Patent Description:
<CIT> (<CIT>) discloses a management device that manages a plurality of pieces of consumer equipment for demand response. The management device includes a storage unit and a calculation unit. The storage unit stores actual power demand data of the pieces of consumer equipment. The calculation unit calculates the available balancing capacity in at least one demand response scenario, based on the actual power demand data. A management system for electric vehicles and corresponding charging stations is disclosed in <CIT>).

Vehicles such as battery electric vehicles (BEVs), plug-in hybrid electric vehicles (PHEVs), and fuel cell electric vehicles (FCEVs) are configured to be charged from and discharged to a power grid when electrically connected to the power grid. A technique of connecting these vehicles to a power grid to make them function as a virtual power plant (VPP) has been proposed (see, for example, <CIT>). According to this technique, power can be supplied from the vehicles to the power grid when the power grid is short of power, and power can be supplied from the power grid to the vehicles when the power grid has surplus power.

One possible way to use this technique is to manage a plurality of vehicles by a server. The server acquires data including location information from those vehicles that are not connected to the power grid out of the plurality of vehicles. The server predicts, for each vehicle, the time when the vehicle becomes connectable to the power grid (connection time), based on the acquired data. An appropriate plan to make each vehicle function as a VPP can be developed by accurately predicting the connection time of each vehicle to the power grid.

The server acquires data from the vehicles through communication, but this can cause excessive data traffic. This problem can be particularly significant when the server manages a very large number of vehicles. Excessive data traffic can increase various communication costs.

The present disclosure provides a server that manages a plurality of vehicles, a vehicle, and a communication control method for managing a plurality of vehicles.

According to the configuration of (<NUM>) or the method of (<NUM>), the communication costs can be reduced as in the configuration of (<NUM>).

According to the present disclosure, the communication costs for the server to manage the vehicles can be reduced.

Hereinafter, an embodiment of the present disclosure will be described in detail with reference to the drawings. The same or corresponding portions are denoted by the same signs throughout the drawings, and description thereof will not be repeated.

In the following embodiment, a configuration in which vehicles are made to function as a VPP will be described by way of example. However, it is not essential to implement a VPP. Charging/discharging may be performed between the vehicles and a power grid regardless of the power supply and demand balance of the power grid. In the present disclosure, "charging/discharging" means either or both of charging and discharging. Charging/discharging may refer to only charging or only discharging.

<FIG> schematically shows an overall configuration of a vehicle management system according to an embodiment of the present disclosure. A vehicle management system <NUM> includes a plurality of vehicles <NUM>, a plurality of user terminals <NUM>, a vehicle management server <NUM>, and a virtual power plant (VPP) server <NUM>. The vehicles <NUM>, the user terminals <NUM>, the vehicle management server <NUM>, and the VPP server <NUM> are connected via a network <NUM> such as the Internet so that they can communicate bidirectionally.

Each of the vehicles <NUM> is a vehicle equipped with a driving battery <NUM> (see <FIG>). More specifically, each vehicle <NUM> is, for example, a battery electric vehicle, a plug-in hybrid electric vehicle, or a fuel cell electric vehicle. A detailed configuration of the vehicle <NUM> will be described with reference to <FIG>.

Each of the user terminals <NUM> is an information terminal carried by the user of a corresponding one of the vehicles <NUM>. The user terminal <NUM> is, for example, a smartphone, a tablet, or a personal computer (PC). The user terminal <NUM> is used to manage the user's schedule. The user terminal <NUM> also includes a Global Positioning System (GPS) receiver, not shown, and is configured to identify the location of the user terminal <NUM>.

The vehicle management server <NUM> is a server that manages the vehicles <NUM>. The vehicle management server <NUM> predicts, for each vehicle <NUM>, the arrival time of the vehicle <NUM> at a destination point. The vehicle management server <NUM> also predicts, for each vehicle <NUM>, the state of charge (SOC) of the battery <NUM> at the arrival time at the destination point. The vehicle management server <NUM> transmits the predicted arrival time and the predicted SOC at the arrival time to the VPP server <NUM>. A detailed configuration of the vehicle management server <NUM> will be described with reference to <FIG>. The vehicle management server <NUM> corresponds to the "server" according to the present disclosure.

