Electronic device and charging control method therefor

Abstract

An electronic device according to an embodiment of the present disclosure may comprise: a memory for storing at least one instruction; and a processor operatively connected to the memory, wherein the processor may, by executing the at least one instruction, acquire battery data related to the states of a plurality of batteries arranged in a vehicle and connected in parallel to each other, acquire charging data related to the state of a charging device for charging the vehicle, input the battery data and the charging data to a machine learning model, and acquire a required charging current of each of the plurality of batteries, output from the machine learning model, the required charging current minimizing a charging time of the charging device for the vehicle.

Description

Electronic device and its charging control method

[0001] This application claims the benefit of priority based on Korean Patent Application No. 10-2024-0124837 filed on September 12, 2024, and all contents disclosed in the document of said Korean Patent Application are incorporated herein as part of this specification.

[0002] The embodiments disclosed in this document relate to an electronic device and a method for controlling the charging of the same.

[0003] Recently, active research and development on secondary batteries has been underway. Here, secondary batteries refer to rechargeable batteries, encompassing conventional Ni / Cd and Ni / MH batteries as well as the more recent lithium-ion batteries. Among secondary batteries, lithium-ion batteries have the advantage of significantly higher energy density compared to conventional Ni / Cd and Ni / MH batteries. Furthermore, lithium-ion batteries can be manufactured in a compact and lightweight manner, making them suitable for use as power sources for mobile devices. Recently, their scope of application has expanded to include electric vehicles, drawing attention as a next-generation energy storage medium.

[0004] As the applications of such secondary batteries expand, the importance of technology regarding management systems for more efficient use and management of secondary batteries is increasing. The status and operation of secondary batteries can be managed and controlled by a battery management system (BMS). The battery management system can be included together with the battery within a single device.

[0005] Meanwhile, in the case of a battery system in which multiple batteries are connected in parallel, a DC-DC converter is required at the point where the batteries are connected in parallel because a voltage difference may occur between the batteries. In particular, battery packs consisting of dual battery systems or multi-battery systems containing two or more batteries of different components have recently been developed. Since the batteries included in such battery packs are connected in parallel with each other, a DC-DC converter must likewise be placed in the battery system.

[0006] In the case of such dual or multi-battery systems, if the same charging current is supplied despite the different components of each battery, the charging time may be unnecessarily prolonged. Accordingly, it is necessary to minimize charging time by controlling the charging current of a battery system containing multiple batteries with different specifications.

[0007] According to one embodiment of the present disclosure, an electronic device and a charging control method thereof can be provided, which can derive a charging current for each battery to minimize the charging time for a vehicle in which a plurality of batteries connected in parallel are arranged.

[0008] The technical problems to be solved by the embodiments of the present disclosure are not limited to the technical problems described above, and other technical problems can be inferred from the following embodiments.

[0009] An electronic device according to one embodiment of the present disclosure includes a memory for storing at least one instruction and a processor operatively connected to said memory, wherein the processor, by executing said at least one instruction, can obtain battery data related to the state of a plurality of batteries arranged in a vehicle and connected in parallel with each other, obtain charging data related to the state of a charging device that performs charging for said vehicle, input said battery data and said charging data into a machine learning model, and obtain a required charging current for each of said plurality of batteries that minimizes the charging time of said charging device for said vehicle, which is output from said machine learning model.

[0010] In an electronic device according to one embodiment of the present disclosure, the battery data includes C-rate data, SOC (State of Charge) data, and temperature data of the plurality of batteries, and the charging data may include power data that the charging device can supply to the vehicle.

[0011] In an electronic device according to one embodiment of the present disclosure, the processor, by executing the at least one instruction, calculates a first parameter based on SOC and a second parameter based on temperature for each of the plurality of batteries based on the battery data, calculates a maximum charging current that the charging device can supply to the vehicle based on the charging data, and inputs the C-rate data of the plurality of batteries, the first parameter, the second parameter, and the maximum charging current to the machine learning model based on the following mathematical formulas 1 and 2.

[0012] [Mathematical Formula 1]

[0013]

[0014] [Mathematical Formula 2]

[0015]

[0016] (In the above mathematical formulas 1 and 2, C-rate1 and C-rate N is the C-rate of the first battery and the Nth battery (where N is a natural number greater than or equal to 2) among the plurality of batteries, respectively, and S1 and S N are respectively the first parameters of the first battery and the Nth battery, and D1 and D N are respectively the second parameters of the first battery and the Nth battery, and I1 and I N is the required charging current of the first battery and the Nth battery, respectively, and I total is the maximum charging current mentioned above.)

