Method of optimizing mobile battery supply chain for electric vehicle and system and server performing the same

Abstract

The present invention is configured to include a method for optimizing a supply chain of a mobile battery for an electric vehicle, performed by a processor, the method comprising: acquiring charging data of a set of battery devices supplying power to an electric vehicle; calculating a time interval for visiting a plurality of battery transport vehicles based on the charging data; and determining an optimal movement path for a plurality of battery transport vehicles transporting the set of battery devices based on the time interval for visiting.

Description

Technology Field

[0001] The present invention relates to a method for optimizing a mobile battery supply chain for electric vehicles and a server and system for performing the same. Background Technology

[0002] As the electric vehicle market expands rapidly, the number of charging stations is also increasing. However, compared to the number of electric vehicles in circulation, the number of charging stations is still woefully insufficient.

[0003] Accordingly, a charging delivery service is being launched in which a service provider's vehicle travels to the location of the electric vehicle to charge the battery, rather than a charging service where the electric vehicle travels to the location of the charging station to charge the battery.

[0004] While charging delivery services can alleviate the anxiety of electric vehicle owners regarding the lack of charging stations, there are still limitations in establishing a smooth charging service system because the number of vehicles capable of providing such services is insufficient compared to the number of electric vehicles requiring charging.

[0005] The background description of the invention is provided to facilitate a better understanding of the present invention. The matters described in the background description should not be construed as an acknowledgment that they exist as prior art. The problem to be solved

[0006] In addition, conventional charging delivery services have limitations in efficiently utilizing finite manpower and vehicle infrastructure because the vehicle equipped with the charging battery and the service provider must wait until the electric vehicle is fully charged.

[0007] Furthermore, if an electric vehicle requesting the charging service is parked in a location that is difficult to access, the vehicle must be moved to a charging area, so service users cannot fully enjoy the convenience of the service.

[0008] Accordingly, a method for optimizing the supply chain of mobile batteries for electric vehicles is required to enhance the operational efficiency and accessibility of electric vehicle charging services.

[0009] As a result, the inventors of the present invention have devised a method to minimize idle batteries and increase the efficiency of charging delivery services by optimizing the movement routes of multiple service providers based on two factors: the charging time of a portable battery for an electric vehicle and the recovery time of a portable battery.

[0010] The problems of the present invention are not limited to those mentioned above, and other unmentioned problems will be clearly understood by those skilled in the art from the description below. means of solving the problem

[0011] To solve the problem described above, a method for optimizing the supply chain of a mobile battery for an electric vehicle according to an embodiment of the present invention is provided. The method is a method for optimizing the supply chain of a mobile battery for an electric vehicle performed by a processor, and is configured to include the steps of: acquiring charging data of a set of battery devices that supply power to an electric vehicle; calculating a time interval for visiting a plurality of battery transport vehicles based on the charging data; and determining an optimal movement path of a plurality of battery transport vehicles that transport the set of battery devices based on the time interval for visiting.

[0012] According to a feature of the present invention, the step of acquiring the charging data may be a step of acquiring a remaining charging time calculated using at least one of the charging data, such as a charging start time, a charging end time, a charging request amount, a charging request ratio, a charging request time, and a charging request amount, using the battery device set.

[0013] According to a feature of the present invention, the step of calculating the visit time interval may be based on the remaining charging time of the battery device set and may include the recovery time range of the battery device set and the effective arrival time range of the battery transport vehicle.

[0014] According to a feature of the present invention, the step of calculating the visit time interval may further include the step of recalculating the visit time interval based on battery data observed or observed in the battery device set.

[0015] According to a feature of the present invention, the step of recalculating the visit time interval may further include the step of calculating the probability of an emergency situation occurring based on the battery data and the step of shortening the remaining charging time of the battery device set or adjusting the recovery time range according to the calculated probability value.

[0016] According to a feature of the present invention, the step of determining the optimal movement path may further include the step of calculating the travel time required to move to the location of the battery device set for each battery station managing the battery device set at a time corresponding to the visit time interval.

[0017] According to a feature of the present invention, the step of calculating the travel time may be a step of calculating the travel time based on the location of the battery station and the battery device set.

[0018] According to a feature of the present invention, the step of calculating the travel time may be a step of calculating the travel time based on the location of at least one battery transport vehicle among a battery transport vehicle located at the battery station, a battery transport vehicle returning to the battery station, and a battery transport vehicle departing from the battery station, and the location of the battery device set.

[0019] According to a feature of the present invention, the step of determining the optimal movement path may be a step of determining the movement path of a plurality of battery transport vehicles using an algorithm capable of solving the Vehicle Routing Problem.

[0020] According to a feature of the present invention, the step of acquiring charging data is a step of acquiring charging data of a charger connected to the electric vehicle and a plurality of battery devices detachably coupled to the charger, and the step of calculating the visit time interval may further include a step of determining a set of battery devices requiring recovery or replacement based on the charging data.

[0021] To solve the problem described above, a supply chain optimization server for a mobile battery device for an electric vehicle according to another embodiment of the present invention is provided. The server comprises a communication interface, a memory, and a processor operably connected to the communication interface and the memory. The processor is configured to acquire charging data of a set of battery devices that supply power to an electric vehicle, calculate a time interval for a visit of a plurality of battery transport vehicles based on the charging data, and determine an optimal movement path for a plurality of battery transport vehicles that transport the set of battery devices based on the time interval for the visit.

