Analysis apparatus and operating method thereof

Abstract

According to embodiments disclosed in the present document, an analysis apparatus may comprise: an information obtainment unit that obtains discharge data of a battery during a charging cycle; and a controller that calculates, for each of previous charging cycles, a probability density function (PDF) on the basis of discharge data corresponding to each charging cycle, calculates a reference usage pattern on the basis of the probability density function for each of the previous charging cycles, calculates a driving usage pattern on the basis of discharge data obtained during a current charging cycle, and calculates, on the basis of the reference usage pattern and the driving usage pattern, usage change information related to the battery.

Description

Analysis device and its operating method

[0001] Cross-citation with related applications

[0002] This invention claims the benefit of priority from Korean Patent Application No. 10-2023-0116929, filed on September 4, 2023, and all contents of the document in that Korean Patent Application are incorporated herein by reference.

[0003] Technology field

[0004] The embodiments disclosed in this document relate to an analysis device and a method of operating the same.

[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] When such batteries are installed in vehicles, individual differences in battery performance, lifespan, safety, and usage can occur depending on the driver's tendencies, habits, and patterns. Therefore, methods for analyzing driver patterns and detecting changes in these patterns are required. In particular, drivers often find it difficult to detect changes in their patterns or the resulting changes in battery usage, requiring a method that can assess these changes and provide the driver with relevant information.

[0007] One purpose of the embodiments disclosed in this document is to analyze and provide a user's usage pattern from battery data.

[0008] Additionally, one purpose of the embodiments disclosed in this document is to provide analysis of changes in patterns and changes in usage amount from a user's usage pattern.

[0009] 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.

[0010] According to an embodiment disclosed in the present document, the analysis device may include an information acquisition unit that acquires discharge data of a battery during a charging cycle, and a controller that calculates a probability density function (PDF) based on discharge data corresponding to each charging cycle for each of the previous charging cycles, calculates a reference usage pattern based on the probability density function of each of the previous charging cycles, calculates a driving usage pattern based on discharge data acquired during a current charging cycle, and calculates usage change information related to the battery based on the reference usage pattern and the driving usage pattern.

[0011] According to an embodiment, the controller may calculate the probability density function by applying Kernel Density Estimation (KDE) to discharge data corresponding to each of the previous charging cycles.

[0012] According to an embodiment, the controller may calculate the reference usage pattern by averaging the probability density function of each of the previous charging cycles.

[0013] According to an embodiment, the controller may calculate the driving usage pattern by applying Kernel Density Estimation (KDE) to discharge data acquired during the current charging cycle.

[0014] According to an embodiment, the controller may update the driving usage pattern by reflecting discharge data for each driving included in the current charging cycle.

[0015] According to an embodiment, the controller can calculate relative entropy for the reference usage pattern and the driving usage pattern.

[0016] According to an embodiment, the controller can determine a tendency of the driving usage pattern based on a change in the relative entropy according to an update of the driving usage pattern.

[0017] According to an embodiment, the controller may provide a notification to the user based on the tendency of the driving usage pattern and the relative entropy.

[0018] According to an embodiment, the controller can calculate the usage change information based on the difference in the area of ​​the graphs represented by each of the reference usage pattern and the driving usage pattern.

[0019] According to an embodiment, the controller may initialize the driving usage pattern when the current charging cycle ends.

[0020] According to an embodiment, the discharge data may include a depth of discharge (DoD) of the battery.

[0021] According to an embodiment disclosed in the present document, a method of operating an analysis device may include: a step of obtaining discharge data of a battery during a charging cycle; a step of calculating a probability density function (PDF) based on discharge data corresponding to each charging cycle for each of the previous charging cycles; a step of calculating a reference usage pattern based on the probability density function of each of the previous charging cycles; a step of calculating a driving usage pattern based on discharge data obtained during a current charging cycle; and a step of calculating usage change information related to the battery based on the reference usage pattern and the driving usage pattern.

