Charging Time Prediction Device and Its Operation Method

Abstract

The charging time prediction device according to an embodiment disclosed in this document includes a communication circuit and a processor. The processor receives, via the communication circuit, first battery information including current values, temperature values, SoC (state of charge), and SoH (state of health) of a battery included in the external electronic device during a specified period of the external electronic device from the external electronic device, receives information regarding the traveling speed of the external electronic device during the specified period and / or information regarding the presence or absence of connection between the external electronic device and an external charger via the communication circuit, extracts a charging section within the specified period based on the first battery information, the information regarding the traveling speed, and / or the information regarding the presence or absence of connection during the specified period, and may be configured to generate a charging time prediction model using the current value, temperature value, SoC, and SoH at a first time point of the charging section and the SoC at a second time point of the charging section.

Description

Technical Field

[0001] The present invention claims the benefit of priority based on Korean Patent Application No. 10-2022-0103001 filed on August 17, 2022, and all the contents disclosed in the document of the Korean patent application are incorporated herein by reference in their entirety.

[0002] The embodiments disclosed in this document relate to a charging time prediction device and an operation method thereof.

Background Art

[0003] In recent years, research and development on secondary batteries have been actively conducted. Here, a secondary battery is a battery that can be charged and discharged, and includes both conventional Ni / Cd batteries, Ni / MH batteries, etc., and recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of having a much higher energy density compared to conventional Ni / Cd batteries, Ni / MH batteries, etc. In addition, since lithium-ion batteries can be manufactured in a small and lightweight manner, they are used as a power source for mobile devices. In recent years, their usage range has been extended to the power source of electric vehicles and they have attracted attention as a next-generation energy storage medium.

[0004] It is important to accurately provide the user with the charging time required for the user to determine whether to continue charging the battery for the time required to charge it to the target SoC (state of charge).

[0005] Conventionally, in order to predict the charging time required, a method of calculating the charging time required that matches the current temperature and SoC of the battery based on a preset table has been used.

Summary of the Invention

Problems to be Solved by the Invention

[0006] However, in the conventional charging time prediction method, the data capacity of the preset table is limited and the battery degradation rate is not considered, so there is a limit in that an error occurs between the calculated charging time and the actual charging time.

[0007] One object of the embodiments disclosed in this document is to provide a charging time prediction device and its operation method that predict the charging time by minimizing the error from the actual charging time.

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

Means for Solving the Problems

[0009] The charging time prediction device according to an embodiment disclosed in this document includes a communication circuit and a processor. The processor receives first battery information including a current value, a temperature value, an SoC (state of charge), and an SoH (state of health) of a battery included in the external electronic device during a specified period from the external electronic device via the communication circuit, receives information regarding the traveling speed of the external electronic device during the specified period and / or information regarding the presence or absence of connection between the external electronic device and an external charger via the communication circuit, extracts a charging section within the specified period based on the first battery information, the information regarding the traveling speed, and / or the information regarding the presence or absence of connection during the specified period, and may be configured to generate a charging time prediction model using the current value, the temperature value, the SoC, and the SoH at a first time point of the charging section and the SoC at a second time point of the charging section.

[0010] According to one embodiment disclosed in this document, the processor may be configured to receive, via the communication circuit, second battery information including a current value, a temperature value, an SoC, and an SoH of the battery at a specified time point from the external electronic device, and predict a charging time required from the SoC at the specified time point to a target SoC based on the charging time prediction model.

[0011] According to one embodiment disclosed in this document, the first battery information includes model information of the battery, and the processor may be configured to generate the charging time prediction model corresponding to the model information.

[0012] According to one embodiment disclosed in this document, the processor may be configured to extract, as the charging section, at least a partial section of a first section in which the current value of the battery is equal to or greater than a specified current value during the specified period.

[0013] According to one embodiment disclosed in this document, the processor may be configured to extract, as the charging section, at least a partial section of a second section in the first section in which the traveling speed of the external electronic device is a specified speed and the external electronic device is connected to the external charger.

[0014] According to one embodiment disclosed in this document, the processor may be configured to extract, as the charging section, at least a partial section of a third section excluding a data interruption section, a section in which the charging time is less than a specified time, and a section in which the charged amount is less than a specified charged amount in the second section.

[0015] According to one embodiment disclosed in this document, the processor may be configured to extract, as the charging section, a section excluding a section in which the current change value of the battery is equal to or greater than a specified change value in the third section.

[0016] According to an embodiment disclosed in this document, the processor may be configured to transmit the predicted charging time to the external electronic device via the communication circuit so as to provide the predicted charging time to the user.