The VPP server <NUM> develops a plan to use various distributed energy resources (DERs), not shown, as a VPP. More specifically, the VPP server <NUM> sets the time of day to use each DER as a VPP, and calculates the electric energy for charging/discharging each DER.

DERs are relatively small-scale power equipment capable of supplying and receiving to and from a power grid. DERs include, for example, power generation DERs and power storage DERs. The power generation DERs may include variable renewable energy and generators. The variable renewable energy refers to power generation equipment whose power output fluctuates depending on weather conditions. The variable renewable energy include, for example, solar power generation equipment and wind power generation equipment. On the other hand, the generators refer to power generation equipment that does not depend on the weather conditions. The generators include, for example, a steam turbine generator, a gas turbine generator, a diesel engine generator, a gas engine generator, a biomass generator, a stationary fuel cell, and a cogeneration system. The power storage DERs may include a power storage system and a heat storage system. The power storage system is a stationary power storage device that stores power generated by the variable renewable energy etc. However, the power storage system may be a power-to-gas device that produces gaseous fuel (hydrogen, methane, etc.) using electric power. The heat storage system includes a heat storage tank that stores a liquid medium warmed by a heat source while keeping the liquid medium warm.

The VPP server <NUM> develops a plan to use the vehicles <NUM> as a VPP, in addition to the DERs. That is, for each vehicle <NUM>, the VPP server <NUM> sets the time of day to use the vehicle <NUM> as a VPP and calculates the electric energy for charging/discharging the vehicle <NUM>, based on the information from the vehicle management server <NUM> (the arrival time of the vehicle <NUM> at the destination point and the SOC at the arrival time).

<FIG> shows in more detail the configurations of the vehicle <NUM> and the vehicle management server <NUM>. The vehicle <NUM> is configured to receive and supply power from and to (be charged/discharged from/to) the power grid via a charging cable (not shown). The vehicle <NUM> includes an inlet <NUM>, a power converter <NUM>, a battery <NUM>, a monitoring unit <NUM>, a navigation system <NUM>, a communication module <NUM>, and an electronic control unit (ECU) <NUM>. The monitoring unit <NUM>, the navigation system <NUM>, the communication module <NUM>, and the ECU <NUM> are connected by an in-vehicle network <NUM> such as a Controller Area Network (CAN) so that they can communicate with each other.

The inlet <NUM> is configured to be connected to a charging cable (not shown) extending from charging equipment such as Electric Vehicle Service Equipment (EVSE). Although not shown in the figure, the charging equipment is electrically connected to the power grid. The charging equipment as used herein is also configured so that the vehicle <NUM> can be discharged to the charging equipment.

The power converter <NUM> is electrically connected between the inlet <NUM> and the battery <NUM>. The power converter <NUM> includes, for example, an alternating current-to-direct current (AC-to-DC) converter (inverter). The power converter <NUM> converts AC power supplied from the charging equipment via the inlet <NUM> to DC power, and supplies the DC power to the battery <NUM>. The power converter <NUM> also converts DC power stored in the battery <NUM> to AC power, and supplies the AC power to the charging equipment via the inlet <NUM>.

The battery <NUM> is an assembled battery including a plurality of cells. Each cell is a secondary cell such as a lithium-ion cell or a nickel metal hydride cell. The battery <NUM> stores power for generating the driving force for the vehicle <NUM>. The battery <NUM> is charged with power generated by a motor generator (not shown) or power supplied from the charging equipment. The battery <NUM> can also discharge the stored power to the charging equipment.

The monitoring unit <NUM> includes a voltage sensor and a current sensor (both not shown). The voltage sensor detects the voltage of the battery <NUM> and outputs the detection result to the ECU <NUM>. The current sensor detects the current flowing through the battery <NUM> and outputs the detection result to the ECU <NUM>. The ECU <NUM> can calculate the SOC of the battery <NUM> based on the detection results from the voltage sensor and the current sensor.