[0022] According to an embodiment, the step of calculating the probability density function may be characterized by calculating the probability density function by applying Kernel Density Estimation (KDE) to discharge data corresponding to each of the previous charging cycles.

[0023] According to an embodiment, the step of calculating the usage pattern may be characterized by calculating the reference usage pattern by averaging the probability density function of each of the previous charging cycles.

[0024] According to an embodiment, the step of calculating the driving usage pattern may be characterized by calculating the driving usage pattern by applying Kernel Density Estimation (KDE) to discharge data acquired during the current charging cycle.

[0025] According to an embodiment, the step of calculating the driving usage pattern may be characterized by updating the driving usage pattern by reflecting discharge data for each driving included in the current charging cycle.

[0026] According to an embodiment, the operating method of the analysis device may further include a step of calculating relative entropy for the reference usage pattern and the driving usage pattern, and a step of determining a tendency of the driving usage pattern based on a change in the relative entropy according to an update of the driving usage pattern.

[0027] According to an embodiment, the step of calculating the usage change information may be characterized by calculating the usage change information based on the difference in the area of ​​a graph represented by each of the reference usage pattern and the driving usage pattern.

[0028] The analysis device and its operation method according to the embodiments disclosed in this document can produce a personalized standard usage pattern of a user.

[0029] In addition, the analysis device and its operating method according to the embodiments disclosed in this document can compare a reference usage pattern with a current driving usage pattern to determine whether it deviates from the reference usage pattern.

[0030] In addition, the analysis device and its operating method according to the embodiments disclosed in this document can effectively determine changes in battery usage and provide adjustment information such as a charging schedule accordingly.

[0031] In addition, various effects may be provided, either directly or indirectly, through this document.

[0032] Figure 1 is a block diagram showing the configuration of an analysis device according to one embodiment disclosed in this document.

[0033] FIG. 2 is a diagram showing an example of calculating a reference usage pattern according to one embodiment disclosed in this document.

[0034] FIGS. 3 and 4 are diagrams showing examples of driving usage patterns according to one embodiment disclosed in this document.

[0035] FIG. 5 is a diagram showing an example of calculating usage change information according to one embodiment disclosed in this document.

[0036] FIG. 6 is a flowchart for explaining an operation method of an analysis device according to one embodiment disclosed in this document.

[0037] FIG. 7 is a flowchart for explaining an operation method of an analysis device according to another embodiment disclosed in this document.

[0038] 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.

[0039] 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.

[0040] 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.

[0041] 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.

[0042] 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).

[0043] 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.

[0044]

[0045] Figure 1 is a block diagram showing the configuration of an analysis device according to one embodiment disclosed in this document.

[0046] Referring to FIG. 1, the analysis device (100) may include an information acquisition unit (110) and a controller (120).

[0047] The analysis device (100) can detect a baseline battery usage pattern of a user (e.g., a vehicle user if the battery is installed in the vehicle), compare the current battery usage pattern with the baseline usage pattern, and notify the user and provide usage change information. The analysis device (100) can detect and compare the user's usage pattern, and if the battery usage exceeds the baseline usage pattern, notify the user of the excess usage and provide information for adjusting the usage (e.g., adjusting the charging schedule, etc.).

[0048] The analysis device (100) can transmit usage change information, notifications, etc. to an external electronic device, and the electronic device may be a user terminal carried by the user. The electronic device may include various types of devices capable of performing data communication. For example, the electronic device (30) may include a portable device such as a smartphone or tablet, a computer device such as a desktop or laptop, a multimedia device, a camera, a wearable device, a VR (virtual reality) device, etc., and is not limited to the devices described above.

[0049] The analysis device (100) may be implemented as a separate physical device or server, and in some cases, may be included in a battery management system (BMS) of a battery and implemented as a component of the battery management system. For example, the analysis device (100) may be implemented as an IC chip, a system-on-chip, etc. configured to perform the function of calculating usage patterns and calculating usage change information in the battery management system (BMS).

