Data analysis device and method

Abstract

According to an embodiment disclosed in the present document, a data analysis device may comprise: an interface unit that obtains capacity data and voltage data of a battery equipped in an electronic device; and one or more processors that preprocess the capacity data and the voltage data to produce differential capacity data, and extract at least one feature for diagnosing the battery on the basis of a value of the differential capacity data in at least one voltage band set on the basis of characteristics of at least one of the battery and the electronic device.

Description

Data analysis device and method

[0001] Cross-citation with related applications

[0002] This application claims the benefit of priority to Republic of Korea Patent Application No. 10-2024-0049482, filed April 12, 2024, the entire contents of which are incorporated herein by reference.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to a data analysis device and method.

[0005] Recently, research and development on secondary batteries has been actively conducted. Here, secondary batteries are rechargeable and include both conventional Ni / Cd and Ni / MH batteries, as well as recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of having a much 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 suitable for use as power sources for mobile devices. Recently, their use has expanded to include power sources for electric vehicles, attracting attention as a next-generation energy storage medium.

[0006] Additionally, secondary batteries can be utilized as battery packs, which typically include battery modules in which multiple battery cells are connected in series and / or parallel. Furthermore, secondary batteries can be utilized as battery racks, which include multiple battery modules and a rack frame that accommodates these battery modules.

[0007] Battery cells, battery modules, battery packs, or battery racks like these can be utilized in a variety of devices. For example, batteries can be used in mobile devices such as cell phones, laptops, smartphones, and tablets, as well as in electric vehicles (EVs, HEVs, PHEVs) and large-capacity energy storage systems (ESS).

[0008] These batteries can have their status and operation managed and controlled by a battery management system (BMS). The BMS can be included with the batteries in a single device.

[0009] Additionally, the battery management system can manage and control the battery while being separated from the device containing the battery. For example, the battery management system can be implemented as a separate server device. In this case, the battery management system can collect battery data and vehicle data from vehicles and other devices, and utilize the collected data to manage and control the battery.

[0010] Meanwhile, if a battery is defective, the risk of damage to devices containing the battery (e.g., EVs, ESS) may increase. Therefore, a method is needed to detect abnormal battery conditions and reduce the risk of damage to devices containing the battery.

[0011] Typically, indicators for battery safety diagnosis are extracted from data with repetitive patterns in a consistent environment (e.g., a laboratory environment). However, field data acquired from batteries in use lacks repetitive patterns, necessitating the development of technologies for extracting indicators for battery diagnosis from field data.

[0012] One purpose of the embodiments disclosed in this document is to provide a data analysis device and method capable of reliably extracting features for battery diagnosis by preprocessing field data.

[0013] 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.

[0014] According to an embodiment disclosed in the present document, a data analysis device may include an interface unit that obtains capacity and voltage data of a battery provided in an electronic device, and one or more processors that preprocess the capacity data and voltage data to produce differential capacity data, and extract at least one feature for diagnosing the battery based on a value of the differential capacity data in at least one voltage band set based on a characteristic of at least one of the battery and the electronic device.

[0015] According to an embodiment, the one or more processors can extract the capacity data and voltage data in the target section, convert the capacity data and voltage data so that the extracted voltage data is differentiable in the target section, and interpolate the converted capacity data and voltage data to produce the differential capacity data.

[0016] According to an embodiment, the one or more processors can identify the target section corresponding to the slow charging section during charging of the battery and extract the capacity data and voltage data.

[0017] According to an embodiment, the one or more processors can classify capacity data having the same voltage value among the extracted voltage data, calculate an average value of the capacity data having the same voltage value, and convert the average value into a capacity value corresponding to the voltage value.

[0018] According to an embodiment, the one or more processors may interpolate the converted capacity data and voltage data in a PCHIP (Piecewise Cubic Hermite Interpolating Polynomial) manner to convert the voltage of the battery to monotonically increase or monotonically decrease.

[0019] According to an embodiment, the one or more processors may smooth the differential capacity data by performing Gaussian filtering with a preset window size.

