KR20240178661A - Method of optimizing mobile battery supply chain for electric vehicle and system and server performing the same - Google Patents

Abstract

The present invention relates to a method for optimizing a supply chain of a mobile battery for an electric vehicle, the method comprising: obtaining charging data of a set of battery devices supplying power to an electric vehicle; calculating visiting time intervals of a plurality of battery transport vehicles based on the charging data; and determining an optimal travel path of a plurality of battery transport vehicles transporting the set of battery devices based on the visiting time intervals.

Description

METHOD OF OPTIMIZING MOBILE BATTERY SUPPLY CHAIN FOR ELECTRIC VEHICLE AND SYSTEM AND SERVER PERFORMING THE SAME

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.

As the electric vehicle market rapidly expands, the number of charging stations for charging electric vehicles is also increasing. However, the number of charging stations is still insufficient compared to the number of electric vehicles distributed.

Accordingly, a charging delivery service is being launched in which a service provider vehicle moves to the location of an electric vehicle to charge the battery, rather than a charging service in which an electric vehicle moves to a location where a charging station is located to charge the battery.

The charging delivery service is a solution that can relieve the anxiety of electric vehicle owners about the absence of charging stations, but there are still not many vehicles that can provide charging delivery services compared to the number of electric vehicles that need to be charged, so there are limitations in establishing a smooth charging service system.

The background technology of the invention has been written to facilitate understanding of the present invention. It should not be understood that the matters described in the background technology of the invention are recognized as prior art.

In addition, conventional charging delivery services have limitations in efficiently operating limited human and vehicle infrastructure, as vehicles equipped with charging batteries and service providers must wait until the electric vehicle is fully charged.

In addition, if an electric vehicle requesting a charging service is parked in a location that is difficult for vehicles to access, the vehicle must be moved to a charging area, making it difficult for users of the service to fully enjoy the convenience of use.

Accordingly, a method for optimizing the mobile battery supply chain for electric vehicles is required to increase the operational efficiency and accessibility of electric vehicle charging services.

As a result, the inventors of the present invention have constructed a method for minimizing idle batteries and increasing the efficiency of a charging delivery service by optimizing the travel routes of multiple service providers based on both the charging time of a mobile battery for an electric vehicle and the recovery time of the mobile battery.

The tasks of the present invention are not limited to the tasks mentioned above, and other tasks not mentioned will be clearly understood by those skilled in the art from the description below.

In order to solve the above-described problem, a method for optimizing a supply chain of a mobile battery for an electric vehicle according to one embodiment of the present invention is provided. The method is a method for optimizing a supply chain of a mobile battery for an electric vehicle, which is performed by a processor, and is configured to include a step of acquiring charging data of a set of battery devices supplying power to an electric vehicle, a step of calculating visiting time intervals of a plurality of battery transport vehicles based on the charging data, and a step of determining an optimal travel path of a plurality of battery transport vehicles transporting the set of battery devices based on the visiting time intervals.

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 charging data from among a charging start time, a charging end time, a charging request amount, a charging request rate, a charging request time, and a charging request amount using the battery device set.

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

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.

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

According to a feature of the present invention, the step of determining the optimal movement path may further include the step of calculating the movement 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.

According to a feature of the present invention, the step of calculating the movement time may be a step of calculating the movement time based on the positions of the battery station and the battery device set.

According to a feature of the present invention, the step of calculating the movement time may be a step of calculating the movement time based on the position 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 position of the battery device set.

According to a feature of the present invention, the step of determining the optimal movement path may be a step of determining the movement paths of a plurality of battery transport vehicles using an algorithm capable of solving a vehicle routing problem.

According to a feature of the present invention, the step of acquiring the 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 that require recovery or replacement based on the charging data.

In order to solve the problem as 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 includes a communication interface, a memory, and a processor operably connected to the communication interface and the memory, and the processor is configured to obtain charging data of a set of battery devices supplying power to an electric vehicle, calculate visiting time intervals of a plurality of battery transport vehicles based on the charging data, and determine an optimal travel path of a plurality of battery transport vehicles transporting the set of battery devices based on the visiting time intervals.

Specific details of other embodiments are included in the detailed description and drawings.

The present invention can minimize idle batteries by optimizing the movement paths of multiple service providers and transport vehicles providing charging services. In particular, the present invention can set the recovery time for optimizing the movement path as a time interval rather than a single point in time. Accordingly, the present invention can minimize the computational consumption of the system by eliminating the need to repeatedly re-set the movement path due to variables that occur during actual battery charging, replacement, and recovery.