[0050] The information acquisition unit (110) may include a communication circuit that can establish a wired communication channel and / or a wireless communication channel between internal and external components of the analysis device (100), such as a server, a vehicle, a battery management system, an OBD (On Board Device), etc., and transmit and receive various data through the established communication channel.

[0051] The information acquisition unit (110) can acquire battery discharge data during a charging cycle. Here, the battery may be a battery mounted on a vehicle associated with the user of the analysis device (100), and may be a battery pack or battery module. The information acquisition unit (110) can acquire battery discharge data from, for example, a battery management system (BMS) installed in the battery pack, a vehicle BMS, an OBD (On Board Device), etc.

[0052] The information acquisition unit (110) can acquire discharge data over multiple charging cycles. Here, a charging cycle may refer to a period between battery charges. For example, the i (i is a natural number) charging cycle may refer to the period from the end of the i-th charge to the start of the i+1-th charge.

[0053] Battery discharge data may include various information related to battery discharge within a charging cycle. For example, battery discharge data may be acquired each time a vehicle equipped with a battery is driven within a charging cycle. That is, the information acquisition unit (110) may acquire discharge data during the battery discharge process for each drive within a charging cycle. For example, if n drives occurred in the ith charging cycle, the information acquisition unit (110) may acquire discharge data for each of the n drives.

[0054] According to an embodiment, the battery discharge data may include the depth of discharge (DoD) of the battery. The information acquisition unit (110) may acquire various battery-related information, such as current, voltage, temperature, and SoC (State of Charge), in addition to the depth of discharge of the battery.

[0055] The information acquisition unit (110) can acquire battery discharge data in the form of time-series data, and the controller (120) can detect the battery discharge period from the time-series data. For example, the controller (120) can detect the start and end of each driving cycle within a charging cycle. The controller (120) can detect the battery discharge period based on various pieces of information acquired from the information acquisition unit (110).

[0056] The controller (120) can calculate a probability density function for each of the previous charging cycles based on the discharge data corresponding to each charging cycle. The controller (120) can detect the start and end of charging from the discharge data acquired in a time-series manner, and can distinguish each charging cycle accordingly. Here, the previous cycle may refer to the charging cycles that occurred before the current cycle, and the current cycle may refer to the current ongoing cycle after the last charging is completed and before the next charging begins.

[0057] The controller (120) can calculate a probability density function (PDF) from discharge data corresponding to each charging cycle section in a divided charging cycle. For example, the controller (120) can calculate a probability density function for parameters of the discharge data (e.g., discharge depth, etc.) in each charging cycle.

[0058] In an embodiment, the controller (120) may utilize discharge data acquired for each discharge section or driving of the battery, and in this case, the value of the discharge data used when calculating the reference usage pattern and the driving usage pattern may be discontinuous. Even if the value of the discharge data used in this way is discontinuous, the controller (120) may apply various operations, such as kernel density estimation described below, to calculate a probability density function for continuous values. For example, the discharge data may include a discharge depth, and the value of the discharge depth consumed for each driving may be discontinuous. In this case, the controller (120) may calculate the discontinuous data as a continuous probability density function for the discharge depth. Separately from the controller (120) utilizing the discontinuous discharge data, the information acquisition unit (110) may of course acquire the discharge data in a time-series manner.

[0059] According to an embodiment, the controller (120) can calculate a probability density function by applying kernel density estimation to discharge data. Kernel density estimation is a technique used to calculate the distribution of data, and can calculate the distribution of data to be analyzed by using a kernel expressed as a probability density. The controller (120) can calculate the distribution of discharge data as a probability density function by using kernel density estimation. The controller (120) can calculate a continuous probability density function even when the discharge data is discontinuous.