[0022] Specific details of other embodiments are included in the detailed description and drawings. Effects of the invention

[0023] The present invention can minimize idle batteries by optimizing the movement paths of transport vehicles and a plurality of service providers offering charging services. In particular, the present invention allows the recovery time for optimizing the movement path to be set as a time interval rather than a single point in time. Accordingly, the present invention eliminates the need to repeatedly reset the movement path due to variables occurring during actual battery charging, replacement, and recovery, thereby minimizing the computational consumption of the system.

[0024] The present invention determines a recovery priority based on the charging time of a plurality of mobile battery device sets and the recovery time of a plurality of mobile battery device sets, thereby enabling the charging of many electric vehicles even with a limited number of mobile battery device sets, and thereby increasing the operational efficiency of the charging delivery service.

[0025] The present invention provides a charging service that supplies and retrieves a set of mobile battery devices capable of charging electric vehicles, rather than a charging service that moves a vehicle equipped with a battery, thereby enabling the provision of a charging service without requiring separate vehicle charging spaces for the service user and the service provider.

[0026] In particular, by configuring only thin, movable battery devices to be movable, charging services can be provided even to electric vehicles placed in narrow parking spaces.

[0027] The effects according to the present invention are not limited to those exemplified above, and a wider variety of effects are included within the present invention. Brief explanation of the drawing

[0028] FIGS. 1 and 2 are schematic diagrams illustrating a supply chain optimization method for a mobile battery for an electric vehicle according to an embodiment of the present invention. FIG. 3 is a block diagram showing the configuration of a supply chain optimization system for a mobile battery for an electric vehicle according to one embodiment of the present invention. FIG. 4 is a block diagram showing the configuration of a portable battery set for an electric vehicle according to one embodiment of the present invention. FIG. 5 is a block diagram showing the configuration of a supply chain optimization server for a mobile battery for an electric vehicle according to one embodiment of the present invention. FIG. 6 is a schematic flowchart of a supply chain optimization method for a mobile battery for an electric vehicle according to one embodiment of the present invention. FIG. 7 is a schematic diagram illustrating a method for calculating a visit time interval according to an embodiment of the present invention. Specific details for implementing the invention

[0029] The advantages and features of the present invention 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 invention is not limited to the embodiments disclosed below but may be implemented in various different forms. These embodiments are provided merely to ensure that the disclosure of the present invention is complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. In connection with the description of the drawings, similar reference numerals may be used for similar components.

[0030] In this document, expressions such as "have," "can have," "include," or "can include" refer to the existence of the relevant feature (e.g., numerical values, functions, actions, or components, etc.) and do not exclude the existence of additional features.

[0031] In this document, expressions such as “A or B,” “at least one of A or / and B,” or “one or more of A or / and B” may include all possible combinations of items listed together. For example, “A or B,” “at least one of A and B,” or “at least one of A or B” may refer to cases including (1) at least one A, (2) at least one B, or (3) both at least one A and at least one B.

[0032] Expressions such as "first," "second," "first," or "second" used in this document may modify various components regardless of order and / or importance, and are used merely to distinguish one component from another without limiting such components. For example, the first user device and the second user device may represent different user devices regardless of order or importance. For example, without departing from the scope of rights set forth in this document, the first component may be named the second component, and similarly, the second component may be renamed the first component.

[0033] Where it is stated that a certain component (e.g., a first component) is "(operatively or communicatively) coupled with" or "connected to" another component (e.g., a second component), it should be understood that the said certain component may be directly connected to the said other component or connected through another component (e.g., a third component). On the other hand, where it is stated that a certain component (e.g., a first component) is "directly connected" or "directly connected" to another component (e.g., a second component), it may be understood that no other component (e.g., a third component) exists between the said certain component and the said other component.

[0034] As used in this document, the expression “configured to” may be replaced, depending on the context, with, for example, “suitable for,” “having the capacity to,” “designed to,” “adapted to,” “made to,” or “capable of.” The term “configured to” does not necessarily mean “specifically designed to” in hardware. Instead, in some situations, the expression “device configured to” may mean that the device is “capable of” in conjunction with other devices or components. For example, the phrase “processor configured to perform A, B, and C” may mean a dedicated processor for performing those operations (e.g., an embedded processor) or a generic-purpose processor (e.g., a CPU or application processor) capable of performing those operations by executing one or more software programs stored in a memory device.

[0035] The terms used in this document are used merely to describe specific embodiments and are not intended to limit the scope of other embodiments. Singular expressions may include plural expressions unless the context clearly indicates otherwise. Terms used herein, including technical or scientific terms, may have the same meaning as generally understood by those skilled in the art described in this document. Terms used in this document that are defined in general dictionaries may be interpreted as having the same or similar meaning as they have in the context of the relevant technology, and are not to be interpreted in an ideal or overly formal sense unless explicitly defined in this document. In some cases, even terms defined in this document may not be interpreted to exclude the embodiments of this document.

