ENERGY SUPPLY SYSTEM AND INFORMATION PROCESSING DEVICE

Abstract

To obtain an energy supply system that easily suppresses a long waiting time by a user waiting for his / her turn to supply energy.SOLUTION: The energy supply system includes an information processing device and an energy supply device. The information processing device includes an acquiring unit, a calculation unit, and a traveling state management unit. The calculation unit calculates an amount of energy required for a mobile body to reach a destination from an energy supply point where the mobile body is located on the basis of location information acquired by the acquiring unit and the destination acquired by the acquiring unit, and calculates a difference between the amount of energy required for the mobile body to reach the destination and a current remaining amount of energy of the mobile body acquired by the acquiring unit. The traveling state management unit identifies a next energy supply point that can be reached with the current remaining amount of energy of the mobile body, and transmits the identified next energy supply point to an information device installed on the mobile body.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] An embodiment of the present invention relates to an energy supply system and an information processing device. [Background technology]

[0002] Information provision systems for EVs (Electric Vehicles) are being considered, for example, that refer to map information when an EV is using a highway (toll road), accurately predict the EV's reachable range, and recommend service / parking areas (SA / PA) that the user should use. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2011-83166 A [Patent Document 2] JP 2003-262525 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, if a user at an SA / PA with charging facilities fully charges the battery of his / her EV regardless of the distance to his / her destination, there is a risk that users waiting their turn to charge will have to wait longer. [Means for solving the problem]

[0005] An energy supply system according to an embodiment includes an information processing device and an energy supply device. The energy supply device is installed at an energy supply point and supplies the energy to a moving object that moves using energy. The information processing device includes an acquisition unit, a calculation unit, an output unit, and a running state management unit. The acquisition unit acquires location information of the moving object located at the energy supply point, a destination of the moving object, and a current remaining amount of the energy stored in the moving object. The calculation unit calculates an amount of the energy required for the moving object to reach the destination from the energy supply point where the moving object is located based on the location information acquired by the acquisition unit and the destination acquired by the acquisition unit, and calculates a difference between the amount of the energy required for the moving object to reach the destination and the current remaining amount of the energy of the moving object acquired by the acquisition unit. The output unit outputs the difference calculated by the calculation unit. The traveling state management unit, while the moving body is moving after the energy is supplied at the energy supply point, identifies the next energy supply point that can be reached with the current remaining amount of the energy of the moving body based on the remaining amount of the energy of the moving body at the time when the supply of the energy at the energy supply point is completed, and transmits the identified next energy supply point to an information device mounted on the moving body. The energy supply device includes an input unit to which the difference output by the output unit is input, and a supply processing unit that performs processing related to the supply of the energy when the difference is input to the input unit. [Brief description of the drawings]

[0006] [Figure 1] FIG. 1 is a block diagram showing a schematic configuration of an information processing system according to an embodiment. [Diagram 2] FIG. 2 is a block diagram of the information processing apparatus according to the embodiment. [Diagram 3] FIG. 3 is a diagram illustrating an example of route information according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of a network structure of route information according to the embodiment. [Diagram 5] FIG. 5 is a diagram illustrating an example of traffic counter management information according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of weather information according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of the road control information according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of vehicle information according to the embodiment. [Figure 9] FIG. 9 is a diagram illustrating an example of the charger information according to the embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of calculating the power consumption amount according to the embodiment. [Figure 11] FIG. 11 is a diagram illustrating another example of calculating the power consumption amount according to the embodiment. [Figure 12] FIG. 12 is a diagram illustrating an example of learning data according to the embodiment. [Figure 13] FIG. 13 is a diagram illustrating an example of the classification rule according to the embodiment. [Figure 14] FIG. 14 is a flowchart of an example of the operation of the model management unit according to the embodiment. [Figure 15] FIG. 15 is a flowchart illustrating another example of the operation of the model management unit according to the embodiment. [Figure 16] FIG. 16 is a diagram illustrating an example of a model management table and a category table according to the embodiment. [Figure 17] FIG. 17 is a diagram illustrating an example of driving data according to the embodiment. [Figure 18] FIG. 18 is a diagram for explaining a specific example of calculating the remaining battery capacity of an EV according to the embodiment. [Figure 19] FIG. 19 is a flowchart of an example of the operation of the prediction evaluation unit according to the embodiment. [Figure 20] FIG. 20 is a flowchart of an example of the operation of the model update management unit according to the embodiment. [Figure 21] FIG. 21 is a flowchart of another example of the operation of the model update management unit according to the embodiment. [Figure 22]FIG. 22 is a flowchart illustrating an example of the operation of the information processing apparatus of the embodiment. [Figure 23] FIG. 23 is a diagram illustrating an example of a system management screen according to the embodiment. [Figure 24] FIG. 24 is a diagram illustrating a hardware configuration of the information processing apparatus according to the embodiment. [Diagram 25] FIG. 25 is a block diagram of a charger according to an embodiment. [Figure 26] FIG. 26 is a flowchart illustrating an example of a charging process of the information processing system of the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Next, the embodiment will be described in detail with reference to the drawings. In the following description, an EV will be taken as an example of a moving body, but the present invention can be applied to moving bodies other than EVs as well. Examples of other moving bodies include trains, hybrid cars, fuel cell cars, airplanes, drones, electric motorcycles, cars with diesel engines, ships, and the like.

[0008] In this embodiment, since the moving body is assumed to be an EV, the energy supply is charging, the supply point is a charging station, and the energy supply device is a charger. However, if the moving body is a gasoline vehicle, the energy supply can be read as refueling, and the supply point can be read as a gas station and a refueling device. Also, if the moving body is an airplane or a drone, the road can be read as an air route, and if it is a ship, the road can be read as a sea route. In this way, the same processing as in this embodiment is possible for moving bodies other than EVs.

[0009] Now, an EV uses the charging power (charging energy) of the battery to travel on roads, which are traffic routes, and moves to various points. The EV point is not limited to an energy supply point, but may be any point on a map, such as a home, a restaurant, a store, or a point arbitrarily designated by the EV user, but the EV point in this embodiment is an energy supply point, which is a point where the EV charges.

[0010] An EV is supplied with power at an energy supply point (hereinafter referred to as a supply point or a charging point), and the supplied power is stored in a battery. The EV then travels using the power stored in the battery. In order for the EV to continue traveling, it must move to the next supply point and receive a supply of power at that supply point before the battery runs out of power. In this way, the EV moves to the destination while charging at each supply point. Note that one or more chargers (energy supply devices) are installed at the supply point, and the EV connects to any charger 221 via wire or wirelessly to receive a supply of power.

[0011] <Information Processing System> FIG. 1 is a block diagram showing a schematic configuration of an information processing system according to an embodiment. As shown in FIG. 1, an information processing system 1 includes: The information processing device 101 includes information devices 201A to 201N, servers 211A to 211N, and chargers 221 to 221N. The information processing device 101 includes: Through the communication network 220 , love The information processing system 1 is connected to the information devices 201A to 201N, the servers 211A to 211N, and the chargers 221A to 221N. Here, the information processing system 1 is an example of an energy supply system, and the chargers 221A to 221N are an example of an energy supply device.

[0012] In the following description, when there is no need to distinguish between the information devices 201A to 201N, they will be collectively referred to as the information device 201. In the following description, the servers 211A to 211N will be collectively referred to as server 211 when there is no need to distinguish between them. In the following description, the chargers 221A to 221N will be collectively referred to as the charger 221 when there is no need to distinguish between them.

[0013] The communication network 220 may be a wired or wireless network or a hybrid network of these. The communication network 220 may include a relay device such as an access point of a wireless LAN. The information devices 201A to 201N are communication devices capable of communicating with this information processing device in real time. The information devices 201A to 201N are, for example, communication terminals (smartphones, tablet devices, mobile phones, notebook PCs, etc.) held by users, in-vehicle devices such as car navigation systems installed in EVs, chargers 221 installed at supply points where EVs are charged, ITS spots (ETC2.0 devices), and other communication devices. Note that the information devices 201A to 201N may be constituted by the chargers 221 installed at supply points where EVs are charged. The ITS spot (ETC 2.0 device) can communicate with at least one of a communication terminal and a car navigation system, and is arranged, for example, at a plurality of predetermined spots. The predetermined spots may be ITS spots, points along a route, service areas on expressways, toll booths, buildings, etc. The ITS spots (ETC devices 2.0) may be provided at the entrances and exits of power supply points, in which case they may detect the entry time, exit time, number of EVs entering, and number of EVs exiting. The communication terminal may be operated by another passenger (e.g., 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 person may drive the EV on his / her behalf. Input to the communication terminal may be manual or voice input.

[0014] The servers 211A to 211N are 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 221 information server that manages charger 221 information, a road control information server that manages road control information, etc. Servers other than those listed here may also be arranged.

[0015] The information processing device 101 is an information providing system for EVs that recommends energy supply points to EVs using expressways (toll roads). The information processing device 101 is installed, for example, in a facility management company or a traffic control station. However, the information processing device 101 may be incorporated in an in-vehicle device such as a car navigation system (hereinafter, car navigation) mounted on an EV, and may also be installed on the roadside. When installed on the roadside, an operator may remotely operate the information processing device.

