WO2024106577A1 - Procédé de gestion de chargeur de véhicule électrique et système associé - Google Patents

Procédé de gestion de chargeur de véhicule électrique et système associé Download PDF

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Publication number
WO2024106577A1
WO2024106577A1 PCT/KR2022/018351 KR2022018351W WO2024106577A1 WO 2024106577 A1 WO2024106577 A1 WO 2024106577A1 KR 2022018351 W KR2022018351 W KR 2022018351W WO 2024106577 A1 WO2024106577 A1 WO 2024106577A1
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Prior art keywords
electric vehicle
charging
charger
vehicle charger
power
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PCT/KR2022/018351
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English (en)
Korean (ko)
Inventor
이재규
최중인
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재단법인차세대융합기술연구원
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Publication of WO2024106577A1 publication Critical patent/WO2024106577A1/fr

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    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/66—Data transfer between charging stations and vehicles
    • B60L53/665—Methods related to measuring, billing or payment
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/30—Constructional details of charging stations
    • B60L53/35—Means for automatic or assisted adjustment of the relative position of charging devices and vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/50—Charging stations characterised by energy-storage or power-generation means
    • B60L53/51—Photovoltaic means
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60L—PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES; SUPPLYING ELECTRIC POWER FOR AUXILIARY EQUIPMENT OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRODYNAMIC BRAKE SYSTEMS FOR VEHICLES IN GENERAL; MAGNETIC SUSPENSION OR LEVITATION FOR VEHICLES; MONITORING OPERATING VARIABLES OF ELECTRICALLY-PROPELLED VEHICLES; ELECTRIC SAFETY DEVICES FOR ELECTRICALLY-PROPELLED VEHICLES
    • B60L53/00—Methods of charging batteries, specially adapted for electric vehicles; Charging stations or on-board charging equipment therefor; Exchange of energy storage elements in electric vehicles
    • B60L53/60—Monitoring or controlling charging stations
    • B60L53/66—Data transfer between charging stations and vehicles
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/40—Business processes related to the transportation industry
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60Y—INDEXING SCHEME RELATING TO ASPECTS CROSS-CUTTING VEHICLE TECHNOLOGY
    • B60Y2200/00—Type of vehicle
    • B60Y2200/90—Vehicles comprising electric prime movers
    • B60Y2200/91—Electric vehicles
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
    • Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
    • Y02T90/10—Technologies relating to charging of electric vehicles

Definitions

  • the present invention relates to an electric vehicle charger management method and system for the same. More specifically, in order to efficiently manage multiple electric vehicle chargers, user information is acquired from the electric vehicle charger, and the acquired user information and charging start time are transmitted to the machine learning engine. Charging strategy information is received from the machine learning engine and charged. Based on strategic information, multiple electric vehicle chargers are controlled to charge, and when a power saving event is detected, at least one electric vehicle charger to save power is selected and sorted through a specific algorithm, and the electric vehicle charger is installed in the sorted order. This relates to an electric vehicle charging management method and system that can control charging by reducing charging power.
  • An electric vehicle is a vehicle that uses electrical energy as a source of energy, and is also called an eco-friendly energy vehicle because it does not use chemical fuel.
  • the popularity of these electric vehicles is increasing day by day, and accordingly, the number of electric vehicle chargers installed in parking facilities within buildings is gradually increasing.
  • a typical electric vehicle charger is used as a slow charger due to its electric capacity and purpose.
  • the charger device itself takes up a relatively large load compared to other load devices (e.g., air conditioning, lighting devices), so a plurality of electricity is used as a temporary charger. If charging is performed and these loads cannot be controlled, instability or power outage in the building system may occur.
  • load devices e.g., air conditioning, lighting devices
  • the present invention targets users who use electric vehicle chargers by acquiring charging information such as the user's charger usage habits (e.g., time, time zone, date, etc., when the user mainly used the charger) and charging start time learned in the past. Based on the information, charging strategy information such as expected charging usage time is generated, and when a situation arises where the load of the entire building needs to be controlled, an electric vehicle charger that will reduce charging power is selected based on the charging strategy information.
  • the purpose is to provide an electric vehicle charger management method that allows users to sequentially reduce charging power.