The present invention determines a recovery priority based on the charging time of a plurality of sets of mobile battery devices and the recovery time of a plurality of sets of mobile battery devices, thereby enabling a large number of electric vehicles to be charged even with a limited number of sets of mobile battery devices, thereby increasing the operational efficiency of a charging delivery service.

The present invention provides 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, thereby enabling the provision of a charging service without having to secure separate vehicle charging spaces for service users and service providers.

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

The effects according to the present invention are not limited to those exemplified above, and more diverse effects are included in the present invention.

FIG. 1 and FIG. 2 are schematic diagrams for explaining a method for optimizing a supply chain of a portable battery for an electric vehicle according to one 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 mobile 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 method for optimizing a supply chain of a portable 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 one embodiment of the present invention.

The advantages and features of the present invention, and the methods for achieving them, will become clearer with reference to the embodiments described in detail below 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, and these embodiments are provided only to make the disclosure of the present invention 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.

In this document, the expressions "have," "can have," "include," or "may include" indicate the presence of a given feature (e.g., a numerical value, function, operation, or component such as a part), but do not exclude the presence of additional features.

In this document, the expressions "A or B," "at least one of A and/or B," or "one or more of A or/and B" can include all possible combinations of the listed items. For example, "A or B," "at least one of A and B," or "at least one of A or B" can all refer to (1) including at least one A, (2) including at least one B, or (3) including both at least one A and at least one B.

The expressions "first," "second," "first," or "second," etc., used in this document can describe various components, regardless of order and/or priority, and are only used to distinguish one component from another, but do not limit the components. For example, a first user device and a second user device can represent different user devices, regardless of order or priority. For example, without departing from the scope of the rights set forth in this document, a first component can be referred to as a second component, and similarly, a second component can also be referred to as a first component.

When it is stated that a component (e.g., a first component) is "(operatively or communicatively) coupled with/to" or "connected to" another component (e.g., a second component), it should be understood that the component can be directly coupled to the other component, or can be connected via another component (e.g., a third component). Conversely, when it is stated that a component (e.g., a first component) is "directly coupled to" or "directly connected to" another component (e.g., a second component), it should be understood that no other component (e.g., a third component) exists between the component and the other component.

The expression "configured to", as used herein, can be used interchangeably 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 something that is "specifically designed to" in terms of hardware. Instead, in some contexts, the expression "a device configured to" can mean that the device is "capable of" doing something in conjunction with other devices or components. For example, the phrase "a processor configured (or set) to perform A, B, and C" can mean a dedicated processor (e.g., an embedded processor) to perform those operations, or a generic-purpose processor (e.g., a CPU or an application processor) that can perform those operations by executing one or more software programs stored in a memory device.

The terms used in this document are only used to describe specific embodiments and may not be intended to limit the scope of other embodiments. The singular expression may include the plural expression unless the context clearly indicates otherwise. The terms used herein, including technical or scientific terms, may have the same meaning as commonly understood by a person of ordinary skill in the art described in this document. Among the terms used in this document, terms defined in general dictionaries may be interpreted as having the same or similar meaning in the context of the related technology, and shall not be interpreted in an ideal or excessively formal meaning unless explicitly defined in this document. In some cases, even if a term is defined in this document, it cannot be interpreted to exclude the embodiments of this document.

The individual features of the various embodiments of the present invention may be partially or wholly combined or combined with each other, and as can be fully understood by those skilled in the art, various technical connections and operations are possible, and each embodiment may be implemented independently of each other or may be implemented together in a related relationship.

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

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

FIG. 1 and FIG. 2 are schematic diagrams for explaining a method for optimizing a supply chain of a portable battery for an electric vehicle according to one embodiment of the present invention.

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

In one embodiment of the present invention, a plurality of battery device sets (100) may be returned to another adjacent battery station (10) depending on the current remaining charging time and vehicle visiting time, even if it is not the battery station (10) from which they were supplied.