[0016] FIG. 2 is a block diagram of the information processing apparatus according to the embodiment. As shown in FIG. 2, the information processing device 101 includes a communication unit 11, a user ID registration unit 12, an EV navigation usage registration unit 13, a user DB (database) 14, a map information management unit 15, a road control information management unit 16, a charger information management unit 17, a weather information management unit 18, a vehicle information management unit 19, a system DB 20, and a control unit 21. The information processing device 101 further includes a model management unit 31, a model DB 32, a prediction unit 41, a driving state management unit 51, a driving management DB 52, a prediction evaluation unit 61, and a model update management unit 71. The information processing device 101 further includes an acquisition unit 81 and a calculation unit 82. Additionally, the information processing device 101 may include an input means for an operator to input instructions or data to the device, and a display means for displaying data to the operator. Examples of the input means include a keyboard, a mouse, a touch panel, and a microphone for voice input. Examples of the display means include an LCD (liquid crystal display), a CRT (cathode ray tube), and a PDP (plasma display).

[0017] The communication unit 11 communicates with the information devices 201A to 201N, the servers 211A to 211N, the chargers 221A to 221N, and the like via the communication network 220.

[0018] The user ID registration unit 12 receives a user registration request from the information device 201 and performs user registration for the service (EV navigation service) of the information provision system for EVs according to this embodiment. For example, the user operates the information device 201 to open an application or web page for user registration for the EV navigation service and access the user ID registration unit 12. The user ID registration unit 12 acquires personal information of the user and information of the EV used by the user (vehicle type, battery capacity, battery deterioration level, cumulative mileage, tire type, etc.), and treats these as user information. The user ID registration unit 12 issues a user ID (EV_ID) to the user, associates the user ID with the user information, and registers it in the user DB 14.

[0019] The EV navigation usage registration unit 13 receives a usage registration request from the information device 201 and performs usage registration to start the EV navigation service. For example, when the user is out, at home or in the EV, he / she operates the information device 201 to open an application or web page for receiving the EV navigation service, and sends a usage registration request including the information necessary to receive the service from the information device 201 to the EV navigation usage registration unit 13. For example, information such as the travel date and time (scheduled date and time of departure from the departure IC (interchange), current date and time, scheduled date and time of departure from home, etc.), departure point (exchange of the expressway, etc.), destination (destination IC, etc.), EV battery capacity (may be omitted if notified at the time of user registration), battery deterioration degree (may be omitted if notified at the time of user registration), EV battery remaining amount (remaining energy), air conditioner usage status (on / off, set mode, temperature setting, 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. are transmitted. If the information device 201 is a car navigation system, information set in the car navigation system may be transmitted, or if it is a smartphone, etc., information input by the user may be transmitted. Such acquired information is referred to as usage registration information.

[0020] The EV navigation usage registration unit 13 issues an ID (driving ID) for this service usage, associates the driving ID with the usage registration information, stores it in the user DB 14, and also notifies the driving state management unit or the model management unit, or both, described below. This device also communicates with the user's information device 201 after user registration to obtain information such as GPS location information, current time, remaining battery power (battery charge), and air conditioner usage status, and notifies a driving state management unit or a model management unit, or both, which will be described later. The timing of communication can be real time, at regular intervals, when a specific IC such as a departure IC registered by the user is passed, when this device issues a request and receives a response, when entering or leaving a service area / parking area (SA / PA), when passing near an SA / PA, etc.

[0021] The user DB 14 holds information registered by the user ID registration unit 12 and the EV navigation usage registration unit 13. The user DB 14 is a hardware storage device, such as a memory device, a hard disk device, or an SSD device.

[0022] The control unit 21 controls each unit of the information processing device 101, thereby controlling the entire information processing device 101 and realizing the operation of the information provision system for EVs.

[0023] The map information management unit 15 acquires map information from the server 211 , and stores and manages the acquired map information in the system DB 20 . Here, map information is information that represents information such as the position (latitude, longitude), size, and range of map elements. For example, it includes altitude information associated with each position on the map, distance information for each route (each route on an expressway, each route on a general road, etc.), gradient, angle of curve, route, direction, KP (kilometre post), shape of JCT (junction), tunnels, lighted areas (places other than tunnels), and road surface conditions (pavement conditions, etc.). Map information also includes the number of lanes on expressways, the locations of interchanges (ICs), and service areas / parking areas (SAs / PAs), etc.

[0024] The map information includes information on the supply points, SA / PA points, ICs, and JCT points as the above map elements. The map information may also include information on the distance of the route and information on the required driving time for the route as route information between the supply points, SA / PA points, ICs, and JCT points. The map information management unit 15 may obtain all or part of the map information from the server 211 at regular intervals or in real time, and update the map information.

[0025] FIG. 3 is a diagram illustrating an example of route information according to the embodiment. The required travel time may be an average of past measured values, or the time required to travel a distance at a specified speed. It may also be information on the required energy or electricity cost required to travel the route. The required energy or electricity cost may be actual statistical values ​​(average, median, etc.), or values ​​calculated using a formula or simulation. The route information may be expressed in a general network structure.

[0026] FIG. 4 is a diagram illustrating an example of a network structure of route information according to the embodiment. The nodes representing the supply points Q1 to Qn are connected by links indicated by dashed lines. The links connect adjacent supply points. It can be seen that the supply point Q1 is adjacent to both the supply points Q3 and Q2. There is also one route from the supply point Q1 to the supply point Q3, and one route from the supply point Q1 to the supply point Q2. The link is assigned the characteristics of the route between the supply points represented by both nodes connected by the link (the route characteristics are omitted in FIG. 4). In this example, a network structure of only supply points is shown, but in addition to the supply points, SA / PA points, ICs and JCTs may also be included as nodes.

[0027] The map information may also include traffic counter (TC) management information that identifies traffic counters (TCs) that exist in the sections between the supply points. A TC is a device that acquires information about traffic in the sections between the supply points. A TC is an example of the map element described above.

[0028] FIG. 5 is a diagram illustrating an example of traffic counter management information according to the embodiment. In the data of the first entry, traffic counters TC1, TC2, ... are arranged in the section between the supply point Qj and the adjacent supply point Qj'. Note that the traffic counters do not have to be arranged in the order of TC1, TC2, ....

[0029] 2, the weather information management unit 18 acquires weather information from the server 211, and stores and manages the acquired weather information in the system DB 20. The weather information management unit 18 may acquire weather information from the server 211 at regular intervals or in real time.

[0030] FIG. 6 is a diagram illustrating an example of weather information according to the embodiment. The weather information includes, for example, temperature for each predetermined region and each date and time, the presence or absence of rain, the amount of precipitation, wind speed, wind direction, total solar radiation, amount of snowfall, road surface temperature, luminosity, visibility (fog), radiation, sudden heavy rain, etc. In addition to past and current weather information, the weather information may also include predicted weather information in the future if such information is available.

[0031] Returning to Fig. 2, the road control information management unit 16 acquires road control information for each route (section) from the server 211, and stores and manages the acquired information in the system DB 20. Examples of road control information include traffic counter (TC) information and ETC information (number of entering vehicles, number of exiting vehicles, EV models, remaining battery power of EVs, etc.). Other information on occurring events includes road closures, congestion, accidents, broken-down vehicles, construction, fallen objects, fires, disasters, speed restrictions, etc. Examples of forecast information include congestion forecasts, accident occurrence forecasts, sudden heavy rain forecasts, and landslide forecasts. The installation locations of the traffic counters are known in advance from the map information mentioned above (see Fig. 5).

[0032] The road control information management unit 16 may acquire road control information from the server 211 at regular intervals or in real time. Also, the road control information may be acquired from the server 211 every time the road control information is updated. The road control information management unit 16 may acquire traffic counter information directly from a traffic counter, not from the server 211. Time is assigned to the row name, and TC ID is assigned to the column name. A speed (average speed) value is stored in each element of the table. In the following, the case of traffic counter information is mainly assumed as the road control information. FIG. 7 is a diagram illustrating an example of the road control information according to the embodiment. The traffic counter information includes, for example, speed [km / h], occupancy [%], traffic volume [vehicles / h], vehicle density [vehicles / km], etc. The speed includes, for example, average speed, maximum speed, minimum speed, etc. for a certain period of time.

[0033] Returning to Fig. 2, the vehicle information management unit 19 acquires information (vehicle information) such as the vehicle type ID, EV manufacturer, battery capacity, battery type (lithium ion battery, etc.), total weight (weight of the EV when it is loaded with passengers or weight of the EV itself), power consumption, battery deterioration rate, release year, etc. from the server 211, and stores and manages the acquired information in the system DB 20. The vehicle information management unit 19 may acquire the vehicle type information from the server 211 at regular time intervals. Alternatively, the vehicle type information may be acquired from the information device 201 instead of the server 211.

[0034] FIG. 8 is a diagram illustrating an example of vehicle information according to the embodiment. The vehicle model ID has a different value for each vehicle model. The server 211 that manages the vehicle information may be a server of the EV manufacturer, or may be a server that collectively manages vehicle model information of multiple EV manufacturers.

[0035] Returning to Fig. 2, the charger information management unit 17 acquires charger 221 information for one or more chargers 221 (energy supply devices) installed at each supply point from the server 211. The charger information management unit 17 holds and manages the acquired charger 221 information in the system DB 20. The charger information management unit 17 keeps track of the number of chargers 221 at each supply point.

[0036] The charger information management unit 17 may obtain the charger 221 information directly from the charger 221, not from the server 211. Alternatively, the user may input the charger 221 information (such as GPS location information and the amount of charged power) to the information device 201, and the charger information management unit 17 may obtain the charger 221 information from the information device 201. Alternatively, the information device 201 (an in-vehicle device such as a car navigation system) may read information on the amount of charged power from the EV, and transmit the charger 221 information to the information processing device 101.