  • the present invention acquires information in real time, such as system information for the entire building, solar power generation amount, presence or absence of a solar generator failure state, and DR signal from a demand response server (e.g., Korea Power Exchange, Korea Electric Power Corporation), and provides information on the entire building.
  • a demand response server e.g., Korea Power Exchange, Korea Electric Power Corporation
  • the purpose is to provide an electric vehicle charger management method that can detect situations in which the load must be controlled.
  • An electric vehicle charger management method in which a charger management system according to the present invention manages a plurality of electric vehicle chargers includes the steps of (a) acquiring user information from the electric vehicle charger; (b) transmitting the user information and charging start time to a machine learning engine; (c) receiving charging strategy information from the machine learning engine; (d) the electric vehicle charger transmitting a control command to perform charging based on the charging strategy information; (e) when a power saving event occurs, selecting and arranging at least one electric vehicle charger capable of reducing charging power through a specific algorithm; and (f) transmitting a control command to the selected electric vehicle charger so that the selected electric vehicle charger lowers the charging power in the sorted order to perform charging.
  • the electric vehicle charger management method before step (e), obtains power system information for the entire building from a building meter, obtains solar power generation from a solar meter placed in the building, and solar power generator It may further include obtaining the solar generator status from.
  • the user information may be information that includes at least one of a user identification ID, vehicle model name, model year, and vehicle number.
  • the user information obtained in step (a) may be information obtained by the user tagging a membership card containing the user information to the electric vehicle charger.
  • the charging strategy information is information about expected charger use time, expected charging completion time, and required charging power generated by the machine learning engine based on the user information and charging start time. It can be characterized.
  • the power saving event is a situation in which the charger management system detects that the load consumed for charging exceeds the maximum building load through the power system information, and the solar power generation amount is calculated from the solar power generation amount. It may be characterized as a situation in which a decrease in photovoltaic power generation is detected, a situation in which a drop in voltage is detected, or a situation in which a demand response event is received from a demand response server.
  • the demand response event may be characterized as a situation in which the demand response server transmits a request to the charger management system to reduce power by a predetermined amount of power in a predetermined time period.
  • the specific algorithm is an algorithm that selects and sequentially sorts the electric vehicle charger in the order that the expected charger usage time is long, the amount of pre-charged power is large, and the amount of power required to complete charging is small. It can be characterized as:
  • step (f) upon completion of charging, generating learning data by comparing and analyzing the expected charger usage time and actual charger usage time; It may further include matching the user information and the charging strategy information to the learning data and transmitting the information to the machine learning engine so that the machine learning engine can learn data for each user.
  • a charger management system for managing a plurality of electric vehicle chargers includes a central processing unit and a memory, and the central processing unit executes an electric vehicle charger management method stored in the memory. Characterized by executing commands to do so, the electric vehicle charger management method includes obtaining user information from the electric vehicle charger; Transmitting the user information and charging start time to a machine learning engine; Receiving charging strategy information from the machine learning engine; Commanding the electric vehicle charger to perform charging based on the charging strategy information; When a power saving event occurs, selecting and arranging at least one electric vehicle charger capable of reducing charging power through a specific algorithm; and commanding charging to be performed by lowering the charging power in the order of the sorted electric vehicle chargers.
  • charging information such as the user's charger usage habits (e.g., time, time zone, date, etc., when the user mainly used the charger) and charging start time learned in the past is provided to users who use an electric vehicle charger.
  • charging strategy information such as expected charging usage time is generated, and when a situation arises where the load of the entire building needs to be controlled, an electric vehicle charger that will save charging power is selected based on the charging strategy information.
  • information such as grid information for the entire building, solar power generation amount, solar generator failure status, and DR signal from demand response servers (e.g. Korea Power Exchange, Korea Electric Power Corporation) are acquired in real time to control the load of the entire building.
  • demand response servers e.g. Korea Power Exchange, Korea Electric Power Corporation
  • Figure 1 is a diagram for conceptually understanding the electric vehicle charger management system of the present invention.
  • Figure 2 is a diagram illustrating through a simple diagram the process by which an electric vehicle charger transmits user information to a charger management system according to the first embodiment of the present invention.
  • Figure 3 is a simple schematic diagram showing the process by which a machine learning engine generates charging strategy information according to the first embodiment of the present invention.