[0017] A method for predicting charging time according to an embodiment disclosed in this document may include: obtaining first battery information including a current value, a temperature value, an SoC, and an SoH during a specified period of a battery included in an external electronic device; obtaining information regarding a traveling speed of the external electronic device and / or information regarding whether the external electronic device is connected to an external charger; extracting a charging section from among the specified period based on the first battery information, the information regarding the traveling speed, and / or the information regarding the connection during the specified period; and generating a charging time prediction model using the current value, the temperature value, the SoC, and the SoH at a first time point of the charging section and the SoC at a second time point of the charging section.

[0018] According to an embodiment disclosed in this document, the first battery information includes model information of the battery, and the operation of generating the charging time prediction model may include an operation of generating the charging time prediction model corresponding to the model information.

[0019] According to an embodiment disclosed in this document, the operation of extracting the charging section may include an operation of extracting, as the charging section, at least a partial section of a first section in which the current value of the battery is equal to or greater than a specified current value from among the specified period.

[0020] According to an embodiment disclosed in this document, the operation of extracting the charging section may include an operation of extracting, as the charging section, at least a partial section of a second section in which the traveling speed of the external electronic device is a specified speed and the external electronic device is connected to the external charger from among the first section.

[0021] According to one embodiment disclosed in this document, the operation of extracting the charging period may include an operation of extracting at least a part of a third period, which excludes a data interruption period, a period in which the charging time is less than a specified time, and a period in which the charged amount is less than a specified charged amount, from the second period, as the charging period.

[0022] According to one embodiment disclosed in this document, the operation of extracting the charging period may include an operation of extracting, as the charging period, a period obtained by excluding, from the third period, a period in which the current change value of the battery is equal to or greater than a specified change value.

Advantages of the Invention

[0023] According to the embodiment disclosed in this document, by predicting the required charging time based on a charging time prediction model generated and / or learned using battery information including the current value, temperature value, SoC, and SoH of the battery, the prediction accuracy of the required charging time can be improved.

[0024] According to the embodiment disclosed in this document, by extracting only the information of the charging period from the battery information, the prediction accuracy of the required charging time of the charging time prediction model can be further improved.

[0025] According to the embodiment disclosed in this document, by providing the predicted required charging time to the user, the convenience of the user can be improved. In addition, various effects directly or indirectly grasped by this document can be provided.

Brief Description of the Drawings

[0026]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Embodiments for Carrying Out the Invention

[0027] Hereinafter, various embodiments of the present invention will be described with reference to the accompanying drawings. However, this is not intended to limit the present invention to specific embodiments, and it should be understood to include various modifications, equivalents, and / or alternatives of the embodiments of the present invention.

[0028] Various embodiments of this document and the terms used therein are not intended to limit the technical features described in this document to specific embodiments, and it should be understood to include various modifications, equivalents, or alternatives of the embodiments. In connection with the description of the drawings, similar reference numerals may be used for similar or related components. The singular form of the noun corresponding to an item may include one or more of the said items, unless clearly indicated otherwise in the relevant context.

[0029] 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 phrase, or all possible combinations of these. Terms such as "first", "second", "first", "second", "A", "B", "(a)", or "(b)" may be used simply to distinguish the component from other components, and do not limit the component in other aspects (e.g., importance or order), unless otherwise stated.

[0030] In this document, when it is mentioned that a certain (e.g., first) component is "coupled", "joined", or "connected" to another (e.g., second) component, with or without the terms "functionally" or "communicatively", or is referred to as "coupled" or "connected", this means that the certain component may be directly (e.g., by wire), wirelessly, or via a third component, connected to the other component.

[0031] According to one embodiment, the methods according to the various embodiments disclosed in this document may be provided included in a computer program product. The computer program product may be traded as a commodity between a seller and a purchaser. The computer program product may be distributed in the form of a machine-readable storage medium (e.g., a compact disc read only memory (CD-ROM)), or may be distributed online (e.g., downloaded or uploaded) via an application store or directly between two user devices. In the case of online distribution, at least a part of the computer program product may be at least temporarily stored in a machine-readable storage medium such as the memory of a manufacturing company's server, an application store's server, or a relay server, or may be temporarily generated.

[0032] According to various embodiments, each of the above-described components (e.g., modules or programs) of the components may include one or more individuals, and some of the plurality of individuals may be separately arranged from other components. According to various embodiments, one or more of the above-described components or operations may be omitted, or one or more other components or operations may be added. Alternatively or additionally, a plurality of components (e.g., modules or programs) may be integrated into one component. In this case, the integrated component may perform one or more functions of each of the plurality of components in the same or similar manner as those performed by the component among the plurality of components before the integration. According to various embodiments, the operations performed by a module, program, or other component may be executed sequentially, in parallel, repeatedly, 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.

[0033] FIG. 1 is a block diagram showing a charging time prediction device according to an embodiment. Referring to FIG. 1, the charging time prediction device 110 may include a communication circuit 111, a memory 113, and / or a processor 115. According to an embodiment, the charging time prediction device 110 may have at least one of the components shown in FIG. 1 omitted or one or more other components added. According to an embodiment, a part of this component may be realized by one integrated circuit.