[0037] FIG. 9 is a diagram illustrating an example of the charger 221 information according to the embodiment. The charger 221 information includes, for example, a user ID (EV_ID) of a user who performed charging at the charger 221, a supply point ID, a start charge amount, an end charge amount, a charging start time, and a charging end time. Other information such as charging efficiency and the number of charges may also be held. The starting charge amount is the amount of power stored in the EV's battery at the time charging starts at the charger 221. The ending charge amount is the amount of power stored in the EV's battery at the time charging ends at the charger 221. The difference between the ending charge amount and the starting charge amount is the amount of power charged in the EV. Instead of the start charge amount and the end charge amount, the charger 221 information may include information on either the start charge amount or the end charge amount, and the amount of charged power. The charger 221 information indicates the energy supply history at the corresponding supply point. The charger information management unit 17 may acquire the charger 221 information from the server 211 at regular intervals or in real time.

[0038] By using charger 221 information for the same user in chronological order, consumption history information can be obtained that indicates the amount of power consumed by the user for each section between supply points, the driving time, the driving speed, etc. As an example, the driving time can be obtained by subtracting the charging end time at the previous supply point from the charging start time at a certain supply point. The driving speed (average driving speed) can be obtained by dividing the driving time by the distance of the section. It is also possible to configure the system to communicate with the EV to obtain information on the driving speed and driving time in real time, and to ascertain the driving speed and driving time from the information obtained from the EV.

[0039] FIG. 10 is a diagram illustrating an example of calculating the power consumption amount according to the embodiment. An example will be given below in which the amount of power consumption for travelling a certain section is calculated from charger 221 information for a certain same EV. A supply point (the supply point of the second entry) having a charging start time closest to the charging end time at a certain supply point (the supply point of the first entry) is identified as the next supply point. That is, the ID of the supply point Qj (the previous supply point) indicated by three black circles and the ID of the next supply point Qj' (the next supply point) indicated by three △ are identified. In addition, the time when charging at the supply point Qj ended (last use end time), the time when charging at the supply point Qj' started (next use start time), the charge amount when charging at the supply point Qj' started (next start charge amount), and the charge amount when charging at the supply point Qj ended (last end charge amount) are specified. The amount of power consumed in moving from the supply point Qj to the supply point Qj' is calculated by subtracting the next start charge amount from the last end charge amount.

[0040] FIG. 11 is a diagram illustrating another example of calculating the power consumption amount according to the embodiment. In this example, information obtained from an information device (smartphone) is used. This information represents an ID (EV_ID) for identifying a user, a GPS information location, and a charge amount. In this case, the power consumption amount is calculated by subtracting the charge amount at a certain location from the charge amount at the next location. The GPS information location may be coordinates obtained by GPS, or may be the name of a place, facility, etc. associated with the coordinates in map information.

[0041] Returning to Fig. 2, the system DB 20 holds information (sometimes called external information) acquired by each of the above-mentioned management units. The external information acquired by each management unit may be held in a buffer within each management unit. The buffer may be, for example, a memory device, a hard disk device, an SSD device, or a hardware storage device. Furthermore, an operating system, an information processing program, and various data used for information processing are stored in the system DB 20. 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 enabling the information processing device 101 to realize each function of information processing, which will be described later.

[0042] The model management unit 31 uses multiple pieces of learning data to generate a model (prediction model) that calculates the predicted power consumption of an EV. The learning data is generated using, for example, all or part of consumption history information (charger 221 information), meteorological information, geographical information, vehicle information, road control information, and information acquired from an information device (smartphone, car navigation system, etc.). This information is acquired from the geographical information management unit 15, road control information management unit 16, charger information management unit 17, meteorological information management unit 18, vehicle information management unit 19, and information device 201.

[0043] The model management unit 31 generates learning data by associating all or part of the acquired information. For example, the power consumption in a certain travel section (a section between supply points), meteorological information (temperature, etc.) for the travel date and time of the route, and geographic information (distance, gradient, etc.) of the route in the certain travel section are associated with each other to generate travel data, which is used as learning data. Travel data acquired and managed by the travel state management unit 51, which will be described later, may be used as learning data. The travel data generated here for learning and the travel data managed by the travel state management unit 51 may be the same or different.

[0044] FIG. 12 is a diagram illustrating an example of learning data according to the embodiment. The history ID is an identifier for identifying learning data. The power consumption is the amount of power used to travel the travel section. For example, this can be obtained from the charger 221 information (history information) mentioned above. The distance is the distance of the travel section traveled, and the speed is the average travel speed. The travel time is the time required to travel the travel section. The battery capacity is the battery capacity (also called the total battery capacity) installed in the EV. Whether the air conditioner is operating is whether or not the air conditioner was turned on while the EV was traveling. The temperature indicates the temperature at the time of travel. Additionally, the vehicle model indicates the type of EV registered by the user. The items shown here are merely examples, and other items may be present, or some of the items shown may not be present. For example, information specifying the travel section, the gradient of the route, humidity, remaining battery power, etc. may be included in the learning data. Each item included in the learning data corresponds to the features of an EV. Speed ​​(average speed), distance, driving time, power consumption, whether the air conditioner is on, etc. represent the operating status of the EV, remaining battery power, degree of battery deterioration, etc. represent the characteristics of the EV, and temperature, gradient, etc. represent the driving environment of the EV. These are examples of information that represent the travel status of an EV.

[0045] The route (travel section) used by the EV to move from the supply point Qj to the next supply point Qj' may be identified from information (GPS information, etc.) acquired by directly communicating with the information device 201. As another method, the route used by the EV can be estimated from the consumption history information and geographic information. When estimating the route used for travel between a supply point Qj (e.g. Q1) where energy is supplied to an EV and a supply point Qj' (e.g. Q6) where energy is next supplied to the EV, the shortest travel route (shortest path) from the supply point Qj to the next supply point Qj' may be estimated as the route used for travel. Methods for solving the shortest path problem include the general Dijkstra algorithm, the Bellman-Ford algorithm, the Gabow algorithm, the Warshall-Floyd algorithm, and the like. The obtained route can be expressed as a list of supply points, for example, (Qj, Q4, ..., Qj').

[0046] The above generation of learning data is merely an example, and the combination of types of information used to create the learning data can be determined arbitrarily. The learning data may be generated by further using at least one of the vehicle information of the EV and the road control information for the date and time when the EV is traveling. The learning data is generated, for example, by using information from a certain period of time in the past.

[0047] The model management unit 31 classifies a plurality of pieces of learning data into a plurality of groups (which may be called clusters) in accordance with classification rules. The classification rules include criteria based on at least one of the following: distance traveled, travel time, travel speed, remaining battery power, gradient information, temperature, vehicle type, and the like.

[0048] One or more classification rules are stored in the model DB 32. The classification rules are created in advance by an operator's operation and stored in the model DB 32. The classification rules may be updated by an operator's operation, or may be created or updated by a process described below. Furthermore, when multiple classification rules are stored, a flag may be set to specify the classification rule to be used for prediction. The classification rule may have any structure, such as a decision tree or a cluster model.

[0049] FIG. 13 is a diagram illustrating a case where a decision tree is used as an example of the classification rule according to the embodiment. It includes nodes (non-terminal nodes) 301 and 302 to which features are assigned, and terminal nodes 303, 304, and 305 to which groups A, B, and C are assigned. Feature conditions (branching conditions) are set for the nodes 301 and 302, and branching to lower nodes occurs depending on which branching condition is satisfied. With this structure, the learning data is finally classified into one of group A, group B, or group C, which correspond to the terminal nodes 303 to 305. For example, if the value of the driving speed (feature) included in the learning data is less than 40 km / h and the temperature (feature) is less than 20 degrees, the learning data is classified into group A. In this example, the features of the nodes of the decision tree are only the driving speed and the temperature, but other items (for example, gradient) may be selected as features. In addition, the depth of the decision tree is 2 in the example shown in the figure, but the depth may be 1 or 3 or more. As will be described later, each group is associated with a prediction model. Therefore, the decision tree is a classification rule that associates multiple first conditions of feature amounts with multiple first prediction models.

[0050] The model management unit 31 generates a prediction model (model parameters) for each group based on the learning data belonging to each group classified by the classification rule. The model management unit 31 stores the generated prediction model in the model DB 32. The model DB 32 stores the prediction model for each group.

[0051] There are many methods for building predictive models, including artificial intelligence, machine learning, black-box modeling, and white-box modeling that defines a physical model. 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. 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, as an example, a prediction model based on the sum of multiple models is shown. In this case, each model constituting the prediction model may be called a sub-model. The type of prediction model can generally be expressed by the following formula (1).

[0052]

number

[0053] ^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 sum of the power consumption calculated by each sub-model ^yi. The number of sub-models may be one. The predicted value ^y of the power consumption indicated by the sub-model is a value that includes the power consumption for a section when the EV is traveling, and the sub-model may also include the power consumption of the air conditioner, the power consumption of the wipers, the power consumption of the car navigation system, and the charging capacity of the smartphone that the EV user charges in the car.

[0054] Examples of sub-models are the regression model, the spec model, and the discrete value model. The regression model and the spec model are examples of the continuous value model that treats continuous values ​​as feature quantities. The discrete value model is an example of the discrete value model that treats discrete values ​​as feature quantities. The model management unit 31 includes a regression modeling means 35 for generating a regression model, a spec modeling means 36 for generating a spec model, and a discrete value modeling means 37 for generating a discrete value model. As an example, ^y1 is a regression model, ^y2 is a spec model, and ^y3 is a discrete value model. Note that, regarding the regression model, the following (1-1) may be used in consideration of the standard deviation of the error between the predicted value and the true value (actual measured value).

