JP2023155476A - Information processing system, information processing apparatus, information processing method, and information processing program - Google Patents

Abstract

To implement the efficient use of a charging facility by increasing the accuracy in managing the use of the charging facility by a preliminarily registered EV and managing also information on an outside EV which is not preliminarily registered.SOLUTION: An information processing system of an embodiment comprises: a mobile information terminal which transmits a reservation request from a first moving body of a person preliminarily registered as a user, including destination information set by the user when the user makes a reservation for a recommended energy supply point out of a plurality of energy supply points; an information processing apparatus which makes a travel plan including the recommended energy supply point on the basis of the destination information included in the received reservation request and acquires the user's place on a waiting list for energy supply; and an information communication terminal which is provided at the energy supply point, presents information of the user's place on the waiting list for energy supply at the energy supply point, receives a request for energy supply at the energy supply point from a second moving body being a moving body which is not preliminarily registered and is not to be managed, and acquires the place on the waiting list for energy supply.SELECTED DRAWING: Figure 1

Description

Embodiments of the present invention relate to an information processing system, an information processing device, an information processing method, and an information processing program.

An information-providing navigation system for electric vehicles (hereinafter referred to as EV) refers to map information when using EVs on expressways (toll roads), accurately predicts the reachable range of EVs, and provides user This system recommends service/parking areas (steps SA/PA) with charging facilities that should be used. It is necessary to create a plan to efficiently use SA/PA with charging equipment so that EVs do not run out of power. A predictive model of EV power consumption is used to estimate the reachable range of the EV.

However, not all EVs using expressways are managed by the information processing system, and some EVs are not registered with the information processing system, and even if they are registered, they are not using the information processing system's services. There may also be EVs that do not exist (hereinafter referred to as external EVs).

Japanese Patent Application Publication No. 2003-262525 Japanese Patent Application Publication No. 2014-038048 Japanese Patent Application Publication No. 2010-267110

However, if you create a plan to use SA/PA charging equipment without considering the existence of external EVs that are not managed by the EV information provision system, it will not necessarily be possible to create an efficient SA/PA charging facility for EVs using this service. /We cannot necessarily recommend the use of charging facilities in PAs.
Similarly, even for external EVs, it is desired that charging facilities in SA/PA can be used efficiently with simple procedures.

The present invention has been made in view of the above, and includes management of the use of charging equipment for EVs that have been pre-registered in an information provision system for EVs when the EVs are used in SA/PA. An information processing system and information system that improves accuracy and manages information on external EVs that use the charging equipment that are not pre-registered in the EV information provision system, allowing efficient use of charging equipment in SA/PA. The purpose of the present invention is to provide a processing device, an information processing method, and an information processing program.

An information processing system according to an embodiment includes a plurality of energy supply points, and upon receiving an energy supply request from a mobile object, makes a reservation for a recommended energy supply point from the plurality of energy supply points. a mobile information terminal that sends a reservation request for energy supply for a first mobile body of a registered user including destination information set by the registered user; Accepting the energy supply reservation request, and creating a travel plan including the energy supply points to which energy supply is recommended to the first mobile object based on the destination information included in the received reservation request. , an information processing device that acquires the energy supply waiting order of the recommended energy supply point; and an information processing device that is provided at the energy supply point and that is generated based on a reservation request from the first mobile object in the information processing device. present the energy supply waiting list information of the energy supply point that has been updated, and accept an energy supply request at the energy supply point for a second mobile object that is an unmanaged mobile object that has not been registered in the system in advance; An information communication terminal that acquires the order of energy supply.

FIG. 1 is a schematic block diagram of an information processing system according to an embodiment. FIG. 2 is a block diagram of the information processing device according to the embodiment. FIG. 3 is an explanatory diagram of a processing procedure in the information processing system of the embodiment. FIG. 4 is an explanatory diagram of an example of the initial display screen of the local information device. FIG. 5 is an explanatory diagram of an example of a charging order status display screen. FIG. 6 is an explanatory diagram of an example of a charging reception screen. FIG. 7 is an explanatory diagram of an example of the password input screen. FIG. 8 is an explanatory diagram of an example of a charging acceptance change/cancellation screen. FIG. 9 is an explanatory diagram of an example of a charging start screen. FIG. 10 is an explanatory diagram of an example of route information between supply points. FIG. 11 is an explanatory diagram of an example of route information between supply points expressed in a network structure. FIG. 12 is an explanatory diagram of traffic counter management information managed by a traffic counter. FIG. 13 is an explanatory diagram of an example of traffic counter information. FIG. 14 is an explanatory diagram of an example of charger information. FIG. 15 is an explanatory diagram of an example of weather information. FIG. 16 is an explanatory diagram of an example of vehicle information. FIG. 17 is an explanatory diagram when calculating power consumption in a certain section as consumption history information. FIG. 18 is an explanatory diagram of another example of calculating power consumption. FIG. 19 is an explanatory diagram of an example of learning data (driving data). FIG. 20 is an explanatory diagram of an example of the classification rule. FIG. 21 is a flowchart of an example of the operation of the model management section. FIG. 22 is a flowchart showing an example of the operation of the model management section 31. FIG. 23 is an explanatory diagram of an example of a model management table. FIG. 24 is an explanatory diagram of an example of a travel management database on an expressway. FIG. 25 is a flowchart of an example of the operation of the virtual EV management unit. FIG. 26 is an explanatory diagram of an example of a travel plan for one virtual EV. FIG. 27 is an explanatory diagram of an example of arrangement of virtual EVs. FIG. 28 is an explanatory diagram of a specific example of creating a travel plan in the first example. FIG. 29 is an explanatory diagram of a specific example of creating a travel plan in the second example. FIG. 30 is a flowchart of an example of a process for generating probability information for each interchange. FIG. 31 is an explanatory diagram of a process of identifying a point reachable by an EV in order to create a charging plan for the EV. FIG. 32 is a diagram illustrating an example of overall charging plan data including a charging plan for only virtual EVs. FIG. 33 is a diagram illustrating an example of data after the system registration EV is added. FIG. 34 is a diagram illustrating an example of recommended data sent to the system-registered EV. FIG. 35 is a flowchart illustrating an example of the operation of the information processing apparatus according to this embodiment. FIG. 36 is a diagram showing the hardware configuration of the information processing device 101 according to this embodiment.

Next, preferred embodiments will be described in detail with reference to the drawings.
In the following description, an EV will be used as an example of a moving object, but the present invention can be similarly applied to moving objects other than EVs. Other moving objects include, for example, trains, hybrid cars, fuel cell cars, airplanes, drones, electric motorcycles, cars with diesel engines, ships, and the like.

In addition, in this embodiment, since it is assumed that the mobile object is an EV, the energy supply is charging, the supply point is a charging stand, and the energy supply device is a charger. However, if the mobile object is a gasoline-powered vehicle, energy replenishment may be replaced with fuel replenishment, and supply points may be replaced with gas stations and refueling stations. Furthermore, if the moving object is an airplane or a drone, the term road can be read as an air route, and if the moving object is a ship, it can be read as a sea route. In this way, the same processing as in this embodiment is possible for moving objects other than EVs.

Now, EVs use the charging power (charging energy) of batteries to travel on roads, which are traffic routes, and move to various locations.
The EV location is not limited to an energy supply point, but may be any location on the map, such as a home, a restaurant, a retail store, or a location arbitrarily designated by the EV user. Although it may be a point, the EV point in this embodiment is an energy supply point where the EV is charged.

EVs are supplied with power at an energy supply point (hereinafter referred to as a supply point or charging point) and store the supplied power in a battery. EVs then use the electricity stored in their batteries to move around. In order to continue moving in an EV, it is necessary to move to the next supply point and receive power at the supply point before the battery runs out of power. In this way, the EV travels to the destination while being charged at each supply point. Note that one or more chargers (energy suppliers) are installed at the supply point, and the EV is connected to any charger by wire or wirelessly to receive power supply.
The communication unit 11 communicates with various information devices, various servers, etc. via a communication network.

FIG. 1 is a schematic block diagram of an information processing system according to an embodiment.
Broadly speaking, the information processing system 100 includes an information processing device 101, information devices 201A to 201N, 211A to 211N, and a communication network 220.

As shown in FIG. 1, the information processing device 101 is connected to information devices 201A to 201N and servers 211A to 211N via a communication network 220.

Next, the configuration of the information processing device will be explained.
FIG. 2 is a block diagram of the information processing device according to the embodiment.
As shown in FIG. 2, the information processing device 101 includes a communication section 11, a user ID registration section 12, an EV navigation usage registration section 13, a user database (DB) 14, a map information management section 15, a road control information management section 16, It includes a charger information management section 17, a weather information management section 18, a vehicle information management section 19, a system database (DB) 20, and a control section 21.

The information processing device 101 also includes a model management unit 31, a model database (DB) 32, a prediction unit 41, a driving state management unit (first management unit) 51, a driving management database 52, and a virtual EV management unit (second management unit). 61, a virtual EV_database (DB) 62, a charging planning section (planning section) 71, and a charging plan database 72.
In addition, the information processing device may include an input device through which an operator inputs instructions or data into the device, and a display device to display data to the operator.

Examples of input devices include a keyboard, a mouse, a touch panel, a microphone for voice input, and the like.
Examples of display devices include LCDs (liquid crystal displays), CRTs (cathode ray tubes), and PDPs (plasma displays).

The information processing device 101 is an information provision system for EVs that recommends energy supply points for EVs that use expressways (toll roads).
Further, the information processing device 101 is installed, for example, at a facility management company or a traffic control station. However, the information processing device 101 can also be incorporated into an EV car navigation system (hereinafter referred to as a car navigation system) or installed on the roadside. When placed on the roadside, an operator may remotely operate the information processing apparatus.

The user ID registration unit 12 of the information processing device 101 receives a user registration request from the information device 201, and performs user registration for the service of the EV information providing system (EV navigation service) according to the present embodiment. For example, the user operates the information device 201 to open an application or web page for registering as a user of the EV navigation service, and accesses the user ID registration section 12.

In addition, the user ID registration unit 12 acquires the user's personal information and the information of the EV used by the user (vehicle model, battery capacity, battery deterioration level, cumulative mileage, tire type, etc.), and uses these as user information. . The user ID registration unit 12 issues a user ID (EV_ID) to the user, associates the user ID with user information, and registers it in the user database 14.

The EV navigation usage registration unit 13 of the information processing device 101 receives a usage registration request from the information device 201 and performs usage registration to start the EV navigation service. For example, a user operates the information device 201 at home or in an EV when going out to open an application or web page for receiving the EV navigation service, and the information device 201 receives information necessary to receive this service. A usage registration request including the above is sent to the EV navigation usage registration section 13.

For example, travel date and time, scheduled departure date and time at the departure IC (interchange) (current date and time, scheduled departure date and time from home, etc.), departure point (expressway departure IC, etc.), destination (destination IC, etc.), EV battery capacity ( (Can be omitted if notified at the time of user registration), battery deterioration level (can be omitted if notified at the time of user registration), EV battery level, air conditioner usage status (on/off, setting mode) , temperature settings, etc.), cumulative mileage (may be omitted if notified at the time of user registration), tire type (may be omitted if notified at the time of user registration), number of passengers, etc. Send.

If the information device 201 is a car navigation system, information set in the car navigation system may be transmitted, or if the information device 201 is a smartphone or the like, information input by the user may be transmitted. This acquired information is referred to as usage registration information. The EV navigation usage registration unit 13 issues an ID (driving ID) for the current service usage, associates the driving ID with the usage registration information, stores it in the user database 14, and also stores it in the user database 14, as well as the driving status management unit or model described later. Notify management or both.

The information processing device 101 also communicates with the user's information device 201 after usage registration to obtain information such as GPS location information, current time, remaining battery power (remaining battery power), and air conditioner usage status. , the driving state management section 51 or the model management section 31, or both thereof, which will be described later, are notified.

The timing of communication is real time, timing at fixed intervals, timing when the user passes through a predetermined IC such as the departure IC registered by the user, timing when this device issues a request and obtains a response, and service area/parking area (step SA). /PA) entry or exit timing, timing of passing near SA/PA, etc.

The user database 14 holds information registered by the user ID registration section 12 and the EV navigation usage registration section 13. The user database 14 is a hardware storage device such as a memory device, hard disk device, or SSD device.

The control unit 21 controls the entire information processing device 101 by controlling each part of the information processing device 101, and realizes the operation of the EV information providing system.

The map information management unit 15 acquires map information from the server 211, and stores and manages the acquired map information in the system database 20.
In this case, the map information is information representing information such as the position (latitude, longitude), size, range, etc. of a map element. For example, elevation information associated with each location on the map, distance information for each route (expressway route, general road route, etc.), gradient, curve angle, route, direction, KP (kilometer post), JCT ( This includes the shape of tunnels, lighted areas (other than tunnels), and road surface conditions (paving conditions, etc.).