[0020] According to an embodiment, the one or more processors may extract, as the feature, at least one of a minimum value and a maximum value of the differential capacity data in the voltage band, if an inflection point of the differential capacity data exists in each voltage band.

[0021] According to an embodiment, the one or more processors may perform a diagnosis of the battery based on a differential capacity data value corresponding to the feature.

[0022] According to an embodiment, the one or more processors may build a database by matching characteristics corresponding to the extracted features with characteristics related to the battery.

[0023] According to an embodiment disclosed in the present document, a data analysis method may include a step of obtaining capacity and voltage data of a battery provided in an electronic device, a step of preprocessing the capacity data and the voltage data to produce differential capacity data, and a step of extracting at least one feature for diagnosing the battery based on a value of the differential capacity data in at least one voltage band set based on a characteristic of at least one of the battery and the electronic device.

[0024] According to an embodiment, the step of calculating the differential capacity data may include the step of extracting the capacity data and voltage data in a target section, the step of converting the capacity data and voltage data so that the extracted voltage data can be differentiable in the target section, and the step of interpolating the converted capacity data and voltage data to calculate the differential capacity data.

[0025] According to an embodiment, the step of extracting at least one feature may be characterized by extracting at least one of a minimum value and a maximum value of the differential capacity data as the feature when an inflection point of the differential capacity data exists in each voltage band.

[0026] According to an embodiment, the method may further include a step of performing a diagnosis of the battery based on a change in a differential capacity value corresponding to the above characteristic.

[0027] The data analysis device and method according to the embodiments disclosed in this document can stably and accurately extract features for diagnosing a battery from the differential capacity data by preprocessing the field data of the battery to produce differential capacity data.

[0028] In addition, various effects may be provided, either directly or indirectly, through this document.

[0029] Figure 1 is a block diagram showing the configuration of a data analysis device according to one embodiment disclosed in this document.

[0030] FIG. 2 is a diagram showing an example of battery data and differential data according to one embodiment disclosed in this document.

[0031] FIG. 3 is a diagram showing a preprocessing process of battery data according to one embodiment disclosed in this document.

[0032] FIG. 4 is a diagram showing an example of feature extraction according to one embodiment disclosed in this document.

[0033] FIG. 5 is a diagram showing an example of information stored in a database according to one embodiment disclosed in this document.

[0034] Figure 6 is a flowchart illustrating a data analysis method according to one embodiment disclosed in this document.

[0035] Figure 7 is a flowchart illustrating a data analysis method according to one embodiment disclosed in this document.

[0036] FIG. 8 is a block diagram showing the hardware configuration of a computing system for performing an operation method of a data analysis device according to one embodiment disclosed in this document.

[0037] Hereinafter, various 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] In this document, the singular form of a noun corresponding to an item may include one or more of said items, unless the context clearly indicates otherwise. In this document, phrases such as "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" may each include any one of the items listed together in that phrase, or all possible combinations thereof. Terms such as "first," "second," or "first" or "second" may be used merely to distinguish the corresponding element from other corresponding elements, and do not limit the corresponding elements in any other respect (e.g., importance or order). When a component (e.g., a first component) is referred to as being “coupled” or “connected” to another component (e.g., a second component), with or without the terms “functionally” or “communicatively,” it means that the component can be connected to the other component directly (e.g., wired), wirelessly, or through a third component.

[0039] Each component (e.g., a module or a program) described in this document may include one or more entities. According to various embodiments, one or more components or operations of the 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 such a 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 integration. According to various embodiments, 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.

[0040] The term "module" or "part" used in this document may include a unit implemented in hardware, software, or firmware, and may be used interchangeably with terms such as logic, logic block, component, or circuit. A module may be an integral component, or a minimum unit or part of such a component that performs one or more functions. For example, according to one embodiment, a module may be implemented in the form of an application-specific integrated circuit (ASIC).