  • Figure 4 is a schematic diagram showing how the charger management system according to the first embodiment of the present invention acquires power information about the building from the entire building meter, solar meter, and solar generator arranged in the building.
  • FIG. 5 is a simple schematic diagram showing the process by which the charger management system according to the first embodiment of the present invention saves charging power of an electric vehicle charger when a demand response event occurs.
  • Figure 6 is a schematic diagram showing how the charger management system according to the first embodiment of the present invention selects and sorts electric vehicle chargers through a specific algorithm when a power saving event occurs and is detected.
  • Figure 7 is a diagram specifically showing an electric vehicle charger management method according to the first embodiment of the present invention.
  • Figure 8 is a diagram specifically showing the electric vehicle charger management method according to the second embodiment of the present invention.
  • Figure 9 is a diagram specifically showing an electric vehicle charger management method according to a third embodiment of the present invention.
  • first and second are used to distinguish one component from another component, and the scope of rights should not be limited by these terms.
  • a first component may be named a second component, and similarly, the second component may also be named a first component.
  • Figure 1 is a diagram illustrating the electric vehicle charger management system 100 of the present invention to conceptually understand it.
  • the electric vehicle charger management system acquires user information from the electric vehicle charger 10 in order to efficiently manage a plurality of electric vehicle chargers 10, and When user information and charging start time are transmitted to the machine learning engine 200, charging strategy information is received from the machine learning engine 200 and a plurality of electric vehicle chargers 10 are controlled to perform charging based on the charging strategy information. can do.
  • the charger management system 100 obtains power system information from the entire building meter 12, obtains solar generator status information (e.g., presence or absence of a breakdown) from the solar generator 13, and solar power meter 14. ), the power saving event can be detected by obtaining the solar power generation amount, and when the power saving event is detected, at least one electric vehicle charger 10 to save power is selected and sorted through a specific algorithm, and in the sorted order.
  • the electric vehicle charger 10 can be controlled to perform charging by reducing charging power.
  • Figure 2 is a diagram illustrating through a simple diagram the process by which the electric vehicle charger 10 transmits user information to the charger management system 100 according to the first embodiment of the present invention.
  • the user information referred to in the present invention may refer to information including at least one user identification ID, vehicle model name, vehicle year, vehicle number, or charging history (number of charging, total charging time, etc.). .
  • User identification ID is a user-specific ID given to the user by the charger management system 100 or the service server when the user registers as a member through a service server (not shown), kiosk, etc. linked/connected to the charger management system 100. can be said.
  • the user identification ID may be data containing at least one number or letter, and may be code data in which a series of data is encrypted, such as a barcode or QR code.
  • the model name and year of the vehicle are data resources used when the machine learning engine 200, which will be described later, generates charging strategy information.
  • the charging strategy information may include information about the expected charger usage time analyzed by the machine learning engine 200 based on data learned in the past.
  • Charging strategy information is information analyzed by the machine learning engine 200 based on the user's past charging habits, and the charging speed can be predicted based on the vehicle's battery capacity and year (older vehicles have slower charging speeds). Bar, the model name and year of the vehicle can be used as a data resource when the machine learning engine 200 generates charging strategy information.
  • the machine learning engine 200 when the machine learning engine 200 generates charging strategy information for the vehicle 20 using the charger management system 100 of the present invention for the first time, there is no past learned data, so the model name and year of the vehicle included in the user information Through this, the expected charger usage time can be derived.
  • the electric vehicle charger 10 in order for the electric vehicle charger 10 to transmit user information to the charger management system 100, first, the user who owns the vehicle 20 tags the membership card to the electric vehicle charger 10 (1). . The user information contained in the membership card must be transmitted to the electric vehicle charger 10. At this time, the electric vehicle charger 10 receives user information from the user's membership card tag and simultaneously transmits the received user information to the charger management system 100 (2).
  • the charger management system 100 records the user information received from the electric vehicle charger 10 and generates learning data for the machine learning engine 200 to learn based on the recorded user information.
  • the user information is remotely transmitted to the electric vehicle charger 10 through an application linked to the charger management system 100.
  • a method of transmitting to the charger management system 100, or a method of transmitting user information to the electric vehicle charger 10 or the charger management system 100 by entering user information in a kiosk linked to the charger management system 100 is utilized. It can be.
