DEVICE AND METHOD FOR CHARGING AND UNCHARGING ELECTRIC VEHICLES FOR LINKING RIDE-SHARING SERVICE AND VEHICLE-TO-GRID

Abstract

An electric vehicle charging and discharging device that connects a ride-sharing service to a vehicle-to-grid system may include a processor and memory that stores software which, when executed by the processor, causes the processor to collect energy data from buildings, predict the energy consumption of the buildings based on the energy data, calculate a time zone in which additional energy is needed for each of the buildings, calculate a required amount of electrical energy based on the collected energy data and the predicted energy consumption, estimate a travel demand of a region in which the buildings are located, and determine a route for a ride-sharing vehicle for each time zone within the region based on the time zone that needs additional energy, the required amount of energy, and the travel demand.

Description

REFERENCE TO RELATED REGISTRATION

[0001] This application claims the priority and benefit of Korean patent application No. 10-2024-0085120, which was filed with the Korean Intellectual Property Office on June 28, 2024, and the entire contents of which are incorporated into this application by reference. BACKGROUND OF THE INVENTION(a) Field of the invention

[0002] The present invention relates to a device and a method for charging and discharging electric vehicles that combine a ride-sharing service and vehicle-to-grid (V2G) technology. In particular, the present invention relates to a device and a method for charging and discharging electric vehicles that combine a ride-sharing service and vehicle-to-grid (V2G) technology and provide a ride-sharing and vehicle-to-grid (V2G) service by using batteries from electric vehicles that can be parked in buildings within a microgrid. (b) Description of the state of the art

[0003] Electric vehicles use battery energy (battery power) to power a motor for locomotion. Electric vehicle batteries are becoming increasingly larger for long-distance journeys, while electric vehicles traveling short distances do not require much energy to reach their destination.

[0004] Therefore, if the excess energy of the battery is discharged in a time zone with high energy consumption, a deterioration of the power supply can be prevented, while the operating rate of the plant can be reduced, and electric vehicle owners can lower the cost of operating the electric vehicle through the discharge fee.

[0005] As information and communication technology continues to develop, buildings are equipped with electronic devices and systems, which gradually increases energy consumption due to the additional equipment.

[0006] Building managers need to increase the capacity of power supply systems (such as transformers) or the contractually agreed energy to cope with rising energy consumption.

[0007] Due to this increase in facilities and updates to contract information, building managers' electricity bills will rise, and technologies will be needed to mitigate this.

[0008] Furthermore, the market for ride-sharing services is also growing with the increasing interest in the sharing economy. SUMMARY OF THE INVENTION

[0009] It is an object of the present invention to provide a device and a method for charging and discharging electric vehicles that combine a ride-sharing service and a vehicle-to-grid and are able to transport passengers to a desired location using electric vehicles via a ride-sharing service and to reduce the energy consumption of a building by discharging the battery of an electric vehicle.

[0010] A device for charging and discharging electric vehicles that combines a ride-sharing service and vehicle-to-grid technology can include a processor and memory that stores software which, when executed by the processor, causes the processor to collect power data (energy data) from buildings, predict the energy consumption of the buildings based on the power data, calculate a time zone in which additional energy is required for each building and calculate a required amount of energy based on the collected energy data and the predicted energy consumption, estimate a traffic demand of a region in which the buildings are located, and determine a route for a ride-sharing vehicle for each time zone within the region based on the time zone that needs the additional energy, the required amount of energy, and the traffic demand.

[0011] The processor can be configured to collect location data of the respective buildings in order to estimate data on past energy consumption, data on power supply systems, contacted electricity data and the traffic demand of the respective buildings for energy forecasting.

[0012] The processor can be configured to predict an energy consumption pattern for each of the buildings during a specific period, based on the collected data on past energy consumption.

[0013] The processor can be configured to estimate the time zone in which each of the buildings needs to be discharged and the amount of electrical energy, taking into account the data from the power supply system and the contacted power data in relation to each of the buildings.

[0014] The processor can be configured to collect data on the demand for public transport and the use of ride-sharing services by region for each time zone in order to generate information on transport demand.

[0015] The processor can be configured to estimate actual traffic demand when the ride-sharing vehicle makes a trip between buildings, taking into account the traffic demand information generated.

[0016] The processor can be configured to estimate travel demand using the following equation 1, taking into account the number of people departing from one departure region and the number of people traveling to another region. XA→B=Arrival quantity B∑n≠AArrival quantity B×Departure quantity A where A and B denote buildings, X denotes a quantity of travel required, X A→Ba quantity required for travel when traveling from A to B, n denotes a set of all buildings, an arrival quantity B The number of people traveling to building B is an arrival quantity. n The number of people traveling to all buildings, and a departure quantity A The number of people departing from building A is [insert value here].