[0055]

number

[0056] When the model is a multiple regression model, the basic function ^y1=f(x) is expressed, for example, as follows:

number

[0057] w0,w1,w2,w3,...,wn are the model parameters to be estimated. x1,x2,x3...,xn are the input variables (features). ^y1 is the output variable. In order to absorb the difference in the measurement units of each input variable, the output variable and all input variables may be normalized to a mean of 0 and a variance of 1 (scaling). Examples of input variables include distance, required time, and outside temperature. These are items included in the learning data. For example, x1 is distance, x2 is required time, and x3 is outside temperature. The input variables may be other values ​​calculated from multiple items included in the learning data. For example, the speed obtained by dividing the distance by the required time may be used as an input variable.

[0058] In the case of the spec model, the basic function ^y2 = f(x) is expressed as in the following equation (3). w is a coefficient or constant to be estimated. x is a feature. ^y2 is an output variable. w may be a fixed value (e.g., 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 power consumption calculated by the spec model depends on, for example, the vehicle model and battery capacity.

number

[0059] (Example where model ^y2 represents the power consumption during driving) The feature quantity x may be distance (km) / electricity consumption (km / kWh). In this case, the electricity consumption may be, for example, a catalog specification value or a coefficient to be estimated. w may be the electricity consumption ratio (electricity consumption of vehicle model A / electricity consumption of vehicle model B), and x may be the power consumption of vehicle model B (a value obtained by a regression model). The electricity consumption ratio may be a coefficient to be estimated. In this case, it is considered that the model corresponds to a group to which vehicle models A and B belong. w may be the degree of battery deterioration, and x may be the amount of power consumption based on a regression model. The degree of battery deterioration may be, for example, the EV battery SoH / average SoH, or the EV cumulative mileage / average cumulative mileage. SoH is the State-of-Health, and is an index that indicates the degree of battery deterioration. w may be the total weight of EV and luggage / total weight of EV (or average total weight), and x may be the power consumption calculated by the regression model. The total weight of EV and luggage / total weight of vehicle (or average total weight) may be determined based on catalog values, or may be a coefficient estimated by learning. The total weight of EV and luggage is, for example, the weight when the maximum number of people and luggage allowed in the EV are loaded.

[0060] (Example where model ^y2 represents the power consumption by an air conditioner) The feature quantity x is the air conditioner usage time (h), and the coefficient w is the coefficient to be estimated, and its unit is kWh / h. The feature quantity x is the temperature difference (Δ°C), and the coefficient w is the coefficient to be estimated, and the unit may be kWh / Δ°C.

[0061] (Example where ^y2 represents the power consumption by the wiper) The feature quantity x is the wiper usage time (h), and the coefficient w is the coefficient to be estimated, and its unit is kWh / h.

[0062] In the case of a discrete value model, the basic function ^y3=f(x) is expressed as the following equation (4).

number

[0063] The model parameter w in equation (2), the coefficient w in equation (3), and the constant C in equation (4) can be obtained by applying a known optimization algorithm such as the maximum likelihood method or the least squares method to equation (1).

[0064] The model management unit 31 generates a prediction model using at least one of the modeling means. A prediction model ^y is generated for each group. The generated prediction model is stored in the model DB 32. The types of sub-models constituting the prediction model may differ for each group. For example, the discretized model included in the prediction model of group A may be the value of the power consumption of an air conditioner, and the discretized model included in the prediction model of group B may be the value of the charge amount of a smartphone. In this case, multiple types are prepared for each submodel, combinations of types are generated between the submodels, and each combination is evaluated. The combination with the highest evaluation for each group can be used. For groups with only a small amount of training data, transfer learning may also be used for the regression model.

[0065] FIG. 14 is a flowchart of an example of the operation of the model management unit according to the embodiment. The operation of this flowchart is performed for each group. One or more candidate methods for constructing a prediction model are determined for the target group (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 and 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 type of each submodel, more construction methods are possible.

[0066] A new prediction model is constructed using the learning data belonging to the target group (S102). The prediction models constructed by each construction method are registered as provisional models in the model DB 32 (S103). It is determined whether or not to provide a trial period for each provisional model (S104). For example, when a new model is constructed, no trial period is provided, and when a prediction model is updated, a trial period is provided. The trial period may be set arbitrarily by an operator. It may also be determined by other methods.

[0067] If a trial period is not set (NO), the provisional model is evaluated using the learning data of the target group (S105). As an example, an evaluation value (model evaluation value) is calculated by the sum of errors (prediction errors) between the output values ​​of the provisional model and the actual values. Then, the provisional model with the highest evaluation (the smallest model evaluation value) is selected as the prediction model (S106). The provisional models not selected may be deleted from the model DB 32.

[0068] Here, each provisional 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 (cross number). K is an integer equal to or greater than 2. One of the pieces is used as test data, and the remaining K-1 pieces are used as training data. A model is constructed using the 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 divided data as test data, and an evaluation value (model evaluation value) is calculated for each. In step S106, statistics (e.g., mean, median, variance, quartile, maximum, minimum, etc.) of the model evaluation values ​​are calculated. Based on the statistics, the provisional model with the highest evaluation among the provisional models is selected as the prediction model. Depending on the evaluation method, the higher the evaluation, the larger the statistical value, or the lower the statistical value.

[0069] On the other hand, if a trial period is to be provided in step S104 (YES), new learning data is collected (S107). Then, each provisional model is evaluated using the newly collected learning data and the previously acquired learning data (S108). The evaluation method is the same as that described in step S105, except that the newly collected learning data is used as the data to be used for evaluation. Note that, if there is a sufficient amount of newly collected learning data for evaluation, a configuration in which evaluation is performed using only the newly collected learning data is not excluded. It is also possible to use the above-mentioned cross-validation method. In this case, since past learning data and new learning data are mixed, the learning data may be divided randomly. For example, a certain amount of test data is randomly sampled, and the remaining data is used as training data. This is repeated K times.

[0070] In the evaluation in step S108, new data may be weighted higher. For example, when calculating the sum of errors, the error calculated for new data may be weighted higher than the error calculated for past data. Additionally, when cross-validation is used, sampling may be performed to increase the likelihood that new data will be selected as test data or training data compared to past data.

[0071] Here, the model evaluation value of the provisional model is the sum of errors between the output value of the provisional model and the actual value in the above example, but is not limited to this. For example, it may be evaluated using the following formula (5). Here, an example of a regression formula is shown, but when using the sum of the regression model and the spec model, it is sufficient to add a value normalized according to the number of samples of the learning data to the following formula, which is the difference between the actual value and the estimated value of the spec model.

number

[0072] Other examples of model evaluation values ​​include Mean Squared Error (MSE), Mean Absolute Error (MAE), R2 value (the square of the correlation coefficient R, also known as the coefficient of determination), Root Mean Square Error (RMSE), and Root Mean Square Percentage Error (RMSPE).

[0073] A model evaluation value may be defined that takes into consideration factors other than the error between the actual power consumption value and the predicted value. For example, the model data size may be multiplied by a certain coefficient, and the model evaluation value may be obtained by adding the multiplied value to the value of the sum of the errors described above. The larger the data size of the model, the longer it takes to calculate a prediction using the model, so by making it easier to select a model with a small size, it is expected that the calculation time and amount of calculation will be reduced.

[0074] The model management unit 31 can use the classification rules given in advance as described above, and can also automatically generate the classification rules. When the classification rules are automatically generated, the classification rules are generated by the method described below, and then a process of constructing a prediction model using the classification rules is performed by the method described above. Here, the classification rules are shown as decision trees, but are not limited to this. When automatically generating classification rules, the generation of classification rules and the construction of a prediction model are performed simultaneously. The decision tree is repeatedly divided in a direction that reduces the variance of the prediction error of the prediction model. Here, splitting a node means generating multiple child nodes for that node. For example, setting the number of divisions for a certain node (parent node) to 2 means generating two child nodes for that node. In other words, it means splitting a data group belonging to the parent node into data groups corresponding to each child node. In this case, it is necessary to determine the feature to be assigned to the parent node and the branching conditions based on the feature value. The feature to be assigned to the parent node may be an item present in the learning data, or an item obtained by calculating multiple items in the learning data. The number of divisions for a parent node does not have to be 2, but may be 3 or more.

[0075] Here, we consider an example of developing a classification rule (decision tree) by repeatedly splitting a node into two. In other words, the classification rule grows (the depth of the decision tree deepens) by splitting the parent group into two groups (left group, right group). SDR (Standard Deviation Reduction) is used to determine which feature to assign to the parent node and the value of the feature that will be the branching condition.

[0076] FIG. 15 is a flowchart showing another example of the operation of the model management unit 31 according to the embodiment. First, the number of divisions is determined (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 according to the number of learning data belonging to the node to be divided or the number of items included in the learning data.

[0077] The feature to be assigned to the parent node and the feature value that serves as the branching condition (feature condition) are determined so as to maximize the SDR (S202). Based on the determined feature condition, the learning data is divided into two child nodes (left child node (left group) and right child node (right group)). As an example, the SDR is expressed by the following formula (6). In this example, power consumption is used as the feature. Alternatively, a prediction model may be constructed first for the parent node and child nodes, and the error between the predicted value and the actual measured value may be used as the feature for the SDR.

[0078]

number

[0079] Multiple feature values ​​are set as candidate conditions, and the above SDR is calculated for the multiple candidates. In this case, the candidates may be sorted by the power consumption amount belonging to the parent node, and all the values ​​may be used as candidates. The pair of the feature with the highest SDR and the feature condition is selected. In this way, two child nodes are provisionally generated for the parent node. Note that the SDR can be calculated in the same way when dividing into three or more. Note that in the case of dividing into three, there are, for example, two feature values ​​that are candidate conditions.