[0060] The controller (120) can calculate a reference usage pattern based on the probability density function of each of the previous charging cycles. For example, the controller (120) can calculate the reference usage pattern from the probability density function of a preset number (e.g., 10) of previous charging cycles. In this case, the preset number of previous charging cycles can be determined in a temporally close order to the current charging cycle.

[0061] According to an embodiment, the controller (120) may calculate a reference usage pattern by averaging the probability density function of each of the previous charging cycles. For example, the controller (120) may calculate a reference usage pattern by averaging the probability density functions of a preset number (e.g., 10) of previous charging cycles. Accordingly, the reference usage pattern may be expressed as a probability density for parameters of the discharge data. The controller (120) may also calculate the reference usage pattern by applying various operations, in addition to the average, to the probability density functions of the previous charging cycles.

[0062] The controller (120) can calculate a driving usage pattern based on discharge data acquired during the current charging cycle. The controller (120) can calculate a driving usage pattern by continuously reflecting discharge data acquired over time during the current charging cycle.

[0063] In an embodiment, the controller (120) can detect the discharge section of the battery included in the current charging cycle. For example, the controller (120) can detect the driving section in the current charging cycle and calculate the driving usage pattern from the discharge data acquired for each driving.

[0064] According to an embodiment, the controller (120) may calculate a driving usage pattern when a preset number of driving (or discharge sections) occurs in the current charging cycle. If the number of driving (or discharge sections) in the current charging cycle is less than the preset number, the controller (120) may not calculate the driving usage pattern until sufficient discharge data is obtained, as the accuracy of the driving usage pattern may be reduced due to insufficient discharge data. The preset number of driving sections may be set to, for example, three times, but is not limited thereto.

[0065] According to an embodiment, the controller (120) can calculate a driving usage pattern by applying kernel density estimation to discharge data acquired during the current charging cycle. Accordingly, the driving usage pattern can be expressed as a probability density for parameters of the discharge data.

[0066] According to an embodiment, the controller (120) may update the driving usage pattern by reflecting discharge data for each driving included in the current charging cycle. For example, if a new driving is detected in the driving usage pattern calculated up to the current point in time, the controller (120) may update the driving usage pattern by reflecting discharge data for the corresponding driving to the previously calculated driving usage pattern. In this way, the controller (120) may update the driving usage pattern by accumulating discharge data in real time. In addition, the controller (120) may track changes in the driving usage pattern.

[0067] The controller (120) can calculate battery usage change information based on a reference usage pattern and a driving usage pattern. For example, the controller (120) can calculate usage change information from battery usage calculated from each of the reference usage pattern and the driving usage pattern. The battery usage change information can include usage exceeding or falling short of the reference usage pattern.

[0068] For example, the controller (120) can calculate usage change information according to [Mathematical Formula 1] or [Mathematical Formula 2] below. The controller (120) can apply [Mathematical Formula 1] when the discharge data value is continuous, and can apply [Mathematical Formula 2] when the discharge data value is discontinuous.

[0069] [Mathematical Formula 1]

[0070] Usage change information =

[0071] [Equation 2]

[0072] Usage change information =

[0073] Here, the discharge data value refers to the discharge data value acquired in the current driving cycle. For example, the discharge data value may refer to the discharge data value acquired for each drive in the current driving cycle. In addition, and represents the probability density of each data value in the standard usage pattern and driving usage pattern, respectively.

[0074] According to an embodiment, the controller (120) can calculate usage change information based on the difference in the area of ​​the graphs represented by the reference usage pattern and the driving usage pattern, respectively. As described above, the reference usage pattern and the driving usage pattern can be expressed as a probability density for parameters of the discharge data, and the discharge data can include DoD. For example, the reference usage pattern and the driving usage pattern can be expressed as a graph representing a probability distribution according to DoD, and the area of ​​each graph can represent the usage of the battery. Here, the area of ​​the graph can mean the area of ​​the region formed by the graph and the x-axis and the y-axis.