  • Figure 3 is a simple schematic diagram showing the process by which the machine learning engine 200 generates charging strategy information according to the first embodiment of the present invention.
  • the charging strategy information referred to in the present invention is information about the expected charger usage time, expected charging completion time, and required charging power generated by the machine learning engine 200 based on user information and charging start time.
  • the charging strategy information is predicted information about how much the user will use the charger, analyzed based on the ‘data about the user’s charging habits’ learned in the past by the machine learning engine 200 and the current charging information.
  • 'data about the user's charging habits' refers to data about the past charger use time/number of times, charger use time compared to the past expected charger use time, or data about the charger use time compared to the past charging date, day, time zone, and weather. It can be.
  • data on the user's charging habits learned in the past by the machine learning engine 200 include the time/number of times the user uses the electric vehicle charger 10 when charging once, the use time of the electric vehicle charger 10 on weekends, and the number of times the user uses the electric vehicle charger 10 when charging. Data on the usage time of the electric vehicle charger 10 on the coming day can be provided.
  • current charging information as used in the present invention may refer to information including user information, charging start time, or charging information selected by the user.
  • the charging information selected by the user may refer to information including the target charging amount and expected return time selected by the user through an interface device (not shown) in the electric vehicle charger 10 or a kiosk linked to the charger management system 100. there is.
  • the charger management system 100 in order for the machine learning engine 200 to generate charging strategy information, the charger management system 100 must first obtain user information from the electric vehicle charger 10 (1). At this time, the charger management system 100 requests the generation of charging strategy information by transmitting user information and charging start time to the machine learning engine 200 (2). Afterwards, the machine learning engine 200 generates charging strategy information based on user information, charging start time, and past learning data (3).
  • FIG 4 shows that the charger management system 100 according to the first embodiment of the present invention receives power information about the building from the entire building meter 12, the solar meter 13, and the solar generator 14 arranged in the building. This is a schematic diagram that simply shows how it is obtained.
  • the charger management system 100 performs analysis based on data on the load of the entire building or the amount of renewable energy generation when managing and controlling the charging power for a plurality of electric chargers 100.
  • an electric vehicle charger (10), other load devices (11), a building-wide meter (12), a solar meter (13), and a solar generator (14) are installed inside and outside the building linked to the charger management system (100). can be placed.
  • load devices 11 may refer to devices other than the electric vehicle charger 10 among devices that consume power within the building.
  • air-conditioning and heating devices, lighting devices, etc. installed in the building 10 may be other devices of the present invention. It can be a load device 11.
  • the building-wide meter 12 is a component that generates power system information by calculating power consumption for the entire building through the electric vehicle charger 10 and other load devices 11 and transmits it to the charger management system 100.
  • the solar meter 13 is a component that acquires solar power generation from the solar generator 14 in real time and transmits it to the charger management system 100.
  • the solar power generator 14 is a device that generates solar power. It generates information about the status of the solar power generator 14 itself, such as information on current failure and whether solar power is being generated, and sends it to the charger management system 100. It is a component that transmits.
  • Figure 5 is a simple schematic diagram showing the process by which the charger management system 100 according to the first embodiment of the present invention saves the charging power of the electric vehicle charger 10 when a demand response event occurs.
  • the demand response event referred to here means that the demand response server 400, such as a power exchange, or the power management server 300, such as Korea Electric Power Company, does not adjust the power supply to meet the current demand for power, but allows electricity users to adjust their power usage on their own. You can talk about a situation where a DR (Demand Response) signal is sent to the user to enable
  • the demand response server 400 or the power management server 300 provides a small amount of compensation to the user in return for contributing to meeting the current strategic demand. can be provided.
  • the demand response server 400 transmits a DR signal to the charger management system 100 (1).
  • the charger management system 100 selects and sorts at least one electric vehicle charger 10 to reduce power through a specific algorithm (2), and then the charger management system 100 sorts.
  • the electric vehicle charger 10 is controlled to reduce charging power and perform charging (3) in the given order.
  • Figure 6 is a schematic diagram showing how the charger management system 100 according to the first embodiment of the present invention selects and sorts the electric vehicle charger 10 through a specific algorithm when a power saving event occurs and is detected. am.