[0036] The features of each of the various embodiments of the present invention may be combined or combined with one another, either partially or wholly, and as will be fully understood by those skilled in the art, various technical interlocking and operation are possible, and each embodiment may be implemented independently of one another or together in an interlocking relationship.

[0037] For clarity in the interpretation of this specification, the terms used in this specification are defined below.

[0038] Hereinafter, the present invention will be described in detail by explaining preferred embodiments of the present invention with reference to the attached drawings.

[0039] FIGS. 1 and 2 are schematic diagrams illustrating a supply chain optimization method for a mobile battery for an electric vehicle according to an embodiment of the present invention.

[0040] Referring to FIG. 1, a supply chain optimization method for a mobile battery for an electric vehicle according to one embodiment of the present invention may provide a method for efficiently recovering a plurality of battery device sets (100) in a charging service for an electric vehicle using a battery device set (100). Specifically, in one embodiment of the present invention, the battery device set (100) may be supplied from a plurality of battery stations (10) located at a certain point. Each of the plurality of battery device sets (100) has a calculated remaining charging time, and an optimal travel path of the vehicle may be determined based on the vehicle travel time between the location of the plurality of battery device sets (100) and the location of the plurality of battery stations (10).

[0041] In one embodiment of the present invention, a plurality of battery device sets (100) may be returned to an adjacent battery station (10) according to the current remaining charging time and vehicle visit time, even if it is not the battery station (10) that was supplied.

[0042] Referring to FIG. 2, in one embodiment of the present invention, the optimal travel path may be determined as follows based on the remaining charging time and the vehicle travel time. For example, if the remaining charging time of a battery device set (100) connected to User A’s electric vehicle is 10-15 minutes, the optimal travel path may be determined so that a battery transport vehicle (200) located at a first battery station (10) that is close to the vehicle travel time can move to the battery device set (100). In another example, if the remaining charging time of a battery device set (100) connected to User B’s electric vehicle is 55-62 minutes and 66-73 minutes, respectively, the optimal travel path may be determined so that the battery device set (100) connected to User C’s electric vehicle, which is far away, can be retrieved first, even if there is a relative margin in the remaining charging time, depending on the vehicle travel time required to arrive at each of the battery device sets (100) from the location of the battery device set (100) connected to User E’s electric vehicle.

[0043] In one embodiment of the present invention, an optimal travel path can be generated through a pre-learned travel path generation algorithm, wherein the vehicle travel time input to the algorithm can be determined as a visit time interval based on the remaining charging time. That is, by using a time range rather than a specific time, flexible application to various variables is possible, thereby increasing the efficiency of algorithm computation.

[0044] FIG. 3 is a block diagram showing the configuration of a supply chain optimization system for a mobile battery for an electric vehicle according to one embodiment of the present invention.

[0045] Referring to FIG. 3, a mobile battery supply chain optimization system (1000) for an electric vehicle (hereinafter referred to as the “supply chain optimization system”) according to one embodiment of the present invention may include a plurality of battery device sets (100) matched to a battery station (10), a battery transport vehicle (20), a mobile battery supply chain optimization server (200) for an electric vehicle (hereinafter referred to as the “supply chain optimization server”), a service user device (300), and an electric vehicle (30).

[0046] A battery station (10) is a base where multiple sets of battery devices (100) are charged and stored, and a battery transport vehicle (20) may be located there as well. Multiple battery stations (10) may be located in an area where there is demand for electric vehicles (30). According to an embodiment, the supply chain optimization system (1000) may further include a battery station manager device (not shown) for a manager who manages the battery station (10). The battery station manager device can monitor the quantity, identification information, and observed and monitored data of the sets of battery devices (100) being charged and stored at the battery station (10), and can monitor the identification information and location of the battery transport vehicle (20) matched to the battery station (10). The battery station manager device can periodically provide monitoring result data to the supply chain optimization server (200).

[0047] A battery device set (100) can be connected to an electric vehicle (30) to supply power to the electric vehicle (30). Specifically, the battery device set (100) may include a charger (110) directly connected to the electric vehicle (30) and a plurality of battery devices (120) that transmit power to the charger (110).

[0048] In this regard, FIG. 4 is a block diagram showing the configuration of a portable battery set for an electric vehicle according to one embodiment of the present invention.

[0049] Referring to FIG. 4, the charger (110) of the battery device set (100) may include a charging connector (111) coupled to the charging port of the electric vehicle (30), a battery connector (112) connecting the charger (110) and the battery device (120), and a cable (121) connecting a plurality of battery devices (120) in parallel. When the battery device set (100) and the electric vehicle (30) are connected through the charging connector (111), the electric vehicle (30) can be fast-charged or slow-charged using the power charged in the plurality of battery devices (120).

[0050] In particular, in one embodiment of the present invention, the battery device set (100) is formed such that the charger (110) and the battery device (120) are thin and movable, allowing the electric vehicle (30) to be moved more easily to the location where it is parked. That is, unlike conventional methods in which there are limitations on the charging location because the battery device set (100) of the present invention must be moved to the location of the electric vehicle (30) to be charged, the charger (110) and a plurality of battery devices (120) are configured to be movable, thereby maximizing the convenience of using the on-demand service.