The map information also includes the number of lanes on the expressway, the location of interchanges (ICs), service areas/parking areas (steps SA/PA), and the like.
Furthermore, the map information includes information on supply points, SA/PA points, IC and JCT points as the above-mentioned map elements.

The map information may also include route distance information and information on the required travel time of the route as route information between supply points, SA/PA points, ICs, and JCT points.

The information devices 201A to 201N are, for example, communication terminals held by users (smartphones, tablet devices, mobile phones, notebook PCs, etc.), in-vehicle devices such as car navigation systems installed in EVs, and devices that EVs are used for charging. This is a communication device installed at a shared communication terminal, charger, ITS spot, etc. installed at the supply point.

In the following description, for ease of understanding, an example will be described in which the information device 201N functions as a shared communication terminal installed at a supply point where EVs are charged.

The information devices 201A to 201N can communicate with the information processing device 101 in real time.

The communication terminal as an example of the information devices 201A to 201N may be operated by another passenger (for example, a person sitting in the passenger seat) of the EV being driven by the user. Alternatively, the user may operate the communication terminal and another representative may drive the EV. Input to the communication terminal may be manual or voice input.

Further, as another example of the information devices 201A to 201N, a device installed on the roadside that complies with the ETC 2.0 standard (hereinafter referred to as an ETC 2.0 device) is a communication device that can communicate with at least one of a communication terminal and a car navigation system. be. ETC2.0 devices are placed, for example, at a plurality of predetermined spots. The predetermined spot may be an ITS spot, a location along a route, a service area of an expressway, a toll booth, a building, or the like.

The ETC 2.0 device may be provided at the entrance and exit of the supply point, and in this case, it may detect the entry time, exit time, number of entering EVs, and number of exiting EVs.

In the following description, if there is no need to identify the information devices 201A to 201N, they will be collectively referred to as information devices 201.

The servers 211A to 211N include a map information management server that manages map information, a weather information management server that manages weather information, a vehicle information management server that manages vehicle information, a charger information server that manages charger information, and a road control information server. This is a road control information server that manages Servers other than those listed here may be arranged. In the following description, these servers 211A to 211N will be collectively referred to as servers 211 when there is no need to identify them.

Communication network 220 is a wired, wireless, or hybrid network. The communication network 220 may include a relay device such as a wireless LAN access point.

FIG. 3 is an explanatory diagram of a processing procedure in the information processing system of the embodiment.
The information processing system 100 of this embodiment functions as an information provision navigation system for EVs. Therefore, in FIG. 3, the information processing system is referred to as an EV charging navigation.

First, the flow of processing in the information processing system 100 differs depending on whether the user is registered in the information processing system 100 (user of system registered EV [=first mobile object]).

First, a processing procedure for a case where the user is a user registered in the information processing system 100 (a system-registered EV user) will be described.

First, if the user is a user registered in the information processing system 100 (step S11; Yes), the user inputs the departure point, destination, and remaining battery level via a mobile information terminal such as a smartphone. and departs (step S12).

Thereby, the information processing device 101 calculates an SA/PA that is reachable by the user (the EV) and in which the charging facility is available (step S13), using a processing procedure that will be described later.

At this stage, the order management database already contains other users (the user of the registered EV (=first mobile object) registered in the information processing device 101, the virtual user of the virtual EV (=third mobile object), and There is a possibility that a charging waiting order (=energy supply waiting information) corresponding to a user (who is a user of an unregistered external EV (second mobile object) whose order has been acquired) is registered in the information processing device 101. The information processing device 101 checks the order (=energy supply waiting information) (step S14).

Next, the information processing device 101 determines whether or not there is a waiting list for charging (step S15).
In the judgment in step S15, if there is a waiting list for charging (step S15; Yes), the information processing device 101 moves to a place where the waiting order is smaller (SA/PA that is reachable and where charging equipment is available). ) is recommended and presented, the order is acquired (step S16), and the order management database is updated (step S18).

On the other hand, in the judgment in step S15, if there is no waiting list for charging (step S15; No), the user (the EV) can reach the first SA/PA where charging equipment is available. The SA/PA that is far away is recommended and presented, the order is acquired (step S17), and the order management database is updated (step S18).

Next, the information processing device 101 sends the user to the location (charging location) where the charging equipment of the SA/PA is installed based on the current location of the user (the EV) acquired for the user. It is determined whether the vehicle has arrived (step S19).

When the user arrives at the location (charging location) where the SA/PA charging equipment is installed in the order determined in step S19, the user is given priority or wireless connection to the charger and starts charging. Start (step S20).

Then, when charging is completed, the user will run the vehicle again (step S21).

On the other hand, in the judgment in step S19, if the user has not yet arrived at the place (charging place) where the charging equipment of the SA/PA that has taken his turn is installed, the information processing device 101 (step S22).

In the determination of step S22, if the user has not yet passed the charging location where the charging equipment of the SA/PA that has taken its turn is installed (step S22; No), the information processing device 101 It is determined whether there are other users waiting for their turn who have already arrived at (step S23).

In the judgment at step S23, if there are no other users waiting in line who have already arrived at the charging location (step S23; No), the user is allowed to continue traveling (step S24), and the process is repeated at step S19. , and repeat the same process as described above.

In the judgment in step S23, if there is another user waiting in line who has already arrived at the charging location (step S23; Yes), the information processing device 101 determines that the user , 10 minutes) to arrive at the charging location (step S25).

In the judgment in step S25, if the user can arrive at the charging location within the predetermined time (10 minutes in the example of FIG. 3), the user is allowed to continue driving (step S24) and the process is repeated. The process moves to step S19, and the same process as described above is repeated.

In the determination at step S25, if it is predicted that the user will not be able to arrive at the charging location within the predetermined time (10 minutes in the example of FIG. 3) (step S25; No), the information processing device 101: Recommend and present a location with a smaller waiting list (SA/PA that is reachable and where charging equipment is available), acquire the queue (step S16), and update the queue management database (step S16). S18).

The information processing apparatus 101 then moves the process to step S13 and repeats the same process as described above.

On the other hand, in the determination in step S22, if the user passes through a charging place where the SA/PA charging equipment for which the order has already been taken is installed (step S22; Yes), the information processing device 101 performs order management. database, cancel the order of the user at the charging location, move the process to step SS13, and repeat the same process as described above.

Next, a processing procedure for a case where the user is a user of an EV (=second mobile object) not registered with the information processing apparatus 101 will be explained.

The user is a user who is not registered with the information processing device 101 (step S11; No), and the user arrives at a location where the charging device is located in an SA/PA where one of the charging facilities at the charging location can be used. If so (step S31), the local information and communication terminal installed at the charging location (charging device with communication function, operation panel with communication function installed adjacent to the charging device, stationary state and The order of waiting for charging is confirmed using a mobile information terminal (such as a portable information terminal) (step S32).

The processing of the information processing device 101 will be described below, including the display on the local information device and the user's operations.
FIG. 4 is an explanatory diagram of an example of the initial display screen of the local information device.
Here, the explanation will be given assuming that the local information device 201N is provided with a touch panel display that functions as an operation section and a display section.

On the initial display screen of the local information device 201N, a menu screen G11 of the charging order management system is displayed as the screen of the information processing system 100.

As shown in FIG. 4, the menu screen G11 of the charging order management system includes a button B11 for viewing the charging order status, a button B12 for starting charging acceptance, a button B13 for changing/cancelling charging acceptance, and a button B13 for changing/cancelling charging acceptance. A button B14 for starting charging, which is operated when starting charging, is displayed.

Since the user is not registered with the information processing device 101 and has not yet acquired the charging order, the user touches, for example, the button B11 for viewing the charging order status.

FIG. 5 is an explanatory diagram of an example of a charging order status display screen.
As a result, the display screen of the local information device 201N transitions from the menu screen G11 of the charging order management system to the charging order status confirmation screen G12.
On the charging order status confirmation screen G12, the number of groups waiting in line at the time (in the case of the example in FIG. 5, 2 cars), the confirmation time, and information on the number of groups waiting in line (in the case of the example in FIG. 5, 8 groups) are displayed. , a return button B21 for returning the display to the menu screen G11 is displayed.

In the case of the example shown in FIG. 5, the waiting information includes information indicating that two sets of reception number "123" with reception order = 1 and reception number "254" with reception order = 2 are waiting for charging. Displayed.
In this case, the user determines whether there is a waiting list for charging in the charging device (step S33).

Specifically, on the charging order status screen G12, if there is a waiting list for charging as in the example of FIG. After confirming the situation, the user presses the return button 21 on the charging order status confirmation screen G12, and touches the charging reception start button B12 to obtain the charging order (step S34).

FIG. 6 is an explanatory diagram of an example of a charging reception screen.
As a result, the display screen of the local information device 201N transitions from the menu screen G11 of the charging order management system to the charging reception screen G13.
The charging reception screen G13 displays the order of reception at the charging device (in the example of FIG. 13, "No. 3" = energy supply queue information), and the password for completing the reception (in the example of FIG. 13). , a 4-digit arbitrary number) is displayed.

FIG. 7 is an explanatory diagram of an example of the password input screen.
In this state, when the user touches the display screen, the display transitions from the charging reception screen G13 to the PIN input screen G14, which displays a numeric input panel P31 for inputting the PIN and a display area D31 for the input PIN. , and an acceptance button B31 for confirming the password are displayed.

Therefore, the user operates the numeric input panel P31 to input the desired PIN number, and when the desired PIN number is displayed in the PIN display area D31, the user touches the reception button B31 to start the reception process. Complete.

In addition, when changing or canceling the reception process after the reception process, touch the button B13 for changing or canceling the reception of charging on the menu screen G11.

FIG. 8 is an explanatory diagram of an example of a charging acceptance change/cancellation screen.
As a result, the display screen of the local information device 201N transitions from the menu screen G11 of the charging order management system to the charging acceptance change/cancellation screen G15, and a numeric input panel P41 for entering the PIN number and the input screen G15. A display area D41 for the password and an accept button B41 for confirming the password are displayed.

Therefore, the user operates the number input panel P31 to input the PIN number set at the time of charging reception, and when the desired PIN number is displayed in the PIN display area D31, the user touches the reception button B41. The process will then proceed to charging acceptance change/cancellation processing.

Now, when the input on the password input screen G14 is completed, the information processing device 101 updates the queue management database and records information on the user's queue (step S35).

The information processing device 101 then determines whether there is a waiting list for charging (step S36).

In the determination at step S36, if there is still a person waiting for their turn to charge (step S36; Yes), the local information device 201N displays a charging order status screen G12, and the user waiting for their turn to charge This will be confirmed.

On the other hand, in the determination at step S36, if the user waiting for charging has reached his or her turn (step S36; No), a button to start charging is displayed on the menu screen G11 of the local information device 201N. Touch button B14 to display the charging start screen.

FIG. 9 is an explanatory diagram of an example of a charging start screen.
On the charging start screen G16, there is a number input panel P41 for inputting a reception number ("125" in the example of FIG. 15), a PIN, a display area D41 for the input PIN, and an instruction to start charging. A charging start instruction button B41 is displayed.

Therefore, the user connects the charging connector (in the case of wired) or faces the power receiving unit to the power transmitting unit (in the case of wireless), operates the numeric input panel P41, and enters the password set by the user. After inputting the number and displaying the desired password in the password display area D41, the user touches the charge start instruction button B41 to instruct the start of charging (step S20).
Then, when charging is completed, the user will run the vehicle again (step S21).

On the other hand, in the charging order status screen G12, if it is determined in step S33 that there is no waiting list for charging (step S33; No), the user who wants to perform the charging reception process checks the charging order status, The user presses the return button 21 on the charging order status confirmation screen G12 and touches the charging reception start button B12 to obtain the charging order (reception order No. 1: charging is possible immediately) (step S38).

As a result, the information processing apparatus 101 updates the turn management database and records information on the user's turn (step S39).
Then, the user connects the charging connector (in the case of wired) or faces the power receiving unit to the power transmitting unit (in the case of wireless), performs the same operation as described above, and then displays the charging start screen G16. Display.

Then, the user operates the numeric input panel P51 of the local information device 201N to input the PIN number that he/she has input and set, and when the desired PIN number is displayed in the PIN display area D51, charging starts. Touch the instruction button B51 to instruct the start of charging (step S20).

Then, when charging is completed, the user will run the vehicle again (step S21).

As described above, according to the present embodiment, even if the EV is not registered for use in the EV information provision system (information processing system), so-called external EV, the on-site information device installed in the charging device Although it is not possible to make a reservation for charging anywhere other than 201N, by using the local information device 201N, it is possible to use the charging equipment at the SA/PA through the same simple procedure as a registered user of the information processing system 100.

In this case, as will be described later, the information processing system 100 assumes the existence of an external EV (second mobile object), uses a virtual EV (third mobile object), and uses the user of the registered EV (first mobile object) to Since a travel plan is generated for the registered user of the information processing system 100, and information is provided to external EV users and registered EV users based on the generated travel plan, any user Even if there is, you won't have to wait any longer than necessary for charging.