[0075] In an embodiment, the controller (120) may calculate relative entropy for a reference usage pattern and a driving usage pattern. In an embodiment, the controller (120) may calculate relative entropy by applying Kullback-Leibler divergence (KLD) to the reference usage pattern and the driving usage pattern. Kullback-Leibler divergence is a method of calculating the difference between two probability distributions, and the difference between the two probability distributions may be calculated as relative entropy. For example, Kullback-Leibler divergence may be expressed as in [Mathematical Equation 3] below.

[0076] [Equation 3]

[0077]

[0078] Here, p(x) and q(x) represent probability distributions, respectively. represents the difference between the probability distribution p and the probability distribution q, i.e., the relative entropy.

[0079] According to an embodiment, the controller (120) can determine the tendency of the driving usage pattern based on the change in relative entropy according to the update of the driving usage pattern. The controller (120) can update the driving usage pattern by reflecting the discharge data of the driving whenever a new driving occurs in the current charging cycle (i.e., whenever a new discharge section occurs). In other words, the driving usage pattern of the current charging cycle can change whenever a new driving occurs. In this case, the controller (120) can calculate the relative entropy with respect to the reference usage pattern whenever the driving usage pattern changes. The controller (120) can track the change in the relative entropy calculated in this way.

[0080] The controller (120) can determine the tendency of the driving usage pattern based on the change in relative entropy. Here, the tendency of the driving usage pattern may include, for example, a tendency toward a reference usage pattern and a tendency toward a trend away from the reference usage pattern. For example, if the relative entropy continuously decreases according to an update of the driving usage pattern, the controller (120) can determine the tendency of the driving usage pattern as a tendency toward a reference usage pattern. If there is a drive in which the relative entropy increases according to an update of the driving usage pattern, the controller (120) can determine the tendency of the driving usage pattern as a tendency toward a trend away from the reference usage pattern.

[0081] According to an embodiment, the controller (120) may provide a notification to the user based on the tendency and relative entropy of the driving usage pattern. For example, if the tendency of the driving usage pattern is determined to be a trend that deviates from the reference usage pattern and there is a driving in which the relative entropy exceeds a threshold, the controller (120) may provide a warning notification to the user. In other words, the controller (120) may provide a warning notification to the user so that the user is aware of the current excessive battery usage. The above-described notification conditions are merely examples, and the controller (120) may provide various notifications according to various conditions. For example, if the tendency of the driving usage pattern is a trend that approaches the reference usage pattern, the controller (120) may provide a notification indicating that the battery usage pattern is good.

[0082] The controller (120) may, for example, provide a notification to a user terminal carried by the user, and the user may check the notification provided by the controller (120) and take action to adjust the battery usage. In an embodiment, the controller (120) may additionally provide adjustment information related to the battery usage (e.g., charging schedule information, etc.) based on the tendency and relative entropy of the driving usage pattern.

[0083] In an embodiment, the controller (120) may initialize the driving usage pattern when the current charging cycle ends. For example, the controller (120) may determine that the current charging cycle has ended if it detects that charging has begun in the current charging cycle. When the current charging cycle ends, the controller (120) may calculate the final driving usage pattern as a probability density function of the current cycle. When charging ends and new discharge data is acquired, the controller (120) may then calculate a new driving usage pattern.

[0084] In this way, the analysis device (100) can calculate a user's personalized baseline usage pattern. Furthermore, the analysis device (100) can compare the baseline usage pattern with the current driving usage pattern to determine whether it deviates from the baseline usage pattern. Furthermore, the analysis device (100) can determine changes in battery usage and effectively provide adjustment information, such as charging schedules, based on the changes.

[0085] FIG. 2 is a diagram showing an example of calculating a reference usage pattern according to one embodiment disclosed in this document.

[0086] Referring to FIG. 2, the controller (120) can derive a reference usage pattern (220) from the probability density functions (210_1, 210_2, …, 210_n) of previous charging cycles. FIG. 2 illustrates an example of deriving a reference usage pattern from the probability density functions of n previous charging cycles, and each graph illustrates an example of a probability density for a discharge depth.