[0041] Various embodiments of the present document may be implemented as software (e.g., a program or an application) including one or more instructions stored in a machine-readable storage medium (e.g., memory). For example, a processor of the device may call at least one instruction among the one or more instructions stored from the storage medium and execute it. This enables the device to operate to perform at least one function according to the at least one instruction called. The one or more instructions may include code generated by a compiler or code executable by an interpreter. The machine-readable storage medium may be provided in the form of a non-transitory storage medium. Here, "non-transitory" only means that the storage medium is a tangible device and does not contain a signal (e.g., electromagnetic waves), and this term does not distinguish between cases where data is stored semi-permanently and cases where it is stored temporarily in the storage medium.

[0042]

[0043] Figure 1 is a block diagram showing the configuration of a data analysis device according to one embodiment disclosed in this document.

[0044] Referring to FIG. 1, the data analysis device (100) may include an interface unit (110) and one or more processors (120).

[0045] A data analysis device (100) can acquire field data on a battery equipped in an electronic device and extract features for diagnosing the battery from the field data. In one embodiment, 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). 10800

[0046] The operation of the data analysis device (100) below can be performed by a BMS (Battery management system) within a vehicle, a battery BMS provided within a battery pack, and can also be performed in various devices such as a server, cloud, charger, or charger / discharger.

[0047] The interface unit (110) establishes a connection between the data analysis device (100) and external components (e.g., electronic devices, BMS of a battery pack, sensors, vehicle BMS, etc.) and can transmit and receive data through the established connection. The connection between the interface unit (110) and the external components 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).

[0048] According to another embodiment, the connection between the battery diagnostic device (100) and the electronic device 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)).

[0049] The interface unit (110) can obtain information about a battery equipped in an electronic device. The battery may be a battery module, a battery pack, or a battery bank, and may also be each battery cell included in each battery module, pack, or bank.

[0050] In one embodiment, when the data analysis device (100) is implemented as a separate component from the electronic device (e.g., a server external to the electronic device), the interface unit (110) can obtain battery information through a communication channel established between the data analysis device (100) and the electronic device.

[0051] In another embodiment, when the data analysis device (100) is implemented as a BMS in an electronic device, the interface unit (110) can obtain information about the battery from at least one sensor that can measure information related to the state of the battery (e.g., voltage, current, temperature, etc.).

[0052] The interface unit (110) can obtain capacity and voltage data of a battery equipped in an electronic device. The battery capacity and voltage data obtained by the interface unit (110) may be time series data.

[0053] In addition to the capacity and voltage of the battery, the interface unit (110) can also obtain additional information related to the battery, such as temperature and SOH.

[0054] The processor (120) may be implemented as one or more processors. Each processor may include a central processing unit, an application processor, a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor.

[0055] The functions and operations of the data analysis device (100) described below may be performed by a single processor, or each function may be separated and performed by at least some of the processors. For convenience of explanation, the operation of the battery diagnosis device (100) will be described below as being performed by a single processor.

[0056] The processor (120) can extract features for battery diagnosis from the battery capacity and voltage data acquired by the interface unit (110). For example, the processor (120) can derive differential capacity data from the battery capacity and voltage data and analyze the differential capacity data to extract at least one feature.

[0057] The processor (120) may utilize capacity and voltage data acquired at a specific time interval, depending on the characteristics of the indicator to be extracted, the diagnostic purpose of the battery (e.g., deterioration diagnosis), etc. For example, the processor (120) may utilize capacity and voltage data acquired during the charging process of the battery.

[0058] The processor (120) can preprocess capacity data and voltage data to produce differential capacity data. The processor (120) can convert capacity data and voltage data obtained from the interface unit (110) into differential capacity data to extract indicators for battery diagnosis from field data.

[0059] According to an embodiment, the processor (120) can extract capacity data and voltage data in the target section. In the case of field data, data may be inaccurate due to various variables such as noise, duplicate data due to sensor errors, etc., depending on the battery charging section, charging conditions, and the environment of the electronic device.