[0080] Using the generated decision tree, the learning data is divided into a plurality of groups, a prediction model is generated by the method described above (S203), and an evaluation value of the prediction model (model evaluation value) is calculated. Based on the model evaluation value of the prediction model of the parent node and the model evaluation value of the prediction models of each child node, it is determined whether the decision tree satisfies the splitting condition (S204). For example, if the model evaluation value of the prediction model of the parent node is greater 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), it is determined that the decision tree satisfies the splitting condition, and if not, it is determined that the decision tree does not satisfy the splitting condition. If the division condition is met (YES), the division of the parent node is confirmed (the above two provisionally generated child nodes are adopted) (S205). Returning to step S202, the same process as above is repeated with each child node as a parent node. Note that when determining candidate features to be assigned to each child node in step S202, features already assigned to upper nodes (including parent nodes) may be excluded.

[0081] If the splitting condition is not satisfied (NO), the provisionally generated child node is deleted (splitting is stopped), the child node is returned to the decision tree before provisional generation (S206), and the process ends. As a result of performing this process recursively for each child node, multiple decision trees may 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 the prediction error of the child node is calculated. For example, statistical values ​​such as the average, median, and maximum of the prediction errors are calculated. The decision tree with the smallest rule evaluation value is selected. When calculating the average, the prediction error may be weighted according to the number of data belonging to the child node (group). In addition, the weight may be reduced for a group whose child node (group) has a larger value obtained by dividing the total battery capacity (total battery capacity for the number of EVs) by its electricity consumption (the larger the divided value, the less likely it is to run out of battery). Alternatively, the number of child nodes whose prediction error is 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 calculated by multiplying the number of child nodes by a certain coefficient and adding the result to the value of the statistical value calculated as above. This makes it easier to select a decision tree with a small number of child nodes, thereby reducing the amount of calculation and the calculation time.

[0082] The end condition of the process may be a condition other than the above-mentioned division condition. For example, the process may be ended when the decision tree reaches a predetermined depth. The process may be ended when the maximum value of the SDR is less than a threshold value. Other conditions may be used.

[0083] The model management unit 31 manages each generated prediction model and classification rule in a model DB 32. The model management unit 31 may manage the model ID of each prediction model and the coefficient of the feature amount used in each prediction model in a model management table.

[0084] FIG. 16 is a diagram illustrating an example of a model management table and a category table according to the embodiment. (A) in FIG. 16 shows the coefficient values ​​to be used for each feature (input variable) such as power consumption and battery capacity. A feature value of 0 means that it is not used. Also, as shown in (B) in FIG. 16, each feature may be managed in a table (category table) indicating the category of continuous or discrete values. This table may be used to determine whether each feature is used in a discrete value model or a continuous value model (regression model, spec model). The classification rules may be stored in the model DB 32 in a format that implements a general tree structure (see FIG. 13), or may be stored in the form of a program. Furthermore, the model management unit 31 may hold in the model DB 32 the program code of each prediction model (the program code of the function that constitutes the prediction model).

[0085] The prediction unit 41 predicts the power consumption of the EV in the travel section to be predicted, using the prediction model and prediction data of the EV. The prediction data is data including items used in each sub-model of the prediction model. However, the items used in a sub-model may be output values ​​of other sub-models. The travel section to be predicted is, for example, a section to a supply point that is the next candidate for charging.

[0086] The driving status management unit 51 manages the driving status of the EV in motion (e.g., the amount of power consumption for each section, etc.). For the EV in motion, the driving status management unit 51 acquires driving data for the driving section between charging bases, and manages the data in the driving management DB 52. The driving data can be generated based on data acquired by each of the management units 15-19 and the EV navigation usage registration unit 13 (information related to the driving status of the mobile object).

[0087] FIG. 17 is a diagram illustrating an example of driving data according to the embodiment. For each EV, the first charging point, second charging point, and various variables are managed. If charging has been performed three or more times, the value of the second charging point is entered in the column for the first charging point, and the value of the third charging point is entered in the column for the second charging point. If charging has been performed four or more times, values ​​are entered in the same way. Time is the time required to travel the traveled section, power consumption is the power consumption (actual value) used in traveling the traveled section, and distance is the distance of the traveled section. Uphill is the length of the route where the traveled section has a gradient of a certain value or more. Downhill is the length of the route where the traveled section has a gradient of less than a certain value (expressed as a negative value). The variables shown here are merely examples, and various types of variables are possible. For example, the total charge amount may be added as the total charge amount. The traveling state management unit 51 may add the power consumption amount predicted for each traveling section of the EV after the EV has completed traveling to the traveling data of the EV. In addition, the first charging point and the second charging point may be any two points for which the amount of power consumption is to be calculated. The two points may be any combination of points, such as between the TC and the IC, between the IC and the charging point, etc.

[0088] The traveling state management unit 51 may also use the prediction unit 41 to find candidate supply points within a range that can be reached based on the remaining battery charge of the EV, and determine the next supply point of the EV from among the found candidates. The start point of the travel section to be predicted may be, for example, an arbitrary supply point, or an arbitrary point during travel. The end point of the travel section is, for example, a candidate supply point. In order to calculate a predicted value of the power consumption of the EV while it is traveling, the travel state management unit 51 may use a predicted value, an average speed, or the like of the travel data, or a real-time current value obtained from road information and weather information. When EV location information is required, it may be obtained by communication, for example, from the GPS of the information device 201. When information such as the remaining power amount (battery remaining capacity) of the EV battery is required, it may be obtained by communication from the information device 201, or the information such as the battery remaining capacity may be calculated from information of the charger 221. Alternatively, the battery remaining capacity may be estimated using the power consumption of the EV. When the remaining battery capacity is required as a feature to be used in a prediction model, the remaining battery capacity thus obtained or estimated can be used.

[0089] The driving state management unit 51 narrows down the supply points that can be reached with the remaining battery power of the EV from the power consumption calculated for each candidate, and determines the next supply point from among them. Any method for determining the supply point may be used as long as it selects a supply point that can be reached with the current remaining battery power. For example, when there is one route to the destination, a supply point with the shortest waiting time may be selected in consideration of the congestion at each supply point, and when there are multiple routes to the destination, a supply point on the shortest route may be selected, or a supply point on the route with the least congestion or the smallest gradient may be selected. A supply point before exiting the destination IC may be determined in consideration of the location of the next reachable supply point after exiting the destination IC. Information on the determined delivery point is transmitted to the user's information device 201 (EV). The transmitted information is displayed on the screen of the information device 201, and the user knows the next delivery point to which he or she should head.

[0090] A specific example of how the traveling state management unit 51 calculates the remaining battery level of an EV will be described with reference to FIG. 18. Supply points A, B, C, D, E, F, and G (hereinafter, points A to G) are located on a highway. These points A to G are SA / PAs located along a route to the EV's destination (destination IC). The EV is charging at point C, and the traveling state management unit 51 knows the remaining battery level (which may be SoC (State of Charge)) after charging is complete from information obtained from the charger 221 or information obtained from the information device 201. The remaining battery level is represented as Ea. The traveling state management unit 51 can also obtain information on the speed and time of the EV from the GPS of the information device 201. The EV is currently traveling and is located between points D and E. The section between two supply points X and Y is described as section XY. This shows an example in which the traveling state management unit 51 calculates the power consumption amounts for sections CD, DE, EF, and FG when the EV is in its current position. As examples of features used in the prediction model, the speed of the section (average speed) and the traveling time required to travel the section are used.

[0091] Section CD is a section that the EV has already traveled in the past. The travel distance DIScd of section CD and the travel distance DISdn from point D to the current position N can be obtained from map information. The time when charging was completed at point C can be obtained from the charger 221 information or information obtained from the information device 201. The time at the current location can be obtained from the timer inside the device. Therefore, if the time when charging is completed at point C is regarded as the departure time from point C, the time tcn required to travel from point C to the current location N can be calculated. The time when the EV departs from point C may be determined based on information obtained from the EV's GPS. If the travel speed (average speed) vcd from point C to point D is calculated as (DIScd+DISdn) / tcn, the time tcd required to travel the section CD can be calculated as DIScd*vcd. Therefore, by inputting vcd, tcd and other necessary features into the prediction model, the power consumption (^ycd) in the section CD can be calculated (predicted). If the time when the vehicle traveled through point D is available, vcd may be calculated by dividing the distance between sections CD by the time required to travel from point C to point D.

[0092] The section DE includes a section DN where the EV has already traveled (a past section) and a section NE where the EV has not yet traveled (a future section). If the speed vdn in the section DN is calculated as (DIScd+DISdn) / tcn, like in the section CD, the time tdn required to travel the section DN can be calculated as DISdn*vdn. Furthermore, the speed vne in the section NE is calculated using the value of the traffic counter (TC) located in that section. For example, the latest value of the TC speed may be used, or the average over a certain period of time in the past (e.g., 30 minutes). The time tne required to travel the section NE can be calculated by DISne / vne, where DISne is the distance of the section NE. Therefore, the time tde required to travel the section DE can be calculated by tdn+tne. In addition, the travel speed (average speed) vde in the section DE can be calculated by (DISdn+DISne) / tde. Note that, instead of DISdn+DISne, the distance DISde in the section DE may be directly obtained from map information. Therefore, by inputting vde, tde and other necessary features into the prediction model, the power consumption (^yde) in the section DE can be calculated (predicted).

[0093] The section EF is a section where the EV has not yet traveled (a future section). The speed vef in the section EF uses the value of the traffic counter (TC) placed in the section. For example, it may be the latest value of the TC speed, or the average value over a certain period of time in the past (e.g., 30 minutes). The time tef required to travel the section EF can be calculated by DISef / vef. Therefore, by inputting vef, tef, and other necessary features into the prediction model, the power consumption (^yef) of the section EF can be calculated (predicted).

[0094] Section FG is a section (future section) where the EV has not yet traveled. Therefore, the power consumption (^yfg) of section FG can be calculated (predicted) in the same way as section FG.