[0087] The probability density function (210_1, 210_2, …, 210_n) of the previous charging cycles and the reference usage pattern (220) can be expressed in the form of a graph showing the probability density for the parameter (e.g., DoD) of the discharge data, as shown in Fig. 2, where the x-axis represents the depth of discharge (DoD) and the y-axis represents the probability density. The value of the depth of discharge can be expressed as a value between 0 and 100%.

[0088] In an embodiment, the controller (120) may calculate a reference usage pattern (220) by averaging probability density functions (210_1, 210_2, …, 210_n) of previous charging cycles. That is, the reference usage pattern (220) may represent an average probability density of each discharge depth.

[0089] FIG. 3 and FIG. 4 are diagrams illustrating examples of driving usage patterns according to one embodiment disclosed in this document. FIG. 3 is a diagram illustrating an example in which a driving usage pattern tends to approach a reference usage pattern, and FIG. 4 is a diagram illustrating an example in which a driving usage pattern tends to move away from a reference usage pattern.

[0090] First, referring to FIG. 3, the driving usage pattern (320, 330, 340, 350) can be confirmed from the kth (k is a natural number) to the k+3rd driving in the current charging cycle. In this way, the controller (120) can update the driving usage pattern for each driving.

[0091] In Figure 3, it can be seen that relative entropy continuously decreases as the number of trips increases (i.e., as the driving usage pattern is updated). In this case, the controller (120) can determine that the driving usage pattern tends to approach the reference usage pattern.

[0092] In Fig. 3, the reference usage patterns (310_1, 310_2, 310_3, 310_4) use different symbols for comparison with the driving usage patterns for each drive, but in reality, they can all be the same. In Fig. 3, the shape of the reference usage patterns may appear different because the y-axis range is different from the k-th drive to the k+3-th drive, but this is only due to the difference in the y-axis range, and the reference usage patterns (310_1, 310_2, 310_3, 310_4) all show the same pattern.

[0093] Next, referring to FIG. 4, it can be confirmed that there exists a run (k+2nd run -> k+3rd run) in which the relative entropy increases as the number of runs increases (i.e., as the run usage pattern is updated). In this case, the controller (120) can determine that the run usage pattern has a tendency to deviate from the reference run usage pattern.

[0094] FIG. 5 is a diagram showing an example of calculating usage change information according to one embodiment disclosed in this document.

[0095] Referring to FIG. 5, the controller (120) can calculate usage change information based on the standard usage pattern (510) and the driving usage pattern (520).

[0096] In Fig. 5, the area of ​​the graph represented by the standard usage pattern (510) is A, and the area of ​​the graph represented by the driving usage pattern (520) is B. When the value of the discharge data is continuous, the controller (120) can calculate usage change information as AB.

[0097] When the value of the discharge data is discontinuous, the controller (120) calculates the battery usage by multiplying the value of the discharge data by the probability density of each data for each of the reference usage pattern (510) and the driving usage pattern (520), and can calculate usage change information from the difference in battery usage.

[0098] For example, if the value of the depth of discharge (DoD) is 1 as shown in Fig. 5, st DoD, 2 nd DoD, 3 rd In the case of discontinuity with DoD, the controller (120) is 1 st DoD value and 1 st Multiply the difference in probability density of the reference usage pattern (510) and the driving usage pattern (520) for the DoD value and divide by 2 equally. nd DoD, 3 rdIt can also be applied to DoD to produce usage change information. In this case, 1 st DoD, 2 nd DoD, 3 rd Each DoD may mean a discharge depth value in the first drive, second drive, and third drive in the current drive cycle that produced the driving usage pattern (520).

[0099] FIG. 6 is a flowchart for explaining an operation method of an analysis device according to one embodiment disclosed in this document.