[0017] In an electronic device according to one embodiment of the present disclosure, the memory stores reference data representing the SOC according to the charging time of each of the plurality of batteries, and the processor, by executing the at least one instruction, for each of the plurality of batteries, a SOC-charge time derivative value (dSOC / dt) at the current SOC based on the reference data stored in the memory. c ) can be calculated using the above first parameter.

[0018] In an electronic device according to one embodiment of the present disclosure, the processor can calculate a temperature decay coefficient based on the current temperature for each of the plurality of batteries as the second parameter by executing the at least one instruction.

[0019] In an electronic device according to one embodiment of the present disclosure, the machine learning model is an artificial intelligence model constructed by modeling the correlation between a learning input data set and a learning output data set, and the learning input data set includes a plurality of battery data sets related to a plurality of states of the plurality of batteries and a plurality of charging data sets related to a plurality of states of the charging device, and the learning output data set may include a set of required charging currents for each of the plurality of batteries that satisfies Equation 2 and minimizes the charging time of Equation 1, generated by reflecting the learning input data set in Equation 1 and Equation 2.

[0020] In an electronic device according to one embodiment of the present disclosure, the processor can control a plurality of DC-DC converters electrically connected to each of the plurality of batteries so that a calculated required charging current flows to each of the plurality of batteries by executing the at least one instruction.

[0021] A charging control method performed by an electronic device according to one embodiment of the present disclosure may include: acquiring battery data related to the state of a plurality of batteries arranged in a vehicle and connected in parallel with one another; acquiring charging data related to the state of a charging device that performs charging for the vehicle; inputting the battery data and the charging data into a machine learning model; and acquiring a required charging current for each of the plurality of batteries that minimizes the charging time of the charging device for the vehicle, which is output from the machine learning model.

[0022] In a charging control method performed by an electronic device according to one embodiment of the present disclosure, the battery data includes C-rate data, SOC (State of Charge) data, and temperature data of the plurality of batteries, and the charging data may include power data that the charging device can supply to the vehicle.

[0023] In a charging control method performed by an electronic device according to one embodiment of the present disclosure, the operation of inputting the battery data and the charging data into the machine learning model may include, based on the battery data, calculating a first parameter based on SOC and a second parameter based on temperature for each of the plurality of batteries; based on the charging data, calculating a maximum charging current that the charging device can supply to the vehicle; and inputting the C-rate data of the plurality of batteries, the first parameter, the second parameter, and the maximum charging current into the machine learning model based on Equation 1 and Equation 2.

[0024] In a charging control method performed by an electronic device according to one embodiment of the present disclosure, the operation of calculating the first parameter comprises, for each of the plurality of batteries, a SOC-charge time derivative value (dSOC / dt) at the current SOC based on reference data representing the SOC according to the charge time of each of the plurality of batteries. c It may include an operation to calculate ) as the first parameter above.

[0025] In a charging control method performed by an electronic device according to one embodiment of the present disclosure, the operation of calculating the second parameter may include, for each of the plurality of batteries, an operation of calculating a temperature decay coefficient based on the current temperature as the second parameter.

[0026] In a charging control method performed by an electronic device according to one embodiment of the present disclosure, the machine learning model is an artificial intelligence model constructed by modeling the correlation between a learning input data set and a learning output data set, the learning input data set includes a plurality of battery data sets related to a plurality of states of the plurality of batteries and a plurality of charging data sets related to a plurality of states of the charging device, and the learning output data set may include a set of required charging currents for each of the plurality of batteries that satisfies Equation 2 and minimizes the charging time of Equation 1, generated by reflecting the learning input data set in Equation 1 and Equation 2.

[0027] A charging control method performed by an electronic device according to one embodiment of the present disclosure may further include an operation of controlling a plurality of DC-DC converters electrically connected to each of the plurality of batteries so that a calculated required charging current flows to each of the plurality of batteries.

[0028] According to the embodiments disclosed in this document, by deriving the optimal charging current for each battery based on battery data and charging data, the charging time for a vehicle having multiple batteries connected in parallel can be minimized.

[0029] The effects of the invention are not limited to those mentioned above, and other unmentioned effects will be clearly understood by a person skilled in the art from the description in the claims.

[0030] FIG. 1 is a block diagram of a charging control system according to one embodiment of the present disclosure.

[0031] FIG. 2 is a block diagram of an electronic device according to one embodiment of the present disclosure.

[0032] FIG. 3 is a circuit diagram of a battery system according to one embodiment of the present disclosure.

[0033] FIG. 4 is a flowchart of the operation of an electronic device according to one embodiment of the present disclosure.

[0034] FIG. 5 is a flowchart of the operation of an electronic device according to one embodiment of the present disclosure.

