Data analysis apparatus and method

Abstract

According to embodiments disclosed herein, a data analysis device can include an interface unit configured to acquire capacity data and voltage data of a battery provided in an electronic device, and one or more processors configured to pre-process the capacity data and the voltage data to calculate 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, the at least one voltage band being set based on characteristics of at least one of the battery and the electronic device.

Description

Technical Field

[0001] Cross-references to related applications

[0002] This application claims priority and benefit to Korean Patent Application No. 10-2024-0049482, filed with the Korean Intellectual Property Office on April 12, 2024, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The embodiments disclosed herein relate to data analysis apparatus and methods. Background Technology

[0004] Recently, research and development of rechargeable batteries have been actively pursued. Here, rechargeable batteries are batteries capable of being charged and discharged, and include conventional Ni / Cd and Ni / MH batteries as well as all recent lithium-ion batteries. Among rechargeable batteries, lithium-ion batteries have the advantage of significantly higher energy density than conventional Ni / Cd and Ni / MH batteries. Furthermore, because lithium-ion batteries can be manufactured in a small and lightweight manner, they are used as power sources for mobile devices, and recently, their application has expanded to power electric vehicles, making them a promising next-generation energy storage medium.

[0005] Additionally, secondary batteries can typically be used as battery packs comprising battery modules in which multiple battery cells are connected in series and / or parallel. Furthermore, secondary batteries can be used as battery racks comprising multiple battery modules and a rack housing the battery modules.

[0006] Battery cells, battery modules, battery packs, or battery racks can be used in a wide variety of devices. For example, batteries can be used not only in mobile devices such as mobile phones, laptops, smartphones, and smart tablets, but also in fields such as electrically powered vehicles (electric vehicles (EVs), hybrid electric vehicles (HEVs), and plug-in hybrid electric vehicles (PHEVs)) or high-power storage devices (electric energy storage systems (ESS)).

[0007] The state and operation of the battery can be managed and controlled by a battery management system (BMS). The BMS can be included in a device along with the battery.

[0008] Furthermore, the BMS can manage and control the battery in a state separate from the device including the battery. For example, the BMS can be implemented as a separate server device. In this case, the BMS can collect battery data and vehicle data from the vehicle, etc., and use the collected data to manage and control the battery.

[0009] Furthermore, the presence of defective batteries may increase the likelihood of damage to devices including those batteries (e.g., EVs, ESSs, etc.). Therefore, a method for detecting abnormal battery conditions is needed to reduce the possibility of damage to devices including batteries. Summary of the Invention

[0010] Technical issues

[0011] Typically, indicators for battery safety diagnostics are extracted from data exhibiting recurring patterns in constant environments (e.g., experimental environments). However, since field data obtained from batteries in use does not exhibit recurring patterns, it is necessary to develop a technique for extracting indicators for battery diagnostics from field data.

[0012] The embodiments disclosed herein aim to provide a data analysis apparatus and method capable of preprocessing field data to reliably extract features for diagnosing batteries.

[0013] The technical objectives of the embodiments disclosed herein are not limited to the above-described technical objectives, and other objectives not described will be clearly understood by those skilled in the art based on the following description.

[0014] Technical solution

[0015] According to the embodiments disclosed herein, a data analysis apparatus may include: an interface unit configured to acquire capacity data and voltage data of a battery disposed in an electronic device; and one or more processors configured to preprocess the capacity data and voltage data to calculate differential capacity data; and to extract at least one feature for diagnosing the battery based on the value of the differential capacity data in at least one voltage band, the at least one voltage band being set based on the characteristics of at least one of the battery and the electronic device.

[0016] According to the implementation, one or more processors can be configured to extract capacity data and voltage data in a target range; transform the capacity data and voltage data such that the extracted voltage data can be differentiated in the target range; and interpolate the transformed capacity data and voltage data to calculate the differentiated capacity data.

[0017] According to an implementation, one or more processors may be configured to identify target intervals corresponding to slow charging intervals during battery charging in order to extract capacity and voltage data.

[0018] According to an implementation, one or more processors can be configured to classify multiple capacity data with the same voltage value among the extracted voltage data; and calculate the average value of the multiple capacity data with the same voltage value and convert the average value into a capacity value corresponding to the voltage value.