[0100] Referring to FIG. 6, the operating method of the analysis device may include a step of obtaining discharge data of a battery during a charging cycle (S100), a step of calculating a probability density function (PDF) based on discharge data corresponding to each charging cycle for each of the previous charging cycles (S200), a step of calculating a reference usage pattern based on the probability density function of each of the previous charging cycles (S300), a step of calculating a driving usage pattern based on discharge data obtained during a current charging cycle (S400), and a step of calculating usage change information related to the battery based on the reference usage pattern and the driving usage pattern (S500).

[0101] In step S100, the information acquisition unit (110) may acquire battery discharge data during a charging cycle. For example, the information acquisition unit (110) may acquire discharge data for each drive in each charging cycle. In an embodiment, the battery discharge data may include the battery's depth of discharge (DoD).

[0102] In step S200, the controller (120) may calculate a probability density function based on the discharge data corresponding to each charging cycle for each of the previous charging cycles. In an embodiment, the controller (120) may calculate the probability density function by applying Kernel Density Estimation (KDE) to the discharge data corresponding to each charging cycle.

[0103] In step S300, the controller (120) may calculate a reference usage pattern based on the probability density function of each of the previous charging cycles. In an embodiment, the controller (120) may calculate the reference usage pattern by averaging the probability density functions of each of the previous charging cycles.

[0104] At step S400, the controller (120) may calculate a driving usage pattern based on discharge data acquired during the current charging cycle. In an embodiment, the controller (120) may calculate a driving usage pattern by applying kernel density estimation to the discharge data acquired during the current charging cycle.

[0105] At step S500, the controller (120) can calculate usage change information related to the battery based on the reference usage pattern and the driving usage pattern. In an embodiment, the reference usage pattern and the driving usage pattern can be expressed as graphs of parameters and probability densities of discharge data, and at this time, the controller (120) can calculate usage change information from the difference in the area of ​​the graphs represented by the reference usage pattern and the driving usage pattern, respectively.

[0106] FIG. 7 is a flowchart for explaining an operation method of an analysis device according to another embodiment disclosed in this document.

[0107] FIG. 7 may further include a step (S600) of calculating relative entropy for a reference usage pattern and a driving usage pattern compared to FIG. 6, and a step (S700) of determining a tendency of a driving usage pattern based on a change in relative entropy according to an update of the driving usage pattern.

[0108] At step S600, the controller (120) can calculate the relative entropy for the reference usage pattern and the driving usage pattern. In an embodiment, the controller (120) can calculate the relative entropy by applying Kullback-Leibler Divergence (KLD) to the reference usage pattern and the driving usage pattern.

[0109] At step S700, the controller (120) can determine the trend of the driving usage pattern based on changes in relative entropy resulting from updates to the driving usage pattern. For example, the trend of the driving usage pattern may include, for example, a trend toward a reference usage pattern and a trend toward a departure from the reference usage pattern.

[0110] Although steps S600, S700, and S800 are depicted in parallel in FIG. 7, each step may be performed simultaneously or sequentially. That is, the controller (120) may update the driving usage pattern for each drive in the current driving cycle, and may also calculate usage change information from the updated driving usage pattern and the reference usage pattern.

[0111] 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.

[0112] 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).

[0113] The MCU (1010) may be a processor that executes various programs (e.g., a battery data acquisition program, a feature extraction program, a correlation coefficient matrix generation program, a pattern extraction program, a classification program, etc.) stored in the memory (1020), processes various information including time series data of a data set through these programs, and performs the functions of the controller included in the data analysis device shown in FIG. 2 described above.

[0114] The memory (1020) can store various programs, such as a battery data acquisition program, a feature extraction program, a correlation coefficient matrix generation program, a pattern extraction program, and a classification program. In addition, the memory (1020) can store various information, including classification results of a data set and abnormality occurrence history.

[0115] 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.

[0116] 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).

[0117] 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 abnormality occurrence details in a data set, from a separately provided external server via the communication I / F (1040).

[0118] 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. 2, for example.