Referring to FIG. 2, in one embodiment of the present invention, an optimal travel path may be determined based on the remaining charging time and the vehicle travel time as follows. For example, if the remaining charging time of the battery device set (100) connected to the electric vehicle of user A is 10-15 minutes, the optimal travel path may be determined so that the battery transport vehicle (200) located at the first battery station (10) with a close vehicle travel time can move to the corresponding battery device set (100). For another example, in a situation where the remaining charging times of the battery device sets (100) connected to the electric vehicles of users B and C are 55-62 minutes and 66-73 minutes, respectively, the optimal travel path may be determined so that the battery device set (100) connected to the electric vehicle of user C, which is farther away, can be recovered first, 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 the electric vehicle of user E, even if there is a relative margin in the remaining charging time.

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 operation.

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.

Referring to FIG. 3, a mobile battery supply chain optimization system (1000) for an electric vehicle (hereinafter referred to as a “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 a “supply chain optimization server”), a service user device (300), and an electric vehicle (30).

The battery station (10) is a base where a plurality of battery device sets (100) are charged and stored, and a battery transport vehicle (20) may be located therewith. A plurality of battery stations (10) may be located in an area where there is a 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) of an administrator who manages the battery station (10). The battery station manager device may monitor the quantity, identification information, observation and observed data of the battery device sets (100) being charged and stored at the battery station (10), and may monitor the identification information and location of the battery transport vehicle (20) matched to the battery station (10). The battery station manager device may periodically provide monitoring result data to the supply chain optimization server (200).

A battery device set (100) can be connected to an electric vehicle (30) and supply power to the electric vehicle (30). Specifically, the battery device set (100) can 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).

In relation to this, FIG. 4 is a block diagram showing the configuration of a mobile battery set for an electric vehicle according to one embodiment of the present invention.

Referring to FIG. 4, the charger (110) of the battery device set (100) may include a charging connector (111) coupled to a charging port of an electric vehicle (30), and may include 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 rapidly or slowly charged using the power charged in the plurality of battery devices (120).

In particular, in one embodiment of the present invention, since the battery device set (100) is configured with a thin and movable form of the charger (110) and the battery device (120), the battery device set (100) can more easily be moved to a location where an electric vehicle (30) is parked. That is, unlike a conventional method in which a vehicle on which a charging facility is installed must be moved to the location of an electric vehicle (30) to be charged, the battery device set (100) of the present invention maximizes the convenience of using an on-demand service by configuring the charger (110) and a plurality of battery devices (120) to be movable.

In addition, in the battery device set (100), the plurality of battery devices (120) may not be devices that are fixedly matched with the charger (110), but may be variable devices that can be changed according to the battery charge amount (or usage). In addition, 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 set 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 a sleep mode (Sleep). Here, the sleep mode may be a state in which power is not supplied and only communication with other devices is possible. In this state, if one battery device (120) is connected to the charger (110) through the charging connector (111), the corresponding battery device (120) may be set as the master battery device (120A). The master battery device (120A) may receive the number of rechargeable battery devices (120) set for charging the electric vehicle (30) from the charger (110) and may compare the received number with the number of previously connected battery devices (120). The master battery device (120A) may determine whether the number of the remaining battery devices (120) including itself is the same as the received number, and if the determination result is the same, may share the setting data from the slave battery device (120B) to the remaining battery devices (120). Here, sharing the setting 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 serial number assigned to the slave battery device (120B), etc., together 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 the number of batteries is not the same as determined by the master battery device (120A), the remaining battery devices (120) including the master battery device (120A) may maintain a sleep mode (Sleep) state. According to the judgment result of the master battery device (120A), the slave battery device (200B) that wakes up from the sleep mode can supply power to the charger (110) by receiving a charging control signal from the electric vehicle (30) through the charger (110). At this time, the power control signal can include a request to stop power supply in addition to a request for power supply.

As described above, a master battery device (120A) and a slave battery device (120B) can be set in the battery device set (100), and the master battery device (120A) can synthesize its own battery data and charging data and the battery data and charging data of the slave battery device (120B) and transmit them to the charger (110). Here, the battery data can include data observed and observed in 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 abnormality, overtemperature, low temperature, and hardware abnormality of the battery module, and status 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 amount (Ah, kWh). Here, if an emergency situation (e.g., abnormal state) is detected or it is determined that an emergency situation will occur according to the monitoring result data and status data, a plurality of device sets (100) may control the power supply to the electric vehicle (30) according to a control signal of the supply chain optimization server (200).