  • the electric vehicle charger management method of the present invention is a method of efficiently managing the power consumed by a plurality of electric vehicle chargers 10. Because the plurality of electric vehicle chargers 10 have a large impact on the building load, temporary If a large number of electric vehicle chargers 10 are charged, a power outage may occur or the power system may become unstable. To prevent this, the charger management system 100 of the present invention automatically detects that a power saving event has occurred. Then, by sorting and selecting the electric vehicle chargers 10 to reduce charging power through a specific algorithm (1), the charging power of the electric vehicle chargers 10 can be reduced in the sorted order (2).
  • the power saving event referred to in the present invention may refer to a situation in which there is a risk of a situation such as power system instability or power outage occurring when consuming power, or a situation in which a power saving request is received from a separate server.
  • a situation in which the charger management system 100 detects that the total load consumed for charging exceeds the building maximum load through power system information obtained from the entire building meter 12, from the solar meter 14 A situation in which a decrease in the obtained amount of solar power generation is detected, a situation in which the solar power generator 14 detects that the solar power generator 14 is currently broken, a situation in which a demand response event is received from the demand response server 400, or We can talk about a situation where a drop in voltage is detected.
  • the specific algorithm referred to in the present invention means that when a power saving event occurs and the charger management system 100 detects it, the power of the electric vehicle charger 10 that has been charged is used to control the overall load of the building. This is an algorithm for sorting and selecting electric vehicle chargers 10 that will save money. If the charging power of the electric vehicle charger 10 is randomly lowered, the fairness of electric vehicle charging may be violated, and the specific algorithm of the present invention is an electric vehicle charger (electric vehicle charger with a high expected charger usage time generated by the machine learning engine 200).
  • a high weight is given to the electric vehicle charger 10 with a large amount of pre-charged power, and a high weight is given to the electric vehicle charger 10 with a small amount of power required to complete charging, so an electric vehicle charger with a high weight is given a high weight.
  • it can be sorted in order of the electric vehicle charger (10) with the highest weight.
  • a first electric vehicle charger (10a), a second electric vehicle charger (10b), and a third electric vehicle charger (10c) are being charged within one building, and the machine learning engine 200 is connected to the first electric vehicle charger (10a).
  • the expected charger use time for each of the second electric vehicle charger 10b and the third electric vehicle charger 10c is generated as measurement strategy information, and the expected charger use time of the first to third electric vehicle chargers 10a, 10b, and 10c are generated as measurement strategy information.
  • the specific algorithm assigns the highest weight to the first electric vehicle charger (10a), the second electric vehicle charger (10b), and the third electric vehicle charger (10c) with the highest expected usage time. is assigned, and if the first electric vehicle charger (10a), the second electric vehicle charger (10b), and the third electric vehicle charger (10c) are sorted in that order, the charger management system 100 will use the first electric vehicle charger (10a).
  • the second electric vehicle charger 10b and the third electric vehicle charger 10c can be controlled to perform charging by reducing charging power in the order arranged by a specific algorithm.
  • the charger management system 100 of the present invention generates learning data for the purpose of supervised learning and transmits the learning data to the machine learning engine 200.
  • the machine learning engine 200 generates charging strategy information by analyzing the expected charging use time of one user or the expected charging use time in the entire parking lot based on the learning data.
  • the learning data is data generated by the charger management system 100 for the purpose of supervised learning, and the charger management system 100 uses a machine learning engine (200) so that the machine learning engine 200 can generate precise and accurate charging strategy information.
  • 200 can refer to data entered arbitrarily.
  • the charger management system 100 converts this information into data, records and creates it, and uses the generated data as learning data to use the machine learning engine 200.
  • the machine learning engine 200 will allow the vehicle to use the charger for 2 hours based on the learning data received from the charger management system 100 prior to generating charging strategy information for the vehicle of Model A. You can generate charging strategy information that you will use.
  • the learning data may be feedback data about charging strategy information previously generated by the machine learning engine 200.
  • the charger management system 100 records charging information such as charger usage time and charging speed, and records charging information and By comparing the charging strategy information, it is possible to analyze how accurate the charging strategy information is, generate feedback information about this, and transmit it to the machine learning engine 200 as learning data.
  • Figure 7 is a diagram specifically showing an electric vehicle charger management method according to the first embodiment of the present invention.