[0035] In describing the embodiments, technical details that are well known in the technical field to which this disclosure belongs and are not directly related to this disclosure are omitted. This is intended to convey the essence of this disclosure more clearly without obscuring it by omitting unnecessary explanations.

[0036] For the same reason, some components in the attached drawings have been exaggerated, omitted, or schematically depicted. Additionally, the size of each component does not entirely reflect its actual dimensions. Identical or corresponding components in each drawing have been assigned the same reference numbers.

[0037] The advantages and features of the present disclosure and the methods for achieving them will become clear by referring to the embodiments described below in detail together with the accompanying drawings. However, the present disclosure is not limited to the embodiments disclosed below but may be implemented in various different forms. The embodiments provided are merely to ensure that the disclosure is complete and to fully inform those skilled in the art of the scope of the invention, and the present disclosure is defined only by the scope of the claims. Throughout the specification, the same reference numerals refer to the same components.

[0038] At this time, it will be understood that each block of the process flow diagrams and combinations of the flow diagrams can be executed by computer program instructions. Since these computer program instructions can be loaded into the processor of a general-purpose computer, a special-purpose computer, or other programmable data processing equipment, the instructions executed through the processor of the computer or other programmable data processing equipment create means for performing the functions described in the flow diagram block(s). Since these computer program instructions can also be stored in computer-available or computer-readable memory that can be directed toward the computer or other programmable data processing equipment to implement functions in a specific way, the instructions stored in such computer-available or computer-readable memory can also produce a manufactured item containing means of instruction for performing the functions described in the flow diagram block(s). Since computer program instructions can also be loaded onto a computer or other programmable data processing equipment, the instructions that execute the computer or other programmable data processing equipment by creating a process that is executed by a computer through a series of operation steps performed on the computer or other programmable data processing equipment can also provide steps for executing the functions described in the flow diagram block(s).

[0039] Additionally, each block may represent a module, segment, or part of code containing one or more executable instructions for executing a specific logical function(s). It should also be noted that in some alternative execution examples, the functions mentioned in the blocks may occur out of order. For instance, two blocks described in succession may actually be executed substantially simultaneously, or the blocks may be executed in reverse order according to their corresponding functions.

[0040] In this embodiment, the term "part" refers to a software or hardware component, such as an FPGA or ASIC, and the "part" performs certain roles. However, the meaning of "part" is not limited to software or hardware. The "part" may be configured to reside in an addressable storage medium or configured to operate one or more processors. Thus, for example, the "part" includes components such as software components, object-oriented software components, class components, and task components, as well as processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functions provided within the components and "parts" may be combined into a smaller number of components and "parts" or further separated into additional components and "parts." Furthermore, the components and "parts" may be implemented to operate one or more CPUs within a device or secure multimedia card.

[0041] The expression “at least one of a, b, and c” described throughout the specification may include ‘a alone’, ‘b alone’, ‘c alone’, ‘a and b’, ‘a and c’, ‘b and c’, or ‘a, b, and c all’.

[0042] The "terminal" mentioned below may be implemented as a computer or portable terminal capable of connecting to a server or other terminal via a network. Here, the computer includes, for example, a notebook, desktop, or laptop equipped with a web browser, and the portable terminal is a wireless communication device that ensures portability and mobility, and may include all types of handheld-based wireless communication devices such as IMT (International Mobile Telecommunication), CDMA (Code Division Multiple Access), W-CDMA (W-Code Division Multiple Access), LTE (Long Term Evolution), communication-based terminals, smartphones, tablet PCs, etc.

[0043] Embodiments of the present disclosure are described below with reference to the attached drawings so that those skilled in the art can easily implement them. However, the present disclosure may be embodied in various different forms and is not limited to the embodiments described herein.

[0044] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the attached drawings.

[0045] FIG. 1 is a block diagram of a charging control system according to one embodiment of the present disclosure.

[0046] Referring to FIG. 1, the charging control system may include a vehicle (10), a charging device (20), and a server (30).

[0047] The vehicle (10) may be an electric vehicle (EV) that receives driving power from a battery that stores electricity, but is not limited thereto, and the technical concept of the present disclosure may also be applied to other electric means of transportation other than the vehicle (10) (e.g., electric scooter, etc.).

[0048] According to one embodiment, a vehicle (10) may include a battery system (100) comprising a first battery (110) that stores power required for driving, an Nth battery (120), and a battery management device (130) that manages the state of the battery or controls its operation. Here, N may be a natural number greater than or equal to 2. That is, the battery system (100) may include two or more batteries.

[0049] According to one embodiment, the battery system (100) may be a battery module or a battery pack comprising a plurality of batteries (110, 120) corresponding to unit batteries. Here, a unit battery may refer to a sub-battery of the battery system (100). For example, if the battery system (100) is a battery module, the plurality of batteries (110, 120) may be battery cells. As another example, if the battery system (100) is a battery pack, the plurality of batteries (110, 120) may be battery cells or battery modules.