Next, a process in which the information processing device 101 determines an SA/PA that is reachable by the user (his/her EV) and in which the charging facility is available will be described in step S13 described above.

FIG. 10 is an explanatory diagram of an example of route information between supply points.
As shown in Figure 3, route information between supply points includes information specifying the supply point adjacent to one supply point, distance information to the adjacent supply point, and slope difference to the adjacent supply point. information, elevation difference information to the adjacent supply point, information on the time required to reach the adjacent supply point (required travel time), information on the required energy required to reach the adjacent supply point, information to reach the adjacent supply point An example is electricity cost information.

In this case, the time required to reach the adjacent supply point may be the average value of past measurements, or the time required to travel the distance at a predetermined speed.

Further, the required energy and electricity cost required for traveling the route may be actual statistical values (average value, median value, etc.), or may be values calculated by a calculation formula or simulation.

In this case, the route information may be expressed using a general network structure.
FIG. 11 is an explanatory diagram of an example of route information between supply points expressed in a network structure.
In this case, nodes corresponding to supply points Q1 to Qn are connected by links corresponding to routes shown by broken lines.

In FIG. 11, links connect mutually adjacent supply points.
Therefore, for example, it can be seen that the supply point Q1 is adjacent to the supply points Q3 and Q2, respectively. It can also be seen that there is one route from the supply point Q1 to the supply point Q3, and there is also one route from the supply point Q1 to the supply point Q2.

Furthermore, each link is represented by both nodes connected by the link, and although the description of the route characteristics is omitted in FIG. 4, the characteristics of the route between the supply points are assigned.

Although the example in FIG. 11 shows the network structure of only supply points, it may also include SA/PA points, ICs, and JCT points as nodes in addition to the supply points.

The map information may also include TC management information that identifies traffic counters (TCs) that exist in the section between each supply point. A traffic counter is a device that obtains information regarding traffic in sections between supply points. Further, the traffic counter is an example of the above-mentioned map element.

FIG. 12 is an explanatory diagram of traffic counter management information managed by a traffic counter.
Traffic counter management information (hereinafter referred to as TC management information) is traffic counter data representing supply point data, adjacent supply point data, and information on traffic counters existing between supply points.

Specifically, as shown in FIG. 12, supply point data = Qj, rinsel supply point data = Qj', traffic counter data = TC1, TC2, . . . are stored. The plurality of traffic counters (TC1, TC2, . . . ) in the traffic counter data do not need to be arranged in the order in which they are stored.

The map information management unit 15 may acquire and update all or part of the map information from the server 211 at regular intervals or in real time.

The road control information management unit 16 acquires road control information regarding each route (section) from the server 211, and stores and manages the acquired information in the system database 20. The road control information includes, for example, traffic counter (TC) information and ETC information (number of incoming vehicles, number of outgoing vehicles, EV vehicle type, remaining battery level of EV, etc.). Other information regarding events that occur includes road closures, traffic jams, accidents, broken vehicles, construction work, fallen objects, fires, disasters, and speed regulations.
Furthermore, examples of predictive information include traffic jam prediction, accident occurrence prediction, torrential rain prediction, and landslide prediction. The installation location of the traffic counter is known in advance from the above map information (see FIG. 5).

FIG. 13 is an explanatory diagram of an example of traffic counter information.
Traffic counter information includes information such as speed information [km/h], occupancy rate information [%], traffic volume information [vehicles/h], vehicle density information [vehicles/km], etc., stored in the order of acquisition time. has been done.
In the above configuration, the speed information is expressed, for example, as an average speed, maximum speed, minimum speed, etc. for each fixed period of time.

Further, the road control information management unit 16 may acquire road control information from the server 211 at regular intervals or in real time. Alternatively, the road control information may be acquired from the server 211 every time the road control information is updated. Note that the road control information management unit 16 may obtain the traffic counter information directly from the traffic counter instead of from the server 211. Time is assigned to the row name, and a traffic counter (TC) ID is assigned to the column name. Each element of the table stores a speed (average speed) value. In the following, we mainly assume the case of traffic counter information as road control information.

The charger information management unit 17 acquires charger information about one or more chargers (energy supply devices) installed at each supply point from the server 211. The charger information management unit 17 retains and manages the acquired charger information in the system database 20. The charger information management unit 17 knows the number of chargers at each supply point.

FIG. 14 is an explanatory diagram of an example of charger information.
For example, as shown in FIG. 14, the charger information includes the user ID (EV_ID) of the user who charged with the charger (EV Navi user), supply point ID, starting charging amount, ending charging amount, and charging start time. , including the charging end time. In addition, information such as charging efficiency and number of times of charging may be held.

In the above configuration, the starting charge amount is the amount of electric power stored in the battery of the EV at the time when the charger starts charging.
The end charge amount is the amount of power stored in the battery of the EV at the time when charging with the charger ends. The difference between the end charge amount and the start charge amount is the amount of electric power charged by the EV.

Instead of the starting charging amount and the ending charging amount, the charger information may include information about either the starting charging amount or the ending charging amount and the charged electric energy. In addition, in the case of an external EV (an EV that has not been registered as a user in the information provision system for EVs, or an EV that has been registered as a user but has not used (not registered for use) the services of the information provision system for EVs), the user Any numerical value can be entered in the ID column. The charger information represents the energy supply history at the corresponding supply point. The charger information management unit 17 may acquire charger information from the server 211 at regular intervals or in real time.

The charger information management unit 17 may acquire the charger information not from the server 211 but from the charger through direct communication. Alternatively, the user may input charger information (GPS position information, amount of charged electric power, etc.) to the information device 201, and the charger information management unit 17 may acquire the charger information from the information device 201. Alternatively, the information device 201 (in-vehicle device such as a car navigation system) may read information on the amount of electric power charged from the EV and transmit the charger information to the information processing device 101.

The weather information management unit 18 acquires weather information from the server 211, and stores and manages the acquired weather information in the system database 20.

FIG. 15 is an explanatory diagram of an example of weather information.
Weather information includes, for example, the temperature of each predetermined region and date and time, the presence or absence of rainfall, the amount of precipitation, wind speed, wind direction, total solar radiation, amount of snowfall, road surface temperature, luminosity, visibility (fog), radiation, and torrential rain. including. The weather information may include not only past and present weather information but also predicted weather information if future predicted weather information can be obtained. The weather information management unit 18 may acquire weather information from the server 211 at regular intervals or in real time.

The vehicle information management unit 19 receives the vehicle model ID, EV manufacturer, battery capacity, battery type (lithium ion battery, etc.), total weight (weight when the EV is loaded to its capacity or the weight of the EV itself), electricity consumption, etc. from the server 211. Information (vehicle information) such as battery deterioration rate and year of release is acquired, and the acquired information is held and managed in the system database 20.

FIG. 16 is an explanatory diagram of an example of vehicle information.
The vehicle type ID has a different value for each vehicle type.
The server 211 that manages vehicle information may be a server of an EV manufacturer, or a server that collectively manages vehicle model information of multiple EV manufacturers. The vehicle information management unit 19 may acquire vehicle type information from the server 211 at regular intervals. Alternatively, the vehicle type information may be obtained from the information device 201 instead of the server 211.

By using charger information about the same user in chronological order, consumption history information representing the user's power consumption, travel time, travel speed, etc. for each section between supply points can be obtained. The travel time can be obtained, for example, by subtracting the charging end time of the previous supply point from the charging start time of a certain supply point. The travel speed (average travel speed) is obtained by dividing the travel time by the distance of the section. Note that a configuration is also possible in which the vehicle communicates with the EV, acquires information on the traveling speed and traveling time in real time, and grasps the traveling speed and traveling time from the information acquired from the EV.

FIG. 17 is an explanatory diagram when calculating power consumption in a certain section as consumption history information.
An example will be shown in which the amount of power consumed for traveling in a certain section is calculated from charger information about the same EV. Another supply point (supply point of the second entry) having a charging start time closest to the charging end time at a certain supply point (supply point of the first entry) is specified as the next supply point.

That is, the ID of the supply point Qj (previous supply point) indicated by three black circles and the ID of the next supply point Qj' (next supply point) indicated by three △ are specified. In addition, the time when charging at supply point Qj ended (previous usage end time), the charging start time at supply point Qj' (next usage start time), and the amount of charge when charging at supply point Qj' started. (Next start charging amount) and the charging amount when charging at the supply point Qj ends (previous ending charging amount) are specified.

Then, by subtracting the next starting charging amount from the previous ending charging amount, the amount of power consumed in moving from the supply point Qj to the supplying point Qj' is calculated.

FIG. 18 is an explanatory diagram of another example of calculating power consumption.
In the example of FIG. 18, information acquired from an information device (smartphone) is used. This information represents an ID (EV_ID) that identifies the user, a GPS information point, and a charging amount.
In this case, power consumption is calculated by subtracting the amount of charge at one point from the amount of charge at the next point. The GPS information point may be the coordinates acquired by GPS, or the name of a place, facility, etc. associated with the coordinates in the map information.

The system database 20 holds information (sometimes referred to as external information) acquired by each of the management units described above. The external information acquired by each management unit may be held in a buffer within each management unit. The buffer is, for example, a memory device, hard disk device, SSD device, or hardware storage device. The system database 20 also stores an operating system, information processing programs, and various data used for information processing. The operating system is a computer program for controlling the overall operation of the information processing device 101. The information processing program is a computer program for the information processing apparatus 101 to realize various information processing functions described below. The system database 20 is, for example, a memory device, a hard disk device, an SSD device, or a hardware storage device.

The model management unit 31 uses a plurality of learning data to generate a model (prediction model) that calculates the predicted power consumption of the EV.
Here, the learning data includes, for example, consumption history information (charger information), weather information, geographic information, vehicle information, road control information, and all or part of information obtained from information devices (smartphones, car navigation systems, etc.). Generate using These pieces of information are acquired from the geographic information management section 15, the road control information management section 16, the charger information management section 17, the weather information management section 18, the vehicle information management section 19, and the information device 201.

The model management unit 31 generates learning data by correlating all or part of the acquired information. For example, it corresponds to the amount of electricity consumed in a certain driving section (section between supply points), the weather information (temperature, etc.) on the driving date and time of the relevant route, and the geographical information (distance, gradient, etc.) of the route in a certain driving section. Then, driving data is generated and used as learning data. Driving data acquired and managed by the driving state management section 51, which will be described later, may be used as the learning data.
The travel data generated for learning here and the travel data managed by the travel state management section 51 may be the same or different.

FIG. 19 is an explanatory diagram of an example of learning data (driving data).
As shown in FIG. 19, the learning data includes, for example, history ID, power consumption, distance, speed, travel time, battery capacity, presence or absence of air conditioner operation, temperature, and vehicle type data.
The history ID is an identifier for identifying learning data.

The amount of power consumed is the amount of power used for traveling in the travel section. For example, it can be obtained from the charger information (history information) described above.
The distance is the distance of the travel section traveled.
Speed is the average speed of travel.
The travel time is the time required to travel the travel section.

Battery capacity is the battery capacity (also called total battery capacity) installed in an EV.
Whether or not the air conditioner is running means whether or not the air conditioner was turned on while the EV was running.
The temperature indicates the temperature at the time of driving.
The vehicle type indicates the type of EV registered by the user.

The items shown here are merely examples; other items may exist, and some of the illustrated items may not exist. For example, the learning data may include information specifying the travel section, route slope, humidity, remaining battery power, and the like.

Each item included in the learning data corresponds to a feature amount of the EV.
In the above example, the speed (average speed), distance, travel time, power consumption, whether the air conditioner is running, etc. represent the operating status of the EV, the remaining battery level, the degree of battery deterioration, etc. represent the characteristics of the EV, and the temperature , slope, etc. represent the driving environment of the EV.

The route used by the EV to move from the supply point Qj to the next supply point Qj' may be specified from information (GPS information, etc.) obtained by direct communication with the information device 201.

Alternatively, it is also possible to estimate the route used by the EV from consumption history information and geographic information. Used for movement between a supply point Qj that supplied energy to a certain EV (for example, supply point Q1 in FIG. 11) and a supply point Qj' that supplied energy next to that EV (for example, supply point Q6 in FIG. 11) When estimating the route used for travel, the shortest travel route (shortest route) from the supply point Qj to the next supply point Qj' may be estimated as the route used for travel.

As a method for solving the shortest path problem, the general Dijkstra method, the Bellman-Ford method, the Gabow method, the Warshall-Froyd method, etc. can be used. The determined route can be expressed as a supply point list such as (Qj, Q4, . . . , Qj'), for example.

The generation of the learning data described above is only an example, and the combination of types of information used to create the learning data can be arbitrarily determined. The learning data may be generated by further using at least one of the vehicle information of the EV and the road control information of the date and time when the EV runs. The learning data is generated using, for example, information from a certain period of time in the past.