Again, referring to FIG. 3, the battery transport vehicle (20) is a vehicle capable of transporting a plurality of battery device sets (100), and may be a vehicle owned by a service provider that actually provides a charging service by connecting the battery device sets (100) and the electric vehicle (30). 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 be provided with a movement path including a departure time range from the supply chain optimization server (200). In addition, the battery transport vehicle (20) or the service provider device may periodically provide location data to the supply chain optimization server (200), and may also be provided with a changed movement path in real time from the supply chain optimization server (200) while moving.

The supply chain optimization server (200) is a server of a service operator for efficiently operating a battery device set (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).

The supply chain optimization server (200) can determine an optimal priority and an optimal movement path according to the priority for recovering the plurality of battery device sets (100) with optimal efficiency by considering the charging status of the plurality of battery device sets (100) and the different movement paths by location and time zone, for each battery transport vehicle (20). The supply chain optimization server (200) can periodically determine an optimal movement path using an algorithm that can solve a vehicle routing problem with limited transport capacity or constraints on transport capacity and time, and can determine the optimal movement path again regardless of the preset cycle in the event of an emergency. However, in one embodiment of the present invention, it goes without saying that an algorithm that can solve the vehicle routing problem can be applied that uses various items applied to the vehicle routing problem as variables in addition to items such as transport capacity and time.

In addition, the supply chain optimization server (200) may not designate the remaining charging time until charging is completed and the visiting time until recovery is completed as a single point in time in the process of determining the optimal movement path. The supply chain optimization server (200) may define the remaining charging time and the visiting time as a time section that includes a certain time range, thereby minimizing the situation in which the optimal movement path must be repeatedly determined due to the occurrence of variables.

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 smart phone, a tablet PC (Personal Computer), a laptop, and a PC. The service user device (300) may request a charging service by installing or executing an application or program provided by the supply chain optimization server (200). For example, the service user device (300) may obtain at least one of the following charging data from the service user: identification data of the electric vehicle (30), location data, charging start time of the electric vehicle (30), charging end time, charging request amount, charging request ratio, charging request time, and charging request amount, and may request a charging service from the supply chain optimization server (200).

An electric vehicle (30) may be connected to a plurality of battery device sets (100) to receive power for driving. The electric vehicle (30) or the battery device set (100) connected thereto may calculate a remaining charging time using charging data up to the present and provide the remaining charging time to the 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 the point in time, and may set a remaining charging time section.

So far, a supply chain optimization system (1000) according to one embodiment of the present invention has been described. According to the present invention, rather than a charging service for moving a vehicle equipped with a battery, a charging service for supplying and retrieving a mobile battery device set capable of charging an electric vehicle is provided, thereby enabling the charging service to be provided without separately securing a vehicle charging space for the service user and the service provider. In addition, after the charging service is terminated, the movement path for retrieval is optimized so that the battery device set can be immediately used for the next charging service, thereby enabling maximum utilization of idle battery resources.

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

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.

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

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 results of monitoring the quantity, identification information, observation, and observed data of the battery device set (100) being charged and stored in the battery station (10), and the results of monitoring the identification information and location of the battery transport vehicle (20) matched to the battery station (10). The communication interface (210) can transmit the optimal movement path determined by the battery station manager device and the identification information of the battery device set (100) to be collected according to the optimal movement path. As another example, the communication interface (210) can receive location data from the battery transport vehicle (20) and transmit the optimal movement path including the departure time section to the battery transport vehicle (20). For another example, the communication interface (210) can receive battery data and charging data together with identification information of the charger (110) and the battery device (120) from the battery device set (100), and transmit a charging control signal to the battery device set (100). For another example, the communication interface (210) can receive a charging service request of the electric vehicle (30) from the service user device (300), and transmit a charging service start and end notification of the electric vehicle (30) to the service user device (300). For another example, the communication interface (210) can receive charging data from the electric vehicle (30), and transmit a charging service start and end control signal to the electric vehicle (30).

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

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), and various charging service-related data received from them. For example, the memory (220) can store identification data of a charger (110) and a battery device (120) included in a battery device set (100), a connection sequence, data on a connected electric vehicle (30), and the like. For another example, the memory (220) can store a movement path generation algorithm and store learning data thereof.

In various embodiments, the memory (220) may include a volatile or nonvolatile storage 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., an SD or XD memory, etc.), a RAM, an SRAM, a ROM, an EEPROM, a PROM, a network storage storage, a cloud, and a blockchain database.

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

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 to control and manage general system operations (e.g., memory management, storage device control, power management, etc.) and may support communication between various hardware, firmware, and software components.