[0019] According to the implementation, one or more processors can be configured to interpolate the converted capacity and voltage data using the Piecewise Cubic Hermitian Interpolation Polynomial (PCHIP) method to convert the battery voltage into a monotonically increasing or monotonically decreasing state.

[0020] According to the implementation, one or more processors can be configured to perform Gaussian filtering on the differential capacity data with a preset window size to smooth the differential capacity data.

[0021] According to the implementation method, when there is an inflection point of differential capacity data in each voltage band, one or more processors can be configured to extract at least one of the minimum and maximum values ​​of the differential capacity data in the voltage band as features.

[0022] According to the implementation, one or more processors can be configured to perform diagnostics on the battery based on the values ​​of differential capacity data corresponding to the features.

[0023] According to the implementation, one or more processors can be configured to match features corresponding to the extracted features with battery-related features to build a database.

[0024] According to the embodiments disclosed herein, a data analysis method may include the following steps: acquiring capacity data and voltage data of a battery disposed in an electronic device; preprocessing the capacity data and voltage data to calculate differential capacity data; and extracting at least one feature for diagnosing the battery based on the value of the differential capacity data in at least one voltage band, wherein the at least one voltage band is set based on the characteristics of at least one of the battery and the electronic device.

[0025] According to the implementation method, the step of calculating differential capacity data may include the following steps: extracting capacity data and voltage data from a target interval; converting the capacity data and voltage data so that the extracted voltage data can be differentiated in the target interval; and interpolating the converted capacity data and voltage data to calculate the differential capacity data.

[0026] According to the implementation method, when there is an inflection point of differential capacity data in each voltage band, the step of extracting at least one feature may include extracting at least one of the minimum and maximum values ​​of the differential capacity data as a feature.

[0027] According to the implementation method, the data analysis method may also include performing diagnostics on the battery based on changes in differential capacity values ​​corresponding to the features.

[0028] Beneficial effects

[0029] The data analysis apparatus and method according to the embodiments disclosed herein can preprocess field data of a battery to calculate differential capacity data, thereby stably and accurately extracting features for diagnosing the battery from the differential capacity data.

[0030] In addition, various effects that can be directly or indirectly identified through this document can be provided. Attached Figure Description

[0031] Figure 1 This is a block diagram illustrating the configuration of a data analysis apparatus according to one embodiment disclosed herein.

[0032] Figure 2 This is a diagram illustrating an example of battery data and differential data according to one embodiment disclosed herein.

[0033] Figure 3 This is a diagram illustrating a battery data preprocessing procedure according to one embodiment disclosed herein.

[0034] Figure 4 This is a diagram illustrating an example of feature extraction according to one embodiment disclosed herein.

[0035] Figure 5 This is a diagram illustrating an example of information stored in a database according to one embodiment disclosed herein.

[0036] Figure 6 This is a flowchart describing a data analysis method according to one embodiment disclosed herein.

[0037] Figure 7 This is a flowchart describing a data analysis method according to one embodiment disclosed herein.

[0038] Figure 8 This is a block diagram illustrating the hardware configuration of a computing system for performing an operation method of a data analysis apparatus according to one embodiment disclosed herein. Detailed Implementation

[0039] In the following description, various embodiments of the present disclosure will be illustrated with reference to the accompanying drawings. However, it should be understood that this is not intended to limit the present disclosure to a particular embodiment, but rather to include various modifications, equivalents, and / or substitutions to the embodiments of the present disclosure.

[0040] Unless the relevant context explicitly specifies otherwise, the singular form of the noun corresponding to an item in this document may include one or more items. In this document, each of the 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 include any one of the items listed together in the corresponding phrases within these phrases, or all possible combinations thereof. Terms such as “first,” “second,” “firstly,” or “secondarily” may be used merely to distinguish the corresponding component from another component and do not limit the corresponding component in any other way (e.g., in terms of importance or order). When a component (e.g., a first) is described, with or without the terms “functionally” or “communically,” as “connected,” “linked,” or “joined” to another component (e.g., a second), it means that the first component may be directly (e.g., wired), wirelessly, or via a third component to the other component.