  • the electric vehicle charger management method first begins with a step (S101) in which the charger management system 100 acquires user information from the electric vehicle charger 10.
  • user information may refer to information including at least one user identification ID, vehicle model name, vehicle year, vehicle number, or charging history (number of charging, total charging time, etc.).
  • step S101 when the user tags the membership card to the card recognition device provided in the electric vehicle charger 10, the electric vehicle charger 10 receives the user information included in the membership card, and the electric vehicle charger 10 receives the received user information. This is the step of transmitting to the charger management system 100.
  • the charger management system 100 transmits user information and charging start time to the machine learning engine 200 to request generation of charging strategy information (S102).
  • the charging strategy information may refer to information about the expected charger usage time, expected charging completion time, and required charging power generated by the machine learning engine 200 based on user information and charging start time.
  • the charger management system 100 receives charging strategy information from the machine learning engine 200 (S103) and controls the electric vehicle charger 10 to perform charging based on the charging strategy information. Send a command (S104).
  • the charger management system 100 selects and sorts at least one electric vehicle charger 10 capable of reducing charging power through a specific algorithm (S105).
  • a power-saving event refers to a situation where there is a risk of power system instability or power outage when consuming power, or a situation in which a power-saving request is received from a separate server, and the specific algorithm is a machine learning engine.
  • the predicted charger generated by (200) gives a high weight to the electric vehicle charger (10) with a large usage time, a high weight to the electric vehicle charger (10) with a large amount of pre-charged power, and an electric vehicle charger with a small amount of power required to complete charging.
  • the charger management system 100 transmits a control command to the electric vehicle charger 10 to reduce charging power and charge the electric vehicle chargers 10 selected and sorted in step S105 (S106).
  • the electric vehicle charger management method according to the first embodiment of the present invention is completed.
  • Figure 8 is a diagram specifically showing the electric vehicle charger management method according to the second embodiment of the present invention.
  • the electric vehicle charger management method according to the first embodiment of the present invention described above is an electric vehicle charger management method mainly explaining the charger management system 100
  • the electric vehicle charger management method according to the second embodiment of the present invention has a more expanded scope.
  • it is an electric vehicle charger management method that not only uses the charger management system 100 as the main subject, but also explains the components linked to the charger management system 100 as another subject.
  • the electric vehicle charger management method first begins with a step (S201) of transmitting user information of the electric vehicle charger 10 to the charger management system 100.
  • the charger management system 100 transmits the user information and charging start time received from the electric vehicle charger 10 to the machine learning engine 200 (S202).
  • the machine learning engine 200 generates charging strategy information based on the received user information, charging start time, and previously learned data (S203), and transmits the generated charging strategy information to the charger management system 100. Do it (S204).
  • the charger management system 100 transmits a control command to the electric vehicle charger 10 so that the electric vehicle charger 10 performs charging based on the charging strategy information (S205), and receives the control command from the charger management system 100.
  • the electric vehicle charger 10 that receives the charge performs charging (S206).
  • the charger management system 100 receives information from the entire building meter 12 that the total load consumed for charging exceeds the maximum load of the building, and that the amount of solar power generation obtained from the solar meter 14 has decreased.
  • Information when information that the solar generator 14 is currently broken is received from the solar generator 14 or a demand response event is received from the demand response server 400 and the occurrence of a 'power saving event' is detected (S207) , the charger management system 100 selects and sorts electric vehicle chargers 10 capable of saving power through a specific algorithm (S208) and sends a control command to save charging power in the order in which the electric vehicle chargers 10 are sorted. 10) (S209), and the electric vehicle charger 10, which has received the charging power control command from the charger management system 100, performs reduced charging (S210).
  • step (S207) in which the charger management system 100 detects the occurrence of a power saving event power system information for the entire building is acquired in real time from the building meter, and solar power generation amount is obtained from the solar meter placed in the building. , and may include obtaining the solar power generator status from the solar power generator.
  • Figure 9 is a diagram specifically showing an electric vehicle charger management method according to a third embodiment of the present invention.
  • the electric vehicle charger management method is after the electric vehicle charger 10 performs reduced charging or normal charging in step S210, the charger management system 100 determines the charging start time and charging completion. This relates to a method of recording charging information that can be obtained during the charging process, such as time and charging speed, and supervised learning of the machine learning engine 200 based on the recorded charging information.