The model management unit 31 classifies a plurality of learning data into a plurality of groups (which may also be called clusters) according to classification rules.
The classification rule has criteria based on at least one of, for example, travel distance, travel time, travel speed, remaining battery power, slope information, temperature, and vehicle type.

One or more classification rules are stored in the model database 32. The classification rules are created in advance by an operator's operation and stored in the model database 32.
Furthermore, the classification rules may be updated by an operator's operation. Further, the classification rule may be created or updated by a process described below. The structure of the classification rule can be selected as appropriate, such as a decision tree or a cluster model.

FIG. 20 is an explanatory diagram of an example of the classification rule.
This classification rule shown in FIG. 20 is an example of a decision tree.
It includes nodes (non-terminal nodes) 301 and 302 to which feature amounts are assigned, and terminal nodes 303, 304, and 305 to which group A, group B, and group C are assigned. Feature conditions (branching conditions) are set for the nodes 301 and 302, and depending on the branching conditions that are met, the nodes 301 and 302 are branched to lower nodes. Based on this structure, the learning data is finally classified into one of group A, group B, and group C corresponding to the terminal nodes 303 to 305.

For example, if the value of the traveling speed (feature quantity) included in the learning data is less than 40 km/h and the temperature (feature quantity) is less than 20 degrees, the learning data is classified into group A. In this example, the feature quantities of the nodes in the decision tree are only the traveling speed and the temperature, but other items (for example, slope) may be selected as the feature quantities. Furthermore, although the depth of the decision tree is 2 in the illustrated example, the depth may be 1 or 3 or more.

As will be described later, each group is associated with a prediction model.
Therefore, a decision tree is a classification rule that associates a plurality of conditions of feature amounts with a plurality of prediction models.

The model management unit 31 generates a predictive model (model parameter) for each group based on learning data belonging to each group classified by the classification rule.
Furthermore, the model management unit 31 stores the generated prediction model in the model database 32. The model database 32 stores prediction models for each group.

There are many ways to build predictive models, including artificial intelligence, machine learning, black box modeling, and white box modeling that defines physical models.

More specifically, black box modeling is a method of modeling using regression, neural networks, support vector machines (SVM), statistics, etc. when the characteristics of the target are unknown.

Furthermore, white box modeling is modeling that is performed by defining a physical model etc. when the characteristics of the target are known. In this embodiment, a prediction model based on the sum of a plurality of models is shown as an example. In this case, each model making up the prediction model may be called a submodel. The type of prediction model can generally be expressed by the following equation (1).

Here, ^yi is the predicted value (output value) of the i-th model, and βi is the weighting coefficient of the i-th model. LP is the number of models. ^y is the output value of the prediction model, and is the weighted total sum of power consumption calculated from each submodel ^yi. The number of submodels may be one. The predicted value of power consumption shown by the sub-model ^y includes the section power consumption when the EV is running, and the sub-model also calculates the power consumption of the air conditioner, the power consumption of the wipers, and the power consumption of the car navigation system. It may also include power consumption, charging capacity of a smartphone that an EV user charges in the car, etc.

Examples of submodels include regression models, spec models, and discrete value models.
A regression model and a spec model are examples of continuous value models that handle continuous values as feature quantities.
A discrete value model is an example of a discrete value model that handles discrete values as feature quantities.
As an example, submodel ^y1 is a regression model, submodel ^y2 is a spec model, and submodel ^y3 is a discrete value model.

When the submodel is a multiple regression model, the basic function ^y1=f(x) is expressed as follows, for example.

Here, w0, w1, w2, w3,..., wn are model parameters to be estimated. x1, x2, x3..., xn are input variables (features). ^y1 is an output variable. Note that in order to absorb differences in the measurement units of each input variable, the output variable and all input variables may be normalized to have a mean value of 0 and a variance of 1 (scaling).
Examples of input variables include distance, travel time, and outside temperature. These are items included in the learning data.
For example, x1 is the distance, x2 is the required time, and x3 is the outside temperature. The input variable may be another value calculated from multiple items included in the learning data. For example, the input variable may be a speed obtained by dividing the distance by the required time.

In the case of the spec model, the basic function ^y2=f(x) is expressed as the following equation (3).

In equation (3), w is a coefficient or constant to be estimated. x is a certain feature amount. ^y2 is an output variable. w may be a fixed value (for example, 1). x may be the value of an item included in the learning data, or may be the output value ^y1 of the regression model in (2). The amount of power consumption calculated using the spec model depends on, for example, the vehicle type and battery capacity.

(Example when model ^y2 represents power consumption due to driving)
The feature amount x may be distance (km)/electricity cost (km/kWh). In this case, the electricity consumption may be, for example, a catalog spec value or a coefficient to be estimated.
The coefficient w may be the power consumption ratio (electricity consumption of vehicle type A/electricity consumption of vehicle type B), and the feature quantity x may be the power consumption amount of vehicle type B (a value determined by a regression model). The electricity consumption ratio may be a coefficient to be estimated. In this case, it is conceivable that the model corresponds to a group to which vehicle types A and B belong.

The coefficient w may be the degree of deterioration of the battery, and the feature x may be the amount of power consumed by the regression model.
The degree of battery deterioration may be, for example, the EV battery SoH/average SoH, or the EV cumulative travel distance/average cumulative travel distance. SoH (State of Health) is an index representing the degree of battery deterioration.

The coefficient w may be the total weight of the EV and luggage/the total weight of the EV (or the average total weight), and the feature amount x may be the amount of power consumed by the regression model.
The total weight of the EV and luggage/total weight of the vehicle (or average total weight) may be determined based on catalog values, or may be a coefficient estimated by learning. The total weight of the EV and baggage is, for example, the weight when the EV is loaded with the maximum number of people and baggage allowed.

(Example when model ^y2 represents power consumption by air conditioner)
The feature quantity x is the air conditioner usage time (h), the coefficient w is a coefficient to be estimated, and the unit is kWh/h.
The feature quantity x is a temperature difference (Δ°C), and the coefficient w is a coefficient to be estimated, and the unit may be kWh/Δ°C.

(Example when ^y2 represents the power consumption by the wiper)
The feature quantity x is the wiper usage time (h), and the coefficient w is a coefficient to be estimated, and the unit is kWh/h.

In the case of a discrete value model, the basic function ^y3=f(x) is expressed as the following equation (4).

For example, as a fixed constant, there is electric power consumption of an EV air conditioner=3 (kWh), or smartphone charging amount=1 (kWh). In the case of a discrete value model, C is a coefficient or constant (such as a catalog spec value) to be estimated.

Item C is included in the learning data. The amount of power consumed by the discrete value model is, for example, the amount of power consumed for purposes other than driving. For the constant C item, it is possible to generate a model corresponding to a specific vehicle type, season, or road surface condition.
If the feature quantity is a discrete value with multiple values (for example, the feature quantity is a seasonal value such as spring, summer, autumn, winter, etc.), by using a commonly known dummy variable, it can be converted into a binary discrete value. can be treated in the same way.

The model parameter w in equation (2), the coefficient w in equation (3), and the constant C in equation (4) are determined by applying a known optimization algorithm such as the maximum likelihood method or least squares method to equation (1). You can find it by doing.

The model management unit 31 generates a predictive model using at least one of each modeling means.
A prediction model ^y is generated for each group. The generated prediction model ^y is stored in the model database 32.

In this case, the types of sub-models forming the prediction model may differ for each group.
For example, the discretization model included in the prediction model of group A may be the power consumption of an air conditioner, and the discretization model included in the prediction model of group B may be the amount of smartphone charging.
Note that for groups with only a small number of training data, transfer learning may also be used for the regression model.

FIG. 21 is a flowchart of an example of the operation of the model management section.
Operations are performed for each group according to the flowchart shown in FIG.
One or more candidates for the predictive model construction method are determined for the target group (step S101).
Examples of construction methods include a model that represents the sum of a regression model, a spec model, and a discrete value model, a model that represents the sum of a regression model, a spec model, and a model that represents the sum of a neural network, a spec model, and a discrete value model. By further changing the types of individual submodels, more construction methods are possible.

A new prediction model is constructed using learning data belonging to the target group (step S102).
The predictive models constructed using each construction method are registered in the model database 32 as temporary models (step S103).

Each temporary model is evaluated using the learning data of the target group (step S104).
As an example, an evaluation value (model evaluation value) is calculated from the sum of errors (prediction errors) between the output value of the temporary model and the actual value. The provisional model with the highest evaluation (the lowest model evaluation value) is selected as the predictive model (step S105). Note that the unselected temporary models may be deleted from the model database 32.

Here, each temporary model may be generated and evaluated using a cross-validation method. In this case, in step S102, the learning data belonging to the target group is divided into K pieces (the number of intersections). K is an integer of 2 or more. One of them is used as test data, and the remaining K-1 pieces are used as training data. Build a model using training data. In step S105, the constructed model is evaluated using test data. Model generation and model evaluation are performed K times using each of the K pieces of divided data as test data, and an evaluation value (model evaluation value) is calculated for each. In step S105, statistical values (eg, mean value, median value, variance, quartile, maximum value, minimum value, etc.) of the model evaluation values are calculated. Based on the statistical values, the tentative model with the highest evaluation among the tentative models is selected as the predictive model.

In this case, depending on the evaluation method, the higher the evaluation, the larger the statistical value, or the higher the evaluation, the lower the statistical value.

Here, an example has been shown in which the model evaluation value of the tentative model is the sum of errors between the output value of the tentative model and the actual value, but it is not limited to this.
For example, the following equation (5) may be used for evaluation. An example of a regression equation is shown here, but when using the sum of the regression model and the spec model, the difference between the actual value of the spec model and the estimated value is calculated in the formula below according to the number of samples of the training data. Just add the normalized values.

Other examples of model evaluation values include mean squared error (MSE), mean absolute error (MAE), and R2 value (which is the square of the correlation coefficient R and is called the coefficient of determination). ), Root Mean Square Error (RMSE), and Root Mean Square Percentage Error (RMSPE).

A model evaluation value may be defined that takes into account factors other than the error between the actual measured value and the predicted value of power consumption. For example, the model evaluation value may be obtained by multiplying the data size of the model by a certain coefficient and adding the multiplied value to a value such as the sum of the errors described above. The larger the data size of a model, the longer it takes to calculate a prediction using the model. Therefore, by making it easier to select a model with a smaller size, it can be expected that the calculation time and amount of calculation will be reduced.

In addition to using classification rules given in advance as described above, the model management unit 31 can also automatically generate classification rules. When automatically generating classification rules, the classification rules are generated using the method described below, and then the process of constructing a predictive model using the classification rules is performed using the method described above.

Here, a case of a decision tree is shown as a classification rule, but the classification rule is not limited to this. When automatically generating classification rules, the generation of classification rules and the construction of a predictive model are performed at the same time. The decision tree is repeatedly divided in a direction that reduces the variation in prediction errors of the prediction model.

In this case, dividing a node means generating a plurality of child nodes for the node. For example, setting the number of divisions of a node (parent node) to 2 means generating two child nodes for the node. In other words, it represents dividing a data group belonging to a parent node into data groups corresponding to each child node.

In this case, it is necessary to determine the feature amount to be assigned to the parent node and the conditions for branching based on the value of the feature amount. The feature amount to be assigned to the parent node may be an item existing in the learning data, or may be an item obtained by calculating a plurality of items in the learning data. The number of divisions of the parent node can also be three or more.

Here, we will consider an example in which a classification rule (decision tree) is developed by repeatedly dividing a certain node into two.
In other words, by dividing the parent group into two groups (left group and right group), the classification rules are grown (the depth of the decision tree is increased). SDR (Standard Deviation Reduction) is used when determining which feature is to be assigned to the parent node and the value of the feature that is the condition for branching.

FIG. 22 is a flowchart showing an example of the operation of the model management section 31.
First, the number of divisions is determined (step S201). Here, the number of divisions is set to 2. The number of divisions may be a predetermined value, or may be a value determined depending on the number of learning data belonging to the node to be divided or the number of items included in the learning data.

The feature amount to be assigned to the parent node and the value of the feature amount (feature amount condition) serving as the condition for branching are determined so that the SDR is maximized (step S202). The learning data is divided into two child nodes (left child node (left group) and right child node (right group)) under the conditions of the determined feature amount.
The SDR is expressed by the following equation (6), for example. In this example, power consumption is used as the feature quantity. Alternatively, a prediction model may be constructed for the parent node and the child node first, and the error between the predicted value and the actual measured value may be used as the SDR feature quantity.

A plurality of feature value values are set as candidates for conditions, and the above SDR is calculated using the plurality of candidates. At this time, the candidates may be obtained by sorting the power consumption amounts belonging to the parent node and using all of the values as candidates.
Then, a set of a feature amount with the largest SDR and a condition for the feature amount is selected. In this way, two child nodes are temporarily generated for the parent node. In addition, when dividing into three or more, SDR can be calculated in the same way. Note that in the case of three divisions, there are, for example, two values of the feature amount that are candidates for the condition.