[0034] The communication circuit 111 can transmit and receive data to and from the first external electronic device 120 and / or the second external electronic device 130 by wire or wirelessly. Here, the first external electronic device 120 is an electric vehicle using electrical energy, and the second external electronic device 130 may be a portable electronic device such as a smart phone or a tablet personal computer.

[0035] According to one embodiment, the communication circuit 111 can receive information regarding the battery included in the first external electronic device 120 from the first external electronic device 120. Here, the information regarding the battery can include model information, current value, temperature value, SoC, and / or SoH of the battery included in the first external electronic device 120. Also, the communication circuit 111 can receive a target SoC set based on a user input from the first external electronic device 120 or the second external electronic device 130. According to one embodiment, the communication circuit 111 can transmit the information received from the first external electronic device 120 and / or the second external electronic device 130 to the processor 115.

[0036] According to one embodiment, the communication circuit 111 can transmit the instruction data transmitted from the processor 115 to the first external electronic device 120 and / or the second external electronic device 130. For example, the instruction data can include information regarding the required charging time predicted by the processor 115. Also, the instruction data can include an instruction to cause the first external electronic device 120 and / or the second external electronic device 130 to output a specified alarm via a user interface (e.g., a display, a speaker).

[0037] The memory 113 may be a volatile memory or a non-volatile memory. As the volatile memory, the memory 113 can be a RAM, a DRAM, an SRAM, etc. As the non-volatile memory, the memory 113 can be a ROM, a PROM, an EAROM, an EPROM, an EEPROM, a flash memory, etc. The examples of the memory 113 listed above are merely illustrative and are not limited to these examples.

[0038] The memory 113 can store various data used by at least one component of the charging time prediction device 110 (for example, the communication circuit 111 and / or the processor 115). For example, the memory 113 can store a charging time prediction model generated by the processor 115. The memory 113 can classify and store the charging time prediction model generated by the processor 115 according to the model information of the battery included in an external electronic device (for example, the first external electronic device 120). For example, the model information of the battery can include the model information of the first external electronic device 120 in which the battery is disposed, the OEM (original equipment manufacturing) information of the battery (for example, the manufacturer information of the battery), and / or the manufacturing year information of the battery.

[0039] The processor 115 can execute software to control at least one other component connected to the processor 115, and can perform various data processing or operations. According to one embodiment, the processor 115 can control at least one other component connected to the processor 115 to control the overall operation of the charging time prediction device 110. The processor 115 can include at least one of processing devices such as ASIC (application specific integrated circuit), DSP (digital signal processor), PLD (programmable logic devices), FPGAs (field programmable gate arrays), CPU (central processing unit), microcontrollers, or microprocessors.

[0040] According to one embodiment, the processor 115 can generate and / or learn a charging time prediction model. According to one embodiment, the processor 115 can generate a charging time prediction model using battery information received from the first external electronic device 120 via the communication circuit 111. After generating the charging time prediction model, the processor 115 can train the charging time prediction model generated using the battery information received from the first external electronic device 120.

[0041] According to one embodiment, the processor 115 can generate a charging time prediction model corresponding to the battery model information included in the acquired battery information. For example, the battery model information can include the model information of the first external electronic device 120 in which the battery is disposed, the OEM information of the battery (e.g., the manufacturer information of the battery), and / or the manufacturing year information of the battery. According to one embodiment, the processor 115 can train the charging time prediction model generated only using the battery information including the same model information.

[0042] According to one embodiment, the processor 115 can acquire first battery information from the first external electronic device 120 via the communication circuit 111. For example, the first battery information can include the current value, temperature value, SoC, and SoH of the battery included in the first external electronic device 120. According to one embodiment, the first battery information may be acquired in real time or periodically.

[0043] According to one embodiment, the processor 115 can receive, via the communication circuit 111, information regarding the traveling speed of the first external electronic device 120 and / or information regarding the presence or absence of connection between the first external electronic device 120 and an external charger from the first external electronic device 120. According to one embodiment, the information regarding the traveling speed and / or the information regarding the presence or absence of connection may be acquired in real time or periodically.

[0044] According to one embodiment, the processor 115 can analyze data during a specified period. According to one embodiment, the processor 115 can extract a charging section by analyzing data (e.g., first battery information measured during the specified period, information regarding the traveling speed, and / or information regarding the presence or absence of a connection) during the specified period. Hereinafter, a method for the processor 115 to extract a charging section from the specified period will be described. In one embodiment, the specified period may be a time window (e.g., one week, one day, one hour, three hours) for the processor 115 to analyze battery information.

[0045] According to one embodiment, the processor 115 can extract a charging section based on the first battery information, information regarding the traveling speed of the first external electronic device 120, and / or information regarding the presence or absence of a connection between the first external electronic device 120 and an external charger during the specified period.