[0060] Accordingly, the processor (120) can extract and utilize data of a target section from among the capacity data and voltage data obtained from the interface unit (110). That is, the processor (120) can set a target section and utilize capacity data and voltage data included in the target section for analysis so as to apply robust data analysis to batteries in various environments.

[0061] In one embodiment, a target section corresponding to a slow charging section during battery charging can be identified to extract capacity data and voltage data. That is, the processor (120) can define a slow charging section during the battery charging process and set the slow charging section as the target section.

[0062] For example, since there may be distortion of voltage data and capacity data due to a temporary decrease in voltage caused by a chemical reaction inside the battery during a rest period after the end of charging of the battery, the processor (120) may extract voltage data and capacity data in a preset target period.

[0063] In one embodiment, the slow charging period may be defined as a charging period in which voltage and capacity change stably during the battery charging process. For example, the slow charging period may be defined as the period from a preset period of time after the start of battery charging to a preset period of time before the end of charging. In another example, the slow charging period may be defined as a period in which the battery voltage is between 2.8 V and 4.2 V.

[0064] According to an embodiment, the processor (120) can convert the capacity data and voltage data so that the extracted voltage data can be differentiated within the target interval. As described above, field data is acquired under different environmental and operating conditions, and the acquired data may contain noise and outliers. Therefore, preprocessing of the acquired data is necessary for robust and accurate feature extraction.

[0065] For example, field data acquired from the interface unit (110) may contain noise, distorted data, or duplicate voltage signals due to sensor errors, and in this case, analysis may be difficult, such as when differential capacity data cannot be calculated. Accordingly, the processor (120) may convert capacity data and voltage data so that the voltage data can be differentiated in the target section.

[0066] In one embodiment, the processor (120) can classify capacity data having the same voltage value among the extracted voltage data. As described above, field data may include duplicate voltage data, in which case differentiation is impossible (differentiation values ​​diverge), and thus differential capacity data can be produced. Accordingly, the processor (120) can classify capacity data and voltage data according to voltage magnitude, thereby classifying capacity data having the same voltage value.

[0067] The processor (120) can calculate an average value of capacity data having the same voltage value and convert the average value into a capacity value corresponding to the voltage value. This is merely an example, and it is of course possible to convert the average value into a mode value, minimum value, maximum value, or a value calculated through other mathematical operations.

[0068] Through this, the processor (120) can enable voltage data and capacity data to be differentiated in the target section.

[0069] According to an embodiment, the processor (120) can interpolate the converted capacity data and voltage data to produce differential capacity data. The processor (120) can interpolate the converted capacity data and voltage data to transform the voltage data and capacity data into a monotonically increasing or monotonically decreasing form, and then produce differential capacity data.

[0070] In one embodiment, the processor (120) can interpolate the converted capacity data and voltage data using a Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) method.

[0071] The PCHIP method maintains the raw data format while increasing data monotonicity, offering the advantage of less distortion in interpolated data compared to other monotonic methods such as smoothing splines. In particular, data with a high probability of noise or anomalies, such as field data, can exhibit less smoothness than experimental data. In these cases, applying the PCHIP method has the advantage of reducing data overshooting and oscillation.

[0072] In one embodiment, the processor (120) can apply the PCHIP method by setting a specified voltage interval. Since the differential capacity data represents the change in capacity per unit voltage (or the change in voltage per unit capacity), the processor (120) can apply the PCHIP method by setting a specified voltage interval for consistent analysis of the data. The processor (120) can set an appropriate voltage interval so as to reduce oscillation in the data while preventing the characteristics from becoming unclear. For example, the specified voltage interval can be set to 5 mV.

[0073] In this case, the processor (120) can interpolate the values ​​of capacity data corresponding to the voltage interval at each specified voltage interval, thereby making the form of the voltage data and capacity data monotonic.

[0074] The processor (120) can produce differential capacity data from capacity data and voltage data interpolated using the PCHIP method. The differential capacity data may be data obtained by differentiating capacity with voltage (dqdv) or data obtained by differentiating voltage with capacity (dvdq).