Using the generated decision tree, the learning data is divided into a plurality of groups, a predictive model is generated by the method described above (step S203), and an evaluation value (model evaluation value) of the predictive model is calculated. Based on the model evaluation value of the prediction model of the parent node and the model evaluation value of the prediction model of each child node, it is determined whether the decision tree satisfies the division condition (step S204).

For example, if the model evaluation value of the prediction model of the parent node is larger than the average of the model evaluation values of the prediction models of the two child nodes (the smaller the model evaluation value, the higher the evaluation), the decision tree will satisfy the splitting condition. If not, it is determined that the splitting condition is not satisfied.
Then, in the judgment in step S204, if the division conditions are satisfied (step S204; Yes), the division of the parent node is determined, and the two provisionally generated child nodes are adopted (step S205).

Next, the process returns to step S202, and the same process as above is repeated using each child node as a parent node.
Note that when performing the process of step S202 again, when determining feature amounts that are candidates to be assigned to each child node, feature amounts that have already been assigned to higher-level nodes (including the parent node) may be excluded.

In the judgment in step S204, if the splitting condition is not satisfied (step S204; No), the temporarily generated child node is deleted (splitting is canceled), and the child node is returned to the decision tree before being temporarily generated (step S206). ), the process ends. As a result of performing this process recursively for each child node, a plurality of decision trees can be identified. In this case, for example, a decision tree is finally selected from among these multiple decision trees. For example, an evaluation value (rule evaluation value) representing a prediction error of a child node is calculated. For example, statistical values such as the mean, median, and maximum of prediction errors are calculated.

Then, the decision tree with the smallest rule evaluation value is selected. When calculating the average, the prediction error may be multiplied by a weight according to the number of data belonging to the child node (group). Also, the weight may be smaller for a group with a larger value obtained by dividing the total battery amount of EVs belonging to a child node (group) (total battery capacity for the number of EVs) by electricity consumption (the larger the division value, the less power is consumed). (meaning difficult to do). Alternatively, the number of child nodes with a prediction error greater than a threshold may be counted, and the decision tree with the smallest number may be selected. Alternatively, the rule evaluation value may be obtained by multiplying the number of child nodes by a certain coefficient and adding it to the statistical value or the like calculated as described above. As a result, a decision tree with a small number of child nodes is more likely to be selected, and the amount of calculation and calculation time can be reduced.

Conditions other than the above-mentioned division conditions may be used as the processing termination condition. For example, the process may be terminated on the condition that the decision tree reaches a predetermined depth. Alternatively, the process may be terminated on the condition that the maximum value of SDR becomes less than a threshold value.

The model management unit 31 manages each generated prediction model and classification rule in the model database 32. The model management unit 31 may manage the model ID of each prediction model and the coefficients of the feature amounts used in each prediction model in a model management table.

FIG. 23 is an explanatory diagram of an example of a model management table.
The value of the coefficient to be used is shown for each characteristic quantity (input variable) such as power consumption and battery capacity. A feature quantity with a value of 0 means that it is not used. Furthermore, the classification rules may be held in the model database 32 in a format that implements a general tree structure (see FIG. 20), or may be held in the form of a program. Further, the model management unit 31 may hold the program code of each prediction model (the program code of the function forming the prediction model) in the model database 32.

The prediction unit 41 uses the prediction model and the EV prediction data to predict the amount of power consumed by the EV in the travel section to be predicted. The prediction data is data that includes items used in each submodel of the prediction model.
However, the items used in a submodel may be the output values of other submodels. The travel section to be predicted is, for example, the section to the next charging candidate supply point.

The running state management unit 51 manages the running state of each running section of the EV during the run. It also manages the performance of the EV (EV that has left the target IC) after completing the run. The running state management unit 51 acquires running data of the running section between the charging supply points for the running EV, and manages the data in the running management database 52 .
The driving data is generated based on data acquired by each of the management units 15 to 19 and the EV navigation usage registration unit 13.

FIG. 24 is an explanatory diagram of an example of a travel management database on an expressway.
The travel management database 52 manages, for example, a first charging point, a second charging point, and various variables for each EV. When charging is performed three or more times, the value of the second charging point is entered in the column of the first charging point, and the value of the third charging point is entered in the column of the second charging point. Even if the battery is charged four or more times, the value will be updated and stored in the same way.

Time is the time required to travel in the travel section, power consumption is the amount of power consumption (actual value) used in travel in the travel section, and distance is the distance of the travel section. The uphill slope is the length of a route that has a gradient greater than a certain value in the traveling section. The downhill slope is the length of a route where the slope is less than a certain value in the travel section (expressed as a negative value).

The variables shown here are just examples, and various types of variables are possible. For example, the total amount of charge from charging at two locations may be added as the total amount of charge. The driving state management unit 51 may add the predicted power consumption amount in each driving section to the driving data of the EV after the EV has completed driving. Further, the first charging point and the second charging point may be any two points at which the amount of power consumption is desired. A plurality of combinations of arbitrary two points can be considered, for example, between traffic counters (TC), between interchanges, between an interchange and a power supply point, etc. The classification rule, the prediction model, or both may be updated using the travel data.

The virtual EV management unit 61 generates a virtual EV (=third mobile object) that is a virtual setting of an EV (external EV) that does not use this EV information provision system among the EVs that use the expressway. , create a virtual EV travel plan.
Therefore, even if an external EV that is not actually using the services of the information processing system 100 is charged in the charging device, the influence on the driving plan of the EV that is using the services of the information processing system 100 is reduced. This allows you to create a travel plan.

Further, the virtual EV management unit 61 may update the created travel plan. Specific examples of external EVs include EVs that are not registered in the EV information provision system, EVs that are registered in the EV information provision system but do not use the services of the EV information provision system, and EV information that will be available in the future. Examples include EVs that are considered to utilize the provision system.

FIG. 25 is a flowchart of an example of the operation of the virtual EV management unit.
This operation may be performed for a certain period of time, for example.
The virtual EV management unit 61 determines whether or not to generate a virtual EV, and when generating a virtual EV, determines the number of virtual EVs to be generated (step S301).
The virtual EV management unit 61 determines to generate a virtual EV if the virtual EV generation conditions are met, and otherwise determines not to generate it.

For example, as a generation condition for a virtual EV, it may be determined that the virtual EV is to be created at a fixed time interval, or it may be determined that the virtual EV is to be created at a time specified by an external party (such as an operator). .
Alternatively, it may be determined to generate a virtual EV at a randomly determined time.

In this case, the number of virtual EVs to be generated may be a predetermined number (for example, one for each charging device), a number specified from the outside (by an operator, etc.), or a randomly determined number. , the number may be preset in consideration of holidays, days of the week, and time zones.

Further, when the charging planning unit 71 (described later) creates a charging plan (energy supply plan) for a specific time period, the time at which a virtual EV is generated may be limited to within the time period.

Furthermore, whether or not to generate virtual EVs or the number of virtual EVs to be generated may be determined depending on the traffic situation on the expressway. For example, the determination is made based on the number of EVs running using an information provision system for EVs and the number of EVs running running ascertained from road control information.

For example, if the ratio between the number of EVs using the EV information provision system and the number of running EVs ascertained from road control information (estimated value of the number of EVs actually running) is less than the threshold. , it is decided to generate a virtual EV. The number of machines to be created is determined according to the difference between these numbers. The larger the difference, the larger the number of units. As an example, the same number of virtual EVs as the difference is generated.

Conversely, if the ratio is equal to or higher than another threshold, a predetermined number (for example, one) of virtual EVs or a number corresponding to the difference may be deleted.

In addition, the utilization rate of chargers at each supply point (if there are multiple chargers at a supply point, the average of the utilization rates of multiple chargers), the utilization rate of each inflow/output point such as IC, or between supply points Based on the average usage rate of the chargers, it may be determined whether to generate virtual EVs or the number of virtual EVs to generate.

The usage rate of the charger is, for example, the ratio of the time that the charger is used out of the time from the reference time to the current time.
In this case, the utilization rate may be calculated in units of fixed time (for example, in units of one hour). The higher the utilization rate, the more congested the expressway or supply point is. As an example, when the utilization rate or average is equal to or higher than a threshold value, it is determined to create a virtual EV. The larger the utilization rate or its average is, the larger the number of virtual EVs to be created may be.

Further, depending on whether the probability that the recommendation by the charging planning unit 71 is successful (recommendation success rate) is less than a threshold, it may be determined whether to generate a virtual EV or the number of virtual EVs to be generated.
For example, when the recommended success rate becomes less than a threshold, it is determined to generate a virtual EV. The smaller the recommended success rate is, the larger the number of virtual EVs to be created may be. The recommended success rate may be calculated for each time period. Here, a successful recommendation means that the user actually charged the battery at the supply point recommended to the user. If a charging time is specified, success may be achieved if charging is performed within the specified time or a time including a margin.

If it is determined in step S301 not to generate a virtual EV (step S301; No), the process ends.
If it is determined in step S301 to generate virtual EVs (step S301; Yes), IDs of virtual EVs (virtual EV_IDs) for the number of virtual EVs to be created are issued (step S302).
As a result, a virtual EV is generated. The virtual EV_ID may be anything as long as it does not overlap with the user-registered EV ID or other virtual EVs.

Next, the virtual EV management unit 61 determines the departure time (departure date and time), departure place (departure IC), destination (destination IC), remaining battery level at the time of departure, etc. for each generated virtual EV (step S303).
Here, the departure time is, for example, the time of arrival at the departure IC (that is, the time of entering the expressway from the departure IC). The items listed here are just examples; vehicle type, driving speed (average driving speed), air conditioner usage status (on/off, heating/cooling, temperature setting, air volume, etc.), cumulative mileage, tire type, total weight, Various items can be considered, such as the route used by the virtual EV to travel to the destination, charging location, charging start time, charging amount, charging end time, rest location, rest start time, and rest time.

Then, the virtual EV management unit 61 generates a travel plan for the virtual EV by associating the value determined in step S303 with the virtual EV_ID issued in step S302 (step S304).

FIG. 26 is an explanatory diagram of an example of a travel plan for one virtual EV.
Although only basic items are shown in FIG. 26, various other items can be included in the travel plan.

The virtual EV management unit 61 stores the created virtual EV travel plan in the virtual EV_database 62.
This virtual EV_database 62 stores a created virtual EV travel plan every time a virtual EV is generated.
The virtual EV for which a travel plan has been created is then treated as traveling to the destination at the above-mentioned travel speed according to the travel plan.

The departure time of the virtual EV may be the current time, the past time, or the future time. In the case of the current time, the virtual EV is located at the starting point, and subsequent movement of the virtual EV may be simulated.
If the departure time of the virtual EV is in the past, movement from the departure time to the current time may be simulated, and subsequent operations may be simulated assuming that the virtual EV exists at that position.
Furthermore, when a charging plan for the virtual EV is created as described later, the operation is simulated based on the charging plan. Alternatively, the position of the virtual EV may be determined first, and then the departure time may be determined.
Furthermore, if the departure time of the virtual EV is in the future, the virtual EV may be treated as existing at the departure position when the future time arrives.

A section in which virtual EVs are to be generated may be determined, and virtual EVs may be arranged at regular intervals in that section. Here, the section may be the entire expressway, the section between the departure point and destination of the EV registered in the EV information provision system, or the section between supply points (for example, charging device installation points). ).
Further, it is also possible to determine whether or not to generate a virtual EV for each section. For example, the traffic situation is checked for each section and it is determined whether to generate a virtual EV for each section. A virtual EV is placed in the section in which it has been decided to generate a virtual EV. That is, the departure time, departure place, destination, etc. are determined so that the virtual EV is placed within the section (or the departure time may be omitted).
Furthermore, it is also possible to randomly determine the section in which the virtual EV is generated.

It is also possible to arrange for a virtual EV to be located at the supply point.
For example, it may be determined for each supply point whether or not to generate a virtual EV based on the utilization rate of the supply point (charger) or its average. Then, if the utilization rate or its average is equal to or greater than the threshold value, a virtual EV may be placed at that supply point.
Furthermore, the departure time may be determined to match the position and time of the placed virtual EV (or the departure time may be omitted).

FIG. 27 is an explanatory diagram of an example of arrangement of virtual EVs.
FIG. 27(A) shows roads on which EVs registered in the EV information provision system (system-registered EVs) are traveling.
In this case, supply points K, L, M, N are located on the highway, and these points K to N are SA/PAs where chargers are located.

In the following description, the section between the two supply points X and Y will be referred to as section XY.
One EV 402 and one EV 403 are running in section KL and section LM, respectively. It is assumed that one EV 401 is staying at supply point K, two EVs 404 and 405 are staying at supply point M, and one EV 406 is staying at supply point N (the purpose of stay of these EVs is charging, resting, (It doesn't matter if it's for any purpose, such as shopping.)