The navigation system <NUM> includes a GPS receiver, not shown. The GPS receiver identifies the location of the vehicle <NUM> based on radio waves from artificial satellites (not shown). The navigation system <NUM> records a travel route of the vehicle <NUM> or find a travel route according to the location of the vehicle <NUM> identified by the GPS receiver.

The communication module <NUM> is an in-vehicle Data Communication Module (DCM) and is configured to allow the ECU <NUM> and the vehicle management server <NUM> to bidirectionally communicate with each other. Hereinafter, information indicating the location of the vehicle <NUM> is sometimes referred to as the "location information. " The location information associates the location of the vehicle <NUM> with time. Information indicating the SOC of the battery <NUM> is hereinafter sometimes referred to as the "SOC information. " The location information and the SOC information are hereinafter sometimes collectively referred to as the "VPP data. " The communication module <NUM> transmits the VPP data to the vehicle management server <NUM>. The location information need not necessarily be transmitted from the vehicle <NUM> to the vehicle management server <NUM>, and may be transmitted from the user terminal <NUM> to the vehicle management server <NUM>.

The ECU <NUM> includes a processor such as a central processing unit (CPU), and a memory such as a read-only memory (ROM) and a random access memory (RAM). The ECU <NUM> controls each device in the vehicle <NUM> so that the vehicle <NUM> is in a desired state, based on detected values from a sensor group (not shown) and programs stored in the memory. The ECU <NUM> also generates various kinds of data to be transmitted to the vehicle management server <NUM>.

The vehicle management server <NUM> includes an application server <NUM>, a database server <NUM>, and a communication device <NUM>. The application server <NUM> includes a processor <NUM> and a memory <NUM>. The processor <NUM> is, for example, a CPU. The processor <NUM> is configured to perform predetermined arithmetic operations described in a program. The memory <NUM> includes, for example, a ROM and a RAM. The memory <NUM> stores programs that are executed by the processor <NUM>. The memory <NUM> also temporarily stores data generated by the processor <NUM> executing the program, and data received from the vehicle <NUM> and/or the user terminal <NUM> (see <FIG>) via the communication device <NUM>.

The application server <NUM> collects the VPP data (location information and SOC information) from each of the vehicles <NUM>. More specifically, in the present embodiment, the application server <NUM> requests the VPP data from each vehicle <NUM>. Each vehicle <NUM> transmits the VPP data to the application server <NUM> in response to the request from the application server <NUM>.

When the vehicle <NUM> is traveling, the application server <NUM> can predict the arrival time at the destination point where the vehicle <NUM> can function as a VPP (typically the arrival time at home), based on the location information. The destination point of the vehicle <NUM> can be set in advance by, for example, the user. In this example, the destination point of the vehicle <NUM> is the user's home where the charging equipment is installed. After the user gets home, the user connects the vehicle <NUM> to the charging equipment (power grid) via the charging cable. This allows the vehicle <NUM> to function as a VPP. Therefore, in the case where the destination point of the vehicle <NUM> is a place where the vehicle <NUM> is connectable to the power grid, the arrival time of the vehicle <NUM> at the destination point is the time when the vehicle <NUM> becomes connectable to the power grid ("connection time" according to the present disclosure). The application server <NUM> can also predict the electric energy the vehicle <NUM> functioning as a VPP can receive or supply from or to the power grid after the user gets home, based on the SOC information (SOC at the arrival time at the destination point).

The database server <NUM> includes a vehicle database (DB) <NUM>, a driving history database (DB) <NUM>, a time prediction model database (DB) <NUM>, and a confidence database (DB) <NUM>.

The vehicle database <NUM> stores the SOC information collected from each vehicle <NUM>. The vehicle database <NUM> also stores information on the vehicle type, year, model, and specifications (such as fuel economy and/or electricity economy) of each vehicle <NUM>.

The driving history database <NUM> stores the location information collected from each vehicle <NUM> as a driving history of the vehicle <NUM>. The driving history database <NUM> stores the days of the week (including whether they are a weekday or a holiday) when the vehicle <NUM> was driven, and also stores weather information (weather, temperature, precipitation, wind speed, etc.) of the area where the vehicle <NUM> was driven during traveling of the vehicle <NUM>. The driving history database <NUM> also stores map information (including traffic congestion information).