[0050] According to one embodiment, a plurality of batteries (110, 120) can be connected in parallel with each other. Accordingly, the positive and negative terminals of the plurality of batteries (110, 120) can be electrically connected to the positive output terminal (not shown) and the negative output terminal (not shown) of the battery system (100), respectively.

[0051] According to one embodiment, a plurality of batteries (110, 120) may be composed of different components and have different chemical properties. For example, the first battery (110) and the Nth battery (120) may be an LFP battery and an NCM battery, respectively.

[0052] According to one embodiment, the battery management device (130) may be implemented as a computer or a similar device (e.g., a battery management system (BMS)) according to hardware, software, or a combination thereof. In terms of hardware, the battery management device (130) may be implemented in the form of an electronic circuit that processes electrical signals to perform control functions, and in terms of software, it may be implemented in the form of a program that drives the hardware battery management device (130). According to one embodiment, the battery management device (130) may be operatively connected to a component (e.g., a switch, a DC-DC converter, etc.) included in the battery system (100) to control the connected component.

[0053] According to one embodiment, the charging device (20) may be a charging device installed at an electric vehicle charging station. According to one embodiment, the charging device (20) can charge a plurality of batteries (110, 120) included in a battery system (100) by supplying power to the plurality of batteries (110, 120) through a connection (101) with the vehicle (10). For example, the charging device (20) may be operated in a manner that supplies power to the vehicle (10), calculates a fee corresponding to the amount of power supplied, and charges the user of the vehicle (10).

[0054] According to one embodiment, the server (30) may be a computing device that processes data received from the charging device (20) and / or the vehicle (10) via connections (102, 103). For example, the server (30) may process charging data received from the charging device (20) via connection (102) and transmit instructions to the charging device (20) to control charging for the vehicle (10). As another example, the server (30) may process battery data received from the vehicle (10) via connection (103) and transmit instructions to the vehicle (10) to manage the state of the battery system (100) or control its operation. In this case, the connections (102, 103) between the server (30), the vehicle (10), and the charging device (20) may be communication connections via wired and / or wireless networks. In one embodiment, the wired network may be based on LAN (local area network) communication or power line communication. In one embodiment, the wireless network may be based on a short-range communication network (e.g., Bluetooth, WiFi (wireless fidelity), or IrDA (infrared data association)), or a long-range communication network (cellular network, 4G network, 5G network).

[0055] FIG. 2 is a block diagram of an electronic device (200) according to one embodiment of the present disclosure.

[0056] The electronic device (200) of FIG. 2 may be implemented as a battery management device (130), a charging device (20), or a server (30) as described in FIG. 1.

[0057] Referring to FIG. 2, the electronic device (200) may include an information acquisition interface (210), a memory (220), and a processor (230). According to an embodiment, the electronic device (200) illustrated in FIG. 1 may further include at least one component (e.g., a display, an input device, or an output device) in addition to the components illustrated in FIG. 1.

[0058] According to one embodiment, the information acquisition interface (210) can acquire status data related to the status of a plurality of batteries (e.g., voltage, current, temperature, SOC, SOH, C-rate, etc.). The plurality of batteries mentioned below may all be a plurality of battery cells, a plurality of battery modules, or a plurality of battery packs included in the same vehicle.

[0059] For example, the information acquisition interface (210) may be implemented as a sensor that measures information (e.g., voltage, current, temperature, etc.) related to the status of multiple batteries. In this case, the information acquisition interface (210) can acquire status data by measuring the status of multiple batteries.

[0060] As another example, the information acquisition interface (210) may be implemented as a communication circuit that establishes a wired communication channel and / or a wireless communication channel between the electronic device (200) and an external electronic device (e.g., a vehicle) and transmits and receives data to and from the external electronic device through the established communication channel. In this case, the information acquisition interface (210) may receive status data from the external electronic device.

[0061] Here, communication, that is, the transmission and reception of data, can be performed via wired or wireless means. To this end, the information acquisition interface (210) may include a wired communication module that communicates with components within an electronic device (200) via a CAN (Controller Area Network) or connects to the internet, etc. via a LAN (Local Area Network), a mobile communication module that transmits and receives data by connecting to a mobile communication network via a mobile communication base station, a short-range communication module that uses a WLAN (Wireless Local Area Network) series communication method such as Wi-Fi or a WPAN (Wireless Personal Area Network) series communication method such as Bluetooth or Zigbee, a satellite communication module that uses a GNSS (Global Navigation Satellite System) such as GPS (Global Positioning System), or a combination thereof.