[0051] In addition, among the battery device set (100), the plurality of battery devices (120) may not be devices fixedly matched with the charger (110), but may be variable devices that can be changed according to the battery charge amount (or usage amount). Furthermore, among the plurality of battery devices (120), the battery device (120) connected to the charger (110) may be set as a master battery device (120A). Specifically, the battery device set (100) may be configured with one master battery device (120A) and the remaining plurality of slave battery devices (120B) for stable operation between the charger (110) and the plurality of battery devices (120). For example, when the plurality of battery devices (120) are connected, the plurality of battery devices (120) may be in sleep mode. Here, sleep mode may be a state in which power is not supplied and only communication with other devices is possible. In this state, when one battery device (120) is connected to the charger (110) via the charging connector (111), the battery device (120) can be set as the master battery device (120A). The master battery device (120A) can receive the number of charging battery devices (120) set for charging the electric vehicle (30) from the charger (110), and can compare the received number with the number of previously connected battery devices (120). The master battery device (120A) determines whether the number of the remaining battery devices (120), including itself, is the same as the received number, and if the result of the determination is the same, it can share the setting data to the slave battery device (120B) with the remaining battery devices (120).Here, sharing configuration data with the slave battery device (120B) may mean that the master battery device (120A) transmits the identification data of the master battery device (120A), the sequence number assigned to the slave battery device (120B), etc., along with a wake-up signal to the slave battery device (120B). In addition, the master battery device (120A) may store the identification data of the slave battery device (120B). If, as a result of the judgment of the master battery device (120A), the number of batteries is not the same, the remaining battery devices (120), including the master battery device (120A), may maintain a sleep mode state. According to the judgment result of the master battery device (120A), the slave battery device (200B) that wakes up from sleep mode can supply power to the charger (110) using a charging control signal provided from the electric vehicle (30) through the charger (110). At this time, the power control signal may include a request to stop power supply in addition to a request for power supply.

[0052] As described above, a master battery device (120A) and a slave battery device (120B) may be configured in the battery device set (100), and the master battery device (120A) may combine its own battery data and charging data with the battery data and charging data of the slave battery device (120B) and transmit them to the charger (110). Here, the battery data may include data observed and observed by the battery device (120). For example, battery data may include sensing data such as voltage, current, temperature, and insulation resistance of a battery module included in a battery device (120), monitoring result data such as overvoltage, undervoltage, overcurrent, cell voltage balancing abnormalities, overtemperature, low temperature, and hardware abnormalities of the battery module, and state data such as State of Charge (SoC) (e.g., battery charge amount (%)), SoC limit, State of Health (SoH) (e.g., battery performance maximum (%)), total usage time, charge and discharge cycles, and cumulative charge and discharge amounts (Ah, kWh). Here, depending on the monitoring result data and state data, if an emergency situation (e.g., abnormal state) is detected or if it is determined that an emergency situation will occur, a plurality of device sets (100) can control power supply to an electric vehicle (30) according to a control signal from a supply chain optimization server (200).

[0053] Referring again to FIG. 3, the battery transport vehicle (20) is a vehicle capable of transporting a plurality of battery device sets (100), and may actually be a vehicle owned by a service provider that connects the battery device sets (100) and the electric vehicle (30) to provide charging services. According to an embodiment, the supply chain optimization system (1000) may further include a service provider device (not shown) owned by the service provider. The battery transport vehicle (20) or the service provider device may receive a travel route including a departure time range from the supply chain optimization server (200). Additionally, the battery transport vehicle (20) or the service provider device may periodically provide location data to the supply chain optimization server (200) and may receive a travel route that has been changed in real time from the supply chain optimization server (200) while moving.

[0054] The supply chain optimization server (200) is a server for a service operator to efficiently operate a set of battery devices (100), which is a limited resource, and may include a general-purpose computer, a laptop, and a data server. The supply chain optimization server (200) may be a cloud-based server and may receive and store various charging service-related data from a plurality of battery device sets (100), service user devices (300), battery stations (10), battery transport vehicles (20), and electric vehicles (30).

[0055] The supply chain optimization server (200) can determine the optimal priority and the corresponding optimal movement path for recovering multiple battery device sets (100) with optimal efficiency for each battery transport vehicle (20) by considering the charging status of multiple battery device sets (100) and different movement paths according to location and time. The supply chain optimization server (200) can periodically determine the optimal movement path using an algorithm that can solve the vehicle routing problem where transport capacity is limited or there are transport capacity and time constraints, and in the event of an emergency, the optimal movement path can be determined again regardless of the preset period. However, in one embodiment of the present invention, the algorithm that can solve the vehicle routing problem may be an algorithm that uses various items applied to the vehicle routing problem as variables, in addition to items such as transport capacity and time.

[0056] In addition, the supply chain optimization server (200) may not specify the remaining charging time until charging is completed and the visit time until retrieval is completed as a specific point in time during the process of determining the optimal movement path. By defining the remaining charging time and visit time as a time interval that includes a certain time range, the supply chain optimization server (200) can minimize the situation where the optimal movement path must be determined repeatedly due to the occurrence of variables.