[0017] The processor can be configured to determine a route that maximizes profitability, and profitability is calculated by the following Equation 2: Profitability = Fee for passenger boarding + Fee for unloading electric vehicles + a service fee for the building manager - Costs for the deterioration of the battery of electric vehicles - Costs for charging electric vehicles - Operating costs of electric vehicles.

[0018] The processor can be configured to use an optimization model that includes reinforcement learning, an auction model, and linear programming to determine the route.

[0019] A device for charging and unloading electric vehicles, which connects ride-sharing services and vehicle-to-grid, can be configured to guide the specified route to a customer terminal and make a reservation for a passenger to use the ride-sharing vehicle according to the route specified for each time zone.

[0020] A method for charging and discharging electric vehicles that combines ride-sharing and vehicle-to-grid may include: collecting building energy consumption data, predicting building energy consumption based on energy consumption data, calculating a time zone in which each building will require additional energy and a required amount of energy based on the collected energy data and predicted energy consumption, estimating the travel demand of a region where the buildings are located, and determining a route for a ride-sharing vehicle for each time zone within the region based on the time zone requiring additional electricity, the required amount of energy, and the travel demand.

[0021] The collection of energy data can include the collection of location data of the respective buildings to estimate past energy consumption data, data on energy systems, contacted electricity data and the traffic demand of the respective buildings for energy forecasting.

[0022] Predicting the energy consumption of the respective buildings can include predicting an energy consumption pattern during a specific period for each of the buildings using the collected past energy consumption data.

[0023] When calculating the time zone that requires additional energy and the required amount of electrical energy, the time zone in which each of the buildings needs to be discharged and the amount of electrical energy are estimated taking into account the data of the energy plant and the contacted energy in relation to each of the buildings.

[0024] Estimating transport demand can involve generating transport demand information by collecting data on public transport demand and ride-sharing service participation data by region for each time zone.

[0025] Furthermore, the estimation of traffic demand may include estimating actual traffic demand when the ride-sharing vehicle makes a journey between buildings, taking into account the traffic demand information generated.

[0026] The estimation of transport demand may also include the estimation of transport demand by the equation 1 below, using the number of people departing from one departure region and the number of people moving to another region. XA→B=Arrival quantity B∑n≠AArrival quantity B×Departure quantity A where A and B denote buildings, X a

[0027] Traffic demand refers to X A→B denotes a traffic demand for a journey from A to B and n denotes a set of all buildings, where an arrival set B The number of people moving into building B is an arrival quantity. n The number of people moving into all buildings, and the number of departures. A The number of people leaving from building A.

[0028] The route setting can include setting a route to maximize profitability, and profitability is calculated by the following equation 2: Profitability = Fee for passenger boarding + Fee for unloading electric vehicles + a service fee for the building manager - Costs for deterioration of the battery of electric vehicles - Costs for charging electric vehicles - Operating costs of electric vehicles.

[0029] The route setting can involve setting the route using an optimization model that includes enhanced learning, an auction model, and linear programming.

[0030] A charging and unloading procedure for electric vehicles to link ride-sharing services and vehicle-to-grid may further include guiding a predetermined path to a customer terminal and making a reservation with respect to a passenger to use the ride-sharing vehicle according to the path specified for each time zone.

[0031] A device and method for charging and discharging electric vehicles, which combines ride-sharing services and vehicle-to-grid according to one embodiment, can transport passengers to desired locations and reduce the energy consumption of the building by discharging the battery of the electric vehicles through an electric vehicle ride-sharing service.

[0032] A device and method for charging and unloading electric vehicles, which combines ride-sharing services and vehicle-to-grid according to one embodiment, can provide very high efficiency in passenger transport and reduce energy consumption by identifying an area where the demand for passenger transport and energy consumption are high.