[0041] Each component described herein (e.g., a module or program) may include a single object or multiple objects. According to various implementations, one or more of the corresponding components described above may be omitted, or one or more other components or operations may be added. Alternatively or additionally, multiple components (e.g., modules or programs) may be integrated into a single component. In this case, the integrated component may perform one or more functions of each of the multiple components in the same or similar manner as the functions performed by the corresponding components of the multiple components prior to integration. According to various implementations, the operations performed by a module, program, or other component may be performed sequentially, in parallel, repeatedly, or heuristically, or may be performed in a different order, or one or more operations may be omitted, or one or more other operations may be added.

[0042] As used herein, the terms "module" or "component" can include units implemented in hardware, software, or firmware and can be used interchangeably with terms such as logic, logic block, component, or circuit. A module can be the smallest unit or part of an integrated component or a component that performs one or more functions. For example, according to one implementation, a module can be implemented as an application-specific integrated circuit (ASIC).

[0043] Various embodiments of this document can be implemented as software (e.g., a program or application) containing one or more machine-readable commands stored in a storage medium (e.g., a memory). For example, a device's processor can retrieve at least one command from one or more commands stored in the storage medium and execute the command. This allows the device to operate to perform at least one function according to at least one retrieved command. The one or more commands may include code generated by a compiler or code executable by an interpreter. The device-readable storage medium may be provided in the form of a non-transitory storage medium. Here, "non-transitory storage medium" means a tangible device and only means that it does not include signals (e.g., electromagnetic waves), and this term does not distinguish between cases where data is stored semi-permanently and cases where data is temporarily stored in a storage medium.

[0044] Figure 1 This is a block diagram illustrating the configuration of a data analysis apparatus according to one embodiment disclosed herein.

[0045] Reference Figure 1 The data analysis device 100 may include an interface unit 110 and one or more processors 120.

[0046] The data analysis device 100 can acquire field data about the battery located in the electronic device and extract features from the field data for diagnosing the battery. In one embodiment, the electronic device may be a mobile device (e.g., a mobile phone, laptop, smartphone, or tablet), an electric vehicle (e.g., an electric vehicle (EV), a hybrid electric vehicle (HEV), a plug-in HEV (HEV), or a fuel cell electric vehicle (FCEV)), an energy storage system (ESS), or a battery swapping system (BSS).

[0047] The following operations of the data analysis device 100 can be performed by the battery management system (BMS) in the vehicle and the battery BMS installed in the battery pack, and can also be performed in various devices such as servers, cloud, chargers, chargers / dischargers, etc.

[0048] Interface unit 110 can establish a connection between data analysis device 100 and external components (e.g., electronic devices, battery pack BMS, sensors, vehicle BMS, etc.) and send and receive data through the established connection. The connection between interface unit 110 and external components can be a wired and / or wireless network communication connection. In one embodiment, the wired network can be based on local area network (LAN) communication or power line communication. In one embodiment, the wireless network can be based on a short-range communication network (e.g., Bluetooth, WiFi, or IrDA) or a telecommunications network (cellular network, 4G network, or 5G network).

[0049] According to another embodiment, the connection between the battery analysis device 100 and the electronic device can be a connection via an inter-device communication method (e.g., bus, general purpose input / output (GPIO), serial peripheral interface (SPI), or mobile industrial processor interface (MIPI)).

[0050] Interface unit 110 can obtain information about the battery disposed in the electronic device. The battery can be a battery module, battery pack, or battery bank, and can be each battery cell included in each battery module, battery pack, or battery bank.

[0051] In one implementation, when the data analysis device 100 is implemented as a component separate from the electronic device (e.g., an external server for the electronic device), the interface unit 110 can obtain information about the battery through a communication channel established between the data analysis device 100 and the electronic device.

[0052] In another embodiment, when the data analysis device 100 is implemented as a BMS in an electronic device, the interface unit 110 can acquire information about the battery from at least one sensor capable of measuring information about the battery's state (e.g., voltage, current, temperature, etc.).

[0053] Interface unit 110 can acquire data regarding the capacity and voltage of a battery installed in an electronic device. The data regarding the battery capacity and voltage acquired by interface unit 110 can be time-series data.

[0054] In addition to the battery's capacity and voltage, the interface unit 110 can also obtain additional information about the battery, such as temperature and state of health (SOH).

[0055] The processor 120 can be implemented as one or more processors. Each of the processors 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.

[0056] The functions and operations of the data analysis device 100 described below can be executed by a single processor, or these functions can be at least partially separated and executed by multiple processors. For ease of description, the operation of the battery diagnostic device 100 will be described below as being executed by a single processor.