[0057] The service user device (300) is a device of a user who wishes to use the charging service of an electric vehicle (30), and may include a smartphone, a tablet PC (Personal Computer), a laptop, and a PC. The service user device (300) may request the charging service by installing or running an application or program provided by the supply chain optimization server (200). For example, the service user device (300) may request the charging service from the supply chain optimization server (200) by obtaining at least one of the following charging data from the service user: identification data of the electric vehicle (30), location data, charging start time, charging end time of the electric vehicle (30), charging request amount, charging request ratio, charging request time, and charging request amount.

[0058] An electric vehicle (30) may be connected to a plurality of battery device sets (100) to receive power for operation. The electric vehicle (30) or the battery device set (100) connected thereto may calculate the remaining charging time using charging data up to that point and provide this to a supply chain optimization server (200). According to an embodiment, the remaining charging time provided by the electric vehicle (30) or the battery device set (100) connected thereto may be a point in time, and the supply chain optimization server (200) may set a time range in which charging is expected to be completed based on that point in time and set a remaining charging time interval.

[0059] Up to this point, a supply chain optimization system (1000) according to one embodiment of the present invention has been described. According to the present invention, by providing a charging service that supplies and retrieves a set of mobile battery devices capable of charging an electric vehicle, rather than a charging service that moves a vehicle equipped with a battery, a charging service can be provided without separately securing a vehicle charging space for the service user and the service provider. In addition, by optimizing the movement path for retrieval so that the set of battery devices can be immediately utilized for the next charging service after the charging service is terminated, idle battery resources can be utilized to the maximum extent.

[0060] Hereinafter, with reference to FIG. 4, a supply chain optimization server (200) that controls the recovery time of a plurality of battery device sets (100) to utilize idle battery resources will be described.

[0061] FIG. 5 is a block diagram showing the configuration of a supply chain optimization server for a mobile battery for an electric vehicle according to one embodiment of the present invention.

[0062] Referring to FIG. 5, the supply chain optimization server (200) may include a communication interface (210), memory (220), I / O interface (230), and a processor (240), and each component may communicate with one or more communication buses or signal lines.

[0063] The communication interface (210) can be connected to a battery station manager device, a battery transport vehicle (20), a battery device set (100), a service user device (300), and an electric vehicle (30) via a wired / wireless communication network to exchange data. For example, the communication interface (210) can receive from the battery station manager device the quantity of the battery device set (100) being charged and stored at the battery station (10), identification information, and the results of monitoring the observed and observed data, as well as the identification information and location monitoring results of the battery transport vehicle (20) matched to the battery station (10). The communication interface (210) can transmit the optimal travel route determined by the battery station manager device and the identification information of the battery device set (100) to be collected according to the optimal travel route. As another example, the communication interface (210) can receive location data from the battery transport vehicle (20) and transmit the optimal travel route including the departure time interval to the battery transport vehicle (20). As another example, the communication interface (210) can receive battery data and charging data along with identification information of the charger (110) and the battery device (120) from the battery device set (100), and can transmit a charging control signal to the battery device set (100). As another example, the communication interface (210) can receive a charging service request for the electric vehicle (30) from the service user device (300), and can transmit a notification of the start and end of the charging service for the electric vehicle (30) to the service user device (300). As another example, the communication interface (210) can receive charging data from the electric vehicle (30) and can transmit a charging service start and end control signal to the electric vehicle (30).

[0064] Meanwhile, a communication interface (210) that enables the transmission and reception of such data includes a wired communication port (211) and a wireless circuit (212), wherein the wired communication port (211) may include one or more wired interfaces, for example, Ethernet, Universal Serial Bus (USB), FireWire, etc. Additionally, the wireless circuit (212) may transmit and receive data with an external device via an RF signal or an optical signal. Furthermore, wireless communication may use at least one of a plurality of communication standards, protocols, and technologies, such as GSM, EDGE, CDMA, TDMA, Bluetooth, Wi-Fi, VoIP, Wi-MAX, or any other suitable communication protocol.

[0065] The memory (220) can store various data used in the supply chain optimization server (200). For example, the memory (220) can store identification information of a plurality of battery device sets (100), service user devices (300), battery stations (10), battery transport vehicles (20), and electric vehicles (30), as well as various charging service-related data received from them. As an example, the memory (220) can store identification data of chargers (110) and battery devices (120) included in the battery device sets (100), connection sequence numbers, data regarding connected electric vehicles (30), etc. As another example, the memory (220) can store a movement path generation algorithm and its training data.

[0066] In various embodiments, the memory (220) may include a volatile or non-volatile recording medium capable of storing various data, commands, and information. For example, the memory (220) may include at least one type of storage medium among a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (e.g., SD or XD memory, etc.), RAM, SRAM, ROM, EEPROM, PROM, network storage, cloud, and blockchain database.

[0067] In various embodiments, the memory (220) may store at least one configuration of an operating system (221), a communication module (222), a user interface module (223), and one or more applications (224).

[0068] An operating system (221) (e.g., embedded operating systems such as LINUX, UNIX, MAC OS, WINDOWS, VxWorks, etc.) may include various software components and drivers for controlling and managing general system operations (e.g., memory management, storage device control, power management, etc.) and may support communication between various hardware, firmware, and software components.

[0069] The communication module (223) can support communication with another device through the communication interface (210). The communication module (220) may include various software components for processing data received by the wired communication port (211) or wireless circuit (212) of the communication interface (210).