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

The user interface module (223) can receive a user's request or input from a keyboard, touch screen, microphone, etc. through an I/O interface (230) and provide a user interface on the display.

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

The I/O interface (230) can connect at least one of an input/output device (not shown) of the supply chain optimization server (200), such as a display, a keyboard, a touch screen, and a 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 a command according to the received input.

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 perform various commands to retrieve a plurality of battery device sets (100) providing a charging service through an application or program stored in the memory (220) and to utilize them for the next charging service.

The processor (240) may correspond to a computational device such as a CPU (Central Processing Unit) or an AP (Application Processor). In addition, the processor (240) may be implemented in the form of an integrated chip (IC) such as a SoC (System on Chip) in which various computational devices are integrated. Alternatively, the processor (240) may include a module for calculating an artificial neural network model such as an NPU (Neural Processing Unit).

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

FIG. 6 is a schematic flowchart of a method for optimizing a supply chain of a portable battery for an electric vehicle according to one embodiment of the present invention.

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 can be obtained according to a charging service request of a service user device (300), and can be obtained from an electric vehicle (30) that is a target of a charging service or a battery device set (100) connected thereto. For example, the charging data can include identification data of the electric vehicle (30) and the battery device set (100) connected thereto, location data, and 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). In addition, 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.

In various embodiments, the processor (240) may determine a battery device set that requires recovery or replacement based on the charging data. Specifically, the processor (240) may determine a battery device set (100) that requires recovery among a plurality of battery device sets (100) providing a charging service so that recovery of the battery device set (100) may be performed periodically. In addition, the processor (240) may obtain charging data of each of the charger (110) and the plurality of battery devices (120) included in the battery device set (100), and may determine a battery device set (100) that requires replacement among the charger (110) and the plurality of battery devices (120) based on the respective charging data. For example, if only the charger (110) or one of the battery devices (120) requires replacement, the processor (240) may determine a battery device set (100) that includes the corresponding component as the battery device set (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 subsequently determined optimal travel path to the battery station manager device or battery transport vehicle (20) to inform the service provider that there are components requiring replacement along the optimal travel path.

After step S110, the processor (240) can calculate the visiting time intervals of the plurality of battery transport vehicles (20) based on the charging data (S120). Here, the visiting time intervals are based on the remaining charging time and can include the recovery time range of the battery device set (100) and the valid arrival time range of the battery transport vehicle (20). To this end, the processor (240) can calculate the recovery time interval and the valid arrival time interval.

In various embodiments, the recovery time range may be a time range required to separate 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 preset by the service operator. However, the recovery time range may be adjusted depending on the progress of the charging service of the battery device set (100).

Specifically, the processor (240) can recalculate the visit time interval based on the battery data observed or observed in the battery device set (100). The battery data is data observed or observed in the charger (110) and the plurality of battery devices (120), and can include sensing data, monitoring result data, and status data. The processor (240) can calculate the probability of occurrence of an emergency situation based on the monitoring result data or status data for the battery device set (100). That is, if the processor (240) determines that an emergency situation has already occurred or will occur based on the calculated probability value, the processor (240) can shorten the remaining charging time of the corresponding battery device set (100) or adjust the recovery time range. Here, the recovery time range can 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.

In various embodiments, the valid arrival time range may be a time range in which the battery transport vehicle (20) may be considered to have arrived. The processor (240) may calculate a 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 valid arrival time range. Here, the arrival location may be a location where the electric vehicle (30) and the battery device set (100) connected thereto are located, and the travel time range may be calculated by considering future traffic volume according to the remaining charging time.

In relation to this, FIG. 7 is a schematic diagram for explaining a method for calculating a visit time interval according to one embodiment of the present invention.

Referring to (a) of FIG. 7, in a normal state where no emergency situation occurs, the processor (240) may determine a reference time for calculating a visit time interval based on the remaining charging time as 3 hours and 20 minutes. Meanwhile, although the remaining charging time is illustrated as a point in time, the remaining charging time may be understood as a fixed time interval. The processor (240) may determine the recovery time interval as 3 hours and 10 minutes to 3 hours and 30 minutes according to a preset recovery time range based on the remaining charging time and time range. The processor (240) may calculate a valid arrival time range and determine a valid arrival time interval that overlaps a start area of the recovery time interval. That is, the processor (240) may determine the valid arrival time interval as 2 hours and 57 minutes to 3 hours and 12 minutes.