[0033] A device and method for charging and discharging electric vehicles, which combines ride-sharing services and vehicle-to-grid according to one embodiment, can use ride-sharing electric vehicles instead of or in addition to private vehicles for the V2G service, thereby facilitating access to service applications. BRIEF DESCRIPTION OF THE DRAWINGS Fig. Figure 1 schematically shows a system for charging and discharging electric vehicles, which combines a ride-sharing service and V2G according to one embodiment. Fig. Figure 2 is a block diagram of a device for charging and discharging an electric vehicle, which combines a ride-sharing service and V2G according to one embodiment. Fig. Figure 3 is a flowchart of a method for charging and discharging an electric vehicle that combines a ride-sharing service and V2G according to one embodiment. Fig. Figure 4 is a drawing showing a signal flow of a process for collecting data from a building participating in a service according to one embodiment. Fig. Figure 5 is a drawing showing a signal flow of a method for charging and discharging an electric vehicle that connects a ride-sharing service and V2G according to one embodiment. Fig. Figure 6 shows diagrams of energy data from buildings according to one embodiment. Fig. Figure 7 shows diagrams of public transport usage by region according to one embodiment. Fig. Figure 8 is a drawing illustrating a device for data processing according to one embodiment. DETAILED DESCRIPTION OF THE EXECUTION FORMS

[0034] One embodiment of the invention is described in more detail below with reference to the accompanying drawings, so that the person skilled in the art can easily implement the embodiment. As those skilled in the art would recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the present invention. For the sake of clarity, sections not pertaining to the description are omitted, and the same elements or equivalents are designated with the same reference numerals throughout the entire DESCRIPTION.

[0035] Furthermore, unless explicitly stated otherwise, the word "include" and variations such as "includes" or "comprehensive" are understood to imply the inclusion of the elements mentioned, but not the exclusion of other elements. Terms encompassing an ordinary number, such as "first" and "second," are used to describe different components, but the components are not restricted by these terms. The terms are used only to distinguish one component from other components.

[0036] Furthermore, in the DESCRIPTION, the terms “unit”, “section” or “part”, “-er” and “module” refer to a unit that processes at least one function or operation which may be implemented by hardware, software or a combination of hardware and software.

[0037] The following describes embodiments of the present invention with reference to the drawings.

[0038] In this description, a device for charging and discharging electric vehicles that combines a ride-sharing service and V2G can often be referred to as a service manager.

[0039] Fig. Figure 1 schematically shows a charging and discharging system for electric vehicles that combines the ride-sharing service and V2G according to one embodiment.

[0040] The charging and discharging system for electric vehicles, which connects the ride-sharing service and V2G, can provide the charging and discharging service for electric vehicles that connects the ride-sharing service and V2G.

[0041] The electric vehicle charging and discharging system that connects the ride-sharing service and V2G can manage the electric vehicle charging and discharging service that connects the ride-sharing service and V2G via the electric vehicle charging and discharging device that connects the ride-sharing service and V2G.

[0042] With reference to Fig. 1 can include the charging and discharging system for electric vehicles, which connects the ride-sharing service and V2G, a ride-sharing vehicle 10, a building manager 20, a customer terminal 30 and a service manager 100.

[0043] The ride-sharing vehicle 10 can include an electric vehicle. The ride-sharing vehicle 10 can be a vehicle intended for the ride-sharing service. The ride-sharing vehicle 10 can be intended to provide the ride-sharing service for a variety of regions, each with a variety of buildings.

[0044] The Building Manager 20 can be a server that contains comprehensive building data. For example, the Building Manager 20 can include a power metering system or power metering equipment for buildings.

[0045] For example, the building manager 20 can be designed as a server that operates the buildings within a micronet MG.

[0046] The building manager 20 can be the operating systems of buildings that are registered to participate in the charging and unloading service for electric vehicles, which combines the ride-sharing service and V2G according to the present invention.

[0047] The Building Manager 20 can exist in multiple versions and in different regions.

[0048] The Building Manager 20 can provide the Service Manager 100 with information, including energy data relating to the building, that is required for the service.

[0049] The customer terminal 30 can include a mobile phone, a tablet, a computer or the like, in which an application is installed that provides the ride-sharing service.

[0050] The customer terminal 30 can provide the customer with the route of the ride-sharing vehicle and allows them to reserve the ride-sharing service as needed.

[0051] The Service Manager 100 can be associated with a Device 100 for charging and discharging electric vehicles, which combines the ride-sharing service and the V2G function. That is, the Service Manager 100 can manage the charging and discharging service for electric vehicles, which combines the ride-sharing service and the V2G function.

[0052] The Service Manager 100 can manage both the ride-sharing service and the V2G service simultaneously. The Service Manager 100 can connect the ride-sharing service and the V2G system.

[0053] The Service Manager 100 can provide and manage a service capable of transporting passengers to a desired location using the ride-sharing service and reducing the building's energy consumption by discharging the electric vehicle's battery via V2G technology.

[0054] Fig. Figure 2 is a block diagram of a device for charging and discharging an electric vehicle, which combines the ride-sharing service and V2G according to one embodiment.