[0062] According to one embodiment, the memory (220) may include volatile memory and / or non-volatile memory.

[0063] According to one embodiment, the memory (220) may store data used by at least one component of the electronic device (200) (e.g., processor (230)). For example, the data may include software (or, an instruction related thereto), input data, or output data. In one embodiment, the instruction may cause the electronic device (200) to perform operations defined by the instruction when executed by the processor (230).

[0064] According to one embodiment, the processor (230) may be implemented as a computer or a similar device according to hardware, software, or a combination thereof. Hardware-wise, the processor (230) may be implemented in the form of an electronic circuit that processes electrical signals to perform control functions, and software-wise, it may be implemented in the form of a program that drives the hardware processor (230). According to one embodiment, the processor (230) may be operatively connected to a component included in the electronic device (200) (e.g., an information acquisition interface (210) and / or a memory (220)) to control the connected component.

[0065] According to one embodiment, the processor (230) may include a central processing unit, an application processor, a graphics processing unit, a neural processing unit (NPU), an image signal processor, a sensor hub processor, or a communication processor.

[0066] Meanwhile, unless otherwise specifically mentioned in the following description, the operation of the electronic device (200) may be interpreted as being performed under the control of the processor (230).

[0067] According to one embodiment, an electronic device (200) can acquire battery data related to the state of a plurality of batteries (110, 120) that are placed in a vehicle (10) and connected in parallel with each other. The battery data may include at least one of voltage data, current data, temperature data, C-rate data, and State of Charge (SOC) data of the plurality of batteries (110, 120). Here, the C-rate data may represent a charge / discharge rate (e.g., 100 A / h, 50 A / h, 25 A / h, etc.) according to the specifications of each of the plurality of batteries (110, 120). Additionally, the voltage data, current data, temperature data, and SOC data may represent the state values ​​over time of each of the plurality of batteries (110, 120).

[0068] For example, if the electronic device (200) is implemented as a battery management device (130), the electronic device (200) can obtain battery data by measuring and processing the status of multiple batteries (110, 120) using an information acquisition interface (210). In this case, regarding C-rate data, the electronic device (200) can obtain it based on specification data of multiple batteries (110, 120) that is stored in advance in memory (220). As another example, if the electronic device (200) is implemented as a charging device (20), the electronic device (200) can obtain battery data from the vehicle (10) through a communication connection with the vehicle (10) (e.g., connection (101)). As yet another example, if the electronic device (200) is implemented as a server (30), the electronic device (200) can obtain battery data through a communication connection with the vehicle (10) (e.g., connection (103)).

[0069] According to one embodiment, the electronic device (200) can acquire charging data related to the state of the charging device (20). The charging data may include power data that the charging device (20) can supply to the vehicle (10).

[0070] For example, if the electronic device (200) is implemented as a battery management device (130), the electronic device (200) can obtain charging data from the charging device (20) through a communication connection (e.g., connection (101)) with the charging device (20). As another example, if the electronic device (200) is implemented as a charging device (20), the electronic device (200) can obtain charging data by checking the output power itself. As yet another example, if the electronic device (200) is implemented as a server (30), the electronic device (200) can obtain charging data from the charging device (20) through a communication connection (e.g., connection (102)) with the charging device (20).

[0071] According to one embodiment, the electronic device (200) can input battery data and charging data into a machine learning model.

[0072] According to one embodiment, the electronic device (200) may preprocess battery data and charging data and then input the preprocessed data into a machine learning model. Based on the battery data, the electronic device (200) may calculate a first parameter based on SOC and a second parameter based on temperature for each of the plurality of batteries (110, 120). Additionally, based on the charging data, the electronic device (200) may calculate the maximum current that the charging device (20) can supply to the vehicle (10). The electronic device (200) may input the calculated first parameter, second parameter, and maximum current into a machine learning model.

[0073] According to one embodiment, the electronic device (200) obtains a SOC-charge time derivative value (dSOC / dt) at the current SOC for each of the plurality of batteries (110, 120) based on reference data stored in the memory (220). c ) can be calculated as the first parameter. Here, the reference data is included in the specification data of each of the plurality of batteries (110, 120) and can represent the SOC according to the charging time. More specifically, the reference data can represent the SOC according to the charging time during charging based on the charging / discharging rate of each battery at the BOL (Begin of Life) of each of the plurality of batteries (110, 120).