  • the electric vehicle charger management method begins when charging of the electric vehicle charger 10 is completed by unplugging the charging cable connected between the vehicle and the electric vehicle charger 10 by the user, and charging As soon as this is completed, the electric vehicle charger 10 transmits a charging completion signal to the charger management system 100 (S302).
  • the charging completion signal may include information acquired while the electric vehicle charger 10 is charging, that is, information such as charging start time, charging speed, and charging power.
  • the charger management system 100 generates learning data for supervised learning of the machine learning engine 200 (S303) and transmits the learning data to the machine learning engine 200 (S304).
  • learning data refers to data randomly input by the charger management system 100 into the machine learning engine 200 so that the machine learning engine 200 can generate precise and accurate charging strategy information, and the charger management system 100 ) may be feedback data for charging strategy information previously generated from the machine learning engine 200.
  • the charger management system 100 compares and analyzes the estimated charger usage time generated by the machine learning engine 200 during charging or before charging begins with the actual charger usage time, and provides feedback data as learning data. can be created.
  • the machine learning engine 200 matches user information and charging strategy information to the learning data to enable learning of data for each user or data for each electric vehicle charger 10 placed throughout the building, thereby creating a machine learning engine ( 200).
  • the electric vehicle charger management method according to the third embodiment of the present invention is completed as the machine learning engine 200 performs data learning for each user or data for each electric vehicle charger 10 placed throughout the building (S305). .

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Abstract

La présente invention concerne un procédé de gestion de chargeur de véhicule électrique et un système associé. Plus précisément, la présente invention concerne un procédé de gestion de charge de véhicule électrique et un système associé, qui acquièrent des informations d'utilisateur en provenance d'un chargeur de véhicule électrique afin de gérer efficacement une pluralité de chargeurs de véhicule électrique, reçoivent des informations de stratégie de charge en provenance d'un moteur d'apprentissage automatique de telle sorte que la pluralité de chargeurs de véhicule électrique effectue une charge en fonction des informations de stratégie de charge si les informations d'utilisateur acquises et si un temps de début de charge sont transmis à un moteur d'apprentissage automatique, sélectionnent et trient, par l'intermédiaire d'un algorithme spécifique, un ou plusieurs chargeurs de véhicule électrique dont la puissance doit être économisée si un événement d'économie d'énergie est détecté, et peuvent vérifier que les chargeurs de véhicule électrique économisent la puissance de charge et effectuent une charge dans l'ordre trié.
PCT/KR2022/018351 2022-11-18 2022-11-18 Procédé de gestion de chargeur de véhicule électrique et système associé WO2024106577A1 (fr)

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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012065432A (ja) * 2010-09-15 2012-03-29 Mazda Motor Corp 電力安定化方法、充電制御方法、充電装置及び電動車両
KR20190023801A (ko) * 2017-08-30 2019-03-08 현대자동차주식회사 차량 및 그 제어방법
KR102150044B1 (ko) * 2020-01-06 2020-08-31 (주)호디 전력계통 보조서비스에 Fast DR이 적용된 전기자동차 충전기 및 전기차 충전 전력 제어 방법 및 장치
KR20210118990A (ko) * 2020-03-17 2021-10-05 (주)멥카 전기차 충전전력 수요관리시스템
KR20220109903A (ko) * 2021-01-29 2022-08-05 현대중공업 주식회사 차량 충전 시스템 및 이를 포함하는 선박

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2012065432A (ja) * 2010-09-15 2012-03-29 Mazda Motor Corp 電力安定化方法、充電制御方法、充電装置及び電動車両
KR20190023801A (ko) * 2017-08-30 2019-03-08 현대자동차주식회사 차량 및 그 제어방법
KR102150044B1 (ko) * 2020-01-06 2020-08-31 (주)호디 전력계통 보조서비스에 Fast DR이 적용된 전기자동차 충전기 및 전기차 충전 전력 제어 방법 및 장치
KR20210118990A (ko) * 2020-03-17 2021-10-05 (주)멥카 전기차 충전전력 수요관리시스템
KR20220109903A (ko) * 2021-01-29 2022-08-05 현대중공업 주식회사 차량 충전 시스템 및 이를 포함하는 선박

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