[0055] In relation to Fig. 2 can include the device for charging and discharging electric vehicles 100, which connects the ride-sharing service and V2G, a data acquisition module 110, a module 120 for predicting building energy, a module 130 for estimating required energy, a module 140 for estimating travel needs and a module 150 for determining the journey route.

[0056] The data acquisition module 110 can record the energy data of buildings.

[0057] The data acquisition module 110 can collect energy data relating to each of the buildings from the building manager 20 (see Fig. 1) The energy data may include various data relating to the electrical energy of the buildings used in the power grid.

[0058] The data acquisition module 110 can collect data on past energy consumption, power supply systems, electricity supply contracts, or similar information for each of the buildings in order to predict energy consumption.

[0059] The data on past energy consumption can include the building's past energy consumption over a preset period. The data on the power supply system can include the capacity of the power supply system. The data on electricity consumption can include the details of the building's electricity contract.

[0060] The Data Acquisition Module 110 can collect location data for each building to estimate the need for trips. This location data can include information about the region where the building is located.

[0061] Module 120, for predicting building energy consumption, can forecast the energy consumption of buildings based on electricity data. Module 120 can predict current and / or future energy consumption based on electricity data that includes historical energy consumption, the capacity of the electrical system, and the electricity contract for each building.

[0062] For example, module 120 can predict the energy consumption of a building by using the collected data on past energy consumption to predict an energy consumption pattern for a specific period for each building.

[0063] Module 120 for predicting the energy consumption of a building can predict the energy consumption pattern of the building for a specific period (for example, one day, 12 hours, or 6 hours) by using the past energy consumption data provided by the building manager 20.

[0064] Depending on the current measuring device (e.g., AMI, current measuring sensor or similar), the prediction unit can vary, for example, 15 minutes, 30 minutes or 1 hour.

[0065] Module 120 for predicting the energy consumption of buildings can predict the energy consumption pattern by applying various prediction models that utilize artificial intelligence from a linear prediction model.

[0066] If the data is insufficient, the Module 120 for predicting the energy consumption of buildings can improve the energy consumption forecast of the associated building by using information about the energy consumption of another building.

[0067] Module 130 for estimating energy demand can calculate a time zone in which additional energy is required for each of the buildings, as well as a required amount of electrical energy based on the collected energy data and the predicted energy consumption.

[0068] Module 130 for estimating energy demand can estimate the time zone in which each of the buildings needs to be discharged and the amount of electrical energy, taking into account the data from the power supply system and the contacted energy data relating to each of the buildings.

[0069] For example, if the predicted energy quantity calculated by module 120 for predicting building energy is higher than the capacity of the energy system or the contractually agreed energy quantity, module 130 for estimating required energy can estimate that an energy quantity is required in that time zone which corresponds to a value obtained by subtracting the contractually agreed energy quantity or the equipment capacity from the predicted energy quantity.

[0070] The Transport Demand Estimation Module 140 can estimate the transport demand of a region where the buildings exist.

[0071] Since the ride-sharing service can be used as one of the public transport options, the transport demand estimation module can collect 140 public transport data points by time and region.

[0072] Although Module 140 can only use conventional public transport data to estimate traffic demand in an initial service, it can also use information about service participation if the service continues.

[0073] This means that module 140 for estimating transport demand can collect data on public transport demand and ride-sharing service participation data by region and for each time zone to generate information on transport demand.

[0074] Module 140 for estimating traffic demand can estimate actual traffic demand when the ride-sharing vehicle makes a movement between buildings, taking into account the generated traffic demand information.

[0075] Module 140 for estimating transport demand can estimate transport demand using Equation 1 below, by using the number of people departing from one departure region and the number of people traveling to another region. XA→B=Arrival quantity B∑n≠AArrival quantity B×Departure quantity A

[0076] Here, A and B denote buildings, X denotes a traffic demand, X A→B denotes a transport demand when moving from A to B, and n denotes a set of all buildings. Here, it is an arrival set. B the number of people moving into building B, an arrival quantity n is the number of people moving into all buildings, and a departure quantity A is the number of people leaving from building A.

[0077] The route setting module 150 can define a route for the ride-sharing vehicle for each time zone within the region based on a time zone that requires additional energy, the required amount of electrical energy, and traffic demand.

[0078] The route setting module 150 can define a route that maximizes profitability, and profitability can be calculated by equation 2 below. Profitability = Passenger boarding fee + Electric vehicle unloading fee + Building manager service fee - Electric vehicle battery degradation costs - Electric vehicle charging costs - Electric vehicle operating costs

[0079] The route setting module 150 can use an optimization model that includes enhanced learning, an auction model, and linear programming to determine the route.