The time prediction model database <NUM> stores time prediction models prepared for each vehicle <NUM>. The time prediction model is used to predict the arrival time of the vehicle <NUM> at the destination point based on the learning results of the movement pattern of the user of the vehicle <NUM>.

The confidence database <NUM> stores data for calculating the confidence of the arrival time predicted using the time prediction model (probability distribution of the arrival time that will be described later). The time prediction model and the confidence will be described later in detail.

Not all of the above databases have to be included in the database server <NUM>. That is, a part or all of the databases may be included in a server other than the vehicle management server <NUM> (e.g., a cloud server).

A very large number of vehicles <NUM> can be under management of the vehicle management server <NUM>. Especially in such a case, data traffic required for the application server <NUM> to collect the VPP data (location information and SOC information) from the vehicles <NUM> may become excessive. This may increase various communication costs such as data communication fees for the vehicles <NUM> and/or the vehicle management server <NUM>, costs for increasing the data processing capability of the application server <NUM>, and costs for increasing the communication performance of the communication device <NUM>.

Therefore, the present embodiment uses a configuration in which the "frequency of communication" of the VPP data from the vehicle <NUM> to the application server <NUM> is variably set. Specifically, the application server <NUM> sets the frequency of communication of the VPP data for each vehicle <NUM>. The frequency of communication is also variably set temporally. For example, when the frequency of communication in a first time period is set to once every <NUM> minutes for a certain vehicle, the application server <NUM> requests the VPP data from this vehicle every <NUM> minutes in the first time period. When the frequency of communication in a second time period is set to once every <NUM> minutes for this vehicle, the application server <NUM> requests the VPP data from this vehicle every <NUM> minutes in the second time period. This vehicle transmits the VPP data to the application server <NUM> in response to a request from the application server <NUM>.

The frequency of communication of the VPP data from the vehicle <NUM> to the application server <NUM> can be thus be switched temporally. The frequency of communication of the VPP data can also be set differently for each vehicle. As will be described below, the communication costs for the vehicle management server <NUM> to manage the vehicles <NUM> can be reduced by determining, for each vehicle <NUM>, an appropriate frequency of communication according to the user's movement history (driving history of the vehicle <NUM>).

<FIG> is a functional block diagram illustrating a part of functions of the vehicle management server <NUM>. The vehicle management server <NUM> includes a time prediction unit <NUM>, a learning unit <NUM>, and a confidence calculation unit <NUM>.

The time prediction unit <NUM> predicts the arrival time of the vehicle <NUM> at the destination point. More specifically, the time prediction unit <NUM> includes a time prediction model <NUM> prepared by, for example, machine learning. The time prediction model <NUM> includes a probability model <NUM> and a parameter <NUM>. The probability model <NUM> is a model that expresses dependencies between or among a plurality of events by probabilities (conditional transition probabilities), and is, for example, a Bayesian network or an nth-order Markov chain model. The parameter <NUM> includes a weighting factor etc. used for machine learning of the probability model <NUM>.

The time prediction unit <NUM> receives as an input the prediction conditions such as the location information of the vehicle <NUM> included in the VPP data (information in which the location of the departure point and/or the travel route is associated with the time), the day of the week, and the weather from the vehicle <NUM>. The time prediction unit <NUM> may receive the user's schedule (e.g., information indicating points to be visited on the way to the destination point) managed using the user terminal <NUM> (see <FIG>). The time prediction unit <NUM> calculates the time when the vehicle <NUM> is predicted to arrive at the destination point (predicted arrival time) using the learned (in this example, the learning unit <NUM> performs additional learning) time prediction model <NUM>. The calculated predicted arrival time is output to the confidence calculation unit <NUM> and the outside (VPP server <NUM>).