[0075] According to an embodiment, the processor (120) may perform Gaussian filtering on the differential capacity data with a preset window size to smooth it. The processor (120) may smooth the differential capacity data through Gaussian filtering to more accurately extract indicators from the differential capacity data.

[0076] The processor (120) can reduce data noise, enhance data trends, and reduce data oscillations through Gaussian filtering. At this time, the processor (120) can set a window size that satisfies an appropriate trade-off so as to reduce data oscillations while preventing the characteristics from becoming unclear.

[0077] The processor (120) can extract at least one feature for diagnosing the battery based on the value of differential capacity data in at least one voltage band set based on characteristics of at least one of the battery and the electronic device.

[0078] That is, since the voltage band in which the characteristics for diagnosing the battery are expressed may vary depending on the type of battery, etc., the processor (120) can identify at least one voltage band set based on the characteristics of at least one of the battery and the electronic device in order to extract the characteristics.

[0079] In one embodiment, the processor (120) may extract at least one of a minimum value and a maximum value of the differential capacity data in each voltage band as a feature when an inflection point of the differential capacity data exists in each voltage band.

[0080] For example, the processor (120) can identify maximum and minimum values ​​of differential capacity data across each voltage band, and identify maximum and minimum values ​​within the voltage band. If the maximum or minimum value is included in an area between the identified maximum and minimum values, the processor (120) can extract at least one of the minimum and maximum values ​​as a feature.

[0081] According to an embodiment, the processor (120) can perform a diagnosis of the battery based on the value of the differential capacity data corresponding to the feature. Since the feature value of the feature extracted as described above (the value of the differential capacity data, the corresponding voltage, etc.) acts as an indicator reflecting the characteristics of the battery (e.g., the degree of deterioration), the processor (120) can perform a diagnosis of the battery based on the value of the differential capacity data corresponding to the feature, i.e., the feature value.

[0082] For example, the processor (120) can directly perform a battery diagnosis from a feature value by analyzing the relationship between the feature value corresponding to the feature and the battery diagnostic items. In another example, the processor (120) can analyze the change in the feature value corresponding to the feature to perform a change in the state or a state diagnosis of the battery. For example, when a battery is installed in a vehicle, the processor (120) can monitor the change in the feature value according to the driving distance of the vehicle, and can diagnose the degree of battery deterioration based on the change in the feature value.

[0083] According to an embodiment, the processor (120) may build a database by matching characteristics corresponding to the extracted characteristics with characteristics related to the battery. For example, characteristics corresponding to the characteristics may include values ​​of differential capacity data in which the characteristics are expressed, voltage bands in which the characteristics are expressed, etc., and characteristics related to the battery may include charging start time, charging end time, charging start SOC, charging end SOC, characteristics of an electronic device equipped with a battery, for example, a vehicle's driving distance, etc.

[0084] The processor (120) extracts features from field data and builds a database, so that the built database can be used for subsequent battery feature extraction and diagnosis.

[0085] FIG. 2 is a diagram showing an example of battery data and differential data according to one embodiment disclosed in this document.

[0086] The graph (210) of Fig. 2 illustrates an example of voltage data and capacity data obtained from the interface unit (110). Referring to the graph (210), the field data of the battery obtained from the interface unit (110) may include noise or distorted data (such as capacity decrease during charging), duplicate voltage signals, etc. due to errors in the sensor itself.

[0087] Graph (220) illustrates an example of calculating differential capacity data from data of graph (210). Referring to graph (220), when calculating differential capacity data without preprocessing redundant voltage signals, etc. among field data, the values ​​of the differential capacity data may diverge as shown in graph (220), making differentiation impossible, or data may be distorted, such as the values ​​of the data coming out as negative numbers.

[0088] Accordingly, the processor (120) can preprocess battery data obtained from the interface unit (110) to prevent distortion of differential capacity data such as the graph (220).

[0089] FIG. 3 is a diagram showing a preprocessing process of battery data according to one embodiment disclosed in this document.