[0080] The device for charging and unloading electric vehicles 100, which connects the ride-sharing service and V2G, can guide a predetermined path to the customer terminal.

[0081] When a user requests a reservation to use the ride-sharing service on the guided route, the electric vehicle charging and unloading device 100, which connects the ride-sharing service and V2G, can make a reservation for a passenger to use the ride-sharing vehicle according to the route specified for each time zone.

[0082] Fig. Figure 3 is a flowchart of a charging and discharging procedure for an electric vehicle that combines the ride-sharing service and V2G according to one embodiment. The charging and discharging procedure for an electric vehicle that combines the ride-sharing service and V2G can be implemented using the charging and discharging device 100 for an electric vehicle according to Figure 3. Fig. 2 will be carried out, which connects the ride-sharing service and V2G.

[0083] In Fig. 3. The device 100 for charging and unloading electric vehicles, which connects the ride-sharing service and V2G, can in step S310 capture the registration and energy data of the building participating in the service.

[0084] Here, the buildings participating in the service can be those that are registered through an application or the like to participate in the electric vehicle charging and unloading service, which combines the ride-sharing service and V2G.

[0085] The device 100 for charging and unloading electric vehicles, which connects the ride-sharing service and V2G, can perform the registration of buildings participating in the service by region.

[0086] The device for charging and discharging electric vehicles 100, which connects the ride-sharing service and V2G, can collect the location data of each of the buildings to estimate the past energy consumption data, the power supply data, the contacted electricity data and the driving requirements of each of the buildings for energy forecasting.

[0087] In step S320, the electric vehicle charging and discharging device 100, which connects the ride-sharing service and V2G, can predict the energy consumption of the buildings participating in the service.

[0088] The device for charging and discharging electric vehicles 100, which connects the ride-sharing service and V2G, can predict the energy consumption of each of the buildings participating in the service by using various predictive models with the aid of artificial intelligence.

[0089] The device for charging and discharging electric vehicles 100, which connects the ride-sharing service and V2G, can output the energy consumption of the building for a specific period by inputting the building's past electricity data into the predictive model.

[0090] In step S330, the device 100 can calculate the time zone that requires the additional energy and the amount of electrical energy for charging and discharging electric vehicles, which connects the ride-sharing service and V2G, taking into account the device capacity and contract information.

[0091] The device for charging and discharging electric vehicles 100, which connects the ride-sharing service and V2G, can estimate the time zone in which each of the buildings needs to be discharged and the amount of electrical energy, taking into account the data of the power supply system and the contacted electricity data in relation to each of the buildings.

[0092] In step S340, the device 100 for charging and unloading electric vehicles, which connects the ride-sharing service and V2G, can estimate the demand for rides in the region where the building participating in the service is located.

[0093] The 100-unit electric vehicle charging and discharging device, which connects the ride-sharing service and V2G, can collect public transport demand and ride-sharing service participation data by region for each time zone to generate traffic demand information. It can then estimate actual traffic demand when the ride-sharing vehicle makes trips between buildings, taking this generated demand information into account. Traffic demand can be represented as the number of people.

[0094] The device for charging and discharging electric vehicles 100, which connects the ride-sharing service and V2G, can estimate traffic demand by equation 1. XA→B=Arrival quantity B∑n≠AArrival quantity B×Departure quantity A

[0095] A and B denote buildings, X denotes a transport demand, X A→B denotes the traffic demand when traveling from A to B, n denotes the set of all buildings. The arrival quantity B is the number of people traveling to building B and the arrival quantity n is the number of people traveling to all buildings, and the departure quantity A is the number of people leaving building A.

[0096] In step S350, the device 100 for charging and unloading electric vehicles, which connects the ride-sharing service and V2G, can determine the route of the electric vehicle so that it goes to the building that needs the electricity, in a time zone where the number of passengers is highest.

[0097] The device for charging and discharging electric vehicles 100, which connects the ride-sharing service and V2G, can define a route that maximizes profitability.

[0098] Profitability can be calculated using the following equation 2. Profitability = Passenger boarding fee + Electric vehicle unloading fee + Building manager's service fee - Electric vehicle battery degradation costs - Electric vehicle charging costs - Electric vehicle operating costs

[0099] This means that profitability can be calculated by subtracting the sum of the costs for the deterioration of the battery of electric vehicles, the costs for charging electric vehicles and the operating costs of electric vehicles from the sum of the fee for passenger boarding, the fee for unloading electric vehicles and the service fee of the building manager.