[0074] According to one embodiment, the electronic device (200) can calculate a temperature attenuation coefficient based on the current temperature for each of the plurality of batteries (110, 120) as a second parameter. Here, the temperature attenuation coefficient can be calculated based on the maximum temperature and critical temperature according to the specifications of each battery. For example, if the maximum temperature according to the specifications of a specific battery is 100°C and the critical temperature is 60°C, the electronic device (200) can calculate the temperature attenuation coefficient as a first value (e.g., 1) when the current temperature of the battery is 60°C or lower, and calculate the temperature attenuation coefficient as a second value (e.g., 0) when the current temperature of the battery is 100°C or higher. Additionally, if the current temperature of the battery is 60°C or higher and 100°C or lower, the electronic device (200) can calculate the temperature attenuation coefficient such that the temperature attenuation coefficient is inversely proportional to the current temperature between the first value and the second value.

[0075] According to one embodiment, the electronic device (200) can calculate the maximum current that the charging device (20) can supply to the vehicle (10) based on charging data, the power that the charging device (20) can currently output, and the total voltage of the battery system (100). For example, if the power that the charging device (20) can currently output is 40 kW / h and the total voltage of the battery system (100) is 400 V, the electronic device (200) can calculate the maximum current that the charging device (20) can supply to the vehicle (10) as 100 A / h.

[0076] According to one embodiment, the machine learning model may be based on the following mathematical formulas 1 and 2.

[0077]

[0078]

[0079] In the above mathematical formulas 1 and 2, C-rate1 and C-rate Nis the C-rate of the first battery (110) and the Nth battery (120) among the plurality of batteries (110, 120), respectively, and S1 and S N are the first parameters of the first battery (110) and the Nth battery (120), respectively, and D1 and D N are the second parameters of the first battery (110) and the Nth battery (120), respectively, and I1 and I N is the required charging current of the first battery (110) and the Nth battery (120), respectively, and I total is the maximum charging current mentioned above.

[0080] According to one embodiment, when the machine learning model receives the C-rate of a plurality of batteries (110, 120) corresponding to input data, a first parameter, a second parameter, and a maximum charging current of a charging device (20), it can output the required charging current of each of the plurality of batteries (110, 120) that satisfies Equation 2 and minimizes the charging time of Equation 1.

[0081] According to one embodiment, the machine learning model may be an artificial intelligence model constructed by modeling the correlation between a training input data set and a training output data set. Here, the training input data set may include a plurality of battery data sets related to a plurality of states of a plurality of batteries (110, 120) and a plurality of charging data sets related to a plurality of states of a charging device (20). Additionally, the training output data set may include a set of required charging currents for each of the plurality of batteries (110, 120) that satisfies Equation 2 while minimizing the charging time of Equation 1.

[0082] According to one embodiment, the electronic device (200) can obtain the required charging current of each of the plurality of batteries (110, 120) that minimizes the charging time of the charging device (20) for the vehicle (10), which is output from a machine learning model.

[0083] According to one embodiment, the electronic device (200) can control a plurality of DC-DC converters electrically connected to each of the plurality of batteries (110, 120) so that a calculated required charging current flows to each of the plurality of batteries.

[0084] FIG. 3 is a circuit diagram of a battery system (100) according to one embodiment of the present disclosure.

[0085] Referring to FIG. 3, the first battery (110) and the Nth battery (120) can be connected in parallel with respect to the positive output terminal (330) and the negative output terminal (340) of the battery system (100). According to one embodiment, the positive output terminal (330) and the negative output terminal (340) can be connected to an external load to supply the output power of the battery system (100) to the external load.

[0086] According to one embodiment, a plurality of DC-DC converters (310, 320) are electrically connected to each of a plurality of batteries (110, 120) to control the output voltage of the plurality of batteries (110, 120) or to control the charge / discharge current.

[0087] According to one embodiment, the electronic device (200) can control a plurality of DC-DC converters (310, 320) so that a calculated required charging current flows to each of the plurality of batteries (110, 120) when charging by the charging device (20). Specifically, the electronic device (200) can control a plurality of DC-DC converters (310, 320) so that a voltage corresponding to each unit, which is connected in parallel with each other and consists of a DC-DC converter (310 or 320) and a battery (110 or 120), is applied to each unit.

[0088] For example, if the electronic device (200) is implemented as a battery management device (130), the electronic device (200) can control a plurality of DC-DC converters (310, 320) that are electrically connected so that the required charging current calculated for each of the plurality of batteries (110, 120) flows. As another example, if the electronic device (200) is implemented as a charging device (20) or a server (30), the electronic device (200) can transmit instructions to the vehicle (10) via a communication connection (e.g., connection (101 or 103)) to control the plurality of DC-DC converters (310, 320) so that the required charging current calculated for each of the plurality of batteries (110, 120) flows.

[0089] FIG. 4 is a flowchart of the operation of an electronic device according to one embodiment of the present disclosure. Since the operation method of FIG. 4 can be performed by the electronic device (200) of FIG. 2, descriptions that overlap with the above-mentioned content may be omitted and may be explained using the configurations of FIG. 1 and FIG. 2.