[0090] Referring to FIG. 3, a graph is shown showing the results of preprocessing battery voltage data through sampling and PCHIP. As illustrated in FIG. 3, raw voltage data contains redundant signals and noise, but the voltage data can be converted into a monotonically increasing form through the sampling process of the processor (120) and the PCHIP interpolation method. Here, the sampling process can be understood as a process of removing redundant voltage signals.

[0091] In Fig. 3, it can be confirmed that the graph exhibits a monotonic increase shape that increases smoothly without noise as a result of applying the PCHIP interpolation method at a specified voltage interval (voltage resolution) to the sampled data, that is, the sampled data from which redundant voltage signals have been removed.

[0092] FIG. 4 is a diagram showing an example of feature extraction according to one embodiment disclosed in this document.

[0093] Referring to FIG. 4, the processor (120) can extract features based on the values ​​of differential capacity data in each voltage band and analyze the feature values.

[0094] Graph (410) of FIG. 4 illustrates four voltage bands (R1 to R4) and features (F1 to F5) extracted from the voltage bands. Each voltage band may be set based on characteristics of at least one of a battery and an electronic device. In one embodiment, the voltage band may be set based on the type of battery, the type of electronic device equipped with the battery (e.g., if the electronic device is a vehicle, the type of vehicle), etc.

[0095] The processor (120) can identify whether an inflection point of the differential capacity data exists in each voltage band. If an inflection point of the differential capacity data exists in each voltage band, the processor (120) can extract at least one of the minimum and maximum values ​​of the differential capacity data in the voltage band as a feature. For example, if the graph represented by the differential capacity data in a specific voltage band is convex upward, the processor (120) can extract the maximum value of the differential capacity data as a feature.

[0096] Referring to the graph (410) of FIG. 4, the processor (120) can identify an inflection point of differential capacity data in the R1 voltage band and extract the data value at the inflection point, i.e., the maximum value of the differential capacity data in the R1 band, as a feature (F1) of the battery. In this way, the processor (120) can produce features (F1 to F5) for battery diagnosis in each band (R1 to R4).

[0097] The processor (120) can perform a diagnosis of the battery based on the value of the differential capacity data corresponding to the extracted feature. For example, the processor (120) can analyze the relationship between the value of the differential capacity data corresponding to the feature of the target battery and the driving distance of the vehicle, as exemplarily illustrated in the graph (420) of FIG. 4. The processor (120) can diagnose the degree of battery degradation from the feature value by analyzing the relationship between the feature value through analysis and the driving distance and the degree of battery degradation. For example, it can be confirmed that as the driving distance increases, i.e., as the degree of battery degradation increases, the value (peak) of the differential capacity data of the feature value tends to decrease.

[0098] Additionally, the processor (120) can further analyze key features that can be utilized for diagnosis according to battery diagnostic items. For example, referring to the graph (420) of FIG. 4, it can be confirmed that the change in feature values ​​according to driving distance is prominent in feature F1. In this case, the processor (120) can prioritize the feature value of feature F1 over other features (F2 to F5) when diagnosing battery deterioration.

[0099] FIG. 5 is a diagram showing an example of information stored in a database according to one embodiment disclosed in this document.

[0100] Referring to FIG. 5, the processor (120) can build a database by matching characteristics corresponding to the extracted features with characteristics related to the battery. For example, as illustrated in FIG. 5, the processor (120) can match the characteristics of the extracted features with the characteristics of the battery for each charge.

[0101] For example, the processor (120) can match and store information such as charging start time, charging end time, start SOC, and end SOC related to data utilized in feature extraction and feature values ​​such as voltage and intensity of the extracted features.

[0102] Figure 6 is a flowchart illustrating a data analysis method according to one embodiment disclosed in this document. The embodiment illustrated in Figure 6 is merely one embodiment, and the order of steps according to various embodiments of the present invention may differ from that illustrated in Figure 6. Some steps illustrated in Figure 6 may be omitted, the order of steps may be changed, or steps may be merged.