The time prediction unit <NUM> calculates the predicted arrival time every time it receives the prediction conditions from the vehicle <NUM>. That is, the predicted arrival time is updated every time the prediction conditions are received (every time the VPP data is collected). The prediction accuracy of the arrival time can thus be improved as the vehicle <NUM> approaches the destination point.

The learning unit <NUM> learns the time prediction model <NUM> using example data and ground truth data. The example data includes, for example, the location information of the vehicle <NUM>, the day of the week, and the weather. The ground truth data is the time when the vehicle <NUM> actually arrived at the destination point (actual arrival time). The learning unit <NUM> adjusts, for each piece of example data, the parameter <NUM> (e.g., the weighting factor) so that the predicted arrival time approaches the actual arrival time (ground truth data). As described above, the vehicle management server <NUM> is configured to update the learned time prediction model <NUM> by additional learning.

The confidence calculation unit <NUM> calculates, for each set of prediction conditions including the location information of the vehicle <NUM>, the day of the week, and the weather, the confidence of the arrival time predicted using the time prediction model <NUM>. The confidence is used to set the frequency of communication of the VPP data.

A method for calculating the confidence will be described with reference to <FIG>. As described above, the time prediction model <NUM> is a model that probabilistically expresses the predicted arrival time at the destination point. Therefore, the prediction results obtained using the time prediction model <NUM> are represented by a probability distribution.

<FIG> is a graph of a probability distribution of the arrival time prediction results (predicted arrival time) obtained using the time prediction model <NUM>. The abscissa represents the predicted arrival time obtained using the time prediction model <NUM>. The ordinate represents the probability for each time. For example, the probability that the vehicle <NUM> will arrive at the destination point at time t1 is predicted to be p1 by using the time prediction model <NUM>.

The confidence is an index of the spread (width) of the probability distribution of the predicted arrival time. The confidence may be, for example, the standard deviation σ of the probability distribution. The confidence may be 2σ or 3σ. The confidence may be the full width at half maximum (FWHM) of the probability distribution.

<FIG> illustrates the level of the confidence. The probability distribution of the predicted arrival time can have various widths. However, the sum of the probabilities of each time (integrated area of the probability distribution) is the same (<NUM>%) regardless of the width of the probability distribution. Therefore, the narrower the width of the probability distribution, the higher the height of the probability distribution. Accordingly, the confidence may be the height (peak) of the probability distribution. As shown in <FIG>, the narrower the width of the probability distribution, that is, the higher the peak of the probability distribution, the higher the confidence. An example in which the confidence is the standard deviation σ of the probability distribution will be described below.

<FIG> shows a specific example of data that is handled by the vehicle management server <NUM>. In the example shown in <FIG>, the arrival time (median of the probability distribution) is predicted to be <NUM>:<NUM> when, for example, it is Monday and sunny, the departure point is the user's office, and the departure time is <NUM>:<NUM>. At this time, the width of the probability distribution of the predicted arrival time is predicted to be <NUM> minutes. The confidence is calculated according to the width of the probability distribution.

In this example, the confidence is divided into levels using symbols. Level A has the narrowest width of the probability distribution (e.g., <NUM> minutes or less) and indicates the highest confidence. Level B has the second narrowest width of the probability distribution (e.g., <NUM> minutes or less) and indicates the second highest confidence. The same applies to the subsequent levels C, D, etc. In the present embodiment, the frequency of communication of the VPP data (location information and SOC information) from the vehicle <NUM> to the vehicle management server <NUM> is set according to the level of the confidence.

<FIG> is a first diagram illustrating a method for determining the frequency of communication according to the level of the confidence. The frequency of communication means, for example, the time interval between one data transmission and a subsequent data transmission. In this example, when the level of the confidence is A, the frequency of communication (time interval) is set to <NUM> minutes. When the level of the confidence is B, the frequency of communication is set to <NUM> minutes. When the level of the confidence is C, the frequency of communication is set to five minutes. The shorter the time interval, the higher the frequency of communication.