[0095] In this way, the EV's power consumption can be calculated when the traveled section is a past section, when it includes both past and future sections, or when it is a future section. Therefore, it is possible to identify a point that can be reached with the EV's current battery remaining power based on the remaining battery power Ea at point C and the power consumption of each section.

[0096] The prediction evaluation unit 61 calculates the difference (error) between the predicted power consumption value and the actual power consumption value for an EV that has completed traveling the section for which the power consumption was predicted by the traveling state management unit 51. Based on the calculated error, an evaluation value (hereinafter, referred to as an update determination evaluation value) for a set of multiple prediction models and classification rules is calculated. The actual value of the power consumption may be calculated from the information of the charger 221. For example, the actual value of the power consumption in section XY can be calculated based on the difference between the amount of power at the end of charging at a certain supply point X and the amount of power at the start of charging at a supply point Y where charging is next performed. The predicted value of the power consumption amount in section XY can be specified from the prediction result of the driving state management unit 51. Section XY may include multiple consecutive sections predicted by the driving state management unit 51. For example, if section XY is section CF, the predicted value of the power consumption amount in section CF can be calculated by adding up the predicted values ​​of sections CD, DE, and EF. If information on the remaining battery charge of the EV can be obtained from the information device 201 through direct communication, it is also possible to obtain this information and calculate the actual value of the power consumption.

[0097] The update determination evaluation value is used to determine whether or not to start processing to update the classification rules and the prediction model. If the update determination evaluation value satisfies an update start condition based on a threshold (for example, if the update determination evaluation value is greater than the threshold), the processing to update the classification rules and the prediction model is started. If the update determination condition is not satisfied (for example, if the update determination evaluation value is less than the threshold), the processing to update the classification rules and the prediction model is not started.

[0098] The update decision evaluation value is calculated based on the errors calculated once or multiple times for multiple EVs. Each time an error is calculated for an EV, the update decision evaluation value is recalculated (updated). Examples of update decision evaluation values ​​include the mean square error (MSE), mean absolute error (MAE), R2 value, root mean square error (RMSE), and root mean square error ratio (RMSPE).

[0099] The update determination evaluation value may be calculated for each group classified by the classification rule. If the number of groups that satisfy the update start condition is equal to or greater than a threshold, it may be determined that the update start condition is satisfied. A statistical value (such as the average, median, or maximum value) of the update judgment evaluation value between groups may be calculated, and if the statistical value is greater than or equal to a threshold value (or less than the threshold value; whether greater than or equal to the threshold value or less than the threshold value is used depends on the type of statistical value), it may be determined that the update start condition is met. When calculating the average of the update determination thresholds between groups, a weighted average may be calculated by setting weights for the groups. The weight may be larger as the number of data items belonging to a group increases. Also, the weight may be decreased for a group whose EVs belong to the group and whose total battery capacity (total battery capacity for all EVs) is divided by their electricity consumption (the larger the divided value, the less likely the EVs are to run out of battery).

[0100] FIG. 19 is a flowchart of an example of the operation of the prediction evaluation unit 61 according to the embodiment. For an EV that has completed traveling a section for which the power consumption has been predicted by traveling state management unit 51, the difference (error) between the predicted power consumption and the actual power consumption is calculated (S301). Based on the calculated errors, an evaluation value (update determination evaluation value) for each set of a plurality of prediction models and classification rules is calculated (S302). It is determined whether the update determination evaluation value satisfies an update start condition based on a threshold (S303), and if the update start condition is satisfied (YES), it is determined to start processing for updating the classification rules and the prediction model (S304). If the update start condition is not satisfied (NO), the process ends without starting the process for updating the classification rules and the prediction model. This flowchart may be performed, for example, each time the actual and predicted power consumption values ​​for one EV are obtained, or may be performed at regular intervals or each time a certain number of errors are calculated.

[0101] When the prediction evaluation unit 61 determines that the update start condition is satisfied, the model update management unit 71 starts a process for updating at least one of the classification rules and the prediction model.

[0102] FIG. 20 is a flowchart of an example of the operation of the model update management unit 71 according to the embodiment. It is determined whether or not to update the classification rules (S401). Whether or not to update the classification rules can be determined by any method.

[0103] As an example, the error of the prediction model is calculated for each group classified by the classification rule. For example, data for a certain period is used to calculate the error. Data obtained after the creation of the prediction model is used as the data used to calculate the error. In addition to the data, the data used to create the prediction model may be used. An evaluation value (rule evaluation value) is calculated based on the error of each group. For example, a statistical value such as the average, median, or maximum value of the error is calculated. If the rule evaluation value is equal to or greater than a threshold, it is determined that the classification rule should be updated, and if it is less than the threshold, it is determined that the classification rule should not be updated. When calculating the above average, the prediction error may be weighted according to the number of data items belonging to the child node (group). Also, the weight may be decreased for groups with a larger value obtained by dividing the total battery capacity (total battery capacity for the number of EVs) belonging to the child node (group) by the power consumption (the larger the divided value, the less likely the group is to run out of power).

[0104] The threshold value may be input by an operator using an input means of the device. Alternatively, the threshold value may be a value obtained by multiplying the average error for a certain period by a certain coefficient. For example, if an error up to β times the average error for the period is allowed, the value of the certain coefficient is set to β.

[0105] As another example of determining whether to update the classification rule, the number of groups with an error equal to or greater than a certain value is counted. If the count value is equal to or greater than a threshold, it is determined that the classification rule should be updated. If the count value is less than the threshold, it is determined that the classification rule should not be updated. In this case, the threshold value may be input by the operator using the input means of the present device. The threshold value may also be a value obtained by multiplying the number of groups by a certain coefficient. For example, if the number of groups with large errors is allowed to be less than 5% of the number of groups, the value of the certain coefficient is set to 0.05.

[0106] If the classification rule is to be updated (YES), the classification rule update process described in Fig. 15 is performed (S402). The classification rule currently being used (referred to as the original classification rule or the first classification rule) is stored in the model DB 32 together with the classification rule newly generated in the classification rule update process (referred to as the updated classification rule or the second classification rule). This is because there is a possibility that the updated classification rule will be discarded and the original classification rule will be restored in the model update process described later. Next, a model update process is performed based on the updated classification rules generated in the classification rule update process (S403). The model update process will be described in detail later.

[0107] On the other hand, if the classification rules are not to be updated (NO), a model update process is performed based on the currently used classification rules (original classification rules) (S403).

[0108] FIG. 21 is a flowchart of another example of the operation of the model update management unit according to the embodiment. It is assumed that update classification rules are used as the classification rules. Steps S101 to S108 are the same as those in FIG.

[0109] The model update management unit 71 determines a prediction model for each group by the processes of steps S101 to S108. The prediction model determined here is called an update prediction model. The update classification rule and the original classification rule are evaluated (S501). That is, a rule evaluation value (update rule evaluation value) is calculated using the update prediction model for each group of the update classification rule. In addition, a rule evaluation value (original rule evaluation value) is calculated for the original classification rule using the original prediction model. The new data collected in step S107 corresponds to the learning data (driving data) acquired after the generation of the update classification rule and each provisional model. The data used in step S105 corresponds to the learning data (driving data) acquired before the generation of the update classification rule and each provisional model.

[0110] The update rule evaluation value is compared with the original rule evaluation value to determine whether the update classification rule and the update prediction model are improved over the original classification rule and the original classification rule (S502). For example, if the update rule evaluation value is smaller than the original rule evaluation value, it is determined that there is an improvement, and if not, it is determined that there is no improvement. If it is determined that there has been an improvement (YES), the original classification rule and the original prediction model are updated with the updated classification rule and the updated prediction model (S503). The prediction model before the update corresponds to the first prediction model, and the prediction model after the update corresponds to the second prediction model. The multiple conditions of the feature amounts included in the updated classification rule correspond to the multiple second conditions of the feature amounts. The multiple second conditions may be the same as the multiple first conditions included in the original classification rule. On the other hand, if it is determined that there is no improvement (NO), it is decided to continue to use the original classification rules (first classification rules) and the original prediction model (first prediction model) (S504). In this case, the updated classification rules and the updated prediction model may be discarded.

[0111] In the above explanation, it is assumed that the updated classification rules are used as the classification rules, but if it is decided to use the original classification rules in the above classification rule update process, the original classification rules are used. In this case, the same process as in the flowchart of Fig. 21 may be performed. However, in step S503, the process of updating the original prediction model with the updated prediction model is the same, but the classification rules are different from the case where the updated classification rules are used in that the original classification rules are used as they are.

[0112] (Another processing example when performing model update processing using the original classification rule as the classification rule) It is possible to determine whether or not to perform an update for each group, and update the prediction model only for the group for which it is decided to perform an update, and not update the prediction model for the group for which it is decided not to perform an update. More specifically, a model evaluation value of the prediction model (original prediction model) of the group is calculated. If the model evaluation value is equal to or greater than a threshold, it is decided to start a process for updating the prediction model, and if it is less than the threshold, it is decided not to start an update process for updating the prediction model. If it is decided to start this process, the processes of S101 to S108 in Fig. 21 are performed to generate a prediction model for the group (update prediction model). The model evaluation value of the generated prediction model (update model evaluation value) is calculated and compared with the model evaluation value of the original prediction model (original model evaluation value). If the update model evaluation value is smaller than the original model evaluation value, it is determined that the update prediction model is an improvement over the original prediction model, and the original prediction model is updated with the update prediction model. The updated forecast model corresponds to the third forecast model. Otherwise, it is decided to use the original forecast model as it is.