[0100] In step S360, the electric vehicle charging and unloading device 100, which connects the ride-sharing service and V2G, can direct a predetermined route to the passenger and make a reservation regarding the passenger to use the ride-sharing vehicle traveling on a corresponding route at a corresponding time.

[0101] Fig. Figure 4 is a drawing showing a signal flow of a process for collecting data from a building participating in a service according to one embodiment. Fig. Figure 4 shows a signal flow between the building manager 20 and the service manager 100.

[0102] In Fig. 4. In step S410, the building manager can measure the energy characteristics of the buildings participating in the service.

[0103] In step S420, the building manager 20 can transfer the measured energy characteristics to the service manager 100.

[0104] In step S430, the service manager can register 100 pieces of information about the building, including the energy characteristics of the buildings participating in the service.

[0105] In step S440, the service manager 100 can train a building prediction model based on the received energy data and the registered information about the building.

[0106] In step S450, the service manager 100 can save the generated energy forecast model. The saved forecast model can be updated during the service process.

[0107] The energy forecasting model can be applied in various ways, transforming a linear forecasting model into a forecasting model using artificial intelligence or the like.

[0108] Fig. Figure 5 is a drawing showing a signal flow of a charging and discharging process for an electric vehicle that connects the ride-sharing service and V2G according to one embodiment.

[0109] Fig. Figure 5 shows a signal flow during the provision of a charging and discharging service for an electric vehicle, connecting the ride-sharing service and V2G between the building manager 20, the service manager 100 and the customer terminal 30.

[0110] The Service Manager 100 can provide charging and discharging services for electric vehicles, connecting ride-sharing services and V2G, and reporting the route in daily increments to the customer terminal 30. The time frame for energy provision can be variably defined depending on the application.

[0111] Service Manager 100 may need to collect the previous day's energy data in order to predict the following day's energy data.

[0112] In Fig. In step S510, the building manager can measure the energy data of the buildings from the previous day, which is one day earlier.

[0113] In step S520, the building manager 20 can transfer the measured energy data from the previous day to the service manager 100.

[0114] In step S530, the service manager 100 can predict the energy data for the following day of the buildings based on the energy data of the previous day.

[0115] The Service Manager 100 can predict the energy consumption of the following day using the energy forecasting model.

[0116] Since the prediction energy may be impaired after a preset period of time since the training of the prediction model, the service manager 100 can perform a relearning process using the data collected during the service period.

[0117] In step S540, the service manager can estimate the traffic demand for each building.

[0118] The Service Manager 100 can use public transport data or demand data generated during the service period to estimate the transport demand for each building.

[0119] The Service Manager 100 can estimate traffic demand (arrival volume, departure volume) for each time zone and for each building and use it for the service.

[0120] In step S550, the service manager 100 can optimize a route based on traffic demand.

[0121] Service Manager 100 can estimate traffic demand and find a route where a large number of passengers are likely to occur. Furthermore, Service Manager 100 can estimate the time when Building Manager 20 will need power and find the route based on this estimate.

[0122] This means that the service manager 100 can determine a route that simultaneously satisfies two route conditions, namely the route on which the largest number of passengers occur, and a route that is able to provide the electrical energy for a building manager 200 at a suitable time, in an optimization procedure.

[0123] Service Manager 100 can use enhanced learning, an auction model, linear programming, or similar methods for path optimization.

[0124] In step S560, the service manager 100 can inform the customer of the optimized route via the customer terminal 30.

[0125] In step S570, the customer can request a seat reservation for the ride-sharing vehicle from the service manager 100 via the customer terminal 30.

[0126] In step S580, the service manager 100 can provide the unloading service to the building manager 20.

[0127] Service Manager 100 can make a seat reservation and arrange for the unloading service, in which the electric vehicle visits the associated building to unload the electrical energy in the time zone where the additional energy is needed, for Building Manager 20.

[0128] Fig. Figure 6 shows diagrams of the energy supply data for the buildings according to one embodiment.

[0129] Each of the diagrams in Fig. Figure 6 shows the capacity of the power supply system (or the contractually agreed electrical energy) and energy consumption per day in relation to the buildings numbered 0 to 19.

[0130] In the diagram, the dashed line can represent the plant capacity or the contractually agreed electrical energy, and the solid line can represent the daily energy consumption. A total of twenty buildings can be distinguished by their building number.

[0131] In one embodiment, the service manager 100 can estimate the time zone that needs to be discharged and the amount of electrical energy, taking into account the collective power system, the contractually agreed electrical energy, or the like for each building.

