle. 150 ) and the difference value (ΔPC) between the principal component values ​​(PC1) in the first diagnostic cycle 150 ) may be included.

[0085] 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 (3.7 V to 3.8 V). Hereinafter, the sixth data (60) is described assuming that it is data including difference values ​​according to a diagnostic cycle for each of the battery cells (121, 122, 123) in a designated voltage range (3.7 V to 3.8 V).

[0086] 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 boundary of the dQ / dV values ​​of each of the battery cells (121, 122, 123) is unclear, whereas in the sixth data (60), the boundary of the data between the battery cells (121, 122, 123) is clear, as it is data converted from the fifth data (50) by extracting principal components with a large dispersion between data in a specified voltage range.

[0087] Based on the above, the battery diagnostic device (10) can diagnose whether the first battery cell (121) is abnormal based on the sixth data (60). For example, 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 within the critical range.

[0088] FIG. 7 is a flowchart illustrating an operation method of a battery diagnostic device according to an embodiment disclosed in this document.

[0089] Referring to FIG. 7, in operation 700, the battery diagnosis device (10) can obtain status data including current values ​​and voltage values ​​of a battery cell for each diagnosis cycle corresponding to a charge or discharge cycle. The battery diagnosis device (10) can obtain status data including current values ​​and voltage values ​​of a first battery cell (121) for each diagnosis cycle. The battery diagnosis device (10) can obtain current values ​​(current data) of the first battery cell (121) over time for each diagnosis cycle, and can obtain voltage values ​​of the first battery cell (121) over time.

[0090] In operation 710, the battery diagnostic device (10) can calculate the similarity between the current data and the reference data. The battery diagnostic device (10) can determine whether the similarity between the current data and the reference data satisfies a specified condition. Here, the specified condition may include a condition regarding whether the similarity exceeds a reference value.

[0091] If the similarity between the current data and the reference data does not satisfy the specified condition (NO), the battery diagnostic device (10) returns to operation 700 and can obtain the status data of the battery cell based on a new diagnostic cycle.

[0092] The battery diagnostic device (10) can perform operation 720 using current data (first current data) that satisfies the specified condition when the similarity between the current data and the reference data satisfies the specified condition (YES).

[0093] In operation 720, the battery diagnostic device (10) can extract first current data whose similarity exceeds a reference value.

[0094] In operation 730, the battery diagnostic device (10) can diagnose whether a battery cell is abnormal based on the first voltage value corresponding to the first current data. The detailed operations of operation 730 are described in detail in FIG. 8 below.

[0095] FIG. 8 is a flowchart showing detailed operations included in operation 730 disclosed in FIG. 7.

[0096] Referring to FIG. 8, operations 732 to 738 may be included in operation 730 of FIG. 7. The following description assumes that the diagnostic cycle mentioned in FIG. 8 is a diagnostic cycle corresponding to the first current data extracted by operation 710 of FIG. 7.

[0097] In operation 732, the battery diagnostic device (10) can generate voltage value-dQ / dV data by calculating dQ / dV based on the first voltage value corresponding to the first current data. The controller (102) can calculate dQ / dV based on the first voltage value corresponding to the first current data among the voltage values ​​of the first battery cell (121) acquired for each charging cycle, and can generate voltage value-dQ / dV data based on the first voltage value.

[0098] 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).

[0099] In one embodiment, the battery diagnostic device (10) can convert voltage value-dQ / dV data into principal component data for each specified voltage section.

[0100] In operation 736, the battery diagnostic device (10) determines the principal component value (PC) corresponding to the nth diagnostic cycle (n: natural number). n ) and the difference value (ΔPC) between the principal component value (PC1) corresponding to the first diagnostic cycle n : PC n-PC1) can be calculated.

[0101] In one embodiment, the battery diagnostic device (10) determines the principal component value (PC) corresponding to the nth diagnostic cycle for each specified voltage section. n ) and the difference value between the principal component value (PC1) corresponding to the first diagnostic cycle can be calculated.

[0102] In operation 738, the battery diagnostic device (10) determines the difference value (ΔPC n ) can be used to diagnose whether the first battery cell (121) is abnormal. In one embodiment, the battery diagnosis device (10) can diagnose whether the first battery cell (121) is abnormal 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 ) can be calculated based on the mean (m) and standard deviation (σ). For example, the critical range may include a range greater than or equal to the critical minimum value (m-2.5σ) and less than or equal to the critical maximum value (m+2.5σ). In another embodiment, the battery diagnosis device (10) 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 ) can be calculated based on the mean (m) and standard deviation (σ).

[0103] In one embodiment, the battery diagnostic device (10) can diagnose whether the first battery cell (121) is abnormal for each specified voltage section. The battery diagnostic device (10) can calculate the average and standard deviation of the difference values ​​for each specified voltage section. 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 specified voltage section.

[0104] FIG. 9 illustrates a computing system that executes operations of a battery diagnostic device according to an embodiment disclosed in this document.

[0105] Referring to FIG. 9, a computing system (90) according to one embodiment disclosed in this document may include an MCU (900), a memory (910), an input / output I / F (920), and a communication I / F (930).

[0106] The MCU (900) may be a processor that executes various programs (e.g., a battery diagnosis program) stored in the memory (910), processes various data from these programs, and performs the functions of the battery diagnosis device (10) shown in FIGS. 1 to 8 described above.

[0107] The memory (910) can store various programs related to the operation of the battery diagnostic device (10). In addition, the memory (910) can store operation data of the battery diagnostic device (10).