Then, an interchange IC01 exists in the section LM, and an interchange IC02 is arranged after the supply point N.
FIG. 27(B) shows an example in which virtual EVs are placed on the road shown in FIG. 27(A).
In the example of FIG. 27(B), the virtual EV 411 is arranged starting from the interchange IC01. Furthermore, a virtual EV 412 is arranged in section MN. Furthermore, a virtual EV 413 is placed at the supply point N.

In the examples of FIGS. 27(A) and 27(B), there is a possibility that an external EV other than the system-registered EV is actually running, but the external EV is not managed by this system. Illustration is omitted.
The virtual EV corresponds to a simulation of this external EV.

The process of creating a virtual EV travel plan in step S303 will be described in detail below.

[First example]
For example, by specifying a period related to the day on which a driving plan is created (the current day), and using the driving history of multiple EVs (EVs registered in the EV information provision system (system-registered EVs)) during the specified period. Then, a virtual EV travel plan is created.

As an example of a simple method, for example, if the current day is a Sunday, specify each Sunday of a certain month in the past (may be the last month, the same month a year ago, or any other month) as the period, The driving history of one vehicle is selected from the driving history of the system-registered EVs during the relevant period using a random number or the like.
Then, items necessary for a travel plan are extracted from the selected travel history, and a travel plan for the virtual EV is created using the values of the extracted items.

FIG. 28 is an explanatory diagram of a specific example of creating a travel plan in the first example.
The upper part of FIG. 28 shows the driving history of the past EV selected from the driving history, and the lower part of FIG. 28 shows the driving plan of the virtual EV created based on the driving history of the past EV. There is.
In the example of FIG. 28, the created virtual EV travel plan has the travel plan items other than the departure date at the departure time set to be the same as the past EV travel history values (year, month, and year of departure). and hours, minutes and seconds).
That is, the departure date in the past EV travel history is "DD1", but the difference is that in the virtual EV travel plan, the departure date is "DD2", which is a later date than "DD1".

Although the past driving history of one EV is used here, the driving history of multiple EVs may be integrated. For example, the hours, minutes, and seconds of the departure time use statistical values such as the average value of past EVs. Furthermore, the departure point and destination may be statistical values such as the mode of a plurality of EVs. At this time, the departure point and the destination may be determined separately, or the departure point and the destination may be set as one set to obtain statistical values such as the mode. Furthermore, statistical values such as the mode of a plurality of EVs may be used as the vehicle type.

Regarding the number of virtual EVs to be determined in step S301, as described above, the number of virtual EVs to be created may be determined based on statistical values such as the average value of the number of EVs per day in a specified period. For example, the number of virtual EVs to be created may be a difference between the average value and the daily average number of EVs running during the period ascertained from the road control information, or a number corresponding to the difference.

[Second example]
For each item included in the travel plan (departure time, departure point, destination, remaining battery level at the departure point, etc.), probabilities of multiple attributes that each item can take are maintained. Based on the probability, the value of each item to be included in the travel plan to be created is determined.

FIG. 29 is an explanatory diagram of a specific example of creating a travel plan in the second example.
An example of determining the departure point (departure IC) in this example will be shown.
FIG. 29A shows an example of a table (probability table) in which probabilities are held for a plurality of ICs (attributes) provided on an expressway.
This probability table is held in the virtual EV_DV62. Note that the interchange IC may be a general IC or a smart IC. In the illustrated example, the probability of interchange IC01 is 0.42, and the probability of interchange IC02 is 0.55. The departure interchange IC is determined using random numbers or the like so that each interchange IC is selected with these probabilities. Although the case of departure point is shown here, a similar probability table may be prepared for other items as well.

Furthermore, the probabilities held in the probability table are merely examples; for example, the table may hold values of the frequencies at which each attribute was actually selected in the past. In this case, each attribute may be selected with a probability according to the magnitude of each frequency value. Note that although an example of a departure point is shown here, probabilities may be similarly held in a probability table for a pair of a departure point and a destination.

In FIG. 29A, the values that an item can take (IC01, IC02, etc.) are discrete values, but if the values that an item can take are continuous values, a probability distribution may be used. For example, the probability distribution used may be a normal distribution. For example, consider traveling speed (average traveling speed) as an item.

FIG. 29(B) shows an example of a normal distribution in which the horizontal axis is the traveling speed and the vertical axis is the probability. Parameters (average value and standard deviation) of such normal distribution are held in advance in the virtual EV_database. The traveling speed is determined so that it is selected with probability according to the normal distribution.

More specifically, the range of normally distributed running speeds is divided into regular intervals, and a representative speed for each section is determined. The section to which the travel speed selected according to the normal distribution belongs is specified, and the representative speed of the specified section is determined as the travel speed of the virtual EV.

Probability information such as the above-mentioned probability table and probability distribution may be generated for all the driving history of the past period, or may be generated by date and time, day of the week, month, hour, minute, second, holiday/weekday, etc. It may be generated with the time type.

In this case, the value of the item (for example, starting point, travel speed, etc.) may be determined using a probability table or probability distribution for the relevant time type. Furthermore, instead of first determining the number of virtual EVs in step S301, IDs of virtual EVs may be issued according to road control information and the probability of occurrence of inflows and outflows in ETC 2.0.

FIG. 30 is a flowchart of an example of a process for generating probability information for each interchange.
Here, an example is shown in which probability information representing the frequency, probability, or probability distribution of each interchange IC being used as a departure interchange IC is generated. However, probability information can be generated for other items in a similar manner.

For example, an index j starting from 1 (the maximum value of j matches the number of target interchange ICs) is temporarily assigned to a plurality of interchange ICs (step S401).

First, an interchange IC with parameter j=1 is identified, and data necessary for processing (target data) is acquired from the past travel history corresponding to the interchange IC corresponding to parameter j=1 (step S402).

Here, at least information regarding the departure interchange IC and date and time is acquired.
Subsequently, an index i starting from 1 is assigned to each attribute of the target time type (step S403).

For example, if the time type is day of the week, each attribute is Sunday to Saturday, and 1 is assigned to Sunday, 2 is assigned to Monday, and 3 is assigned to Tuesday. First, data regarding the parameter i=1 is extracted from the target data (step S404), and based on the extracted data, the frequency, probability, or probability distribution that the interchange IC with the parameter j=1 is used as the departure interchange IC is calculated. (Step S405).

Similarly, frequencies or probabilities are calculated for days of the week after parameter i=2 (steps S404 and S405).
As a result, for example, if the time type is a day of the week, probability information representing the frequency, probability, or probability distribution of each day of the week from Sunday to Saturday can be obtained. The obtained probability information is stored in the virtual EV_database 62 (step S406).
The same process is repeated for the interchange ICs after parameter j=2 (steps S402 to S406). As a result, probability information for each day of the week can be obtained for each interchange IC.

In the above explanation, probability information for each interchange IC was generated for each day of the week, but probability information for each IC may be calculated for each vehicle type and day of the week, or probability information may be calculated by adding other items. It's okay.

In addition, although we have treated days of the week as time types, it can also be each day (1st to 31st), each month (January to December), each time (00:00 to 23:00), or morning, It may be during the day, evening, or late at night.

In addition, although the probability of each interchange IC being selected as a departure interchange IC has been shown, it is possible to calculate the probabilities of other items in the same manner as described above.

The charging planning unit 71 uses this information provision system for EVs to determine the running status of the EV (system-registered EV) based on the driving state of the system-registered EV, the driving plan of the system-registered EV, and the driving plan of the virtual EV. A charging plan (energy supply plan) between the virtual EV and the virtual EV is created. In creating the charging plan, the usage registration data notified by the system-registered EV and the travel plan of the virtual EV are used.

Furthermore, the power consumption history (charging history) since the system-registered EV entered the departure IC or the current driving state of the system-registered EV (current position, remaining battery level, etc.) may be used.

Further, external information (for example, road control information, charger information) etc. obtained in real time may be used. The charging plan is, for example, information about a supply point that can be reached with the current remaining battery level of the EV, the congestion status of the reachable supply point (whether the waiting time is long or short), etc.
The route used by the system-registered EV may be notified from the information device 201, or may be estimated by this device from the departure point and destination. Examples of estimation include the method of estimating the shortest route described above.

Since charging plans cannot be recommended for virtual EVs, first create a charging plan for virtual EVs, and then create a charging plan for system-registered EVs assuming that virtual EVs will be charged at the supply points according to the charging plan. Create.
In this case, considering the case where the system-registered EV does not charge as recommended, the charging plan may be created by treating the virtual EV and the system-registered EV as the same.

Regarding the system-registered EVs, a charging plan may be created in the order of system-registered EVs with earlier departure times. When a new system-registered EV or a new virtual EV is added, a charging plan is created for the added system-registered EV or virtual EV, and a charging plan is created for the previously created system-registered EV and virtual EV. may be updated or maintained as is, or all charging plans may be recreated (updated) each time a new system-registered EV or virtual EV is added.

FIG. 31 is an explanatory diagram of a process of identifying a point reachable by an EV in order to create a charging plan for the EV.
In this case, the target EV is a system-registered EV or a virtual EV.

In FIG. 31, it is assumed that supply points A, B, C, D, E, F, and G (hereinafter referred to as points A to G) are located on an expressway.
These points A to G are chargeable SA/PAs arranged along a certain route to the EV destination (destination IC).

It is assumed that the target EV is charged at point A and is currently traveling between points B and C.

Also in the following description, the section between the two supply points X and Y will be referred to as section XY.
The charging planning unit 71 calculates the power consumption amount of the section CD, the section DE, the section EF, and the section FG, and calculates the remaining battery level of the EV at the supply points C, D, E, F, and G (here, SoC (State of Charge). )).

The charging planning unit 71 knows the current position of the EV and the remaining battery level.
In this case, the current location may be determined, for example, from information (GPS information, etc.) acquired through real-time communication with the information device.

In addition, the current battery level is determined by calculating the amount of power consumed up to the current position using variables such as the distance from the supply point where the last charge was performed to the current position and a prediction model. The amount of power consumption may be subtracted from the remaining battery power. Alternatively, the amount of power consumed may be calculated using information on electricity consumption, which is spec data of the EV, and the amount of power consumed may be subtracted from the remaining battery level after the previous charge.

The remaining battery power at point C may be calculated by subtracting the power consumption for traveling from the current position to point C from the current remaining battery power. The amount of power consumed can be calculated based on variables such as the distance from the current location to point C, and a prediction model (the amount of power consumed is calculated using information on electricity costs, which is the spec data of the EV, and The amount of power consumption may be subtracted from the remaining battery power after that (the same applies hereafter).
If the prediction model includes traveling speed or the like as a variable, for example, information obtained from the outside, such as the speed between sections CD indicated by traffic counter information, may be used.

The remaining battery power at point D may be calculated by subtracting the power consumption ^ycd for traveling in section CD from the calculated remaining battery power at point C. The power consumption ^ycd for traveling in section CD may be calculated based on variables such as the distance from point C to point D and a prediction model.

Similarly, the power consumption ^yde for traveling in section DE is calculated, and the power consumption ^yde is subtracted from the remaining battery power at point D to calculate the remaining battery power at point E. Further, the power consumption ^yef for traveling in section EF is calculated, and the power consumption ^yef is subtracted from the remaining battery power at point E to calculate the remaining battery power at point F.

Further, the power consumption ^yfg for traveling in section FG is calculated, and the power consumption ^yfg is subtracted from the remaining battery power at point F to calculate the remaining battery power at point G. For points after point G, the remaining battery power is calculated in the same manner.

If the calculated remaining battery level is less than a certain value, it is determined that the location cannot be reached with the current remaining battery level. A point before that point is identified as a point within reachable range. For example, suppose that after point G, the remaining battery power is calculated for point H, and the remaining battery power calculated for point H (not shown) becomes less than a certain value. In this case, points C to G are specified (the following explanation assumes this case).

Based on the method described above, the charging planning unit 71 first creates a charging plan for each virtual EV among the virtual EVs and system-registered EVs. First, a plurality of points reachable by the virtual EV are specified, and a charging point for the EV is determined based on the distribution of remaining battery power at the specified points. As an example, among the plurality of identified points, the first point located after the remaining battery level becomes less than the threshold value is set as the charging point.

For example, if the threshold value is SOC=30, the first point after the SOC becomes less than 30 is point E.
Therefore, in this case, point E is determined as the charging point. The charging planning unit 71 determines that the virtual EV will be charged at this point, and calculates the time when the virtual EV will arrive at the charging point (for example, calculated from the traveling speed of the virtual EV and the distance to the charging point).

Assuming that the virtual EV will use the charger at this point for a certain period of time from the earliest available time (the earliest available time after the arrival time), plan the charging of the virtual EV and include the charging schedule in the virtual EV travel plan. to add.
In this case, if there is a charger to which a virtual EV is not assigned at the time corresponding to the arrival time (the arrival time or 5 minutes after the arrival time, etc.), the arrival time becomes the earliest available time. It may be determined that they match.