[0057] The processor 120 can extract features for diagnosing the battery 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.

[0058] The processor 120 can use capacity and voltage data acquired over a specific time period, depending on the characteristics of the metrics to be extracted, the purpose of battery diagnostics (e.g., degradation diagnosis), etc. For example, the processor 120 can use capacity and voltage data acquired during the battery charging process.

[0059] Processor 120 can preprocess capacity and voltage data to calculate differential capacity data. Processor 120 can convert capacity and voltage data acquired from interface unit 110 into differential capacity data to extract indicators for battery diagnosis from field data.

[0060] According to the implementation, the processor 120 can extract capacity data and voltage data from the target range. In the case of field data, depending on the battery's charging range and charging conditions, the environment of the electronic device, etc., the data may be inaccurate due to various variables such as noise, duplicate data due to sensor errors, etc.

[0061] Therefore, the processor 120 can extract and use data within a target range from the capacity and voltage data acquired from the interface unit 110. In other words, the processor 120 can set a target range and use the capacity and voltage data included within that range for analysis, enabling reliable data analysis to be applied to batteries in various environments.

[0062] In one implementation, a target range corresponding to a slow charging range during battery charging can be identified to extract capacity and voltage data. That is, the processor 120 can define a slow charging range and set it as the target range during the battery charging process.

[0063] For example, during a rest period after the battery is fully charged, since voltage and capacity data may be distorted due to a temporary drop in voltage caused by chemical reactions inside the battery, the processor 120 can extract voltage and capacity data within a preset target range.

[0064] In one implementation, a slow charging range can be defined as a charging range in which the voltage and capacity change steadily during the battery's charging process. For example, a slow charging range can be defined as the period from the start of charging through a preset time interval to a specified time before the end of charging. As another example, a slow charging range can be set to a battery voltage range of 2.8V to 4.2V.

[0065] According to the implementation, the processor 120 can convert capacity data and voltage data so that the extracted voltage data can be differentiated within a target range. As described above, since the field data is acquired under different environmental and operating conditions and may include noise and outliers, it is necessary to preprocess the acquired data to extract reliable and accurate features.

[0066] For example, the field data acquired from interface unit 110 may include noise, distorted data, or repetitive voltage signals due to sensor errors, and in such cases, analytical difficulties may arise, such as the inability to calculate differential capacity data. Therefore, processor 120 can convert capacity data and voltage data so that the voltage data can be differentiated within a target range.

[0067] In one implementation, the processor 120 can classify capacity data with the same voltage value among the extracted voltage data. As mentioned above, field data may include repetitive voltage data, and in this case, differentiation is impossible (differential values ​​diverge), so differential capacity data can be calculated. Therefore, the processor 120 can classify capacity data and voltage data according to the magnitude of the voltage to classify multiple capacity data with the same voltage value.

[0068] Processor 120 can calculate the average of multiple capacity data with the same voltage value and convert the average to a capacity value corresponding to the voltage value. This is merely illustrative, and it is obvious that conversion to the mode, minimum, maximum, or other mathematically calculated values ​​besides the average is possible.

[0069] Therefore, the processor 120 can make the voltage data and capacity data differentiable in the target range.

[0070] According to an implementation, processor 120 can interpolate the converted capacity data and voltage data to calculate differential capacity data. Processor 120 can also interpolate the converted capacity data and voltage data to convert them into a form that is monotonically increasing or monotonically decreasing, and then calculate the differential capacity data.

[0071] In one implementation, the processor 120 may use the Piecewise Cubic Hermitian Interpolation Polynomial (PCHIP) method to interpolate the converted capacity and voltage data.

[0072] The PCHIP method preserves the original data form and improves its monotonicity, thus offering the advantage of less data distortion compared to other monotonic methods such as smoothed splines. In particular, data such as field data, which are prone to noise or outliers, may exhibit non-smoothness compared to experimental data; in such cases, applying the PCHIP method can reduce overshoot or oscillations.

[0073] In one implementation, processor 120 can set a specified voltage interval to apply the PCHIP method. Since differential capacity data represents the capacity change per unit voltage (or voltage change per unit capacity), processor 120 can set a specified voltage interval for consistent data analysis, thereby applying the PCHIP method. Processor 120 can set an appropriate voltage interval to reduce data oscillations and prevent characteristics from becoming unclear. For example, the specified voltage interval can be set to 5mV.