[0132] For example, if the predicted amount of electrical energy calculated from module 120 for predicting building energy (see Fig. 2) is higher than the capacity of the energy plant or the contractually agreed amount of electrical energy as in building no. 5 and / or building no. 14, module 130 can be used to estimate the required energy (see Fig. 2) estimate that the electrical energy is as high as the predicted electrical energy in this time zone - contractually agreed / plant capacity.

[0133] Fig. Figure 7 shows diagrams of public transport usage by region according to one embodiment.

[0134] The diagrams show the travel needs of people in different regions by time zone.

[0135] Since the ride-sharing service of the present invention can be used as one of the public transport options, the collection of public transport data by region and time zone is necessary.

[0136] The Service Manager 100 can use the travel demand information for each recorded region to estimate the travel demand of passengers between buildings between regions.

[0137] For example, the service manager 100 can estimate the amount of travel demand by using the number of people leaving the departure region and the number of people traveling to another region.

[0138] Fig. Figure 8 is a drawing illustrating a calculating device according to one embodiment.

[0139] With reference to Fig. 8 The device and method for charging and discharging electric vehicles, which combine the ride-sharing service and V2G according to embodiments, can be implemented by using a computing device 900.

[0140] The computing device 900 can comprise at least one processor 910, one memory 930, one input interface device 940, one output interface device 950, and one storage device 960, which communicate via a bus 920. The computing device 900 can also comprise a network interface 970, which is electrically connected to a network 90. ​​The network interface 970 can send or receive signals with other units via the network 90.

[0141] The Processor 910 can be implemented in various ways, for example as a microcontroller unit (MCU), application processor (AP), central processing unit (CPU), graphics processing unit (GPU), neural processing unit (NPU), and the like, and can be any type of semiconductor device capable of executing instructions stored in the Memory 930 or the Memory Device 960. The Processor 910 can be configured to perform the functions and methods described above with respect to Fig. 1 to Fig. 7 implemented.

[0142] The memory 930 and the storage device 960 can comprise various types of volatile or non-volatile storage media. For example, the memory can comprise a read-only memory (ROM) 931 and a working memory (RAM) 932. In this embodiment, the memory 930 can be located inside or outside the processor 910, and the memory 930 can be connected to the processor 910 by various known means.

[0143] In some embodiments, at least some configurations or functions of a device and method for charging and discharging an electric vehicle, which combine a ride-sharing service and vehicle-to-grid according to an embodiment, can be implemented as a program or software that can be executed by the computing device 900, and the program or software can be stored in a computer-readable medium.

[0144] In some embodiments, at least some configurations or functions of a device for charging and discharging an electric vehicle and a method linking a ride-sharing service and a vehicle-to-grid connection according to an embodiment can be implemented by using hardware or circuits of the computing device 900 or can also be implemented as separate hardware or circuits that can be electrically connected to the computing device 900.

[0145] Although this invention has been described in connection with what are currently regarded as practical embodiments, it is to be understood that the invention is not limited to the disclosed embodiments, but on the contrary is intended to cover various modifications and equivalent arrangements which encompass the spirit and scope of the attached claims. QUOTES INCLUDED IN THE DESCRIPTION

[0000] This list of documents cited by the applicant was automatically generated and is included solely for the reader's convenience. The list is not part of the German patent or utility model application. The DPMA accepts no liability for any errors or omissions. Cited patent literature

[0000] KR 10-2024-0085120

[0001]