In addition, if all the charging devices (chargers) have been allocated at the time of arrival, then the time will be set at the first free time when the charger becomes free (the time when the charger becomes free, or even 5 minutes after the time when the charger becomes free). ) may be set as the earliest available time. Although the charging time is assumed to be a predetermined time regardless of the remaining battery level, the charging time may be determined to be longer as the battery level decreases.
Then, the SOC after charging may be calculated by adding the amount of power according to the charging time to the remaining battery level at the time of starting charging.

As another method for determining a charging point for a virtual EV, the charging planning unit 71 may determine a point after the remaining battery level becomes less than a threshold value as a candidate point, and then determine a charging point from among the candidate points. good.
For example, if the threshold value is SOC=30, the candidate points are points E, F, and G.

In this case, it may be added as a requirement for the candidate point that the remaining battery level is equal to or higher than the lower limit value.
For example, if the lower limit is SOC=18, the candidate points are points E and F. Then, a charging point is selected from a plurality of candidate points. The selection method may be random. Alternatively, the selection may be made based on the congestion situation at the candidate point or the surrounding traffic situation (presence or absence of traffic congestion, etc.).

For example, the congestion situation includes the utilization rate of chargers at each candidate point. When there are multiple chargers at a candidate point, the average usage rate of the multiple chargers on the day may be used, for example. For example, the utilization rate may be calculated from the charger information in FIG. 16 using the method described above. The supplementary point with the lowest utilization rate or the candidate point with the utilization rate less than a threshold value among the plurality of candidate points is determined as the charging point.

Further, as an example using surrounding traffic conditions, the traffic volume in the section before reaching each candidate point may be compared, and the candidate point with the least traffic volume may be determined as the charging point.

The charging planning unit 71 then determines charging schedules for all virtual EVs. This creates data (overall charging plan data) including a virtual EV charging plan for each charger for each supply point.

This overall charging plan data can be used as a prediction of how many external EVs will arrive at each supply point and at what times in the future for charging.
Moreover, the charging plan of the system-registered EV is also added to the overall charging plan data, as will be described later. Charging planning section 71 stores the entire charging planning data in charging planning database 72 .

The charging plan database 72 holds overall charging plan data. The charging plan database 72 is realized by a hardware storage device such as a memory device, a hard disk device, or an SSD device.

FIG. 32 is a diagram illustrating an example of overall charging plan data including a charging plan for only virtual EVs.
The overall charging plan data in FIG. 32 is in the form of graphical data, but may be configured in other formats such as a table format or a list format.

Further, this overall charging plan data may be displayed to the operator via a display device. The horizontal axis shows supply points E to H (other supply points are omitted), and the vertical axis represents time. For each supply point, a rectangle (shaded rectangle) representing the virtual EV is arranged along the time axis direction. The vertical length of the rectangular symbol representing the virtual EV represents the time (start time and end time) during which the virtual EV charges.

Further, the position of the bottom side of the rectangular symbol represents the charging start time, and the position of the top side represents the charging end time. For example, at supply point F, it is shown that charging is performed in the order of virtual EV2, virtual EV3, virtual EV4, and virtual EV5.

In other words, in the case of the example shown in FIG. 32, it is predicted that the external EV will actually arrive at the supply point F and be charged at the start time of charging each of virtual EV2 to virtual EV5.

In the example of FIG. 32, two supply points are determined for each of the virtual EVs 1 to 5. In the example of FIG. 32, there is one charger at each supply point, but if there are multiple chargers, a virtual EV rectangle may be arranged for each charger along the time axis.

Then, by using the overall charging plan data in the charging plan database 72, the charging planning unit 71 determines a supply point that requires less waiting time for the EV registered in the system. That is, a charging schedule for the system-registered EV is created using the charging schedule for the virtual EV shown in the overall charging plan data as a constraint (that is, without changing the charging schedule for the virtual EV).

The charging planning unit 71 also identifies candidate charging points for the system-registered EV from among points within the reachable range in the same manner as described above. The method for identifying candidate charging points may be the same as in the case of virtual EVs. For example, a point after the remaining battery level becomes less than a threshold is determined as a candidate point.

Specifically, if the threshold value is SOC=30, the candidate points are points E, F, and G. A candidate point may be required to have a remaining battery level equal to or higher than a lower limit value. For example, if the lower limit is SOC=18, the candidate points are points E and F. Then, a recommended charging point (recommended point) is selected from a plurality of candidate points.

As an example, the charging planning unit 71 selects the supply point with the shortest waiting time or less than a threshold value as the charging point among the plurality of candidate points.
The charging planning unit 71 then calculates the predicted arrival time at each candidate point.
It is assumed that the system-registered EV can be charged if the charger is available from the predicted arrival time or a time corresponding thereto.

Under this condition, the supply point with the least waiting time or below a threshold is determined. Suppose that the candidate points for system-registered EVs are E and F, and from the predicted arrival times of candidate points E and F for system-registered EVs and the overall charging plan data, it is determined that candidate point E has the shortest waiting time. do.

In this case, candidate point E is determined as the recommended point. The charging time length may be a predetermined length of time, or the charging time may be determined so that it becomes longer as the remaining battery power decreases.If the user specifies the charging amount, the charging time may be set to a specified amount. It can take as long as you need.
The SOC after charging may be calculated by adding the amount of power according to the charging time to the remaining battery level at the time of starting charging. Alternatively, the charging time length may be determined by the user.
The charging planning unit 71 adds the predicted data of the determined charging time of the system-registered EV to the overall charging plan data as a charging plan of the system-registered EV.

FIG. 33 is a diagram illustrating an example of data after the system registration EV is added.
As shown in FIG. 33, a charging plan for the system-registered EV (EV11 in the figure) has been added to supply point E. The time determined as the system registration EV is represented by a white rectangle representing the system registration EV. Further, by repeating the same process as above, the next supply point G is determined as the recommended point for the system registered EV11, and a charging plan is also added to the supply point G.

The charging planning unit 71 sends recommended data indicating recommended points determined for the system-registered EV (in the example of FIG. 33, two supply points), charging time at the recommended points (start time and end time), etc. It is transmitted to the user's information device 201 via the communication unit 11.

FIG. 34 is a diagram illustrating an example of recommended data sent to the system-registered EV.
The recommended data may include information regarding the predicted arrival time at the recommended point and the waiting time until charging starts at the recommended point (wait for ○ minutes, no waiting time, etc.). Here, the recommended locations are shown for two times (the next supply point and the next supply point), but it may be recommended for only one time (only the next supply point).

Charging planning section 71 similarly determines recommended points for other system-registered EVs.
In this case, based on the latest overall charging plan data, it is assumed that not only the virtual EV but also the system-registered EV will be charged at the recommended supply point, and with these as constraints, the other system-registered EVs are recommended. Decide on the location. By planning the charging of the virtual EV and other system-registered EVs in this way, it is possible to recommend supply points with no or little waiting time for the system-registered EVs.

In the explanation using FIGS. 32 and 33 above, the charging plan for the virtual EV was performed first, but as described above, even if the order of the charging plan is determined without distinguishing between the virtual EV and the system-registered EV, good.
In this case, for example, charging plans may be performed in order of earliest departure time, or the order may be determined at random. In addition, as in the method described in Patent No. 5364768, we utilize the mountain collapse method and other mathematical optimization methods to minimize the peak waiting time and load to minimize the system registered EV and virtual It is also possible to formulate a plan to achieve overall optimization including EV.

Further, when the user agrees to a recommended spot recommended to the user, the recommended spot may be reserved for the user. For example, reservation information including user information is transmitted to a reservation system (not shown) of a charging facility (charger) at a recommended point. The reservation system makes a reservation for the user based on the reservation information. As a result, the time for the reservation is secured for the user at the recommended location. Therefore, the user can charge the EV at the scheduled time. Note that the user's consent can be obtained by inputting consent data into the information device 201 and transmitting the input data to the charging planning section 71. The charging planning unit 71 generates reservation information based on the data and transmits it to the reservation system. The information processing device may include the function of the reservation system.

(Update of virtual EV driving plan)
The charging planning unit 71 may update the data of the virtual EV in the virtual EV_database 62 based on information on system-registered EVs and information obtained in real time from the server 211 (traffic control information, charger information, etc.).

[First example]
Based on charger information obtained in real time, a virtual EV that is scheduled to be charged at a certain supply point during a certain time period (for example, from 13:00 to 15:00, which is sufficiently longer than one charging time) is identified. The number of EVs actually charged within the relevant time period is calculated.

Next, the ratio between the calculated number of EVs and the number of virtual EVs is calculated. If this ratio is less than the threshold, it is determined that the accuracy (reliability) of these virtual EVs is low, and these virtual EVs are deleted. The process described herein may also be performed for other supply points and other time periods. After deleting the virtual EV, the charging planning unit may re-plan the remaining virtual EVs and system-registered EVs.

For example, a charging plan for the remaining virtual EVs is recreated, and a charging plan for system-registered EVs is created using this as a constraint. Although all of the corresponding virtual EVs are deleted here, only the number of EVs corresponding to the ratio may be deleted. For example, the smaller the ratio, the more virtual EVs are deleted. Alternatively, the difference between the above calculated number and the number of virtual EVs may be calculated, and the number of virtual EVs equal to the difference may be deleted. In these cases, the virtual EVs to be deleted may be selected at random, for example, in the order in which they were created, or in any other order.

[Second example]
Using ETC information (number of incoming vehicles, number of outgoing vehicles, EV vehicle type, remaining EV battery level, etc.), the actual number of incoming vehicles at a certain location (for example, IC, supply point) at a certain time is calculated.

Next, the number of system-registered EVs that have flowed into the same location during the same time period is subtracted from the actual number of vehicles that have flown in. The number of virtual EVs coming in at the same location in the same time zone is calculated. The ratio between the number after the subtraction and the number of virtual EVs flowing in is calculated. If this ratio is less than the threshold, the accuracy of these virtual EVs is determined to be low, and these virtual EVs are deleted. The processing described herein may also be performed at other times and locations.

In the above explanation, all of the corresponding virtual EVs are deleted, but only the number of corresponding virtual EVs may be deleted depending on the ratio. For example, the smaller the ratio, the more virtual EVs are deleted. Alternatively, the difference between the number after the above subtraction and the number of virtual EVs may be calculated, and the number of virtual EVs equal to the difference may be deleted. In these cases, the virtual EVs to be deleted may be selected randomly, for example.

[Third example]
For a period of predetermined length (such as one year), statistical values of the frequency (number of cars flowing in, number of cars being charged) of a certain event (flowing into an interchange, charging, etc.) at a certain place in a certain time period are calculated. Although statistical values include, for example, an average value, a median value, and a maximum value, the average value is used here.
If the average value is equal to or greater than the threshold value, it is determined that the virtual EV is highly accurate for that location in that time period. In that case, the virtual EV related to the event may be determined to be valid for that time period and that location, and may be excluded from the deletion targets in the first and second examples.

(First modification)
In the embodiment described above, the information regarding the various probabilities or frequencies described above (probability information) is calculated based on past data obtained at that time, but new data collected after that and past data The probability information may be recalculated based on the following. Alternatively, probability information calculated based on past data may be updated by Bayesian updating using new data.

(Second modification)
In the above-described embodiment, among the EVs registered in the system, EVs that do not follow the recommendations of the information processing system 100 (EVs that do not charge at the recommended supply points) or EVs that have a high probability of not following the recommendations of the information processing system 100 are This becomes a factor that disrupts the schedule for EVs that follow the recommendation or EVs that have a high probability of following the recommendation. Therefore, a virtual EV that imitates such an EV (disturbance EV) may be generated.

First, the probability of following the recommendation for the system-registered EV is calculated from the past driving history (history of driving data managed by the driving state management section 51, etc.). If the probability is less than the threshold, the system-registered EV is regarded as a disturbance EV, and the positional relationship between the supply points recommended so far and the supply points where charging is actually performed is calculated.

In this case, examples of the positional relationship include statistical values (average values, etc.) or distributions (probability distributions, etc.) of distance differences. If the actual charging point is further along the direction of travel than the recommended charging point, the difference will be a negative value, and if it is earlier, the difference will be a positive value (or the positive/negative relationship can be reversed). good). Note that the example of the positional relationship is not limited to this, and may be the number of supply points along the route between the recommended point and the supply point where the battery was actually charged.

For the disturbance EV, a charging plan is created using the method described above at the time of next usage registration. In the charging plan for the virtual EV, charging points for the virtual EV are determined based on the recommended points recommended in the charging plan for the disturbance EV and the statistical value or distribution of the difference.

This virtual EV charging point may be generated, for example, on the condition that the disturbance EV has been registered for use. As an example of determining the virtual EV charging point, a position is obtained by adding the statistical value of the difference to the recommended point of the disturbance EV, and the point closest to this position is determined as the final recommended point.