[0070] The user interface module (223) can receive user requests or inputs from a keyboard, touch screen, microphone, etc. through the I / O interface (230) and provide a user interface on the display.

[0071] The application (224) may include a program or module configured to be executed by one or more processors (240). Here, the application for determining the optimal travel path may be on a server farm.

[0072] The I / O interface (230) can connect at least one of the input / output devices (not shown) of the supply chain optimization server (200), such as a display, keyboard, touch screen, and microphone, to the user interface module (223). The I / O interface (230) can receive user input (e.g., voice input, keyboard input, touch input, etc.) together with the user interface module (223) and process commands based on the received input.

[0073] The processor (240) is connected to a communication interface (210), a memory (220), and an I / O interface (230) to control the overall operation of the supply chain optimization server (200), and can execute various commands to retrieve a set of multiple battery devices (100) currently providing a charging service and to utilize them for the next charging service through an application or program stored in the memory (220).

[0074] The processor (240) may correspond to a computing device such as a CPU (Central Processing Unit) or an AP (Application Processor). Additionally, the processor (240) may be implemented in the form of an Integrated Chip (IC), such as a System on Chip (SoC) that integrates various computing devices. Alternatively, the processor (240) may include a module for computing artificial neural network models, such as a Neural Processing Unit (NPU).

[0075] Below, a method for the processor (240) to recover a plurality of battery device sets (100) will be described.

[0076] FIG. 6 is a schematic flowchart of a supply chain optimization method for a mobile battery for an electric vehicle according to one embodiment of the present invention.

[0077] Referring to FIG. 6, the processor (240) can obtain charging data of a battery device set (100) that supplies power to an electric vehicle (30) (S110). Here, the charging data may be obtained in accordance with a charging service request from a service user device (300) and may be obtained from an electric vehicle (30) that is the target of the charging service or from a battery device set (100) connected thereto. For example, the charging data may include identification data and location data of the electric vehicle (30) and the battery device set (100) connected thereto, along with the charging start time, charging end time, charging request amount, charging request ratio, charging request time, and charging request amount of the electric vehicle (30). Additionally, the processor (240) can obtain a remaining charging time calculated using the charging data from the electric vehicle (30) or the battery device set (100) connected thereto.

[0078] In various embodiments, the processor (240) may determine a set of battery devices that require recovery or replacement based on charging data. Specifically, the processor (240) may determine a set of battery devices (100) that requires recovery among a plurality of battery device sets (100) that are providing charging services, so that the recovery of the battery device set (100) can be performed periodically. Additionally, the processor (240) may acquire charging data for each of the charger (110) and the plurality of battery devices (120) included in the battery device set (100), and may determine a set of battery devices (100) that requires replacement among the charger (110) and the plurality of battery devices (120) based on each charging data. For example, if only the charger (110) or one of the battery devices (120) needs replacement, the processor (240) may determine a set of battery devices (100) that includes the corresponding component as a set of battery devices (100) that requires replacement. However, the processor (240) may provide identification information of the battery device set (100) and components requiring replacement, along with the optimal travel route subsequently determined, to the battery station manager device or battery transport vehicle (20) in order to notify the service provider that there are components requiring replacement in the optimal travel route.

[0079] After step S110, the processor (240) can calculate the visit time intervals of a plurality of battery transport vehicles (20) based on charging data (S120). Here, the visit time intervals are based on the remaining charging time and may include the recovery time range of the battery device set (100) and the effective arrival time range of the battery transport vehicles (20). To this end, the processor (240) can calculate the recovery time intervals and the effective arrival time intervals.

[0080] In various embodiments, the recovery time range may be the time range required to disconnect the battery device set (100) connected to the electric vehicle (30) from the electric vehicle (30) and to load the battery device set (100) onto the battery transport vehicle (20). The recovery time range may be pre-set by a service operator. However, the recovery time range may be adjusted according to the progress of the charging service of the battery device set (100).

[0081] Specifically, the processor (240) can recalculate the visit time interval based on battery data observed or observed from the battery device set (100). The battery data is data observed or observed from the charger (110) and a plurality of battery devices (120), and may include sensing data, monitoring result data, and status data. The processor (240) can calculate the probability of an emergency situation occurring based on the monitoring result data or status data for the battery device set (100). That is, depending on the calculated probability value, if the processor (240) determines that an emergency situation has already occurred or is likely to occur, it can shorten the remaining charging time of the battery device set (100) or adjust the recovery time range. Here, the recovery time range may be maintained or shortened depending on whether the entire battery device set (100) is recovered or only some components of the battery device set (100) are recovered and replaced. For example, the processor (240) can maintain the recovery time range when the entire battery device set (100) is recovered, and can shorten the recovery time range when only some components are recovered and replaced.

[0082] In various embodiments, the effective arrival time range may be a time range in which the battery transport vehicle (20) can be considered to have arrived. The processor (240) may calculate the travel time range required for the battery transport vehicle (20) to enter a preset radius centered on the arrival location, and determine the calculated travel time range as the effective arrival time range. Here, the arrival location may be where the electric vehicle (30) and the set of battery devices (100) connected thereto are located, and the travel time range may be calculated by considering future traffic volume based on the remaining charging time.