<FIG> is a second diagram illustrating a method for determining the frequency of communication according to the level of the confidence. The frequency of communication may be the number of data transmissions that are performed while the vehicle <NUM> travels from the departure point to the destination point. When the level of the confidence is A, the frequency of communication (number of data transmissions) is set to three times. When the level of the confidence is B, the frequency of communication is set to four times. When the level of the confidence is C, the frequency of communication is set to five times. The larger the number of data transmissions, the higher the frequency of communication.

<FIG> is a graph showing a first example of the relationship between the confidence and the frequency of communication. The abscissa represents the confidence. The ordinate represents the frequency of communication. The same applies to <FIG> that will be described later.

As shown in <FIG>, the vehicle management server <NUM> can set the frequency of communication to a lower value as the confidence is higher. That is, the vehicle management server <NUM> can determine the frequency of communication in such a manner that the higher the confidence, the longer the time interval between two consecutive data transmissions or the smaller the number of data transmissions performed from the departure point to the destination point.

When the confidence is low, the width of the probability distribution of the predicted arrival time is great. This means that the predicted arrival time varies greatly. The predicted arrival time may therefore deviate greatly from the actual arrival time. Accordingly, it is desirable to set the frequency of communication to a high value when the confidence is low. As described above, the vehicle management server <NUM> collects the VPP data from the vehicle <NUM> at the frequency of communication set according to the confidence, and updates the predicted arrival time every time the vehicle management server <NUM> collects the VPP data from the vehicle <NUM>. Since the predicted arrival time is updated based on the latest VPP data, the prediction accuracy of the arrival time can be improved.

On the other hand, when the confidence is high, the width of the probability distribution of the predicted arrival time is narrow, and the predicted arrival time does not vary so much. It can therefore be said that the predicted arrival time is less likely to deviate greatly from the actual arrival time. Accordingly, the frequency of communication can be set to a low value when the confidence is high. The communication costs can thus be reduced without excessively reducing the prediction accuracy of the arrival time.

<FIG> shows an example in which the frequency of communication changes stepwise (in steps) according to the confidence. However, the frequency of communication may change linearly or curvilinearly according to the confidence.

<FIG> is a graph showing a second example of the relationship between the confidence and the frequency of communication. As shown in <FIG>, the vehicle management server <NUM> may set the frequency of communication in such a manner that the lower the confidence, the lower the frequency of communication when the confidence is lower than R1, and that the higher the confidence, the lower the frequency of communication when the confidence is higher than R2. R2 has a higher value than R1.

The reason why the frequency of communication is set to a lower value as the confidence increases when the confidence is higher than R2 is similar to the reason described with reference to <FIG>. That is, when the confidence is high, the predicted arrival time is less likely to deviate greatly from the actual arrival time. Therefore, the frequency of communication is set to a low value to reduce the communication costs.

The reason why the frequency of communication is set to a lower value as the confidence decreases when the confidence is lower than R1 will be described below. When the confidence is low, the predicted arrival time varies greatly and may greatly deviate from the actual arrival time. Even when the predicted arrival time is updated based on the latest VPP data, the confidence based on the new VPP data may not increase significantly, and as a result, the prediction accuracy of the arrival time may remain low. Therefore, when the confidence is low, the frequency of communication need not necessarily be increased, and the communication costs can be reduced by reducing the frequency of communication. It can also be said that, when the confidence is low, priority is given to reducing the communication costs over improving the prediction accuracy of the arrival time.

<FIG> is a graph showing a third example of the relationship between the confidence and the frequency of communication. <FIG> shows an example in which when the confidence is lower than R1, the frequency of communication is set to be lower as the confidence decreases (see dashed line in <FIG>). However, as shown in <FIG>, the frequency of communication may be constant when the confidence is lower than R1. In this case, when the confidence is higher than R2, the communication costs can be reduced as in <FIG>. On the other hand, when the confidence is lower than R1, a certain level of prediction accuracy of the arrival time can be obtained without increasing the communication costs, unlike the case of <FIG>.

<FIG> is a flowchart of a process that is performed in the vehicle management system <NUM> according to the present embodiment. The process shown in this flowchart is performed when a predetermined condition is satisfied (e.g., every predetermined cycle). Each step is implemented by software processing by the vehicle management server <NUM> (application server <NUM>), but may be implemented by hardware (electric circuit) located in the vehicle management server <NUM>. The term "step" is hereinafter abbreviated as "S.