[0046] According to one embodiment, the processor 115 can extract a charging section based on the current value of the battery included in the first battery information. According to one embodiment, the processor 115 can extract a charging section based on a section in which the current value of the battery is equal to or greater than a specified current value based on the first battery information. According to one embodiment, the processor 115 can extract at least a partial section of a section in which the current value of the battery is equal to or greater than a specified current value as a charging section based on the first battery information. Here, the specified current value may be 0 A. When the current value of the battery exceeds 0 A, it may mean that the battery is being charged, and when the current value of the battery is less than 0 A, it may mean that the battery is discharging.

[0047] According to one embodiment, the processor 115 can extract a charging period based on information regarding the traveling speed of the first external electronic device 120 and information regarding the presence or absence of connection between the first external electronic device 120 and an external charger. According to one embodiment, the processor 115 can extract a charging period based on a period in which the traveling speed of the first external electronic device 120 is a specified speed and the first external electronic device 120 is connected to an external charger. According to one embodiment, the processor 115 can extract at least a part of a period in which the traveling speed of the first external electronic device 120 is a specified speed and the first external electronic device 120 is connected to an external charger as a charging period. Here, the specified speed may be 0 km / h.

[0048] According to one embodiment, the processor 115 can extract a charging period based on the first battery information, excluding a data interruption period, a period in which the charging time is less than a specified time, and a period in which the charged amount is less than a specified charged amount within a specified period.

[0049] According to one embodiment, the processor 115 can extract a charging period excluding a period in which the current change value of the battery is equal to or greater than a specified change value. When the current change value of the battery during charging is equal to or greater than a specified change value, it may mean that noise has occurred in the measured current.

[0050] According to one embodiment, the processor 115 can extract a first period in which the current value of the battery is equal to or greater than a specified current value within a specified period. The processor 115 can extract a second period in which the traveling speed of the external electronic device is a specified speed and the external electronic device is connected to an external charger from the first period. The processor 115 can extract a third period excluding a data interruption period, a period in which the charging time is less than a specified time, and a period in which the charged amount is less than a specified charged amount from the second period.

[0051] According to one embodiment, the second interval may include a data interruption interval. For example, among the third interval or the fourth interval included in the second interval, the fourth interval may include a data interruption interval. In the fourth interval, if the interval remaining after excluding the data interruption interval is less than the specified time, the processor 115 can extract the third interval excluding the fourth interval as a charging interval. Here, the data interruption interval may mean an interval in which data cannot be acquired from the first external electronic device 120 for a certain period of time or more.

[0052] According to one embodiment, the processor 115 can extract a charging interval excluding an interval less than a specified charge amount based on the amount of change in the SoC. For example, among the third interval or the fourth interval included in the second interval, if the amount of change in the SoC of the fourth interval is less than the specified amount of change, the processor 115 can extract the third interval excluding the fourth interval as a charging interval.

[0053] According to one embodiment, the processor 115 can extract, as a charging interval, an interval in the third interval excluding an interval in which the current change value of the battery is equal to or greater than the specified change value.

[0054] According to one embodiment, the processor 115 can generate and / or train a charging time prediction model using the current value, temperature value, SoC, and SoH of the battery during the extracted charging interval. Hereinafter, a method for the processor 115 to generate and / or train a charging time prediction model will be described. In one embodiment, the charging time prediction model may be a machine learning model that uses the current value, temperature value, SoC, SoH, and target SoC at the start time of battery charging as input data and obtains the charging time required as output data.

[0055] According to one embodiment, the processor 115 can identify the current value, temperature value, SoC, and SoH of the battery during the charging interval extracted from the specified period. According to one embodiment, the processor 115 can identify the current value, temperature value, SoC, and SoH at at least two time points in the charging interval.

[0056] According to one embodiment, the processor 115 can train a charging time prediction model based on the current value, temperature value, SoC, and SoH at a first time point and the SoC at a second time point among two time points. For example, the processor 115 can train the charging time prediction model by using the current value, temperature value, SoC, and SoH at the first time point and the SoC at the second time point among two time points as the input data of the charging time prediction model.

[0057] In one embodiment, the processor 115 can adjust the parameters (or weights) of the hidden layer of the charging time prediction model based on the output data of the charging time prediction model based on the current value, temperature value, SoC, and SoH at the first time point and the SoC at the second time point and the difference value between the first time point and the second time point. In one embodiment, the processor 115 can adjust the parameters (or weights) of the hidden layer of the charging time prediction model so that the error between the output data of the charging time prediction model and the difference value indicates a value within a specified error.

[0058] According to one embodiment, the processor 115 can repeatedly train the charging time prediction model by using the data in the charging interval. According to one embodiment, the processor 115 can repeatedly train the charging time prediction model by using the current value, temperature value, SoC, and / or SoH at all time points in the charging interval. For example, the charging time prediction model can be trained by using the data at the first time point and the second time point in the charging interval, and the charging time prediction model can be trained by using the data at the third time point and the fourth time point in the charging interval.