Referring to (b) of FIG. 7, in an abnormal state where an emergency situation occurs or is expected to occur, the processor (240) may shorten the remaining charging time and determine the reference time for calculating the visit time interval as 1 hour and 20 minutes. Here, the shortened time may vary depending on the type and degree of the emergency situation and may be preset by the service operator. In the case of retrieving 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 as 1 hour and 13 minutes to 1 hour and 27 minutes. The processor (240) may calculate the valid arrival time range and determine the valid arrival time interval overlapping the start area of the recovery time interval. That is, the processor (240) may determine the valid arrival time interval as 12:55 minutes to 1:15 minutes.

Referring back to FIG. 6, after step S120, the processor (240) may determine an optimal movement path of a plurality of battery transport vehicles (20) transporting the battery device sets (100) based on the visit time interval. Specifically, the processor (240) may calculate the movement time required to reach the location of the battery device set (10) for each location of the plurality of battery stations (10), considering that the recovery and supply of the battery device sets (100) may not be performed at the same battery station (10). The processor (240) may calculate the movement time at a point in time corresponding to the visit time interval calculated in step S120. That is, the processor (240) may calculate the movement time considering future traffic volume, the number of recovery and location of the battery device sets (100).

The processor (240) can one-to-one match a plurality of battery stations (10) and a plurality of battery device sets (100) that need to be recovered, from a starting point to an ending point, so as to determine an optimal movement path. The processor (240) can generate a start-end table that indicates movement times according to the locations of the battery stations (10) and the battery device sets (100), as shown in [Table 1] below.

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

…

The processor (240) may determine the movement paths of a plurality of battery transport vehicles (20) based on a departure-arrival table using a vehicle routing problem (VRP) model. For example, the processor (240) may determine the movement paths of a plurality of battery transport vehicles (20) using a model or algorithm that can solve a vehicle routing problem with limited transport capacity or with limited transport capacity and time. For example, the processor (240) may use an algorithm that determines an optimal movement path in a situation where a departure-arrival time limit of the battery transport vehicles (20) is set. As another example, the processor (240) may use a vehicle operation path optimization model or a heuristic-based vehicle operation path optimization algorithm or models derived therefrom to output an optimal movement path by inputting a departure-arrival table. Meanwhile, the algorithm for determining the movement paths of a plurality of battery transport vehicles (20) is not limited thereto, and various known route problem solving models or algorithms may be applied.

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

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 a battery transport vehicle (20) returning to the battery station (10) and a battery transport vehicle that departed from the battery station (10), in addition to the battery transport vehicle (20) located at the battery transport station (10), and the location of the battery device set (100). That is, the processor (240) may also utilize a battery transport vehicle (20) moving to supply the battery device set (100) as a resource for recovery, thereby increasing the recovery efficiency of the battery device set (100) by using a limited number of battery transport vehicles (20).

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 a movement path as a time interval rather than a point in time, there is no need to repeatedly re-set the movement path due to variables occurring during actual battery charging, replacement, and recovery, so that the computational consumption of the system can be minimized.

Although the 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 various modifications may be made without departing from the technical idea of the present invention. Accordingly, the embodiments disclosed in the present invention are not intended to limit the technical idea of the present invention, but to explain it, and the scope of the technical idea of the present invention is not limited by these embodiments. Therefore, it should be understood that the embodiments described above are exemplary in all aspects and not restrictive. The protection scope of the present invention should be interpreted by the following claims, and all technical ideas within a scope equivalent thereto should be interpreted as being included in the scope of the rights of the present invention.

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 (20)