[0074] In this case, for each specified voltage interval, the processor 120 can interpolate the value of the capacity data corresponding to the voltage interval, thereby making the form of the voltage data and capacity data monotonic.

[0075] The processor 120 can calculate differential capacity data based on capacity data and voltage data interpolated using the PCHIP method. The differential capacity data can be data obtained by differentiating the capacity with respect to the voltage (dqdv) or data obtained by differentiating the voltage with respect to the capacity (dvdq).

[0076] According to the implementation method, the processor 120 can perform Gaussian filtering on the differential capacity data with a preset window size to smooth the differential capacity data. The processor 120 can use Gaussian filtering to smooth the differential capacity data to extract indicators more accurately from the differential capacity data.

[0077] Processor 120 can reduce data noise, increase trend, and reduce data oscillations through Gaussian filtering. In this case, processor 120 can set a window size that satisfies an appropriate trade-off, thereby reducing data oscillations and preventing characteristics from becoming unclear.

[0078] 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, the at least one voltage band being set based on the characteristics of at least one of the battery and the electronic device.

[0079] In other words, since the voltage band that reflects the characteristics of the battery for diagnosis may vary depending on the type of battery, 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 features.

[0080] In one implementation, when there is an inflection point in the differential capacity data 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.

[0081] For example, processor 120 can identify the maximum and minimum values ​​of differential capacity data in all voltage bands and identify local maximum and local minimum values ​​in the corresponding voltage bands. When the maximum or minimum value is included in the region between the identified local maximum and local minimum values, processor 120 can extract at least one of the minimum and maximum values ​​as a feature.

[0082] According to the implementation, the processor 120 can perform diagnostics on the battery based on the value of differential capacity data corresponding to the feature. Since the feature values ​​(values ​​of differential capacity data, corresponding voltage, etc.) of the features extracted as described above serve as indicators reflecting the characteristics of the battery (e.g., the degree of degradation), the processor 120 can perform diagnostics on the battery based on the value of differential capacity data (i.e., feature values) corresponding to the feature.

[0083] For example, processor 120 can directly perform battery diagnostics by analyzing the relationship between the characteristic value corresponding to the feature and the battery's diagnostic items. In another example, processor 120 can analyze changes in the characteristic value corresponding to the feature to perform battery state change or state diagnostics. For example, when a battery is installed in a vehicle, processor 120 can monitor changes in the characteristic value based on the vehicle's travel distance and diagnose the degree of battery degradation based on the changes in the characteristic value.

[0084] According to the implementation, the processor 120 can construct a database by matching characteristics corresponding to the extracted features with battery-related characteristics. For example, characteristics corresponding to the features may include values ​​of differential capacity data reflecting the features, voltage bands reflecting the features, etc., and battery-related characteristics may include charging start time, charging end time, charging start state of charge (SoC), charging end SoC, characteristics of electronic devices with batteries (e.g., vehicle driving range), etc.

[0085] The processor 120 can extract features from field data and build a database of features for use in subsequent battery feature extraction and diagnostics.

[0086] Figure 2 This is a diagram illustrating an example of battery data and differential data according to one embodiment disclosed herein.

[0087] Figure 2Figure 210 shows an example of voltage and capacity data acquired from interface unit 110. Referring to Figure 210, noise or distortion data caused by errors in the sensor itself (such as capacity reduction during charging), repetitive voltage signals, etc., may appear in the field data of the battery acquired from interface unit 110.

[0088] Figure 220 shows an example of calculating differential capacity data based on the data in Figure 210. Referring to Figure 220, when calculating differential capacity data without preprocessing the repetitive voltage signal from the field data, the value of the differential capacity data may diverge as shown in Figure 220, making differentiation impossible, or the data value may be distorted into a negative number.

[0089] Therefore, the processor 120 can preprocess the battery data obtained from the interface unit 110 to prevent distortion of the differential capacity data as shown in Figure 220.

[0090] Figure 3 This is a diagram illustrating a battery data preprocessing procedure according to one embodiment disclosed herein.