[0059] According to one embodiment, the processor 115 can terminate the training of the charging time prediction model based on the output data of the charging time prediction model. In one embodiment, the processor 115 can terminate the training of the charging time prediction model when the error between the output data of the charging time prediction model and the difference value between at least two time points in the charging interval is within a specified error.

[0060] According to one embodiment, the processor 115 can generate and / or train a charging time prediction model using a machine learning model (e.g., a gradient boosting machine (GBM) or a distributed random forest (DRF)).

[0061] Hereinafter, a method for predicting the charging time based on the charging time prediction model learned by the processor 115 will be described. According to one embodiment, the processor 115 can predict the charging time based on the generated and / or learned charging time prediction model.

[0062] According to one embodiment, the processor 115 can receive second battery information from the first external electronic device 120 via the communication circuit 111. For example, the second battery information can include the current value, temperature value, state of charge (SoC), and state of health (SoH) of the battery at a specified time included in the first external electronic device 120.

[0063] According to one embodiment, the processor 115 can predict the charging time required from the SoC included in the second battery information to the target SoC based on the charging time prediction model. Here, the target SoC can mean one of the SoCs between the SoC at the specified time of the battery included in the second battery information and the SoC when the battery is fully charged. According to one embodiment, the processor 115 can obtain the target SoC set based on user input from the first external electronic device 120 or the second external electronic device 130. In this case, the processor 115 can output an interface (e.g., the first screen 510 in FIG. 5) via the first external electronic device 120 or the second external electronic device 130 to obtain user input for selecting the target SoC.

[0064] According to one embodiment, the processor 115 can provide the predicted charging time to the user. According to one embodiment, the processor 115 can transmit instruction data to the first external electronic device 120 and / or the second external electronic device 130 via the communication circuit 111. For example, the instruction data can include information regarding the predicted charging time. Also, the instruction data can include an instruction for the first external electronic device 120 and / or the second external electronic device 130 to output a specified alarm (e.g., the second screen 520 in FIG. 5) via the user interface.

[0065] FIG. 2 is an operation flowchart of a charging time prediction device according to one embodiment. FIG. 2 can be described using the configuration of FIG. 1. The embodiment shown in FIG. 2 is only one embodiment, and the order of steps according to various embodiments of the present invention may be different from that shown in FIG. 2, some steps shown in FIG. 2 may be omitted, the order between steps may be changed, or steps may be combined.

[0066] According to one embodiment, operations 205 to 215 can be understood as being performed by the processor 115 of the charging time prediction device 110. Referring to FIG. 2, in operation 205, the charging time prediction device 110 can generate and / or train a charging time prediction model.

[0067] According to one embodiment, the charging time prediction device 110 can generate a charging time prediction model using the battery information acquired from the first external electronic device 120. After generating the charging time prediction model, the charging time prediction device 110 can train the charging time prediction model generated using the battery information acquired from the first external electronic device 120.

[0068] According to one embodiment, the charging time prediction device 110 can generate a charging time prediction model corresponding to the battery model information included in the acquired battery information. For example, the battery model information can include the model information of the first external electronic device 120 in which the battery is disposed, the OEM information of the battery (for example, the manufacturer information of the battery), and / or the manufacturing year information of the battery. According to one embodiment, the charging time prediction device 110 can learn a charging time prediction model generated using only battery information including the same model information.

[0069] The operation of the charging time prediction device 110 to generate and / or learn a charging time prediction model can be specifically described with reference to FIGS. 3 and 4 described later.

[0070] In operation 210, the charging time prediction device 110 can predict the charging time required based on the charging time prediction model generated and / or learned in operation 205.

[0071] According to one embodiment, the charging time prediction device 110 can acquire second battery information from the first external electronic device 120. For example, the second battery information can include the current value, temperature value, SoC, and SoH of the battery included in the first external electronic device 120 at a specified time point.

[0072] According to one embodiment, the charging time prediction device 110 can predict the charging time required from the SoC included in the second battery information to the target SoC based on the charging time prediction model. Here, the target SoC may mean one of the SoCs between the SoC at the designated time of the battery included in the second battery information and the SoC when the battery is fully charged. According to one embodiment, the charging time prediction device 110 can acquire the target SoC set based on the user input from the first external electronic device 120 or the second external electronic device 130. In this case, the charging time prediction device 110 can output an interface (for example, the first screen 510 in FIG. 5) via the first external electronic device 120 or the second external electronic device 130 in order to acquire the user input for selecting the target SoC.

[0073] In operation 215, the charging time prediction device 110 can provide the user with the charging time predicted in operation 210. According to one embodiment, the charging time prediction device 110 can transmit command data to the first external electronic device 120 and / or the second external electronic device 130. For example, the command data can include information regarding the charging time predicted in operation 210. Also, the command data can include an instruction for the first external electronic device 120 and / or the second external electronic device 130 to output a designated alarm (for example, the second screen 520 in FIG. 5) via the user interface.