[0083] In this regard, FIG. 7 is a schematic diagram illustrating a method for calculating a visit time interval according to an embodiment of the present invention.

[0084] Referring to FIG. 7(a), in a normal state where no emergency occurs, the processor (240) may determine a reference time of 3:20 to calculate the visit time interval based on the remaining charging time. Meanwhile, although the remaining charging time is depicted as a specific point in time, the remaining charging time can be understood as a specific time interval. Based on the remaining charging time and the time range, the processor (240) may determine the recovery time interval to be from 3:10 to 3:30 according to a preset recovery time range. The processor (240) may calculate the valid arrival time range and determine the valid arrival time interval that overlaps with the starting area of ​​the recovery time interval. That is, the processor (240) may determine the valid arrival time interval to be from 2:57 to 3:12.

[0085] Referring to FIG. 7(b), in the event of an emergency or abnormal condition expected to occur, the processor (240) may shorten the remaining charging time and determine the reference time for calculating the visit time interval to be 1:20. Here, the shortened time may be variable depending on the type and severity of the emergency and may be pre-set by the service operator. In the case of recovering and replacing some components of the battery device set (100), the processor (240) may shorten the preset recovery time range and determine the recovery time interval to be from 1:13 to 1:27. The processor (240) may calculate the effective arrival time range and determine the effective arrival time interval that overlaps with the starting area of ​​the recovery time interval. That is, the processor (240) may determine the effective arrival time interval to be from 12:55 to 1:15.

[0086] Referring again to FIG. 6, after step S120, the processor (240) can determine the optimal travel route of a plurality of battery transport vehicles (20) transporting a battery device set (100) based on the visit time interval. Specifically, the processor (240) can calculate the travel time required to reach the location of the battery device set (10) for each location of the plurality of battery stations (10), taking into account that the retrieval and supply of the battery device set (100) may not be performed at the same battery station (10). The processor (240) can calculate the travel time at a point in time corresponding to the visit time interval calculated in the previous step S120. That is, the processor (240) can calculate the travel time considering future traffic volume, the number of retrievals of the battery device set (100), the location, etc.

[0087] The processor (240) can match multiple battery stations (10) and multiple battery device sets (100) that need to be recovered one-to-one from start to end so as to determine an optimal travel path. The processor (240) can generate a Start-End table indicating travel time according to the location of the battery station (10) and battery device set (100) as shown in [Table 1] below.

[0088] S \ E Location 1 Location 2 Location 3 … Station A 50 mins 10 mins 30 mins … Station B 70 mins 27 mins 90 mins … Station C 60 mins 33 mins 12 mins … …

[0089] The processor (240) can determine the movement paths of multiple battery transport vehicles (20) based on a departure-arrival table by using a Vehicle Routing Problem (VRP) model. For example, the processor (240) can determine the movement paths of multiple battery transport vehicles (20) by using a model or algorithm capable of solving vehicle routing problems where transport capacity is limited or where transport capacity and time constraints apply. For example, the processor (240) can use an algorithm to determine the optimal movement path in a situation where the departure-arrival time limit of the battery transport vehicles (20) is set. As another example, the processor (240) can use a vehicle route optimization model, a heuristic-based vehicle route optimization algorithm, or models derived therefrom to output the optimal movement path using a departure-arrival table as input. Meanwhile, the algorithm for determining the movement paths of multiple battery transport vehicles (20) is not limited to these, and various known route problem solving models or algorithms may be applied.

[0090] Meanwhile, the processor (240) can determine a suitable movement path for multiple battery transport vehicles (20) by using various algorithms capable of solving vehicle path problems in addition to the above example.

[0091] In various embodiments, the processor (240) may generate a departure-arrival table corresponding to the location of at least one battery transport vehicle (20) among the battery transport vehicle (20) located at the battery transport station (10), the battery transport vehicle (20) returning to the battery station (10), and the battery transport vehicle departing from the battery station (10), and the location of the battery device set (100). That is, the processor (240) can increase the recovery efficiency of the battery device set (100) by utilizing the battery transport vehicle (20) that is moving to supply the battery device set (100) as a resource for recovery, thereby using a limited number of battery transport vehicles (20).

[0092] So far, a supply chain optimization server (200) according to one embodiment of the present invention has been described. According to the present invention, by setting the recovery time for optimizing the movement path as a time interval rather than a single point in time, there is no need to repeatedly reset the movement path due to variables occurring during actual battery charging, replacement, and recovery, thereby minimizing the computational consumption of the system.