[0113] The threshold value used to determine whether to start the update process may be input by an operator using an input means. Alternatively, the threshold value may be a value obtained by multiplying the average error for a certain period by a certain coefficient. For example, if an error up to β times the average error for the period is allowed, the value of the certain coefficient is set to β.

[0114] In addition, only a part of the classification rules may be updated in the above-mentioned classification rule update process. For example, the model evaluation value of the prediction model for each group of the classification rules is calculated, and a group with a model evaluation value equal to or greater than a threshold is identified. The nodes below the parent node of the child node to which the identified group belongs are targeted for update. In the example of FIG. 13, it is assumed that the model evaluation values ​​of groups A and B are equal to or greater than the threshold, and the model evaluation value of group C is less than the threshold. In this case, the nodes below the parent node of "Temperature < 20" are subject to update. The feature value of "Speed ​​< 40" and the feature value (= 40) that specifies the branching condition are maintained. The model update process may be the same as the process of FIG. 21 described above, but it may be decided to continue using the prediction model for group C as is and exclude it from the update target, or a prediction model for update may be generated according to the process of the flowchart of FIG. 21.

[0115] FIG. 22 is a flowchart illustrating an example of the operation of the information processing apparatus of the embodiment. The driving state management unit 51 acquires driving data of a driving EV from the information device 201 (smartphone, car navigation system, etc.) via the communication unit 11 (S601). The driving state management unit 51 stores the acquired driving data in the driving management DB 52.

[0116] The prediction unit 41 calculates a predicted value of the power consumption for the travel section indicated by the travel data by using the acquired travel data, the classification rule, and a plurality of prediction models (prediction models for each group) (S602). That is, the prediction model to be used is specified based on the travel data and the classification rule, and the predicted value of the power consumption is calculated based on the specified prediction model and the travel data.

[0117] The traveling state management unit 51 acquires a performance value of the amount of power consumption of the EV in the traveling section (S603). The performance value may be acquired from the charger 221 information or from the information device 201.

[0118] The prediction evaluation unit 61 calculates an evaluation value (update determination evaluation value) based on the calculated predicted value of the power consumption and the acquired actual value of the power consumption (S604).

[0119] The control unit 21 judges whether the end condition of this process is satisfied (S605). For example, when the operator inputs an end instruction, it judges that the end condition is satisfied. If the end condition is satisfied (YES), this process is terminated, and if not satisfied (NO), the process proceeds to step S606.

[0120] The prediction evaluation unit 61 judges whether to start processing for updating the classification rules and the prediction model based on the evaluation value calculated in step S604 (S606). Specifically, it judges whether the evaluation value satisfies the update start condition. For example, if the evaluation value is equal to or greater than a threshold, it judges that the update start condition is satisfied, and if it is less than the threshold, it judges that the update start condition is not satisfied. If it is judged that the condition is not satisfied (NO), it returns to step S601.

[0121] If it is determined that the update start condition is met (YES), the model update management unit 71 performs a classification rule update process (see FIG. 15) (S607). Then, the model update process (see FIG. 21) is performed (S608), after which the process returns to step S601.

[0122] The results of the classification rule update process and the model update process may include cases where neither the classification rules nor the prediction model are updated, cases where only one of them is updated, cases where some of the classification rules and some of the prediction model are updated, and cases where only some of the prediction model is updated.

[0123] FIG. 23 is a diagram illustrating an example of a system management screen according to the embodiment. The figure above shows the trend data of the prediction error of the prediction models of each group of classification rules (prediction models 1 to 4). The construction date and time of each model, the latest prediction error, and the date and time of discontinuation of use are shown in the table below the figure. Models 1 and 2 are used until time t1, and the prediction error of model 1 rises sharply at time t1. Due to the rise in the prediction error of model 1 at time t1, classification rule update processing and model update processing are performed by the operation of this embodiment. Models 3 and 4 are generated as models for updating model 1. Model 2 is determined not to need to be updated, and continues to be used. During the trial period, Model 3 and Model 4 are used in parallel with Model 1. The trial period ends at time t2 (= YYYY / MM / DD HH:MM:SS2). Because the average model evaluation value of Model 3 and Model 4 was smaller than the model evaluation value of Model 1, use of Model 1 was discontinued, and Model 3 and Model 4 will be used from time t2 onwards. Model 2 will continue to be used from time 2 onwards. Note that in the table below the figure, the current prediction error of Model 1 is the value at time t2 (date and time use was discontinued).

[0124] The acquisition unit 81 and the calculation unit 82 of the information processing device 101 will be described in detail later.

[0125] 24 shows a hardware configuration of an information processing device 101 according to this embodiment. The information processing device 101 according to this embodiment is configured with 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 connected to each other by a bus 157.

[0126] A CPU (Central Processing Unit) 151 executes a computer program that realizes each of the above-mentioned functional components of the information processing device 101 on a main storage device 155. Each functional component is realized by the CPU 151 executing the computer program.

[0127] 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 device 101.

[0128] The display device 153 displays data or information output from the information processing device 101. The display device 153 is, for example, but not limited to, an LCD (liquid crystal display), a CRT (cathode ray tube), or a PDP (plasma display). The data or information output from the computer device 100 can be displayed by this display device 153.

[0129] The communication device 154 is a circuit for the information processing device 101 to communicate with an external device wirelessly or via wire. Information can be input from the external device via the communication device 154. The information input from the external device can be stored in the DB. The communication unit 11 can be built on the communication device 154.

[0130] The main memory device 155 stores a program for implementing the processing of this embodiment, data required for executing the program, data generated by executing the program, and the like. The program is deployed and executed on the main memory device 155. The main memory device 155 is, for example, a RAM, a DRAM, or an SRAM, but is not limited to these. The storage unit in each embodiment may be constructed on the main memory device 155.

[0131] The external storage device 156 stores the above programs, data required for executing the programs, and data generated by executing the programs. These programs and data are read into the main storage device 155 during processing of this embodiment. The external storage device 156 is, for example, a hard disk, an optical disk, a flash memory, and a magnetic tape, but is not limited to these. The storage unit in each embodiment may be constructed on the external storage device 156.

[0132] The above-mentioned program may be pre-installed in the computer device 100, or may be stored in a storage medium such as a CD-ROM. Also, the program may be uploaded onto the Internet.

[0133] The computer device 100 may include one or more of each of a CPU 151, an input interface 152, a display device 153, a communication device 154, and a main memory device 155, and may be connected to peripheral devices such as a printer and a scanner.

[0134] Furthermore, the information processing device 101 may be configured as a single computer device 100, or may be configured as a system made up of a plurality of computer devices 100 connected to each other.

[0135] <Charger 221> 25 is a block diagram of a charger 221 according to an embodiment. The charger 221 is an example of an energy supplying device. The charger 221 includes a control unit 310 , an operation unit 311 , an output unit 312 , a charging unit 313 , and a communication interface unit 314 .

[0136] The operation unit 311 includes a display device with a touch panel, etc., and receives operations from a user. The output unit 312 includes a display device, a speaker, etc., and outputs various types of information by display, sound, etc. The charging unit 313 includes a charging circuit and is electrically connected to the battery of the EV to supply power to the battery. The communication interface unit 314 communicates with the information processing device 101, the information devices 201A to 201N, and the servers 211A to 211N via the communication network 220.

[0137] The control unit 310 is configured by, for example, a computer having a CPU (Central Processing Unit) and a storage unit. The storage unit of the control unit 310 includes a ROM (Read Only Memory) and a RAM (Random Access Memory) and stores programs executed by the CPU. The control unit 310 controls each unit of the charger 221 and performs various calculations. In addition, the control unit 310 realizes functional units such as a communication unit 320, a charging control unit 321, and a notification unit 322 by the CPU executing the programs stored in the storage unit.

[0138] The communication unit 320 transmits and receives information to and from the information processing device 101, the information devices 201A to 201N, and the servers 211A to 211N, etc., via the communication interface unit 314. The charging control unit 321 controls charging by the charging unit 313 to the battery of the EV. The notification unit 322 notifies various types of information via the output unit 312.

[0139] <Charging process> Next, an example of a method for charging the battery of an EV at a supply point will be described. When charging the battery of an EV, the information processing system 1 executes a charging process. In the information processing device 101, the communication unit 11, the acquisition unit 81, and the calculation unit 82 perform part of the charging process, and in the charger 221, the communication unit 320, the charging control unit 321, and the notification unit 322 perform part of the charging process. The communication unit 11 of the information processing device 101 is an example of an output unit, and the communication unit 320 of the charger 221 is an example of an input unit.

[0140] The acquisition unit 81 of the information processing device 101 acquires calculation information including the location information of the EV located at the supply point, the destination of the EV, and the remaining amount of power (remaining battery power) stored in the EV. At this time, for example, the user operates a charge start button or the like displayed on the information device 201 owned by the user at the supply point. Then, the user inputs the remaining battery power of the EV displayed on the display unit of the EV into the information device 201 (manual input). The remaining battery power thus input is transmitted as charging start request information including the current location of the information device 201, a user ID (EV_ID), and the like, from the information device 201 to the information processing device 101. The current location of the information device 201 is also the current location of the EV (the location of the supply point where the EV is located). The acquisition unit 81 acquires calculation information other than that included in the charge start request information from the user DB 14, the driving management DB, and the system DB. When the EV battery is electrically connected to the charger 221, the charger 221 may obtain the remaining battery charge of the EV and the user ID (EV_ID) from the EV and transmit the information to the information processing device 101 (automatic input).