[0074] FIG. 3 is a flowchart of the operation of the charging time prediction device according to one embodiment. FIG. 3 can be described using the configuration of FIG. 1. The embodiment shown in FIG. 3 is only one embodiment, and the order of the steps according to various embodiments of the present invention may be different from that shown in FIG. 3, some of the steps shown in FIG. 3 may be omitted, the order between steps may be changed, or the steps may be combined.

[0075] According to one embodiment, operations 305 to 315 can be understood to be performed by the processor 115 of the charging time prediction device 110. Referring to FIG. 3, in operation 305, the charging time prediction device 110 can obtain first battery information from the first external electronic device 120. For example, the first battery information can include the current value, temperature value, SoC, and SoH of the battery included in the first external electronic device 120. According to one embodiment, the first battery information may be obtained in real time or periodically.

[0076] According to one embodiment, the charging time prediction device 110 can analyze data during a specified period. According to one embodiment, the charging time prediction device 110 can extract a charging section by analyzing data (for example, the first battery information measured during the specified period, information regarding the traveling speed, and / or information regarding the presence or absence of a connection) during the specified period. Hereinafter, a method for the charging time prediction device 110 to extract a charging section from the specified period will be described. In one embodiment, the specified period may be a time window (for example, one week, one day, one hour, three hours) for the charging time prediction device 110 to analyze battery information.

[0077] In operation 310, the charging time prediction device 110 can extract a charging section from the specified period. According to one embodiment, the charging time prediction device 110 can obtain information regarding the traveling speed of the first external electronic device 120 and / or information regarding the presence or absence of a connection between the first external electronic device 120 and an external charger from the first external electronic device 120. According to one embodiment, the information regarding the traveling speed and / or the information regarding the presence or absence of a connection may be obtained in real time or periodically. According to one embodiment, the charging time prediction device 110 can extract a charging section based on the first battery information, the information regarding the traveling speed of the first external electronic device 120, and / or the information regarding the presence or absence of a connection between the first external electronic device 120 and an external charger during the specified period.

[0078] According to one embodiment, the charging time prediction device 110 can extract a charging section based on the current value of the battery included in the first battery information. According to one embodiment, the charging time prediction device 110 can extract a charging section based on a section in which the current value of the battery is equal to or greater than a specified current value based on the first battery information.

[0079] According to one embodiment, the charging time prediction device 110 can extract a charging section based on information regarding the traveling speed of the first external electronic device 120 and information regarding the presence or absence of connection between the first external electronic device 120 and an external charger. According to one embodiment, the charging time prediction device 110 can extract a charging section based on a section in which the traveling speed of the first external electronic device 120 is a specified speed and the first external electronic device 120 is in a state of being connected to an external charger.

[0080] According to one embodiment, the charging time prediction device 110 can extract a charging section based on the first battery information, excluding a data interruption section, a section in which the charging time is less than a specified time, and a section in which the charge amount is less than a specified charge amount within a specified period.

[0081] According to one embodiment, the charging time prediction device 110 can extract a charging section excluding a section in which the current change value of the battery is equal to or greater than a specified change value. When the current change value of the battery during charging is equal to or greater than a specified change value, it may mean that noise occurs in the measured current.

[0082] The operation of the charging time prediction device 110 for extracting a charging section can be specifically described with reference to FIG. 4 described later. In operation 315, the charging time prediction device 110 can generate and / or train a charging time prediction model using the current value, temperature value, SoC, and SoH of the battery during the charging section extracted in operation 310. In one embodiment, the charging time prediction model may be a machine learning model that uses the current value, temperature value, SoC, SoH, and target SoC at the start time of battery charging as input data and obtains the required charging time as output data.

[0083] According to one embodiment, the charging time prediction device 110 can identify the current value, temperature value, SoC, and SoH of the battery during a charging period extracted from a specified period. According to one embodiment, the charging time prediction device 110 can identify the current value, temperature value, SoC, and SoH at at least two time points during the charging period.

[0084] According to one embodiment, the charging time prediction device 110 can train a charging time prediction model based on the current value, temperature value, SoC, and SoH at the first time point and the SoC at the second time point among the two time points. For example, the charging time prediction device 110 can train the charging time prediction model by using the current value, temperature value, SoC, and SoH at the first time point and the SoC at the second time point among the two time points as input data for the charging time prediction model.

[0085] In one embodiment, the charging time prediction device 110 can adjust the parameters (or weights) of the hidden layer of the charging time prediction model based on the output data of the charging time prediction model based on the current value, temperature value, SoC, and SoH at the first time point and the SoC at the second time point, and the difference value between the first time point and the second time point. In one embodiment, the charging time prediction device 110 can adjust the parameters (or weights) of the hidden layer of the charging time prediction model so that the error between the output data of the charging time prediction model and the difference value indicates a value within a specified error.