[0119]

[0120] 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.

[0121] 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.

[0122] 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 information acquisition unit that acquires discharge data of a battery during a charging cycle; and For each of the previous charging cycles, a probability density function (PDF) is calculated based on the discharge data corresponding to each charging cycle, Calculate a reference usage pattern based on the probability density function of each of the above previous charging cycles, Calculate driving usage patterns based on discharge data acquired during the current charging cycle, An analysis device including a controller that calculates usage change information related to the battery based on the above-mentioned standard usage pattern and the above-mentioned driving usage pattern.

2. In paragraph 1, The above controller, An analysis device that calculates the probability density function by applying Kernel Density Estimation (KDE) to discharge data corresponding to each of the above-mentioned previous charging cycles.

3. In paragraph 1, The above controller, An analysis device that calculates the reference usage pattern by averaging the probability density function of each of the previous charging cycles.

4. In paragraph 1, The above controller, An analysis device that calculates the driving usage pattern by applying Kernel Density Estimation (KDE) to discharge data acquired during the current charging cycle.

5. In paragraph 4, The above controller, An analysis device that updates the driving usage pattern by reflecting discharge data for each driving included in the current charging cycle.

6. In paragraph 5, The above controller, An analysis device that calculates relative entropy for the above-mentioned standard usage pattern and the above-mentioned driving usage pattern, 7. In paragraph 6, The above controller, An analysis device that determines the tendency of the driving usage pattern based on the change in the relative entropy according to the update of the driving usage pattern.

8. In paragraph 7, The above controller, An analysis device that provides a notification to a user based on the tendency of the driving usage pattern and the relative entropy.

9. In paragraph 1, The above controller, An analysis device that calculates the usage change information based on the difference in the area of ​​the graphs indicated by the above-mentioned standard usage pattern and the above-mentioned driving usage pattern, respectively.

10. In paragraph 1, The above controller, An analysis device that resets the driving usage pattern when the current charging cycle ends.

11. In paragraph 1, An analysis device wherein the discharge data includes the depth of discharge (DoD) of the battery.

12. A step of acquiring discharge data of a battery during a charging cycle; A step of calculating a probability density function (PDF) based on discharge data corresponding to each charging cycle for each of the previous charging cycles; A step of calculating a reference usage pattern based on a probability density function of each of the previous charging cycles; A step of calculating a driving usage pattern based on discharge data acquired during the current charging cycle; and An operating method of an analysis device, comprising a step of calculating usage change information related to the battery based on the above-mentioned standard usage pattern and the above-mentioned driving usage pattern.

13. In paragraph 12, The step of calculating the above probability density function is: An operating method of an analysis device, characterized in that the probability density function is calculated by applying Kernel Density Estimation (KDE) to discharge data corresponding to each of the previous charging cycles.

14. In paragraph 12, The steps for calculating the above usage pattern are: A method of operating an analysis device, characterized in that the reference usage pattern is calculated by averaging the probability density function of each of the previous charging cycles.

15. In paragraph 12, The step of calculating the above driving usage pattern is: An operating method of an analysis device, characterized in that the driving usage pattern is calculated by applying Kernel Density Estimation (KDE) to discharge data acquired during the current charging cycle.

16. In paragraph 15, The step of calculating the above driving usage pattern is: An operating method of an analysis device, characterized in that for each drive included in the current charging cycle, the driving usage pattern is updated by reflecting the discharge data for the drive.

17. In paragraph 16, A step of calculating relative entropy for the above standard usage pattern and the above driving usage pattern; and A method of operating an analysis device, further comprising a step of determining a tendency of the driving usage pattern based on a change in the relative entropy according to an update of the driving usage pattern.

18. In paragraph 12, The step of calculating the above usage change information is: An operating method of an analysis device, characterized in that the usage change information is calculated based on the difference in the area of ​​the graphs indicated by the above-mentioned standard usage pattern and the above-mentioned driving usage pattern, respectively.

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