A method for optimizing the supply chain of a portable battery for an electric vehicle performed by a processor,
A step of acquiring charging data of a set of battery devices supplying power to an electric vehicle;
A step of calculating the visiting time interval of multiple battery transport vehicles based on the above charging data; and
A method for optimizing the supply chain of mobile batteries for electric vehicles, comprising: a step of determining an optimal travel path of a plurality of battery transport vehicles transporting the battery device set based on the visiting time interval; In the first paragraph,
The step of obtaining the above charging data is:
A method for optimizing the supply chain of a portable battery for an electric vehicle, the method comprising: obtaining a remaining charging time calculated using at least one of charging data from among a charging start time, a charging end time, a charging request amount, a charging request rate, a charging request time, and a charging request amount using the above battery device set. In the second paragraph,
The steps for calculating the above visit time interval are:
A method for optimizing the supply chain of a mobile battery for an electric vehicle, the method comprising: calculating a time range for recovery of the battery device set and a time range for valid arrival of the battery transport vehicle based on the remaining charging time of the battery device set. In the third paragraph,
The steps for calculating the above visit time interval are:
A method for optimizing the supply chain of a mobile battery for an electric vehicle, further comprising the step of recalculating the visit time interval based on battery data observed or observed in the set of battery devices. In paragraph 4,
The step of recalculating the above visit time interval is:
A step of calculating the probability of occurrence of an emergency situation based on the above battery data, and
A method for optimizing the supply chain of a portable battery for an electric vehicle, further comprising a step of shortening the remaining charging time of the set of battery devices or adjusting the recovery time range according to the calculated probability value. In the first paragraph,
The step of determining the above optimal movement path is:
A method for optimizing the supply chain of a mobile battery for an electric vehicle, further comprising: a step of calculating a travel time required to move to a location of the battery device set for each battery station managing the battery device set at a time corresponding to the visit time interval; In Article 6,
The steps for calculating the above travel time are:
A method for optimizing the supply chain of a mobile battery for an electric vehicle, the method comprising: calculating a travel time based on the location of the battery station and the battery device set. In Article 6,
The steps for calculating the above travel time are:
A method for optimizing the supply chain of mobile batteries for electric vehicles, comprising: calculating a 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. In any one of paragraphs 7 and 8,
The step of determining the above optimal movement path is:
A method for optimizing the supply chain of mobile batteries for electric vehicles, the method comprising: a step of determining a movement path of the plurality of battery transport vehicles based on the movement time using a vehicle routing problem (VRP) model. In the first paragraph,
The step of obtaining the above charging data is:
A step of obtaining charging data of a charger connected to the electric vehicle and a plurality of battery devices detachably coupled to the charger,
The steps for calculating the above visit time interval are:
A method for optimizing the supply chain of a mobile battery for an electric vehicle, further comprising the step of determining a set of battery devices that require recovery or replacement based on the charging data. communication interface;
memory; and
a processor operably connected to the communication interface and the memory;
The above processor,
A supply chain optimization server for a mobile battery device for an electric vehicle, configured to obtain charging data of a set of battery devices supplying power to an electric vehicle, calculate visiting time intervals of a plurality of battery transport vehicles based on the charging data, and determine an optimal travel path of a plurality of battery transport vehicles transporting the set of battery devices based on the visiting time intervals. In Article 11,
The above processor,
A supply chain optimization server for a mobile battery for an electric vehicle, configured to obtain a remaining charging time calculated using at least one of charging data from among a charging start time, a charging end time, a charging request amount, a charging request rate, a charging request time, and a charging request amount using the above battery device set. In Article 12,
The above processor,
A supply chain optimization server for mobile batteries for electric vehicles, configured to calculate based on a remaining charging time of the battery device set and including a recovery time range of the battery device set and an effective arrival time range of the battery transport vehicle. In Article 13,
The above processor,
A supply chain optimization server for mobile batteries for electric vehicles, further configured to recalculate the visit time interval based on battery data observed or observed from the set of battery devices. In Article 14,
The above processor,
A supply chain optimization server for a mobile battery for an electric vehicle, further configured to calculate an emergency situation occurrence probability based on the above battery data and, based on the calculated probability value, shorten the remaining charging time of a set of battery devices or adjust the recovery time range. In Article 11,
The above processor,
A supply chain optimization server for mobile batteries for electric vehicles, further configured to calculate a travel time required to move to a location of the battery device set for each battery station managing the battery device set at a time corresponding to the above visit time interval. In Article 16,
The above processor,
A supply chain optimization server for mobile batteries for electric vehicles, further configured to calculate a travel time based on the locations of the battery stations and the battery device sets. In Article 16,
The above processor,
A supply chain optimization server for mobile batteries for electric vehicles, configured to calculate a travel time based on a 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 a location of the battery device set. In any one of Articles 17 and 18,
The above processor,
A supply chain optimization server for mobile batteries for electric vehicles, configured to determine a movement path of a plurality of battery transport vehicles based on a departure-arrival table using a Vehicle Routing Problem (VRP) model. In Article 11,
The above processor,
Acquire charging data of a charger connected to the electric vehicle and a plurality of battery devices detachably coupled to the charger,
A supply chain optimization server for mobile batteries for electric vehicles, further configured to determine a set of battery devices that require recovery or replacement based on the charging data above.