In S11, the vehicle management server <NUM> acquires the VPP data (location information and SOC information) from the vehicle <NUM>. The vehicle <NUM> transmits the VPP data to the vehicle <NUM> at the frequency of communication set in advance. The frequency of communication set in advance may be a predetermined initial value of the frequency of communication, or may be a value set as a result of the previous series of steps. Although not shown in the figure, the vehicle management server <NUM> separately acquires the day of the week, the weather in the area around the vehicle <NUM>, etc..

In S12, the vehicle management server <NUM> determines whether the vehicle <NUM> satisfies a VPP participation condition. More specifically, the vehicle management server <NUM> can determine that the vehicle <NUM> satisfies the VPP participation condition when a contract in which the vehicle <NUM> participates in the VPP has been concluded in advance. Information on this contract is stored in the vehicle database <NUM> (see <FIG>). The vehicle management server <NUM> can also determine that the vehicle <NUM> satisfies the VPP participation condition when the user of the vehicle <NUM> has performed to the user terminal <NUM> a predetermined operation for the vehicle <NUM> to participate in the VPP (e.g., an operation to allow the vehicle <NUM> to participate in the VPP during a specific period or during a specific time period of the day). When the vehicle <NUM> satisfies the VPP participation condition (YES in S12), the routine proceeds to S13. When the vehicle <NUM> does not satisfy the VPP participation condition (NO in S12), the vehicle management server <NUM> ends the process without performing the subsequent steps.

In S13, the vehicle management server <NUM> determines whether the vehicle <NUM> has started traveling toward the destination point or whether the vehicle <NUM> is traveling toward the destination point, based on the location information of the vehicle <NUM>. When the vehicle <NUM> has started traveling toward the destination point or is traveling toward the destination point (YES in S13), the routine proceeds to S14. The subsequent steps will not be performed before departure of the vehicle <NUM> (NO in S13). When the vehicle <NUM> arrives at the destination point, the vehicle management server <NUM> determines NO in S13.

In S14, the vehicle management server <NUM> predicts the arrival time of the vehicle <NUM> at the destination point (e.g., arrival time at home) from the location information of the vehicle <NUM>, day of the week, weather, etc. by using the time prediction model <NUM> (see <FIG>) corresponding to the vehicle <NUM>.

In S15, the vehicle management server <NUM> predicts the SOC of the battery <NUM> of the vehicle <NUM> at the predicted arrival time. The vehicle management server <NUM> can calculate the amount of power (Wh) that will be used by the vehicle <NUM> to reach the destination point, based on the distance (km) of a predicted travel route of the vehicle <NUM> and the electricity economy (km/kWh or Wh/km) of the vehicle <NUM>. Therefore, the vehicle management server <NUM> can predict the SOC at the arrival time from the current SOC and the amount of power that will be used by the vehicle <NUM> to reach the destination point.

The vehicle management server <NUM> transmits the predicted arrival time and the predicted SOC of the battery <NUM> at the predicted arrival time to the VPP server <NUM>. The VPP server <NUM> uses the predicted arrival time of the vehicle <NUM> and the predicted SOC to develop a VPP plan.

In S16, the vehicle management server <NUM> calculates the confidence of the predicted arrival time obtained in S14. As described in detail above with reference to <FIG>, the vehicle management server <NUM> can calculate the confidence based on the width or height of the probability distribution of the predicted arrival time.

In S17, the vehicle management server <NUM> sets the frequency of communication of the VPP data with the vehicle <NUM> according to the confidence of the predicted arrival time. The vehicle management server <NUM> can set the frequency of communication from the confidence by using, for example, the maps of <FIG> defining the relationship between the confidence and the frequency of communication. The vehicle management server <NUM> may use a data table, a function, a relational expression, etc. instead of the maps.