[0093] Although embodiments of the present invention have been described in more detail with reference to the attached drawings, the present invention is not necessarily limited to these embodiments and may be modified in various ways within the scope of the technical spirit of the present invention. Accordingly, the embodiments disclosed in the present invention are intended to explain, not limit, the technical spirit of the present invention, and the scope of the technical spirit of the present invention is not limited by these embodiments. Therefore, the embodiments described above should be understood as illustrative in all respects and not restrictive. The scope of protection of the present invention shall be interpreted by the claims below, and all technical spirits within an equivalent scope shall be interpreted as being included within the scope of rights of the present invention. Explanation of the symbols

[0094] 1000: Mobile Battery Supply Chain Optimization System for Electric Vehicles 10: Battery Station 20: Battery transport vehicle 30: Electric vehicles 100: Battery device set 110: Charger 120: Battery Device 111: Charging connector 112: Battery connector 120A: Master battery device 120B: Slave battery device 121: Cable 200: Supply Chain Optimization Server 210: Communication Interface 211: Wired communication port 212: Wireless circuit 220: Memory 221: Operating System 222: Communication Module 223: User Interface Module 224: Application 230: I / O Interface 240: Processor 300: Service user device

Claims

Claim 1 A method for optimizing the supply chain of a mobile battery for an electric vehicle, performed by a processor, comprising: acquiring charging data of a battery device set that supplies charging power to an electric vehicle; calculating a visit time interval of a plurality of battery transport vehicles, including a recovery time range of the battery device set and an effective arrival time range of the battery transport vehicle, based on a remaining charging time calculated from the charging data; calculating a probability of an emergency situation occurring based on battery data observed or observed in the battery device set; recalculating the visit time interval by shortening the remaining charging time of the battery device set or adjusting the recovery time range according to the calculated probability value; calculating a travel time interval required to move to the location of the battery device set for each battery station managing the battery device set based on the recalculated visit time interval; and determining an optimal travel path of a plurality of battery transport vehicles transporting the battery device set based on the recalculated visit time interval and the travel time interval. Claim 2 A method for optimizing the supply chain of a mobile battery for an electric vehicle, wherein, in claim 1, the step of acquiring the charging data is the step of acquiring the remaining charging time calculated using at least one of the charging data among the charging start time, charging end time, charging request amount, charging request ratio, charging request time, and charging request amount using the battery device set. Claim 3 delete Claim 4 delete Claim 5 delete Claim 6 delete Claim 7 A method for optimizing the supply chain of a mobile battery for an electric vehicle, wherein, in claim 1, the step of calculating the travel time is the step of calculating the travel time based on the location of the battery station and the battery device set. Claim 8 A method for optimizing the supply chain of a mobile battery for an electric vehicle, wherein the step of calculating the travel time is a step of calculating the travel time based on the location of at least one battery transport vehicle among a battery transport vehicle located at the battery station, a battery transport vehicle returning to the battery station, and a battery transport vehicle departing from the battery station, and the location of the battery device set. Claim 9 A method for optimizing the supply chain of a mobile battery for an electric vehicle, wherein, in either claim 7 or 8, the step of determining the optimal travel path is the step of determining the travel path of the plurality of battery transport vehicles based on the travel time using a Vehicle Routing Problem (VRP) model. Claim 10 A method for optimizing the supply chain of a mobile battery for an electric vehicle, wherein, in claim 1, the step of acquiring charging data is a step of acquiring charging data of a charger connected to the electric vehicle and a plurality of battery devices detachably coupled to the charger, and the step of calculating the visit time interval is a step of determining a set of battery devices requiring recovery or replacement based on the charging data. Claim 11 A supply chain optimization server for a mobile battery device for an electric vehicle, comprising: a communication interface; a memory; and a processor operably connected to the communication interface and the memory; wherein the processor acquires charging data of a battery device set that supplies charging power to an electric vehicle, calculates a visit time interval of a plurality of battery transport vehicles including a recovery time range of the battery device set and an effective arrival time range of the battery transport vehicle based on the remaining charging time calculated from the charging data, calculates the probability of an emergency situation occurring based on battery data observed or observed in the battery device set, shortens the remaining charging time of the battery device set or adjusts the recovery time range according to the calculated probability value to recalculate the visit time interval, recalculates the travel time interval required to move to the location of the battery device set for each battery station managing the battery device set based on the recalculated visit time interval, and determines the optimal travel path of a plurality of battery transport vehicles transporting the battery device set based on the recalculated visit time interval and the travel time interval. Claim 12 A supply chain optimization server for a mobile battery for an electric vehicle according to claim 11, wherein the processor is configured to obtain the remaining charging time calculated using at least one of charging data including a charging start time, a charging end time, a charging request amount, a charging request ratio, a charging request time, and a charging request amount using the battery device set. Claim 13 delete Claim 14 delete Claim 15 delete Claim 16 delete Claim 17 In claim 11, the processor is further configured to calculate travel time based on the location of the battery station and the battery device set, supply chain optimization server for a mobile battery for an electric vehicle. Claim 18 A supply chain optimization server for a mobile battery for an electric vehicle, wherein, in claim 11, the processor is configured to calculate travel time based on the location of at least one battery transport vehicle among a battery transport vehicle located at the battery station, a battery transport vehicle returning to the battery station, and a battery transport vehicle departing from the battery station, and the location of the battery device set. Claim 19 A supply chain optimization server for mobile batteries for electric vehicles, wherein, in either one of claims 17 and 18, the processor is configured to determine the movement paths of the plurality of battery transport vehicles based on a departure-arrival table using a Vehicle Routing Problem (VRP) model. Claim 20 A supply chain optimization server for a mobile battery for an electric vehicle, wherein, in claim 11, the processor is further configured to acquire charging data of a charger connected to the electric vehicle and a plurality of battery devices detachably coupled to the charger, and to determine a set of battery devices requiring recovery or replacement based on the charging data.

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