[0103] Referring to FIG. 6, the data analysis method may include a step (S100) of obtaining capacity and voltage data of a battery equipped in an electronic device, a step (S200) of preprocessing the capacity data and the voltage data to produce differential capacity data, a step (S300) of extracting at least one feature for diagnosing a battery based on a value of the differential capacity data in at least one voltage band set based on characteristics of at least one of the battery and the electronic device, and a step (S400) of performing a diagnosis of the battery based on a change in the differential capacity value corresponding to the feature.

[0104] At step S100, the interface unit (110) can obtain capacity and voltage data of a battery equipped in an electronic device. For example, the interface unit (110) can obtain time series data on capacity and voltage during the battery charging process.

[0105] At step S200, one or more processors (120) can preprocess capacity data and voltage data to produce differential capacity data.

[0106] Referring to FIG. 7, a process of calculating differential capacity data by one or more processors (120) according to one embodiment may include a step of extracting capacity data and voltage data in a target section (S210), a step of converting the capacity data and voltage data so that the extracted voltage data can be differentiable in the target section (S220), and a step of calculating differential capacity data by interpolating the converted capacity data and voltage data (S230).

[0107] In step S210, one or more processors (120) may extract capacity data and voltage data from a target section. In one embodiment, one or more processors (120) may define a slow charging section during a battery charging process as a target section, and extract capacity data and voltage data corresponding to the slow charging section.

[0108] In step S220, one or more processors (120) may convert the capacity data and voltage data so that the extracted voltage data can be differentiated in the target section. In one embodiment, the one or more processors (120) may classify capacity data having the same voltage value among the extracted voltage data, calculate an average value of the capacity data having the same voltage value, and convert the calculated average value into a capacity value corresponding to the voltage value.

[0109] At step S230, one or more processors (120) may interpolate the converted capacity data and voltage data to produce differential capacity data. In one embodiment, one or more processors (120) may interpolate the converted capacity data and voltage data using the PCHIP method. One or more processors (120) may differentiate the interpolated voltage data and capacity data to produce differential capacity data.

[0110] In step S300, one or more processors (120) may extract at least one feature for diagnosing a battery based on the value of differential capacity data in at least one voltage band set based on characteristics of at least one of the battery and the electronic device. In one embodiment, when an inflection point of the differential capacity data exists in each voltage band, the one or more processors (120) may extract at least one of a minimum value and a maximum value of the differential capacity data in the voltage band as a feature.

[0111] At step S400, one or more processors (120) may perform a battery diagnosis based on a differential capacity value corresponding to a feature. For example, one or more processors (120) may perform a battery diagnosis by analyzing a differential capacity value corresponding to a feature.

[0112] FIG. 8 is a block diagram showing the hardware configuration of a computing system for performing an operation method of a data analysis device according to one embodiment disclosed in this document.

[0113] Referring to FIG. 8, a computing system (1000) according to one embodiment disclosed in the present document may include an MCU (1010), a memory (1020), an input / output I / F (1030), and a communication I / F (1040).

[0114] The MCU (1010) may be a processor that executes various programs stored in the memory (1020), processes various information including battery data through these programs, and performs the functions of the processor included in the data analysis device shown in the aforementioned FIG. 1.

[0115] The memory (1020) can store various programs for performing the functions of the data analysis device. In addition, the memory (1020) can store various information, including battery data (voltage data, capacity data, etc.), differential capacity data, etc., and can include a constructed database.

[0116] Such memories (1020) may be provided in multiple numbers as needed. The memories (1020) may be volatile memories or non-volatile memories. As volatile memories (1020), RAM, DRAM, SRAM, etc. may be used. As non-volatile memories (1020), ROM, PROM, EAROM, EPROM, EEPROM, flash memories, etc. may be used. The examples of the memories (1020) listed above are merely examples and are not limited to these examples.

[0117] The input / output I / F (1030) 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 (1010).

[0118] The communication I / F (1040) 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 data analysis device can transmit and receive various information, including battery data, from a separately provided external server via the communication I / F (1040).