[0091] Reference Figure 3 The figure illustrates the results of preprocessing battery voltage data through sampling and PCHIP. Figure 3 As shown, the raw voltage data includes repetitive signals and noise, but through the sampling process of processor 120 and the PCHIP interpolation method, the voltage data can be converted into a monotonically increasing form. Here, it can be understood that the sampling process is the process of removing repetitive voltage signals.

[0092] exist Figure 3 In the graph, it can be confirmed that, as a result of applying the PCHIP interpolation method to the sampled data (i.e., sampled data from which repetitive voltage signals have been removed) at specified voltage intervals (voltage resolution), the graph shows a smooth, monotonically increasing form without noise.

[0093] Figure 4 This is a diagram illustrating an example of feature extraction according to one embodiment disclosed herein.

[0094] Reference Figure 4 The processor 120 can extract features and analyze feature values ​​based on the values ​​of differential capacity data in each voltage band.

[0095] Figure 4Figure 410 illustrates four voltage bands R1 to R4 and features F1 to F5 extracted from the voltage bands. Each voltage band can be set based on the characteristics of at least one of the battery and the electronic device. In one embodiment, the voltage bands can be set based on the type of battery, the type of electronic device having the battery (e.g., the type of vehicle when the electronic device is a vehicle), etc.

[0096] Processor 120 can identify whether there is an inflection point in the differential capacity data within each voltage band. When an inflection point in the differential capacity data exists within each voltage band, processor 120 can extract at least one of the minimum and maximum values ​​of the differential capacity data within the voltage band as a feature. For example, when the graph representing the differential capacity data in a particular voltage band bulges upwards, processor 120 can extract the maximum value of the differential capacity data as a feature.

[0097] Reference Figure 4 According to Figure 410, the processor 120 can identify the inflection point of the differential capacity data in voltage band R1 and extract the data value at the inflection point (i.e., the maximum value of the differential capacity data in band R1) as the characteristic F1 of the battery. In this way, the processor 120 can calculate the characteristics F1 to F5 for diagnosing the battery in each of the frequency bands R1 to R4.

[0098] The processor 120 can perform diagnostics on the battery based on the values ​​of differential capacity data corresponding to the extracted features. For example, such as Figure 4 As illustrated in Figure 420, processor 120 can analyze the relationship between the values ​​of differential capacity data corresponding to the characteristics of the target battery and the vehicle's driving distance. Processor 120 can analyze the relationship between the analyzed characteristic values, driving distance, and the degree of battery degradation, and diagnose the degree of battery degradation based on the characteristic values. For example, it can be confirmed that the value (peak) of the differential capacity data of the characteristic values ​​tends to decrease with increasing driving distance (i.e., with increasing battery degradation).

[0099] Additionally, the processor 120 can analyze key features that can be used for diagnosis based on battery diagnostic items. For example, refer to... Figure 4 As shown in Figure 420, the change in the feature value based on driving distance is more significant in feature F1. In this case, when diagnosing battery degradation, processor 120 can use the feature value of feature F1 (instead of the other features F2 to F5) as the primary analysis target.

[0100] Figure 5 This is a diagram illustrating an example of information stored in a database according to one embodiment disclosed herein.

[0101] Reference Figure 5The processor 120 can construct a database by matching characteristics corresponding to the extracted features with battery characteristics. For example, such as Figure 5 As shown, for each charge, the processor 120 can match the characteristics of the extracted features with the characteristics of the battery.

[0102] For example, processor 120 can match information related to the data used during feature extraction, such as charging start time, charging end time, start SOC, and end SOC, with feature values ​​such as the voltage and intensity of the extracted features and store the matching results.

[0103] Figure 6 This is a flowchart describing a data analysis method according to one embodiment disclosed herein. Figure 6 The illustrated embodiment is only one embodiment, and the operational sequence of the various embodiments of this disclosure can be compared with... Figure 6 The order shown is different, and can be omitted. Figure 6 Some of the operations shown can change the order of operations or can be combined.

[0104] Reference Figure 6 The data analysis method may include the following operations: acquiring capacity data and voltage data of a battery disposed in an electronic device (S100), preprocessing the capacity data and voltage data to calculate differential capacity data (S200), extracting at least one feature for diagnosing the battery based on the value of the differential capacity data in at least one voltage band, the at least one voltage band being set based on the characteristics of at least one of the battery and the electronic device (S300), and performing a diagnosis on the battery based on the change in the differential capacity value corresponding to the feature (S400).