The vehicle management server <NUM> switches the timing to request the VPP data from the vehicle <NUM> at the set frequency of communication. That is, when the frequency of communication changes, the vehicle management server <NUM> changes the time interval to request the VPP data from the vehicle <NUM> (or the number of times the vehicle management server <NUM> requests the VPP data from the vehicle <NUM> until the vehicle <NUM> reaches the destination point). The vehicle <NUM> transmits the VPP data to the vehicle management server <NUM> in response to the request from the vehicle management server <NUM>.

Alternatively, the vehicle management server <NUM> may transmit the set frequency of communication to the vehicle <NUM>. The vehicle <NUM> may voluntarily transmit the VPP data to the vehicle management server <NUM> at the frequency of communication, instead of every time the vehicle <NUM> receives a request from the vehicle management server <NUM>.

As described above, in the present embodiment, when the vehicle <NUM> is not home etc. and is not connected to the power grid, the vehicle management server <NUM> predicts the arrival time at home when the vehicle <NUM> becomes connectable to the power grid. The time prediction model <NUM> that probabilistically calculates the arrival time at home is used to predict the arrival time at home. The arrival time at home predicted using the time prediction model <NUM> is expressed as a probability distribution. The vehicle management server <NUM> calculates the confidence of the arrival time at home to be higher as the width of the probability distribution of the arrival time at home is narrower (as the height of the probability distribution is greater). The vehicle management server <NUM> sets the frequency of communication of the VPP data according to the confidence of the arrival time at home. For example, the vehicle management server <NUM> sets the frequency of communication of the VPP data to a lower value as the confidence of the arrival time at home is higher. This is because when the confidence of the arrival time at home is high, the arrival time at home can be accurately predicted without acquiring the VPP data frequently. Therefore, according to the present embodiment, the communication costs required for communication of the VPP data can be reduced.

An example in which the vehicle management server <NUM> performs all the steps is described in the embodiment (see <FIG>). However, the vehicle <NUM> may perform a part of the steps.

<FIG> is a flowchart of a process that is performed in the vehicle management system <NUM> according to a modification of the embodiment. A process that is performed by the vehicle <NUM> is shown on the left side of the figure, and a process that is performed by the vehicle management server <NUM> is shown on the right side of the figure.

In S21, the vehicle <NUM> transmits the VPP data including location information and SOC information to the vehicle management server <NUM>.

In S31, the vehicle management server <NUM> determines whether the vehicle <NUM> has started traveling toward the destination point or is traveling toward the destination point. When the vehicle <NUM> has started traveling toward the destination point or is traveling toward the destination point (YES in S31), the vehicle management server <NUM> performs S32 to S34. S32 to S34 correspond to S14 to S16 in the embodiment. The vehicle management server <NUM> transmits the confidence calculated in S34 to the vehicle <NUM>.

The vehicle <NUM> sets the frequency of communication of the VPP data to the vehicle management server <NUM> according to the confidence received from the vehicle management server <NUM> (S22). From then on, the vehicle <NUM> can voluntarily transmit the VPP data to the vehicle management server <NUM> at the frequency of communication set by the vehicle <NUM> itself.

As described above, the vehicle <NUM> and the vehicle management server <NUM> may cooperatively perform the series of steps. According to this modification as well, as in the embodiment, the communication costs required for communication of the VPP data can be reduced.

Claim 1:
A server (<NUM>) that manages a plurality of vehicles (<NUM>), each of the vehicles being configured to perform either or both of charging and discharging from and to a power grid when electrically connected to the power grid, the server comprising:
a communication device (<NUM>) that acquires data including location information of a target vehicle through communication, the target vehicle being a vehicle not connected to the power grid out of the vehicles (<NUM>); and
a processor (<NUM>) that sets a frequency of communication at which the data is acquired from the target vehicle, wherein
the processor (<NUM>) acquires the data from the target vehicle,
the processor (<NUM>) predicts connection time from the acquired data according to a prediction model (<NUM>) and calculates confidence of the predicted connection time, the connection time being time when the target vehicle becomes connectable to the power grid, and the prediction model (<NUM>) being a model that predicts the connection time based on the data from the target vehicle, and
characterised in that
the processor (<NUM>) sets the frequency of communication according to the confidence