[0086] According to one embodiment, the charging time prediction device 110 can repeatedly train the charging time prediction model using the data of the charging period. According to one embodiment, the charging time prediction device 110 can repeatedly train the charging time prediction model using the current value, temperature value, SoC, and / or SoH at all time points of the charging period. For example, the charging time prediction model can be trained using the data at the first and second time points of the charging period, and the charging time prediction model can be trained using the data at the third and fourth time points of the charging period.

[0087] According to one embodiment, the charging time prediction device 110 can finish the learning for the charging time prediction model based on the output data of the charging time prediction model. In one embodiment, when the error between the output data of the charging time prediction model and the difference value between at least two time points in the charging period is within the specified error, the charging time prediction device 110 can finish the learning for the charging time prediction model.

[0088] According to one embodiment, the charging time prediction device 110 can generate and / or learn the charging time prediction model by a machine learning model (for example, GBM or DRF).

[0089] FIG. 4 is an operation flowchart of the charging time prediction device according to one embodiment. FIG. 4 can be described using the configuration of FIG. 1. FIG. 4 can be understood as a drawing that embodies operation 310 of FIG. 3.

[0090] The embodiment shown in FIG. 4 is only one embodiment, and the order of steps according to various embodiments of the present invention may be different from that shown in FIG. 4. Some steps shown in FIG. 4 may be omitted, the order between steps may be changed, or steps may be combined.

[0091] According to one embodiment, operations 405 to 415 can be understood as being performed by the processor 115 of the charging time prediction device 110. Referring to FIG. 4, in operation 405, the charging time prediction device 110 can extract a first section in a specified period during which the current value of the battery is equal to or greater than a specified current value. Here, the specified current value may be 0 A. When the current value of the battery exceeds 0 A, it means that the battery is being charged, and when the current value of the battery is less than 0 A, it may mean that the battery is being discharged.

[0092] In operation 410, the charging time prediction device 110 can extract, from the first interval extracted in operation 405, a second interval in which the traveling speed of the external electronic device is the specified speed and the external electronic device is in a state of being connected to an external charger. Here, the specified speed may be 0 km / hr.

[0093] In operation 415, the charging time prediction device 110 can extract, from the second interval extracted in operation 410, a third interval excluding a data interruption interval, an interval in which the charging time is less than the specified time, and an interval in which the charge amount is less than the specified charge amount.

[0094] According to one embodiment, the second interval extracted in operation 410 can include a data interruption interval. For example, among the third interval or the fourth interval included in the second interval, the fourth interval can include a data interruption interval. If, in the fourth interval, the interval remaining after excluding the data interruption interval is less than the specified time, the charging time prediction device 110 can extract, as a charging interval, the third interval excluding the fourth interval. Here, the data interruption interval may mean an interval in which data cannot be acquired from the first external electronic device 120 for a certain period of time or more.

[0095] According to one embodiment, the charging time prediction device 110 can extract a charging interval excluding an interval in which the charge amount is less than the specified charge amount based on the amount of change in the SoC. For example, among the third interval or the fourth interval included in the second interval, if the amount of change in the SoC of the fourth interval is less than the specified amount of change, the charging time prediction device 110 can extract, as a charging interval, the third interval excluding the fourth interval.

[0096] In operation 420, the charging time prediction device 110 can extract, as a charging interval, an interval excluding an interval in which the current change value of the battery is equal to or greater than the specified change value from the third interval extracted in operation 415. If the current change value of the battery during charging is equal to or greater than the specified change value, it may mean that noise occurs in the measured current.

[0097] FIG. 5 is a diagram showing an interface provided by the charging time prediction device according to an embodiment to the user. FIG. 5 can be described using the configuration of FIG. 1. The charging time prediction device 110 can display the first screen 510 and / or the second screen 520 according to FIG. 5 via the display of the first external electronic device 120 and / or the display of the second external electronic device 130.

[0098] According to an embodiment, the charging time prediction device 110 can output the first screen 510 via the display of the first external electronic device 120 or the display of the second external electronic device 130 in order to obtain a user input for selecting a target SoC. The first screen 510 can include an interface that guides the user to select whether to receive a notification of the target SoC and / or the required charging time via a touch input. The charging time prediction device 110 can determine whether to send a notification of the target SoC and / or the required charging time based on the user input entered via the display on which the first screen 510 is displayed.

[0099] According to an embodiment, the charging time prediction device 110 can output the second screen 520 via the display of the first external electronic device 120 or the display of the second external electronic device 130 in order to provide the user with the predicted required charging time. According to an embodiment, when the user selects to receive a notification of the required charging time via the display on which the first screen 510 is displayed, the charging time prediction device 110 can output the second screen 520 when the battery charging of the first external electronic device 120 starts. The second screen 520 can include information regarding the presence or absence of the start of charging of the battery of the first external electronic device 120, the target SoC, and the predicted required charging time.