[0105] In operation S100, interface unit 110 can acquire capacity and voltage data of the battery installed in the electronic device. For example, interface unit 110 can acquire time-series data on capacity and voltage during the battery charging process.

[0106] In operation S200, one or more processors 120 can preprocess the capacity data and voltage data to calculate differential capacity data.

[0107] Reference Figure 7 According to one embodiment, the process of calculating differential capacity data by one or more processors 120 may include the following operations: extracting capacity data and voltage data in a target interval (S210), transforming the capacity data and voltage data such that the extracted voltage data can be differentiated in the target interval (S220), and interpolating the transformed capacity data and voltage data to calculate differential capacity data (S230).

[0108] In operation S210, one or more processors 120 can extract capacity data and voltage data from the target range. In one embodiment, one or more processors 120 can define a slow charging range during the battery charging process as the target range and extract capacity data and voltage data corresponding to the slow charging range.

[0109] In operation S220, one or more processors 120 can convert capacity data and voltage data such that the extracted voltage data can be differentiated within a target range. In one embodiment, one or more processors 120 can classify capacity data with the same voltage value among the extracted voltage data, calculate the average value of multiple capacity data with the same voltage value, and convert the calculated average value into a capacity value corresponding to the voltage value.

[0110] In operation S230, one or more processors 120 may interpolate the converted capacity data and voltage data to calculate differential capacity data. In one embodiment, one or more processors 120 may use the PCHIP method to interpolate the converted capacity data and voltage data. One or more processors 120 may differentiate the interpolated voltage data and capacity data to calculate differential capacity data.

[0111] In operation S300, one or more processors 120 may extract at least one feature for diagnosing the battery based on the values ​​of differential capacity data in at least one voltage band, the at least one voltage band being set based on the characteristics of at least one of the battery and the electronic device. In one embodiment, when there is an inflection point of differential capacity data in each voltage band, one or more processors 120 may extract at least one of the minimum and maximum values ​​of the differential capacity data in the voltage band as a feature.

[0112] In operation S400, one or more processors 120 can perform diagnostics on the battery based on differential capacity values ​​corresponding to features. For example, one or more processors 120 can analyze differential capacity values ​​corresponding to features to perform diagnostics on the battery.

[0113] Figure 8 This is a block diagram illustrating the hardware configuration of a computing system for performing an operation method of a data analysis apparatus according to one embodiment disclosed herein.

[0114] Reference Figure 8 The computing system 1000 according to one embodiment disclosed in this document may include a microcontroller unit (MCU) 1010, a memory 1020, an input / output I / F 1030 and a communication I / F 1040.

[0115] MCU 1010 can be a processor configured to execute various programs stored in memory 1020, through which various information, including battery data, is processed, and execution is performed, including... Figure 1 The functions of the processor in the data management device shown above.

[0116] The memory 1020 can store various programs for performing the functions of the data management device. Additionally, the memory 1020 can store various information including battery data (voltage data, capacity data, etc.), differential capacity data, and includes a constructed database.

[0117] The memory 1020 can be configured as multiple memories as needed. The memory 1020 can be volatile or non-volatile. As volatile memory, the memory 1020 can use RAM, DRAM, SRAM, etc. As non-volatile memory, the memory 1020 can use ROM, PROM, EAROM, EEPROM, flash memory, etc. The examples of memory 1020 listed above are merely illustrative and are not limited to these examples.

[0118] The input / output I / F 1030 can be an interface for connecting input devices (not shown), such as a keyboard, mouse, or touch panel, and output devices (not shown), such as a display, to the MCU 1010 and allowing the input and output devices, as well as the MCU 4610, to send and receive data.

[0119] The Communication I / F 1040 is a component capable of sending and receiving various types of data to and from a server, and can be any device capable of supporting wired or wireless communication. For example, a data management device can use the Communication I / F 1040 to send and receive various information, including battery data, to and from a separately configured external server.

[0120] As described above, a computer program according to one embodiment disclosed herein can be implemented for executing Figure 1 The module with the shown function, for example, is recorded in memory 1020 and processed by MCU 1010.

[0121] As stated above, although all components constituting the embodiments disclosed herein are described as operating by or in connection with each other, the embodiments disclosed herein are not necessarily limited to these embodiments. In other words, one or more of the components may operate by selective connection without departing from the purpose of the embodiments disclosed herein.