[0119] In this way, a computer program according to one embodiment disclosed in this document may be implemented as a module that is recorded in a memory (1020) and processed by an MCU (1010) to perform each function illustrated in FIG. 1, for example.

[0120]

[0121] Although all components constituting the embodiments disclosed in this document have been described as being combined or operating in combination as one, the embodiments disclosed in this document are not necessarily limited to such embodiments. That is, within the scope of the purpose of the embodiments disclosed in this document, all of the components may be selectively combined and operated one or more times.

[0122] In addition, terms such as "include," "comprise," or "have" described above, unless specifically stated otherwise, mean that the corresponding component can be included, and therefore should be interpreted to include other components rather than excluding 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 belong, unless otherwise defined. Commonly used terms, such as terms defined in a dictionary, should be interpreted to be consistent with the contextual meaning of the relevant technology, and shall not be interpreted in an idealized or overly formal sense, unless explicitly defined in this document.

[0123] 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 unit for obtaining capacity and voltage data of a battery provided in an electronic device; and The above capacity data and voltage data are preprocessed to produce differential capacity data, A data analysis device comprising one or more processors for extracting at least one feature for diagnosis of the battery based on a value of the differential capacity data in at least one voltage band set based on a characteristic of at least one of the battery and the electronic device.

2. In paragraph 1, One or more of the above processors, Extract the above capacity data and voltage data in the target section, Converting the capacity data and voltage data so that the extracted voltage data can be differentiated in the target section, A data analysis device that interpolates the converted capacity data and voltage data to produce the differential capacity data.

3. In paragraph 2, One or more of the above processors, A data analysis device that identifies the target section corresponding to the slow charging section during charging of the battery and extracts the capacity data and voltage data.

4. In paragraph 2, One or more of the above processors, Classify capacity data having the same voltage value among the extracted voltage data, A data analysis device that calculates an average value of capacity data having the same voltage value and converts the average value into a capacity value corresponding to the voltage value.

5. In paragraph 2, One or more of the above processors, A data analysis device that interpolates the above-mentioned converted capacity data and voltage data using the PCHIP (Piecewise Cubic Hermite Interpolating Polynomial) method to convert the voltage of the battery to monotonically increase or monotonically decrease.

6. In paragraph 1, One or more of the above processors, A data analysis device that smoothes the above differential capacity data by performing Gaussian filtering with a preset window size.

7. In paragraph 1, One or more of the above processors, A data analysis device that extracts, as the feature, at least one of the minimum value and the maximum value of the differential capacity data in the voltage band when an inflection point of the differential capacity data exists in each voltage band.

8. In paragraph 1, One or more of the above processors, A data analysis device that performs diagnosis of the battery based on differential capacity data values ​​corresponding to the above characteristics.

9. In paragraph 1, One or more of the above processors, A data analysis device that builds a database by matching characteristics corresponding to the extracted features with characteristics related to the battery.

10. A step of obtaining capacity and voltage data of a battery equipped in an electronic device; A step of preprocessing the above capacity data and voltage data to produce differential capacity data; and A data analysis method comprising a step of extracting at least one feature for diagnosing the battery based on a value of the differential capacity data in at least one voltage band set based on a characteristic of at least one of the battery and the electronic device.

11. In paragraph 10, The step of calculating the above differential capacity data is: A step of extracting the capacity data and voltage data in the target section; A step of converting the capacity data and voltage data so that the extracted voltage data can be differentiated in the target section; and A data analysis method comprising a step of interpolating the converted capacity data and voltage data to produce the differential capacity data.

12. In paragraph 10, The step of extracting at least one feature above is: A data analysis method characterized in that, when an inflection point of the differential capacity data exists in each voltage band, at least one of the minimum value and the maximum value of the differential capacity data is extracted as the feature.

13. In paragraph 10, A data analysis method further comprising a step of performing a diagnosis of the battery based on a change in a differential capacity value corresponding to the above characteristic.

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