[0108] Such memories (910) may be provided in multiples as needed. The memories (910) may be volatile memories or non-volatile memories. As volatile memories (910), RAM, DRAM, SRAM, etc. may be used. As non-volatile memories (910), ROM, PROM, EAROM, EPROM, EEPROM, flash memories, etc. may be used. The memories (910) listed above are merely examples and are not limited to these examples.

[0109] 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).

[0110] The communication I / F (930) is a component capable of transmitting and receiving various data with the server, and may be any device capable of supporting wired or wireless communication. For example, a program for diagnosing abnormalities or various data (e.g., status values) may be transmitted and received from a separately provided external server via the communication I / F (930).

[0111] The terms "include," "comprise," or "have" used herein, unless otherwise specifically stated, imply that the corresponding component may be included, and therefore should be interpreted to include other components rather than to exclude other components. All terms, including technical or scientific terms, have the same meaning as commonly understood by a person of ordinary skill in the art to which the embodiments disclosed in this document pertain, unless otherwise defined. Commonly used terms, such as terms defined in dictionaries, should be interpreted to be consistent with their contextual meaning in the relevant art, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

[0112] The above description is merely an example of the technical idea disclosed in this document, and those skilled in the art to which the embodiments disclosed in this document pertain may make various modifications and variations without departing from the essential characteristics of the embodiments disclosed in this document. Therefore, the embodiments disclosed in this document are not intended to limit the technical idea of ​​the embodiments disclosed in this document, but to explain it, and the scope of the technical idea disclosed in this document is not limited by these embodiments. The scope of protection of the technical idea disclosed in this document should be interpreted by the claims below, and all technical ideas within a scope equivalent thereto should be interpreted as being included in the scope of rights of this document.

Claims

1. An interface for acquiring status data including current and voltage values ​​of battery cells for each diagnostic cycle corresponding to a charge cycle or discharge cycle; and Calculate the similarity between the current data including the above current value and the reference data, Extracting the first current data among the above current data whose similarity exceeds the reference value, A controller that diagnoses whether the battery cell is abnormal based on a first voltage value corresponding to the first current data, Battery diagnostic device.

2. In claim 1, The above controller, Calculating the similarity between the current data and the reference data based on the cosine similarity or Pearson similarity calculation technique. Battery diagnostic device.

3. In claim 1, The above reference value is an arbitrary value within the range of 0.85 or more and 0.95 or less. Battery diagnostic device.

4. In claim 1, The above controller, Based on the first voltage value, dQ / dV is calculated to generate voltage value-dQ / dV data, The voltage value-dQ / dV data is converted into principal component data based on principal component analysis (PCA), The 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 ) and the difference value (ΔPC) between the principal component values ​​(PC1) included in the principal component data corresponding to the first diagnostic cycle n : PC n -PC1) is calculated, The above difference value (ΔPC n ) to diagnose whether the battery cell is abnormal, Battery diagnostic device.

5. In claim 4, The above controller, Calculate the average (m) and standard deviation (σ) of the difference values ​​of multiple battery cells for each of the first diagnostic cycles, Diagnosing whether the battery cell is abnormal based on the difference value, the average, and the standard deviation. Battery diagnostic device.

6. In claim 5, The above controller, A threshold value is calculated based on the above average and standard deviation, Diagnosing whether the battery cell is abnormal based on the difference value and the threshold value. Battery diagnostic device.

7. In claim 5, The above controller, If the above difference value is not included in the critical range of (m-2.5σ) or more and (m+2.5σ) or less, the battery cell is diagnosed as an abnormal cell. Battery diagnostic device.

8. An operation of acquiring status data including current values ​​and voltage values ​​of a battery cell over time for each diagnostic cycle corresponding to a charge cycle or discharge cycle; An operation for calculating the similarity between current data including current values ​​according to the above time and reference data; An operation of extracting first current data among the current data whose similarity exceeds a reference value; and An operation for diagnosing whether the battery cell is abnormal based on a first voltage value corresponding to the first current data, Method of operation of a battery diagnostic device.

9. In claim 8, The operation of calculating the above similarity is: An operation for calculating a similarity between the current data and the reference data based on a cosine similarity or Pearson similarity calculation technique, Method of operation of a battery diagnostic device.

10. In claim 8, The above reference value is an arbitrary value within the range of 0.85 or more and 0.95 or less. Method of operation of a battery diagnostic device.

11. In claim 8, The above diagnostic action is, 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). The 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 above n ) and the difference value (ΔPC) between the principal component values ​​(PC1) included in the principal component data corresponding to the first diagnostic cycle n : PC n -PC1) The action of producing, and The above difference value (ΔPC n ) including an operation of diagnosing whether the battery cell is abnormal or not. Method of operation of a battery diagnostic device.

12. In claim 11, The above diagnostic action is, An operation of calculating the average (m) and standard deviation (σ) of the difference values ​​of multiple battery cells for each of the first diagnostic cycles, and An operation for diagnosing whether the battery cell is abnormal based on the difference value, the average, and the standard deviation, Method of operation of a battery diagnostic device.

13. In claim 12, The above diagnostic action is, An operation of calculating a threshold value based on the above average and the above standard deviation, and An operation for diagnosing whether the battery cell is abnormal based on the difference value and the threshold value, Method of operation of a battery diagnostic device.

14. In claim 12, The above diagnostic action is, Including an operation of diagnosing the battery cell as an abnormal cell if the above difference value is not included in a critical range of (m-2.5σ) or more and (m+2.5σ) or less. Method of operation of a battery diagnostic device.

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