[0141] The calculation unit 82 of the information processing device 101 calculates a recommended remaining battery level (recommended remaining power) for the EV at the supply point where the EV is located, according to the EV's destination. In other words, the calculation unit 82 calculates a recommended remaining battery level for the EV according to the movement of the EV from the supply point where the EV is located to the EV's destination. In detail, the calculation unit 82 calculates the distance from the EV's current position (supply point) to the destination. The calculation unit 82 calculates the amount of power consumption required for the EV to reach the calculated distance to the destination. At this time, the calculation unit 82 can calculate the necessary power consumption based on various information (battery capacity, battery type, total weight), power consumption, battery deterioration rate, release year, and other information (vehicle information) stored in the system DB. The calculation unit 82 calculates the difference between the calculated power consumption and the current remaining battery charge of the EV, and sets this difference as the recommended remaining battery charge. In other words, the recommended remaining battery charge is, for example, the amount of power required (sufficient) for the EV to travel to the destination. Furthermore, the calculation unit 82 calculates the power supply time required to bring the current battery level of the EV to the recommended battery level (hereinafter also referred to as the required supply time) based on the power supply performance of the charger 221. Note that the calculation unit 82 may use the method of calculating the power consumption performed by the prediction unit 41. That is, the prediction unit 41 may also function as the calculation unit 82. The calculation method of the recommended remaining power of an EV is not limited to the above. For example, the recommended remaining power of an EV may be calculated according to the degree of deterioration of the battery. In addition, since the charging efficiency may be extremely poor when the battery is nearly fully charged, the recommended remaining power of an EV may be calculated according to the time required for power supply, for example. Furthermore, the recommended remaining power level of the EV may be calculated by combining the above examples. The recommended remaining battery level is an example of a recommended remaining energy level.

[0142] The communication unit 11 of the information processing device 101 outputs the recommended battery remaining amount and the necessary supply time calculated by the calculation unit 82. Specifically, the communication unit 11 transmits the recommended battery remaining amount and the necessary supply time to the charger 221.

[0143] The communication unit 320 of the charger 221 receives the recommended battery remaining amount and the necessary supply time transmitted by the communication unit 11 of the information processing device 101. That is, the communication unit 320 receives the recommended battery remaining amount and the necessary supply time output by the communication unit 11 of the information processing device 101.

[0144] When the recommended battery remaining amount is input to communication unit 320, notification unit 322 of charger 221 performs the following process related to power supply. That is, notification unit 322 notifies the recommended battery remaining amount and the necessary supply time via output unit 312. Note that notification unit 322 may notify one of the recommended battery remaining amount and the necessary supply time via output unit 312. That is, notification unit 322 may notify at least one of the recommended battery remaining amount and the necessary supply time via output unit 312.

[0145] When the recommended battery remaining capacity is input to the communication unit 320, the charging control unit 321 of the charger 221 performs the following processing related to power supply. That is, the charging control unit 321 starts supplying power to the battery of the EV, and ends the supply of power to the EV when the remaining power of the EV reaches the recommended battery remaining capacity while power is being supplied to the EV. At this time, the charging control unit 321 determines whether the remaining power of the EV has reached the recommended battery remaining capacity based on, for example, the elapsed time from the start of charging and the required charging time. As another example, the charging control unit 321 may determine whether the remaining power of the EV has reached the recommended battery remaining power based on the difference between the current remaining power of the EV and the recommended battery remaining power of the EV, and the voltage and current of the power supplied by the charger 221.

[0146] Next, an example of a charging process executed by the information processing system 1 will be described with reference to FIG. FIG. 26 is a flowchart showing an example of a charging process of the information processing system 1 of the embodiment.

[0147] The acquisition unit 81 of the information processing device 101 acquires calculation information (S701). Next, the calculation unit 82 of the information processing device 101 calculates the recommended remaining battery level in the EV (S702). At this time, the calculation unit 82 also calculates the required power supply time.

[0148] Next, the communication unit 11 of the information processing device 101 transmits the recommended remaining battery level and the required power supply time calculated by the calculation unit 82 to the charger 221 (S703).

[0149] The communication unit 320 of the charger 221 receives the recommended remaining battery level and the required power supply time from the communication unit 11 of the information processing device 101 (S711).

[0150] Next, the notification unit 322 of the charger 221 notifies the recommended remaining battery level and the required supply time via the output unit 312 (S712).

[0151] Next, the charging control unit 321 of the charger 221 starts supplying power to the battery of the EV (S713). The charging control unit 321 continues charging until the remaining power of the EV reaches the recommended battery remaining power (S714: No). If the remaining power of the EV reaches the recommended battery remaining power (S714: Yes), the charging control unit 321 ends the supply of power to the EV (S715).

[0152] As described above, in this embodiment, the information processing system 1 (electricity supply system) includes an information processing device 101 and a charger 221 (energy supply device) that is installed at a supply point (energy supply point) and supplies electric power (energy) to an EV that moves using electric power. The information processing device 101 includes an acquisition unit 81, a calculation unit 82, and a communication unit 11 (output unit). The acquisition unit 81 acquires calculation information including location information of the EV located at the supply point, the destination of the EV, and the remaining amount of electric power stored in the EV. Based on the calculation information, the calculation unit 82 calculates A recommended battery level (recommended remaining energy level) for the EV at the supply point where the EV is located, according to the EV's destination, is calculated. The communication unit 11 outputs the recommended battery level calculated by the calculation unit 82. The charger 221 includes a communication unit 320 (input unit), a notification unit 322 (supply processing unit), and a charging control unit 321 (supply processing unit). The recommended battery level output by the communication unit 11 is input to the communication unit 320. The notification unit 322 and the charging control unit 321 (supply processing unit) perform processing related to the supply of power when the recommended battery level is input to the communication unit 320.

[0153] According to the present embodiment, the calculation unit 82 calculates the recommended remaining battery level of the EV at the supply point where the EV is located according to the destination of the EV, and can notify the user of the recommended remaining battery level or stop charging when the recommended remaining battery level is reached. This can prevent the user from waiting for a power supply to be too long.

[0154] In this embodiment, the notification unit 322 notifies at least one of the recommended battery level and the power supply time required to bring the remaining power of the EV to the recommended battery level. Thus, the user can adjust the amount of power supply based on the recommended battery level and / or the power supply time required to bring the remaining power of the EV to the recommended battery level.

[0155] In this embodiment, the charging control unit 321 stops supplying power to the EV when the remaining power of the EV reaches the recommended battery remaining power during power supply to the EV. Therefore, the user does not need to adjust the amount of power supply. Also, the information processing system 1 can manage the amount of charge of the EV.

[0156] In the above embodiment, in the process of Fig. 26, an example is shown in which the charging control unit 321 ends the supply of power to the EV when the remaining power of the EV reaches the recommended battery remaining power, but this is not limiting. For example, even if the remaining power of the EV reaches the recommended battery remaining power, the EV may be charged to an amount (e.g., full charge) specified by the user via the charger 221 or the information device 201, subject to payment of an additional fee or the like.

[0157] Although some embodiments of the present invention have been described, these embodiments are presented as examples 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 modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and spirit of the invention, and are included in the scope of the invention and its equivalents described in the claims. [Explanation of symbols]

[0158] 1...information processing system (power supply system), 11...communication unit (output unit), 51...driving state management unit, 81...acquisition unit, 82...calculation unit, 101...information processing device, 221...charger (energy supply device), 320...communication unit (input unit), 322...notification unit (supply processing unit), 321...charging control unit (supply processing unit).

Claims

1. An information processing device; an energy supplying device that is installed at an energy supply point and supplies the energy to a moving body that moves using the energy; Equipped with The information processing device includes: an acquisition unit that acquires location information of the moving object located at the energy supply point, a destination of the moving object, and a current remaining amount of the energy stored in the moving object; a calculation unit that calculates an amount of energy required for the moving body to reach the destination from the energy supply point where the moving body is located based on the location information acquired by the acquisition unit and the destination acquired by the acquisition unit, and calculates a difference between the amount of energy required for the moving body to reach the destination and a current remaining amount of the energy of the moving body acquired by the acquisition unit; an output unit that outputs the difference calculated by the calculation unit; a travel state management unit that, while the moving body is moving after the energy is supplied at the energy supply point, identifies a next energy supply point that can be reached with the current remaining amount of energy of the moving body based on the remaining amount of energy of the moving body at the time when the supply of energy at the energy supply point is completed, and transmits the identified next energy supply point to an information device mounted on the moving body; Equipped with The energy supply device comprises: an input unit to which the difference output by the output unit is input; a supply processing unit that performs a process related to the supply of the energy when the difference is input to the input unit; Equipped with Energy supply system.

2. The supply processing unit notifies, as the process, at least one of the difference and a supply time of the energy required to make the remaining amount of the energy of the moving object the required amount of energy. The energy supply system according to claim 1 .

3. the supply processing unit, as the process, terminates the supply of the energy to the moving body when a remaining amount of the energy of the moving body reaches the amount of the required energy during the supply of the energy to the moving body.

3. The energy supply system according to claim 1 or 2.

4. an acquisition unit that acquires location information of a moving body that moves using supplied energy and is located at an energy supply point, a destination of the moving body, and a current remaining amount of the energy stored in the moving body; a calculation unit that calculates an amount of energy required for the moving body to reach the destination from the energy supply point where the moving body is located based on the location information acquired by the acquisition unit and the destination acquired by the acquisition unit, and calculates a difference between the amount of energy required for the moving body to reach the destination and a current remaining amount of the energy of the moving body acquired by the acquisition unit; an output unit that outputs the difference calculated by the calculation unit; a travel state management unit that, while the moving body is moving after the energy is supplied at the energy supply point, identifies a next energy supply point that can be reached with the current remaining amount of energy of the moving body based on the remaining amount of energy of the moving body at the time when the supply of energy at the energy supply point is completed, and transmits the identified next energy supply point to an information device mounted on the moving body; Equipped with Information processing device.

Images