[0090] The embodiment illustrated in FIG. 4 is merely one example, and the sequence of operations according to various embodiments of the present disclosure may differ from that illustrated in FIG. 4, and some operations illustrated in FIG. 4 may be omitted, the order of operations may be changed, or operations may be merged.

[0091] Referring to FIG. 4, in operation 410, the electronic device (200) can obtain battery data related to the state of a plurality of batteries (110, 120) that are placed in a vehicle (10) and connected in parallel with each other. The battery data may include at least one of voltage data, current data, temperature data, C-rate data, and SOC (State of Charge) data of the plurality of batteries (110, 120).

[0092] In operation 420, the electronic device (200) can obtain charging data related to the state of the charging device (20) that performs charging for the vehicle (10). The charging data may include power data that the charging device (20) can supply to the vehicle (10).

[0093] In operation 430, the electronic device (200) can input the battery data obtained in operation 410 and the charging data obtained in operation 420 into a machine learning model.

[0094] In operation 440, the electronic device (200) can obtain the required charging current of each of the plurality of batteries (110, 120) that minimizes the charging time of the charging device (20) for the vehicle (10), which is output from a machine learning model.

[0095] FIG. 5 is a flowchart of the operation of an electronic device according to one embodiment of the present disclosure. Since the operation method of FIG. 5 can be performed by the electronic device (200) of FIG. 2, descriptions that overlap with the foregoing content may be omitted and may be explained using the configurations of FIG. 1 and FIG. 2.

[0096] The embodiment illustrated in FIG. 5 is merely one example, and the sequence of operations according to various embodiments of the present disclosure may differ from that illustrated in FIG. 5, and some operations illustrated in FIG. 5 may be omitted, the order of operations may be changed, or operations may be merged.

[0097] In addition, since operations 510 to 540 of FIG. 5 are identical to operations 410 to 440 of FIG. 4, a description thereof may be omitted.

[0098] In operation 550, the electronic device (200) can control a plurality of DC-DC converters electrically connected to each of the plurality of batteries so that the required charging current of each of the plurality of batteries (110, 120) calculated in operation 540 flows.

[0099] Various embodiments of the present disclosure may be represented by functional block configurations and various processing steps. These functional blocks may be implemented by various numbers of hardware and / or software configurations that execute specific functions. For example, the embodiments may employ integrated circuit configurations such as memory, processing, logic, look-up tables, etc., which can execute various functions by the control of one or more microprocessors or other control devices. Similar to how components may be implemented as software programming or software elements, the embodiments may be implemented in programming or scripting languages ​​such as C, C++, Java, assembler, etc., including various algorithms implemented as combinations of data structures, processes, routines, or other programming configurations. Functional aspects may be implemented as algorithms executed on one or more processors. Additionally, the embodiments may employ prior art for electronic configuration, signal processing, and / or data processing. Terms such as “mechanism,” “element,” “means,” and “configuration” may be used broadly and are not limited to mechanical and physical configurations. The above terms may include the meaning of a series of software processes (routines) in conjunction with processors, etc.

[0100] The aforementioned embodiments are merely examples, and other embodiments may be implemented within the scope of the claims set forth below.

Claims

1. In an electronic device, Memory for storing at least one instruction; and It includes a processor operatively connected to the above memory, and The above processor, by executing the above at least one instruction, Obtain battery data related to the status of multiple batteries arranged in a vehicle and connected in parallel, and Acquire charging data related to the status of a charging device that performs charging for the above vehicle, and Input the above battery data and the above charging data into a machine learning model, An electronic device for obtaining the required charging current of each of the plurality of batteries, which minimizes the charging time of the charging device for the vehicle, output from the machine learning model.

2. In Paragraph 1, The battery data above includes C-rate data, SOC (State of Charge) data, and temperature data of the plurality of batteries, and The above charging data is an electronic device comprising power data that the charging device can supply to the vehicle.

3. In Paragraph 2, The above processor, by executing the above at least one instruction, Based on the battery data above, for each of the plurality of batteries, a first parameter based on SOC and a second parameter based on temperature are calculated, and Based on the above charging data, the maximum charging current that the charging device can supply to the vehicle is calculated, and An electronic device that inputs the C-rate data of the plurality of batteries, the first parameter, the second parameter, and the maximum charging current to the machine learning model based on the following mathematical formulas 1 and 2. [Mathematical Formula 1] [Mathematical Formula 2] (In the above mathematical formulas 1 and 2, C-rate1 and C-rate N is the C-rate of the first battery and the Nth battery (where N is a natural number greater than or equal to 2) among the plurality of batteries, respectively, and S1 and S N are respectively the first parameters of the first battery and the Nth battery, and D1 and D N are respectively the second parameters of the first battery and the Nth battery, and I1 and I N is the required charging current of the first battery and the Nth battery, respectively, and I total is the maximum charging current mentioned above.) 4. In Paragraph 3, The above memory stores reference data representing the SOC according to the charging time of each of the plurality of batteries, and The above processor, by executing the above at least one instruction, For each of the plurality of batteries, based on the reference data stored in the memory, the SOC-charge time derivative value (dSOC / dt) at the current SOC c An electronic device that calculates ) as the first parameter above.