[0122] Furthermore, unless otherwise stated, terms such as "comprising," "constituting," or "having" as used above mean that the corresponding component may be inherent and should therefore be interpreted as including another component rather than excluding another component. Unless otherwise defined, all terms, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiments disclosed herein pertain. Unless expressly defined herein, commonly used terms, such as those defined in dictionaries, should be interpreted as consistent with the context of the relevant art and should not be interpreted in an ideal or overly formal sense.

[0123] The above description is merely an exemplary description of the technical spirit disclosed herein, and those skilled in the art to which the embodiments disclosed herein pertain will be able to make various modifications and changes to this document without departing from the essential characteristics of the embodiments disclosed herein. Therefore, the embodiments disclosed herein are not intended to limit the technical spirit disclosed herein, but are for illustrative purposes, and the scope of the technical spirit disclosed herein is not limited by these embodiments. The scope of the technical spirit disclosed herein should be interpreted by the appended claims, and all technical spirit within the equivalent scope should be interpreted as including within the scope of this document.

Claims

1. A data analysis device, the data analysis device comprising: An interface unit configured to acquire capacity and voltage data of a battery disposed in an electronic device; as well as One or more processors, wherein the one or more processors are configured to: The capacity data and voltage data are preprocessed to calculate the differential capacity data; and At least one feature for diagnosing the battery is extracted based on the value of the differential capacity data in at least one voltage band, the at least one voltage band being set based on the characteristics of at least one of the battery and the electronic device.

2. The data analysis device according to claim 1, wherein, The one or more processors are configured to: Extract the capacity data and voltage data from the target range; The capacity data and the voltage data are transformed so that the extracted voltage data can be differentiated within the target range; and The converted capacity and voltage data are interpolated to calculate the differential capacity data.

3. The data analysis device according to claim 2, wherein, The one or more processors are configured to identify the target interval corresponding to a slow charging interval during the charging of the battery in order to extract the capacity data and the voltage data.

4. The data analysis device according to claim 2, wherein, The one or more processors are configured to: The extracted voltage data is then categorized into multiple capacity data sets with the same voltage value. and Calculate the average value of the plurality of capacity data having the same voltage value and convert the average value into a capacity value corresponding to the voltage value.

5. The data analysis device according to claim 2, wherein, The one or more processors are configured to interpolate the transformed capacity and voltage data using the Piecewise Cubic Hermitian Interpolation Polynomial (PCHIP) method to convert the battery voltage into a monotonically increasing or monotonically decreasing state.

6. The data analysis apparatus according to claim 1, wherein, The one or more processors are configured to perform Gaussian filtering on the differential capacity data with a preset window size to smooth the differential capacity data.

7. The data analysis apparatus according to claim 1, wherein, When an inflection point of the differential capacity data exists in each voltage band, the one or more processors are configured to extract at least one of the minimum and maximum values ​​of the differential capacity data in the voltage band as the feature.

8. The data analysis apparatus according to claim 1, wherein, The one or more processors are configured to perform diagnostics on the battery based on values ​​of differential capacity data corresponding to the feature.

9. The data analysis apparatus according to claim 1, wherein, The one or more processors are configured to match features corresponding to the extracted features with features related to the battery to build a database.

10. A data analysis method, the data analysis method comprising the following steps: Acquire the capacity and voltage data of the battery installed in the electronic device; The capacity data and voltage data are preprocessed to calculate the differential capacity data; as well as At least one feature for diagnosing the battery is extracted based on the value of the differential capacity data in at least one voltage band, the at least one voltage band being set based on the characteristics of at least one of the battery and the electronic device.

11. The data analysis method according to claim 10, wherein, Calculating the differential capacity data includes the following steps: Extract the capacity data and voltage data from the target range; Transform the capacity data and the voltage data such that the extracted voltage data is differentiable within the target range; and The converted capacity and voltage data are interpolated to calculate the differential capacity data.

12. The data analysis method according to claim 10, wherein, When there is an inflection point in the differential capacity data in each voltage band, the step of extracting the at least one feature includes extracting at least one of the minimum and maximum values ​​of the differential capacity data as the feature.

13. The data analysis method according to claim 10, further comprising performing a diagnosis on the battery based on the change in the differential capacity value corresponding to the feature.

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