Alternatively, the charging point is determined according to the distance between the position where the statistical value of the difference is added and each reachable supply point, so that the closer the distance is, the higher the probability of selection is. Moreover, instead of the statistical value of the difference, the value of the difference may be selected using random numbers or the like in the above distribution, and the charging point may be determined using the selected difference.

(Third modification)
Based on the past driving history of this information provision system for EVs, the driving pattern from each departure point to each destination and its probability are calculated. The driving pattern includes, for example, a departure point, a destination, a supply point used for charging, a route (driving section) used for movement, and the like. In addition, items such as vehicle type, tire type, temperature, and slope may also be included. A driving pattern is selected from among these driving patterns according to probability, and a virtual EV is generated using the selected driving pattern.

For example, the same values as the selected travel pattern are used except for the departure time of the virtual EV. As a result, an EV that will be registered for use in this EV information provision system and driven from now on can be generated as a virtual EV.

FIG. 35 is a flowchart illustrating an example of the operation of the information processing apparatus according to this embodiment.

The driving state management unit 51 refers to the user database 14 and grasps EVs that have been registered for use (system-registered EVs) (step S501). The driving state management unit 51 determines whether or not each EV that has been identified is running (step S502).

In the judgment in step S502, if the EV is running (including the case where it is staying at an SA/PA on the way) (step S502; Yes), the prediction unit 41 is used to The amount of power consumed in a section (for example, a section between supply points) is predicted (step S503).

In this case, the power consumption amount for a certain number of sections ahead may be predicted, or the power consumption amount for all sections up to the destination may be predicted.
Note that this step may be performed every time this flowchart is repeated to update the predicted power consumption amount one by one. Note that the route to the destination may be estimated and determined by, for example, searching for the shortest route, or information on the route to the destination may be obtained from the EV user's information device and used in this information. The route to the destination may be specified based on the information.

In addition, in the judgment in step S502, if the EV is not running (step S502; No), that is, if the EV has passed the destination, the driving performance of the EV is saved as a driving history in the driving management database 52. (Step S504).

The virtual EV management unit 61 generates one or more virtual EVs based on the virtual EV generation conditions (step S505).
Next, items such as the departure time, departure point, destination, remaining power amount, and route used to travel to the destination of the generated virtual EV are determined. Furthermore, for the virtual EV, the power consumption amount in each section (for example, the section between supply points) up to the destination is predicted using the prediction unit 41 (step S506).
In this case, the power consumption amount for a certain number of sections ahead may be predicted, or the power consumption amount for all sections up to the destination may be predicted. Note that the predicted power consumption amount may be sequentially updated by performing the process of step S506 each time this flowchart is repeated.

The charging planning unit 71 determines a charging point based on each predicted power consumption amount for each virtual EV (step S507), and determines the charging time (start time and end time) of charging.
That is, a charging plan for each virtual EV is created. The charging time is determined based on, for example, the predicted time when the virtual EV will arrive at the charging point, the remaining battery level, and the like. At this time, the charging time is determined so as not to overlap with the charging time determined for other virtual EVs (and if there is a system-registered EV for which a charging plan has been created, also the charging time of the charging plan).
Thereby, the charging planning unit 71 generates overall charging schedule data including a charging plan for each virtual EV.

Then, the charging planning unit 71 determines recommended points for the system-registered EV based on the overall charging plan data (step S508).

The charging planning unit 71 also determines the charging time (start time and end time) at the determined recommended point. For example, to avoid overlapping the charging time of the virtual EV registered in the overall charging plan data (and also of the system-registered EV if charging plans for other system-registered EVs are already registered), the recommended points and charging Determine the time. At this time, the supply point where the waiting time is the least or the waiting time is less than the threshold value is selected.

In other words, it is assumed that an external EV will actually charge according to the charging plan of the virtual EV, and that other system-registered EVs will actually charge according to the charging plan recommended to other system-registered EVs that have already been recommended. In this case, the supply point that requires less waiting time is determined as the recommended point of the EV registered in the system.

Then, the charging planning unit 71 adds the determined charging plan for the system-registered EV to the overall charging schedule data.
Further, the charging planning unit 71 transmits recommendation data including the determined recommended points to the user's information device (step S509).

Subsequently, the control unit 21 determines whether the termination condition for this process is satisfied (step S510).
For example, when the operator inputs a termination instruction, it is determined that the termination condition is satisfied.

In the determination of step S510, if the end condition is not satisfied (step S510; No), the process moves to step S501.
If a newly registered EV is generated, the same process as above is repeated for the generated EV.
In addition, in the process of step S505, if the virtual EV generation conditions are satisfied, a virtual EV is generated, and the previously generated virtual EV is inherited as is unless it is deleted by the method described above or unless the driving is completed. It will be done.

In the determination of step S510, if the termination condition is satisfied (step S510; Yes), this process is terminated.

As described above, according to this embodiment, it is assumed that an external EV that does not use this system is treated as a virtual EV and a charging schedule for the virtual EV is generated, and that the external EV actually charges according to this charging schedule. Then, a charging schedule for the system-registered EV is generated.

Thereby, uncongested supply points (steps SA/PA) can be recommended with high accuracy to users of system-registered EVs. Since supply points can be recommended to the user with high accuracy, the probability that the user will follow the recommendations of this EV information provision system can be improved. In other words, the success rate of guidance can be increased.

FIG. 36 is a diagram showing the hardware configuration of the information processing device 101 according to this embodiment.
The information processing device 101 according to this embodiment is configured by a computer device 100. The computer device 100 includes a CPU 151, an input interface 152, a display device 153, a communication device 154, a main storage device 155, and an external storage device 156, which are interconnected by a bus 157.

The CPU (central processing unit) 151 executes a computer program that implements each of the above-described functional configurations of the information processing device 101 on the main storage device 155. Each functional configuration is realized by the CPU 151 executing the program.

The input interface 152 is a circuit for inputting operation signals from input devices such as a keyboard, a mouse, and a touch panel to the information processing apparatus 101.

The display device 153 displays data or information output from the information processing device 101.
Here, the display device 153 includes, for example, an LCD (liquid crystal display), an organic EL display, etc., but is not limited thereto.
Furthermore, data or information output from the computer device 100 can be displayed on the display device 153.

The communication device 154 is a device for the information processing device 101 to communicate with an external device wirelessly or by wire.
Information can be input from an external device via the communication device 154. Information input from an external device can be stored in various databases (DB). The communication unit 11 can be built on the communication device 154.

The main storage device 155 stores a program that implements the processing of this embodiment, data necessary for executing the program, data generated by executing the program, and the like. The program is expanded on the main storage device 155 and executed. The main storage device 155 is, for example, RAM, DRAM, or SRAM, but is not limited thereto. The storage unit in each embodiment may be constructed on the main storage device 155.

The external storage device 156 stores the program, data necessary for executing the program, data generated by executing the program, and the like. These programs and data are read into the main storage device 155 during processing of this embodiment. Examples of the external storage device 156 include a hard disk, an optical disk, a flash memory, and a magnetic tape. However, it is not limited to these.
Furthermore, the storage unit in each embodiment may be constructed on the external storage device 156.

Note that the above-mentioned program may be installed in advance on the computer device 100, or may be stored in a storage medium such as a CD-ROM. Further, the program may be uploaded onto the Internet.

Note that the computer device 100 may include one or more of each of a processor 151, an input interface 152, a display device 153, a communication device 154, and a main storage device 155, or may be connected to peripheral devices such as a printer or a scanner. You can leave it there.

Further, the information processing device 101 may be configured by a single computer device 100. Alternatively, it may be configured as a system consisting of a plurality of computer devices 100 interconnected via a communication network.

Although several embodiments of the invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These novel embodiments can be implemented in various other forms, and various omissions, substitutions, and changes can be made without departing from the gist of the invention. These embodiments and their modifications are included within the scope and gist of the invention, as well as within the scope of the invention described in the claims and its equivalents.

11: Communication Department 12: User ID Registration Department 13: EV Navi Usage Registration Department 14: User Database (DB)
15: Map information management section 16: Road control information management section 17: Charger information management section 18: Weather information management section 19: Vehicle information management section 20: System database (DB)
21: Control unit 31: Model management unit 32: Model database (DB)
41: Prediction unit 51: Traveling state management unit 52: Traveling management database (DB)
61: Virtual EV management unit 62: Virtual EV database (DB)
71: Charging plan section 72: Charging plan DB
100: Information processing system 101: Information processing devices 201A to 201N: Information devices 211A to 211N: Server 220: Communication network

Claims (10)

In an information processing system that includes a plurality of energy supply points and, upon receiving an energy supply request from a mobile object, makes a reservation for a recommended energy supply point from the plurality of energy supply points,
a mobile information terminal that transmits a reservation request for energy supply for a first mobile object of a user who has been registered in advance, including destination information set by the registered user;
the energy supply point that receives the energy supply reservation request for the first mobile body and recommends energy supply to the first mobile body based on the destination information included in the received reservation request; an information processing device that creates a travel plan including: and obtains the energy supply waiting order of the recommended energy supply point;
The energy supply point is provided at the energy supply point, and presents energy supply waiting information of the energy supply point, which is generated in the information processing device based on a reservation request from the first mobile object, and is not registered in the system in advance. an information communication terminal that receives an energy supply request at the energy supply point for a second mobile body that is an unmanaged mobile body and obtains an energy supply order;
An information processing system equipped with The information processing device includes a turn management database that stores the energy supply waiting information provided to the first mobile body and the second mobile body for each energy supply point.
The information processing system according to claim 1. When the information processing device creates the travel plan, the information processing device determines how the first mobile body can travel based on route information between the energy supply points and the required amount of energy required for the first mobile body to reach the energy supply points. Create a travel plan that includes an energy supply plan that recommends energy supply points that are reachable and have charging facilities available;
The information processing system according to claim 1. When creating the travel plan, the information processing device generates a third mobile object that is a virtual mobile object that is assumed to be traveling on a route corresponding to the route information without using the information processing system. creating an energy supply plan for the first mobile body, assuming that the first mobile body travels on the route and charges at any of the energy supply points;
The information processing system according to claim 3. receiving an energy supply reservation request for a first mobile object of a user who has been registered in advance, including destination information set by the registered user;
Based on the destination information included in the reservation request, create a travel plan including energy supply points recommended for energy supply to the first mobile object;
obtaining the energy supply waiting order of the recommended energy supply point;
Information processing device. The information processing device includes a turn management database that stores information on the energy supply queue provided to the first mobile body and the second mobile body at each energy supply point.
The information processing device according to claim 5. When the information processing device creates the travel plan, the information processing device generates a virtual movement that is assumed to be traveling along a route corresponding to the route information without using the information processing system configured by the information processing device. creating a travel plan including an energy supply plan for the first mobile body, assuming that a third mobile body, which is a body, travels on the route and charges at any of the energy supply points;
An information processing device according to claim 5 or claim 6. The information processing device creates an energy supply plan for the first mobile body using the energy supply plan for the third mobile body as a constraint.
The information processing device according to claim 7. A reservation processing method for, upon receiving an energy supply request from a mobile object, reserving a recommended energy supply point from a plurality of energy supply points, the method comprising:
The information processing device is configured to provide information about the energy supply point of the first mobile body of the first mobile body of the user registered in advance, including destination information set by the registered user. Procedures for accepting reservation requests;
A step of creating a travel plan including the energy supply point at which energy supply is recommended to the first mobile object based on the destination information included in the received reservation request;
A procedure for obtaining the energy supply waiting order of the recommended energy supply point;
A step of presenting, in an information communication terminal provided at the energy supply point, energy supply waiting information of the energy supply point, which is generated in the information processing device based on a reservation request from the first mobile object;
Receive an energy supply request at the energy supply point for a second mobile body that is an unmanaged mobile body that is not registered in the system in advance, obtain the energy supply order, and set up the energy supply at the energy supply point. Procedures to provide to information communication terminals;
An information processing method comprising: It is connected via a communication network to a plurality of mobile information terminals and an information communication terminal provided at each of a plurality of energy supply points, and when an energy supply request from a mobile object is received, energy supply is recommended from the plurality of energy supply points. An information processing program executed by an information processing device that makes a spot reservation,
Steps for receiving a reservation request for the energy supply point for the first mobile body of a user who has been registered in advance, including destination information set by the registered user. and,
A step of creating a travel plan including the energy supply point at which energy supply is recommended to the first mobile object based on the destination information included in the received reservation request;
A procedure for obtaining the energy supply waiting order of the recommended energy supply point;
A step of presenting, in an information communication terminal provided at the energy supply point, energy supply waiting information of the energy supply point, which is generated in the information processing device based on a reservation request from the first mobile object;
Receive an energy supply request at the energy supply point for a second mobile body that is an unmanaged mobile body that is not registered in the system in advance, obtain the energy supply order, and set up the energy supply at the energy supply point. Procedures to provide to information communication terminals;
An information processing program for causing an information processing device to execute.

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