Claims

[1] A device for charging and discharging electric vehicles for linking a ride-sharing service and vehicle-to-grid, comprising: a processor; and a memory that stores software which, when executed by the processor, causes the processor to: Collecting energy data from buildings, to predict the energy consumption of buildings based on the energy data, to calculate a time zone in which additional energy is required for each of the buildings, and to calculate a required amount of electrical energy based on the collected energy data and the predicted energy consumption, to estimate the transportation needs of a region where the buildings are located, and to determine a route for a ride-sharing vehicle for each time zone within the region based on the time zone requiring additional energy, the required amount of energy, and the travel demand. [2] Device according to claim 1, wherein the processor is configured to collect location data of the respective buildings for estimating past energy consumption data, power supply system data, contacted energy data and the driving requirements of the respective buildings for energy forecasting. [3] Device according to claim 2, wherein the processor is configured to predict an energy consumption pattern during a specific period for each of the buildings using the collected data of past energy consumption. [4] Device according to claim 2, wherein the processor is configured to estimate the time zone in which each of the buildings needs to be discharged and the amount of electrical energy, taking into account the data of the power supply system and the data on contacted energy in relation to each of the buildings. [5] Device according to claim 1, wherein the processor is configured to collect data on the demand for public transport and data on characteristics of participation in ride-sharing services by region for each time zone in order to generate information on the demand for rides. [6] Device according to claim 5, wherein the processor is configured to estimate actual traffic demand when the ride-sharing vehicle makes a movement between buildings, taking into account the traffic demand information generated. [7] Device according to claim 6, wherein: the processor is configured to estimate traffic demand using Equation 1 below, taking into account the number of people departing from one departure region and the number of people traveling to another region. XA→B=Arrival quantity B∑n≠AArrival quantity B×Departure quantity A where A and B denote buildings, X denotes a quantity of travel required, X A→B A travel requirement quantity is defined as when traveling from A to B, n denotes a set of all buildings, an arrival quantity B The number of people traveling to building B is an arrival quantity. n The number of people traveling to all buildings, and a departure quantity A The number of people departing from building A is [insert value here]. [8] Device according to claim 1, wherein the processor is configured to determine a route that maximizes profitability, and wherein the profitability is calculated by equation 2 below, Profitability = Fee for passenger boarding + Fee for unloading electric vehicles + a service fee for the building manager - Costs for the deterioration of the battery of electric vehicles - Charging costs for the electric vehicle - Operating costs for the electric vehicle. [9] Device according to claim 1, wherein the processor is configured to use an optimization model comprising reinforcement learning, an auction model and linear programming to determine the driving path. [10] Device according to claim 1, wherein the processor is configured to follow the predetermined path to a customer terminal and to make a reservation with respect to a passenger for use of the ride-sharing vehicle according to the predetermined path for each time zone. [11] A method for charging and discharging an electric vehicle for linking a ride-sharing service and vehicle-to-grid, comprising: Collecting energy data from buildings; Predictions of energy consumption for buildings, each based on energy data; Calculating a time zone in which additional energy is required for each of the buildings, and a required amount of electrical energy based on the collected energy data and the predicted energy consumption; Estimating the transportation needs of a region where the buildings are located; and Establishing a route for a ride-sharing vehicle for each time zone within the region based on the time zone requiring additional energy, the required amount of electrical energy, and the travel demand. [12] Method according to claim 11, wherein the collection of energy data includes the collection of location data of the respective buildings to estimate data on past energy consumption, data on power supply systems, data on contacted energy and the driving requirements of the respective buildings for energy forecasting. [13] Method according to claim 12, wherein the prediction of the energy consumption of the respective buildings comprises predicting an energy consumption pattern during a specific period for each of the buildings using the collected data of past energy consumption. [14] Method according to claim 12, wherein, in calculating the time zone requiring additional energy and the required amount of electrical energy, the time zone requiring the discharge of each of the buildings and the amount of electrical energy are estimated taking into account the data on power supply systems and the data on contacted energy in relation to each of the buildings. [15] Method according to claim 11, wherein the estimation of travel needs comprises generating travel need information by collecting data on the demand for public transport and data on characteristics of participation in ride-sharing services by region for each time zone. [16] Method according to claim 15, wherein the estimation of travel needs further comprises estimating actual travel needs when the ride-sharing vehicle performs a movement between buildings, taking into account the generated travel need information. [17] Method according to claim 16, wherein the estimation of travel demand further comprises the estimation of travel demand by the following equation 1 using the number of persons departing from one departure area and the number of persons traveling to another area, XA→B=Arrival quantity B∑n≠AArrival quantity B×Departure quantity A where A and B denote buildings, X denotes a quantity of travel required, X A→B a travel requirement quantity is defined when traveling from A to B, and n denotes a set of all buildings, where an arrival quantity B The number of people traveling to building B is an arrival quantity. n The number of people traveling to all buildings, and a departure quantity A The number of people departing from building A. [18] Method according to claim 11, wherein determining the route comprises determining a route that maximizes profitability, wherein profitability is calculated by equation 2 below: Profitability = Fee for passenger boarding + Fee for unloading electric vehicles + a service fee for the building manager - Costs for the deterioration of the battery of electric vehicles - Costs for charging electric vehicles - Operating costs of electric vehicles. [19] Method according to claim 11, wherein determining the route comprises determining the route using an optimization model that includes reinforcement learning, an auction model and linear programming. [20] The method of claim 11, further comprising guiding a predetermined route to a customer terminal and making a reservation with respect to a passenger for use of the ride-sharing vehicle according to the predetermined route for each time zone.

Images