[0100] Terms such as "including", "comprising", or "having" described above shall, unless otherwise stated to the contrary, be construed to mean that the component can be inherent, and thus shall not exclude other components, but may further include other components. All terms, including technical or scientific terms, shall have the same meaning as commonly understood by those with ordinary knowledge in the technical field to which the embodiments disclosed in this document belong, unless otherwise defined. Commonly used terms such as those defined in a dictionary shall be construed to be consistent with the meaning in the context of the related art, and shall not be construed in an ideal or overly formal sense unless clearly defined in this document.

Description of Reference Numerals

[0101] 110 Charging Time Prediction Device 111 Communication Circuit 113 Memory 115 Processor 120 First External Electronic Device 130 Second External Electronic Device

Claims

1. A charging time prediction device, comprising a communication circuit, a processor, and the processor is configured to: receive first battery information including a current value, a temperature value, an SoC (state of charge), and an SoH (state of health) of a battery included in the external electronic device from the external electronic device via the communication circuit during a specified period (time period); receive information regarding a traveling speed of the external electronic device during the specified period and / or information regarding whether the external electronic device is connected to an external charger via the communication circuit; extract a charging section within the specified period based on the first battery information, the information regarding the traveling speed, and / or the information regarding the connection during the specified period; and generate a charging time prediction model using a current value, a temperature value, an SoC, and an SoH at a first time point in the charging section and an SoC at a second time point in the charging section. A charging time prediction device configured as such.

2. The processor is configured to: generate the charging time prediction model by a machine learning model. The charging time prediction device according to claim 1.

3. The processor is configured to: receive second battery information including a current value, a temperature value, an SoC, and an SoH of the battery at a specified time point (time point) from the external electronic device via the communication circuit; predict a charging time required from the SoC at the specified time point to a target SoC based on the charging time prediction model. The charging time prediction device according to claim 1.

4. The first battery information includes model information of the battery, and the processor is configured to: The charging time prediction device according to claim 1, configured to generate the charging time prediction model corresponding to the model information.

5. The processor is configured to extract, as the charging section, at least a partial section of a first section in which a current value of the battery is equal to or greater than a specified current value during the specified period in the charging time prediction device according to claim 1.

6. The processor is configured to extract, as the charging section, at least a partial section of a second section in which a traveling speed of the external electronic device is a specified speed and the external electronic device is connected to the external charger among the first section in the charging time prediction device according to claim 5.

7. The processor is configured to extract, as the charging section, at least a partial section of a third section excluding a data interruption section, a section in which a charging time is less than a specified time, and a section in which a charged amount is less than a specified charged amount among the second section in the charging time prediction device according to claim 6.

8. The processor is configured to extract, as the charging section, a section excluding a section in which a current change value of the battery is equal to or greater than a specified change value among the third section in the charging time prediction device according to claim 7.

9. The processor is configured to transmit the predicted charging time to the external electronic device via the communication circuit so as to provide the predicted charging time to the user in the charging time prediction device according to claim 1.

10. A method for predicting charging time, comprising: an operation of obtaining first battery information including a current value, a temperature value, an SoC, and an SoH of a battery included in an external electronic device during a specified period; An operation of obtaining information regarding the traveling speed of the external electronic device and / or information regarding the presence or absence of connection between the external electronic device and an external charger, An operation of extracting a charging section within the specified period based on the first battery information, the information regarding the traveling speed, and / or the information regarding the presence or absence of connection during the specified period, An operation of generating a charging time prediction model using the current value, temperature value, SoC, and SoH at a first time point in the charging section and the SoC at a second time point in the charging section, A method for predicting charging time, including:

11. The first battery information includes the model information of the battery, The operation of generating the charging time prediction model includes an operation of generating the charging time prediction model corresponding to the model information. The method for predicting charging time according to claim 10.

12. The operation of extracting the charging section includes an operation of extracting at least a partial section of a first section in the specified period, where the current value of the battery is equal to or greater than a specified current value, as the charging section. The method for predicting charging time according to claim 10.

13. The operation of extracting the charging section includes an operation of extracting at least a partial section of a second section in the first section, where the traveling speed of the external electronic device is a specified speed and the external electronic device is in a state of being connected to the external charger, as the charging section. The method for predicting charging time according to claim 12.

14. The operation of extracting the charging section includes an operation of extracting at least a partial section of a third section in the second section, excluding a data interruption section, a section where the charging time is less than a specified time, and a section where the charging amount is less than a specified charging amount, as the charging section. The method for predicting charging time according to claim 13.

15. The operation of extracting the charging period includes an operation of extracting, as the charging period, a period obtained by excluding, from the third period, a period in which a current change value of the battery is equal to or greater than a specified change value, according to the method for predicting charging time according to claim 14.

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