5. In Paragraph 3, The above processor, by executing the above at least one instruction, An electronic device that calculates a temperature attenuation coefficient based on the current temperature as a second parameter for each of the plurality of batteries.

6. In Paragraph 3, The above machine learning model is an artificial intelligence model constructed by modeling the correlation between a training input data set and a training output data set, and The above-mentioned learning input data set includes a plurality of battery data sets related to a plurality of states of the plurality of batteries and a plurality of charging data sets related to a plurality of states of the charging device, and An electronic device comprising a set of required charging currents for each of the plurality of batteries, which is generated by reflecting the learning input data set in the mathematical formula 1 and the mathematical formula 2, and which satisfies the mathematical formula 2 while minimizing the charging time of the mathematical formula 1.

7. In Paragraph 1, The above processor, by executing the above at least one instruction, An electronic device that controls a plurality of DC-DC converters electrically connected to each of the plurality of batteries so that a calculated required charging current flows to each of the plurality of batteries.

8. In a charging control method performed by an electronic device, An operation to acquire battery data related to the status of multiple batteries arranged in a vehicle and connected in parallel with each other; An operation to acquire charging data related to the state of a charging device that performs charging for the above vehicle; The operation of inputting the above battery data and the above charging data into a machine learning model; and A charging control method comprising the operation of obtaining the required charging current of each of the plurality of batteries, which minimizes the charging time of the charging device for the vehicle, output from the machine learning model.

9. In Paragraph 8, The battery data above includes C-rate data, SOC (State of Charge) data, and temperature data of the plurality of batteries, and A charging control method wherein the charging data includes power data that the charging device can supply to the vehicle.

10. In Paragraph 9, The operation of inputting the above battery data and the above charging data into the machine learning model is, Based on the battery data above, for each of the plurality of batteries, an operation of calculating a first parameter based on SOC and a second parameter based on temperature, An operation to calculate the maximum charging current that the charging device can supply to the vehicle based on the above charging data, and A charging control method comprising the operation of inputting the C-rate data of the plurality of batteries, the first parameter, the second parameter, and the maximum charging current to the machine learning model based on the following mathematical formulas 1 and 2. [Mathematical Formula 1] [Mathematical Formula 2] (In the above mathematical formulas 1 and 2, C-rate1 and C-rate N is the C-rate of the first battery and the Nth battery (where N is a natural number greater than or equal to 2) among the plurality of batteries, respectively, and S1 and S N are respectively the first parameters of the first battery and the Nth battery, and D1 and D N are respectively the second parameters of the first battery and the Nth battery, and I1 and I N is the required charging current of the first battery and the Nth battery, respectively, and I total is the maximum charging current mentioned above.) 11. In Paragraph 10, The operation of calculating the first parameter above is, For each of the plurality of batteries, based on reference data representing the SOC according to the charging time of each of the plurality of batteries, the SOC-charge time derivative value (dSOC / dt) at the current SOC c A charging control method comprising the operation of calculating ) as the first parameter.

12. In Paragraph 10, The operation of calculating the above second parameter is, A charging control method comprising, for each of the plurality of batteries, calculating a temperature attenuation coefficient based on the current temperature as the second parameter.

13. In Paragraph 10, The above machine learning model is an artificial intelligence model constructed by modeling the correlation between a training input data set and a training output data set, and The above-mentioned learning input data set includes a plurality of battery data sets related to a plurality of states of the plurality of batteries and a plurality of charging data sets related to a plurality of states of the charging device, and A charging control method comprising: the above-mentioned learning output data set, which includes a set of required charging currents for each of the plurality of batteries generated by reflecting the above-mentioned learning input data set in the above-mentioned mathematical formula 1 and the above-mentioned mathematical formula 2, satisfying the above-mentioned mathematical formula 2 while minimizing the charging time of the above-mentioned mathematical formula 1.

14. In Paragraph 8, A charging control method further comprising the operation of controlling a plurality of DC-DC converters electrically connected to each of the plurality of batteries so that a calculated required charging current flows to each of the plurality of batteries.

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